Abstract
The boom in the real estate market necessitate easier and modern prediction models to accurately estimate properties' values. In this research, seven machine learning algorithms for house price prediction on residential houses in Bolton, Greater Manchester, were evaluated to cover the research gap in local UK market research. Following the CRISP-DM guideline, analysis of 13,933 transactions of 2,930 unique properties over a period of 12 months based on 17 property attributes with GDPR compliance were made. The pipeline for preprocessing end-toend comprised correlation analysis, detection of outliers using IQR, log transformation for skewness correction, and Min-Max normalization. Seven different models were experimented with: Linear SVR, Polynomial SVR, RBF SVR, K-Nearest Neighbors, Decision Tree, Random Forest implementations (RF-100, RF-200, RF-500), and XGBoost. Performance assessment with 70/30 traintest split showed Random Forest with 500 estimators yielded superior results ( R^{2}=0.93 , MAE =\mathbf{0. 1 0 8 6} , MSE =\mathbf{0. 0 3 3 2} , RMSE =0.1822 ), then XGBoost ( R^{2}=0.92 ). Total Living Area was the superior predictor ( r=0.67 ), confirming rudimentary real estate appraisal fundamentals. These findings offer proof of the capacity of machine learning to enhance property valuation accuracy, with implications for applied utilization by real estate professionals, financial institutions, investors, and policymakers in regional market analysis.