Algorithmic Underwriting in High Risk Mortgage Markets

80 Pages Posted: 10 Nov 2023 Last revised: 26 Dec 2023

See all articles by Janet Gao

Janet Gao

McDonough School of Business

Hanyi (Livia) Yi

Boston College - Carroll School of Management

David Zhang

Rice University - Jesse H. Jones Graduate School of Business

Date Written: October 14, 2023

Abstract

We study the effects of a policy that shifted from pure human underwriting to human-augmented algorithmic underwriting for low-credit-score, high-leverage mortgage borrowers. Estimating the bunching of loans around the policy's debt-to-income threshold, we find a large credit expansion to affected borrowers with little changes in default risks or interest rates among the affected group. Such effects are more pronounced among non-Hispanic White borrowers and higher-income borrowers. Consequently, low-credit-score households are more likely to move to better school districts. We use a structural approach to quantify the welfare implications of the policy change and isolate the credit supply channel. Overall, our results suggest that automated underwriting systems (AUS) can help increase financial inclusion while controlling risk. However, it can also generate disparate impact across racial groups and along the income distribution.

Keywords: Algorithmic Underwriting, FinTech, Household Leverage, Racial Inequality in Mortgage Markets, Mobility, Financial Inclusion.

JEL Classification: G18, G21, G51, O33

Suggested Citation

Gao, Janet and Yi, Hanyi and Zhang, David, Algorithmic Underwriting in High Risk Mortgage Markets (October 14, 2023). Available at SSRN: https://ssrn.com/abstract=4602411 or http://dx.doi.org/10.2139/ssrn.4602411

Janet Gao (Contact Author)

McDonough School of Business ( email )

Washington, DC 20057
United States

Hanyi Yi

Boston College - Carroll School of Management ( email )

140 Commonwealth Avenue
Chestnut Hill, MA 02467
United States

David Zhang

Rice University - Jesse H. Jones Graduate School of Business ( email )

6100 South Main Street
P.O. Box 1892
Houston, TX 77005-1892
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
159
Abstract Views
572
Rank
337,277
PlumX Metrics