Covariate Selection for Mortgage Default Analysis Using Survival Models

Abstract

The mortgage sector plays a pivotal role in the financial services industry, and the U.S. economy in general, with the Federal Reserve, St. Louis, reporting Households and Nonprofit Organizations for One-to-Four-Family Residential Mortgages Liability Level at $10.8T in Q3 2020. It has been in the interest of banks to know which factors are the most influential predicting mortgage default, and the implementation of survival models can utilize data from defaulted obligors as well as non-default obligors who are still making payments as of the sampling period cutoff date. Besides the Cox proportional hazard model and the accelerated failure time model, this paper investigates two machine learning-based models, a random survival forest model, and a Cox proportional hazard neural network model DeepSurv. We compare the accuracy of covariate selection for the Cox model, AFT model, random survival forest model, and DeepSurv model, and this investigation is the first research using machine learning based survival models for mortgage default prediction. The result shows that Random survival forest can achieve the most accurate, and stable, covariate selection, while DeepSurv can achieve the highest accuracy of default prediction, and finally, the covariates selected by the models can be meaningful for mortgage programs throughout the banking industry.