Predicting real estate prices with AI: a comparative study of machine learning models - Lim, Eng-Lian; Wong, Doris Hooi-Ten; Masrom, Maslin (Universiti Teknologi MARA, Perak, 2025)
Accurate house price prediction is vital for economic, financial, and policy decision-making, impacting homebuyers, investors, financial institutions, and government agencies. This study employs data-driven machine learning approach to forecast residential property prices, with a particular focus on high-rise properties in Kuala Lumpur. Real-world housing data comprising 12,735 transactions (2021–August 2024) were collected from the National Property Information Centre (NAPIC), preprocessed, and analyzed using exploratory data analysis (EDA) to understand the influence of various property attributes on prices. Multiple predictive models, including traditional regression, ensemble methods (Random Forest, Gradient Boosting Machines), and deep learning (Artificial Neural Networks), were developed and rigorously compared. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 on an 80:20 training-testing split. Hyperparameter tuning and K-fold cross-validation were applied to optimize accuracy, prevent overfitting, and ensure model generalizability. The Random Forest model was the best-performing predictor, with the lowest error values and highest R2 score compared to other tested algorithms. This research offers practical insights into key price-influencing features and highlights the efficacy of machine learning for robust and interpretable house price prediction in the Malaysian real estate market.