Property Price Prediction: Utilizing Machine Learning for Real Estate Valuation

Abstract—One of the high-profile uses of machine learning in real estate is as a substitute to appraisals to predict property prices and to generate data-driven investment decisions. This manuscript describes an ensemble real estate valuation approach that integrates gradient boosting, random forest, and ridge regression methods yielding high predictive accuracy for real estate values across different types of real estate and geographies. The model has an extensive feature engineering component to transform raw property attributes into meaningful predictors within spatial, temporal, and qualitative contexts. Innovations included city-specific multipliers, property quality ratings, debt and equity investment potential rankings, and location-based coefficients accounting for the complex real estate setting of India. The application demonstrates strong valuation performance with cross-validated accuracy exceeding 95% and strong R² scores, ratifying a strong predictive capacity of recommended property against valuation. The machine-learning model is able to predict across unobserved locations via location correspondence to control for property characteristics, market tiers, and geographic econometric factors. Also, the model provides confidence metrics and feature importance for transparency during the valuation process. This method is particularly useful in emerging markets with limited historical data, where traditional appraisal practices may not be satisfactory. The practical cases involve different property types in Kerala, Tamil Nadu, Karnataka, and other states in India, demonstrating how machine learning can change the real estate valuation process into a precise and data-driven process while addressing market dynamics.