Real estate decision-making: precision in price prediction through advanced machine learning algorithms

07.Mar.2025

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This study aims to enhance real estate price prediction accuracy using advanced machine learning models, minimizing biases and inconsistencies inherent in traditional appraisal methods. By leveraging support vector regression (SVR) and gradient boosting machine (GBM), this study provides a data-driven approach to property valuation, improving decision-making for buyers, sellers and policymakers. This study also seeks to bridge the gap in machine learning applications for emerging markets like Jordan. This study’s research’s broader goal is to offer a transparent, efficient and reliable tool for property valuation that improves market efficiency and reduces transaction uncertainty.

Design/methodology/approach

This study uses machine learning techniques – SVR and GBM – to predict real estate prices in Amman, Jordan. Data was collected from the Department of Lands and Survey, covering residential …​