Unlocking PropTech Potential: Artificial Intelligence, Cybersecurity, and Real Estate Marketing in Lagos, Nigeria
Adeyemi Aromolaran () and
David Mhlanga ()
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Adeyemi Aromolaran: University of South Wales
David Mhlanga: University of Johannesburg
A chapter in Behavioral Cybersecurity and Ethical AI in Relational Economics Context, 2026, pp 391-411 from Springer
Abstract:
Abstract Artificial intelligence (AI) technologies possess transformative potential in the real estate industry, organizing vast data ecosystems into accurate, secure, and accessible information. AI enhances real estate marketing by streamlining customer targeting, engagement, and data management. While AI adoption is extensive in North America and expanding in Europe, its presence in African markets, particularly in Lagos, Nigeria, Africa's most expensive real estate market, is limited. A study found that in 2021, only 20% of Lagos-based real estate practitioners used AI for agency-related activities, increasing to 24% in 2023 for marketing applications. Adoption rates were similar in both luxury and regular property segments. However, AI integration raises cybersecurity concerns like data breaches and smart home vulnerabilities, as these systems handle sensitive financial transactions and are prime targets for cyberattacks. The study assesses cybersecurity measures, including encryption and compliance with data protection regulations like GDPR and NDPR. It recommends blockchain adoption, AI-driven threat monitoring, and enhanced security frameworks. Addressing these challenges is essential for secure and effective AI integration in Lagos’ real estate sector. The study concludes with recommendations for bridging research, theoretical, and practical gaps to create a more secure and technologically advanced market.
Keywords: Artificial intelligence (AI); Real estate; Cybersecurity; Property marketing; Emerging markets (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-01214-2_17
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DOI: 10.1007/978-3-032-01214-2_17
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