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14 29
Do Tenant- and Place-Based Rental
Housing Programs Complement Each
Other? Evidence from Ohio
Brett Barkley, Amy Higgins, and
Francisca G.-C. Richter
FEDERAL RESERVE BANK OF CLEVELAND
Working papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to
stimulate discussion and critical comment on research in progress. They may not have been subject to the
formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views stated
herein are those of the authors and are not necessarily those of the Federal Reserve Bank of Cleveland or of
the Board of Governors of the Federal Reserve System.
Working papers are available on the Cleveland Fed’s website at:
www.clevelandfed.org/research.
Working Paper 14-29
November 2014
Do Tenant- and Place-Based Rental Housing Programs
Complement Each Other? Evidence from Ohio
Brett Barkley, Amy Higgins, and Francisca G.-C. Richter
We characterize Housing Choice Voucher (HCV) use in Low-Income Housing
Tax Credit (LIHTC) units with the intent to explore whether the subsidy overlap
responds to needs unmet by the HCV program alone. Lacking the data to contrast
HCV use in and out of LIHTC units, we turn to a comparison of HCV users in
LIHTC units relative to the overall HCV population. Our analysis of 2011 tenantlevel LIHTC data from Ohio and HCV data from HUD suggests placed-based
vouchers, which must be redeemed in an LIHTC unit, are more likely allocated
to extremely low-income or special-needs households. On the other hand, HCV
users who freely choose to redeem their voucher in an LIHTC unit are similar to
the overall HCV population in terms of incomes and ethnicity, although they tend
to be older. There is little evidence that using both programs in concert enables
access to better neighborhoods for HCV users: households across both programs
live in neighborhoods that tend to have poverty rates above 20 percent, with
HCV-LIHTC users actually living in higher-poverty neighborhoods in most urban
Ohio counties when compared to the HCV population as a whole.
JEL Codes: R38, H71, R23.
Keywords: Affordable Housing, Housing Choice Vouchers, Low-Income Housing
Tax Credit, Housing Policy, Neighborhood Quality, Poverty.
Suggested citation: Barkley, Brett, Amy Higgins, and Francisca G.-C. Richter,
2014. “Do Tenant- and Place-Based Rental Housing Programs Complement Each
Other? Evidence from Ohio,” Federal Reserve Bank of Cleveland, working paper
no. 14-29.
Brett Barkley and Amy Higgins are at the Federal Reserve Bank of Cleveland,
and Francisca G.-C. Richter is at the Federal Reserve Bank of Cleveland and Case
Western Reserve University. The authors thank Dr. Holly A. Holtzen and the staff
of the Ohio Housing Finance Agency for providing de-identified tenant-level
data and for helpful comments. The paper also benefited from conversation with
Gregory D. Johnson, CEO of the Cincinnati Housing Authority.
1. Introduction
The longstanding debate over cost-effectiveness in the provision of rental housing subsidies
is influenced by whether the housing goods offered by tenant-based and place-based programs are
considered substitute or complement products. Assuming that both programs provide equal access
to housing units and environments of similar quality, researchers show that tenant-based programs
are less costly than their place-based counterparts (Olsen 2009; GAO 2002). Thus, under the
substitutability assumption, it is possible to house a larger number of eligible households when
allocating funds away from place-based and into tenant-based programs. Alternatively, when placebased programs supply housing in a tight rental market, deliver human or educational services, or
support neighborhood revitalization efforts, they may reach subpopulations whose needs would be
unmet under a tenant-based program alone. Thus, some argue that the programs should be best
seen as complementary in the provision of housing for low-income households. These programs can
also be complementary at the individual level. For instance, an elderly, extremely low income
individual may need the human services provided through a place-based program, and the deep
rental subsidy provided by a housing voucher. Under the complementarity assumption, the goal is to
optimally allocate resources across both programs in order to cover the heterogeneous needs of
eligible families.
While, to the extent of our knowledge, there is not an explicit coordination of programs at
the federal level, the number of households using vouchers in units produced by place-based
programs is not insignificant. By 2011, 34% of Housing Choice Vouchers (HCVs) were being used to
subsidize the rent of Low-Income Housing Tax Credit (LIHTC) units in Ohio. Based on a sample from
ten states, O’Regan & Horn (2013) report that between 33.7% and 49.6% of low-income qualifying
LIHTC tenants received additional rental assistance-- HCV or otherwise. Such overlap is not
necessarily a recent phenomenon; two older studies, GAO (1997) and Abt (Buron et al. 2000),
reported that 37%-39% of LIHTC tenants receive additional project-based or tenant-based rental
assistance as of 1994. In order to understand to what extent such programs complement each other,
it is important to identify and characterize the subset of users who receive simultaneous subsidies
from both types of programs.
Previous economic and policy research focusing on LIHTC users has documented the
incidence of using a voucher in LIHTC but has not looked into whether this subsidy overlap provides
housing services unmet by either program alone (Galvez 2010). For that, a comparison of HCV users
in and out of LIHTC would be needed. Lacking the data required for such approach, we resort to a
comparison between the entire HCV population in Ohio and its subset of HCV holders living in LIHTC.
The time period analyzed spans 2011 and 2012. A discussion of the relevant literature precedes the
analysis and the last section provides some concluding thoughts. We use subsidized tenant level
data in Ohio for 2011, provided by the Ohio Housing Finance Agency (OHFA) and HUD’s Public Use
Microdata Sample (PUMS) for the year 2012. Tract level data for HCV users is from HUD’s A Picture of
Subsidized Households, 2011, and neighborhood quality variables are from the Census Bureau 1, 2010.
2. The incidence of voucher use in LIHTC units
The Low Income Housing Tax Credit Program is the main source of subsidies for the
construction of rent-controlled housing units. LIHTC is designed so one of the following affordability 2
conditions apply: at least 40% of its units are affordable to households with incomes at 60% of area
median income (AMI), or at least 20% of units are affordable to households with incomes at 50% of
AMI. The Housing Choice Voucher program is the main source of tenant-based subsidies. As
conceived, the program relies on the market supply of rental units at the Fair Market Rent (FMR) 3
and requires the voucher holder to pay 30% of their income on housing. The remaining portion of the
rent is covered by the voucher, conditional on the unit and tenant meeting certain requirements
specified by HUD. Thus, by design, housing should be affordable (or close to affordable) and
accessible to all HCV users, provided the market supplies enough units satisfying HUD requirements
to meet demand.
While LIHTC units are not affordable to individuals with incomes below 50% of AMI in the
absence of additional subsidies, some argue that the program is important in broadening the supply
of quality units offered at or around the FMR. In fact, under minimum requirements, LIHTC owners
may not refuse to rent to HCV holders 4, whereas landlords in the private market can freely choose to
go through the HUD certification process in order to accept HCV tenants or abstain from renting to
HCV holders. O’Regan & Horn (2013) argue that the HCV program has a significant role in allowing
households with extremely low income to access LIHTC developments. Their data consists of
480,000 LIHTC units from fifteen state housing finance agencies (HFAs) for 2009 and 2010,
2010 data in 2000 tract boundaries by Geolytics, Inc.
Housing affordability refers to being able to afford rent and utilities with 30% or less of income
3 The Fair Market Rent is set at the 40th percentile of gross rent for typical, non-substandard units in a given
local housing market.
4 Internal Revenue Code § 42(h)(6)(B)(iv)
1
2
representing approximately 30% of the national LIHTC housing stock. The analysis finds that states
with higher rates of HCV use in LIHTC also have higher rates of extremely low-income households
served by LIHTC, suggesting more focused resources for the most disadvantaged populations. They
also point out that LIHTC households without additional rental assistance experience higher levels of
instability (i.e. occupancy turnover) than LIHTC households using a HCV.
Williamson et al. (2009) analyze 2004 tenant level data from Florida to determine the extent
of the HCV-LIHTC overlap in general. They are able to identify HCV holders in LIHTC units, but cannot
discriminate between project-based vouchers (PBVs) and tenant-based vouchers (TBVs). PBVs are
HCVs allocated by the local Public Housing Authority to units in certain LIHTC projects. A tenant
making use of a PBV cannot leave the LIHTC unit without losing her voucher. On the other hand,
TBVs denote vouchers assigned to tenants, who can then choose to rent a LIHTC unit. The study
finds that a significant portion of HCV-LIHTC users (30%) are housed in LIHTC units located in
Qualified Census Tracts (QCTs), usually low-income neighborhoods where developers receive 1.3
times the tax credit that would be allocated outside of QCTs. In a subsequent study, Williamson
(2011) finds that LIHTC tenants also receiving HCV assistance are much less likely to be cost burdened
than other LIHTC tenants. Her analysis shows that 65% of HCV-LIHTC users do not experience a
housing cost burden compared to only 9% of LIHTC households without a voucher.
The aforementioned studies characterize the HCV-LIHTC population in relation to other LIHTC
tenants. However, such analyses are unable to inform on whether the subsidy overlap responds to
needs unmet by the HCV program alone. The relevant counterfactual analysis for such test would
imply comparing the housing conditions of HCV households within a locality, with and without the
availability of LIHTC rentals. As Williamson et al. (2009) suggest, it is possible that the use of HCV
holders in LIHTC units is a response to the scarcity of HUD-certified affordable housing units in the
private rental market. It may also signal household preferences for newer, higher quality units than
what are typically available to HCV users -- all LIHTC units have been built since 1987 (Galvez 2010).
HCV users may also choose LIHTC units in search for better neighborhoods or the provision of special
need services within the living environment.
Focusing on the potential issue around the supply of FMR units, Eriksen (2010) suggests that
LIHTC developments may actually crowd out unsubsidized rental housing that would have otherwise
been built. But the extent of such an effect is not entirely clear. Baum-Snow and Marion (2009) find
that LIHTC projects crowd out new rental construction in areas where property values are rising but
do not have the same effect in stable or declining areas. One of the largest experiments conducted
by HUD in 1973 provides valuable insight on private rental market responses to an expansion of
tenant-based housing subsidies (Frieden 1980). The Experimental Housing Allowance Program
(EHAP) consisted of three components: a demand-side experiment (how families would make use of
their allowances), a supply-side experiment (local housing market responses to increased demand),
and an administrative experiment (to test effectiveness of different administrative arrangements).
Despite the fear that discriminatory practices would affect take-up rates, the experiment showed
that minority and “hard to house” families were able to find housing of acceptable quality. Female
headed-households and families on welfare assistance were the main participants, but participation
by the elderly was low. Still, only about half of families eligible for allowances participated and the
program had small effects on mobility and quality of housing. Partly due to the low take-up rate,
there was virtually no increase in rents following the open enrollment policy.
While the use of HCV in LIHTC units might be a response to tight rental markets, desirability
of newer units, or need for special services, it could also represent an opportunity for HCV
households to access better neighborhoods in close proximity to their social networks.
Neighborhood environment influences can have a profound impact on the lives of individuals. The
program design of both HCV and LIHTC consider the importance of neighborhood quality. HCVs were
originally designed to allow for movement out of public housing units by providing neighborhood
choice to poor households, and many states preference LIHTC credits to projects that are part of a
broader community revitalization plan in an effort to improve neighborhoods.
Deng (2007) studied HCV and LIHTC programs in six Metropolitan Statistical Areas (MSAs),
San Jose, New York, Boston, Cleveland, Atlanta, and Miami. She finds that both HCV and LIHTC units
are predominantly located in areas with low school quality. On the other hand, Newman and Schnare
(1997) find slightly better prospects for HCV than LIHTC households. Compared to public housing,
LIHTC units provide poor households access to better quality neighborhoods; but when compared to
HCVs, LIHTC unit dwellers experience lower neighborhood quality.
Lens et al. (2011) investigate whether HCVs assist tenants in moving to neighborhoods with
less crime. Their analysis makes use of two datasets, 2000 census tract level crime data for 91 cities
and census tract data from 1998-2008 for Austin, Chicago, Cleveland, Denver, Indianapolis,
Philadelphia, and Seattle. The authors find that voucher households occupied lower crime
neighborhoods compared to tenants occupying LIHTC and public housing units. Residents living in
LIHTC and public housing units experienced crime rates comparable to poor renters in general.
Richter et al. (2013), consider neighborhood quality of HCV and LIHTC households in 2000 and
the 2006- 2009 period for Cuyahoga, Franklin, and Hamilton counties in Ohio, as well as Allegheny
County in Pennsylvania (quality is a function of poverty, education, and employment indicators).
They find that Allegheny County neighborhoods with LIHTC projects placed in service by 2000
improved in quality relative to other poor neighborhoods without LIHTC projects. And the level of
quality experienced by LIHTC users in 2006-2009 was comparable to that of HCV users. However,
these results did not hold for any of the Ohio counties studied.
3. Characteristics of the HCV and LIHTC populations in Ohio
HCV is the largest subsidized housing program in Ohio, with 33% of all subsidized households
falling under this program in 2011. LIHTC comprises the third largest component of subsidized
housing in Ohio, at 23% in 2011 (Figure 1.2). While the number of participants across both programs
adds to about 154,000, the number of unique households covered is less than 124,000 due to the fact
that about 30,000 households use a voucher in a LIHTC unit.
HCVs may be used in a LIHTC unit via a place-based voucher (PBV) or a tenant-based voucher
(TBV). While PBV holders can only use their subsidy in the unit attached to the voucher, TBV users are
unrestricted, but choose to redeem their voucher in a LIHTC unit. In Ohio, during 2011, 34% of HCVs
were being used in a LIHTC unit, with 23% of them being PBVs and 11% TBVs (Figure 1.2). Relative to all
LIHTC households, PBV holders represented 34% of that pool, while 15% of LIHTC households were
TBV users. 51% of LIHTC users were not subsidized under the HCV program (denoted as “LIHTConly.”)
We compare PBV, TBV, LIHTC-only, and all HCV users (includes PBV, TBV, and HCV-non LIHTC)
by the characteristics of individuals they house. HCV data is from PUMS, for the year 2012, while PBV
and TBV data is from OHFA for the year 2011 5. Our comparisons are limited to those characteristics
collected in both data sets. Figure 2.1 shows a higher incidence of elderly (62 and older) among LIHTC
users as compared to the entire HCV population. However, non-elderly head of households with a
disability are clearly more represented in the HCV program than in any of the LIHTC categories.
Comparing TBV holders with the entire HCV population, it is possible to say that the elderly are more
likely to use their voucher in a LIHTC unit, while the reverse is true for the non-elderly, disabled
population of HCV holders.
5
The PUMS for subsidized households is not available in 2011.
The income distribution of subsidized households across programs shows that TBV holders
are very similar to the overall population of HCV users with a median income of about $10 000 (Figure
3.1). On the other hand, PBV holders tend to be on the lower end of the distribution, with a median
income under $8 000 and about 10% of its population receiving no income at all. Other characteristics
of the PBV population suggest this group may lack family support. Over fifty percent of PBV users
have a household size of 1, compared to a larger household size, on average, for all HCV users.
Accordingly, PBV users also tend to live in much smaller units—nearly fifty percent are one or zero
bedroom apartments. As expected, LIHTC dwellers with no additional housing subsidies are less
needy. In Ohio, the median income for this group was about $20 000 in 2011.
We also look at the distribution of households by race under each subsidy combination
(Figure 2.2). Issues such as discrimination in the private rental market could lead to a higher share of
African-American households using their HCVs in LIHTC units when compared to the HCV holder
population as a whole. However, the data only tells us that the shares of African-American head of
households in the HCV population and the population of TBV holders is comparable, at about 60%.
3.1 Characterizing voucher use within LIHTC
While we are limited in our comparison of HCV users in and out of LIHTC units, data does
allow for a richer comparison of HCV users within LIHTC. We specify a multinomial logit model to
characterize households belonging to the following three groups: TBV holders, PBV holders and
other LIHTC users.
(1)
𝑃{𝑦𝑖 = 𝑗} =
′
𝛽𝑗 )
exp(𝑥𝑖𝑖
′
′
𝛽2 � + exp(𝑥𝑖3
𝛽3 )
1 + exp�𝑥𝑖2
,
𝑗 = 1, 2, 3
The model expresses the probability of being in each of the three categories (indexed by j) as a
function of several explanatory variables associated with households in each housing category (xij
vector). Specifically, we use the age, race, and gender of household head; number of children;
homelessness status; disability status; and an income from employment dummy. We compute
marginal effects for the model that allow comparing households using PBVs relative to those using
TBVs or no additional subsidy.
Table 1. Average Marginal Effects estimate the average change in probability of being a PBV user due
to a one unit increase in the explanatory variable. Age is in years and the remaining variables are
dichotomous (1=yes, 0=no). These estimates are for relative risk ratio estimates in the multinomial
logit of equation 1, with TBV is the base outcome.
age of household head
PBV
-0.019
Std. Err.
0.001
z
-29.56
P>|z|
0
age of household head^2
0.000
0.000
21.7
0
0.000
0.000
female household head
0.033
0.006
5.9
0
0.022
0.044
household with children
-0.019
0.002
-7.9
0
-0.024
-0.014
disabled head or spouse
0.188
0.007
27.23
0
0.175
0.202
income from employment
-0.312
0.005
-62.78
0
-0.321
-0.302
homeless
0.109
0.012
9.4
0
0.087
0.132
95% CI
-0.021
-0.018
Results confirm that PBV holders are clearly a more vulnerable population relative to other
LIHTC dwellers. All else equal, having been previously homeless increases the probability of being a
PBV user by 11%. Among all LIHTC dwellers, disability status also increases the probability of being a
PBV holder, whereas the probability decreases for male-headed households or for those that have
income from employment. Altogether, the data suggests that PBVs are purposely allocated to the
most vulnerable individuals among LIHTC users. Unfortunately, we cannot examine how or to what
extent this population is served by the voucher program outside of LIHTC.
Figure 3.2 shows the 2011 share of elderly and disabled Ohio LIHTC tenants by year in which
the units were placed in service. Between 30% to 50% of subsidized LIHTC units placed in service since
2007 are inhabited by elderly residents. The fact that newer buildings house a higher share of elderly
suggests that over time, the program has purposely offered housing units and services that are more
attractive or suitable for this population.
3.2 Neighborhood Quality for HCV users in LIHTC units relative to all HCV users
Previous work comparing neighborhood quality under place- and tenant-based programs has
not identified neighborhood quality for the group of households subsidized under both programs.
There is no way of uncovering the complex decision making process by which individuals decide
where to locate with a voucher. However, we can see whether those selecting into a LIHTC unit –via
a TBV or PBV- experience better neighborhood quality as compared to the entire population of HCV
users. We know that the HCV population is made of individuals living in and out of LIHTC units and
that those in LIHTC units represented about 34% of the HCV population in Ohio, 2011.
HCV_pop= TBV_pop + PBV_pop + HCV-non LIHTC_pop.
Unable to identify HCV users not living in LIHTC units (HCV_non LIHTC_pop), we compare average
neighborhood quality for the voucher population in LIHTC (TBV_pop+PBV_pop) with that of the HCV
population as a whole (HCV_pop).
Figure 4 shows the average neighborhood quality for voucher users in select Ohio counties
versus that for the subset of TBV and PBV holders, jointly. Figure 5 compares neighborhood quality
for TBV and PBV holders. Neighborhood quality is measured at the census tract level and it is relative
to all tracts in the United States in 2010. We use quantiles of the first principal component estimated
from the following variables: percent poverty, percent in the labor force, percent with a high school
diploma, percent with a bachelor’s degree, and percent employed. 6 Thus, the highest quality
neighborhood in the US would have an index of 100 and the median quality neighborhood according
to our measure would have an index of 50. The average neighborhood quality is calculated for all
tracts within a county, weighted by the number of HCV, TBV or PBV holders per tract, accordingly.
Counts of HCV holders within each tract are from the Picture of Subsidized Households, 2011 and
counts for TBV and PBV holders are from the OHFA – 2011 LIHTC data 7. Similarly, average poverty
rates experienced by HCV, TBV and PBV users at the county level are presented in Figures 6 and 7.
The size of the bubble in these graphs reflects the relative share of TBV and PBV users in the HCV
The first component explains 67% of the variation in the 2010 data.
Because these two data sets are collected at different points in time, and low-income populations are highly
mobile, we cannot expect alignment of counts at the census tract level. However, the data does give us a
snapshot of the distribution of voucher holders across neighborhoods and thus, we can compare
neighborhood quality for all three groups (HCV, PBV, TBV) aggregated to the county level. Counties included in
our study had a population greater than 50,000 to primarily capture urban areas, per US Census Bureau
definition, and greater than 50 PBV & 50 TBV holders to ensure low voucher populations aren’t given undue
weight.
6
7
populations. The larger the bubble, the more TBV and PBV holders there are in the county relative to
the total number of HCV holders.
By focusing on the bottom left quadrant of figure 4, we see that on average, HCV holders in
Ohio counties live in neighborhoods of below median quality; and for those voucher holders living in
a LIHTC unit (TBV+PBV holders), neighborhood quality tends to remain below the median. Figure 5
shows that in some counties PBV holders experience higher average neighborhood quality than TBV
holders, yet in these counties, both groups remain below the median of quality (bottom left
quadrant). There are, however, a few counties in which subsidized households experience above the
median neighborhood quality (right side quadrants). In these counties, it is most likely that TBV
holders are located in better neighborhoods than their PBV counterparts.
To complement the rank-based analysis of neighborhood quality we provide graphs using
actual neighborhood poverty rate averages experienced by the subsidized populations within
counties. Here, we divide the plots into four quadrants to mark average poverty rates below and
above 0.2 or 20%. It is worth noting that the average neighborhood poverty rate for the HCV
population is above 10% for all counties studied and HCV-LIHTC households do not benefit from living
in less poor neighborhoods relative to the overall HCV population (Figure 6). Figure 7 shows that
compared to PBV users, TBV users -- those who chose to live in LIHTC unit -- tend to live in less poor
neighborhoods, but only in a handful of counties the average neighborhood poverty for TBV users is
below 20%.
Conclusions
Evidence from Ohio suggests that HCV and LIHTC programs exhibit some degree of
complementarity, particularly, when serving populations with very low incomes and need for special
services. The overlap of programs is not at all insignificant: In Ohio, during 2011, 34% of LIHTC
households received place-based voucher assistance and another 15% of LIHTC dwellers were using
their tenant-based voucher to live in a tax-credit unit. The characteristics of the PBV population seem
to reflect the desire of local housing administrators to combine the deep subsidy of the HCV with the
supportive services that some LIHTC projects provide in order to serve those most needy. On the
other hand, the population of TBV users, who freely choose to live in LIHTC units, is similar to the
overall HCV population in terms of incomes and ethnicity, but they tend to be older. Such
observations are consistent with the trend that 30% to 50% of subsidized LIHTC units placed in service
since 2007 are occupied by elderly tenants.
While our analysis of HCV users in LIHTC points to benefits of the overlap for certain
populations, there is very little evidence to support PBV or TBV usage enables access to better
quality neighborhoods relative to other HCV users. In the Ohio counties studied, the average HCV
user and the average voucher holder living in LIHTC units, occupy neighborhoods which are below
the 50% median neighborhood quality index, with poverty rates of above 20%. There are only a few
Ohio counties—Clermont (Cincinnati MSA) and Delaware (Columbus MSA)—where PBV or TBV
users, on average, reside in better neighborhoods than the HCV population as a whole, and at the
same time, experience above median neighborhood quality. Unfortunately, households across both
programs live in neighborhoods that tend to have above 20% poverty rates, with PBV and TBV users
actually living in higher poverty neighborhoods in the most urban counties (e.g., Cuyahoga,
Hamilton, Summit, and Franklin) when compared to the HCV population as a whole.
Voucher use in LIHTC units can be perceived as beneficial insofar as the most disadvantaged
populations are provided access to important supportive services and quality rental units that would
otherwise not be available. However, it is important for housing administrators and policy makers to
address the low neighborhood quality that prevails for users of both programs. A further exploration
of the county-level variation in neighborhood quality and poverty illustrated by our analysis may shed
some light into how to better coordinate the provision of rental housing subsidies to improve the
living conditions and opportunities of low-income families.
References
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Developments on Neighborhoods,” Journal of Public Economics, 2009, 93 (5-6), 654–666.
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O’Regan, Katherine M. and Keren M. Horn, “What Can We Learn About the Low-Income Housing Tax
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Richter, Francisca G.-C. and Mark Sniderman, with Matthew Klesta and Frank Manzo, “Low-IncomeRental-Housing Programs in the Fourth District,” 2013. Working Paper 13-11, Federal Reserve Bank of
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---, “Tax Credits: Opportunities to improve oversight of the low-income housing program,” 1997.
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Williamson, Anne R., “Can They Afford the Rent? Resident Cost Burden in Low Income Housing Tax
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---, Marc T. Smith and Marta Strambi-Kramer, “Housing Choice Vouchers, the Low-Income Housing
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0
500,000
1,000,000
1,500,000
2,000,000
Subsidized Housing in US, 2011
PB Sec 8
Public
HCV
LIHTC
Zoom on HCV and LIHTC
80,000
100,000
All programs
0
20,000
20,000
40,000
40,000
60,000
60,000
80,000
100,000
Subsidized Housing in Ohio, 2011
0
HCV
PB Sec8
HCV
HCV or LIHTC only
Public
LIHTC
PBV
LIHTC
TBV
Figure 1.1 & 1.2: Subsidized Housing in the US and Ohio. 2011 LIHTC data is from the Ohio
Housing Finance Agency. 2011 Ohio PB Sec 8, Public and HCV data and all US-level data
are from the Center of Budget and Policy Priorities.
Share of Elderly or Disabled HH Heads
0
.1
.2
.3
by type of assistance, 2011-12
HCV
LIHTC-only
PBA
62 or older
TBA
disabled, under 62
Share of HH Heads by Race/Ethnicity
0
.2
.4
.6
by type of assistance, 2011-12
HCV
LIHTC-only
Black
PBA
White
TBA
Hispanic
Figure 2.1 & 2.2: Share of Household Head by Demographic. 2011 LIHTC data is from the
Ohio Housing Finance Agency. 2012 HCV data is from the Public Use Microdata Sample.
Income Distribution of Rent-Subsidized Tenants
0
.2
.4
.6
.8
1
Tenant level data for Ohio, 2011-12
0
10000
LIHTC-PBA
LIHTC-TBA
LIHTC-only
HCV
30000
20000
annual income in dollars
40000
50000
Share of Elderly and Disabled LIHTC Tenants in 2011
0
.1
.2
.3
.4
.5
By year in which the building was placed in service
1995
2000
2005
date
2010
62 or older
disabled
Fitted values
Fitted values
2015
Figure 3.1 & 3.2: Income Distribution of Rent-Subsidized Tenants and Share of Elderly
and Disabled LIHTC Tenants. 2011 LIHTC data is from the Ohio Housing Finance Agency.
2012 interval income data of HCV users is from the Public Use Microdata Sample.
Average Neighborhood Quality for HCV and HCV-LIHTC Users
80
County averages - Ohio, 2011
Delaware
Miami
40
Lorain
Butler
Trumbull
Allen
Clark
20
TBV+PBV users
60
Fairfield
Athens
Cuyahoga
Lucas
Jefferson
Franklin
Hancock
Portage
Warren
Montgomery
Hamilton
Summit
Stark
0
Mahoning
Greene
Clermont
0
20
40
60
80
All HCV holders in county
Figure 4: Average Neighborhodd Quality for HCV (x-axis) and HCV-LIHTC Users (y-axis).
Neighborhood quality measured as quantiles of first principal component of census tract level
variables from Census 2010. We include %poor, %employed, %in labor force, %high school, %bachelors.
Bubble size represents relative share of HCV use in LIHTC units across counties. 2011 LIHTC data is
from Ohio Housing Finance Agency. 2011 HCV data is from A Picture of Subsidized Housing.
Average Neighborhood Quality for TBV and PBV Users
80
County averages - Ohio, 2011
60
Clermont
Fairfield
Hancock
Delaware
40
Miami
Athens
Lorain
Trumbull
Clark
Allen
Jefferson
Franklin
Hamilton
Montgomery
Summit
Cuyahoga
Lucas
Greene
Warren
Mahoning
20
PBV holders
Portage
Butler
0
Stark
0
20
40
60
80
100
TBV holders
Figure 5: Average Neighborhodd Quality for TBV and PBV Users. Neighborhood quality measured as
quantiles of first principal component of census tract level variables from Census 2010. We include
%poor, %employed, %in labor force, %high school, %bachelors. Bubble size represents relative share of
HCV use in LIHTC units across counties. 2011 LIHTC data is from Ohio Housing Finance Agency.
Average Neighborhood Poverty Rate for HCV and HCV-LIHTC Users
County averages - Ohio, 2011
.4
Stark Lucas
Hamilton
Cuyahoga
Mahoning
.2
.3
TBV+PBV users
Summit
Franklin
Warren
Athens
Clark
Montgomery
Trumbull
Greene Jefferson
Lorain
Butler
Allen
Portage
Hancock
Fairfield
0
.1
Miami
Clermont
Delaware
0
.1
.2
.3
.4
All HCV holders in county
Figure 6: Average Neighborhood Poverty Rate for HCV and HCV-LIHTC Users. Census tract
neighborhood poverty rates are from the Census 2010. Bubble size represents relative share of HCV
use in LIHTC units across counties. 2011 LIHTC data is from Ohio Housing Finance Agency. 2011 HCV
data is from A Picture of Subsidized Housing.
Average Neighborhood Poverty Rate for TBV and PBV Users
.5
County averages - Ohio, 2011
Lucas
.3
Butler
Greene
Jefferson
Stark
Summit
Franklin
Montgomery
Athens
Clark
Mahoning
Lorain
Trumbull
Warren
.2
PBV holders
.4
Hamilton
Cuyahoga
Allen
Hancock
Fairfield
Clermont
.1
Delaware
Miami
0
.1
.2
Portage
.3
.4
.5
TBV holders
Figure 7: Average Neighborhood Poverty Rate for TBV and PBV Users. Census tract neighborhood
poverty rates are from the Census 2010. Bubble size represents relative share of HCV use in LIHTC
units across counties. 2011 LIHTC data is from Ohio Housing Finance Agency.