Borrowing constraints and access to owner-occupied housing

Regional

Science

and Urban

Economics

24 (1994) 301-322.

North-Holland

Borrowing constraints and access to owner-occupied housing John V. Duca” and Stuart

S. Rosenthalb9*

a Research Department, Federal Reserve Bank of Dallas, Dallas, TX 75256, USA b Faculty of Commerce and Business Administration, University of British Columbia, Vancouver, BC, V6T IZZ, Canada Received

April 1992, final version

received

February

1993

Cross-sectional data from the 1983 Survey of Consumer Finances (SCF) enable us to identify two groups of households: those whose housing tenure status is unaffected by borrowing constraints, and those whose housing tenure status may be affected by borrowing constraints. Using these data, a bivariate probit model is estimated to evaluate the joint probability of whether families prefer to live in owner-occupied housing and whether borrowing constraints affect access to owner-occupied housing. Findings indicate that borrowing constraints have a significant negative effect on homeownership rates. Indeed, if borrowing constraints had not been binding in our sample period (the early 198Os), ceteris paribus, owner-occupancy rates in the United States would have risen from 64.5% to nearly 73%. Moreover, borrowing constraints appear to have a disproportionate effect on the ability of younger families and non-white families to own a home, consistent with popular perceptions. Key words: Credit

constraints;

Housing;

Tenure

choice

JEL classification: Dl; E5; 57; R2

1. Introduction Theoretical models of loan markets suggest that lenders may use non-rate terms to ration credit on the basis of default risk [Jaffee and Russell (1976), Stiglitz and Weiss (1981), and Williamson (1986)], while a number of recent empirical studies of the mortgage market provide support for that

Correspondence to: S.S. Rosenthal, Faculty of Commerce and Business Administration, University of British Columbia, Vancouver, BC, V6T 122, Canada. *We thank Larry Jones and two anonymous referees for helpful comments on an earlier draft. We are also grateful to Robert Avery and Arthur Kennickell for giving us access to their cleaned data tape of the 1983 Survey of Consumer Finances. The views expressed here are those of the authors and do not reflect the views of the Federal Reserve Bank of Dallas or the Federal Reserve System. Rosenthal gratefully acknowledges support from the Social Science and Humanities Research Council of Canada and the Real Estate Institute of British Columbia. 0166-0462/94/$07.00 0 1994 Elsevier SSDI 0166-0462(93)02041-Z

Science B.V. All rights

reserved

302

J. K Duca and S.S. Rosenthal, Borrowing constraints

hypothesis.’ An important implication of these studies is that the imposition of binding borrowing constraints (or credit rationing) may be the ‘second best’ solution in a competitive loan market characterized by asymmetric information, adverse selection, and moral hazard. But despite theoretical motivation for borrowing constraints, fundamental empirical questions concerning the extent and manner in which borrowing constraints affect household behavior remain unanswered.’ For example, given that most household debt is used to finance the purchase of a home, much of the impact of borrowing constraints on household behavior is likely to be transmitted through housing tenure decisions. However, the extent to which borrowing constraints affect housing tenure decisions and owner-occupancy rates is largely unknown. Because home equity accounts for the overwhelming bulk of household savings among homeowners, anything that would affect the timing and size constraints - could have an of home purchase - such as borrowing important impact on the pattern and composition of savings over a family’s life cycle. Such effects have typically been ignored in life-cycle models of household behavior. In that regard, evidence that borrowing constraints have a substantial effect on owner-occupancy rates could have important implications for future modelling of household savings and consumption decisions. Such evidence could also be important for government programs designed to reduce the effect of borrowing constraints on access to owner-occupied housing. For example, Fannie Mae recently initiated a new low downpayment loan program to increase homeownership rates among ‘low and moderate income families’ [see Reuters (1991)l.j Similarly, various government agencies are considering policies to increase access to mortgage credit among minority households, partly in response to persistent allegations of racial discrimination in mortgage markets4 In both cases, a careful ‘Quigley and Van Order (1991) for example, find that the loss rate on conventional mortgage defaults for lenders averaged 35”/, of the original mortgage value in the late 1970s. Given such high loss rates, one would expect lenders to&count fo; default risk when setting the mortgage contract. Moreover, Rosenthal, Duca and Gabriel (1991) Gabriel and Rosenthal (1991), and Duca and Rosenthal (1991) all provide qualitative evidence than lender concerns about default risk give rise to credit rationing in the mortgage market. 2As a result, controversy remains as to the empirical relevancy of borrowing constraints [e.g. the debate between Riley (1987) and Stiglitz and Weiss (1987)]. 3Fannie Mae’s policy, in part, reflects concerns that many young prospective ‘first-time homebuyers cannot meet downpayment and payment-to-income requirements imposed by mortgage lenders, particularly given the run-up in real housing values that occurred in much of the U.S. up through the mid-1980s. 41n May 1988, the Atlanta Constitution published a four part series, ‘The Color of Money’, while the Detroit Free Press published a similar series in July 1988. More recently, numerous press reports have focused attention on data from the Home Mortgage Disclosure Act (HMDA) that indicate that mortgage rejection rates for blacks are 2.4 times as large as those for white families with similar income [see Canner and Smith (1991) for a detailed description of the HMDA data]. We should note, however, that questions have been raised about the extent to which these differences are indicative of racial discrimination in the mortgage market given that

J.K Duca and S.S. Rosenthal,

Borrowing

constraints

303

accounting of the number and type of families whose housing tenure is affected by borrowing constraints could have implications for the direction and design of government programs. Unfortunately, previous attempts to evaluate the effect of borrowing constraints on household behavior have been hampered by three fundamental problems. Researchers must identify which families are credit constrained,, they must evaluate how those families would behave if borrowing constraints were relaxed, ceteris paribus, and they must control for all possible types of constraints that lenders might impose on prospective borrowers. Housing studies by Zorn (1989) Linneman and Wachter (1989), and Brueckner and Follain (1990), address these problems by assuming that prospective homeowners with more than a 28% house payments-to-income ratio are credit constrained.’ However, many lenders have house payments-to-income limits in excess of 28x, while families with a bad credit history are likely to face tighter-than-average credit standards [see, for example, Boyes, Hoffman, and Lowe (1990)].6 In addition, the 28% criterion does not control for downpayment constraints or total debt service-to-income constraints which could also be binding for many prospective homebuyers. In contrast, studies on household savings and consumption by Hall and Mishkin (1982), Hayashi (1985) and Zeldes (1989), argue that families with large amounts of wealth or high wealth-to-income ratios are not credit constrained. Sample selection methods are then used to evaluate the effect of borrowing constraints in a manner that allows for all possible forms of constraints that lenders might impose. However, if the demand for debt increases with wealth and income, many well-to-do families may still be credit constrained as shown by Jappelli (1990) using the 1983 Survey of Consumer Finances.’ This paper addresses the effect of borrowing constraints on homeownership rates by drawing on several unique features of the 1983 Survey of Consumer Finances (SCF) in a manner that avoids several of the measurement problems of previous studies. As in Jappelli (1990), a household is defined as being ‘credit constrained’ if a lender had turned down or not fully the HMDA data do not include information on borrower credit history and wealth [e.g. Rehm (1991a, b)]. Nevertheless, partly in response to these reports and related community pressure, the Federal Reserve Board has recently approved several large bank mergers conditional on merger applicants meeting lending goals in minority neighborhoods [see for example, the description of Bank of America’s merger with Security Pacific in Thomas (1992, p. A6)]. ‘This assumption derives, presumably, from secondary mortgage market criteria that generally prohibit the securitization of mortgages with house payments-to-income ratios in excess of 28%. 6Trans Data Corporation data on official credit standards for primary mortgage lenders across the United States (in 1986) indicate that many lenders have house payments-to-income limits above 28%. ‘Jappelli (1990) shows that many wealthy families are turned down for loans or receive smaller loans than requested. Given that the consumption/savings studies above depend critically on their ability to exclude credit-constrained families from the unconstrained group, such measurement error could lead to biased results.

304

J.K Duca and S.S. Rosenthal, Borrowing constraints

granted a household’s loan request (and the household did not successfully reapply at an alternate lender), or if the family did not apply for credit because it thought that it would be turned down. Conversely, families are defined as ‘unconstrained’ if the response to each of these questions is negative. Two points must be carefully considered when identifying constrained and unconstrained households in this fashion. First, 35% of credit constrained households in the sample own their homes. Assuming that these families have the option of renting, their tenure status is unaffected by borrowing constraints, even though the level of debt held by such families is limited by lenders. Second, the SCF does not provide information on the type of loan credit constrained households are unable to obtain. Hence, some renters may be credit constrained because they were turned down for an auto loan, a consumer loan, or some other form of nonmortgage financing. Even if borrowing constraints were relaxed, some of these families might still prefer to rent if they expect to move relatively soon [see, for example, Rosenthal (1988) or Zorn (1989)], if they are not sufficiently adept at maintaining an owner-occupied dwelling [Henderson and Ioannides (1983)], or if they view housing as an excessively risky asset [Henderson and Ioannides (1983), Fu (1991)]. Accordingly, the housing tenure status of credit constrained renters may or may not be affected by borrowing constraints. Bearing these points in mind, our strategy is to split the sample into two groups in a manner analogous to Hall and Mishkin (1982), Hayashi (1985) and Zeldes (1989). Group 1 contains only households whose tenure status is unaffected by borrowing constraints, i.e. homeowners and unconstrained renters. Group 2 contains families whose tenure status may or may not be affected by borrowing constraints; this group is comprised of credit constrained renters. The probability that families prefer to own is then estimated using only Group 1 households, controlling for group-related selection effects through bivariate probit methods (in a manner to be clarified shortly). Results are used to predict the tenure preferences of all families - both Groups 1 and 2 - and to compare the predicted preferred owner-occupancy rate in the sample to the actual rate of homeownership. The approach used in this paper has several advantages over previous studies, First, our definition of who is or is not credit constrained allows for all possible forms of borrowing constraints, in contrast to models that rely on a specific constraint, such as the 28% house payments-to-income criterion. Second, given the manner in which unconstrained families are defined, it seems less likely that Group 1 households would contain families whose

*In addition, comparing the bivariate probit tenure model to a housing model that does not control for borrowing constraints enables us to construct a formal test of whether borrowing constraints affect housing tenure decisions.

305

J.K Duca and S.S. Rosenthal, Borrowing constraints

tenure status is affected by borrowing constraints than if we had relied on wealth levels or wealth-to-income ratios to define who is or is not credit constrained. Finally, the 1983 SCF is representative of the United States. Hence, as long as Group 1 households contain only families whose tenure status is unaffected by borrowing constraints, our results provide a consistent estimate of the effect of borrowing constraints on owner-occupancy rates in the United States. This paper is organized as follows: Section 2 presents the empirical model, Section 3 describes the data, Sections 4 and 5 present the empirical findings and simulations, and Section 6 gives our conclusions.

2. Econometric

model and estimation

method

We assume that families choose their housing tenure based on a two-stage process. Families first evaluate the level of housing they would choose if they were to rent (H,J and if they were to own (HO); then they choose the tenure that yields the highest level of utility. In solving this problem, H, and H, are determined subject to the family’s budget constraint plus a variety of credit constraints that limit access to non-mortgage debt (such as auto and consumer loans). In addition, H, must conform to constraints imposed by mortgage lenders. When considering tenure choice in this fashion, we should recognize that the feasible budget space of most families is quite complicated because households face many different types of borrowing constraints as described above. As a result, it is difficult to determine which credit constraints may be binding with respect to H,. Moreover, it is possible that borrowing constraints could be binding with respect to H, while having no effect on housing tenure. To allow for all possible types of borrowing constraints when evaluating the effect of credit constraints on housing tenure, we specify two reduced form equations. The first equation is an unobservable index that reflects the difference in utility between owning and renting when borrowing constraints are not binding, with respect to the tenure decision, ceteris paribus, I, =mt+u,.

(1)

This equation determines a family’s preferred tenure status, as households favor the tenure that yields the highest level of utility. The likelihood that a family’s tenure status is not affected by borrowing constraints is governed by a second unobservable index, I, =zg+u*, where

m

and

z are

the

systematic

determinants

of (1)

and

(2)

with

J. r/: Duca and S.S. Rosenthal, Borrowing constraints

306

corresponding parameters t and g.’ The observable discrete analogues of (1) and (2) are given by T and C, where, T=l, 0,

I,>O*prefertoown, I,
(3)

and C = 1, I, > 0 * tenure unaffected by borrowing constraints (owner-occupiers and renters that are not credit constrained), (4) 0,

I, < 0 =S tenure may be affected by borrowing constraints (credit constrained renters who may or may not prefer to own).

Given the manner in which T and C are defined, T is observed only when we are certain a family’s tenure status is unaffected by borrowing constraints (C= 1). Hence, there are only three distinct cells in the model, T = C= 1, T = 0 and C = 1, and C = 0. To simplify estimation, we assume that [u,, UJ are distributed normal with mean zero and variance (V),”

The log likelihood function for this model is given by,l’ I((1 -

C).logCF(-dl+ C. T.logCGW,zg, ~7u1,u2)1

+ C.(l-

T).logCG( -mm-~,,,,,)I),

(5)

where F( *) and G( .) are the standard unit and bivariate normal distributions, respectively. To clarify how our model enables us to evaluate the impact of borrowing constraints on housing tenure status, we should emphasize that housing tenure preferences, as defined by T and I,, are not sensitive to whether a family’s tenure status is actually affected by borrowing constraints. Note also, that if u1 is independent of u2, then the expected value of I, is mt regardless of whether a family is or is not credit constrained. In that case, one could ‘Note that as tenure preferences likely affect the propensity of borrowing constraints to influence a family’s actual tenure status, all of the exogenous variables in (1) should be included in (2). “‘Note that the variances of ur and a2 are normalized to one because the parameters of the bivariate probit model can only be estimated up to a scale factor [see Maddala (1993) for further discussion]. “Boyes et al. (1989) estimate a similar bivariate probit model with three cells. For a more general discussion of bivariate probit models with censoring see Tunali (1986).

J.V Duca and S.S. Rosenthal, Borrowing constraints

307

obtain consistent estimates of t by running a univariate probit model of housing tenure status using only unconstrained families. But more generally, if there are unobserved components that affect both u1 and u2 (e.g. idiosyncratic differences in either interest rates or preferences), then univariate probit estimates of t based only on unconstrained families would be biased and inconsistent.r2 The bivariate probit model controls for such problems by allowing crul,u2 to differ from zero in (5).r3 This allows us to obtain consistent estimates of t (denoted 2) even though expression (1) is estimated only over unconstrained families. Moreover, assuming that unconstrained households intend to pay back their loans, Z is indicative of housing tenure preferences when families abide by their budget constraints.r4 Given Z we can simulate the percentage of the population that prefers to own by computing the mean of F(mZ) over the entire sample. Comparing that estimate to the actual frequency of owner-occupiers gives an estimate of the impact of borrowing constraints on homeownership rates. In addition, we can estimate housing tenure status based on a univariate probit model using the full sample without distinguishing between ‘credit constrained’ and ‘unconstrained’ households as in previous studies [e.g. Rosen (1979)]. Let estimates from this model be denoted t. If borrowing constraints do not affect housing tenure status, f should be similar to 2 which is testable.

3. Data and variables The main data source for the study is the 1983 SCF which contains 4303 households. From these households we excluded individuals with wealth over 1 million dollars (in 1982 dollars), any observations with relevant missing values, and households that belong to a special high income group that was over sampled in the survey. The remaining sample is representative of the United States in 1983 for households with wealth under 1 million dollars. As discussed earlier, households were asked whether they ‘had a request for credit turned down by a particular lender or creditor in the past few years, or had been unable to get as much credit as he/she had applied for’. Families that had been turned down or received less credit than desired were further asked whether they had successfully reapplied for the desired level of credit at an alternative lender. In addition, households were asked whether ‘there had been any time in the past few years that he/she (or their spouse) had thought about applying for credit at a particular place, but changed r21n contrast, consistent estimates of g can be obtained from a simple probit model using the full sample. However, more efficient estimates of g are obtained based on the likelihood function in (5). ‘%ee, for example, Maddala (1983) or Boyes et al. (1989). t41n contrast, the reduced form specification for eq. (2) makes it difftcult to interpret the parameters of the credit model (g). However, it should be stressed that the primary reason for estimating (2) is to control for possible selection effects when evaluating (1).

308

J. K Duca and S.S. Rosenthal, Borrowing constraints

their mind because [the household] thought it might be turned down’. Based on these questions, a household was defined to be credit constrained in the 198&1983 period (CREDIT=O) if the household had not applied for credit because it thought that it would be turned down, or if a lender had turned down or not fully granted a household’s loan request and the household did not successfully reapply for the desired level of credit. Recall also that all owner-occupiers have the option of renting, while credit-constrained renters may or may not prefer to own (given that the SCF does not indicate the type of loan constrained households were unable to obtain). Hence, C = 1 for homeowners and unconstrained renters, while C=O for credit constrained renters. Somewhat more simply, housing tenure status (T) is taken directly from the SCF, with T= 1 for owners and T =0 for renters. In conducting our analysis it is important to recognize that tastes and preferences for living in owner-occupied housing could differ markedly across age groups. In addition, capital gains tax laws can have quite different effects on different age groups, at least with respect to the decision to live in owneroccupied housing.’ ’ To allow for age-related differences, the bivariate probit model was estimated separately for families under age 35 (YOUNG), families from age 35 through age 54 (MIDAGE), and families age 55 and over (OLDER). Focusing on the tenure status equation, results from Henderson and Ioannides (1983) in conjunction with Fu (1991) suggest that, in the absence of tax effects and borrowing constraints, wealth has an ambiguous effect on housing tenure preferences (where wealth is formed here as the difference between non-pension assets and debt). l6 Note also, that tenure status could influence the observed level of wealth held by a family, particularly given the rapid increase in home prices just prior to our sample period. To control for possible simultaneity effects, wealth is regressed on all of the exogenous variables in the model, as well as some additional variables taken from the SCF. The fitted value from the wealth equation (What) in $100,000 units was

IsThe tax code allows homeowners to avoid paying tax on the capital gain on their home if they buy up when they move. In addition, owners age 55 and over are allowed a one-time exemption from capital gains on their primary residence. These tax provisions likely have little effect on younger families who are deciding whether to purchase their first home. However, the tax code could encourage many middle age homeowners to remain in owner-occupied housing in order to avoid paying a capital gains tax. The tenure decisions of older families would be relatively less affected since those households are eligible for the one-time exemption. r6Henderson and Ioannides (1983) argue that households prefer to be owner-occupiers when their consumption demand for housing is sutliciently less than their investment demand for housing, where the investment demand for housing is determined by equating risk-adjusted rates of return across alternative assets. Fu (1991) shows that both the investment and consumption demands for housing increase with wealth, which suggests that wealth has an ambiguous effect on housing tenure preferences.

J.l! Duca and S.S. Rosenthal, Borrowing constraints

309

then included in the tenure status function. (Results from the wealth regressions by age group are provided in Appendix B.) Total household income in $100,000 units (INC82) and INC82 squared (INCSQ82) were also included in the tenure equation. In addition to capital gains tax effects (which are sensitive to income through the marginal income tax rate), the value of other federal tax provisions that favor owner-occupied housing increase with the marginal income tax rate [e.g. Rosen (1979)l.l’ Hence, the relative cost of owning to renting declines as the family’s federal marginal income tax rate goes up, which implies an increasing relationship between income and the propensity to live in owner-occupied housing. The relative cost of owning to renting may also vary regionally because of idiosyncratic differences in local housing markets. Such variation is proxied using county wide data from the 1980 decennial census; for each household in our sample the county wide ratio of the median house value of owners to the median monthly rent of renters is calculated (MDHVRNT). An increase in MDHVRNT suggests that it is more difficult to buy a home for any given level of income, wealth, and other household characteristics. Accordingly, MDHVRNT is expected to have a negative sign in the tenure status model.” Various demographic variables are also included in the tenure status equation to proxy preferences for living in owner-occupied housing, as well as to further proxy household mobility given that more mobile households typically are more likely to rent, ceteris paribus.” These variables include the age of the household head (AGE), marital status [MARR (1 if married)], sex of the household head [SEX (1 if male)], household size (HSIZE), education of the household head [ED (1 if high school or more)], and race of the household head [RACE (1 if non-white)]. All of the demographic and financial variables in the tenure model are included in the credit model to proxy household preferences for mortgage debt, as well as debt limits set by lenders. In addition, the credit model contains variables based on whether households felt it was ‘all right for someone like [the respondent] to borrow money .. . to finance auto or furniture purchases (DUR) or to finance luxury items (LUX)‘. The unemployment rate in 1982 of the household head’s profession (UNEMP) was included to proxy income security, as was the size of the urban population (URBANPOP) in the household’s county (based on the 1980 decennial census) which proxies cost of living differentials. The credit model also “Federal tax laws allow homeowners to deduct mortgage interest and property tax payments (although maintenance expenses are not deductible), while imputed rent is not taxed. “Specifying MDHVRNT in this manner implicitly assumes that choice of county is exogenous to the tenure choice decision which seems reasonable as a first approximation. “Owner-occupiers typically pay substantial realtor and legal fees upon moving while renters do not. Rosenthal (1988) shows that such differences greatly increase the likelihood that mobile households rent.

310

J.PT Duca and S.S. Rosenthal, Borrowing constraints

includes several variables indicative of default risk that are typically requested on loan applications, such as the number of years the household head has worked for the current employer (CUREMP), whether the household has received public assistance (WELFARE), and whether the household has had problems making loan payments in the past 3 years (BADHST).20 Although a rich set of variables is available for the credit model, the reduced form character of the model complicates interpretation of its coefficients. However, we should emphasize that the primary purpose for estimating the credit model is to control for selection effects when evaluating housing tenure preferences [expression (l)]. For that reason, results from the credit model are presented in Appendix C, which allows us to focus the remainder of the discussion on the tenure model.

4. Results Results from the bivariate probit tenure model for young, midage, and older households are presented in Table 1. Also included in Table 1 are results from three univariate probit models; these models evaluate the probability that a household lives in owner-occupied housing using the full sample for each respective age group without controlling for borrowing constraints. A series of Wald tests based on the bivariate and univariate probit models reject the null that the coefficients in the two models are alike for the young and midage households, although the null cannot be rejected for the older households.21 Particularly for young households where the test statistic was quite large, these results suggest that borrowing constraints have a significant effect on access to owner-occupied housing, and that failing to control for borrowing constraints leads to biased estimates of housing tenure preferences.22 Comparing columns (1) and (2) provides a more detailed picture of how “‘In preliminary runs MDHVRNT, INCSQ82, and CHECK (1 if the household has a checking account and 0 otherwise) were included in the credit model, while UNEMP, DUR, LUX and CUREMP were included in the tenure model. However, these variables were jointly (and individually) not significant for young households. In addition, although CHECK (in the credit model) and DUR (in the tenure model) were significant for the midage and older households, omitting these variables had little effect on the remaining coefficients for those age groups or on results from Table 2. For that reason, these variables were omitted from the final model to simplify the presentation. “The Wald test statistics are distributed X2(11) which has a critical value at the 5% level equal to 19.7. In contrast, the test statistics for the young, midage, and older households are 210.3, 35.09 and 8.54, respectively. “In addition, note that oul.u2 is negative and generally significant for each of the age groups. This implies that the tenure status of families with a strong idiosyncratic taste for renting (u, is large and negative) is less likely to be affected by borrowing constraints (u2 is large and positive). As suggested in Section 2, under these conditions a univariate probit model of housing tenure based on a sample that omits credit constrained renters would yield biased estimates of housing tenure preferences.

J.K Duca and S.S. Rosenthal, Borrowing constraints

Table Probit

model

Young Variable CONST

tenure

estimates

families

Bivariate probit - 1.14247

( - 2.702)

1

(numbers

Midage

in parentheses

families

Univariate probit

Bivariate probit

- 1.78248 ( - 4.975)

( - 1.026)

- 1.35146

( - 3.023)

0.37029 (1.148)

1.21341 (4.691)

0.038969 (0.242)

0.16364 (1.187)

INC82

3.11107 (3.409)

4.21366 (5.721)

2.58176 (3.722)

2.74136 (5.273)

AGE ED SEX RACE

- 2.08749 (-2.112)

- 3.52692 (-4.763)

-0.80157 (- 1.498)

families

Bivariate probit

Univariate probit

-0.51891

are t-ratios)’ Older

WHAT

INCSQ82

311

-0.98360 ( - 4.476)

Univariate probit

1.92272 (4.185)

1.33165 (2.876)

0.52433 (4.158)

0.59097 (5.331)

-0.57671

-0.76388

( - 0.778)

(- 1.215)

-0.0094183 (-0.035) - 0.0090788

0.018364 (0.128)

0.02948 1 (2.359)

0.027923 (2.407)

0.027243 (3.053)

0.039262 (4.775)

(- 1.519)

( - 0.524)

- 0.003098

0.19397 (1.416)

0.10213 (0.782)

0.18301 (1.510)

0.22151 (2.060)

-0.12126 (-1.111)

- 0.070569 (-0.676)

-0.10769 (- 1.163)

-0.077354 (-0.891)

-0.32002 ( - 2.942)

-0.32033 (-3.280)

-0.16144 - 1.495)

( - 2.459)

- 0.2544

-0.17211 (- 1.286)

-0.31953 (-2.552)

-0.37923 ( - 2.799)

-0.45235 ( - 3.832)

-0.21794 (- 1.659)

-0.27554 (-2.231)

MARR

0.17616 (1.241)

0.45015 (3.793)

0.49414 (3.810)

0.54730 (4.747)

0.39916 (3.067)

0.51729 (4.309)

HSIZE

0.18475 (4.239)

0.11200 (2.915)

0.091132 (2.809)

0.073232 (2.350)

0.10479 (2.083)

0.11208 (2.187)

-0.0042599

-0.0044302 (-5.139) -

-0.0053191 (- 5.772) -0.85288 ( - 2.220)

MDHVRNT

-0.0035731

(- 3.808) 0”1.“2 Smp. Size Log (L) W(11)2

-0.70483

-0.0041179

( - 4.749)

( - 4.426)

_ _

937 - 1,044.7 _

1,224 -606.81 210.33

( - 4.472) -0.50437 (-1.411) 1,185 - 745.8 _

’ Results from the credit model corresponding Appendix C. ‘The Wald statistic is based on a comparison and is distributed X*(11).

1,281 - 565.73 35.089

to the bivariate of results

tenure

1,129 - 623.2 equation

from the two tenure

-0.0052313

( - 5.662) _ _ 1,163 - 542.4 8.543 are presented status

equations

in

312

J.V. Duca and S.S. Rosenthal, Borrowing constraints

borrowing constraints affect the housing tenure of younger families.23 A striking result in columns (1) and (2) is that wealthy families, white households, and married couples, are all significantly more likely to live in owner-occupied housing [column (2)]. However, these variables are not significant predictors of who prefers to live in owner-occupied housing once credit effects are removed [column (l)]. Hence, much of the effect of wealth, race, and marital status, on who lives in owner-occupied housing appears to stem from credit market constraints, at least for young households.24 Moreover, our wealth result is consistent with theoretical findings by Fu (1991) which indicate that after controlling for tax effects (which is done here by including income and income squared) and borrowing constraints, the effect of wealth on housing tenure preferences is ambiguous, a priori. In contrast, both with regard to preferences for living in owner-occupied housing and actual tenure status, households are more likely to live in owner-occupied housing if they are older (AGE), if family size is larger (HSIZE), and if the relative cost of owning to renting is lower (as proxied by INC82, INCSQ82, and MDHVRNT). Although it is difficult to interpret the effect of HSIZE, the AGE result is consistent with evidence that younger families typically are more mobile and that more mobile families are less likely to own, while the coefficients on income and MDHVRNT indicate that families are more likely to own as the relative cost of owning declines.25

5. Simulations For each household in the sample the probability of wanting to reside in owner-occupied housing, F(mZ), was calculated using coefficients from the bivariate probit model for the appropriate age group. Averaging F(mZ) over all households provides a consistent estimate of the percentage of households in the United States that would prefer to reside in owner-occupied housing (in 1983). 26 The simulated preferred owner-occupancy rate is presented in Table 2 along with the actual frequency of credit constrained renters 23Note that borrowing constraints have a similar qualitative effect on the tenure coefficients of all three age groups. However, the effect of borrowing constraints appears to be substantially stronger among younger families. For that reason, we focus our discussion on younger households. 24These results are consistent with findings from Duca and Rosenthal (1993) which indicate that lenders adjust total debt limits upwards for more wealthy families but apply tighter total debt limits to single households and non-white families. 25The negative coefftcient on INCSQ82 is roughly consistent with a non-linear and declining marginal effect of income on the relative cost of owning, as marginal income tax rates first increase with income and then level off. Z61t should be emphasized that our simulations are partial equilibrium in nature. If credit effects were removed, presumably demand for housing and mortgage credit would increase and housing prices and mortgage rates would rise (at least in the short run). In that respect, our estimates likely provide an upper bound on the impact of borrowing constraints on homeownership rates.

313

J.V. Duca and S.S. Rosenthal, Borrowing constraints Table 2 The percentage

of owner

Full sample (3,568 observations) Stratified by age Under 35 years (1,124 observations)

occupiers

in the United

Percent credit constrained renters

Actual percent owning

Percent that prefer to own

11.0

64.5

12.9

23.4

41.2

57.9

74.3

80.7

76.1

78.9

67.8

75.2

43.1

58.9

45.1 23.4

59.9 48.8

35-54 years (1,281 observations) Over 54 years (1,163 observations)

States in 1983 by age and race.

2.9

Stratified by race White (3,276 observations) Non-white (588 observations) Stratified by age and race Under 35 years White (1,002 observations) Non-white (222 observations) 35-54 years White (1,078 observations) Non-white (203 observations)

5.9 16.3

78.5 52.2

83.8 64.5

Over 54 years White (1,001 observations) Non-white (162 observations)

2.4 6.2

78.9 58.6

81.1 65.6

[households whose tenure status may or may not be affected by borrowing constraints (C =O)], and the actual owner-occupancy rate for the sample. Observe that 11% of the sample is comprised of credit constrained renters. Thus, even before employing careful econometric methods, it is clear that 11% is the upper limit of the effect of borrowing constraints on homeownership rates since some credit-constrained renters may prefer to rent.27 In contrast, our model predicts that 72.9% of the sample would prefer to live in owner-occupied housing, 8.4 percentage points higher than the actual frequency of owner-occupiers. Further review of Table 2 suggests that the effect of borrowing constraints differs considerably across age groups. Among midage and older households, for example, there is little difference in the preferred and actual rate of “In principle, therefore, our homeownership rates by less than

model should predict 1 lx, which is consistent

that borrowing constraints with results from Table 2.

depress

314

J. K Duca and S.S. Rosenthal, Borrowing constraints

homeownership. But among families under age 35, roughly 58% prefer to own while only 41% actually live in owner-occupied housing.28 Hence, borrowing constraints appear to have a disproportionate effect on young families. Even more dramatic is the effect of borrowing constraints on young nonwhite households. Among those families, 49% would like to own but only 23.4% actually live in owner-occupied housing. Moreover, while the difference in actual owner-occupancy rates between young white and young nonwhite households is 21.7 percentage points, the difference in preferred owneroccupancy rates between these groups is 11.1 percentage points. This 11 point gap can be attributed to racial differences in socioeconomic status among young households, given that race has little effect on housing tenure preferences (as noted earlier) and our model controls for credit market constraints. In contrast, the residual 10.6 point difference in owner-occupancy rates between young white and young non-white households (21.7- 11.1) can be attributed to the effect of borrowing constraints. To properly interpret this result note that two explanations exist for why lenders may vary borrowing constraints across households of different race: (i) lenders may respond to differences in socioeconomic and credit history characteristics that happen to be correlated with race, and (ii) lenders may vary borrowing constraints on the basis of race per se (i.e. racial discrimination). Unfortunately, our data do not allow us to carefully distinguish between these two different explanations, at least as they relate to the effect of borrowing constraints on homeownership rates. However, related work by Gabriel and Rosenthal (1991) and Duca and Rosenthal (1993) suggests that racial differences in socioeconomic status and credit history account for much of the difference in the manner in which lenders vary borrowing constraints across racial lines, although a significant intrinsic race effect remains.

6. Conclusions This study takes advantage of a unique set of variables in the 1983 Survey of Consumer Finances (SCF) to evaluate the effect of credit constraints on access to owner-occupied housing. Our results indicate that failing to control for borrowing constraints leads to biased estimates of household preferences for living in owner-occupied housing, especially among younger families. Moreover, simulations suggest that if borrowing constraints were relaxed, 28The disproportionate effect of borrowing constraints on younger households (relative to preferences for living in owner-occupied housing), may reflect that younger households, aside from having less wealth for downpayment, have a more limited credit history. Such an interpretation would be consistent with evidence from Boyes et al. (1989) and Duca and Rosenthal (1993) that lenders relax debt limits for borrowers that pose less credit risk.

J.V Duca and S.S. Rosenthal, Borrowing constraints

315

ceteris paribus, owner-occupancy rates in the United States (in the early 1980s) would increase from 64.5% to nearly 73%. These findings are consistent with evidence from Cox and Jappelli (1993) and Duca and Rosenthal (1993), that many credit constrained households would hold substantially more debt in the absence of borrowing constraints. We also find that borrowing constraints have a disproportionate effect on young families and non-white households, consistent with popular perceptions, with the largest effect on non-white families under age 35. Accordingly, if a goal of policymakers is to increase access to owner-occupied housing for young and/or non-white households, programs that improve access to credit for these families would likely be effective. We should emphasize, however, that the cost of providing mortgage credit to households that have been rejected by competitive lenders could be large given the market’s implicit judgement that such mortgages yield below market rates of return.

Appendix Selected

A variable

definitions

and summary statistics

Own equals 1 if the household owns their home in 1983 and 0 otherwise. Credit equals 1 if the household is not credit constrained and 0 if the family is credit constrained. C equals 1 if the family’s tenure status is unaffected by borrowing constraints (owner-occupiers and unconstrained renters) and 0 if the family’s tenure status may be affected by borrowing constraints (constrained renters). Wealth equals household net worth (non-pension assets minus debt) in 1982 in current dollars (in 100,000 dollar units). INC82 equals total household income in 1982 dollars (in 100,000 dollar units). AGE equals the age of the household head. ED equals 1 if the household head has a high school degree or more. SEX equals 1 if the household head is male. RACE equals 1 if the household head is non-white. MARR equals 1 if married. HSIZE equals the number of people in the household. MEDHVRNT equals the ratio of the county wide median house value to the county wide median monthly rent (based on the household’s county in 1980). UNEMP equals the 1982 unemployment rate of the household head’s profession. LUX equals 1 if the household head felt it was ‘all right for someone like [the respondent] to borrow money to finance the purchase of a fur coat, boat, or other luxury items’. DUR equals 1 if the household head felt it was ‘all right for someone like

316

J.I/: Duca and S.S. Rosenthal, Borrowing constraints

[the respondent] to borrow money to finance the purchase of furniture or a car’. CUREMP equals the number of years working at current employer. BADHST equals 1 if the household had problems making loan payments in the last 3 years. WELFARE equals 1 if the household received public assistance in 1982. URBANPOP equals the urban population of the household’s county in 1980.

Variable

OWN CREDIT C Wealth INC82 AGE ED SEX RACE MARR HSIZE MDHVRNT UNEMP LUX DUR CUREMP BADHST WELFARE URBANPOP

Young

families

0.41176 0.69281 0.76552 0.28774 0.21616 27.47 1 0.86356 0.53676 0.18137 0.59804 2.7672 208.85 5.4107 0.29085 0.92402 3.0809 0.20343 0.13235 730.85

mean

( 1,224 Obs) S.D. 0.49235 0.46152 0.42384 0.58887 0.16373 4.1539 0.34339 0.49885 0.38548 0.49049 1.4783 52.736 6.0732 0.45434 0.26508 3.4856 0.40272 0.33901 277.34

Selected

A.1

0.743 17 0.84387 0.92506 0.88041 0.31017 43.728 0.76034 0.52849 0.15847 0.69477 3.3646 205.05 5.0443 0.22014 0.89696 7.8954 0.14520 0.09289 714.72

SD. 0.43706 0.36312 0.26340 1.2688 0.24598 5.7607 0.42704 0.49938 0.36532 0.46068 1.6204 49.755 5.5128 0.41450 0.30414 8.2207 0.35244 0.29040 285.85

statistics.

Midage families (1,281 Obs) mean

summary

Table

0.76096 0.94497 0.97077 1.0573 0.20332 67.071 0.48925 0.47635 0.13929 0.53998 1.9433 198.86 2.3535 0.09630 0.72571 5.2502 0.03353 0.10404 682.33

Older families (1,163 Obs) mean

SD. 0.42668 0.22814 0.16854 1.4948 0.28063 8.4270 0.50010 0.49966 0.34640 0.49861 1.0708 47.204 4.63 15 29513 0.44635 10.226 0.18010 0.30545 296.63

Variable

CONST INC82 INCSQ82 PENINC UNEMP AGE ED SEX RACE MARR HSIZE AVERSE CONSUMP LUX DUR EMERG CUREMP BADHST SOMHST WELFARE

families

-0.25513 1.47887 0.25560 - 0.56491 -0.43333E-02 0.970328-02 0.70149E-01 -0.26629E-01 -0.63128E-01 -0.60487E-01 0.40073E-01 -0.62875E-01 -0.360758-01 -0.46132E-01 0.79178E-02 -O.l4073E-01 0.29040E-01 -0.80236E-01 0.69454E-01 -0.73888E-01

coefficient

Young

- 1.662 4.555 0.996 -4.816 - 1.563 2.403 1.555 -0.828 - 1.499 - 1.423 2.910 - 2.050 -0.910 - 1.375 0.142 -0.305 5.797 -2.196 1.631 - 1.450

T-ratio

Ordinary

Table regressions.’

T-ratio - 1.950 12.180 1.937 - 11.340 -2.200 2.659 2.439 - 3.368 -2.318 1.932 1.653 - 1.218 1.730 - 1.275 ~ 1.813 1.333 6.130 - 2.935 1.582 - 2.306

coeffkient

families

wealth

B.l

- 0.62452 3.35050 0.29541 - 1.83106 -O.l2299E-01 O.l3027E-01 0.17028 -0.19793 -0.18650 0.14302 0.31673E-01 -0.68626E-01 0.13666 -0.84361E-01 -0.16280 0.10131 0.242548-01 -0.22365 0.13289 -0.25010

Midage

least squares

T-ratio - 2.505 23.090 - 15.250 - 8.462 -2.179 2.639 2.978 0.174 - 1.228 1.357 -2.619 -3.146 0,345 - 1.317 - 1.601 1.071 5.882 - 0.932 2.448 -0.355

coefficient

families

- 1.01417 6.97058 - 1.02721 -2.04133 -O.l9324E-01 O.l1803E-01 0.21078 O.l2382E-01 -0.12274 0.11167 -0.90571E-01 -0.20108 0.38188E-01 -0.14287 -0.11931 0.84506E-01 0.23899E-01 -0.16013 0.36428 -0.39696E-01

Older

0.33055E-01 -O.l8560E-01 0.36354E-01 -0.76585E-01 -0.32782 0.70565 -O.l5697E-03 -0.29322 -O.l6704E-03 1224 0.288 0.494 0.295

0.849 - 0.349 0.610 - 1.482 - 1.655 3.566 -0.500 - 0.246 -2.910

-0.22296E-01 - 0.25026 0.17312 0.87238E-01 0.37806 0.90927E-01 0.44258E-03 2.25502 - O.l4704E-03 1281 0.880 0.915 0.479

-0.271 - 2.386 1.233 0.760 1.455 0.310 0.774 0.984 - 1.422

0.12791 -0.921862E-01 0.58794lE-02 0.398220 - 1.22122 0.797967 0.773863E-03 3.16623 -0.440115E-03 1163 1.06 1.02 0.536

1.435 - 0.670 0.030 3.200 - 3.829 2.208 1.100 1.146 - 3.864

‘Variables in the wealth model that are not defined in Appendix A include: CHECK equals 1 if the household has a checking account; PENINC equals INC82 multiplied by a dummy variable (PEN), where PEN equals 1 if either the household head or spouse expect to receive pension income upon retirement; AVERSE equals 1 if the household was not willing to take on any risk in investing family savings; CONSUMP equals 1 if the household head felt it was ‘all right for someone like [the respondent] to borrow money to finance a vacation’; EMERG equals 1 if the household head felt it was ‘all right for someone like [the respondent] to borrow money to finance medical expenses or to finance living expenses when income is cut’; SOMHST equals 1 if the household has a nonmortgage loan outstanding that was originated prior to 1980; FULLTIME equals 1 if the household head is currently working fulltime; EXPINHER equals 1 if the household anticipates receiving a ‘large’ inheritance; INHERIT equals 1 if the household has received a ‘large’ inheritance; FULLINC equals FULLTIME multiplied by INC82; EXPINC equals EXPINHER multiplied by INC82; PCTURBAN equals the percentage of the county population that lives in an urban area (based on the household’s county in 1980).

Obs. Mean dep. var. Std. err. regr. Adjusted R-squared

CHECK FULLTIME EXPINHER INHERIT FULLINC EEXPINC MDHVRNT PCTUBAN URBANPOP

Mean dep. var. Sample size Log (0

CONST WHAT lNC82 UNEMP AGE ED SEX RACE MARR HSIZE LUX DUR CUREMP BADHST WELFARE URBANPOP

Variable

families T-ratio 3.171 1.367 3.124 - 1.266 0.198 - 0.472 0.945 - 1.171 1.456 0.025 - 2.293 - 2.420 1.709 - 1.358 -3.933 -4.163

0.756 1224 - 1044.7

1.18381 0.53843 2.32952 -O.l0311E-01 0.23919E-02 -0.61199E-01 O.Y0961E-01 -0.20612 0.18528 0.10331E-02 - 0.22274 -0.37305 0.29874E-01 -0.13917 - 0.50722 -0.83053E-03

coefficient

Young

Bivariate

Table

-0.65559 -0.20535 1.98686 -0.75597E-02 0.46537E-01 0.18699 - 0.27040 -0.30833 0.20662 0.67402E-01 -0.20801E-01 -O.l6034E-01 0.2436YE-01 -0.29181 -0.54276 -0.64886E-03

T-ratio

0.925 1281 - 745.8

- 1.041 - 0.865 2.706 -0.568 3.934 1.238 - 1.863 ~ 1.881 1.112 1.532 -0.126 - 0.086 2.028 - 1.834 - 3.066 - 2.661

model.

families

credit

C.l

coefficient

Midage

probit Older

0.84068 0.21529 -0.79422E-01 0.41297E-02 0.25227E-01 0.30565 - 0.46093 -0.10128 0.50714 0.10331 0.34209 -0.19384 0.157218-02 -0.21367 - 0.82776 -O.l0313E-02 0.971 1163 - 623.2

families

coefficient

0.690 0.487 -0.035 0.113 1.724 1.075 - 1.826 -0.346 1.575 0.830 0.792 -0.823 0.080 - 0.628 -3.103 - 2.260

T-ratio

J.I/ Duca and S.S. Rosenthal, Borrowing constraints

321

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