Analisis Harga Rumah Berdasarkan Faktor Fisik dan Lingkungan Menggunakan Metode Regresi Linier

Abstract

This study aims to analyze the influence of physical and environmental factors on house prices using the multiple linear regression method and to implement the analysis results into the Property Analyzer system. The physical factors analyzed include building area, land area, number of bedrooms, number of bathrooms, and number of garages, while environmental factors are represented by accessibility to schools, markets, shopping centers, and toll road entrances. The study utilizes a 2022–2024 house price dataset sourced from Kaggle, comprising 1,010 data points. The research stages include data collection, preprocessing, regression assumption testing, parameter estimation using Ordinary Least Squares (OLS), result analysis, and model implementation into the Property Analyzer system. The results indicate that building area, land area, number of bedrooms, number of bathrooms, and garage capacity significantly influence house prices (p-value < 0.05). Land area exhibits the strongest statistical relationship with house prices, with a t-statistic value of 22.221. The model yielded a coefficient of determination (R²) of 0.6554, indicating that 65.54% of the variation in house prices is explained by the independent variables in the model, while 34.46% is influenced by factors outside the model. The implementation of Property Analyzer successfully applied the multiple linear regression model to facilitate a more objective, rapid, and structured house price estimation process. The findings demonstrate that multiple linear regression serves as a viable approach for analyzing factors related to house prices and supporting property analysis processes.