Download The User Cost of Low-Income Homeownership

JRAP 43(2): 123-137. © 2013 MCRSA. All rights reserved.
The User Cost of Low-Income Homeownership
Sarah F. Riley, HongYu Ru, and Qing Feng
University of North Carolina at Chapel Hill – USA
Abstract. Empirical research examining whether owning a home is less costly than renting for
low-income households is largely lacking. We use detailed property information provided by
a set of low-income homeowners who participated in the Community Advantage Panel Survey, along with a matched sample of similar rental properties from the American Housing
Survey, to determine whether low-income homeowners in the United States would have experienced lower housing costs by renting between 2003 and 2011. We calculate the homeowners’ user costs directly from the survey data, and we derive hedonic measures of equivalent
rent for these homeowners via pooled regressions of house prices and rents on housing characteristics, from which we obtain capitalization rates. For the median homeowner in our
sample, we find that owning was less costly than renting a comparable property between 2003
and 2011.
1. Introduction
Although government efforts to foster lowincome homeownership have been ongoing for decades, it is still an open question as to whether and
when such policies actually generate benefits for
low-income households in financial or social terms
(Dietz and Haurin, 2003; Shlay, 2006). From a financial perspective, proponents of low-income homeownership sometimes observe that low-income
homeowners, on average, tend to accumulate positive wealth, while comparable renters do not
(Boehm and Schlottman, 2004b; Turner and Luea,
2009). In particular, homeownership can be viewed
as a savings commitment mechanism, and evidence
also suggests that the return on investment from
leveraged homeownership often dwarfs the unleveraged returns to other common investment vehicles, such as stocks and bonds (Stegman et al., 2007;
Hasanov and Dacy, 2009).
However, the extent to which individual lowincome households accumulate or lose wealth
through homeownership depends greatly on the
location and timing of the house purchase, as well as
on the length of time for which homeownership is
sustained (Case and Marynchenko, 2002; Duda and
Belsky, 2002). Transaction costs are often a larger
proportion of total homeownership costs for lowincome households, who have been historically likely to hold their houses for shorter periods of time
(Boehm and Schlottman, 2004a,b; Turner and Smith,
2009). Moreover, any tax benefits from mortgage
interest deductions are widely recognized to be
small, if not negligible, for low-income households,
especially when the standard deduction is considered (Beracha and Tibbs, 2010; Poterba and Sinai,
2011). Finally, surveys of financial literacy have
called into question the general competence of
American households, and especially the lowincome population, in making rational and informed
financial decisions (Bucks and Pence, 2008; Lusardi
and Tufano, 2009). Thus, it has been suggested that
government programs that facilitate and promote
low-income homeownership may do more financial
harm than good to the households involved, and
that low-income households would have been better
off renting than owning during the recent housing
price bubble and subsequent financial crisis (Baker,
2005).
124
Despite the longevity and vigor of the debate
about whether homeownership makes sense for
low-income households, empirical research examining directly whether owning is, in practice, less costly than renting for this population is largely lacking
(Duda and Belsky, 2002). To address this shortcoming of the existing literature, we use detailed
information provided by a set of community reinvestment mortgage recipients to assess whether
low-income homeowners would have been financially better off renting during the period from 2003
to 2011 from the perspective of the ex post user cost
of capital.
Our primary data set comes from the Community Advantage Panel Survey (CAPS) and comprises
information about a sample of primarily urban lowincome homeowners who all originally received 30year, fixed-rate mortgages at near-prime terms
through the Community Advantage Program (CAP),
which is a secondary mortgage market demonstration program for mortgages that meet the terms of
the lending test of the Community Reinvestment Act
(CRA). The CRA encourages lenders to provide
credit services as well as depository services to the
communities in which they do business. Large
lenders, in particular, are evaluated on the amount
of residential credit that they extend to borrowers
who either have household incomes of at most 80%
of the metropolitan statistical area median income
(MSAMI) or who live in Census tracts where the
median household income is at most 80% of
MSAMI.
CAP was initiated in 1998 by the Ford Foundation, Fannie Mae, and Self-Help, a non-profit financial institution located in North Carolina, with the
intention of demonstrating the viability of a secondary mortgage market for CRA-eligible mortgages.
Under this program, Self-Help has purchased existing portfolios of eligible loans from originating
lenders who have been otherwise unable to sell their
loans in the secondary market, and then has resold
them to Fannie Mae while initially retaining recourse. Overall, about half of the borrowers who
have participated in the CAP program had household incomes of 60% of MSAMI or less at the time of
loan origination, and about 40% are racial or ethnic
minorities. CAP borrowers took out a median loan
of $81,000 (or about 2.6 times median annual income) and purchased houses that were valued at a
median of $85,200 at loan origination, for a median
loan-to-value ratio of 97%.
The Ford Foundation provided the original underwriting capital for the CAP purchasing arrange-
Riley, Ru, and Feng
ment and continues to fund the Community Advantage Panel Survey (CAPS), an ongoing annual
survey that collects detailed financial, social, and
demographic information from a subset of 3,743 of
these borrowers who received CAP mortgages between 1999 and 2003. The data set contains information about changes in housing tenure status (i.e.,
tenancy vs. owner occupancy), incurred maintenance costs, itemization of tax deductions, and
mortgage terms. The data set also contains quarterly
zip-code-level house price estimates provided by
Fannie Mae for the period 2003-2011. The CAP data
are described in detail by Riley et al. (2009),
who find that, with respect to income and race distributions, the CAPS participants are largely representative of the low-income homeowners who
participated in the May 2003 Current Population
Survey. Thus, using these data, we seek to inform
policy discussions that concern CRA lending and the
low-income and minority population in the U.S. that
would be eligible for such lending.
In brief, our methodological approach involves
the following steps. We combine the CAPS data
with a sample of matched rental properties from the
American Housing Survey and use these combined
data to derive tenure-pooled hedonic estimates of
capitalization rates and equivalent rents for CAPS
properties using methods developed by Linneman
(1980) and Crone et al. (2009) that make use of the
property attributes of owned and rented properties.
We then also calculate ex post user costs for the
CAPS owners directly from the survey data. We
compare the estimated user costs with the estimated
equivalent rents for the CAPS owners to assess
whether owning was more costly than renting for
these households between 2003 and 2011. We also
then evaluate annual break-even house price appreciation by equating the user cost of owned housing,
exclusive of observed appreciation, with the estimated equivalent rent in a given year.
The user cost literature most closely related to
this paper comprises the work of Elsinga (1996),
Belsky et al. (2005), and Garner and Verbrugge
(2009). Elsinga (1996) compares the ex post user cost
of owner-occupied and rental housing units in six
neighborhoods in Holland for the period from 1978
to 1993 based on survey data. Belsky et al. (2005)
simulate the user costs of renting versus owning
between 1983 and 2001 for low-income homeowners
and renters in Boston, Chicago, Denver, and Washington, DC. Garner and Verbrugge (2009) also use
survey data to compare user costs to rents for the
median housing structure in New York City,
User Cost of Low-Income Homeownership
Philadelphia, Chicago, Houston, and Los Angeles
between 1982 and 2002. Like much of the large existing user cost literature, these three analyses emphasize the primary importance of timing and market price movements in determining whether owning is less costly than renting. In addition, user costs
for renters tend to be less volatile than those for
owners as a result of fluctuations in house prices.
Secondary factors that are found to reduce the relative user cost of owning include better mortgage
terms, better property location, higher household
income (due to the greater tax benefit of deducting
mortgage interest), refinancing to obtain better
mortgage terms, and government subsidies for
owned housing. Our paper complements this existing work by considering more recent and more
detailed data for known low-income households1 in
the U.S., both before and during the recent housing
market decline that began in 2006.
Because investment decisions are often evaluated
from an ex ante perspective in economic research, it
is worth mentioning up front that we are interested
in the ex post user costs during the survey period for
a couple of reasons. First, Shiller (2007) suggests
that house price expectations during this period may
have been driven by psychological factors rather
than economic fundamentals. Because rational ex
ante investment decisions are predicated on rational
expectations, “irrational exuberance” in the housing
market causes problems for accurately assessing the
user cost of owned housing. These problems are
compounded by the possibility (noted above) that
low-income borrowers may be less financially literate and thus prone to making financial mistakes.
Second, as noted by Oulton (2007), the choice of an
ex ante or ex post approach to measuring the user cost
of capital is best informed by what one is trying to
accomplish. In historical growth accounting exercises, for example, the ex post approach is often preferable because one is interested in what actually
happened, rather than what was expected to happen. In this spirit, one can interpret our analysis as
an historical evaluation of what happened when
traditionally high-risk2 borrowers were given the
Most analyses for low-income households tend to rely on houseprice proxies, under the assumption that low-income households
will buy less expensive houses, rather than making use of actual
income data.
2 CAP borrowers did have to provide full documentation and
demonstrate an ability to repay their loans in order to qualify for
the program. Therefore, these borrowers do represent somewhat
lower risk than many subprime borrowers in the more general
population who received subprime loans, which were sometimes
made without any verification of borrower income or assets.
125
opportunity to buy houses financed with low-cost,
high-leverage mortgages right before one of the
most volatile periods in U.S. economic history. Understanding the evolution of realized housing costs
for these borrowers during this period may help to
inform future housing policy for urban low-income
households.3
For the period of 2003-2011, we estimate median
cumulative owner user costs of about $51,700 and
capitalization rates of 8-10%. In comparison, we
estimate a median cumulative equivalent rent of
approximately $78,700, with a standard error of
about $7,200. Thus, our analysis suggests that, at the
median, homeownership was less costly than renting a comparable property for CRA homeowners
during the period from 2003 to 2011. Decomposing
these results by year indicates that median annual
user costs were generally lower than median equivalent rents before 2007/2008 and were higher thereafter. Thus, because of when they purchased their
homes, the households in our sample experienced
gains from appreciation prior to the market downturn that were, at the median, sufficient to offset the
relatively higher user costs that they have experienced since the decline began. In addition to these
overall figures, we observe regional differences,
with the discrepancy between median cumulative
user costs and median cumulative equivalent rents
being largest in the West, smallest in the Midwest,
and intermediate in the South and Northeast. Overall, we estimate that annual house price appreciation
of less than 5% was necessary to ensure that owning
was no more costly than renting a comparable property for 95% of owners during the period considered.
The remainder of the paper is organized as follows. In the next section, we further describe the
data set and provide details about the calculation of
the owners’ equivalent rents and user costs. In the
third and fourth sections, we present results and
discuss the robustness of our estimates and the limitations of our analysis. In the final section, we conclude and suggest directions for future research.
1
We do also consider ex ante measures of the user cost based on
expectations defined as some average of observed appreciation in
prior years, but defining house price expectations in this way
simply strengthens our general results in favor of homeownership, because rents tend to be relatively stable and house price
appreciation rates have fallen in the most recent years, causing
expected appreciation to exceed actual appreciation. Therefore,
we present only our more conservative ex post analysis in this
paper.
3
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2. Data and methods
2.1. Samples and matching
The survey data collected via CAPS forms the basis of our analysis. However, we restrict our initial
sample to that subset of respondents who have not
moved since their original baseline interview. This
restriction allows us to make use of the property
characteristics information, such as the number of
bathrooms, that was collected by the survey beginning in 2008. As described in greater detail below,
we use these property characteristics in a hedonic
regression to estimate the average capitalization rate
for this segment of the housing market and then calculate equivalent rents for these homeowners.
However, deriving an accurate measure of the capitalization rate is complicated by the fact that, on average, the properties occupied by renters tend to be
of a different quality and type than those of owneroccupiers. For example, owner-occupied properties
in the U.S. are more likely to be single-family detached units, while renters are more likely to reside
in multifamily housing.
The substantial discrepancy between the characteristics of owned and rented properties is potentially a problem for two reasons. From a statistical perspective, this mismatch can cause regression analysis to perform poorly and coefficient estimates to be
unreliable. From an economic perspective, the hedonic approach to deriving market capitalization
rates rests on the assumption that the marginal contribution of property characteristics to the market
prices of those properties is the same regardless of
whether the property is owned or rented; however,
this assumption of a single market is unlikely to be
met if owned and rented properties have very different characteristics.
Therefore, to create a counterfactual for these
owners, we make use of matched properties from
the American Housing Survey (AHS) that have
characteristics that are similar to the properties
owned by the CAP survey participants. For each
year for which the AHS is available (2003, 2005,
2007, 2009, and 2011), we use one-to-one nearest
neighbor propensity score matching to select a cohort of AHS rental properties that are roughly comparable to the CAP properties. In generating the
propensity scores, we make use of the property
characteristics variables that are available for both
CAPS and the AHS: house type (single-family detached vs. other), the number of bedrooms, the
number of bathrooms, house quality (scale of 0-10),
neighborhood quality (scale of 0-10), geographic
Riley, Ru, and Feng
region, square footage, the year of construction, and
the year in which the respondent moved into the
property. As noted by Rubin (2001) and Stuart
(2010), the balance in two samples matched via propensity score methods, as measured by the standardized difference in the means of the propensity
scores, should fall below 0.25 in order for subsequent regression analysis to provide reliable estimates. For our sample, the initial balance in these
variables before the propensity score match varied
by year between 0.17 and 0.18, and we achieve a
final balance for each year below 0.14.4
Summary statistics for the CAP properties and
the matched 2003 AHS rental properties are presented in Table 1. Although the number of matched
properties (i.e., sample size) varies somewhat from
year to year due to variations in the AHS data, the
distributions of property characteristics in the subsequent years are similar to those presented here.
Approximately 58% of the properties owned by
CAPS respondents are located in the South, and 35%
are located in the Midwest. The corresponding percentages for the AHS renters are 37% and 19%,
respectively. About 4% of CAPS owners are located
in the West, and 2% are located in the Northeast.
Comparable percentages for the AHS rental properties are 26% and 18%, respectively.
With regard to housing structure, 86% of the
owner households live in single-family detached
housing, compared with 72% of the AHS renter
households. About 55% of CAPS properties have
three bedrooms, compared with about 47% of AHS
rental properties. Similarly, 48% and 45% of CAPS
and AHS properties have 1.5-2 bathrooms, respectively. Most CAPS properties (63%) and AHS rental
properties (55%) have between 1,000 and 2,000
square feet. About 21% of CAPS properties have
more than 2,000 square feet, compared with 16% of
AHS properties. About 22% of the properties in
each sample were constructed between 1950 and
1970, with an additional 25% constructed between
1970 and 1990. About 23% of the CAPS properties
and 16% of the AHS properties were constructed
after 1990.
Most CAPS owners moved into their residences
between 2000 and 2002, while more than half of the
AHS renters started occupying their properties before the year 2000. On a scale of 1 to 10, 91% of
As a robustness check, we also replicate our analysis using Mahalanobis metric matching with propensity-score calipers, as
described by Feng et al. (2006), but we find that the simple propensity score match provides slightly better balance for our data.
4
User Cost of Low-Income Homeownership
127
CAPS homeowners rate their homes at 7 or above,
compared with 86% of AHS respondents. Similarly,
84% of CAPS owners give their neighborhoods a
rating of 7 or above, compared with 83% of AHS
renters. The next several sections discuss the construction of the equivalent rent, user cost, and breakeven appreciation measures for the CAPS owners.
Table 1. Housing characteristics by matched sample for 2003.
Variable Name
CAPS Owners Sample (N=925)
AHS Renters Sample (N=925)
N
%
N
%
Residence Type
Single-family
797
86
669
72
Other
128
14
256
28
Bedrooms
0-1
14
1
54
6
2
193
21
250
27
3
509
55
439
47
4+
209
23
184
20
Bathrooms
0-1
274
30
358
51
1.5-2
445
48
320
45
2.5+
206
22
38
4
House Quality Rating
(Scale of 0-10)
0-4
8
1
26
3
5
28
3
48
5
6
47
5
58
6
7
161
17
143
16
8
345
37
263
28
9
153
17
138
15
10
183
20
249
27
Neighborhood Quality Rating
(Scale of 0-10)
0-4
28
3
32
4
5
52
6
69
7
6
64
7
51
6
7
178
19
130
14
8
307
33
229
25
9
152
16
142
15
10
144
16
272
29
Region
Midwest
318
35
173
19
Northeast
19
2
165
18
South
549
59
346
37
West
39
4
241
26
128
Riley, Ru, and Feng
Table 1 (continued). Housing characteristics by matched sample for 2003.
Variable Name
CAPS Owners Sample (N=925)
AHS Renters Sample (N=925)
N
%
N
%
Square Feet
<= 1,000
148
16
267
29
1,000-1,500
355
38
329
36
1,500-2,000
233
25
174
19
>2,000
189
21
155
16
Year of Construction
Before 1930
151
16
153
17
1930-1950
138
15
185
20
1950-1970
201
22
206
22
1970-1990
224
24
231
25
After 1990
211
23
150
16
Year Moved into Residence
Before 2000
34
4
481
52
2000
316
34
69
7
2001
294
32
108
12
2002
229
25
143
16
After 2002
52
5
124
13
2.2. Measuring equivalent rent
Using methods developed by Linneman (1980)
and Crone et al. (2009) and the matched CAPS-AHS
data sets, we derive capitalization rates for each year
by estimating tenure-pooled regressions of property
values and rents on respondent tenure status and
property characteristics. Using these estimated capitalization rates, we then calculate the equivalent
rents for the homeowners sample.
More formally, in the conventional hedonics
framework, the annual rent 𝑅𝑖𝑡 of renter-occupied
property 𝑖 at time 𝑡 is a function of property characteristics 𝑋𝑖𝑡 and a random error term 𝑢𝑖𝑡 ~𝒩(0, 𝜎 2 ),
as follows:
𝑙𝑛(𝑅𝑖𝑡 ) = 𝛾𝑡 𝑋𝑖𝑡 + 𝑢𝑖𝑡
(1)
Noting that 𝑅𝑖𝑡 = 𝐶𝑡 𝑉𝑖𝑡 , where 𝐶𝑡 is the capitalization rate at time 𝑡 and 𝑉𝑖𝑡 is the property value, one
can derive a comparable hedonic model for owned
property as
𝑙𝑛(𝑉𝑖𝑡 ) = − 𝑙𝑛(𝐶𝑡 ) + 𝛾𝑡 𝑋𝑖𝑡 + 𝑢𝑖𝑡
(2)
Using these two expressions, one can create a pooled
hedonic for both rented and owned properties that
allows the estimation of 𝐶𝑡 . Specifically, the relationship of house values and rents to property characteristics can be expressed as
𝑙𝑛(𝑌𝑖𝑡 ) = − 𝑙𝑛(𝐶𝑡 )𝐷0 + 𝛾𝑡 𝑋𝑖𝑡 + 𝑢𝑖𝑡
(3)
where 𝐷0 is an indicator for an owner-occupied
property and where
𝑌𝑖𝑡 = {
𝑉𝑖𝑡 , 𝑖𝑓 𝐷0 = 1
𝑅𝑖𝑡 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(4)
The average capitalization rate 𝐶𝑡 can then be obtained from the estimated coefficient on 𝐷0 . Using
this approach, we derive average capitalization rates
for each year between 2003 and 2011 for which AHS
data are available. We present our estimation results for these hedonic regressions in Table 2. From
among the variables that were used in the propensity score match, we retain only those variables that
continue to be significant predictors of property
value or annual rent: property type, number of bathrooms, and geographic region. Including the additional property characteristics variables does not
User Cost of Low-Income Homeownership
129
substantively influence the results. We observe
some variation in the estimated coefficients over
time as a result of temporal changes in economic
conditions. For intermediate years for which AHS
data are not available, we take the average of the
estimated capitalization rates for adjacent years; for
example, the capitalization rate for 2004 is the average of the 2003 rate and the 2005 rate. For simplicity, we refer to this series of values as CapRate.
The validity of this estimation approach rests on
the assumption that γt is the same for both owned
and rented properties. As noted above, we attempt
to ensure that this is a reasonable assumption by
limiting our analysis to owned and rented properties
with similar characteristics. Note that the propensity score match does not eliminate the need for a
multivariate framework in calculating the equivalent
rent; rather, similar to the way in which multivariate
methods are often used in analyzing data from randomized experiments in order to control for residual
variation in potential confounders, matching methods and multivariate regression methods are complementary and improve the robustness of estimates
from observational studies when used together (Rubin, 2001; Stuart, 2010).
After deriving CapRate, we create corresponding
measures of equivalent rent (EquivRent) for CAPS
properties as the product of the capitalization rate
and house value in each year. In an effort to capture
the greatest amount of local variation in capitalization rates while working within sample size limitations, as well as to illustrate the sensitivity of our
estimates to sample aggregation, we estimate
CapRate and EquivRent both for the sample as a
whole (i.e., a single national market) and separately
for regional subsamples representing local markets.
Table 2. Pooled hedonic estimation results for 2003-2011 (odd years).
Variable Name
2003
2005
2007
2009
2011
Owner occupied
2.32 (0.02)***
2.39 (0.03)***
2.49 (0.03)***
2.32 (0.03)***
2.28 (0.03)***
Single-family residence
0.05 (0.03)
-0.01 (0.04)
0.05 (0.04)
0.20 (0.04)***
0.14 (0.04)***
Two or more bathrooms
0.36 (0.02)***
0.36 (0.03)***
0.43 (0.03)***
0.46 (0.03)***
0.52 (0.03)***
Northeast
0.15 (0.04)***
0.24 (0.05)***
0.17 (0.05)**
0.23 (0.05)***
0.31 (0.05)***
Midwest
-0.06 (0.03)**
-0.06 (0.03)*
-0.11 (0.03)***
-0.12 (0.03)***
-0.09 (0.03)**
West
0.23 (0.03)***
0.36 (0.04)***
0.34 (0.04)***
0.23 (0.04)***
0.21 (0.04)***
N
1,850
1,420
1,452
1,764
1,530
R2
0.86
0.84
0.86
0.82
0.83
Region:
Notes: *** indicates p ≤ 0.01; ** indicates p ≤ 0.05; * indicates p ≤ 0.10. The dependent variable is the logged house price
(for owner-occupied CAPS properties) or the logged annual rent (for renter-occupied AHS properties).
2.3. Measuring the user cost
For each owner household 𝑖 in year 𝑡, we construct the user cost 𝑈𝐶𝑖𝑡 as
𝑈𝐶𝑖𝑡 = 𝑀𝑖𝑡 + 𝑟𝑡 𝐸𝑖𝑡 + 𝐾𝑖𝑡 + 𝑑𝑡 𝑉𝑖𝑡 − [𝑇𝑖𝑡 + ∆𝑉𝑖𝑡 ] (5)
where the notation is as follows:
 𝑀𝑖𝑡 is the annual mortgage payment, including property taxes and insurance.5
While we do observe the actual change in the unpaid principal
balance for those households that retained their original CAP
mortgages, we do not observe this amount for those who refinanced. Thus, for consistency, we do not subtract out the principal contributions in either case. However, the contribution of
5
 𝐸𝑖𝑡 is equity in the house as of the third quarter of year 𝑡. The survey has been administered annually during the summer and fall, so
the beginning of the third quarter falls roughly at the midpoint of the survey completion
dates.

𝑟𝑡 is the after-tax interest rate that could be
earned by investing in something other than
housing. We set 𝑟𝑡 equal to the return on a 10year Treasury bill, reduced by the taxes that
the household would have paid on such interest. This provides a counterfactual for
holding home equity in a virtually risk-free
principal payments to the user cost is small, given the relatively
recent origination and high leverage of these loans.
130
Riley, Ru, and Feng
but similarly illiquid asset.6 This choice of alternative interest rate has previously been
adopted by Garner and Verbrugge (2009),
among others.
if claimed.8 Beracha and Tibbs (2010) argue
that the tax refund associated with claiming
the mortgage interest tax deduction is often
overstated in analyses of the user cost of
homeownership. In particular, they suggest
that only the portion of the tax refund derived
from claiming a deduction in excess of the
standard deduction should be considered in
calculating the user cost for homeowners.
Therefore, our user cost measure addresses
this concern and considers only that portion
of the refund that exceeds what would have
been received anyway under the standard
deduction. This choice makes our results
somewhat conservative relative to similar
measures considered in other papers that
have found homeownership to be less costly
than renting.
 𝐾𝑖𝑡 is the sum of all other miscellaneous expenses, including mortgage closing costs and
origination fees, homeowners association fees,
and maintenance expenditures. Note that all
CAP mortgages were originated directly by
lenders, so no brokerage fees were incurred.
Data on all maintenance expenditures that
were incurred between loan origination and
2008 were collected in 2008, and those for the
period from 2009 to 2011 were collected in
2012. We spread the former costs evenly
across the years 2003-2008 and similarly assign the four-year average of costs reported in
2012 to each year from 2009 to 2011.
 ∆𝑉𝑖𝑡 is the house price appreciation observed
between the beginning of the third quarter of
year 𝑡 and the beginning of the third quarter
of year 𝑡 + 1. Note that this term is meant to
replace the term for house price expectations
that commonly appears in ex ante expressions
of the user cost.
 𝑑𝑡 is annual depreciation, which is not already
included in observed house price changes
(see ∆𝑉𝑖𝑡 below), because the house price index used to obtain estimates of house value
assumes constant quality of the housing stock
over time. Based on work by Poterba (1992)
and Harding et al. (2007), we set 𝑑𝑡 = 0.02.
 𝑉𝑖𝑡 is the observed property value as of the
third quarter of year 𝑡. With the exception of
the purchase price at loan origination, which
we obtain from Self-Help’s database, we use
the quarterly house price estimates provided
for these properties by Fannie Mae. These
estimates are based on a zip-code-level
constant-quality house price index, which is
then adjusted for refinance bias and information concerning property characteristics
and taxes.7
 𝑇𝑖𝑡 is the tax refund received in year 𝑡 from
claiming the mortgage interest tax deduction,
We also consider other conventional interest rate assumptions,
including the 6-month T-bill rate and the mortgage note rate, but
the amount of equity held in the house is very small for this sample, so the choice of an external rate of return has little effect on
the results.
7 Further details about how these estimates were constructed is
not available, because Fannie Mae uses an internal, proprietary
process. However, we perform a robustness check of the data by
comparing these estimates with actual sale prices for the 499
CAPS owners who sold their CAP properties during the survey
period. We match these sale prices with the closest house price
estimates based on the sale date and find a correlation of 0.82
between these two measures. The price estimates over-estimate
the actual sale price for two-thirds of these observations and under- estimate the market value in the remaining cases. The median discrepancy is about $3,000, or about 3% of the final sale price.
6
2.4. Measuring break-even appreciation
Because the rate of house price appreciation is
generally recognized as the primary driver of the
user cost of owner-occupied housing, we also calculate the amount of house price appreciation that
would be required to make the user cost equal to the
equivalent rent. Specifically, we calculate the breakeven appreciation rate 𝑟𝑏 as
𝑟𝑏 =
𝑏
∆𝑉𝑖𝑡
𝑉𝑖𝑡
(6)
where ∆𝑉𝑖𝑡𝑏 is the dollar amount of appreciation necessary to equate the user cost (exclusive of actual
appreciation) and the equivalent rent for household
𝑖 at time 𝑡, as follows:
∆𝑉𝑖𝑡𝑏 = 𝑈𝐶𝑖𝑡 + ∆𝑉𝑖𝑡 − 𝐸𝑞𝑢𝑖𝑣𝑅𝑒𝑛𝑡𝑖𝑡
(7)
We observe only gross income and must make assumptions
about marital filing status and the number of deductions claimed
for dependents based on reported household structure. Therefore, the tax refund may be overstated in some cases.
8
User Cost of Low-Income Homeownership
131
3. Results
Complete user cost information for each year of
the survey is available for 604 CAPS owners. Therefore, we restrict our comparison of user costs and
equivalent rents to this subset of owners to achieve a
constant sample size across time. The estimated average capitalization rates, median user costs, and
median equivalent rents for these CAPS owners are
presented in Table 3 for each year of the survey,
2003-2011. We present both user cost and equivalent
rent estimates based on the national sample and estimates separately obtained from regional estimations based only on the properties located in each
region.
Table 3:. Capitalization rates, ex post user costs, and equivalent rents by year (N=604).
Median Annual
UserCost
EquivRent (Std Error)
CapRate
2003
9.85
$3,604
$8,325 ($642)
$3,604
$8,325 ($642)
2004
9.51
$3,464
$8,376 ($685)
$6,753
$16,715 ($1,344)
2005
9.17
$550
$8,467 ($748)
$7,365
$25,206 ($2,064)
2006
8.74
$2,341
$8,463 ($786)
$9,497
$33,758 ($2,898)
2007
8.31
$5,209
$8,250 ($834)
$13,776
$42,032 ($3,731)
2008
9.08
$9,503
$8,815 ($869)
$22,400
$50,889 ($4,638)
2009
9.85
$10,827
$9,105 ($819)
$32,873
$60,170 ($5,514)
2010
10.03
$7,564
$9,189 ($845)
$40,803
$69,491 ($6,353)
2011
10.22
$10,498
$9,026 ($877)
$51,699
$78,712 ($7,194)
The estimated average capitalization rates are in
the neighborhood of 8-10% throughout the period.
For the user costs and equivalent rents, we present
medians, rather than means, because the user cost
distribution tends to be skewed and have long tails.
We use the standard nonparametric bootstrap with
200 repetitions to generate standard errors for the
median equivalent rents.9
On an annual basis, the median user cost was
approximately $3,600 for these owners in 2003 and
fell slightly through 2006, after which it rose to
$5,200 in 2007 and reached above $10,000 in 2009. In
comparison, the median annual equivalent rent was
about $8,300 in 2003 and remained relatively stable
through the 2003-2011 period, reaching a maximum
of about $9,200 in 2010, with an annual standard
error between $600 and $900. Thus, relative to median equivalent rents, annual median user costs
were generally lower than annual median equivalent rents between 2003 and 2007/2008 but tended to
be higher thereafter. In other words, homeownership was less costly than renting on an annual basis
during the period of housing market appreciation,
while the reverse has been true since the market
For a discussion of bootstrapping methods and sufficient repetitions, see Efron and Tibshirani (1986), Andrews and Buchinsky
(2000), and MacKinnon (2006).
9
Median Cumulative
UserCost
EquivRent (Std Error)
Year
downturn began. On a cumulative basis, the median user cost was about $51,700, while the median
equivalent rent was about $78,700, with a standard
error of about $7,200. Therefore, the median homeowner, on the whole, experienced lower costs from
owning than renting during this period.
Table 4 presents similar estimates for each of the
four regions. The regional variation that we observe
largely reflects underlying housing market trends.
Median user costs for Western homeowners on an
annual basis were substantially negative for the beginning of the period as a result of the high appreciation observed in those markets prior to 2007. In
contrast, for Midwestern, Southern, and Northeastern homeowners, annual median user costs were
positive, or very nearly so, during each year of the
study period. In the West, the annual median user
cost ranged from a minimum of -$4,435 in 2003 to a
maximum of $46,771 in 2009. In the other regions,
the variation was less pronounced: between $1,563
and $11,642 in the Midwest, between $656 and
$10,723 in the South, and between -$54 and $11,704
in the Northeast. The discrepancy between the annual median user costs and equivalent rents was
also most volatile in the West. In 2005, the median
equivalent rent exceeded the median user cost by
more than $50,000, while in 2009 the median user
cost exceeded the median equivalent rent by more
132
Riley, Ru, and Feng
than $32,000. This volatility reflects the underlying
volatility in regional house prices that was present
during the period, and the corresponding differences were smaller in the other regions. On a cumu-
lative basis, the extent to which median equivalent
rents exceeded median user costs was largest in the
West ($51,152), followed by the Northeast ($39,818),
South ($28,784), and Midwest ($14,699).
Table 4. Capitalization rates, ex post user costs, and equivalent rents by year and region.
Median Annual
UserCost
EquivRent (Std Error)
-$4,435
$10,480 ($603)
Median Cumulative
UserCost
EquivRent (Std Error)
-$4,435
$10,480 ($603)
Region
West
Year
2003
CapRate
9.09
(N=34)
2004
7.77
-$17,664
$14,611 ($1,400)
-$18,213
$25,211 ($1,774)
2005
6.46
-$41,339
$11,131 ($1,060)
-$53,397
$36,140 ($2,654)
2006
6.14
-$8,808
$11,550 ($1,106)
-$69,364
$47,778 ($3,781)
2007
5.83
$20,126
$10,963 ($1,050)
-$43,617
$58,785 ($4,868)
2008
7.66
$46,771
$14,399 ($1,379)
-$5,995
$73,246 ($6,322)
2009
9.49
$40,753
$11,837 ($1,423)
$32,826
$85,522 ($6,733)
2010
10.03
$12,298
$18,855 ($1,806)
$49,612
$104,427 ($8,661)
2011
10.57
$16,151
$12,038 ($1,833)
$65,759
$116,911 ($9,197)
Midwest
2003
10.15
$3,750
$7,657 ($564)
$3,750
$7,657 ($564)
(N=138)
2004
10.15
$4,116
$8,120 ($589)
$7,286
$15,831 ($1,170)
2005
10.15
$1,563
$8,118 ($589)
$9,564
$23,966 ($1,751)
2006
9.90
$5,341
$7,921 ($574)
$13,802
$31,889 ($2,320)
2007
9.66
$7,576
$7,700 ($451)
$20,435
$39,488 ($2,811)
2008
10.47
$11,642
$8,378 ($607)
$31,633
$47,862 ($3,436)
2009
11.29
$9,082
$8,190 ($539)
$40,871
$55,954 ($3,744)
2010
11.47
$6,760
$9,174 ($665)
$50,013
$65,134 ($4,420)
2011
11.65
$9,191
$8,057 ($551)
$58,414
$73,113 ($4,706)
South
2003
9.46
$3,814
$8,195 ($586)
$3,814
$8,195 ($586)
(N=413)
2004
9.12
$3,612
$9,201 ($737)
$7,361
$17,513 ($1,403)
2005
8.78
$656
$8,342 ($689)
$7,495
$25,874 ($2,101)
2006
8.48
$1,641
$8,556 ($685)
$8,828
$34,463 ($2,788)
2007
8.19
$4,214
$8,526 ($689)
$13,075
$43,009 ($3,457)
2008
8.74
$8,718
$8,812 ($705)
$20,214
$51,843 ($4,154)
2009
9.29
$10,723
$9,113 ($677)
$30,183
$60,993 ($4,834)
2010
9.28
$7,785
$9,364 ($750)
$38,114
$70,322 ($5,597)
2011
9.28
$10,654
$8,659 ($666)
$49,343
$79,127 ($6,313)
Northeast
2003
15.07
$2,860
$8,685 ($1,578)
$2,860
$8,685 ($1,578)
(N=19)
2004
14.78
-$54
$9,771 ($1,954)
$1,646
$18,591 ($3,442)
2005
14.49
-$181
$9,863 ($2,026)
$581
$28,478 ($5,450)
2006
11.59
$3,378
$7,663 ($1,532)
$1,887
$36,141 ($6,963)
2007
8.69
$4,498
$6,592 ($975)
$5,491
$42,689 ($7,996)
2008
8.81
$11,704
$5,823 ($1,164)
$11,299
$48,512 ($9,140)
2009
8.92
$8,834
$6,667 ($893)
$17,289
$55,201 ($10,159)
2010
10.15
$4,568
$6,708 ($1,341)
$25,818
$61,910 ($11,483)
2011
11.37
$5,605
$8,057 ($773)
$30,253
$70,071 ($12,526)
User Cost of Low-Income Homeownership
133
To assess how much appreciation would have
been necessary for these low-income households to
face equivalent housing costs from renting comparable properties, we also calculate break-even appreciation rates. These estimates are presented in Tables 5 and 6. We find that, at the median, breakeven appreciation rates are negative for the entire
period and range between 1% and 3% in absolute
value. Positive appreciation of less than 1% would
have would have ensured that 75% of CAPS owners
found owning no more expensive than renting; at
the 95th percentile, the required appreciation rate
jumps to around 5%. Regional variation also exists
in the break-even appreciation rates, with higher
rates of appreciation required in the Midwest and
Northeast and lower rates generally needed in the
South and West.
Overall, these results illustrate the key role that
local economic conditions and market timing play in
driving the relative cost of homeownership. Most of
the owner households in our sample experienced
gains from appreciation prior to the market downturn that were sufficient to offset the relatively higher user costs that they have experienced since the
decline began.
Table 5. Break-Even appreciation rate (%) quintiles by year (N=604).
Year
5th Percentile
25th Percentile
Median
75th Percentile
95th Percentile
2003
-3.57
-2.26
-0.93
1.14
6.43
2004
-9.36
-3.22
-1.97
-0.52
4.12
2005
-10.89
-8.10
-2.81
-1.15
2.87
2006
-4.97
-2.53
-1.33
0.21
4.78
2007
-4.50
-2.45
-1.17
0.43
4.91
2008
-5.23
-3.08
-1.70
0.21
4.85
2009
-5.67
-3.56
-2.04
-0.37
4.28
2010
-5.92
-3.84
-2.16
-0.33
4.67
2011
-7.56
-4.03
-2.34
-0.09
4.98
4. Robustness and limitations
4.1. Mobility
Because of data limitations, we have chosen to
focus our analysis on those CAPS owners who did
not move during the sample period. However, if
user costs influence mobility, then restricting the
sample in this fashion could bias our results; in particular, if those households with relatively higher
user costs are the same households who moved,
then our sample will be biased in favor of those
households who had lower relative user costs and
thus found it cost effective to remain homeowners.
In an effort to check for bias, we first investigate
whether absolute user costs were higher for movers
than for non-movers in the year prior to the move,
and we do not find any evidence of systematically
higher user costs for movers. Therefore, if the properties that non-movers would have occupied, if they
had moved, are similar to those actually occupied by
movers, we should not expect any systematic bias in
the user costs of these two groups relative to the cost
of comparable rental housing. As a second means of
checking for mobility bias, we investigate whether
the movers experienced systematically lower user
costs after moving, but we do not find that user costs
are substantially different after the move than before
the move. Therefore, we infer that user costs may
not be the primary driver of mobility decisions for
these homeowners. Consistent with this inference,
and in a related but more comprehensive analysis of
the drivers of CAPS homeowner mobility during the
period 2003-2011, Riley et al. (2012) find that CAPS
homeowners moved out of the CAP residence primarily for family-related reasons, such as divorce or
the birth of a child, with housing costs playing only
a secondary role in mobility decisions. In light of
these findings, we do not believe that our results
suffer from systematic bias with respect to mobility.
134
Riley, Ru, and Feng
Table 6. Break-Even appreciation rate (%) quintiles by year and region.
Sample
Year
5th Percentile
25th Percentile
Median
75th Percentile
95th Percentile
West
2003
-3.98
-2.31
-1.55
0.12
14.93
(N =34)
2004
-11.77
-6.65
-4.86
-3.40
1.44
2005
-8.30
-7.46
-4.33
-1.21
0.42
2006
-3.21
-2.48
-1.52
-0.83
3.11
2007
-3.42
-2.02
-1.41
-0.49
2.75
2008
-9.76
-5.74
-3.64
-1.85
0.73
2009
-6.35
-3.45
-2.19
-0.82
2.05
2010
-22.49
-13.36
-9.13
-4.32
-1.77
2011
-12.46
-5.02
-3.13
-1.01
3.15
Midwest
2003
-3.32
-1.92
-0.28
1.19
8.92
(N =138)
2004
-11.28
-3.46
-2.06
-0.19
5.80
2005
-12.03
-9.91
-3.51
-1.48
2.91
2006
-4.23
-2.32
-1.00
0.60
6.91
2007
-4.53
-2.33
-1.06
0.87
6.82
2008
-5.93
-3.11
-1.24
0.73
7.49
2009
-6.69
-3.87
-2.21
-0.25
5.74
2010
-7.37
-4.91
-3.15
-1.37
5.09
2011
-6.82
-4.17
-2.40
0.41
6.77
South
2003
-3.23
-1.93
-0.68
1.23
5.62
(N =413)
2004
-9.14
-3.99
-2.78
-1.46
2.50
2005
-10.48
-5.95
-2.26
-0.96
2.79
2006
-4.34
-2.28
-1.33
-0.05
4.18
2007
-4.10
-2.32
-1.30
-0.08
3.58
2008
-6.04
-2.73
-1.68
-0.37
3.27
2009
-5.38
-3.15
-1.92
-0.39
3.90
2010
-7.86
-3.68
-2.17
-0.49
3.47
2011
-7.22
-3.21
-1.77
0.03
4.62
Northeast
2003
-10.97
-6.33
-3.12
1.22
11.88
(N =19)
2004
-16.78
-9.14
-7.08
-3.45
0.66
2005
-16.49
-14.92
-6.81
-3.81
-1.01
2006
-7.16
-3.91
-1.16
0.61
17.32
2007
-5.75
-4.04
-0.43
3.34
4.69
2008
-5.89
-1.81
0.51
4.23
10.32
2009
-5.15
-3.87
-0.81
1.12
12.82
2010
-10.86
-5.17
-0.07
1.09
8.17
2011
-13.26
-7.03
-3.99
-3.21
6.78
User Cost of Low-Income Homeownership
4.2. Terminal transactions cost
Some user cost analyses that seek to compare the
relative costs of owning and renting include a term
for the eventual transaction costs of property liquidation. We do not include such a measure because
all of the properties continue to be occupied by their
CAPS owners and we are interested in the costs incurred to date. However, if one considers homeownership as a fixed-term investment, there is certainly scope for incorporating the transaction costs
associated with moving. If a 5-10% liquidation cost
(equivalent to standard real estate brokerage commissions) were added to the user cost, the difference
between the user cost and equivalent rent for the
median homeowner would be reduced by as much
as 50%.
4.3. Risk
While some existing user cost analyses also incorporate a risk premium for owned housing relative to other investment assets or relative to rental
housing (e.g., Poterba, 1992; Himmelberg et al., 2005;
Poterba and Sinai, 2011), others do not (e.g., Poterba,
1984; Elsinga, 1996; Quigley and Raphael, 2004;
Beracha and Tibbs, 2010; Haffner and Heylen, 2011),
and it is not clear whether or why one method
should be preferred over the other. Moreover, determining the magnitude and direction of a risk
premium for investment in owned housing is complicated by the fact that this risk premium varies
across markets, which vary in their supply and demand conditions, and that homeownership can actually serve to hedge both housing-related risks and
portfolio risks associated with other investment assets (Sinai and Souleles, 2005; Hasanov and Dacy,
2009; Sinai, 2011; Han, 2011). Therefore, for simplicity, we have omitted any explicit consideration of a
risk premium from our current analysis. We hope to
extend our analysis in future research to examine
this issue.
4.4. Maintenance
An important limitation to keep in mind is that
the median CAP borrower in our sample reported
spending less than 1% of the house value on renovations and maintenance on an annual basis during the
survey period considered. In fact, only about 25% of
these homeowners spent at least 2% on home repairs
and renovations. So our assumed depreciation rate
of 2% may understate the true extent of depreciation
on these properties. This inference is consistent with
the results of Van Zandt and Rohe (2011), who find
that many low-income homeowners face challenges
135
in sustaining homeownership and maintaining their
property values as a result of maintenance and repair expenses that they do not foresee when purchasing their houses. To the extent that CAP properties may require higher levels of maintenance in
the future, it is unclear whether the user costs that
we have estimated for the period of 2003-2011 may
underestimate the longer-term trend in this regard.
4.5. Market segmentation
A further limitation is that, while we have calculated equivalent rents for comparable properties, in
practice finding rental equivalents of the owneroccupied housing stock is difficult or impossible in
many locations. The housing units that are available
for owner occupation often tend to be of greater
quality and of a different type (e.g., single-family
detached vs. apartment complex) than available
rental housing. If the quality of rental and owneroccupied housing differs systematically, which
would be consistent with the fact that renters tend to
spend a smaller fraction of their incomes on housing
than do owners (Sinai, 2011), then the cost of renting
(given lower housing quality) may be systematically
lower than that of owning. Thus, we infer that the
owners in our sample have likely benefitted from
higher quality housing, as well as possibly somewhat lower housing costs given the quality of that
housing, as a result of the decision to become homeowners.
5. Conclusion
Using data from the Community Advantage
Panel Survey and matched data from the American
Housing Survey, we have compared the user cost of
homeownership with hedonic estimates of equivalent rent for low-income households in the United
States who received community reinvestment mortgages between 1999 and 2003. We find that owning
was less costly than renting a comparable property
for the median homeowner in our sample during the
period from 2003 to 2011. The median annual user
cost was less than the median equivalent rent before
2007/2008 and greater thereafter, but the initial period of house price appreciation has been sufficient
to offset the more recent higher user costs for the
period as a whole. Some regional variation exists in
the extent to which equivalent rents exceeded user
costs during the period, but the direction of the results remains robust by region. Furthermore, we
estimate that annual house price appreciation of less
than 5% was sufficient to ensure that owning was no
136
more costly than renting a comparable property for
95% of the homeowners in our sample between 2003
and 2011.
Our results are driven in part by the high original
loan-to-value ratio (median of 97%) associated with
all of the loans considered here. A low down payment on the loan has a couple of effects that tend to
make owning more attractive relative to renting: the
increased leverage raises the benefit from even small
amounts of house price appreciation while simultaneously reducing the opportunity cost of equity.
Thus, while low-income households may generally
tend to derive less financial benefit from homeownership than more affluent borrowers, our results
suggest that this difference may be partly offset under reduced down-payment requirements. This observation may partly explain why community reinvestment mortgages tend to permit such low down
payments: these not only reduce entry costs for new
homeowners but can also help to contain user costs
during the initial period for which the property is
held, thus increasing the likelihood that homeownership will be sustained. Thus, in future research,
we hope to continue to track the experiences of these
households and to investigate the role that relative
housing costs may play in the decisions of lowincome households to sustain or exit homeownership.
Acknowledgements
We thank participants at the 2011 Southern Economic Association annual conference, participants at the
2011 Urban Affairs Association annual conference,
participants at the Federal Reserve Bank of Cleveland's 2011 policy summit, and researchers at the
UNC Center for Community Capital for helpful
comments on earlier versions of this paper. We
thank the Ford Foundation for financial support. All
opinions and any errors remain our own.
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