Research On Credit Risk Evaluation Model Of Real Estate Listed Companies In China

In China,banks have large credit risk in corporate finance,and the non-performing loan ratio of commercial banks shows an upward trend.With the deepening of the marketization process of China's banking industry and the rise of Internet finance pioneered by third-party payment,the areas and scales involved in credit risk have also expanded rapidly.Credit risk management has become an important issue for commercial banks.Large foreign commercial banks have es-tablished a series of risk management methods suitable for their own.Compared with foreign countries,China's technology in credit risk quantification is relatively backward.As an important pillar of China's economic development,the real estate industry is one of the main indicators to measure the Chinese economy.However,there are many enterprises involved in the real estate market in China,which are not well-balanced.Due to the economic downturn and fierce competition in the industry,a large number of companies with weak competitiveness and low capital operation will face financial crisis and bankruptcy risks.This paper will introduce the principle of two credit risk models and their application,in listed companies in the real estate industry from the perspective of commercial banks.In the selection of the model,we choose the KMV model,which is the representative of the structural models.Here we discuss the prediction effect of three default points,and obtain the default point setting method which is most suitable for the real estate company in China.Then,we introduce a method based on statistical analysis model—logistic regression model,and combine the factor analysis method with logistic regression model to solve the dimensional disaster.In the selection of financial indicators,according to the financial structure characteristics of China's real estate industry,we choose seven types of financial indicators as the main factors for assessing risks.Then build the logistic regression model and test the predictive effects of the model.The results show that the two methods have good predictive effects in the credit risk assessment of listed companies in the real estate industry.