Ömer Alkan

Determinants of housing demand in the Erzurum province, Turkey

Purpose - – The main aim of this study is to determine the factors that influence the housing demand of households in Erzurum, northeastern Turkey. Housing demand is generally affected by several factors including housing prices, individuals’ income, expectations and choices and so on, as a means of its demographic and socio-psychological contexts. Design/methodology/approach - – A questionnaire-based cross-sectional survey was carried out, in which the outcome variable had binary responses such as whether to invest in housing or not. A binary logistic regression analysis was performed to estimate the underlying data. Findings - – The questionnaire was conducted in 2,927 households living in Erzurum city center, and 47 per cent of the respondents claimed that they would consider investing in housing in the future. The estimation results reveal that demographic or socio-economic factors that may possibly influence housing demand of the respondents are as follows: household head’s and spouse’s occupation, monthly income, the number of individuals in the family and car ownership. Originality/value - – This paper involves the most comprehensive survey addressing the housing demand in the East Anatolian Region, Turkey. Additionally, this paper aims to contribute to the existing housing literature through establishing the statistical analysis of housing demand in an unstudied territory of the world.

An investigation of export–import ratios in Turkey using spline regression models

Abstract This paper examines the use of spline functions in linear, squared, and cubic spline regression models and exhibits the estimation of spline parameters from data by ordinary least squares. Determination of the number and the location of knots is central to spline regression. In this paper, we initially propose a method based on the coefficient of determination R2 related to the estimation of knots in spline regression. This proposed method as applied to export–import ratio distributions in Turkey for the years 1923–2010 determines the knots, and linear, quadratic, and cubic spline regression models are established accordingly. Results reveal that spline regression models offer better results than polynomial regression models, and that the quadratic spline regression model is the best explanatory model for export–import ratio distributions in the smoothest spline regression models.