Applying Machine Learning in Real Estate Prediction: The Case in Vietnam

Abstract

The complexity of housing price forecasting in Vietnam, stemming from multifarious influencing factors and elusive nonlinear relationships, poses a significant challenge for conventional econometric and statistical models. Although substantial previous studies have harnessed machine learning methods to forecast housing prices accurately, their tailored application within the Vietnamese domain remains notably limited. By harnessing Vietnamese data on the largest Internet data between June and August 2023, this paper focuses on identifying the most effective machine learning model for precise housing price prediction in Vietnam. Our findings reveal that Random Forest emerges as the most effective model for housing price prediction, with housing areas identified as the primary factor exerting a significant influence on the Vietnamese real estate market.