| China’s Real estate industry became an independent economic sector since the 20th century, early 90s. In 20 years of developing, The real estate industry has achieved rapid development. According to the Commission’s classification of listed companies, it is a total of 114 companies specializing in real estate industy in Shanghai and Shenzhen Stock Exchange Market. Real Estate and the national economy closely linked to a variety of industries, the statistics show that the real estate industry can pull iron、steel、building materials、home appliances and other 60 related industries. The real estate industry in the national economy plays an important role.The real estate industry in the national economy plays a basic position, and its products is to meet the needs of the people’s basic living necessities, the basic characteristics of the real estate industry, in addition to follow the decision of the development of the market, but also to be National macro-control a large extent. Adjustment in the industry, companies often face a shortage of funds, the risk of living beyond their means so that companies suffer huge financial risk, and even lead to financial crisis.At present, in studying of the financial crisis early warning model, most scholars focus on the test results which more accurately forecast financial indica-tors, and on behalf of the domestic data directly into the existing models, ignoring the different domestic and international environments, the model of science resis-tance to be verified. At the same time, the lack of combination forecasting model thinking, few build traditional statistical analysis methods of combining forecast-ing model, while most domestic scholars directly selected sample companies as a financial crisis in ST companies, though easy to do sampling, but the limitations of equally clear. Since the formation of the emergence of the financial crisis is often not sudden, but rather a slow evolution. ST companies have been dealing with the general financial crisis that has developed to the outbreak of the final stage of de-terioration, or even to whether it was financial position ST as a listed company or health standard is not precise, and select appropriate sample or regression results would have a very significant impact.This article draws scholars of the past experience, building principal compo-nent analysis and Logistic regression analysis of the combination of early warning models, while the principal component factor of the samples classified by the fi-nancial crisis in the sample group and control group, so can greatly increase the accuracy of the model. Principal Component Logistic regression prediction model can change the static regulation of real estate companies, after the regulatory mod-el behind the regulation, the formation of dynamic early warning mechanism so that the real estate enterprise is able to keep the financial risk for their own, to take reasonable and effective measures to avoid falling into financial crisis. Therefore, the early warning model for the real estate business and enhance their own ability to resist risks, reduce losses caused by bankruptcy, has strong practical signifi-cance.This article is divided into six parts:Chapter1:Introduction. First set out the background and significance of the research topics. Research ideas and then illustrate the structural system.Chapter2:Literature Reviews. Systematic review of the financial crisis, in-cluding the definition of research status and other early warning model of financial distress, including theory and critical analysis on the basis of these results, pro-posed this view that the establishment of financial distress Logistic regression analysis of the main components model.Chapter3:The real estate business theory of financial crisis. In this section, the system reviews the development process of the four real estate industry; a com-prehensive summary of the characteristics of the real estate industry, from basic, volatility, cyclical, right dominant, constraints, regional, differential profitability, capital-intensive, high risk, and several other aspects of the comprehensive ex-planation; from the external objective environment, the internal environment of the real estate business two levels of financial crisis, the reasons for, and eventually come to the characteristics of the real estate business financial crisis, that the fi-nancial crisis the accumulation of an objective process of development, financial crises has burst, financial crisis, diversity, predictability, especially in the last fea-ture, as the financial distress model this study provides a theoretical basis.Chapter4:an overview of research model. On principal component analysis and two methods for system Logistic explanations, including modeling concepts, ideas, calculation steps and the meaning of statistics, and the model is constructed ideas.Chapter5:Building early warning model for empirical analysis. Select the real estate development in Shanghai and Shenzhen are also listed as the analysis sam-ple, and for the real estate industry characteristics, including the solvency of se-lected indicators, development capacity, operational capacity indicators, profitabil-ity indicators, cash generating capacity of 24 indicators, including financial vari-ables indicators, financial indicators will be 24 by the significance test to validate the sensitivity of the financial crisis, the sensitivity of the 11 selected strong finan-cial indicators, and then integrated into 5 principal component analysis principal component factors. This four principal components as the sample group and Lo-gistic regression analysis of standards. First, discriminant analysis by grouping the sample is divided into financial crisis group and normal group, and then principal components factor into the model fitting Logistic regression model, and form the final prediction model. Finally, early warning models to predict the effects of in-spection and test of goodness to be, and to demonstrate the practical value of early warning models. Model prediction accuracy of the financial crisis group rate was 84.9% prediction accuracy on the normal rate of 94.9%,92.1% overall prediction accuracy, this is a very high predictive accuracy, indicating that the early warning model can be applied in practice, high practical value.Chapter6, Conclusions. Summarizes the results of this study, combined with the lack of findings of the study, we propose the following research prospects:1.The real estate enterprises of different sizes may be differences in the finan-cial indicators there are different, the model of the building will have an impact. Therefore, future studies should try the real estate firm size classification of enter-prises, for the characteristics of enterprises of different sizes, build more targeted, accuracy of financial distress model, so that the financial ealy warning model can truly become a real estate business decision-making. 2.This study is the principal component based on the Logistic model, the lack of comparison between the different model ideas, so easy for readers to compare object because of lack of model accuracy in predicting the progress of the lack of perceptual awareness, it is In future research scholars in the set into the previous model, compare the advantages and disadvantages of different models.3. Due to a number of factors both inside and outside the real estate industry under the influence, therefore, introduced in the model such as the macroeconomic situation, the manager dummy variable indicators of ability, may make further im-prove the prediction accuracy, prediction better.4.Different industries, sub-critical value of Logistic Regression to determine an impact. Therefore, the scientific method should be adopted to determine the critical value of the re-analysis to determine more precisely the critical value. Oth-erwise, if the critical value error, misjudgment, such as the company appears nor-mal or crisis, the company misjudged the crisis into the companies are listed as a direct result of the impact of discrimination,... |
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