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Predicting House Prices Using DMA Method: Evidence from Turkey

Author

Listed:
  • Nuri Hacıevliyagil

    (Faculty of Economics and Administrative Sciences, Inonu University, No. 44210, Battalgazi, 44000 Malatya, Turkey)

  • Krzysztof Drachal

    (Faculty of Economic Sciences, University of Warsaw, ul. Długa 44/50, 00-241 Warszawa, Poland)

  • Ibrahim Halil Eksi

    (Faculty of Economics and Administrative Sciences, Gaziantep University, Şehitkamil, 27310 Gaziantep, Turkey)

Abstract

The aim of this study is to analyze the dynamics of the housing market in Turkey’s economy and to examine the impact of variables related to housing prices. Preferred by many international housing investors, Turkey hosts profitable real estate investments as one of the developing countries with a shining housing market. This study applies the dynamic model averaging (DMA) methodology to predict monthly house price growth. With the increasing use of information technologies, Google online searches are incorporated into the study. For this purpose, twelve independent variables, with the Residential Property Price Index as the dependent variable, were used in the period January 2010–December 2019. According to the analysis results, it was observed that some variables, such as bond yields, the level of mortgages, foreign direct investments, unemployment, industrial production, exchange rates, and Google Trends index, are determinants of the Residential Property Price Index.

Suggested Citation

  • Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
    • Handle: RePEc:gam:jecomi:v:10:y:2022:i:3:p:64-:d:768364
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