Research On Default Risk Measurement Of Chinese Listed Real Estate Companies Based On KMV-Logit Model

The real estate industry plays a very important role in China’s national economy.After the COVID-19 pandemic outbreak,debt defaults of Chinese real estate enterprises occurred frequently,which had a huge impact on the stability and development of China’s capital market.In May 2022,China Banking and Insurance News published an article titled "Persevere in Preventing and Defusing Major Financial Risks" written by the Committee of the China Banking and Insurance Regulatory Commission.The article pointed out that due to the multiple spread of COVID-19 and proactive policies,the upward pressure on the macro leverage ratio has increased.We must strengthen our response to risks related to the rise in the macro leverage ratio,in particular,guard against risks arising from excessive debt growth of non-financial enterprises and local government sectors.We will monitor and give early warning of debt defaults by large enterprises,formulate plans for further financing and debt restructuring in advance,and properly cope with any rebound in non-performing assets.Adhere to the position of "no speculation in housing",support local governments to improve real estate policies based on local conditions,support and improve housing demand,and promote the steady and healthy development of the real estate market.Based on the importance and difficulty of default risk management in real estate industry,it is of great practical and theoretical significance to study how to measure bond default risk accurately and how to detect the measurement results dynamically and timely.Starting with the traditional default risk measurement models Z-Score model,KMV model and Logit model,this thesis selects the actual default data of Chinese listed real estate enterprises from 2017 to 2022,and studies the applicability of different models to the default risk measurement of Chinese listed real estate enterprises.The results show that Z-Score model and KMV model can distinguish well the sample characteristics of the default group and the control group.However,since the Z-Score model itself is a financial index scoring model,and the default probability calculated by the KMV model is greatly affected by the default distance parameters,accurate parameters cannot be obtained in the case of lack of empirical data.As a result,neither Z-Score nor KMV model can accurately calculate a meaningful probability of default.The default probability calculated based on the Logit model and KMV-Logit model is basically consistent with the actual default situation of China’s listed real estate enterprises,that is,the samples with high predicted default probability have a high degree of fitting with the actual default situation,which is tested by ROC curve.It is found that Logit model and KMV-Logit model have good discriminating ability to the default risk of listed real estate enterprises,and adding the default distance DD calculated by KMV model on the basis of Logit model can further improve the accuracy of the model.Finally,the author puts forward the following suggestions.Firstly,real estate enterprises need to strengthen liquidity management to avoid liquidity crisis.Secondly,at the regulatory level,it is necessary to establish a sound enterprise credit information database to improve the standardization,accuracy,integrity and timeliness of credit information.Thirdly,the supervisor needs to strengthen the management of information disclosure of listed companies.Fourthly,it is necessary to improve the credit rating mechanism,promote the high-quality and rapid development of the credit rating industry,and improve the credibility of the overall market.At last,the regulatory departments should carry out reasonable implementation of supervision to ensure adequate financing of real estate enterprises.