A Study On Credit Risk Of The Real Estate Based On BP Neural Network Model

The real estate industry as the pillar industry of China, is constantly active with the rapid development of economic. At the same time, the real estate industry also is capital-intensive industry, and adequate financial support is an important guarantee for the development of this industry. However, China's real estate enterprises'financial situation have a lot of problems like single financial structure, serious shortage of own funds, easily influenced by macroeconomic policy. Based on this background, this article proposes a new idea about building credit risk evaluation system.This text use some theories and technical methods about credit risk early warning, propose the early warning methods of real estate industry credit risk based on BP neural network. There are five parts of this article:The first part describe the financial status of real estate companies, and explain the significance of the research topics, the structure and the content of this article, as same as innovation points. The second part contrast and analyze a number of topic-related research results by scholars at home and abroad, such as financial crisis and credit risk of the real estate industry, commercial bank's credit risk research, neural network's application in credit risk. The third part define and describe the concepts and theories involved in this text, including : risk, credit risk, financial risk, real estate credit, neural network model, corporate finance theory, credit risk management theory, and the application theory of neural network model. The fourth part is an empirical analysis part of the topic. The quantitative analysis can be divided into three steps: First, use the KMV model to calculate probability of default based on the market information of the sample. Second, do correlation analysis of financial indicators and the probability of default. Then, use the probability of default as output, and use significantly related indicators as input, to build neural network model for verifying the effectiveness of the overall indicator system. Finally, obtain the conclusion of empirical research. In order to prevent credit risks from the source, this article in the fifth section presents recommendations and research prospects. This section presents some suggestions of the financial problem about how to avoid the credit risk in real estate industry, The countermeasure proposed from many standpoints like government's, the bank's, the enterprise's, and future development angle. At last, illustrates the limitations in the research process of the text, and look into the future research.