Prediction And Influencing Factors Analysis On Housing Price In Beijing

The real estate industry is one of the pillar industries in China's national economy.The rapid rise or decline of real estate prices will cause a series of social and economic problems.To ensure the fluctuation of house prices in a reasonable range is an important part of ensuring the stable development of the country's economy.Therefore,it is of great significance to study the influencing factors of real estate prices and predict them for understanding the trend of real estate prices and controlling them.In this paper,the housing price in Beijing is taken as an example.Firstly,the H-P filtering method is used to study the trend of housing price in Beijing.It is found that the housing price in Beijing presents an overall upward trend.With the passage of time,the frequency of housing price fluctuation increases,and the range of fluctuation increases.Next,this paper uses principal component regression analysis,grey correlation degree and VAR model to study the influencing factors of Beijing's housing prices.It is found that Beijing's housing prices are affected by many factors,and GDP from the economic level has the greatest impact on housing prices.Finally,the grey prediction GM(1,1)model,VAR model and ARIMA model are used to predict the housing prices in Beijing.It is found that the residual of ARIMA model will be more uniformly distributed near the zero value when making intra-sample prediction.When making out-of-sample prediction,the predicted values of VAR model and grey prediction model will continue to rise,while the predicted values of ARIMA model will rise or fall.Obviously,the prediction of ARIMA model is more realistic,and the prediction effect is slightly better than the other two models.