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Open Access

House Price Prediction: A Multi-Source Data Fusion Perspective

Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 999077, China
School of Economics and Management, Beihang University, Beijing 100191, China
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Abstract

House price prediction is of utmost importance in forecasting residential property prices, particularly as the demand for high-quality housing continues to rise. Accurate predictions have implications for real estate investors, financial institutions, urban planners, and policymakers. However, accurately predicting house prices is challenging due to the complex interplay of various influencing factors. Previous studies have primarily focused on basic property information, leaving room for further exploration of more intricate features, such as amenities, traffic, and social sentiments in the surrounding environment. In this paper, we propose a novel approach to house price prediction from a multi-source data fusion perspective. Our methodology involves analyzing house characteristics and incorporating factors from diverse aspects, including amenities, traffic, and emotions. We validate our approach using a dataset of 28550 real-world transactions in Beijing, China, providing a comprehensive analysis of the drivers influencing house prices. By adopting a multi-source data fusion perspective and considering a wide range of influential factors, our approach offers valuable insights into house price prediction. The findings from this study possess the capability to improve the accuracy and effectiveness of house price prediction models, benefiting stakeholders in the real estate market.

Keywords

price predictionreal estatedata miningdata fusionmachine learning

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 603-620
DOI: 10.26599/BDMA.2024.9020019
Cite this article:
Zhao Y, Zhao J, Lam EY. House Price Prediction: A Multi-Source Data Fusion Perspective. Big Data Mining and Analytics, 2024, 7(3): 603-620. https://doi.org/10.26599/BDMA.2024.9020019

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Received: 24 November 2023
Revised: 12 March 2024
Accepted: 22 March 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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