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As machine learning (ML), geospatial analytics, and generative AI take the place of manual evaluation and intuition-driven investment, the real estate industry is undergoing a fundamental transformation. Current research limits practical adoption by treating forecasting, valuation, and decision assistance as distinct activities. A five-layer AI system that combines multi-source data intake, feature engineering using spatial and visual signals, an ensemble valuation engine, a temporal market-forecasting module, and an explainable decision-support layer is proposed in this study. We provide a replicable evaluation procedure, formalize each component, and address governance concerns such as privacy, bias, and regulatory compliance. The framework provides researchers and practitioners with a standard architecture for developing transparent, scalable, and auditable AI systems for real estate markets.
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Gopinath et al. (2026) studied this question.
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