Real estate is an asset class that plays a crucial role in home ownership, economic stability, wealth accumulation, and investment portfolio management. Therefore, predicting prices and future market trends is important for home buyers, investors, and policymakers as they help in making informed decisions. Machine learning (ML) has emerged as a useful tool for predictive modeling in financial decisions. The primary objective of this study is to compare and identify the ML algorithms which provide the most accurate predictions for residential housing prices. To achieve this objective, we utilized the Housing Price Index (HPI) from Canada and Australia to analyze performance and influencing factors in this study. Key economic indicators included in the dataset are price-to-income ratio, population growth, interest rates, yield of the 10-year government bond, household real disposable income, and the impact of Covid-19. The data collected span from September 2003 to December 2022, encompassing an analysis of market fluctuations for both markets for approximately two decades. Multiple machine learning algorithms for predictive modeling were used in this study, including K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Bayesian Regression, and Extreme Gradient Boosting (XGBoost). We evaluated model performance and standard error metrics using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) to measure accuracy. The results indicated that Bayesian Regression outperformed all other algorithms, followed by XGBoost, for all datasets in terms of accurate prediction. Overall, our study demonstrates the potential of integrating machine learning into real estate analytics and highlights its importance for improving investment strategies and decisions in the residential housing market.