Research on the Influencing Factors of Beijing Housing Price

Abstract

This paper aims to develop a house price prediction system using a multiple linear regression model to improve the accuracy of predicting housing prices by analyzing the linear relationships among various influencing factors. Fluctuations in the real estate market have significant impacts on urban development and residents' quality of life, making precise house price forecasting essential for both real estate research and informed decision-making. Traditional prediction methods are often rooted in economic theories, but advancements in data science have introduced more data-driven approaches that can leverage vast datasets to enhance predictive power. Multiple linear regression, a classic statistical technique, is particularly suitable for examining the linear connections between multiple independent variables and a dependent variable, in this case, housing prices. This study incorporates factors such as location, infrastructure, economic indicators, and property characteristics to construct a predictive model, subsequently validating its effectiveness through real-world data analysis and the effect of Variance Inflation Factor (VIF) on the model is discussed.