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作者

Raffaella Calabrese;Meagan McCollum;R. Kelley Pace

来源

REGIONAL SCIENCE AND URBAN ECONOMICS,Vol.76,Issue1,P.103-114

语言

英文

关键字

Binary imbalanced samples;Spatial econometrics;Generalized extreme value distribution;Mortgage default decisions

作者单位

Business School, University of Edinburgh, UK;College of Business, University of Tulsa, USA;LREC Endowed Chair of Real Estate, Department of Finance, Louisiana State University, USA;Business School, University of Edinburgh, UK;College of Business, University of Tulsa, USA;LREC Endowed Chair of Real Estate, Department of Finance, Louisiana State University, USA

摘要

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.