Research On Influencing Factors Of Urban Housing Sales Based On Partial Functional Model

The real estate products provided by the real estate industry are both living materials and production materials,which are indispensable basic elements of social and economic activities.Therefore,it can be said that the real estate industry is the basic industry of the national economy.Relevant data show that the real estate narrow industry chain accounts for about 13%of GDP;in the national household's per capita wealth,the net value of real estate accounts for 65.99%.For the Chinese economy,any fluctuation in house prices may cause huge changes in the social economy,which in turn affects the various economic behaviors and lifestyles of family members.Different from other research methods,this paper applies the statistical semi-parametric function linear regression model to the housing price influencing factors,and proposes a new estimation method to estimate the unknown parameters in the model.Firstly,the model needed in this paper is established,and the corresponding estimation methods are proposed for the unknown parameters in the model.The required influencing factors are selected and the outliers are tested and processed.Secondly,from the perspective of different city levels,the housing price influencing factors of the third-and fourth-tier cities are studied separately.Based on this,the housing sales volume is used as the dependent variable and the housing price is used as the independent variable to study the factors affecting the housing sales.Finally,in order to study in detail the influence of quantile points on house prices,a semi-parametric quantile regression model was constructed and applied to the factors affecting housing prices.The research finds that:1)The proposed model has better estimation effect than the traditional semi-parametric function model,the absolute error value is smaller,and the estimation procedure is simpler.2)There are similarities and differences in the factors affecting housing prices in different cities.In the third-tier cities,the biggest factor affecting housing prices is the urban population.In the fourth-tier cities,the GDP has the greatest impact.In the third-tier or fourth-tier cities,the interest rates are maintained.Negatively related relationships.3)Among the factors affecting housing sales,the biggest influencing factors in the third-tier cities are urban GDP and urban development index,while the fourth-tier cities are urban GDP and urban comprehensive competitiveness.In the third-and fourth-tier cities,house prices and interest rates are expressed.Negative correlation.4)The semi-parametric function linear model proposed in this paper has good expandability,and can be applied to other industries in combination with corresponding data.