IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2010-58.html
   My bibliography  Save this paper

Does credit scoring produce a disparate impact?

Author

Listed:

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
    as

    Download full text from publisher

    File URL: http://www.federalreserve.gov/pubs/feds/2010/201058/201058abs.html
    Download Restriction: no

    File URL: http://www.federalreserve.gov/pubs/feds/2010/201058/201058pap.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    2. David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
    3. 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.
    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. 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.
    6. Robert M. Hunt, 2005. "A century of consumer credit reporting in America," Working Papers 05-13, Federal Reserve Bank of Philadelphia.
    7. 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, April.
    8. Berkovec, James A & Canner, Glenn B. & Gabriel, Stuart A. & Hannan, Timothy H., 1994. "Race, Redlining, and Residential Mortgage Loan Performance," The Journal of Real Estate Finance and Economics, Springer, vol. 9(3), pages 263-294, November.
    9. Mitchell Stengel & Dennis Glennon, 1999. "Evaluating Statistical Models of Mortgage Lending Discrimination: A Bank‐Specific Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 27(2), pages 299-334, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Dubravka Ritter & David Skanderson, 2014. "Fair lending analysis of credit cards," Consumer Finance Institute discussion papers 14-2, Federal Reserve Bank of Philadelphia.
    4. 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.
    5. 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.
    6. 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.
    7. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    8. Liming Brotcke, 2022. "Time to Assess Bias in Machine Learning Models for Credit Decisions," JRFM, MDPI, vol. 15(4), pages 1-10, April.
    9. 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.
    10. Vitaly Meursault & Daniel Moulton & Larry Santucci & Nathan Schor, 2022. "One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas," Working Papers 22-39, Federal Reserve Bank of Philadelphia.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Ballance, Joshua & Clifford, Robert & Shoag, Daniel, 2020. "“No more credit score”: Employer credit check bans and signal substitution," Labour Economics, Elsevier, vol. 63(C).
    16. Ha Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," Working Papers hal-04141336, HAL.
    17. Laura Blattner & Scott Nelson, 2021. "How Costly is Noise? Data and Disparities in Consumer Credit," Papers 2105.07554, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan Zhang, 2013. "Fair Lending Analysis of Mortgage Pricing: Does Underwriting Matter?," The Journal of Real Estate Finance and Economics, Springer, vol. 46(1), pages 131-151, January.
    2. Patrick Bayer & Fernando Ferreira & Stephen L. Ross, 2018. "What Drives Racial and Ethnic Differences in High-Cost Mortgages? The Role of High-Risk Lenders," The Review of Financial Studies, Society for Financial Studies, vol. 31(1), pages 175-205.
    3. Song Han, 2011. "Creditor Learning and Discrimination in Lending," Journal of Financial Services Research, Springer;Western Finance Association, vol. 40(1), pages 1-27, October.
    4. James B. Kau & Lu Fang & Henry J. Munneke, 2019. "An Unintended Consequence of Mortgage Financing Regulation – a Racial Disparity," The Journal of Real Estate Finance and Economics, Springer, vol. 59(4), pages 549-588, November.
    5. Patrick Bayer & Fernando Ferreira & Stephen L. Ross, 2014. "Race, Ethnicity and High-Cost Mortgage Lending," NBER Working Papers 20762, National Bureau of Economic Research, Inc.
    6. Song Han, 2004. "Discrimination in Lending: Theory and Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 29(1), pages 5-46, July.
    7. Stephen L. Ross, 2003. "What Is Known about Testing for Discrimination: Lessons Learned by Comparing across Different Markets," Working papers 2003-21, University of Connecticut, Department of Economics, revised Nov 2003.
    8. Manthos D. Delis & Panagiotis Papadopoulos, 2019. "Mortgage Lending Discrimination Across the U.S.: New Methodology and New Evidence," Journal of Financial Services Research, Springer;Western Finance Association, vol. 56(3), pages 341-368, December.
    9. Chan, Sewin & Haughwout, Andrew & Tracy, Joseph, 2015. "How Mortgage Finance Affects the Urban Landscape," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 987-1045, Elsevier.
    10. Judith Clarke & Nilanjana Roy & Marsha Courchane, 2009. "On the robustness of racial discrimination findings in mortgage lending studies," Applied Economics, Taylor & Francis Journals, vol. 41(18), pages 2279-2297.
    11. Yan Zhang, 2018. "Assessing Fair Lending Risks Using Race/Ethnicity Proxies," Management Science, INFORMS, vol. 64(1), pages 178-197, January.
    12. Ghent, Andra C. & Hernández-Murillo, Rubén & Owyang, Michael T., 2014. "Differences in subprime loan pricing across races and neighborhoods," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 199-215.
    13. repec:max:cprpbr:24 is not listed on IDEAS
    14. Diaz-Serrano, Luis & Raya, Josep M., 2011. "Is there Discriminatory Mortgage Pricing against Immigrants in the Spanish Lending Market?," IZA Discussion Papers 5578, Institute of Labor Economics (IZA).
    15. Nolan Kopkin, 2018. "The conditional spatial correlations between racial prejudice and racial disparities in the market for home loans," Urban Studies, Urban Studies Journal Limited, vol. 55(16), pages 3596-3614, December.
    16. Ping Cheng & Zhenguo Lin & Yingchun Liu, 2011. "Do Women Pay More for Mortgages?," The Journal of Real Estate Finance and Economics, Springer, vol. 43(4), pages 423-440, November.
    17. Randall Campbell & Brandon Roberts & Kevin Rogers, 2008. "An Evaluation of Lender Redlining in the Allocation of Unsecured Consumer Credit in the US," Urban Studies, Urban Studies Journal Limited, vol. 45(5-6), pages 1243-1254, May.
    18. Marsha J. Courchane & Stephen L. Ross, 2019. "Evidence and Actions on Mortgage Market Disparities: Research, Fair Lending Enforcement, and Consumer Protection," Housing Policy Debate, Taylor & Francis Journals, vol. 29(5), pages 769-794, September.
    19. Díaz Serrano, Lluís & Raya, Josep Maria, 2011. "Is there Descriminatory Mortgage Pricing against Immigrants in the Spanish Lending Market?," Working Papers 2072/151811, Universitat Rovira i Virgili, Department of Economics.
    20. Blanchard, Lloyd & Zhao, Bo & Yinger, John, 2008. "Do lenders discriminate against minority and woman entrepreneurs?," Journal of Urban Economics, Elsevier, vol. 63(2), pages 467-497, March.
    21. Su, Qing, 2015. "Determinants of mortgage pricing: A quantile regression analysisAuthor-Name: Al-Bahrani, Abdullah," Journal of Housing Economics, Elsevier, vol. 30(C), pages 77-85.

    More about this item

    Keywords

    Credit scoring systems; Mortgage loans; Discrimination in consumer credit;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedgfe:2010-58. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ryan Wolfslayer ; Keisha Fournillier (email available below). General contact details of provider: https://edirc.repec.org/data/frbgvus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.