House Value Estimation using Different Regression Machine Learning Techniques

Yıl 2024, , 245 - 259, 31.12.2024

Tarek Ghamrawi , Muesser Nat

https://doi.org/10.26650/acin.1543650

Öz

This study investigates the effectiveness of various regression algorithms in estimating house values using a dataset sourced from Zillow.com, encompassing 15,000 residential properties from Denver, Colorado. Comparisons of different models such as linear regression, Ridge regression, Lasso regression, Elastic Net, Decision Tree, Random Forest, Gradient Boosting, and XGBoost. The models were evaluated using R-squared (R²) and Mean Absolute Error (MAE) as performance metrics. The results demonstrated that the Random Forest Regressor and XGB Regressor outperformed other models, achieving the highest R² scores and the lowest MAE values. These findings underscore the potential of these models for accurate house price estimation, which can be instrumental for the real estate market. Accurate valuations can help prevent overpricing, which causes properties to remain unsold for extended periods, and under-pricing, leading to financial losses. Implementing these regression models can enhance pricing strategies, ensuring efficient buying and selling processes and contributing to the overall financial health of the real estate market. Future research will explore the use of a broader range of regression models with fewer features to assess their performance and robustness in house price prediction.

Anahtar Kelimeler

“House Price Estimation”, “Machine Learning”, “ElasticNet”, “Lasso Regression”, “Decision Tree Regressor”, “Random Forest Regressor”, “Linear Regression”, “Ridge Regressor”, “Gradient Boosting Regressor”, “XGB Regressor”

Kaynakça

• Ahtesham, M., Bawany, N., & Fatima, K. (2020). House price prediction using machine learning algorithm - The case of Karachi City. Pakistan, 1-5. doi:10.1109/ACIT50332.2020.9300074 google scholar
• Ali, S., Mohammad, H., Fatemeh, A., Christopher, J. P. (2022). Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms. Cities, 131, 103941. https://doi.org/10.1016/j.cities.2022.103941. google scholar
• Binu, J. (2020). A data analytics model for extended real estate comparative market analysis. Pace University, New York. google scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. doi:10.1023/A:1010950718922 google scholar
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. doi:10.1145/2939672.2939785 google scholar
• Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. IMS 1999 Reitz Lecture. Modified March 15, 2000, April 19, 2001. google scholar
• Geron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O’Reilly Media. google scholar
• Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity: The lasso and generalizations. CRC Press. google scholar
• IBM. What is underfitting? Retrieved from https://www.ibm.com/cloud/learn/underfitting google scholar
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer. google scholar
• Li, C. (2023). House price prediction using machine learning. Proceedings of the 4th International Conference on Signal Processing and Machine Learning. School of International Education, GuangDong University of Technology, Guangzhou, China. doi:10.54254/2755-2721/53/20241426 google scholar
• Li, D. (2020, December 25). Overcoming data scarcity and privacy challenges with synthetic data. InfoQ. Retrieved from https://www.infoq. com/articles/overcoming-privacy-challenges-synthetic-data/#idp_register/ google scholar
• Madhuri, C. R., Anuradha, G., & Pujitha, M. V. (2019). House price prediction using regression techniques: A comparative study. 2019 International Conference on Smart Structures and Systems (ICSSS), Chennai, India, pp. 1-5. doi:10.1109/ICSSS.2019.8882834 google scholar
• Marcin, H., Piotr, T., & Mateusz, S. (2024). Prediction of residential real estate price on primary market using machine learning. Procedia Computer Science, 246, 3142-3147. https://doi.org/10.1016/j.procs.2024.09.358. google scholar
• Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis (6th ed.). Wiley. google scholar
• Quang, T., Minh, N., Hy, D., & Bo, M. (2020). Housing Price Prediction via Improved Machine Learning Techniques. Procedia Computer Science, 174, 433-442. https://doi.org/10.1016/j.procs.2020.06.111. google scholar
• Rana, V. S., Mondal, J., Sharma, A., & Kashyap, I. (2020). House price prediction using optimal regression techniques. 2020 2nd Interna-tional Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India, pp. 203-208. doi:10.1109/ICACCCN51052.2020.9362864 google scholar
• Thomas, D. (2023). The importance of data in property valuation and the key role of comparative method. doi:10.13140/RG.2.2.35313.86881 google scholar
• Wang, C., & Wu, H. (2018). A new machine learning approach to house price estimation. New Trends in Mathematical Sciences, 6(4). google scholar
• Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320. doi:10.1111/j.1467-9868.2005.00503. google scholar