Abstract
The purpose of this paper is to reduce the default rate of personal housing loan and accurately predict whether or not the borrower defaults. Based on the data of individual housing loan, this paper employs a proximal support vector machine (PSVM) to explore the credit risk factors. Then the paper constructed the credit risk assessment system of individual housing loan. The data of individual housing loan was from China Construction Bank of Shaanxi branch in Xi’an market. The empirical results not only show that PSVM can accurately predict credit risk assessment of personal housing loan, but also can quickly and accurately judge whether or not the borrower break a contract.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lambrecht, B., Perraudin, W., Satchell, S.: Time to Default in the UK Mortgage Market. Economic Modelling 10, 485–499 (1997)
Morton, T.G.: A Discriminant Function Analysis of Residential Mortgage Delinquency and Foreclosure. Areuea. 3, 73–90 (1975)
George, W., Gau, A.: Taxonomic Model for the Risk-Rating of Residential Mortgages. Business 51(4), 687–706 (1978)
Kau, K.: An Overview of the Option-Theoretic Pricing of Mortgages. Housing Research 6(2), 217–244 (1995)
Quercia, S.: Residential Mortgage Default: A Review of the Literature. Housing Research 3(2), 341–379 (1992)
Wang, f., Jia: An Empirical Study of the Factors Influencing Residential Mortgage Defaults: The Case of Hangzhou. China Economic Quarterly 4(3), 739–752 (2005)
Wang, x.: Research on Risk Management of Commercial Bank of Family House Mortgage Loan Based on Logistic regression. Finance and Economy 12, 109–110 (2008)
Tian, k.: The Risk Management of Our Commercial Bank’s Personal Housing Mortgage. Technology Information 27, 418–420 (2008)
Fung, G., Mangasarian, O.L.: Proximal Support Vector Machine Classifiers. KDD, San Francisco (2001)
Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hou, J., Xue, Q. (2011). The Empirical Study of Individual Housing Loan Credit Risk Based on Proximal Support Vector Machine. In: Zhou, Q. (eds) Applied Economics, Business and Development. ISAEBD 2011. Communications in Computer and Information Science, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23023-3_74
Download citation
DOI: https://doi.org/10.1007/978-3-642-23023-3_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23022-6
Online ISBN: 978-3-642-23023-3
eBook Packages: Computer ScienceComputer Science (R0)