Using High-Frequency Evaluations to Estimate Discrimination: Evidence from Mortgage Loan Officers

Giacoletti, Marco; Heimer, Rawley Z.; Yu, Edison

doi:10.21799/frbp.wp.2021.04

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…The large gap of 7 to 8 days in processing time for Black borrowers in the GSE segment may result from the observation that a larger portion of Black applicants do not get the approval from the GSEs' automated underwriting systems and need to go through manual underwriting with additional analysis before obtaining final approval of their loan applications. For instance, Giacoletti et al (2020) show that Black applicants are recommended for approval approximately 6 percentage points less frequently in the post-2018 HMDA data containing the recommendations of the automated underwriting systems. The GSEs' procedural underwriting process may be less flexible to speed up the timeline, which can explain the persistent disparities in processing time.…”

Section: Disparities Across Mortgage Purchaser Typesmentioning

confidence: 99%

“…Munnell et al (1996) reach a similar conclusion after controlling for borrower characteristics collected from loan applications in Boston in 1990, which are unavailable in the HMDA data. Giacoletti et al (2020) argue that at least half of the observed approval gap for Black borrowers is attributable to within-month variation in loan officers' subjectivity. Conditional on the sample of originated mortgages, studies have shown that minority borrowers have to bear a higher cost (Courchane and Nickerson, 1997;Black et al, 2003;Ghent et al, 2014;Cheng et al, 2015;Reid et al, 2017;Bayer et al, 2018;Delis and Papadopoulos, 2018;Ambrose et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Racial Disparities in Mortgage Lending: New Evidence Based on Processing Time

Bin

Zhao²

2022

The Review of Corporate Finance Studies

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This paper examines racial disparities in mortgage processing time prior to the global financial crisis. We find that Black borrowers are underrepresented and experience a longer processing time than white borrowers among the mortgages securitized by government-sponsored enterprises (GSEs). At the same time, Black borrower are overrepresented and face a similar processing time among privately securitized (PLS) mortgages. Additionally, Black borrowers are strongly associated with faster segments of the mortgage markets, faster lenders within each segment, and the types of loan products that are processed faster, all of which subsequently experienced higher defaults.

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Section: Disparities Across Mortgage Purchaser Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Racial Disparities in Mortgage Lending: New Evidence Based on Processing Time

Bin

Zhao²

2022

The Review of Corporate Finance Studies

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“…and Fang (2006), Charles and Guryan (2008), Price and Wolfers (2010)), including racial disparities in access to financial services (see, for example, Tootell (1996), Bayer, Ferreira, and Ross (2018), Bhutta and Hizmo (2021), Ambrose, Conklin, and Lopez (2020), Giacoletti, Heimer, and Yu (2021), Begley and Purnanandam (2021), ). Most directly relevant is work on the role of race in small business lending (Blanchflower, Levine, and Zimmerman (2003), Robb and Robinson (2018), Fairlie and Robb (2007), Asiedu, Freeman, and Nti-Addae (2012), Bellucci, Borisov, and Zazzaro (2013), Fairlie, Robb, and Robinson (2020)).…”

mentioning

confidence: 99%

Lender Automation and Racial Disparities in Credit Access

HOWELL,

KUCHLER,

SNITKOF

et al. 2024

The Journal of Finance

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Process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision‐making. We document these findings amid the Paycheck Protection Program (PPP), where private lenders faced no credit risk but decided which firms to serve. Black‐owned firms obtained PPP loans primarily from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black‐owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, pre‐existing banking relationships, firm performance, or fraud rates.This article is protected by copyright. All rights reserved

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Algorithmic discrimination in the credit domain: what do we know about it?

2023

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The widespread usage of machine learning systems and econometric methods in the credit domain has transformed the decision-making process for evaluating loan applications. Automated analysis of credit applications diminishes the subjectivity of the decision-making process. On the other hand, since machine learning is based on past decisions recorded in the financial institutions’ datasets, the process very often consolidates existing bias and prejudice against groups defined by race, sex, sexual orientation, and other attributes. Therefore, the interest in identifying, preventing, and mitigating algorithmic discrimination has grown exponentially in many areas, such as Computer Science, Economics, Law, and Social Science. We conducted a comprehensive systematic literature review to understand (1) the research settings, including the discrimination theory foundation, the legal framework, and the applicable fairness metric; (2) the addressed issues and solutions; and (3) the open challenges for potential future research. We explored five sources: ACM Digital Library, Google Scholar, IEEE Digital Library, Springer Link, and Scopus. Following inclusion and exclusion criteria, we selected 78 papers written in English and published between 2017 and 2022. According to the meta-analysis of this literature survey, algorithmic discrimination has been addressed mainly by looking at the CS, Law, and Economics perspectives. There has been great interest in this topic in the financial area, especially the discrimination in providing access to the mortgage market and differential treatment (different fees, number of parcels, and interest rates). Most attention has been devoted to the potential discrimination due to bias in the dataset. Researchers are still only dealing with direct discrimination, addressed by algorithmic fairness, while indirect discrimination (structural discrimination) has not received the same attention.

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Using High-Frequency Evaluations to Estimate Discrimination: Evidence from Mortgage Loan Officers

Cited by 9 publications

References 44 publications

Racial Disparities in Mortgage Lending: New Evidence Based on Processing Time

Racial Disparities in Mortgage Lending: New Evidence Based on Processing Time

Lender Automation and Racial Disparities in Credit Access

Algorithmic discrimination in the credit domain: what do we know about it?

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