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Deep Learning for Mortgage Risk

75 Pages Posted: 23 Jun 2016 Last revised: 5 Oct 2017

Justin Sirignano

Imperial College London - Department of Mathematics; University of Illinois at Urbana-Champaign

Apaar Sadhwani

Stanford University

Kay Giesecke

Stanford University - Management Science & Engineering

Date Written: October 4, 2017

Abstract

We develop a deep learning model of multi-period mortgage risk and use it to analyze an unprecedented dataset of origination and monthly performance records for over 120 million mortgages originated across the US between 1995 and 2014. Our nonparametric estimators of term structures of conditional probabilities of prepayment, foreclosure and various states of delinquency incorporate the dynamics of a large number of loan-specific as well as economic and demographic variables at national, state, county and zip-code levels. The estimators highlight the importance for mortgage risk of local economic conditions, in particular state unemployment. The behavior of mortgage risk can vary strongly depending upon the geographic region. Moreover, the relationship between factors and mortgage risk is often highly nonlinear. Higher-order interactions between multiple factors are prevalent. By incorporating these nonlinear effects, our deep learning estimators offer superior out-of-sample predictions of multi-period mortgage risk at loan- and pool-levels and enable the selection of performing mortgage investment portfolios.

Keywords: Deep Learning, Machine Learning, Mortgages, Loans, Credit Risk

Suggested Citation

Sirignano, Justin and Sadhwani, Apaar and Giesecke, Kay, Deep Learning for Mortgage Risk (October 4, 2017). Available at SSRN: https://ssrn.com/abstract=2799443

Justin Sirignano (Contact Author)

Imperial College London - Department of Mathematics ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

HOME PAGE: http://jasirign.github.io

University of Illinois at Urbana-Champaign ( email )

601 E John St
Champaign, IL 61820
United States

Apaar Sadhwani

Stanford University ( email )

Stanford, CA 94305
United States

Kay Giesecke

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States
(650) 723 9265 (Phone)
(650) 723 1614 (Fax)

HOME PAGE: http://www.stanford.edu/~giesecke/

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