Study On The Trend And Analysis Of Factors Affecting For Housing Prices In Chongqing Municipality

As the pillar industry, the healthy of real estate development is related to the people's livelihood. Real estate development in the health of the judge, the price is undoubtedly the most important criterion. At present, China is facing a problem that real estate prices have continued rising. How to play real estate in the role of the pillars of the national economy, but also to prevent a economy bubble, has become the focus of attention .even in the relatively backward house market of Chongqing municipality, make some theoretical study, to guide the future house market, is extremely urgent and full of meaning.In the study of real estate prices, because of application of the quantitative models Forecast and control technology makes the results more intuitive, more convincing. The paper through the application of quantitative models, aims to get forecast data of housing prices in the future in Chongqing municipality, and provides some useful ideas for real estate development and investment, price regulation, public consumption. Papers mainly describe three aspects.First, on the basic research level, mainly make a note and analysis on real estate prices and real estate price formation mechanism.This was followed by the qualitative analysis level; the part of the contents on the main purpose is to show real estate prices in Chongqing to the status quo, and to identify the reasons for its essence. To this end, the paper adopted the comparative study, and other methods to achieve. Through the city of Chongqing and Shanghai, Beijing, Chengdu and other places of the real estate prices, a description of Chongqing City real estate prices in the status quo, combined with the Chongqing City real estate prices important factor behind the analysis of the status quo-oriented factors.Finally, the Chongqing City real estate prices quantitative prediction to increase the reliability of predicted data, the paper according to the characteristics of historical data, used three types of forecasting model, and given their proper weight factor, Consolidated that forecast data.