THE EMERGING PARADIGM OF PROPERTY VALUATION IN NIGERIAN URBAN MARKETS: INTEGRATING ARTIFICIAL INTELLIGENCE, SPARTIAL ANALYTICS AND URBAN DYNAMICS

Abstract

Property valuation in Nigeria and other emerging markets is undergoing a major shift from static, retrospective methods toward a dynamic, data-driven approach. Traditionally, practitioners have relied on comparative, cost, and income methods. However, these conventional tools fail to capture real-time market dynamics, behavioral factors, or systemic risks. While recent innovations like artificial intelligence (AI), geographic information systems (GIS), and automated valuation models (AVMs) have emerged, they remain fragmented and detached from the complex realities of modern urban infrastructure. To bridge this gap, this study introduces the Dynamic Integrated Real Estate and Infrastructure Valuation Engine (DIREV-E). Designed as an adaptive, multi-layered framework, DIREV-E unifies financial, geospatial, environmental, and regulatory data into a single analytical platform. By leveraging predictive AI, digital twin technology, and scenario-based simulations, the engine delivers continuous value updates and real-time decision support for both traditional real estate and complex infrastructure systems. Ultimately, this research reframes valuation as a self-adaptive ecosystem where value is dynamically generated through the interplay of market actors, policies, and environmental factors. Our findings show that embedding behavioral analytics, feedback loops, and ESG criteria significantly improves valuation accuracy and strategic relevance. This study provides a scalable pathway to transform valuation from a passive estimation exercise into an intelligent decision-support engine for investors, urban planners, and policymakers in rapidly evolving urban economies.