Optimising Real Estate Investment Through Technology

References

[1] Adewale, T. T., Olorunyomi, T. D., & Odonkor, T. N. (2023). Big data-driven financial analysis: A new paradigm for strategic insights and decision-making. Journal of Financial Innovation and Analytics, 1(1), 1-15.

[2] Almquist, P., & Gustafsson, N. (2018). Building Information Modelling: Perceived value and opportunities An investigation of real estate owners and appraisal firms.

[3] Anastasios, P., & Maria, G. (2024). Predictive AI in business intelligence enhancing market insights and strategic decision-making. American Journal of Technology Advancement, 1(8), 72-90.

[4] Andriole, S. J. (Ed.). (2008). Technology Due Diligence: Best Practices for Chief Information Officers, Venture Capitalists, and Technology Vendors: Best Practices for Chief Information Officers, Venture Capitalists, and Technology Vendors. IGI Global.

[5] Balan, B., Ni, N., Ma, Y., Ren, M., Huang, J., Zhu, S., & Balan, B. (2025). AI-Driven M&A-How Algorithms are Influencing Target Identification and Valuation.

[6] Battisti, E., Creta, F., & Miglietta, N. (2020). Equity crowdfunding and regulation: implications for the real estate sector in Italy. Journal of Financial Regulation and Compliance, 28(3), 353-368.

[7] Baum, A. (2021). Tokenization—The future of real estate investment. The Journal of Portfolio Management, 47(10), 41-61.

[8] Celestin, M. (2023). How digital twins are enhancing supply chain resilience against market disruptions. Brainae Journal of Business, Sciences and Technology (BJBST), 7(4), 989-999.

[9] Ciflikli, C., & Kahya‐Özyirmidokuz, E. (2012). Enhancing product quality of a process. Industrial Management & Data Systems, 112(8), 1181-1200.

[10] Conway, J. J. E. (2018). Artificial intelligence and machine learning: Current applications in real estate.

[11] Edu, W. S. O. (2021). PropTech 3.0: the future of real estate. Retrieved May, 10, 2018-07.

[12] Emrich, T. (2024). The Next Dimension: How to Use Augmented Reality for Business Growth in the Era of Spatial Computing. John Wiley & Sons.

[13] Hafeez, S. (2025). Exploring determinants of funding success in crowdfunding campaigns.

[14] Hamilton, T. (2011). Real estate market dynamics during capital market imbalances. Journal of Property Investment & Finance, 29(4-5), 359-371.

[15] Indunil, K., Ekanayake, U., Gunawardana, K., & De Alvis, K. (2024). Smart Contracts and FIDIC: Automating Dispute Resolution and Payment Clauses Using Blockchain.

[16] Jahid, M. S. R. (2024). Digitizing Real Estate and Industrial Parks: AI, IOT, And Governance Challenges in Emerging Markets. International Journal of Business and Economics Insights, 4(1), 33-70.

[17] Joshi, S. (2025). Generative AI in Investment and Portfolio Management: Comprehensive Review of Current Applications and Future Directions. Investment and Portfolio Management: Comprehensive Review of Current Applications and Future Directions (April 01, 2025).

[18] Krivorotov, G., & LaCour-Little, M. (2021). AVMs versus Appraisals in Refinances: Evaluating Anchoring Bias through Relative Risk Identification. Available at SSRN 3603327.

[19] Marson, M. (2024). The Smart Building Advantage: Unlocking the value of smart building technologies. Emerald Group Publishing.

[20] McGrath, P., Desai, K., & Junquera, P. (2019). Resistance is futile: How corporate real estate companies can deploy artificial intelligence as a competitive advantage. Corporate Real Estate Journal, 9(2), 121-129.

[21] Minoli, D., Sohraby, K., & Occhiogrosso, B. (2017). IoT considerations, requirements, and architectures for smart buildings—Energy optimization and next-generation building management systems. IEEE Internet of Things Journal, 4(1), 269-283.

[22] Moita, T. D. S. B., da Silva, A. A. C. A., de Albuquerque, C. V. C. C., Ramos, J. M. G. C., & da Silva Jesus, S. M. (2021). Real Estate Risk Management at BPI GA (Master's thesis, Universidade NOVA de Lisboa (Portugal)).

[23] Mulero-Palencia, S., & Monzon Baeza, V. (2023). Detection of vulnerabilities in smart buildings using the Shodan tool. Electronics, 12(23), 4815.

[24] Munawar, H. S., Qayyum, S., Ullah, F., & Sepasgozar, S. (2020). Big data and its applications in smart real estate and the disaster management life cycle: A systematic analysis. Big Data and Cognitive Computing, 4(2), 4.

[25] Nasirzada, N. (2025). ERP System Integration to Optimize Financial Reporting in Real Estate Management. Universal Library of Business and Economics, 2(1).

[26] Oladiran, O., & Dickins, L. (2024). PropTech and real estate innovations: A guide to digital technologies and solutions in the built environment. Routledge.

[27] Omrany, H., Al-Obaidi, K. M., Husain, A., & Ghaffarianhoseini, A. (2023). Digital twins in the construction industry: a comprehensive review of current implementations, enabling technologies, and future directions. Sustainability, 15(14), 10908.

[28] Oza, V. J., Nikte, A., Bhanushali, V., & Rote, U. (2024, April). Smart contracts and tokenization: revolutionizing real estate transactions with blockchain technology. In 2024 International Conference on Inventive Computation Technologies (ICICT) (pp. 1598-1604). IEEE.

[29] Peiser, R., & Wei, J. The Evolution of Proptech in the 21st Century: Evidence from Early-Stage Investment Data. Available at SSRN 5238235.

[30] Rane, N. (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. Engineering and Construction (AEC) Industry: Challenges and Future Directions (September 24, 2023).

[31] Rane, N., Choudhary, S., & Rane, J. (2023). Artificial Intelligence (Ai) and Internet of Things (Iot)–based sensors for monitoring and controlling in architecture, engineering, and construction: Applications, challenges, and opportunities. Engineering, and construction: applications, challenges, and opportunities (November 20, 2023).

[32] Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE access, 8, 21980-22012.

[33] Raza, M., Jahangir, Z., Riaz, M. B., Saeed, M. J., & Sattar, M. A. (2025). Industrial applications of large language models. Scientific Reports, 15(1), 13755.

[34] Riabchenko, Y., Onyshchenko, A., Kudin, V., Kononets, O., & Holdskyi, V. (2025). Digital assets and property rights: Regulation and legal implications within the EU and globally. Statute Law Review, 46(3), hmaf029.

[35] Saiz, A. (2020). Bricks, mortar, and proptech: The economics of IT in brokerage, space utilization and commercial real estate finance. Journal of Property Investment & Finance, 38(4), 327-347.

[36] Sirigiri, P. (2024). Identification of Distressed Real Estate Properties Using Natural Language Processing/Machine Learning.

[37] Sjöblom, H., Palomäki, S., Halonen, M., Lankiniemi, S., & Oy, G. C. (2018). Climatic risks and opportunities in real estate portfolio management.

[38] Skačkauskienė, I., Bytautė, S., & Leonavičiūtė, V. (2025). Assessment of potential tenants and decision support systems: from systematic literature review to future research guidelines.

[39] Sudrajat, R. (2023). Web-based information system framework for the digitization of historical databases and endowments. International journal of data and network science.

[40] Taofeek, A. (2025). Smart Compliance: Opportunities and Pitfalls in Deploying AI-Blockchain Systems for Real-Time AML Monitoring.

[41] Tekouabou, S. C., Gherghina, Ş. C., Kameni, E. D., Filali, Y., & Idrissi Gartoumi, K. (2024). AI-Based on Machine Learning Methods for Urban Real Estate Prediction: A Systematic Survey: SCK Tekouabou et al. Archives of Computational Methods in Engineering, 31(2), 1079-1095.

[42] Topraklı, A. Y. (2025). AI-driven valuation: a new era for real estate appraisal. Journal of European real estate research, 18(1), 105-120.

[43] Torre‐Bastida, A. I., Del Ser, J., Laña, I., Ilardia, M., Bilbao, M. N., & Campos‐Cordobés, S. (2018). Big Data for transportation and mobility: recent advances, trends and challenges. IET Intelligent Transport Systems, 12(8), 742-755.

[44] Tshitangano, T. (2024). An explorative case study of blockchain as a means to enhancing land registry governance to uphold property and land restitution in South Africa.

[45] Tsirovasilis, E., Katafygiotou, M., & Psathiti, C. (2025). Comparative Assessment of LEED, BREEAM, and WELL: Advancing Sustainable Built Environments. Energies, 18(16), 4322.

[46] Van Nieuwerburgh, S., Stanton, R., & de Bever, L. (2015). A review of real estate and infrastructure investments by the Norwegian Government Pension Fund Global (GPFG). Report to the Norwegian Ministry of Finance.

[47] Walacik, M. (2024). Property appraisal via lens of property registration abundance–real estate market asymmetry assessment. International Journal of Strategic Property Management, 28(6), 393-410.

[48] Yeoh, P. (2021). The sustainability of environmental, social and governance (ESG) reporting in the US and the UK. Business Law Review, 42(6).

[49] Zubizarreta, H. V., & Aquino, D. T. (2026). Environmental, Social, and Governance (ESG) in Office Real Estate: A Systematic Literature Review.

@article{1719762,
      author = {Olatunde Badmus, Dayo Jegede, Yusuf Babatunde Adejumo, Njoku Francis Kelechi},
      title = {Optimising Real Estate Investment Through Technology},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {1372-1386},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719762.pdf},
      abstract = {This study will review the impact of technology on optimizing real estate investment and explain the emergence of new digital technology and solutions that are transforming real estate investment decision-making, asset management, transaction processes and real estate portfolio performance. The study highlights the influence of artificial intelligence (AI), machine learning, big data analytics, blockchain technology, PropTech platforms, smart buildings, Internet of Things (IoT) systems and digital twin technologies on the current real estate investment landscape, based on current industry developments, examples and technological trends. The study shows that AI tools for valuation analysis, predictive data analysis, and automated due diligence procedures boost the accuracy, speed, and efficiency of investment decisions. By processing large volumes of data, big data analytics facilitates market intelligence, allowing investors to make more accurate and precise decisions on investment opportunities. Moreover, blockchain technology and tokenization open up new avenues for transparency, liquidity, and fractional ownership, making real estate investment assets more accessible to investors. Moreover, the research underscores the increasing significance of digital investment platforms in promoting the democratization of property markets and streamlining transactions. Smart building technologies and digital twins also help improve the operations of the building, its sustainability, and long-term asset value creation. Despite these advantages, there are still concerns about the limitations with the data quality, potential security breaches, regulatory uncertainty, algorithmic bias, and relying too heavily on automated systems. The study has found that technology has become a strategic need for real estate investment and not just a supportive tool. The ability of investors who successfully combine technological innovation with expertise is poised to help them create better risk-adjusted returns, efficiency, and sustainable long-term growth in a digital real estate landscape.},
      keywords = {Real Estate Investment, PropTech, Artificial Intelligence, Big Data Analytics, Blockchain Technology, Smart Buildings, Digital Twins, Asset Management.},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719762}
  }