<![CDATA[House prices are an important indicator in the property sector and are influenced by various factors, including physical characteristics of the building and location. This study aims to analyze and predict house prices using a linear regression method by utilizing several variables, namely land area, building area, number of bedrooms, number of bathrooms, parking availability, and distance to the city center. The data used in this study are secondary data collected through a web scraping process and are focused on houses with a price range of 300700 million rupiah to represent the middle-market segment. The research stages include data preprocessing, Pearson correlation analysis, multicollinearity testing, multiple linear regression modeling, and model performance evaluation using the coefficient of determination (R²) and Root Mean Square Error (RMSE). The dataset is divided into 80% training data and 20% testing data. The results show that the constructed linear regression model achieves an R² value of 0.3078, indicating that the independent variables are able to explain 30.78% of the variation in house prices. The RMSE value of 117,482,242 indicates that prediction errors remain relatively high due to the wide variation in house prices. The correlation analysis results reveal that the number of bathrooms and the distance to the city center have a relatively stronger relationship with house prices compared to other variables. This study demonstrates that linear regression can be used as an initial approach for house price prediction; however, it still has limitations in explaining overall price variations. Therefore, future research is expected to improve prediction performance by incorporating additional variables or applying more advanced modeling methods.]]>
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