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Does Credit Scoring Produce a Disparate Impact?

Author

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  • Robert B. Avery
  • Kenneth P. Brevoort
  • Glenn 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.
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Suggested Citation

  • Robert B. Avery & Kenneth P. Brevoort & Glenn Canner, 2012. "Does Credit Scoring Produce a Disparate Impact?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 40, pages 65-114, December.
    • Handle: RePEc:bla:reesec:v:40:y:2012:i::p:s65-s114
    DOI: j.1540-6229.2012.00348.x
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    Cited by:

    1. Liu, Zilong & Liang, Hongyan, 2025. "Are credit scores gender-neutral? Evidence of mis-calibration from alternative and traditional borrowing data," Journal of Behavioral and Experimental Finance, Elsevier, vol. 47(C).
    2. Schwarting, Rena & Ulbricht, Lena, 2022. "Why Organization Matters in “Algorithmic Discrimination” [Warum Organisationen einen Unterschied bei „algorithmischer Diskriminierung“ machen]," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 74(S1), pages 307-330.
    3. Ryan M. Goodstein & Alicia Lloro & Sherrie L.W. Rhine & Jeffrey M. Weinstein, 2021. "What accounts for racial and ethnic differences in credit use?," Journal of Consumer Affairs, Wiley Blackwell, vol. 55(2), pages 389-416, June.
    4. David Nickerson & Robert Jones, 2017. "Collateral Risk and Demographic Discrimination in Mortgage Market Equilibria," Review of Economics & Finance, Better Advances Press, Canada, vol. 9, pages 13-28, August.
    5. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    6. Fumiko Hayashi & Joanna Stavins, 2012. "Effects of credit scores on consumer payment choice," Public Policy Discussion Paper 12-1, Federal Reserve Bank of Boston.
    7. Liming Brotcke, 2022. "Time to Assess Bias in Machine Learning Models for Credit Decisions," JRFM, MDPI, vol. 15(4), pages 1-10, April.
    8. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    9. 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.
    10. Laura Blattner & Scott Nelson, 2021. "How Costly is Noise? Data and Disparities in Consumer Credit," Papers 2105.07554, arXiv.org.
    11. Vitaly Meursault & Daniel Moulton & Larry Santucci & Nathan Schor, 2025. "One threshold doesn't fit all: Tailoring machine learning predictions of consumer default for lower‐income areas," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 44(3), pages 792-815, June.
    12. Zilong Liu & Hongyan Liang, 2025. "Racial Disparities in Conforming Mortgage Lending: A Comparative Study of Fintech and Traditional Lenders Under Regulatory Oversight," FinTech, MDPI, vol. 4(1), pages 1-23, February.
    13. David Gaddis Ross & Dong Hyun Shin, 2024. "Do financial market frictions hurt the performance of women‐led ventures? A meta‐analytic investigation," Strategic Management Journal, Wiley Blackwell, vol. 45(3), pages 507-534, March.
    14. Robert Clifford & Daniel Shoag, 2016. "“No more credit score”: employer credit check bans and signal substitution," Working Papers 16-10, Federal Reserve Bank of Boston.
    15. 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.
    16. Ballance, Joshua & Clifford, Robert & Shoag, Daniel, 2020. "“No more credit score”: Employer credit check bans and signal substitution," Labour Economics, Elsevier, vol. 63(C).
    17. Dubravka Ritter & David Skanderson, 2014. "Fair lending analysis of credit cards," Consumer Finance Institute discussion papers 14-2, Federal Reserve Bank of Philadelphia.
    18. Ulbricht, Lena, 2020. "Algorithmen und Politisierung [Algorithms and politicization]," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 0, pages 255-278.
    19. 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.
    20. Ha Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," Working Papers hal-04141336, HAL.

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