Full text of Economic Policy Review (Federal Reserve Bank of New York) : Volume 9, Number 2 : Policies to Promote Affordable Housing: Proceedings of a Conference Cosponsored by the Federal Reserve Bank of New York and New York University's Furman Center For Real Estate and Urban Policy, February 7, 2002

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William J. McDonough

Opening Remarks

am delighted to welcome you to the Federal Reserve Bank
of New York. Today’s conference, “Policies to Promote
Affordable Housing,” has been organized by this Bank and the
Furman Center for Real Estate and Urban Policy of New York
University. I would also like to recognize Ronay Menschel’s
leadership in the development of today’s program. Ronay is
Chairman of Phipps Houses, a major provider of low- and
moderate-income housing, and a member of the Board of
Directors of this Bank.
As the title of the conference suggests, today we intend to
advance our understanding of the issue of affordable housing:
the cost burdens that housing places on low- and moderateincome households, the policies that are designed to lower the
cost of housing for these households, and the policies that—in
pursuit of some other worthy goal—may have exacerbated the
lack of affordable housing. Many of the papers presented today
will discuss the issue from a national perspective, but we will
also focus on the unique conditions of New York City and the
surrounding metropolitan area.
To help set the stage for today’s discussion, let me provide a
broad overview of what we know about affordable housing, or
the lack thereof. We have been involved in this issue for some
time through the work of our Office of Regional and
Community Affairs, headed by Elizabeth Rodriguez-Jackson,
and through past conferences, internal research, and the
volunteer activities of our staff.

First, it is noteworthy that the words “housing quality” are
not included in the title of this conference. An analysis of
longer term trends at the national level, presented at a
conference held here in May of 1999, indicated that relatively
few housing units in the United States meet the criteria of
“severely physically inadequate” or “overcrowded.” By this, we
mean that, with the growth of the U.S. economy over the post–
World War II period, most housing units in the United States
are safe and provide the basic comforts of life. Of course, what
is deemed to be physically adequate would not necessarily
appeal to the people in this room. Housing quality problems
have not been completely eliminated, but we have certainly
made great strides in this area relative to where we were in
1950.
Again at the national level, housing affordability has
improved for the population as a whole over the past decade.
The proportion of household income devoted to housing costs
increased from the mid-1970s to the mid-1980s, a period of
relatively high inflation and high nominal interest rates. It then
declined from the mid-1980s through the mid-1990s as
inflation and interest rates declined, ending the 1990s at
roughly the same level it held in the early 1970s. Indeed, buying
a home has become vastly more affordable over the past
decade, with the result being that the rate of homeownership
climbed to a record 68 percent by the second half of 2001.
Because homeownership makes people stakeholders, builds

William J. McDonough is the president of the Federal Reserve Bank
of New York.

FRBNY Economic Policy Review / June 2003

wealth, and enhances social cohesion, increasing the
homeownership rate has long been a goal of U.S. domestic
policy. The rise in the homeownership rate is even more
noteworthy in that many of these new homeowners are
minorities with moderate incomes.
Nonetheless, for those in the lowest income quintile,
housing affordability has not improved over the past decade.
For the nation as a whole, housing costs for this group rose
from around 40 percent of income in the mid-1970s to around
60 percent by the mid-1980s and have stayed at roughly that
level since then. Those who can least afford it must pay what I
regard as an unconscionable share of their income for what
must surely be basic shelter. It is part of a broader problem that
the incomes of these families have risen at rates well below
average.
This is not an abstract statistical issue. There is growing
evidence that poor housing outcomes are associated with poor
outcomes in other aspects of life, such as health, education, and
the incidence of crime. As we have seen time and again, the
problems of poorer communities very quickly become
everyone’s problems.
Because the New York area is such an attractive place to live
and conduct business, the housing affordability problem here
extends much further up the income distribution. Over the
period from 1997 through 2001, employment in this area grew
at a compound annual rate of 2.1 percent, the fastest growth of
any five-year period for which we have reliable data. According
to the 2000 census, the population of New York City has
surpassed its previous peak, in 1970. But because the area is
already so densely populated and new construction is so
expensive, even middle-income professionals struggle to pay
the rent or the mortgage while still being able to afford life’s
other necessities. Imagine the difficulties of those on the first
rungs of the income ladder.
Our understanding of the appropriate role for government
in alleviating the unduly high housing cost burdens faced by
low- and moderate-income households has evolved
dramatically over the past fifty years. Government
construction or financing of high-density housing in general
did not work and in some cases produced disastrous results. In
the worst cases, such housing was isolated from employment
opportunities as well as health and social services. More
recently, this housing has begun to be replaced by lower density
homes that are developed as part of a broader community plan
and that, in many cases, offer ownership opportunities.

Opening Remarks

While the lessons learned have been hard ones, it is now
widely recognized that tax incentives and subsidies can be
effective in encouraging economic development, provided they
are appropriately structured. At the macro level, we use tax
policy to encourage many things, including homeownership,
research and development, and historic preservation. Local
governments provide tax rebates, build or improve roads, and
make other infrastructure investments using bonding
authority to make their regions more enticing to companies.
Providing tax incentives and subsidies to make housing more
affordable and thereby keep communities growing and vibrant
is an equally important role for government at all levels.
Moreover, it is the right thing to do. Our job is to discover the
most effective and efficient designs for these incentives.
Today’s conference is part of that process.
Now, you might ask why the central bank—the institution
charged with setting monetary policy and maintaining
financial stability—is involved in this issue. One reason is that
it matters to us as people. I have been active in this area for a
long time, both in my native Chicago and here in New York.
I am a firm believer that disparities in the distribution of
wealth and income threaten the social fabric of the United
States. It is in every citizen’s self-interest to address the
inequalities that exist in our society and to strive to eliminate
the permanent underclass.
Furthermore, the Federal Reserve is concerned with
economic growth in all sectors of the economy. Growth of the
national economy is nothing more than the sum total of
growth in the nation’s numerous local economies. At the
Federal Reserve Bank of New York, we work with the private,
nonprofit, and government sectors to furnish information
about new ideas and models to help address local issues. We
bring together key players in neutral forums and act as a
catalyst for the exchange of ideas.
Your attendance today is evidence of your commitment,
interest, and willingness to help your fellow citizens and
improve our local communities. It is my sincere hope that this
conference will further advance our understanding of how best
to achieve these honorable goals. As you all know well, there is
no magic formula. But we must ensure that there is concrete
hope and economic opportunity for all in order for our society
to prosper. The fundamental strength of our economy offers a
unique opportunity to bring disadvantaged people and
communities into the social and economic mainstream.

Michael H. Schill and Glynis Daniels

State of New York City’s
Housing and Neighborhoods:
An Overview of Recent Trends
1. Introduction

ew York City is well-known for the special challenges it
faces in providing the largest urban population in the
United States with quality affordable housing. The city’s
housing problems are frequently the subject of intense debate.
It is sometimes said that housing problems in New York City
are exceptional and cannot be compared with those of other
cities. In this paper, we provide this comparative perspective
through an examination of certain housing indicators for New
York City, the nation as a whole, and several comparison cities.
Our results suggest that New York is not as exceptional as some
might think.
Many housing and neighborhood indicators improved
substantially in New York City over the late 1990s. Although a
large number of New Yorkers live in poor-quality housing or
pay extraordinarily high proportions of their incomes for rent,
housing problems by and large either stabilized or, in some
instances, moderated during the late 1990s. Nevertheless,
significant housing problems remain and not all improvements
were felt everywhere in the city.
Much of the information on New York City presented here
is taken from our recent report, “State of New York City’s
Housing and Neighborhoods 2001.”1 In that report and in this
presentation, we derive many indicators from the New York
City Housing and Vacancy Survey (HVS). This survey, which is
modeled on the Census Bureau’s American Housing Survey

Michael H. Schill is a professor of law and urban planning at New York
University and director of the university’s Furman Center for Real Estate and
Urban Policy; Glynis Daniels is associate director and a research scholar at the
Furman Center for Real Estate and Urban Policy.

(AHS), is conducted every two to three years and is based on a
sample of approximately 18,000 housing units—a substantially
larger sample than the metropolitan area surveys of the AHS,
which range from 1,300 to 3,500 housing units. Because the
HVS is unique to New York City, AHS data for New York, the
United States, and six comparison cities are also presented to
place the city’s housing situation in context.2

2. Vacancy Rates and Housing
Creation
The scarcity of housing in New York City is well-known. As
shown in Chart 1, rental vacancy rates in New York are
consistently lower than rates for the United States as a whole,
reflecting the fact that the city has one of the tightest housing
markets in the nation. According to the HVS, from 1996 to
1999, rental vacancy rates in New York declined from
4.0 percent to 3.2 percent. This decline may be an indication of
a reversal of the generally upward trend in the vacancy rate
since 1984, when only 2 percent of rental units were vacant and
available. The current vacancy rate is well below the 5 percent
level that statutorily constitutes an official housing emergency
in the city. As shown in the chart, the decline in New York
City’s vacancy rate contrasts with the change in the nation as a
whole. According to the AHS, from 1995 to 1999, the

The views expressed are those of the authors and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

FRBNY Economic Policy Review / June 2003

Chart 1

Chart 2

Rental Vacancy Rates

Rental Vacancy Rate: New York City
Sub-Borough Areas, 1999

Percent vacant
8
7

1995-96

;

Bronx
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1999
Percent vacant
0-2
2-4
4-6
More than 6

6
5
4
3

Brooklyn-Queens
border

Queens

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2
1
0
United States

New York City

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Manhattan

nationwide rental vacancy rate increased slightly, from
7.2 percent to 7.4 percent.
New York City’s housing market is not the tightest in the
nation, however (Table 1). According to the 2000 U.S. census,
two other cities—San Francisco (2.5 percent) and Boston
(3.0 percent)—had lower rental vacancy rates. Los Angeles also
had a very low vacancy rate of 3.5 percent. At the other extreme
are Philadelphia, which has experienced substantial population
loss and has a relatively high vacancy rate of 7.0 percent, and
Houston, an expanding city, which has the highest vacancy rate
of the cities examined, 8.7 percent.
Within New York City, there is substantial variation in
rental vacancy rates.3 As Chart 2 indicates, the areas of

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Brooklyn

Staten Island

Source: 1999 New York City Housing and Vacancy Survey.

Table 1

Housing Units, Vacancies, and Crowding in the United States and Selected Cities
Area

Year

Personsa

Households

Housing Units

Vacancy Rate (Percent)a

Severe Crowding (Percent)

United States
New York City
Chicago
Los Angeles
Boston
San Francisco
Philadelphia
Houston

1999
1999
1999
1999
1998
1998
1999
1998

281,421,906
8,008,278
2,896,016
3,694,820
589,141
776,733
1,517,550
1,953,631

102,803,000
2,868,415
1,061,928
1,099,000
228,300
307,300
582,300
642,800

112,292,000
3,038,796
1,152,868
1,337,706
251,935
346,527
661,958
782,009

7.4
3.2b
5.7
3.5
3.0
2.5
7.0
8.7

0.4
3.0b
1.5
4.0
0.1
2.1
0.0
1.6

Source: American Housing Survey.
a

Source: 2000 United States Census.
Source: New York City Housing and Vacancy Survey.

State of New York City’s Housing and Neighborhoods

Chart 3

New Housing Units Issued Certificates of Occupancy:
New York City Community Districts, 1991-2000
Bronx
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Number of certificates
13-2,139
2,140-4,266
4,267-6,393
6,394-8,520
Brooklyn-Queens
border

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;;;Airports
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Queens

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New York that have the most vacancies are generally those
neighborhoods with high populations of low- and moderateincome families, such as the South Bronx and Central
Brooklyn. One exception is southern Staten Island, where land
is more available and construction levels are relatively high.
Low vacancy rates can be thought of as reflecting strength or
weakness. On the one hand, the extremely tight housing
market indicates high demand for residence in the City of
New York. People flocked to New York City during the 1990s,
largely because of immigration and the attraction of a booming
economy. According to the 2000 census, the city’s population
grew by 686,000 people over the 1990s. Roughly one-half of
this increase was probably attributable to the efforts of the City
Planning Commission to find people who were always there
but had gone uncounted. Nevertheless, the city probably grew
by about 300,000 people, or about 122,000 households, over
the decade, resulting in a growth rate of between 4.1 percent
(using an adjusted 1990 population) and 9.4 percent (using
unadjusted data). New York City’s population did not grow as
fast as the nation’s (13.2 percent growth over the decade), but
the relatively strong growth in population confirmed a
turnaround in the trends of population loss and decline in
desirability of urban residential location that has plagued
New York and other older cities since the 1950s.
On the other hand, the less desirable implication of low
vacancy rates is that housing supply did not keep up with the
demand for residence in the city. Over the decade, the city
issued certificates of occupancy for only 81,000 new units of
housing. That total is less than half the average number of
housing units built in the 1970s and only one-fifth the number
completed in the 1960s.
As Chart 3 indicates, the bulk of the production in the city
was in Manhattan south of Ninety-Sixth Street, Staten Island,
Jamaica, and East New York. The development in Manhattan
and Staten Island was primarily market-driven; the
development in Jamaica and East New York, however, was
largely subsidized.
One unique factor in New York City housing production is
the large role that government has played in financing and
supporting the creation of affordable housing, particularly
through the city’s capital programs. Since 1987, the city has
produced nearly 28,000 new units of housing designated for
low- and moderate-income residents. In addition, these
programs have rehabilitated another 155,000 units of housing.
Some distressed neighborhoods have been affected
tremendously by these efforts (Chart 4). Neighborhoods in
the South Bronx, for example, have had from 18 percent to
35 percent of their currently existing housing units created or
rehabilitated through these programs.

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Manhattan

Brooklyn

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Staten Island

Source: New York City Department of City Planning.

One impact of the tight housing market is crowding.
According to HVS data, severe crowding (1.5 persons or more
per room) grew worse in New York City during the late 1990s,
increasing from 2.7 percent of all households in 1996 to
3.0 percent in 1999. This is much higher than the nationwide
incidence of severe crowding observed in the AHS data, which
actually decreased from 0.5 percent in 1995 to 0.4 percent in
1999 (Chart 5). Among our six comparison cities, only Los
Angeles (4.4 percent) had a higher rate of severe crowding than
New York. San Francisco had about 2 percent of housing units
with severe crowding, Chicago and Houston each had about
1.5 percent severe crowding, while Boston and Philadelphia
had severe crowding rates of less than 1 percent.
Some crowded households are actually two households
doubled up in one housing unit. According to estimates from

FRBNY Economic Policy Review / June 2003

Chart 4

Percent of Total Housing Units Assisted through
New York City’s Capital Programs: New York City
Community Districts, 1987-2000
Bronx
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Percent of existing units
0.0-8.7
8.8-17.3
17.4-25.9
26.0-34.5
Brooklyn-Queens
border

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Queens

the 1999 HVS, there were 25,295 households in New York City
that contained one or more persons who had doubled up with
other households. Of these, 44 percent (11,177 households)
doubled up specifically for affordability reasons. This is a
decrease of about 5,000 households since 1996. Although the
number of doubled-up households in New York is only a small
percentage of total households (slightly less than 1 percent),
the figure is troubling nonetheless because doubling up is an
indicator that a household may be on the verge of
homelessness.

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Manhattan

Brooklyn

3. Housing Affordability

Housing affordability is a major concern in New York City.
As Chart 6 indicates, median gross rent (out-of-pocket rent
exclusive of subsidies) in New York is substantially higher than
the national averages. According to the American Housing
Survey, median gross rent grew by 8.7 percent from 1995 to
1999 in New York City. Over the same period, the national
median rent grew at a faster rate, 10.9 percent, to reach $580
per month, but it was still substantially lower than the median
monthly rent of $640 paid by New Yorkers.
Median rent varies widely across New York City
neighborhoods. Chart 7 displays median contract rent data
from the 1999 HVS. Three very desirable neighborhoods—the
Upper East Side, Stuyvesant Town/Grammercy/Turtle Bay,
and Greenwich Village/SoHo/Financial District—each had
median contract rents above $1,000 per month. Certain
neighborhoods, including those in Central and East Harlem,

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Staten Island

Source: New York City Department of Housing Preservation
and Development.

Chart 5

Chart 6

Severe Crowding

Median Gross Rent

Percent crowded
4.0
3.5

Median rent (dollars)
800
1995-96

1999

3.0

600

2.5

500

2.0

400

1.5

300

1.0

200

0.5

100
0

0.0
United States

New York City

State of New York City’s Housing and Neighborhoods

1995

700

United States

1999

New York City

Chart 7

;

Median Monthly Rent: New York City
Sub-Borough Areas, 1999

Bronx
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Median rent in dollars
0-500
500-750
750-1,000
More than 1,000
Brooklyn-Queens
border

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the South Bronx, and Central Brooklyn (Bedford-Stuyvesant,
Brownsville, and Bushwick) had median rents below $500 per
month.
Perhaps surprisingly, New York does not have the highest
average rent among all the cities we examined (Table 2).
San Francisco had the highest median gross rent ($839) and
Boston residents paid an average of $750 per month for rental
housing. Los Angeles ($613), Chicago ($586), Philadelphia
($559), and Houston ($527) each had lower average rents than
New York.
Despite rising housing costs, severe affordability problems
declined in New York City in the late 1990s (Chart 8).
According to the 1999 AHS, more than one in five New York
renter households (22.4 percent) experienced a severe rent
burden, defined as paying more than 50 percent of household
income for rent and utilities.4 This represents an
improvement—it is more than 6 percent less than the
proportion with severe rent burdens in 1996 (28.7 percent)—
reflecting the fact that incomes rose faster than rents as a result
of the economic expansion. However, it also means that more
than 600,000 New Yorkers pay a staggering proportion of their
income in rent.
This decline in severe rent burdens made the New York
picture nearly comparable to the national picture. In 1999,
21.4 percent of renter households spent 50 percent or more of
their income on rent nationwide, just 1 percent less than the
figure for New York City. This national figure also represents a
decrease of about 1 percent from the 1995 level of 22.3 percent.
Also surprisingly, all but one of the comparison cities had
higher levels of severe rent burdens than New York. Houston,

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Manhattan

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Staten Island

Source: 1999 New York City Housing and Vacancy Survey.

Table 2

Rents and Rent Burdens in the United States
and Selected Cities

Chart 8

Severe Rent Burdens
Area
United States
New York City
Chicago
Los Angeles
Boston
San Francisco
Philadelphia
Houston

Year
1999
1999
1999
1999
1998
1998
1999
1998

Median
Rent
(Dollars)
580
640
586
613
750
839
559
527

Severe Rent
Burden
(Percent)
21.4
22.4
26.1
29.2
30.6
26.7
25.9
20.5

Median Rent
Burden
(Percent)
28
27
27
30
30
28
27
24

Percent of households
35
30

1995

1999

25
20
15
10
5
0

Source: American Housing Survey.

United States

FRBNY Economic Policy Review / June 2003

New York City

with 20.5 percent of households paying more than 50 percent
of their income for rent, experienced lower levels than either
New York City or the nation. The other comparison cities had
greater proportions of households with severe rent burdens
than New York. Boston had the highest level of severe rent
burden (30.6 percent), followed by Los Angeles (30.0 percent),
San Francisco (26.7 percent), Chicago (26.1 percent), and
Philadelphia (25.9 percent).
According to the AHS data, the national median rent
burden, defined as the median percentage of household
income spent on rent and utilities, was 28 percent in 1999
(Table 2). Most of the cities we examined fall within 2 percent
of this figure. The highest median percentage of income spent
on rent is found in Boston and Los Angeles, where residents
typically pay 30 percent of their income for rent. The median
rent burden is substantially lower in Houston, only 24 percent;
this may well be related to the low rents and high vacancy rates
found in that city. In New York, the median rent burden was
27 percent.
High rent burdens mean different things to different
households. A 50 percent rent-to-income ratio would be
difficult for affluent families, but for them sufficient income
would be available for essential expenses such as food, clothing,
and medical care after paying for housing. In New York City,
however, most households with severe rent burdens are not
affluent. According to the 1999 HVS, about nine out of ten
renters with severe rent burdens are low-income (80 percent of
median) and 62 percent are below the poverty level.
The lower prevalence of severe rent burdens in New York
City—compared with Boston, Chicago, Los Angeles,
Philadelphia, and San Francisco—can be attributed, at least in
part, to high levels of rent subsidies and rent regulation in the
city. As shown in Table 3, data from the 1999 HVS and the
New York City Housing Authority indicate that nearly threequarters of all New York City renters either receive some form
of rent subsidy or have their rents regulated.5 This is more than
three times the national rate of rent subsidy and/or regulation
reported in the 1999 American Housing Survey. And it is the
highest level of relief from market-rate rents found in any of
the cities we examined. The only city that has similarly high
rates of rent relief is San Francisco, where 67.6 percent of
renters are protected from market rents through regulation or
subsidy. In Boston and Los Angeles, about one-quarter of
renters are protected from market rents. In Chicago, nearly
one-fifth of renters receive protection or subsidy, and in
Houston and Philadelphia, only about 13 percent of renters
receive relief from market-rate rents.

State of New York City’s Housing and Neighborhoods

Table 3

Rent Regulation and Subsidies in the United States
and Selected Cities

Area

Year

United States
1999
New York Citya 1999
Chicago
1999
Los Angeles
1999
Boston
1998
San Francisco 1998
Philadelphia
1999
Houston
1998

Rent
Regulation
(Percent)

Public
Housing
(Percent)

Subsidized
(Percent)

Total
(Percent)

2.7
55.4
0.0
9.9
0.7
54.3
0.0
0.0

5.6
5.9
7.5
2.5
13.0
3.9
6.2
2.1

13.1
10.3
12.2
11.9
12.1
9.4
6.7
11.0

21.4
71.6
19.8
24.3
25.8
67.6
13.0
13.1

Source: American Housing Survey.
a

Sources: New York City Housing and Vacancy Survey; New York City
Housing Authority.

4. Housing Quality
One of the nation’s great achievements over the past century
has been the improvement of housing quality. Housing quality
improved so much that we actually had to change the previous
definition of substandard housing used in 1940 (units lacking
full plumbing) because virtually all housing now meets that
standard. Data from the Housing and Vacancy Survey indicate
that housing quality in New York City continued to improve
between 1996 and 1999. According to the HVS, the proportion
of units with severe maintenance deficiencies—defined as five
or more of seven deficiency criteria—declined from
4.5 percent to 3.1 percent.6 The pattern of housing-quality
problems in New York City suggests that most of the units with
multiple deficiencies are in low- and moderate-income
neighborhoods. For example, Chart 9 shows that the
proportion of units with five or more maintenance deficiencies
is greatest in Harlem, the South Bronx, and Central Brooklyn.
Similar patterns exist for serious housing code violations
(Chart 10).
The American Housing Survey provides two other
indicators of housing quality: the percent of units with a severe
physical problem and the percent of units with a moderate
physical problem. The specific physical deficiencies used to
create the AHS measures vary somewhat from the HVS, but the

primary difference is that for the AHS indicators, housing units
experiencing any of the criteria of physical problems are
counted as having physical problems. The HVS maintenance
deficiency measure we utilize requires that a unit have five or
more problems simultaneously.
The AHS indicators of physical problems present a different
picture of housing quality in New York City. As shown in
Chart 11, the percent of housing units with serious physical
problems actually increased from 6.1 percent in 1995 to

7.6 percent in 1999.7 These figures are substantially higher than
the incidence of severe physical problems nationwide, which
decreased slightly from 2.1 percent in 1995 to 2.0 percent in
1999.
In fact, the AHS data indicate that New York City has the
highest incidence of severe physical problems of the cities we
examined (Table 4). San Francisco has the next highest rate,
with 6.5 percent of units experiencing severe problems,
followed by Los Angeles, with 5.0 percent. Houston has the

Chart 9

Chart 10

Percent of Housing Units with Five or More
Maintenance Deficiencies: New York City
Sub-Borough Areas, 1999

Serious Housing Code Violations per 1,000 Rental
Units: New York City Community Districts, 2000

;

Bronx
yyy
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border

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yyy
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yyyy
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yy
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yy
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yy

;
;
;
;
; ;;
;
;
;
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Percent of housing units
0-3
4-6
7-9
10-13

Violations per 1,000 units
Under 40
40-80
80-120
More than 120

Brooklyn-Queens
border

yyy
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Queens

yy
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Manhattan

Brooklyn

yy
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yy

yyyy
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yyyy
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yyyy
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yyyy
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yyyy

Manhattan

Brooklyn

yy
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yy

Staten Island

Source: 1999 New York City Housing and Vacancy Survey.

Source: New York City Department of Housing Preservation
and Development.

FRBNY Economic Policy Review / June 2003

Chart 11

Severe Physical Problems
Percent of housing units
9
8

1999

1995

7
6
5
4
3
2
1
0
United States

New York City

lowest rate, 1.9 percent. The picture is very different, however,
when moderate physical problems are examined.8 New York
actually has the lowest incidence of moderate physical
problems (6.2 percent) of the seven cities. The highest rate of
moderate problems is found in Houston (12.1 percent),
followed by San Francisco, with 9.4 percent. This suggests that
although there is a substantial core of lower quality housing in
New York City, housing deficiencies are largely limited to this
group of substandard housing units and are not widespread
throughout the housing stock.

Table 4

Physical Problems of Housing Units
in the United States and Selected Cities

Area
United States
New York City
Chicago
Los Angeles
Boston
San Francisco
Philadelphia
Houston

Year

Severe
Problems
(Percent)

Moderate
Problems
(Percent)

Units Built
before 1930
(Percent)

1999
1999
1999
1999
1998
1998
1999
1998

2.0
7.6
3.8
5.0
2.5
6.5
3.2
1.9

4.7
6.2
7.6
7.1
7.1
9.4
6.9
12.1

13.6
40.9
37.1
11.4
52.0
39.0
44.3
3.2

Source: American Housing Survey.

State of New York City’s Housing and Neighborhoods

The age of New York’s housing stock is certainly one factor
contributing to its higher rates of physical problems. In 1999,
about 41 percent of all units in the city were built before 1930.
This means that two out of every five housing units were
seventy years old or more. Nationwide, only 13.6 percent
of housing units were built before 1930. Only Boston
(52.0 percent) and Philadelphia (44.3 percent) had a greater
proportion of their housing stock built before 1930. By
contrast, in Los Angeles and Houston, the proportions are
11.4 percent and 3.2 percent, respectively.
Another factor impacting the quality of New York’s housing
stock is the legacy of housing abandonment and disinvestment
that plagued the city from the late 1960s through the 1970s.
During this time of financial crisis and social unrest, many
middle- and working-class households fled the city. From 1970
to 1980, the city lost more than 800,000 people—more than
10 percent of its population. Entire communities were
devastated, and many landlords walked away from their
buildings.
Over the past two decades, tremendous progress has been
made in New York as a result of a strengthening economy
combined with a variety of housing investment programs and
anti-abandonment policies. The city took ownership of many
abandoned buildings through in rem legal actions. A
substantial proportion of these properties have been
rehabilitated and returned to the private sector through the
capital programs mentioned earlier. Many other properties
that were never taken in rem have been rehabilitated or
constructed. But a core of problem buildings in distressed
neighborhoods still exists.
Two of the best indicators of fiscal distress and potential
abandonment are long-term property tax delinquencies and
high lien-to-value ratios. From 1996 to 2000, both the number
and proportion of New York City properties with tax
delinquencies persisting longer than one year fell
substantially—from 9.5 percent to 3.5 percent (Chart 12).
Similarly, tax delinquencies that constitute more than half of a
property’s market value also declined from 4.2 percent of all
properties to 3.8 percent. Among the reasons for these declines
in tax delinquency are the program of tax lien sales instituted
by the city in the mid-1990s, the city’s anti-abandonment
policies, and the resurgence of the city’s economy.
Nevertheless, despite these improvements, some neighborhoods still have extremely high rates of tax delinquency. For
example, Chart 13 shows the high rates of tax delinquencies
of one year or more that exist in the northern portion of

Chart 12

Property Tax Delinquencies in New York City
Percent of residential properties
10
9
8
7
6
5
4
3
2
1
0
One year or longer

1996

2000

Staten Island, the South Bronx, Harlem, Morningside Heights
in Manhattan, and Central Brooklyn. Similar patterns exist for
delinquencies in excess of 50 percent of property value
(Chart 14). This is a similar, though not identical, geographic
pattern as was seen in the map of severe maintenance
deficiencies. One implication is that neighborhoods such as
northern Staten Island and Manhattan’s Morningside Heights
might be at risk of further disinvestment if the current
problems with the fiscal health of their housing stock are
not reversed.

Value greater than 50 percent

Chart 14
Chart 13

Rental Properties with Tax Delinquencies of One Year
or More: New York City Community Districts, 2000

Percent delinquent one year
1.1-7.5
7.5-13.9
13.9-20.3
20.3-26.7
Brooklyn-Queens
border

3.6-11.6
11.6-19.7
19.7-27.7
27.7-35.7
Queens

yy
;;
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yy
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yy
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yy

Bronx
yyy
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Percent with lien
greater than 50 percent

Brooklyn-Queens

border
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Queens

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Bronx
yyy
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yyy
yyyy
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yyy
;;;;
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yyyy
yy
;;

Rental Properties with Tax Delinquency Amounts
Greater Than 50 Percent of Property Value:
New York City Community Districts, 2000

yyyy
;;;;
;;;;
yyyy
;;;;
yyyy
;;;;
yyyy
;;;;
yyyy

Manhattan

Brooklyn

yy
;;
;;
yy

yyyy
;;;;
;;;;
yyyy
;;;;
yyyy
;;;;
yyyy
;;;;
yyyy

Manhattan

Brooklyn

yy
;;
;;
yy

Staten Island

Source: New York City Department of Finance.

FRBNY Economic Policy Review / June 2003

5. Homeownership and Mortgage
Finance
According to the Housing and Vacancy Survey, homeownership rates edged up slightly in New York City, from
30.0 percent in 1996 to 31.9 percent in 1999.9 As shown in
Chart 15, national homeownership rates also increased slightly,
from 65.0 percent in 1995 to 66.9 percent in 1999. New York’s
homeownership rate remains less than half that of the nation as
a whole, and New York has the lowest rate of homeownership
among the cities we examined (Table 5). San Francisco
(33.3 percent), Boston (33.7 percent), and Los Angeles
(38.1 percent) also have low rates of homeownership; Chicago
(45.4 percent) and Houston (46.3 percent) have somewhat

higher homeownership rates; and Philadelphia’s homeownership rate (61.9 percent) approaches that of the nation.
Nevertheless, housing investment, at least as reflected in
home purchase loans, boomed in New York City in the second
half of the 1990s. Between 1996 and 1999, the number of home
purchase loan originations increased by 44 percent. This rise is
much higher than the 4.9 percent increase in home purchase
loans in the nation’s metropolitan areas identified in the Joint
Center for Housing Studies’ “State of the Nation’s Housing”
report over the same period. Each borough in New York
enjoyed significant increases, with Staten Island leading the
way, followed by Manhattan and the Bronx.
In terms of the dollar amount of home purchase lending in
New York, the increase was even greater, 77 percent. As
Chart 16 illustrates, the total dollar amount of home purchase

Chart 15

Homeownership Rates
Percent homeowners
80
70

Chart 16
1995-96

1999

60
Amount (millions of dollars)
12-237
237-462
462-687
687-912

50
40
30
20

Brooklyn-Queens
border

0
United States

yyy
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Bronx
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;
;
;
;
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;

Total Dollar Amount of Home Purchase Mortgage
Loans: New York City Sub-Borough Areas, 1999

New York City

yyyy
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yyyy

Manhattan

Table 5

Homeownership Rates in the United States
and Selected Cities
Area

Year

United States
New York Citya
Chicago
Los Angeles
Boston
San Francisco
Philadelphia
Houston

1999
1999
1999
1999
1998
1998
1999
1998

Brooklyn

yy
;;
;;
yy

Homeownership Rate (Percent)
66.9
31.9
45.4
38.1
33.7
33.3
61.9
46.3

Staten Island

Source: American Housing Survey.
a

Source: New York City Housing and Vacancy Survey.

State of New York City’s Housing and Neighborhoods

Source: Home Mortgage Disclosure Act.

mortgage loans was predictably greatest in Manhattan south of
Ninety-Sixth Street, Staten Island, and parts of the more
affluent sections of Queens and Brooklyn. Loan originations
per 1,000 properties, however, were more evenly distributed
across neighborhoods (Chart 17).
One possible problem area related to the increase in loan
origination is predatory lending. Almost all of these loans are
made by subprime lenders. Although the share of home
purchase loans made by subprime lenders decreased from 1996
to 1999—from 7.2 percent to 3.8 percent—in some

neighborhoods in the city, as much as 25 percent of home
purchase loans were made by subprime lenders in 1999. Again
predictably, Chart 18 shows that the proportion of home
purchase loans made by subprime lenders is much higher in
many of the city’s poorest areas. Even more pronounced
patterns exist for subprime refinance loans (Chart 19). It is
important to underscore, however, that not all subprime
lending is undesirable. In many instances, poor families would
not be able to access the credit market without a subprime
lender.

Chart 17

Chart 18

Home Purchase Mortgage Loan Originations
per 1,000 Homeowner Units: New York City
Sub-Borough Areas, 1999

Percent Subprime Home Purchase Mortgage
Loan Originations: New York City
Sub-Borough Areas, 1999

;

Bronx
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yyy
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;;;;
;;;;
yyyy
yy
;;

Loans per 1,000
homeowner units
22-41
42-60
61-79
80-99
Brooklyn-Queens
border

Queens

yy
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yy
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yy
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Brooklyn-Queens
border

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Percent of loans
from subprime lenders
0-6
7-13
14-20
21-27

yyyy
;;;;
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yyyy
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yyyy
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yyyy
;;;;
yyyy

Manhattan

Brooklyn

yy
;;
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yy

Staten Island

Source: Home Mortgage Disclosure Act.

yyyy
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yyyy
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yyyy
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yyyy
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yyyy

Manhattan

Brooklyn

yy
;;
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yy

Staten Island

Source: Home Mortgage Disclosure Act.

FRBNY Economic Policy Review / June 2003

6. Conclusion

Chart 19

Percent Subprime Refinance Mortgage
Loan Originations: New York City
Sub-Borough Areas, 1999

;

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Percent of dollars
from subprime lenders
0.5-16.1
16.1-31.6
31.6-47.2
47.2-62.7
Brooklyn-Queens
border

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Queens

Recent data suggest that although substantial numbers of
New Yorkers experience rather severe housing problems, the
intensity of these problems did not increase in the late 1990s.
Indeed, over the last half of the 1990s, as the city’s economy
boomed and its massive investment in housing bore fruit, levels
of severe housing cost burdens and substandard housing
moderated slightly. Similarly, homeownership rates crept up,
mortgage capital flowed more freely, and tax delinquency
declined.
Somewhat surprisingly, data from the American Housing
Survey indicate that the housing situation of New Yorkers is
better in some respects than that of residents of several other
large cities. Although substandard housing is more prevalent
in New York, the rate of severe affordability problems is
somewhat lower. At least part of the reason for New York’s
relatively favorable comparative performance on affordability
is the fact that a large proportion of the housing stock is either
rent-regulated or subsidized. Furthermore, even though rates
of severe affordability problems among renters may be
somewhat lower in New York City than in other large cities,
these other cities typically have much higher rates of owneroccupancy. Therefore, the absolute number and proportion of
all households in the city with affordability problems are likely
to be as great or greater in New York than in these cities.
Most of the data examined in this paper were collected in
1999 or 2000. New York’s financial picture is much different
today. The national recession hit the New York area shortly
before September 11. Since then, the city has lost jobs,
businesses, and tax revenues. It will be a substantial challenge
for New York City to maintain the gains of the 1990s. Things
will be especially challenging for low-income New Yorkers,
who are more dependent on government subsidies, are more
likely to have lost jobs or wages after September 11, and may
face a loss of income subsidies as a result of the five-year time
limits enacted in the Welfare Reform Act of 1996. The strength
and speed of the hoped-for economic recovery—combined
with the ability of local, state, and federal governments to find
ways to provide support to the neediest New Yorkers—will
determine whether New York City is able to maintain its hardwon improvements, or whether it will reexperience a
downward cycle of housing abandonment and neighborhood
degradation.

yyyy
;;;;
;;;;
yyyy
;;;;
yyyy
;;;;
yyyy
;;;;
yyyy

Manhattan

Brooklyn

yy
;;
;;
yy

Staten Island

Source: Home Mortgage Disclosure Act.

State of New York City’s Housing and Neighborhoods

Endnotes

1. The report is available at <http://www.law.nyu.edu/
realestatecenter>.
2. In some instances, HVS data for New York City are compared with
indicators from the AHS. When these comparisons are made, care is
taken to utilize similar computational methods so that the indicators
are comparable.
3. Care should be taken when interpreting data from the HVS for
sub-borough areas. For some indicators, small sample sizes render the
estimates statistically unreliable.
4. Calculations from the HVS, as reported in our “State of New York
City’s Housing and Neighborhoods 2001” report, resulted in a severe
rent burden of 24.3 percent in 1999, down slightly from 25.3 percent
in 1996. The differences between the HVS and AHS are due to
differences in the measurement of rent used in the calculations (gross
rent in the AHS and contract rent in the HVS) and differences in the
measurement of income. When calculating rent burdens, the AHS
uses family income as reported in a single question; the HVS uses
household income derived from a series of income questions detailed
by source.
5. Data from the American Housing Survey yield significantly lower
numbers of rent-regulated housing units, 21.8 percent instead of the
55.4 percent reported in the HVS. The AHS probably underestimates
the number of households whose rent is kept stable through
regulation. This discrepancy may result from the fact that the AHS
uses the wording “rent control” to describe rent-regulated
apartments. In New York City, the term rent control refers to a strict
form of rent regulation that was phased-out beginning in the 1970s
and now covers about 3 percent of rental units. However, many
New York apartments, 51.9 percent, are covered by the city’s rent
stabilization law, under which allowable rent increases are determined
annually by a rent guidelines board.

infestation; cracks/holes in walls, ceilings, or floors; broken plaster or
peeling paint larger than 8 ½ by 11 inches; toilet breakdowns; or water
leaking from outside the unit.
7. The indicators of severe physical problems in the AHS are:
plumbing (lacking hot or cold piped water or lacking both bathtub
and shower, all inside the structure); heating (having been
uncomfortably cold last winter because the heating equipment broke
down, and it broke down at least three times last winter for at least six
hours each time); electric (having no electricity, or all of the following
three problems: exposed wiring, a room with no working wall outlet,
and three blown fuses or tripped circuit breakers in the last ninety
days); hallways (having all of the following four problems in the public
areas: no working light fixtures, loose or missing steps, loose or
missing railings, and no working elevator); and upkeep (having any
five of the following six problems: water leaks from the outside, leaks
from inside the structure, holes in the floors, holes or open cracks in
the walls or floors, more than 8 by 11 inches of peeling paint or broken
plaster, or signs of rats in the last ninety days).
8. The AHS definition of moderate physical problems is having any of
the following five problems, but none of the severe problems:
plumbing (on at least one occasion during the last three months, all
the flush toilets were broken down for at least six hours); heating
(having unvented gas, oil, or kerosene heaters as the primary heating
equipment); kitchen (lacking a kitchen sink, refrigerator, or cooking
equipment inside the structure for the exclusive use of the unit);
hallways (having any three of the four problems listed in endnote 7);
and upkeep (having any three or four of the six problems listed in
endnote 7).
9. AHS data for New York City indicate a slight decline in
homeownership rates, from 29.8 percent in 1995 to 29.3 percent in
1999. There is no clear reason for the difference in HVS and AHS
results. We rely on homeownership data from the HVS because of its
substantially larger sample size.

6. The seven criteria of maintenance deficiencies in the HVS are:
heating equipment breakdowns; additional heat required; rodent

The views expressed are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York
or the Federal Reserve System. The Federal Reserve Bank of New York provides no warranty, express or implied, as to the
accuracy, timeliness, completeness, merchantability, or fitness for any particular purpose of any information contained in
documents produced and provided by the Federal Reserve Bank of New York in any form or manner whatsoever.
FRBNY Economic Policy Review / June 2003

Edward L. Glaeser and Joseph Gyourko

The Impact of Building
Restrictions on Housing
Affordability
1. Introduction

chorus of voices appears to proclaim unanimously that
America is in the midst of an affordable housing crisis.
Housing and Urban Development Secretary Andrew Cuomo
asserted the existence of such a crisis in his introduction to a
March 2000 report that documents a continuing and growing
housing affordability crisis throughout the nation. Indeed,
Secretary Cuomo regularly justified aggressive requests for
funding by pointing to this crisis. Advocacy groups for the poor
such as the Housing Assistance Council pepper their
documents with assertions that “the federal government
should commit to a comprehensive strategy for combating the
housing affordability crisis in rural America.” Trade
associations such as the National Association of Home Builders
decree that “America is facing a silent housing affordability
crisis.” The National Association of Realtors agrees: “there is a
continuing, growing crisis in housing affordability and
homeownership that is gripping our nation.”
Does America actually face a housing affordability crisis?
Are home prices high throughout the United States, or are
there just a few places where housing prices become extreme?
In those places that are expensive, why are home prices so high?
Is subsidized construction a sensible approach to solving this
problem—relative to other, deeper reforms? This paper

Edward L. Glaeser is a professor of economics at Harvard University and
a faculty research fellow at the National Bureau of Economic Research;
Joseph Gyourko is the Martin Bucksbaum Professor of Real Estate and Finance
at the University of Pennsylvania’s Wharton School.
<eglaeser@harvard.edu>
<gyourko@wharton.upenn.edu>

examines whether America actually does face an affordable
housing crisis, and why housing is expensive in high-price
areas.
In general, housing advocates have confused the role of
housing prices with the role of poverty. Both housing costs and
poverty matter for the well-being of American citizens, but
only one of these factors is a housing issue per se. Certainly, the
country should pursue sensible antipoverty policies, but if
housing is not unusually expensive, these policies should not be
put forward as a response to a housing crisis.1 To us, a housing
affordability crisis means that housing is expensive relative to
its fundamental costs of production—not that people are poor.
Therefore, we will focus entirely on housing prices, not on the
distribution of income.
A second key concept in thinking about a housing
affordability crisis is the relevant benchmark for housing costs.
Affordability advocates often argue for the ability to pay (for
example, some percentage of income) as a relevant benchmark,
but this again confuses poverty with housing prices. We believe
that a more sensible benchmark is the physical construction
costs of housing. If we believe that there is a housing crisis, then
presumably the correct housing response would be to build
more housing. Yet the social cost of that new housing can never
be lower than the cost of construction. For there to be a “social”
gain from new construction, housing must be priced
appreciably above the cost of new construction.

The authors are grateful to Albert Saiz; Jesse Shapiro; and their discussant,
Brendan O’Flaherty, for comments. The views expressed are those of the
authors and do not necessarily reflect the position of the Federal Reserve Bank
of New York or the Federal Reserve System.

FRBNY Economic Policy Review / June 2003

This argument is not meant to deny that the existence of
poor people who cannot afford housing is a major social
problem. However, if housing does not cost appreciably more
than new construction, then it is hard to understand why
policies oriented toward housing supply would be the right
response to this problem. Hence, we focus on the gap between
housing costs and construction costs.
To look at the housing affordability issue, we use the R.S.
Means Company’s data on construction costs in various U.S.
metropolitan areas (hereafter, the Means data). These data give
us information (based on the surveying of construction
companies) on the costs of building homes with various
characteristics. As a basic number, the Means data suggest that
construction costs for the lowest of the four quality types they
track (termed an economy home) are about $60 per square foot.
Construction costs for the next highest quality type (termed an
average home) are about $75 per square foot. Ultimately, we
compare this information with data on housing prices.
To get a better sense of the distribution of housing prices
throughout the United States, we turn to the American
Housing Survey (AHS), but for a quick look at the affordability
issue, it is useful to examine the 2000 U.S. census. The census
indicates that the self-reported median home value is
$120,000.2 Sixty-three percent of single-family detached homes
in America are valued at less than $150,000. Seventy-eight
percent of these homes are valued at less than $200,000. The
American Housing Survey reports that the median size of a
detached owned home is 1,704 square feet. The construction
costs of an average home imply that this home should cost
about $127,500 to build, with a lower quality economy home
costing $102,000 to construct.3
Together, these numbers provide us with the first important
lesson from housing markets. The majority of homes in this
country are priced—even in the midst of a so-called housing
affordability crisis—close to construction costs. The value of
land generally seems modest, probably 20 percent or less of the
value of the house. To us, this means that America as a whole
may have a poverty crisis, but its housing prices are basically
being tied down by the cost of new construction. Unless state
intervention can miraculously produce houses at far less than
normal construction costs, such programs are unlikely to
reduce the distribution of housing costs in America radically.
If housing costs in the United States are so low, why the
horror stories? What about the tear-downs going for millions
in Palo Alto? What about the multi-million-dollar apartments
in Manhattan? The American Housing Survey allows us to see
the distribution of house prices across the country. In addition,
this source improves on the census by providing much better

The Impact of Building Restrictions on Housing Affordability

information on housing characteristics. Thus, we can better
compare the self-reported value of a house with the cost of
building a home from scratch. When combined with the Means
data, the American Housing Survey allows us to examine
housing prices in a wide range of cities as well as the gap
between these prices and new construction costs.
These data suggest that America can be divided into three
broad areas. First, there are a number of places where housing
is priced far below the cost of new construction. These areas are
primarily central cities in the Northeast and the Midwest, such
as Detroit and Philadelphia. In these places, which were the
subject of our previous work (Glaeser and Gyourko 2001),
there is almost no new growth. In general, these places had
significant housing price appreciation over the 1990s, but
values are still below construction costs.
In the second category of housing, in large areas of the
country, costs are quite close to the cost of new construction.
These places generally have robust growth on the edges of
cities, where land is quite cheap. These areas represent the bulk
of American housing, although they seem to be somewhat
underrepresented in the AHS.
Finally, there is a third category of cities and suburbs where
the price of homes is much higher than the cost of new
construction; Manhattan and Palo Alto are two examples.
Indeed, many of these places are in California, but the 1990s
saw an increase in such areas in the Northeast and South as
well. Although there are a number of such places with
extremely expensive homes, they do not represent the norm for
America. Both poor and nonpoor people suffer from higher
housing costs in such areas.
In this paper, after first surveying housing costs in the
United States, we examine why the expensive places have such
high housing costs. High-cost places generally have either very
attractive local amenities (great weather or good schools) or
strong labor markets. The Rosen (1979) and Roback (1982)
framework has proved useful in such studies, and one of us
(Gyourko and Tracy 1991) has written on this topic.
It is noteworthy that we do not focus here on the housing
demand side of the cost equilibrium. Instead, we focus on the
role of housing supply. What is it that creates places where the
cost of housing is so much higher than the physical
construction costs? We offer two basic views. First, there is the
classic economics approach, which argues that houses are
expensive because land is expensive. According to this view,
there is a great deal of demand for certain areas, and land, by its
very nature, is limited in supply. As such, the price of housing
must rise. Traditional models, such as the classic AlonsoMuth-Mills framework, take this view.

Our alternative view is that homes are expensive in highcost areas primarily because of government regulation, that is,
zoning and other restrictions on building. According to this
view, housing is expensive because of artificial limits on
construction created by the regulation of new housing. It
argues that there is plenty of land in high-cost areas, and in
principle new construction might be able to push the cost of
houses down to physical construction costs. This is not to
imply that high prices exist in areas with weak demand
fundamentals. A strong demand, because of attractive
amenities or a thriving labor market, is essential. However, this
hypothesis implies that land prices are high, not due to some
intrinsic scarcity, but because of man-made regulations.
Hence, the barriers to building create a potentially massive
wedge between prices and building costs.
We present three pieces of evidence that attempt to
differentiate between these two hypotheses. First, we look at
two different ways of valuing land. The first, classic way, is to
use a housing hedonic and compare the price of comparable
homes situated on lots of different sizes. With these
comparisons, we are, in principle, able to back out the value
that consumers place on larger lots. Our second methodology
is to subtract the construction costs from the home value and
divide by the number of acres. This gives us another per-acre
value of land that is implied by the home price. The first, or
hedonic, methodology can be thought of as giving the intensive
value of land—that is, how much land is worth on the margin
to homeowners. The second methodology gives the extensive
margin—or how much it is worth to have a plot of land with a
house on it.
The two hypotheses outlined above offer radically different
predictions about the relationship of these two values. The
neoclassical approach suggests that land should be valued the
same using either methodology. After all, if a homeowner does
not value the land on his plot very much, he would subdivide
and sell it to someone else. The regulation approach suggests
that the differences can be quite large. Empirically, we find that
the hedonic estimates produce land values that often are about
10 percent of the values calculated with the extensive methodology. We believe that this is our best evidence for the critical
role that building limitations may play in creating high housing
costs.
Our second empirical approach is to look at crowding in
high-cost areas. The neoclassical approach tells us that if these
are areas with a high cost of land, then individuals should be
consuming less land. The regulation approach argues that
highly regulated areas will have large lots and high prices. Our
evidence suggests that there is little connection across areas

between high prices and density. This again is consistent with a
critical role for regulation.
Our third approach is to correlate measures of regulation
with the value of housing prices. This approach is somewhat
problematic because high values of land may themselves create
regulation. Nonetheless, we find a robust connection between
high prices and regulation. Almost all of the very high-cost
areas are extremely regulated—even though they have fairly
reasonable density levels. Again, we interpret this as evidence of
the importance of regulation.
As a whole, our paper concludes that America does not
uniformly face a housing affordability crisis. In the majority of
places, land costs are low (or at least reasonable) and housing
prices are close to (or below) the costs of new construction. In
the places where housing is quite expensive, building
restrictions appear to have created these high prices.
One implication of this analysis is that the affordable
housing debate should be broadened to encompass zoning
reform, not just public or subsidized construction programs.
Although poor households almost certainly are not consuming
the typical unit in areas with extremely high prices, we suspect
that most filtering models of housing markets would show that
they too would benefit from an increased focus on land-use
constraints by affordability advocates.
That said, we have done nothing to assess the possible
benefits of zoning (well discussed by Fischel [1992], for
example). So we cannot suggest that zoning should be
eliminated. However, we believe that the evidence suggests that
zoning is responsible for high housing costs, which means that
if we are thinking about lowering housing prices, we should
begin with reforming the barriers to new construction in the
private sector.

2. Housing Prices in the
United States
We start with an analysis of housing prices across the United
States. This work follows the methodology of Glaeser and
Gyourko (2001). In this paper, we use the American Housing
Survey and the U.S. census to gather data on housing
characteristics and values; we use the R.S. Means data for
construction costs. We then create measures relating home
prices to construction costs.
R.S. Means monitors construction costs in numerous
American and Canadian cities. The Means Company reports
local construction costs per square foot of living area. Its data

FRBNY Economic Policy Review / June 2003

on construction costs include material costs, labor costs, and
equipment costs for four different quality types of single-unit
residences. No land costs are included.4
The Means data contain information on four quality types
of homes—economy, average, custom, and luxury. The data
are broken down further by the size of living area (ranging from
600 to 3,200 square feet), the number of stories in the unit, and
a few other differentiators. We focus on costs for a one-story,
economy house with an unfinished basement, with the mean
cost associated with four possible types of siding and building
frame, and with small (less than 1,550 square feet), medium
(1,550 to 1,850 square feet), or large (1,850 to 2,500 square feet)
living areas. Generally, our choices reflect low to modest
construction costs. This strategy will tend to overestimate the
true gap between housing prices and construction costs. If the
relevant benchmark is an average-quality unit, not an
economy-quality unit, construction costs should generally be
increased by about 20 percent.
The housing price data used in this paper to create the
relationship between home prices and construction costs come
from the American Housing Survey. We focus on observations
of single-unit residences that are owner-occupied and exclude
condominiums and cooperative units in buildings with
multiple units, even if they are owned.
Excluding apartments simplifies our analysis, but in some
ways the connection between construction costs and home
prices is easier with apartments. In general, the marginal
construction cost of an apartment is the price of building up.
For example, other data from R.S. Means show that the price
per square foot of building in a typical high-rise of from eight
to twenty-four stories was nearly $110 per square foot in New
York City in 1999.5 This implies that the purely physical costs
of construction for a new 1,500-square-foot unit in New York
City are about $166,500. Anyone familiar with the New York
housing market knows that a large number of Manhattan
apartments trade at many multiples of this amount.
Because house price will be compared with construction
costs, and the latter are reported on a square-foot basis, the
house price data must be put in similar form. This is
straightforward for the AHS, which contains the square footage
of living areas. For every single unit reported in the 1999 or
1989 AHS, we can then compute the ratio of house value to
construction costs (as long as it is in an area tracked in the
Means data).6 From this, we can calculate the distribution of
homes priced above and below construction costs and can do
so for nearly forty cities in both 1989 and 1999. We look at two
measures: the first is the share of housing in the area that costs
at least 40 percent more than new construction. These are the
homes in the area where land is actually a significant share of

The Impact of Building Restrictions on Housing Affordability

new construction costs. If the appropriate benchmark is an
economy home, then for these homes land is about 40 percent
or more of the value. If the appropriate benchmark is an
average home, then for these homes land is approximately
20 percent of the value of the home. Our view is that homes
below this cutoff are sitting on relatively cheap land. We also
calculate the share of homes with prices that are more than
10 percent below the cost of new construction.
Table 1 shows the distribution of homes—relative to
construction costs—for the nation as a whole and for the four
main census regions. These data highlight the point that at least
half of the nation’s housing is less than 40 percent more
expensive than economy-quality home construction costs, or
no more than 20 percent more expensive than average-quality
home construction costs. They also suggest that a large share of
the nation’s housing has its price determined roughly by the
physical costs of new construction, as most of the housing value
is within 40 percent of physical construction costs. That said,
the regional breakdowns reported in Table 1 emphasize that
much land in Western cities looks to be relatively expensive.
Charts 1 and 2 give an overall impression of the underlying
data. In Chart 1, for central cities, we have graphed the share of
homes with prices that are more than 40 percent above
construction costs in the 1999 American Housing Survey on
the share of comparable homes in the 1989 AHS. The straight
line in the chart is the 45-degree line. In Chart 2, we have
repeated this procedure for the suburban parts of the
metropolitan areas.

Table 1

Distribution of Single-Family House Prices Relative
to Construction Costs
American Housing Survey Data: 1989 and 1999,
Central-City Observations
1989

1999

Fraction
Fraction
Fraction
Fraction
of Units
of Units
of Units
of Units
Valued below Valued above Valued below Valued above
90 Percent of 140 Percent of 90 Percent of 140 Percent of
Construction Construction Construction Construction
Costs
Costs
Costs
Costs
Nation
Midwest
Northeast
South
West

0.17
0.41
0.12
0.11
0.05

Source: Authors’ calculations.

0.46
0.14
0.58
0.50
0.69

0.17
0.30
0.37
0.13
0.04

0.50
0.27
0.34
0.46
0.77

Chart 1 makes two major points. First, there is a great deal
of permanence in these measures. The correlation coefficient
between the 1989 and 1999 measures is 82 percent. The average
of this variable across central cities was 47.8 percent in 1989
and 50.2 percent in 1999, so it does not look like the 1990s were
a watershed in terms of housing price changes. Second, there is a
great deal of heterogeneity across places. A number of places—
primarily in California—have almost no homes that cost less

than 1.4 times construction costs. However, in a number of
places, almost all of the homes cost less than this benchmark.
Chart 2 makes similar points. The correlation between the
1989 and 1999 measures is lower, but remains high at 0.70.
There is also heterogeneity across space in suburban areas,
but in general these places are more likely to have land values
that are substantially higher than construction costs. The
unweighted mean across the thirty-seven suburban areas was

Chart 1

House Prices/Construction Costs over Time
Central Cities
1989
1.0

Anaheim
Los Angeles

San
Francisco

0.9
Norfolk
New York City

0.8

0.7

San Diego
Raleigh

Albuquerque

Phoenix

0.6

Greensboro
Sacramento

Dallas
Jacksonville

Philadelphia
0.5

Denver

New Orleans

San Antonio
Baltimore

Tucson

Tampa

Fort Worth

0.4

Seattle

Austin

Little Rock
Tulsa
Oklahoma City
Las Vegas
Chicago

El Paso
0.3

Minneapolis

0.2
Toledo

Wichita

Columbus

Omaha
0.1

Milwaukee

Kansas City

Detroit
0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1999
Note: The x-axis (y-axis) denotes the share of homes in central cities with prices that are more than 40 percent above construction costs in the 1999
(1989) American Housing Survey.

FRBNY Economic Policy Review / June 2003

States, there are many areas with extremely cheap housing.
However, in this sample, only Philadelphia and Detroit had
extremely large values of this measure in 1999.7 We should note
that our previous work using the 1990 census suggests that
there is a greater amount of cheaper housing than is indicated
by the AHS. Our suspicion is that the census is more
representative, but we leave further examination of these
discrepancies to future work.

61 percent in 1989 and 63 percent in 1999. We suspect that one
reason for the higher fractions of expensive housing is that
suburban homes are newer and are likely to be of high quality.
A second reason is that suburban homes have more land and
suburban land is more expensive.
The data by local area are shown in Tables 2 and 3. These
tables also report the share of the housing stock that is priced at
least 10 percent below construction costs. Across the United

Chart 2

House Prices/Construction Costs over Time
Suburban Areas
1989
1.0

Oxnard
San Francisco
San Diego Anaheim

Newark
0.9

Riverside

Boston
New York City

Los Angeles

Sacramento

0.8

Philadelphia

Fort Lauderdale
Orlando

0.7

Miami

Atlanta
Rochester

Baltimore

Albany

0.6

Dallas
Fort Worth
Birmingham

Seattle

Chicago
Phoenix
Tampa

New Orleans
0.5
Columbus
0.4

Milwaukee
St. Louis

0.3
Houston

Minneapolis

Cincinnati

Kansas City

0.2

Detroit
Cleveland

Salt Lake City

Pittsburgh

0.1

0
0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1999
Note: The x-axis (y-axis) denotes the share of homes in suburban areas with prices that are more than 40 percent above construction costs in the 1999
(1989) American Housing Survey.

The Impact of Building Restrictions on Housing Affordability

1.0

Table 2

Table 3

Distribution of House Prices/Construction Costs

City Areas, 1989 and 1999

Suburban Areas, 1989 and 1999

1989

City
Albuquerque
Anaheim
Austin
Baltimore
Chicago
Columbus
Dallas
Denver
Detroit
El Paso
Fort Worth
Greensboro
Houston
Indianapolis
Jacksonville
Kansas City
Las Vegas
Little Rock
Los Angeles
Milwaukee
Minneapolis
NashvilleDavidson
New Orleans
New York City
Norfolk
Oklahoma
City
Omaha
Philadelphia
Phoenix
Raleigh
Sacramento
San Antonio
San Diego
San Francisco
Seattle
Tampa
Toledo
Tucson
Tulsa
Wichita

1999

1989

0.82
1.00
0.46
0.41
0.28
0.18
0.56
0.60
0.05
0.34
0.40
0.59
0.40
0.22
0.55
0.09
0.29
0.36
0.93
0.10
0.21

0.03
0.00
0.06
0.30
0.16
0.12
0.13
0.08
0.54
0.02
0.26
0.00
0.25
0.24
0.11
0.40
0.03
0.08
0.04
0.27
0.20

0.83
0.93
0.71
0.27
0.44
0.29
0.47
0.86
0.20
0.28
0.29
0.69
0.27
0.22
0.43
0.12
0.45
0.40
0.89
0.22
0.30

0.02
0.02
0.04
0.01

0.69
0.49
0.81
0.87

0.05
0.03
0.11
0.02

0.56
0.57
0.56
0.66

0.13
0.21
0.10
0.02
0.06
0.00
0.12
0.07
0.00
0.06
0.09
0.27
0.06
0.07
0.18

0.30
0.15
0.52
0.69
0.81
0.55
0.48
0.88
0.97
0.49
0.43
0.16
0.43
0.36
0.21

0.16
0.30
0.60
0.05
0.02
0.03
0.30
0.03
0.04
0.02
0.13
0.40
0.04
0.08
0.13

0.41
0.21
0.16
0.65
0.81
0.72
0.26
0.93
0.96
0.86
0.49
0.23
0.61
0.38
0.48

City
Albany
Anaheim
Atlanta
Baltimore
Birmingham
Boston
Chicago
Cincinnati
Cleveland
Columbus
Dallas
Detroit
Fort
Lauderdale
Fort Worth
Houston
Kansas City
Los Angeles
Miami
Milwaukee
Minneapolis
Newark
New Orleans
New York City
Orlando
Oxnard
Philadelphia
Phoenix
Pittsburgh
Riverside
Rochester
Sacramento
Salt Lake City
San Diego
San Francisco
Seattle
St. Louis
Tampa

1999

0.63
0.96
0.67
0.66
0.56
0.87
0.67
0.29
0.23
0.47
0.58
0.26

0.00
0.03
0.06
0.01
0.12
0.02
0.05
0.10
0.05
0.03
0.06
0.08

0.40
0.96
0.58
0.61
0.53
0.86
0.74
0.47
0.58
0.61
0.52
0.58

0.00
0.09
0.23
0.15
0.04
0.05
0.05
0.08
0.01
0.10
0.03
0.03
0.00
0.03
0.02
0.23
0.05
0.01
0.03
0.10
0.04
0.01
0.02
0.11
0.03

0.76
0.59
0.24
0.22
0.91
0.72
0.39
0.29
0.96
0.53
0.85
0.70
1.00
0.78
0.65
0.19
0.87
0.63
0.83
0.22
0.92
0.98
0.72
0.34
0.57

0.00
0.09
0.08
0.05
0.04
0.00
0.08
0.05
0.01
0.06
0.09
0.04
0.04
0.11
0.00
0.25
0.02
0.09
0.05
0.02
0.05
0.02
0.01
0.21
0.05

0.85
0.49
0.31
0.33
0.89
0.73
0.53
0.43
0.72
0.61
0.78
0.61
0.93
0.47
0.76
0.21
0.76
0.28
0.72
0.86
0.88
0.97
0.90
0.34
0.66

FRBNY Economic Policy Review / June 2003

Our focus here is not on the cheap areas, however, but on
the expensive ones. Moreover, we believe that these data
confirm that there are some areas of the country that do indeed
have a dearth of affordable housing. Still, for much of the
country, prices are determined by new construction costs.
As we discussed, this means that there is not an affordable
housing crisis in such areas. The problem probably lies in the
labor market, not the land market. We now consider why home
prices are high relative to construction costs in the areas that
are expensive.

3. Discussion: Demand for Land
versus Zoning
Housing prices are determined by both demand and supply
concerns. High housing prices must reflect high consumer
demand for a particular area. However, they must also reflect
some sort of restriction on supply. Data from sources such as
Means suggest that physical houses can be supplied almost
perfectly elastically. As such, the limits on housing supply must
come from the land component of housing. The usual urban
economics view of housing markets suggests that the
restriction on housing supply is the availability of land. Because
land is ultimately inelastically supplied, this naturally creates a
limit on the supply of new housing at construction costs. An
alternative view is that land itself is fairly abundant, but zoning
authorities make new construction extremely costly. These
costs can take the form of classic impact fees or Byzantine
approval processes that slow or put up costly hurdles to
construction. Obviously, there could be some truth to both
views. In this section, we provide an analytical framework for
our attempts to distinguish empirically between the two views
of expensive land: intrinsic scarcity versus zoning. Section 4
then examines a variety of data to determine if the weight of the
evidence more strongly supports one view over the other.
As noted, we have decided to ignore the housing demand
component of the housing prices. Two reasons underpin this
decision. First, housing demand has been studied much more
extensively than housing supply. A distinguished literature,
including Alonso (1964), Muth (1969), Rosen (1979), and
Roback (1982), has considered the determinants of housing
demand. Labor market demand and consumption amenities,
such as weather and schools, are both important causes of
particular demand for some areas. We have little to add to these
findings. Second, policy responses to housing prices are
unlikely to change housing demand. Increasing supply is a

The Impact of Building Restrictions on Housing Affordability

much more natural policy response to high housing prices than
is reducing demand.
To clarify the issues, let us consider a jurisdiction with a
supply of land equal to A. Assume that the construction cost for
a home is K. Here we are not interested in the margin of
interior space. The free market price of land equals p. We
represent zoning and other building restrictions with a tax T on
new construction. In principle, zoning could also work by
limiting the total number of homes in the area to a fixed
number or, equivalently, by constraining lot size to be greater
than a given amount. As we assume homogenous residents, a
minimum lot size and a constraint on the number of residents
will be equivalent. Also, as we are not interested in the
incidence of the policy, a tax and a quantity limit will yield the
same outcomes.
As such, the supply price of building a house with L units of
land will be K + T + pL . We will not generally directly observe
either p or T . The sales price of the home will be denoted
P ( L ) , where P ( L ) refers to the price of a home with L units
of land. In equilibrium, P ( L ) must equal K + T + pL so
P′ ( L ) = p .
Our primary interest is in the relevant magnitudes of pL
and T in creating expensive housing. We do not directly
observe either p or T , but we do observe P ( L ) and K . As such,
we can compute P ( L ) – K , which gives us an estimate of
T + pL . Using standard hedonic analysis, we can estimate
P′ ( L ) , which is the amount the housing price increases within
a given neighborhood as the amount of land rises. By
estimating P′ ( L ) , we are estimating p —the implicit price of
land. Even in communities where new houses are not being
built, the hedonic value of land still gives us an implicit price of
land. We can then compare p with ( P ( L ) – K ) ⁄ L , which
equals p + T ⁄ L . The difference between these two values gives
us a sense of the relative importance of land prices and building
restrictions.
A second test of the model requires us to look across
communities with different levels of some local amenity that we
denote as B. In this case, we write the home price function as
P ( L, B ) . If we differentiate across communities and T changes
dT
dP ( L ,B ) dp
across communities but K does not, then --------------------- = ------ L + ------- .
dB
dB
dB
The value of T might differ across communities because
impact fees differ, but more likely T will differ if zoning takes
the form of quantity controls. If zoning takes the form of minimum lot size or maximum residents, then the implicit tax will
be higher in high-amenity communities. In a sense, our interest
dT
dp
lies in determining the relative magnitudes of ------ L and ------- .
dB
dB

One way to examine this is to look at our implied measures of
p and T found using the methodology discussed above.
Another way is to look at land densities. We specify utility as
a function of the location-specific amenity B , consumption of
land, and consumption of a composite commodity, denoted C ,
which is equal to income (denoted Y ) minus housing costs. Thus,
total utility equals U ( B, L, Y – P ( L, B ) ) . This implies an optimal
level of land, denoted L∗ , which satisfies U L = P′ ( L∗ )U c
(where Ux denotes the derivative of U (.,.,.) with respect
to an argument X ). For simplicity, we assume that
U ( B, L, Y – P ( L, B ) ) equals W ( B ) + V ( L ) + Y – P ( L, B ) ,
so the first-order condition for land becomes V′ ( L∗ ) = p .
Differentiating this with respect to B then yields
α
dL∗ ⁄ dB = ( dp ⁄ dB ) ⁄ V ′′( L∗ ) . If V ( L ) equals vL , then this
log ( v α )
1
tells us that log ( L ) = -------------------- – ----------- log ( p ) . This yields the
1–α
1–α
clear implication that if dp ⁄ dB is big, we should expect there
to be lower densities in areas with large amenities and high
costs. Conversely, if there is no connection between housing
costs and density, then this is more evidence for the zoning
model rather than the neoclassical housing price model.
Our third empirical approach relies on the existence of
zoning. If we have measures of the difficulty of obtaining
building permits in a particular area, then we should expect
them to drive up housing costs (holding B constant). This is
just documenting that dP ⁄ dT > 0. Obviously, this approach is
likely to be compromised if high-amenity areas impose more
stringent zoning. Nonetheless, a connection between the
strength of zoning rules and housing prices seems like a final
test for the zoning view. As an added test, if we have measures
of zoning controls across communities, we would expect the
estimated value of T ⁄ L to be higher.

4. Evidence on Zoning: The
Intensive Margin and
the Extensive Margin
As our first test, we follow the framework and attempt to
estimate p: the market price of land, and T ⁄ L : the implicit
zoning tax. Using data from the 1999 American Housing
Survey, we begin by estimating p using the standard hedonic
methodology in a regression of the following specification:
(1) housing price = p∗ land area + other controls.
The other controls include the number of bedrooms, the
number of bathrooms, the number of other rooms, an

indicator variable that takes on a value of 1 if the home has a
fireplace, an indicator variable that takes on a value of 1 if the
home has a garage, an indicator variable that takes on a value of
1 if the home is in a central city, an indicator variable that takes
on a value of 1 if the home has a basement, an indicator variable
that takes on a value of 1 if the home has air conditioning, and
the age of the home. We ran each regression separately for
26 metropolitan areas for which there were 100 observations
so that trait prices would reasonably be precisely
estimated.8
Column 1 of Table 4 reports the hedonic price of land for
different metropolitan areas using this linear specification. The
hedonic literature has generally argued that non-normal error
terms make a logarithmic specification more sensible. As such,
we have also estimated logarithmic equations of the following
form:
( 1′ ) log(home price) = p′∗ log(land area) + other controls.
To transform the estimate of p′ , which is an elasticity, into
a value of land, we take this coefficient and multiply it by the
ratio of mean home price to mean land area. After this
transformation, our elasticity-based estimates should be
comparable to those in column 1, and we report them in
column 2.
The two hedonic estimates are strongly correlated ( ρ = .5),
although the implicit prices arising from the logged
specification tend to be slightly higher. In any event, functional
form does not lead one to materially different conclusions
regarding the value of a small change in lot size about the
sample mean in these areas. In general, the hedonic estimates
suggest that land is relatively cheap on this margin. In some
cities, the estimated price is below $1 per square foot. Although
estimates in those places tend not to be precise, the t-statistics
reported still do not imply really high prices, even at the top
end of the 95 percent confidence interval. In places where the
point estimate is reasonably precise, land prices tend to be
between $1 and $2 per square foot. In these areas, this implies
that an average homeowner would be willing to pay between
$11,000 and $22,000 for an extra quarter-acre of land.9 The
estimates are higher in some cities, primarily in California. For
example, in San Francisco, it appears that homeowners are
willing to pay almost $80,000 for an extra quarter-acre of
land.10 Although we do not have very good benchmarks against
which to compare these prices, intuitively they seem reasonable
to us as a whole.
To implement our first test, we must compare these prices
with the implicit price of land found by computing the

FRBNY Economic Policy Review / June 2003

Table 4

Land Price on the Extensive and Intensive Margins

City
Anaheim
Atlanta
Baltimore
Boston
Chicago
Cincinnati
Cleveland
Dallas
Detroit
Houston
Kansas City
Los Angeles
Miami
Milwaukee
Minneapolis
Newark
New York City
Philadelphia
Phoenix
Pittsburgh
Riverside
San Diego
San Francisco
Seattle
St. Louis
Tampa

Hedonic Price of
Land/Square Foot,
Linear Specification
$2.89
(1.54)
$0.23
(0.50)
$1.15
(2.53)
$0.07
(0.10)
$0.79
(2.43)
$0.89
(1.92)
$0.26
(0.95)
-$0.83
(-1.14)
$0.14
(0.92)
$1.43
(2.61)
$2.06
(2.75)
$2.19
(4.63)
$0.37
(0.45)
$1.44
(3.08)
$0.29
(0.93)
$0.42
(0.62)
$0.84
(1.09)
$1.07
(6.41)
$1.89
(3.88)
$2.28
(6.26)
$1.35
(3.55)
$0.58
(0.97)
$0.97
(0.76)
-$0.68
(-0.69)
$0.63
(1.91)
$0.19
(0.36)

Hedonic Price of Land/Square
Foot, Log-Log Specification
$3.55
(1.34)
-$0.30
(-0.70)
$5.21
(2.31)
$0.55
(0.67)
$0.80
(1.96)
$0.50
(1.14)
$0.24
(0.81)
$0.21
(0.27)
$0.45
(2.31)
$1.62
(2.66)
$1.65
(2.11)
$2.60
(3.53)
$0.18
(0.24)
$0.95
(1.90)
$0.35
(1.09)
$0.10
(0.11)
$1.62
(1.60)
$0.77
(5.28)
$1.86
(3.26)
$1.71
(4.55)
$1.60
(2.95)
$1.29
(1.33)
$7.84
(2.42)
$0.48
(0.06)
$0.07
(1.55)
$0.89
(1.30)

Note: t-statistics are in parentheses.

The Impact of Building Restrictions on Housing Affordability

Imputed Land Cost from
R.S. Means Company Data
(Intensive Margin)

Mean House Price

$38.99

$312,312

$3.20

$150,027

$4.43

$152,813

$13.16

$250,897

$14.57

$184,249

$2.71

$114,083

$4.13

$128,127

$5.42

$117,805

$5.10

$138,217

$4.37

$108,463

$1.92

$112,700

$30.44

$254,221

$10.87

$153,041

$3.04

$130,451

$8.81

$149,267

$17.70

$231,312

$32.33

$252,743

$3.20

$163,615

$6.86

$143,296

$3.08

$106,747

$7.92

$149,819

$26.12

$245,764

$63.72

$461,209

$18.91

$262,676

$1.74

$110,335

$6.32

$101,593

difference between home prices and structure costs.
Subtracting structure costs (provided by the Means data) from
reported home values and then dividing by the amount of land
generates an estimate of p + T ⁄ L , as described above—the
value of land including the implicit tax on new construction.
These average values of p + T ⁄ L for each metropolitan area
appear in column 3 of Table 4.
Comparing columns 1 and 2 with column 3 illustrates the
vast differences in our estimates of the intensive and extensive
prices of land, or p and p + T ⁄ L . In many cases, our estimate
of p + T ⁄ L is about ten times larger than p. For example, in
Chicago, our imputed price of land per square foot from the
extensive margin methodology is $14.57. This means that a
home on a quarter-acre plot (or 10,890 square feet) will cost
more than $140,000 above construction costs. In San Diego, this
quarter-acre plot is implicitly priced at nearly $285,000. The
analogous figure is even higher in New York City, at slightly
more than $350,000. In San Francisco, the plot is apparently
worth just under $700,000.
This is our first piece of evidence on the relative importance
of classic land prices and zoning. In areas where the ratio is
10:1, the findings suggest that for an average lot, only 10 percent of the value of the land comes from an intrinsically high
land price as measured by hedonic prices.11
Although the hedonic land prices from the linear
specification (column 1) are not significantly correlated with
the mean house prices reported in column 4 of Table 4, both
the hedonic prices from the logged model (column 2) and the
extensive margin prices (column 3) are strongly positively
correlated with mean prices. Simple regressions of each of the
three land price series on mean house price find that the dollar
impact of house price with respect to land price is far larger for
the series that reflects the implicit development tax. Specifically, a one-standard-deviation increase in house price (which
equals $82,239 in this twenty-six-city sample) above its mean is
associated with a $13.82 increase in land price as reflected in
our p + T ⁄ L measure. The analogous standardized effect with
respect to our measure of p arising from the logged hedonic
model is $1.10.12 Although these results are based on an
admittedly small sample, we believe that the difference in the
scale of the changes provides evidence consistent with the
hypothesis that high home prices appear to have more to do
with regulation than with the operations of a free market
for land.

5. Evidence on Zoning: Density
and Housing Costs
Our second test is to look at the connection between housing
prices and density. As described in the model, the neoclassical
land model strongly suggests that there should be a positive
connection between density and housing prices. The free land
market view suggests that higher amenities will lead to higher
land prices and lower consumption of land. The zoning view
suggests that higher amenities will just lead to a higher implicit
zoning tax. This zoning tax does not impact the marginal cost
of additional land and there should therefore be little
connection between the cost of land and density.
To test this implication, we correlate land density within a
central city with our various measures of housing prices within
that city. Because the framework suggested the relationship
log ( v α )
1
log ( L ) = -------------------- – ------------log ( p ), we estimate a logarithmic
1–α
1–α
equation. We use as our land area measure the logarithm of the
land area in the city divided by the number of households.13
Obviously, density is higher the lower the value of this variable.
Table 5 presents the results from a series of regressions
exploring the relationship of our density measure with the
index of expensive homes and land in our sample of cities. In
regression 1, we use as the independent variable our measure of
the share of houses that cost at least 40 percent more than
construction does. In this case, the relationship is negative, so a
higher concentration of expensive homes is associated with
greater density. However, there still is no meaningful statistical
relationship. Chart 3 plots the relationship with the regression
line included. The chart highlights the extraordinary amount of
heterogeneity in the relationship between density and the
distribution of house prices. For example, Detroit, Seattle, and
Los Angeles have similar land densities per household, but
radically different fractions of units sitting on expensive land.
Analogously, New York City and San Diego have similarly high
fractions of expensive land, but very different residential
densities.
In regression 2, we control for median income in the city in
1990 to help account for the possibility that richer people live
in expensive areas and demand more land. However, there still
is no really strong relationship between density and the fraction
of expensive land and homes. Density is slightly higher in more
expensive areas on average, but the relationship is tenuous even
when controlling for income. In regression 3, median house
price in 1990 is used as the independent variable. There is a
statistically significant negative relationship between density
and price in this case, with the elasticity being -0.56. However,
there is much heterogeneity here too. The statements above

FRBNY Economic Policy Review / June 2003

regarding Detroit, Seattle, Los Angeles, New York City, and
San Diego still hold true when median price is on the righthand side of the regression.
For regressions 4, 5, and 6, we take the model more seriously
and use an amenity to look at the impact of housing costs and
land consumption. We focus on a particularly well-studied
amenity—average January temperature. In regression 4, we
show that there is a strong positive relationship between the
fraction of expensive homes and land and average January
temperature. This relationship is necessary for this variable to
qualify as an instrument. In regression 5, we regress the
logarithm of land area per household on January temperature.
In this case, the relationship is much less strong statistically.
The t-statistic is 1.6. Taken together, these results show that a
warmer January temperature may raise housing prices,14 but
there is no strong evidence that it increases densities—at least
not by very much. Indirectly, this suggests that it is not raising
the marginal cost of land by much.
In regression 6, we follow the spirit of the framework most
closely. We regress the logarithm of land area per household on

the distribution of housing prices using average January
temperature as an instrument. January temperature is meant to
represent the exogenous variation in amenities that causes
prices to rise. Not only is there no statistically meaningful
connection between prices and land consumption, but these
instrumental-variables results imply that higher prices are
associated with lower, not higher, densities. One possibility is
that incomes are higher in these areas and that richer people are
demanding more land. Consequently, we redid the analysis
adding median family income as a control, but the results were
largely unchanged. That is, there is no statistically significant
relation between instrumented prices and density, and the
point estimate still is slightly positive (albeit small). Although
we acknowledge that the sample is small and that there could
be other omitted factors, these results suggest to us that higher
prices have more to do with zoning than a higher marginal cost
of land.
As a final test of this view, we regress our two measures of
land costs from Table 4 with average January temperature. We
only have twenty-six observations, but the results are still quite

Table 5

Density and the Distribution of House Prices in Cities, 1990
Dependent Variable

Fraction of units valued at or above
140 percent of construction costs

Log Land Area
per Household

-0.510
(0.451)

-0.576
(0.507)

Log median family income, 1989

Log Land Area
per Household

Log Land Area
per Householda
1.177
(0.880)

-0.565
(0.225)

Mean January temperature

R
Number of observations

Log Land Area
per Household

0.266
(0.895)

Median house price, 1990

Intercept

Fraction of Units
Valued at or above
140 Percent
of Construction Costs

-7.050
(0.245)
0.01
40

-9.784
(9.191)
-0.02
40

-0.959
(2.536)
0.12
40

0.013
(0.003)
-0.021
(0.113)
0.34
40

0.015
(0.009)
-7.882
(0.387)
0.04
40

-17.254
(8.678)
40

Notes: Standard errors are in parentheses. Density is defined as the log of the ratio of square miles of land in the city divided by the number of households.
a

Two-stage least squares: Mean January temperature as instrument.

The Impact of Building Restrictions on Housing Affordability

Chart 3

Density and the Distribution of House Prices
Central Cities, 1990
Land area per household, 1990
-5.5

Jacksonville

Oklahoma City
-6.0

Nashville-Davidson
Kansas City

-6.5

Fort Worth
El Paso
Little Rock
Austin
Greensboro
Tulsa
San
Antonio
Tucson
Tampa
New Orleans
Las Vegas
Houston
Dallas
Denver
Sacramento

Indianapolis
-7.0

Wichita
Omaha

Columbus
Toledo

-7.5

Phoenix

Raleigh
San Diego

Albuquerque

Norfolk
Milwaukee

Anaheim

Los Angeles

Detroit

Seattle

-8.0

Minneapolis
Baltimore
Chicago

-8.5

Philadelphia
San Francisco

-9.0
New York City
-9.5
0

0.1

0.2

0.3

0.4

0.6

0.5

0.7

0.8

0.9

1.0

Central cities, 1989
Note: The x-axis denotes the share of homes in central cities with prices that are more than 40 percent above construction costs in the 1989 American
Housing Survey.

illuminating. A standard-deviation increase of 14.7 degrees in
mean January temperature is associated with a $5.02 higher
construction-cost-based price of land. The same increase in
warmth is associated with only a $0.47 higher hedonic-based
price of land.15 Once again, amenities seem to have more
of an effect on the implicit zoning tax than on the marginal
cost of land.

6. Evidence on Zoning: Housing
Costs and Zoning
Our last perspective on the role of zoning comes from an
examination of the correlation between land prices and
measures of zoning. Such data are very difficult to obtain. Our
measures of zoning come from the Wharton Land Use Control

FRBNY Economic Policy Review / June 2003

Survey. This survey, which took place in 1989, covers
jurisdictions in sixty metropolitan areas. Because of the
limitations of our American Housing Survey data, we are
forced to consider only observations on the central cities of
forty-five metropolitan areas.
The variable we focus on here is a survey measure of the
average length of time between an application for rezoning and
the issuance of a building permit for a modest size, singlefamily subdivision of fewer than fifty units. This measure can
take on values ranging from 1 to 5: a value of 1 indicates the
permit issuance lag is less than three months, a value of 2
indicates the time frame is between three and six months, a
value of 3 indicates a seven-to-twelve-month lag, a value of
4 indicates the lag is between one and two years, and 5 indicates
a very long lag of more than two years. Before proceeding to a
regression, we note that the correlation of the permit length
variable with the fraction of housing stock priced more than
40 percent above the cost of new construction is fairly high at
0.43. The mean fraction of high-cost housing among the cities
with permit waiting times of at least six months (that is, a value
of 3 or more for this variable) is 0.75. Difficult zoning seems to
be ubiquitous in high-cost areas.16
Table 6 reports regression results using the permit length
variable. In the first column, we regress our housing cost

measure (again using the share of the city’s housing stock
priced more than 40 percent above the cost of new
construction) on the first zoning measure—the time required
to get a permit issued for a rezoning request. We find a strong
positive relationship, so that when the index increases by one,
15 percent more of the housing stock becomes quite expensive.
This positive relationship also survives controlling for
population growth during the 1980s and median income, as
shown in the second column.17
In the final column of Table 6, we return to our implied
zoning tax—T/L from above. This value is calculated using the
data in Table 4. Specifically, we subtract the cost of land
estimated in the nonlinear hedonic equation (that is, p from
column 2 of Table 4) from the cost of land implied by subtracting structure cost from total home value (that is, p+T/L
from column 3 of Table 4). We then regress this variable on our
zoning measure. As the results show, the implied zoning tax is
strongly increasing in the length of time it takes to get a permit
issued for a subdivision. Increasing a single category in terms of
permit issuance lag is associated with a nearly $7 per-squarefoot increase in the implicit zoning tax. If the dependent
variable is logged, the results imply that a one-unit increase in
the index is associated with a 0.50-log-point increase in the
implicit zoning tax.18

Table 6

Zoning Regulations and the Distribution of House Prices
Dependent Variable

Time to permit issuance for rezoning request

Fraction of Units Valued at or above
140 Percent of Construction Costs

T/L from Table 4
(Implied Zoning Tax)

0.150
(0.051)

0.112
(0.044)
0.260
(0.255)
1.080
(0.411)
-2.512
(2.634)
0.40
40

6.796
(3.048)

Log median family income, 1989
Percentage population growth, 1980-90
Intercept
2

R
Number of observations

0.111
(0.120)
0.16
40

-3.527
(7.732)
0.15
22

Note: The independent zoning variable is a categorical measure of time lag between the application for rezoning and the issuance of a building permit
for development of a modest size, single-family subdivision.

The Impact of Building Restrictions on Housing Affordability

Conclusion

America is not facing a nationwide affordable housing crisis. In
most of the country, home prices appear to be fairly close to the
physical costs of construction. In some of the country, home
prices are even far below the physical costs of construction.
Only in particular areas—especially New York City and
California—do housing prices diverge substantially from the
costs of new construction.
In the areas where houses are expensive, the classic urban
model fares relatively poorly. These areas are not generally
characterized by substantially higher marginal costs of land, as
estimated by a hedonic model. The hedonic results imply that
the cost of a house on 10,000 square feet is usually pretty close
in value to a house on 15,000 square feet. In addition, these
high prices often are not associated with extremely high
densities. For example, there is as much land per household in
San Diego (a high-price area) as there is in Cleveland (a lowprice area).
The bulk of the evidence marshaled in this paper suggests
that zoning, and other land-use controls, are more responsible
for high prices where we see them. There is a huge gap between
the price of land implied by the gap between home prices and

construction costs and the price of land implied by the price
differences between homes on 10,000 square feet and homes on
15,000 square feet. Measures of zoning strictness are highly
correlated with high prices. Although all of our evidence is
suggestive, not definitive, it seems to suggest that this form of
government regulation is responsible for high housing costs
where they exist.
We have not considered the benefits of zoning, which could
certainly outweigh these costs. However, if policy advocates are
interested in reducing housing costs, they would do well to start
with zoning reform. Building small numbers of subsidized
housing units is likely to have a trivial impact on average
housing prices (given any reasonable demand elasticity), even
if well targeted toward deserving poor households. However,
reducing the implied zoning tax on new construction could
well have a massive impact on housing prices.
The positive impact of zoning on housing prices may well be
zoning’s strongest appeal. If we move to a regime with weaker
zoning rules, then current homeowners in high-cost areas are
likely to lose substantially. To make this politically feasible, it is
crucial that any political reform also try to compensate the
losers for this change.

FRBNY Economic Policy Review / June 2003

Appendix: Creation of the House Value/Construction Cost Ratio

A number of adjustments are made to the underlying house
price data in the comparison of prices and construction costs.
These include imputation of the square footage of living area
for observations from the Integrated Public Use Microdata
Series for the 1980 and 1990 census years. However, because
the results reported in this paper do not include census data, we
omit the description of that imputation. See Glaeser and
Gyourko (2001) for those details.
Two adjustments have been made to the American Housing
Survey (AHS) house price data to account for the depreciation
that occurs on older homes and to account for the fact that
research shows that owners tend to overestimate the value of
their homes. The remainder of this appendix provides the
details.
As noted, one adjustment takes into account the fact that
research shows that owners tend to overestimate the value of
their homes. Following the survey and recent estimation by
Goodman and Ittner (1992), we presume that owners typically
overvalue their homes by 6 percent.19
Empirically, the most important adjustment takes into
account the fact that the vast majority of homes are not new
and have experienced real depreciation. Depreciation factors

The Impact of Building Restrictions on Housing Affordability

are estimated using the AHS. More specifically, we regress
house value per square foot (scaled down by the Goodman and
Ittner [1992] correction) in the relevant year on a series of age
controls and metropolitan area dummies. The age data are in
interval form so that we can tell if a house is zero to five years
old, six to ten years old, eleven to twenty-five years old, twentyfive to thirty-six years old, and more than forty-five years old.
The coefficients on the age controls are each negative, as
expected, and represent the extent to which houses of different
ages have depreciated in value on a per-square-foot basis.
Finally, we note that our procedure effectively assumes that
units with a basement in the AHS have unfinished basements,
so that we underestimate construction costs for units with
finished basements. Having a basement adds materially to
construction costs, according to data from R.S. Means
Company. Depending on the size of the unit, those with
unfinished basements have about 10 percent higher
construction costs. Units with finished basements have up to
30 percent higher construction costs, again depending on the
size of the unit. After these adjustments have been made, house
value is then compared with construction costs to produce the
distributions reported in our paper.

Endnotes

1. This is not to say that housing vouchers might not be a sensible part
of an antipoverty program. However, if housing is not expensive, then
policies should be thought of as a response to poverty and not a
response to a housing affordability crisis.
2. Goodman and Ittner (1992) document that self-reported values tend
to be about 7 percent higher than true sale prices.
3. Another relevant issue is change over time. The census reports a
significant (15 percent) increase in the median value of a home over
the 1990s. However, when we look at repeat-sales indices, which
control for housing quality, we see much less of an increase over the
1990s.
4. Two publications are particularly relevant for greater detail on the
underlying data: R. S. Means Company’s Residential Cost Data, 19th
ed., and Square Foot Costs, 21st ed.
5. See R. S. Means Company (2002).
6. The actual computation is more complicated, as adjustments are
made to correct for depreciation, inflation, the fact that owners tend
to overestimate the value of their homes, and regional variation in the
presence of basements. See the appendix for details. We also
performed the analysis using the 1991 AHS; the results are virtually
unchanged from 1989’s results.
7. The Philadelphia numbers for 1989 are not typos. They reflect a
small sample bias associated with the number of units with basements.
This is a statistical oddity that does not show up in other samples,
whether in the AHS or decennial censuses.
8. There are only ninety-six observations in the Baltimore
metropolitan area, which is the smallest number across all cities.
Visual inspection of the findings found sensible results for most
traits when the number of observations was at or above 100.
9. There are 43,560 square feet in an acre of land.
10. The estimate from the linear specification is much lower, but
logging materially improves the overall hedonic in the case of
San Francisco.
11. This ratio obviously is sensitive to biases in our hedonic estimates.
We need to be concerned especially about the possibility that the

quantity of land is correlated with price and (omitted) amenities. It is
easy to construct examples in which the bias goes in opposite
directions. For example, land undoubtedly costs different amounts in
different parts of a given metropolitan area. Although our hedonic
model includes a control for whether the observation is located within
the central city of an area, this may only imperfectly capture a
location-specific amenity that reflects, say, distance from a key
employment node. Thus, people could be buying bigger lots in those
parts of the metropolitan area with lower costs, and by not being able
to control for this fully, our hedonic land price estimates will be biased
downward.
That said, it is not at all clear that the net bias will be in that
direction. We find it at least equally plausible that richer households,
who tend to have larger lots, end up congregating in higher amenity
(and higher price) areas. In this case, our estimated hedonic price of
land would be biased upward. Although we cannot be certain what the
net bias is, we find it highly unlikely that our estimates are so severely
skewed downward that bias could account for the huge differential
reported between land prices on the intensive and extensive margins.
Our estimates would have to be off by an order of magnitude for that
possibility to be relevant.
12. The coefficients are precisely estimated in the underlying
regressions and are available upon request. Because the hedonic land
price arising from the linear model is virtually uncorrelated with mean
house price, the analogous impact is near zero for that land price
series.
13. Using population per square mile yields similar results.
14. There is a statistically and economically significant positive
relationship between mean January temperature and median house
price. Those results are not reported here, but are available from the
authors upon request.
15. We use the price series from the nonlinear hedonic in the
underlying regression. Only the regression involving the constructionbased land prices (column 3 of Table 4) yields statistically significant
results at conventional levels.
16. Other measures in the database include the analogue to this
rezoning question, except that the permit length time applies to a
completely new subdivision that does not require rezoning. We
examined this and other variables and found correlation patterns
similar to those presented below.

FRBNY Economic Policy Review / June 2003

Endnotes (Continued)

17. Adding region dummies to the specification eliminates any
significant positive correlation between this zoning control and the
fraction of expensive housing in the area.
18. Finally, similar results are obtained if other approval-time
variables are used (such as those for a new subdivision).

The Impact of Building Restrictions on Housing Affordability

19. This effect turns out to be relatively minor in terms of its
quantitative impact on the results.

References

Alonso, William. 1964. Location and Land Use: Toward a
General Theory of Land Rent. Cambridge: Harvard University
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Roback, Jennifer. 1982. “Wages, Rents, and the Quality of Life.”
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Fischel, William. 1992. “Property Taxation and the Tiebout Model:
Evidence from the Benefit View of Zoning and Voting.” Journal
of Economic Literature 30, no. 1 (March): 171-7.

Rosen, Sherwin. 1979. “Wage-Based Indices of Urban Quality of Life.”
In Mahlon R. Straszheim and Peter M. Mieszkowski, eds.,
Current Issues in Urban Economics. Baltimore: Johns
Hopkins University Press.

Glaeser, Edward L., and Joseph Gyourko. 2001. “Urban Decline and
Durable Housing.” NBER Working Paper no. 8598, November.

R. S. Means Company. 2000a. Residential Cost Data. 19th ed.
Kingston, Mass.

Goodman, John L., and J. B. Ittner. 1992. “The Accuracy of Home
Owners’ Estimates of House Value.” Journal of Housing
Economics 2, no. 1 (March): 339-57.

———. 2000b. Square Foot Costs. 21st ed. Kingston, Mass.

Gyourko, Joseph, and Joseph Tracy. 1991. “The Structure of Local
Public Finance and the Quality of Life.” Journal of Political
Economy 99, no. 4 (August): 774-806.
Muth, Richard F. 1969. Cities and Housing: The Spatial Pattern
of Urban Residential Land Use. Chicago: University of
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———. 2002. Building Construction Cost Data. 60th ed.
Kingston, Mass.
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U.S. Census Bureau. Various years. “American Housing Survey.”
Data tapes.

Brendan O’Flaherty

Commentary

dward Glaeser and Joseph Gyourko present a good paper,
but the paper is not what it claims to be, at least for lay
readers who do not interpret words literally. For most people,
“affordable housing” has something to do with housing for the
poor. This conference, according to the program, aimed to
“explore . . . strategies easing the housing problems of low- and
moderate-income families.” This connection very likely takes
liberties with the English language, but the connection has been
made, and it makes good sense to respect it.
Therefore, arguments for affordable housing policies ought
to show either that poor people would be better off as they
perceive it, or that the poor would be better off according to
some metric not tied to desire/satisfaction. (Thus, housing
policies for poor people are targeted not only at high supply
prices or at poverty, but also at intrahousehold or interhousehold externalities—just as homeowner tax preferences
are.) Although Glaeser and Gyourko acknowledge that such a
link probably can be made—a contention that I think is
plausible—they do not make it, and so they leave the paper
incomplete as an affordable housing paper as the term is
commonly (and probably mis-) understood.
The paper’s real interest lies in the finding that in some
cities, land is very expensive—more expensive than people
appear willing to pay for it. This finding makes Glaeser and
Gyourko’s study important in that it is likely to spur a great
deal of further research.

Brendan O’Flaherty is an associate professor of economics
at Columbia University.

Basically, Glaeser and Gyourko fit an hedonic equation:
P = αS + βL ,

where S represents structure and L land. The authors find that
α > c , the known construction cost of structure in some
metropolitan areas. They conclude that zoning is holding up
the price of land, and provide evidence that zoning is more
restrictive in areas where the difference is greatest.
The step from α > c to “zoning is the problem” is a very big
one. There is an instructive analogy in the study of household
economics. Different methods (for example, replacement
versus opportunity cost) produce radically different estimates
of the hourly value of time devoted to household work. But that
does not imply that a government policy, perverse or not, is
causing the discrepancy. I think the consensus now is that the
theories that imply no discrepancy are the ones that are wrong,
even in the absence of government intervention. Similarly, we
should look further at why the combination of Glaeser and
Gyourko’s statistical methods and accepted urban economics
theory fails to work before concluding that government
policies are the only possible explanation. (The correlation—
absent regional dummies—between high estimated land prices
and restrictive zoning, although suggestive, is certainly not
definitive. Jewelry stores with more expensive wares spend
more on security, but we do not think that the security
expenditures are driving the value of the jewelry.)

The views expressed are those of the author and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

FRBNY Economic Policy Review / June 2003

Thus, we can look at two kinds of possible alternatives to the
zoning conclusion—statistical and theoretical.

1. Statistical Alternatives
When examining this type of alternative, there might be
omitted variable errors, collinearity problems, or measurement
issues. To consider omitted variable errors, suppose that the
true model is
P = αS + βL + γb ,

where b represents some other attribute, such as proximity to a
train station or a school, or a scenic view. Neoclassical theory
would lead us to think that the covariance between S and b
would be positive and that the covariance between L and b
would be negative. Both of these covariances imply that α
should be too big and that β should be too small. The Glaeser
and Gyourko regressions have very poor measures of amenities
and location.
In a note to their paper, Glaeser and Gyourko speculate that
covariances might very well go in the opposite direction—that
neoclassical urban theory might be wrong. This adds to the
possibilities for new theories.
Collinearity problems could arise if land and structure, or
aspects of structure, were correlated. This is not unlikely, since
the aspects of structure that the authors measure include the
presence of a garage and the number of rooms. I will give a not
terribly implausible example below that shows how this
collinearity can lead to serious underestimates of β.
Finally, the estimation procedure relies on construction
costs and depreciation being the same in all metropolitan areas.
There are several reasons why construction costs can differ,
aside from differences in construction laws and regulations.
Weather is one: colder climates entail more insulation, more
solid windows and doors, and greater interest and scheduling
costs due to winter delays. Notice that this supply
interpretation works in the opposite direction from the
demand-side January temperature used by Glaeser and
Gyourko to measure amenities. Labor is another source of
variation: because wages vary between metropolitan areas and
wages are a substantial part of building costs, the cost of
building identical structures will vary between metropolitan
areas. Other inputs, such as electricity, also vary in price.
Depreciation is also likely to vary, because the rate at which
houses depreciate depends on economic decisions about

Commentary

maintenance, repair, and home improvement. Demand shocks
that make housing in certain metropolitan areas more
expensive may be correlated with greater home maintenance
and home improvement expenditures. A thirty-five-year-old
Cape Cod with 1,700 square feet in New York may, on average,
be a very different house in ways unobservable to the
econometrician in a similar house in Dallas.

2. Theoretical Alternatives
The basic premise of the Glaeser and Gyourko paper is that if
you know the square footage of a lot, the price per square foot
of land, and the construction costs of the structure, you know
everything you need to find the price that would prevail in a
market without zoning. This idea is probably wrong, although
Glaeser and Gyourko are probably correct in interpreting this
premise as an implication of standard urban economics. There
are several reasons for this.
First, all relevant costs of a house are not incurred on the lot.
The costs of roads, sewers, gas and electric, telephones, cable,
and other infrastructure are quite hefty relative to the costs of a
private structure—roads are going to account for at least
20 percent of land in a new development, and the materials
used in them are not cheap. In equilibrium, the (marginal) cost
of new developments is going to be the replacement cost of
existing houses, so the price of installed infrastructure is going
to be part of the price of land—even without zoning. On the
other side, some part of the capitalized value of property taxes
is going to be subtracted from a house’s value. Infrastructure
pricing practices, like taxes, may vary between metropolitan
areas. Combined with the uncertainty about structure costs
introduced by variations in construction costs and
depreciation, these add up to a hefty uncertainty about the
value of land.
Second, lumpiness and selection present problems. There
are serious increasing returns to scale in housing, for example,
from the 2/3 rule, the sharing of utility connections, and the
sharing of furniture. The restriction to single-family detached
houses further reduces the possibilities for using very small
pieces of land. This means that small pieces of unused land are
not going to be very valuable.
Consider a simple example. Suppose land is only one
dimension, you are a profit-maximizing developer without any
zoning constraints, and the marginal product of a plot of land
of size x is x - x2. Assume you are working with a plot of land of
size z. If z = 3/4, or any multiple, you will build one house (or

the multiple), and the usual optimizing condition of marginal
equals average will hold. But if z is not a multiple of 3/4,
marginal will not equal average at the optimum. Let D(z) =
average profit minus marginal profit, assuming optimal-sized
lots. Then for z < 1,
D(z) = z (2/3 z - 1/2),
which rises to 1/6 at z = 1. In general, D(z) goes up and down,
crossing zero at 3/4 n for every n, and decreasing in amplitude
as n increases. But for small n—the likely condition for small
developers with physical constraints and existing buildings
around them—marginal is likely to be very different from
average. It could be bigger or it could be smaller. The hedonic
equation measures at best the marginal value of land, while the
construction-cost measures back out the average.
Third, land is not a quantity. I am not indifferent between
my 5,000 contiguous rectangular square feet of New Jersey and
720,000 randomly chosen square inches spread across the face
of the earth.
One distinction that matters is frontage versus depth
(assuming that plots are roughly rectangular, which is
endogenous). Frontage is more costly to construct and is
probably more valuable because it sets the minimum distance
to neighbors. Depth is less valuable. Land area is the product of
the two, and there is probably more variation in depth than in
frontage. If that is the case, the hedonic is picking up the less
valuable dimension.
To see how this can be compounded by collinearity, suppose
a community has two kinds of houses—those with garages and
those without. Houses with garages are on lots with greater
frontage, otherwise all structures are identical. Frontage is
much more valuable than depth. All houses of each type have
the exact same frontage, but depth varies randomly. An
hedonic regression with the presence of garages and the square
footage of the lot would conclude that land was valueless, or
close to it, no matter what it was really worth. The value of
frontage would show up in the coefficient on garages.
Land also varies in topography and physical characteristics.
Some land is just lousy to build on or live on (due, for instance,
to the presence of rock outcroppings, steep slopes, or bad
swamps). People who want land for less valuable purposes
(privacy rather than construction) are likely to end up holding

such land. Differences in lot size within a community therefore
are also likely to reflect differences in bad rather than good
land. This is similar to the frontage scenario.
Fourth, with two dimensions and physical obstacles, the
optimal subdivision problem becomes very difficult. In
operations-research terms, it is a suitcase problem. One
interesting result of these difficult problems is that “greedy
algorithms”—the sort of myopic hill-climbing you could
expect from a bunch of independent developers—usually do
not produce optimality. So it is not clear that in the absence of
zoning, optimal subdivisions would occur. Without optimal
subdivisions, there is no chance that marginal cost will equal
average cost even with regard to a large problem.
Finally, even if neighborhoods were constructed originally
with marginal cost equal to average cost of land on every lot,
they would not stay that way for long. Unanticipated shocks
would destroy this equality and change optimal density. Of all
the ways to increase density in an existing neighborhood,
increasing the number of single-family homes on existing
single-family land is the most drastic and the most expensive.
To make small changes, you have to move all of the existing
houses. This is not easy: the arrangement of houses and lots in
a neighborhood is not likely to change much unless everything
is torn down. Thus, the equality of marginal and average cost of
land upon which the Glaeser and Gyourko paper is based will
not be observed very often in neighborhoods more than a few
years old, even in the best of all possible cases.

3. Summary
Glaeser and Gyourko are probably correct in observing that
excessive zoning in certain jurisdictions makes life worse for
poor people who do not live there. In that regard, their paper
did not dramatically change my view. The paper’s actual
contribution is much more novel and much more fundamental: the authors have raised very deep questions about how
urban economists think about land and land markets. It will
probably be a long time before these questions are answered
properly.

The views expressed are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York
or the Federal Reserve System. The Federal Reserve Bank of New York provides no warranty, express or implied, as to the
accuracy, timeliness, completeness, merchantability, or fitness for any particular purpose of any information contained in
documents produced and provided by the Federal Reserve Bank of New York in any form or manner whatsoever.
FRBNY Economic Policy Review / June 2003

C. Tsuriel Somerville and Christopher J. Mayer

Government Regulation and
Changes in the Affordable
Housing Stock
1. Introduction

n terms of housing issues, the primary public policy focus of
economists has been the affordability of homes, mortgage
availability, land-use regulation, and rent control. Studies of
land-use regulation focus on the effects of regulation on the
price of owner-occupied housing. Work on low-income
housing has concerned itself more with issues of measurement
and the debate over supply-side versus demand-side subsidies.
In this paper, we look at the relationship between these two
issues to examine how government regulation affects the
dynamics of the low-income housing stock. We find that,
consistent with theoretical models of housing, restrictions on
the supply of new units lower the supply of affordable units.
This occurs because increases in the demand for higher quality
units raise the returns to maintenance, repairs, and renovations
of lower quality units, as landlords have a stronger incentive to
upgrade them to a higher quality, higher return housing
submarket. This result is disturbing because it highlights how
policies targeted toward new, higher income owner-occupied
suburban housing can have unintended negative consequences
for lower income renters.
Our research differs from most studies of affordable
housing in that we are not concerned with identifying the size
of the affordable stock or matching it to the number of lowincome households. The gap between the housing needs of
low-income households and the stock of units deemed

C. Tsuriel Somerville is an associate professor of real estate at the University of
British Columbia; Christopher J. Mayer is an associate professor of real estate
at the University of Pennsylvania’s Wharton School.
<tsur.somerville@commerce.ubc.ca>
<mayerc@wharton.upenn.edu>

affordable has been demonstrated in a considerable amount of
other research.1 Here, we build on the Somerville and Holmes
(2001) study of the effects of the unit, neighborhood, and
market characteristics on the probability that a unit will stay in
the stock of rental units affordable to low-income households;
we do so by looking at how government regulations affect this
probability. Our approach is to look at individual units in
successive waves of the American Housing Survey (AHS)
metropolitan area sample. In doing so, we follow Nelson and
Vandenbroucke (1996) and Somerville and Holmes (2001),
who use the panel nature of the AHS metropolitan area survey
data to chart the movements of individual units in and out of
the low-income housing stock.
The remainder of the paper is structured as follows. First, we
lay out the theoretical framework for our analysis. We follow with
a discussion of our data. Finally, we present our empirical results,
both for measures of constraints on the supply of new residential
units and for the pervasiveness of rent control in an area.

2. Theoretical Framework
We model movements of units in and out of the stock of
affordable housing as the filtering down of units through
successive housing submarkets. The filtering model describes
the housing market as a series of submarkets differentiated by

This paper received assistance and useful comments from Joseph Tracy,
Richard Peach, the conference participants, and especially Jack Goodman;
their insights are greatly appreciated. Special thanks are also due Cynthia
Holmes for able research assistance. The views expressed are those of the
authors and do not necessarily reflect the position of the Federal Reserve
Bank of New York or the Federal Reserve System.
FRBNY Economic Policy Review / June 2003

unit quality. Rents fall as quality declines, so units that are
lower on the quality ladder have lower rents than units of the
same size in the same location at the top. Without expenditures
on maintenance, renovation, and repairs, units decline in
quality as they depreciate physically and technologically. As
this occurs, the units move down the quality ladder. The cost to
maintain a given level of quality is assumed to increase with
unit age. Extra expenditures on maintenance and renovation
can move units back up the ladder. Relative rents in the
different submarkets vary with the distribution of income
across households (demand) and the supply of units in that
submarket. When quality is least expensive to provide at the
time units are built, new units will be of high quality. The
supply of the most affordable, lowest quality units will be those
units built in earlier periods that have been allowed to
depreciate and move down—to filter down—the quality
ladder. Landlords will choose a level of maintenance to
maximize profits, and that choice determines into which
housing submarket their unit will fall. When incomes,
population, and the housing stock raise rents in the submarket
for higher quality units relative to those in the submarket for
lower quality units, landlords in the latter submarket have a
greater incentive to increase maintenance, renovation, and
repair expenditures to cause units to filter up, that is, to move
to the higher quality submarket. Reducing the supply of lowend affordable units can potentially exacerbate affordability
problems for the least well-off. Although this may occur when
the entire demand curve for a neighborhood’s amenities shifts
out, we do not formally model neighborhood gentrification,
focusing instead on unit-specific decisions.
The focus of this paper is on use of the filtering model to
explain the effect of restrictions on new construction and rent
control on the movement in units in and out of the low-income
housing stock. We expect that factors that lower the market’s
new-construction-supply response to increases in demand will
reduce the affordable housing stock. This occurs because the
increase in demand that is unmet with new construction raises
the returns to landlords for moving units up the quality ladder.
These factors can include explicit government land-use
regulations that constrain the new supply or an area’s market
supply elasticity, which for reasons such as unobserved
regulation, land supply, and builder industry organization can
differ across markets.
One of the major forms of government regulation of housing
markets with important implications for the affordable housing
stock is rent control. The question of interest for this paper is
what effect rent control has on the uncontrolled affordable
housing stock. We know from Early and Phelps (1999) and Fallis
and Smith (1984) that rent control lowers the supply of

Government Regulation and Changes

uncontrolled affordable housing because excess demand for
units raises rents in this segment. This suggests that it raises the
probability that in any time period the uncontrolled units that
remain affordable will be more likely to filter up. Alternatively,
there may be reasons why these units remain affordable and
cannot filter up easily. The units could be of particularly low
quality or there may be negative neighborhood effects from
surrounding, poorly maintained rent-controlled buildings.
Finally, an application of the labor markets’ efficiency wage
model suggests that some landlords who prefer to keep rents low
to give themselves the advantage of selecting from a larger pool
of prospective tenants increase their ability to weed out those
who may be more likely to be bad tenants.

3. The Existing Literature
This paper draws from a wide variety of existing work. There is a
literature on filtering stretching back to Ratcliff ’s (1949)
discussion of the phenomenon. Government land-use regulation
as it applies to new construction has spawned a voluminous
theoretical and empirical literature looking at zoning restrictions
on use and density, development fees, greenbelts, growth
controls, and factors that delay and slow the new supply response
to demand shocks. Furthermore, in an area where economists
mostly agree with one another, there is a copious literature on
rent control and its effect on rents, maintenance, and housing
market equilibria. All of this work bears on our paper.
Sweeney (1974) is credited with the first thorough theoretical
treatment of filtering, where the level of maintenance affects the
rate of depreciation. The theoretical literature includes papers
that expand his model to include other issues.2 Most of the
recent empirical filtering literature does not examine individual
units directly, but looks for outcomes consistent with filtering.
Phillips (1981) uses cross-sectional data to compare mean
neighborhood income with descriptive statistics of the
neighborhood housing stock. Weicher and Thibodeau (1988),
using aggregate data, test for the effect of new construction on
the low-income housing stock. A more targeted study is Susin’s
(1999) examination of the effect of Section 8 housing vouchers
on rents for the least expensive third of units. Using the AHS
neighborhood sample, he finds a fairly inelastic supply curve and
little downward filtering as rents are clearly higher in the
presence of vouchers. The notable exception to these studies
with aggregate data is Somerville and Holmes (2001). They use
micro data to describe the relationship between individual unit,
neighborhood, and market characteristics, and the probability
that units will filter up or down.

Here, we look at the effect of land-use regulations on filtering.
Although no work has done this explicitly, a considerable body
of research has studied the theoretical and empirical effects of
various land-use regulations on urban form, development
patterns, and the price of housing. Nearly all of the existing
empirical work (see Fischel [1990] for a review) explores the
impact of regulation on house prices, with the bulk of the papers
finding that increased local regulation leads to higher house
prices. Constraints on supply result in higher house prices, but so
too does the capitalization of benefits that regulations provide
for local residents. A much smaller literature looks specifically
for the effects of regulation on new construction, and finds lower
levels of construction in the presence of higher regulatory
barriers and fees.3 This latter literature is relevant for our analysis
because we expect that restrictions on new development will
affect the supply of affordable units from the existing stock by
creating excess demand in the market for newer and higher
quality units, which increases the incentives for landlords to
upgrade their units.
We also examine the relationship between rent control and
filtering. There is a copious literature that highlights aspects of
the aggregate welfare losses associated with rent control.4 Olsen
(1998) provides a brief of summary of the economics of rent
control; other important work is Glaeser (1996) and Glaeser
and Luttmer (1997) on the welfare losses from the misallocation of housing under rent control, and the seminal
empirical analysis by Olsen (1972).

4. Data Description
We use the AHS metropolitan surveys to create a data set of
individual rental units in metropolitan statistical areas (MSAs)
from 1984 to 1994 for those MSAs for which we have land-use
regulation data. An “observation” is an individual rental unit
that is included in two successive surveys. Each MSA is
surveyed every three or four years in waves of approximately
eleven MSAs per survey, so that we have potentially two
observations per unit for twenty-three of the MSAs and one
observation per unit for the remaining twenty-one. As a result,
our time periods of analysis are not constant across MSAs.
However, our right-hand-side variables are either surveyperiod-specific or assumed to be time-invariant within an
MSA. Observations per unit are constrained by the
introduction of a new survey questionnaire in 1984 and a new
sample in 1995.5 When examining rent control, we look only at
those MSAs that include jurisdictions that impose significant
rent control.

In this paper, we define the affordable housing stock as those
units for which the gross rents are less than or equal to 30 percent of household income for a household with 35 percent of
the median MSA household income. We map this cutoff to
different unit sizes using the Department of Housing and
Urban Development’s methodology for calculating differences
in fair market rents by unit size.6 Throughout, we use rent to
refer to gross rents.7 Although there are a variety of approaches
to defining affordability, we have a taken a naïve approach. We
do not believe that how we define the housing stock should
cause problems. Our test is of the effect of a vector of variables
on the probability that a unit will cross a threshold, relative to
not doing so. How we define the threshold only matters if the
effect of explanatory variables varies systematically along the
quality ladder.
This study analyzes how restrictions on new construction
and rent control affect the evolution of the affordable stock.
Units must appear in at least two surveys to be included in our
sample. As a result, we exclude units that for whatever reason
appear in only one survey. A unit identified as affordable in the
first survey year can have one of four outcomes in the
subsequent survey year, assuming that the occupants respond
to the second survey. First, it can remain affordable. Second,
the unit’s rent can exceed the affordability cutoff, that is, filter
up. Third, a unit can become owner-occupied. Fourth, it can
either be abandoned, or demolished or converted.8 For rental
units that were identified as unaffordable in the first survey
year, we have a similar set of possible outcomes, except that the
baseline remains unaffordable and option two is to filter down
and become affordable.
We employ a mixed strategy to private-market units where
the occupant receives a subsidy. Work by McArdle (n.d.)
indicates that in many cases in the AHS, one cannot distinguish
between the actual gross rent and the gross rent paid (net of the
subsidy). We choose to exclude units where the occupant
receives a subsidy in the first survey year. However, a unit
whose occupying household did not receive a subsidy in the
first survey, but did in the second survey, is considered to be
affordable in the second survey. This approach does not result
in bias, as treating subsidized units as a separate category into
which units can move does not qualitatively change our results.
Table 1 shows the frequency of each outcome for
movements out of the affordable housing stock and out of the
unaffordable stock between any two AHS metropolitan
surveys. Similar to Nelson and Vandenbroucke (1996), we find
substantial movement in and out of the affordable stock. Not
surprisingly, units in the unaffordable stock are less likely to
become government-subsidized or be demolished, but are
more likely to convert to owner-occupancy than are units
initially classified as affordable. These figures show an increase

FRBNY Economic Policy Review / June 2003

Table 1

rental units subject to rent control varies widely, from a low of
4 percent in Boston to a high of more than 25 percent in San
Francisco. The principal determinant appears to be whether the
central city itself imposes rent control. Even in cities with little
rent control, there is at least one zone for which rent-controlled
units make up more than 10 percent of the rental stock.
In the analysis, we include unit and neighborhood variables
that enter into the landlord’s optimal maintenance and
renovation decision as well as the MSA land-use and supply
restriction variables. All regressions also include a set of control
variables. We include unit characteristics such as a dummy
variable for the unit if it is defined as adequate by AHS
standards, unit age, a dummy for multiunit buildings, and the
number of units in the structure. Adequacy is an AHS-coded
summary variable based on responses to questions about
physical problems in the unit. The lack of hot piped water or a
flush toilet would classify a unit as severely inadequate, while
multiple leaks and holes in the floor and walls would classify
the unit as moderately inadequate.
Neighborhood effects enter the decision to invest in a unit’s
quality. We use AHS zones—socioeconomically homogeneous
areas of approximately 100,000 people—as our definition of a
neighborhood. Although larger than a neighborhood, this is the
most geographically disaggregated variable available in the AHS
metropolitan survey. For each zone, we estimate the ratio of
rental units to all units, affordable units to all rental units, public
housing units to all rental units, and subsidized units to all rental
units in the zone. We also measure the average age of the rental
stock, the percentage of households headed by an AfricanAmerican, and the median household income in the zone.
Both market and unit measures act as control variables. The
first controls for the effect of aggregate MSA changes in house
prices and rents in causing movements of individual units into
and out of the affordable stock. We use DiPasquale and
Somerville’s (1995) methodology to generate hedonic price

Changes in the Affordable Housing Stock
Number

Percentage

Units beginning as affordable
Remain affordable
Become unaffordable
Become subsidized
Become owner-occupied
Are demolished or converted
Total

4,171
2,928
760
506
837
9,202

45.3
31.8
8.3
5.5
9.1

Units beginning as unaffordable
Remain unaffordable
Become affordable
Become subsidized
Become owner-occupied
Are demolished or converted
Total

54,298
6,007
3,185
4,703
1,369
69,562

78.1
8.6
4.6
6.8
2.0

Notes: Only units that had observations for two consecutive years are
included; units that were initially government subsidized or classified as
public housing are excluded. A unit is defined as affordable if the sum of
rent and utilities is less than 30 percent of household income for a household at 35 percent of the median income for four-person families for that
year in that city. To account for different unit sizes, we make an adjustment based on the number of bedrooms. These aggregate data are likely
to underestimate the number of units that become unaffordable because
rents tend to increase more when tenants change, but new tenants are less
likely to become American Housing Survey respondents.

of approximately 1,700 units. This result may be misleading
because the AHS will tend to exclude units with a change in
occupants in successive surveys; this leads to bias because these
are the units most likely to experience rent increases.9
In Table 2, we present the distribution of rent-controlled
units for those MSAs with rent-control policies. The number of

Table 2

Rent-Control Descriptive Statistics
Percentage of Rent-Controlled Units in Rental Stock
Percentage of Rent-Controlled Rental Units in Zone
Metropolitan Statistical Area (MSA)
Boston
Los Angeles
New York
San Francisco
San Jose
Washington, D.C.

MSA Mean Number of Zones
(Percent)
in MSA
4.0
25.0
17.1
25.5
10.1
9.3

Average
across Zones

25th Percentile
across Zones

Median
across Zones

75th Percentile
across Zones

90th Percentile
across Zones

2.3
19.2
11.6
17.6
9.2
6.6

0.0
2.1
0.0
0.9
5.5
0.8

0.0
8.0
9.3
4.2
6.9
2.0

1.8
39.0
18.0
36.1
12.7
4.1

5.7
47.9
28.7
56.4
16.5
25.9

31
44
83
22
10
23

Government Regulation and Changes

and rent series from the AHS, with mean values of the
affordable stock used to describe the bundle. The second is the
ratio of a unit’s rent to the affordability conditions that the
most marginally affordable units are more likely to filter up.
Data on land-use regulation come from the Wharton Urban
Decentralization Project Data Set (Linneman and Summers
1991). These data summarize surveys sent to local planners in a
sample of sixty MSAs, of which we have price data and American
Housing Survey information for thirty-eight. We include two
measures of regulation, a count of the number of ways in which
growth management techniques have been introduced in the
MSA, and whether development or impact fees are imposed in
the cities in the MSA. The number of growth management
techniques is the sum of five different dummy variables, each of

which indicates whether one of the following approaches to
introducing growth management policies is prevalent in the
MSA: citizen referendum; legislative action by municipalities,
counties, and the state; and administrative action by public
authorities. We assume that the more types of actions taken and
the greater the number of groups that act to control
development, the more constrained the regulatory environment.
These variables vary by MSA, but are constant over time. This
forces us to assume that the regulatory environment described by
these variables is time-invariant.
In Table 3, we present descriptive statistics for these
variables separately for affordable units and unaffordable units.
Comparing these two sets, we note that the difference of means
t-tests rejects equality of means for nearly all variables.

Table 3

Descriptive Statistics
Affordable Units

Unaffordable Units

Count

Mean

Standard
Deviation

Count

Mean

Standard
Deviation

t-Test on Mean
Difference

Unit
Adequacy of unit (1 if adequate, 0 otherwise)
Age of unit
Unit is part of multiunit building (1 if yes, 0 if no)
Number of units in building

9,202
9,202
9,202
9,202

0.72
46.56
0.70
8.35

0.45
19.58
0.46
19.00

69,562
69,562
69,562
69,562

0.90
27.91
0.76
13.63

0.30
20.64
0.43
29.19

37.44
85.33
12.43
23.25

Neighborhood
Ratio of subsidized units to rental units in zone
Average age of rental units in zone
Ratio of public housing units to rental units in zone
Ratio of rental units to all units in zone
Ratio of affordable units to rental units in zone
Percentage African-American heads of household in zone
Median household income in zone

9,202
9,202
9,202
9,202
9,202
9,202
9,202

0.11
37.15
0.07
0.48
0.31
0.27
21,487

0.06
13.67
0.07
0.17
0.17
0.30
8,665

69,562
69,562
69,562
69,562
69,562
69,562
69,562

0.10
28.28
0.04
0.44
0.14
0.13
27,650

0.06
12.92
0.05
0.15
0.13
0.18
8,998

19.52
58.85
39.15
21.53
92.42
44.67
63.83

Regulation
New single-family permits—supply elasticity
Jurisdictions in MSA use impact fees (dummy)
Number of approaches to growth management
Percentage rent control in zone greater than 10 percent (1 if yes, 0 if no)
Percentage rent control in zone

7,502
8,571
8,215
761
761

15.96
0.36
0.54
0.47
0.14

8.64
0.48
0.83
0.50
0.16

56,552
61,708
59,713
8,302
8,302

14.37
0.51
0.69
0.30
0.10

7.38
0.50
0.89
0.46
0.14

15.25
27.35
14.66
9.04
6.67

Control
Hedonic price change in MSA (affordable units)
Hedonic rent change in MSA (affordable units)
Number of years current resident has occupied unit
Ratio of rent to cutoff of affordability

9,202
9,202
7,878
9,202

0.07
0.23
6.33
0.76

0.38
0.11
8.60
0.20

69,562
69,562
60,907
69,562

0.08
0.21
2.92
1.62

0.34
0.12
4.96
0.46

1.95
19.54
34.39
319.24

Variable

Notes: Only units that were included in two consecutive surveys are included; units that dropped out of the sample in successive surveys are excluded. All
price and rent changes are measured in nominal dollars. The mean values in the affordable units column and the unaffordable units column for the hedonic
price and rent changes differ because these two categories of units are not distributed identically across metropolitan statistical areas (MSAs). Rent-control
variables are only for Boston, Los Angeles, Newark, San Francisco, San Jose, and Washington, D.C., American Housing Surveys. Supply elasticities and
regulation variables are only available for thirty-eight of forty-four American Housing Survey MSAs.

FRBNY Economic Policy Review / June 2003

Qualitatively, affordable units are in poorer condition and in
older and smaller buildings. Tenants have a notably longer
mean stay in the affordable units, 6.3 versus 2.9 years.
Affordable units are both more concentrated in space than are
rental units in general and are much more likely to be in areas
with a higher proportion of African-Americans. Although
other differences are statistically significant, they are not
meaningful. The rent changes, which are calculated at the zone
rather than at the unit level, differ by class because affordable
and nonaffordable units do not have the same distribution
across space, while price and rent changes vary by area. Those
MSAs with more affordable units are likely to have higher
supply elasticities and less land-use regulation.

5. Empirical Results
We estimate the model using a multinomial logit specification
where any observation i = 1 to n can fall into one of k groups.
For a unit currently in the low-income stock, these groups are
remaining in the low-income stock, filtering up (defined as
having a rent that surpasses the affordability threshold),
converting to owner-occupied, or being demolished. For each
observation, we have a probability:
X βj

(1)

e
- for all k = 1 to 4 groups.
Pr ( i ∈ j ) = -----------------k
Xβ
e k

∑

j=1

X β1

Equation 1 is unidentified unless we set e
= 1 . The
standard procedure is to present the odds ratio, the ratio of the
probability that i ∈ k ( k ≠ 1 ) relative to the probability that
i ∈ 1. For instance:
Xβ

(2)

1
Pr ( i ∈ 2 -) ---------------------------e 2 - ---------------------------- = e x β2 .
---------------------=
⁄
k
k
X βk
X βk
Pr ( i ∈ 1 )
1+
e
1+
e

∑

j=2

∑

j=2

The multinomial regression results are presented in the
appendix. There, Tables A1 and A2 show the effects of land-use
regulation on affordable and unaffordable units, while Table A3
does the same for the effect of the rent-control variables. The
relatively small number of degrees of freedom at the MSA level
causes us to separate these two into distinct tables.
Multinomial logit regression output can be difficult to
interpret. The coefficients are both exponentiated and relative
to the baseline outcome, which, in our case, is when the unit’s
affordability status remains unchanged. We present the results
in a set of tables that show the sensitivity of relative
probabilities to given changes in the values of right-hand-side
variables. These describe the percentage-point change in the

Government Regulation and Changes

probability of outcome i, relative to remaining affordable, for a
10 percent change in the explanatory variables. These results
are like elasticities, but are applied to relative rather than to
absolute probabilities.
Table 4 shows the effects of the unit characteristics,
neighborhood quality measures, and control variables. Adding
the government regulation variables to these variables does not
change the results, so for clarity of presentation, we show them
just once. The results in column 1 describe the sensitivity that an
affordable unit filters up, relative to staying affordable. Several
factors stand out. Older units are less likely to filter up, as the cost
of improving quality is higher. Neighborhood effects matter:
filtering up is more likely to occur in neighborhoods with lots of
rental units, but less likely if those units are mostly affordable.
The control variables matter: units are more likely to become
unaffordable if rents are rising in the market and if the unit’s
initial survey rent is closer to the cutoff. Being in better shape
relative to the neighborhood also matters. From columns 2 and
3, the older the zone average, controlling for the unit’s own age,
the more likely the unit is to become owner-occupied, and the
less likely it is to be demolished, though conversion to owneroccupancy is falling and demolition or conversion is rising in the
unit’s own age. For units initially unaffordable—columns 3-6—
median zone income and market conditions are extremely
important. Units are dramatically less likely to filter down or be
demolished/converted the higher the median zone income is and
the greater the increase in rents is.
Table 5 presents the effects of changes in regulation
measures on changes in the stock of affordable units. All of the
regression specifications used in Table 5 include the full set of
unit, neighborhood, and control variables in Table 4. The
results here are consistent with the filtering model: the more
constrained the supply response for new residential units to
demand shocks, the greater the probability that an affordable
unit will filter up and out of the affordable stock relative to
staying in the stock. Explicitly, the greater the supply elasticity
of new single-family construction, the lower this relative
probability will be, as builders are able to respond much more
quickly to demand shocks. With more units coming in more
quickly in response to an increase in demand, relative rents
between high- and low-quality markets diverge less, reducing
the returns to upgrading a unit so that it can filter up. The sign
is robust across specifications, though the coefficient is not
uniformly statistically different from zero. We find this a
compelling result, clearly identifying the linkage between
construction of new high- and standard-quality homes and the
affordable stock consisting of lower quality units.
In regressions 2 and 3, we add the two measures of
government land-use regulation, the presence of impact fees,
and measures of the number of growth management

Table 4

Percentage Change in Relative Probabilities
10 Percent Change in Mean Values
Affordable Units

Variable
Adequacy of unit
Age of unit
Unit is part of multiunit building
Number of units in building
Ratio of subsidized units to all units in zone
Average age of rental units in zone
Ratio of public housing units to rental units in zone
Ratio of rental units to all units in zone
Ratio of affordable units to rental units in zone
Percentage African-American heads of household in zone
Median income in zone
Hedonic price change in MSA (affordable units)
Hedonic rent change in MSA (affordable units)
Number of years current resident has occupied unit
Ratio of rent to cutoff of affordability

Unaffordable Units

Filters up— Converts to
Becomes
OwnerConverted or
Unaffordable Occupied
Demolished
(1)
(2)
(3)
2.28
-5.03
1.24
-0.68
NS
NS
NS
6.89
-4.62
-0.96
0.00
0.13
4.89
-0.90
5.29

NS
-6.38
-10.82
NS
NS
5.98
NS
NS
NS
-2.17
0.00
NS
2.64
—
NS

-5.26
8.35
-2.37
NS
NS
-10.38
NS
NS
NS
NS
NS
NS
NS
-1.10
-6.27

Filters down—
Becomes
Affordable
(4)

Converts
to OwnerOccupied
(5)

Converted or
Demolished
(6)

NS
1.90
-2.91
0.26
2.32
1.00
0.25
-1.22
0.89
0.45
-24.16
0.35
-39.01
0.17
0.00

NS
NS
-14.55
NS
NS
NS
0.37
-1.97
1.48
-0.80
NS
NS
13.62
0.28
0.00

-7.36
9.63
-5.46
NS
NS
-7.66
NS
NS
1.03
0.94
-24.16
NS
-26.17
-0.54
0.00

Notes: The table reports changes in the odds ratios due to a 10 percent increase from the mean and due to an increase equal to one standard deviation from
the mean. The odds ratios are relative to the outcome with the unit remaining affordable or becoming subsidized. The metropolitan statistical area (MSA)
dummies are used in specification 1 but are not reported. NS indicates that the variable was not significant at the 5 percent level; the dash indicates that the
variable was not used in this specification.

techniques used in the MSA. We argue that both describe
constraints on supply. In both cases, greater regulation results
in an increase in the probability that an affordable rental unit
will filter up to become unaffordable. This is consistent with
the predictions of the filtering model, as the constraints on new
development can be expected to increase the returns to
maintenance and renovation because with less new
construction, relative rents for units of higher quality will be
greater. The effects of elasticity and regulation variables on the
relative probability of conversion to owner-occupied status or
being demolished or converted are not statistically different
from their effect on a unit remaining affordable.
We believe that the negative effect of supply regulations is
more pronounced than is suggested by the absolute magnitude
of these coefficients. When we compare the quasi-elasticities in
Table 5 with those in Table 4, 10 percent increases in each of the
elasticity and regulations variables have no more than onequarter the effect of a similar increase in unit age and less than
half the effect for unit quality. The effect is also less than onequarter that of the neighborhood measures, mix of rental,

owner-occupied, and affordable units in the zone. However, to
say that the effects of regulations are unimportant would be
erroneous. Our regulation measures are quite crude, yet they
still provide robust, theoretically compelling results. More
important, an increase in these measures affects all units in the
affordable stock, so that even with a small effect per unit, the
aggregate effect on affordable housing can be substantive. In
contrast, unit age or quality affects the unit alone.
In Table 6, we present the same results for units
unaffordable to low-income renters. Regulation variables have
no effect on the relative probability that one of these will leave
the stock. However, the new-construction-supply elasticity
does matter. Higher end rental units are less likely to become
owner-occupied and less likely to be demolished or converted
when the supply response to a given demand shock is greater.
This is consistent with the spirit of the filtering model,
particularly if we think of the purchase of an existing rental unit
and its conversion to an owner-occupied unit and the
redevelopment of an existing structure as inferior to new
greenfield development.

FRBNY Economic Policy Review / June 2003

Table 7 presents the effects of rent control. Our prior is that
in a rent-controlled environment, uncontrolled units are more
likely to filter up. Early and Phelps (1999) and Fallis and Smith
(1984) demonstrate that rent control increases the rents for
uncontrolled rental units. However, we find that an
uncontrolled unit in an area with more rent control is less likely
to filter up or become owner-occupied and more likely, though
the effect is not statistically different from zero, to be
demolished or converted. In trying to explain this outcome, the
other results do shed some light on the apparent paradox.
Although not robust in significance, as the percentage of rental

Table 5

units subject to rent control in an area rises, uncontrolled units
are less likely to convert to ownership, relative to remaining
affordable, and more likely to be demolished or converted.
Given that rents for uncontrolled units will be higher, and that
rent control is typically imposed in locations where rents are
high and rising, this suggests two possible explanations. First,
uncontrolled units that remain affordable in the presence of
rent control are more likely to be very low-quality units,
suggesting selection bias. Despite the presence of rent control,
the quality of these units indicates that they are less appealing
for owner-occupants, unable to filter up, and more likely to be

Table 6

Effect of a 10 Percent Change
in Regulation Variables

Affordable Units

Unaffordable Units
Specification (Percent)

Variable

Filters up
New single-family permits—
supply elasticity
Jurisdictions in MSA use impact fees
(dummy)
Number of approaches to growth
management
Converts to owner-occupied
New single-family permits—
supply elasticity
Jurisdictions in MSA use impact fees
(dummy)
Number of approaches to growth
management
Demolished or converted
New single-family permits—
supply elasticity
Jurisdictions in MSA use impact fees
(dummy)
Number of approaches to growth
management

-1.19* -0.53

-1.23**

0.92***
0.33*

1.46

1.55

1.40

0.15
-0.28

0.83

1.20

0.80

0.50
-0.34

Specification (Percent)
Variable
Filters down
New single-family permits—
supply elasticity
Jurisdictions in MSA use
impact fees (dummy)
Number of approaches to growth
management
Converts to owner-occupied
New single-family permits—
supply elasticity
Jurisdictions in MSA use impact
fees (dummy)
Number of approaches to growth
management
Demolished or converted
New single-family permits—
supply elasticity
Jurisdictions in MSA use impact
fees (dummy)
Number of approaches to growth
management

-0.38

-0.27

-0.38

0.24
0.10

-0.92**

-0.88**

-0.92**

0.09
0.00

-1.25

-1.48*

-1.26*

-0.58
-0.18

Notes: The table reports the percentage change in the odds ratios due to a
10 percent increase from the mean. The odds ratios are relative to the outcome with the unit remaining affordable or becoming subsidized. MSA is
metropolitan statistical area.

***Statistically significant at the 1 percent level.

**Statistically significant at the 5 percent level.

*Statistically significant at the 10 percent level.

Government Regulation and Changes

Table 7

reluctant without a better sense of the data to reach any strong
conclusion from this result, and we caution readers to use
discretion when interpreting it.

Effect of a 10 Percent Change
in Rent-Control Measures
Affordable Units
Specification
(Percent)
Variable

Filters up
Percentage of units in zone that are
rent-controlled is greater than 10 percent
Percentage of units in zone that are
rent-controlled

-3.65***

Converts to owner-occupied
Percentage of units in zone that are
rent-controlled is greater than 10 percent
Percentage of units in zone that are
rent-controlled

-4.99*

Demolished or converted
Percentage of units in zone that are
rent-controlled is greater than 10 percent
Percentage of units in zone that are
rent-controlled

0.32

-2.18*

-5.25

1.02

Notes: All regressions have metropolitan statistical area (MSA) fixed
effects and a dummy if the unit is in the MSA’s central city. The table
reports the percentage change in the odds ratios due to a 10 percent
increase from the mean. The odds ratios are relative to the outcome
with the unit remaining affordable or becoming subsidized.
***Statistically significant at the 1 percent level.
**Statistically significant at the 5 percent level.
*Statistically significant at the 10 percent level.

demolished. Second, if there are strong negative neighborhood
externalities from being in an area with an undermaintained
rent-controlled stock, this might reduce the returns to
maintenance and renovation on uncontrolled units. Even
though there is an incentive for the rents to rise, this second
effect would work in the opposite direction. Both of these
approaches allow for uncontrolled rents to be higher, while the
returns to maintenance, for filtering up, to be lower. We are

6. Conclusion
This paper takes a new approach to studying the effects of landuse regulation. Instead of focusing on the effects of supply
restrictions, both explicit and implicit, on new construction,
we examine how they affect the filtering process. This allows us
to examine the dynamics of the relationship between housing
affordable to low-income households and local-governmentimposed land-use regulations. Our approach, which borrows
from Somerville and Holmes (2001), looks at how regulation
affects the probability that a rental unit currently deemed
affordable will become unaffordable, owner-occupied, or
demolished, relative to staying affordable.
We find that regulation does matter: when new
construction is more constrained, as measured either by a
lower supply elasticity or the presence of certain regulations,
affordable units are more likely to filter up and become
unaffordable, relative to remaining in the affordable stock. We
find this result to be quite compelling and to offer an important
lesson for policymakers. The effects of land-use regulation are
not limited to raising the price of owner-occupied housing and
reducing access to homeownership. They also have a clear
negative impact on the most vulnerable. Given the ample
efforts to document the difficult and worsening affordability
crisis for the least well-off, this has to be a concern.
There are a number of aspects of this paper that should
caution against using this work to predict the effects of any new
policies on the affordable stock. We examine the dynamics of
the stock, but our supply control variables are MSA-specific
and time-invariant. Consequently, we know little of the timing
of these processes. Given the long-run nature of the filtering
process, this suggests that the outcome of short-run changes in
policy would be hard to predict. Still, through our examination
of changes in the stock of affordable units across MSAs—rather
than the size of the MSA stock itself—we are able to avoid some
of the more egregious problems of MSA-level, excludedvariable bias.

FRBNY Economic Policy Review / June 2003

Appendix: Multinomial Regression Results

Table A1

Affordable Rental Units
Multinomial Logit/Excluded Option/Remain Affordable
Specification 1
Pseudo R2 = 7.94 Percent
Rent
Rises

Variable

Owner- Demolished/
Occupied Converted

Specification 2
Pseudo R2 = 8.01 Percent
Rent
Rises

Owner- Demolished/
Occupied Converted

Specification 3
Pseudo R2 = 7.98 Percent
Rent
Rises

Owner- Demolished/
Occupied Converted

Adequacy of unit
(1 if adequate, 0 otherwise)

1.4121
(4.26)

1.2719
(1.54)

0.5504
(5.38)

1.4045
(4.19)

1.2731
(1.54)

0.5488
(5.40)

1.4134
(4.27)

1.2773
(1.56)

0.5507
(5.37)

Average resident’s evaluation of unit
(scale of 1-10: 1 is worst, 10 is best)

0.9936
(0.48)

1.0340
(1.28)

0.8649
(7.21)

0.9945
(0.41)

1.0341
(1.28)

0.8652
(7.20)

0.9940
(0.46)

1.0334
(1.26)

0.8643
(7.24)

Age of unit

0.9899
(5.16)

0.9856
(3.92)

1.0198
(5.70)

0.9900
(5.09)

0.9856
(3.91)

1.0198
(5.71)

0.9899
(5.17)

0.9857
(3.89)

1.0200
(5.74)

Unit is part of multiunit building
(1 if yes, 0 if no)

1.1901
0.2005
(2.40) (11.80)

0.7236
(2.79)

1.1924
0.2006
(2.42)
(11.80)

0.7245
(2.78)

1.1894
(2.39)

0.2006
(11.80)

0.7245
(2.78)

Number of units in building

0.9930
(3.93)

0.9902
(1.78)

0.9975
(0.73)

0.9927
(4.06)

0.9902
(1.78)

0.9973
(0.78)

0.9929
(3.96)

0.9903
(1.77)

0.9977
(0.66)

Ratio of subsidized units to rental units
in zone

1.6727
(0.85)

2.2670
(0.72)

0.4304
(0.81)

1.2908
(0.42)

2.1206
(0.65)

0.3801
(0.92)

1.6012
(0.78)

2.3343
(0.74)

0.4278
(0.81)

Average age of rental units in zone

0.9982
(0.45)

1.0118
(1.49)

0.9752
(3.65)

1.0003
(0.08)

1.0121
(1.50)

0.9764
(3.40)

0.9975
(0.60)

1.0121
(1.52)

0.9757
(3.56)

Ratio of public housing units to rental
units in zone

0.6161
(0.74)

0.9293
(0.06)

5.2494
(1.73)

0.6336
(0.70)

0.9471
(0.04)

5.5030
(1.78)

0.5255
(0.98)

1.0082
(0.01)

6.0647
(1.86)

Ratio of rental units to all units in zone

4.0005
(4.96)

1.5240
(0.72)

0.8627
(0.30)

3.3228
(4.20)

1.4826
(0.66)

0.7920
(0.47)

3.8044
(4.76)

1.6012
(0.79)

0.8900
(0.23)

Ratio of affordable units to rental units
in zone

0.1852
(6.08)

0.8163
(0.39)

0.6334
(1.04)

0.1771
(6.22)

0.8132
(0.40)

0.6191
(1.09)

0.2046
(5.61)

0.7675
(0.50)

0.5854
(1.19)

Average resident’s evaluation
of neighborhood
(scale of 1-10: 1 is worst, 10 is best)

1.0298
(0.41)

1.3643
(2.13)

0.8603
(1.22)

1.0852
(1.11)

1.3724
(2.11)

0.8874
(0.94)

1.0296
(0.41)

1.3590
(2.10)

0.8596
(1.23)

Percentage African-American heads
of household in zone

0.7339
(2.03)

0.4705
(2.44)

0.9249
(0.31)

0.8793
(0.79)

0.4789
(2.23)

1.0162
(0.06)

0.7493
(1.89)

0.4635
(2.48)

0.9080
(0.38)

Median income in zone

1.0000
(3.22)

1.0000
(0.52)

1.0000
(0.20)

(0.79)
(0.79)

1.0000
(0.44)

1.0000
(0.02)

1.0000
(3.04)

1.0000
(0.61)

1.0000
(0.27)

Hedonic price change in MSA
(affordable units)

0.9855
(0.12)

1.8291
(2.50)

0.8197
(0.87)

(0.79)
(0.79)

1.8519
(2.54)

0.8390
(0.77)

0.9892
(0.09)

1.8245
(2.50)

0.8283
(0.83)

Hedonic rent change in MSA
(affordable units)

6.6865
(5.16)

2.5710
(1.36)

0.7265
(0.56)

(0.79)
(0.79)

2.6750
(1.36)

0.8462
(0.28)

6.3513
(4.99)

2.6997
(1.42)

0.7892
(0.41)

Government Regulation and Changes

Appendix: Multinomial Regression Results (Continued)

Table A1 (continued)

Affordable Rental Units
Multinomial Logit/Excluded Option/Remain Affordable
Specification 1
Pseudo R2 = 7.94 Percent
Variable
New single-family permits—
supply elasticity

Rent
Rises
0.9925
(1.96)

Owner- Demolished/
Occupied Converted
1.0091
(1.23)

1.0052
(0.78)

Jurisdictions in MSA use impact fees
(dummy)

Specification 2
Pseudo R2 = 8.01 Percent
Rent
Rises

Owner- Demolished/
Occupied Converted

(0.79)
(0.79)

1.0097
(1.23)

1.0075
(1.06)

(0.79)
(0.79)

1.0421
(0.26)

1.1484
(1.00)

Number of approaches to growth
management

Specification 3
Pseudo R2 = 7.98 Percent
Rent
Rises

Owner- Demolished/
Occupied Converted

0.9923
(2.01)

1.0087
(1.19)

1.0050
(0.76)

1.0623
(1.69)

0.9490
(0.71)

0.9395
(0.97)

Number of years current resident
has occupied unit

0.9877
(3.23)

1.0044
(0.65)

0.9823
(2.57)

0.9876
(3.26)

1.0043
(0.65)

0.9823
(2.57)

0.9874
(3.30)

1.0047
(0.70)

0.9826
(2.52)

Ratio of rent to cutoff of affordability

2.0477
(4.16)

0.8783
(0.40)

0.3351
(4.07)

2.1028
(4.31)

0.8805
(0.39)

0.3388
(4.03)

2.0585
(4.19)

0.8782
(0.40)

0.3331
(4.09)

Notes: Number of observations: 6,168. The dependent variable has four possible values: 1) an affordable rental unit can remain affordable, 2) become unaffordable because of increases in its rent relative to the affordability cutoff, 3) become owner-occupied, or 4) be demolished or converted to another use. The
excluded (base) outcome is to remain affordable. The top number reported is the unit odds ratio e b ; the bottom number (in parentheses) is the Z-statistic.
The odds ratio is the probability of outcome i divided by the probability of the null (or excluded) outcome, and is equal to e XB. The unit odds ratio is the
odds ratio for a one-unit increase to the independent variable. Thus, it is not b that is reported in the table, but eb. The Z-statistic is based on the null
b
hypothesis that b = 0, which is equivalent to the unit odds ratio e = 1 . MSA is metropolitan statistical area.

FRBNY Economic Policy Review / June 2003

Appendix: Multinomial Regression Results (Continued)

Table A2

Unaffordable Rental Units
Multinomial Logit/Excluded Option/Remain Unaffordable
Specification 1
Pseudo R2 = 14.58 Percent
Rent Falls/
Subsidized

Variable

Rent Falls/
Subsidized

OwnerOccupied

Demolished/
Converted

Specification 3
Pseudo R2 = 14.58 Percent
Rent Falls/ Owner- Demolished/
Subsidized Occupied Converted

Adequacy of unit
(1 if adequate, 0 otherwise)

0.8675
(2.93)

1.0149
(0.19)

0.4966
(7.11)

0.8685
(2.91)

1.0153
(0.19)

0.4953
(7.13)

0.8680
(2.92)

1.0150
(0.19)

0.4966
(7.11)

Average resident’s evaluation
of unit (scale of 1-10:
1 is worst, 10 is best)

1.0016
(0.22)

1.0284
(2.71)

0.9096
(5.48)

1.0018
(0.25)

1.0285
(2.71)

0.9092
(5.51)

1.0017
(0.24)

1.0284
(2.70)

0.9094
(5.50)

Age of unit

1.0082
(8.58)

1.0021
(1.66)

1.0336
(13.61)

1.0082
(8.59)

1.0021
(1.65)

1.0336
(13.60)

1.0082
(8.59)

1.0021
(1.66)

1.0337
(13.61)

0.6930
(10.14)

0.1333
(43.85)

0.4521
(9.27)

0.6931
(10.14)

0.1333
(43.84)

0.4516
(9.29)

0.6932
(10.14)

0.1333
(43.85)

0.4517
(9.28)

Number of units in building

1.0016
(2.74)

0.9991
(0.94)

1.0007
(0.35)

1.0016
(2.74)

0.9991
(0.95)

1.0007
(0.37)

1.0016
(2.75)

0.9991
(0.94)

1.0007
(0.35)

Ratio of subsidized units
to rental units in zone

8.0568
(7.44)

0.6546
(1.08)

0.2030
(1.94)

7.7836
(7.29)

0.6535
(1.08)

0.2141
(1.87)

7.9195
(7.36)

0.6545
(1.08)

0.2078
(1.90)

Average age of rental units
in zone

1.0032
(1.64)

1.0028
(1.05)

0.9732
(5.34)

1.0036
(1.83)

1.0029
(1.09)

0.9720
(5.48)

1.0031
(1.57)

1.0028
(1.05)

0.9734
(5.29)

Ratio of public housing units
to rental units in zone

1.3729
(0.88)

1.2864
(0.43)

0.0738
(2.73)

1.3555
(0.84)

1.2731
(0.41)

0.0734
(2.73)

1.3328
(0.79)

1.2862
(0.43)

0.0777
(2.66)

Ratio of rental units to all units
in zone

0.6826
(2.79)

0.4465
(4.06)

0.8638
(0.41)

0.6644
(2.95)

0.4429
(4.08)

0.9040
(0.28)

0.6763
(2.85)

0.4465
(4.06)

0.8693
(0.39)

Ratio of affordable units
to rental units in zone

2.6278
(5.88)

4.1712
(5.47)

3.7485
(3.15)

2.5791
(5.74)

4.1394
(5.43)

3.9768
(3.27)

2.6867
(5.93)

4.1674
(5.39)

3.5933
(3.00)

Average resident’s evaluation
of neighborhood (scale of
1-10: 1 is worst, 10 is best)

1.0327
(0.89)

0.9513
(0.95)

0.9293
(0.77)

1.0430
(1.14)

0.9561
(0.83)

0.9040
(1.03)

1.0344
(0.93)

0.9511
(0.95)

Percentage African-American
heads of household in zone

1.4736
(4.54)

0.5119
(4.18)

1.8318
(2.84)

1.5326
(4.70)

0.5225
(3.86)

1.6618
(2.22)

1.4840
(4.60)

0.5118
(4.15)

1.8133
(2.79)

Median income in zone

1.0000
(6.80)

1.0000
(1.31)

1.0000
(4.44)

1.0000
(6.92)

1.0000
(1.36)

1.0000
(4.24)

1.0000
(6.84)

1.0000
(1.31)

1.0000
(4.41)

Hedonic price change in MSA
(affordable units)

1.3389
(4.94)

1.1121
(1.37)

1.0197
(0.12)

1.3478
(5.04)

1.1140
(1.39)

1.0070
(0.04)

1.3404
(4.95)

1.1122
(1.37)

1.0225
(0.14)

Hedonic rent change in MSA
(affordable units)

0.1328
(13.40)

1.8894
(3.24)

0.3134
(3.10)

0.1379
(12.90)

1.9044
(3.26)

0.2891
(3.26)

0.1305
(13.38)

1.8912
(3.19)

0.3240
(2.98)

Unit is part of multiunit
building (1 if yes, 0 if no)

Owner- Demolished/
Occupied Converted

Specification 2
Pseudo R2 = 14.59 Percent

Government Regulation and Changes

0.9267
(0.80)

Appendix: Multinomial Regression Results (Continued)

Table A2 (Continued)

Unaffordable Rental Units
Multinomial Logit/Excluded Option/Remain Unaffordable
Specification 1
Pseudo R2 = 14.58 Percent
Variable
New single-family permits—
supply elasticity

Rent Falls/
Subsidized
0.9973
(1.37)

Owner- Demolished/
Occupied Converted
0.9936
(2.32)

0.9913
(1.64)

Jurisdictions in MSA use
impact fees (dummy)

Specification 2
Pseudo R2 = 14.59 Percent
Rent Falls/
Subsidized

OwnerOccupied

Demolished/
Converted

0.9981
(0.92)

0.9939
(2.11)

0.9897
(1.89)

1.0474
(1.26)

1.0184
(0.39)

0.8917
(1.23)

Number of approaches to
growth management
Number of years current
resident has occupied unit
Ratio of rent to cutoff
of affordability

Specification 3
Pseudo R2 = 14.58 Percent
Rent Falls/ Owner- Demolished/
Subsidized Occupied Converted
0.9974
(1.33)

0.9936
(2.32)

0.9912
(1.66)

1.0141
(0.84)

0.9996
(0.02)

0.9743
(0.58)

1.0105
(3.92)

1.0162
(4.04)

0.9610
(3.89)

1.0104
(3.88)

1.0161
(4.03)

0.9612
(3.88)

1.0104
(3.89)

1.0162
(4.04)

0.9612
(3.87)

0.1101
(45.98)

1.9436
(15.53)

0.5795
(5.07)

0.1100
(45.99)

1.9413
(15.46)

0.5827
(5.01)

0.1099
(45.95)

1.9438
(15.43)

0.5829
(4.99)

Notes: Number of observations: 48,347. The dependent variable has four possible values: 1) an unaffordable rental unit can remain unaffordable, 2) become
affordable because of decreases in its rent relative to the affordability cutoff, 3) become owner-occupied, or 4) be demolished or converted to another use.
The excluded (base) outcome is to remain unaffordable. The top number reported is the unit odds ratio e b ; the bottom number (in parentheses) is the
Z-statistic. The odds ratio is the probability of outcome i divided by the probability of the null (or excluded) outcome, and is equal to e XB. The unit odds
ratio is the odds ratio for a one-unit increase to the independent variable. Thus, it is not b that is reported in the table, but eb. The Z-statistic is based on the
b
null hypothesis that b = 0, which is equivalent to the unit odds ratio e = 1 . MSA is metropolitan statistical area.

FRBNY Economic Policy Review / June 2003

Appendix: Multinomial Regression Results (Continued)

Table A3

Affordable Rental Units
Multinomial Logit/Excluded Option/Remain Affordable
Specification 1
Pseudo R2 = 10.79 Percent
Rent Rises

OwnerOccupied

Demolished/
Converted

Rent Rises

OwnerOccupied

Demolished/
Converted

Adequacy of unit (1 if adequate, 0 otherwise)

2.1860
(2.74)

1.2212
(0.29)

0.4240
(1.74)

2.0122
(2.47)

1.0494
(0.07)

0.4315
(1.71)

Average resident’s evaluation of unit
(scale of 1-10: 1 is worst, 10 is best)

0.9240
(1.86)

1.1128
(0.95)

0.9087
(1.12)

0.9282
(1.77)

1.1147
(0.97)

0.9095
(1.11)

Age of unit

0.9976
(0.37)

0.9697
(2.11)

1.0114
(0.84)

0.9973
(0.41)

0.9698
(2.10)

1.0117
(0.86)

Unit is part of multiunit building (1 if yes, 0 if no)

1.7279
(2.19)

0.1558
(3.03)

0.2353
(2.77)

1.6745
(2.09)

0.1617
(3.02)

0.2357
(2.75)

Number of units in building

0.9911
(1.77)

0.9973
(0.33)

0.9954
(0.42)

0.9915
(1.72)

0.9968
(0.39)

0.9950
(0.45)

Ratio of subsidized units to rental units in zone

0.7921
(0.13)

0.0002
(2.05)

0.2915
(0.28)

0.4865
(0.41)

0.0001
(2.20)

0.2465
(0.32)

Average age of rental units in zone

0.9698
(1.66)

0.9700
(0.67)

0.9290
(1.76)

0.9735
(1.42)

0.9829
(0.36)

0.9262
(1.78)

Ratio of public housing units to rental units in zone

0.0040
(2.39)

0.0318
(0.73)

0.1173
(0.43)

0.0122
(1.97)

0.0915
(0.53)

0.0888
(0.50)

Ratio of rental units to all units in zone

0.7950
(0.23)

4.9530
(0.71)

104.5796
(1.99)

0.9647
(0.03)

8.5545
(0.92)

77.8929
(1.83)

Ratio of affordable units to rental units in zone

0.7486
(0.25)

11.9466
(0.81)

1.9007
(0.23)

0.5920
(0.45)

13.4601
(0.84)

1.9701
(0.24)

Average resident’s evaluation of neighborhood
(scale of 1-10: 1 is worst, 10 is best)

1.1137
(0.49)

1.2297
(0.43)

1.1343
(0.27)

1.1639
(0.66)

1.4238
(0.69)

1.0564
(0.11)

Percentage African-American heads of household in zone

0.4929
(1.11)

0.0373
(1.82)

0.2794
(0.88)

0.4870
(1.12)

0.0352
(1.79)

0.2909
(0.87)

Median income in zone

0.9999
(2.63)

0.9999
(1.49)

1.0000
(0.21)

0.9999
(2.46)

0.9999
(1.33)

1.0000
(0.22)

Hedonic price change in MSA (affordable units)

0.7749
(0.43)

0.2247
(1.07)

5.0766
(0.98)

0.7991
(0.38)

0.2322
(1.05)

4.9279
(0.96)

Hedonic rent change in MSA (affordable units)

0.0368
(0.51)

0.0000
(1.23)

0.0000
(0.93)

0.2773
(0.20)

0.0000
(0.99)

0.0000
(0.96)

Dummy variable = 1 if percentage of units in zone
that are rent-controlled is greater than 10 percent

0.4516
(3.04)

0.3349
(1.85)

1.0714
(0.12)
0.2057
(1.66)

0.0210
(1.62)

2.0694
(0.35)

Variable

Percentage of units in zone that are rent-controlled

Specification 2
Pseudo R2 = 10.35 Percent

Dummy variable = 1 if zone is in central city

1.4884
(1.33)

2.7934
(1.50)

1.3968
(0.50)

1.2578
(0.78)

2.5738
(1.39)

1.3405
(0.45)

Dummy variable = 1 for Washington, D.C.

0.4480
(0.68)

0.0682
(0.99)

0.1614
(0.87)

0.5596
(0.50)

0.1001
(0.84)

0.1613
(0.87)

Government Regulation and Changes

Appendix: Multinomial Regression Results (Continued)

Table A3 (Continued)

Affordable Rental Units
Multinomial Logit/Excluded Option/Remain Affordable
Specification 1
Pseudo R2 = 10.79 Percent

Specification 2
Pseudo R2 = 10.35 Percent

Rent Rises

OwnerOccupied

Demolished/
Converted

Rent Rises

OwnerOccupied

Demolished/
Converted

Dummy variable = 1 for New York City

0.6799
(0.55)

0.5997
(0.32)

0.6003
(0.37)

0.6866
(0.52)

0.4254
(0.52)

0.7762
(0.17)

Dummy variable = 1 for San Francisco

0.4021
(0.97)

0.0914
(1.10)

0.2025
(0.94)

0.4563
(0.84)

0.1195
(0.96)

0.1935
(0.97)

Dummy variable = 1 for San Jose

0.3275
(1.26)

0.1017
(1.04)

0.2308
(0.88)

0.4102
(1.00)

0.1489
(0.86)

0.2342
(0.87)

Dummy variable = 1 for Boston

1.2361
(0.34)

5.1691
(1.14)

3.2279
(0.88)

1.2339
(0.32)

3.4391
(0.83)

4.1158
(0.94)

Number of years current resident has occupied unit

0.9990
(0.11)

0.9866
(0.55)

1.0083
(0.39)

0.9982
(0.19)

0.9859
(0.58)

1.0080
(0.38)

Ratio of rent to cutoff of affordability

0.6567
(0.90)

1.0654
(0.06)

1.1459
(0.13)

0.6721
(0.85)

1.1626
(0.14)

1.0885
(0.08)

Variable

Notes: Number of observations: 592. The dependent variable has four possible values: 1) an affordable rental unit can remain affordable, 2) become unaffordable because of increases in its rent relative to the affordability cutoff, 3) become owner-occupied, or 4) be demolished or converted to another use. The
excluded (base) outcome is to remain affordable. The top number reported is the unit odds ratio e b ; the bottom number (in parentheses) is the Z-statistic.
The odds ratio is the probability of outcome i divided by the probability of the null (or excluded) outcome, and is equal to e XB. The unit odds ratio is the
odds ratio for a one-unit increase to the independent variable. Thus, it is not b that is reported in the table, but eb. The Z-statistic is based on the null
b
hypothesis that b = 0, which is equivalent to the unit odds ratio e = 1 . The excluded metropolitan statistical area (MSA) dummy is for Los Angeles.

FRBNY Economic Policy Review / June 2003

Endnotes

1. Among the many papers in this literature are Bogdon, Silver, and
Turner (1994) on the relationship between affordability and
adequacy, Nelson (1994) on the association between the affordable
stock and low-income households, O’Flaherty (1996) on the
economics of homelessness, and especially Nelson and
Vandenbroucke’s (1996) seminal work charting the size of and change
in the aggregate low-income housing stock.
2. The older empirical treatments of filtering are well surveyed by
Brzeski (1977). Arnott, Davidson, and Pines (1983) allow for
maintenance and rehabilitation, and Braid (1981) studies filtering in
rental housing markets. Among a number of their papers on this topic,
Bond and Coulson (1989) analyze neighborhood change in a model
where the value of housing is related to neighborhood characteristics.
3. Mayer and Somerville (2000b) formally test the effects of regulation
on the dynamics of the supply response to demand shocks.
4. An exception is Arnott (1995), who identifies several potential
welfare benefits of rent control.
5. DiPasquale and Somerville (1995) demonstrate how to merge the
1974-83 AHS data with those from 1984-94, but the earlier period
does not report precise rents. Combining the two sets would bias our
results because we must set a precise cutoff for affordability.

Government Regulation and Changes

6. Rents are a percentage of the four-person family, 30 percent cutoff
as follows: zero bedrooms, 70 percent; one bedroom, 75 percent; two
bedrooms, 90 percent; three bedrooms, 104 percent; four bedrooms,
116 percent; then increasing by 12 percentage points for each
additional bedroom up to fourteen bedrooms.
7. In 1989, the survey question about utility costs was changed,
resulting in a shift in responses. To correct for this change, we follow
Nelson and Vandenbroucke (1996) and adjust reported utility costs
for 1989 and later years.
8. The category “demolished or converted” includes units that were
converted to business use, eliminated in a conversion, abandoned,
destroyed by disaster, demolished, or condemned. It also includes
units with an interior now exposed to the elements and mobile-home
sites that no longer have a home on them.
9. We expect that a new occupant is less likely to respond to the AHS
than an occupant who has responded in the past. Rents for a unit tend
to increase more with unit turnover. Thus, we are likely to undercount
units whose rents rise, resulting in an undercount of those units that
move out of the affordable stock because the new rent exceeds the
affordability cutoff.

References

Arnott, Richard. 1995. “Time for Revisionism on Rent Control.”
Journal of Economic Perspectives 9, no. 1: 99-120.
Arnott, Richard, Russell Davidson, and David Pines. 1983. “Housing
Quality, Maintenance, and Rehabilitation.” Review of
Economic Studies 50: 467-94.
Bogdon, A., J. Silver, and M. Turner. 1994. “National Analysis of
Housing Affordability, Adequacy, and Availability: A Framework
for Local Housing Strategies.” HUD-1448-PDR. Washington,
D.C.: U.S. Department of Housing and Urban Development.

Fallis, George, and Lawrence B. Smith. 1984. “Uncontrolled Prices in a
Controlled Market: The Case of Rent Control.” American
Economic Review 74, no. 1: 193-200.
Fischel, William. 1990. Do Growth Controls Matter? A Review
of the Empirical Evidence on the Effectiveness and
Efficiency of Local Government Land-Use Regulation.
Cambridge: Lincoln Institute of Land Policy.
Fraser Institute. 1975. Rent Control: A Popular Paradox.
Vancouver, British Columbia: The Fraser Institute.

Bond, Eric W., and N. Edward Coulson. 1989. “Externalities, Filtering
and Neighborhood Change.” Journal of Urban Economics 26:
231-49.

Fu, Yuming, and C. Tsuriel Somerville. 2001. “Site Density Restrictions:
Measurement and Empirical Analysis.” Journal of Urban
Economics 49, no. 2: 404-23.

Braid, Ralph M. 1981. “The Short-Run Comparative Statics of a Rental
Housing Market.” Journal of Urban Economics 10, no. 3:
286-310.

Glaeser, Edward L. 1996. “The Social Costs of Rent Control Revisited.”
NBER Working Paper no. 5441.

Brueckner, Jan K. 1997. “Infrastructure Financing and Urban
Development: The Economics of Impact Fees.” Journal of
Public Economics 66, no. 3: 383-407.
Brzeski, Wladyslaw. 1977. “An Annotated Bibliography of the
Literature on Filtering and Related Aspects of Urban Housing.”
University of British Columbia Faculty of Commerce, Urban Land
Economics Bibliography, Series 1.
Coulson, N. Edward, and Eric W. Bond. 1990. “A Hedonic Approach to
Residential Succession.” Review of Economics and Statistics
72: 433-43.
DiPasquale, Denise, and C. Tsuriel Somerville. 1995. “Do House Price
Indexes Based on Transacting Units Represent the Entire Stock?
Evidence from the American Housing Survey.” Journal of
Housing Economics 4: 195-229.
Early, Dirk W., and Jon T. Phelps. 1999. “Rent Regulations’ Pricing
Effect in the Uncontrolled Sector: An Empirical Investigation.”
Journal of Housing Research 10, no. 2: 267-85.
Engle, R., P. Navarro, and R. Carson. 1992. “On the Theory of Growth
Controls.” Journal of Urban Economics 32, no. 3: 269-83.

Glaeser, Edward L., and Erzo F. P. Luttmer. 1997. “The Misallocation
of Housing under Rent Control.” NBER Working Paper no. 6220.
Linneman, Peter, and Anita Summers. 1991. “Wharton Urban
Decentralization Project Data Set.” University of Pennsylvania,
Wharton Real Estate Unit.
Margolis, Steve E. 1982. “Depreciation of Housing: An Empirical
Consideration of the Filtering Hypothesis.” Review of
Economics and Statistics 64: 90-6.
Mayer, Christopher J., and C. Tsuriel Somerville. 2000a. “Residential
Construction: Using the Urban Growth Model to Estimate
Housing Supply.” Journal of Urban Economics 48: 85-109.

———. 2000b. “Land-Use Regulation and New Construction.”
Regional Science and Urban Economics 30, no. 6: 639-62.
McArdle, Nancy. N.d.“Survey Changes Affecting Rent for Subsidized
Units.” Unpublished paper, Harvard University Joint Center for
Housing Studies.
Metzger, John T. 2000. “Planned Abandonment: The Neighborhood
Life-Cycle Theory and National Urban Policy.” Housing Policy
Debate 11, no. 1: 7-40.

FRBNY Economic Policy Review / June 2003

References (Continued)

National Housing Task Force. 1988. “A Decent Place to Live: The
Report of the National Housing Task Force.” Washington, D.C.
Nelson, Kathryn P. 1994. “Whose Shortage of Affordable Housing?”
Housing Policy Debate 5, no. 4: 401-42.
Nelson, Kathryn P., and David A. Vandenbroucke. 1996. “Affordable
Rental Housing: Lost, Stolen, or Strayed?” Unpublished paper,
Department of Housing and Urban Development, Office of Policy
Development and Research.
O’Flaherty, Brendan. 1996. The Economics of Homelessness.
Cambridge: Harvard University Press.
Olsen, Edgar O. 1972. “An Econometric Analysis of Rent Control.”
Journal of Political Economy 80, no. 5: 1081-1100.
———. 1998. “Economics of Rent Control.” Regional Science
and Urban Economics 28, no. 6: 673-8.
Phillips, Robyn S. 1981. “A Note on the Determinants of Residential
Succession.” Journal of Urban Economics 9, no. 1: 49-55.
Ratcliff, Richard U. 1949. “Filtering Concept.” In Urban Land
Economics. New York: McGraw-Hill.

———. 2001. “Permits, Starts, and Completions: Structural
Relationships versus Real Options.” Real Estate Economics 29,
no. 1: 161-90.
Somerville, C. Tsuriel, and Cynthia Holmes. 2001. “Dynamics of the
Affordable Housing Stock: Micro-Data Analysis of Filtering.”
Journal of Housing Research 12, no. 1: 115-40.
Stone, Michael E. 1993. Shelter Poverty: New Ideas on Housing
Affordability. Philadelphia: Temple University Press.
Susin, Scott. 1999. “Rent Vouchers and the Price of Low-Income
Housing.” New York University Furman Center for Real Estate
and Urban Policy Working Paper no. 99-4.
Sweeney, James L. 1974. “Quality, Commodity Hierarchies, and
Housing Markets.” Econometrica 49: 147-67.
U.S. Department of Housing and Urban Development, Office of Policy
Development and Research. 1996. “Rental Housing Assistance at a
Crossroads.” Washington, D.C.: U.S. Government Printing Office.
Weicher, John C., and Thomas G. Thibodeau. 1988. “Filtering and
Housing Markets: An Empirical Analysis.” Journal of Urban
Economics 23, no. 1: 21-40.

Riddiough, Timothy J. 1997. “The Economic Consequences of
Regulatory Taking Risk on Land Value and Development
Activity.” Journal of Urban Economics 41, no. 1: 56-77.
Somerville, C. Tsuriel. 1999. “Residential Construction Costs and the
Supply of New Housing: Finding Consistent Effects of Structure
Costs on Homebuilding Activity.” Journal of Real Estate
Finance and Economics 19, no. 1: 43-62.

Government Regulation and Changes

Jack Goodman

Commentary

ousing affordability is a wide-ranging topic, and the
conference organizers have wisely chosen to organize the
program sessions around different themes. The theme of this
session is housing markets, but it is really about housing
markets as they are affected by local regulation. It is an
appropriate and important focus.
I will do two things in these comments. First, I offer some
thoughts on the paper by Tsuriel Somerville and Christopher
Mayer, by way of the mandatory critique, and then go on to
discuss some broader issues related to the topic of their paper.
The authors use a sample of rental housing units from
thirty-eight metropolitan areas in the 1980s and 1990s to
examine the effects of regulation on housing affordability. They
find that regulation and other constraints on new construction
put upward pressure on rents in the existing housing stock and
cause units to filter up and out of the affordable stock. This is
not a surprise. Their finding on rent control is a surprise,
however, in that they estimate that uncontrolled units are less
likely to leave the affordable stock in areas where rent control is
more prevalent. This finding is at odds with previous findings
and common sense, and as the authors indicate, they think it is
due to the characteristics of these units.
There is a lot to like about this paper. First is its focus on
regulation as an influence on housing affordability. There are
two other ways by which governments influence housing
affordability: demand subsidies to give people money or tax

breaks to help them buy or rent housing, and supply subsidies
to reduce the cost of building or renovating housing. We know
a fair amount about these two forms of government action to
promote affordability. One thing we know is that they cost a lot
of money. Regulation is different in that it involves neither cash
outlays nor credit guarantees from governments.
But, with the exception of rent control, we do not know
much about regulation’s effects on housing affordability in the
existing housing stock. There are many opinions and
anecdotes, but little hard evidence, in part because it is difficult
to quantify regulation. It is a tough topic to tackle empirically,
and the authors are to be commended for taking it on.
Another attraction of this research is that it offers a new
approach: following individual housing units over time and
relating their performance to their characteristics and to the
local market and regulatory structure around them. The
research looks at multiple possible outcomes for affordable
units—another innovation. And the authors explain how it fits
into the literature. The paper is a logical extension of previous
work by Somerville and Mayer and their coauthors.
Lastly, the data source is potentially quite powerful. The
same questions are asked of statistically valid samples in a large
number of metro areas. The data provide the opportunity to go
way beyond case studies and anecdotes, which are useful but
are hard to generalize with confidence.

Jack Goodman is president of Hartrey Advisors.

The views expressed are those of the author and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

FRBNY Economic Policy Review / June 2003

These are all strengths of the research. Yet the authors face a
number of research challenges with this work as well.
One challenge to all researchers on housing affordability is
to define what affordable housing is. The Somerville and Mayer
study adopts a fairly conventional standard in terms of
household income and how much of it can be allocated to
housing. But affordability is an inherently subjective notion on
which reasonable people can and do disagree. Yet even if
people disagree on what affordable housing is, they may be able
to agree on whether housing is getting more or less affordable
over time. For this reason, counting units that cross a threshold
(which is the approach in this study) can be less controversial
than selecting the threshold itself. Picking another threshold
would likely have produced qualitatively similar results.
Note that the authors only look at rental housing. This does
not mean that owner-occupied housing presents no
affordability issues, but renters have lower incomes on average
than owners, and therefore appropriately receive special
attention in policy discussions. In addition, measurements of
housing costs, market dynamics, and government programs all
differ between rental and owner-occupied housing. For all
these reasons, it is sensible to study rental housing on its own.
A second challenge is to quantify regulation. It is very tough
to boil regulation down to a ten-point scale or anything similar.
Much of regulation’s effect on housing affordability comes down
to land-use controls, and the authors rightly focus on this effect.
Another challenge is to use the American Housing Survey
(AHS) data fully, but to avoid pushing the data beyond their
limits. I have used the AHS data a lot, and I know that these
data are not easy to link longitudinally or to aggregate across
the different metro surveys. Much behind-the-scenes work was
needed to get the data to where the authors have them, and
Somerville and Mayer should be credited for that work.
But I am concerned that the resulting data set is a bit of a
grab bag. It mixes time periods, jurisdictional differences
within metropolitan areas, and different sampling fractions
across metro areas. And the timing of the growth management
survey does not necessarily match the timing of the housing
unit observations to which it is linked.
Without getting into the econometrics, let me just say that
these characteristics of the sample put pressure on the model to
include all the relevant variables so that influences ascribed to
one variable are not really reflecting the influence of a variable
left out of the model. Some of these data issues, as well as simple
misreporting of rent control and subsidy status in the AHS,
may help explain the counterintuitive rent control results. The
interpretation given by the authors is not inconsistent with the
data, but it seems just a little too easy and convenient.

Commentary

Separate from these data issues is the paper’s approach of
using long-run differences across areas to explain short-run
dynamics. In particular, land-use regulations are used to
explain movement of units across the affordability threshold. It
seems more appropriate to look at the regulations’ effects on
the proportion of units above and below the threshold. The
model’s specification calls for caution in drawing conclusions.
For example, one cannot project from these results that, if
regulations were changed, a jurisdiction would experience
within that same three- or four-year period the changes in
filtering estimated by the model.
A last comment specifically about the paper regards the
summary statement that regulation is less important than unit
or neighborhood characteristics in determining filtering. I take
exception to this as a portable conclusion that can be applied
elsewhere. It is very specific to the variables used in this
analysis, their calibration, and the model specification. This
will always be the case, so it is unlikely that any general
statement about the relative importance of regulation, housing
unit, and neighborhood characteristics in the filtering process
is a meaningful statement.
The paper is about housing filtering. Let me offer a
framework and set of charts that I think capture the authors’
approach and will help me to illustrate some more general
points: Every housing unit in a local market can be defined in
terms of a quality index and a price index. The quality index (q)
is a single-dimensional summary of all the size, amenity, and
locational attributes that are valued in housing. The price index
(p) measures the price per unit of housing quality paid for that
house or apartment. This price index will vary from house to
house and from apartment to apartment even within a local
housing market due to segmentation of the market and various
market “imperfections.” Speaking loosely, this price index can
be viewed as a profitability index from the supplier’s
perspective and as an (inverse) “good deal” index from the
consumer’s perspective. Chart 1 offers an illustration, where
each dot represents a house or apartment. Apartments A and B
provide the same quality housing, but Apartment A is more
expensive. Similarly, Apartments B and C have the same price
per unit of quality, but unit B is of higher quality.
To be in the housing stock, units must meet two criteria:
a minimum quality standard, set by government through code
enforcement, zoning, and occupancy standards; and a price
(loosely a proxy for profitability) threshold, set by the market. In
Chart 1, these two minimums are indicated by the hash marks.
When people think about affordable housing, many think
about modest but decent housing that is not too expensive and
fits within a family’s budget. A household’s expenditure on
housing is the product of how much housing they consume (q)

and the price per unit of quality (p) that they pay. A fixed budget
for housing is consistent with various combinations of q and p.
All households hope, of course, to get a good deal on housing so
that their housing expenditure gives them a lot of q at a low p.
The triangle in Chart 1 defines the housing units with
combinations of p and q that meet all three requirements for
affordable housing: minimum standards, minimum
profitability, and within a moderate-income household’s
budget constraint. The downward slope to the hypotenuse
indicates that households that get a better deal (lower p) on
their housing can consume more housing (higher q) without
exceeding their housing budget. Drawn here for simplicity as a
straight line, the combinations of p and q consistent with a
fixed budget actually trace out a line that bows inward (concave
to the origin).
Filtering in its simple form is represented by horizontal
movement over time of individual housing units in the chart.
Units increase or decrease in housing quality, but with no
change in the “profitability” of the units. Vertical movement, in
contrast, indicates a change in housing price or profitability,
but with no change in physical characteristics.
Gentrification, shown in Chart 2, can be represented by a
unit filtering up in quality level, with a profit incentive driving
the upgrading, indicated here by the upward tilt to the line.
Housing can also be lost from the affordable stock if its
profitability turns negative due to insufficient demand relative
to available supply. This phenomenon is depicted in Chart 3 by

the price index falling below the threshold level for the site and
structure to avoid abandonment or redevelopment into
nonresidential use. Redevelopment can occur on any residential
site providing any level of housing quality, but it typically occurs
where the existing structures are reaching the end of their
economic life and often are in the affordable triangle.
Lastly, housing can be lost to the affordable stock through
government action. Local governments establish and enforce
the zoning ordinances, building codes, and occupancy
standards that set the minimum quality level of housing in a
neighborhood. If units fall below that threshold, as shown in
Chart 4, they are subject to removal from the stock, regardless
of their profitability.
In this paper and in a previous one, Somerville and Mayer
show that neighborhood influences are especially important in
determining whether housing filters up and out of the
affordable stock. They find that, all else equal, units are more
likely to filter up if they are surrounded by higher value
housing. In other words, it is hard to maintain housing
heterogeneity in neighborhoods with strong housing demand.
Let me say a few things about neighborhood heterogeneity.
It is a value judgment, to be sure, but many people want
diversity in their local populations and housing. Despite
“NIMBYism,” many communities promote diversity, if not
within blocks, then diversity within neighborhoods, or at least
within local jurisdictions.

Chart 1

Chart 2

The Affordable Housing Triangle

Gentrification

Rent per unit of housing quality (price index)

Apartment A
Apartment C

Apartment B

Housing quality

FRBNY Economic Policy Review / June 2003

Neighborhood is important to housing affordability
because mixed, diverse neighborhoods are where a lot of the
affordable stock is found. But neighborhood diversity tends to
be transitional, a nonequilibrium condition. Some diverse
neighborhoods are on their way up, growing in demand and
being redeveloped into newer, higher density places. Other
mixed neighborhoods are on their way down, characterized by
outmigration by those who can leave and by housing
abandonment. Affordable housing is lost in both instances.
The challenges of maintaining a housing mix in
neighborhoods and communities growing in popularity are
different from those that are declining. If citizens should charge
their government with maintaining a housing mix, what can
government do to achieve that objective?
Here, I am talking about local governments. Each of the
three levels of government has a distinct role, I would argue, in
promoting housing affordability. First, the federal government
is the program designer and financier for most of the country’s
largest demand- and supply-side affordability initiatives.
Second, state governments are the gatekeepers that provide
legislative authority to local jurisdictions and allocate funds
from some federal and state revenues. Third, local
governments are the enablers/implementers that run or
oversee programs and control development and property
operations through zoning and building codes.
Local governments have a lot of sticks and carrots that can
be brought to bear on maintaining housing diversity. But these
tools work better in growing areas than in declining ones. In
declining neighborhoods, government intervention is a bit like

Chart 3

Chart 4

Lost through Insufficient Demand

Lost through Government Regulation

Rent per unit of housing quality (price index)

Housing quality

pushing on a string. Regulation usually means keeping people
from doing something, and you cannot keep people from
moving out of a neighborhood.
In growing areas, depending on state laws, local governments may be able to mandate that development be of a certain
type and include affordable housing. In other jurisdictions, a
“carrot” approach of offering density bonuses or other
regulatory incentives for inclusion of on-site affordable
housing may be more appropriate. The bonus density
approach will not always result in diversity in housing types,
but it can retain diversity in neighborhood incomes.
There is another, potentially powerful but much more
controversial, tool that local governments have at their disposal
for promoting housing affordability: housing-quality
standards can be relaxed. The housing affordability problem in
large part is an income problem. People do not have enough
money to pay rent for the housing that is available. And that
housing is constrained not only by the cost of building and
maintaining it, but also by restrictions placed by government
on the types of housing that can be offered in the community.
These government restrictions force some residents to consume
more housing than they would choose to, given their resources.
“Reduce housing-quality standards,” is a phrase certain to
raise blood pressures among some in the local electorate. But
closely related policy prescriptions include “eliminate
exclusionary zoning” and “remove barriers to affordable
housing.” The latter, by the way, is very close to the name of the
presidentially mandated Advisory Commission on Regulatory
Barriers to Affordable Housing, which issued its report in 1991.

Commentary

Chart 5

Effect of Government Easing of Quality Constraint
Rent per unit of housing quality (price index)

Housing quality

A policy focus on housing-quality standards is not a new or
radical idea, but one that may need reinforcing.
Housing standards typically are set at levels way above those
required to ensure safety and sanitation. Zoning and building
code restrictions on lot sizes and required interior space per
housing unit are good examples of regulations that can force
overconsumption or exclusion. Easing standards can have
significant effects on the availability of affordable housing.
Within the triangle framework, this potential is illustrated in
Chart 5.
In conclusion, any way you look at it, local governments,
through their regulations, directly and indirectly affect the
affordable housing stock and changes to it. The paper by
Somerville and Mayer and others similar to it shed light on this
local government role and help to calibrate it, and by doing so
provide a valuable resource to the policy debate.

Ingrid Gould Ellen, Michael H. Schill, Amy Ellen Schwartz, and Ioan Voicu

Housing Production Subsidies
and Neighborhood
Revitalization:
New York City’s Ten-Year
Capital Plan for Housing
1. Introduction

perennial question in housing policy concerns the form
that housing assistance should take. Although some argue
that housing assistance should be thought of as a form of
income support and advocate direct cash grants to needy
households, others favor earmarked assistance—but they differ
over whether subsidies should be given to the recipients as
vouchers or to developers as production subsidies.
The appropriate composition of housing assistance has
recently taken on particular import. In 2000, Congress created
the Millennial Housing Commission and gave it the task of
evaluating the “effectiveness and efficiency” of methods to
promote housing through the private sector. As part of its
mandate, the commission is examining changes to existing
programs as well as the creation of new production programs
to increase affordable housing.
This paper reexamines the debate over the appropriate form
of housing assistance. First, we briefly summarize and evaluate

Ingrid Gould Ellen is an assistant professor of public policy and urban planning
at New York University; Michael H. Schill is a professor of law and urban
planning at New York University and director of the university’s Furman Center
for Real Estate and Urban Policy; Amy Ellen Schwartz is an associate professor
of public policy at New York University; Ioan Voicu is the Furman Fellow at
New York University’s Furman Center for Real Estate and Urban Policy.

arguments in favor of demand-oriented housing subsidies
(such as Section 8 vouchers) and supply-oriented housing
subsidies (such as production subsidies). We conclude that
although demand-oriented subsidies are preferable to supplyoriented subsidies on a number of grounds, government
support for production may, at least theoretically, be justified
as a way to promote positive spillover effects and neighborhood revitalization. Whether sufficient spillovers exist is, in the
end, an empirical question. Although much of the existing
research finds little evidence of spillover effects, our findings on
the New York City experience suggest that spillovers may be
significant and large enough to justify government support for
production.
Next, we describe the most extensive experiment in the
United States in which a city used supply-oriented subsidies to
rebuild neighborhoods—New York City’s Ten-Year Capital
Plan for Housing (the “Ten-Year Plan”). Born out of the
necessity to rebuild communities devastated by years of
abandonment and arson, the program, launched by New York
The authors thank the Fannie Mae Foundation, the Lincoln Institute for Land
Policy, and the Furman Center for Real Estate and Urban Policy for funding
this research. They also express gratitude to Jerilyn Perine, Richard Roberts,
Harold Shultz, Calvin Parker, Ilene Popkin, and Harry Denny of the New York
City Department of Housing Preservation and Development for providing the
data necessary to complete this research. Finally, thanks are due Felice
Michetti for comments on a previous draft. The views expressed are those of
the authors and do not necessarily reflect the position of the Federal Reserve
Bank of New York or the Federal Reserve System.
FRBNY Economic Policy Review / June 2003

City in 1986, ultimately led to the investment of more than
$5.1 billion in housing in many of the city’s poorest
neighborhoods.
Finally, we describe the results of several empirical studies
we have recently completed on the effect of the Ten-Year Plan
on property values in New York City. Our results suggest that
the use of production subsidies can indeed generate positive
spillovers and contribute to neighborhood revitalization.
Furthermore, by comparing and contrasting New York City’s
experiences with those of other cities, we explain why New
York was so successful, and identify aspects of its program that
could be transplanted to other cities.

2. Justifications for Housing
Assistance: Revisiting the
Supply-versus-Demand Debate
Although housing subsidies have become commonplace in the
United States, it is still worthwhile to consider whether
household financial assistance might be tied to housing rather
than just provided as unrestricted cash grants. If the only
housing-related problem facing Americans was insufficient
income among poor families to purchase adequate housing,
then a strong argument could be made that unrestricted cash
grants would be best. In a liberal society dedicated to free
choice, allowing individuals to make their own decisions with
respect to consumption would generally seem desirable.
Furthermore, considerable evidence suggests that unrestricted
cash grants would lead to increases in housing consumption
that fall short of the grant amount (Polinsky and Ellwood
1979). The implication is that earmarking subsidies for housing
would be a less efficient way than cash grants to enhance
household welfare. Finally, earmarked housing assistance
carries an additional inefficiency—the cost of administration
necessitated by the requirement that the money be spent on a
specific good.
Despite the inefficiency, since the end of World War II,
federal, state, and city governments have repeatedly tied
subsidies to housing consumption. A number of justifications
might be offered for this. First, consumers may have
incomplete information about the benefits and importance of
adequate housing, leading them to spend too little on it. People
who choose other goods and services before a minimum level
of shelter may do so because they lack sufficient information or
are unable to assess rationally the true worth of decent housing,
thereby justifying societal paternalism. Second, efforts to

72 Housing Production Subsidies and Neighborhood Revitalization

provide a minimum level of housing consumption may be
necessary to protect children from irresponsible parents, who
would, without government intervention, provide inadequate
housing for their children. Third, taxpayers may derive utility
merely from the knowledge that people are not living in
desperately deteriorated and unhealthy accommodations
(Aaron 1972; Schill 1990; Olsen 2001). Thus, taxpayers may
prefer that their tax dollars subsidize someone’s shelter
directly, since it yields a greater increase in housing
consumption per public dollar spent than do cash transfers,
even if housing subsidies are less useful to the recipient than
cash transfers.
In addition to achieving redistributive and/or paternalistic
goals, earmarked housing assistance may be preferable to cash
transfers in addressing other economic and social objectives.
Such goals might include lessening adjustment lags in supply
and demand, ameliorating the impact of discrimination in the
housing market, improving the locations in which families
live, and promoting positive spillovers and neighborhood
redevelopment (see Ellen, Schill, Schwartz, and Voicu [2001]).
The observation that earmarked housing assistance may
further some or all of these objectives does not, however,
suggest what form this assistance should take. In the remainder
of this section, we examine what we have learned about the
relative merits of different approaches. In particular, we discuss
the advantages and disadvantages of supply- and demandoriented housing subsidy programs.
According to recent estimates, the federal government
provides housing assistance to roughly 5.2 million renter
households. An additional 9 million households qualify for
assistance but do not receive it because housing subsidies are
neither an entitlement nor a fully funded social welfare
program (U.S. General Accounting Office 2001). This scarcity
of subsidies makes efficient deployment of government
resources crucial. Thus, it is important to begin by noting that
virtually every empirical study performed over the past twentyfive years has found that demand-oriented subsidies (that is,
vouchers and certificates) are more cost-effective than supplyoriented programs that subsidize the production of housing
(including the public housing program, the Section 8 new
construction program, and the low-income housing tax
credit).1
A 2001 study by the U.S. General Accounting Office (GAO),
for example, compared the cost, both in total and in the
amount borne by the federal government, of housing vouchers
over a thirty-year period with the cost of housing built using
the low-income housing tax credit, the HOPE VI program,
Section 202, Section 811, and Section 515. According to the
analysis, the total per-unit costs for housing production

programs ranged from 12 percent to 27 percent more than the
cost of voucher programs (U.S. General Accounting Office
2001, p. 2). In terms of the cost to the federal government, the
production programs were between 15 percent and 38 percent
more expensive.2
In addition to being cheaper than production programs,
housing vouchers have typically led to better locational
(neighborhood) outcomes. Supply-oriented programs operate
with a built-in contradiction: programs that try to target scarce
resources to the neediest recipients (such as the public housing
program) end up creating intensely concentrated poverty. And
there is growing and persuasive evidence that concentrations of
poverty are related to a wide variety of social problems,
including high crime, dropout, welfare receipt, and teenage
pregnancy rates.3 Programs with less effective targeting (such
as HOPE VI or the low-income housing tax credit) foster more
economically integrated environments—but the cost is vertical
inequity.
Housing vouchers resolve this contradiction. Because the
voucher recipient can rent housing in the private market
(restricted only by maximum fair market rents), the more
narrowly a voucher program is targeted to the poor, the more
likely it is that deconcentration will occur. Indeed, research has
typically shown that the neighborhood outcomes of voucher
recipients dominate those who live in housing supported by
production subsidies; voucher recipients see greater
improvement in their neighborhood conditions than do public
housing recipients. As an example, using data from the 1990
census, Newman and Schnare (1997) conclude that projectbased assistance programs “do little” to improve the quality of
recipients’ neighborhoods (and, in the case of public housing,
“appear to make things significantly worse”), while certificate
and voucher programs reduce the probability that a family will
live in the most economically and socially distressed areas
(pp. 726-7). They provide a powerful argument in favor of
vouchers.
In some housing markets, however, vouchers may not live
up to their promise. In markets with extremely low vacancy
rates, such as New York City in the late 1990s, voucher
recipients might experience significant difficulties identifying
standard-quality housing with rents below federally prescribed
maximum levels.4 Although this imbalance of supply and
demand might be a short-term phenomenon caused by a
sudden exogenous increase in demand for housing, it might be
chronic and attributable to barriers (including regulatory
barriers) in the housing market (Salama, Schill, and Stark
1999).
In such tight housing markets, production subsidies can, in
principle, enable households to obtain housing faster and more

cheaply than vouchers can. In practice, however, governmentsupported development is frequently slowed by bureaucratic
delays, neighborhood opposition, and political pressure.
Moreover, if regulatory barriers are the problem, direct
government provision is hardly the ideal response—instead, a
much better solution would be to remove the barriers that
interfere with the smooth operation of the housing market.
Subsidizing production can also, again in principle, be
justified as a method of eliminating or ameliorating the effects
of discrimination in the housing market.5 Discriminatory
treatment may increase search costs, drive up the cost of
housing for its victims, and interfere with optimal residential
location decisions. Since government provision should be
nondiscriminatory, direct provision of housing by government
may be proposed as a partial solution to the problem of
housing discrimination. Unfortunately, some of the most
blatant acts of discrimination by landlords in the United States
have been committed by government agencies and some of the
most segregated housing developments in the nation are
owned by public housing authorities (Hirsch 1983).
Furthermore, even if government could be relied upon to
operate in a nondiscriminatory manner, it is unclear whether
production programs would be the most effective way to
ameliorate the effects of housing discrimination. Instead, more
vigorous enforcement of antidiscrimination laws may be more
effective and preferable.
Although production programs do not have a comparative
advantage over vouchers in cost-effectiveness or improving
locational outcomes, and the case for relying upon them to deal
with market failures such as adjustment lags and discrimination seems weak, production programs may be justified
by their ability to promote neighborhood development.
Production programs may generate positive external benefits
to the neighborhoods in which they are located above and
beyond the benefits received by the housing consumers
themselves.
Because housing is fixed in space, its condition influences
the value of neighboring properties. A dilapidated structure,
for instance, can reduce the value of neighboring homes and
may lead to disinvestment in the neighborhood. Introducing a
high-quality building might, however, generate positive
spillovers and increase values and confidence in the area.
Adding new housing might also bring new people to a
neighborhood, which may, in turn, improve neighborhood
safety and fuel demand for retail services. If building owners do
not bear all of the costs (or benefits) generated by their
properties, the private sector will underinvest in housing.
Public intervention, such as slum clearance or rehabilitation
assistance, may therefore be appropriate.

FRBNY Economic Policy Review / June 2003

Similarly, production programs may generate informational externalities. Housing developers may be averse to
investing in distressed urban neighborhoods because they have
little information about the demand for new housing in the
area. Housing investment in distressed neighborhoods, then,
may be delayed or be insufficient because each developer
hesitates to make the first move. Government, through
subsidies and planning, can, in principle, encourage developers
to make the first move, provide information, and thereby
reduce risk (Caplin and Leahy 1998).
If any form of housing subsidy is likely to be capable of
generating positive spillovers and catalyzing neighborhood
development, it would seem to be production subsidies rather
than vouchers. Indeed, the key shortcoming of production
subsidies—their concentration in spatially defined areas—
becomes an advantage when it comes to neighborhood
revitalization. Although vouchers increase demand and may
well stimulate a supply response (including both new units and/
or housing rehabilitation to meet minimum standards), their
reliance upon individual decisionmaking limits their
effectiveness in achieving spatially targeted goals. Individual
voucher recipients choosing where to rent housing do not take
into account the effect their choice will have on the surrounding
neighborhood and thus are unlikely to choose the locations
where external benefits are maximized. Housing agencies and
community-based nonprofit organizations responsible for
locating and implementing production programs, however, are
more likely to consider the interests and needs of entire
communities rather than just individual tenants.
It is unclear whether or not public officials and nonprofit
developers do, in fact, successfully deploy production subsidies
to create housing that generates positive spillover effects. As the
remainder of this paper demonstrates, until recently, there has
been little evidence that government housing programs
generate positive spillover effects and successfully promote
neighborhood revitalization. Nevertheless, our analysis of
New York City’s Ten-Year Capital Plan for Housing,
specifically designed to revitalize neighborhoods devastated by
years of abandonment, has yielded strong evidence that these
spillover effects may be significant.

3. New York City’s Ten-Year
Capital Plan for Housing
The results of our research on the spillover effects of affordable
housing investment differ substantially from those of earlier
studies. To some extent, these differences derive from the

74 Housing Production Subsidies and Neighborhood Revitalization

particular circumstances and features of the programs
composing the Ten-Year Plan. Thus, this section describes
these programs, paying particular attention to those features
that may have been especially important in driving spillover
effects.
Throughout the twentieth century, New York City has been
among the leading innovators in housing policy. In 1935, New
York was the first city in the United States to build public
housing. New York’s Fair Housing Practices Act of 1957 was
the first law to make illegal discrimination against racial
minorities by private landlords. In addition, the Act’s MitchellLama Middle Income Housing Program became a model for
Congress when it passed the first below-market interest rate
programs, in the 1960s.
Thus, New York City Mayor Ed Koch’s announcement of
the Ten-Year Plan in 1985 was not entirely unprecedented.
Indeed, many of the programs that would be encompassed in
the plan were already in existence in 1985, albeit at substantially
lower rates of activity. The rough contours of the plan were first
announced in the Mayor’s State of the City Speech (Koch 1985,
p. 8). The goal was to renovate or build 252,000 units and make
a financial commitment of $5.1 billion (City of New York
1988). To fund the program, Koch proposed using money
from the World Trade Center to finance approximately
$1 billion in bonds. Other revenues would come from the city’s
Housing Development Corporation and its capital budget.
Certainly, a principal objective of the Ten-Year Plan was to
create additional housing opportunities for low- and
moderate-income families as well as the homeless. In addition,
a focus on neighborhood revitalization was evident from the
beginning of the plan. According to the mayor, “first, we intend
to undertake a major effort to rebuild entire neighborhoods of
perhaps 15 to 25 square blocks throughout the City . . . it is
anticipated that such concentrated revitalization would
provide the hub for further development” (Koch 1985, p. 11).
A 1989 report by the New York City Department of Housing
Preservation and Development (HPD) made the point even
more explicitly: “We’re creating more than just apartments—
we’re re-creating neighborhoods. We’re revitalizing parts of
the city that over the past two decades had been decimated by
disinvestment, abandonment, and arson.”
In New York City’s Ten-Year Plan, the location of housing
investments was, to some extent, dictated by where the city
owned property. During the late 1970s, the city had taken
ownership of more than 100,000 vacant and occupied apartments
as a result of tax foreclosure. This so-called in rem housing,
named after the legal action that vested title in the city, would
provide the raw material for the lion’s share of the program.
Over time, HPD created a vast array of programs that
enlisted a wide variety of actors. Because neighborhood

preservation and revitalization were important objectives of
the plan, the city implemented programs that made
community-based nonprofit organizations the major
stakeholders in housing production. According to Felice
Michetti, a former HPD commissioner and one of the principal
architects of the plan, “when the Ten-Year Plan began, there
were about twelve not-for-profits in the City of New York that
were actively involved in housing . . . . By the time I left HPD,
there were over a hundred not-for-profits involved in the TenYear Plan, and involved not in the traditional federal role of
sponsoring projects, but actively involved [in development]”
(New York City Department of Housing Preservation and
Development 2000, p. 25). For-profit housing developers were
also active participants, attracted by the development fees or
the promise of long-term property value appreciation. Local
financial institutions and intermediaries were active
participants as well.
Over the course of the Ten-Year Plan, the city utilized at
least 105 programs, many of which produced only a handful of
units. Although the majority of these programs involved
renovation of occupied housing, our focus in this paper is on
the 66,147 new housing units created—through either new
construction or the gut rehabilitation of formerly vacant
buildings.6 In most instances, the city’s subsidy for housing was
not limited to capital dollars. Most newly constructed or
rehabilitated housing also qualified for property tax
abatements and/or exemptions.7 We divide these programs
into four categories, based on whether they involved new

Table 1

Distribution of Ten-Year-Plan New Housing Units
by Program Class
Units
Number

Percentage
of Total

Owner-oriented programs
Rehabilitation of vacant buildings
New construction
Total owner-oriented programs

2,801
16,813
19,614

4.2
25.4
29.7

Renter-oriented programs
Rehabilitation of vacant buildings
New construction
Total renter-oriented programs
Total—all classes

41,484
5,049
46,533
66,147

62.7
7.6
70.3
100.0

Program Class

Note: Figures include all Ten-Year-Plan new housing projects in the
New York City Department of Housing Preservation and Development
database.

construction or gut rehabilitation and whether they were slated
for homeownership or rental use. Table 1 shows the distribution of Ten-Year-Plan units across these four categories. The
bulk of the units were rental, created from the gut rehabilitation
of formerly vacant buildings.

4. Evidence of Spillover Effects:
New York City and Elsewhere
Here, we review the results of our recent empirical work on
the effect of the New York City’s Ten-Year Capital Plan for
Housing on property values in the city. We compare and
contrast New York City’s experiences with those of other cities
to explain why New York was so successful as well as which
aspects of its program might be successfully transplanted to
other cities.

4.1 Evidence from New York City
Using a unique administrative data set, we have completed a
series of studies on New York City’s Ten-Year Capital Plan for
Housing (Ellen, Schill, Susin, and Schwartz 2001; Schill, Ellen,
Schwartz, and Voicu 2001; Ellen, Schill, Schwartz, and Voicu
2001). Although each of our studies has differed in focus, our
core objective was to examine whether investments in placebased housing programs have an effect on the value of homes
in surrounding neighborhoods and to derive estimates of the
sign and significance (both substantive and statistical) of these
effects. All three studies found evidence of positive and
significant spillover effects.
Our first study explored the effects of the Nehemiah Plan
and the New Homes Program of the New York City
Partnership, both of which subsidize the development of
affordable, owner-occupied homes in distressed urban
neighborhoods (Ellen, Schill, Susin, and Schwartz 2001). In the
second study, we expanded the analysis to consider the effects
of a wider range of housing subsidized through the Ten-Year
Plan; for instance, we analyzed the effects of rental and
homeownership programs and renovation and rehabilitation
as well as new construction programs (Schill, Ellen, Schwartz,
and Voicu 2001). For the third study, we restricted our analysis
to the effects of newly created units, investigated differences in
spillover effects across types of housing programs, and
provided some evidence to suggest how the magnitude of the
spillover benefits generated by these units compared with their
approximate costs (Ellen, Schill, Schwartz, and Voicu 2001).

FRBNY Economic Policy Review / June 2003

For consistency with other analyses (which typically focus
on new units) and for brevity, we mainly review the methods
and results of our most recent study of newly created units. Our
basic empirical strategy in all of these studies, however, was the
same: we used a difference-in-difference model to compare the
sales prices of properties within 500-foot rings of Ten-YearPlan sites to the prices of comparable properties in the same
census tracts (but outside the rings). We then compared the
magnitude of this difference before and after the completion of
a Ten-Year-Plan project to estimate the effect of the housing
investment on property values.
More formally, we used a fixed-effects hedonic price model,
adapted from Galster, Tatian, and Smith (1999), which
controls for structural characteristics of the property. In this
model, the fixed effects are specified as census tract, quarterspecific fixed effects.8 In other words, we effectively included a
separate dummy variable for each census tract for each of the
seventy-nine quarters in our data.9 This allowed us to control
for neighborhood-specific price changes over our time period.

The core equation we estimated is shown below, where
ln P ict is the log of the sales price (per unit) of property i in
census tract c in quarter t; X it is a vector of property-related

characteristics, including age and structural characteristics
(square footage, lot size, garage); and Z it is a vector of
locational attributes—specifically, a set of what we call “ring”
variables: whether a sale is within 500 feet of a Ten-Year-Plan
site, whether any units are completed within this distance, and,
if so, the number and mix of the completed units. Finally,
I ct are a series of dummy variables indicating the quarter and
census tract of the sale.10
(1)

ln P ict = α + β X it + γ Z it + Σρ ct I ct + εit .

To help explain our identification strategy, Table 2 provides
a list of ring variables. First, we include a series of in-ring
dummy variables, which indicate whether a property sold is
within 500 feet of a particular type of Ten-Year-Plan project,
whether completed or not. Because different kinds of projects

Table 2

Main Ring Variables
Variable
In ring, new units, owner but not renter
1-100 units
101+ units

In ring, new units, renter but not owner
1-100 units
101+ units

In ring, new units, owner and renter
1-100 units
101+ units

Post ring, new units
Number of new units at time of sale
(Number of new units at time of sale)2
Share of multifamily new units at time of sale
Share of rental new units at time of sale
Share of new construction units at time of sale
Tpost, new units
Tpost*(number of new units at time of sale)

Definition
1 if the property sold is within 500 feet of 1-100 homeownership new units, whether completed or not,
but not of rental new units; 0 otherwise
1 if the property sold is within 500 feet of more than 100 homeownership new units, whether completed
or not, but not of rental new units; 0 otherwise

1 if the property sold is within 500 feet of 1-100 rental new units, whether completed or not, but not
of homeownership new units; 0 otherwise
1 if the property sold is within 500 feet of more than 100 rental new units, whether completed or not,
but not of rental new units; 0 otherwise

1 if the property sold is within 500 feet of 1-100 homeownership and rental new units, whether
completed or not, but not of rental new units; 0 otherwise
1 if the property sold is within 500 feet of more than 100 homeownership and rental new units, whether
completed or not, but not of rental new units; 0 otherwise
1 if the property sold is within 500 feet of any completed new units; 0 otherwise
Number of completed new units within 500 feet of the property sold
Squared number of new units at time of sale
Share of completed new units within 500 feet of the property sold that are in multifamily buildings
Share of completed new units within 500 feet of the property sold that are rentals
Share of completed new units within 500 feet of the property sold that are in newly constructed
buildings
Years since earliest completion of new units within 500 feet of the property sold; 0 if no new units were
completed before sale
Interaction term

Note: “New units” is defined as newly constructed units and rehabilitated (formerly) vacant units.

76 Housing Production Subsidies and Neighborhood Revitalization

may have been located in different kinds of neighborhoods, we
defined six mutually exclusive in-ring variables—properties
within 500 feet of large homeownership projects, small
homeownership projects, large rental projects, and so on.
Second, we included a post-ring variable that indicates if there
are any completed units within 500 feet of the sale. The
coefficient on this variable indicates the extent to which, after
the completion of a development of any size, sales prices rise in
the vicinity relative to the average increase in the larger census
tract. Third, we controlled for the number of completed units
within this distance and the share of completed units that were
in multifamily structures, were rentals, and were in newly
constructed buildings. Finally, we include Tpost, which indicates the years since completion, and Tpost interacted with
number of completed units to see if the effect changed over
time and whether this change was shaped by the size of the
project.
To estimate this model, we used a combination of three geocoded administrative data sets. First, we used detailed data on
the location (down to the block level) of all housing built or
renovated through the Ten-Year Plan. Second, through an
arrangement with the New York City Department of Finance,
we obtained a database that contains sales transaction prices for
all apartment buildings, condominium apartments, and singlefamily homes over the 1980s and 1990s.11 We used GIS
techniques to measure the distance from each sale to all TenYear-Plan sites. Our final sample in the three studies ranges
from 234,000 to 294,000 property sales, a very large sample size
compared with much of the literature.
Third, we supplemented these transaction data with
building characteristics from an administrative data set
gathered for the purpose of assessing property taxes (the RPAD
file). The RPAD data contain information about buildings but
do not contain much information about the characteristics of
individual units in apartment buildings (except for condominiums). Nonetheless, these building characteristics explain
variations in prices surprisingly well (our final R2s exceeded
0.87), suggesting that the data are rich enough for estimating
hedonic price equations.
Our results consistently show that the completion of new
housing units under the Ten-Year Plan was associated with
increased sales prices of nearby properties. For example,
Charts 1 and 2 show the regression-adjusted percentage
difference between prices in the ring and prices in the larger
census tract, before and after the completion of a project.
Specifically, Chart 1 shows how prices in the ring changed after
completion of a Ten-Year-Plan homeownership project of
three different sizes. The first set of bars shows that before the
completion of a ten-unit homeownership project, the sales

price of a property located within 500 feet of a future site was
on average 6.8 percent lower than the price of a comparable
property sold in the same quarter in the same census tract.
After completion, the gap shrunk so that prices in the ring were
only 3.1 percent lower than prices in the larger census tract.
As can be seen from Chart 1, the impact appears to be
greater for larger projects. The second set of bars shows that,
before completion of a project with 100 homeownership units,
the sales price of a property located within 500 feet of the future
site was, on average, 6.8 percent lower than the price of a
comparable property sold in the same quarter in the same
census tract.12 After completion, prices in the ring actually
ended up higher than those in the surrounding census tract.
Similarly, for properties within 500 feet of homeownership
sites with 200 units, the ring/census tract gap shifted from an
8.4 percent shortfall in the ring to a 3.9 percent “premium”
after completion.
For properties within 500 feet of renter-oriented Ten-YearPlan projects, we obtained very similar results (Chart 2). The
one key difference is the very large price gap for properties
located within 500 feet of a site that will ultimately hold 200
rental units. We estimated that before completion, prices of
properties near such large rental project sites were a full
17 percent lower on average than prices of comparable
properties located outside the ring, but in the same census
tract. After completion, the gap decreased by more than
12 percentage points.
There are several points to highlight here. First, in all cases,
quality-controlled property values were lower for properties

Chart 1

Percentage Price Differences in 500-Foot Ring
and Surrounding Tracts, by Number of Units Built
Rings with Homeownership Units Only
Price relative to rest of tract
6
4

Price gap before completion
Price gap after completion

2
0
-2
-4
-6
-8
-10
10 units

100 units

200 units

Note: Price gaps are for before and after the completion of Ten-YearPlan new units.

FRBNY Economic Policy Review / June 2003

Chart 2

Percentage Price Differences in 500-Foot Ring
and Surrounding Tracts, by Number of Units Built
Rings with Rental Units Only
Price relative to rest of tract
10
5

Price gap before completion
Price gap after completion

0
-5

project was made up of one-to-four-unit buildings or
multifamily apartment buildings.
In summary, we found that the units created through the
Ten-Year Plan generated significant and sustained positive
spillover effects on neighboring properties, indeed, benefits
that were quite large relative to city subsidies (Ellen, Schill,
Schwartz, and Voicu 2001). We next review evidence from
other cities, then speculate as to whether our positive results
might be unique to New York City and the particular efforts
made under the Ten-Year Plan.

-10
-15
-20
10 units

100 units

200 units

Note: Price gaps are for before and after the completion of Ten-YearPlan new units.

located within 500 feet of Ten-Year-Plan sites than for
comparable properties located beyond this distance but in the
same census tract. Ten-Year-Plan housing, in other words, was
typically located in the most distressed micro-neighborhoods
within a census tract. Furthermore, the larger the project, the
more that distressed property values tended to be in the
vicinity, and rental projects appear to have been sited in even
more distressed neighborhoods than homeownership projects.
These projects, in other words, were not randomly located,
emphasizing the need to control for these baseline conditions
when estimating effects.
In addition, the value of properties near Ten-Year-Plan sites
typically rose significantly relative to prices in their census tract
after completion of a project, and this increase was sustained
over time. (The coefficient on the post-completion time trend
in the ring was statistically insignificant.13) A final, notable
point is that the greater the number of units, the greater the
effect. With this said, we found a relatively large, positive
“fixed” effect common to projects of all sizes. One interpretation of this result is that much of the positive spillover
effect may derive from the elimination of existing blight; the
scale or size of the project is less important than the fact that at
least some units were built.
Consistent with this interpretation, we found that the type
of project made little difference in determining effects. We
found no statistically different effects between rental and
ownership projects, or between units created through the
rehabilitation of vacant buildings and those built through
new construction. Structure type was also irrelevant—the
magnitude of the spillover effect was unchanged whether the

78 Housing Production Subsidies and Neighborhood Revitalization

4.2 Evidence on the Effects of Other
Supply-Side Programs
Although several studies have attempted to quantify the
spillover effects of place-based subsidized housing, few have
found statistically significant effects. Some studies have found
small, positive effects (De Salvo 1974; Rabiega, Lin, and
Robinson 1984), yet the general conclusion has been that the
development of subsidized housing has had little or no effect
on surrounding neighborhoods (Nourse 1963; Schafer 1972;
see Matulef [1988] and Goetz, Lam, and Heitlinger [1996] for
a review of the literature). Indeed, attempts to quantify the
effect of housing quality more generally on the value of
neighboring properties have largely yielded insignificant
results. As Mills and Hamilton (1994) write, researchers “have
almost uniformly failed to find significant and consistent
effects of neighboring activities on property values.” Although
economists have not rejected the possibility of spillover effects,
they speculate that such effects operate mainly in high-density
neighborhoods, are probably highly localized, and only matter
when housing is badly deteriorated or abandoned (Mills and
Hamilton 1994).
During the 1990s, three studies were published suggesting
that proximity to subsidized housing can affect neighboring
property values, but the effects were typically negative, at least
in the case of federally subsidized rental developments (Lyons
and Loveridge 1993; Goetz, Lam, and Heitlinger 1996; Lee,
Culhane, and Wachter 1999). Other recent studies have
suggested no significant effect (Briggs, Darden, and Aidala
1999; Cummings, DiPasquale, and Kahn 2001).
One recent paper comes to a more hopeful conclusion
about place-based subsidies. Santiago, Galster, and Tatian
(2001) used a hedonic model with localized fixed effects to
study whether the purchase and renovation of property by the
Denver Housing Authority, and its conversion into subsidized
housing, influenced the subsequent sales prices of surrounding

single-family homes. The authors found that proximity to
dispersed public housing units was, on average, associated with
a modest increase in the prices of single-family homes. But they
found that these positive benefits were weakest in the poorest
areas. Indeed, the effects were consistently negative in
substantially black neighborhoods. This contrasts sharply with
our research on New York City, which found substantial
positive effects in the city’s poorest neighborhoods.

4.3 Why Are New York City’s
Results Stronger?
We have several hypotheses for why our results suggest larger
and more positive spillover effects: differences in data and
methods, more favorable housing market conditions, a more
favorable mix of housing, a greater level of municipal
commitment, and a greater focus on neighborhood
revitalization. Note that another possible difference is timing—
most prior research examined large-scale federal housing
programs from an earlier era. There may be common
macroeconomic, sociological, or political explanations for
different outcomes in those earlier periods. Thus, when
comparing our results with those for other cities, we pay
particular attention to six studies that have focused on more
recent housing programs: Lyons and Loveridge (1993), Goetz,
Lam, and Heitlinger (1996), Lee, Culhane, and Wachter

(1999), Briggs, Darden, and Aidala (1999), Cummings,
DiPasquale, and Kahn (2001), and Santiago, Galster, and
Tatian (2001). Table 3 provides summary information on these
studies.

Data and Methods
It is possible that the differences in results are rooted in
differences in data and methods. Our study is based on an
extraordinarily rich data set. The large number and variety of
housing units built, the long time frame, and the large volume
of sales data allow us to employ a data-intensive methodology
that incorporates many of the best features of previous studies.
The most important methodological challenge in estimating
the effect of subsidized housing is identifying the appropriate
counterfactual. One approach is to compare price levels in
areas receiving subsidized housing with comparable properties
that have no subsidized housing. This yields an unbiased
estimate of the effect if the only difference between the areas is
the housing investment—which is difficult to determine. If the
prices of homes tend to be lower near subsidized housing sites,
is this because the development of subsidized housing
depressed housing values or because the subsidized housing
was located in a more distressed area? A second approach
compares property values before and after housing investment,
which yields an unbiased estimate of the effect if there is no

Table 3

Projects and Units in the Analyses of Assisted Housing Effects

Author

Housing Program

City

Briggs et al. (1999)
Santiago et al. (2001)
Cummings et al. (2000)
Lyons and Loveridge (1993)

Dispersed
Dispersed
Homeownership
Multiple federally assisted

Goetz et al. (1996)
Lee et al. (1999)

Nonprofit developed
Multiple federally assisted

Yonkers, New York
Denver
Philadelphia
Ramsey County,
Minneapolis
Minneapolis
Philadelphia

Number of
Home Sales/
Residential
Properties

Number of
Subsidized
Units

Number of Projects/
Developments

Study Period

200
118a
311
12,864

7
92
2
120

1985-96
1987:1-1997:3
1986-97
1991

3,101
43,361
146,053

476
NAc

1994
1989-91

22,156
18,062

26,503b

a
This is an estimate based on average number of households per site reported in the authors’ Table 1, “Selected Characteristics of 1989-1997 Vintage
Dispersed Housing Sites” (p. 75).
b

This is a 25 percent sample of the 128,010 nonsubsidized residential units in Ramsey County.

c
The authors do not report total number of units; however, they do include dummy variables for large and high-rise public housing developments.
“Large” is not defined.

FRBNY Economic Policy Review / June 2003

other force shaping the growth in property values at the same
time as the housing investment. But again, there may be other
forces affecting the target neighborhood that coincide with
development of subsidized housing, complicating the effort to
disentangle the specific effect of subsidized housing. Finally,
effects can be investigated by constructing and estimating an
econometric model that fully specifies the determinants of
property values, including the neighborhood characteristics
and housing investments. Here, unbiased impact estimates
can only be obtained if the model includes all relevant
neighborhood characteristics—a formidable challenge. (See
Galster, Tatian, and Smith [1999] for a fuller discussion of
alternative approaches to estimating impacts of subsidized
housing.)
Using more detailed data and a clever methodology,
Santiago, Galster, and Tatian (2001) are able to sort out
causality more persuasively than the other studies, and
therefore we place more weight on their results. They use a
hedonic model with localized fixed effects and, in contrast to
earlier research, they also control for past trends in housing
prices in the immediate vicinity of a project. That is, they
control for both past levels and trends in housing prices in the
baseline neighborhood and therefore control for any tendency
of the housing authority to develop housing in neighborhoods
where prices were already rising.
We adapt their methodology in our approach, and our
results are, in some sense, most comparable to theirs. As noted
earlier, we estimate effects based upon the assumption that in
the absence of the Ten-Year-Plan units, properties within
500 feet of the sites would have appreciated in value at the same
rate as comparable properties in the same census tract, but
outside of the 500-foot ring. That seems particularly reasonable
given the small size of these rings. Put differently, our estimates
are identified as the difference in the growth in property values
before and after the housing investment relative to the growth
in prices in a comparable area—outside the ring but in the
same census tract. Thus, our methodology combines the best of
the alternative strategies described above and, as a result, our
findings are less likely to be biased. (Our estimates will be
biased only if there was some force affecting property values
differentially inside and outside the ring at the same time as the
housing investment.)
Equally important, our analyses are based on a rich data set
including information on an extraordinarily large number of
transactions and an enormous number of units. As shown in
Table 3, earlier studies typically examined the effect of several
hundred subsidized units, spread across a number of projects.
By contrast, we examined the effect of approximately 66,000
new subsidized units, developed at different times over several

80 Housing Production Subsidies and Neighborhood Revitalization

years, in a wide range of neighborhoods. Thus, it is harder to
believe that some other contemporaneous phenomenon was
responsible for lifting property values in the proximity of the
Ten-Year-Plan units while leaving properties outside the ring
but in the same neighborhood unaffected. One would have to
believe that this phenomenon occurred at different times in
different neighborhoods at the same time as the housing
investment.
Note that the small number of subsidized units examined in
many of the other earlier studies has made it difficult to form
sharp estimates. Although estimated effects may have been
positive, standard errors are large. Briggs, Darden, and Aidala
(1999) and Cummings, DiPasquale, and Kahn (2001), for
instance, found that subsidized housing had a positive but
statistically insignificant effect on surrounding property values.
It may be that a larger number of projects would have yielded
smaller standard errors and found positive and statistically
significant effects. (It is also possible, of course, that expanding
the number of projects would have revealed negative and
significant effects.)

Housing Market Conditions
A second possible explanation for the difference in findings is
that housing market conditions were simply more propitious
in New York City than elsewhere. During this time, the city was
gaining population largely fueled by enormous waves of
immigration, in sharp contrast to Philadelphia (where two of
these earlier studies were undertaken), which lost 4 percent of
its residents between 1990 and 2000. Vacancy rates were also
quite low in New York City during this time—the rental
vacancy rate in the city fell to 3.2 percent in 1999 (Daniels and
Schill 2001). Vacancy rates in the Philadelphia metropolitan
area were, by comparison, more than 8 percent—and
undoubtedly higher still in the city itself. As noted above, placebased housing programs are likely to be most effective in tight
housing markets, where they can help to meet growing
demand. Thus, the difference in findings may reflect what
common sense (and economics) suggests. In cities like
Philadelphia in the 1990s, with a shrinking population and
high vacancy rates, housing investment is likely to have (at
best) little effect on values of neighboring properties—an
infusion of new housing was probably not what the city’s
distressed neighborhoods needed. Indeed, additional housing
may have promoted filtering and the removal of buildings from
the housing stock. In growing New York City, with very little
vacant housing and a preponderance of structural barriers that
inhibit construction of affordable, private housing (Salama,

Schill, and Stark 1999), public housing investment may have
been a highly effective spur to neighborhood economic
development.
Alternatively, New York’s extraordinarily high density may
also have contributed to the larger effects. Clearly, we would
expect spillover effects to be larger in neighborhoods with
higher densities. In 1990, population density was more than
twice as high in New York City than in Philadelphia and three
and a half times as high as it was in Minneapolis—the site of
three of our comparison studies.

Mix of Housing
A third possible explanation for New York’s difference concerns the type of housing built by the city. That is, the mix of
housing built in New York may have been disproportionately
composed of the type that would generate larger neighborhood
spillover effects. Although plausible, this explanation is
undermined to some extent by the fact that our research found
no differences in spillover effects across different types of
housing. In addition, New York’s focus on income mixing may
have made a difference. Rather than concentrating the very
poorest households in particular neighborhoods or projects,
the city generally aimed to create housing with a mix of incomes.

Level of Commitment
New York City’s Ten-Year Plan may have had a greater effect
than initiatives of other cities because of New York’s level of
commitment. Mayor Koch, in announcing the Ten-Year Plan,
placed his prestige and that of his housing agency on the line in
committing the city to an effort of unprecedented magnitude
and scope. This commitment, together with the quality of the
staff assembled at the housing agency, may have generated
confidence on the part of neighborhood residents, financial
institutions, and investors, encouraging them to contribute
their own resources and time to revitalization activities.

Focus on Neighborhood Revitalization
Finally, the explanation may lie in New York City’s explicit
emphasis on neighborhood revitalization. As noted above, one
of the key objectives of the Ten-Year Plan (if not the key
objective) was to reclaim parts of the city that had been
destroyed by arson and disinvestment during the 1970s. In the

programs evaluated in other cities, this aim was far less central.
In the scattered-site public housing initiatives, for instance, the
goal was to offer housing opportunities to poor families in
lower poverty communities (Briggs, Darden, and Aidala 1999;
Santiago, Galster, and Tatian 2001). Therefore, it is perhaps not
surprising that New York appears to have been more successful
in developing housing that benefited the surrounding
communities.14
Furthermore, New York City chose sites (either buildings or
vacant land) that were extremely blighted, so that even modest
improvements may have been able to generate dramatic
improvements in the blocks surrounding them. Many of the
cities examined by other researchers were unlikely to have
faced such pockets of abandonment. If they did, the studies
may not have so explicitly targeted them. Indeed, in Denver
and in Yonkers, New York, the aim was to select sites in
middle-class neighborhoods. These were hardly areas
characterized by the same devastation as the neighborhoods
studied in New York City.

4.4 Evidence on the Effects of
Demand-Oriented Subsidies
Ideally, we would like to obtain estimates of the spillover effects
of tenant-based vouchers to compare with the housing built
under the Ten-Year Plan. Unfortunately, such estimates are
unavailable. Nevertheless, for the reasons discussed above (for
example, tenants are likely to be dispersed and the aim of
voucher programs is typically not to revitalize neighborhoods),
it is unlikely that vouchers would deliver spillover effects of the
magnitude we found generated by the Ten-Year Plan.
This expectation is modestly supported by other research.
Galster, Tatian, and Santiago (1999), for example, examine the
effects of Section 8 tenants on neighboring properties in the
suburbs surrounding Baltimore. They find, in general, that
proximity to a small number of Section 8 tenants is linked to
positive changes in property values. But closer inspection
showed that these small positive effects were limited to
properties within 500 feet of no more than six voucher holders.
For properties close to larger numbers, the net effect proved to
be negative, and these negative effects were quite substantial for
the largest concentrations of tenants (more than fifty tenants).
Moreover, when looking across different types of neighborhoods, the authors find that the positive effects were in fact
limited to high-value, largely white neighborhoods, as was the
case in their analysis of scattered-site public housing in Denver.
In short, the authors conclude that Section 8 demand-side
subsidies can be used to generate neighborhood externalities,

FRBNY Economic Policy Review / June 2003

but only in higher valued, appreciating, largely white
communities. The irony, of course, is that these are hardly the
sorts of neighborhoods where we are likely to be very
concerned about improving neighborhood quality.
Two other studies examine the effect of voucher households
on property values: Lyons and Loveridge (1993) find no effect
on surrounding property values and Lee, Culhane, and
Wachter (1999) uncover slight negative effects on surrounding
property values. In short, prior research provides little support
for the notion that vouchers are likely to lead to the same large,
positive spillover effects on surrounding properties that we
estimate were generated by the Ten-Year Plan.

5. Conclusion
Since the mid-1970s, the central debate among housing policy
analysts and government officials has revolved around the
relative advantages and disadvantages of housing vouchers
versus supply-oriented subsidies. Study after study
demonstrated the comparative advantage of vouchers on a
variety of grounds—ranging from their lower cost to the better
neighborhoods they enable their recipients to live in. Economic
theory has suggested that production programs might do better
than housing vouchers in generating positive spillovers and
neighborhood revitalization, but empirical studies have never
quite supported this theory.

82 Housing Production Subsidies and Neighborhood Revitalization

New York City’s Ten-Year Capital Plan for Housing provides advocates of production programs with more optimistic
results. Our findings suggest that New York’s unprecedented
expenditure of $5.1 billion on housing production programs
has generated substantial positive spillovers and contributed to
neighborhood revitalization. The rebuilding of extraordinarily
depressed neighborhoods in the South Bronx, Central Harlem,
and Central Brooklyn seems to have been achieved not just as a
result of a booming economy and a growing population, but
also because of an innovative and massive investment of public
dollars.
Although our research on the utility of production
programs as a neighborhood revitalization tool in New York
provides some evidence of the contributions that production
programs can make in distressed neighborhoods, more
research is needed. First, our study did not directly compare the
spillovers generated by production programs with those that
might accompany housing vouchers. Second, whether the
success in New York City can be replicated elsewhere remains
very much an open question. Third, production programs such
as those utilized by New York City are extremely costly. Our
research suggests that the benefits achieved in terms of
increased property values may outweigh the costs of the
subsidies, yet much more work remains to be done before that
conclusion can be stated with any level of assurance.

Endnotes

1. For an overview of the theoretical and empirical evidence on the
relative cost-effectiveness of housing vouchers and certificates, see
Schill (1993). One recent article has made a counterargument
(McClure 1998); Shroder and Reiger (2000) have challenged
McClure’s methodology.

have begun and funding commitments for the developments may
have been made before the announcement of the plan in 1985.

2. According to the report, these estimates of the cost differential
between voucher and production programs were conservative. They
did not include the value of tax abatements granted by localities for
new construction, nor did they include funding of capital reserves.
The authors estimated that including these costs would have increased
the differences between the two types of subsidy programs by about
10 percent.

8. Note that Galster, Tatian, and Smith (1999) include census-tract
fixed effects instead, which assumes neighborhood fixed effects are
constant over time—an assumption that seems unrealistic over a time
period as long as ours.

3. For a summary of the literature on the neighborhood effects of
concentrated poverty, see Ellen and Turner (1997).
4. A recent paper by Bahchieva and Hosier (2001) indicates that
between October 1999 and June 2000, 2,263 vouchers issued by the
New York City Housing Authority for nonemergency reasons were
picked up by applicants. Only 1,339 applicants successfully rented a
unit with their vouchers; 1,124 failed to obtain a unit before expiration
of their vouchers.
5. Recent evidence suggests that black and Latino homeseekers
encounter unfavorable treatment approximately half of the time they
transact in the housing market (Ondrich et al. 1999).
6. In this paper, units built or rehabilitated under the Ten-Year Plan
are defined to include only projects completed between January 1987
and June 2000. The January 1987 beginning date was selected because
of the long lag time associated with housing construction. It is likely
that buildings completed in 1986 were planned and financed long
before the announcement of the plan. In addition, when we count
units produced through the plan, we do not include housing units
built under federal programs such as public housing, Section 8, and
Section 202 housing. In certain respects, our definition of the TenYear Plan is therefore both under- and overinclusive. Federal housing
programs that made use of city resources such as city-owned land
would not be included in our totals. In addition, it is possible that
completions after 1986 would be included even though planning may

7. For more details on financing, see Schill, Ellen, Schwartz, and Voicu
(2001).

9. Ellen, Schill, Susin, and Schwartz (2001) used ZIP code fixed effects.
10. In Ellen, Schill, Susin, and Schwartz (2001), we also estimate a
number of alternative specifications (for instance, providing year-byyear estimates of post-completion effects), but all rely on the same
fundamental difference-in-difference approach.
11. Because sales of cooperative apartments are not considered sales of
real property, they are not recorded and were thus not included in our
analyses. We should also note that most of the apartment buildings in
our sample are rent-stabilized. Given that legally allowable rents are
typically above market rents outside of affluent neighborhoods in
Manhattan and Brooklyn, we do not think that their inclusion biased
our results (see Pollakowski [1997]).
12. Our specification allowed the precompletion price gap to differ
only for projects above and below 100 units.
13. In our first paper, we found that the impact of Partnership and
Nehemiah homes declined over time within the 500-foot ring. Effects
on properties somewhat more distant from the subsidized homes were
persistent, however, suggesting that impacts may have diffused
outward over time.
14. Interestingly, Goetz, Lam, and Heitlinger (1996) found that
housing developed by community-based nonprofits had positive
spillover effects, while that developed by the housing authority had
negative effects. This may be because the community-based
nonprofits they examined in Minneapolis were more sensitive to
community effects.

FRBNY Economic Policy Review / June 2003

References

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Bahchieva, Raisa, and Amy Hosier. 2001. “Section 8 in a Tight Market,
the Case of New York City.” Unpublished paper, New York City
Housing Authority.
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Wake of Desegregation: Early Impacts of Scattered-Site Public
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the American Real Estate and Urban Economics Association,
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Housing and Neighborhoods 2001.” New York University Furman
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Ellen, Ingrid Gould, Michael H. Schill, Amy Ellen Schwartz, and Ioan
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University Wagner School of Public Service Conference Honoring
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Ellen, Ingrid Gould, Michael H. Schill, Scott Susin, and Amy Ellen
Schwartz. 2001. “Building Homes, Reviving Neighborhoods:
Spillovers from Subsidied Construction of Owner-Occupied
Housing in New York City.” Journal of Housing Research 12,
no. 2: 185-216.
Ellen, Ingrid Gould, and Margery Austin Turner. 1997. “Does
Neighborhood Matter? Assessing Recent Evidence.” Housing
Policy Debate 8, no. 4: 833-66.

84 Housing Production Subsidies and Neighborhood Revitalization

Galster, George C., Anna M. Santiago, Peter Tatian, and Robin Smith.
1999. “Assessing Property Value Impacts of Dispersed Housing
Subsidy Programs.” Report to the U.S. Department of Housing
and Urban Development. Washington, D.C.
Galster, George C., Peter Tatian, and Robin Smith. 1999. “The Impact
of Neighbors Who Use Section 8 Certificates on Property Values.”
Housing Policy Debate 10, no. 4: 879-917.
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the Neighborhood? The Impact of Subsidized Multi-Family
Housing on Urban Neighborhoods.” Center for Urban and
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Hirsch, Arnold. 1983. Making the Second Ghetto: Race and
Housing in Chicago, 1940-1960. New York: Cambridge
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Koch, Edward I. 1985. “The State of the City: Housing Initiatives.”
Remarks, January 30.
Lee, Chang-Moo, Dennis P. Culhane, and Susan M. Wachter. 1999.
“The Differential Impacts of Federally Assisted Housing Programs
on Nearby Property Values: A Philadelphia Case Study.” Housing
Policy Debate 10, no. 1: 75-93.
Lyons, Robert F., and Scott Loveridge. 1993. “An Hedonic Estimation of
the Effect of Federally Subsidized Housing on Nearby Residential
Property Values.” University of Minnesota, Department of
Agriculture and Applied Economics Staff Paper no. P93-6.
Matulef, Mark. 1988. “The Effects of Subsidized Housing on Property
Values.” Journal of Housing, November/December: 286-7.
McClure, Kirk. 1998. “Housing Vouchers versus Housing Production:
Assessing Long-Term Costs.” Housing Policy Debate 9, no. 2:
355-71.
Mills, Edwin S., and Bruce W. Hamilton. 1994. Urban Economics.
5th ed. New York: HarperCollins.
Newman, Sandra J., and Ann B. Schnare. 1997. “. . . And a Suitable
Living Environment: The Failure of Housing Programs to Deliver
on Neighborhood Quality.” Housing Policy Debate 8, no. 4:
703-41.

References (Continued)

New York City Department of Housing Preservation and Development.
1989. “The 10 Year Plan.”

———. 2000. “Building on a Solid Foundation: Conference

Salama, Jerry J., Michael H. Schill, and Martha E. Stark. 1999.
“Reducing the Cost of New Housing Construction in New York
City.” New York University Furman Center for Real Estate and
Urban Policy.

Proceedings Summary.”
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Nourse, Hugh O. 1963. “The Effects of Public Housing on Property
Values in St. Louis.” Land Economics 39: 433-41.
Olsen, Edgar O. 2001. “Housing Programs for Low-Income
Households.” NBER Working Paper no. 8208, April.
Ondrich, Jan, Alex Stricker, and John Yinger. 1999. “Do Landlords
Discriminate? The Incidence and Causes of Racial Discrimination
in Rental Housing Markets.” Journal of Housing Economics 8,
no. 3: 185-204.

Santiago, Anna M., George C. Galster, and Peter Tatian. 2001.
“Assessing the Property Value Impacts of the Dispersed Housing
Subsidy Program in Denver.” Journal of Policy Analysis and
Management 20, no. 1: 65-88.
Schafer, Robert. 1972. “The Effect of BMIR Housing on Property
Values.” Land Economics 48: 282-6.
Schill, Michael H. 1990. “Privatizing Federal Low Income Housing
Assistance: The Case of Public Housing.” Cornell Law Review
75: 878-948.

———. 1993. “Distressed Public Housing: Where Do We Go from
Here?” University of Chicago Law Review 60, no. 2: 497-554.

Polinsky, Mitchell A., and Ellwood T. David. 1979. “An Empirical
Reconciliation of Micro and Grouped Estimates of the Demand for
Housing.” Review of Economics and Statistics 61, no. 2:
199-205.
Pollakowski, Henry O. 1997. “The Effects of Rent Deregulation
in New York City.” Massachusetts Institute of Technology Center
for Real Estate.
Rabiega, William A., Ta-Win Lin, and Linda Robinson. 1984. “The
Property Value Impacts of Public Housing Projects in Low and
Moderate Density Residential Neighborhoods.” Land Economics
60: 174-9.

Schill, Michael H., Ingrid Gould Ellen, Amy Ellen Schwartz, and Ioan
Voicu. 2001. “Revitalizing Inner-City Neighborhoods: New York
City’s Ten-Year Plan for Housing.” Paper presented at the MidYear Meetings of the American Real Estate and Urban Economics
Association, Washington, D.C.
Shroder, Mark, and Arthur Reiger. 2000. “Vouchers versus Production
Revisited.” Journal of Housing Research 11, no. 1: 91-107.
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What They Cost and What They Provide.” GAO-01-901R, July 18.
Washington, D.C.: U.S. Government Printing Office.

Joseph M. Harkness and Sandra J. Newman

Effects of Homeownership
on Children: The Role of
Neighborhood Characteristics
and Family Income
1. Introduction

recent press release from the U.S. Department of Housing
and Urban Development (HUD) captures the wideranging benefits increasingly being attributed to
homeownership: “Homeowners accumulate wealth as the
investment in their homes grows, enjoy better living
conditions, are often more involved in their communities, and
have children who tend on average to do better in school and
are less likely to become involved with crime. Communities
benefit from real estate taxes homeowners pay, and from stable
neighborhoods homeowners create” (U.S. Department of
Housing and Urban Development 2000). This credo
undergirds the last decade’s push to extend homeownership to
all Americans, particularly low-income families and racial
minorities. Because it is believed to strengthen not only families
but communities, homeownership is being promoted as an
important strategy for regenerating distressed urban
neighborhoods.
Enormous amounts of money, both public and private, are
being invested in increasing the homeownership rate. From the
$2 trillion “American Dream Commitment” of Fannie Mae, to
the multimillion-dollar homeownership programs of the
Enterprise Foundation, the Local Initiatives Support
Corporation, and the Neighborhood Reinvestment

Joseph M. Harkness is a research statistician at the Institute for Policy Studies
at Johns Hopkins University; Sandra J. Newman is a professor of policy studies
and the director of the Institute for Policy Studies.

Corporation, to the millions of dollars of programs and
incentives under HUD’s control, a consistent view of
homeownership as a “silver bullet” has emerged. Incentives for
homeownership even appear in the welfare reform plans of a
number of states.
Despite this significant investment, there is remarkably little
known about the real effects of homeownership on either
homeowners, their children, or their communities. This paper
focuses on one aspect of homeownership: its potential longterm effects on children. Several recent studies have found that
growing up in a homeowning family exerts positive effects on
children’s development and outcomes (Green and White 1997;
Aaronson 2000; Boehm and Schlottman 1999; Haurin, Parcel,
and Haurin 2000). But what accounts for these positive effects,
and whether other features may either strengthen or weaken
them, is unclear. One such feature is the neighborhood. Since
many families who will become new homeowners under
current policies promoting homeownership for the poor will
purchase homes in areas traditionally thought of as troubled or
distressed, it is important to understand whether neighborhood characteristics play a role in the effects of homeownership
on children’s outcomes.
To our knowledge, only Aaronson (2000) has explored this
link. He finds that parental homeownership in low-income
census tracts has a more positive effect on high-school
The authors gratefully acknowledge the Fannie Mae Foundation for its
support of this research; the helpful insights of their discussant, Frank Braconi;
and Sally Katz, Amy Robie, and Laura Vernon-Russell for research assistance
and production help. Certain copyrighted material is used with the permission
of the Fannie Mae Foundation. The views expressed are those of the authors
and do not necessarily reflect the position of the Federal Reserve Bank of
New York or the Federal Reserve System.
FRBNY Economic Policy Review / June 2003

graduation than it does in high-income census tracts. This
intriguing result suggests that homeownership may buffer
children against the damaging effects of growing up in
distressed neighborhoods. But Aaronson also finds that
neighborhood residential stability enhances the positive effects
of homeownership on high-school graduation, which suggests
that at least some of the positive effects of homeownership
found in other studies may be attributed to the greater residential stability of the neighborhoods where homeowners live.
Very different policy recommendations emerge from these
two results. According to the first, homeownership should be
promoted even—or especially—in very low-income
neighborhoods. According to the second, neighborhoods that
are residentially stable are preferred, and efforts to stabilize
distressed neighborhoods by encouraging low-income families
to purchase homes there may carry significant risks for the
“pioneers,” the first homeowners in a distressed area.
Another neighborhood feature that may play a role is the
homeownership rate, which has largely been ignored in the
sizable and growing body of research on the effects of distressed
neighborhoods on the life chances of children (see reviews by
Jencks and Mayer [1990], Haveman and Wolfe [1995],
Gephart [1997], Ellen and Turner [1998], and Moffitt [2001]).1
But if the silver-bullet view of homeownership benefiting not
only the immediate homeowning family but also the
surrounding community is correct, then the positive effects of
homeownership on children’s outcomes may be attributed to
the tendency for homeowning families to live in neighborhoods of homeowners—not to the family’s homeownership
status, per se.
This scenario also raises important policy concerns. As with
neighborhood residential stability, if the homeownership rate
in a neighborhood is responsible for the improved outcomes of
children who live there, then policies encouraging poor
families to purchase homes in areas where there are few
homeowners may be good for the neighborhood but bad for
the individual family. Since moving a neighborhood from a low
to a high rate of homeownership is likely to be a long-term
process, the early “pioneer” homeowners would derive few or
no benefits and, in fact, may bear considerable costs such as low
property values, high crime rates, poor schools, and, perhaps
most importantly, the inability to move elsewhere easily (that
is, selling a home is much more difficult than breaking a lease).
A second feature that may alter the effects of
homeownership on children is family income. Interest in this
topic is also motivated by policy concerns, since most
homeownership promotion policies target low-income
families. Previous research has examined this question only
indirectly, and results are conflicting. For example, Green and
White (1997) report estimates from one data set showing that

Effects of Homeownership on Children

family income matters more for children of renters than for
children of homeowners. They interpret this to mean that the
positive effects of homeownership on children erode with
higher incomes. But using another data set, Green and White
find that ownership of a more expensive home is more
beneficial to children, consistent with Aaronson’s (2000)
finding that greater home equity is associated with better
outcomes. Since higher income families tend to both live in
more expensive housing and have more equity in their homes,
these results suggest that homeownership primarily benefits
children of higher income families.
This exploratory paper first tests whether homeownership
has equally positive effects for children of low-income and
higher income families. Focusing on the low-income group, it
then examines whether, and how, these homeownership effects
are influenced by neighborhood attributes. The next section
reviews theories of the ways in which homeownership could
benefit children and how these benefits could be modified by
neighborhood characteristics. We then describe our data,
methods, and results. A discussion of the findings and their
policy implications follows.

2. Background
There are three broad sets of explanations for the effects of
homeownership on children’s outcomes. According to the
first, there is a direct link between family homeownership and
children’s outcomes. The second set, in contrast, posits that
differences in neighborhoods, not family homeownership,
explain why children of homeowners have better outcomes.
The third set speculates that neither homeownership nor
neighborhoods by themselves are the key explanatory factors,
but rather that homeownership is associated with more
favorable outcomes only under certain neighborhood
characteristics. We refer to these as direct, indirect, and
interactive homeownership effects, respectively.

2.1 Direct Homeownership Effects
The literature suggests four paths through which parental
homeownership could affect children’s outcomes: 1) parenting
practices, 2) physical environment, 3) residential mobility, and
4) wealth.
Haurin, Parcel, and Haurin (2000) find that homeowning
parents provide a more stimulating and emotionally
supportive environment for their children, which significantly

improves cognitive ability and reduces behavioral problems.
They attribute the improved parenting of homeowners to
either their greater investment in their properties or residential
stability, both of which are explored below. Another
explanation, supported by some empirical evidence, is that
homeownership produces greater life satisfaction or selfesteem for adults, which, in turn, provides a more positive
home environment for children (Balfour and Smith 1996;
Rossi and Weber 1996; Rohe and Basolo 1997; Rohe and
Stegman 1994b). Sherraden (1991) argues that the
psychological benefits of homeownership for adults derive
from its function as an asset. Green and White (1997) offer
several wide-ranging hypotheses of the potential links between
homeownership and children’s outcomes, including the
possibility that experience with contractors and repair
personnel may improve homeowning parents’ interpersonal
and management skills, which may transfer to their children.
Except for gross, health-threatening inadequacies, little is
known about how children are affected by their dwellings’
conditions.2 But it is plausible that the physical features of
owned versus rental housing may also affect children’s
development. More than four-fifths of owned homes are
single-family, detached structures, compared with less than
one-fourth of rental properties.3 These environments may be
better for children because, for example, they are likely to be
more spacious and private. Owned homes are also likely to be
in better physical condition because owner occupants are more
likely to invest in the quality of their dwellings (Galster 1987;
Mayer 1981; Spivack 1991). Since higher quality housing is
generally more expensive, the previously cited findings of
Green and White (1997) and Aaronson (2000)—that more
expensive housing has favorable long-term effects on
children—lend support to the view that the physical quality of
housing matters. But their findings also suggest that the lower
quality housing affordable to low-income homebuyers may not
benefit their children significantly.
Several studies demonstrate that moving can harm
children’s educational outcomes (Haveman, Wolfe, and
Spaulding 1991; Astone and McLanahan 1994; Jordan et al.
1996; Hanushek, Kain, and Rivkin 1999), and there is
substantial evidence that homeowners move far less often than
renters (Barrett, Oropes, and Kanan 1994; Hanushek and
Quigley 1978; Newman and Duncan 1979; Quigley and
Weinberg 1977). Included here are recent studies that detect a
causal, not merely correlational, impact of homeownership on
a reduced likelihood of moving (Ioannides and Kan 1996; Kan
2000). Aaronson (2000) investigates this issue, and finds that
much of the positive effect of homeownership on childhood
outcomes can be attributed to its impact on residential stability.

Home equity is the most significant asset held by most
American families, and for many, their only asset. One
function of assets is that they can be leveraged during times of
need, which could benefit children. For example, homeowning
parents can borrow money against the equity in their home to
finance a child’s college education. In addition, inheritable
wealth constitutes a child’s claim on the future, enabling longterm planning and higher expectations (Conley 1999).
Empirical evidence suggests a link between home value or
equity and favorable youth outcomes (Aaronson 2000; Boehm
and Schlottman 1999; Conley 1999), such as the likelihood of
acquiring a college education. However, these estimates could
be biased upward because they are likely to be picking up at
least some of the impact of neighborhood characteristics,
which are not controlled for in these studies. In addition,
homeownership as an asset-building tool could fail to benefit
poor children if the down payment and ongoing maintenance
costs absorb resources that might otherwise be invested in
children’s development. The tax advantages of homeownership
are also disproportionately reaped by the more affluent, which
could lead to better outcomes for their children.

2.2 Indirect Homeownership Effects
A second perspective is that the findings of previous studies on
the benefits of homeownership are spurious because it is the
better neighborhoods and schools experienced by children of
homeowners—not growing up in an owned home—that
account for their better outcomes.4 Because homeowners
generally live in communities characterized by higher incomes,
higher rates of homeownership, and greater residential
stability, their children will benefit from these positive
neighborhood externalities.
Homeownership may generate positive neighborhood
externalities through its effect on either physical or social
capital. As noted, owner-occupied houses appear to be better
maintained than rental properties (Galster 1987; Mayer 1981;
Spivack 1991), providing one form of neighborhood amenity
that may benefit children. But theory also suggests that because
homeowners’ financial stake in their properties is illiquid and
not easily extracted, homeowners will be more active in
maintaining or improving the quality of their neighborhoods,
not just their own houses.
A substantial body of research suggests that homeowners are
more attached to their communities and more active in
community affairs (Rossi and Weber 1996; DiPasquale and
Glaeser 1999; Blum and Kingston 1984; Austin and Baba 1990).

FRBNY Economic Policy Review / June 2003

Greater community involvement could plausibly lead to
greater community social capital. Sampson et al. (1997)
provide strong evidence to support this link. These researchers
show that homeownership, in conjunction with residential
stability, generates social capital in the form of “collective
efficacy,” which may produce better outcomes for children.
However, residential stability has also been shown to be an
important determinant of community involvement (Kasarda
and Janowitz 1974; Sampson 1988). A question raised by this
body of evidence is whether homeownership itself—or the
residential stability it is correlated with—is more responsible
for the positive effects of homeownership on community
participation. DiPasquale and Glaeser (1999) explore this issue,
and find that length of residence is more important than
homeownership across several key measures of community
involvement. Because residentially stable neighborhoods of
renters may be as beneficial to children as neighborhoods of
homeowners, it is critical to distinguish analytically between a
neighborhood’s homeownership rate and its residential
stability.

2.3 Interactive Homeownership Effects
Finally, a third view is that the effects of homeownership on
children’s outcomes vary depending on the type of
neighborhood. Homeownership could buffer the effects of a
distressed neighborhood if, for example, homeowning parents
more aggressively monitor their children’s activities, have
higher expectations for their children, or have more social
capital to draw on. But the child-rearing practices of
homeowners living in more prosperous neighborhoods may
differ little from those of neighboring renters. This buffering
hypothesis is consistent with Aaronson’s (2000) finding that
growing up in a homeowning family in a low-income
neighborhood has a stronger positive effect on the probability
of graduating from high school than homeownership in a highincome neighborhood.
Alternatively, children of homeowners might be more,
not less, affected by the conditions in their neighborhoods than
renter children because of homeowners’ relatively greater
residential stability. Greater residential stability reduces or
eliminates the need to change schools and increases the
opportunity to develop closer ties to neighbors. As a result, the
characteristics of their neighborhoods—both good and bad—
could exert a particularly strong influence.5 Aaronson’s (2000)
finding that homeownership has more positive effects on highschool graduation in residentially stable neighborhoods is
consistent with this speculation.

Effects of Homeownership on Children

3. Data and Methods
This study extends and refines previous work on the effects of
homeownership on children’s outcomes in several ways.
Earlier investigations have focused on educational attainment
effects (Green and White 1997; Aaronson 2000; Boehm and
Schlottman 1999).6 We extend the set of outcomes to include
teen unwed births, idleness, wage rates, and welfare receipt.
Examining multiple outcomes is important because the effects
of homeownership may vary by outcome. Children of
homeowners may attend higher quality schools than children
of renters, for example, so that identical educational
attainment by the two groups may not translate into identical
earnings or welfare receipt.
Second, the analysis compares results for low-income and
higher income families, with “low-income” defined as having
parental earnings below 150 percent of the federal poverty
line.7 Although all previous studies on the effects of
homeownership have controlled for income, none has
explicitly tested for different effects of homeownership
between low-income and higher income groups. An analytical
focus on low-income families is appropriate because they are
the primary target of homeownership promotion policies, and
pooling low-income with higher income families could
produce misleading results.8
The third way in which this paper differs from previous
work is that we examine the effects of neighborhood
characteristics both as independent factors and as factors that
may change the way homeownership influences outcomes.
Since homeowners and renters may live in very different kinds
of neighborhoods, and children’s outcomes may be affected by
these different neighborhoods, the failure to control for them
could produce estimates that mistakenly attribute
neighborhood effects to homeownership.9
We test for the simultaneous effects of three measures of
neighborhood characteristics: the poverty rate, the
homeownership rate, and residential stability. We include the
poverty rate because we are interested in the effects of
homeownership in distressed neighborhoods on children’s
outcomes, and the poverty rate is a widely used indicator of
neighborhood distress. The neighborhood poverty rate is also
almost perfectly correlated (negatively) with neighborhood
median income, which ensures comparability with the results
of Aaronson (2000). We include the homeownership rate to
distinguish between the effects of homeownership by a child’s
parents from the homeownership level of the neighborhood.
Finally, we control for neighborhood residential stability
because a neighborhood’s homeownership rate is plausibly
linked to residential stability (Rohe and Stewart 1996), and we

want to determine whether it is neighborhood homeownership
or neighborhood stability that is responsible for neighborhood
effects on children’s outcomes.

3.1 Sample
The analysis uses data from the 1968-93 waves of the geocoded
Panel Study of Income Dynamics (PSID). Begun in 1968, the
PSID is an ongoing longitudinal survey of U.S. households
conducted by the Survey Research Center at the University of
Michigan. All original household members have been followed
over time. Recent research confirms that despite considerable
attrition, the PSID remains representative of the population
(Fitzgerald, Gottschalk, and Moffitt 1998a, 1998b; Zabel 1998).
The analysis is performed on a sample of individuals with
PSID family data available each year between ages eleven and
fifteen, born between 1957 and 1973. Results are first compared
for two samples: 1) a low-income sample of children from
families with parental earnings below 150 percent of the federal
poverty threshold for at least three of the five years between
ages eleven and fifteen and 2) a higher income sample
comprised of the children not in the low-income sample.10 We
then shift the analysis to focus exclusively on the low-income
group and further restrict the sample to children whose parents
were either always homeowners or always renters when the
child was between ages eleven and fifteen. This latter
restriction, which eliminates about 20 percent of cases, enables
us to derive meaningful coefficients on the effects of
homeownership while testing interactions between tenure
status and neighborhood characteristics (see Appendix A for
further discussion of the methodology).

3.2 Approach
We examine the effects of living in an owned home as a child
on seven outcomes: 1) giving birth as an unmarried teenager
(women only), 2) idleness (not working, attending school, or
caring for children) at age twenty, 3) years of education at age
twenty, 4) high-school completion at age twenty,
5) acquisition of post-secondary education at age twenty,
6) average hourly wage rates between ages twenty-four and
twenty-eight, and 7) receipt of welfare—Aid to Families with
Dependent Children (AFDC), food stamps, or other cash
assistance—between ages twenty-four and twenty-eight.11

We estimate three sets of models, corresponding to the three
broad conceptualizations of homeownership effects outlined
earlier. The first set of models tests for the direct effects of
homeownership on children’s outcomes without controls for
neighborhood features. Estimates are obtained separately and
compared for low-income and higher income groups. Next,
we test for indirect effects by adding controls for average
neighborhood characteristics experienced between ages eleven
and fifteen using the low-income sample. If neighborhood
differences between homeowners and renters account for a
substantial portion of the beneficial effects of homeownership,
the homeownership effect estimates produced by these models
should be much smaller than those produced by the direct
effect models. The third set of models tests for the interaction
of tenure status and neighborhood characteristics by specifying
interaction terms between tenure status and each of the three
neighborhood characteristics (stability, homeownership rate,
and poverty rate), also performed on the low-income sample
only.
The analysis uses ordinary least squares to estimate the effect
of homeownership on years of education and wage rates. The
models for the effects of homeownership on high-school
completion, acquisition of post-secondary education, idleness,
and welfare receipt, which are binary (that is, whether high
school was completed or not), use probit.12
A major difficulty in identifying the effects of homeownership and neighborhoods on children is that they may be
associated with parental characteristics that are not measured
in the data and, therefore, cannot be controlled for in statistical
models. The standard technique for dealing with such
unmeasured variable problems is to use “instruments,”
variables that are correlated with the key analytical variables
(homeownership and neighborhood characteristics, in this
paper) but are independent of the unmeasured characteristics.
However, while finding plausible instruments for homeownership is possible and has been done in other studies (Green and
White 1997; Haurin, Parcel, and Haurin 2000; Aaronson 2000;
Harkness and Newman 2002), it is difficult to identify credible
instruments for the three neighborhood indicators tested
here (Duncan, Connell, and Klebanov 1997; Duncan and
Raudenbusch 1998; Moffitt 1999). Because this paper focuses
on homeownership and neighborhoods, results based on
instrumenting for homeownership alone would not be
interpretable. In discussing the results, however, we argue
that conclusions would be unlikely to change if controls for
unmeasured family characteristics were added.

FRBNY Economic Policy Review / June 2003

3.3 Policy Variables
The measure of homeownership is whether a child always lived
in an owned home between ages eleven and fifteen. Three
neighborhood features are included: the poverty rate, the
percentage of families owning their home, and residential
stability, the last being measured as the percentage of families
living in the same housing unit for five or more years.13
Interactive effects between housing tenure and neighborhood
are obtained by multiplying the homeownership variable by
each of the neighborhood variables. In the interaction model,
the neighborhood variables are specified in mean-deviation
form.14 This implies that the coefficient on homeownership in
these models can be readily interpreted as the effect of
homeownership in the average sample neighborhood.

nor obtained through public assistance.17 We control for city
size because Page and Solon (1999) have demonstrated “the
importance of being urban” on adult earnings. State dummy
variables are included to account for the fact that unmeasured
features of states, such as quality of education or labor market
conditions, may affect outcomes (Moffitt 1994).
Although children’s outcomes may be affected by a family’s
home equity and residential mobility, as described earlier, we
did not include controls for these factors in the initial models
because both are also likely to be affected by whether a family
owns its home, as well as neighborhood characteristics.
Consequently, the estimates for the effects of homeownership
and neighborhoods will include the effects that operate
through home equity and residential moves, and they should
be interpreted accordingly. After reviewing the main results, we
conduct a supplementary analysis using these excluded
variables.

3.4 Control Variables
All models control for the following characteristics: 1) race,
2) gender, 3) year born, 4) age of mother when born,
5) educational attainment of household head, 6) number of
children in family, 7) years in a two-parent family, 8) average
annual earnings, 9) whether there is any, and the amount of,
parental income (not including public assistance) in excess of
earnings (average annual), 10) number of years the family
relied on AFDC, food stamps, or other cash assistance
(excluding Supplemental Security Income), 11) years in a city
of 500,000 or more, 12) years in a city of 100,000 to 500,000,
and 13) the child’s primary state of residence.15
For educational outcomes, about 25 percent of cases are
missing data on grades completed at age twenty, but have data
on grades completed at some other age. In these cases, we
substituted educational attainment in the closest year after age
twenty, if available, and in the closest year before age twenty
otherwise. Because educational attainment is affected by age,
the models also include a control variable for the age to which
the educational attainment measure applies. Monetary values
are expressed in 1997 dollars using the CPI-U, the consumer
price index for all urban consumers. City sizes come from the
PSID census geocode.16
Each of these variables is plausibly related to one or more
outcomes examined here, and most have been used extensively
in other research on determinants of children’s outcomes. The
exceptions are controls for wealth other than home equity, and
city size. Based on Conley’s (1999) finding that parental wealth
has significant effects on children’s outcomes, we control for
wealth by including a measure of income that is neither earned

Effects of Homeownership on Children

4. Sample Characteristics
Table 1 shows the mean differences in outcomes, neighborhood characteristics, and family background characteristics
between children of homeowners and those of renters. The
differences are stark. Relative to homeowner children, renter
children are 40 percent more likely to give birth as an
unmarried teenager, and they are nearly twice as likely to be
idle at age twenty and to rely on welfare as an adult. Their highschool graduation rate is 19 percent lower than that of
homeowner children, they are only half as likely to acquire
some post-secondary education, and their average hourly wage
is a dollar less. These differences are all statistically significant.
Differences in the family backgrounds of renter and owner
children are also dramatic. The parental income of renter
children is half that of owner children, and renter children are
twice as likely to grow up in a single-parent household or be on
welfare. They experience an average neighborhood poverty rate
of 24 percent, compared with 18 percent for owner children,
and a substantially lower neighborhood homeownership rate
(56 percent versus 72 percent, respectively). Surprisingly, there
is little difference in the residential stability of the neighborhoods of these two groups. In renter neighborhoods,
57 percent of families had lived in the same residence for
five years or more, compared with 58 percent in homeowner
neighborhoods. The neighborhood poverty and homeownership rates experienced by the sample children are
somewhat negatively correlated (r =-.45), but the correlation

Table 1

between neighborhood residential stability and homeownership rates is surprisingly weak (r =.25), as is the correlation
between residential stability and poverty rates (r = .11).18

Sample Means for Renters and Homeowners
Renters
Outcomes
Gave birth as unwed teen
(women only)
Idle at age twenty
Years of education at age twenty
Graduated from high school
by age twenty
Obtained some post-secondary
education by age twenty
Average hourly wage, ages
twenty-four to twenty-eight
Received any welfare, ages
twenty-four to twenty-eight
Neighborhood conditions
Mean neighborhood poverty rate
Mean neighborhood
homeownership rate
Mean neighborhood percentage
did not move in five or more years
Individual and family background
features
Female
Black
Year born
Mother’s age when born
Whether income is greater than
earnings plus transfers
Parental earnings
Mean amount of family income is
greater than earnings plus
transfers
Years in two-parent family
Mean number of children in
family
Years receiving AFDC, food
stamps, or “other” cash welfare
Household head graduated from
high school
Household head had some
post-secondary education
Fraction of years in a city of
100,000-500,000
Years in a city of more than
500,000
Number of observations

Homeowners p-value

0.14
0.25
11.30

0.10
0.14
12.0

*
***
***

0.57

0.70

***

0.12

0.23

***

9.16

10.35

0.34

0.18

***

23.9

17.9

***

56.0

72.2

***

56.7

58.0

***

0.52
0.44
1966
25.2

0.52
0.21
1966
26.8

***
*
***

0.55
11,080

0.81
20,920

***
***

2,380
2.25

8,070
3.65

***
***

3.64

3.45

0.62

0.22

0.36

0.49

0.18

0.30

1.12

0.73

***

1.31

0.53

***

1,495

1,081

***

Source: Panel Study of Income Dynamics (PSID), 1968-93.
Notes: Monetary figures are expressed in 1997 dollars. Statistical significance indicators refer to one-tailed t-test results for differences in means,
unequal variances assumed. Values are weighted using age fifteen PSID
individual weights. AFDC is Aid to Families with Dependent Children.
* Value is less than .05.
** Value is less than .01.
*** Value is less than .0001.

5. Regression Results
5.1 Direct Effects: Low-Income
and Higher Income Samples
Estimates from the direct effects models performed on the
low-income and higher income samples are presented in
Table 2. With the low-income sample, homeownership has
statistically significant benefits for all outcomes except for teen
unwed childbearing, where homeownership has a favorable but
not significant effect. In contrast, with the higher income
sample, homeownership has positive, statistically significant
effects only on the acquisition of post-secondary education and
total years of education. These results indicate that the benefits
of homeownership for children are reaped primarily by the less
affluent. For this reason, and because policy interest centers on
the low-income group, the remainder of this paper focuses on
the low-income sample alone.19

5.2 Models with Controls
for Neighborhood Features
Table 2 also presents estimates for the policy variables obtained
from the indirect effects models, which control for
neighborhood features. The inclusion of neighborhood
controls has modest effects on some model estimates, but
overall, there is little effect. Even with neighborhood controls,
homeownership has strong, favorable effects on most
outcomes. Thus, the beneficial effects of homeownership on
children’s long-term outcomes appear to be only marginally, if
at all, attributable to the better neighborhood characteristics
experienced by children of homeowners. The estimates for
educational outcomes and welfare receipt are particularly
strong. In the direct effects models, children of homeowners
are estimated to complete almost half a year more of education,
have a high-school graduation rate that is 13 percentage points
higher, a likelihood of acquiring post-secondary education that
is 6 percentage points greater, and a chance of receiving welfare
between ages twenty-four and twenty-eight that is 9 percentage

FRBNY Economic Policy Review / June 2003

Table 2

Effects of Homeownership on Early Adult Outcomes
Age Twenty-Four to
Twenty-Eight Outcomes

Age Twenty Outcomes

Direct effects
No controls for neighborhood features
Homeowner family, ages eleven to
fifteen, income below 150 percent
of poverty
Homeowner family, ages eleven to
fifteen, income above 150 percent
of poverty

Idle
(Probit)

Years of
Schooling
(Ordinary
Least Squares)

High-School
Graduate
(Probit)

-0.030
(0.285)

-0.066
(0.038)

0.417
(0.000)

0.131
(0.000)

0.058
(0.002)

0.698
(0.018)

-0.091
(0.009)

0.011
(0.237)

-0.011
(0.583)

0.209
(0.030)

0.015
(0.564)

0.101
(0.003)

0.767
(0.124)

-0.020
(0.416)

-0.045
(0.153)
0.005
(0.715)
-0.017
(0.145)
-0.005
(0.737)
0.238

0.039
(0.000)
-0.048
(0.164)
-0.003
(0.913)
0.040
(0.254)
0.327

0.124
(0.000)
-0.016
(0.176)
-0.005
(0.672)
0.020
(0.122)
0.298

0.052
(0.006)
-0.007
(0.365)
0.000
(0.960)
0.012
(0.115)
0.300

0.514
(0.090)
-0.172
(0.133)
0.072
(0.525)
0.227
(0.098)
0.106

-0.095
(0.008)
0.023
(0.072)
0.016
(0.191)
-0.006
(0.664)
0.295

1,364

2,404

2,397

2,391

1,240

1,902

Teen Unwed
Birth
(Probit)

Indirect effects
With controls for neighborhood features
Homeowner family, ages eleven
-0.037
to fifteen
(0.198)
Neighborhood poverty rate
0.002
(0.878)
Neighborhood homeownership
0.010
rate
(0.324)
Neighborhood percentage staying
-0.025
five or more years
(0.035)
Joint significance of neighborhood
0.151
features (p-value of f-test)
Number of observations

844

Any
Post-Secondary
Wage Rate
Education
(Ordinary
(Probit)
Least Squares)

Received
Welfare
(Probit)

Source: Panel Study of Income Dynamics, 1968-93.
Notes: In all probit estimates, the coefficient is transformed to indicate marginal effects with all independent variables set to their means. Wage rates are in
1997 dollars. Huber-White standard errors are used to account for nonindependence of sibling observations. Neighborhood coefficients show the effects
of a 10-percentage-point change in neighborhood conditions. p-values are in parentheses.

points lower. All of these estimates are highly statistically
significant (p =.01), and they decline only slightly, if at all, when
controls for neighborhood features are added.
The estimated effects of homeownership on children’s
subsequent idleness and wage rates are also favorable, but
somewhat less impressive. In the direct effects models, idleness
at age twenty among children of homeowners is reduced by
7 percentage points, and their average wage rates between ages
twenty-four and twenty-eight increase by $0.70 relative to
children of renters. Both of these estimates are statistically
significant (p<.05), but when controls for neighborhood
features are added, they decline by about 30 percent and are of
only moderate statistical significance (p = .15 for idleness and

Effects of Homeownership on Children

p = .09 for wage rates). The estimates for the effects of
homeownership on teen out-of-wedlock childbearing are also
favorable, but weak (p = .29) in the direct effects estimate.
The smaller samples used to estimate homeownership
effects on idleness, wage rates, and teen unwed childbearing
partially explain the weaker results for these outcomes.20 There
may also be greater measurement error for these outcomes,
which could produce a downward bias, compared with
education or welfare receipt.21 Thus, it would be hazardous to
conclude that the effects of homeownership on education and
welfare receipt are, in reality, stronger than they are for the
other outcomes examined. Instead, homeownership appears to
be associated with positive effects across-the-board, although

these effects are statistically significant at conventional levels
only for outcomes that are precisely measured and tested using
the largest samples.
The estimated effects of neighborhood characteristics are
weak.22 Only in the model for wage rates do they jointly attain
a moderate level of statistical significance (p = .11). The
estimated effects of neighborhood residential stability and
poverty, but not the homeownership rate, have the expected
sign for virtually all outcomes. Neighborhood residential
stability exhibits the strongest effects, with a statistically
significant (p<.05) impact on reduced teen out-of-wedlock
childbearing and modestly significant (p<.13) positive effects
of high-school graduation, acquisition of post-secondary
education, and wage rates. Neighborhood poverty is a weaker
determinant of long-term outcomes, with a moderate (p<.10)
effect on increased probability of welfare receipt and some
weak, deleterious effects on other outcomes. Estimates for the
effects of neighborhood homeownership are inconsistent and
weak. For four of the seven outcomes, it has an unexpected
sign, suggesting deleterious effects, and it is not statistically
significant for any outcome. Contrary to expectations, these
results indicate that there are no spillover benefits of
homeownership to the neighborhood beyond the immediate
homeowning family. Instead, they suggest that residential
stability may foster a neighborhood’s social capital, with
beneficial effects on children.23
The finding that the beneficial effects of homeownership
cannot be attributed to the better neighborhood characteristics
of homeowners may be surprising. It arises because residential
stability—the neighborhood characteristic that matters most
for children’s outcomes—is nearly identical for homeowners
and renters in this sample, as shown in Table 1. Differences in
the neighborhood poverty rate, which also appears to affect
outcomes, are also fairly modest, at 6 percentage points on
average. Only the neighborhood homeownership rate differs
substantially between owner and renter families, but this
feature has virtually no effect on children’s outcomes. Thus, on
the dimensions that matter most for children’s outcomes, the
neighborhood characteristics of owner and renter families are
very similar, and they differ substantially only on the
dimension that matters least, at least in this sample.

5.3 Models with Tenure/
Neighborhood Interactions
Table 3 shows the results for models testing the interaction of
tenure status and neighborhoods.24 The indirect effects models

imposed the assumption that neighborhood characteristics
have identical effects on children of homeowners and renters.
In the present results, this assumption is relaxed; that is, in the
interaction models, the effects of homeownership are allowed
to depend upon characteristics of the neighborhood.
The key result of these models is that homeownership does
not buffer children against the deleterious effects of bad
neighborhoods. If anything, the pattern of results points in the
opposite direction—toward an amplification effect.
Homeowner children appear to be more adversely affected by
neighborhood poverty than renter children, and to benefit
more from neighborhood homeownership and residential
stability. Effects of neighborhood residential stability, in
particular, appear to be better for children of homeowners than
for children of renters.
The first row of coefficients in Table 3 shows that in a
neighborhood with average sample characteristics (27 percent
poverty, 59 percent homeownership, and 57 percent
residential stability), the estimated effects of homeownership
are nearly the same as in the direct and indirect effects models.
Subsequent rows in the table show how these average effects are
modified by neighborhood characteristics. For example, the
coefficient on homeownership (first row) in the wage rate
model is $0.397. A 10-percentage-point increase in the poverty
rate of the neighborhood where the child lived between ages
eleven and fifteen is estimated to reduce the early adult wage
rate of homeowner children by $0.322 and of renter children by
$0.102, with a net difference of $0.22. Thus, homeownership in
a neighborhood with a 37 percent poverty rate, rather than the
sample mean of 27 percent, would raise a child’s early adult
wage rate by $0.177 ($0.397 minus $0.22), rather than $0.397.
Comparing coefficients in this way indicates that
neighborhood poverty generally has worse effects on the
outcomes of homeowner children than on renter children, and
neighborhood homeownership and residential stability
generally have better effects. But none of the differences
between the estimated effects of neighborhoods on children of
homeowners and renters are highly statistically significant. In
the strongest case, a 10-percentage-point increase in
neighborhood residential stability is associated with a
statistically significant $0.43 increase in the wage rates of
homeowner children (p<.05), but it has no effect on the wage
rates of renter children. However, the difference between these
two estimates is statistically significant at only a moderate level
(p = .10). In another case, the difference between owner and
renter children in the impact of neighborhood residential
stability on teen out-of-wedlock childbearing is modest
(p =.16). None of the other differences is statistically
distinguishable at even this weak level.

FRBNY Economic Policy Review / June 2003

Despite this lack of statistical significance in differences,
however, the pattern of homeowner children being more
adversely affected by neighborhood poverty and more
favorably affected by neighborhood stability and
homeownership is consistent. Although the statistical evidence
to support the neighborhood amplification effect of
homeownership is modest, the underlying theory (that is, that
children of homeowners may develop closer ties with other
community members and, therefore, be more affected by
them) is consistent with the data used here, where renter
children experienced 40 percent greater variability in
neighborhood characteristic than children of homeowners.

If there was truly no difference in the impacts of neighborhoods on homeowner and renter children, we would expect a
more random pattern of results. In addition, tests of an additive
(admittedly crude) neighborhood quality index25 reveal that
on three of the seven outcomes (high-school graduation,
acquisition of post-secondary education, and wage rates), the
difference between the estimated effects on homeowner and
renter children is moderately significant (p<.10). On balance,
these results suggest that neighborhood characteristics may
have different effects on owner and renter children, but these
differences are weak and require further exploration.

Table 3

Results of Housing Tenure/Neighborhood Interaction Models
Age Twenty-Four to
Twenty-Eight Outcomes

Age Twenty Outcomes

Homeowner family, ages eleven
to fifteen
Neighborhood poverty rate
Homeowners
Renters
Neighborhood homeownership rate
Homeowners
Renters
Neighborhood stability rate
Homeowners
Renters
Tests for equality of neighborhood
coefficients
Poverty rate
Homeownership rate
Stability rate

Idle
(Probit)

Years of
Schooling
(Ordinary
Least Squares)

High-School
Graduate
(Probit)

-0.041
(0.190)

-0.041
(0.210)

0.369
(0.000)

0.11
(0.001)

0.045
(0.023)

0.397
(0.209)

-0.086
(0.022)

-0.002
(0.926)
-0.004
(0.761)

-0.003
(0.895)
0.010
(0.492)

-0.092
(0.111)
-0.026
(0.502)

-0.034
(0.072)
-0.010
(0.466)

-0.015
(0.192)
-0.003
(0.734)

-0.322
(0.051)
-0.102
(0.478)

0.019
(0.352)
0.027
(0.068)

0.014
(0.345)
0.008
(0.517)

-0.024
(0.190)
-0.011
(0.365)

-0.006
(0.893)
-0.001
(0.973)

0.001
(0.942)
-0.008
(0.548)

0.003
(0.787)
-0.003
(0.761)

0.094
(0.564)
0.038
(0.770)

0.002
(0.929)
0.023
-0.085

-0.041
(0.011)
-0.010
(0.545)

-0.013
(0.574)
0.001
(0.971)

0.065
(0.239)
0.023
(0.594)

0.037
(0.052)
0.009
(0.596)

0.019
(0.052)
0.005
(0.652)

0.431
(0.012)
0.039
(0.835)

-0.001
(0.952)
-0.007
(0.678)

0.886
0.742
0.155

0.605
0.521
0.619

0.303
0.925
0.517

0.274
0.626
0.227

0.355
0.647
0.274

0.277
0.759
0.097

0.729
0.303
0.816

Teen Unwed
Birth
(Probit)

Any
Post-Secondary
Wage Rate
Education
(Ordinary
(Probit)
Least Squares)

Received
Welfare
(Probit)

Source: Panel Study of Income Dynamics, 1968-93.
Notes: In all probit estimates, the coefficient is transformed to indicate marginal effects with all indepencent variables set to their means. Wage rates are in
1997 dollars. Huber-White standard errors are used to account for nonindependence of sibling observations. Neighborhood coefficients show the effect
of a 10-percentage-point change in neighborhood conditions. p-values are in parentheses.

Effects of Homeownership on Children

6. Discussion
6.1 Unmeasured Variable Bias
As discussed earlier, the results presented here could be
erroneous if the unmeasured characteristics of families that
choose different tenure and neighborhood combinations were
driving them. In particular, the concern here is that the
homeownership coefficients may have much larger upward
biases than the neighborhood coefficients. If so, the findings of
the preceding analysis would be spurious. However, previous
research indicates that estimates for the effects of homeownership and neighborhoods have roughly the same upward bias.
Using instrumental variable techniques, Green and White
(1997), Haurin, Parcel, and Haurin (2000), and Aaronson
(2000) all find a modest upward bias in homeownership effect
estimates, while sibling difference analyses and other attempts
to gauge the extent of bias associated with neighborhood effect
estimates (Aaronson 1997; Duncan, Connell, and Klebanov
1997) also find a modest upward bias associated with
neighborhood poverty. These results suggest that conclusions
drawn from the uninstrumented results will be qualitatively

correct, although the point estimates may be overstated. In
contrast to other studies of homeownership effects, Harkness
and Newman (2002) find that homeownership coefficients are
biased downward for children of low-income families; that is,
the effects of homeownership are even larger than estimates
provided by the uninstrumented models.

6.2 Policy Implications
One possible implication of this analysis is that under certain
adverse neighborhood characteristics, homeownership could
result in worse, not better, outcomes for children, compared
with renting. To gain a sense of what these conditions might be,
we used the coefficients from the interaction model results to
calculate the effects of homeownership if the three neighborhood characteristics considered here were worsened by one
standard deviation from their means, both individually and
simultaneously, with the results presented in Table 4.26 With
one exception—the effect of reduced neighborhood residential
stability on earnings—all of the estimated effects of homeownership remain favorable. For educational outcomes and welfare
receipt, many of these effects remain statistically significant

Table 4

Effects of Homeownership on Early Adult Outcomes under Different Neighborhood Conditions
Age Twenty-Four to
Twenty-Eight Outcomes

Age Twenty Outcomes

Poverty rate, increase of one
standard deviation
Homeownership rate, decrease
of one standard deviation
Residential stability, decrease
of one standard deviation
Worsen all neighborhood features
by one standard deviation

Teen Unwed
Birth
(Probit)
-0.037
(0.374)
-0.052
(0.335)
-0.005
(0.876)
-0.013
(0.800)

Idle
(Probit)

Years of
Schooling
(Ordinary
Least Squares)

High-School
Graduate
(Probit)

-0.059
(0.248)
-0.015
(0.779)
-0.026
(0.562)
-0.018
(0.774)

0.273
(0.054)
0.379
(0.014)
0.320
(0.006)
0.235
(0.170)

0.076
(0.103)
0.092
(0.092)
0.078
(0.066)
0.026
(0.660)

Any
Post-Secondary
Wage Rate
Education
(Ordinary
(Probit)
Least Squares)
0.027
(0.351)
0.035
(0.270)
0.028
(0.270)
0.001
(0.998)

0.079
(0.850)
0.284
(0.610)
-0.049
(0.903)
-0.480
(0.412)

Received
Welfare
(Probit)
-0.097
(0.062)
-0.043
(0.485)
-0.092
(0.040)
-0.061
(0.357)

Source: Panel Study of Income Dynamics, 1968-93.
Notes: The table uses the coefficients from the interaction models (in Table 3) to show the estimated effects of homeownership when the neighborhood
measures are worsened by one standard deviation from their mean values, both individually and simultaneously. In all probit estimates, the coefficient is
transformed to indicate marginal effects with all independent variables set to their means. Wage rates are in 1997 dollars. Huber-White standard errors are
used to account for nonindependence of sibling observations. p-values are in parentheses.

FRBNY Economic Policy Review / June 2003

near conventional levels when individual neighborhood
features are worsened. None remain significant when all
neighborhood features are simultaneously worsened by one
standard deviation, but these sorts of neighborhood
characteristics—a poverty rate of 42 percent, a homeownership
rate of 39 percent, and only 46 percent of residents remaining
in their dwellings for five years or more—roughly represent
the worst quintile of neighborhoods in the sample. It is
noteworthy that even with these extremely poor neighborhood
characteristics, and under the assumption that owner children
are, in fact, more adversely affected by these conditions than
renter children, effects of homeownership on children’s
outcomes tend to be positive.

6.3 Comparison with the Results
of Aaronson (2000)
Because this paper uses a different approach than Aaronson
(2000) to examine the role of neighborhood in homeownership
effects, it is important to compare results. Although both
analyses find that neighborhood residential stability enhances
the positive effect of homeownership on children’s outcomes,
findings on the effect of neighborhood poverty disagree.
Aaronson finds that homeownership has a more positive effect
on high-school graduation in low-income neighborhoods; we
find that neighborhood poverty reduces the positive effect of
homeownership on high-school graduation and other
outcomes.27
When we attempt to replicate Aaronson’s results using a
sample unrestricted by income, our results are consistent with
his: homeownership in a high-poverty neighborhood has a
significantly more positive effect on high-school graduation
than homeownership in a low-poverty neighborhood.
Aaronson’s result therefore appears to be attributable to the
inclusion of higher income families in the sample. In our
results using the low-income sample, homeownership is
estimated to increase the probability of high-school graduation
by about 10 percentage points, roughly equal in magnitude to
the effect Aaronson finds in low-income neighborhoods.
Because the families living in low-income neighborhoods in
Aaronson’s sample probably have low incomes themselves and,
therefore, roughly match the sample we use, our results are
consistent with his. Excluded from our sample are the wealthier
families who live in the most affluent neighborhoods and for
whom homeownership has no effect on children’s high-school
graduation, according to Aaronson’s results. Thus, the
difference Aaronson finds in high- versus low-income

Effects of Homeownership on Children

neighborhoods may, in fact, be attributable to differences in
the type of families that live in such neighborhoods, not the
neighborhoods themselves.

6.4 Supplementary Models
Measures of home equity and the family’s history of residential
mobility were not included in the foregoing models because
they could be affected by homeownership or neighborhood
characteristics, as discussed earlier. However, when
supplementary models that include these measures were tested,
the effects of home equity were not statistically significant for
any outcome except wage rates. A history of frequent
residential moves was associated with the most adverse effects
for outcomes, and these effects were statistically significant for
all educational outcomes and for wage rates. Like Aaronson
(2000), we find the positive effects of homeownership to be
weaker when residential moves are added to the model, which
suggests that these effects can be partially attributed to the
reduced residential mobility of homeowners. But even after we
controlled for residential moves, homeownership continued to
exhibit statistically significant (p<.05) favorable effects on all
three educational outcomes and on reduced welfare usage. It
thus appears that the impacts of homeownership on other
features, not simply residential stability, need to be examined
in order to explain the beneficial effects of homeownership on
children.

7. Conclusions
The key finding of this paper is that homeownership is
beneficial to children’s outcomes in almost any neighborhood.
However, because better neighborhoods are associated with
better outcomes for homeowner children, homeownership in
better neighborhoods is an even stronger combination.
Residentially stable neighborhoods are particularly beneficial
to homeowner children, and low neighborhood poverty also
increases the benefits of homeownership. Interestingly,
however, the neighborhood homeownership rate has no effect.
Are better neighborhoods also better for renter children?
The answer appears to be “no.” One possible explanation is
that because renter families move more often, renter children
do not develop close ties with others in their community and
consequently are influenced less by them. The one compensation

is that distressed neighborhoods may also be less deleterious for
them, since renters’ children appear to be influenced less by
their neighborhoods—good or bad.
These provocative findings imply that the children of most
low-income renters would be better served by programs that
help their families become homeowners in their current
neighborhoods instead of helping them move to better
neighborhoods but remain renters. The best evidence to date
on the effects of neighborhoods on renter children comes from
the Moving-to-Opportunity (MTO) demonstration program.
In the program, one group of families living in public housing
in highly distressed neighborhoods was offered a Section 8
certificate, counseling, and assistance to help them move out of
public housing and into rental housing in very low-poverty
neighborhoods. Another group was offered a Section 8
certificate, but no additional assistance, to move as they chose.
This latter group generally moved to somewhat better
neighborhoods than those of their former public housing
residence, but much worse than the experimental group that
received assistance in moving to very low-poverty
neighborhoods. The early MTO results demonstrate a variety
of benefits to both groups of families moving out of public
housing. But it is not yet evident whether the children whose
families moved to low-poverty neighborhoods are faring much
better than those whose families generally remained in fairly

distressed neighborhoods. For example, Ludwig, Duncan, and
Ladd (2001) report significant gains in reading scores for both
Section 8 mover groups, whether they moved to a low-poverty
neighborhood or not. Thus, while it seems clear that helping
families to move out of public housing in highly distressed
neighborhoods is beneficial, the MTO research has not yet
demonstrated that neighborhoods matter significantly for
children of renters.28
The research reported here is only an initial strep toward
understanding the role of neighborhood characteristics in the
effects of homeownership on children. But the research is
limited by its small sample size and methodological issues—
including the likelihood of upwardly biased estimates because
of failure to control for important family characteristics—that
render the results of this analysis extremely tenuous. Further
research, preferably using an experimental design, is therefore
necessary to measure solidly the relative benefits of
homeowning and renting for children with a variety of
neighborhood characteristics.
Finally, homeownership may generate broader social
benefits beyond its favorable effects on children, such as a more
active and informed citizenry (DiPasquale and Glaeser 1999)
and more residentially stable neighborhoods. The case for
greater investment in homeownership must take this full range
of potential benefits into account.

FRBNY Economic Policy Review / June 2003

Appendix A: Discussion of Sample Restrictions and Implications

Suppose we want to estimate how the neighborhood poverty
rate differentially affects children of homeowners and renters.
Some children are always homeowners between ages eleven
and fifteen, some are always renters, and some experienced
both forms of tenure. One solution might be to specify
homeownership as years in a homeowning family and
multiplicatively interact this variable with the average
neighborhood poverty rate experienced over the period. But
for those with mixed tenure, the average neighborhood poverty
rate comprises both the neighborhood poverty rate while
renting and the neighborhood poverty rate while owning,
which are two quantities whose effects we want to estimate
separately.
Another solution might be to specify separately the average
neighborhood poverty rate/level experienced while owning
and the average neighborhood poverty rate experienced while
renting. The problem here is that average neighborhood
poverty rate while owning (renting) is undefined for renters
(owners). To correct for this problem, we can set the average
neighborhood poverty rate while owning (renting) to zero for
renters (owners) and introduce a dummy variable to control
for the fact that this substitution has been made. But the
dummy variables introduced also act as indicators of zero and
five years of homeownership between ages eleven and fifteen,

100

Effects of Homeownership on Children

which means that the model estimates for the effects of
homeownership rely solely upon the relatively few cases with
mixed tenure status over the period.
The most likely effect of eliminating from the sample
children of mixed tenure status between ages eleven and
fifteen would be to overestimate the favorable effects of
homeownership on children’s outcomes because homeownership is generally indicative of better household conditions, and
families that did not become homeowners until their children
were age eleven or older are more likely to have been worse off
in financial and other ways compared with families that
became homeowners earlier. Likewise, families that were
already homeowners and became renters after their children
were age eleven or older are likely to be undergoing serious
difficulties, such as job loss or divorce. (The question of
whether homeownership is good for children in families
undergoing serious stress is an important one, but it is not
examined here.) Thus, the estimates obtained by eliminating
families of mixed tenure status should produce the most
favorable picture of homeownership effects on children’s
outcomes. Tests of basic models (that is, those without tenure/
neighborhood interactions) using the full low-income sample
support this expectation.

Appendix B: Data

For intercensus years, we interpolated using the values from the
two bracketing decennial censuses; for census years and for
cases where the data from only one of the bracketing censuses
were available, we used values from a single census. (From 1986
on, the Panel Study of Income Dynamics geocode match
provided data from the 1990 census only.) Data from two
censuses were used in 79 percent of the cases; one census was
used for 21 percent of the cases.
Approximately 68 percent of the two-census interpolations
were obtained from tract data alone, 10 percent used ZIP code
data alone, and 4 percent used a combination of tract and ZIP
code measures. In the remaining 18 percent of the two-census
cases, data at the tract or ZIP code level were available for only
one of the bracketing censuses. For these, we used the value at

the tract or ZIP code level that was available relative to the
minor civil division (MCD) value for that census to impute a
tract or ZIP code value for the missing census based on its
MCD value. That is, we imputed z1 = Z1 * z2 /Z2, where z1 is
the missing ZIP code or tract level datum from census year 1,
z2 is the available ZIP code or tract level datum from census
year 2, and Z1 and Z2 are the MCD level values. (The MCD
corresponds roughly to a township or a quarter of a county.
Values for the MCD, or something conceptually similar to it,
were available for all years.) About 0.4 percent of two-census
interpolations used MCD values for both bracketing census
years. Of the single-census cases, 73 percent used tract level
data, 21 percent used ZIP code level data, and 6 percent used
MCD level values.

FRBNY Economic Policy Review / June 2003

101

Appendix C: Alternative Probit Estimates

Alternative Probit Estimates for Indirect Effects Model

Unfavorable family background
Homeowner family, ages eleven to fifteen
Neighborhood poverty rate
Neighborhood homeownership rate
Neighborhood percentage staying five or more years
Favorable family background
Homeowner family, ages eleven to fifteen
Neighborhood poverty rate
Neighborhood homeownership rate
Neighborhood percentage staying five or more years

Idle
(Probit)

High-School
Graduate
(Probit)

Any
Post-Secondary
Education
(Probit)

Received Welfare
(Probit)

-0.057
(0.176)
-0.003
(0.879)
0.016
(0.324)
-0.038
(0.041)

-0.056
(0.145)
0.006
(0.715)
-0.020
(0.142)
-0.006
(0.737)

0.128
(0.000)
-0.017
(0.178)
-0.005
(0.672)
0.020
(0.122)

0.029
(0.029)
-0.004
(0.378)
0.000
(0.960)
0.007
(0.132)

-0.101
(0.007)
0.024
(0.070)
0.017
(0.192)
-0.006
(0.665)

-0.016
(0.403)
-0.001
(0.878)
0.004
(0.443)
-0.011
(0.340)

-0.014
(0.373)
0.001
(0.727)
-0.005
(0.362)
-0.001
(0.739)

0.076
(0.010)
-0.010
(0.216)
-0.003
(0.672)
0.012
(0.151)

0.096
(0.006)
-0.013
(0.363)
-0.001
(0.960)
0.023
(0.122)

-0.072
(0.038)
0.017
(0.103)
0.012
(0.218)
-0.004
(0.666)

Teen Unwed
Birth
(Probit)

Source: Panel Study of Income Dynamics, 1968-93.
Notes: In all probit estimates, the coefficients were transformed to indicate marginal effects with all variables set to their means. The table shows how these
estimates remain stable with different choices for the values of the independent variables. For the “unfavorable family background” estimates, maternal age
at birth was set to fifteen, parental earnings to zero, parental education to no high school, years of childhood welfare usage to 100 percent, and asset income
to zero. For the “favorable family background” estimates, maternal age at birth was set to thirty, parental earnings to $30,000 annually, parental education
to college, years of childhood welfare usage to zero, and asset income to $1,000 annually. Variables other than those mentioned were set to their means.
Wage rates are in 1997 dollars. Huber-White standard errors are used to account for nonindependence of sibling observations. p-values are in parentheses.

102

Effects of Homeownership on Children

Endnotes

1. Distressed neighborhoods are typically defined as those with high
rates of poverty, unemployment, and dependence on public
assistance, though researchers differ in their specific operationalizations. Some analysts use an index of factors (for example, the
Ricketts-Sawhill definition of underclass neighborhoods) or factor
analysis scores (for example, the papers collected in Brooks-Gunn,
Duncan, and Aber [1997]). Others rely primarily on the poverty rate,
though the cutoff point for “distress” varies from 20 percent (used by
the census to define poverty areas) to 40 percent. These different
definitions are substantively quite similar, because the factors that
characterize distressed neighborhoods are highly interrelated. Most
researchers rely on census tracts as proxies for neighborhoods.
2. See Sandel et al. (1998) for a discussion of health-threatening
conditions in substandard housing. We are aware of only one study
that investigates the effects of milder forms of physical deprivation on
children’s development. Using the National Longitudinal Survey of
Youth (NLSY) child data set, Mayer (1997) constructs a “housing
environment” index, based on whether the interviewer observed the
respondent’s home to be “dark and perceptually monotonous,”
“minimally cluttered,” or “reasonably clean.” She found almost no
effect of this index on young children’s cognitive test scores or
behavioral problems.

and above 150 percent of the poverty line, indicating that it is not
appropriate to pool the two samples.
9. Green and White (1997) and Haurin, Parcel, and Haurin (2000)
include some rough proxies for neighborhood characteristics in their
models, but acknowledge weaknesses in these proxies. Aaronson
(2000) examines the interaction effects of homeownership by retesting
models on samples split by residence in high- versus low-income
neighborhoods and in high- versus low-stability neighborhoods. But
this technique could produce misleading results for the interactive
effect of homeownership and neighborhood characteristics if the
difference in neighborhood characteristics experienced by
homeowners and renters was unequally distributed in the split
samples.
10. We also experimented with defining low income as having
parental earnings below the regional median for at least two-thirds of
observed years, using the four census-defined regions. This definition
has the advantage of providing a more geographically balanced
sample. It is also more consistent with definitions of low-income
families used in other housing studies, which are usually based on the
median income of the metropolitan area. However, it does not adjust
for family size as does the poverty formula. The two definitions
produce almost identical results.

3. Data were tabulated from the 1999 American Housing Survey.
4. The better socioeconomic features of homeowning families may be
another factor explaining the improved outcomes of homeowner
children, but all previous studies control for income and other family
features.
5. This speculation follows from the collective socialization and
epidemic models of neighborhood effects (Jencks and Mayer 1990).
6. Green and White (1997) also examine the effect of homeownership
on teen unwed childbearing in one of the three data sets they consider.
Boehm and Schlottman (1999) simulate the indirect effect of
homeownership on lifetime earnings via its impact on educational
attainment, and they also test whether children of homeowners are
more likely to become homeowners themselves.
7. For a four-person, two-child family, 150 percent of the 2001
poverty line was $26,940.
8. Chow tests confirm structural differences between model estimates
obtained from samples of children from families with incomes below

11. Two other outcomes—whether there are any, and number of,
hours employed between ages twenty-four and twenty-eight—were
also tested and found to be unaffected by parental homeownership.
Results on these two outcomes are not reported below. Hourly wage
rates were constructed by dividing total earnings by work hours. Six
outliers with calculated wage rates of more than $40 an hour and less
than 300 average annual hours of work were excluded from the wage
rate model.
12. Huber-White standard errors are used because the data include
siblings, which may not be independent.
13. Each of these measures was extracted from the PSID census
geocode and averaged over observed years. Census tract level measures
were available for roughly 70 percent of cases, and ZIP code areas were
available for the remainder. Direct census measures were only
obtained for decennial census years. For intercensus years, we linearly
interpolated between the two closest decennial censuses. For example,
for 1975, we interpolated between the 1970 and 1980 census values for
the tract (or ZIP code area). (Appendix B provides more detail on the
construction of neighborhood measures.)

FRBNY Economic Policy Review / June 2003

103

Endnotes (Continued)

14. That is, each neighborhood variable is transformed by subtracting
off its sample mean.
15. A variety of nonlinear specifications for several of these variables
(such as parental earnings, maternal age when born) were tested and
found to have no impact on the key results, and diagnostics for
colinearity problems with these variables using the techniques of
Belsley, Kuh, and Welsch (1980) revealed no such evidence.
16. Annual city size values were obtained by logarithmically
interpolating between place size values in the two closest decennial
census years.
17. The PSID did not begin collecting detailed data on assets until
1984.
18. Diagnostics revealed no colinearity problems with these
neighborhood variables and the other control variables.
19. Harkness and Newman (2002) find that the positive effects of
homeownership on the educational outcomes of the higher income
group are not sustained when instrumental variable techniques are
used to account for unmeasured family background variables. In
contrast, positive effects of homeownership are sustained in the
low-income sample.
20. The smaller sample for teen unwed births is attributable to missing
data and the restriction of the sample to women. A substantial portion
of the data needed to construct the idleness measure is also missing.
The sample used for the wage rate model is smaller because there are
fewer cohorts with data for ages twenty-four to twenty-eight, when
wage rates were measured, and also because it is restricted to cases with
nonzero work hours. (Six cases with fewer than 300 annual average
work hours and wage rates above $40 per hour were also excluded
from the wage rate sample.)
21. An individual’s average wage rate between ages twenty-four and
twenty-eight is likely to be difficult to measure accurately because

104

Effects of Homeownership on Children

earnings and work hours (from which we constructed the wage rate
variable) can be quite volatile from month to month (Duncan 1988),
and it may be difficult for individuals to recall accurately their wage
rates when surveyed annually (as in the PSID). The variables for teen
unwed childbearing and idleness were also constructed from other,
more basic variables in the PSID, which could also introduce
measurement error.
22. For expository purposes, the coefficients on the neighborhood
variables are scaled to represent the effect of a 10-percentage-point
change.
23. It may be that, by fostering greater residential stability, homeownership could play an indirect role in creating neighborhood
characteristics beneficial to children’s development. This role appears
to be weak, however. In supplementary models that exclude neighborhood residential stability, the estimated effects of neighborhood
homeownership are only slightly more favorable than those shown
in Table 2.
24. In these results, all interactions were tested simultaneously, not in
separate models or entered in the same model sequentially.
25. This index was formed by adding the homeownership and
residential stability rates and subtracting the poverty rate.
26. These standard deviations are 14, 20, and 11 percentage points for
the poverty rate, homeownership rate, and residential stability rate,
respectively.
27. These findings can be compared because neighborhood poverty
and income are almost perfectly negatively correlated.
28. Complete documentation of the MTO research to date can be
found at <http://www.mtoresearch.org>.

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Network Electronic Paper Collection. Available at <http://
papers.ssrn.com/paper.taf?abstract_id=218969>.
Haveman, Robert, Barbara Wolfe, and James Spaulding. 1991.
“Childhood Events and Circumstances Influencing High-School
Completion.” Demography 28, no. 1: 133-57.
Haveman, Robert, and Barbara Wolfe. 1995. “The Determinants of
Children’s Attainments: A Review of Methods and Findings.”
Journal of Economic Literature 33: 1829-78.
Ioannides, Yannis J., and Kamhon Kan. 1996. “Structural Estimation of
Residential Mobility and Housing Tenure Choice.” Journal of
Regional Science 36, no. 3: 335-63.
Jencks, Christopher, and Susan Mayer. 1990. “The Social Consequences
of Growing up in a Poor Neighborhood.” In Laurence Lynn and
Michael McGeary, eds., Inner-City Poverty in the United
States, 111-86. Washington, D.C.: National Academy Press.
Jordan, Will J., et al. 1996. “Exploring the Causes of Early Dropout
among Race, Ethnic, and Gender Groups.” Youth and
Society 28, no. 1: 62-94.
Kan, Kamhon. 2000. “Dynamic Modeling of Housing Tenure Choice.”
Journal of Urban Economics 46: 46-69.
Kasarda, John, and Morris Janowitz. 1974. “Community Attachment
in Mass Society.” American Sociological Review 39: 328-39.

106

Effects of Homeownership on Children

Mayer, Susan. 1997. What Money Can’t Buy: Family Income and
Children’s Life Chances. Cambridge: Harvard University Press.
Moffitt, Robert. 1994. “Welfare Effects on Female Headship with Area
Effects.” Journal of Human Resources 29, no. 2: 621-36.
———. 2001. “Policy Interventions, Low-Level Equilibria, and Social
Interactions.” In Steven Durlauf and Peyton Young, eds., Social
Dynamics, 45-82. Cambridge: MIT Press.
Newman, Sandra J., and Greg J. Duncan. 1979. “Residential Problems,
Dissatisfaction, and Mobility.” Journal of the American
Planning Association 45: 154-66.
Page, Maryann, and Gary Solon. 1999. “Correlations between Brothers
and Neighboring Boys in Their Adult Earnings: The Importance of
Being Urban.” Paper presented at the conference “Neighborhood
Effects on Low-Income Families.” Joint Center for Poverty
Research, Chicago, September 9-10.
Quigley, John, and Daniel Weinberg. 1977. “Intra-Urban Residential
Mobility: A Review and Synthesis.” International Regional
Science Review 2, no. 2: 41-66.
Rohe, William M., and Victoria Basolo. 1997. “Long-Term Effects of
Homeownership on the Self-Perceptions and Social Interaction of
Low-Income Persons.” Environment and Behavior 29, no. 6:
793-819.
Rohe, William M., and Michael A. Stegman. 1994. “The Impacts of
Homeownership on the Self-Esteem, Perceived Control, and Life
Satisfaction of Low-Income People.” Journal of the American
Planning Association 60, no. 1: 173-84.

References (Continued)

Rohe, William M., and Leslie S. Stewart. 1996. “Homeownership and
Neighborhood Stability.” Housing Policy Debate 7, no. 1:
37-81.
Rossi, Peter H., and Eleanor Weber. 1996. “The Social Benefits of
Homeownership: Empirical Evidence from National Surveys.”
Housing Policy Debate 7, no. 1: 1-35.
Sampson, Robert J. 1988. “Local Friendship Ties and Community
Attachment in Mass Society: A Multilevel Systemic Model.”
American Sociological Review 53: 766-79.
Sampson, Robert J., Stephen W. Raudenbusch, and Felton Earls. 1997.
“Neighborhoods and Violent Crime: A Multilevel Study of
Collective Efficacy.” Science 277: 918-23.

Sherraden, Michael. 1991. Assets and the Poor: A New American
Welfare Policy. Armonk, N.Y.: M. E. Sharpe.
Spivack, Richard. 1991. “Determinants of Housing Maintenance and
Upkeep: A Case Study of Providence, Rhode Island.” Applied
Economics 23: 639-46.
U.S. Department of Housing and Urban Development. 2000. “Cuomo
Sets Goal of Boosting Black and Hispanic Homeownership above
50 Percent in Three Years.” Available at <http://www.hud.gov:80/
pressrel/pr00-132.html>.
Zabel, Jeffry E. 1998. “An Analysis of Attrition in the Panel Study of
Income Dynamics and the Survey of Income and Program
Participation with an Application to a Model of Labor Market
Behavior.” Journal of Human Resources 33, no. 2: 479-506.

Sandel, Megan, Joshua Sharfstein, Seth Kaplan, Tracy King, and Mary
Pulaski. 1998. “Not Safe at Home: How America’s Housing Crisis
Threatens the Health of Its Children.” Boston Medical Center and
Children’s Hospital Doc4Kids Project report. Available at
<http://www.bmc.org/program/doc4kids/contents.htm>.

107

Frank Braconi

Commentary

he paper by Joseph Harkness and Sandra Newman
combines two important issues in community
development research: the questions of “neighborhood
effects” and of “homeowner effects.” It is interesting that
researchers widely accept the notion that neighborhood
characteristics influence the outcomes of children and
adolescents, although the empirical evidence of such effects
remains spotty (Ginther, Haveman, and Wolfe 2000; Evans,
Oates, and Schwab 1992). However, there is widespread
skepticism that homeownership effects are real, although the
statistical evidence for them is fairly robust. The Harkness and
Newman paper is one of the first to explore those hypothesized
influences in combination, an avenue of inquiry that can
potentially have significant policy implications.

A number of researchers have found that parental homeownership is associated with substantially improved outcomes
for children (Green and White 1997; Aaronson 2000). Of
course, it is natural and prudent for researchers to question
whether the improved outcomes are due to homeownership
per se, or to unobservable characteristics of the parents that
cause them to both self-select into homeownership and to rear

more successful kids. The standard techniques for dealing with
the problem are to seek a more complete set of parental control
variables, or to instrument for homeownership. As might be
expected, when this is done, the simple estimated effects of
homeownership tend to diminish somewhat, but heretofore
have remained stubbornly positive and significant. Harkness
and Newman choose to deal with this issue in a related
paper, presenting a thorough analysis of how instrumentation
changes, or does not change, their basic conclusions.
In the present paper, they assume that the interaction of
homeownership effects and neighborhood effects should be
relatively unbiased in single-equation probit models. That
appears to be a sensible approach, given that the alternatives
would be methodologically complex and might risk obscuring
the policy implications.
Of course, all statistical studies also involve database choice
and sample selection trade-offs, and it would be useful here to
note some of those implicit in the Harkness and Newman
paper. The data are drawn from the Panel Study of Income
Dynamics (PSID), which provides unparalleled information
on family structure and living arrangements throughout the
individual’s childhood and adolescence. That provides an
excellent set of parental and, with the PSID geocoding,
neighborhood control variables. Unfortunately, one trade-off
that is inevitable is sample size. Apparently because of sample
size considerations, Harkness and Newman have combined

Frank Braconi is executive director of the Citizens Housing and Planning
Council and an assistant professor of real estate economics at New York
University’s Real Estate Institute.

The views expressed are those of the author and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

1. Trade-Offs in Research Design

FRBNY Economic Policy Review / June 2003

109

males and females in most of their basic regressions, with a
dummy variable to indicate gender. However, I generally
prefer the segregation of the sexes, at least in statistical samples.
My own research indicates that homeownership, as well as
other characteristics of a family’s housing and neighborhood
situation, have differential effects on young men and women.
This is intuitively plausible, insofar as the socialization and
expectations of adolescent males and females are so
different. A thorough understanding of neighborhood and
homeownership effects, I am convinced, will require separate
investigations of their effects on boys’ and girls’ development.
Harkness and Newman also make an effort to analyze
homeownership and neighborhood effects on a variety of child
outcomes. Much of the research so far, including my own, has
focused more narrowly on the effects on high-school
graduation or on teenage or out-of-wedlock births. Expanding
the inquiry to include a number of other outcome variables is a
useful step at this stage in the game. This broadening of the
agenda also involves trade-offs, however. In particular,
different characteristics of neighborhoods may influence child
development in different ways, requiring a proliferation of
neighborhood control variables that may be highly correlated
with one another. One of the pioneering papers in the field
(Case and Katz 1991) suggests that families and neighborhoods
influence youths along like dimensions; for instance, a youth’s
likelihood of completing high school will be most directly
influenced by family and peer propensities toward school
completion. The neighborhood variables Harkness and
Newman test may not be specific enough to capture all of the
particular neighborhood effects on each of their outcome
variables, and hence omitted variable bias may be present. It
would be interesting to know what other neighborhood
variables the authors tested.

2. Implications for Ownership
Programs
With the methodological caveats duly considered, the research
of Harkness and Newman addresses pressing questions in
community development policy. Promotion of homeownership opportunities has been a favorite policy
prescription of government officials, private financial leaders,
and policy analysts for a number of years, especially since the
large-scale rental production programs, typified by public
housing, fell into disfavor. By facilitating, even encouraging,
low-income families’ purchases of homes in distressed areas,
are we doing them, and their children, harm? Those families

110

Commentary

are probably among the more capable and motivated, and left
to their own devices, might well migrate toward more stable
communities. Do we do them a disservice by anchoring them
to troubled neighborhoods with homeowner incentives?
The intriguing result obtained by Harkness and Newman
is that neighborhoods do appear to affect the children of
homeowners and renters differently. They find that children
of homeowners appear to be more adversely affected by
neighborhood poverty and more favorably affected by
neighborhood stability and homeownership rates. While the
authors’ estimates indicate that, even in distressed
neighborhoods, the net effect of homeownership on children is
positive, their findings should not be taken too casually. Many
of the New York neighborhoods in which homeownership
projects have been completed are actually much worse than the
worst case estimated by Harkness and Newman. For example,
in some of the Bronx neighborhoods in which affordable
homes were built in the early 1990s, the poverty rates exceeded
50 percent and the homeownership rate was less than 5 percent.
Moreover, the more telling comparison might not be with
renter children in the same neighborhood, but with renter
children in the neighborhood the family lived in prior to
becoming homeowners, or in the neighborhood they might
have moved to if left to their own devices.
These concerns should be mitigated, to an extent, if
homeownership projects are undertaken on a large scale. In the
South Bronx between 1988 and 1997, more than 3,200 units in
one- and two-family homes were built, often in large clusters,
through the New York City Partnership and Nehemiah
programs. Inner-city homeownership development on that
scale can change the character of the neighborhoods
themselves, possibly diluting the effects of bad neighborhoods
on the children of the homeowners. A more disturbing policy
conclusion could be drawn, however, if the homeowner effect
turned out to be illusory. If the measured gains to children’s
outcomes are actually due to unobservable characteristics of
homeowner parents themselves, public policies that
facilitate ownership would actually contribute nothing to
the children’s outcomes, and could harm them if the
ownership opportunities are in more adverse neighborhood
environments than the families would otherwise choose.

3. Broader Policy Implications
Harkness and Newman add to a growing body of literature
relating to the most fundamental question in housing policy:
Is the issue of affordable housing simply a question of rent

burdens? If so, it may be better addressed through income
policies, such as minimum wages or earned income tax credits.
Conventional economic models suggest that, if given the
equivalent income supplements, most low-income families
would not spend as much on housing as housing programs
would implicitly have them spend. So, skewing the
consumption of the poor toward housing, through affordable
housing programs, can only be justified if there are societal
benefits that are not apparent to the beneficiaries themselves. If
housing conditions affect health, educational attainment,
and other important outcomes in subtle and sometimes
imperceptible ways, then a justification exists for giving the poor
more housing than they would otherwise choose to purchase.
Homeownership and neighborhood effects each can be used
to justify government programs that give the poor more
housing, or more stable neighborhoods, rather than an
equivalent amount of money. But much more research needs
to be done to target housing programs effectively. What
produces the homeownership effect and what characteristics of
neighborhoods promote good outcomes for children? Harkness
and Newman further this effort by exploring the interaction
between housing tenure and neighborhood context.
The authors note that, as they move from simple to more
complete specifications, “the inclusion of neighborhood
controls has modest effects on some model estimates, but
overall, there is little effect. Even with neighborhood controls,
homeownership has strong, favorable effects on most
outcomes.” Those results are consistent with my own research
findings on housing conditions and high-school completion.
In fact, approaching it from the other direction, I first tested a
model with only parental and neighborhood controls, then one

that added housing variables such as homeownership,
mobility, overcrowding, and maintenance condition. I found
that the housing variables actually dominate the neighborhood
variables. That finding has led me to wonder if some of the
neighborhood effects commonly reported are not, in fact,
actually due to missing housing variables for which the
neighborhood variables are proxying. At the least, I believe that
more research is needed to understand the effects of the
physical aspects of the home environment on children, some of
which could affect kids’ educational attainment through their
health and school attendance.
Researchers have found that residential mobility can
adversely affect the educational attainment of children
(Haveman, Wolfe, and Spaulding 1991), and that the housing
stability that usually accompanies homeownership may
account for some, though probably not all, of the positive effect
that homeownership seems to have (Aaronson 2000). There
are other housing conditions that may plausibly affect
children’s outcomes that have received less research attention.
Overcrowding was thought by early housing reformers to have
adverse effects on children, but there is surprisingly little
research into the issue. Poor maintenance conditions,
including insufficient heat, inoperable plumbing, or rodent
infestation, could also adversely affect the health or study
habits of children. The effects of such physical deficiencies on
children’s development and behavior need to be investigated
more thoroughly. A better understanding of which housing
and neighborhood conditions maximize children’s chances for
success would assist in formulating public programs that not
only improve housing conditions, but contribute to solving
other social problems as well.

FRBNY Economic Policy Review / June 2003

111

References

Aaronson, Daniel. 2000. “A Note on the Benefits of Homeownership.”
Journal of Urban Economics 47, no. 3 (May): 356-69.
Case, Anne C., and Lawrence F. Katz. 1991. “The Company You Keep:
The Effects of Family and Neighborhood on Disadvantaged
Youths.” NBER Working Paper no. 3705, May.

Green, Richard K., and Michelle J. White. 1997. “Measuring the
Benefits of Homeowning: Effects on Children.” Journal of
Urban Economics 41, no. 3: 441-61.
Haveman, Robert, Barbara Wolfe, and James Spaulding. 1991.
“Childhood Events and Circumstances Influencing High-School
Completion.” Demography 28, no. 1: 133-57.

Evans, William, Wallace Oates, and Robert Schwab. 1992. “Measuring
Peer Group Effects: A Study of Teenage Behavior.” Journal of
Political Economy 100, no. 5: 966-91.
Ginther, Donna, Robert Haveman, and Barbara Wolfe. 2000.
“Neighborhood Attributes as Determinants of Children’s
Outcomes.” Journal of Human Resources 35, no. 4: 603-42.

Commentary

John Goering

The Impacts of New
Neighborhoods on Poor
Families: Evaluating the
Policy Implications of the
Moving to Opportunity
Demonstration
1. Introduction

he U.S. Department of Housing and Urban Development’s
(HUD) Moving to Opportunity for Fair Housing
Demonstration, or MTO, is a large, federally funded social
experiment designed to test whether improved neighborhood
opportunities may significantly affect the life chances of lowincome public housing residents. This paper provides the first
systematic overview of the design of the MTO and describes its
key features. The paper also offers the first cross-site analysis of
research findings and explores the MTO’s relevance to social
science research concerning housing and neighborhood effects.
We begin with the social science background to MTO and
discuss the purposes of the demonstration. We then describe
the key features of the demonstration and how its experimental
design addresses methodological issues that have long limited

John Goering is a professor of public affairs at the School of Public Affairs
of Baruch College and at the Graduate Center of the City University
of New York.

neighborhood effects research. The implementation of the
demonstration and how that implementation shapes and
limits the research is discussed next, followed by a description
of the major research results from a number of MTO studies.
We conclude with a discussion of future research needs and
policy issues.

1.1 Research Background
Research over the last decade has shown that poverty in the
United States has become increasingly concentrated in “highpoverty” neighborhoods, and that such concentrations appear
to have a range of detrimental effects on the well-being and
future opportunities of residents of those areas (Jargowsky
1997; Wilson 1987, 1996; Brooks-Gunn, Duncan, Klebanov,

This paper received assistance from Judith D. Feins, Principal Associate of Abt
Associates, and Todd M. Richardson of the U.S. Department of Housing and
Urban Development’s Office of Research. Earlier versions were presented at a
June 2000 conference of the European Network for Housing Research, held at
Gaavle, Sweden, and at a New School University faculty seminar in March
2001. The author thanks the Fannie Mae Foundation for permission to cite
material. The views expressed are those of the author and do not necessarily
reflect the position of the Federal Reserve Bank of New York, the Federal
Reserve System, the U.S. Department of Housing and Urban Development,
or Abt Associates.
FRBNY Economic Policy Review / June 2003

113

and Saland 1993; Aneshensel and Sucoff 1996; Sampson 2000;
Morenoff, Sampson, and Raudenbush 2001; Catsambis and
Beveridge 2001). The harmful effects of high-poverty areas are
thought to be especially severe for children; their behavior,
choices, and prospects may be particularly susceptible to
neighborhood-based events and characteristics, such as peer
group influence, school quality, and the level of violent crime
(Galster and Killen 1995; Ellen and Turner 1997; Leventhal and
Brooks-Gunn 2001).
Social scientists have also focused recently on the possible
theoretical causes of both the positive and negative effects of
neighborhoods (Manski 1993, 2000; Galster and Killen 1995;
Galster, Quercia, and Cortes 2000; Leventhal and BrooksGunn forthcoming). The core question is whether there are
clear, independent effects from a neighborhood. If so, then
social science must next attempt to identify the causes and
processes through which such effects appear in the lives of
children, adolescents, or adults. While there has long been
social science evidence of the harmful effects of living in
concentrated-poverty neighborhoods, evidence and discussion
about how neighborhood environments may exert positive
influences on behavior and life chances are more recent
(Brooks-Gunn, Duncan, and Aber 1997; Sampson, Morenoff,
and Gannon-Rowley 2002).
Galster and Killen (1995) have noted the complexity of the
causal influences linking metropolitan and neighborhoodbased opportunities; they point out the dynamic nature of
opportunities, and the critical issue of residents’ willingness
and ability to take advantage of contextually positioned
resources. Ellen and Turner’s (1997) summary of the literature
in this area suggests various mechanisms by which middle-class
(often predominantly white) neighborhoods shape, or reshape,
the lives of residents. The effects of neighborhood appear to
be more pronounced for children rather than for adults.
Leventhal and Brooks-Gunn (2001) offer evidence that
neighborhood influences on achievement measures—such
as IQ—are most important below five years of age.
Despite considerable progress over the last decade,
researchers have only a limited understanding of which
neighborhood effects are most likely to appear first, in what
types of households or family members they may appear, under
what circumstances, and with what durability or persistence.
This paper provides evidence that there are such effects, that
they are clearest for children and teenagers, and that there is
little evidence of positive neighborhood effects on adults to
date.
We also do not know whether there are effective policy tools
for improving the life chances of those who move into betteroff neighborhoods. Among the research issues that have
received minimal attention is whether public housing or other

114

The Impacts of New Neighborhoods on Poor Families

forms of federal housing assistance for the poor can alter the
present or future opportunities of program participants
(Newman and Harkness 2002). Interest is relatively recent
concerning whether moving families from heavily racially and
poverty-concentrated neighborhoods can generate positive
changes in attitudes and subsequent behavior (Rubinowitz and
Rosenbaum 2000; U.S. Department of Housing and Urban
Development 2000; Goetz 2001). And there has been a notable
absence of experimentally designed research to address
persistent policy and research questions about the positive or
negative effects of concentrations of assisted housing (Galster
and Daniell 1996).
Following the Experimental Housing Allowance Program
begun in 1970, MTO was the first attempt to design and
operate a random-assignment program aimed at testing the
effects of HUD’s major current forms of housing assistance—
public housing and tenant-based Section 8 rental assistance—
compared with an economically based, deconcentrated form of
rental assistance (U.S. Department of Housing and Urban
Development 2000). Specifically, MTO is the first systematic
test of whether shifting to tenant-based assistance and altering
the neighborhood may noticeably improve the life chances of
low-income residents who formerly lived in distressed, innercity public-housing developments.
The first research suggestion that housing mobility or
deconcentration may have important social and educational
effects appeared in the late 1980s, prompted by a federal courtordered racial desegregation program in Chicago. Under the
name of tenant-activist Dorothy Gautreaux, applicants and
residents of Chicago public housing brought a class-action
housing segregation lawsuit against HUD and the Chicago
Housing Authority (CHA) in 1966 (Davis 1993; Rubinowitz
and Rosenbaum 2000). After years of litigation, which went all
the way to the Supreme Court, the courts ordered HUD and
the local CHA to remedy the extreme racial segregation that
they had imposed on public-housing applicants and residents.
Starting in the late 1970s, these agencies had to provide (among
other remedies) a housing mobility option throughout the
Chicago region for about 7,100 black families.
The Gautreaux program took shape as a result of the Court’s
ruling. “Gautreaux families,” as they became known, were
helped to move out of racially isolated areas through the (thennew) tenant-based Section 8 program. Families chosen for the
Gautreaux program received Section 8 certificates that
required them to move to either predominantly white or
racially mixed neighborhoods. They also received assistance
from housing counselors to make these moves. Roughly threequarters of all the families were required to move to
predominantly white (usually suburban) areas, while about
one-quarter were allowed to move to more racially mixed city

neighborhoods. Families unwilling to make these moves did
not receive the housing subsidy. While the eligibility criteria, as
well as the forms of housing counseling offered participants,
varied somewhat over the roughly twenty years of the
program’s operation, the required move to a nonsegregated
neighborhood persisted until the completion of the program in
1998 (Rubinowitz and Rosenbaum 2000).
Beginning in the late 1980s, research on the Gautreaux
program suggested that the moves to less segregated suburban
locations were associated with measurable improvements in
the lives of participating children. Changes were reported for
small samples of children who had been living in less segregated
neighborhoods for periods of seven to ten years. Such children
were less likely to drop out of school and were more likely to
take college-track classes than their peers (in a comparison
group) who moved within the City of Chicago rather than to
suburban areas. The city neighborhoods were poorer and more
racially segregated than the suburban locations. After
graduating from high school, the Gautreaux children were also
more likely than their city peers to attend a four-year college or
to become employed full-time (Rubinowitz and Rosenbaum
2000).

1.2 MTO’s Purpose
The promising Gautreaux results, as well as increasing concern
about the high levels of racial and economic isolation of many
public housing families (Hirsch 1983; Newman and Schnare
1997), led Congress to initiate a demonstration program aimed
at offering better neighborhood opportunities to publichousing residents living in distressed inner-city areas. Dimond
(2000, p. 259) outlines the antipoverty argument for MTO:
Isolating poor persons in inner-city ghettos and barrios
does not help them connect to the rising demand for
more workers throughout the local regional labor
markets. . . . Thus federal, state, and local governments act
irresponsibly and waste taxpayer dollars whenever they
limit housing and job-training subsidies to particular
projects or places—public or private—rather than putting
such subsidies directly in the hands of poor families so
they can choose for themselves where best to live and
learn in order to find new and better jobs.
In 1992, these factors—the concentration and persistence
of urban poverty and the awareness of the Gautreaux
program findings—led a coalition of Democratic and
Republican policymakers to propose offering public-housing
residents the chance to move to private rental housing in

more affluent communities by means of a housing voucher.
The demonstration they envisioned would test whether
HUD’s main tenant-based housing program, the Section 8
rental assistance program, could be used effectively to assist
poor, largely minority families in successful relocation to
private rental housing in working-class or middle-class
neighborhoods—in which landlords were unaccustomed to
renting to poor families.
MTO is a planned social experiment making use of HUD’s
Section 8 rental subsidy program to facilitate the residential
mobility of families out of inner-city public-housing
developments in five cities across the country. The MTO
demonstration was authorized by the Housing and
Community Development Act of 1992 to “assist very lowincome families with children who reside in public housing or
housing receiving project-based assistance under Section 8 of
the Housing and Community Development Act of 1937 to
move out of areas with high concentrations of persons living in
poverty to areas with low concentrations of such persons.”
High concentrations of poverty were defined as census tracts
where 40 percent or more of the residents were poor in 1990.
Low-poverty areas were defined as census tracts where less
than 10 percent of the population lived in poverty in 1990.
The 40 percent threshold follows a social science standard for
defining deeply poor (“underclass”) neighborhoods (Jargowsky
1997; Brooks-Gunn, Duncan, and Leventhal 1997). The
10 percent threshold for “low poverty” corresponds to the
median tract-level poverty rate across the United States in 1990.
Congress appropriated $20 million in Section 8 rental
assistance for fiscal year 1992 and another $50 million for fiscal
year 1993 for MTO. Congress also stipulated that HUD should
conduct evaluations of the demonstration to determine shortand long-term impacts. HUD decided that the most effective
means for reliably answering questions about such impacts was
to establish a social experiment, including a randomassignment process that would allocate, by a computerized
lottery, families who volunteered into different treatment
groups.

2. MTO’s Design
2.1 Methodological Shortcomings
of Prior Research
The problem of selection bias has been recognized by social
scientists for over a decade as a crucial limitation on the

FRBNY Economic Policy Review / June 2003

115

Gautreaux research and most other research on neighborhood
effects (Mayer and Jencks 1989; Crane 1991; Case and Katz
1991; Lehman and Smeeding 1997, p. 262). Jencks and Mayer
(1990, p. 119) caution:
The most fundamental problem confronting anyone
who wants to estimate neighborhood’s effects on
children is distinguishing between neighborhood effects
and family effects. This means that children who grow
up in rich neighborhoods would differ to some extent
from children who grow up in poor neighborhoods even
if neighborhoods had no effect whatever.
People typically select their neighborhoods to match their
needs and resources. Therefore, researchers restricted to crosssectional, nonexperimental evidence must try to separate the
impact of personal factors affecting choice of neighborhood
from the effects of the neighborhood. But it is difficult—if not
impossible—to measure all these socioeconomic, personal, and
local characteristics well enough to distinguish their effects.
The answers sought are often hidden in unmeasured factors
and unexplained variations.
Issues of selection bias notably limited the credibility of the
findings from the Gautreaux research. First, there was evidence
that families self-selected to participate in the program. There
was also evidence that the program screened participants for
suitability to particular neighborhoods or communities. In the
early years of Gautreaux, for example, program managers and
counselors identified the families with the potential to succeed
in the suburbs, and matched them with landlords and
communities there. Other families, judged to be less suitable
for suburban locations, were not placed by the program or were
placed in city neighborhoods. Second, because of the limited
information gathered and kept about the families who joined
Gautreaux but did not move, the differences in families’
demographic or personal characteristics that affected success in
moving could not be investigated. Third, some evidence of
positive mobility effects in the Gautreaux program is based
upon small, nonrepresentative fractions of the families
enrolled—those who could be found a number of years later
(Popkin, Buron, Levy, and Cunningham 2000).
The direct solution to the problem of selectivity bias is to
remove people’s ability to select their neighborhoods by
randomly assigning them to a community. This detaches the
individual’s personal characteristics and preferences from the
neighborhoods’ potential impacts (Brooks-Gunn, Duncan,
Leventhal, and Aber 1997, p. 286). Jencks and Mayer (1990,
p. 119) describe this requirement:
From a scientific perspective, the best way to estimate
neighborhood effects would be to conduct controlled

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The Impacts of New Neighborhoods on Poor Families

experiments in which we assigned families randomly to
different neighborhoods, persuaded each family to remain
in its assigned neighborhood for a protracted period, and
then measured each neighborhood’s effects on the
children involved.
However, until MTO, there had never been an initiative to
design and implement this type of controlled experiment.

2.2 MTO’s Experimental Design
From September 1994 to July 1998, public- and assistedhousing families, who volunteered and were found to be
eligible, were randomly assigned to one of three groups:
1.

The MTO treatment group, which received Section 8
certificates or vouchers usable only in areas of less than
10 percent poverty. Families in this group were also
provided counseling assistance from a local nonprofit
organization in finding a private rental unit.

A Section 8 comparison group, which received regular
Section 8 certificates or vouchers with no special
geographic restrictions or counseling.

An in-place control group, which continued to receive its
current project-based assistance.

The Section 8 comparison group was established in order to
allow measurement of the extent to which the routine
operation of the Section 8 program generates changes in
location and in family outcomes that can be compared with
changes for the treatment-group population. The in-place
control group was created to measure the behavioral outcomes
for children and adults who remained in public-housing
developments in deeply poor communities to permit
comparison of their outcomes with the other two groups.
Although MTO was targeted to a specific population (very lowincome families with children, living in public or assisted
housing in concentrated-poverty areas), its participants share
many characteristics with families who have worst-case
housing needs, families excluded from the economic
mainstream, and families in poverty (U.S. Department of
Housing and Urban Development 2001).
The random-assignment design embedded in the MTO
demonstration program seeks to test the effects of
neighborhood experimentally and avoid selection bias. MTO
uses a carefully designed and strictly implemented randomassignment process to ensure that nothing about an individual
or family could influence the group assignment. Assignment of
families among the three groups was carried out under uniform
procedures across the five sites, with thorough monitoring and

recordkeeping. As a result, the research findings concerning
MTO address whether willing poor families with children—
given the opportunity to improve their neighborhood
conditions—may benefit significantly from an atypical change
in residential location.
The experimental design of MTO not only permits analyses
of impacts in a variety of domains (such as child educational
achievement, adult employment and earnings, youth risktaking, and the physical and mental health of family members)
but also permits multiple-method or tiered assessments of
cross-cutting questions that will help verify or enhance what
has been learned about neighborhood impacts on families,
adults, and children. Answering these questions is possible
because MTO is an ongoing, longitudinal research project
designed to address some questions that only the passage of
time can answer.

MTO provides an estimate of the effectiveness of the offer of
the experimental treatment in improving the lives of publichousing residents as a group. The intent-to-treat (ITT)
estimates reported in this paper recognize that some members
of the target group did not use the Section 8 subsidy. The
measured ITT effects include the outcomes not only for those
who moved, but also for those who were randomly assigned to
receive the treatment but did not relocate. However, even if the
ITT effects are statistically significant, the larger the proportion
of those who fail to move then the less effective a program like
MTO would be in improving the lives of additional publichousing families.
Next, we describe the results of the implementation stage of
the demonstration and discuss the characteristics of the MTO
volunteers. We then present the research results on the effects
of the experiment on the children, teenagers, and adults who
participated, focusing essentially on ITT effects.

2.3 MTO’s Research Hypothesis
MTO’s design includes three phases of evaluation. The first
phase, conducted by seven teams of social scientists operating
in single MTO sites and with their own research strategies,
constitutes the bulk of the evidence synthesized in this paper.
The second stage is a major cross-site evaluation, currently in
the field, from which results are expected by 2003. The third
and final stage of MTO research will occur approximately
six years from now—a final impact evaluation of the
demonstration.
MTO’s research value is rooted in the fact that it is the first
experimentally designed panel study aimed at understanding
the effects that neighborhoods may have upon low-income
residents of public and assisted housing. The experiment has
been designed to show whether the negative impacts of
distressed neighborhoods on families can be reversed by
offering public-housing families the choice to volunteer to
move to more affluent neighborhoods. The core hypothesis is
that MTO will have positive and statistically significant effects
on the lives of the experimental-group families when compared
with the lives of the in-place control-group members.
Contrasts with any effects experienced by the Section 8
comparison group will reveal whether tenant-based rental
assistance—without any geographical restriction—can achieve
similar results. The MTO hypothesis is that the offer of a move
from a poor to a nonpoor neighborhood will significantly
improve the neighborhood conditions of the families, and will
affect their longer run prospects in areas such as education,
health, risky behavior, and criminal activity.

3. MTO Implementation—
Characteristics and Limitations
In this section, we turn to the specifics of how the MTO
demonstration was conducted. These details provide
information on demonstration selection criteria and on some
of the characteristics of the programs design that affect the
interpretation of the research findings reported in the next
section.

3.1 Initial Implementation
MTO implementation began with HUD’s issuance of a notice
of funding availability (NOFA) in September 1993 soliciting
sites for the demonstration. The NOFA laid out the statutory
criteria for MTO site selection and the general outline of
program operations. In March 1994, HUD selected five local
public-housing authorities (PHAs) to participate in running
the MTO demonstration. The sites selected were Baltimore,
Boston, Chicago, Los Angeles, and New York. In its
application, each PHA identified the public-housing and
Section 8 project-based developments in high-poverty census
tracts from which it would recruit families with children under
eighteen. The PHAs also named a partner nonprofit agency to
counsel the families assigned to the MTO treatment group.
The selected PHAs and nonprofit agencies were required to
follow a general set of uniform rules and procedures for the

FRBNY Economic Policy Review / June 2003

117

management of most key aspects of the demonstration—
particularly research requirements. The core administrative
responsibilities for implementing MTO were:
• outreach to landlords and families,

3.2 Implementation Results1

Intake

• enrollment of families and creation of waiting lists,
• determination of family eligibility,
• random assignment, and
• counseling assistance for treatment-group families.
The PHAs and counseling agencies also helped implement
MTO’s experimental design—including the collection of data
on the participants and the program. Based on their prior
experience and on the availability of local funding to
supplement HUD’s grants, the counseling agencies varied the
form and amount of counseling assistance offered to clients
(Feins et al. 1997). This variation in treatment constitutes one
of the limitations of MTO implementation.
The PHAs began MTO operations by informing all eligible
residents of the targeted public- and assisted-housing projects
in high-poverty census tracts about what MTO offered and
how to apply. In most instances, there were meetings of groups
of tenants to explain the program and answer questions.
Waiting lists of applicants were then established in each city,
and small groups of applicants (working down from the top of
the lists) were invited to orientation sessions. At these sessions,
the applicants were informed about the experiment: that they
would be randomly (or by lottery) assigned to one of three
groups; that they had a chance of being offered Section 8 by
joining; and that—if they were chosen by lottery for the
treatment group—they would be provided training,
counseling, and housing search assistance in order to move to
a low-poverty area in the city or suburbs. Families were also
informed that they were only required to remain in the lowpoverty area for the length of their first one-year lease; after
that, they were permitted to move to any area under regular
Section 8 rules.
The applicants were also informed of the screening criteria
established by the PHA, including the fact that all tenants had
to be current in their rent payments and that there could be no
criminal record for any family member. Families who enrolled
agreed in writing to cooperate with the information gathering
and research needed for the demonstration, and they filled out
a lengthy baseline survey. Random assignment occurred only
after the eligibility checking, screening, and initial data
collection were finished.

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The Impacts of New Neighborhoods on Poor Families

In MTO, among the families eligible to apply, about onequarter chose to do so; roughly 5,300 families volunteered in
the five cities. The families were then screened for eligibility
with respect to: 1) having a child under eighteen in the family,
2) being tenants in good standing (up-to-date in rent
payments), 3) having all family members on the current lease,
and 4) being without criminal background or history, as
required (with some variation) by the local Section 8 program
rules. In total, across the five sites, 4,608 families were found
eligible and randomly assigned. With approximately 285
vouchers for HUD to allocate per site, this was a sufficient
number of volunteers for the demonstration.
Fear of crime and the experience of criminal victimization
were the major factors in families’ decisions to participate in
the MTO demonstration. When applicants were asked during
their baseline interviews why they wanted to move away from
the public-housing developments in which they lived, more
than half (54.8 percent) identified the fear of crime, gangs, and
drugs as the principal motivation.
In answer to whether those who volunteered for the MTO
demonstration were typical of other residents from their
public-housing developments, we learned that MTO
households, compared with public-housing families who chose
to remain, were somewhat different. They were younger (with
heads of household thirty-five versus forty-one years old),
more often female-headed (93 versus 78 percent), and less
likely to be Hispanic (39 versus 45 percent). They were also
slightly poorer (with an $8,200 versus $8,600 median income).

Lease-Up
Prior research has shown that not all Section 8 certificate and
voucher holders have been able to use their housing assistance,
and that successful lease-up is influenced by applicant
characteristics, market features, and market conditions (U.S.
Department of Housing and Urban Development 2000; Finkel
and Buron 2001). Lease-up success rates also vary over time
and among cities. For MTO, the lease-up rate for families in the
demonstration’s Section 8 comparison group was roughly
60 percent, while the rate for MTO treatment-group families
across the five cities was 47 percent. Rates varied from a high of

more than 61 percent in Los Angeles to a low of only 34 percent
in Chicago.
There are a number of possible explanations for these lower
rates, including the fact that families in MTO were already
securely housed with project-based housing subsidies. They
were much less needy than emergency applicants and
significantly less burdened by housing costs than were other
low-income renters without subsidies. Therefore, despite the
high levels of crime reported by MTO families, the incentive to
lease-up through MTO was apparently lower than that of the
typical Section 8 applicant. The lower lease-up rates achieved
with MTO clearly will affect any future replicability of the
demonstration.
Understanding the characteristics and motivations of
families that succeeded in renting an apartment through the
MTO demonstration can also help researchers to generalize
from MTO to the larger universe of public-housing families.
For all five sites, Shroder (2002) shows that success in leasingup in MTO was positively associated both with families’
dissatisfaction with their original neighborhoods, and with
their degree of confidence (at baseline) about finding a new
unit. The level of housing counseling received by the treatment
families also helped in achieving lease-up.

Would MTO Families Remain
in Low-Poverty Areas?
Did the families who moved out of public housing to lowpoverty areas remain there, or did they move back into more
familiar, higher poverty communities after the one-year
requirement was fulfilled? The answer to this question matters
because the potential benefits of moves to communities of
opportunity may take years to accrue. Social science literature
suggests that positive effects on child development, educational
outcomes, and adult prospects (compared with continued life
in public housing in deeply poor areas) might occur in a fiveto-ten-year time frame, but only if the families remained in
distinctly different neighborhoods (Leventhal and BrooksGunn 2001).
An examination of data from a 1997 HUD-funded survey of
all the MTO families who joined the program from 1994 to
1996 shows that more than a third (34.5 percent) of the MTO
treatment group—but just 10.6 percent of the comparison
group, and less than 3 percent of the in-place group—was
living in low-poverty neighborhoods. Although roughly
45 percent of the treatment group was living in high-poverty
areas, those tenants were largely the nonmovers (those who
remained in their initial public-housing developments),

compared with 38 percent of the Section 8 comparison group
and 74 percent of the control group.

4. MTO Research Findings to Date:
First-Stage Research
Research results concerning MTO to date derive from studies
conducted by seven HUD-commissioned teams of social
scientists; each team worked in one of the five MTO locations.
These teams used a number of different data sources, including
HUD administrative data; baseline survey data; data from
follow-up surveys of enrolled families; some qualitative
interviews; and some administrative data on juvenile crime,
labor-market outcomes, and school performance. The initial
studies covered various topics, used differing approaches,
and were carried out by researchers from a range of
disciplines.
As each team made use of differing analytic and
methodological strategies, the resulting lack of comparability
across sites is a limitation of MTO research to date. Further,
initial research projects focused on establishing whether any
early effects would appear soon after the transitions from
inner-city projects. They did not focus on which institutions or
processes caused improvement in the lives of children or adults
(Sampson, Morenoff, and Gannon-Rowley 2002).
A number of statistically significant ITT results, for the
groups as assigned, have been found in the early research
undertaken on MTO families. Tables 1-3 present findings from
different single-site research projects that have tested for
statistically significant differences between the two treatment
groups and the control group. The tables provide an overview
of research results for three sets of issues: Table 1 presents
findings on neighborhoods, Table 2 on outcomes for children,
and Table 3 on outcomes for adults.
The focus here is on ITT effects, which are measured by
considering the difference between the average outcome for the
entire MTO treatment group, or the entire Section 8
comparison group, and the outcome for the control group. For
example, the average poverty rate for census tracts occupied by
members of the treatment group was 32.3 percent in 1997. The
intent-to-treat effect is the difference between that rate and the
control group’s average poverty rate (48.1 percent); thus, the
ITT effect is 15.8 percent. The treatment-on-treated (TOT)
effect—that is, the estimated effect on those persons who
successfully leased up under MTO—is generally higher, as it is
measured for only those participants who actually took up the
treatments (that is, moved with Section 8). In the analysis

FRBNY Economic Policy Review / June 2003

119

below, we mainly focus on intent-to-treat effects, noting that
whenever ITT effects are statistically significant, TOT effects
are typically significant and stronger.

4.1 Neighborhood and School
Characteristics
Table 1 reports differences in the neighborhood and school
characteristics of the areas in which MTO participants live.
Three critical outcomes follow from this research.

MTO Families Live in More Economically
and Racially Mixed Communities
The 1997 survey of families at all five MTO sites enables us to
examine whether residential locations differed significantly
among the randomly assigned groups. After their initial moves
and one-year leases, treatment-group families were no longer
constrained to live in low-poverty areas. Despite this, one to
three years after random assignment, treatment-group families
lived in significantly more affluent and more racially mixed

Table 1

Early Evidence of MTO Impacts: Differences in Characteristics of Neighborhoods
and Schools Where MTO Participants Live

Type of Impact
Differences in neighborhood after
one to three yearsa
Poverty percentage of current location
Median income of current location
Percentage black population of current location

MTO
Site
All sites

Population

Los Angeles

Households in MTO
as of 12/18/96

Differences in average test scores for schools attended
by MTO children in 1997c
School’s percentile, reading test score
School’s percentile, math test score

Boston

Households in MTO
as of 5/96

Baltimore

Differences in perceived safety
of current neighborhoode
Percentage reporting neighborhood has drug and crime problems

Baltimore

Differences in perceived safety
of current neighborhoodf
Percentage reporting very safe neighborhood

Los Angeles

Section 8
Comparison
Group

In-Place
Control
Group

32.3**
$24,075**
38.2**

33.4**
$21,246**
40.3

48.1
$13,920
48.6

6,137.25**

5,984.21**

8,018.40

All households in
MTO as of 12/31/96

Differences in total crime rate per 100,000 population
in census tractb

Differences in resources and characteristics scores for schools attended
by MTO children after random assignment and initial relocationd
Percentage children receiving free lunch
Fifth-grade raw reading test pass rate
Fifth-grade raw math test pass rate

MTO
Treatment
Group

15.9**
16.0**

10.9
12.6

8.3
9.9

66.82**
11.84**
18.40**

80.82*
7.84**
15.40**

84.82
5.84
12.40

27.8**

60.8

—

27.5*

6.7

10.1

School-age children of
all households in MTO

Adults in MTO as
of 9/4/97

Adults in MTO
as of 12/18/96

Notes: MTO is the Moving to Opportunity for Fair Housing Demonstration. Differences reported are based on intent-to-treat comparisons (full group)
rather than adjusted treatment-on-treated results.
a

Source: Feins (2000, Exhibit 9).
Source: Hanratty, McLanahan, and Pettit (2001, Table 6).
c
Source: Katz, Kling, and Liebman (2001, Table 4).
d
Source: Ludwig and Ladd (forthcoming, Table 9).
e
Source: Norris and Bembry (2001, Table 16).
f
Source: Hanratty, McLanahan, Pettit (2001, Table 7).
b

*Statistically significant difference from in-place control group (intent-to-treat effect) at p less than the .10 level.
**Statistically significant difference from in-place control group (intent-to-treat effect) at p less than the .05 level.

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The Impacts of New Neighborhoods on Poor Families

communities than either the Section 8 comparison-group or
the in-place control-group families.
Late in 1997, the average poverty rate of residential locations
for the MTO treatment-group families and the Section 8
comparison-group families was significantly lower (by 15 to
16 percentage points) than the poverty rates of areas in which
in-place control-group families lived. Moreover, median
incomes in the treatment-group families’ neighborhoods were
73 percent higher than median incomes in the control-group
neighborhoods and they were 53 percent higher in the
Section 8-only group locations compared with the controls.
There were also significant differences in the racial
composition of the areas. In each of the five metropolitan sites
in 1997, the MTO treatment-group families lived in less
segregated neighborhoods than either the Section 8
comparison-group families or those who remained in place.
Using the percentage black population as an indicator, there
was a statistically significant 10-percentage-point reduction in
black population in the treatment-group families’ locations—
compared with the locations of control-group families. But
there was no significant difference for Section 8-only families
(Feins forthcoming). Future analyses will make use of census
2000 tract-level data to examine how much the new
neighborhoods have changed since 1990.

MTO Families Live in Areas with Lower Crime Rates
Measured at the census-tract level, in total crimes per 100,000
population, the places where MTO treatment-group families
and Section 8 comparison-group families were living had
significantly fewer crimes in Los Angeles. The reduction was
23 percent for the former and 25 percent for the latter group.
The fact that regular Section 8 families benefited from moves
from high-poverty projects is an important finding mirrored in
some other early outcomes.

Schools Currently Attended by MTO Children
Are Better
Research teams in both Boston and Baltimore demonstrated
that schoolwide reading and math scores or pass rates were
significantly better in treatment-group children’s schools
relative to the schools attended by children of in-place controlgroup families. In Baltimore, these indicators were also
significantly better for the schools of children from Section 8only families.

Families’ Views of Their Neighborhoods
Have Improved
The early MTO research has also demonstrated significant
betterment in families’ views of their neighborhoods. These
views contrast with the higher levels of fear and dissatisfaction
expressed by MTO applicants at baseline.

MTO Families Have Become Less Fearful
As noted earlier, many families enrolled in MTO because of
their fear of the crime conditions surrounding them in their
public-housing or Section 8 project-based developments. Most
of the MTO research teams reported that freedom from this
fear is among the earliest, clearest outcomes.
As shown in Table 1, significantly fewer Baltimore families
in the treatment group reported neighborhood problems with
drugs and crime, compared with reports from the Section 8
comparison group. A significantly higher proportion of MTO
treatment-group members in Los Angeles reported very safe
neighborhoods at follow-up, compared with those in the
control group, but the difference between the Section 8
comparison group and the in-place control group was not
found to be significant. In Chicago, MTO mothers were asked
about the risks and opportunities their current locations
offered to teenagers. Those in the MTO treatment group
reported significantly reduced risks in comparison with their
old locations, but those in the Section 8 comparison group did
not.

4.2 Outcomes for Children
Turning to early evidence of MTO impacts on individuals in
the demonstration, we present in Table 2 findings on children’s
behavior, health, and educational achievement, as well as
results concerning youth involvement in violent crime.
The Boston research team found that there were
significantly fewer behavior problems among boys in both the
MTO treatment and the regular Section 8 groups relative to
boys in the in-place group. A significantly higher proportion of
girls in both treatment groups reported at least one close friend
in the neighborhood. Treatment-group children were also less
likely to be injured or to have an asthma attack. In fact, among
children with asthma in Boston, there was a substantial
reduction in the number of attacks requiring medical attention
over the prior six-month period.

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121

There Have Been Educational Improvements

There Have Been Declines in Juvenile Crime

In addition to the signs indicating that the children are
attending better schools (Table 1), Ludwig, Ladd, and Duncan
(2001) report direct evidence of the effect of MTO in Baltimore
upon the school performance of individual children. The
researchers used standardized reading and math scores
(obtained from schools) for a sample of Baltimore children and
matched them to identifying information for the MTO
subjects. Despite data limitations, the results revealed
statistically significant improvements for the treatment group.
However, in the early research, no direct educational testing of
children in MTO families was conducted. Such testing is now
under way, and results should become available in 2004.

In another Baltimore study, researchers using outcome
measures from juvenile arrest records taken from
administrative (police and court) data reported that providing
families with the opportunity to move to lower poverty
neighborhoods reduced arrests for violent criminal behavior by
teenagers in those families. They showed that one to one-anda-half years after random assignment, arrests for violent crime
of male juveniles in the treatment group declined relative to
those in the control group. But the difference for boys from the
Section 8-only group was not statistically significant.
Reductions in robbery accounted for about half of this decline.
The research also examined whether teens in the treatment

Table 2

Early Evidence of MTO Impacts—Outcomes for MTO Children

MTO
Site

Type of Impact
Differences in child
behaviora
Percentage with seven behavior problems, boys
Percentage with seven behavior problems, girls
Percentage with at least one close friend in neighborhood, boys
Percentage with at least one close friend in neighborhood, girls

Boston

Differences in child
healtha
Percentage with any asthma attack requiring medical
attention in past six months
Percentage with any accident or injury requiring medical
attention in past six months

Boston

Differences in number of arrests per 100 juveniles
ages eleven to sixteenb
Arrests for violent crimes
Differences in school test scores

Baltimore

Population

MTO
Treatment
Group

Section 8
Comparison
Group

In-Place
Control
Group

23.6**
17.0
73.8
67.7**

21.3**
14.3
72.8
63.3**

32.6
19.3
74.7
82.3

4.7*

9.4

9.8

4.6*

6.8

10.5

1.4**

1.6*

3.0

32.47**
36.25**

31.52**
30.25

25.13
28.77

Children ages six to fifteen in households in MTO as of 5/96

Children ages six to fifteen
in households in MTO as of 5/96

Children ages eleven
to sixteen in all MTO households

Children ages five to twelve in all
MTO households

Elementary school CTBS percentile reading scores
Elementary school CTBS percentile math scores

Notes: MTO is the Moving to Opportunity for Fair Housing Demonstration; CTBS is the Comprehensive Test of Basic Skills. Differences reported are based
on intent-to-treat comparisons (full group) rather than adjusted treatment-on-treated results.
a

Source: Katz, Kling, and Liebman (2001, Table 6).
Source: Ludwig, Duncan, and Hirschfield (2001, Table 3).
c
Source: Ludwig, Ladd, and Duncan (2000, Table 6).
b

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The Impacts of New Neighborhoods on Poor Families

group had higher rates of property-crime arrests relative to the
control group. The result was not statistically significant once
differences in preprogram characteristics were controlled. The
issue of whether or not property crime increases in receiving
neighborhoods has been raised repeatedly by opponents of
mobility programs (see, for example, Husock [2000]), and it is
an ongoing research issue for MTO.

4.3 Outcomes for Adults
in MTO Families
There are also some significant early impact findings on the
well-being of MTO adults (Table 3).

Table 3

Early Evidence of MTO Impacts—Outcomes for MTO Adults

MTO
Site
Health effects
Differences in depressive behaviorsa

Population

MTO
Treatment
Group

Welfare and labor market effects
Differences in welfare and labor market effects
for household headsc
Average percentage on welfare
Average percentage employed
Average number of weekly hours worked
Differences in rate of welfare
receipte
Average percentage of household heads on welfare during
thirteen quarters after random assignment

In-Place
Control
Group

New York All mothers in MTO through
12/31/98

Percentage unhappy, sad, or depressed
Differences in adult healthb
Percentage reporting overall health is good or better

Section 8
Comparison
Group

Boston

All sites

Baltimore

33.0**

46.2

50.6

69.3**

74.0**

57.8

58.0
35.0
33.3

58.0
34.0
31.5

57.0
37.0
33.9

38.0**

41.0

44.0

33.1*

37.2*

26.8

49.9

46.0

49.5

44.4

46.3

43.4

Adults in MTO as of 5/96

Adults in MTO surveyed via
1997 long-form canvassd

Adults in all MTO
households

Differences in weekly hours
workedf

Los
Angeles

Adults in MTO
as of 12/18/96

Differences in adult economic outcomesg
Percentage adults receiving public assistance seven to nine quarters
after random assignment
Percentage adults with employment earnings seven to nine quarters
after random assignment

Boston

Adults in MTO as of 5/96

Notes: MTO is the Moving to Opportunity for Fair Housing Demonstration. Differences reported are based on intent-to-treat comparisons (full group)
rather than adjusted treatment-on-treated results.
a

Source: Leventhal and Brooks-Gunn (forthcoming, Table 6).
Source: Katz, Kling, and Liebman (2001, Table 9).
c
Source: Goering, Feins, and Richardson (2002).
d
The long form was administered to households participating in the MTO under the original random-assignment ratio.
e
Source: Ludwig, Duncan, and Pinkston (2000).
f
Source: Hanratty, McLanahan, and Pettit (2001, Table 8).
g
Source: Katz, Kling, and Liebman (2001, Table 7).
b

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123

Adults Have Experienced Improved Physical
and Mental Health
In New York, parents in the MTO treatment group reported
significantly better health and emotional well-being than those
in the control group, while Section 8 comparison-group
parents enjoyed more modest improvements. Treatmentgroup mothers were much less likely to report being depressed
or feeling tense. Treatment-group parents also provided more
structure for their children and were less restrictive in
parenting. These effects were measured using standard
batteries of interview questions, developed and tested in
previous child and family research. Improvements in adult
health were found in Boston, too. There adults in both the
treatment and regular Section 8 groups were more likely to
report that their overall health was good or better. There were
also indications of reduced stress.

Changes in Welfare Status and Wages
When MTO was designed, it was expected that moving from a
high-poverty community to a low-poverty community would
have a gradual positive effect on employment for adults, since
social science evidence suggests that a complicated set of factors
is involved in improving the work situations and wages of
inner-city minority families. Job discrimination in new
communities, poor access to jobs by public or private
transportation, and limited human capital (skills) all could be
involved in constraining the possibility of a poor person’s
obtaining a better paying job (O’Regan and Quigley 1999,
p. 458). Simply relocating families to a community whose
residents are employed at good jobs will not necessarily, or
quickly, translate into increased human capital for newcomers.
Nor did the Gautreaux research suggest that poor families from
public housing could be easily or quickly absorbed into local
labor markets, particularly given the decline in the 1980s of
well-paid jobs available to persons with limited education and
skills (Duncan and Rodgers 1991, p. 549).
When MTO was authorized, there was also little expectation
for major reform of welfare laws. However, following the end
of the Aid to Families with Dependent Children program, and
the inception of the Temporary Assistance for Needy Families
program, the number of families on welfare nationwide
dropped by roughly half, at least partially as a result of the
enactment of new welfare statutes (Schoeni and Blank 2000;
Weaver 2000). In 1994, 5.5 percent of the total U.S. population
was receiving welfare, while by 1999 the proportion had
declined to 2.3 percent (Kaushal and Kaestner 2000).

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The Impacts of New Neighborhoods on Poor Families

Before MTO began, only 44 percent of single mothers
nationwide were employed; by 1999, the proportion had
increased to 65 percent. This transformation is the subject of
several major research projects that are investigating whether
former welfare recipients, like most of the MTO family heads,
are leaving welfare for work (Kaushal and Kaestner 2000,
pp. 2-3). And this transformation may have affected
participants in MTO across all three randomly assigned
groups.
Have MTO mothers experienced any changes in their
welfare and economic situation? Research on the wage growth
of low-income workers suggests that only modest changes can
be expected. Low-wage workers typically earn wage increases of
only 4 to 6 percent for a year of full-time employment, and
such increases are often less for both black men and women
(Gladden and Taber 2000, p. 189).
MTO researchers at two sites have examined these issues
(Table 3). Researchers in Baltimore used state unemployment
insurance records to learn whether MTO families there had
experienced any detectable change in welfare status or
earnings. Their data covered the period from 1985 to 1999, or
an average of 3.8 years of post-program information on the
MTO families. The researchers found that the number of
treatment-group families on welfare during the post-program
period was 6 percentage points lower than the number for the
in-place control group. In addition, the Section 8 comparison
group’s rate of welfare receipt was 5 percentage points lower
than that of the in-place control group in the first program
year. This latter margin dissipated in subsequent years, while
the gap between the treatment and control groups grew to
nearly 10 percentage points by the third year. That is,
assignment to the treatment group reduced welfare receipt
relative to controls—but assignment to the Section 8 group had
little effect beyond the first year.
The researchers did not, however, find any significant
change in either employment or earnings. This was somewhat
unexpected, since the treatment group reported in interviews
that there were better job and training opportunities in their
new neighborhoods (Ludwig, Duncan, and Pinkston 2000,
p. 31). The authors conclude that “these differences in welfareto-work transitions are . . . not reflected in quarterly earnings
data from the state UI [unemployment insurance] system,
because many of the jobs and earnings changes are not
captured by the UI data” (p. 29).
In Boston, the receipt of public assistance by MTO families
dropped by half, and employment for all groups increased by
more than half. Employment rates for the full MTO population
increased from 27 percent at the time of baseline interview to
43 percent one to three years later. However, the MTO
treatment had no significant impact on the employment or

earnings of household heads, as revealed in Massachusetts
administrative earnings data on household heads. Nor did
MTO treatment affect welfare receipt in the three years after
random assignment up through December 1998.
Multisite data from the 1997 MTO canvass also serve as a
test of short-term impacts of MTO on employment, public
assistance, hours worked, and weekly wages for heads of
household. The data show that an average of 2.4 years after
random assignment, substantially more heads of household
across the sites were employed, and many fewer were receiving
public assistance. Employment rates for MTO heads of
household rose 14 percentage points in that interval, while
public assistance rates fell 16 percentage points. However,
Table 3 shows that despite (or perhaps because of) these
dramatic changes in employment and welfare rates, there was
no significant difference between the three groups in terms of
employment rates, hours worked per week, or use of public
assistance at the time of the 1997 canvass.

5. Current Research Limitations
and Future Research Needs
There are a number of limitations to the MTO design and
research that need to be kept in mind in evaluating the study
results reported earlier in this paper. The families who
volunteered to join MTO were somewhat different from others
in the same public-housing developments that chose not to
join. In addition, PHA screening requirements may have
caused some families to decide against applying, thus
eliminating a number of other families during eligibility
determination. Moreover, the relatively low lease-up rates
achieved for both of the random-assignment groups receiving
Section 8 certificates or vouchers are important because ITT
effects are measured across entire groups. The effects of better
neighborhoods can only be experienced by families who move
and—for the group as a whole—such effects are “diluted” by
the portion of the group that does not move. Thus, the lease-up
rates are also central to the detection of program effects.
There are also limitations to a demonstration program that
delivers benefits to only half the families who join. The regular
Section 8 lease-up rate for MTO families was only 60 percent,
considerably lower than the rate in the overall program in the
same cities at that time. The lease-up rate for the MTO
experimental group was lower still. Comparing just the
experimental and regular Section 8 groups, Shroder (2002)
estimates that for the MTO demonstration as a whole, the
locational constraint—even with effective counseling—

reduced the probability of lease-up by roughly 14 percentage
points.
Also, in the period of MTO enrollment, particularly
1994-95, central-city crime rates were quite high. Drive-by
shootings, gang wars, and drug-related violence were a
common feature of life in the neighborhoods where MTO
families were living. These phenomena likely affected the
motivation to join MTO and may well have made people more
interested in joining the demonstration than they might
otherwise have been.
Another consequence of MTO’s mid-decade timing was
that the census data used to identify high-poverty areas (from
which to recruit families) and low-poverty areas (to which
experimental group families could move) were outdated. MTO
housing counselors in MTO sites frequently raised questions
about the suitability of certain census tracts that technically
met the low-poverty definition. Use of the poverty rate as the
sole criterion for identifying opportunity areas also has
limitations, and this may have been particularly misleading at
mid-decade. When census 2000 tract-level data become fully
available in 2002, it may turn out that some of the areas chosen
by experimental-group families were not actually low-poverty
communities.
As noted earlier, because each of the initial MTO research
studies was based upon a unique design, results are often
applicable to only one MTO site, and sample sizes are quite
small. As tests of statistical significance are strongly affected by
sample sizes, it is possible that different conclusions would be
reached in MTO research if the tests could be conducted on
larger, multisite samples.
Certain other aspects of the demonstration’s implementation
also limit the ability to generalize from MTO results. In MTO,
the treatment received by families assigned to the experimental
group included both a location-restricted housing voucher and
some form of counseling to assist in leasing-up. The services
provided by the nonprofit counseling organizations to the
treatment-group families varied in breadth, depth, and
intensity across the sites (Feins et al. 1997), a factor that
might lead to some differences in program impacts. For
example, differences in counseling affected lease-up rates
(Feins et al. 1997; Shroder 2002) and perhaps also affected
how well families in the treatment group adapted to their new
neighborhoods and how long they remained in low-poverty
areas. In three sites, a single nonprofit provided counseling
throughout the demonstration period. The effects of any
distinctive practices at these three agencies could easily be
confounded with the effects of the site-specific housing market
and other factors.
Finally, while considerable evidence has been gathered from
the work of the early research teams about what changes have

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occurred as a result of participation in MTO, little is known
about why and how these changes took place. That is, there is
currently a dearth of information about the neighborhood
processes related to reported outcomes.
Yet larger samples and a clearer understanding of causality
are not sufficient for MTO to be counted among the small
number of successful policy experiments. Crane (1998,
pp. 1-2) lists the criteria he judges relevant in deciding whether
a new social program has been successful. These include
“unusually convincing evidence that the program delivers
substantial benefits regardless of cost . . . convincing evidence
of long-term effects; and new hope of making progress to solve
a seemingly intractable social problem.” He also includes
measures of the program’s cost-benefit relationships as another
central concern.2
For MTO to be counted a clear policy success, it must
demonstrate major long-term impacts achieved in a costeffective manner. MTO’s average counseling costs of roughly
$3,000 per family (those who leased-up a unit) would need to
be offset by evidence concerning reductions in such
expenditures as health care costs, unemployment, welfare
enrollment, crime reduction, improvements in educational
attainment and labor force engagement, and other measurable
impacts. MTO’s long-term research plan, as it is currently
configured, has the capability to generate the evidence
necessary to assess how well the program works.

5.1 The Next Stage in the Evaluation
of MTO’s Effects
Before discussing the specific issues and questions that appear
to warrant further inquiry, it is helpful for the reader to
appreciate that MTO was designed with a research plan
consisting of a number of stages of interconnected data
collection and analysis. Each stage is oriented toward the
completion of a final impact evaluation and data release. The
first stages have either been completed or have received
funding from HUD and other agencies. Design and
implementation, including random-assignment procedures,
were completed by 1998. The results from the small-grant
research projects at each of the five MTO locations are reported
in this paper. Two waves of regular surveys of MTO families to
determine their current location have already been conducted.
Recently, a multimillion-dollar midterm evaluation has
been funded and is under way. The only remaining portion of
the MTO research plan is the final, longer term impact
assessment. In the following section, we briefly outline
suggestions as to the key research and evaluation issues that

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emerge from the first set of analyses of the outcomes from the
MTO experiment.

For Which Social Outcomes Are There Comparable,
Statistically Powerful Results?
Persevering to make full use of the longitudinal character of
MTO’s panel design will permit, for the first time, the
answering of questions about the power and role of
neighborhoods in affecting the lives of deeply poor families
across all five MTO sites. The next stage of research will make
use of standardized, common instruments—rather than the
unique research plans and instruments that were used in the
first stage of MTO research. The full MTO sample can be used
to learn whether statistically meaningful effects occur across all
sites and what those effects are. This analysis will permit an
understanding of whether there are major differences between
types of families and the sites in the ways in which families
respond to the MTO treatment.

Are the Changes in Parents’ and Children’s Lives
Long-Lasting or Reversible?
Time will also permit us to understand the extent to which any
positive effects persist, diminish, or grow in strength. It is
unclear whether we can confidently predict that once a child or
parent has achieved some degree of positive improvement in,
say, employment, health, or education, that these changes will
continue. Are parents’ and children’s lives permanently and
irreversibly altered by MTO, or is there some degree of reversal
or “backsliding”? Do treatment-group children’s futures
dramatically improve as they move on to college and better
paying jobs compared with their control-group colleagues? Or
does the appeal of low-poverty areas wear thin? And do families
retreat to their former, more familiar communities? Do the
appeal and benefits of more affluent neighborhoods become
depleted if parents’ isolation and loneliness overwhelm them?

Will Parents as Well as Children Benefit from,
or Be Harmed by, MTO?
The bulk of the research reported in this collection suggests
that children’s and teenagers’ behavior and health have more
likely benefited from MTO than have their parents’ behavior

and health. Although many mothers feel better and appear
more positive about their futures, we still do not know if
previously unemployed adults’ employment situations and
wages will improve. The absence of any experimental change in
labor-market outcomes is an area where more time might
result in learning whether this crucial outcome is amenable to
MTO-driven change. Perhaps MTO was not the right, or
sufficient, demonstration to improve the employment
potential and incomes of deeply poor mothers because we
know from studies of labor-market programs that there are a
host of complicated interventions that might be required
before we can legitimately expect to see major improvements in
the job situations of low-income adults from poor
communities (Haveman 1994; O’Regan and Quigley 1999,
pp. 458-9).
The opposite of these questions is clear: will MTO prove
harmful to significant numbers of adults or children? Will
mothers or grandmothers who moved from their former
neighborhood find themselves lonely and isolated in a
community without friends, religious groups, or other familiar
ties that they spent decades acquiring? Will teenagers be
subjected to more police scrutiny and risk as a result of moving
to areas unaccustomed or resistant to their presence? Will
landlords in the new communities treat their new Section 8
tenants with indifference, or worse? What, if any, harm has
been caused to families who moved, how severe is it, and how
long-lasting might the effects be?

Why Have Changes Occurred?
For many of the statistical and quantitative statements in this
collection, we have only a limited sense of why they have
happened. Quantitative measures of school, health, and
criminal outcomes do not tell us the reasons for positive change
and personal transformation. Why have teenagers in Baltimore
stopped committing as much violent crime? Why has there
been a decline in asthma cases in Boston? How did younger
children in the treatment group achieve such improvements in
their reading tests? Ellen and Turner (1997) are also curious
about what has caused families’ lives to change, and to what
degree their neighborhoods are the cause. Qualitative or
ethnographic research is one tool needed to look inside the
“black box” of experimental effects to understand better those
institutions, networks, and processes that have leveraged
change in adults, children, or both.

Will There Be Any Significant Negative Impacts
on the Surrounding Neighborhood?
Galster (forthcoming), among a number of social scientists, asks
whether MTO families might affect the overall rate of problematic
behavior in both the sending and the receiving neighborhoods. He
assumes that moving those families will not have a major impact,
but wonders whether the move of a low-income family from
one neighborhood to another will result in a corresponding
shift of problematic behavior from sending to destination
neighborhoods. Are changes in socially problematic behavior
“capitalized” into corresponding changes in neighborhood
property values, and thus indirectly measurable through these
means? Is there a neighborhood concentration “threshold,” he
asks, of low-income families, after which rates of problematic
behavior increase (Turner, Popkin, and Cunningham 2000)?
Has MTO done any measurable harm to the communities
into which MTO families have moved that can be causally
attributed to the demonstration? Can an impact on the tiny
scale of the MTO movers, roughly 285 families in each of the
five sites, be detected reasonably amidst the welter of other
social, economic, racial, and attitudinal alterations that
normally occur in the life-course of any neighborhood?
It is essential for future researchers to develop measures of
actual or perceived impacts to address how the receiving
communities or neighborhoods react to small numbers of lowincome, largely minority, public-housing families. We may
learn that the receiving community neighbors and
neighborhood organizations are not all alike (Guhathakurta
and Mushkatel 2002). They might well have different
thresholds of tolerance and acceptance for children and adults
of varying racial and ethnic groups, depending on their own
racial and ethnic composition, their perceived vulnerability or
susceptibility to other changes, and their access to social
resources and programs that might be useful to new families.

5.2 Understanding the Costs and Benefits
One potential result of future research will be a clearer
understanding of the net costs of an MTO program, including
an appreciation of savings that result from improved outcomes
for treatment-group families. How does the cost of MTO
counseling compare with other social and economic costs and
benefits to families? Are improved test scores, lower levels of
welfare use, and lower violent crime rates common across all
sites? If so, what do these improvements “save” government
agencies compared with the higher costs for treatment-group
families (Johnson, Ladd, and Ludwig 2001)?

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At the end of this research, it will likely be important to
recall that experimental research projects will almost certainly
have problems of external validity. Manski (2000, p. 126) has
cautioned, “the groups whose interactions are observed are
formed artificially for the sake of the experiment. This raises
obvious questions about extrapolating findings from
experimental settings to populations of interest.” Higher levels
of attention by PHAs to tenants during the recruitment stage of
a demonstration such as MTO may result in attracting families
unlike those not involved. There is some risk, therefore, that
results that emerge from MTO may not readily translate into a
national program for remaining families. “It may be hazardous
to generalize from the treatment effects on members of the
experimental sample to some larger population” (Shroder
2000, p. 256).

6. Policy Issues and Concerns
One response to this paper’s positive results might be to build
the MTO model into something closer to a national program to
link intensive housing counseling to geographically limited
housing vouchers. To others, the improved level of
employment reported for all MTO families may reflect the
impact that macroeconomic improvements can have on the
lives of most Americans, suggesting that overall economic
improvement is a policy priority (Haveman 1994, p. 440;
Danziger 2002). Yet some may find the single-site results
reported within this collection unpersuasive. Should MTO,
then, be abandoned as a policy option, or is there enough
relevant information to warrant proposing that MTO be
adopted on a more permanent basis as a tool for local housing
agencies?
An important predicate for attempting to answer these
questions is to appreciate the fact that HUD was implementing
alternative opportunities for public-housing families at the
same time that MTO was being implemented. Among the key
alternative policy options was, and is, the Hope VI program.

6.1 The Option to Stay: Rebuilding
Inner-City Projects
A necessary part of the context for appreciating MTO’s design
and implementation was the fact that it was not “the only game
in town” for public-housing families in 1994. One of the

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parallel programs whose purpose and implementation directly,
if inadvertently, affected MTO was an initiative demolishing
many of the worst public-housing projects in larger cities—the
very projects from which some MTO families would be enabled
to move. The new program was Hope VI.3 The program’s goal
was to enable families to relocate, using Section 8 so that some
proportion of them would return to their old communities
after their public-housing buildings had been fully refurbished.
With congressional backing, HUD provided funding for
Hope VI to demolish the most troubled urban public-housing
projects and replace them with rebuilt mixed-income
communities. The initial goal was to tear down roughly
100,000 units (U.S. Department of Housing and Urban
Development 1996). Such rebuilding efforts, however,
encountered problems in regenerating their communities. In
part, this was because the communities remained troubled with
crime and gangs, relocation efforts were sometimes badly
managed, and some tenants resisted efforts to move them from
their familiar neighborhoods (Popkin 2000, pp. 181-90).
In several cities that were selected for MTO, families had the
choice of remaining or returning to a remodeled publichousing development. MTO-eligible families, beginning in
1994, frequently knew that they had the choice to stay and wait
for better housing or to relocate. Families in Boston, for
example, had seen the drawings of their soon-to-berefurbished public-housing development and often opted to
remain because the refurbished housing appeared attractive. In
Baltimore, several family housing projects near the downtown
core of the city were being demolished and replaced with
mixed-income housing as MTO began tenant selection. Some
families told us they preferred to remain and see what would
result.
MTO was not designed to be the “silver bullet” to end ghetto
poverty, nor was it intended to be the only choice available to
public-housing residents. It was but one of a set of choices that
public-housing applicants and residents could and should be
offered, including the right to stay in place and the option to
move into nonpoor neighborhoods (Brown and Richman
1997; Downs 1994, pp. 112-4). Whether the outcomes of
Hope VI will result in a net advantage for former residents is
yet to be determined (Salama 1999; Goetz 2000; Dimond 2000,
p. 260). Perhaps Hope VI and MTO will only work best in
aiding residents when larger policies and economic forces—
including welfare reform and a strong economy—provide
simultaneous reinforcement (Weisberg 2000). Only time and
carefully conducted research will provide answers to the
question of what mix of rebuilding and mobility is right for
particular cities and families.

6.2 Going “to Scale”?
Perhaps the most frequently asked question when MTO is
discussed among social scientists and policy analysts is what
might “bringing MTO to scale” be like? Thompson (1999,
p. 126), for example, appears certain that MTO could not
become a general, large-scale program—at least in the New
York area. “Given the fierce resistance,” he argues, “to even
modest public-housing development in nearby Yonkers, the
notion that significant portions of the NYCPHA [New York
City Public Housing Authority] population could be integrated
into Long Island and Westchester is fanciful. Political problems
aside, HUD’s entire $70 million national MTO budget would
have only a minor impact on deconcentrating public housing
in New York City.”
Political opposition and costs have been familiar obstacles
to prior HUD efforts to promote either economic or racial
mobility. Heclo (1994, p. 422) also reminds us of this: “dealing
in any realistic way with this socioeconomic catastrophe
(poverty) is going to be costly and will demand a long-term
commitment to people whom many Americans would not
want as neighbors. This is the dirty little secret buried in the
shelves of social science poverty studies.”
Another potential obstacle to the future of a demonstration
like MTO is what future the Section 8 program will have.
Husock (2000), a frequent critic, states that “in the blue-collar
and middle-class neighborhoods where voucher holders
increasingly live, longtime residents hate the program. It
undermines and destabilizes their communities by importing
social problems into their midst. . . .” Part of his solution is to
leave families in conventional public-housing projects fixed so
that residents would be both time-limited and required to get
“instruction in parenting.” If this does not work, he argues, “no
system at all would be better than Section 8 vouchers.” Such
criticism, however, now appears to be marginal to the
mainstream public policy debate over federal housing policy.
There are, however, fairly constant complaints about undue
concentration of voucher recipients. There is the growing sense
that the Section 8 program, left to its own devices, will create
submarkets or niches within which Section 8 families will be
served—just as project-based housing has done (Stegman
2000, p. 93). If, and as, the Section 8 program continues to
grow, it may be subject to increasing criticism for contributing
to such concentrations of poverty. The program seems likely to
need a new generation of policy tools to help families who wish
to move to communities with lower levels of poverty.
In addition, some worry that the regular Section 8 program
already has too high a level of failure in moving families into
private rental apartments. Even the fact that only 80 to

85 percent of families can lease-up under the regular program
appears a cause for concern. Stegman (2000, p. 93), for
example, argues that “because a voucher can be a ticket out of
a ghetto into a middle-class neighborhood, with better schools
and services, we should be concerned about the 15 percent of
families who cannot use their voucher to find acceptable
housing in the private sector.” How to promote access to better
neighborhoods and to also increase lease-up rates is a major
part of the ongoing policy conundrum for which MTO does
not provide an answer. Lease-up rates of roughly 50 to
60 percent are not the solution to moving large numbers of
families promptly into the rental market.
There is then an explicit policy trade-off between getting
needy families into private rental housing quickly at a high
lease-up rate versus getting them access to low-poverty areas at
a lower success rate. If, for example, a family with average
characteristics in a city like Los Angeles can receive regular
Section 8 assistance with no counseling services, it has a leaseup probability of roughly 70 percent based upon MTO
evidence. If, however, another MTO family receives the highest
intensity counseling services and is required to lease-up in a
low-poverty area, its lease-up probability is roughly
50 percent—a 20-percentage-point reduction. This appears to
be a considerable cost. Whatever positive results are traded off
against it, the decisions about MTO’s future will not be simple.
Some families will rightly be unwilling to voluntarily cede their
ability to locate in higher poverty areas except on the same basis
that they did in MTO; that is, they would otherwise have no
access to a Section 8 subsidy.
To address the policy question of whether the lease-up rates
in MTO were “too low,” current evidence is needed about how
well the general Section 8 program succeeds in leasing-up
families without any restrictions or counseling assistance. How
well does the regular program succeed in cities such as New
York and Los Angeles? A recent report suggests interestingly
that there has been a notable overall drop in the ability of
families to make use of their rental vouchers (Finkel and
Buron 2001). Lease-up rates declined from more than 80 percent
in 1993, just as MTO was being planned, to only 69 percent
in 2000. The report notes that, “PHAs generally attribute the
decline in success rates between 1993 and 2000 to a tightening
of rental markets during the intervening years” (Finkel and
Buron 2001, p. 1). While the national rate was roughly
70 percent, lease-ups in New York and Los Angeles occurred
at, again, a reduced rate. In New York, only 57 percent of
families, and in Los Angeles, only 47 percent, were able to find
and lease a rental unit. The MTO lease-up rate in Los Angeles
(averaged over several years), surprisingly, was higher, at
61 percent, for the treatment group, while in New York it was
somewhat lower, at 45 percent.

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Finkel and Buron (2001) also explore the types of program
activities that occurred alongside tightening markets. Local
agencies that required tenant screening and counseling
typically achieved higher rates of lease-ups compared with
those that did not (Finkel and Buron 2001, pp. 3-19). It appears
clear from this evidence that lease-up rates are constrained by
larger market forces but are also, within some margin,
malleable. Programmatic tools and interventions appear
relevant and reasonable for assisting tenant clients to find a
rental unit in a timely manner. The MTO intervention appears
less anomalous and boutique-like under tightened market
circumstances.
An additional part of the answer to whether lower lease-up
rates are an acceptable cost of administering an MTO-like
extension will rest on clear research evidence of the effects that
lower poverty neighborhoods will have on Section 8 families’
futures. If it should turn out in the 2000 census that the
neighborhoods to which many regular Section 8 families
moved are in deep poverty and distressed, and we become
reasonably certain that the long-term prospects for these
families are not good, the option to expand MTO will become
more attractive. That those tenants may be slightly better off
than they would be in public housing will mean little, since
their opportunities for positive change are seriously
constrained. Thus, there would be little self-sufficiency gained
from such higher lease-up rates.
If MTO treatment-group families are shown to still be
living largely in low-poverty locations that now look notably
better than those into which regular Section 8 families moved,
and if positive outcomes continue, there should be less
opposition to allowing local jurisdictions to offer an MTO-like
counseling effort—with some restrictions on vouchers to move
into low-poverty areas. Should treatment-group families look
much better off than in-place public-housing families, the
arguments in favor of an MTO-like expansion could appear
even more appealing.
Researchers will also need to debate which of several
possible administrative agencies are best suited to deliver
MTO-like Section 8 assistance. Some will argue that the 1930sera PHAs are outmoded and ineffective mechanisms for
responding to interwoven housing and employment needs on
a regional basis. Housing programs, such as Section 8, need not
be managed by funding the traditional 3,000 or more PHAs.
Set-asides of funding could be awarded competitively to those
communities that can create effective sets of administrative
tools that will permit cost-effective options for regionwide
housing mobility as part of their programming.
Better delivery of programs, for example, might be
accomplished by linking real estate brokerage services to
nonprofit counseling agencies. PHAs might offer income

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verification and housing inspections if they do so consistently,
efficiently, and promptly. Some local PHAs have already
recognized the advantages of linking information and services
across their wider region, using their annual and five-year plans
to assess how to best offer a regionwide, diverse range of
neighborhood choices to their clients (Tegler, Hanley, and
Liben 1995). State and local PHAs, as well as other program
providers, could be offered incentives to make affordable
regional housing markets materialize and function at a
controlled scale so that the PHAs, local landlords, and
neighborhood associations all become comfortable with their
role in managing a “fair” share of the city’s poor, assisted
families (Katz and Turner 2001). Such policy transitions may
take a decade or more in communities resistant to the poor and
public housing, but they may move more quickly if private
organizations, nonprofit groups, and PHAs throughout the
region combine their skills and resources.
There need not, then, be one-size-fits-all programming.
Funds could be allocated for a three-to-ten-year period with
periodic verification required of how well families were
provided options for housing mobility out of ghetto projects.
Hope VI redevelopment options could be included as part of
the mix of choice offered to families, so that options did not
remain narrowly limited. Housing policies must be capable of
managing multiple program options to meet the needs of local
families, since in the past fifty years, one-size-fits-all markets
have often proved ruinously inflexible and inept (Haveman
1994, p. 444; Downs 1994, p. 99).
MTO evidence to date suggests that only when a range of
choices is available to the inner-city poor can agencies begin to
effectively undo the damage to those living in concentrated
poverty. Subsequently, when careful observation and data
collection tell us who chose what action, why, and with what
result, we will be more confident that going to scale is a
necessary and even cost-effective program option. How might
MTO be extended to other cities or expanded to a somewhat
larger scale? A few preliminary policy suggestions to support
the decision to increase the size and scale of an MTO-like set of
program requirements and restrictions follow.

Do It Slowly and with Greater Public Involvement
Among the lessons from neighborhood opposition to MTO in
one Baltimore suburb in 1994 (Ihlanfeldt 1999) is the sense that
with better notice to the affected communities, and at a slower
pace, opposition might have been lessened if not altogether
mollified. The hurry to implement the demonstration meant
that the normal caution that might be expected to accompany

a racial and class integration program was not taken by either
HUD or the local administering agencies. The explicit and upfront exclusion of areas that did not have 10 percent or less
poverty should have been announced and publicized more
clearly, because a nontrivial number of protestors came from
areas that were not eligible sites for MTO family relocation.

Explain It Better
The imbroglio in Baltimore County in the first months of
MTO’s existence suggests that HUD and local PHAs could do
a better job of explaining the potential links between any largescale public-housing demolition programs, such as Hope VI,
and MTO housing mobility. Housing mobility options should
not become the political patsy for badly administered tenantrelocation programs tied to Hope VI. This may well mean that
MTO-like options cannot be implemented concurrently with
inner-city demolition programs, or not until the public
throughout the region understands and accepts the role that
screening and counseling will play in allocating families to their
communities. Addressing the complex intersection of race
and class will have to be undertaken by multiple levels of
government, and on a sustained basis, with MTO-like evidence
offering relevant input.

volunteered for MTO in five cities suggests that they are not
alone in their fear of crime and desire to move out of the
projects. MTO could be expanded into a program of modest
size, offered in a wider range of metropolitan areas and over
multiple years, to ensure that the operation of regional
counseling and restricted vouchers remains effective.
It is also possible, as MTO’s results become more widely
established and accepted, that the differences between
“volunteers” for any future program and those remaining
behind may narrow. Agencies may learn to explain and
motivate families better in the sending as well as the receiving
communities. This is of course a fundamental, treacherous
assumption at the heart of “normalizing” MTO. It is also
subject to at least two qualifications:
• Crime rates and interest in MTO: Given the critical
importance of fear of crime as a root source for families’
interest in MTO, will the apparent decline in urban
crime rates since the mid-1990s mean that fewer families
will be impelled to seek to get out through an MTO-like
program (Blumstein 2000; Fountain 2001)? Or will
crime rates return to higher levels and sustain interest in
the option to move out?
• Hope VI and enrollments: To what extent will inner-city
revitalization programs, such as Hope VI, result in more
families wanting to return to their old neighborhoods in
newly refurbished housing units? Will the presence of
more viable inner-city choices reduce interest in housing
options that send families far from their old neighbors?

MTO Does Not Appear Appropriate for All,
but Can Assist Additional Families
It May Not Be Relevant for Every City
MTO appears not to be suited for everyone. It has attracted
certain types of families with specific characteristics and levels
of motivation. Motivation helps set movers apart from those
who failed to move and appears to be a key to MTO’s future
(Popkin and Cunningham 2000). Those who volunteered
for MTO and then found a private-market apartment are
somewhat different from other poor public-housing residents
still living in deeply poor neighborhoods. Additional research
may help us appreciate the full extent to which MTO families
differ from others. This is true for the movers, whose motivations and opportunities enabled them to move to a new
neighborhood either as part of the experimental or Section 8
control group. To whom do the positive outcomes found in the
research reported in this paper best apply? To what universe of
public-housing families do they generalize?
Although MTO does not appear to be a relevant option for
all public-housing residents, the fact that thousands of families

There is suggestive evidence in MTO site-based research that
MTO has worked slightly differently in various regions. For
example, an MTO option may appear to be of far greater
benefit to families in Baltimore than in Boston. Site differences
may prove of interest and importance in subsequent program
implementation.
Whether MTO is relevant for a specific metropolitan area
may depend on whether the rental housing market includes
enough landlords willing to rent to low-income, former publichousing families. In looser markets, more landlords appear
available and willing to wait while the local PHA completes
paperwork and inspections. In tighter markets, it is clear that
Section 8 in general, and more likely MTO, will find it difficult
to achieve reasonable rates of lease-up. Analysis of the causes of
variation in demonstration effects between sites may help in
appreciating the scale and reasons for cross-site variation.

FRBNY Economic Policy Review / June 2003

131

Counseling Appears to Help
Although it is not statistically certain whether the restricted
vouchers or counseling had a greater effect in achieving the
effects shown to date, housing counseling (Shroder 2002;
Finkel and Buron 2001) has benefits in promoting lease-ups in
low-poverty areas. Unlike many PHAs, such as New York’s,
which provide “almost no assistance to tenants in the housing
search” (Kamber 2000, pp. 6, 30), an extended MTO option
would require that poor families receive help in searching
widely enough to make dispersed housing choices possible and
meaningful to the family.

Restrictions and the Meaning of Opportunity
There are both pros and cons associated with restricting the use
of Section 8 rental assistance to low-poverty communities.
Galster’s (2002) comments offer reason for policymakers to
examine the precise percentages of poverty and affluence that
might best facilitate the least harmful process for selecting
receiving neighborhoods to ensure that there will never be too
many Section 8 families allocated into any one vulnerable area.
It is also important to ensure that 10 percent poor does not
remain the sole definition of an area of opportunity. Future
expansion of MTO could include labor market and school
characteristics as among the variables that can assist in selecting
a set of neighborhoods for MTO-like counseling.
Balancing limits or temporary quotas with the principle that
Section 8 families should have the freedom to choose whatever
neighborhood they would like will require a new generation of
policy thinking within both HUD and Congress, especially as
long as the Section 8 program continues to serve as “the only
(housing) game in town” (Quigley 2000).

Make Timely Use of the Analysis of Costs and Benefits
When future research provides a clearer understanding of the
total costs and the social and economic benefits of MTO,
policymakers will likely find it easier to justify the cost of
funding for additional housing mobility vouchers and
counseling. Until that time, improvements in reading scores
and reductions in childhood asthma appear to offer adequate
justification for allowing PHAs to offer such a choice without
waiting. If, in a clinical medical experiment, patients were
found to benefit from a trial medication in the way that MTO
has allowed, there would likely be justification for permitting
other lives to be aided. There remain ample reasons for caution,

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The Impacts of New Neighborhoods on Poor Families

but the chance that some children’s lives can be substantially
improved by the choice of a different neighborhood suggests
that additional families should be offered this choice and
allowed to decide for themselves.

7. Concluding Observations
Based upon the research reported in this paper, it is possible to
draw one clear policy conclusion and one provisional, although
important, research conclusion. First, MTO’s operations
demonstrate that it is possible for HUD and local PHAs to
operate successfully an economic and racial desegregation
program using Section 8 rental assistance in differing
metropolitan markets. It has shown that, on a small scale, you
can reverse the historical practice of concentrating poor
minority households in poor minority neighborhoods, limiting
their housing choices, and exacerbating problems of economic
and racial isolation. It is, however, important to note, as the
research by Feins (forthcoming) points out, that the lowpoverty neighborhoods into which experimental-group
families moved were often heavily minority. MTO was
successful in providing a “mixed-income” neighborhood
rather than offering communities that are predominantly
white. MTO families who moved live in less racially segregated
communities than in-place control-group families but, then,
the latter live in neighborhoods that are among the most
racially and economically segregated in the United States.
Second, preliminary research on MTO’s effects on families
demonstrates that beneficial, statistically significant changes
have occurred in families’ lives within two to four years of their
participation in MTO. The first phase of MTO research reveals
that households in the treatment group, as well as some Section 8
comparison-group families, have experienced improvements
in multiple measures of well-being relative to the in-place
control group. This has included better health for adults and
fewer behavior problems among boys. Treatment-group family
members experienced declines in depression and asthma
following their moves from public housing, and male children
were much less likely to pose disciplinary problems.
In the area of education, despite the potential difficulties of
making the transition out of poor neighborhoods and their
schools, there is evidence of improvements in one MTO site.
Treatment-group children ages five through twelve have
experienced substantial gains in academic achievement as
measured by standardized test scores, compared with children
in the control group. If these results are borne out in
subsequent research, the demonstration will have achieved
major educational benefits for younger children much earlier

than anticipated. The unclear effects for older children compel
further research as part of the cross-site MTO evaluations.
Qualitative research conducted in 2001suggests that a number
of parents in that sample did not move their children to new
schools, but kept them in the schools serving their original
high-poverty neighborhoods (Popkin, Harris, and
Cunningham 2002). The extent to which families have made
moves into low-poverty communities but not taken advantage
of “local” resources and institutions represents a crucial
question for the next stage in MTO research.
MTO not only provides a clearer understanding of how
residential mobility programs can operate, but has clarified the
temporally sequenced, quantifiable effects that this change in
neighborhood has on the lives of parents and children who
would likely otherwise remain in “ghetto” neighborhoods.
These changes appear to have occurred in some areas of social
and economic life more clearly than in others; and in some
cities, more surely than in others.
Achieving improvements in education performance,
reductions in criminal behavior, improvements in adults’
mental and physical health, as well as a reduction in welfare
dependence, is a nontrivial initial policy and research
contribution. MTO’s ability to document the conditions under
which large numbers of poor families’ lives may be improved as
a result of a change in their neighborhood is potentially among
the most significant social science and policy legacies that HUD
will have for the next decade or more.
There are, nonetheless, a critical number of important
research and policy issues that need to be addressed by future
research aimed at clearer appreciation of the consequences of
life in high-poverty public-housing developments compared
with life in less concentrated Section 8 comparison- and
treatment-group neighborhoods. Such research should also

help establish the conditions under which a programmatic
extension of the MTO program might best be developed.
Knowing in which communities and neighborhoods, and for
which types of families, such a program may work best will
greatly aid in offering alternatives to life within high-rise, highpoverty communities. If Downs (1999, p. 967) is correct in
observing that most efforts to revitalize deeply poor
communities through community development have “almost
universally failed,” then some form of regional housing
mobility effort such as MTO is a necessary accompaniment to
other development strategies (Katz and Turner 2001).
Some neighborhoods, families, and policy analysts will
continue to oppose agencies such as HUD and its Section 8
program to protect what they feel is theirs from perceived or
actual threats (Husock 2000). Such opposition can, however,
be better managed to reduce its occurrence or effects. The
worst consequence of acquiescing fully to such opposition
would be to leave in place public housing as a “federally
funded, physically permanent institution for the isolation of
black families by race and class” (Massey and Kanaiaupuni
1993, p. 120). Heclo (1994, p. 427) sagely reminds us of
additional obstacles to expanding MTO: “Full-scale attacks on
ghetto poverty will inevitably mean targeting resources
disproportionately on minorities. Whether such efforts are
seen as pro-black preferences or an act of solidarity with the
country’s children and its future will depend heavily on how
political leaders help educate the public.” Few political leaders
of either party have done, or have been able to do, much to
address this concern. MTO offers policymakers, for the first
time, necessary if not yet sufficient evidence that children’s
lives have been notably benefited and that parts of the “ghetto”
poverty problem can be redressed.

FRBNY Economic Policy Review / June 2003

133

Endnotes

1. For additional details, see Goering, Feins, and Richardson (2002).
2. Regarding the latter, he notes: “Determining the cost-benefit
relationship is easier said than done. Although the costs are usually
easy enough to measure, determining the monetary value of the
benefits is often difficult” (Crane 1998, p. 3). See also Brooks-Gunn,
Berlin, Leventhal, and Fuligni (2000).

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The Impacts of New Neighborhoods on Poor Families

3. A National Commission on Severely Distressed Public Housing
had, in 1989, recommended a strategy for the elimination of the worst
projects in forty of the country’s largest cities. The program derived
from this recommendation, Hope VI, was enacted at the same time as
MTO. Congress allocated $1.6 billion for this program from 1993 to
1995 (U.S. Department of Housing and Urban Development 1996).

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Suburbia. Chicago: University of Chicago Press.
Salama, Jerry. 1999. “The Redevelopment of Distressed Public
Housing: Early Results from Hope VI Projects in Atlanta, Chicago,
and San Antonio.” Housing Policy Debate 10, no. 1: 95-142.

Tegler, Philip, Michael Hanley, and Judith Liben. 1995. “Transforming
Section 8: Using Federal Housing Subsidies to Promote Individual
Choice and Desegregation.” Harvard Civil Rights-Civil
Liberties Law Review 30 (summer): 451-86.
Thompson, Phillip. 1999. “Public Housing in New York City.” In
Michael H. Schill, ed., Housing and Community Development
in New York City, 119-42. Albany: State University of New York.

Sampson, Robert. 2000. “The Neighborhood Context of Investing in
Children: Facilitating Mechanisms and Undermining Risks.”
In Sheldon Danziger and Jane Waldfogel, eds., Securing the
Future: Investing in Children from Birth to College,
205-27. New York: Russell Sage Foundation.

Turner, Margery, Susan Popkin, and Mary Cunningham. 2000.
Section 8 Mobility and Neighborhood Health: Emerging
Issues and Policy Challenges. Washington, D.C.: Urban
Institute Press.

Sampson, Robert, Jeffrey Morenoff, and Thomas Gannon-Rowley.
Forthcoming. “Assessing Neighborhood Effects: Social Processes
and New Directions in Research.” Annual Review of
Sociology.

U.S. Department of Housing and Urban Development. 1996. “An
Historical and Baseline Assessment of Hope VI: Cross-Site
Report.” HUD-1620-PDR, August. Washington, D.C.: Office
of Policy Development and Research.

Schoeni, Robert, and Rebecca Blank. 2000. “What Has Welfare Reform
Accomplished? Impacts on Welfare Participation, Employment,
Income, Poverty, and Family Structure.” NBER Working Paper
no. 7627, March.

———. 1999. “Moving to Opportunity for Fair Housing Demonstration: Current Status and Initial Findings.” Washington, D.C.:
Office of Policy Development and Research.

Shroder, Mark. 2000. “Social Experiments in Housing.” Cityscape 5,
no. 1: 237-59.
———. 2002. “Locational Constraint, Housing Counseling, and
Successful Lease-up in a Randomized Housing Voucher
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Smith, Jeffrey, and Petra Todd. 2000. “Does Matching Overcome
Lalonde’s Critique of Nonexperimental Estimators?” Unpublished
paper, University of Western Ontario and University of
Pennsylvania, November.
Stegman, Michael. 2000. “Comments.” In William Gale and Janet
Rothenberg Pack, eds., Brookings-Wharton Papers on Urban
Affairs 2000, 89-93. Washington, D.C.: Brookings Institution.

———. 2000. “Section 8 Tenant-Based Housing Assistance: A Look
Back after 30 Years.” March. Washington, D.C.
———. 2001. “A Report on Worst Case Housing Needs in 1999.”
January. Washington, D.C.: Office of Policy Development
and Research.
Waldinger, Roger. 1996. Still the Promised City? AfricanAmericans and New Immigrants in Postindustrial
New York. Cambridge: Harvard University Press.
Waldinger, Roger, and Medhi Bozorgmehr, eds. 1994. Ethnic
Los Angeles. New York: Russell Sage Foundation.
Weaver, R. Kent. 2000. Ending Welfare as We Know It.
Washington, D.C.: Brookings Institution.

FRBNY Economic Policy Review / June 2003

139

References (Continued)

Weisberg, Jacob. 2000. “For the Sake of Argument.” New York Times
Magazine, November 5.

———. 1996. When Work Disappears: The World of the
New Urban Poor. New York: Alfred A. Knopf.

Wilson,William Julius. 1987. The Truly Disadvantaged: The Inner
City, the Underclass, and Public Policy. Chicago: University
of Chicago Press.

The views expressed are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York,
the Federal Reserve System, the U.S. Department of Housing and Urban Development, or Abt Associates. The Federal
Reserve Bank of New York provides no warranty, express or implied, as to the accuracy, timeliness, completeness,
merchantability, or fitness for any particular purpose of any information contained in documents produced and provided by
the Federal Reserve Bank of New York in any form or manner whatsoever.
140

The Impacts of New Neighborhoods on Poor Families

Lance Freeman

Commentary

ohn Goering does an excellent job summarizing the early
results of the Moving to Opportunity for Fair Housing
Demonstration (MTO). His paper serves as an excellent
reference for anyone interested in learning about the
motivation, design, and preliminary outcomes of MTO. The
findings summarized by Goering also settle and raise a number
of important policy-related questions. In this commentary,
I place the MTO experiment in the context of housing policy as
well as summarize some of the lessons learned and the
remaining questions relevant to affordable housing policy.
The MTO experiment is significant not only because of the
lessons it offers on how neighborhoods affect individuals but
also because it represents a major effort to use social science to
inform housing policy. Compared with many other policy
domains, such as health or welfare, housing has been somewhat
of a laggard in using social science to inform policy. Politics,
ideology, and the latest fads have often carried the day instead.
This is not to say that politics can or should be removed from
policymaking. Rather, social science can inform such decisions,
but in order to do that, rigorous social science evidence of the
type provided by the MTO experiment is required. Without
such evidence, we are left with only ideology to guide us. Thus,
MTO may represent the advent of social science playing an
important role in the crafting of housing policy. This would
certainly be welcome.
The MTO experiment is also significant, of course, for its
lessons on neighborhood effects. The quasi-experimental

evidence thus far is consistent in showing that neighborhoods
do in fact affect a number of behavioral outcomes. MTO’s
results represent the strongest findings to date that
neighborhoods do indeed matter. Living in a high-poverty
neighborhood seems to inhibit upward mobility. The question
of whether neighborhoods matter is certainly closer to
becoming questions of how, and now what do we do, as a result
of the evidence produced by MTO.
The mechanisms through which neighborhoods exert their
effects still remain something of a black box, although there
are a number of plausible theories. The evidence from the
qualitative analyses of the MTO demonstration suggests that
the positive examples set by residents of low-poverty
neighborhoods and the better schools available in these
neighborhoods may be the primary mechanisms through
which program participants in low-poverty neighborhoods
achieve improved outcomes (Popkin, Harris, and
Cunningham 2002). More in-depth qualitative research is
necessary before we can draw any definitive conclusions on the
“how” of neighborhood effects.
MTO’s findings also force us more than ever to confront the
implications of neighborhood effects and housing policy.
Affordable housing policy in the United States has been
predicated on the notion that it improves the physical
characteristics of recipients’ housing, increases affordability,
and, implicitly at least, enhances the neighborhood
environment. To date, however, our policy has failed on the

Lance Freeman is an assistant professor of urban planning at Columbia
University.

The views expressed are those of the author and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

FRBNY Economic Policy Review / June 2003

141

last account. Indeed, the evidence suggests that in some
respects, housing assistance has worsened neighborhood
conditions, at least in terms of living in concentrated-poverty
neighborhoods (Newman and Schnare 1997). The early results
of the MTO demonstration show that neighborhood
environment is indeed important. As others have suggested,
neighborhoods help shape the opportunity structure
confronting individuals (Galster and Killen 1995). These
results suggest that when we craft affordable housing policy, we
should take neighborhood quality into account.
Before acting on this, however, we need to consider the
following questions:
• When designing housing policy, are the magnitudes of
the observed effects large enough to warrant taking into
consideration neighborhood effects?
• Assuming the impacts are substantial and long-lasting,
how might the findings of MTO inform affordable
housing policy?
• Should integration of all the poor—either through
dispersal or mixed-income revitalization—be a goal?

142

Commentary

• Should neighborhood quality, like physical housing
conditions, be a standard for housing assistance
eligibility?
• Might we expect neighborhood effects to work in
reverse? That is, will mixed-income housing in
neighborhoods undergoing revitalization produce
similar benefits for the poor? HOPE VI is predicated on
the assumption that this is indeed the case. But
neighborhood effects may operate differently for poor
households who do not seek out more affluent
neighbors. This is certainly an area worthy of further
study.
The MTO demonstration cannot, of course, provide the
answers to these questions. But it increasingly moves policy
debates in the direction of addressing these issues. To continue
to ignore them in the face of convincing evidence of the
importance of neighborhood effects would not only be
intellectually dishonest but morally bankrupt as well.

References

Ellen, Ingrid Gould, and Margery A. Turner. 1997. “Does
Neighborhood Matter? Assessing Recent Evidence.” Housing
Policy Debate 8, no. 4: 833-66.
Galster, George, and Sean P. Killen. 1995. “The Geography of
Metropolitan Opportunity: A Reconnaissance and Conceptual
Framework.” Housing Policy Debate 6, no. 1: 7-43.

Newman, Sandra J., and Ann B. Schnare. 1997. “. . . And a Suitable
Living Environment: The Failure of Housing Programs to Deliver
on Neighborhood Quality.” Housing Policy Debate 8, no. 4:
703-44.
Popkin, Susan J., Laura E. Harris, and Mary K. Cunningham. 2002.
Families in Transition: A Qualitative Analysis of the
MTO Experience. Washington, D.C.: Urban Institute Press.

143

Denise DiPasquale, Dennis Fricke, and Daniel Garcia-Diaz

Comparing the Costs
of Federal Housing
Assistance Programs
1. Introduction

or more than sixty years, the federal government has
provided assistance to improve the condition and reduce
the cost of rental housing for low- and very low-income
households.1 The focus of federal assistance has changed over
time, as illustrated by the major policy reviews of the last four
decades—the Kaiser Committee in 1968, the President’s
Commission on Housing in 1982, and the National Housing
Task Force of 1988. The focus of these reviews shifted from
increasing the physical quality of the housing stock in the
Kaiser Committee, to increasing housing affordability in the
President’s Commission on Housing, to addressing housing
availability and affordability in the National Housing Task
Force.2 Production programs dominated federal housing
policy until the early 1980s. Since then, the voucher program
has been one of the fastest growing federal housing assistance
programs.
Although there is little debate that vouchers will remain a
dominant form of housing assistance, there is still considerable
debate concerning the appropriate role for production
programs. A major concern with production programs is their
cost, particularly when compared with vouchers. Much of the
housing cost literature cited in this debate is more than twenty
years old and evaluates production programs that are no longer
active. In this paper, we describe the housing provided by
vouchers and five active federal production programs, and
Denise DiPasquale is president of City Research; Dennis Fricke is a retired
assistant director and Daniel Garcia-Diaz a senior housing analyst at the
U.S. General Accounting Office.

estimate the total costs of each program. In addition, we
examine who pays the costs of each program.
Today, six active federal housing programs continue to
increase the number of households assisted. These programs
include the Housing Choice Voucher program (housing
vouchers)—the largest source of federal funds for housing
assistance—and five production programs, which currently
receive federal funds to construct or substantially rehabilitate
units. In this paper, we examine the characteristics of the
housing provided and the total costs of providing that housing
under these six active programs:3
• Housing vouchers (produced about 1.6 million
households) supplement tenants’ rental payments in
privately owned, moderately priced apartments chosen
by the tenants.
• Low-income housing tax credits (produced about
700,000 units) provide tax incentives for private equity
investment and are often used in conjunction with other
federal, state, and local government and private
subsidies in the production of new and rehabilitated
affordable housing units consistent with statedetermined housing priorities.
• HOPE VI (produced about 65,000 units) provides
grants—coupled with funds from other federal, state,
local, and private sources—to revitalize severely
distressed public housing, support community and
social services, and promote mixed-income
communities.4
This paper is based on “Federal Housing Assistance: Comparing the
Characteristics and Costs of Federal Programs” (U.S. General Accounting
Office report no. 02-76, 2002). We thank Susan Wachter and the participants
in the Policies to Promote Affordable Housing Conference. The views
expressed are those of the authors and do not necessarily reflect the position
of the Federal Reserve Bank of New York, the Federal Reserve System, or the
U.S. General Accounting Office.
FRBNY Economic Policy Review / June 2003

147

• Section 202 (produced about 66,000 units) provides
grants to develop supportive housing for the elderly and
project-based rental assistance.5
• Section 811 (produced about 18,000 units) provides
grants to develop supportive housing for persons with
disabilities and project-based rental assistance.
• Section 515 (produced about 485,000 units) provides
below-market loans to support the development of
housing for families and the elderly in rural areas and
project-based rental assistance through the Section 521
program.
The housing provided under the six active federal programs
can be quite diverse, varying in age, type, size, and in level of
services and amenities provided. We find that for units of the
same size and in the same general location, the total costs of
production programs are greater than the total costs of
vouchers, but the difference in costs is smaller than suggested
in earlier literature. In addition, these cost differences generally
diminish as unit size increases.
Compared with vouchers, we estimate that the average total
thirty-year costs of one-bedroom units in metropolitan areas
range from 8 percent more under the Section 811 program to
19 percent more under the tax credit program. For threebedroom units in metropolitan areas, tax credit units cost an
average of just 4 percent more than vouchers. HOPE VI is the
most expensive production program; we estimate that
HOPE VI units exceed voucher costs by 36 percent. With the
exception of HOPE VI, total costs are generally similar among
the production programs. The federal government pays the
largest share of total costs for all of the housing programs,
except for tax credits, in which the tenants pay the largest share.
We also find that the production programs are more expensive
than housing vouchers for the federal government.
Our work raises a number of housing policy issues. All
federal housing programs provide benefits beyond housing
people with low and very low incomes. For example, vouchers
can increase household mobility while production programs
can be important components of community development
strategies. These benefits must be weighed when assessing
program costs. Analysis of the full costs and benefits of federal
housing programs require comprehensive, consistent data that
are not readily available. For example, there is no centralized
national database that includes information on costs for tax
credits—the largest housing production program.
In this paper, we first provide background information on
program expenditures and a brief review of the literature. Next,
we describe the housing provided under each program and our
methodology for estimating costs. We then present our total
cost estimates along with estimates of the share of those costs

148

Comparing the Costs of Federal Housing Assistance Programs

paid by the various actors in the programs. We conclude with a
discussion of the policy issues raised by our work.

2. Background
Of the approximately 5.2 million renter households assisted by
the federal government in 1999, about 2.7 million were assisted
by programs that no longer receive appropriations to produce
additional units. We refer to these programs as “inactive.”
Appropriations are, however, provided to fund project-based
rental assistance, interest reduction payments, and operating
subsidies for the units developed under these programs in
previous years. The remaining 2.5 million units are subsidized
under the six active programs that receive appropriations both
to add new units and to subsidize units funded in previous
years. This figure accounts for units that receive subsidies from
more than one program. More than 10 percent of the total
units (2.9 million) under the active programs benefit from
overlapping subsidies. For the tax credit program alone, nearly
40 percent of the units receive overlapping subsidies from
various Section 8 rental assistance programs.
In fiscal year 1999, the federal government spent about
$28.7 billion, including $3.5 billion in tax credits, for both the
active and inactive housing programs. Of this combined
amount, about $15.1 billion supported units funded under the
inactive programs, and about $13.6 billion in budgetary
outlays and tax credits supported the active programs. Less
than one-third of the total expenditures went toward the
construction, rehabilitation, or modernization of affordable
housing. As shown in Chart 1, the voucher program is the
largest of the active programs, accounting for about 52 percent
of federal funding for them. The tax credit program accounts
for about 26 percent of the federal funding for active programs,
the HOME program about 10 percent, the Section 202 and
Section 811 programs together about 5 percent, the
Section 515 program about 5 percent,6 and the HOPE VI
program about 2 percent.
Previous studies on the relative costs of housing programs
have generally found that vouchers are less expensive and more
cost-effective than production programs. Weicher (1990)7
reviews the housing cost literature and finds that production
programs are more expensive than vouchers. Using data provided
in Wallace et al. (1981),8 Weicher estimates that the Section 8
New Construction program was 40 percent to 50 percent more
expensive than the Section 8 Existing Housing program. Olsen
(2000, 2001)9 also reviews the housing cost literature but uses a
different approach: he evaluates cost-effectiveness of housing

Chart 1

Budgetary Outlays and Tax Expenditures for
Active and Inactive Housing Assistance Programs
Fiscal Year 1999, in Millions of Dollars
Active
Programs

Inactive
Programs
Section 8
project-based
$8,190

Public housing
$6,940

Vouchers
$7,010
Tax credits
$3,500

Other
$1,690
HOME
$1,350
Notes: The total equals $28.7 billion in budgetary outlays and tax
expenditures. Outlays for Section 8 project-based include new
construction/substantial rehabilitation, loan management set-aside,
property disposition, Section 236, and rent supplement. Outlays for
“Other” include Section 202, Section 811, Section 515, Section 521,
and HOPE VI. HOME is the Home Investment Partnerships program.

programs by comparing their total cost of providing assisted
housing and their estimated market value. Olsen (2001) finds
that the studies reviewed unanimously conclude that vouchers
are more cost-effective than production programs such as
Public Housing, Section 8 New Construction, and
Section 236.10 His review concludes, “whether there are any
market conditions under which construction programs are
more cost-effective than vouchers is surely one of the most
important unanswered questions in housing policy analysis.”
The reviews by Weicher and Olsen illustrate that much of the
housing cost literature is more than twenty years old, and, as a
result, focuses on older production programs that are no longer
active. Little recent work has been done to compare costs across
current programs, in part because of the lack of consistent,
detailed cost data across these programs, as we will discuss.
A goal of this paper is to begin to fill that void.

3. Housing Characteristics
of Federal Housing Programs
Housing vouchers are used almost exclusively in existing
properties whose median age nationwide is about thirty-five
years, ranging from about sixty-five years in the Northeast to
about thirty years in the West. According to U.S. Department
of Housing and Urban Development (HUD) data, about three-

quarters of vouchers are used in multifamily dwellings, and the
remainder is used in single-family homes. Production program
properties are either newly constructed or substantially
rehabilitated. For example, the HOPE VI program replaces or
renovates severely distressed public housing developments as
part of a broader community revitalization strategy. The new
or rehabilitated properties often include special design features
that are intended to integrate the public housing community
with the neighborhood. HOPE VI properties, which have an
average of nearly 300 units, span the full range of building
types, from detached homes to row houses to elevator
buildings.
The tax credit and Section 811 programs also provide newly
constructed and substantially rehabilitated properties. Most
tax credit properties are multifamily buildings, including
single-room-occupancy dwellings, walk-up apartments, town
houses and row houses, and elevator buildings, and have an
average of seventy-five units.11 Section 811 properties are
predominantly of two types—independent living projects and
group homes. Independent living projects generally provide
separate apartments with individual kitchens and bathrooms,
while group homes typically include a bedroom for each
resident and a common kitchen, dining, and living area.
Section 811 properties range from single-family dwellings to
walk-up apartments and have an average of about twelve units.
Section 811 group homes normally do not house more than six
persons.
Finally, the Section 202 and Section 515 programs primarily
provide newly constructed properties. Section 202 properties
are generally mid- and high-rise buildings with elevators,
averaging forty-five units nationwide, whereas most
Section 515 properties are walk-up apartments and often
consist of no more than twenty-four units, which is a size
consistent with the lower population densities of rural areas.
Across the six active programs, units vary in their average
size (as measured by the number of bedrooms) and
distribution across size, as shown in Chart 2. The average
number of bedrooms ranges from 1.0 for the Section 202 and
Section 811 programs to 2.4 for the HOPE VI program.
Vouchers and tax credits provide higher percentages of larger
family units, while the Section 515 program includes a
combination of larger units for families and smaller units for
the elderly.
Most assisted housing is in metropolitan areas but the
location of properties varies somewhat by program. As Chart 3
indicates, all HOPE VI units are in metropolitan areas, with
about 90 percent in central cities. In addition, about 94 percent
of tax credit units12 and about 80 percent of voucher,
Section 202, and Section 811 units are in metropolitan areas.
For all of these programs, the majority of the metropolitan area

FRBNY Economic Policy Review / June 2003

149

Chart 2

Distribution and Average Size of Units
in the Six Active Housing Programs

Percent

Efficiency
1 bedroom
2 bedrooms
3+ bedrooms

100
80
60
40
20
0
Rental Vouchers Tax HOPE VI Section Section Section
housing (2.2)
credits
(2.4)
202
811
515
stock
(1.9)
(1.0)
(1.0)
(1.6)
(2.0)
Note: The average number of bedrooms appears in parentheses.

units are in central cities. By contrast, nearly 70 percent of
Section 515 units are in rural nonmetropolitan areas, with the
balance in the rural parts of metropolitan areas.
The neighborhoods where assisted housing is located also
vary. The census tracts where HOPE VI units are found are
poorer than the census tracts where other program units are
located. HOPE VI census tracts also have higher percentages of
minority households and lower percentages of homeowners.
In general, the demographic characteristics of the census tracts

where other program properties are located are fairly similar,
as shown in Chart 4.
In addition to providing a range of property types with units
of different sizes in different locations, the six active programs
vary in the extent to which they make supportive services
and amenities available to assisted households. In general,
supportive services are not an integral part of the voucher, tax
credit, and Section 515 programs. However, when individual
tax credit and Section 515 properties serve households with
special needs, such as the elderly or persons with disabilities,
they may provide services and amenities similar to those
provided in Section 202 and Section 811 properties.
Section 202 properties typically include congregate dining
facilities, and both Section 202 and Section 811 properties
include common rooms and may make transportation,
housekeeping, and health care services available. The HOPE VI
program emphasizes services, allowing up to 15 percent of the
HOPE VI grant to be used for community and supportive
services. For example, HOPE VI developments often include
employment or job training centers as well as facilities for
children. Production program units are more likely to have
modern amenities, whereas voucher units typically have
amenities characteristic of older rental properties. In addition,
although it is expected that new units under the production
programs start out in better condition than the older units
under the voucher program, over time, the condition of these
new units, as well as of existing units, depends on the level of
maintenance and reinvestment.

Chart 4

Demographic Characteristics of Neighborhoods
Where Assisted Housing Is Located

Chart 3

General Location of Units in the Six Active
Housing Programs
Percent

Nonmetro
Metro-suburban
Metro-central city

100

Percent
100

Section 811
Section 202
Tax credits
Vouchers
HOPE VI

80
60

0
Poverty

Minority households

Homeowners

20
0
Rental Vouchers Tax
housing
credits
stock

150

HOPE Section Section Section
VI
202
811
515

Comparing the Costs of Federal Housing Assistance Programs

Notes: The data for poverty indicate the percentage of neighborhood
households with incomes below a certain threshold adjusted for
family size as determined by the U.S. Bureau of the Census. The chart
excludes data for Section 515 units because the addresses of
Section 515 properties were not readily available.

4. Methodology for Comparing
Program Costs
For this analysis, we constructed the total costs of a unit under
each program, regardless of who bears the costs. In the private
rental housing market, rents cover the total costs of providing a
housing unit. The total costs include operating expenses (for
example, administrative expenses, utilities, routine maintenance,
and property taxes), debt service, deposits to a replacement
reserve for major capital improvements over time, and a
market return to equity investors. We defined the total costs of
vouchers as the present discounted value (PDV) of the total
rent paid by both the federal government and the assisted
household plus the fee paid by HUD to the local housing
authority to administer the program:
total voucher costs = PDV (rents + administrative fee).
For production programs, costs are more complicated
because an asset with a long useful life is produced. In the
private housing market, the value of the housing equals the
PDV of the net rental income stream over the useful life:13
value = PDV (net rental income).
The rental income stream must cover the total costs:14
PDV (rental income) = total costs = total development costs
+ PDV (operating costs).
In the private market, if the PDV of market rents does not
cover total costs, the housing development will not be built.
Federal production programs generally provide housing at
below-market rents or provide housing in locations where
market rents would be insufficient to cover costs. In either case,
the difference between total rents paid and total costs is covered
by development subsidies. Therefore, for production
programs:
total production program costs = PDV (rental income)
+ PDV (development subsidies).
For both vouchers and the production programs, our
estimates of total costs recognize that rents are paid over many
years and development subsidies are paid either up front or
over many years. Vouchers are short-term commitments to
provide housing assistance, while production programs
provide units with certain restrictions to ensure that the units
will remain affordable in the future, often more than thirty
years. To account for differences in the timing of investments
under the various programs, we estimated their thirty-year lifecycle costs. Longer time frames for the life cycle tend to favor

production programs in terms of costs because of the impact of
rent inflation over time.15
Vouchers and the production programs are subject to and
insulated from different cost risks over time. Whereas vouchers
are vulnerable to inflation in market rents, the production
programs are less vulnerable because of federal regulations or
limits on rents associated with development subsidies.
However, the production programs can pose substantial cost
risks if capital reserves are underfunded, as they often have
been in the past. Vouchers pose no such risk because the federal
government has no commitment to specific units.
Both the voucher and the production programs are subject
to cost-containment guidelines. For the voucher program,
HUD sets payment standards that are based on fair market
rents for more than 2,700 market areas, taking into account
unit size (by number of bedrooms). These payment standards
are intended to give assisted households a selection of units and
neighborhoods while containing costs. Public housing
authorities can ask HUD to increase the payment standard if
they believe increases are warranted. For the production
programs, the cost-containment guidelines are designed to
provide properties of modest design. These guidelines may
establish cost limits that vary by location, type of building (for
example, elevator or garden-style), and unit size, or they may
simply require assurances that the costs of proposed properties
are reasonable.
Table 1 presents the average total development costs for the
production programs by general location and for seven
metropolitan areas. Information on housing vouchers does not
appear in the table because the program relies on existing
housing. Nationally and in most metropolitan areas, the total
development costs are considerably higher for HOPE VI than
for the other production programs. It is important to note that
HOPE VI is a small program with few projects per metropolitan area; the HOPE VI figures for most of our seven
metropolitan areas incorporate data for only two developments. As a result, the average for a particular metropolitan
area can be skewed by the presence of large projects with high
or low development costs. In the New York City metropolitan
area, for example, one very large HOPE VI development
involved rehabilitation, which can cost much less than new
construction, and, consequently, the average HOPE VI
development cost for New York City is unusually low. At the
same time, three HOPE VI properties in the Baltimore metropolitan area involving new construction had development costs
very similar to each other.
For some programs, the entire development cost is
subsidized with up-front grants, while for others, it is
subsidized over time with tax credits or below-market interestrate loans. Table 2 shows our estimates of the present

FRBNY Economic Policy Review / June 2003

151

Table 1

Average Total Development Costs per Unit by General Location and for Seven Metropolitan Areas
In 1999 Dollars
HOPE VIa
Location
Nation
Metro
Nonmetro
Seven metro areas
Baltimore
Boston
Chicago
Dallas-Fort Worth
Denver
Los Angeles
New York City

Tax Credits

Housing-Related

All Costs

Section 202

Section 811

Section 515

73,510
75,430
60,270

70,430
73,020
63,120

58,280

80,250
94,160
75,020
52,390
72,160
94,360
101,730

69,420
96,000
71,370
66,710
74,640
97,520
116,180

73,590
75,690
62,010

117,920
117,920

143,450
143,450

77,360
116,710
79,340
60,100
72,650
104,750
111,580

166,380
197,000
102,470
78,920
102,170
113,060
76,710

221,210
261,610
108,950
96,460
126,440
154,310
107,010

58,280

b
b
b

b
b

a
The total development costs for HOPE VI reflect mostly planned figures. Housing-related costs exclude the costs of remediation, demolition, the
construction of housing and community facilities, relocation, and community-based planning and participation, most of which are not applicable to the
other housing programs. These other expenses are included in the “All Costs” column.
b

The program generally does not build units in these areas.

Table 2

Average Present Discounted Value of Development Subsidies per Unit by General Location
and for Seven Metropolitan Areas
In 1999 Dollars
HOPE VIa
Location
Nation
Metro
Nonmetro
Seven metro areas
Baltimore
Boston
Chicago
Dallas-Fort Worth
Denver
Los Angeles
New York City

Tax Credits

Housing-Related

All Costs

Section 202a

Section 811a

Section 515

50,350
52,790
44,690

117,920
117,920

143,450
143,450
b

70,430
73,020
63,120

41,730

73,510
75,430
60,270

51,780
50,630
62,190
31,470
29,080
81,380
111,780

166,380
197,000
102,470
78,920
102,170
113,060
76,710

221,210
261,610
108,950
96,460
126,440
154,310
107,010

80,250
94,160
75,020
52,390
72,160
94,360
101,730

69,420
96,000
71,370
66,710
74,640
97,520
116,180

41,730

b
b
b
b
b
b

For the HOPE VI, Section 202, and Section 811 programs, total costs are paid entirely up front and no debt service payments are made for these units.
As a result, the total development subsidies equal the total development costs.
b

152

The program generally does not build units in these areas.

Comparing the Costs of Federal Housing Assistance Programs

discounted value of the average development subsidies per unit
in 1999 for the five production programs, both for the nation
and for seven metropolitan areas. For HOPE VI, Section 202,
and Section 811, the federal government pays the total
development costs up front with grants; as a result, the
development subsidies are equal to the total development costs.
Section 515 provides below-market fixed-rate loans of
1 percent with fifty-year terms. To estimate the value of the
subsidy provided through a below-market interest-rate loan,
we took the present discounted value of the difference in the
interest payments over thirty years between the rate on the
constant-maturity treasuries—which is a very conservative
indicator of market interest rates—and the actual loan. We
assumed the project would be sold in year thirty. For tax
credits, the federal government provides investors with a flow
of tax credits over ten years. In addition, state and local
governments or private entities may provide grants or belowmarket loans. For tax credits, the present discounted value of
the development subsidies is the sum of the present discounted
value of the flow of the tax credits, any grants provided, and the
present discounted value of the flow of the interest subsidies on
any below-market loans.16
As shown in Table 2, the development subsidies for the tax
credit and Section 515 programs are generally lower than for
the HOPE VI, Section 202, and Section 811 programs, whose

total development costs are covered by federal grants. However, the development subsidies for tax credit properties in the
New York City metropolitan area are quite high. In New York
City, the city government provides all first mortgages on tax
credit projects at steep discounts, substantially increasing the
level of development subsidies. In the Los Angeles metropolitan area, state and local governments have given priority to
tax credit proposals for single-room-occupancy developments
and have provided substantial subsidies.
The development subsidies provided with production
programs have resulted in below-market rents. Although
deeper development subsidies can cover the cost of building
in certain markets or of additional amenities, deeper
development subsidies can also lower rents, making units
affordable for lower income tenants. For the HOPE VI,
Section 202, and Section 811 programs, rents need only cover
operating costs and replacement reserves, since up-front
federal grants pay the total development costs. For the tax
credit and Section 515 programs, under which rents must
cover debt service payments for the portion of the development
costs that are financed, rents are somewhat higher than for the
other production programs but are still generally below market
rents. As shown in Table 3, voucher rents, which include both
the tenant and federal contributions, are higher than rents for
the five housing production programs.

Table 3

Average Monthly Rents by General Location and for Seven Metropolitan Areas
In 1999 Dollars
Production Program
Location

Housing Vouchers

Tax Credits

HOPE VI

Section 202

Section 811

Section 515

340
350
300

320
340
280

380

380
420
470
310
290
380
490

250
470
450
310
350
440
550

Nation
Metro
Nonmetro

610
650
440

540
530
450

430
430

Seven metro areas
Baltimore
Boston
Chicago
Dallas-Fort Worth
Denver
Los Angeles
New York City

630
880
640
650
710
730
750

510
820
500
670
700
440
430

c
c
c
c
c
c

380

d
d
d
d
d
d

For vouchers, the average rent does not include a monthly administrative fee, which, at the national level, averages about $48 per unit and, in the seven
metropolitan areas, ranges from $42 per unit in Denver to $61 per unit in Los Angeles.
b
c

Our estimate of HOPE VI rent is based on the national average operating subsidy plus tenant contribution.

For individual metropolitan areas, reliable cost data were not available.

Because Section 515 units are located in rural areas, rent data are presented for nonmetropolitan areas only.

FRBNY Economic Policy Review / June 2003

153

Unlike the production program rents, which have been
reduced by development subsidies, the voucher rents are
consistent with market rents. The size of the voucher subsidy is
determined generally by the difference between the unit’s
rent—generally not to exceed the fair market rent (FMR)—and
30 percent of tenant income. FMRs are set by HUD for local
markets countrywide to reflect the rent for modest housing.
They represent the 40th percentile of the distribution of rents
paid by recent movers for units of a given size. For example, in
1999, the FMR for a two-bedroom unit in Chicago was $735;
rents for units occupied by voucher recipients averaged about
$605 for a two-bedroom unit in Chicago, 18 percent below the
FMR.

5. Total Costs of Production
Programs and Vouchers
In both metropolitan and nonmetropolitan areas, the average
total per-unit cost of each of the production programs exceeds
the cost of providing a voucher for a unit with the same
number of bedrooms. To control the impact of unit size on
costs, we compared the costs of units with the same number of
bedrooms across programs. We focused on one- and twobedroom units because they are provided under most of the
programs and generally account for more than 60 percent of
each program’s units. (We could not include HOPE VI, the
program with the largest average unit size, in this analysis
because data were not available to present total cost by unit
size.) As shown in Chart 5, in metropolitan areas, the total

thirty-year life-cycle costs range from $139,520 for vouchers
to $166,610 for tax credits. Compared with vouchers, the
production programs cost from 8 percent more for Section 811
units to 19 percent more for tax credit units. In nonmetropolitan areas, the life-cycle costs range from $95,890 for
vouchers to $138,060 for tax credits, and, compared with
vouchers, the production programs cost from 35 percent more
for Section 811 units to 44 percent more for tax credit units.17
The drop in total cost from metropolitan to nonmetropolitan areas for one-bedroom units is greatest for the voucher
program. Vouchers in nonmetropolitan areas cost 31 percent
less than vouchers in metropolitan areas. For the production
programs, nonmetropolitan units cost from 14 percent less
than metropolitan units under Section 811 to 17 percent less
under tax credits.
As shown in Chart 6, examining the costs of two-bedroom
units yields similar results. In metropolitan areas, the total
thirty-year life-cycle costs range from $161,650 for the voucher
program to $184,130 for the tax credit program. Compared
with vouchers, the production programs cost from 6 percent
more for Section 811 units to 14 percent more for tax credit
units. In nonmetropolitan areas, the production programs cost
from 20 percent more for Section 515 units to 38 percent more
for tax credit units. Again, the drop in total costs from
metropolitan to nonmetropolitan areas for two-bedroom units
is greatest for the voucher program.
For units with more than two bedrooms, cost data were
available for two programs—tax credits and vouchers. We
estimate that the total cost of three-bedroom units in
metropolitan areas is about $203,510 for tax credits and

Chart 6

Estimated Total Thirty-Year Costs of Two-Bedroom
Units by General Location

Chart 5

Estimated Total Thirty-Year Costs of One-Bedroom
Units by General Location

Thousands of dollars
200

Metro

Nonmetro

Metro

Nonmetro

Section 811

Section 515

150

150
100
100
50
50
0
Vouchers

Tax credits

0
Vouchers

Tax credits

Section
202

Section
811

Section
515

Note: Because Section 515 is a rural program, we present our cost
estimate of Section 515 for nonmetropolitan areas only.

154

Comparing the Costs of Federal Housing Assistance Programs

Notes: Section 202 is not included in this analysis because it produces
mainly efficiencies and one-bedroom units. Because Section 515 is
a rural program, we present our cost estimate of Section 515 for
nonmetropolitan areas only.

$196,470 for vouchers—a difference of about 4 percent. In
nonmetropolitan areas, the total cost is about $179,400 for tax
credits and $131,580 for vouchers—a difference of about
36 percent.
In the seven metropolitan areas we reviewed, one- and twobedroom production program units are also more expensive
than one- and two-bedroom voucher units, respectively.
However, as Chart 7 shows, there is considerable variation
across metropolitan areas. In Boston, for example, the
differences in costs between vouchers and production
programs are small; the costs of one-bedroom tax credit units,
on average, are 7 percent greater than the costs for onebedroom voucher units. In contrast, in Denver, tax credit units
are nearly 40 percent more costly than voucher units. Across
production programs, total costs are quite similar in Baltimore
and Boston. In Denver and Los Angeles, however, the variation
in production program costs is considerably greater.
We could not include the HOPE VI program in Charts 5-7
because data were not available to present total costs by unit
size. However, the total cost of an average HOPE VI unit, with
2.4 bedrooms, is $223,190, which includes only housingrelated construction costs. We estimate that the average
voucher cost of a 2.4-bedroom voucher unit is $175,577.18
According to these estimates, the HOPE VI program is about
27 percent more expensive than the voucher program. If the
cost of remediation, demolition, construction of housing and
community facilities, relocation, and community-based
planning and participation—in addition to housing-related
construction costs—were included, the total thirty-year cost of
the program would be $248,720, or 42 percent more expensive
than vouchers.

Chart 7

Estimated Total Thirty-Year Costs of One-Bedroom
Units for Seven Metropolitan Areas
Thousands of dollars
250

Vouchers

Tax credits

Section 202

Section 811

200
150
100
50
0
e
or

lt
Ba

sto

ica

s
r
srk
ele
lla rth enve
Yo
ng
Da t Wo
w
D
A
e
r
s
N
Fo
Lo

With the exception of HOPE VI, the average total costs are
very similar across production programs. For one-bedroom
units in metropolitan areas, the average thirty-year cost of the
most expensive program (tax credits) is 10 percent greater than
that of the least expensive one (Section 811). In nonmetropolitan areas, the difference in the average total cost for
one-bedroom units between the most expensive program (tax
credits) and the least expensive one (Section 811) is even
smaller—only 6 percent. The average total costs of twobedroom units are also similar across production programs in
metropolitan and nonmetropolitan areas.
The total cost of HOPE VI, however, varied greatly from the
other production programs. When we estimated only housingrelated construction costs, the average total cost for all units
under the HOPE VI program was about 35 percent greater
than a two-bedroom tax credit unit and 10 percent greater than
a three-bedroom tax credit unit. If all other construction costs
were included, it would increase the spread in total cost
between HOPE VI and tax credits by roughly 15 percentage
points.
Total per-unit costs of the voucher and production
programs vary across individual properties, even within the
same metropolitan area. This is primarily because of variations
in the rents charged for the voucher program and the
development costs for the production programs.19 For
example, in the Boston metropolitan area, the market rents for
two-bedroom voucher units range from about $540 to $1,300
per month, and the average total development costs of twobedroom tax credit units range from about $44,800 to $293,340
per unit.
Neighborhood characteristics may influence market rents
and total development costs (in particular, the value of land).
Under the voucher program, variations in market rents within
a metropolitan area for similar-sized units may be influenced
by neighborhood differences such as quality of schools, crime
rates, pollution, and proximity to jobs and shopping centers.20
Market rents may also be influenced by the quality of the
property and the amenities and services offered. Under the
production programs, variations in total development costs
within a metropolitan area reflect not only differences in
neighborhoods but also in property and unit amenities, project
sponsors, program requirements, and a host of other factors.21
For HOPE VI and tax credits, we find high-cost properties
located in very low-income neighborhoods where market rents
would be insufficient to generate new construction. Often,
production programs, by design, build housing in
neighborhoods where the market would not. There may be
additional costs of building in these neighborhoods. Additional
costs may also result from compliance with federal wage and
hiring regulations. According to HUD, all HOPE VI

FRBNY Economic Policy Review / June 2003

155

developments must follow these federal regulations, including
the Davis-Bacon Act and Section 3 requirements to hire small
and minority contractors. In addition, HOPE VI must follow
resident participation requirements. For example, in an
interview with the authors, HOPE VI program officials report
that Davis-Bacon alone, which requires construction workers
to receive locally prevailing wages and fringe benefits, can
increase construction costs by as much as 25 percent,
depending on the local construction labor market. Finally,
higher costs can result from participation of less experienced
developers, such as housing authorities or neighborhood
groups that may be less efficient than larger developers who
have better construction management capacity.22 For example,
HOPE VI officials recognized that, unlike private-sector
developers, many housing authorities hire program managers
and construction managers to oversee HOPE VI
developments, which can increase costs. Nonetheless, it is
doubtful that these factors alone account for the high costs of
the most expensive projects in our database, some of which
exceed $200,000 per unit.
Actual total costs for the production programs are
somewhat higher than our estimates because our estimates do
not reflect the value of abated property taxes or shortfalls in
capital reserves. Under each production program, some
properties receive tax abatements, and, historically, sufficient
reserves for capital replacements and improvements have not
been set aside.23 Although data were not available to estimate
the additional costs of property tax abatements and capital
reserve shortfalls for individual properties, we estimated, on
the basis of industry averages, that under a worst-case scenario
(for example, full tax abatements and no payments to reserves),
the thirty-year total costs would be understated by nearly
15 percent.24 This scenario is most applicable to the HOPE VI
program, in which full property taxes are not paid and capital
reserves are not fully funded. Under the other four production
programs, many properties fund capital reserves and pay full
property taxes. For these programs, our cost estimates are likely
to be understated by less than 15 percent.
Overall, our cost comparisons show the voucher program to
be less expensive than production programs, a result consistent
with the previous literature. However, in general, our results
show a smaller gap between voucher costs and production costs
than in many of the previous studies. This difference may be
due, at least in part, to differences in methodology. Many of the
earlier studies compared costs in the first year rather than over
the life cycle. For example, Mayo (1980) estimated that the
costs of new construction programs exceeded existing housing
by 82 percent, a figure often cited. This estimate is based on
first-year costs. However, that study also provides forty-year
life-cycle estimates that show production costs ranging from

156

Comparing the Costs of Federal Housing Assistance Programs

29 percent to 46 percent more than existing housing.25 In
addition, the production programs examined in previous
studies are very different from those included in this analysis.
Today’s production programs may be more efficient than
previous production programs.

6. The Federal Government and
Tenants Pay the Largest Shares
of Total Costs
The federal government pays most of the total costs for all
of the programs with the exception of tax credits, for which
tenants pay the largest share of total costs. As Chart 8 shows,
the federal share, as a percentage of total thirty-year costs, is
about 65 percent for vouchers; 60 percent for Section 515; and
70 percent for HOPE VI, Section 202, and Section 811. The
federal share is the smallest for tax credits—about 40 percent.
As Chart 8 also shows, tenants contribute between
21 percent (HOPE VI) and 54 percent (tax credits) of the total
housing costs over a period of thirty years. The tenant share for
each of the programs is dependent on the average income of the
households served and the average portion of this income paid
for rent. The more the assisted households pay, the less the
federal government needs to contribute.

Chart 8

Shares of Total Thirty-Year Costs of One-Bedroom
Units Paid by the Federal Government, Tenants,
and Others
Other
Tenant
Federal

Percent
100
80
60
40
20
0
Vouchers

Tax
credits

HOPE VI Section
202

Section
811

Section
515

Notes: The cost shares for HOPE VI are for all units, not onebedroom units, because the program does not break out costs by the
number of bedrooms. The chart presents data on average cost shares
for the nation, which are similar to those for metropolitan and
nonmetropolitan areas. “Other” includes state, local, and private
funding sources.

As Chart 9 indicates, compared with the other programs, tax
credit households have the highest average income, about
$14,150 (in 1999 dollars),26 and pay the largest portion of their
income for rent—about 35 percent overall—compared with
about 30 percent for most of the households assisted through
the other programs.27 As a result, the tenant share of total costs
is the largest for the tax credit program. The other active
housing programs target households with lower average
incomes, and, therefore, tenants in these programs pay a
smaller share of the average total per-unit costs. Most of these
households receive rental assistance and generally pay about
30 percent of their income for rent, leaving the federal
government and, to a far lesser extent other subsidy providers,
to cover the remaining costs. Chart 9 displays the average
incomes of the households assisted through the six active
programs.
If we assume that voucher households have incomes equal
to those in the tax credit program28 and if both groups of
tenants pay the same percentage of their income for rent, it
would cost the federal government about 30 percent more for
the tax credit program than for housing vouchers for a onebedroom unit in metropolitan areas (Chart 10). Similarly, if
the average incomes of the other production programs and
voucher households are equal and if both groups of tenants pay
the same percentage of their income for rent, it would cost the
federal government, in metropolitan areas, from 7 percent
more for Section 811 to 16 percent more for Section 202 for
one-bedroom units over thirty years. For two-bedroom units,
it costs the federal government, in metropolitan areas,

2 percent more for Section 811 and 15 percent more for tax
credits. The federal cost of an average-size HOPE VI unit
(2.4 bedrooms) is 24 percent more than vouchers, and if all
costs including housing-related expenses were considered, the
federal cost of HOPE VI would be 43 percent more.29 We also
estimated the federal cost of three-bedroom units, where data
were available, and found that tax credit units in metropolitan
areas cost the federal government 3 percent less than vouchers.
In nonmetropolitan areas, the differences in the
comparative federal cost of vouchers and production programs
are greater. For example, the federal cost of one-bedroom tax
credit units is about 180 percent more than the federal cost of
vouchers in nonmetropolitan areas, compared with about
30 percent more in metropolitan areas (Chart 11). The thirtyyear federal costs for the other production programs are from
57 percent (Section 811) to 67 percent (Section 202) greater
than for vouchers in nonmetropolitan areas. For two-bedroom
units, it costs the federal government, in nonmetropolitan
areas, 103 percent more for tax credits. For the other programs,
the federal costs in nonmetropolitan areas are 28 percent
greater for Section 811 and 39 percent greater for Section 515.
Finally, the federal cost of three-bedroom tax credit units in
nonmetropolitan areas is 102 percent more than vouchers.
Contributions from state, local, and private sources, as
shown in Chart 8, cover a small share of the total costs of the
production programs.30 At the national level, these contri-

Chart 10

Comparison of Federal Cost of One-Bedroom
Units in Metro Areas
Production Programs versus Vouchers Adjusted
for Household Income and Rent Burden

Chart 9

Average Annual Incomes of Households Served
under the Six Active Programs

Thousands of dollars
200

Thousands of dollars

Adjusted vouchers

Production programs

16
150

14
12

100

10
8

6
4

2
0

Tax credits
Tax
credits

Vouchers Section HOPE VI Section
202
515

Section
811

Sources: U.S. Department of Housing and Urban Development,
Multifamily Tenant Characteristics System and A Picture of
Subsidized Households; Rural Housing Service agency officials;
GAO/GGD/RCED-97-55.

HOPE VI

Section 202

Section 811

Notes: Because Section 515 properties are located in rural areas, they
are not included in this chart. Due to data limitations, HOPE VI cost
data reflect the average for all units, not one-bedroom units. It is not
appropriate to compare across production programs because
the assumed tenant rental contribution for housing vouchers is
different for each production program.

FRBNY Economic Policy Review / June 2003

157

Chart 11

Comparison of Federal Cost of One-Bedroom
Units in Nonmetro Areas
Production Programs versus Vouchers Adjusted
for Household Income and Rent Burden
Thousands of dollars
120

Adjusted vouchers

Production programs

100
80
60
40
20
0
Tax credits

Section 202

Section 811

Section 515

Note: Because HOPE VI properties are located exclusively in metro
areas, they are not included in this chart. It is not appropriate to
compare across production programs because the assumed tenant
rental contribution for housing vouchers is different for each
production program.

butions do not exceed, on average, 7 percent over thirty years.
This percentage, however, would be somewhat higher if data
were available to account for the impact of property tax
abatements, as previously discussed in this paper.
Even though the share of total costs paid by these sources is,
on average, small, we identified state and local subsidies that, in
certain locations, had a significant impact on rents or federal
costs. For example, a comparison of the subsidies provided to
properties in the New York City and Boston metropolitan areas
demonstrates the impact of a significant nonfederal subsidy. As
shown in Table 4, the average contribution from state, local,
and private sources for a two-bedroom tax credit unit was
more than five times greater in New York City than in Boston.

At the same time, both the total and federal per-unit costs were
about the same for both cities. Because of the difference in
subsidies from state, local, and private sources, the average
monthly rent paid by a tax credit household was about $850 in
Boston and about $450 in New York City—a difference of
nearly 90 percent. The primary reason for the difference in tax
credit rents is that New York City provides virtually all of the
mortgages for tax credit properties, at rates averaging about
1 percent—a very significant subsidy. Conversely, in the
Boston metropolitan area, the state provides about two-thirds
of the mortgages at interest rates that are very close to market
rates. In addition, rent reductions resulting from state and local
subsidies present opportunities to lower the federal cost of
providing rental assistance to these units.
Our data also allow us to compare the total government
(federal, state, and local) costs of production programs and
vouchers, while making the same assumptions concerning
household income and rent burdens as in the federal cost
comparisons.31 Given the emphasis placed on “leveraging”
different sources of funding by many of the production
programs (including, most recently, Section 202), analyzing
total government costs offers some perspective on public
expenditures on affordable housing. Compared with vouchers,
total government costs for a one-bedroom unit under the
production programs in metropolitan areas are higher by
12 percent for Section 811, 20 percent for Section 202, and
53 percent for tax credits. The total government costs for an
average-size unit under HOPE VI are 37 percent greater than
the cost for vouchers. In nonmetropolitan areas, the total
government costs for a one-bedroom unit under the
production programs, compared with vouchers, are higher by
60 percent for Section 811, 67 percent for Section 202,
75 percent for Section 515, and 214 percent for tax credits. The
differentials in total government costs are similar for twobedroom units.

7. Housing Policy Issues
Table 4

Impact of Contributions from State, Local, and
Private Sources on Thirty-Year Average Costs of
Two-Bedroom Units for Tax Credit Properties
In Dollars

158

Location

Federal

State, Local,
and Private

Tenant

Total

Boston
New York City

100,060
92,450

10,180
58,520

153,740
81,730

263,980
232,700

Comparing the Costs of Federal Housing Assistance Programs

If costs were the only consideration, our estimates would
suggest that the production programs should be replaced with
vouchers. However, federal housing programs deliver
additional benefits that must be taken into account when
addressing costs. Voucher recipients can choose housing in
neighborhoods that offer better educational and employment
opportunities, or they can choose to remain in place while
paying less for rent. In many markets, production programs are

the only sources of new affordable rental units, and restrictions
on use will keep these units affordable for decades to come,
limiting the impact of market forces. These units can be crucial,
especially when housing markets are tight or landlords are
unwilling to rent to voucher recipients. Certain housing
authorities have found that the fair market rents in some
metropolitan areas are too low, making it difficult for voucher
recipients to find housing. As a result, vouchers are being
returned to housing authorities. A 2001 HUD study found that,
based on a sample of forty-eight metropolitan areas, about
one-third of the households who received vouchers in 2000
were not able to lease a unit—a substantial increase from
HUD’s 1993 estimate of 19 percent.32
In addition, there are substantial differences in the housing
and services provided under each of the production programs
that must also be considered. For example, the Section 202 and
Section 811 programs make available services that are not
readily found in affordable housing in the private rental
market. These services can be particularly important for frail,
elderly residents or persons with disabilities for whom housing
vouchers are probably not a reasonable alternative. As the
nation’s population ages, production programs for the elderly
may become an even more important part of national housing
policy. Finally, in many urban areas, the production programs
have formed an integral part of an overall community
development strategy. As a matter of public policy, the benefits
of mobility, increasing the supply of affordable units,33
providing additional services for special-needs populations, or
revitalizing distressed communities must be weighed against
the costs of these efforts.
As shown in this paper, the federal government and tenants
cover the majority of costs for both the voucher and
production programs. The share of costs covered by the federal
government increases as tenant income declines. The bottom
line is that housing very low-income households is expensive
for the federal government under both the voucher and
production programs because those tenants can shoulder only
a very small portion of the costs. To shift more of the cost
burden to tenants without creating an affordability problem,
the programs would have to serve higher income households.
In some instances, increasing contributions from state and
local sources may be an option for limiting federal expenditures for some of the production programs, as our discussion
of New York City’s mortgage interest subsidy indicates.
Substantial subsidies from these sources could eliminate or
reduce the need for federal rental assistance, freeing federal
funds to assist other households. However, state and local
governments vary in their ability and willingness to support
affordable housing. Federal incentives, such as additional tax

credit or grant awards for major financial commitments, might
promote greater nonfederal participation.
Further research on the adherence of projects to costcontainment guidelines could identify opportunities for
controlling development costs. Our data on the production
programs show wide variation in the development costs of
projects under the same program in the same metropolitan
area. Although the higher costs of some units reflect the cost
differential between new construction and rehabilitation or the
premiums paid for special features, the reasons for the higher
costs of other units are less obvious. Understanding the
considerable variation in per-unit costs requires more research
on the determinants of development costs and the effectiveness
of current cost-containment guidelines. To the extent that a
property’s development costs can be contained and a
production program’s objectives still achieved, federal dollars
can go further.
Further research on the adequacy of the production
programs’ capital replacement reserves would put the federal
government in a better position to manage potential long-term
cost risks. As we previously noted, the production programs
could pose a cost risk to the federal government if capital
reserves are underfunded. The experience with modernization
programs for public housing and other production programs
suggests that this cost risk can be large. It is still too early to tell
whether tax credit properties will suffer from capital shortfalls
as the properties age. However, even if there are shortfalls, the
structure of the tax credit program may limit the risk to the
federal government. The government does not own the units or
hold the mortgages on most of them. As a result, it is not clear
what the potential role of the federal government would be if
these units were to need an infusion of capital. It is possible
that, as the ownership of tax credit properties changes over
time, new owners will apply for tax credits to rehabilitate the
properties. However, their applications will have to be assessed
by the relevant state agencies, which will have no statutory
obligation to provide the credits.
Our analysis for this paper, which required detailed,
consistent data on housing characteristics, services, and costs
for the six active programs, relied on information collected and
centralized by HUD and the Rural Housing Service but was
hampered by gaps in the data for some programs. For example,
HUD’s centralized data on the Section 202 and Section 811
programs do not include information on the sources of funds
other than the capital advance. For the HOPE VI program, data
were available on total costs and on HUD’s portion of the total
costs, but information on tax credits and state, local, and
private funds was limited.34 To varying degrees, HUD and RHS
have data on tenant characteristics and on property revenues

FRBNY Economic Policy Review / June 2003

159

and expenses. Cooperation and coordination across federal
agencies to establish standards for collecting data on housing
programs would facilitate the development of information to
further our understanding of federal housing programs.
For the tax credit program, no federal agency is responsible
for collecting and centralizing data from the state and local
housing finance agencies that administer the program.
Although the Internal Revenue Service oversees compliance
with the federal regulations for using tax credits, it does not
oversee the program’s impact on national housing policy,
including its relationship to other federal housing programs.
Recognizing the importance of the tax credit program, HUD
established a limited national database on tax credit properties.
This database has information, which the housing finance
agencies have voluntarily reported to HUD, on the properties
placed in service through 1998, including their location,
number of units, number of bedrooms per unit, type of
construction (new or rehabilitated), and type of sponsor
(nonprofit or for-profit). However, HUD’s database does not
include information on tenant characteristics, project costs,
and property operating revenues and expenses. These data,
though generally available from the housing finance agencies,
have not been centralized, making analysis and evaluation of

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Comparing the Costs of Federal Housing Assistance Programs

the program difficult. As a result, for this paper, we relied on a
database constructed by a private research firm.
Given the size of the tax credit program—soon to exceed
$4 billion per year—it is important to monitor and evaluate the
program’s impact on national housing policy. However, no
federal agency has been designated to perform this role, and no
requirements have been established for state finance agencies
to report data on project costs and households served.
Accordingly, there is a need for a national, centralized database
on the tax credit program to serve as the basis for evaluating the
program’s success in serving various populations, assessing
how federal funds are being used, determining to what extent
other sources of funding are being leveraged, gauging projects’
compliance with cost-containment guidelines, and monitoring
projects’ ongoing and long-term financial viability. To develop
this database, a federal agency would have to be explicitly
designated as responsible for collecting the information and
establishing reporting requirements for the housing finance
agencies that manage the program. The costs and benefits of
designating such an agency and requiring more detailed
reporting by the housing finance agencies would have to be
weighed before any action is taken.

Appendix: Data Sources

Housing Vouchers
We obtained from the U.S. Department of Housing and Urban
Development (HUD) data on gross rents, housing assistance
payments, tenant contributions, and incomes for the housing
voucher and certificate programs for about 1.4 million
households participating in the programs in 2000 from the
Multifamily Tenant Characteristics System. We also collected
information from HUD and individual housing authorities on
the average administrative fee paid to housing authorities.

properties in the seven metropolitan areas constituted about
20 percent of the units in our HOPE VI inventory. The HOPE VI
program also funds various types of activities (for example,
property demolition, tenant relocation, and community services)
in addition to housing-related construction. We estimated both
housing-related and all costs for the HOPE VI program.
In general, HUD does not have public housing data on
revenues and expenses on a property-by-property basis. This
information is also not available for the HOPE VI program.
Consequently, to estimate a national rent for the HOPE VI
program, we obtained from HUD the average tenant rental
contribution and operating subsidy paid by HUD for all public
housing units. Together, these payments constitute an
approximation of a traditional rental payment.

Low-Income Housing Tax Credits
The tax credit program is decentralized by nature, which means
there is no national database to evaluate the program’s
characteristics, including costs. Consequently, we relied
extensively on rent and development cost data collected and
analyzed by City Research, a private research firm in Boston.
City Research assembled and analyzed detailed data on more
than 2,500 tax credit properties, with more than 150,000 units,
that were acquired by four national syndicators.35 These units
were estimated to represent about 25 percent to 27 percent of
those generated under the program from 1987 through 1996.36
City Research’s data were supplemented with data we collected
on tax credit properties placed in service in 1999 within the
seven metropolitan areas.

HOPE VI
We obtained from HUD data on the total development costs for
130 planned and completed HOPE VI developments, which
contained about 63,560 planned units as of 2000. Approximately
10 percent of these properties were either completed or
substantially completed. HOPE VI properties use multiple
sources of funding, but the data were not sufficiently detailed to
break out funding by individual sources other than HUD. For
properties in the seven metropolitan areas, we contacted public
housing authorities and were able to obtain complete data on
their sources of funds. For our national cost estimate, we based
the distribution of costs paid by state, local, and private entities on
the actual cost shares in our seven metropolitan areas. The

Section 202 and Section 811
HUD identified about 135 properties, comprising about 6,040
units that were placed in service nationwide in fiscal year 1998
under the Section 202 program, and about 115 properties,
comprising about 1,420 units, under the Section 811 program.
From the list provided, we contacted thirty-nine HUD field
offices to get detailed data on the properties’ total development
costs and the sources of funds used to pay these costs. We also
obtained data from the field offices on properties’ rents. Most of
the seven metropolitan areas did not have enough properties
placed in service in 1998 for us to compute meaningful averages
for development costs and rents. Consequently, we asked the
field offices to identify the properties placed in service from 1996
to 1999 to ensure that we would have at least four properties
under each program to compute such averages better.

Section 515
Rural Housing Service state offices identified 53 Section 515
properties, containing about 1,250 units, which were placed in
service in fiscal year 1998. The state offices provided data on
total development costs, including the sources and terms of
funds used to finance these costs. The state offices also
provided information on 1999 rents. We excluded Section 515
from our analysis of the seven metropolitan areas because it is
a rural program.

FRBNY Economic Policy Review / June 2003

161

Endnotes

1. Federal rental assistance programs define “low-income” households as those with incomes below 80 percent of the area median
income and “very low-income” households as those with incomes
below 50 percent of the area median income.
2. See Keyes and DiPasquale (1990).
3. This analysis does not treat the HOME Investment Partnerships
program as a separate production program because HOME grants are
often used in conjunction with other housing production programs.
The HOME funds provided with the production programs discussed
in this paper are included in our analyses of these programs’ costs.
4. HOPE VI is actually a modernization program. In this paper, we
classify HOPE VI as a production program because it is currently the
only major construction effort in public housing. Since 1996, public
housing has not received new appropriations to fund the development
of new, incremental units.
5. The Section 202 Direct Loan program, which is no longer active,
developed more than 200,000 units for elderly households and, to a
lesser extent, for persons with disabilities. In 1990, Congress converted
Section 202 to a grant program and established the Section 811
program to provide housing for persons with disabilities.
6. We include outlays for rental assistance provided to Section 515
units under the Section 521 program. Section 521 is a Rural Housing
Service (RHS) program within the U.S. Department of Agriculture
that provides rental assistance to nearly all units currently developed
under Section 515.
7. See Weicher (1990).
8. See Wallace et al. (1981).
9. See Olsen (2000, 2001).
10. The studies reviewed include U.S. Department of Housing and
Urban Development (1974), Mayo et al. (1980), Olsen and Barton
(1983), and Wallace et al. (1981).
11. This average does not include tax credit properties with
Section 515 mortgages. The average size of tax credit properties with
Section 515 mortgages is thirty-three units. The average size of all tax
credit properties is fifty-nine units.

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Comparing the Costs of Federal Housing Assistance Programs

12. This percentage excludes tax credit units in properties with
Section 515 mortgages because we included these units in our
calculations for the Section 515 program. If these units were included
in our calculations for tax credits, the percentage of units in nonmetropolitan areas would increase to about 22 percent from about
6 percent.
13. For all of the present value calculations, we assumed a discount
rate of 6 percent, which was the government cost of funds according
to 1999 data published by the Office of Management and Budget.
14. We did not include the costs incurred by federal agencies (HUD,
the Rural Housing Service, and the Internal Revenue Service) to
administer and monitor the programs, since these costs are not
identified in sufficient detail in the agencies’ records. However, we
believe these costs to be extremely small relative to those costs that we
have accounted for. In addition, we did not include the cost to the
government in forgone taxes due to depreciation because the rationale
for the depreciation deduction in tax law is to permit investors to
realize the real costs associated with a structure’s wearing out over
time. However, to the extent that a building’s tax life (27.5 years) is
generally shorter than its economic life, some portion of the
depreciation benefit may be viewed as a subsidy.
15. For this analysis, we assumed a 3 percent rate of annual rent
inflation based on a ten-year average national rate for rental housing
according to the consumer price index. Although we assumed the
same annual rate of rent inflation for both production programs and
vouchers, production program rents tend to be lower than voucher
rents because of development subsidies (see Table 3). As a result,
voucher costs rise more with rent inflation than production costs.
With rent inflation, increasing the number of years for the analysis
decreases the difference in total costs between production programs
and vouchers.
16. We estimated the interest subsidies using the same procedure we
used for Section 515 below-market loans.
17. As discussed in the previous section, these estimates assume
annual rent inflation of 3 percent. In U.S. General Accounting Office
(2002), we estimate program costs using different assumptions about
the rate of rent inflation. Assuming a higher rate of rent inflation
narrows the gap in costs between vouchers and the production
programs; lower rent inflation widens the gap.

Endnotes (Continued)

18. We took the actual voucher rents for two- and three-bedroom
units and interpolated a rent consistent with the average bedroom size
of 2.4 for the HOPE VI program.
19. For some of the programs reviewed, variances in the costs of
individual properties in certain locations can also be due to their small
sample sizes.
20. A detailed discussion of the impact of housing characteristics and
public amenities on housing rents is found in DiPasquale and
Wheaton (1996, chapters 3, 4, and 14).
21. For a discussion of the impact of property and neighborhood
characteristics on total development costs for the tax credit program,
see Cummings and DiPasquale (1999) and U.S. General Accounting
Office (1999). For more information, HUD’s Office of Policy
Development and Research (1982) measured the differences in total
development costs among the inactive housing production programs.

25. Life-cycle analysis narrows the gap between voucher costs and
production costs because of the impact of rent inflation on voucher
costs. The U.S. General Accounting Office (2002, p. 54) reported
first-year and life-cycle costs for each of the programs by unit size. The
total first-year costs for two-bedroom tax credit units in metro areas
were 35 percent greater than the same costs for two-bedroom voucher
units. The total thirty-year life-cycle costs for two-bedroom tax credit
units were 20 percent more than the same costs for two-bedroom
voucher units.
26. The tax credit program serves two distinct groups. The first group,
which we estimate includes about 40 percent of tax credit households,
has an average income of $8,350 (in 1999 dollars), comparable to the
average incomes of households assisted through the other active
programs. This group receives rental assistance and pays about
30 percent of its income for rent. The second group, however, has a
larger average income of $17,750, does not receive rental assistance,
and faces much higher rent burdens, sometimes exceeding 50 percent
of its income. See U.S. General Accounting Office (1997).

22. Also see Cummings and DiPasquale (1999, pp. 260-1).
23. One HUD study estimates that modernization needs of public
housing are nearly $20,000 per unit. If these needs were met, the
ongoing annual accrual needs of public housing are estimated at
almost $1,700 per unit. See Finkel et al. (2000). However, given the
unique nature of public housing, its history may not shed much light
on the future of other current programs. Perhaps more relevant,
another HUD study estimates that the annual accrual needs of Federal
Housing Association (FHA)-insured multifamily properties are
almost $1,100 per unit. In addition, see Finkel et al. (1998).
24. This percentage represents an increase of $35,220 to the total
thirty-year cost of $223,190 for the HOPE VI program. Our estimate
of this increase is based on the national average property tax rate of
$11 per $1,000 of property value, according to the 1999 American
Housing Survey (U.S. Census Bureau 1999), and an annual set-aside
of $600 per unit. About 25 percent of this increase is attributable to
shortfalls in capital reserves and 75 percent to property tax
abatements. Interviews with industry officials indicate that annual
set-asides for new construction under the tax credit program are about
$300 per unit. HUD officials, however, argue that the history of public
housing and other federal multifamily housing programs suggests that
a set-aside of about $1,000 per unit is more appropriate. When an
annual shortfall of $300 per unit is assumed and no changes are made
to the property tax abatement estimates, our total thirty-year cost
estimate increases by 14 percent. When $1,000 per unit is assumed,
our total thirty-year cost estimate increases by 18 percent.

27. According to the U.S. General Accounting Office (2000, pp. 6-7)—
its most recent report on tax credits—about 57 percent of tax credit
households paid 30 percent or less of their income for rent, about
21 percent paid between 31 and 40 percent, about 8 percent paid
between 41 and 50 percent, about 8 percent paid more than
50 percent, and 5 percent paid an unknown percentage.
28. Since differences in household incomes and rent burdens can have
a significant impact on federal costs, we adjusted the rent paid by the
voucher household to equal the rent paid by the tax credit household.
We also made similar adjustments for the comparisons between
vouchers and the other production programs.
29. Because data for the HOPE VI program are not available by unit
size, we followed the approach used in U.S. General Accounting Office
(2001) to estimate the program’s federal cost. For the other programs,
we were able to compare costs across different unit sizes.
30. These contributions are not applicable to the voucher
program.
31. Our estimate of total government costs may include private
subsidies. However, these subsidies generally make up a very small
fraction of the total cost of the programs.
32. See U.S. Department of Housing and Urban Development
(2001).

FRBNY Economic Policy Review / June 2003

163

Endnotes (Continued)

33. A 1999 study measured the impact of subsidized housing for
moderate-income households and for low-income households. It
found that moderate-income, subsidized housing most likely adds
little or nothing to the total housing stock. In contrast, low-income
subsidized housing (public housing) has steadily added to the total
stock of housing since its inception in 1935. See Murray (1999).
34. HOPE VI program officials, however, are revising their data
collection procedures to provide more details on all sources of funds.
35. The four syndicators were Boston Capital Partners, Inc., Boston
Financial, Enterprise Social Investment Corporation, and the National

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Comparing the Costs of Federal Housing Assistance Programs

Equity Fund, Inc. Each of these syndicators has a national portfolio
and has been active in the tax credit market throughout the tax credit
program’s history.
36. See City Research (1998) for results of its analysis of these data and
Cummings and DiPasquale (1999). Comparisons of the City Research
data with those collected by the U.S. General Accounting Office (1997)
indicate that City Research’s data are quite representative of the
program nationally.

References

City Research. 1998. “Building Affordable Rental Housing: An
Analysis of the Low-Income Housing Tax Credit.” February.
Available at <http://www.cityresearch.com>.

Olsen, Edgar O., and David M. Barton. 1983. “The Benefits and Costs
of Public Housing in New York City.” Journal of Public
Economics 20: 299-332.

Cummings, Jean L., and Denise DiPasquale. 1999. “The Low-Income
Housing Tax Credit: The First Ten Years.” Housing Policy
Debate 10, no. 2: 257-67.

U.S. Census Bureau. 1999. “American Housing Survey for the United
States: 1999.” Current Housing Reports, Series H150/99.
Washington, D.C.: U.S. Government Printing Office.

DiPasquale, Denise, and William C. Wheaton. 1996. Urban
Economics and Real Estate Markets. Upper Saddle River,
N.J.: Prentice Hall.

U.S. Department of Housing and Urban Development. 1974. “Housing
in the Seventies.” Washington, D.C.: U.S. Government Printing
Office.

Finkel, Meryl, Donna DeMarco, Hin-Kin Lam, and Karen Rich. 2000.
Capital Needs of the Public Housing Stock in 1998.
Cambridge, Mass.: Abt Associates.

———. 2001. “Study on Section 8 Voucher Success Rates, Vol. 1.”
Washington, D.C.: U.S. Government Printing Office.

Finkel, Meryl, Donna DeMarco, Deborah Morse, Sandra Nolden,
and Karen Rich. 1998. Status of HUD-Insured (or Held)
Multifamily Rental Housing in 1995. Cambridge, Mass.:
Abt Associates.
Keyes, Langley C., and Denise DiPasquale. 1990. “Housing Policies
for the 1990s.” In Denise DiPasquale and Langley C. Keyes, eds.,
Building Foundations: Housing and Federal Policy.
Philadelphia: University of Pennsylvania Press.
Mayo, Stephen K., et al. 1980. Housing Allowances and Other
Rental Assistance Programs—A Comparison Based on the
Housing Allowance Demand Experiment, Part 2: Costs and
Efficiency. Cambridge, Mass.: Abt Associates.
Murray, Michael P. 1999. “Subsidized and Unsubsidized Housing
Stocks 1935 to 1987: Crowding Out and Cointegration.” Journal
of Real Estate Finance and Economics 18, January.
Olsen, Edgar O. 2000. “The Cost-Effectiveness of Alternative Methods
of Delivering Housing Subsidies.” Thomas Jefferson Center for
Political Economy Working Paper no. 351, December. Available
at <http://www.virginia.edu/~econ/TJpapersx.htm>.

U.S. Department of Housing and Urban Development, Office of Policy
Development and Research. 1982. “The Costs of HUD Multifamily
Housing Programs.” Washington, D.C.: U.S. Government
Printing Office.
U.S. General Accounting Office. 1997. “Opportunities to Improve
Oversight of the Low-Income Housing Program.” GAO/GGD/
RCED-97-55. Washington, D.C.: U.S. Government Printing
Office.
———. 1999. “Tax Credits: Reasons for Cost Differences in Housing
Built by For-Profit and Nonprofit Developers.” GAO/RCED-99-60,
March 10. Washington, D.C.: U.S. Government Printing Office.
———. 2000. “Tax Credits: Characteristics of Tax Credit Properties
and Their Residents.” GAO/RCED-00-51R, January 10.
Washington, D.C.: U.S. Government Printing Office.
———. 2001. “Federal Housing Programs: What They Cost and
What They Provide.” GAO-01-901R, July 18. Washington, D.C.:
U.S. Government Printing Office.
———. 2002. “Federal Housing Assistance: Comparing the
Characteristics and Costs of Federal Programs.” GAO-02-76.
Washington, D.C.: U.S. Government Printing Office.

———. 2001. “Housing Programs for Low-Income Households.”
NBER Working Paper no. 8208, April.

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References (Continued)

Wallace, James E., Susan Philipson Bloom, William L. Holshouser,
Shirley Mansfield, and Daniel H. Weinberg. 1981. Participation
and Benefits in the Urban Section 8 Program: New
Construction and Existing Housing. Cambridge, Mass.:
Abt Associates.

Weicher, John C. 1990. “The Voucher/Production Debate.” In Denise
DiPasquale and Langley C. Keyes, eds., Building Foundations:
Housing and Federal Policy. Philadelphia: University of
Pennsylvania Press.

The views expressed are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York,
the Federal Reserve System, or the U.S. General Accounting Office. The Federal Reserve Bank of New York provides no
warranty, express or implied, as to the accuracy, timeliness, completeness, merchantability, or fitness for any particular
purpose of any information contained in documents produced and provided by the Federal Reserve Bank of New York in any
form or manner whatsoever.
166

Comparing the Costs of Federal Housing Assistance Programs

Susan M. Wachter

Commentary

he paper by Denise DiPasquale, Dennis Fricke, and Daniel
Garcia-Diaz addresses a central question for the future
direction of federal housing policy: how do the costs of
delivering housing assistance vary by program? Costs are now
central to the debate on the future of federal housing programs
for several reasons. First, there appears to be agreement among
many policymakers and academics on the threshold issue of the
rationale for federal housing programs.
Market forces alone cannot assure that household incomes
are sufficient to deliver what society views as minimally
adequate for low-income households—even when these
households participate in the labor market as full-time
workers. Moreover, there is, if anything, an increased sense of
urgency to the need to address housing outcomes, since
affordable housing problems appear to be worsening, as
evidenced by recent trends. (In five of the past six years,
housing price and rent increases have exceeded overall
inflation rates.)
Second, although additional funding has been provided for
housing subsidies in recent federal budgets, far from being an
entitlement program, federal housing expenditures at current
levels reach less than one-third of those who qualify, resulting in
horizontal inequity in the delivery of federal housing subsidies.
Third, congressional leadership and the Office of
Management and Budget increasingly focus on the costeffectiveness of the delivery of all social programs. Housing is a

major federal government expenditure, as it includes the
approximately $30 billion Department of Housing and Urban
Development budget and the $3.5 billion tax credit cost of the
Low Income Housing Tax Credits (LIHTC) production
program. The efficacy of delivery has increasingly become the
center of policymakers’ attention—thus, the importance of the
authors’ findings.
Despite the salience of the question, there has been no
formal work done on this issue in the past twenty years. Indeed,
the major conclusion of the literature of the 1980s—that
vouchers are a less expensive way to deliver housing subsidies
than production programs—is partly responsible for the
cessation of the historical production programs on which these
studies were based. A different tax-incentive-based production
approach, LIHTC, was instituted in the mid-1980s. Thus, the
void in the recent literature is not because the issue has been
settled. Rather, surprisingly, there have been no public data
available to evaluate the relative costs of new production
programs and vouchers. Hence, a major contribution of this
paper is its use of a private database that allows for the
comparative analysis of these programs.
The authors make methodological strides and are
exhaustive in the implementation of the necessarily complex
process of comparing costs across very different programs. The
task is daunting. In particular, they undertake the comparison
of the ongoing rental costs of voucher programs with the

Susan M. Wachter is a professor of real estate and finance at the University
of Pennsylvania’s Wharton School.

The views expressed are those of the author and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

FRBNY Economic Policy Review / June 2003

167

construction costs (and ongoing subsidization) of productionbased programs. The process requires the appropriate
discounting and treatment of a number of cost streams
associated with the different programs. The outcomes are
subject to the discount rate chosen; there is no avoiding this.
Differences in the size and location of housing must also be
accounted for by program, to the extent that the data allow.
DiPasquale and her coauthors conclude that vouchers are less
expensive than production-based delivery of housing
subsidies, a result that is qualitatively similar to past findings.
Nonetheless, they estimate a differential that is far lower than
that found in earlier studies.
However, questions remain. First, while it is necessary to
make key assumptions to carry out the analysis—and the paper
has made its assumptions explicit with painstaking clarity—it
would be useful to undertake and present an analysis of how
sensitive the results are to the key assumptions. Besides the
discount rate, the other major assumptions that will make a
difference in outcomes are how local property taxes and setasides for capital costs are treated. Second, there are puzzling
geographic variations in the relative costs of programs. In

particular, vouchers are far less expensive than production
programs, as a group, in nonmetro areas as compared with
metro areas, where they are only somewhat less costly.
Interestingly, in Boston, a very tight market, the difference
between voucher costs and production program costs is very
small. Third—and a key question—is why is there a difference
in these results compared with earlier findings? Is it due to
differences, over time, in the structure of programs or in
market conditions, or are there methodological differences
across studies that could account for differing results?
The intriguing regional differences suggest that the tightness
of the market, and particularly whether rents have reached
construction-feasible levels, may have important effects. But
part of the explanation may lie in the evolution of the programs
themselves. As noted, the major production program analyzed
in this paper is LIHTC, which the literature suggests is both
more efficient than past public housing production programs
and is itself becoming more efficient over time. While these are
questions for subsequent studies, the authors’ empirical
findings will contribute to the current debate over the future
of housing policy.

Commentary

William C. Apgar and Mark Duda

The Twenty-Fifth
Anniversary of the
Community Reinvestment
Act: Past Accomplishments
and Future Regulatory
Challenges
1. Introduction and Summary1

he U.S. Congress passed the Community Reinvestment
Act (CRA) in 1977 to encourage depository institutions to
meet the credit needs of lower income neighborhoods. The
CRA was built on the simple proposition that deposit-taking
banking organizations have a special obligation to serve the
credit needs of the communities in which they maintain
branches. At the time of the CRA’s passage, banks and thrifts
originated the vast majority of home purchase loans. The
CRA’s initial focus on areas where CRA-regulated institutions
maintained branches made sense because restrictions on
interstate banking and branching activities were limiting the
geographic scope of mortgage lending operations.
Today, the CRA continues to provide significant incentives
for CRA-regulated institutions to expand the provision of
credit to lower income and/or to minority communities where

William C. Apgar is a lecturer in public policy at Harvard University’s John F.
Kennedy School of Government and the senior scholar at Harvard’s Joint
Center for Housing Studies; Mark Duda is a research analyst at the Joint
Center for Housing Studies.

those institutions maintain deposit-taking operations. Yet in
the quarter century since the act’s passage, dramatic changes
have transformed the financial services landscape, especially in
home mortgage lending. These changes have combined to
weaken the link between mortgage lending and the branchbased deposit gathering on which the CRA was based. Today,
less than 30 percent of all home purchase loans are subject to
intensive review under the CRA. In some metropolitan areas,
this share is less than 10 percent.
With a substantial portion of home purchase lending no
longer subject to detailed scrutiny under the CRA, the issue of
how best to modernize the CRA has emerged as an important
public policy challenge. Some argue that the CRA’s costs
exceed its benefits. Others advocate expanding regulatory
oversight. Congress considered changes to the CRA in the
debate leading up to the passage of the 1999 Gramm-LeachBliley Financial Modernization Act (GLBA), but in the end it

The views expressed are those of the authors and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

FRBNY Economic Policy Review / June 2003

169

did little to make the CRA conform to the realities of the
financial services marketplace. Although the CRA continues to
provide significant benefits to lower income households and
communities, reform is needed for the act to encourage
financial services providers to meet the continuing needs of the
communities they serve.

1.1 Summary of Key Findings
This paper draws on a more extensive Joint Center for Housing
Studies assessment of the CRA, funded by the Ford
Foundation. The larger study not only assesses the impact of
the CRA on home purchase and home refinance lending, it also
presents commentary on the CRA’s impact on small-business
and multifamily lending, as well as on the provision of financial
services more generally. In addition, the Ford Foundation
study presents qualitative findings concerning the CRA’s
impact on the operation of banks and mortgage lenders as well
as the impact on the relationship between mortgage lenders
and community-based advocacy organizations.
Our paper focuses on the regulatory and legislative
challenges that confront the act at age twenty-five. In
addition to providing a brief review of the evolution of CRA
regulations, we document the impact that the CRA has had
on home mortgage lending to lower income people and
communities and assess changes in industry structure. We
conclude with a discussion of current legislative and
regulatory challenges.

The CRA Has Expanded Access to Mortgage Capital
Working in combination with the Home Mortgage Disclosure
Act (HMDA) and the closely related Fair Housing and Fair
Lending Legislation, the CRA continues to expand access to
capital for CRA-eligible borrowers. Here, CRA-eligible
borrowers include those with an income of less than
80 percent of the area median income and/or those living in
census tracts with a median income of less than 80 percent of
the area median. CRA-regulated lenders refer to federally
regulated banks and thrifts as well as their mortgage company
and finance company affiliates.
• In both 1993 and 2000, CRA-regulated lenders operating
in their assessment areas (areas where they maintain
deposit-taking operations) had shares of conventional,
conforming prime home purchase loans to CRA-eligible
borrowers that exceeded the equivalent shares for outof-area lenders or noncovered organizations.

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• The CRA-eligible share of conventional prime lending to
blacks is as much as 20 percentage points higher for
CRA-regulated lenders operating in their assessment
areas than for independent mortgage companies. For
Hispanics, the equivalent gap is 16 percentage points.

The Changing Mortgage Industry Structure Reduces
the CRA’s Impact
Dramatic changes in the structure of the financial services
industry—and particularly in mortgage banking—have
combined to weaken the link between mortgage lending and
the branch-based deposit gathering on which the CRA was
based. Consequently, this may also be reducing the CRA’s
effect on the mortgage market.
• In 2000, the twenty-five largest lenders each made
more than 25,000 home purchase loans and accounted
for 52 percent of all home purchase loans made that
year. In contrast, only fourteen organizations made
more than 25,000 loans in 1993 and accounted for only
23.5 percent of all home purchase lending.
• Banking organizations operating out of their assessment
areas have expanded rapidly and today constitute the
fastest growing segment of the residential mortgage
market. As a result, between 1993 and 2000, the
number of home purchase loans made by CRAregulated institutions in their assessment areas as a
share of all home purchase loans fell from 36.1 percent
to 29.5 percent.
• Assessment-area lending varies from one market area
to the next. Of the 301 metropolitan areas examined in
this study, the assessment-area share of lending varies
from 6 percent in Denver, Colorado, to 74 percent in
Dubuque, Iowa.

The CRA Fails to Keep Pace with the Changing
Industry Structure
The changing industry structure, along with the fact that over
time the CRA may have expanded the capacity of all industry
players to serve lower income borrowers, has eroded CRAregulated entities’ lead in the conventional prime home
purchase market. When Congress modernized financial
services through the GLBA, it did little to bring the CRA into
conformance with the rapidly evolving world of financial
services. Reform could follow one or both of two distinct
pathways:

• Reform could build on the CRA’s traditional mortgage
lending focus by expanding assessment areas to cover a
larger share of lending by banking organizations subject
to CRA and by extending the act to include independent
mortgage companies and other newly emerging
nonbank lenders.
• Retail banking services and community-development
lending arguably remain most closely linked to the
branch banking mechanism through which CRA
obligations are defined and implemented. Reform could
therefore build on the CRA’s traditional branch banking
focus and reposition the act to give greater emphasis to
providing financial services to lower income people and
to promoting the development of lower income
communities.
Before turning to a more detailed discussion of these
findings, we briefly review the methodology used to generate
these results.

1.2 Methodology
The work presented here uses the Joint Center for Housing
Studies Enhanced HMDA Database, which combines loanlevel data on borrower and loan characteristics with data on
lender characteristics and branch locations from the Board of
Governors of the Federal Reserve System. The Federal
Reserve’s lender file contains information that facilitates
aggregation of individual HMDA reporters into commonly
owned or commonly controlled institutions that can be analyzed
as integrated units. The Board’s branch-location data are the
source of assessment-area definitions used in the analyses
presented here. For a reasonable approximation of true
assessment areas, this report assumes that if a lending entity
subject to the CRA has a branch office in a particular county,
then that county is part of that entity’s assessment area.
Loans made in counties where the lending entity does not
have a branch are assumed to fall outside of that entity’s
assessment area.
Other information on metropolitan area and neighborhood
characteristics was linked to the HMDA loan-level data to
assess the way economic, demographic, and housing market
trends influence lending. These data included U.S.
Department of Housing and Urban Development (HUD)
data used to classify loans based on both the income of the
applicant and the income of the census tract where the
property is located. HUD was also the source for the annual
listing of HMDA reporters specializing in subprime or
manufactured-home lending.

In addition to quantitative analyses, this paper draws on
qualitative information gathered during a series of discussion
groups and in-depth interviews. In the spring of 2000, the Joint
Center for Housing Studies held eleven discussion groups with
more than 100 experts in four cities—three each in Atlanta, New
York, and San Francisco, and two in Washington, D.C. (Belsky
et al. 2000). The Joint Center also conducted in-depth interviews
with more than 100 individuals in the Baltimore, Birmingham,
Chicago, and Los Angeles metropolitan areas, as well as in rural
Colorado. These interviews examined the CRA in the context of
the changing organization of the mortgage industry, the
growth of new affordable lending tools, and the resulting
changes in the provision of credit to lower income borrowers.

1.3 HMDA Data Quality
This paper utilizes HMDA data to illustrate trends in mortgage
lending. HMDA data have been collected since 1977, but
because they were not reported at the loan level by
nondepository lenders until 1993, the discussion focuses on the
1993-2000 period. Even over this period, however, HMDA data
have a number of limitations. Perhaps most critical is the fact
that the HMDA’s coverage of the mortgage market changed
over the 1993-2000 period. One source of this differential
coverage is the fact that although nondepository lenders were
first required to report in 1993, some subset either did not, or
did so haphazardly for several years. Consequently, HMDA
data are likely to overstate somewhat actual lending growth for
the 1993-2000 period.
Potentially more serious is the fact that the change in
reporting requirements may differ by lender type, based on the
specialization of each lender. Therefore, some of the growth in
lending to lower income households relative to that to higher
income households could simply reflect differential reporting if
lenders specializing in lower income lending increased the
reliability of their reporting over the period.
Counterbalancing these limitations is the fact that the
HMDA database is a large and fairly rich microlevel data source
at the individual loan application level. No other data source
affords the opportunity to analyze lending patterns and trends
by borrower income, race/ethnicity, and gender in such detail.
Further, HMDA loans are geocoded to census tracts, allowing
a thorough exploration of the CRA’s impact on lending in
lower income, minority, or other historically underserved
market areas. These strengths and limitations also suggest the
importance of disaggregating the results by lender and
borrower characteristics in an effort to control for reporting
differentials across the various mortgage industry segments.

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171

2. The Regulatory Environment
This section examines issues associated with the CRA and
related legislation. We begin by discussing the early history and
rationale of the act and then consider the evolution of the CRA
and related legislation in the 1980s and 1990s. Despite
numerous changes over its nearly twenty-five-year history, the
CRA continues to focus on the presumed spatially determined
link between retail deposit-gathering activities and a depository
institution’s obligation to meet community credit needs.

2.1 Early History and Rationale
The CRA directed federally insured depository institutions to
help meet the credit needs of the communities in which they
operate.2 This focus on depository institutions reflected the
fact that, at a time when intra- and interstate branching was
largely proscribed, depositories were responsible for the
majority of home mortgage and small-business lending in
communities across the country.
The CRA directed bank regulators to evaluate the
effectiveness of depository institutions in meeting the credit
needs of their communities, including those of lower
income borrowers and neighborhoods, consistent with safe
and sound banking operations.3 It also required depository
institutions to post in their offices a CRA notice, and to
maintain and make available upon request a public file that
included specified information about the institution’s CRA
performance. Two of the act’s provisions that later proved
most important required regulators to allow public
comment on the institution’s community lending record
and to consider an institution’s CRA performance in
evaluating consolidation and expansion applications.
Despite these lofty pronouncements, the act provided little
guidance as to how bank regulators should evaluate bank
performance in this regard and how often these examinations
should take place. Moreover, it granted the regulators little
direct enforcement authority, other than stipulating that a
bank’s CRA record can be used as a basis to deny the bank’s
application to expand operations.

2.2 The 1980s and a Renewed Focus
on Fair Lending
After a decade, there was a growing sense among community
advocates, and ultimately in the U.S. Congress, that the

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performance assessments and ratings specified in the initial
legislation had done little to expand lending in underserved
markets. In 1988, Senator William Proxmire, Senate
Banking Committee Chair, held a highly visible hearing
where he challenged the regulatory agencies to be more
aggressive in their efforts to encourage banks to expand
credit access to lower income borrowers. Despite the
apparent rigor of the criteria, fully 97 percent of the
institutions examined over the period received one of the
two highest ratings (on a five-point scale). Indeed,
testimony revealed that in some years in the 1980s, certain
regulators conducted no CRA examinations at all (Matasar
and Pavelka [1998], as reported by Zinman [2001]).
This is not to say that the CRA had no impact in the early
years. Armed with a legislative mandate that a bank should
serve the “the credit needs of its entire community, including
low- and moderate-income (LMI) neighborhoods” and with
Home Mortgage Disclosure Act data on lending patterns,
community activists confronted banks and demanded that they
expand lending (Bradford and Cincotta 1992). Not all banks
responded, but some did engage with community groups and
began to experiment with new loan underwriting criteria and
with new mortgage products designed to expand access to
credit in many underserved communities. Arrangements
between community groups and lenders often were codified
into formal commitments, or “CRA agreements,” where banks
pledged to meet specific lending or service delivery targets
(Fishbein 1992).
Despite this progress, there could be little doubt that more
needed to be done to expand credit access to lower income
communities. This awareness was heightened by the
publication in 1988 of the Atlanta Journal-Constitution Pulitzer
Prize–winning “Color of Money” (Dedman 1988) series
documenting the disparities in mortgage lending between
blacks and whites in Atlanta. This not only generated
discussion of the failure of banks to serve “community needs,”
but also linked CRA and fair lending in the public debate. The
Fair Lending Act of 1968 prohibited discrimination in
mortgage lending—a prohibition that was enhanced with the
passage of the Equal Credit Opportunity Act of 1974 and the
Community Reinvestment Act of 1977.4 Stimulated in part by
the continuing community activism around racial disparities
in lending, Congress enacted the Fair Housing Amendments
Act of 1988. This law, passed twenty-five years after the initial
legislation, significantly expanded the scope of the initial
legislation and strengthened its enforcement mechanism
(Schill and Friedman 1999).

2.3 Changes in the Late 1980s and the
Financial Institutions Reform, Recovery,
and Enforcement Act
The failure of the Community Reinvestment Act to have a
more pronounced effect on lower income lending lay largely in
its failure to provide regulators with tools to punish poor
performance or reward successful behavior. The CRA’s
strongest provision—the ability of regulators to condition or
deny a merger—had little weight in an era of limited banking
consolidation, and in any case was never implemented in the
first decade following the act’s passage. Furthermore, both
lenders and advocates perceived the examination process as
capricious. Lender accountability was limited because lenders
were evaluated on the strength of their plans to serve lower
income areas rather than on the outcome of these plans on
improving conditions in lower income markets. Additionally,
any reputational risk and public scrutiny faced by lenders for
poor performance was minor because examiners’ ratings were
not made public. This was to change, as the combination of
additional regulations and changing market conditions gave
new bite to the CRA in the late 1980s and early 1990s.
In 1989, Congress strengthened both the HMDA and the
CRA in several key ways through the Financial Institutions
Reform, Recovery, and Enforcement Act (FIRREA). The act
enhanced HMDA disclosure requirements to include the race,
ethnicity, gender, and income of mortgage loan applicants, and
the disposition of mortgage loan applications. These additional
data—when combined with census data on the racial
composition; median family income; and central-city,
suburban, or rural location of the property—provided a greatly
enhanced statistical basis for analyzing the geographic and
demographic distribution of home mortgage loans. FIRREA
also mandated public disclosure of each institution’s CRA
rating and performance evaluation, established a four-tiered
descriptive rating system5 to replace a numeric scale, and
required banking regulators to prepare a detailed written
evaluation of the institution’s CRA record.
Heightened congressional concern over the effectiveness of
CRA oversight also coincided with bank regulators’ more
aggressive use of authority. In 1989, the Federal Reserve denied
on CRA grounds an application by the Continental Bank
Corporation to acquire Grand Canyon Bank of Scottsdale. The
Federal Reserve ruled that in light of inaccurate filings and a
lack of significant efforts to ascertain the credit needs of its
community or advertise its products—with no compensating
activities—Continental Bank’s commitments to improve its
CRA performance did not absolve it for a weak CRA record. In
an equally significant move and on the same day that it
announced its decision regarding the Continental Bank

Corporation, the Federal Reserve released a policy statement
outlining a more aggressive stance concerning the CRA,
including a checklist of items that regulators should consider
when deciding whether to approve an application to merge and
a statement acknowledging the importance of public hearings
and community input in the decision-making process.
The combination of the new policy statement and the fact
that the Continental case marked the first time a merger was
rejected on CRA grounds sent shock waves through the
banking community. These events focused senior banking
executives on the role of CRA compliance in an organization’s
competitive position, particularly in the consolidationoriented environment surrounding the demise of many savings
and loans at that time. It also awakened community advocates
to the potential gains from focusing protests on consolidating
institutions. The fact that CRA performance is a meaningful
criterion in approvals of consolidation and expansion activity
became even more important later in the decade as the pace of
such activity accelerated after passage of the Riegle-Neal
Interstate Branching and Efficiency Act of 1994.
The growing congressional concern about lending
discrimination also prompted the U.S. Department of Justice
to expand its fair lending enforcement activity (Galster 1999).
In a high-profile case, the Justice Department accused
Decatur Federal Savings and Loan Association of Decatur,
Georgia, of redefining its market area to exclude AfricanAmericans and of rarely advertising its products in AfricanAmerican communities. The Justice Department also sued
the Shawmut Mortgage Company of Boston, Massachusetts,
in 1993, alleging discriminatory treatment in loan approval.
In 1994, the Justice Department accused Chevy Chase Federal
Savings Bank of Washington, D.C., of violating fair-lending
laws by failing to extend services to African-American
neighborhoods. The Justice Department prevailed in each of
these high-visibility actions. Settlements ranged from
requiring banks to give aggrieved borrowers specific relief, to
requiring the banks to expand lending to minority borrowers
by enhancing outreach and marketing, altering underwriting
procedures, and creating special loan packages for lower
income applicants.

2.4 Further CRA Changes in the 1990s
The changes in the CRA continued into the 1990s as the
banking industry and community advocates complained that
CRA evaluations still relied too heavily on efforts to meet the
needs of their communities, rather than on results. In 1995,
federal banking regulators refined CRA enforcement

FRBNY Economic Policy Review / June 2003

173

procedures to focus explicitly on covered depository
institutions’ success in meeting their obligations under the
CRA by examining actual performance in their assessment
areas—the geographic areas where the institution has its main
office, branches, and deposit-taking ATMs—and neighboring
areas in which the institution originates or purchases
substantial portions of its loans.
The 1995 regulations provided for specific tests for three
different lender types, sizes, and businesses (large retail, small
retail, and wholesale/limited-purpose institutions). The 1995
regulations went furthest toward standardizing, quantifying,
and objectifying performance criteria for large retail
depositories.6 For these institutions, the CRA examination
consists of three distinct tests: lending, investment, and service.
Lending is the most heavily weighted component in the
overall rating equation and is most widely scrutinized by
community advocates. Regardless of point values, no
institution can receive a composite rating of “satisfactory”
unless it receives a minimum rating of “low-satisfactory” on
the lending test. Furthermore, an institution rated
“outstanding” on the lending test is assured an overall
“satisfactory” rating, even if it receives substantial
noncompliance on the other two components. In addition to
formal CRA examinations, public access to detailed mortgage
loan data under the HMDA allows community organizations
to monitor the activities of lenders.
Despite the effort to focus on quantitative results, the CRA
examination remains largely subjective, as examiners are
directed to apply the relevant test in the context of the
particular institution and the market in which it operates. This
“performance context” is defined to include information about
the economic and demographic characteristics of the
institution’s assessment area; lending, investment, and service
opportunities in that area; the institution’s product offerings
and business strategy; its capacity and constraints; its past
performance and the performance of similarly situated lenders;
information and public commentary contained in the
institution’s public CRA file; and any other information the
regulator deems relevant. The new rules also attempted to
reduce both paperwork and subjectivity. For all types of
institutions, public comment is encouraged by requiring that
each banking regulator publish a list of banks that are
scheduled for CRA examinations in the upcoming quarter.
In a nod to the changing structure of the banking industry,
the 1995 regulations also recognized that many banking
organizations included both depository institutions and
affiliated mortgage companies or subsidiaries. For example, the
1995 changes gave each institution the discretion to include or
exclude the activities of affiliated mortgage companies in the

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The Twenty-Fifth Anniversary

CRA examination for specific assessment areas. Recognizing
that some mortgage company affiliates specialize in serving
lower income markets, while others serve a broader market,
this feature arguably weakened the CRA’s inducement to
expand lower income lending by allowing institutions to select
the combination of reporting that will produce the most
favorable lending record.
Interestingly, the revised lending test, which gives lenders
credit for certain mortgage loans regardless of the
characteristics of the areas in which the loans are made,
represented a movement away from the initial spatial focus of
the CRA. Similarly, small-business lending is evaluated
primarily on the size of the loan and the applicant’s business
rather than on the income characteristics of the neighborhood.
At the same time, the regulations continued to focus on
assessment-area residential mortgage lending as well as the
spatial distribution of the provision of banking services to
assessment-area neighborhoods. As a result, more than two
decades after enactment, the CRA still maintains a clear focus
on the presumed spatially determined link between retail
deposit-gathering activities and a depository institution’s
obligation to meet community credit needs.

2.5 CRA and the Gramm-Leach-Bliley Act
of 1999
The most recent changes to the Community Reinvestment
Act occurred with the Gramm-Leach-Bliley Financial
Modernization Act of 1999. The GLBA mandates that
depository institutions must have satisfactory CRA ratings
before the institution, or its holding company, affiliates, or
subsidiaries, can engage in any of the expanded financial
activities permitted under the law. The GLBA’s “sunshine”
provision requires public disclosure of agreements entered into
by depository institutions and community organizations or
other entities in fulfillment of CRA obligations. The GLBA also
changed the frequency of small banks’ examinations to once
every five years for institutions with an outstanding rating,
every four years for those with a satisfactory rating, and as
deemed necessary for institutions whose last rating was less
than satisfactory. These small banks, however, also remain
subject to CRA review at the time of any application for merger,
to open or close a branch, or at the discretion of the regulators
for reasonable cause at any time. Finally, the GLBA also raised
important concerns about the privacy of borrowers and placed
limits on the use of credit history reports for purposes other
than credit scoring.

3. The Impact of the CRA
on Residential Mortgage Lending
This section summarizes an analysis of the effect of the CRA on
regulated lenders by comparing their home purchase lending
record with that of other lenders. Since CRA-regulated lenders
and other lenders were influenced by the same changes in the
marketplace, the comparison has the potential to highlight the
independent effects of the CRA on lending patterns. The
analysis suggests that CRA-regulated entities continue to lead
the market in the provision of prime, conventional residential
mortgage loans to lower income people and neighborhoods,
particularly in terms of their greater outreach to minority
borrowers.

3.1 CRA Expands Access to Mortgage Credit
Chart 1 shows the share of all conventional, conforming prime
loans made to CRA-eligible borrowers. Lenders are divided
into three groups: CRA-regulated banking organizations
lending in their assessment areas, CRA-regulated banking
organizations lending outside their assessment areas, and nonCRA-regulated entities. Here, banking organizations include
commercial banks and savings associations and their mortgage
and finance company affiliates, while non-CRA-regulated
organizations include independent mortgage companies and

Chart 1

Assessment-Area Lenders Lead in the Provision
of Conventional, Conforming Prime Loans
CRA-eligible share of home purchase lending (percent)
40
35

1993

2000

30
25
20
15
10
5
0
Banking
Banking
Non-CRA-regulated
organizations inside organizations outside
organizations
assessment areas
assessment areas

Source: Joint Center for Housing Studies Enhanced Home Mortgage
Disclosure Act Database.

credit unions. Chart 1 excludes prime loans with government
backing, including loans insured by the Federal Housing
Administration (FHA), as this lending is mostly a pass-through
operation, with loans largely originated by mortgage brokers
and sold into the secondary market. Finally, limiting this
assessment to conforming loans avoids giving undue weight to
those lenders operating chiefly in the jumbo market.
The chart confirms that CRA-regulated entities operating in
their assessment areas make a higher share of these
conventional, conforming prime loans to CRA-eligible
borrowers than do either CRA lenders outside their assessment
areas or non-CRA lenders. It also shows that the gap across
lender types is closing, possibly in response to an enhanced
understanding of how to lend to these markets profitably
through experience acquired by CRA-regulated lenders in
response to CRA obligations.
Table 1 extends this analysis to examine racial and ethnic
variations in lending patterns. It highlights the fact that loans to
blacks and Hispanics are much more likely to be CRA-eligible,
presumably because these groups have lower average incomes
than whites and are more likely to live in lower income census
tracts.
At the same time, it is important to note that in 2000, the
CRA-eligible share of conventional prime lending to blacks and
Hispanics by CRA-regulated entities operating in their
assessment areas was higher than the lending to blacks and
Hispanics by regulated entities operating outside the
assessment area as well as the lending by non-CRA lenders. For
whites, the difference is minimal, but for blacks, assessmentarea lenders have CRA-eligible shares that are 17 percentage
points (38 percent) higher than shares for lenders outside
assessment areas and 20 percentage points (48 percent) higher
than shares for non-CRA lenders. For Hispanics, the CRAeligible share for assessment-area lenders is 13 percentage
points (28 percent) higher than that for outside-assessmentarea lenders and 16 percentage points (39 percent) higher than
that for non-CRA lenders.
Even twenty-five years after its enactment, the CRA
continues to encourage CRA-regulated entities to extend
conventional prime lending to historically underserved
segments of the market. Other lenders, and indeed CRAregulated entities themselves, are increasingly using other loan
products, including government-backed loans and subprime
loans, to manage the increased risks inherent in serving these
markets. But in addition to their growing use of alternative
lending products, CRA-regulated entities continue to lead the
market in extending prime conventional loans to lower income
people and communities.

Note: CRA is the Community Reinvestment Act.

FRBNY Economic Policy Review / June 2003

175

Table 1

CRA-Eligible Share Varies by Race, Loan, and Lender Type
Banking Organizations
In Assessment Area

Non-CRA-Regulated Organizations

Out of Assessment Area

1993

2000

1993

2000

1993

2000

All prime lending
Whites
Blacks
Hispanics
Other
All races

29.6
58.6
52.5
29.1
31.9

30.9
62.6
56.7
27.2
33.7

28.3
52.1
49.5
27.8
30.5

30.4
56.9
54.0
27.6
33.1

26.7
48.0
44.4
24.6
28.6

31.2
53.6
52.1
28.1
34.1

Conventional prime lending
Whites
Blacks
Hispanics
Other
All races

27.4
59.2
51.1
27.4
29.7

28.9
60.6
54.4
25.9
31.4

22.0
42.4
38.9
22.9
23.1

25.5
43.7
42.6
23.4
26.4

19.3
29.4
31.6
19.4
20.0

25.6
40.9
38.8
23.0
26.3

Government
Whites
Blacks
Hispanics
Other
All races

43.3
57.2
60.2
40.7
45.4

50.1
67.5
68.5
45.0
54.2

41.5
57.4
58.2
39.0
44.2

48.8
66.9
65.5
44.8
53.9

41.1
55.8
54.0
36.7
43.6

45.4
60.3
60.1
40.7
49.5

Source: Joint Center for Housing Studies Enhanced Home Mortgage Disclosure Act Database.
Notes: “Other” includes Asian, Native American, and all other groups, and loans where the applicant and co-applicant were of different races.
CRA is the Community Reinvestment Act.

3.2 Multivariate Analyses Confirm
CRA’s Effect
Detailed econometric analyses discussed at length in the larger
Ford Foundation study confirm that the CRA continues to
have an important effect on mortgage lending. In particular,
the act appears to have encouraged CRA-regulated lenders to
originate a higher proportion of loans to lower income people
and communities than they would have if the act did not exist.
Moreover, CRA-regulated entities appear to have gained
market share in the provision of loans to lower income people
and communities, in effect crowding out lenders falling outside
of the CRA’s regulatory reach. Finally, lower income
neighborhoods targeted by the CRA have had more rapid price
increases and higher property turnover rates than other
neighborhoods, a finding that is consistent with the
proposition that the CRA has expanded the provision of credit
in these neighborhoods.

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The Twenty-Fifth Anniversary

These econometric studies also suggest that CRA-regulated
entities respond both to the regulatory requirements set forth
by the act as well as to pressure from community-based
organizations that the act has enabled. As a result, the
econometric models suggest that even controlling for other
mortgage lending supply and demand factors, CRA-regulated
entities originate a higher share of their loans to lower income
people and communities in their assessment areas—the areas
under the most intense CRA scrutiny. Moreover, lower income
lending is greater in areas covered by agreements made with
community groups that commit CRA-regulated entities to
expand their outreach.
Interestingly, both effects seem to be waning. Just as the
growth of large banking organizations has fostered rapid
growth of nonassessment-area lending, so too has the growth
of these organizations changed the ability of community
organizations to extract concessions from lenders operating in
their neighborhoods. As in the case of the simple descriptive

statistics presented earlier, the econometric analyses confirm
that CRA-regulated lenders continue to outperform other
lenders in the lower income lending arena, but the CRA
effect appears to be on the decline. For example, the
econometric models suggest that from 1993 to 2000, the act
may have increased the share of loans to CRA-eligible borrowers
by 2.1 percentage points (or from 30.3 to 32.4 percent).
Estimates for individual years suggest, however, that the
CRA impact has declined from 3.7 percentage points in 1993
to 1.6 percentage points in 2000.

4. The Changing Mortgage Industry
Structure
The mortgage industry has witnessed a dramatic restructuring
in the past decade. It has experienced an explosion of new
forms of lending, the ascendancy of large lending
organizations, the expanding share of loans originated through
mortgage brokers and mortgage banking operations, the
migration of some bank and thrift mortgage lending to
separately incorporated affiliates, and the growth of secondary
mortgage markets with its attendant reduction in the share of
lending funded by bank deposits. This section summarizes
these significant trends and assesses their implications for the
evolution of mortgage markets.

4.1 The Growing Importance
of Securitization and the Rise
of Mortgage Banking
Historically, deposit-taking institutions (thrifts and
commercial banks) dominated mortgage originations. As
recently as 1980, nearly half of all one-to-four-family home
mortgages were originated by thrift institutions. An additional
22 percent were originated by commercial banks (U.S.
Department of Housing and Urban Development 1997). That
same year, mortgage companies and other lenders accounted for
the remaining 29 percent of all one-to-four-family mortgage
loans. That distribution reflected the fact that deposits, and
hence deposit-taking institutions (particularly thrifts), were the
main source of funds for mortgage debt. Depository lenders held
the loans they originated in portfolios because underwriting
standards and mortgage documents varied considerably and
third-party investors were reluctant to purchase mortgages that
lacked adequate credit enhancements and standard features.

Over the subsequent two decades, this system changed
dramatically. Although banks and thrifts continue to originate
loans and hold some of them in portfolio, mortgage brokers
and retail mortgage bankers now originate a majority of
mortgage loans. In 1997 (the last year that HUD conducted its
Survey of Mortgage Lending Activity), mortgage companies
were the dominant (56 percent) originator of one-to-fourfamily mortgages loans. Their rise came at the expense of
thrifts, which captured only 18 percent of loans in 1997, while
commercial banks were up slightly, to a 25 percent share of all
originations. Further marking the change in industry structure,
43 percent of originations by banks and thrifts flowed through
their mortgage banking subsidiaries.
The rise to dominance of nondepository lenders has been
facilitated by the rise of secondary-market institutions. The
ability to package and sell loans in the secondary market
reduces the need to hold deposits (or other sources of cash) to
fund mortgage loans because investors in the mortgage-backed
securities that the government-sponsored enterprises (GSEs)
and private conduits issue replace deposits as the source of
funds for these loans. Fannie Mae and Freddie Mac—by
mandating the standardization of loan contracts and through
their sheer scale—have played a role in streamlining and
rationalizing the mortgage market role that extends beyond
incorporating additional sources of funding within it.
In addition to Ginnie Mae, an organization created to
securitize the government-insured portions of the market,
private market entities are also now active in the securitization
business. While the largest share of conventional conforming
loans (those made at standard terms for amounts below the
federally determined ceiling for GSE purchases) is typically sold
to Fannie Mae and Freddie Mac, nonconforming mortgages
(or “jumbos”) are also commonly pooled and sold as privatelabel securities, mostly by Wall Street investment banks.
Individual loans underlying both GSE and private-label issues
that are made at high loan-to-value ratios carry private
mortgage insurance, but issuers of jumbo packages tend to
provide additional credit enhancements beyond those of the
conventional conforming GSE issues.
Securitization has largely affected the market for prime
mortgages—those made at the most favorable rates and terms
to borrowers who present lenders and investors with small and
manageable credit and collateral risks. Prior to the 1990s,
subprime mortgages were chiefly extended by large finance
companies, which financed them with secured and unsecured
debt. Recently, however, securitization has also been
aggressively extended into the subprime sector. Indeed, a
joint report by the U.S. Department of the Treasury and the
U.S. Department of Housing and Urban Development
(2000) notes that the securitization of subprime loans

FRBNY Economic Policy Review / June 2003

177

increased from $11 billion in 1994 to $83 billion in 1998,
before easing back to $60 billion in 1999. Issuers of
subprime mortgage-backed securities have tended to be
private firms, because, until recently, Fannie Mae and
Freddie Mac purchased only prime loans.

4.2 The Rise of Large Banking Organizations
Paralleling the rise of mortgage brokers and the securitization of
mortgage loans has been the rise of large banking organizations
and their affiliated mortgage lending organizations. A study by
the Federal Reserve noted that from 1975 to 1997, the number of
banking institutions dropped 40 percent as a result of industry
consolidation and a substantial number of bank failures (Avery
et al. 1999). Following the shakeout in the late 1980s and early
1990s, the number of liquidations slowed, but the number of
mergers and acquisitions continued to rise, stimulated by the
globalization of financial services and efforts to increase
efficiency, reduce costs, or gain competitive advantages.
Regulatory changes also supported the consolidation of the
financial services industry as most state-level restrictions in the
1980s on intrastate banking were removed or relaxed. At the
federal level, interstate banking became a reality in the 1990s.
This opened up opportunities for commercial banks to expand
beyond boundaries that had been in place since the Depression
and allowed larger organizations to enhance the scale and scope
of their operations further through mergers and acquisitions.
Federal Reserve System data indicate the scale of consolidation
in the mid-1990s. From 1993 to 1997 alone, the number of
banking institutions obtained in a merger or acquisition
totaled 2,829, or 21 percent of the total. Over the same period,
431 new institutions were formed.
To understand the ongoing concentration in mortgage
lending, it is necessary to understand trends within the mortgage
sector and in the broader financial services industry (Avery et al.
1997). Among the various financial services provided by banks
and related businesses, consumer and mortgage lending require
extensive marketing, customer support, account management,
and servicing operations. Large-scale operations are able to
spread the high fixed costs associated with these tasks across a
larger customer base. In addition to these classic “scale
economies,” larger organizations benefit from “scope
economies” that allow them to use data and information
gathered from a large customer base to develop and cross-sell
specialized, and potentially more profitable, consumer products
to mortgage customers. Similarly, the organizations can reduce
the average costs of mortgage originations by capturing the
mortgage activity of their other customers.

178

The Twenty-Fifth Anniversary

Finally, technological advances also spurred major changes in
the structure of the mortgage industry. The link today between
the location of the borrower and the location of the lender is less
important than it was even a decade ago because loan origination
systems increasingly started to operate via telephone, fax, and
now the Internet. As a result, many banks have abandoned
conducting some or all of their residential mortgage lending
operations out of “sticks-and-bricks” branches, but instead have
created or acquired large mortgage banking subsidiaries that
utilize technology to operate from centralized locations that
serve entire metropolitan areas or larger regions. Moreover,
electronic loan processing and underwriting, including the
growing use of automated credit scoring and automated
appraisal and underwriting tools, have reduced the costs of loan
origination and loan servicing and have allowed lenders to
reduce costs by managing risk better.
For the most part, the new technology requires high fixed
investment by firms, but once installed, it operates at extremely
low marginal costs. As a result, increased technological
sophistication in mortgage lending tends to favor larger
lending organizations and has helped to foster consolidation in
the mortgage business. At the same time, these trends have also
supported the growth of mortgage brokers, who, working on a
fee-for-service basis, handle the front end of the mortgage
application process, a function that still may benefit from a
presence in a local market area, and some face-to-face
communication with loan applicants. Here, scale economies
are decidedly less significant, and relatively small organizations
continue to thrive as mortgage brokers.
In 2000, only twelve lending organizations made more
than 50,000 home purchase loans, but these twelve accounted
for 39 percent of all such loans made that year (Table 2). In
1993, only four organizations topped 50,000 loans, and they
accounted for only 11 percent of all home purchase lending.
The number of lenders making between 25,000 and 50,000 loans
per year also increased, though their share of the overall market
was flat. Together, the top twenty-five home purchase lenders
originated fully 52 percent of all home purchase loans in 2000.
Table 2 divides the lending organizations into two categories:
banking organizations (that is, commercial banks and savings
associations with their mortgage and finance company affiliates)
and other organizations (independent mortgage and finance
companies and credit unions). The table indicates that banking
organizations led the growth of large organizations. By 2000,
home purchase lending for the ten largest banking organizations
totaled more than 1.1 million loans, and the top twenty
combined for a total of 1.5 million loans.
The emergence of large bank lending operations reflects, in
large measure, forces that prompted dramatic consolidation of
retail banking operations within and across individual

Table 2

Large Banking Organizations Lead Mortgage Lending Growth
Banking Organizations
Lenders

Non-CRA-Regulated Organizations
Loans

Number of Loans

1993

2000

More than 50,000
25,000 to 49,999
10,000 to 24,999
5,000 to 9,999
1,000 to 4,999
500 to 999
250 to 499
100 to 249
Fewer than 100
Total

2
5
21
26
141
138
254
619
3,175
4,381

10
10
18
21
109
134
194
456
2,844
3,796

1993
155,085
149,018
301,236
189,288
302,513
97,277
88,734
99,128
86,561
1,468,840

Lenders
2000

1,161,815
341,556
286,624
146,278
240,739
92,231
67,856
71,437
82,183
2,490,719

Loans

1993

2000

1993

2
5
11
11
117
122
161
193
1,163
1,785

2
3
9
20
140
125
169
290
1,483
2,241

105,686
153,294
160,837
78,140
243,394
90,307
58,602
31,334
24,075
945,669

2000
282,306
129,399
127,884
141,509
300,327
87,170
58,106
48,011
34,100
1,208,812

Source: Joint Center for Housing Studies Enhanced Home Mortgage Disclosure Act Database.
Notes: Banking organizations include all commercial banks, savings associations, and their mortgage and finance company affiliates. Non-CRA-regulated
organizations include mortgage companies and credit unions. CRA is the Community Reinvestment Act.

metropolitan market areas. Within-market consolidations
reflect the increasing economies to scale of retail banking, and
the trend for larger, more efficient banking operations to
acquire smaller banks or otherwise increase their presence in a
particular market. Growth of regional and even national
banking operations also reflects the efforts of larger banks to
capitalize on potential scale economies and name recognition
as well as to reduce risk by diversifying across numerous
spatially distinct market segments (Avery et al. 1999).
At the same time, several large independent mortgage and
finance companies competed head to head against banking
organizations in mortgage markets across the country. These
included the two largest, Countrywide Home Loans and Cendant
Mortgage, each of which made more than 50,000 home purchase
loans in 2000. But many other independent mortgage banking
operations either failed to grow over the period or merged with or
were acquired by a large banking operation. This latter category
includes such large operators as North American Mortgage,
which was acquired by Dime Savings Bank, and Norwest
Mortgage, which merged with Wells Fargo & Company.
At the other end of the spectrum, the data confirm that the
number of banking organizations originating fewer than 100
loans shrank by 10 percent between 1993 and 2000. This
category of lender also made slightly fewer loans in 2000
than in 1993. In contrast, the number of smaller independent mortgage companies and credit unions was on the rise.

For example, over the period, the number of independent
mortgage companies and credit unions making fewer than
100 home purchase loans rose 28 percent (from 1,163 to
1,483) and the number of home loans made by these
organizations rose 42 percent.
Consolidation among home refinance lenders was also
strong, as the effect of technological advances and related
developments that have reduced the costs of home purchase
lending had an equally strong impact on the costs of providing
home refinance loans. For example, lending institutions
making more than 10,000 refinance loans in 2000 accounted
for 57 percent of all home refinance loans, compared with only
51 percent in 1993, with much of the growth again
concentrated among large banking institutions.
It remains to be seen whether the dominance of larger
organizations helps or hinders the provision of affordable
home loans. Many housing advocates argue that smaller,
community-based institutions have an enhanced capacity to
better understand and address the credit needs of the
communities they serve (Immergluck and Smith 2001). Others
argue that the efficiencies associated with large-scale
operations, as well as the ability of larger organizations to offer
a wider and more diverse product mix and to access low-cost
funds on the world capital market, are advantages that more
than neutralize any disadvantages. In any case, there seems to
be little doubt that the trends of consolidation in the mortgage

FRBNY Economic Policy Review / June 2003

179

industry and the declining importance of deposits as a source
of mortgage capital have yet to run their course.
Continued technological change should further enhance the
competitive advantage of larger players. New automated
systems require substantial initial investments, and smaller
companies unable to afford such investments are finding it
increasingly difficult to remain competitive in the mortgage
market. At the same time, since these technologies operate at
low marginal or incremental costs, they foster fierce
competition among those firms operating in the market. Going
forward, the result will likely be both a continued consolidation
of mortgage lending activities and a growing reliance on
mortgage brokers to take loan applications. In addition, the
continued evolution of better products, services, and pricing
can be expected, as large firms seek to identify and exploit
competitive advantage in their pursuit of customers in an
increasingly competitive marketplace.

5. Industry Structure and Current
Regulatory Issues
Changes in the structure of the financial services industry,
particularly in mortgage banking, have combined to weaken
the link between mortgage lending and the branch-based
deposit-taking on which the Community Reinvestment Act
was based. This section discusses these trends at the national
level and their implication for the CRA’s impact on lending to
lower income borrowers and communities, as well as their
implication for the variation in the act’s regulatory reach across
metropolitan areas and individual lenders.

The fact that loans made by CRA-regulated institutions in
their designated assessment areas as a percentage of all loans
(or assessment-area share) has declined has several
implications. First, a large and growing share of the mortgage
lending industry (independent mortgage companies, finance
companies, and credit unions) falls entirely outside the CRA’s
regulatory reach. Next, even among CRA-regulated
institutions, the fastest growth has been in out-of-area lending,
or lending that takes place outside the markets where these
organizations maintain deposit-gathering branches, and hence
is not subject to the most stringent aspects of the CRA
examination process.
Equally noteworthy is the fact that each of these broad types
of lending (in-assessment-area lending by CRA-regulated
banking organizations; out-of-assessment-area lending by
CRA-regulated banking organizations; and lending by
noncovered organizations) differs in terms of its product mix
and market orientation. As a result, the extent of detailed CRA
examination of loans varies significantly by loan type, borrower
type, and location. For example, in 2000, CRA-regulated
depository institutions and affiliates operating in their
assessment areas made 38 percent of all prime conventional
home purchase loans. In contrast, in the rapidly growing
subprime segment, only 3 percent of all loans were made by
CRA-regulated organizations within their assessment areas. In
addition, the vast majority of HMDA-reported manufacturedhome lending was not subject to CRA assessment-area review.
Significant differences also appear in the home
refinancing market, where assessment-area lending by CRAregulated institutions captured 32 percent of all lending in
2000 and 42 percent of all conventional prime lending

Chart 2

5.1 The CRA and the Changing Industry
Structure
The increasing share of loans by the mortgage banking
subsidiaries or affiliates of bank holding companies and by
independent mortgage companies has brought a concomitant
decline in the share of mortgage loans originated by deposittaking institutions in the assessment areas where they maintain
branch banking operations. An increasing share of all loans is not
subject to detailed CRA review because the act mandates the
most extensive review of assessment-area lending. Between 1993
and 2000, the number of home purchase loans made by CRAregulated institutions in their assessment areas as a share of all
home purchase loans fell from 36.1 to 29.5 percent (Chart 2).

180

The Twenty-Fifth Anniversary

Assessment-Area Lending Has Fallen Steadily
Percent
50

Home purchase

Refinance

40
30
20
10
0
1993

Source: Joint Center for Housing Studies Enhanced Home Mortgage
Disclosure Act Database.

(indicating that depositories’ branch networks remain
advantageous in this market). Even so, the vast majority
(96 percent) of all subprime refinance loans are made by
independent mortgage companies and out-of-area lenders, and
as a result fall largely outside the CRA’s regulatory reach.
The relative importance of assessment-area lending by
depository institutions covered by the CRA also varies by
borrower and neighborhood income. For example, the CRA’s
regulatory reach is lowest for the nation’s historically
disadvantaged minority groups. In 2000, assessment-area
lending accounted for only 23 percent of all home purchase
loans to black households and 26 percent to Hispanic
households, as opposed to 32 percent for whites. For home
refinancing, the assessment-area share for blacks stands at
21 percent; the figure is higher for Hispanics (32 percent),
but still trails the share of assessment-area lending for whites
(36 percent).

Chart 3

The Assessment-Area Share of Home Purchase
Originations Varies Widely
Number of metropolitan statistical areas (MSAs)
100
80
60
40
20
0
Fewer
than 10

10-19

20-29

30-39

40-49

50-59

60 or
more

Assessment-area lenders’ share
of total home purchase originations in the MSA, 2000
Source: Joint Center for Housing Studies Enhanced Home Mortgage
Disclosure Act Database.

5.2 Metropolitan-Area Variation
in Assessment-Area Lending
Significant variation in assessment-area lending also exists
across metropolitan statistical areas (MSAs). The demand for
mortgage credit will depend in part on the relationship
between home prices and incomes in a given area. In areas
where housing costs are high relative to income, there may be
little opportunity to lend to lower income families.
Accompanying this housing market variability is equally
significant metropolitan-area variation in banking and
mortgage industry organization. These differences are a result
of the long-term economic performance of the area, the
strength and national ambitions of locally based lenders,
demand for mortgage credit, and state-level banking
regulations, among other factors.
In some MSAs, only a handful of loans are originated by CRAregulated entities operating in their assessment areas, while in
other MSAs, well over half are (Chart 3). From a CRA perspective,
there are two important implications of metropolitan-area
variation in housing and banking markets. First, CRA-eligible
lending is significantly more challenging for lenders in some
MSAs than in others. Second, vastly different shares of lending
pass through the CRA-regulatory apparatus in more places than
others. Consequently, the CRA’s effect from one MSA to another
varies substantially based on MSA characteristics and the MSAspecific structure of the mortgage industry there.
Table 3 extends this analysis and displays the ten metro areas
with the lowest share and the ten metro areas with the highest
share of assessment-area lending. At the extreme, the

assessment-area share of lending varies from a low of 6 percent
in Denver to a high of 74 percent in Dubuque. Although there is
a slight tendency for smaller metropolitan areas to have
somewhat higher assessment-area shares, at least one large MSA
and a complement of medium and smaller ones are included in
the list of MSAs with the highest and lowest shares. For example,
San Francisco’s 60 percent share is some ten times higher than
Denver’s share. Similarly, Brazoria, Texas, with one of the lowest
shares, had a much smaller share of assessment-area lending
than Lincoln, Nebraska, which is in the top ten, though the two
MSAs had nearly identical numbers of home purchase
originations in 2000.
This MSA variation also bears little relationship to the share
of lending that is CRA-eligible. For instance, Denver, where
only 6 percent of loans are made in assessment areas, has a
relatively high CRA-eligible lending share of 40 percent.
Conversely, San Francisco, where 60 percent of loans are made
inside assessment areas, has the seventh-lowest CRA-eligible
share, at just 21 percent.7 These two markets present almost
completely opposite characteristics with respect to their shares
of lending that are CRA-eligible and the shares that are actually
originated by a CRA-regulated entity.
Table 3 does suggest, however, that the variation in
assessment-area shares may relate to state-level banking
regulations and the idiosyncratic characteristics of the individual
markets. All six of Colorado’s MSAs are among the eleven MSAs
with the lowest assessment-area shares in the country. Note that
Colorado was one of the last states to deregulate its banking

FRBNY Economic Policy Review / June 2003

181

Table 3

Top and Bottom Metropolitan Statistical Areas
(MSAs) for Assessment-Area Home Purchase
Lending Originations in 2000
MSA

Assessment-Area Share

Total Loans

Lowest shares
Denver, CO
Greeley, CO
Boulder, CO
El Paso, TX
Colorado Springs, CO
Tucson, AZ
Lawton, OK
Brazoria, TX
Anchorage, AK
Pueblo, CO

5.9
7.1
7.9
8.2
8.6
9.0
9.8
10.2
10.5
11.9

63,755
5,735
9,306
7,244
12,699
17,244
1,208
4,276
5,022
2,212

Highest shares
San Francisco, CA
Grand Forks, ND
Williamsport, PA
Pittsfield, MA
Wheeling, WV
Decatur, IL
Bloomington, IL
Lincoln, NE
Enid, OK
Dubuque, IA

59.6
60.1
60.2
60.4
60.5
64.7
69.7
70.6
71.0
73.9

22,228
639
1,250
1,563
1,379
1,748
2,942
4,278
801
1,063

Selected others
Las Vegas, NV
Atlanta, GA
Baltimore, MD
Washington, D.C.
Birmingham, AL
Chicago, IL
San Diego, CA
Los Angeles, CA
New York, NY
San Jose, CA

14.4
16.6
20.3
24.5
25.8
30.4
32.6
36.7
45.7
54.8

37,035
94,537
44,343
113,740
14,861
146,434
54,357
114,254
59,118
27,565

Source: Joint Center for Housing Studies Enhanced Home Mortgage
Disclosure Act Database.

industry, putting branch-based mortgage operators at a
disadvantage relative to independent and affiliated mortgage
companies. Moreover, a wrenching regional recession in the
1980s led to the collapse of many Denver-based banking
operations. Today, Denver and other metropolitan areas in
Colorado are experiencing explosive growth, but this growth is
largely being served by national mortgage companies—both
bank affiliates and independent mortgage companies.

182

The Twenty-Fifth Anniversary

5.3 The Diversity of Mortgage Lenders
Against these general trends stand the rich and varied stories of
the rise of individual organizations. The twenty-five largest
home purchase lenders depicted in Table 4 illustrate this
substantial diversity. These are the organizations that made
52 percent (1.9 million loans) of all home purchase loans in
2000. With respect to mortgage lending, there are strikingly few
similarities these organizations share. Among large independent
mortgage companies, Countrywide Home Loans operates
nationally and focuses on lending to lower income, first-time
home buyers. In contrast, Cendant Mortgage serves customers
with slightly higher incomes through a unique marketing
approach that yields a mixture of applicants, while Conseco
Finance specializes in funding subprime and manufacturedhome loans for lower income borrowers. These different
business models and plans translate into substantially different
specializations. For instance, of the independent mortgage
companies in Table 4, the share of refinancing loans ranges from
6 to 36 percent of total loans.
The banking organizations in Table 4 are equally diverse.
Overall, the banking organizations in the top twenty-five
originate about a quarter of their loans inside their CRA
assessment areas. For refinancings, the share is 33 percent. In
contrast, Bank of America, which has a nationwide network of
branches, originated more than 80 percent of its more than
240,000 home purchase and refinancing loans in its CRA
assessment areas. At the other end of the spectrum, J.P. Morgan
Chase and Company, which originated nearly as many total
loans, did so primarily through its mortgage banking
subsidiary in counties where the company did not operate
branches. Only 13 percent of Chase’s home purchase loans and
10 percent of its refinancings took place in the bank’s CRA
assessment areas.
The top banking organizations also have significantly
different home purchase and refinance lending shares.
Chase is again extreme, with refinancing loans accounting
for 18 percent of its loans. In contrast, Citigroup (55 percent)
and Bank One Corporation (78 percent) made well over half of
their originations through refinance lending, even in 2000’s
relatively high-interest-rate environment.
These comparisons illustrate just some of the distinct blends
of mortgage banking and retail banking operations. Although
physical location—sticks and bricks—within a particular
community can boost a mortgage lending operation, it is not
an essential feature. As a result, many mortgage companies that
have emerged over the past several decades operate
electronically through a network of brokers with limited
physical presence in a given market area. IndyMac, a lender

that made more than 10,000 loans in 2000, is an interesting
example of these trends. Once an independent mortgage
company, IndyMac recently purchased a small thrift in the
Los Angeles area and now operates with an organizational
structure best described as an “inverted” mortgage company.
Such a structure allows IndyMac to tap into traditional
secondary-market sources, while also diversifying its funding by
raising deposits in Los Angeles as well as in the national capital
market through the Internet and other electronic channels.

Also contributing to the growing diversity of the industry
are the mortgage banking subsidiaries of “nonbanks,”
including mortgage companies that operate as subsidiaries of
large insurance companies and financial services companies.
Similarly, mortgage banking subsidiaries of major home builders
and manufactured-home producers are included in the top tier
of mortgage lenders in the growth regions of the country
(Kaufman & Broad Mortgage, NVR Mortgage Finance,
Oakwood Acceptance Corp, and the Pulte Mortgage Company).

Table 4

Assessment-Area Lending Varies Significantly among the Top Mortgage Lenders in 2000
Assessment-Area Shares
(Percent)
Organization
Wells Fargo and Co.
J.P. Morgan Chase and Co.
Countrywide Home Loans
Bank of America Corp.
National City Corp.
Cendant Mortgage
Washington Mutual Bank, FA
Standard Federal Bank
Dime Savings Bank of New York, FSB
World Savings Bank, FSB
Citigroup Inc.
Suntrust Banks Inc.
GMAC Mortgage
First Union Corp.
Greenpoint Financial Corp.
Old Kent Financial Corp.
Conseco Finance Servicing Corp.
CTX Mortgage Co.
Flagstar Bank, FSB
FleetBoston Financial Corp.
PNC Financial Services Group
Ohio Savings Bank
Bank One Corp.
California Federal Bank
Irwin Financial Corp.
Total for top lenders

Total Home
Purchase Loans
219,623
184,102
173,531
152,810
147,146
108,775
91,843
89,670
76,579
75,927
72,015
52,100
49,650
45,862
42,217
41,886
40,573
39,176
34,036
33,798
32,918
29,633
28,775
27,147
25,284
1,915,076

CRA-Eligible Loan Shares
(Percent)

Total Home
Refinance Loans

Home Purchase

Home Refinance

Home Purchase

Home Refinance

74,118
39,788
53,578
91,053
42,920
6,989
43,680
41,051
25,396
28,679
88,671
13,398
28,097
48,118
18,055
18,094
15,641
12,376
21,512
21,941
22,624
11,005
102,462
9,800
7,051
886,097

19.1
12.9
0.0
83.0
11.7
0.0
63.6
32.8
4.5
71.7
15.9
57.0
0.0
64.6
1.0
15.9
0.0
0.0
18.9
33.9
38.0
14.5
10.0
70.4
7.2
25.7

52.0
10.1
0.0
80.6
17.9
0.0
64.6
32.4
4.6
77.1
6.6
48.7
0.0
46.6
2.2
45.2
0.0
0.0
16.3
51.6
65.5
8.5
19.2
71.7
2.8
32.6

27.8
33.4
32.7
40.6
40.7
30.6
24.6
32.8
34.6
20.2
49.2
29.7
32.3
42.5
46.1
39.4
68.0
39.5
35.7
39.0
30.4
27.7
33.9
22.0
50.4
34.8

30.4
39.5
45.4
41.7
39.9
32.6
24.5
38.0
35.8
25.9
56.2
34.9
33.5
46.2
25.2
37.7
44.9
64.2
43.8
33.2
25.0
30.0
37.6
24.4
36.8
38.9

Source: Joint Center for Housing Studies Enhanced Home Mortgage Disclosure Act Database.
Notes: Top lenders are the twenty-five organizations that made at least 25,000 home purchase loans in 2000 based on activity in metropolitan statistical
areas (MSAs) included in this study. Lenders are aggregated at the holding company level. CRA-eligible loan shares include loans to borrowers earning less
than 80 percent of the area median income and/or loans made on properties in census tracts to borrowers with incomes less than 80 percent of the MSA
median as of 1990. CRA is the Community Reinvestment Act.

FRBNY Economic Policy Review / June 2003

183

6. Regulatory Challenges
In recent years, Congress, through the Gramm-Leach-Bliley
Act, has focused on financial services modernization, but
little has been done to help the CRA conform to the rapidly
evolving world of mortgage banking and financial services.
During the debate on the GLBA, some sought to scale back
the CRA, and called for, among other things, the creation of
a “safe harbor” that would limit CRA challenges for banks
with a satisfactory or better rating. Advocates pushed to
expand the CRA by extending its reach to all segments of the
financial services industry, including nonbanks that were
involved in the provision of financial services. In the end, the
GLBA left the CRA more or less where it had been, although
discussion continues about the need to “modernize CRA”
(Goldberg 2000).

6.1 CRA Assessment of Mortgage Loans
Is Uneven and Often Ineffective
The growth of large and diverse lending organizations poses
regulatory challenges to the CRA. In their Advanced Notice of
Proposed Rulemaking (ANPR), issued in 2001, federal
regulators requested comments on how best to improve the
efficacy of the current regulations. One central issue is how best
to define “assessment area,” or otherwise determine which
loans should be subject to detailed CRA review. At present,
assessment areas are defined in terms of where a CRAregulated entity maintains deposit-taking operations. These
rules reflect the original CRA philosophy that financial
institutions had an obligation to meet the mortgage credit
needs of those areas where they gather deposits. At the time the
CRA was enacted, this focus made sense because locally based
depository institutions dominated mortgage lending.
Today, the assessment-area concept results in an
unevenness of application of CRA oversight. Detailed CRA
review is conducted on virtually all loans made by some smaller
depository institutions operating in a single area, but scant
review is applied to the fastest growing segment of home
purchase lending, namely, those loans made outside areas
where organizations maintain deposit-taking operations.
Furthermore, no review of loans is made by the independent
mortgage companies not covered by the act from the
beginning. As noted earlier, under current rules, CRA oversight
has declined steadily over time and varies significantly from
one market area to the next.
The diversity of mortgage lending operations and the
decline in the share of all loans made by CRA-regulated lenders

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in CRA assessment areas have spawned numerous proposals to
alter the CRA focus on traditional deposit-taking entities
operating from a network of branch locations. Some argue that
the current definition of assessment areas makes little sense in
a world of electronic banking and national-scale mortgage
lending operations (Thomas 1998). The ANPR generated
numerous proposals for expanding assessment areas for CRAregulated institutions to include markets where regulated
entities maintain deposit-gathering operations as well as all
places where they conduct mortgage lending operations. For
example, the National Association of Homebuilders (2001)
advocates that assessment areas be defined as areas where CRAregulated entities deliver retail banking services, whether or not
they have physical deposit-gathering branches or ATMs in that
locale. In a similar fashion, the National Community
Reinvestment Coalition (2001) proposes expanding
assessment areas to include those metropolitan areas where a
lending institution accounts for at least one-half of 1 percent of
all home purchase and/or refinancing loans.
Other proposals call for the extension of the CRA to all
financial services organizations, including nondepositories. One
commonly suggested approach is to extend CRA obligations to
independent mortgage companies and consumer finance
companies that currently fall entirely out of the regulatory reach
of CRA (Campen 2001). These comments suggest that despite
the multiyear congressional debate on how best to “modernize”
the financial services industry, Congress should continue to
assess critical aspects of the CRA, including the act’s original
focus on assessment areas linked to deposit-gathering activities.

6.2 One Size Doesn’t Fit All
Much of the CRA examination process continues as if the
examination is being applied to activity in a single neighborhood or community where a bank or thrift has branch activity.
In this context, lending, investment, or service activity can
reasonably be compared with the activity of others operating in
the same area.
The growth of large and diverse lending organizations poses
regulatory challenges to the CRA. Despite these differences in
the scale of operations, current CRA regulations attempt to
apply a relatively simple set of rules to a diverse set of
depository institutions. Although the distinction between
“small” and “large” banking organizations represents a nod
toward developing separate rules for organizations of differing
scale, the asset threshold (greater than $250 million) used to
define “large banks” lumps together “small large banks,” that
often make fewer than 1,000 loans in a single assessment area,

with national-scale financial institutions making as many as
200,000 home purchase loans in assessment areas scattered
across the country.
Faced with the challenge of evaluating entities with many
distinct assessment areas, regulators have adopted a number of
sampling concepts that select just a subset of areas for “full
scope review.” Since selection criteria appear to be weighted
toward more densely populated assessment areas, these rules
focus limited attention on smaller market areas, including rural
areas. Moreover, for lenders with multiple assessment areas,
current CRA practices “roll up” individual assessment-area
scores into an overall average for operations in a given state. As
a result, the current system permits an entity to obtain an
overall satisfactory rating, even when the organization’s
performance in a particular assessment area was rated as “needs
to improve.”
Proposed modifications include the addition of criteria that
would mandate “full scope reviews” in rural areas or
assessment areas that are generally deemed to be
“underserved.” The National Training and Information Center
(2001) calls for “localized CRA ratings,” so that CRA-regulated
institutions have an incentive to perform consistently well in all
locations. Another approach would be to develop a multistage
sampling procedure. This approach would first review HMDA
and other readily available data to obtain an initial series of
indicators of a given institution’s performance in each
assessment area. Then, “full scope reviews” would be
conducted in all areas where these initial indicators suggest that
the lender’s performance may fall in the low range of
satisfactory or below, while at the same time continuing to
target for review a sample of other areas as well. Whatever
method of selection is developed, other proposals call for
specific penalties if a lender fails to obtain a rating of
satisfactory or higher in any single assessment area that is
reviewed.

equality of access to branches in lower—as compared with
higher—income areas where the bank operates branches. It
also focuses on the pattern of branch openings and closings
according to neighborhood income since the previous
examination.
Lenders clearly perceive the community-development
services portion as onerous to document, if not comply with.
For example, lenders are responsible for undertaking the highly
subjective task of documenting the charitable activities of their
employees as evidence of their service to the community.
Lenders also must take on the somewhat tedious task of
describing the location of ATMs and documenting decisions
concerning bank branch closings. Yet, beyond possibly
constraining their ability to close branches in lower income
markets, the service test appears to have little impact on the
provision of financial services to lower income individuals.
Despite the apparent weakness of the service test, the
examination’s component on retail banking services is
arguably the most closely linked to the branch-banking
mechanism through which CRA obligations are defined and
operated. In contrast, mortgage lending is almost entirely
decoupled from branch locations as underwriting decisions on
the vast majority of loans are made by automated systems that
can be located just about anywhere.
Meanwhile, many people in lower income areas frequently use
check-cashing businesses, buy money orders at the post office,
and get above-market-rate used-car loans from unscrupulous
finance companies. Reacting to this situation, some have
suggested that the CRA may provide an opportunity to encourage
banks to meet the financial services needs of lower income people,
who today are underserved with respect to many other financial
services to a greater degree than they are with respect to
mortgage lending (Stegman, Cochran, and Faris 2001).

6.4 Small-Business Lending
6.3 Service Test
During the GLBA debate, numerous proposals surfaced about
how to alter the CRA service test to account for the dramatic
shifts in the provision of financial services (Goldberg 2000). By
most accounts, the service test component of the examination
is the least well developed of the three. Review of the CRA
examinations for the banks interviewed for this study suggests
that regulators in general spend little time on this element of
the examination. In a typical CRA examination report, the
service test gets a fraction of the space devoted to the lending
test. The test focuses largely on the hours of operation and

Prior to the 1995 changes to CRA regulations, limited data
existed for tracking small-business lending. Although
assessments of banks’ mortgage lending benefited from
relatively detailed information reported under HMDA, the
assessment of small-business lending was subject to a lower
level of scrutiny. Since 1996, small-business data reporting and
public dissemination requirements for CRA lenders have
improved the ability to track and evaluate lending patterns for
this component of the examination, although small-business
data remain less detailed and comprehensive than HMDA
filings. In addition, the small-business data collected and
distributed pursuant to the CRA include limited information

FRBNY Economic Policy Review / June 2003

185

on business characteristics, failing in particular to report on the
race and gender of business owners. These factors combine to
limit the effectiveness of the CRA’s oversight of small-business
lending and limit its impact.
Among the weaknesses of current regulations is the fact
that only institutions with assets greater than $250 million
(those subject to the large bank examination) report smallbusiness data. A greater proportion of mortgage lenders file
HMDA reports because the asset threshold stands at a much
lower $31 million. In addition, the HMDA mandates
reporting by most nondepository residential mortgage lenders,
but only depository lenders file small-business data. Also,
unlike the HMDA, lenders report only on originated smallbusiness loans, not ones that they reject. Furthermore, the
“location” of a small business is ambiguous and could
potentially be the owner’s residence, mailing address, or
location of management offices or other firm facilities. This
ambiguity may enable potential borrowers to “game the
system” by using an address on their loan application that is
located in a CRA-eligible area in an effort to improve the
chances of their loan being approved.

6.5 Regulatory Toughness
Focus on the effectiveness of the implementation of the smallbusiness lending or the service-test portions of the CRA is part
of a larger set of issues relating to the uniformity of CRA
enforcement by the four regulatory agencies. The regulatory
agencies do coordinate their activities through the Federal
Financial Institutions Examination Council, but in practice there
is wide variation in how the CRA is enforced. In 1995, a U.S.
General Accounting Office study (1995) reviewed forty CRA
evaluations and found general evidence of inconsistent grading
from one examiner to another. Similarly, Thomas (1998)
reviewed 1,407 CRA examinations and found significant
variation both between and within regulatory agencies. Using
data from the Thomas study, Zinman (2001) found not only that
there was clear evidence of differing degrees of “regulator
toughness” from one regulator to the next, but also from one
geographical region to the next. Moreover, Zinman concluded
that this variation in the degree of toughness mattered, in that
banks with tougher regulators were more likely to expand the
provision of small-business loans.
Findings such as these continue to fuel the ongoing debate
as to how best to implement CRA provisions in the evolving
world of financial services. Absent further regulatory reform,
many bankers will continue to push for legislative relief,
arguing that the CRA is “unfairly” administered. At the same

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time, housing advocates will counter by noting that when
“properly implemented,” the CRA does produce clear benefits
and that there is significant room to extend the reach of the
CRA beyond the world of residential mortgage lending. In
short, the debate over how to implement the CRA effectively is
likely to continue into the foreseeable future.

6.6 HMDA Data Collection
Closely related to the ongoing discussion of CRA enforcement
is the discussion of HMDA data collection. The structure of the
large-bank CRA examination formally makes the lending test
as important as the investment and service tests combined.
Anecdotal evidence suggests that of the three lending test
components, mortgage lending carries the most weight. To the
extent that this is true, it is a reflection of the fact that analysis
of mortgage lending is supported by HMDA data, which, while
imperfect, are more widely accessible, comprehensive, and
available over a longer duration than data for small-business or
community-development lending. It also reflects the large
share of all lending in lower income market sectors that is
devoted to housing.
HMDA data have also been the primary empirical tool used
to complement street-level activism by community advocates.
These groups have used the HMDA to evaluate and in some
instances lodge protests with regulators about the performance
of lenders in their communities. However, despite its important
role in the struggles of the 1980s and the first half of the 1990s,
HMDA’s usefulness waned as reporting requirements failed to
keep pace with the rapid restructuring of the mortgage lending
industry. Among the key changes are the growth of subprime
lending, the increased prominence of manufactured housing as
a tenure choice for lower income people, and the growth of loans
by consumer lending organizations.
The area where current HMDA data perhaps lagged the
market most was in the HDMA’s failure to collect data that
would allow loans to be distinguished as being for
manufactured housing or made at terms below the “A” rate.
Current practice by many analysts supplements public HMDA
data with a lender “specialization” list available from HUD that
makes it possible to classify loans as being made by an
institution that focuses on prime, subprime, or manufacturedhousing lending. Given the diversity of products offered by
large and even relatively small lenders, this constitutes a coarse
method of sorting loans. Many subprime lending specialists
also make prime loans, just as banks and mortgage lenders may
make subprime or manufactured-home loans, although the
bulk of their business may be in conventional prime lending.

Analysis of lending patterns for manufactured housing is
hampered by a lack of information on property characteristics,
making it impossible to determine whether a loan by a
manufactured-housing specialist involved the acquisition of a
unit placed on rented land or the purchase of a manufactured
home and associated land. Because the potential financial
outcome of the transaction for the typical owner of
manufactured housing rests in large part on whether or not he
or she owns the land, knowing the property characteristics
would allow regulators to assess differentially banks’ lending of
each type during the examination. Although this information is
known to the lender at the time the loan is made, many bankers
argue that including this information in the HMDA would be
prohibitively costly.
Subprime lending raises even thornier issues for regulators
attempting to assess an institution’s lower income mortgage
lending performance. Currently, regulators can obtain
information about the terms and pricing of mortgage contracts
that goes beyond what appears in HMDA reports. But review of
CRA evaluations suggests that most CRA examinations do not
take advantage of this potential. As a result, most examinations
merge all loans to lower income people and communities to
produce an aggregate lending total. This results, for example, in
equal credit being awarded in examinations for loans to lower
income people and areas made at the “A” rate and the “B” or
“C” rate, or for loans that do and do not reflect practices, such
as inclusion of single-premium credit insurance, that are
widely considered predatory. Meanwhile, the rise of new
players in the home mortgage market, including independent
consumer finance companies engaged in mortgage lending, has
served to limit the share of all home lending covered by HMDA
reporting.
Given the importance of understanding more fully the
implications of the rapid expansion of mortgage product
offerings—particularly as they relate to lower income
households and communities—in January 2002, the Federal
Reserve issued a rule to expand the number of nondepository
institutions subject to HMDA reporting requirements. The
rule also called for disclosing pricing data on higher cost loans
and identifying loans on manufactured homes. In particular,
the new rule extends HMDA coverage to nondepository
institutions making more than $25 million in mortgage loans.
Currently, nondepository lenders report under the HMDA
only if their residential mortgage lending (including home
purchase and refinance loans) during the previous year equaled
or exceeded 10 percent of their total loan originations. In
addition, the new rule requires lenders to identify whether the
loan is “high-cost,” as defined by the Home Ownership and
Equity Protection Act, and to report the spread between the
annual percentage rate and the yield on the comparable

Treasury security when this spread exceeds 3 percent for firstlien loans and exceeds 5 percentage points for subordinate-lien
loans. Finally, the new regulation requires lenders to report
whether the loan involves a manufactured home.

7. Conclusion
On this twenty-fifth anniversary of the Community
Reinvestment Act’s enactment, reform is needed to ensure that
the act keeps pace with dramatic shifts in mortgage lending and
the financial services industry. Reform could come either as a
result of new rulemaking by federal regulators or new
legislation. In either case, there appear to be two major
pathways to reform: 1) reform could maintain and improve
upon the CRA’s historical focus on residential mortgage
lending, or 2) reform could reposition the CRA to give more
emphasis to community-development activities and the
provision of banking services to lower income people and
communities more generally.
Residential mortgage lending has been central to the CRA
since its passage, yet the act’s historical focus on assessment
areas linked to deposit-taking activities makes little sense
today. Limiting detailed CRA scrutiny to assessment-area loans
arguably distorts the efficient operation of the marketplace.
Minimally, it seems unfair for the CRA to mandate detailed
scrutiny of a relatively large share of home loans made in some
metropolitan areas and by some lenders, while at the same time
devoting so little attention to the vast majority of loans made in
other areas and by other lenders. In order to extend the CRA’s
legacy of expanding home-buying opportunities to lower
income people and communities, federal regulators should
consider expanding assessment-area definitions to include
loans made by the CRA-regulated entities operating outside the
areas where they maintain deposit-taking branches. In
addition, Congress should also consider expanding the CRA to
include the residential mortgage lending operations of a diverse
set of nondepository organizations now playing an increasingly
important role in lending to lower income people and
communities.
Alternatively, if Congress and/or the federal regulators
choose to focus the most extensive CRA-imposed obligations
only on the CRA-regulated entities operating in assessment
areas defined by the location of deposit-taking branches, then
the CRA needs to be “repositioned” to better reflect what these
organizations actually do. Given the growth of large banking
organizations, many smaller banks and thrifts have abandoned
their historical residential lending operations, focusing instead

FRBNY Economic Policy Review / June 2003

187

on other forms of lending, including small-business and
community-development lending. In this regard, retail
banking services are arguably most closely linked to the
branch-banking mechanism through which CRA obligations
are defined and operated. Going forward, new CRA regulations
could expand the CRA’s focus on small-business and
community-development lending and investment as well as the
provision of banking services.

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The Twenty-Fifth Anniversary

In any event, the Community Reinvestment Act must
change. It is hoped that Congress, having finished work on the
Gramm-Leach-Bliley Act, will continue to work with housing
advocates, industry representatives, and regulators to craft a
consensus on “CRA modernization” and how best to address
the ongoing needs of lower income communities for improved
access to credit and financial services.

Endnotes

1. This paper draws on research funded by the Ford Foundation and
contained in the Joint Center for Housing Studies report, “The
Twenty-Fifth Anniversary of the Community Reinvestment Act:
Access to Capital in an Evolving Financial Services System.” See also
previous work completed by the Joint Center and the Brookings
Institution for the U.S. Department of the Treasury (Litan et al. 2000,
2001). An earlier version of this paper, “The Evolution of CRA:
Changing Industry Structure and CRA Regulations,” was presented at
the American Real Estate and Urban Economics Association Annual
Meeting in January 2002.
2. Insured depository institutions include any bank or savings
association, the deposits of which are insured by the Federal Deposit
Insurance Corporation (FDIC). CRA does not apply to credit unions
and independent mortgage companies.
3. The federal banking regulators responsible for administering the
statute are the Office of the Comptroller of the Currency for national
banks; the Board of Governors of the Federal Reserve System for statechartered banks that are members of the Federal Reserve System and

for bank holding companies; the Federal Deposit Insurance
Corporation for state-chartered banks and savings banks that are not
members of the Federal Reserve System and whose deposits are
insured by the FDIC; and the Office of Thrift Supervision for savings
associations whose deposits are insured by the FDIC and for savings
association holding companies.
4. For an excellent collection of essays on the cause and extent of
mortgage lending discrimination, see Goering and Wienk (1996).
5. The four-tiered rating system was: outstanding, satisfactory, needs
to improve, substantial noncompliance.
6. Institutions are defined as those with $250 million or more in assets
or those belonging to a holding company with $1 billion or more in
assets.
7. The shares for Oakland and San Jose are 25 percent and 28 percent,
respectively.

FRBNY Economic Policy Review / June 2003

189

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Harvard University Joint Center for Housing Studies.

191

Ronay Menschel

Preservation First

would like to begin by thanking Christine Cumming and
Michael Schill and their staffs for organizing this conference.
It certainly has enhanced our understanding of the issues, and
hopefully will lead to a more informed and therefore more
effective policy to address the affordable housing needs of this
city and the region.
A central theme of the presentations today is the need
to learn from past mistakes as well as past successes. As
Commissioner Perine observed, there have been many
mistakes made in the past. She clearly is someone who is
carefully learning from the past as she shapes how we move
forward.
The key issue for New York City and the region is
preservation of the existing affordable housing stock.
Commissioner Perine mentioned how much of that stock
has been lost in past years—how far behind we have gotten
because we allowed so much of the old affordable housing
stock to slip through our fingers in the 1970s and 1980s.
Of course, new housing construction rates also have been
far short of demand.
Housing advocates had hoped that the region’s economic
growth of the 1990s would continue, with associated rapid
growth in tax revenues. In addition, we were all looking at
excess revenues from the sale of the World Trade Center and
from Battery Park City to provide additional resources to
address affordable housing issues. Instead, we are now
confronted with a very constrained economic environment.

My sense is that Mayor Bloomberg understands the importance of affordable housing in any economic development
strategy, and that is significant. And it is noteworthy that
Commissioner Perine reports to Daniel Doctoroff, Deputy
Mayor for Economic Development. Affordable housing will
therefore be well represented in the entire policy mix.
Regrettably, we will not have as many resources as we once
thought we would. As Assistant Secretary Bernardi noted, the
federal budget is holding up reasonably well, and that is
helpful—although affordable housing for some years has not
been the funding priority at the federal level that it should be.
As we consider what needs to be preserved, we have to look
at housing created with public-sector dollars and private
dollars. And we have to be mindful that capital has to be
available to property owners—capital that they can access even
in more difficult times—so that their properties do not
deteriorate further. We also have to look at the incentives given
to those owners to maintain their buildings.
On the federal front, we have a large portfolio of what are
called “older-assisted” properties. This is one of my favorite
topics. Phipps Houses has two older-assisted properties, more
than twenty-five years old, in need of capital renewal. These
buildings receive very hard use, as do all older-assisted
buildings, at least in New York. And they have been undercapitalized by HUD through the years. The question is how to
put capital into those buildings.

Ronay Menschel is chairman of Phipps Houses.

The views expressed are those of the author and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

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HUD’s Mark-to-Market program does not offer a solution
for many of the older-assisted properties because it is directed
at newer, federally assisted properties with rents that exceed
120 percent of an area’s fair market rent. Ironically, olderassisted properties generally have a lower rent scale, and while
in greater need of capital repair, they do not qualify for the
program. Mark-to-Market is directed more at reducing the
federal government’s Section 8 burden than putting up capital
for renewal. Mark-up-to-Budget holds greater promise, in that
properties with lower rents can qualify, but they must pursue a
tortuous process to gain HUD’s approval of increased subsidies
to service increased debt. I listened with some envy when
Assistant Secretary Bernardi said that some rules and
regulations were put aside for a $700 million community
development block grant to the city. Whatever the refinancing
program, HUD’s lending and grant-making process needs to
be accelerated.
I know that the Millennial Commission is looking at
revisions to the exit tax. That may provide some opportunities
and incentives to investors of twenty-five years ago to transfer
their properties to not-for-profits without suffering negative
tax consequences. The recipient not-for-profits can protect the
properties and bring to bear new financial resources. In
addition, not-for-profits are usually vested in the community
for the long term and have a broad, comprehensive agenda that
includes community preservation.
During John Goering’s presentation on the Moving to
Opportunity Demonstration, I was interested to hear that some
significant results were obtained. Of course, they were realized
in very extreme situations in the Chicago Housing Authority,
where you had people living in terrible conditions. My
response to this strategy is that it can be exercised only on a
relatively small scale—this is a point Lance Freeman also made.
We cannot move everybody out. We have to make our lowincome communities work. That is how leadership by not-forprofits has demonstrated positive results.
This is especially true of the affordable housing program in
New York, where you have the involvement of not-for-profit
community-development organizations. Investments by
community-based organizations are comprehensive: the
organizations are concerned about education, local health care,
youth development, and public safety. Their leadership brings
community residents together to advocate for themselves and
for individuals to be mutually supportive. We have seen that
homeownership, interspersed within these communities, has
proved effective again in stabilizing neighborhoods and in
improving both their physical condition and their social fabric.
An earlier presentation described the positive effects that
investment in a property or in new construction has had—a
certain “halo” effect. Likewise, studies presented today

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Preservation First

illustrated the impact of neighborhood conditions on public
safety and on children. Children’s development is affected by
neighborhood conditions. And as Lance said, families need
social support. That support has to accompany physical
changes.
The City of New York, of course, has its own housing stock
in need of preservation. First, the city-managed stock needs to
be brought up to standards and fully utilized. We know that
many city-owned and -managed buildings are only partially
occupied. A priority is to make those buildings not only more
habitable, but fully occupied.
I found Glynis Daniels’ description of areas with high
concentrations of HPD violations—which obviously mirror
the high delinquency rates—to be very interesting. To me, it
suggested where the city’s priorities might lie in terms of future
investment: low-interest loans to private owners for repairs,
third-party transfers, and the use of tax credits to help finance
improvements to buildings. In addition, these are communities
for which city social service investments should be designed to
complement brick-and-mortar investments so as to maximize
the benefits of each. The current administration realizes that it
has to coordinate the work of all agencies that affect housing.
So you have Deputy Mayor Doctoroff, City Planning, the
Department of Buildings, the Department of Finance, the
Department of Housing Preservation and Development, and
even the Human Resources Administration all concerned with
housing. Recently, the Human Resources Administrator called
together the leaders of each agency that has an impact on the
homeless and on people who receive Temporary Assistance for
Needy Families to discuss this particular population’s housing
needs. That is the type of coordinated approach that is required
and is being pursued.
Finally, I would like to comment on the issue of vouchers
and their effect on production, a topic that was addressed in
some of the presentations. Vouchers have very limited, if any,
effect on housing production in New York City, where it is hard
to find an apartment to rent using a voucher. The voucher is
given to the individual, not a developer. While there is a steady
flow of voucher funding by HUD, this revenue stream cannot
be used to finance new housing—a lost opportunity. We need
to be able to obligate vouchers to rental properties in development, just as vouchers can now be used for first-time
homeownership. Hopefully, this is something that can be
examined in greater detail.
In short, in times of limited resources, we have to be more
ingenious and learn from the past. It is paramount to preserve
what we have and to achieve higher utilization from it. In
today’s world, we have to look to a mix of funding sources,
blending subsidies, low-interest loans, and tax credits with
market rate financing. Important too is identifying early trends

of tax delinquencies and multiple building code violations, and
providing assistance (often modest dollars) before such
deterioration overwhelms a community. Ultimately, New York
City will have to devote a greater share of public resources to

increase the inventory of affordable housing available to lowand middle-income people and families if it is to continue to be
a city of growth and opportunity.

197

Richard Roberts

The Building Blocks
for Private Investment
in New York City’s
Underserved Communities
I

t is good to see everybody, and I trust you are having an
informative day. First, I want to thank Christine Cumming
and Michael Schill for inviting me to participate in this
conference. I thought I would share with you briefly the
perspective of someone who invests in the neighborhoods
and communities that are the focus of today’s discussions. We all
bring different perspectives to this issue, either from government,
the nonprofit sector, or the private sector. I think that I add an
interesting view: that of someone who is focused on generating
a fair, risk-adjusted return on the firm’s capital.
Goldman Sachs’ Urban Investment Group is an
opportunity fund that specializes in making investments in a
broad range of opportunities that we refer to as the urban
emerging market. We invest in minority-owned businesses,
which for the most part are located in or provide goods and
services to core urban areas: generally low- and moderateincome areas. In addition, we are investors in urban real estate.
We are a comprehensive real estate investor in the sense that we
focus not only on housing but on other types of real estate as well.
There are, of course, more traditional sources of privatesector capital for these markets. First among them is the
Community Reinvestment Act. As we heard earlier, and as
many of us know firsthand, the act has had a dramatic effect in
terms of directing private-sector resources into urban

neighborhoods. The government-sponsored mortgage
enterprises—Fannie Mae and Freddie Mac—are another
traditional source. For many years, there has also been a host of
tax-motivated incentives, such as the low-income housing tax
credit and other types of tax partnerships. Quite frankly, the
biggest of these tax-motivated sources has been the mortgage
interest deduction, which encourages people to become
homeowners no matter where they live. The deduction has had
a strong effect on directing private-sector resources into urban
neighborhoods, although its reach is limited to those capable of
becoming homeowners.
One of the things we have found is that there has been
tremendous pressure on corporate earnings over the past year
or two, making it very difficult for us to invest our money. Even
with the increases in the low-income housing tax credit,
syndicators report that it has been very difficult to raise tax
credit equity for projects. For those of us who historically have
been developing these projects, we do not see that pressure.
Now, you may see it in pricing and other areas, but it has been
very tough to raise tax credit equity. That is just something to
consider when you are heavily dependent upon these types of
mechanisms to attract resources. That being said, all of this tends
to be supplementary to the capital that the government and the
not-for-profits and philanthropic organizations provide.

Richard Roberts is managing director of Goldman Sachs’ Urban
Investment Group.

The views expressed are those of the author and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve
System.

FRBNY Economic Policy Review / June 2003

199

These traditional private-sector sources of capital in
combination with government have fueled tremendous
investment and change in many of these neighborhoods and
communities. More recently, opportunity funds have started to
appear. These funds, which are separate and apart from
distressed investors, look for dislocations or problems in the
market, and there are always some. We are investors who
believe that there is value in these markets and that in a fair and
appropriate way it is possible to earn adequate risk-adjusted
returns. Opportunity funds have emerged because of the
increased number of people focusing on the commercial
opportunities in these neighborhoods. For example, Porter
began by focusing on the retail disallocation in low-income
neighborhoods and how density in these neighborhoods might
create real opportunities. With this success, others began to
realize that, from an opportunity fund standpoint, investing in
these neighborhoods might actually make sense.
From a housing standpoint, we are dependent upon several
things. One is a vibrant for-sale market, because we tend not to
focus on being a long-term holder. However, it should be noted
that long-term investors in multifamily housing have not done
so badly. That is probably one of the best performing asset
classes over time. But when compared with other asset classes,
it tends to represent a much longer hold on your money. In
addition, there is an emphasis on, for obvious reasons, market
rate opportunities because, as a general matter, we think we can
do better with respect to our returns.
There are many different funds that have focused on similar
investments. CPC now has a fund that focuses on opportunities
here in New York. Both Jerry Salama and Magic Johnson have
opportunity funds focused on the inner city. Magic’s tends to
be more focused on the commercial front while Jerry’s is more
of a multifamily, affordable housing fund. In addition, Fannie
Mae has a very important and aggressive equity fund in this
arena: the American Communities Fund. All of these examples
represent people attracting institutional money with fair and
very aggressive rates of return. That leads to some conclusions.
First, the money tends to be very expensive. We are not
looking for 9 percent tax credit yields. If we could earn
9 percent in New York, we would be ecstatic. But as a general
matter, we are looking for something much more aggressive.
We are obviously willing to assume some real risks, which has
not been the case with other investors. Why are we so willing?
One reason is that we believe that these markets are strong. We
have been looking at the research of Porter and others in terms
of the underlying strength of the markets, and we believe that
there is a good amount of value there.
Second, the quality-of-life improvements that have
occurred in low- and moderate-income areas throughout the
country have made the areas much more attractive candidates

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The Building Blocks for Private Investment

for investment. Third, we recognize the type of first-loss
position that the government and not-for-profit sectors have
assumed. The massive public investment that has occurred in
places like Harlem and the South Bronx has created a platform
for us to start looking at other potential investments.
The demographic trends are undeniable. When you look at
the growth of immigrant communities and communities of
color throughout the country, and the fact that they are
disproportionately located in urban areas, you can conclude
that there are strong investment opportunities not only in real
estate, but also in a host of commercial activities ranging from
cable television to radio to retail.
The prospect of attractive returns for investors like us is
based on the strong likelihood of rising economic fortunes in
these areas. But also, quite frankly, in tough economic times,
pricing tends to come down—and the idea is to buy low and
hopefully sell high. So if you believe the demographic trends
and the density story, then do not worry about the fact that the
macroeconomic environment is not ideal. Because if you can
buy economically and invest economically, you ultimately will
earn your returns.
From a policy standpoint, some things must occur for this
trend of more aggressive investment to continue. I will focus on
New York because it is the area I know best. For one, there
needs to be continued emphasis on quality-of-life
improvements. The favorable underlying trends, such as
declining crime, have made these communities attractive places
for investment. Should there be a reversal in these trends and
crime rates start to rise again, these areas will quickly become
much less attractive for what I call unassisted equity capital.
There also needs to be greater emphasis on regulatory
reform and cost reduction. We have seen a number of projects
where people come in and say, for instance, that the time is
right for a hotel in a particular underserved community or
market. The first thing we ask them is whether they have a site
plan and whether the site is entitled. If the site is not entitled, it
can take fifteen months or more just to determine whether the
project can be built on a proposed site. By that time, all of the
other things that we are looking at in terms of our economic
and financial analysis will have changed. From the standpoint
of committing capital, you have to be able to move with some
degree of certainty and you have to be able to move relatively
quickly. There are many opportunities to invest. Why wait on
a particular project to be entitled when you can invest
elsewhere and earn a fair and appropriate return?
That strategy applies not only to land-use planning and site
designation, but also to the allocation of the particular groups
with whom the government decides to work. We have seen a
number of projects that were very worthwhile and appropriate.
Because we can invest anywhere, we are going to invest with

people who we think can actually make the project take shape.
But if the city or the state or some other governmental entity is
wedded to a certain organization or group because of other
considerations, it is very difficult for us to think about
committing capital to that particular project.
People ask me why, as the former Housing Preservation
and Development commissioner, did I decide to go to an
opportunity fund? I often answer that we have been able to
move an agenda of affordable housing and community
development very far, and I feel very fortunate to have been a
part of the most recent history of that agenda. Government has

played a role in advancing that agenda, as have the nonprofits
and the private sector. However, there needs to be a more wideranging discussion. That is to say, I do not think that
opportunity funds or funds like the ones operated by Goldman
Sachs are by any stretch of the imagination the complete
answer or right for every project. But I do believe that people
who willingly invest in low- and moderate-income areas, rather
than in a range of other opportunities where capital can flow,
need to be at the table to participate in the discussion. I say this
because private capital can go a long way toward stretching the
resources of the other players.

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FRASER