Comparative Analysis of Machine Learning with Regression Algorithms in Housing Price Prediction

Abstract

Housing price prediction serves as a critical instrument for property industry stakeholders to determine fair market values. This research aims to perform a comprehensive comparative analysis of four machine learning algorithms: Linear Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost). The study utilizes the Melbourne Housing Dataset, comprising 34,857 data rows and 21 features. Experiments were conducted across three primary scenarios: (1) XGBoost pipeline without imputation, (2) model comparison with a 70:15:15 data ratio, and (3) model comparison with an 80:10:10 data ratio. The methodology encompasses systematic preprocessing phases, including ANOVA testing (F-Score > 50) for feature significance, context-based missing value imputation, and selection of the top 10 features based on Pearson correlation coefficients. The results demonstrate that ensemble and boosting-based algorithms deliver superior performance, with XGBoost achieving the highest R² value of 0.7678 at the 80% training data ratio. This research contributes to determining the optimal dataset configuration and model selection for property price estimation, offering minimal error rates for both developers and potential buyers. Furthermore, the findings highlight the critical importance of non-linear algorithms in capturing the complex dynamics of the property market. This study offers actionable technical recommendations for industry stakeholders seeking to enhance the objectivity and precision of their financial decision-making processes through data-driven approaches.