Real estate price model based on multiple linear regression and analysis of influencing factors of regional economic vitality development

Abstract

Using this statistical analysis method, which regards one variable as a dependent variable and one or more other variables as independent variables, the quantitative relations of linear or nonlinear mathematical models between multiple variables are established and analyzed by using sample data. Economic vitality is the regional support to its social economy. By analyzing the influencing factors of economic vitality, the evaluation index system of regional economic vitality is constructed. SPSS software is used to analyze the selected data, so as to judge the change of gross national product, which is of great significance for improving the efficiency of government policy implementation, stimulating urban economic vitality and realizing regional sustainable development. Taking Guangzhou as an example, through multiple regression analysis, this paper establishes a model of influencing factors from the perspectives of human capital level, government intervention, consumption, industrial structure and opening up, and puts forward an action plan to improve regional economic vitality. This paper studies the relationship between the average sales price of commercial housing and the sales area of commercial housing, real estate housing investment, year-end resident population, regional gross domestic product and the number of real estate development enterprises. a multiple linear regression model for predicting commercial housing prices is established, and the model is tested and tested for multicollinearity, serial correlation and heteroscedasticity, and the optimal model is obtained. In addition, based on the grey correlation degree, this paper analyzes the impact of population trend and enterprise vitality on the economic vitality of Guangzhou, and studies the long-term and short-term impact of economic policy transformation on the provincial economy.