ARTIFICIAL INTELLIGENCE AND SPECIALIZED ASSETS VALUATION: ASSESSING THE ADEQUACY, PREPAREDNESS AND PROFESSIONAL DEXTERITY OF ESTATE SURVEYORS & VALUERS IN NIGERIA IN THE VALUATION OF STADIA, HYDROELECTRIC FACILITIES, MARITIME VESSELS, AIRPORTS & AIRCRAFT

Abstract

Valuing specialized infrastructure assets such as stadia, hydroelectric dams, ports, airports, and aircraft is highly complex due to intricate operational systems, regulatory constraints, and unique revenue functions that traditional valuation methods fail to capture. In Nigeria, reliance on the depreciated replacement cost method often overlooks operational revenue, risk-adjusted forecasting, and scenario modeling, leading to inaccurate economic valuations. This study proposes a conceptual framework for AI-assisted valuation of specialized assets in Nigeria, merging human professional competence with advanced data analytics to enhance predictive capacity and reliability. The framework integrates four interactive layers: (1) Specialized Asset Categories, (2) Professional Capacity Pillars (valuer adequacy, preparedness, and dexterity), (3) an AI Analytical Engine (leveraging machine learning, big data, GIS, and digital twins for risk-adjusted forecasting and operational simulations), and (4) a Valuation Output and Feedback System for lifecycle cost projections and continuous iterative learning. While global evidence confirms that AI-driven frameworks outperform conventional methods by optimizing lifecycle costs and enabling real-time asset monitoring, adoption in Nigeria remains low due to gaps in professional readiness and institutional support. This framework bridges these gaps, providing an economically robust, operationally realistic approach to sustainable asset management, investment appraisal, and public-private sector policy development for capital-intensive national assets.