Research on housing prices prediction based on multiple linear regression

Abstract

With the steady development of social economy, commercial housing, as an important real estate, occupies a large proportion in family assets. According to the China Household Wealth Survey Report (2018) compiled by the Social China Economic Trends Institute, household net worth accounts for 70% of household wealth, including housing prices in Beijing and Shanghai. In higher cities, the proportion is as high as 80%. This paper analyzes the transaction data of about 10,000 second-hand houses in Beijing, constructs a multiple regression model with SPSS software, and obtains the dependent variable (housing price per unit area). The dataset used in this paper is fetched from the Kaggle website (Housing Price in Beijing). The results show that the relationship between the elevator, the floor situation, the decoration method, the administrative division and other independent variables. Also, it is shown that the correlation between the two is significant, so the model can be used. This paper provides reference for the actual transaction of second-hand housing in Beijing.