Application of Machine Learning Using Linear Regression Algorithm for House Price Prediction

Abstract

The price of properties has been indicated to increase annually in the primary market, according to Bank Indonesia's (BI) Residential Property Price Survey (SHPR) for the first quarter of 2022. The IHPR (Residential Property Price Index) rose across almost all house types. Growth for small house types was 2.01% year-on-year, for medium house types 2.18%, and for large house types 1.11% . Meanwhile, in the second quarter of 2022, growth is expected to be limited to 1.16%. Property price predictions will be highly sought after by the public, especially those interested in acquiring properties with their desired specifications. This study predicts house prices by applying machine learning with a case study of data from Pondok Sabilulungan Permai. The data used spans from 2018 to 2023. The objective of this study is to develop a house price prediction application. RMSE (Root Mean Squared Error) was used to calculate errors. The prediction results using linear regression showed an error of 0.66. Based on the linear regression model results, an accuracy of 90.08% was achieved, indicating that the system is performing well.