Forecasting house prices using data-driven techniques: insights from structured real estate data

The real estate industry makes a large contribution to the growth of the economy, and house prices are determined by a large number of factors such as location, amenities, and property type. Proper forecasting of house prices is crucial for investors, buyers, and sellers to make effective decisions. This paper is about an in-depth study of several machine learning models to forecast house prices based on an Indian residential property data set with 14,620 records. Comprehensive feature engineering was undertaken to identify salient property features. Various regression models such as linear regression, elastic net, support vector regression (SVR), AdaBoost, gradient boosting, and XGBoost were analyzed based on mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R2 values. Among these, XGBoost performed the best with an R² value of 0.892 and the minimum RMSE, reflecting better predictive efficiency. The research exhibits the promise of state-of-the-art ensemble approaches to real estate price prediction and provides directions for further research, such as the integration of geospatial information, natural language text descriptions, and explainability methods to increase model interpretability.