research paper

Deep Learning for Mortgage Risk

Kay Giesecke

Founder, Chairman and Chief Scientist at Infima, Professor at Stanford University

Justin Sirignano

University of Oxford

Apaar Sadhwani

Google Health AI

This paper examines the behavior of mortgage borrowers over several economic cycles using an unprecedented dataset of origination and monthly performance records for over 120 million mortgages originated across the US between 1995 and 2014. Our deep learning model of multi-period mortgage delinquency, foreclosure, and prepayment risk uncovers the highly nonlinear influence on borrower behavior of an exceptionally broad range of loan-specific and macroeconomic variables down to the zip-code level. In particular, most variables strongly interact. Prepayments involve the greatest nonlinear effects among all events. We demonstrate the significant implications of the nonlinearities for risk management, investment management, and mortgage-backed securities.

This paper is published in the Journal of Financial Econometrics, volume 19(2), pages 313–368, 2021.

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