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Does credit scoring produce a disparate impact?

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  • Robert B. Avery
  • Kenneth P. Brevoort
  • Glenn B. Canner

Abstract

The widespread use of credit scoring in the underwriting and pricing of mortgage and consumer credit has raised concerns that the use of these scores may unfairly disadvantage minority populations. A specific concern has been that the independent variables that comprise these models may have a disparate impact on these demographic groups. By "disparate impact" we mean that a variable's predictive power might arise not from its ability to predict future performance within any demographic group, but rather from acting as a surrogate for group membership. Using a unique source of data that combines a nationally representative sample of credit bureau records with demographic information from the Social Security Administration and a demographic information company, we examine the extent to which credit history scores may have such a disparate impact. Our examination yields no evidence of disparate impact by race (or ethnicity) or gender. However, we do find evidence of limited disparate impact by age, in which the use of variables related to an individual's credit history appear to lower the credit scores of older individuals and increase them for the young.

Suggested Citation

  • Robert B. Avery & Kenneth P. Brevoort & Glenn B. Canner, 2010. "Does credit scoring produce a disparate impact?," Finance and Economics Discussion Series 2010-58, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2010-58
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    References listed on IDEAS

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    1. Robert M. Hunt, 2005. "A century of consumer credit reporting in America," Working Papers 05-13, Federal Reserve Bank of Philadelphia.
    2. Marsha J. Courchane, 2007. "The Pricing of Home Mortgage Loans to Minority Borrowers: How Much of the APR Differential Can We Explain?," Journal of Real Estate Research, American Real Estate Society, vol. 29(4), pages 399-440.
    3. Stephen L. Ross & John Yinger, 2002. "The Color of Credit: Mortgage Discrimination, Research Methodology, and Fair-Lending Enforcement," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262182289, March.
    4. Munnell, Alicia H. & Geoffrey M. B. Tootell & Lynn E. Browne & James McEneaney, 1996. "Mortgage Lending in Boston: Interpreting HMDA Data," American Economic Review, American Economic Association, vol. 86(1), pages 25-53, March.
    5. James A. Berkovec & Glenn B. Canner & Stuart A. Gabriel & Timothy H. Hannan, 1994. "Race, redlining, and residential mortgage loan performance," Proceedings, Federal Reserve Bank of Philadelphia, pages 263-298.
    6. Black, Harold A. & Boehm, Thomas P. & DeGennaro, Ramon P., 2003. "Is there discrimination in mortgage pricing? The case of overages," Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1139-1165, June.
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    Cited by:

    1. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    2. Patrick Bayer & Fernando Ferreira & Stephen L. Ross, 2016. "The Vulnerability of Minority Homeowners in the Housing Boom and Bust," American Economic Journal: Economic Policy, American Economic Association, vol. 8(1), pages 1-27, February.
    3. Skanderson, David & Ritter, Dubravka, 2014. "Fair lending analysis of credit cards," Payment Cards Center Discussion Paper 14-2, Federal Reserve Bank of Philadelphia.
    4. repec:bap:journl:170302 is not listed on IDEAS
    5. Fumiko Hayashi & Joanna Stavins, 2012. "Effects of credit scores on consumer payment choice," Research Working Paper RWP 12-03, Federal Reserve Bank of Kansas City.
    6. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.

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    Keywords

    Credit scoring systems ; Mortgage loans ; Discrimination in consumer credit;

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