<![CDATA[In the era of data-driven decision-making, predictive analytics has become an essential approach for transforming raw business data into strategic managerial insights, particularly in the property sector. This study develops an interpretable predictive analytics model for estimating house prices using Multiple Linear Regression (MLR) based on physical and financial housing attributes. Using a housing dataset of 120 records and six variables processed through RapidMiner and Tableau, the findings reveal that building tax amount (β = 0.995, p < 0.001) and building area (β = 1.826, p = 0.022) are the most influential predictors. The number of bathrooms produced a negative coefficient (β = −1665.021, p = 0.017), indicating multicollinearity, while year completed was statistically insignificant (p = 0.276). The model achieved excellent performance with R² = 0.998, MAE = 2,547, and RMSE = 3,215 IDR. This study integrates regression interpretation, predictive analytics, and managerial insight within a practical analytical framework, contributing to property valuation and data-driven decision-making in real estate management.]]>
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