Analysis On The Influence Factors Of Real Estate Price And Prediction On The Real Estate Price In China

The influence factors of real estate price of China are analysed by the clustering analysis method, factor analysis method and improved factor analysis method. The housing sales price index model is established by the nonparametric autoregressive model and bayes dynamic prediction model. The main contents of the thesis are as follows:(1) This thesis utilizes methods of the clustering analysis and non-parameter test to select indicators of real estate price factors in China.(2) Based on the indicators of selection,through the factor analysis method, comprehensive evaluation and the rank of regions on the real estate industry are given. But currently this method of factor analysis has the irrational distribution of index weights with the evaluation function,which leads to the shortcoming that evaluation results do not accurately reflect the actual situation,then this thesis utilizes an improved comprehensive evaluation model with examples indicating validity of the model. For the further development and gradually improved comprehensive evaluation method,it provides reasonable information.(3) Based on housing sales price index data published by the national bureau of statistics (NBS), nonparametric autoregressive model of housing sales price index is established based on nonparametric autoregressive model,and the housing sales price index is forecasted.(4) Bayes dynamic model of housing sales price index is established based on bayes method,and the housing sales price index is forecasted.Comparing the bayes dynamic prediction model to nonparametric autoregressive model,the results show that the bayes dynamic prediction model is superior the nonparametric autoregressive model.