Research On Pricing Model Of Our Country’s Real Estate

Since1998, with canceling of the housing distribution system in our country, the price ofcommercial housing rise continually. National continue to control the sales of commercialproperty prices, but the real estate price continues to rise quickly. It has greatly influencedpeople’s life in China and the whole national economy steady development, commercialhousing prices has become a widely concerned social problems and economic problems.Factors that affect real estate price is very complicated，and at present the analysis of theaffecting factors of commercial housing sales price way varied also. In this paper, qualitativeanalysis and quantitative analysis are the main method. Though the analysis the influencefactors are important, the real estate pricing is the most concern. There are many kinds ofmethods of the real estate price. In this article, the multiple linear regression model, GM(1,1)model, BP neural network model and grey neural network model are studied and chose. Thespecific six parts are as follow.The first part is the introduction. In this part introduced this article research backgroundand research significance, put forward some problems, described the research ideas andmethods and key and difficult points and the innovation and deficiency of this paper arepointed out.The second part is the review. In the study of some typical references, on the basis ofanalysis and comparison, respectively, introduced the domestic and foreign research status ofrelated issues.The third part is the analysis of influence factors. Firstly, the qualitative analysis is usedfor the influence factors of commodity house average sales price. It is divided into internalfactors and external factors. The internal factors contain the cost and marketing goals, whilethe external factors have analyzed the four factors of the economic environment factors,social environmental factors administrative factor and other factors. Then, on the basis ofqualitative analysis, the multiple linear regression model has been used to quantitativeanalysis of the relevant factors by the article. By using the software of Eview6.0to make thestepwise regression of the multiple linear regression model, the multi-co-linearity betweenthe factors are eliminated. At last, It determines the completed housing cost. And the biggestimpact on commodity house average sales price is the total population of the country.At thesame time, the results of quantitative analysis and qualitative analysis are sure to beconsistent.The fourth part mathematical modeling is carried out on the commercial housing price. Respectively using GM(1,1) model, BP neural network model and grey neural network modelto simulate and predict our country commercial housing sales prices, through the modelsimulation test, to determine the applicability of the three kinds of models in the price ofhouse in China.The fifth part is comparison and selection of the model. Mainly by comparing theadvantages and disadvantages of the model itself and combine with the actual to choosemodel, we know that multiple linear regression model is suitable for determining influencefactors, GM(1,1) model is suitable for short-term prediction of high accuracy of the samplesize is little, the BP neural network is suitable for large sample of medium-and long-termforecasting and grey forecasting model is suitable for prediction of high accuracy of thesample size is little.The sixth part is summary and outlook of this paper.