Credit Rating Model For Real Estate Companies Based On Logistic Regression Method

Since China took the real estate industry as the pillar industry in 2003,the real estate industry has ushered in a rapid development period for nearly 20 years.There are also many real estate enterprises in the list of the world's top 500.During this period,the house prices have also reached new highs.However,in recent years,the policy orientation has changed,and on the national level has always stressed the "deleveraging" of the real estate industry,In August 2020,new regulations on real estate financing supervision were introduced.In order to control the growth of interest-bearing debts of real estate enterprises,"three red lines" have been set to prevent the real estate industry from causing systemic financial risks.With the establishment of the fundamental principle of real estate development that "the house is for living,not for speculation",the asset liability structure and credit management of the real estate industry are facing more stringent supervision than ever before.From the macro-economic situation,it has great practical significance to study the credit risk rating of real estate enterprises.Real estate enterprises have the characteristics of high asset-liability ratio,single financing structure and financing source,and large capital demand,so the potential credit risk is huge.Due to the co VID-19 epidemic,the sales of real estate enterprises are generally not ideal this year,in the next three years,real estate enterprises will generally meet the peak of debt repayment,and the pressure of credit default risk of real estate enterprises is increasing.In this context,it has great significance to fully reveal and warn the risk situation to real estate enterprises,bond investors of real estate enterprises,and other creditors of real estate enterprises in advance.Give early warning before the real default of real estate enterprises breaks out on a large scale is helpful,on the one hand,it is helpful for real estate enterprises to measure their own credit risk level and do a good job in credit risk management;on the other hand,it is helpful for investors and creditors to judge risks in advance and effectively control risk exposure.Based on the data disclosed by the real estate listed companies of Shanghai and Shenzhen Stock Exchange,the real estate listed companies of the National Small and Medium-sized Enterprise Share Transfer System,and the issuers of bonds issued in the inter-bank market and trading market,through the process of data acquisition,data cleaning,default marking,indicator screening,model establishment and model validation,five credit scoring models based on logistic regression method are constructed based on five different index screening methods,and using ROC curve and KS curve to evaluate the effectiveness of the five models,it is concluded that the better model are generalized cross validation method,stepwise regression method and boruta method.Based on the score card constructed by these three methods and the distribution characteristics of the batch rating results of the real estate industry,the income regulation is as follows the real estate companies with small model,weak profitability,high level of assets and liabilities and slow cash flow recovery are more likely to default.From the risk ranking results of the models constructed by these three methods,30 entities with higher default risks are selected for risk warning.After further comparison,12 subjects repeatedly appeared in the risk prompt list.This also shows that the model constructed by the three methods is effective,reliable and practical.Finally,based on the research results,this article proposes systematic credit risk management recommendations,such as constructing a black-and-white list of various businesses with the help of quantitative results as business access standards,and granting stable credit to the entities that have been admitted.