House Price Prediction Leveraging Feature Engineering and Model Comparison: A Case Study of Ames and Iowa Datasets

Developing a precise prediction of house prices has been an unsettled task for many years. It is the social as well as economic need for the welfare and comfort of the citizens. The objective and aim of the research is to provide digital-based solutions to real estate prices, which focuses on economic, geographic, and demographic variables. The paper uses Advanced Regression Techniques on datasets from Ames, Iowa. The study identifies significant correlations between housing prices and 79 explanatory variables describing every aspect of residential homes. For example, the Lot configuration. Physical locations within Ames city limits, original construction date, and so on. The paper employs Lasso, Ridge, XGBoost and LightGBM to verify the most suitable model in practice in this field, offering insights for policymakers, investors, and homebuyers. This research contributes to the existing literature by providing a comparative analysis across different housing markets and suggesting strategies for price stabilisation.