Abstract

This paper aims to investigate the potential factors influencing housing prices in Beijing and optimize the prediction model. Using the Kaggle dataset "Housing Price in Beijing", multiple factors affecting housing prices were examined, including square footage, bedroom count, bathroom count, follower count, the presence of an elevator, and subway proximity. The study initially established a multiple linear regression model (MLR) and then optimized the model through variable selection and hypothesis testing. The final model indicates that square footage, bathroom count, follower count, elevator presence, and subway accessibility significantly impact housing prices. Additionally, subway proximity positively correlates with housing prices, while increased square footage is negatively associated with price, possibly due to lower unit costs for larger properties and the higher market demand for smaller homes. By validating the model's performance using a test dataset, the final model demonstrates effective predictive capability and offers insights for future model improvements.

Keywords: 

housing prices, Beijing real estate, multiple linear regression, model optimization, subway accessibility

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