Analysis Of Factors Influencing China's Large And Medium Cities Housing Prices Based On Partial Least Squares Regression

The price of the real estate in our country has remained high over the past ten years.The literature review indicates that many research studied the influence of political factor,demographic factor and revenue factor.However,these research studies were rarely carried out for the influence of education resources and medical resources.In this study,we attempt to analysis the real estate price from the perspective of educational resources and medical resources.Data from 30 provincial capital cities were selected for horizontal analysis.On this basis,three regions were developed as central,western,and eastern regions.In addition,added to the regression model to test whether different regions have an impact on real estate prices The model error could be expanded if multiple linear regression is used to establish the model.Therefore,partial least squares regression analysis was applied due to its ability to eliminates the effects of multicollinearity and therefore gives a relatively reliable conclusion.The mechanism of this process is to extract the irrelevant principal components that are highly correlated with dependent variables from the independent variables.The extract principal components can then be subjected to regression analysis with the depend variables.The results indicated that both educational resources and medical resources had a major influence on the real estate price.Both educational resources and medical resources can affect the urban population aggregation and demand of quality resources which causing side effects on the supply and demand of real estate.This paper uses simca-p software for data analysis to obtain model cross-validity,variable projections,and other important indicators to analyze the impact of various factors on the price of real estate.According to the analysis results,suggestions on the distribution of educational resources and medical resources are proposed to slow down the phenomenon of high real estate prices.