READINESS OF ESTATE MANAGEMENT FIRMS FOR AI-BASED RENT PRICING SYSTEMS IN SOUTHWESTERN NIGERIA

Abstract

Artificial intelligence (AI) is increasingly transforming global real estate practice, particularly in rent pricing where predictive algorithms allow for enhanced efficiency, accuracy, and transparency. Rent pricing, once a human-centered process dependent on comparative market surveys and subjective judgment, is now shifting toward automated models capable of analyzing big data for market responsiveness. In mature markets such as the United States and the United Kingdom, platforms like Zillow and Rightmove demonstrate how AI-driven systems are shaping landlord–tenant interactions and reducing pricing disputes. However, in developing economies like Nigeria, the question of readiness for such technological disruptions remains unresolved. This study explores the readiness of estate management firms in Southwestern Nigeria to adopt AI-based rent pricing systems. The study draws on the Technology Readiness Index (TRI) and the Technology Acceptance Model (TAM) to assess multiple dimensions of readiness, including technological infrastructure, financial capacity, human resource competence, and organizational culture. Readiness is conceptualized not only as the presence of infrastructure but also as “the willingness and ability of firms to deploy and sustain new technologies in their operations”. A mixed-methods approach was employed, combining structured questionnaires, interviews, and secondary data analysis. Findings reveal that while firms demonstrate moderate awareness of AI concepts, substantial gaps exist in infrastructural preparedness, funding capability, and staff training. For example, many firms, especially those outside Lagos, lack reliable broadband and analytical tools essential for AI adoption. Organizational resistance to change also emerged as a critical barrier, consistent with previous research that “cultural inertia within professional service firms often undermines innovation adoption”. Despite these limitations, estate managers recognize opportunities for AI to improve transparency, attract foreign investment, and enhance competitiveness in Nigeria’s dynamic rental market. The study concludes that readiness among estate management firms in Southwestern Nigeria is still emerging and uneven across states. Lagos demonstrates relatively higher preparedness due to its stronger technological ecosystem and concentration of larger firms, while states such as Osun and Ekiti lag behind. Policy interventions, professional training, and industry–technology partnerships are recommended to accelerate AI adoption. By addressing these readiness gaps, Nigeria’s estate management profession can align more closely with global best practices, thereby improving market efficiency and strengthening the credibility of rent pricing systems.