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Keywords:Credit score 

Working Paper
How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions

We assess racial discrimination in mortgage approvals using new data on mortgage applications. Minority applicants tend to have significantly lower credit scores, higher leverage, and are less likely than white applicants to receive algorithmic approval from race-blind government automated underwriting systems (AUS). Observable applicant- risk factors explain most of the racial disparities in lender denials. Further, we exploit the AUS data to show there are risk factors we do not directly observe, and our analysis indicates that these factors explain at least some of the residual 1-2 ...
Finance and Economics Discussion Series , Paper 2022-067

Working Paper
Credit Score Doctors

We study how the existence of cutoffs in credit scores affects the behavior of homebuyers. Borrowers are more likely to purchase houses after their credit scores cross over a cutoff to qualify them for a higher credit score bin. However, the credit accounts of these individuals (crossover group) are more likely to become delinquent within four years following home purchases than the accounts of those who had stayed in the same bin (non-crossover group). The effect is not only concentrated in subprime bins, but in other bins as well. It is neither limited to pre-crisis period nor curtailed by ...
Working Paper Series , Paper WP-2020-07

Working Paper
The Unintended Consequences of Employer Credit Check Bans for Labor Markets

Over the last 15 years, 11 states have restricted employers’ access to the credit reports of job applicants. We estimate that county-level job vacancies have fallen by 5.5 percent in occupations affected by these laws relative to exempt occupations in the same counties and national-level vacancies for the same occupations. Cross-sectional heterogeneity suggests that employers use credit reports as signals of a worker’s ability to perform the job: vacancies fall more in counties with a large share of subprime residents, while they fall less for occupations with other commonly available ...
Research Working Paper , Paper RWP 20-04

Working Paper
How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions

We assess racial discrimination in mortgage approvals using new data on mortgage applications. Minority applicants tend to have significantly lower credit scores, higher leverage, and are less likely than white applicants to receive algorithmic approval from race-blind government automated underwriting systems (AUS). Observable applicant- risk factors explain most of the racial disparities in lender denials. Further, we exploit the AUS data to show there are risk factors we do not directly observe, and our analysis indicates that these factors explain at least some of the residual 1-2 ...
Finance and Economics Discussion Series , Paper 2022-067

Working Paper
How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions

We assess racial discrimination in mortgage approvals using new data on mortgage applications. Minority applicants tend to have significantly lower credit scores, higher leverage, and are less likely than white applicants to receive algorithmic approval from race-blind government automated underwriting systems (AUS). Observable applicant- risk factors explain most of the racial disparities in lender denials. Further, we exploit the AUS data to show there are risk factors we do not directly observe, and our analysis indicates that these factors explain at least some of the residual 1-2 ...
Finance and Economics Discussion Series , Paper 2022-067

Working Paper
How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions

We assess racial discrimination in mortgage approvals using new data on mortgage applications. Minority applicants tend to have significantly lower credit scores, higher leverage, and are less likely than white applicants to receive algorithmic approval from race-blind government automated underwriting systems (AUS). Observable applicant- risk factors explain most of the racial disparities in lender denials. Further, we exploit the AUS data to show there are risk factors we do not directly observe, and our analysis indicates that these factors explain at least some of the residual 1-2 ...
Finance and Economics Discussion Series , Paper 2022-067

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