DEVELOPMENT OF AN INTELLIGENT SYSTEM FOR HOUSING PRICE PREDICTION BASED ON DIGITAL PLATFORM DATA
Downloads
Objective: The primary aim of the study is to create and apply an intelligent model that would correctly forecast prices of houses based on different factors that affect the prices, including location, size of the property, number of rooms, infrastructure and market trends. Method: The information employed in this research is presumed to be gathered on popular online platforms, which offer real-time and a wide variety of data. The predictive performance of several machine learning algorithms, such as Linear Regression, Random Forest, and Gradient Boosting, is taken into consideration. Results: The suggested system not only increases the accuracy of predictions, but it also increases the process of making decisions among buyers, sellers and real estate professionals. The experimental findings indicate that the ensemble-based models are more accurate and robust than the traditional statistical methods. Novelty: The present paper is devoted to the creation of an intelligent algorithm for price forecasting of houses depending on the data obtained from Internet platforms. The paper can be seen as an addition to the increasing body of research in the area of intelligent real estate analytics since it provides an efficient and scalable method of housing price forecasting.
N. Kok, P. Monkkonen, and J. M. Quigley, “Economic geography, jobs, and regulations: The value of land and housing,” Journal of Urban Economics, 2017.
S. Mullainathan and J. Spiess, “Machine learning: An applied econometric approach,” Journal of Economic Perspectives, 2017.
Z.-H. Zhou, Machine Learning. Cham: Springer Nature, 2021.
S. Zheng et al., “Urban housing price prediction based on big data and machine learning,” IEEE Access, 2018.
H. Li and J. Wu, “Real estate price prediction with deep learning: A review,” IEEE Transactions on Knowledge and Data Engineering, 2020.
A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Sebastopol, CA: O’Reilly Media, 2022.
M. Kuhn and K. Johnson, Feature Engineering and Selection. Boca Raton, FL: CRC Press, 2019.
G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning. Cham: Springer, 2021.
L. Breiman, “Random forests,” Machine Learning, 2001.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. New York, NY: Springer, 2017.
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. ACM SIGKDD Int. Conf., 2016.
Z. Zhang et al., “Housing price prediction using machine learning algorithms,” IEEE Access, 2021.
Y. Li et al., “Feature importance analysis in real estate prediction models,” Expert Systems with Applications, 2022.
X. Wang et al., “Real estate price prediction based on ensemble learning,” Applied Sciences, 2020.
G. Biau and E. Scornet, “A random forest guided tour,” Test, 2016.
E. L. Glaeser et al., “Housing market dynamics and urban economics,” Journal of Economic Perspectives, 2018.
S. García et al., Data Preprocessing in Data Mining. Cham: Springer, 2019.
N. Kok and P. Monkkonen, “Big data in real estate? From manual appraisal to automated valuation,” Journal of Portfolio Management, 2020.
Copyright (c) 2026 Elmurotov Rustam Ergash o‘g‘li
This work is licensed under a Creative Commons Attribution 4.0 International License.
