The aim of this study is to develop a Washington, D.C. house price prediction model based on prior transactions in a very interesting and dynamic market. Therefore, we develop predictive models using the richness of the historical housing dataset concerning geographic location: neighborhood and quadrants, property size, number of rooms and other amenities offered, and factors such as distance from access to public transport and schools. K-means clustering was then used to address the class imbalance by defining meaningful subgroups, and thus doing random oversampling in low-represented categories for balancing. The machine learning models that have been used include Random Forest and Gradient Boosting, whose performances were then compared based on RMSE and R 2 metrics. The ensemble methods, in turn, have outperformed the more straightforward models in predictive accuracy, thus proving to be effective in capturing the complexities of housing price prediction. The comparison of machine learning approaches thus forms a valuable foundation for future studies and practical applications in real estate.analytics.