Exploring the Role of Artificial Intelligence in Predicting Property Value Trends: A Systematic Review of Machine Learning Applications in Real Estate Pricing and Risk Assessment

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) systems have revolutionised the real estate industry's property value evaluation system and market risk management. The research systematically evaluates fifteen peer-reviewed studies from 2011 to 2025 that assess AI/ML applications for property valuation with investment analysis, fraud detection, and innovative property management systems. Through a structured synthesis process, the review determines that supervised learning algorithms represented by Random Forest and XGBoost, plus Support Vector Machines, maintain dominance as modern substitutes for conventional valuation evaluation. Numerous studies show that AI solutions surpass traditional methods and demonstrate better predictive power, high adaptability, and analytical strength in complex non-linear data sets. AI technology produces valuable property value estimation results and exceptional results in risk assessments, transaction fraud discovery, and time-based market structure development. Many property operations now utilise AI systems to achieve predictive maintenance while optimising energy use, monitoring ESG data, and enhancing tenant interaction through automated solutions. The review describes ongoing hurdles researchers face, such as the need for more interpretable models, privacy data protection and common data infrastructure standards in different real estate markets. The conclusion establishes that real estate stakeholders receive many benefits from AI and ML.