Commercial Shop Price Assessment: Comparing Machine Learning and Ordinary Least Squares for Improved Accuracy
Keywords:
Commercial Valuation, Machine Learning, Random Forest, Ordinary Least SquaresAbstract
This study compares the predictive performance of OLS and five ML algorithms in valuing commercial shop properties using 2,480 transactions from Kuala Lumpur from 2013 to 2023. While OLS showed limited predictive power, the Random Forest algorithm, applied with log-transformed target variables, achieved superior accuracy (R² = 0.9974, RMSE = 0.03, MAPE = 0.02%). These findings support the use of machine learning as a reliable and efficient alternative for property valuation, offering enhanced precision and scalability in commercial real estate assessment.
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Copyright (c) 2025 Junainah Mohamad, Intan Faiqah Hamizah Mohd Firazan, Suraya Masrom, Abdul Rehman Gila
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