Enhancing House Price Predictability: A Comprehensive Analysis of Machine Learning Techniques for Real Estate and Policy Decision-Making

Abstract

Accurate house price prediction is crucial for stakeholders in real estate markets and economic policy formulation. This research investigates the application of sophisticated machine learning (ML) algorithms to improve the precision of house price forecasting. By analyzing existing literature, we explore the methodologies employed in house price prediction using ML approaches. We emphasize the significance of precise predictions for various stakeholders, including homebuyers, sellers, investors, and policymakers. Additionally, this abstract critically evaluates the strengths and limitations of different ML techniques in predicting housing prices Our goal is to enhance predictability of models through rigorous analysis, thus facilitating informed decision-making when it comes to housing transactions, investments, and policy implementations through our research.