A Data-Driven Approach to Predicting Melbourne Housing Prices Using Advanced Machine Learning Models

This research rigorously explores the intricate correlations between property attributes and housing prices in Melbourne, aiming to refine price prediction accuracy. Positive correlations observed with ‘Rooms,’ ‘Bedroom2,’ ‘Bathroom,’ ‘Landsize,’ and ‘BuildingArea’ underscore the intuitive link between property size and price, while negative associations with ‘Distance,’ ‘Y earBuilt,’ and ‘PropertyCount’ emphasize the influential role of location, age, and market demand. Multiple modeling approaches were employed and compared for their predictive prowess. Among these, the Random Forest model (Approach 5) stands out with an exceptional R-squared value of 0.81, showcasing its adeptness in capturing complex data relationships effectively. Notably, the inclusion of categorical variables and leveraging localized data in conjunction with Random Forest (Approach 6) also yielded an impressive R-squared value of 0.81, underlining the pivotal role of nuanced information in precise predictions. Furthermore, XGBoost (Approach 7) demonstrated commendable performance, achieving an R-squared value of 0.80, showcasing the versatility of boosting techniques. The comparison of these approaches emphasizes the significance of methodical analysis and model selection in predicting housing prices accurately. The findings underscore the superiority of ensemble methods, especially Random Forest, and the importance of incorporating categorical and localized information for precise price estimations in Melbourne's dynamic real estate market. This comprehensive investigation highlights the exceptional performance of Random Forest and underscores the significance of leveraging nuanced data for superior predictive accuracy. Recommendations stemming from this study advocate for the adoption of ensemble methods, specifically Random Forest and XGBoost, while emphasizing the critical role of categorical variables and localized insights for refining housing price predictions in Melbourne. These insights offer a robust roadmap for enhancing predictive accuracy and guiding informed decisions in real estate.