Real Estate Price Prediction Using Machine Learning

Real Estate Price Prediction Using Machine Learning

Pavithra S, Preethi G, Rajasekar U, Roobhasri S

ABSTRACT
Real estate price prediction is vital for stakeholders like investors, homeowners, and policymakers, relying on factors such as location, property size, age, and economic indicators. This study combines traditional econometric models with advanced machine learning techniques, including linear regression, decision trees, random forests, and neural networks, evaluated using metrics like MAE, RMSE, and R-squared. Ensemble and deep learning models excel at capturing complex, non-linear patterns, while GIS and spatial analysis improve accuracy. Practical applications include property valuation, mortgage risk assessment, and urban planning. Ethical concerns like data privacy and bias are addressed, with future research exploring real-time data and emerging technologies like IoT and blockchain.

Keywords: Machine learning, GIS, deep learning, spatial analysis, ensemble methods.