Real-Estate Price Analysis and Prediction Using Ensemble Learning

Accurate real-estate price estimation is crucial for buyers, sellers, investors, lenders, and policymakers, yet traditional valuation practices often rely on subjective judgment, inconsistent expertise, and incomplete market information. With the increasing availability of digital property listings, large volumes of structured real-estate data can now be leveraged to build objective, data-driven valuation systems. This project develops a comprehensive analytical framework for predicting different types of properties prices using real-world listing data collected from 99acres.com across major Indian cities. The workflow includes automated web scraping, extensive data cleaning, normalization of heterogeneous property attributes, and exploratory data analysis to identify important pricing patterns and structural trends within the dataset. A multi-stage learning pipeline is designed—consisting of feature preparation, hyperparameter tuning, cross-validation, and performance evaluation—to ensure that the final predictive system is both reliable and generalizable. In addition to the core prediction engine, the project proposes a future extension using Retrieval-Augmented Generation (RAG) with Large Language Models(LLM’s) to provide transparent, context-aware explanations for each valuation. Overall, this work establishes the foundation for a scalable, interpretable, and data-centric real-estate valuation platform capable of supporting informed decision-making in diverse market contexts.