The Comparative Study on Credit Risk Evaluation Models of Real-estate for Chinese Commercial Banks

[Abstract] The assessment of credit risk of real-estate of Chinese banks has become a hot point nowadays. Traditional gross risk-evaluation methods are mainly Discriminant Analysis (DA) and logistic regression, and the most common approach is a credit rating system based on logistic regression. Yet the daily ripening neural network technique has opened up a new idea for experts in fields of bank credit and credit evaluation. In this paper, two methods—the logistic model and the BPneural network technique, are compared in their application in the evaluation of credit risk of realestate in Chinese banks and then tested with samples of listed real estate agencies. Results show that the BP-neural network technique has superiority over the logistic model in quantizing and classifying gross risk of real estate agencies.