IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-44916-1_19.html
   My bibliography  Save this book chapter

Applications of Artificial Neural Networks in the Identification of Real Estate Cycles: Evidence from China

In: Proceedings of the 18th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Yang Li

    (Tsinghua University)

  • Hong Zhang

    (Beijing University of Civil Engineering and Architecture)

  • Fei Yang

    (Tsinghua University)

  • Yue Wang

    (Tsinghua University)

Abstract

Deep understandings of the cyclical changes in real estate market have significant meanings for market participants to make appropriate investment decisions. This paper innovatively applied artificial neural networks to identify real estate cycles in China, and accurately predicted its development phases with a well-trained artificial neural network based on 1993–2008 historical training samples. The results indicate that, China’s real estate market has oscillational characteristics and the performance of the artificial neural networks reaches high accuracy. In the context of continuously deepening governmental interventions, the volatility in real estate cycles has become more evident since 2008, when the market reached its peak in 2009, but quickly plunged into recession in 2010, and then approached to its trough in 2011. Therefore, a series of governmental macro-control policies since 2008 have tremendous impacts on the duration and frequency of China’s real estate cycles, by adjusting the expansion speed of real estate business.

Suggested Citation

  • Yang Li & Hong Zhang & Fei Yang & Yue Wang, 2014. "Applications of Artificial Neural Networks in the Identification of Real Estate Cycles: Evidence from China," Springer Books, in: Donglang Yang & Yanjun Qian (ed.), Proceedings of the 18th International Symposium on Advancement of Construction Management and Real Estate, edition 127, chapter 0, pages 185-195, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-44916-1_19
    DOI: 10.1007/978-3-642-44916-1_19
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-642-44916-1_19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.