An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market
An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market
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.
boosting, random forests, semi-parametric models, mortgages, credit scoring
427-439
Fitzpatrick, Trevor
b3d78774-8c4d-4f7d-875c-8483843da9ef
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
1 March 2016
Fitzpatrick, Trevor
b3d78774-8c4d-4f7d-875c-8483843da9ef
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
Fitzpatrick, Trevor and Mues, Christophe
(2016)
An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market.
European Journal of Operational Research, 249 (2), .
(doi:10.1016/j.ejor.2015.09.014).
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.
Other
1-s2.0-S0377221715008383-main.pdf__tid=b7f07864-6b55-11e5-9255-00000aab0f26&acdnat=1444045340_74ea767bc4f6f8d9a9423587019171e3
- Accepted Manuscript
More information
Accepted/In Press date: 8 September 2015
e-pub ahead of print date: 16 September 2015
Published date: 1 March 2016
Keywords:
boosting, random forests, semi-parametric models, mortgages, credit scoring
Organisations:
Centre of Excellence in Decision, Analytics & Risk Research
Identifiers
Local EPrints ID: 381624
URI: http://eprints.soton.ac.uk/id/eprint/381624
ISSN: 0377-2217
PURE UUID: 2e652152-8a14-4600-8fda-ab1bfd4291a2
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Date deposited: 05 Oct 2015 11:41
Last modified: 15 Mar 2024 05:21
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Author:
Trevor Fitzpatrick
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