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Demography

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23.07.2020

A Framework for Explaining Black-White Inequality in Homeownership Sustainability

verfasst von: Chunhui Ren

Erschienen in: Demography | Ausgabe 4/2020

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Abstract

To explain racially differential housing outcomes, previous studies have tended to concentrate on discriminatory processes within the mortgage market while ignoring homeowning families’ broad socioeconomic challenges. This study proposes a conceptual framework for understanding Black-White inequality in homeownership sustainability, which emphasizes Black homeowners’ socioeconomic challenges that are external to mortgage market evaluations, with a particular focus on the mediating role of liquid assets. Based on the Panel Study of Income Dynamics, the framework is put to an empirical test on the differential exit rates between Black and White homeowners in the United States during the recent housing crisis. The findings indicate that the racial gap in homeownership exit is eliminated after liquid wealth is controlled in the model alongside other covariates and that the inclusion of liquid wealth renders all mortgage-oriented variables nonsignificant with regard to their explanatory power for Black-White inequality in exit rates. Policy implications of the findings are also discussed.

Vorheriger Artikel The Dynamics of U.S. Household Economic Circumstances Around a Birth

Nächster Artikel Measuring Housing Stability With Consumer Reference Data

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Compared with European welfare societies, where public rental housing plays a more significant role, U.S. housing policies have consistently focused on increasing private homeownership as part of a new so-called ownership society. For a discussion on the historical evolvement of the ownership society experiment as well as its contemporary challenges in the post-recession era, see Clark (2013) and Forrest and Yip (2011).

Although Hispanic/Latino homeowners were also hit by the housing crisis (Faber 2013; Rugh 2015), the residential experiences of Hispanic/Latino families are different from those of their African American counterparts in terms of both historical experiences (Jargowsky 1997) and contemporary challenges (Rugh 2015). Thus, this study leaves the specific issues facing Hispanic/Latino homeowners to be explored by other research.

This is particularly true when it comes to racial inequality: minority homeowners tend to have limited financial resources, benefit less from tax deductions, and have to deal with unexpected home maintenance costs (Herbert and Belsky 2008).

Insurance coverage serves as a security against health-related financial shocks, which reduces the pressure on home equity (Davidoff 2010). Berger et al. (2015) included health insurance as a predictor variable for homeownership exit, but they did not test its role in explaining racial inequality.

Hilber and Liu (2008) included parental wealth in modeling the cross-sectionally measured overall racial homeownership gap. Hall and Crowder (2011) discovered significant effects of extended-family resources on racial disparity in transition to homeownership, although no analysis was performed on sustainability.

Although homeownership sustainability broadly defined implies both the risk of exit and the duration of ownership status, my empirical model tests homeownership exit only, mainly because of this study’s narrow temporal focus on the Great Recession. See Haurin and Rosenthal (2004, 2005) for similar studies on homeownership duration.

Recent empirical tests based on more specialized surveys showed that race plays a significant role in mortgage-lending decisions even after wealth and asset variables are controlled (Munnell et al. 1996).

Past studies have tended to handle this by reporting robust standard errors (Berger et al. 2015; Sharp and Hall 2014). Although robust standard errors raise the bar for accepting significant coefficients, they do not correct model misspecification (Fomby and Murfin 2005). That is, robust standard errors help validate a misspecified model only if the model is justified by strong theoretical reasons, which is apparently not the case for this study.

Haurin and Rosenthal (2004, 2005) defined an observation window as a completed housing tenure spell, which begins at the time of homeownership entry and ends at the time of exit. Turner and Smith (2009) employed a six-year fixed window to observe whether homeownership exit occurs.

Because PSID surveys collect the information of the year before, the 2007–2013 period reflects an observation window from 2006 to 2012. The national mortgage delinquency rate started to ascend in 2006 and showed no sign of decline until the end of 2012 (Board of Governors of the Federal Reserve System 2018).

Prior research on racial wealth gaps has sometimes treated the omission of renters as a sample selection bias (Flippen 2001). When it comes to homeownership sustainability, however, taking renters into account faces difficulty in interpreting results: if renters are considered part of the sample, the findings (on homeownership sustainability) should apply to renters as well, which makes little intuitive sense. For that reason, this study deems the renting-to-owning transition as a separate process and strictly focuses on homeowners as the target population.

Results concerning the potential resampling biases indicate that the final sample is representative of the original sample (online appendix, Table A1).

Black homeowners account for 25.5% of the sample, far exceeding their representation in the general population. The PSID purposely oversamples African Americans to improve the analytical reliability on issues where Black representation is low (Wilson et al. 2015). Thus, it is a common practice to not apply weights in race-oriented homeownership research (Berger et al. 2015; Boehm and Schlottmann 2004, 2009; Hall and Crowder 2011; Sharp and Hall 2014), and I follow this approach.

Another option is to apply a survival model, which explores the variation (by two-year intervals) in homeownership duration. However, because this study’s conceptual framework requires (1) a focus on the housing crisis (six-year limited window) and (2) a conceptualization of liquid assets as a time-fixed variable, a survival model will not materialize its major strength—the ability to explore longer-term processes with time-varying predictors—but instead lose the benefits associated with the logistic regression (e.g., compatibility with the conceptual framework, policy convenience, and the ability to assess predictor variables’ mediating effects).

The sample includes 84 (<3%) families who exited ownership in intermediate years (2009 and 2011) but returned to owning in 2013. These cases with only intermediate tenure changes are considered as “not exiting homeownership” during the study period. Also, excluding them from the sample does not lead to any change in major findings.

When the dependent variable is measured as a change during a PSID interval, explanatory variables should be measured in their beginning-of-period values to avoid endogeneity. For example, Sharp and Hall (2014) regressed housing tenure change during one PSID window on the changes of a set of predictor variables (e.g., divorce) during the same window, creating a simultaneous causality problem because it is impossible to determine whether divorce causes tenure change or divorce occurs as a result of it.

Prior research has shown that older families tend to exhibit a higher likelihood of maintaining ownership, but the positive effects of age on sustaining homeownership decline after passing retirement age (Painter and Lee 2009).

I do not apply an age restriction on the sample because doing so runs the risk of underappreciating elderly homeowners’ financial stress (Danziger et al. 2013). Instead of implementing an arbitrary threshold age, I retain all cases but specify the family head’s labor-market status. In addition, I reestimated the model with different scenarios of age-restricted samples, and findings were consistent (online appendix, Table A2).

Bank accounts includes money in checking or savings accounts, money market funds, certificates of deposit, government savings bonds, and Treasury bills.

This measuring strategy minimizes the influence of outlier cases by the PSID’s self-reported data (Rohe et al. 2002) while retaining maximum information. I also tried log-transforming the asset variables, although it caused a substantial sample reduction (3,117 to 2,326) because of the eliminated cases with zero/negative wealth. The two versions of findings are nevertheless consistent. Detailed results are available upon request.

About 24% of the homeowners in the sample were not holding a mortgage.

This is not an ideal measurement because housing markets do not operate regionally but within each metropolitan area. Unfortunately, the PSID’s public-access data do not contain metro-specific locational information. Given this study’s focus on nonspatial analyses, the rough measure of geographic controls can be tolerated.

For a detailed discussion on the KHB method and other solutions, see Karlson et al. (2012) and Williams (2012).

All analyses of this study are performed using Stata 13. All data sets, syntax, and detailed Stata outputs are available upon request.

Detailed results are available upon request.

For a detailed discussion of the baby bond proposal, see Hamilton and Darity (2010).

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Titel: A Framework for Explaining Black-White Inequality in Homeownership Sustainability
verfasst von: Chunhui Ren
Publikationsdatum: 23.07.2020
Verlag: Springer US
Erschienen in: Demography / Ausgabe 4/2020
Print ISSN: 0070-3370
Elektronische ISSN: 1533-7790
DOI: https://doi.org/10.1007/s13524-020-00894-4

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