Enhance Real Estate Prediction System Using Advanced Machine Learnings Models and Dynamic Location Data

The prediction of real estate prices forms an important part of the property market to help buyers, investors, and policymakers in decision making for informed decisions. Traditional models have static property attributes and do not capture dynamic location-based factors, which results in poor predictive accuracy. Therefore, the study envisages Enhanced Real Estate Price Prediction System which comprises advanced machine learning models with real-time localization of data to improve predictive accuracy. Stacked ensemble learning approach incorporating XGBoost, LightGBM, and artificial neural networks (ANNs) have been used in this system to capture complex relationships present in the real estate price. Furthermore, context feature engineering and real-time data integration (for example proximity to amenities, crime rates, and transportation access) also improve this robustness of prediction system. A comparative study shows that the proposed model includes 10-15% reductions in the value of Mean Squared Error (MSE) compared to existing models and up to an accuracy of 95-99% as compared to other models, which are based on regression methods. This research closes the gap of static valuation techniques with AI-enabled market predictions, rendering property valuation accurate, adaptive, and data-driven.