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Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application

Author

Listed:
  • Jasmina Ćetković
  • Slobodan Lakić
  • Marijana Lazarevska
  • Miloš Žarković
  • Saša Vujošević
  • Jelena Cvijović
  • Mladen Gogić

Abstract

Using an artificial neural network, it is possible with the precision of the input data to show the dependence of the property price from variable inputs. It is meant to make a forecast that can be used for different purposes (accounting, sales, etc.), but also for the feasibility of building objects, as the sales price forecast is calculated. The aim of the research was to construct a prognostic model of the real estate market value in the EU countries depending on the impact of macroeconomic indicators. The available input data demonstrates that macroeconomic variables influence determination of real estate prices. The authors sought to obtain correct output data which show prices forecast in the real estate markets of the observed countries.

Suggested Citation

  • Jasmina Ćetković & Slobodan Lakić & Marijana Lazarevska & Miloš Žarković & Saša Vujošević & Jelena Cvijović & Mladen Gogić, 2018. "Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    • Handle: RePEc:hin:complx:1472957
    DOI: 10.1155/2018/1472957
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    References listed on IDEAS

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    1. Limsombunchai, Visit, 2004. "House Price Prediction: Hedonic Price Model vs. Artificial Neural Network," 2004 Conference, June 25-26, 2004, Blenheim, New Zealand 97781, New Zealand Agricultural and Resource Economics Society.
    2. Carlo Bagnoli & Halbert C. Smith, 1998. "The Theory of Fuzzy Logic and its Application to Real Estate Valuation," Journal of Real Estate Research, American Real Estate Society, vol. 16(2), pages 169-200.
    3. Renigier-Biłozor Małgorzata & Wiśniewski Radosław, 2012. "The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe," Folia Oeconomica Stetinensia, Sciendo, vol. 12(2), pages 103-125, December.
    4. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
    5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    6. Joe Peek & James A. Wilcox, 1991. "The baby boom, \\"pent-up demand\\" and future house prices," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
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    Cited by:

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