Statistical Forecasts Of Selling Price Of Houses

After becoming our country pillar of the national economy since in 2003 real estate business is formal, the effect contributing to economic growth increases with each passing days, becomes new growth point of national economy and consumption hot spots gradually. In recent years china's real estate prices are rising rapidly, part area house price continues clambering rising, especially greatly like Beijing, Shanghai, Guangzhou and other big cities, rise extent sharply exceeds the overall increase in economic growth of industry and other product is rising. With too quick house price increase, not only affects the quality of life the urban resident also gives entire national economy sustained, stable, speedy development to have brought about an unsteady factor. House price problem catch regard of the fellow countrymen, therefore broad having also aroused the in the homeland scholar to the analysis affecting the house price relevance factor's pay close attention to.This article first select the influence house price 8 targets from the influence house price's numerous factors to take the explanatory variable, and obtain the accurate data from CEINET and DRCNET to construct a regression model of selling price of houses. Through carries on the variable coefficient significance T-test, collinearity diagnostics, the model overall remarkable F-test and the variance analysis to the model, finally obtains that house price mainly decided by the average per person gross national product index and completion house construction cost, and the same direction.Then the introduction real estate policy variable, establish the regression model with policy variables, and draw the real estate policy had the inhibitory effect on housing prices, the effect is not obvious, but long-term perspective, prices subject to policy impact of the steady decline. Finally, through to the above two model's analysis, the relative error which forecast is big, in order to optimize, carry on the time series analysis, establish the house selling price index ARIMA model, and carry on the optimum linear prediction, thus obtain the forecast the effect to be good.