Abstract
We use a large data set of over 12 million residential mortgages observed over time to investigate the loan default behavior in several European countries. We model the occurrence of default as a function of borrower characteristics, loan-specific variables, and a set of local economic conditions. Given the high geographical heterogeneity in default and its drivers, we carry out the analysis at the regional level. We adopt boosting algorithms from the machine learning literature and compare their performance relative to the logistic regression. With respect to the logistic benchmark, boosting models perform significantly better in providing predictions. The most important variables in explaining loan default are the interest rate currently applied to the mortgage and the local economic characteristics, while other loan- or borrower-specific features are less relevant. Our results indicate the existence of relevant geographical heterogeneity in the importance of the variables, pointing at the need for regionally tailored risk assessment and policies in Europe.
Highlights
LiteratureThere is a substantial empirical literature on the determinants of mortgage delinquency and default, mainly focused on the United States
Our results indicate that the current interest rate and LTV have a significant impact on default occurrence and that they are highly nonlinear, explaining the better performance of boosting models related to the logistic regression
A better understanding of the default drivers is of primary importance for policymakers in order to reduce the societal costs associated with a default and avoid the inefficient allocation of resources
Summary
There is a substantial empirical literature on the determinants of mortgage delinquency and default, mainly focused on the United States. Bajari, Chu, and Park (2008) study the relative importance of the various drivers behind subprime borrowers decision to default. They point at the role of the nationwide decrease in home prices and increase in borrowers with high payment to income ratios as main drivers of default. Campbell and Cocco (2015) propose a model of mortgage default for the United States in the presence of labor income, house price, inflation, and interest rate risk to show how different shocks contribute to the default decision. One important result from this study is that negative home equity tends to occur when house prices decline in a low-inflation environment and for moderate levels of negative home equity, default is more likely as borrowing constraints bind more tightly on households. Babii, Chen, and Ghysels (2019) analyze the spatial dependence among commercial and residential default using Generalized Autoregressive Score(GAS) models
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