# RECORD c27bb162a21e02e24af6ca45e7eacffb91547138
# TITLE: Application of Multiple Linear Regression Algorithm for House Price Estimation Based on Building Location and Area to Improve Predictive Accuracy in Real Estate Valuation
# YEAR: 2025.0  VENUE: Riau Jurnal Teknik Informatika  DOI: 10.30606/rjti.v4i3.3993
# AUTHORS: Kecitaan Harefa
# PAGES: 5  CANDIDATE TABLE PAGES (1-based): []
# ABSTRACT: Significant differences in home prices, even on properties with similar building sizes and locations, pose a major challenge in accurately determining property valuations. The discrepancy between the actual market price and the estimated value makes it difficult for potential buyers, sellers, and developers to make the right decision. To overcome these problems, this study applied the Multiple Linear Regression (MLR) algorithm in the Decision Support System (DSS) to estimate house prices based on the location and area of the building. The dataset used consists of 545 housing data points with variables such as house prices, locations, and building areas. The research stages include data collection, pre-processing (data cleaning and normalization), model development using MLR, and model performance evaluation. The evaluation was carried out using the division of trained data and test data with an 80:20 ratio, so that the model was tested using data that was not previously trained. The results showed that the model produced a Mean Absolute Error (MAE) of 1,474,748.13, a Root Mean Squared Error (RMSE) of 1,917,103.70, and a coefficient of determination (R²) of 0.273. A relatively low R² value indicates that the location and area variables of the building are not sufficient to explain the overall variation in house prices, so the addition of other variables—such as the number of rooms, facilities, and environmental conditions—is needed to improve the accuracy of the prediction and produce a more representative price estimate.


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Lisensi  
Lisensi Internasional Creative Commons Attribution-ShareAlike 4.0. 
 
436 
 
 
Terbit online pada laman web jurnal: https://journal.upp.ac.id/index.php/rjti/index 
 
 
Vol. 4 No. 3 (2025) 436 – 440 | ISSN: 2829-2529(Media Online)   
Application of Multiple Linear Regression Algorithm for House Price 
Estimation Based on Building Location and Area to Improve Predictive 
Accuracy in Real Estate Valuation 
 
Kecitaan Harefa1 
1Teknik Informatika, Fakultas Ilmu Komputer, Universitas Pamulang 
1dosen00842@unpam.ac.id 
Abstract  
Significant differences in home prices, even on properties with similar building sizes and locations, pose a major challenge in 
accurately determining property valuations. The discrepancy between the actual market price and the estimated value makes 
it difficult for potential buyers, sellers, and developers to make the right decision. To overcome these problems, this study 
applied the Multiple Linear Regression (MLR) algorithm in the Decision Support System (DSS) to estimate house prices 
based on the location and area of the building. The dataset used consists of 545 housing data points with variables such as 
house prices, locations, and building areas. The research stages include data collection, pre-processing (data cleaning and 
normalization), model development using MLR, and model performance evaluation. The evaluation was carried out using the 
division of trained data and test data with an 80:20 ratio, so that the model was tested using data that was not previously 
trained. The results showed that the model produced a Mean Absolute Error (MAE) of 1,474,748.13, a Root Mean Squared 
Error (RMSE) of 1,917,103.70, and a coefficient of determination (R²) of 0.273. A relatively low R² value indicates that the 
location and area variables of the building are not sufficient to explain the overall variation in house prices, so the addition 
of other variables—such as the number of rooms, facilities, and environmental conditions—is needed to improve the 
accuracy of the prediction and produce a more representative price estimate. 
Keywords: House Price Estimation, Multiple Linear Regression, Location, Building Area. 
Abstrak 
Perbedaan harga rumah yang signifikan, bahkan pada properti dengan luas bangunan dan lokasi yang serupa, 
menimbulkan tantangan besar dalam menentukan penilaian properti secara akurat. Ketidaksesuaian antara harga 
pasar aktual dan nilai estimasi menyebabkan kesulitan bagi calon pembeli, penjual, maupun pengembang dalam 
mengambil keputusan yang tepat. Untuk mengatasi permasalahan tersebut, penelitian ini menerapkan algoritma 
Multiple Linear Regression (MLR) dalam Decision Support System (DSS) untuk memperkirakan harga rumah 
berdasarkan lokasi dan luas bangunan. Dataset yang digunakan terdiri atas 545 data perumahan dengan variabel 
harga rumah, lokasi, dan luas bangunan. Tahapan penelitian meliputi pengumpulan data, pra-pengolahan 
(pembersihan dan normalisasi data), pembangunan model menggunakan MLR, serta evaluasi kinerja model. 
Evaluasi dilakukan menggunakan pembagian data latih dan data uji dengan rasio 80:20, sehingga model diuji 
menggunakan data yang tidak dilatih sebelumnya. Hasil penelitian menunjukkan bahwa model menghasilkan 
Mean Absolute Error (MAE) sebesar 1.474.748,13, Root Mean Squared Error (RMSE) sebesar 1.917.103,70, 
dan koefisien determinasi (R²) sebesar 0,273. Nilai R² yang relatif rendah mengindikasikan bahwa variabel 
lokasi dan luas bangunan belum cukup untuk menjelaskan variasi harga rumah secara keseluruhan, sehingga 
diperlukan penambahan variabel lain—seperti jumlah kamar, fasilitas, dan kondisi lingkungan—untuk 
meningkatkan akurasi prediksi dan menghasilkan estimasi harga yang lebih representatif. 
Kata kunci:  Estimasi Harga Rumah, Regresi Linear Berganda, Lokasi, Luas Bangunan.  
 
1. Introduction  
The development of the property sector in Indonesia 
has increased rapidly in recent years. The community’s 
need for decent and strategic housing continues to drive 
fluctuations in house prices, especially in urban areas 
such as Greater Jakarta [1]. A major challenge that 
arises is determining the selling or buying price of a 
house that accurately reflects market conditions, as 
prices are influenced by factors such as location, 
building area, land area, and available facilities. These