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 BP-neural network technique, are compared in their application in the evaluation of credit risk of real-estate 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.
[Keywords] Credit risk in real estate agencies; logistic model; BP-neural network
Along with the capitalization process of housing and urbanization, real estate is improving. That real estate fever keeps warming has gradually engendered bad assets for several commercial banks. As a result, they are likely to adopt a discreet and conservative strategy when getting involved in housing credit. In view of this, it is necessary to select an appropriate evaluation method for credit rating to effectively warn of housing credit risks.
Traditional risk assessment methods include Discriminant Analysis (DA) and Logistic Regression. Altaian (1968) built a famous warning model of multi- variables, the Z-model, by using multi- variate discriminate analysis and then revised it as the ZETA model. Ohlson (1980) is the first one who used the logistic regression model to warn of financial risks. Wiginton (1980) applied a logistic regression model and discriminant analysis to credit rating and then compared the two methods, which showed that the former was better than the latter. Tang Youyu (2002) tested the accuracy of the Logistic Model by sampling 5 listed companies with good financial conditions and 5 companies with bad conditions from the Shanghai and Shenzhen Securities markets. Qi Zhiping, Yu Miaozhi (2002) got satisfactory results by building a Logitstic Model with quadratic terms and intercross terms. However, the common limitations of these methods are that they focus on financial analysis of companies, but ignore the facts of projects involved. Good projects will help enterprises with poor performance get out of bad conditions, but those methods tend to reject loans. On the other hand, the generality of these models is bad. They need to re-estimate the parameters of samples when the environment and time change.
Through continuous learning, neural networks can now find the law from a large number of complex data in an unknown pattern. Continuous learning overcomes the complexity of traditional analysis and the difficulties of selecting an appropriate function, since it puts forward a higher request in Basel Treaty on total risk evaluation models about the classification of credit rating and the grades have to fall into the five categories as banks have noted. A neural network approach meets these needs well, and credit risk can be better involved in this model. In 1992, Jensen (1992) used a BP neural network to categorize loan corporations, and the accuracy of rating was up to 76%-80%. Trippi and Turba (1996) introduced the application of neural networks in finance and investment. In 2000, David West modeled five different neural networks to investigate the accuracy of credit rating marked by commercial banks. Malhotra (2002) used neuron-fuzzy system to differentiate "credit-good" and "credit-bad" borrowers.
Wang Chunfeng (1999) adopted the neural network technique to evaluate the credit risks of commercial banks. Yu Ruifeng (2007) introduced an index system to firm credit risk assessment using the BP neural network based on supply chains to evaluate loan applicants' total solvency. …