<![CDATA[The real estate sector functions as a critical barometer of a nation’s economic performance; however, its inherent volatility and intricate pricing mechanisms often hinder precise valuation—particularly in developing urban markets. In the context of Indonesia, where the property industry contributes substantially to national GDP, deriving fair and data-driven housing price estimates remains a persistent challenge. Traditional appraisal methods, which rely predominantly on subjective human judgment, frequently fall short in reflecting market dynamics accurately. This research seeks to construct an interpretable machine learning framework for predicting residential housing prices by employing a Decision Tree Regression (DTR) model. The DTR method was chosen for its transparent and hierarchical structure, allowing for a clear understanding of how individual property characteristics affect price outcomes. The study utilizes a public dataset from Kaggle containing key housing attributes, including land area, building size, number of rooms, and location variables. The methodological steps encompass data preprocessing (cleaning and encoding using One-Hot Encoding), data partitioning into training and testing sets with an 80:20 ratio, and model performance evaluation using standard regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). The model attained an R² value of 0.385, suggesting that the selected features explain approximately 38.5% of the variance in housing prices. While this indicates moderate predictive capability, the DTR model offers valuable interpretive insights—particularly in identifying land area as the most influential predictor of price. The findings highlight that interpretable machine learning approaches can serve as effective analytical tools for property valuation in emerging markets, balancing predictive accuracy with transparency. Moreover, this study lays the groundwork for the future development of ensemble and hybrid predictive models, as well as the integration of AI-based analytics into decision-support systems for property valuation, investment forecasting, and urban development planning in Indonesia’s evolving real estate landscape.]]>
