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Mortgage Lending Discrimination Across the U.S.: New Methodology and New Evidence

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Abstract

Is there discrimination in mortgage-loan origination and pricing? If so, does the level of discrimination differ before and after the eruption of the subprime crisis? Using data from 6.5 million loan applications from 2004 through 2013, we propose a novel approach aiming to substantially lower the notorious omitted-variable bias of the Home Mortgage Disclosure Act (HMDA) database and identify the level of racial, ethnic, and gender discrimination in mortgage lending across the United States. In stark contrast with previous studies, we find, on average, very little discrimination in loan origination. Although discrimination increases somewhat after 2007, its probability remains well below 1%. In contrast, we find that white (non-Hispanic) applicants pay a lower spread on the originated loans by 0.37 (0.11) basis points, a result that almost entirely comes from the pre-crisis period.

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Notes

  1. Between 2007 and 2009 the National Association for the Advancement of Colored People (NAACP) filed numerous class action lawsuits against a number of the United States’ largest lenders for discriminatory lending practices (Sen 2012) including, among others, Wells Fargo & Co, HSBC Finance Corporation, CitiMortgage, SunTrust Mortgage, JP Morgan, First Horizon, Ameriquest Mortgage Company, Fremont Investment & Loan, Option One Mortgage Corporation, WMC Mortgage Corporation, Long Beach Mortgage Company, BNC Mortgage, Accredited Home Lenders, Bear Stearns Residential Mortgage Corporation, Encore Credit, First Franklin Financial Corporation and Washington Mutual, Inc. The class action lawsuits alleged that the financial institutions violated the Fair Housing Act, the Equal Credit Opportunity Act, and the Civil Rights Act of 1866 engaging in systematic, institutionalized racism and discriminatory practices in home mortgage lending. A more detailed analysis on the aforementioned lawsuit cases is available at: http://www.naacp.org.

  2. The Fair Housing Act was enacted in 1968 by the Office of Fair Housing and Equal Opportunity in the U.S. Department of Housing and Urban Development (HUD) to prohibit discrimination based on race (Hubbard et al. 2012).

  3. The Home Mortgage Disclosure Act (HMDA) and the Community Reinvestment Act (CRA) were enacted by Congress in 1975 and 1977, respectively, to monitor lending institutions’ practices toward minorities and low-income borrowers and neighborhoods (Hubbard et al. 2012).

  4. Empirically distinguishing between these forces is very hard because of a lack of specialized data.

  5. There is also a relatively smaller number of studies that focus on geographical redlining against minority neighbourhoods (Holmes and Horvitz 1994; Turner and Skidmore 1999) or the default rates of minority applicants (Ferguson and Peters 1995).

  6. Ladd (1998), LaCour-Little (1999), Turner and Skidmore (1999), Ross and Yinger (2002, 2006), and Ross (2005) provide an exhaustive review of the earlier literature on mortgage lending discrimination. For more recent studies, see Pager and Shepherd (2008), Hubbard et al. (2012), Sen (2012), and Wheeler and Olson (2015) and references therein.

  7. According to the Pew Research Centre (2011), from 2008 to 2010, unemployment increased among the white population by 4.7% (from 4.9% to 9.6%), among the African-American population by 7.9% (from 9.4% to 17.3%), and among the Asian population by 5.2% (from 3.2% to 8.4%). Further, African-American and Hispanic households lost 53% and 66% of their wealth, respectively, between 2005 and 2009. During the same period, white households’ wealth dropped by 16%. Also, Hoynes et al. (2012) provide evidence that the effect of the 2007–2009 recession in the labor market differs across demographic groups, with African-American and Hispanic workers being more affected. Experience from earlier crises suggests that these differences are stable through all the recessionary periods for the last three decades.

  8. The census tract is an area roughly equivalent to a neighborhood established by the Bureau of Census for analyzing populations. Census tracts generally encompass a population between 2500 to 8000 people, and represent the smallest territorial unit for which population data are available.

  9. There are a number of issues we examine to ensure that estimation with the linear probability model is not problematic. The two main problems of the LPM vis-à-vis the probit or logit models are heteroscedasticity and the out-of-bound (out of the 0 and 1 bounds) predictions (Wooldridge 2009). Heteroscedasticity is easily resolved with the estimation with robust standard errors. The out-of-bound problem is not that large in our sample, where for e.g. the baseline regression with the full sample, 3045 observations out of the 6,452,279 are below 0 and 127 are above 1. This is less than 0.05% of our sample. Thus, we anticipate very little econometric problems with the OLS estimation. Our best bet to actually show that this is indeed the case, would be to show that results from probit or logit models without fixed effects are similar to results from the LPM and no fixed effects. Unfortunately, even the simple logit or probit models do not converge in our data set.

  10. As suggested by Erickson et al. (2014), we examine the sensitivity of the estimated elasticities on a case-by-case basis when using the higher-order cumulants (from 3 to 8) of the mismeasured regressor, which in our case is the residuals from Eqs. (2) and (3).

  11. In fact, econometrically it is not even possible to include \( {u}_i^D \) in Eq. (6) because \( {L}_i^D={L}_i^S \) and, if included, \( {u}_i^h \) drops out from the regression due to perfect collinearity.

  12. The estimates become even more economically significant if we remove the regional fixed effects, and they decrease by about 10% if we include bank fixed effects.

  13. To further compare our findings with the existing literature, and especially the seminal study of Munnell et al. (1996), we collect HMDA data for Boston (Suffolk County) in 1990. As in Munnell et al., we identify 1200 black and Hispanic applicants and choose a random sample of white applicants to produce roughly equal numbers of white and minority denials (10% rejection rate and 3300 applications by whites). Using that sample, we run a basic model, which includes race, income, gender, and census tract fixed effects as explanatory variables. We find a discrimination estimate of 11.4%. Recall that Munnell et al. find an estimate of 8% when adding 38 more variables, which are not available to us (data are confidential). Then, we repeat the analysis using our method and find an estimate of 0.9%. We report these results in Table 13 of the Appendix. Thus, we must conclude that there is still a lot of omitted-variable bias in the study by Munnell et al. (1996).

  14. As we showed earlier, including or excluding income in the first stage of the model does not significantly affect our results. From this point onward, we retain the specification with income included in the first stage of the model.

  15. Available online in http://mcdc2.missouri.edu/websas/geocorr2k.html.

  16. For the mean applicant, the spread is 5.07 basis points, implying that white applicants pay approximately a 7.3% lower spread.

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Author information

Authors and Affiliations

  1. Montpellier Business School - Montpelier Research in Management, 2300 Avenue des Moulins, 34080, Montpellier, France

    Manthos D. Delis

  2. Surrey Business School, University of Surrey, Guildford, GU2 7XH, UK

    Panagiotis Papadopoulos

Authors
  1. Manthos D. Delis
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  2. Panagiotis Papadopoulos
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Correspondence to Manthos D. Delis.

Appendix

Appendix

The tables in this appendix are for online use only. They include the following information:

  • Table 12 reports the results from the estimation of Eq. (5) without the previous estimation of Eqs. (2)–(4) and the inclusion of the component e among the regressors in Eq. (5).

  • Table 13 reports the results from an analysis similar to Munnell et al. (1996). See also footnote 12 of the main text.

  • Table 14 reports the results from the analysis on income misreporting, which includes only zip codes with mortgage-loan misrepresentation values at the lowest 5% centile of the distribution of the misrepresentation measure compiled by Piskorski et al. (2015).

  • Table 15 reports the results after orthoganilizing the three discrimination dummy variables and \( {u}_i^D \) and \( {u}_i^S \).

  • Table 16 compares the results from the logit model without fixed effects (marginal effects) with the equivalent OLS results (again without fixed effects).

Table 12 Results from the estimation of Eq. (5) without a first stage. The table reports coefficient estimates and t-statistics (in parentheses) from the estimation of Eq. (5). The dependent variable is Action type, and all variables are defined in Table 1. Equation (5) includes year and census-tract fixed effects. Column I reports the results for the period 2004–2013, Column II the results for the pre-crisis period (2004–2007), and Column III for the post-crisis period (2008–2013)
Full size table
Table 13 Comparison of our baseline results with those of Munnell et al. (1996) for the year 1990. The table reports coefficient estimates and t-statistics (in parentheses) from the estimation of Eq. (5) using data for the year 1990 for Boston (Suffolk County), as in Munnell et al. (1996). We identify 1200 black and Hispanic applicants and choose a random sample of white applicants to produce roughly equal numbers of white and minority denials (10% rejection rate and 3300 applications by whites). Using that sample, we first run a basic probit model (Column I). Then, we repeat the analysis using our method (Column II). The dependent variable is Action type, and all variables are defined in Table 1. Equation (5) includes census-tract fixed effects
Full size table
Table 14 Evidence from zip codes with low mortgage-loan misrepresentation values. The table reports coefficient estimates and t-statistics (in parentheses) from the estimation of Equation (5), given estimates from Eqs. (2) through (4). The sample includes only zip codes with mortgage-loan misrepresentation values at the lowest 5% centile of the distribution of the misrepresentation measure compiled by Piskorski et al. (2015). The dependent variable is Action type, and all variables are defined in Table 1. Equations (2), (3), and (5) include year and census-tract fixed effects. Column I reports the results for the period 2004–2013, Column II the results for the pre-crisis period (2004–2007), and Column III for the post-crisis period (2008–2013). In all regressions, income is included in the first stage of the model
Full size table
Table 15 Results after orthogonalization of discrimination dummies and errors from Eqs. (2) and (3). The table reports coefficient estimates and t-statistics (in parentheses) from the estimation of Equation (5), given estimates from Eqs. (2) through (4). Prior to the estimation, we orthogonalize Race, Gender, Ethnicity relative to \( {u}_i^D \). The dependent variable is Action type, and all variables are defined in Table 1. Equations (2), (3) and (5) include year and census-tract fixed effects. Column I reports the results for the period 2004–2013, Column II the results for the pre-crisis period (2004–2007), and Column III for the post-crisis period (2008–2013)
Full size table
Table 16 Discrimination in loan pricing: Results from a truncated regression model. The table reports coefficient estimates and t-statistics (in parentheses) from the estimation of Eq. (6), given estimates from Eq. (3). Estimation method is the truncated regression model. The dependent variable is Spread, and all variables are defined in Table 1. Equations (3) and (6) include year and census-tract fixed effects. Column I reports the results for the period 2004–2013, Column II the results for the pre-crisis period (2004–2007), and Column III for the crisis and post-crisis period (2008–2013). In all regressions, income is included in the first stage of the model
Full size table

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Delis, M.D., Papadopoulos, P. Mortgage Lending Discrimination Across the U.S.: New Methodology and New Evidence. J Financ Serv Res 56, 341–368 (2019). https://doi.org/10.1007/s10693-018-0290-0

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