Mathematical Models Research On Housing Supply,Demand And Prices

The real estate industry is a pillar industry of national economy. Its steady growth, sustainable and healthy development has an important impact on the stability of the financial system, the development of the national economy, and the improvement of people’s living standards. In recent years, China’s real estate industry develops rapidly, which contributes to the development of the national economy, and improves people’s living standards. However, there are still many problems. For example, the housing price rises rapidly; in some areas, the scale of investment in real estate is continually expanding; the real estate market is a bit confusing; the supply and demand is imbalanced.From the perspective of the real estate supply and demand, this thesis deeply analyses the characteristics of the real estate supply and demand and its impact on real estate prices. With the relevant economical data collected, using several statistic methods, such as correlation analysis, multiple linear regression analysis, gray relational analysis, ridge regression analysis, etc., this thesis builds housing demand model, housing supply model and housing prices model of china’s real estate industry. In the end, this thesis makes forecasts about the current situation and future development of China’s real estate industry, and makes several suggestions on China’s real estate development and regulation.In housing demand model, this thesis chooses real estate annual sales area as the dependent variable, processes relevant data and builds the linear regression model via matlab. After mistakenly setting analysis and multicollinearlity revision, we learn that real estate annual sales area shows a linear correlation of the average wage of workers. In housing supply model, this thesis chooses real estate annual completion area as the dependent variable, uses the statistic software SPSS for correlation analysis and multiple linear regression analysis, finally comes to the conclusion that real estate annual completion area is linearly related to total urban population, real estate sales prices, per capita consumption expenditure, real estate sales area. In housing prices model, this thesis chooses real estate annual sales price as the dependent variable, uses gray relational analysis to calculate the relational degree between housing price and its factors. Via SPSS we build the multiple linear regression model, handle the multicollinearlity, and finally build the ridge regression model.