A Study On The Model Of Warning System Against Corporate Financial Risk Of Real Estate In China

The Real Estate Industry, as is known to all, is characterized by huge investment, high risks, high returns and a prolonged cycle. In China the industry is still quite young; besides, the domestic financial system is not sound yet; as a result, the industry is facing such problems as small scales and a poor system of industrial standards. For example, quite a few real estate developers have a higher proportion of advance receipt account, a high loan-to-value ratio which leads to an above 70% debt-to-assets ratio, sometimes even as high as 90%. The high indebtness ratio has brought about enormous financial risks for the developers, resulting in many unfinished projects in many cases. Hence, the urgent problem is how to prevent real estate companies from such financial crises."The Warning System against Corporate Financial Risks" has been a hot topic in recent years, on which studies from home and abroad are abundant in number. Yet the majority of researchers have focused on companies with public stocks in the stock markets, and few have paid attention to one specific line of business. After absorbing some advanced theories of warning system, the author of this dissertation will be the first to apply the financial warning system theory to the Real Estate Industry in China. A model of a warning system against financial risks designed for real estate corporations will be constructed, based on the features of domestic companies and the macro economic climate. Some cases of real estate companies with public stocks will be quoted as samples. The author would like to employ a combination of analytical methods, Principal Components Analysis and a Logistic Regression Analysis. The selection of the Logistic Retrospective Analytical Method is supported by various advantages of the method itself. It is highly precise and requires few hypotheses; the variants don't have to fall into the normal deviation scope. The method has already been widely used in the related studies. However, certain shortcomings are inevitable, such as its inability to solve multi-collinearity and multi-dimensional problems. Therefore, Principal Components Analysis is jointly employed to make up for the deficiency. The two methods together will ensure the integrity of statistics, and well explain the financial status of corporations.The dissertation is comprised of five chapters,Chapter 1 Introduction; The definition and the necessity of a Warning System against Financial Risks will be given, based on the review of related literature of home and abroad. The main purpose of the dissertation is to study the model of Warning System by employing two methods, the main-factor analysis and logistic retrospective analysis. Chapter 2 A Summary of Financial Risks of Real Estate Corporation. The financial risks confronted by domestic real estate companies will be discussed here. Risks fall into two categories, the systematic risk and non-systematic risk. Systematic risks include periodic risks, and risks from legal and political aspect, interest rate, inflation, demand-supply changes, discounting, etc. Non-systematic risks are closely related to a company's style of operating, capital structure, financial management, consciousness of risks, etc. Risks are ubiquitous in all activities of any corporation.Chapter 3 A General Introduction of the model. In this part the author would deliberate on the two analytical methods, the main-factor analysis and the logistic retrospective analysis. The construction of the model has the following steps: first, selection of samples; second, selections of financial indicators; third, T test; fourth, multi-linear test; fifty, main-factor analysis; sixth, logistic retrospective analysis; seventh, the construction of the model; eighth, validity test of the model.Chapter 4 The analysis of the effects of such a warning system model. The author has chosen 78 real estate companies as research samples, among which 35 have financial problems and 41 are normal. Data spanning a year are employed. By applying the two analytical methods the author manages to establish a warning system. The model will finally be tested for its real effects and, in order to prove the practical value of such a model.Chapter 5 Conclusion and the Potential Application.