Deep Learning for Mortgage Risk

Apaar Sadhwani, Kay Giesecke, Justin Sirignano

Research output: Contribution to journalArticlepeer-review


We examine 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 United States 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.

Original languageEnglish (US)
Pages (from-to)313-368
Number of pages56
JournalJournal of Financial Econometrics
Issue number2
StatePublished - 2021

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


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