AI-Driven Asset Valuation in Real Estate: Trends, Contributions, and Future Research Directions

This article presents a comprehensive bibliometric and critical review of 450 peer-reviewed publications on artificial intelligence (AI) applications in the real estate sector between 2021 and 2025. Using VOSviewer and Bibliometrix tools, the study maps the intellectual, conceptual, and thematic evolution of the field. The findings indicate a consistent growth in research output, with China (218 publications), the United States (57), and the United Kingdom (37) emerging as the leading contributors. The dominant research areas include Engineering (35.8%), Computer Science (21.8%), and Business & Economics (20.7%), underscoring the field’s interdisciplinary character. The analysis identifies three primary thematic clusters: (1) AI-based property valuation and price prediction, (2) smart property management and sustainability, and (3) data-driven investment and decision analytics. More recent studies increasingly focus on explainable AI, generative models, and smart city integration, reflecting a shift toward transparency, interpretability, and technological innovation. The study contributes theoretically by framing AI-driven real estate through the lens of digital institutionalism, linking technological adoption with institutional and behavioral theories of asset valuation. Practically, it demonstrates how AI enhances accuracy, efficiency, and sustainability in real estate operations, offering valuable insights for advancing both academic research and professional practice.