A Comparative Study of Machine Learning Algorithms for Predicting Housing Prices in Chinese Cities

Abstract

This study conducts a comparative analysis of machine learning algorithms for predicting housing prices in major Chinese cities, addressing the growing complexity and economic significance of the real estate market. With housing affordability becoming a critical socioeconomic issue, accurate price prediction models are essential for policymakers, investors, and urban planners. The research evaluates the performance of four algorithms—Linear Regression, Decision Trees, Random Forests, and Gradient Boosting Machines—using a dataset comprising historical transaction records, macroeconomic indicators, and location-based features from 10 major Chinese cities. Results indicate that ensemble methods, particularly Gradient Boosting Machines, achieve the highest predictive accuracy, with a mean absolute error reduction of up to 18% compared to traditional linear models. The findings underscore the importance of incorporating non-linear relationships and feature interactions in housing price modeling. This study not only provides a practical framework for real estate valuation but also highlights the potential of advanced machine learning techniques in addressing urban economic challenges.