Advance Methodology for Predicting Residential Property Value

Abstract

This paper presents a machine learning-based approach for predicting residential property values by analyzing key factors influencing house prices. Accurate property valuation is critical in real estate markets, as inaccuracies can result in poor decision-making by buyers, sellers, and investors. Traditional methods often rely on expert judgment and heuristic techniques, which can need more consistency and be time-consuming. This study explores the potential of machine learning models to provide a more systematic and precise valuation framework. Key attributes are analyzed, including location, size, age, and market trends. Various algorithms, such as linear regression, decision trees, random forests, and gradient boosting, are trained on a comprehensive dataset and evaluated using performance metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results reveal that advanced ensemble techniques, particularly random forests and gradient boosting, significantly outperform traditional methods in predictive accuracy. These findings underscore the potential of data-driven approaches to revolutionize property valuation, offering scalable, objective, and efficient tools for real estate stakeholders. By leveraging machine learning, this framework can support more informed decision-making, reduce valuation inconsistencies, and enhance market strategies, paving the way for innovative solutions in the real estate domain.