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Machine Learning-Based Beijing Housing Price Prediction System

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Machine Learning-Based Beijing Housing Price Prediction System


Zihao Wang | Hao Zhang | Tianshuang Han | Xiuyi Yang | Yinuo Liu



Zihao Wang | Hao Zhang | Tianshuang Han | Xiuyi Yang | Yinuo Liu "Machine Learning-Based Beijing Housing Price Prediction System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-5, October 2025, pp.389-392, URL: https://www.ijtsrd.com/papers/ijtsrd97504.pdf

With rapid socioeconomic development, the real estate market faces dual challenges of increasing homebuying pressure and rising investment risks. As a unique commodity combining residential attributes and asset value, the formation mechanism of real estate prices has grown increasingly complex. It is not only influenced by multiple factors but also exhibits significant nonlinear characteristics in its overall trend. As a first-tier city in China, Beijing's housing prices are influenced by various factors, including economic policies, geographical location, educational resources, and transportation accessibility. Accurately forecasting housing price changes holds significant importance for homebuyers, investors, real estate developers, and government policymakers. This project aims to leverage machine learning and data analytics to propose a multi-source data fusion framework. By collecting local environmental data such as air quality and noise pollution, it breaks the boundaries of traditional real estate datasets. For the first time, dynamic environmental indicators are incorporated into the housing price evaluation system. This approach systematically quantifies the impact mechanism of human living environment quality on real estate value. A baseline model, XGBoost, is constructed to handle high-dimensional features, establishing a model capable of predicting Beijing housing prices. This assists users in understanding price trends and supports decision-making.

Machine Learning; Housing Price Prediction; XGBoost.


IJTSRD97504
Volume-9 | Issue-5, October 2025
389-392
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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