Predicting Housing Prices in Riyadh: A Comparative Analysis on Machine Learning and Hedonic Regression

Abstract

This study investigates the performance of hedonic regression against artificial neural networks and random forest for real estate price prediction in Riyadh, Saudi Arabia. Using data from a major property platform collected in Q3 2024, the analysis spans 2,997 residential units across five major districts. The study is situated within the context of Vision 2030 and addresses the challenges of real estate valuations in a rapidly evolving urban environment. Twelve locational and structural features were analyzed using a 70/30 training-testing split. Random Forest led with test R²=0.9065, RMSE=0.1911, MAE=0.1353, outperforming Artificial Neural Network (R²=0.8968, RMSE=0.2008, MAE=0.1450) and Hedonic Regression (R²=0.8034, RMSE=0.2673, MAE=0.1970). Random Forest feature importance: North of Riyadh (0.768659), West of Riyadh (0.612986), building type (0.180799), entrances (0.171134), size (0.127103). Random Forest and Artificial Neural Network excelled in capturing non-linear and spatial price trends in Riyadh’s housing market. This study supports the incorporation of machine learning models into workflows for predicting property prices, especially in cities like Riyadh that are quickly urbanizing. The research provides a useful foundation for improving real estate valuation procedures in accordance with Saudi Arabia's Vision 2030 goals by demonstrating the predictive benefits of RF over conventional methods.