<p>Being one of the most populous regions in Indonesia, Greater Tangerang experiences high property prices, particularly for new houses, due to limited land and high demand. As an alternative, purchasing a second-hand house is a viable option as it tends to be more affordable and readily available for occupancy. However, potential buyers often struggle to obtain reliable information and estimate the price of second-hand houses with existing specifications. Previous research has mainly focused on predicting second-hand property prices in different markets such as Pakistan, China, and Spain, making this study original by predicting second-hand property prices in an Indonesian city where such research is scarce. This study presents a model for predicting second-hand house prices using the Extreme Gradient Boosting (XGBoost) algorithm. A dataset of 3,234 second-hand houses was divided into training and test data, achieving an accuracy of 91.34% by fine-tuning various XGBoost parameters. The XGBoost model outperformed the Random Forest (90%) and Decision Tree (88%) models in terms of accuracy. The implications of this study are significant for both developers and individuals seeking to predict second-hand house prices in Greater Tangerang, Indonesia. The findings reveal that factors such as land area, building area, and region play a crucial role in the XGBoost model. Surprisingly, the proximity to public services such as schools and markets did not significantly influence the price of second-hand properties.</p>