Early Warning The Price Of Commercial Housing And Study Space Evolution Based On EMH

With the rapid development of real estate, real estate prices continue to rise,bringing the majority of social problems. In this study, the factors affectingcommercial housing prices as a starting point, focus on the spatial variation ofcommercial housing prices, predict future price and alert, propose policyrecommendations to enhance Xiamen residential market’s effectiveness. Researchresults can provide a reference for the healthy development of residential market inXiamen, for urban planning departments and government agencies to develop policies,to provide the basis for developers to invest in the site, to provide consumers with thepurchase criterion.Firstly, combine the real estate market development theory and the efficient markettheory, using ADF unit root test, autocorrelation test and run test to test2006-2012monthly average price of commercial housing price samples in Xiamen, the resultsshow that the commercial housing market in Xiamen City is in weak non-effective.Secondly, analyze the factors affected commercial housing prices of Xiamen fromthe macro and micro level.(1)Macro level: based on the result of EMH test, combine with previous researchexperience to study annual commercial housing prices of Xiamen from2000to2012,choose early warning indicators, establish early warning indicator system ofcommercial housing prices and build early warning models, real-time monitorcommercial housing prices and predict future prices’ movements.(2)Micro level: collect the information about Xiamen Island(Huli and SimingDistrict) residential real estate transactions of all sold out from2000to2012(exceptvillas, affordable housing and other small property listings). Based on ArcGISsoftware platform, establish spatial database, determine price trends throughexploratory spatial data analysis(ESDA), exduding outliers, study spatial changes ofresidential price and development law; build commercial housing price GWR modelof Xiamen Island based on Geographically Weighted Regression(GWR) model,analyze the micro factors impacting residential price with the regression, and compare with the traditional OLS regression results, conclude the GWR model is more suitablefor residential micro-factors analysis.Finally, deep analyze the result of research factors, find policy recommendations toenhance Xiamen Island commercial housing market effectiveness at the macro level:the government can appropriately increase land supply, curb speculation, control realestate investment, while strengthening market information build, and ensure markettransparency; at the micro level, propose advice that balanced the area supportingconstruction, ensure commercial, educational, medical and other supporting resourcesreasonable configuration.