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The Trend of Average Unit Price in Taipei City

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  • Hong-Yu Lin
  • Kuentai Chen

Abstract

The volatility of real estate prices is one of the key factors to the decision making of financial institutions, as well as to a country¡¯s economic development. Therefore how to catch the trend of this real estate price has been an important issue for Governments and financial institutions. In this study, we discuss the trend of average unit price in a capital city, in hope of establishing a decent predicting model and key factors for this price. Other than traditional statistic methods, Neural Networks (NN) and Support Vector Regression (SVR) have demonstrated their advantages in previous research, and thus are applied and compared in this study. Variables are first summarized and concluded from earlier research and then selected by stepwise procedure. The result shows that SVR outperformed NN and stepwise procedure is valid in variable selections, and the key factors are previous trading price, Money supply M2 and New House-purchasing Loans.

Suggested Citation

  • Hong-Yu Lin & Kuentai Chen, 2015. "The Trend of Average Unit Price in Taipei City," Research in World Economy, Research in World Economy, Sciedu Press, vol. 6(1), pages 133-142, March.
    • Handle: RePEc:jfr:rwe111:v:6:y:2015:i:1:p:133-142
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    References listed on IDEAS

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    1. Kaashoek, Johan F & van Dijk, Herman K, 2002. "Neural Network Pruning Applied to Real Exchange Rate Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(8), pages 559-577, December.
    2. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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