Revolutionizing Home Price Forecasting Through Machine Learning

Awais Azam and Sakshi Rai

SAP Land and Architecture, 2025

Abstract: Introduction; This study develops a data-driven framework for accurate house price prediction using machine learning techniques. Method; We implement a comprehensive methodology involving rigorous data preprocessing, exploratory visualization through multiple chart types, and comparative evaluation of predictive models. Our approach demonstrates the effectiveness of combining analytical visualization with algorithmic modeling for real estate valuation. Result; The research contributes to both academic discourse and practical applications by establishing robust data cleaning protocols and validating model performance. Results indicate significant improvements in prediction accuracy, offering valuable insights for homeowners, investors, and urban planners. Conclusion; This work advances the field of property analytics while providing a replicable methodology for housing market analysis in different socioeconomic contexts.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:laarti:la2025153

DOI: 10.56294/la2025153

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