The global real estate market, particularly within the rapidly urbanizing corridors of India, has long been characterized by extreme information asymmetry and a lack of pricing transparency. For the average home-seeker or small-scale investor, determining the "fair market value" of a rental property is often an exercise in guesswork which depends on broker evaluations and current market conditions. This research addresses these systemic inefficiencies by proposing and implementing an intelligent data-driven framework for real estate valuation which connects advanced machine learning concepts with user learning needs. The framework includes a strong predictive system which operates through Random Forest Regression technology. The Random Forest algorithm provides an effective solution for urban housing challenges because it can understand the complex relationships between property features and local geographical patterns which include square footage and BHK configuration. Our model was developed using a complete data collection that included various Indian metropolitan areas and it went through an extensive preprocessing procedure which involved converting "Furnishing" and "City" data into categorical formats and creating a special "Luxury Score" assessment tool. This score measures the total effect of secondary facilities which include balconies and bathrooms to help the model differentiate between standard and premium listings with accurate statistical results. The research introduces a new method for prediction which replaces static prediction methods with dynamic simulation techniques. Users frequently encounter "what-if" scenarios so we created an AI-driven Feature Simulator. Users can change property features through this tool which provides interactive property variable manipulation.
Real Estate Valuation, Random Forest Regression, Indian Housing Market, Predictive Analytics, Interactive Simulation.
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