Figure 4.
The framework contains six numbered stages connected by directional arrows. Stage 1 is Analysis of key stakeholder needs. Its PESTLE requirements and needs are investment optimisation, affordability analysis, market trend analysis, regulations and policies, fair and unbiased valuation, regulatory compliance, real time predictive analytics, model accuracy, climate risk assessment, and sustainability driven property insights. Stage 2 is Identify A I approaches for valuation. Machine learning approaches include D T for simple rule based predictions, R F for improved accuracy using multiple methods, G B M for enhanced price prediction, X G Boost for fast and efficient boosting, and CatBoost for handling categorical data. Deep learning approaches include A N N for detecting complex pricing patterns and C N N for analysing images for valuation. The flow branches down to Hybrid A I Model Selection. A related note describes a hybrid A I approach combining M L and D L to improve accuracy through higher accuracy, better data utilisation, improved predictions, and handling complex relationships. The main flow continues from Stage 2 to Stage 3, A I Model Development. Data collection and processing gathers property, market, and environmental data. A I model application uses M L for price estimation and trends and D L for image analysis and risk detection. Market and risk assessment evaluates property value, affordability, and investment risks. Environmental analysis identifies climate risks and sustainability factors. Stage 3 leads to Stage 4, Model Effectiveness and Trust Factors. Evaluation criteria cover accuracy and reliability through model precision and accuracy, stakeholder usability and trust through interpretability, explainability, bias reduction, and fairness, and efficiency and performance through model processing time and real time adaptability to market fluctuations. Stage 4 leads down to Stage 5, A I Trustworthiness and Decision Integration. Regulatory and governance requirements include transparency, explainability, accountability, data privacy, and legal compliance. A V M model satisfaction covers transparency and interpretability by ensuring A I decisions are explainable, and user satisfaction by providing confidence in A I based valuation insights. Stage 5 leads to Stage 6, Stakeholder Trust and Regulatory Confidence. The hybrid model selection also leads to Stage 6. Stage 6 leads down to Regulatory Approval and Implementation, while a return path leads back to Stage 1.Sequential hybrid AI valuation framework integrating trust and governance factors