Predicting House Prices Using DMA Method: Evidence from Turkey

Nuri Hacıevliyagil, Krzysztof Drachal and Ibrahim Halil Eksi
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Nuri Hacıevliyagil: Faculty of Economics and Administrative Sciences, Inonu University, No. 44210, Battalgazi, 44000 Malatya, Turkey
Ibrahim Halil Eksi: Faculty of Economics and Administrative Sciences, Gaziantep University, Şehitkamil, 27310 Gaziantep, Turkey

Economies, 2022, vol. 10, issue 3, 1-27

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.

Keywords: housing price prediction; RPPI; DMA; Google Trends index; Turkey (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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