Full text of Working Papers (Federal Reserve Bank of Chicago) : Why Has Home Ownership Fallen Among the Young?, Working Paper 2009-01

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

Federal Reserve Bank of Chicago

Why Has Home Ownership
Fallen Among the Young?
Jonas D.M. Fisher and Martin Gervais

REVISED
February 12, 2010
WP 2009-01

Why Has Home Ownership
Fallen Among the Young?∗
Jonas D.M. Fisher
Federal Reserve Bank of Chicago
jfisher@frbchi.org

Martin Gervais
University of Southampton
gervais@soton.ac.uk

February 12, 2010

Abstract
We document that home ownership of households with “heads” aged 25–44
years fell substantially between 1980 and 2000 and recovered only partially during the 2001–2005 housing boom. The 1980–2000 decline in young home ownership occurred as improvements in mortgage opportunities seemingly made it
easier to purchase a home. This paper uses an equilibrium life-cycle model
calibrated to micro and macro evidence to understand why young home ownership fell over a period when it became easier to own a home. A trend toward
marrying later mechanically lowers young home ownership after 1980. We show
that the large rise in earnings risk which occurred after 1980 can easily account
for the remaining decline in young home ownership.
Journal of Economic Literature Classification Numbers: E0; E2; J1; R21.
Keywords: Housing, home ownership, tenure choice, first-time home-buyers,
marriage, income risk

∗

We thank anonymous referees, Francesco Caselli, Morris Davis, Fran¸ois Ortalo-Magn´, Filippo
c
e
Scoccianti, and seminar participants at various institutions for their comments. We are grateful to
Faisal Ahmed, Nishat Hasan, and Eric Vogt for excellent research assistance. The views expressed
herein are those of the authors and do not necessarily represent those of the Federal Reserve Bank
of Chicago or the Federal Reserve System.

1

Introduction

Increasing home ownership has long been a high priority of policy makers. This strong
interest has led to a proliferation of legislated institutions and regulations intended
to make home ownership easier. Despite these efforts, home ownership of households
with “heads” aged 25–44 years declined substantially between 1980 and 2000, even
as the aggregate home ownership rate was rising. Young ownership recovered only
partially during the 2001–2005 housing boom. The declines in young home ownership
after 1980 occurred as government intervention and private innovation in mortgage
markets should have made it easier to purchase a home. This paper seeks to understand why home ownership of the young declined so much during this period when
owning should have become easier.
Our explanation is driven by changes in marriage and idiosyncratic earnings risk.
Below we document that marriage and home ownership are tightly linked. This fact
underlies why, for any given cohort and age, the married tend to own more than
the unmarried. Because of this tendency, a decline in the incidence of marriage
mechanically lowers home ownership. Between 1980 and 2000 marriage rates for
individuals aged 25–44 fell by 15 percentage points. But, this fall in marriage rates
does not account for all of the decline in young home ownership: combining the 2000
shares of unmarried and married with the corresponding 1980 home ownership rates
accounts for only half of the decline. Another indication that something else must be
going on is that home ownership has fallen for young, married households as well.1
The other main source of decline in young home ownership we point to is a rise
in household earnings risk. There is ample empirical evidence that individual home
ownership declines with higher earnings risk.2 Furthermore, there is powerful evidence
that earnings risk has increased since the 1970s. With the most recent data Moffitt
and Gottschalk (2008), Meghir and Pistaferri (2004) and Cunha and Heckman (2007)
confirm earlier findings reviewed by Katz and Autor (1999). In their recent survey of
1

We are not the first to notice the decline in home ownership of the young or the potential for
marriage to play a role in this decline. See, for example, Haurin et al. (1988) and Haurin et al.
(1996).
2
Several empirical studies based on micro data find an unambiguous negative effect of earnings
uncertainty on home ownership, including Diaz-Serrano (2005), Fu (1995), Haurin (1991), Haurin
and Gill (1987), and Robst et al. (1999).

2

the evidence, Moffitt and Gottschalk (2009) conclude that there has been a substantial
rise in earnings uncertainty after 1980 compared to the 1970s, and that much of this
increase occurred in the early part of the 1980s. There are differences depending
on educational attainment and whether income is measured for males, females or
families, but the overall trend is unmistakable. Higher earnings risk has two opposing
effects on home ownership. Precautionary savings increase with earnings risk, and
this should ease the transition to home ownership. In our analysis we find this effect
is dominated by the impact of risk on the value of delaying home ownership. Other
things being equal, an increase in earnings risk reduces the incentive to own a home
when there are proportional adjustment costs. It is well-known that such costs exist
for housing transactions. In the presence of proportional transactions costs, the option
of delaying the first home purchase until the household is possibly wealthier, and can
afford a larger house, has value. An increase in household earnings risk increases the
value of this option, thereby delaying the transition to home ownership and lowering
the home ownership rate. This effect is analogous to impact of a firm’s revenue
uncertainty on partially irreversible investment studied by Dixit and Pindyck (1994),
Abel and Eberly (1996), and many others.
Several factors a priori should have worked to raise young home ownership between 1980 and 2000 and make the observed decline seem puzzling. We have already
emphasized the possible impact of mortgage innovations and government intervention
on home ownership. Another important factor is the greater participation and improved outcomes of young females in the labor market after 1980. From 1980 to 1990
the employment rate of 25–44 year old females rose by close to 10 percentage points.
Concurrently, the male–female average wage premium was shrinking. So, households
with at least one female worker, other things unchanged, are richer, which can increase home ownership. Like Caucutt et al. (2002), we suspect that these changes in
female labor market outcomes also drive the decline in marriage. However, we do not
account explicitly for this connection in our analysis.
We disentangle the effects of the competing factors driving young home ownership with an equilibrium life-cycle model of consumption, saving, and housing. As in
Kiyotaki et al. (2007) and Ortalo-Magn´ and Rady (1999) home ownership is desired
e
because the utility from renting is discounted relative to owning the same house.
Marriage is modeled as the middle stage of a three stage life-cycle and the link be3

tween marriage and home ownership is captured by allowing the rental discount to be
greater when married compared to the first stage of life. We account for less marriage
by reducing the rate of transition to the middle stage of life, since evidence we discuss below suggests delayed entry into marriage is the main determinant of declining
marriage rates. Higher household earnings risk is accounted for by increasing the
variance of the household’s idiosyncratic earnings process.
We calibrate the model so that its stationary equilibrium is consistent with key
features of the aggregate U.S. economy in the years leading up to 1980. At the calibrated parameters the model is consistent with microeconomic evidence that income,
wealth, marriage, and age are significant predictors of home ownership. We then
compare the 1980 calibration with the stationary equilibrium incorporating various
structural changes affecting home ownership in 2000, including delayed marriage,
higher earnings risk, improved female labor market outcomes, lower growth in the
number of households, relaxed credit constraints, and higher real house prices. Holding prices fixed, the effects of delayed marriage and higher earnings risk are large
enough to offset the other structural changes, which boost home ownership. Using a
conservative calibration of risk, the changes in marriage and risk account for between
3/5 and 4/5 of the decline in young home ownership. Easier access to mortgage credit
has a relatively small positive impact on home ownership in the model.
Our paper contributes to the original home ownership literature and two additional
emerging literatures. Theoretical work on housing spans models which seek to explain
why owning and rental housing coexists, for example Henderson and Ioannides (1983);
models which explore the impact of risky house prices, income, and credit constraints
on housing choices, such as Ortalo-Magne and Rady (2002); and models that treat
housing within an asset portfolio choice framework, including Berkovec and Fullerton
(1992), Flavin and Yamashita (2002) and Piazzesi and Schneider (2008). There is
also a large empirical literature on home ownership, some of which we have already
cited. Much of this literature focuses on the impact of credit constraints on home
ownership, including Haurin et al. (1996) and Engelhardt and Mayer (1998).
There is an emerging literature on the aggregate implications of rising idiosyncratic
earnings risk. Blundell et al. (2008), Krueger and Perri (2006), and Heathcote et al.
(2008) have begun to investigate the aggregate implications of rising idiosyncratic

4

earnings risk for labor supply and the weaker rise in consumption inequality since
1980. This literature has yet to address how rising earnings risk is connected to
trends in home ownership.
The second emerging literature studies housing choices within the context of equilibrium life-cycle models. Ortalo-Magn´ and Rady (1999) and Ortalo-Magne and
e
Rady (2006) describe how a downpayment constraint, fixed housing supply and the
housing property ladder interact to propagate shocks through house prices. Given our
focus on the long run, it is natural to assume housing is in perfectly elastic supply
and this is what we do. However, we do consider the affect of a higher real price of
housing. Gervais (2002) examines the preferential tax treatment of houses on tenure
choice. We abstract from this issue, but do discuss the possible role of taxes in the
decline in young home ownership. Chambers et al. (2009) study how changes in
mortgages can account for the large increase in overall home ownership after World
War II and the additional increase between 1995 and 2005. They find a large role for
the down payment constraint after the War, but attribute the post-1995 increase to
other mortgage features which became prevalent in that period. We think these latter
factors have a limited impact on the 2000 home ownership rate we are interested in.
Kiyotaki et al. (2007) explore the global run-up in house prices. They find relaxing a
down-payment-like constraint in their model has a large impact on home ownership.
They assume housing is equity-only financed, which is non-standard, and so makes
ıaz
it difficult to compare our findings. D´ and Luengo-Prado (2008) examine various
factors which influence the lifetime pattern of housing choices including the role of
idiosyncratic earnings risk. Consistent with our analysis, they find an important role
for earnings risk.
The rest of the paper proceeds as follows. In section 2 we document the trends
in home ownership and marriage as well as the impact of marriage on the propensity
to own a home. In section 3 we describe our life-cycle model. Section 4 describes
our calibration and individual decision making this implies. Section 5 decomposes
the decline in home ownership of the young into the influence of the various factors
discussed above assuming that housing prices do not change. Section 6 considers other
possible explanations for the decline in young home ownership, including changes in
house prices, mobility, tax policy, and inflation. Section 7 concludes.

5

2

Evidence on Home Ownership and Marriage

This section documents the decline in young home ownership and discusses the role
marriage seems to have played in this decline.

2.1

Trends in Home Ownership, 1960-2007

Figure 1 displays home ownership rates for the economy as a whole and households
with heads designated by the Census Bureau aged 25 to 44 years.3 These rates are
calculated using the Census of Population and Housing for the years 1960–2000, and
the American Community Survey for 2001–2007 (ACS). We use the Census Bureau
definition of the home ownership rate for a household with particular characteristics
such as age: the number of households with those characteristics who own divided by
all households with those characteristics.
The overall home ownership rate grew by 3 percentage points between 1960 and
1980, dropped by about 1 percentage point between 1980 and 1990, and rose another
3 percentage points between 1990 and 2006, before dipping slightly in 2007. Key
factors driving the post-1990 rise in aggregate home ownership are higher ownership
rates for households over age 65 and an aging population. We discuss this further
below. For the young the time path is quite different. There is a large drop between
1980 and 1990, which takes the young home ownership rate two percentage points
below its 1960 level. Young ownership peaks two years earlier than the aggregate
rate and in 2007 is essentially back to its 2000 level. The trough in home ownership
in 1990 undoubtedly is driven in part by the large housing recession. In addition,
e
we describe below how the short run model of Ortalo-Magn´ and Rady (1999) might
be used to understand the decline in home ownership between 1980 and 1990. Their
model is less relevant for the long run analysis we are interested in. We think other
factors must be in play as well because the young home ownership rate never again
attains its 1980 level.
For concreteness, we focus our analysis on the years 1980 and 2000. The year
3
Before 1980 the husband was always classified as the head when he and his wife were living
together. In 1980 and after the head could be any household member in whose name the property
was owned or rented. If no such person was present, any adult could be selected.

6

.56

.58

.6

.62

.64

.66

.68

Figure 1: Home Ownership Rates, 1960-2007

1960

1970

1980

1990

2000

2007

Year
All Ages

25−44

Source: Our estimates for 1960–2000 are based on the Census
of Population and Housing and for 2001–2007 the ACS.
1980 is the year with the highest young home ownership rates. We use the year 2000
for three reasons. First, by this time the structural changes we emphasize are firmly
in place, including the developments that have made it easier to purchase a home.
Second, unlike 1990, cyclical factors should be playing a limited role at this time.
Third, the years after 2000 involve unique driving forces which are beyond the scope
of this paper.
The Census is not the only source for aggregate home ownership rates. It is instructive to consider two other sources as well: the Current Population Survey (CPS)
and the American (formerly Annual) Housing Survey (AHS). Table 1 displays changes
in home ownership rates, by age, between the years 1980 and 2000 for the Census
and CPS and between 1980 and 1999 for the AHS. Interestingly there are noticeable
differences in the levels of the home ownership rates across the three datasets, with
home ownership rates the highest in the CPS. However, the qualitative patterns are
quite similar, except that the CPS indicates a slight decline in the overall home ownership rate, while the other two data sources show a small increase. Most importantly,
7

Table 1: Changes in Home Ownership Rates by Age between 1980 and 2000
Source

All Ages

25-29

30-34

35-39

40-44

45-64

65+

65.0
66.2
1.2

43.4
36.0
-7.4

60.7
53.0
-7.7

69.7
63.4
-6.3

74.3
69.1
-5.2

78.0
76.8
-1.2

70.8
77.5
6.7

1980
2000
Change

68.0
67.2
-.8

46.6
37.1
-9.5

63.5
54.6
-8.9

72.8
63.6
-9.2

76.3
70.4
-5.9

80.7
77.9
-2.8

74.1
80.5
6.4

AHS
1980
1999
Change

66.5
66.9
0.4

44.2
37.0
-7.2

62.3
54.4
-5.9

72.3
64.2
-8.1

75.1
70.7
-4.6

79.1
77.8
-1.3

72.9
80.3
7.4

Census
1980
2000
Change
CPS

Note: Home ownership rates and their changes are reported in percentage points.
regardless of the source of the evidence, the changes for the young age groups 25-29,
30-34, 35-39, and 40-44 are all large and negative. We conclude that, regardless of
the source of the data, the evidence strongly suggests there has been a substantial
decline in home ownership among the young between 1980 and 2000. For simplicity,
we base our analysis on the Census.4
All three datasets indicate the home ownership rate for the 45–64 age group declined by a small amount, while the rate for the over 65 age group rose by a large
amount. The structural changes we have in mind should have smaller effects on these
two age groups. We think their home ownership rates are primarily influenced by
decisions made younger in life. This last point is easiest to see with the over 65s. The
2000 over 65 age group corresponds to the 45-64 age group in 1980, and, consistent
4

We are more confident in the Census home ownership numbers because home ownership is a
primary measurement target for this data source. The Census Bureau appears to place more weight
on the Census when it uses it to re-calibrate the CPS periodically. The CPS uses weights designed
to improve the precision of its labor market variables, not home ownership rates. Similarly, the AHS
is designed to measure home sales, not home ownership rates.

8

with older home ownership being driven by decisions made when younger, their home
ownership rates are very close. Green and Hendershott (1995) emphasize the improved health and wealth of later cohorts. These trends make prior ownership more
persistent. We do not think it is necessary to model these features of the data as
long as we can establish that our findings are robust to ignoring the impact of older
individuals on equilibrium outcomes. We do this by considering partial equilibrium
experiments where the interest rate is held fixed.
Table 2 demonstrates that the decline in young home ownership between 1980 and
2000 is broad-based. It breaks out the decline in ownership of young households by
different household characteristics. For all distinguishing characteristics but four, the
home ownership rate has fallen between 1980 and 2000. These include, race, number
of children, number of adults, region, educational attainment, and income quintile.
The increases are concentrated among single females living alone. We suspect these
increases are mainly due to the wealth effect discussed in the introduction.

2.2

Trends in the Mortgages of First-Time Home Buyers

The decline in home ownership among the young is striking because it came during
a time when mortgage opportunities for young families seem to have expanded dramatically. Many papers document the development of the mortgage market and the
regulatory changes since the early 1980s.5 Public and private initiatives expanded
mortgage opportunities by lowering transactions costs, the underlying real interest
rate, and the required down-payment, among other factors. We now briefly document how mortgage criteria for the young became less stringent after the 1970s,
concurrent with the changes to mortgage markets.
Table 3 describes borrowing characteristics of first-time home buyers over the
period 1976–1999. The mortgage market changes should have their largest impact
on first-time home buyers, because they are more likely to have lower income and
wealth and greater credit constraints than other buyers. Also, first-time home buyers
5

Florida (1986) contains several essays describing mortgage market deregulation. Gerardi et al.
(2009) provide a recent overview of how the mortgage market has evolved. Edelberg (2006) discusses
the expanded use of sophisticated credit scoring methods in the mid-1990s. Ryding (1990) and
Van Order (2000) describe the evolution of the secondary mortgage market. Chambers et al. (2009)
describe the evolution of mortgages after World War II.

9

Table 2: Young Home Ownership by Household Characteristic
Characteristic

1980

Difference

64.1
38.2
48.2

63.4
36.6
41.4

-0.7
-1.6
-6.8

67.2
36.0

63.9
43.1

-3.3
7.1

33.0
74.0

38.2
71.9

5.2
-2.1

41.4
63.3
72.5

45.1
60.4
67.0

3.8
-3.0
-5.5

30.9
69.6
71.7

36.2
66.4
61.2

5.3
-3.2
-10.5

54.4
66.4
62.4
56.0

53.5
63.8
59.1
50.5

-0.9
-2.6
-3.3
-5.5

49.8
62.1
64.8

40.2
57.4
62.7

-9.5
-4.5
-2.1

29.9
45.2
64.3
77.7
86.4

Head’s Race
White
Black
Other
Head’s Sex
Male
Female
Head’s Marital Status
Not Married
Married
Children in Household
None
One
Two or more
Adults in Household
One
Two
Three or more
Region
East
Midwest
South
West
Head’s Education
< High School
High School or Some College
College
Head’s Income Quintile
1
2
3
4
5

2000

27.6
43.0
59.1
73.5
83.9

-2.3
-2.2
-5.2
-4.2
-2.5

Source: Census of Population and Housing, 1980 and 2000.

10

Table 3: Characteristics of First-Time House Buyers
Statistic

1976-80

Median Price/Median Income
Mean Down-payment/Price
Mean Monthly Payment/After-Tax Income

1981-90

1991-99

2.0
.18
.29

2.1
.16
.34

2.4
.14
.35

Source: Various issues of The Guarantor, 1978-1999.
are typically within the young age groups we focus on. Table 3 indicates that firsttime buyers financed their house purchases with progressively larger value to income
ratios, lower down-payments, and higher monthly payments. In the 1976-80 period
the median house price averages 2.0 times median income, in the 1981-90 period the
multiple is 2.1, and over the period 1991-99 the multiple is 2.4. These houses are
purchased with an average down-payment of just 14 percent of the house value over
the period 1991-99, compared to 16 percent in 1981-90 and 18 percent in 1976-80.
To acquire the higher value houses relative to income, first-time buyers increase the
share of income they devote to mortgage servicing, rising from .29 in 1976-80 to
.35 in 1991-99. We interpret Table 3 as reflecting mortgage criteria for first-time
home buyers becoming more flexible after the 1970s, making larger houses feasible.
Another interpretation is that real house prices have gone up and households have
chosen, constrained by a down payment, to spend more on housing. The strength of
this mechanism relies on the strength of the down payment constraint. If mortgages
have become easier to obtain then this would mitigate the effect of higher prices
via the down payment constraint. Ultimately a structural model is needed to fully
understand which factor underlies the outcomes described in the table.

2.3

Marriage and Home Buying

To understand the changes in young home ownership, we need to assess what drives
the house purchase decision of the young. To start our analysis we use the National
Longitudinal Survey of Youth (NLSY) for the 1979 cohort of about 13,000 individuals
14-22 years of age. This is a dataset of individuals that also has information on family

11

Table 4: Linear Probability Model of Young Home ownership
Coefficient Standard Error
Real net assets (000s)
Real household income (000s)
Married (versus not married)
Female (versus male)
Race is White (versus not white)
Education is more (versus less) than college
Age (versus under 25)
25-29
30-34
35-39
Adults in household (versus single)
2
>3
Children in household (versus none)
1
2
>2
Number of Observations
R2

.006
.003
.23
.01
.02
-.03

.0002
.0001
.005
.003
.004
.01

.04
.09
.14

.004
.01
.01

.013
-.09

.004
.004

.05
.08
.04

.005
.005
.01
52,233
.34

Source: Our estimates using the NLSY.
level variables. The sample years are 1985-1994, 1996, and 1998.6 Since we have
relatively few observations for individuals aged 40 and over, we restrict the sample
to those aged 21–39. We use the NLSY because it has many more years of net worth
data over our sample compared to other datasets, and wealth is an important factor
for home ownership.
We begin by estimating a linear probability model of young home ownership using
the variables in Table 4 plus year effects as regressors. Assets and income are deflated
by the CPI.7 The table displays coefficient estimates with robust standard errors.
6

Wealth data are not available prior to 1985.
Net assets and income include those for the survey respondent and their spouse, if the spouse
lives with the respondent. Income is labor plus transfer income. As in Zagorsky (1999), net assets
are defined as the sum of home value, cash, stockholdings, trust holdings, business equity, car value,
7

12

The coefficients on the categorical variables are interpreted as marginal effects on
probability relative to the indicated omitted category. The coefficients on real wealth
and assets are interpreted as the effect of an extra $1,000 on the probability of owning.
Every variable is highly significant. Marriage stands out as having a particularly large
effect. The coefficient on marriage says that in our sample if you are married, then
you are 23% more likely to own compared to someone of the same assets, income,
sex, race, education, age, family structure, and year who is not married.
Does the connection between home ownership and marriage indicated by Table 4
reflect causality from marriage to ownership or the other way round? We use Figure 2
to address this question. This plots conditional probabilities of home ownership in
the years surrounding an individual’s first marriage. The probabilities are estimated
by regressing a dummy variable for whether the respondent is a home owner on a set
of dummy variables for the years before, during, and after the first marriage, plus
dummies for year, age, household size, educational attainment, and sex. The figure
plots the fitted values and 95% confidence intervals for the year relative to year of
first marriage for estimates based on two samples from the Panel Study of Income
Dynamics (PSID), 1968-1986 and 1979-1997. The omitted category is individuals
who are never observed to marry for which we have at least fifteen years of data on
whether they are married.
Figure 2 suggests causality running from marriage to home ownership and that this
relationship has been roughly stable over time.8 It is important for our theory that the
relationship can be viewed as stable. In the years leading up to the first marriage an
individual’s marginal likelihood of ownership is flat and less than 5 percent in both the
early and the late sample period. In the year of marriage the probability rises a little,
and after marriage it rises substantially and significantly. Four years after marriage
individuals are about 30 percent more likely to own a house compared to individuals
with the same characteristics who are not observed to marry. This pattern of flat
IRA holdings, certificates of deposit, 401(k) holdings, and non-car durables goods, less the sum of
mortgage and other property debt, car debt, and any other debt. Top coding affects about 10 percent
of the sample in the later years of the survey. Since individual items of net assets are top-coded, we
drop observations with any top-coding. We use the weights provided by the NLYS in our estimation
to correct for the oversampling of the poor and members of the military.
8
When we include ”Years before first child” in the regressions underlying Figure 2, there is little
impact on the marriage coefficients and the child coefficients are relatively flat and close to zero.

13

0

.1

Probability
.2

.3

.4

Figure 2: Home Ownership Around First Marriage

−4

−2

0
Years from marriage
1968−1986

2

4

1979−1997

Note: These are our estimates from the PSID. The coefficients
corresponding to -4 to 4 years after the first marriage are plotted
with 95% confidence intervals.
and low home ownership before marriage and rising home ownership after marriage
is what we expect if marriage is a significant driver of the home ownership decision.9
If home ownership caused marriage then we would expect a rise in the likelihood of
home ownership before marriage, not after.

2.4

Delayed Marriage and Trends in Home Ownership

Taken together, the empirical findings strongly suggest that any trends in marriage
will be important for understanding the decline in young home ownership. This leads
us to study trends in marriage and their impact on young home ownership using the
Census of Population and Housing.
The key trend in marriage is that young people are much less likely to be married
9

The analogous plot of the likelihood of marriage around the first home purchase reveals a
pattern consistent with this view: the likelihood of marriage rises before the first purchase and is
flat afterwards.

14

Figure 3: Marriage Rates in 1980 and 2000

.9
.8
.7
Percent
.4 .5 .6
.3
.2
.1
0

0

.1

.2

.3

Percent
.4 .5 .6

.7

.8

.9

1

B. Females

1

A. Males

25 27 29 31 33 35 37 39 41 43
Age

25 27 29 31 33 35 37 39 41 43
Age

Note: Solid line – 1980, Dashed line – 2000. These are our
estimates using the 1980 and 2000 Census of Population and
Housing.
at any given age today than they were in 1980. This is demonstrated in Figure 3.
For males, in 1980 there was roughly a 50% chance that you were married at age
25. By 2000 you would have to be 30 years old to have the same chance. Females
behave similarly, but not identically, since the age distribution of marriage matches
is different for males and females.
The marriage rate reflects two effects: entry into and exit from the state of marriage. Stevenson and Wolfers (2007) describe evidence indicating increased exit is
not the key factor underlying declining marriage rates since 1980, so we focus on
entry into the state of marriage. The primary determinant of entry into marriage is
the timing of the first marriage. As we just documented, the first home purchase is
tightly connected to the first marriage. So, delayed entry into the state of marriage
for the first time should delay the transition to home ownership as well.
Figure 4 demonstrates that the first marriage is substantially delayed in 2000
compared to 1980 for both males and females. It displays the fraction of males and
15

Figure 4: Never Married Rates in 1980 and 2000

.6
.5
Percent
.3
.4
.2
.1
0

0

.1

.2

Percent
.3
.4

.5

.6

.7

B. Females

.7

A. Males

25 27 29 31 33 35 37 39 41 43
Age

25 27 29 31 33 35 37 39 41 43
Age

Note: Solid line – 1980, Dashed line – 2000. These are our
estimates using the 1980 and 2000 Census of Population and
Housing.
females of a given age who have never been married. The upward shift in the profile of
“Never married” rates in 2000 compared to 1980 indicates that individuals are much
less likely to have ever married in 2000 compared to 1980. The magnitude of the shift
is similar to that for marriage rates shown in Figure 3. This points toward delayed
marriage being the primary proximate cause of the decline in marriage among the
young.
Figure 5 confirms that delayed marriage must lower home ownership rates. This
figure displays home ownership rates by marital status in 1980 and 2000. There was
essentially no change in home ownership between 1980 and 2000 for unmarried household heads. This presumably reflects our discussion of Table 2 that home ownership
has increased among single females and decline for males. Since the home ownership
rate of married household heads is always higher than for the unmarried at a particular age, it follows that the decline in the marriage rate mechanically leads to a
decline in home ownership. However, the fact that the home ownership rate of the
16

Figure 5: Home ownership Rates in 1980 and 2000

.8
Percent
.4
.6
.2
0

0

.2

Percent
.4
.6

.8

1

B. Married

1

A. Unmarried

25 27 29 31 33 35 37 39 41 43
Age

25 27 29 31 33 35 37 39 41 43
Age

Note: Solid line – 1980, Dashed line – 2000. These are our
estimates using the 1980 and 2000 Census of Population and
Housing.
married falls between 1980 and 2000 implies that something else must be involved.
This motivates our consideration of the role of heightened earnings risk.

3

The Model Economy

In this section we describe our life-cycle model of tenure choice with idiosyncratic
earnings risk. The model consists of households, goods producing firms, and financial
intermediaries. For parsimony, we do not model marriage explicitly.10 Households
experience a three stage life-cycle, where the second stage of life is interpreted as
marriage. They derive utility from consumption and housing services, are subject
to an exogenous, stochastic flow of labor income, and via intermediaries invest in
10

This modeling choice reflects our empirical findings that suggest a stable causal relationship
running from marriage to ownership. We view the factors driving changes in marriage rates as
distinct from the home ownership decision so that modeling the marriage decision is not crucial for
understanding the evolution of home ownership rates.

17

non-residential and housing capital. We assume that rental housing yields services at
a discount relative to owned housing and that the discount is larger when married.
Changing rental housing is costless, but owning requires a down-payment, and buying
and selling an owner-occupied house involves transactions costs. We now describe the
model in detail.

3.1

Households

Preferences The economy has a large number of ex-ante identical households who
forever repeat the same three-stage life-cycle of being single, having a family, and
retirement. The transitions between the stages of life occur with fixed and known
probabilities. Households care about their future selves as much as they care about
their current self and so preferences are represented by
∞

β j−t u(cj , ψj hj ),

Ut = Et

0 < β < 1.

(1)

j=t

For the incarnation of the household alive in period j, cj denotes the quantity of
goods consumed and hj is the quantity of housing the household occupies and either
rents or owns. The parameter ψj determines how much the household prefers to own
rather than rent. When the household rents its home ψj < 1 and when the household
owns its home ψj = 1. The parameter β is the household’s time discount factor. We
assume a time period equals one year. For simplicity, below we drop time subscripts.
With a couple of exceptions, the prime symbol denotes the current value of a choice
variable and the absence of this symbol indicates the previous period’s value of the
same variable.
Stages of the Life-Cycle The state variable s controls both the life-cycle status
and labor earnings of a household. Let s ∈ S = Y ∪ F ∪ R = {1, 2, . . . , N } ∪
{N + 1, N + 2, . . . , 2N } ∪ {2N + 1, 2N + 2, . . . , 3N }. Households go through three
stages of life. When s ∈ Y, a household is a single type whose housing services
when renting are discounted by ψ(s, 0) = ψy < 1. When s ∈ F, a household is a
family type. For this household type rented housing services are discounted at the
rate ψ(s, 0) = ψf . We have in mind that ψf < ψy , to capture the tight connection
18

between marriage and home ownership we have documented. This would also be
consistent with the empirical phenomenon that many housing services desired by
families, such as proximity to good schools and parks, are harder to obtain in rental
housing. However in our calibration we do not impose this condition. Finally, when
a household’s state transits to s ∈ R, the household retires and the rental discount
reverts to ψ(s, 0) = ψy .
Non-retired households supply one unit of labor inelastically and face uninsurable
idiosyncratic uncertainty with respect to their labor productivity. A household in
state s ∈ Y ∪ F is endowed with e(s) efficiency units of labor, each unit being
paid after-tax wage rate w = (1 − τw )w, where τw is a labor income tax and w
ˆ
ˆ
is the before-tax wage rate. The revenues from the labor income tax are used to
operate a pay-as-you-go social security system. All retired households are entitled to
a social security payment equal to a fraction, θ, of average before-tax earnings of the
working population. To keep the notation consistent with working households, we let
e(s) = θe/(1−τw ) if s ∈ R, where e is the average labor productivity of the workingage population. Given the simple structure of this social security system, it can easily
be shown that τw = θµR /(1 − µR ), where µR is the fraction of the population that is
retired.
The process governing a household’s state over time is described by the Markov
matrix Π,


ΠYY ΠYF 0N


Π =  0N
ΠFF ΠFR  ,
GΠRY 0N ΠRR
where 0N denotes an N × N matrix of zeros and the other terms are non-zero N × N
matrices. We use πss to denote individual elements of Π. Since households need to
go through an entire life-cycle, the probability of going from set Y to set R is zero.
Similarly, the probabilities of transiting from set F to set Y and set R to set F are
also zero. The elements of matrix ΠYY and those of matrix ΠFF control how efficiency
units supplied by single and family households evolve over time. The matrices ΠF R
and ΠRR are diagonal. The matrix ΠRY controls the probability of dying and the
magnitude of intergenerational earnings persistence. At the same time as death, a
new generation of households of size G are born, where G > 1 determines the rate at
which the number of households grows.
19

Labor efficiency of the newborn is controlled by the elements of the matrix ΠRY
as follows:


θ1 δ · · · θN δ


.
,
.
ΠRY = 
.


θ1 δ · · · θN δ
where δ is the probability of dying, and [θ1 , . . . , θN ] is the part of the invariant distribution of Π associated with the single stage of life. As written, the matrix ΠRY
assumes that there is no intergenerational earnings persistence because each household has the same probability of being any of the N types of single households,
regardless of the parent’s type at the time of death.
Housing We use the housing tenure variable x to indicate whether the household
rents or owns in the current period, and if it owns, the quantity of housing services
consumed. Households that currently own and occupy a house of size hi have x = i
and households who currently rent have x = 0.
Owned houses must be chosen from a finite grid,
¯
G = {hi , i = 1, 2, ..., M : hi ∈ [h, h]}.
Households that rent may choose a continuous quantity of housing for houses smaller
than h, but are confined to the set G for larger houses. The parameter h is important for reconciling home ownership rates with the quantity of owned housing in the
economy. We summarize the set of possible house choices in the current period as
follows:
h ∈ H(x ),
(2)
where
H(x ) =

(0, h) ∪ G, if x = 0;
G,
if x > 0,

and
x ∈ X = {0, 1, 2, ..., M } .

(3)

All houses depreciate at the rate δh ∈ [0, 1]. To accommodate the housing grid, we
assume that each house requires maintenance equal to depreciation each period in
order to be habitable.
20

In addition to assuming that there is a minimum sized house that can be owned, h,
we also assume that owning a house involves two kinds of costs. First, we assume that
to own a house the household must have an exogenously determined minimum equity
stake in the house the first year the house is occupied, i.e. it faces a down-payment
constraint. After the first year, and as long as the household does not change the
size of its house, the down-payment constraint does not apply. Second, if a household
changes the size of its owned and occupied house, it faces costs of buying and selling
that are proportional to the size of the house involved, τb and τs . Transactions costs
are given by

 τb hx ,
if x = 0 and x > 0;



 τ h + τ h , if x > 0, x > 0 and x = x ;
b x
s x
τ (x, x ) =
 τ s hx
if x > 0 and x = 0;



 0,
otherwise.
Saving Households accumulate wealth with two types of assets: owner-occupied
houses and a generic asset called deposits, d, which pay interest r. We assume the
interest is paid during the current period and the deposit is returned at the beginning
of the next period. Let a denote the household’s net worth at the beginning of the
period. All households face a non-negative savings restriction, a ≥ 0. In addition,
homeowners may borrow against their house by acquiring a mortgage at the interest
rate r. Consistent with deposits, the interest is paid during the current period and
the principal is paid at the beginning of the following period. Because households
borrow and lend at the same interest rate, they are indifferent between paying down
their mortgage and accumulating financial assets. We assume that households pay
down their mortgage before accumulating any financial assets.
The down-payment constraint says that a mortgage acquired in the current period,
m , is limited to be no more than a fraction γd of the value of the home so that
m ≤ (1 − γd )h . Current savings of a household that chooses to be a homeowner next
period are a = d +h −m . It follows that in the year the mortgage is acquired, savings
must be at least as big as the minimum down-payment on the house: a ≥ γd h . We
summarize the constraint on savings as follows
a ≥ γ(x, x ),
21

(4)

where
γ(x, x ) =

0,
if x = 0 or x > 0 and x = x ;
γd hx , if x > 0 and x = x .

Recursive Formulation of the Household Problem The problem faced by
households is to choose sequences of consumption, asset holdings, housing tenure,
and housing services to maximize (1), subject to (2)–(4), c > 0, and the budget
constraint
c + ph h + a + τ (x, x ) = we(s) + a + ra ,
(5)
where ph is the price of housing services determined by a no-arbitrage condition
described below.
To address how to allocate assets of retired households who die between periods,
we introduce annuities. We assume households face a 100% estate tax upon death
so they would never bequeath any wealth to their children. To avoid accidental
bequests, households participate in annuity markets. All retired households (the only
households that have a positive probability of dying) pool their net worth together in
the current period and divide that pool among the survivors in the following period
according to their proportion of the pooled net worth. Since each unit of net worth
has the same probability of surviving, 1 − δ, each retired household ends up with
1/(1 − δ) of their net worth tomorrow should they survive.
Let V (s, x, a) denote the value function of a household that enters a period with
state variables s, x and a. The recursive representation of the household’s problem is
as follows:
V (s, x, a) =

max

c>0,x ∈X ,
a ≥γ(x,x ),
h ∈H(x )

U (c, ψ(s, x )h ) + β

πss V (s , x , ϕ(s)a )

(6)

s ∈S

subject to (5), where ϕ(s) = 1 unless the household is retired in the current period,
in which case it equals 1/(1 − δ).

3.2

Producers

Firms maximize profits
f (k, l) − wl − pk k,
22

where f (k, l) is a constant returns production function, k denotes non-residential
capital used in production, l denotes the quantity of labor employed, measured in
efficiency units, and pk denotes the rental price of non-residential capital. We assume that producers’ output can be costlessly transformed into consumption goods,
new residential capital or new non-residential capital. Consequently, the prices of
these goods are all equal to one in a competitive equilibrium. Non-residential capital
depreciates at the rate δk ∈ [0, 1].

3.3

Financial Intermediaries

Non-residential investment and investment in rental housing is undertaken by overlapping generations of two-period-lived risk-neutral financial intermediaries. In their
first period, intermediaries accept deposits from households, Df , which they use to
purchase from the previous generation of intermediaries non-residential capital, Kf ,
and rental housing capital, Hf , and to issue mortgages to homeowners, Mf . During
the period the newly purchased non-residential capital is rented to producers and
the housing is rented to households.11 Interest on deposits is paid at the end of the
first period. At the beginning of the second period, the capital is sold to the new
generation of intermediaries, the mortgage principal is repaid, and the deposits are
returned to households. The problem of a financial intermediary is:
max

(pk − δk )Kf + (ph − δh )Hf + rMf − rDf

{Kf ,Hf ,Mf ,Df }

(7)

subject to the constraint
Kf + Hf + Mf ≤ Df .

(8)

The solution to this maximization problem yields the following no-arbitrage conditions:
pk = r + δk ;
ph = r + δh .

(9)

It follows that financial intermediaries are indifferent at the margin between their
asset holdings, and liabilities and they make zero profits in equilibrium.
11

As in Kiyotaki et al. (2007), we assume that new capital is productive immediately, i.e. there
is no time-to-build. This assumption is made to treat non-residential capital symmetrically with
housing. Since the time period in the model is one year we think this is a reasonable assumption.

23

3.4

Stationary Competitive Equilibrium

A stationary competitive equilibrium consists of a value function V (s, x, a), decision
rules for savings ga (s, x, a), tenure choice gx (s, x, a) and housing services gh (s, x, a),
prices {r, w, ph , pk }, a fiscal policy {τ, θ}, aggregate quantities {K , H , L}, an allocation for financial intermediaries {Df , Kf , Hf , Mf }, and a measure over household
types λ(s, x, a) such that
1. Given prices and the fiscal policy, the value function and associated policy rules
solve the household problem as given by (5) and (6);
2. Given prices and the fiscal policy, producers maximize profits. This implies
factors are paid their marginal products: pk = f1 (K , L), w = f2 (K , L), where
ˆ
L is the aggregate demand for labor by producers;
3. Given prices and the fiscal policy, {Df , Kf , Hf , Mf } solves the financial intermediaries’ problem given by (7) and (8). This implies (8) holds with equality
and the no-arbitrage conditions (9) hold;
4. Aggregates are consistent with individual behavior: λ(s, x, a) is generated by
λ(s , x , a ) =

 0,










if s ∈ R, s ∈ Y, a > 0
s∈Y

+
s∈S

πss
s∈R

πss

M
x=0

πss
M
x=0

λ(s, x, da)

a∈A(a ,x )
M
x=0 a≥0 λ(s, x, da),
a∈A(a ,x )

if s ∈ Y, x = a = 0

λ(s, x, da), otherwise

where
A(a , x ) = {(a, x) : ga (s, x, a) ≤ a , gx (s, x, a) = x };
5. The social security system is self-financed: τw = θµR /(1 − µR );

24

6. Markets clear:
M

M

Df =

ga λ(s, x, da) −
s∈S x=0

a≥0

gh λ(s, x, da)
s∈S x=1

a≥0

M

+
s∈S x=0

{a:gh >ga and

gx >0}

[gh − ga ] λ(s, x, da);

M

H

=

gh λ(s, x, da);
s∈S x=0

Hf =

a≥0

gh (s, 0, a)λ(s, 0, da);
s∈S

a≥0
M

Mf =
s∈S x=0

{a:gh >ga and

gx >0}

[gh − ga ] λ(s, x, da);

Kf = K ;
2N

L =

θs e(s),
s=1

where we have suppressed the arguments of the decision rules when there is
no ambiguity about what they are. These expressions are the clearing conditions for the deposit market, the aggregate housing market, the rental housing
market, the mortgage market, the non-residential capital market, and the labor
market. These conditions should be transparent except for the deposit market
condition. This condition says all households’ net worth minus total equity in
owner occupied housing must equal deposits at financial intermediaries. If all
these conditions are satisfied, then the goods market must clear by Walras’ law.

4

Calibration

We use stationary equilibria of our model to quantify the role of structural change on
single home ownership. Our baseline scenario is designed to capture the environment
faced by households in the years leading up to 1980. We compare this baseline
to one that embodies the structural changes which occurred after 1980 and are a
feature of the environment faced by households in the years leading up to 2000. This
section describes how we assign values to the model’s parameters in the 1980 and
25

2000 calibrations. At the end of this section we discuss household behavior in the
model at the calibrated parameter values.

4.1

1980 Calibration

We assume the functional form of the utility function is
(ψ(s, x)h)1−σ − 1
u(c, ψ(s, x)h) = ln(c) +
,
1−σ

σ ≥ 0.

and the functional form of the production function is
f (k, l) = Ak α l1−α .
For σ → 1 the preferences are homothetic. In this case spending on housing is a
fixed share of income, consistent with the findings in Davis and Ortalo-Magne (2010)
for US renters in the cross-section and over time. We end up calibrating σ = 2. At
this setting, housing is a necessity so that expenditures on housing rise with the real
price of houses. We were unable to obtain a calibration with any individuals at the
down payment constraint with homothetic preferences, but with σ = 2 10 percent of
“first-time” home buyers are at the borrowing constraint.
We set the number of income states in each of the two working stages of life
to N = 9 and the number of houses to M = 20. Our results are not sensitive to
¯
increasing the number of houses. The upper limits on house size and assets, h and a,
¯
are also chosen so that increasing their magnitudes does not affect our results.
The parameters we need to calibrate include those governing the income process,
{Π, e, θ, τ, G}, preferences, {β, η, σ, ψy , ψf }, production, {A, α, δh , δk }, and housing
{h, τb , τs γd }. Our calibration strategy is to first use direct evidence to assign values
to the income process and some housing and preference parameters, and then to
choose the remaining parameters to bring the model as close as possible to a short
list of statistics. Table 5 displays parameter values which are held fixed across the
1980 and 2000 calibrations. Table 6 later in this section displays parameters which
change between the two calibrations to account for various structural changes.
The income process involves three key inputs into our analysis: the speed of
transition to “marriage”, differences in income over the life-cycle, and idiosyncratic
26

Table 5: Parameters Constant Across the 1980 and 2000 Calibrations
Preferences
β 0.951
ψy 0.9544

σ
ψf

2.0
0.87

η

0.879

Housing
τb 0.0163

τs

0.0776

h

1.15

Production
α 0.257

δk

0.082

δh

0.044

Social Security
θ
0.4

τ

0.061

Income Process
Expected age at transition to retirement:
Expected lifetime:
Income ratio of family versus single:
Autocorrelation of income:

65
75
1.47
.95

risk. We assume that within each of the first two life stages, income follows a Tauchen
and Hussey (1991) approximation to a first-order auto-regressive process. It follows
that the income process is completely specified by the following elements: the average
duration of each life stage, the mean, innovation variance, and serial correlation of
income in the single and family stages of life, the replacement ratio for the retired life
stage, and the growth rate for the number of households. We now describe how we
calibrate these characteristics.
We interpret the transition from the single to family type as the event of marriage.
The duration of the first stage of life is selected so that the fraction of individuals
who do not marry, that is transit to the second stage of life, by age 27, corresponds
to the estimate for the cohort born in the period 1948–1957 reported in Table IV of
Caucutt et al. (2002). Life is assumed to begin at age 18 and we assume the average
durations for the three stages are 6, 39, and 9 years. On average, the single stage of
life is 18-24, the family stage is 25-64, and the retirement stage is 65-74.
Household income jumps significantly around the time of marriage. To capture
27

this phenomenon we assume that average income of the family type is higher than
for the single type. We calibrate this increase in income by estimating the average
amount by which family income rises upon first marriage using data from the NLSY.12
We normalize average income over the single and family stages of life to one and use
our estimate of the marriage income increase, 47%, to determine average income in
the two stages of life.
The third key feature of the income process is risk. Income risk is governed
by the autocorrelation coefficient and innovation variance for the single and family
stages of life. We use the life-cycle income process estimated from the PSID from
1968 to 2004 by Storesletten et al. (2004) to guide our selection of these parameters.
Storesletten et al. (2004) assume the autocorrelation of income does not change over
the working years of the life-cycle. Accordingly, we fix the autocorrelation for the
two working life-cycle stages at a value, .95, which is within the range of estimates
reported in Table 2 of Storesletten et al. (2004). Given the evidence discussed below
that idiosyncratic risk has risen between 1980 and 2000, we cannot directly use the
variance estimates in Storesletten et al. (2004). Instead, we assume the life-cycle
conditional variances they report are an equally weighted sum of variances from the
two halves of their sample, corresponding to our 1980 and 2000 calibrations. Using
an assumption, discussed below, of how much the conditional variances increase, we
calculate conditional variances for both sub-samples. We take the average variance of
earnings for the under 25 and the 26-55 age groups, .3 and .5, to calculate the single
and family cross-sectional variances. Once we have the cross-sectional variances, we
calculate the innovation variances using our assumed autocorrelation coefficient.
To complete the specification of income, we need to assign values to the social security replacement ratio, the labor tax, and the rate at which the number of households
grows. The replacement ratio for retirees is θ = 0.4, which is taken from Mitchell and
Phillips (2006). We set the labor tax, τw , to be the value which finances the social
security system. Since labor is supplied inelastically, the labor tax is really just a
lump sum tax and so does not affect any decisions at the margin. The growth factor,
12

Specifically, we regress percent changes in income on dummy variables for year, age, education,
household size, sex, and a dummy variable indicating the years before, during, and after the year of
first marriage. The estimate for our calibration is the coefficient on the dummy variable for year of
first marriage. The income variable includes earnings income of the individual and, when relevant,
their spouse. We get similar results using the PSID.

28

G, is set to 2.35, corresponding to a growth rate of 2 percent, which is the mean
growth rate of households from 1960 to 1980 as reported by the Census Bureau.
Housing transactions costs are very important for our analysis since they determine the magnitude of the effect of income risk on the option value of delaying a
housing transaction. There are several kinds of housing transactions costs, often
called “closing costs,” including real estate agent fees, fees and taxes associated with
recording an official record of the transaction, attorney fees, real estate transfer taxes,
title search, and title insurance. Some of these costs vary widely by jurisdiction and
the magnitude and complexity of the transaction. In addition, while the convention
for real estate agents’ fees is 6% of the property value, agents are sometimes willing
to reduce their rate to close a deal. We obtain our estimates of average transactions
costs from globalpropertyguide.com. This is a firm specializing in selling information relevant to real estate investors. They estimate U.S. housing transactions costs
as a percentage of property values to be in the range 1.05% – 2.2% for buyers and
6.51% – 9% for sellers. These costs include those mentioned above, but exclude other
costs such as appraisal fees, home insurance, mortgage and bank-related fees, and
inspection fees. We use the mid-point of the ranges: τb = .0163 and τs = .0776.
The down-payment parameter, γd , is set to .2 in the 1980 calibration. This value is
commonly used in the literature because of its important role empirically. Specifically,
a down-payment of at least 20% is required to avoid paying mortgage insurance.
The remaining parameters are β, η, σ, ψy , ψf , A, α, δh , δk , and h. The discount rate
β is chosen so that the interest rate is 5% and A is normalized to 1. The parameters
α, δh , and δk are chosen to match empirical estimates of the nonresidential plus
residential investment to output ratio (.26), non-residential capital to output ratio
(1.95), and the residential capital to output ratio (.97). The parameters η, ψy , ψf ,
and h are chosen to match the share of owned residential capital in total residential
capital (.7), the share of housing services in total consumption (.11), the overall home
ownership rate of the 25–44 age group (60.6%), and the home ownership rate of the
never married among the 25–44 age group (25.1%).13 Table 5 indicates that the
13

The home ownership rates are estimated from the Census of Population and Housing and the
remaining statistics are estimated using NIPA data (described in the appendix) for the period 1955–
1980. In practice, we do not exactly match the home ownership rate targets. Our target for the share
of consumption spending that is on housing services is small compared to that used by some other

29

Table 6: Parameters Governing Differences in the 1980 and 2000 Calibrations
1980
Minimum down-payment requirement (γd )
Expected age at transition to family stage
Variance of innovations during single stage
Variance of innovations during family stage
Productivity effect (A)
Population growth (G)

2000

0.200
25
0.025
0.042
1.000
0.020

0.133
27
0.033
0.056
1.041
0.013

calibrated value of ψf is smaller than that for ψy , consistent with the link between
marriage and home ownership we have documented. Finally, σ is chosen, within a
range consistent with estimated income elasticities of housing demand, to match the
life-cycle profile of home ownership. By this latter criterion we mean the increase in
home ownership from the 25–29 age group to the 40–44 age group of all households
and never married households. We found σ = 2 to do the best along these two
dimensions. The implied income elasticity of housing demand in the model is .33,
which is toward the low end of estimates in the literature, e.g. Hansen et al. (1998).

4.2

2000 Calibration

The 2000 calibration embodies several structural changes that should have influenced
young home ownership between 1980 and 2000. These include a lower down-payment
constraint, delayed marriage, slower growth in the number of households, heightened
idiosyncratic income risk, and greater aggregate productivity due to the changes in
female labor market outcomes. Recall that Table 6 displays the model parameters
which change across the two calibrations.
The down-payment constraint is set to .13, which is 2/3 of the value used in the
1980 calibration. The 2/3 value roughly corresponds to the ratio of the average downpayment in the 1990s compared to the 1970s. To approximate the phenomenon of
authors, for example Davis and Ortalo-Magne (2010). Their share is based on including household
operation in housing services and computing it only for renters. Our measure of housing services
excludes expenditures on household operations.

30

delayed marriage after 1980, we assume that the average duration of the single stage
is 2 years longer in the 2000 calibration. This implies the same value for the fraction
of individuals who do not marry by age 27 for the cohort born in the period 1958–1967
reported in Table IV of Caucutt et al. (2002), 34%. We set G = 1.83 to match the
rate of growth in the number of households over the period 1980-2000, 1.3%.
Moffitt and Gottschalk (2008), Moffitt and Gottschalk (2009), Meghir and Pistaferri (2004) and Cunha and Heckman (2007) all provide information on the percentage
increase in idiosyncratic risk. We base our estimates on Cunha and Heckman (2007).
This paper studies white males born between 1957 and 1964, surveyed starting in
1979, and an earlier sample born between 1941 and 1952, surveyed starting in 1966.
Between the two samples, earnings uncertainty, defined as the variance of the unforecastable component of earnings, rises by 11% for college graduates and 52% for high
school graduates. They also find uncertainty in the rate of return to schooling rises
by 33%. Using the 1980 and 2000 Census we find the percentage of individuals aged
25-44 who were high school graduates rose from 59% to 61%. The college graduate
share rose from 22% to 27%. These changes plus the findings in Cunha and Heckman
(2007) translate to an increase in earnings uncertainty of 39% for the weighted average of these income components alone. This is our assumption on how much earnings
risk rises in our 2000 calibration compared to our 1980 calibration.
We think this estimate is conservative for two reasons. First Moffitt and Gottschalk
(2008) and Meghir and Pistaferri (2004) find that the increase in idiosyncratic earnings risk is diminishing in educational attainment. So including less than high school
educational attainment would raise our estimate. The estimate is also low relative to
those reported by Moffitt and Gottschalk (2008) for males, females, and families and
Meghir and Pistaferri (2004) for white and non-white males. Their estimates could
be used to justify very large increases in earnings risk anywhere from 50 percent to
150 percent.
Two developments in the labor market experience of women likely affected young
home ownership. First, the gender wage premium has declined. For example, Heathcote et al. (2010) use CPS data to show that the average wage paid to men relative
to women for the period 1967–1980 was about 1.6, whereas between 1980 and 2000
this ratio averaged about 1.5. Second, women worked outside the home more after

31

1980. The data compiled by Francis and Ramey (2009) indicate that average weekly
hours worked per female over age 14 rose from 12.4 for the 1955–1980 period to 17.5
for the 1981–2000 period. In terms of our model, these changes imply a larger effective supply of labor per household. We model this as a change in the productivity
parameter A from 1 to 1.041.14

4.3

Comparing Our Model with Micro Data

The macroeconomic predictions of our model depend on the underlying microeconomic behavior. Therefore it is important that our model resembles key features of
the micro data. We address this issue with data generated from our model under
the 1980 calibration (similar results are obtained with the 2000 calibration.) As in
section 2.3, we focus on the reduced form determinants of home ownership and the
dynamics of home ownership around marriage. We confirm that our model is reasonably successful at reproducing key features of the micro data important for our
macroeconomic analysis.
Table 7 compares a linear probability model of home ownership estimated from
simulated data generated with our model with the one we estimated on data from
the NLSY displayed in Table 4. For convenience, we reproduce the corresponding
point estimates from Table 4. There are fewer sources of heterogeneity in our model
compared to the data and so fewer explanatory variables are included in the estimation
based on simulated data. The model’s estimates are based on a sample of similar size
to the one underlying the empirical estimates. Prior to estimation we normalize the
dollar amounts from our model to the average income level in our NLSY sample. Since
the standard errors are very small we do not report these. Table 7 confirms that our
model is consistent with microeconomic evidence that income, wealth, marriage and
age are significant predictors of home ownership. The magnitudes of the coefficients
are similar in the model and the data.
14

The 4.1% increase in productivity equals the ratio of population share weighted average weekly
pay for males and females in the second sub-sample relative to the first. The average pay is calculated
as follows: for the early sample, .47 × 30.28 + .53 × 12.42/1.6 and for the later sample, .48 × 27.49 +
.52 × 17.2/1.5. The population shares are from the Census Bureau, the average hours worked per
male and female are from Francis and Ramey (2009), and the relative wages are from Heathcote
et al. (2010).

32

Table 7: Reduced Form Determinants of Home Ownership in the Model and Data
Data
.006
.003
.23

.003
.005
.20

.04
.09
.14

Real net assets (000s)
Real household income (000s)
Married (versus not married)
Age (versus under 25)
25-29
30-34
35-39

Model

.09
.15
.19

Source: Table entries are estimates from linear probability models of home ownership. The model-based estimates are based on
simulated data from our model under the 1980 calibration and
the data-based estimates are taken directly from Table 4.
Figure 6 displays the dynamics of home ownership around marriage in our model
and in the data. The data-based estimates are taken from the 1968-1986 sample displayed in Figure 2. The model-based regressions include age and year-before-marriage
dummies only. Figure 6 demonstrates that our model is reasonably successful matching the micro data along this dimension. As in the data, the model displays low
and flat probabilities of home ownership before marriage and rising ones afterwards.
The main discrepancies are that the level is too low before marriage and there is a
counterfactually large increase in the probability of home ownership in the year of
marriage in our model. The latter reflects our stark income process. Still, the model
does roughly match the average likelihood of ownership after marriage.

4.4

Impact of Income Risk on Housing Choices

We now discuss how income risk influences household behavior in our calibrated
model. Figure 7 displays the housing service decision rule for a household in the
single stage of life that rented in the previous period, under the 1980 calibration,
“Low Income Risk,” and with 1980 prices but income risk set according to the 2000
calibration, “High Income Risk.” The household’s level of income is almost identical
in both cases, so essentially the only difference to the household’s environment is the

33

−.1

0

Probability
.1
.2

.3

.4

Figure 6: Home Ownership Around Marriage in the Data and the Model

−4

−2

0
Years from marriage
Data

2

4

Model

income risk it faces.15 On the horizontal axis is the beginning of period level of net
worth, a, and on the vertical access is the housing service choice. Figure 8 shows
the housing decision of the same household except that it owned the smallest house,
h, in the previous period. The domain of assets considered in each figure differs to
highlight different features of the decision rules.
Consider the low risk case for a renter. This shows that for assets less than about
a = 2 this household chooses to rent. The amount rented rises continuously with
wealth. Near asset level a = 2 this household switches from renting to owning the
minimum size house, h = 1.15. The tenure choice is not evident in the figure, but the
household chooses to own all houses equal to or exceeding the smallest house. Due
to the discreteness in house sizes and the transactions costs, there is an interval of
assets for which the minimum size house is still chosen. For assets of about a = 5,
the household’s desired level of housing services increases. The step function form
of the policy rule continues to the right of a = 5. Another interesting feature of
15
Due to the way the income process is constructed, increasing income risk changes the level of
income in each income state. Figure 7 displays the decision rules for the fourth highest income state
which turns out to be very close across the two cases.

34

.9

.95

1

Housing Services
1.05 1.1 1.15

1.2

1.25

Figure 7: Housing Decision of a Household that Rented in the Previous Period

0

1

2

3

4
5
Net Wealth

Low Income Risk

6

7

8

9

High Income Risk

the decision rule is that the jump in housing services from renting to the smallest
house is larger than the other jumps in the figure. Similarly, the interval of assets
for which the household chooses to own the smallest house is wider than for larger
houses. These characteristics arise because the switch from renting to owning ends
the discounting due to renting which mitigates the transactions costs, while the other
switches only involve the transactions costs. Therefore, the household is willing to
incur the transactions cost of the move earlier than otherwise, that is with lower
net wealth.16 The basic form of the decision rule of this household holds for all
households in the model. All that changes are the asset cut-off values determining
when the household selects a different level of housing services.
The impact of raising income risk is to delay switching from renting to owning.
This is indicated in the figure by the fact that the dashed line lies to the right of the
solid line. The rightward shift of the decision rule implies the household switches from
renting to owning at a higher level of assets in the high income risk case. These higher
16
After the second housing service jump, the interval of assets for which the housing choice is fixed
gets larger as the size of the house increases. This is an artifact of the log linear manner in which
the housing grid was constructed.

35

1.2

Housing Services
1.4
1.6

1.8

Figure 8: Housing Decision of a Household that Owned in the Previous Period

10

15

20
Net Wealth

Low Income Risk

25

30

High Income Risk

assets take longer to accumulate on average and so higher income risk leads to delay
in acquiring the first house and, other things being equal, lower home ownership.
The intuition for this is straightforward. In the presence of proportional adjustment costs and income risk, there is value to delaying the home purchase or sale until
the household is possibly wealthier and can afford a larger house. Higher income risk
increases the value of this option, thereby delaying home ownership and lowering the
home ownership rate. An increase in risk also raises the amount of precautionary
saving. Other things remaining the same, higher wealth eases the transition to home
ownership and so this effect should raise the home ownership rate. As we see in the
next section, at our calibrated parameter values the option value effect dominates the
precautionary saving effect.
In Figure 8 the household always chooses to own in both the low and high risk
cases. Consequently the behavior of this household does not directly impact the home
ownership rate. However, Figure 8 illustrates another effect of raising income risk,
namely that it leads to delay in moving into larger houses. Note that for assets a < 10
the household chooses to continue owning the smallest house. When the household

36

Table 8: Young Home Ownership in 1980 and 2000

Age Group

1980

Data
2000 Change

1980

Model
2000

Change

All Individuals
25–29
30–34
35–39
40–44

43.4
60.7
69.7
74.3

36.0
53.0
63.4
69.1

-7.4
-7.7
-6.3
-5.2

48.1
59.9
67.6
72.9

43.0
55.2
63.1
68.6

-5.1
-4.7
-4.5
-4.3

25–44

60.6

57.3

-3.3

60.3

56.1

-4.2

Unmarried Individuals
25–29
30–34
35–39
40–44

18.3
28.1
35.5
42.1

21.1
32.4
42.3
46.9

2.8
4.3
6.8
4.8

22.1
29.3
34.4
38.0

19.8
28.2
34.3
38.6

-2.3
-1.1
-0.1
0.6

25–44

25.1

33.2

8.1

25.4

25.0

-0.4

enters the period with more assets, larger houses are chosen. Delay is indicated by
the fact that the asset level for which the switch to the next sized houses occurs is
higher in the high risk compared to low risk case. The intuition for this effect of
raising income risk is similar to before.

5

Findings

We now discuss the impact of structural change on the home ownership rates of the
young implied by our 1980 and 2000 calibrations. Table 8 displays home ownership
rates for the young age groups of interest in the U.S. data and under the stationary
equilibrium corresponding to each calibration. The empirical values for 1980 and
2000 are taken from Table 2.
It is clear from Table 8 that the model goes a long way toward accounting for
37

the reduction in home ownership rates by age and for the 25–44 category as a whole.
By age group our model accounts for 3/5–4/5 of the fall in home ownership and,
as in the data, the effects are larger for the younger age groups than for the older
age groups. The model implies a larger drop for the 25–44 age group than in the
data, but smaller drops for the individual age groups. The larger drop for the young
group as a whole is because the change in the age distribution in the model from
1980 to 2000 is not identical to the data, despite our attempt to take changes in
the rate of household formation into account. The model is qualitatively successful
with unmarried individuals. The changes for these young individuals are less negative
than for all young individuals. However the sign is incorrect. We could improve the
model along this dimension by shifting out the production function further in the
2000 calibration.
Table 9 sheds light on the factors driving our model’s ability to account for a
large fraction of the decline in young home ownership. This table displays differences in home ownership by age between the 1980 calibration and versions of the
2000 calibration where just one of the five structural changes is imposed, for all and
unmarried individuals. In each case we calculate the home ownership rates from the
corresponding stationary equilibrium. This table indicates that heightened income
risk and delayed marriage are the driving forces behind our findings for all individuals. These effects lower the home ownership rate substantially for each age group.
Income risk lowers home ownership rates by age by between 3.37 and 4.59 percentage
points. Marriage delay lowers the rates by between 1.58 and 3.98 percentage points.
The reasons for heightened income risk lowering home ownership were described at
the end of the last section. Marriage delay lowers home ownership mechanically since,
as in the data, the non-married have lower home ownership rates than the married in
our model.
The other factor having a large impact on home ownership is the productivity
increase. Recall that this is our way of modeling the higher wages and market work
of women after 1980. Not surprisingly higher productivity has a substantial positive
impact on home ownership. Still, when all the structural changes are incorporated,
the productivity increase is dominated by the effects of marriage delay and heightened
income risk.

38

Table 9: Effects of Individual Structural Changes on Young Home Ownership

Age Group

Down-payment
Constraint

Household
Formation

Income
Risk

Marriage
Delay

Productivity
Increase

All Individuals
25–29
30–34
35–39
40–44

0.20
0.22
0.21
0.19

0.69
0.54
0.33
0.13

-3.37
-3.86
-4.32
-4.59

-3.98
-3.08
-2.26
-1.58

4.87
4.50
3.92
3.37

25–44

0.20

0.80

-3.95

-2.83

4.28

Unmarried Individuals
25–29
30–34
35–39
40–44

0.29
0.32
0.31
0.31

0.95
0.59
0.31
0.10

-8.38
-6.89
-5.82
-4.90

-1.73
-1.82
-1.83
-1.46

8.78
8.33
7.76
7.18

25–44

0.30

0.90

-7.71

-0.74

8.54

Lowering the down-payment constraint has a small positive impact on home ownership. This is despite the fact that in the 1980 calibration 10% of those switching
from renting to owning are at the down-payment constraint and this percentage falls
to zero with the reduction in the down-payment constraint. Lowering the downpayment even further has a very small additional effect on home ownership. That
relaxing credit constraints has a small impact on home ownership in our model does
not rule out the possibility that such constraints play an important role in cyclical
fluctuations.
The reduction in the rate of household formation also has a small positive impact
on home ownership. The lower rate of household formation raises the share of households in the family and retired stages compared to the single stage. This raises home
ownership because of the higher home ownership rates of the married and retired
compared to the single in the model. A side-effect of this change in the underlying
distribution of households is that wealth accumulation is greater and so the equilib39

rium interest rate is lower. The impact of the lower rate of household formation on
young home ownership is similar in magnitude to the effect of general equilibrium on
the total impact of the structural changes on home ownership in the 2000 calibration. In particular, if we do not impose market clearing in the 2000 calibration and
keep the interest rate at its 1980 level, then home ownership rates would be about .5
percentage points lower than when we impose market clearing.
This last result indicates our findings do not depend on assuming equilibrium
in the capital market. We view this as providing indirect support for our decision
to leave the changes in home ownership among older individuals unexplained. To
the extent that we have failed to account for changes in home ownership of older
individuals due to an inadequate modeling of their wealth, this should not necessarily
matter for our findings on young home ownership. This conclusion relies on housing
supply being perfectly elastic across the two steady states we consider.
With the exception of marriage delay, the marginal impact of each structural
change is generally larger for unmarried individuals compared to all individuals. The
impact of income risk and the productivity increase are much larger.

6

Other Possible Explanations

We now address some other potential alternative explanations for the decline in young
home ownership. We consider changes in house prices, household mobility patterns,
tax policy and inflation.

6.1

House Prices

So far house prices have been fixed. This was justified by our interest in steady state
equilibria. Are they important for understanding the changes in home ownership?
We consider three possible channels through which they might, involving the level of
real house prices, price-rent ratios, and the riskiness of houses as an asset.
There is evidence that real house prices were higher in the years leading up to
2000 compared to 1980. The FHFA (formerly OFHEO) house price index deflated
by the CPI for urban consumers is 4 1/2 percent higher in 2000 compared to 1980.
40

Excluding housing services from the CPI raises the increase to about 10 percent. The
impact on home ownership of house prices is ambiguous in our model. In particular,
it depends on preferences and the housing grid.
Suppose house prices rise by 10 percent across the 1980 and 2000 calibrations. We
accomplish this in our model by changing the technological rate at which consumption
goods can be transformed into units of housing. If we treat the grid as literally houses,
then an increase in the price makes the smallest house that can be owned more
e
expensive. As Ortalo-Magn´ and Rady (1999) show, this can lower home ownership
in the presence of a down payment constraint. However, suppose we reinterpret the
grid as being in terms of expenditures on housing. Essentially this means shifting the
grid in terms of housing units to the left by 10 percent. We view this as the opposite
extreme as holding the grid on houses fixed. It allows for individuals to economize
on the size of the house it purchases when faced with a higher price.
It can easily be shown that with homothetic preferences, such as constant relative
risk aversion in the Cobb-Douglas aggregate of consumption and housing services,
then housing expenditures are invariant to the price of housing. In this case raising
the price by 10 percent with the left-shifted grid has no impact on home ownership.
We do not have homothetic preferences. Our preferences imply that housing is a
necessity; an increase in the real price of housing raises expenditures on housing and
therefore shifts out the demand for housing. This means that a 10 percent increase in
prices actually raises home ownership rates in our model by several percentage points
when we shift the grid to the left. When we hold the housing grid fixed then home
ownership rates decline by several percentage points due to the mechanism described
e
by Ortalo-Magn´ and Rady (1999). We think the truth lies somewhere between the
two extremes of fixed and flexible house sizes, which suggests a roughly neutral effect
of prices on home ownership in our model.
This does not mean that prices were not a causal factor for home ownership at
any point between 1980 and 2000. Indeed we suspect that the sharp decline in home
ownership between 1980 and 1990 might be be explained by the interaction of house
prices and the down payment constraint due to the short run inelasticity of house
supply. For example, in the model of Ortalo-Magn´ and Rady (1999) an exogenous
e
increase in the wealth of the old can cascade through the property ladder, raising

41

the price of starter homes for the young. If young incomes are unchanged and these
agents are down payment constrained, then an increase in the real price must lower
home ownership of the young.17 We do not think this mechanism can explain why
the home ownership remained so low in 2000 because its strength relies heavily on a
fixed housing supply.
The second way prices may influence home ownership is through the price-rent
ratio. A substantial rise in the price of an owner-occupied house relative to the cost
of rental housing should induce a shift away from owning toward renting. Piazzesi
and Schneider (2008) find that the price–rent ratio actually declined from 25 to 22
between 1980 and 2000. Still, the value of the ratio in 2000 was about where it
had been in the mid-1970s. Davis et al. (2008) find that the price–rent ratio was
essentially the same in 1980 and 2000. We conclude that there have not been large
enough changes in the price–rent ratio to have a material impact on home ownership.
The final way prices may be important for home ownership is if their volatility
changed. If house price risk increased, then this would increase the value of delaying
a home purchase and lower home ownership. Aggregate measures of house prices
suggest that if there has been a trend, it has been toward less volatility in real house
prices.18 We suspect this is driven by the fact that consumer prices now are less
volatile than in 1980. Sinai and Souleles (2005) convincingly argue that rent risk is
more important than house price risk in the tenure choice decision. This arises due
to the role of housing as a hedge against variation in rental rates. If rent risk were
to decline, then there would be an incentive to substitute away from owned toward
rented housing. This is an intriguing possibility, but we leave it as an open question
for this paper.

6.2

Mobility

A second possible reason for a decline in ownership is that households’ mobility rates
may have changed. If, for whatever reason, households were to move more frequently,
17

We thank an anonymous referee for highlighting this possibility.
We have studied regional price indexes published by the Census Bureau, Office of Federal Housing Enterprise Oversight and Freddie Mac. We have been unable to obtain house prices for finer
geographical classifications, such as by city, that begin early enough for our purposes.
18

42

then, holding all else equal, this should lower home ownership due to the additional
costs involved with moving when a home is owned. In fact, mobility reports by
the Census Bureau point toward little change in mobility rates during the period in
question. For the young age groups we study in this paper, the probability that an
individual lives at a different address from the previous year was 22% in the mid-1970s
and 20% in the mid-1990s.19

6.3

Tax Policy

One candidate for tax policy having an impact on home ownership is the 1986 tax
reform. Poterba (1992) postulates that the reductions in high income marginal tax
rates in 1986 lowered the benefits to the mortgage interest tax deduction, thereby
lowering the incentive to own a home. While there is a direct effect of lowering the
top marginal tax rates on the tax implications of mortgage interest deductability, it
is far from clear that this translates into a substitution from owned to rental housing.
Gervais (2002) shows that even the elimination of mortgage interest deductibility
would have only modest consequences for home ownership. Similarly, Gervais and
Pandey (2008) argue that relatively high income families do not benefit from mortgage
interest deductibility nearly as much as conventionally believed when the family’s
budget constraint is taken into account. There were other changes to the tax code
in 1986 which increased the incentive to build multi-family rental units. An increase
in the relative supply of rental housing should induce a substitution away from home
ownership. There was a brief period in the mid-1980s when investment in multi-family
units grew at a faster pace than single family homes. However, capital stock data
from the Bureau of Economic Analysis suggest that the overall effect on the relative
supply of rental housing was small.20
While there do not seem to have been any tax policy changes with a substantial direct impact on home ownership, this does not discount the possibility that taxes have
19

This is based on the CPS and reported in various issues of “Geographical Mobility,” a publication
of the Census Bureau.
20
Changes to the tax code in 1998 affected the amount of capital gains on selling the primary
residence that is exempted from taxes. This should affect the timing of home sales and would make
ownership more desirable since the expected after-tax returns from owning are higher, which should
raise home ownership slightly.

43

played a role in lowering young home ownership. Consider the wedge between taxation of owner-occupied housing and other capital. Both rental and owner-occupied
housing are subject to property taxes, but the service flow from rental housing is
also subject to capital income taxes while it is not for owner-occupied housing. Nonresidential capital of course is subject to capital income taxes. Therefore, lowering
capital income taxes lowers the wedge between returns on housing and non-housing
assets, and therefore could lower the home ownership rate. There is evidence supporting the view that capital income taxes fell between 1980 and 2000. Eichenbaum
and Fisher (2005) find that average capital taxes are generally lower after 1980 than
before. McGrattan and Prescott (2005) find that the same is true for measures of
average marginal capital taxes. While consistent with the decline in home ownership
among the young, the tax explanation seems hard to square with the increase in home
ownership among the old, on whom we should observe the greatest impact of changes
in capital taxes due to their higher wealth levels. So, while tax changes may help
explain the decline in young home ownership, any explanation involving taxes must
simultaneously account for the behavior of older households. We leave this to future
work.

6.4

Inflation

Piazzesi and Schneider (2008) document large swings in the share of owner-occupied
real estate in the portfolio of the U.S. household sector. Their figure 2 indicates that
the share fell from 55% in 1952 to under 50% in the late 1960s, it grew to over 70%
by 1980, and declined back to its late 1960s values by 2000. Piazzesi and Schneider
(2008) argue that inflation and inflation expectations induced these swings in the
household sector’s portfolio of assets. Their analysis does not say anything explicitly
about home ownership, but it is suggestive. We think inflation in the 1970s probably
had some role to play in the home ownership rate reaching such high levels in the
1980 Census. However, as with taxes, any explanation for home ownership trends
driven by inflation must confront the fact that young and old households’ ownership
trends differ when there are similar incentive effects of inflation.

44

7

Conclusion

In this paper we have documented a striking decline in home ownership among the
young between 1980 and 2000, a period during which home ownership should have
become easier. We have established, using a calibrated equilibrium life-cycle model,
that the decline in young home ownership can be explained mostly by a slower rate
of marriage formation and heightened income risk. Part of the reason these factors
are able to account for the decline is that relaxing the down-payment constraint on
mortgages has a small impact on home ownership rates.
To our knowledge, our paper is the first to draw the connection between heightened
income risk and lower home ownership. We think it has important implications for
public policy. To the extent that heightened income risk is here to stay, then we should
expect aggregate home ownership rates to begin to decline going forward. If such a
decline is the optimal response of households to an increase in risk, then policy makers
should be wary of introducing market distortions to offset the decline. On the other
hand, if high rates of home ownership are viewed as a desirable public policy objective,
for example as discussed in Glaeser and Shapiro (2003), then existing institutions and
regulations designed to boost home ownership will need to be rethought.
Our model abstracts from many interesting features of the tenure choice decision.
One important omission from our analysis is that we did not model marriage explicitly.
We think abstracting from marriage was justified for the purposes of this paper. Still,
many of the trends in home ownership seem connected to factors which have influenced
marriage. We think our model has shown the likely quantitative importance of some
of these factors and points toward the value of future research in this area.

45

Appendix: Data Underlying Estimates of Aggregate Ratios
Except where noted, all expenditure data is from the National Income and Product Accounts. The capital stock data are from the Bureau of Economic Analysis
publication, “Fixed Assets and Consumer Durable Goods.”
• Output is measured as GDP plus the service flow of consumer durables obtained
from the Federal Reserve Board.
• Non-residential capital includes producer durable equipment and non-residential
structures, plus the stock of consumer durables and the stock of non-residential
government capital.
• Residential capital is the stock of private and public residential capital.
• Owned residential capital is the stock of privately owned residential capital.
• Housing services are the flow of housing services component of consumer expenditure on services.
• Consumption includes non-housing services plus non-durable expenditures plus
government consumption plus the service flow from consumer durables.

46

References
Abel, A. and J. Eberly (1996). Optimal investment with costly reversibility. Review
of Economic Studies 63 (4), 581–593.
Berkovec, J. and D. Fullerton (1992). A general equilibrium model of housing, taxes,
and portfolio choice. Journal of Political Economy 100 (2), 390–429.
Blundell, R., L. Pistaferri, and I. Preston (2008). Consumption inequality and partial
insurance. American Economic Review 98 (5), 1887–1921.
Caucutt, E., N. Guner, and J. Knowles (2002). Why do women wait? Matching,
wage inequality, and the incentives for fertility delay. Review of Economic Dynamics 5 (4), 815–855.
Chambers, M., C. Garriga, and D. E. Schlagenhauf (2009). Accounting for changes
in the homeownership rate. International Economic Review 50 (3), 677–726.
Cunha, F. and J. J. Heckman (2007). The evolution of inequality, heterogeneity and
uncertainty in labor earnings in the U.S. economy. IZA Discussion Paper No. 3115.
Davis, M. and F. Ortalo-Magne (2010). Household expenditures, wages, rents. Review
of Economic Dynamics, Forthcoming.
Davis, M. A., A. Lehnert, and R. F. Martin (2008). The rent-price ratio for the
aggregate stock of owner-occupied housing. Review of Income and Wealth 54, 279–
284.
D´
ıaz, A. and M. J. Luengo-Prado (2008). On the user cost and homeownership.
Review of Economic Dynamics 11 (3), 584–613.
Diaz-Serrano, L. (2005). On the negative relationship between labor income uncertainty and home ownership: Risk aversion vs. credit constraints. Journal of Housing
Economics 14 (2), 109–126.
Dixit, A. K. and R. S. Pindyck (1994). Investment under Uncertainty. Princeton:
Princeton University Press.
Edelberg, W. (2006). Risk-based pricing of interest rates for consumer loans. Journal
of Monetary Economics 53 (8), 2283–2298.
Eichenbaum, M. and J. D. Fisher (2005). Fiscal policy in the aftermath of 9/11.
Journal of Money, Credit and Banking 37 (1), 1–22.

47

Engelhardt, G. V. and C. J. Mayer (1998). Intergenerational transfers, borrowing
constraints, and saving behavior: Evidence from the housing market. Journal of
Urban Economics 44, 135–157.
Flavin, M. and T. Yamashita (2002). Owner-occupied housing and the composition
of the household portfolio. American Economic Review 92 (1), 345–362.
Florida, R. L. (Ed.) (1986). Housing and the New Financial Markets. Center for
Urban Policy Research.
Francis, N. and V. A. Ramey (2009). A century of work and leisure. American
Economic Journal: Macroeconomics 1 (2), 189–224.
Fu, Y. (1995). Uncertainty, liquidity, and housing choices. Regional Science and
Urban Economics 25 (2), 223–236.
Gerardi, K., H. S. Rosen, and P. Willen (2009). The impact of deregulation and
financial innovation on consumers: The case of the mortgage market. Journal of
Finance, Forthcoming.
Gervais, M. (2002). Housing taxation and capital accumulation. Journal of Monetary
Economics 49 (7), 1461–1489.
Gervais, M. and M. Pandey (2008). Who cares about mortgage interest deductibility?
Canadian Public Policy 34 (1), 1–24.
Glaeser, E. L. and J. M. Shapiro (2003). The benefits of the home mortgage interest
deduction. Tax Policy and the Economy 17, 37–82.
Green, R. K. and P. H. Hendershott (1995). Age, housing demand, and real house
prices. Regional Science and Urban Economics 26, 465–480.
Hansen, J. L., J. P. Formby, and W. J. Smith (1998). Estimating the income elasticity
of demand for housing: A comparison of traditional and Lorenz-concentration curve
methodologies. Journal of Housing Economics 7 (4), 328–342.
Haurin, D. R. (1991). Income variability, homeownership, and housing demand. Journal of Housing Economics 1 (1), 60–74.
Haurin, D. R. and H. L. Gill (1987). Effect of income variability on the demand for
owner occupied housing. Journal of Urban Economics 22, 136–150.
Haurin, D. R., P. H. Hendershott, and D. Ling (1988). Home ownership rates of
married couples: An econometric investigation. Housing Finance Review 7 (2),
85–108.
48

Haurin, D. R., P. H. Hendershott, and S. M. Wachter (1996). Wealth accumulation
and housing choices of young households: An exploratory investigation. Journal of
Housing Research 7 (1), 33–57.
Heathcote, J., F. Perri, and G. L. Violante (2010). Unequal we stand: An empirical analysis of economic inequality in the United States, 1967–2006. Review of
Economic Dynamics 13 (1), 15–51.
Heathcote, J., K. Storesletten, and G. L. Violante (2008). The macroeconomic implications of rising wage inequality in the United States. Unpublished manuscript.
Henderson, J. and Y. Ioannides (1983). A model of housing tenure choice. American
Economic Review 73 (1), 98–113.
Katz, L. F. and D. H. Autor (1999). Changes in the wage structure and earnings
inequality. In O. Ashenfelter and D. Card (Eds.), Handbook of Labor Economics,
Handbooks in Economics, pp. 1463–1555. North-Holland: Elsevier Science.
Kiyotaki, N., A. Michaelides, and K. Nikolov (2007). Winners and losers in housing
markets. Unublished manuscript.
Krueger, D. and F. Perri (2006). Does income inequality lead to consumption inequality? Evidence and theory. Review of Economic Studies 73 (1), 163–193.
McGrattan, E. R. and E. C. Prescott (2005). Taxes, regulations and the value of U.S.
and U.K. corporations. Review of Economic Studies 72 (3), 767–796.
Meghir, C. and L. Pistaferri (2004). Income variance dynamics and heterogeneity.
Econometrica 72 (1), 1–32.
Mitchell, O. S. and J. W. R. Phillips (2006). Social security replacement rates for alternative earnings benchmarks. University of Michigan Retirement Research Center
WP 2006-116.
Moffitt, R. and P. Gottschalk (2008). Trends in the transitory variance of male
earnings in the U.S., 1991-2003: Preliminary evidence from LEHD data. Boston
College manuscript.
Moffitt, R. and P. Gottschalk (2009). The rising instability of U.S. earnings. Journal
of Economic Perspectives 23 (4), 3–24.
Ortalo-Magn´, F. and S. Rady (1999). Boom in, bust out: Young households and the
e
housing price cycle. European Economic Review 43, 755–766.
Ortalo-Magne, F. and S. Rady (2002). Tenure choice and the riskiness of non-housing
consumption. Journal of Housing Economics 11, 266–279.
49

Ortalo-Magne, F. and S. Rady (2006). Housing market dynamics: On the contribution
of income shocks and credit constraints. Review of Economic Studies 73, 459–485.
Piazzesi, M. and M. Schneider (2008). Inflation and the price of real assets. Unpublished manuscript.
Poterba, J. M. (1992). Taxation and housing: Old questions, new answers. American
Economic Review Papers and Proceedings 82 (2), 237–242.
Robst, J., R. Deitz, and K. McGoldrick (1999). Income variability, uncertainty and
housing tenure choice. Regional Science and Urban Economics 29 (2), 219–229.
Ryding, J. (1990). Housing finance and the transmission of monetary policy. Federal
Reserve Bank of New York Quarterly Review , 42–55.
Sinai, T. and N. S. Souleles (2005). Owner-occupied housing as a hedge against rent
risk. The Quarterly Journal of Economics 120 (2), 763–789.
Stevenson, B. and J. Wolfers (2007). Marriage and divorce: Changes and their driving
forces. Journal of Economic Perspectives 21 (2), 27–52.
Storesletten, K., C. I. Telmer, and A. Yaron (2004). Cyclical dynamics in idiosyncratic
labor-market risk. Journal of Political Economy 112 (3), 695–717.
Tauchen, G. and R. Hussey (1991). Quadrature-based methods for obtaining approximate solutions to nonlinear asset pricing models. Econometrica 59 (2), 371–396.
Van Order, R. (2000). The U.S. mortgage market: A model of dueling charters.
Journal of Housing Research 11 (2), 233–255.
Zagorsky, J. L. (1999). Young baby boomers’ wealth.
Wealth 45 (2), 135–156.

50

Review of Income and

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
U.S. Corporate and Bank Insolvency Regimes: An Economic Comparison and Evaluation
Robert R. Bliss and George G. Kaufman

WP-06-01

Redistribution, Taxes, and the Median Voter
Marco Bassetto and Jess Benhabib

WP-06-02

Identification of Search Models with Initial Condition Problems
Gadi Barlevy and H. N. Nagaraja

WP-06-03

Tax Riots
Marco Bassetto and Christopher Phelan

WP-06-04

The Tradeoff between Mortgage Prepayments and Tax-Deferred Retirement Savings
Gene Amromin, Jennifer Huang,and Clemens Sialm

WP-06-05

Why are safeguards needed in a trade agreement?
Meredith A. Crowley

WP-06-06

Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-06-07

A New Social Compact: How University Engagement Can Fuel Innovation
Laura Melle, Larry Isaak, and Richard Mattoon

WP-06-08

Mergers and Risk
Craig H. Furfine and Richard J. Rosen

WP-06-09

Two Flaws in Business Cycle Accounting
Lawrence J. Christiano and Joshua M. Davis

WP-06-10

Do Consumers Choose the Right Credit Contracts?
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles

WP-06-11

Chronicles of a Deflation Unforetold
François R. Velde

WP-06-12

Female Offenders Use of Social Welfare Programs Before and After Jail and Prison:
Does Prison Cause Welfare Dependency?
Kristin F. Butcher and Robert J. LaLonde
Eat or Be Eaten: A Theory of Mergers and Firm Size
Gary Gorton, Matthias Kahl, and Richard Rosen

WP-06-13

WP-06-14

1

Working Paper Series (continued)
Do Bonds Span Volatility Risk in the U.S. Treasury Market?
A Specification Test for Affine Term Structure Models
Torben G. Andersen and Luca Benzoni

WP-06-15

Transforming Payment Choices by Doubling Fees on the Illinois Tollway
Gene Amromin, Carrie Jankowski, and Richard D. Porter

WP-06-16

How Did the 2003 Dividend Tax Cut Affect Stock Prices?
Gene Amromin, Paul Harrison, and Steven Sharpe

WP-06-17

Will Writing and Bequest Motives: Early 20th Century Irish Evidence
Leslie McGranahan

WP-06-18

How Professional Forecasters View Shocks to GDP
Spencer D. Krane

WP-06-19

Evolving Agglomeration in the U.S. auto supplier industry
Thomas Klier and Daniel P. McMillen

WP-06-20

Mortality, Mass-Layoffs, and Career Outcomes: An Analysis using Administrative Data
Daniel Sullivan and Till von Wachter

WP-06-21

The Agreement on Subsidies and Countervailing Measures:
Tying One’s Hand through the WTO.
Meredith A. Crowley

WP-06-22

How Did Schooling Laws Improve Long-Term Health and Lower Mortality?
Bhashkar Mazumder

WP-06-23

Manufacturing Plants’ Use of Temporary Workers: An Analysis Using Census Micro Data
Yukako Ono and Daniel Sullivan

WP-06-24

What Can We Learn about Financial Access from U.S. Immigrants?
Una Okonkwo Osili and Anna Paulson

WP-06-25

Bank Imputed Interest Rates: Unbiased Estimates of Offered Rates?
Evren Ors and Tara Rice

WP-06-26

Welfare Implications of the Transition to High Household Debt
Jeffrey R. Campbell and Zvi Hercowitz

WP-06-27

Last-In First-Out Oligopoly Dynamics
Jaap H. Abbring and Jeffrey R. Campbell

WP-06-28

Oligopoly Dynamics with Barriers to Entry
Jaap H. Abbring and Jeffrey R. Campbell

WP-06-29

Risk Taking and the Quality of Informal Insurance: Gambling and Remittances in Thailand
Douglas L. Miller and Anna L. Paulson

WP-07-01

2

Working Paper Series (continued)
Fast Micro and Slow Macro: Can Aggregation Explain the Persistence of Inflation?
Filippo Altissimo, Benoît Mojon, and Paolo Zaffaroni

WP-07-02

Assessing a Decade of Interstate Bank Branching
Christian Johnson and Tara Rice

WP-07-03

Debit Card and Cash Usage: A Cross-Country Analysis
Gene Amromin and Sujit Chakravorti

WP-07-04

The Age of Reason: Financial Decisions Over the Lifecycle
Sumit Agarwal, John C. Driscoll, Xavier Gabaix, and David Laibson

WP-07-05

Information Acquisition in Financial Markets: a Correction
Gadi Barlevy and Pietro Veronesi

WP-07-06

Monetary Policy, Output Composition and the Great Moderation
Benoît Mojon

WP-07-07

Estate Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-07-08

Conflict of Interest and Certification in the U.S. IPO Market
Luca Benzoni and Carola Schenone

WP-07-09

The Reaction of Consumer Spending and Debt to Tax Rebates –
Evidence from Consumer Credit Data
Sumit Agarwal, Chunlin Liu, and Nicholas S. Souleles

WP-07-10

Portfolio Choice over the Life-Cycle when the Stock and Labor Markets are Cointegrated
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-07-11

Nonparametric Analysis of Intergenerational Income Mobility
with Application to the United States
Debopam Bhattacharya and Bhashkar Mazumder

WP-07-12

How the Credit Channel Works: Differentiating the Bank Lending Channel
and the Balance Sheet Channel
Lamont K. Black and Richard J. Rosen

WP-07-13

Labor Market Transitions and Self-Employment
Ellen R. Rissman

WP-07-14

First-Time Home Buyers and Residential Investment Volatility
Jonas D.M. Fisher and Martin Gervais

WP-07-15

Establishments Dynamics and Matching Frictions in Classical Competitive Equilibrium
Marcelo Veracierto

WP-07-16

Technology’s Edge: The Educational Benefits of Computer-Aided Instruction
Lisa Barrow, Lisa Markman, and Cecilia Elena Rouse

WP-07-17

3

Working Paper Series (continued)
The Widow’s Offering: Inheritance, Family Structure, and the Charitable Gifts of Women
Leslie McGranahan
Demand Volatility and the Lag between the Growth of Temporary
and Permanent Employment
Sainan Jin, Yukako Ono, and Qinghua Zhang

WP-07-18

WP-07-19

A Conversation with 590 Nascent Entrepreneurs
Jeffrey R. Campbell and Mariacristina De Nardi

WP-07-20

Cyclical Dumping and US Antidumping Protection: 1980-2001
Meredith A. Crowley

WP-07-21

The Effects of Maternal Fasting During Ramadan on Birth and Adult Outcomes
Douglas Almond and Bhashkar Mazumder

WP-07-22

The Spending and Debt Response to Minimum Wage Hikes
Daniel Aaronson, Sumit Agarwal, and Eric French

WP-07-23

The Impact of Mexican Immigrants on U.S. Wage Structure
Maude Toussaint-Comeau

WP-07-24

A Leverage-based Model of Speculative Bubbles
Gadi Barlevy

WP-08-01

Displacement, Asymmetric Information and Heterogeneous Human Capital
Luojia Hu and Christopher Taber

WP-08-02

BankCaR (Bank Capital-at-Risk): A credit risk model for US commercial bank charge-offs
Jon Frye and Eduard Pelz

WP-08-03

Bank Lending, Financing Constraints and SME Investment
Santiago Carbó-Valverde, Francisco Rodríguez-Fernández, and Gregory F. Udell

WP-08-04

Global Inflation
Matteo Ciccarelli and Benoît Mojon

WP-08-05

Scale and the Origins of Structural Change
Francisco J. Buera and Joseph P. Kaboski

WP-08-06

Inventories, Lumpy Trade, and Large Devaluations
George Alessandria, Joseph P. Kaboski, and Virgiliu Midrigan

WP-08-07

School Vouchers and Student Achievement: Recent Evidence, Remaining Questions
Cecilia Elena Rouse and Lisa Barrow

WP-08-08

4

Working Paper Series (continued)
Does It Pay to Read Your Junk Mail? Evidence of the Effect of Advertising on
Home Equity Credit Choices
Sumit Agarwal and Brent W. Ambrose

WP-08-09

The Choice between Arm’s-Length and Relationship Debt: Evidence from eLoans
Sumit Agarwal and Robert Hauswald

WP-08-10

Consumer Choice and Merchant Acceptance of Payment Media
Wilko Bolt and Sujit Chakravorti

WP-08-11

Investment Shocks and Business Cycles
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

WP-08-12

New Vehicle Characteristics and the Cost of the
Corporate Average Fuel Economy Standard
Thomas Klier and Joshua Linn

WP-08-13

Realized Volatility
Torben G. Andersen and Luca Benzoni

WP-08-14

Revenue Bubbles and Structural Deficits: What’s a state to do?
Richard Mattoon and Leslie McGranahan

WP-08-15

The role of lenders in the home price boom
Richard J. Rosen

WP-08-16

Bank Crises and Investor Confidence
Una Okonkwo Osili and Anna Paulson

WP-08-17

Life Expectancy and Old Age Savings
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-08-18

Remittance Behavior among New U.S. Immigrants
Katherine Meckel

WP-08-19

Birth Cohort and the Black-White Achievement Gap: The Role of Health Soon After Birth
Kenneth Y. Chay, Jonathan Guryan, and Bhashkar Mazumder

WP-08-20

Public Investment and Budget Rules for State vs. Local Governments
Marco Bassetto

WP-08-21

Why Has Home Ownership Fallen Among the Young?
Jonas D.M. Fisher and Martin Gervais

WP-09-01

5