Mortgage default decisions in the presence of non-normal, spatially dependent disturbances

We develop a flexible binary choice model for mortgage default decisions that incorporates neighborhood effects in the disturbances. The main advantage of the model lies in its performance in providing improved estimates of the probability of default for risky mortgage loans. In addition, it can be applied to portfolios with a high number of loans. Assuming mortgage decisions with spatially dependent disturbances, the proposed approach uses the generalized extreme value distribution to flexibly model the error terms. To estimate the model on a large sample size, we use a variant of the Geweke-Hajivassiliou-Keane algorithm. We apply the proposed model and its competitors to a large dataset on almost 300,000 mortgages in Clark County, which includes Las Vegas, over 2009–2010. The results show that our proposal greatly improves the predictive accuracy of identifying loans that will default. Moreover, the competitor models underestimate credit Value at Risk.