Using Linear Regression, Ridge Regression, Lasso Regression, and Elastic Net Regression for Predicting Real Estate Price

Zhaorui Zeng

2025

Abstract

In the contemporary economic landscape, the real estate market wields significant influence, with housing prices being a crucial factor affecting individuals, industries, and the overall economy. Fluctuations in housing prices can impact people's living standards, investment decisions, and the stability of related industries. Thus, accurately predicting housing sales prices is of utmost importance. This paper focuses on predicting housing sales prices using linear regression, ridge regression, Lasso regression, and elastic net regression. The principles and applications of the four regression models in price prediction are explored in detail. By calculating evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), the performance of each model is compared. The results show that linear regression has an MAE of 0.092129, indicating normal performance. Ridge regression can address multicollinearity issues with an MAE of 0.091513, but may overfit. Lasso regression and elastic net regression, with MAEs of 0.087802 and 0.087756 respectively, can simplify models through feature selection, reducing overfitting risks and improving the ability of generalization. Future research could expand data sources, incorporate external variables, adopt advanced models, and utilize big data and deep learning technologies. This research provides valuable references for real estate-related decision-making and promotes the development of real estate price prediction research.

Paper Citation

in Harvard Style

Zeng Z. (2025). Using Linear Regression, Ridge Regression, Lasso Regression, and Elastic Net Regression for Predicting Real Estate Price. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 509-515. DOI: 10.5220/0013700300004670

in Bibtex Style

@conference{icdse25,
author={Zhaorui Zeng},
title={Using Linear Regression, Ridge Regression, Lasso Regression, and Elastic Net Regression for Predicting Real Estate Price},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={509-515},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013700300004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Using Linear Regression, Ridge Regression, Lasso Regression, and Elastic Net Regression for Predicting Real Estate Price
SN - 978-989-758-765-8
AU - Zeng Z.
PY - 2025
SP - 509
EP - 515
DO - 10.5220/0013700300004670
PB - SciTePress