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Subprime lending over time: the role of race

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Abstract

In light of the increased scrutiny of the subprime market nationally and the concerns raised that low- and moderate-income and minority homeowners are targeted for high-cost loans, this paper examines the extent to which subprime lending occurs in selected states and the role that race plays in obtaining prime versus subprime loans. It focuses on explaining the gap in subprime rates between African–Americans and whites and estimating its change over time (1999 to 2006) for the study states. We use a unique data set comprised of data from several data sources, including loan-level information, which allows for better controls over factors correlated with race so that better inferences can be drawn. Also, an estimating procedure is employed that fine-tunes the influence of race in the allocation of mortgage capital between the prime and subprime markets. After taking into accounts various controls, the results suggest the possibility of bias in mortgage lending for the prioed studied.

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Notes

  1. These concerns were raised at outreach meetings conducted by the staff of the Reserve Bank’s Community Affairs Department. During outreach meetings, the Reserve Bank’s staff members meet with representatives of financial institutions, government agencies, nonprofit organizations, and consumer advocates to determine mutual areas of interest and activity as well as to discuss any finance-related issues that concern them. The targeting of certain segments of the population for subprime loans is thought to occur, in part, because unscrupulous mortgage brokers work closely with some subprime lenders. While not specifically mentioned during the outreach meetings, some correspondent banks, it is worth noting, have also made questionable high-cost loans.

  2. As Chris Henderson (2007) so aptly points out, these nontraditional loans are legal and intended for savvy borrowers. Potential problems arise when these complex products are obtained by unsophisticated borrowers.

  3. In 2007, the homeownership rate declined slightly to 68.1%. See http://en.wikipedia.org/wiki/Homeownership_in_the_United_States.

  4. For a discussion of legislation signed by Wisconsin Governor Jim Doyle to address this issue, see “Doyle to Sign Legislation Against Predatory Lenders (April 2004).”

  5. See http://www.ffiec.gov/hmda/default.htm.

  6. HMDA data includes information on home purchase, improvement, and refinancing loans. More specifically, the data contain the type and amount of loan, whether the loan resulted in an origination or was denied, type of property (such as single-family or multi-family unit) and location, as well as the ethnicity, race, sex, and income of the applicant.

  7. For a related line of inquiry that focuses on the role that neighborhood characteristics play in the loan-decision process via information externalities, see Lang and Nakamura (1993) and Blackburn and Vermilyea (2007).

  8. We chose 1999 because it was close to the 2000 census data and 2007 because it was the most recent wave of HMDA data available when we began the study.

  9. The data are 58% of the total market and a third of the subprime market.

  10. During this period, HMDA did not include any information that could be used to systematically classify loans as subprime.

  11. Even though the HUD list and the HMDA higher-priced designations for subprime lenders affect the estimates of loan originations, their use in the regressions estimated in this study where they overlap did not appreciably affect the results. For a similar result, see Mayer and Pence (2009).

  12. A list of the variables can be found in appendix Table 8.

  13. This is a FICO score.

  14. We were unable to use the loan-to-value ratio variable, since it does not include second liens on the property.

  15. This variable is equal to the number of home-purchase loans from HMDA divided by the number of owner-occupied housing units from the census.

  16. This variable is equal to the number of applications denied for non-subprime conventional loans divided by the number of applications for non-subprime conventional loans.

  17. The analysis in this study focuses only on African-Americans and whites.

  18. In addition to Bocian et al. (2006), several other studies have relied on the logit model to examine the subprime market. For example, see Lax et al. (2004); Anshasy et al. (2006); Courchane (2007); and Courchane et al. (2004).

  19. The regressions for the remaining years of the data used here are available from the authors upon request.

  20. The Credit Reinvestment Act (CRA) “directs the federal banking regulatory agencies to encourage the institutions they regulate to meet the credit needs of the entire communities, including low- and moderate- income areas, to the extent consistent with safe and sound banking practices.” Consequently, in response to CRA and other forces, “prime lenders intensified their efforts to reduce barriers to mortgage financing, through establishment or expansion of affordable programs. These programs featured flexible underwriting standards, resulting in increased credit risk exposure, and risk mitigation activities, such as credit counseling…In most cases, eligibility was restricted to low- or moderate-income borrowers, first-time homebuyers, or households purchasing a home in a low- or moderate-income neighborhood.” Some of these neighborhoods are in minority tracts. See Calem et al. (2009).

  21. We found that estimates from using 200 simulations were identical to the 4th decimal place as using 2,000 simulations.

  22. “The subprime loan securitization rate [grew] from less than 30% in 1995 to over 58% in 2003.” See Chomsisengphet and Pennington-Cross 2006), p. 37.

  23. This is predicated on the notion, as stated above, that some analysts regard the unexplained portion as an approximation of the degree of bias, while others maintain that it might reflect unmeasurable or unobserved factors.

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Acknowledgement

We thank Loretta Mester, Rick Lang, Robert Hunt, Michell Berlin, Chris Henderson and Dede Myers for thier valuable comments, John Wackes for his assistance with the maps and the research assistance of Brian Tyson. We also benefited from the comments of two anonymous reviewers. The views expressed here are those of the authors and do not necessarily represent the views of the Federal Reserve Bank of Philadephia or the Federal Reserve System.

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Correspondence to Marvin M. Smith.

Appendix

Appendix

Table 8 Definition of regression analysis and decomposition variables

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Smith, M.M., Hevener, C.C. Subprime lending over time: the role of race. J Econ Finan 38, 321–344 (2014). https://doi.org/10.1007/s12197-011-9220-9

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