A Genetic Algorithm-Based Multivariate Grey Model in Housing Demand Forecast in Turkey

A Genetic Algorithm-Based Multivariate Grey Model in Housing Demand Forecast in Turkey

Miraç Eren (Atatürk University, Turkey), Ali Kemal Çelik (Atatürk University, Turkey) and İbrahim Huseyni (Şirnak University, Turkey)
DOI: 10.4018/978-1-5225-9276-1.ch024
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Housing sector is commonly considered as a very strong economic industry in terms of both its contribution to creating employment and its impact on other associated sectors. By means of its featured characteristics, the sector also plays an important role on economic growth and development of emerging countries. In this respect, any evidence that determines factors affecting housing investments and future demand behavior may be remarkably valuable for monitoring possible future excess supply and deficits. This chapter attempts to determine factors affecting housing demand in Turkey during a sample period of 2003-2011 using a genetic algorithm-based multivariate grey model. Housing demand forecasts are also employed until the year 2020. Results reveal that several factors including M2 money supply, consumer price index and urbanization rate have an impact on housing demand. According to housing demand forecasts, a significant housing demand increase is expected in Turkey.
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In the multivariable grey optimization models, the determination of input variables that may possible influence the output variable is performed. Afterwards, the quantitative analysis related to the initial qualitative analysis is established. Thus, system modeling takes place in four steps:

  • Step 1: Determination of the input variables affecting output variable

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