Purpose The integration of Artificial Intelligence (AI) in property management transactions is transforming the real estate sector via improved automation, predictive analytics, intelligent property management and enhanced decision-making. This study investigates how AI enhances property management transactions as well as the significant barriers to its implementation. Design/methodology/approach This research employs a systematic literature review (SLR) and NVivo-based qualitative analysis to discern significant trends, innovations and obstacles in the adoption of AI. The study analyzes existing literature and industry reports to identify patterns, challenges and emerging solutions in AI-driven property management. Findings The results indicate that AI markedly enhances efficiency (automation and predictive analytics), tenant engagement (behavior analysis and intelligent communication), property value (AI-driven assessments) and sustainability (energy optimization and waste minimization). Nevertheless, obstacles to widespread adoption persist, including data privacy issues, legal and ethical challenges, budgetary limitations and opposition from stakeholders. Smaller real estate enterprises have heightened hurdles stemming from the digital divide, security vulnerabilities and algorithmic prejudice. Research limitations/implications The study is mostly based on secondary data from literature and industry sources, which may limit the findings' applicability to real-world scenarios. Future research could use empirical data, such as case studies or surveys, to confirm AI’s practical influence in a variety of property markets. Practical implications The findings offer valuable insights for real estate professionals, investors and AI developers on how to effectively integrate AI into property management. Key areas for practical implication include predictive maintenance relating to IoT usage; property valuation automation; AI-powered tenant screening; Site selection and market forecasting; Chabot and NLP for leasing; and blockchain integration and fraud detection. To achieve effective integration, industry stakeholders must emphasize ethical AI governance, stringent data security and cooperation between AI and humans. Additionally, AI’s synergy with cloud computing, blockchain and the Internet of Things (IoTs) may enhance transparency, security and efficiency in real estate transactions. Social implications The adoption of AI in property management has broader societal consequences, including the possibility of job displacement and the necessity for reskilling initiatives to assist real estate workers. An equitable strategy that encourages innovation, reduces risks and increases worker flexibility is required to realize AI’s full potential in property management. This study emphasizes the importance of collaboration among researchers, real estate companies, legislators and AI technologies developers. Originality/value This study contributes to the expanding body of knowledge on AI in real estate by providing a structured qualitative synthesis of AI uses, barriers and future potential. Unlike prior studies that have focused only on AI benefits, this study offers a balanced evaluation of both the promise and constraints of AI-driven property management transactions.
Adediran et al. (Tue,) studied this question.