Before revision:  The basic building block is a deep neural network which addresses the nonlinear relationship between the explanatory variables and loan performance. Our likelihood estimators, which are based on 3.5 billion borrower-month observations, indicate that mortgage risk is strongly influenced by local economic factors such as zip-code level foreclosure rates. The out-of-sample predictive performance of our deep learning model is a significant improvement over linear models such as logistic regression. Model parameters are estimated using GPU parallel computing due to the computational challenges associated with the large amount of data. The deep learning model's superior accuracy compared to linear models directly translates into improved performance for investors. Portfolios constructed with the deep learning model have lower prepayment and delinquency rates than portfolios chosen with a logistic regression .
After revision:  The basic building block is a deep neural network which addresses the nonlinear relationship between the variables and borrower behavior, especially prepayment. They also highlight the effects of local economic conditions on borrower behavior. State unemployment has the greatest explanatory power among all variables, offering strong evidence of the tight connection between housing finance markets and the macroeconomy. The sensitivity of a borrower to changes in unemployment strongly depends upon current unemployment. It also significantly varies across the entire borrower population, which highlights the interaction of unemployment and many other variables. These findings have important implications for mortgage-backed security investors, rating agencies, and housing finance policymakers .