Imputing Borrower Heterogeneity and Dynamics in Mortgage Default Models

The determinants of mortgage default have been an area of rising interest since the 2008 recession. There are two distinguishing features of mortgage default analysis. First, predictor variables are often only recorded at origination. However, many important variables such as credit scores vary over time. Second, there are omitted variables (such as borrower’s income and job security). If omitted variables are correlated with included regressors or if only origination values are used in a dynamic model, then biases may be present in econometric models for default risk. Our focus is to develop a ridge regression model to impute the dynamics of time-varying predictors and to capture unobserved borrower heterogeneity. The model is evaluated using cross-validation, and the relevant parameters are tuned to maximize out-of-sample predictive performance. After allowing for imputed dynamics and borrower heterogeneity, we find that the loan-to-value ratio becomes a larger signal of default risk and that credit scores as well as full documentation become smaller signals of default risk. These changes primarily are driven by imputing static variables, rather than dynamics, and may pertain to either omitted liquidity factors or strategic factors.