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Combined Grey Relational Analysis and Weighted Synthesis for Housing Price Prediction

Author

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  • WEIPENG TAN

    (Chaoyang University of Technology, Taiwan)

  • TSUNG-NAN CHOU

    (Chaoyang University of Technology, Taiwan)

Abstract

The objective of this paper is to evaluate the performance of the grey relational analysis in the forecast of housing price for the real estate market of Taiwan. An instance-based approach which used k-nearest neighbor classifier was also applied for performance comparison. The grey relational analysis was modified to calculate the weighted synthesis of the top ten matching instances through various weighting strategies. The experimental results in this paper concluded that the grey relational analysis outperformed the instance-based approach in terms of the mean absolute error and root mean square error. In addition, the synthesis strategy with descending weights performed better than the averaging weights during the integration process of matching instances. The result also suggested that the performance was slightly decreased if the top ten matching instances were reduced to five instances. The grey relational analysis integrated with the weighted synthesis model can assist both buyers and owners in identifying opportunities and estimating the potential risks in a worsening real estate market.

Suggested Citation

  • Weipeng Tan & Tsung-Nan Chou, 2016. "Combined Grey Relational Analysis and Weighted Synthesis for Housing Price Prediction," International Journal of Business and Administrative Studies, Professor Dr. Bahaudin G. Mujtaba, vol. 2(3), pages 81-88.
    • Handle: RePEc:apa:ijbaas:2016:p:81-88
    DOI: 10.20469/ijbas.2.10005-3
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    References listed on IDEAS

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    3. Xin J. Ge & G. Runeson, 2004. "Modeling Property Prices Using Neural Network Model for Hong Kong," International Real Estate Review, Global Social Science Institute, vol. 7(1), pages 121-138.
    4. Chao Jiang & Zijiang Yang, 2015. "Internet Advertisements Prediction," Springer Books, in: Zhenji Zhang & Zuojun Max Shen & Juliang Zhang & Runtong Zhang (ed.), Liss 2014, edition 127, pages 409-414, Springer.
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