Artificial Intelligence (AI) in Real Estate Valuation: Developing an Explainable AI-Based Automated Valuation Model (AVM)

Abstract

Abstract Property valuation remains fundamental to real estate markets, influencing investment decisions, mortgage lending, taxation, and financial reporting. Traditional valuation approaches relying on professional judgment have been increasingly criticized for subjectivity, inconsistency, and limited capacity to process growing property data volumes. Artificial Intelligence and Automated Valuation Models offer transformative potential for mass appraisal with enhanced speed and accuracy. However, the "black box" nature of many AI systems raises significant concerns regarding transparency, interpretability, and regulatory compliance in valuation practice. The Nigerian real estate market faces unique challenges including data scarcity, inconsistent property records, limited technological adoption, and pronounced gaps in academic literature regarding AI applications in emerging market contexts. Existing research predominantly focuses on developed economies with established property data infrastructures, leaving developing economies underserved by appropriate AI valuation frameworks. The lack of Explainable AI in existing models creates a trust deficit among stakeholders unable to verify AI-generated valuations. This conceptual study develops an Explainable AI-based Automated Valuation Model framework tailored to Nigerian residential property market conditions, integrating hedonic pricing theory, machine learning, and XAI techniques. Adopting a conceptual research design, the study synthesizes theoretical foundations from property economics, ensemble learning algorithms (XGBoost, Random Forest), and explainability techniques (SHAP, LIME) to develop a comprehensive five-layer architectural framework. The proposed framework integrates data integration, feature engineering, predictive modeling, explainability, and output presentation layers. SHAP analysis enables global and local explanations for valuation transparency. Contextual adaptation strategies address Nigerian market realities including data limitations and technological constraints. The framework provides a practical roadmap for developing transparent, interpretable AI valuation systems appropriate for emerging markets, supporting professional validation, regulatory compliance, and stakeholder confidence in AI-enabled property valuation practice. Keywords: Explainable AI; Automated Valuation Model; Real Estate Valuation; Machine Learning; Property Market