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The Color of Credit: Mortgage Discrimination, Research Methodology, and Fair-Lending Enforcement
Abstract
An analysis of current findings on mortgage-lending discrimination and suggestions for new procedures to improve its detection. In 2000, homeownership in the United States stood at an all-time high of 67.4 percent, but the homeownership rate was more than 50 percent higher for non-Hispanic whites than for blacks or Hispanics. Homeownership is the most common method for wealth accumulation and is viewed as critical for access to the most desirable communities and most comprehensive public services. Homeownership and mortgage lending are linked, of course, as the vast majority of home purchases are made with the help of a mortgage loan. Barriers to obtaining a mortgage represent obstacles to attaining the American dream of owning one's own home. These barriers take on added urgency when they are related to race or ethnicity. In this book Stephen Ross and John Yinger discuss what has been learned about mortgage-lending discrimination in recent years. They re-analyze existing loan-approval and loan-performance data and devise new tests for detecting discrimination in contemporary mortgage markets. They provide an in-depth review of the 1996 Boston Fed Study and its critics, along with new evidence that the minority-white loan-approval disparities in the Boston data represent discrimination, not variation in underwriting standards that can be justified on business grounds. Their analysis also reveals several major weaknesses in the current fair-lending enforcement system, namely, that it entirely overlooks one of the two main types of discrimination (disparate impact), misses many cases of the other main type (disparate treatment), and insulates some discriminating lenders from investigation. Ross and Yinger devise new procedures to overcome these weaknesses and show how the procedures can also be applied to discrimination in loan-pricing and credit-scoring.
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Citations
Cited by
Racial Segregation and the American Foreclosure Crisis
Rugh, J. S., & Massey, D. S. (2010) · American Sociological Review · doi:10.1177/0003122410380868 · Cited by 686
Predictably Unequal? The Effects of Machine Learning on Credit Markets
FUSTER, A., GOLDSMITH‐PINKHAM, P., RAMADORAI, T., & WALTHER, A. (2022) · The Journal of Finance · doi:10.1111/jofi.13090 · Cited by 872
A multidisciplinary survey on discrimination analysis
Romei, A., & Ruggieri, S. (2014) · The Knowledge Engineering Review · doi:10.1017/s0269888913000039 · Cited by 288
Race, Space, and Cumulative Disadvantage: A Case Study of the Subprime Lending Collapse
Rugh, J. S., Albright, L., & Massey, D. S. (2015) · Social Problems · doi:10.1093/socpro/spv002 · Cited by 286
Housing and wealth inequality: Racial-ethnic differences in home equity in the United States
Krivo, L. J., & Kaufman, R. L. (2004) · Demography · doi:10.1353/dem.2004.0023 · Cited by 274
Smith, S. S. (2010) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102526 · Cited by 254
Handling Conditional Discrimination
liobaite, I., Kamiran, F., & Calders, T. (2011) · 2011 IEEE 11th International Conference on Data Mining · doi:10.1109/icdm.2011.72 · Cited by 125
Double Jeopardy: Why Latinos Were Hit Hardest by the US Foreclosure Crisis
Rugh, J. S. (2015) · Social Forces · doi:10.1093/sf/sou107 · Cited by 93
Metadata
- Authors
- Ross, S. L., & Yinger, J.
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2002-11-08
- OpenAlex
- W109511496
- Cited by
- 267
- References
- 0
- FWCI
- 10.65
- Type
- book
- Language
- en
- Open access
- closed
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