Stakeholder trust in artificial intelligence (AI) for property valuation: insights from a systematic literature review

Abstract

This study aims to address the growing concerns surrounding the use of artificial intelligence (AI) in property valuation, particularly issues of transparency, trust and accuracy. This study focuses on aligning AI models with expectations to foster responsible adoption in real estate decision-making. A systematic literature review (SLR) of 44 peer-reviewed studies published between 2018 and 2025 was conducted. NVivo software was used for qualitative coding, and the political, economic, social, technological, legal and environmental framework guided the analysis of external factors influencing AI adoption. The study examined both technical model performance and stakeholder concerns. Random forest and support vector machines were most frequently applied in structured valuation tasks, while artificial neural networks were reported in contexts involving non-linear modelling and complex data patterns. Despite demonstrated predictive capabilities, policymakers and professional stakeholders placed greater emphasis on transparency, explainability and legal accountability. Identified trust-related challenges included algorithmic bias, limited model interpretability, regulatory ambiguity and insufficient integration of contextual factors. These findings informed the development of a hybrid AI valuation framework that integrates technological performance with governance mechanisms, contextual calibration and professional judgement to strengthen property valuation quality. This study is limited by the absence of primary data from stakeholder interviews. The findings are based solely on published literature, which may not fully capture real-time industry perspectives or emerging on-the-ground challenges. The proposed framework offers a transparent, data-driven solution for valuers, investors and regulators, supporting better-informed decisions and encouraging ethical AI adoption in real estate. This study synthesises technical and stakeholder dimensions of AI in property valuation using a structured qualitative approach via SLR. It proposes a novel hybrid framework that integrates stakeholder trust factors with model precision to enhance both reliability and acceptance of AI tools.