An analysis of risk classifications for residential mortgage loans

An analysis of risk classifications for residential mortgage loans Aims to distinguish among different levels of default risk for residential mortgage loans and to examine the significant factors for the different levels of default risk. Classifies the sample into default and non‐default groups and analyses the original mortgage loan data by factor and cluster analyses based on borrower characteristics, property characteristics and microeconomic variables in order to derive risk classifications from various likelihoods of default. Furthermore, applies logit, probit and discriminant analyses to examine the significant factors for all three clusters. The empirical results show that the three clusters may be ranked as follows, in order of risk, from the least to greatest likelihood of default: the owner‐occupied housing buyer, invester group and young buyer clusters. In addition, the factor “borrower’s education level” has negative impact for all three clusters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Property Finance Emerald Publishing

An analysis of risk classifications for residential mortgage loans

Journal of Property Finance, Volume 8 (3): 19 – Sep 1, 1997

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Publisher
Emerald Publishing
Copyright
Copyright © 1997 MCB UP Ltd. All rights reserved.
ISSN
0958-868X
DOI
10.1108/09588689710175033
Publisher site
See Article on Publisher Site

Abstract

Aims to distinguish among different levels of default risk for residential mortgage loans and to examine the significant factors for the different levels of default risk. Classifies the sample into default and non‐default groups and analyses the original mortgage loan data by factor and cluster analyses based on borrower characteristics, property characteristics and microeconomic variables in order to derive risk classifications from various likelihoods of default. Furthermore, applies logit, probit and discriminant analyses to examine the significant factors for all three clusters. The empirical results show that the three clusters may be ranked as follows, in order of risk, from the least to greatest likelihood of default: the owner‐occupied housing buyer, invester group and young buyer clusters. In addition, the factor “borrower’s education level” has negative impact for all three clusters.

Journal

Journal of Property FinanceEmerald Publishing

Published: Sep 1, 1997

Keywords: Mortgages; Multivariate analysis; Residential property management; Risk management; Taiwan

References

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