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'On the Take': The Black Box of Credit Scoring and Mortgage Discrimination
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Subprime credit, a relatively new method of risk-based pricing, has been hailed as a way to open up markets and provide access to credit to those who would otherwise be excluded. Evidence suggests that subprime mortgage segmentation increases rather than reduces exclusionary practices in lending. Furthermore, what is unclear is how lenders determine who qualifies as a subprime borrower. This concern became manifested when studies demonstrated that minority borrowers, regardless of creditworthiness, are more likely to receive expensive, sub-prime loans. The disparity is properly attributed to lenders’ credit pricing policies which included discretionary increases despite the objectively-determined risk-based interest rate as well as minority borrowers’ search costs and asymmetric information about prices in this market. This Article reviews the theory and history of credit scoring in mortgage lending and argues that lenders’ practices arguably justified by "legitimate business need" as well as lenders’ credit scoring models exacerbate lending disparities. It argues that the failure of competitive forces to disallow these unjustified and illegal increases also speaks to regulatory failure. It proposes that lenders operating in noncompetitive subprime mortgage markets address how their practices interact with lending disparities even apart from their complicity in creating conditions that worsen economic disadvantage.
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“Comparable problems can be found in the literature on credit scoring, where the place of residence can have a negative effect on creditworthiness and thus discriminate against people in selected regions (Andreeva et al., 2004; Fernandes and Artes, 2016; Havard, 2010; Marron, 2007).”
From Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems (2024) · cites this paper as Havard, 2010
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Research works printed but not linked: 42
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- Similarly, researchers also presume that any deviations from assigned weights are random, instead of indicating bias
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- Ross and Yinger are highly critical of the current enforcement regime and posit that it not only fails to deter unfair lending, but can easily result in an unfair enforcement system. See Ross & Yinger, supra note 215
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- The estimating equation for disparate impact when the variable is known is Yij = I,jf3j*Xij + I,j(f3j -(3j*)Xij + yMi + See Ross & Yinger, supra note 2IS
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- 2010, The dynamics of credit reporting are discretionary. What is most beneficial to the average consumer is to have comprehensive credit re� porting. Comprehensive credit files contain information across all sectors. These are compared to segmented files, which are restricted to a single sector in which the con� sumer has a credit line. Finally, there are privately-owned credit bureaus owned by more detail below, the variables and weights may disadvantage certain types of borrowers. See, e.g" Finkelstein, supra note 260, at 36, 263 See generally STAFF OF II, The best structures are those which are comprehensive, full-file, rather than segmented, reporting from reporting bureaus
- The probability of mistaken characterizations of borrowers, e.g., a low-risk borrower mistaken as a high-risk borrower, occurs when insufficient infonnation is reported, Computerized Credit Scoring's Effect on the Lending Industry
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- & Ross, 277-286; see generally Mark Schreiner, Benefits and Pitfalls of Statistical Credit Scoring for Microflnance, 28 SAVINGS AND DEVELOPMENT 63 (discussing personal knowledge as a basis for making loan decisions in poor countries and how credit scoring can improve risk prediction), 28
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Cited by
Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems
Neifer, T., Bossauer, P., Pakusch, C., Boehm, L., & Lawo, D. (2024) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-022-09461-4 · Cited by 6
“Comparable problems can be found in the literature on credit scoring, where the place of residence can have a negative effect on cred-itworthiness and thus discriminate against people in selected regions (Andreeva et al., 2004; Fernandes and Artes, 2016; Havard, 2010; Marron, 2007).”
Metadata
- Authors
- Havard, C.
- Affiliations
- University of Baltimore (US)
- Corresponding author
- Havard, Cassandra
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2010
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W1806306520
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2020: 1 · 2018: 1 · 2017: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- University of Baltimore Law Forum (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Housing Market and Economics
- Social Sciences › Sociology and Political Science › Urban, Neighborhood, and Segregation Studies
- Social Sciences › Economics, Econometrics and Finance › Finance › Banking stability, regulation, efficiency
- Keywords
- subprime lending, credit scoring, mortgage discrimination, racial discrimination in lending, risk-based pricing, minority borrowers, search costs, information asymmetry, credit scoring models, regulatory failure, access to credit
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