The Research To The Influence Factors Of Taiyuan Residential Market Demand And The Demand Forecast

With the accelerating process of urbanization in China, the housing industry has entered aperiod of marketization and commercialization and the residential system has also beenreformed continuously. At present, the housing market has begun to take shape and the realestate industry has occupied a dominant position in the national economy. As the mostimportant component of real estate industry, it plays a pivotal important role in relatedindustries of the whole country or region, growth in socio-economic, beautifying the cityscenery and growth in consumption. Therefore, it is of great importance to analyze andpredict the demand of residential market scientifically and rationally.The main purpose of this study is to analysis and predict the development of Taiyuanresidential market, through the use of GM (1,1) model in the grey correlation analysismethod,and the multiple linear regression model in the regression analysis, doing it canexplorat the analysis method to the residential market demand, enrich and develop theapplication of the theory and method, and provide a few basises for that the government andenterprises make the science decision.As a dominant factor in the residential market, housing demand is the driving forces tocause changes in housing supply and is the starting point and destination of housing supply.Only if we grasp the development trends and patterns based on analyzing and judgment ofresidential demand can the government departments obtain help in formulating thecorresponding macro-control policies. Therefore, the study chose residential market ofTaiyuan as the object, and examined related concepts and theories. Then the author analyzedinfluencing factors quantitatively using Grey Relational model after introducing developmentstatus of Taiyuan residential market. And then point out the main influencing factors as wellas predict the needs of the residential market in the next three years by making good use ofthe gray model and multiple linear regression models. At last, the author made relevanttargeted recommendations for local and real estate companies under the premise of current situation and recent trends.