Traditional or advanced machine learning approaches: Which one is better for housing price prediction and uncertainty risk reduction?

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Abstract

Predicting housing prices is particularly of interest to many scholars and policymakers. However, housing prices are highly volatile and difficult to predict. This study used both traditional and advanced machine learning (ML) approaches to address the issue of housing price prediction. This study involves and compares the predictive power between advanced ML models, including random forest, gradient boosting, k-nearest neighbors (KNN), bagged classification and regression trees (CART), and traditional ML models based on linear regression and its modifications. Notably, in this study, we employed both performance metrics, including the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and k-fold cross-validation (CV) procedure in order to investigate the predictive performance of each model. Empirically, based on a dataset comprising 78,704 real estate sales in Hanoi, Vietnam, we find that advanced ML approaches outperform traditional approaches. Specifically, advanced ML models enhance the accuracy of house price prediction and the decision-making process related to housing buying and selling activities. Our findings also reveal that among advanced ML algorithms, the random forest algorithm performs better than the other models in predicting housing prices.

Keywords: Housing Price, Price Prediction, Machine Learning Models, Traditional Statistical Models, Performance Evaluation Framework

Authors’ individual contribution: Conceptualization — L.P.T. and H.D.L.; Methodology — T.T.P. and D.C.N.; Formal Analysis — L.P.T. and D.C.N.; Writing — Original Draft — L.P.T. and D.C.N.; Writing — Review & Editing — L.P.T.; Supervision — H.D.L.; Funding Acquisition — H.D.L.

Declaration of conflicting interests: The Authors declare that there is no conflict of interest.

JEL Classification: G17

Received: 06.03.2024
Accepted: 02.01.2025
Published online: 06.01.2025

How to cite this paper: Tran, L. P., Le, H. D., Phuong, T. T., & Nguyen, D. C. (2025). Traditional or advanced machine learning approaches: Which one is better for housing price prediction and uncertainty risk reduction? Risk Governance and Control: Financial Markets & Institutions, 15(1), 27–36. https://doi.org/10.22495/rgcv15i1p3