The Research on Factors Influencing Housing Prices-Take Beijing as an Example

This article aims to identify those factors that have an impact on housing prices. The method of Multiple Linear Regression is used to analyze the significant factors with 400 samples of Beijing from 2011 to 2017. Based on an assumption, 12 variables that were chosen do correlate with housing prices. This paper also considers the interaction effects between house squares and the number of living rooms, drawing rooms or bathrooms, and uses Forward Stepwise Regression to solve the covariance problem caused by adding interaction terms. In order to test the effectiveness of this operation, the research compares the VIF value and significance of those variables. It turns out that square, building type, elevator, construction time, renovation condition, and subway have a significant linear relationship with prices, while the number of living rooms, drawing rooms and bathrooms, building structure, property rights, and district fail the significance test. Overall, the volatility of housing prices in Beijing can be considered by the extent to which these factors affect them.