Assessment on Credit Risk of Real Estate Based on Logistic Regression Model

DOI

10.2991/emeit.2012.75How to use a DOI?

Keywords

Real estate credit, Logistic regression model, Loan risk, Assessment

Abstract

Real estate industry, whose volatility will bring about the fluctuations of other related industries, is a basic industry highly associated with the national economy. With the development of real estate market in recent years, the non-rational growth of real estate credit of the commercial banks in China has increased the risk of real estate credit. The paper assesses the credit risk of listed real estate companies based on logistic regression model. The results show that on the whole the logistic regression model can predict accurately. The results of principal component analysis suggest that a company’s capability to make profits is an important basis for the evaluation of credit risk. In addition, the assessment results of logistic regression model demonstrates that the prediction is asymmetric because the model makes a high predictive accuracy for credit non-default group, while a low predictive accuracy for credit default group.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Hongli Li
AU  - Liwei Song
PY  - 2012/09
DA  - 2012/09
TI  - Assessment on Credit Risk of Real Estate Based on Logistic Regression Model
BT  - Proceedings of the 2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT 2012)
PB  - Atlantis Press
SP  - 374
EP  - 382
SN  - 1951-6851
UR  - https://doi.org/10.2991/emeit.2012.75
DO  - 10.2991/emeit.2012.75
ID  - Li2012/09
ER  -