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An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market

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  • Fitzpatrick, Trevor
  • Mues, Christophe

Abstract

This paper evaluates the performance of a number of modelling approaches for future mortgage default status. Boosted regression trees, random forests, penalised linear and semi-parametric logistic regression models are applied to four portfolios of over 300,000 Irish owner-occupier mortgages. The main findings are that the selected approaches have varying degrees of predictive power and that boosted regression trees significantly outperform logistic regression. This suggests that boosted regression trees can be a useful addition to the current toolkit for mortgage credit risk assessment by banks and regulators.

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  • Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    • Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:427-439
    DOI: 10.1016/j.ejor.2015.09.014
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