Purpose: This study identifies key challenges to adopting smart real estate (SRE) technologies and offers insights and recommendations to enhance decision-making for stakeholders, including buyers and property investors. Design/methodology/approach: To achieve the aim of the study, a rigorous research approach was employed, conducting an in-depth analysis of 41 academic papers utilising PRISMA guidelines and checklists. The chosen methodology also applies a PEST (Political, Economic, Social and Technological) framework to identify factors influencing technology adoption in the real estate sector. Findings: The study uncovers critical challenges to adopting smart real estate technologies, such as regulatory ambiguity, high implementation costs, and societal resistance. PEST analysis reveals that unclear standards and guidelines, coupled with the high financial burden of technology implementation, are significant obstacles. Socially, resistance to change and difficulties in integrating new technologies are prevalent. The study also underscores the potential of artificial intelligence (AI) for predictive analytics and blockchain for secure transactions and records, though their adoption is currently hindered by inadequate infrastructure and regulatory challenges. These findings underscore the need for strategic interventions to address these challenges and facilitate the effective integration of advanced technologies in the real estate sector, thereby enhancing industry innovation and competitiveness. Practical implications: The study offers insights for real estate stakeholders to embrace technology effectively, with a conceptual framework contributing to industry advancements. Originality/value: The study’s key contribution is offering real estate stakeholders execution tactics and recommendations to navigate challenges and utilise technology, thereby driving industry innovation and enhancing competitiveness.

Property valuation is a critical component of real estate management, influencing decisions on investments, sales, and financing. Traditional methods, such as Linear Regression and Multiple Regression Analysis, often struggle to address the complexities of modern property markets. In response, Artificial Intelligence (AI) offers innovative solutions by utilising advanced models for more accurate predictions of property value. This paper presents a Systematic Literature Review (SLR) using Scopus database, focusing on 37 selected papers. It evaluates the effectiveness of key AI models in property valuation, assessing their predictive power, interpretability, robustness, and flexibility, and examines how well these models generate reliable valuations and their accuracy. The key models were referred to as the “Valuation Four”: Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Regression Models (RM). The review highlights each model’s ability to handle complex real estate data, with SVM and RF demonstrating superior accuracy. At the same time, DT excels in interpretability, making it more user-friendly for decision-making. Regression-based models continue to serve as useful benchmarks but are less effective for intricate datasets. The findings of this study provide valuable insights for real estate professionals and policymakers by identifying the AI models that support precise and reliable property valuation on basis of dataset and factors considerations. This contributes to the ongoing evolution of automated valuation methods and helps advance data-driven decision-making in real estate.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Practical implications: 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. Originality/value: 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.