THE ROLE OF ARTIFICIAL INTELLIGENCE IN OPTIMIZING PROPERTY MANAGEMENT PROCESSES IN THE UAE REAL ESTATE SECTOR
DOI:
https://doi.org/10.70301/Keywords:
AI ROLE IN PROPERTY MANAGEMENTAbstract
Abstract
The real estate sector in the United Arab Emirates (UAE) is an important factor in the national development, yet it still suffers underlying inefficiencies in property management, including manual processes, tenant delays, and operational risks. This paper examines the connection between Artificial Intelligence (AI) and its use in the optimization of property management focusing on six main applications: work automation, data analysis, predictive maintenance, tenant interaction, operational effectiveness, and risk management. Based on the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the study applied a mixed-methods approach, integrating the results of the survey conducted among 300 industry professionals and a qualitative case study of Better Homes, a powerful UAE-based property management company.
The quantitative findings show that AI is very effective in enhancing all six property management aspects, with data analysis (R²=0.750) and risk management (R²=0.703) showing the greatest effects. The moderation analysis also indicates that technological infrastructure boosts the effects of AI in work automation, interaction with tenants, and risk management. These results are validated by qualitative data, as the participants mention efficiency benefits due to automation, active maintenance planning, and enhanced tenant satisfaction due to AI-based communication devices.
Theoretically, the study adds to TAM and RBV by applying them to the UAE real estate setting, whereas practically it can provide the companies, policy-makers, and investors with the information on how to use AI as a competitive edge. Despite limitations, the findings highlight AI as a revolutionary force, which can be used to optimize the workflow, minimize risks, and enhance the experiences of tenants in a fast-changing environment.
Keywords: Artificial Intelligence, Property Management, UAE Real Estate, Technological Infrastructure, Operational Efficiency
References
References
Adel, I. (2022). The Impact of the Real Estate Market in the Urban Development of Cities and Driving Economic Growth in Dubai / Cairo. [Master thesis, Metropolia University of Applied Sciences]. Theseus.
Al Hosani, Y., & Jabeen, F. (2019). Barriers in Post-Merger Integration in the Real Estate Development Companies in the United Arab Emirates: An AHP Approach. Polish Journal of Management Studies, 19(2), 33–47.
Antwi-Afari, M. F., Li, H., Pärn, E. A., & Edwards, D. J. (2018). Critical success factors for implementing building information modelling (BIM): A longitudinal review. Automation in Construction, 91, 100–110.
Bibri, S. E., & Krogstie, J. (2017). The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis. Journal of Big Data, 4(1).
Chen, J., Hui, E. C. M., & Wang, Z. (2011). Perceived Risk, Anticipated Regret and Post-Purchase Experience in the Real Estate Market: The Case of China. Housing Studies, 26(3), 385–402.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world: Does not start with moon shots. Harvard Business Review, 96, 108–116.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003.
Faccia, A., Le Roux, C. L., & Pandey, V. (2023). Innovation and E-Commerce Models, the Technology Catalysts for Sustainable Development: The Emirate of Dubai Case Study. Sustainability, 15(4).
Jaiswal, A., Arun, C. J., & Varma, A. (2021). Rebooting employees: upskilling for artificial intelligence in multinational corporations. The International Journal of Human Resource Management, 33(6), 1–30.
Jia, M., Komeily, A., Wang, Y., & Srinivasan, R. S. (2019). Adopting Internet of Things for the development of smart buildings: A review of enabling technologies and applications. Automation in Construction, 101, 111–126.
Kaganova, O., & Nayyar-Stone, R. (2000). Municipal Real Property Asset Management: An Overview of World Experience, Trends and Financial Implications. Journal of Real Estate Portfolio Management, 6(4), 307–326.
Koelemaij, J., and Janković, S. (2019). Behind the frontline of the Belgrade waterfront: a reconstruction of the early implementation phase of a transnational real estate development project. In Experiencing post socialist capitalism: urban changes and challenges in Serbia (pp. 45-65). University of Belgrade. Faculty of Philosophy. Institute for Sociological Research.
Kowalski, S. (2018). Establishing Appropriate Best Practices in Intellectual Property Management and Technology Transfer in the United Arab Emirates: Building Human Capital, Global Networks and Institutional Infrastructure to Drive Sustainable Knowledge-Based, Innovation-Driven Development. Indian Journal of Law and Technology, 77.
Na, S., Heo, S., Choi, W., Kim, C., & Whang, S. W. (2023). Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross-National Perspective Using the Technology Acceptance Model. Buildings, 13(10), 2518.
Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Khan, M. S. (2020). The adoption of artificial intelligence and robotics in the hotel industry: Prospects and challenges. Electronic Markets, 31(3).
Naz, F., Kumar, A., Upadhyay, A., Chokshi, H., Trinkūnas, V., & Magda, R. (2022). Property Management Enabled by Artificial Intelligence Post Covid-19: An Exploratory Review and Future Propositions. International Journal of Strategic Property Management, 26(2), 156–171.
Rafiei, M. H., and Adeli, H. (2016). A novel machine-learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2), 04015066.
Rane, N., L. (2023). Integrating Leading Edge Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies for Smart and Sustainable Architecture, Engineering and Construction (AEC) Industry: Challenges and Future Directions. International Journal of Data Science and Big Data Analytics, 3(2), 73–95.
Sapkota, K. (2019). Artificial Intelligence in property management automation: technologies, current applications, and challenges.
Seagraves, P. (2023). Real Estate Insights: Is the AI revolution a real estate boon or bane? Journal of Property Investment & Finance.
Seetharaman, A., Saravanan, A. S., Patwa, N., & Bey, J. M. (2017). The Impact of Property Management Services on Tenants’ Satisfaction with Industrial Buildings. The Journal of Asian Finance, Economics and Business, 4(3), 57–73.
Shaw, J. (2018). Platform Real Estate: theory and practice of new urban real estate markets. Urban Geography, 41(8), 1–28.
Siniak, N., Kauko, T., Shavrov, S., & Marina, N. (2020). The impact of proptech on real estate industry growth. IOP Conference Series: Materials Science and Engineering, 869(6), 062041.
Statista. (2023). Real estate in the UAE. Statista.
Statista. (2023). Real estate worldwide. Statista.
Starr, C. W., Saginor, J., and Worzala, E. (2021). The rise of PropTech: Emerging industrial technologies and their impact on real estate. Journal of Property Investment and Finance, 39(2), 157-169.
Tchuente, D., and Nyawa, S. (2022). Real estate price estimation in French cities using geocoding and machine learning. Annals of Operations Research, 1-38.
Ullah, F., Sepasgozar, S., & Wang, C. (2018). A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms. Sustainability, 10(9), 3142.
Wachsberger, K. (2021). Opportunities and Challenges for Israel-UAE Economic Cooperation. Mitvim Institute.
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