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Can machine learning algorithms associated with text mining from internet data improve housing price prediction performance?

    Jian-qiang Guo Affiliation
    ; Shu-hen Chiang   Affiliation
    ; Min Liu Affiliation
    ; Chi-Chun Yang Affiliation
    ; Kai-yi Guo Affiliation

Abstract

Housing frenzies in China have attracted widespread global attention over the past few years, but the key is how to more accurately forecast housing prices in order to establish an effective real estate policy. Based on the ubiquitousness and immediacy of Internet data, this research adopts a broader version of text mining to search for keywords in relation to housing prices and then evaluates the predictive abilities using machine learning algorithms. Our findings indicate that this new method, especially random forest, not only detects turning points, but also offers prediction ability that clearly outperforms traditional regression analysis. Overall, the prediction based on online search data through a machine learning mechanism helps us better understand the trends of house prices in China.


First published online 10 June 2020

Keyword : housing frenzies, Internet search, text mining, machine learning

How to Cite
Guo, J.- qiang, Chiang, S.- hen, Liu, M., Yang, C.-C., & Guo, K.- yi. (2020). Can machine learning algorithms associated with text mining from internet data improve housing price prediction performance?. International Journal of Strategic Property Management, 24(5), 300-312. https://doi.org/10.3846/ijspm.2020.12742
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Aug 14, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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