The Study On The Credit Risk Measurement Of Real Estate Customers

Based on the data from Chinese listed real estate companies and non-listed company data from one large machine manufacture corporation with foreign capital, whose main customers are real estate companies, use traditional non-linear estimation method two-classify Logit model to derive a quantitative credit risk model for measuring and monitoring credit risk of real estate companies. The theory of what is the essence of company default tells us payoff will and payoff ability are the necessary and sufficient conditions for the company to keep his promise, so use the financial parameters to reflect the payoff ability, and use non-financial parameters to reflect the payoff will. This paper applies the default logo is "due to the overdue payment, or unable to pay debts, real estate company is litigated and lost", namely actual default. Then adopt classification statistics to compare the power of different models, and gets abundant empirical conclusions.Based on the empirical analysis, it is found that Logit models have good default prediction power generally, the Logit models based on the financial data of last year are better, and the Logit models include both financial parameters and non-financial parameters have the greatest power. The Logit models based on the data from Chinese listed real estate companies have acceptable default prediction power for non-listed companies.As the credit risk mainly concerned in this thesis is commercial credit risk about credit sales, based on the business credit management status in China, and the severe environment of the suppliers of real estate companies, in the last part of the thesis, advise how to manage the real estate customers’ credit risk, and combining credit management work experience of the author, give the suitable proposal of credit risk management process for action, and some detail advices for implementation, especially focus on how the real estate customer credit risk measure model is applied to the practice of credit risk management process.