Predicting Property Prices With Machine Learning: A Case Study in the Moroccan Real Estate Market

Abstract

In this study, the authors aim to explore the potential of machine learning (ML) in real estate valuation, particularly in Morocco where challenges include intelligent and sustainable valuation methods and transitioning to smart urban planning aligned with the eleventh sustainable development goal. To tackle these, they analyzed, processed, and tested seven ML architectures using real estate ads from Casablanca and Rabat collected over three months (April to June 2022). Support vector regression (SVR) led with 92.6% accuracy, followed by neural networks at 90%, then random forest, gradient boosting, XGBoost, and ridge and lasso regressions. SVR, a validated model, produced predictions depicted in an interactive thematic map showing their distribution across the two cities, underscoring the influence of digital real estate on conventional valuation methods.