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

Trevor Fitzpatrick and Christophe Mues

European Journal of Operational Research, 2016, vol. 249, issue 2, 427-439

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.

Keywords: Boosting; Random forests; Semi-parametric models; Mortgages; Credit scoring (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:249:y:2016:i:2:p:427-439

DOI: 10.1016/j.ejor.2015.09.014

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