Konut Talebinin Dinamikleri: Van İli Örneği, Türkiye

Year 2020, Volume: 9 Issue: 5, 3697 - 3722, 29.12.2020

Mehmet Akif Arvas , Haluk Yergin , Kerem Özen , Cemalettin Levent

https://doi.org/10.15869/itobiad.746333

Cited By: 1

Abstract

Küresel olarak, son yıllarda inşaat sektöründeki büyümeyle birlikte konut arzındaki büyük artış tüketicilerin satın alma davranışını etkilemiştir. Emlak sektörünün ilerlemesinde, tüketicilerin artan refahı ve düşen faiz oranları da etkili olmuştur. Pek çok tanımı olmasına rağmen, mesken, bireylerin hayatlarını sürdürebilmelerini sağlayan mutfak, içme suyu tesisatları ve atık sistemi gibi alanların toplamı olarak tanımlanmıştır. Sonuç olarak, bir ev sosyal, kültürel, ekonomik, yasal ve teknolojik faktörler gibi çok yönlü bileşenlere sahip bir bütündür. Konut piyasası kavramı son yıllarda yerel ve merkezi hükümetler için önem kazandığından, bu konuda çeşitli araştırma ve çalışmalar yapılmıştır. Literatürde konut piyasası ile ilgili çalışmalar son yıllarda önemli bir artış göstermiştir. Küresel finansal piyasalarda faiz oranlarındaki düşüş ve artan likidite nedeniyle, konut yatırımları bu alana yapılan sermaye akımlarının bir kısmını çekmiştir. Konut piyasasının gerçek etkileri gelir artışı, genel tasarruf ve yatırım seviyesi, istihdam ve işgücü hareketliliği düzeyi açısından incelenmektedir. Konut talebine ilişkin ampirik çalışmalar temel olarak konut fiyatları ile bazı makroekonomik göstergeler arasındaki ilişkiyi araştırmayı amaçlamaktadır. Bu nedenle, konut piyasasında il düzeyinde yürütülen çalışma sayısı oldukça düşüktür. Bu bağlamda, bu çalışmanın temel amacı, Türkiye'nin Van ilinde yakın gelecekte ev satın alma kararlarını etkileyen temel faktörleri araştırmaktır. Talep denkleminde yer alan cinsiyet, medeni durum, gelir, yaş, hane halkı büyüklüğü, evin yeri, evin tipi, cazibe merkezlerine yakınlık gibi bazı hedonik (fiyat dışı) faktörlerin konut talebine etkileri analiz edilmiştir. Bu amaçla, tüketicilerin satın alma kararlarını tahmin etmek için şehir merkezinde yaşayan ve rasgele seçilmiş olan 450 kişiye anket uygulanmıştır. Logit model tahmin sonuçlarına göre, cinsiyet, medeni durum, yaş, çalışma durumu, eğitim ve gelir gibi faktörlerin bir ev sahibi olma olasılığını arttırdığı görülmüştür. Ayrıca merkezdeki evler daha çok talep edilirken, işyeri, okul ve hastane gibi yerlere yakınlık, bu talepte güvenlik, kira geliri ve yatırım faktörlerinden çok daha az etkili olmuştur.

Keywords

Logit Model, Konut Talebi, Konut Piyasası, Van, Türkiye

Supporting Institution

Van Yüzüncü Yıl Üniversitesi Bilimsel Araştırmalar Birimi

Project Number

SBA-2018-7244

Thanks

Araştırmaya olan maddi katkılarından dolayı Van Yüzüncü Yıl Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP)'ne teşekkür ederiz.

Dynamics of Housing Demand: The Case of Van Province, Turkey

Abstract

A house is a whole with versatile components such as social, cultural, economic, legal, and technological factors. Because of the decline in interest rates and increasing liquidity in the globalized financial markets, housing investments have attracted some of the capital flows to this field. The real effects of the housing market are examined in terms of the increases in income, the general level of savings and investments, and the level of employment and labour mobility. Empirical studies on the housing demand have mainly aimed to investigate the relationship between the prices of houses and some macroeconomic indicators. Therefore, the number of studies conducted at the provincial level in the housing market is quite low. In this context, the main purpose of this study is to investigate the key factors that affect the decisions of individuals to buy a house in the near future, in Van province, Turkey. In the demand equation, the effects of a number of hedonic (non-price) factors such as gender, marital status, income, age, household size, location of the house, type of the house, proximity to attraction centres on the housing demand are analysed. For this purpose, in order to analyse the purchasing decisions of consumers in the housing demand function, a questionnaire was applied to 450-randomly selected people who live in the city centre. According to logit model estimation results, it is observed that factors such as gender, marital status, age, working status, education, and income increased the likelihood of owning a house. Further, while the houses in the centre are more demanded, proximity to the places such as workplace, school, and the hospital is much less effective than the security, rental income, and investment factors, in such demand.

Keywords

Housing Demand, Housing market, Van, Turkey, Logit Model

Project Number

SBA-2018-7244

References

• Acolin, A., & Wachter, S. (2017). Opportunity and Housing Access. Cityscape, 19(1), 135-150. Retrieved May 16, 2020, from www.jstor.org/stable/26328303.
• Agnello, L. & Schuknecht, L. (2010). Booms and bust in housing markets: Determinants and implications, Journal of Housing Economics, 20, 171-190.
• Aktürk, E. & Tekman, N. (2016). Housing demand and factors affecting housing acquisition decisions of consumers in centre of Erzurum. Atatürk University Journal of Economics and Admin. Sci., 30(2), 423-440.
• Alakbar, A. and Eren. E. (2007). Application of hedonic pricing model to the Turkish passenger car sector”, IIF Journal, 22(261), 22-37.
• Alkan, Ö, Karaaslan, A., Abar, H., Çelik, A. & Oktay, E. (2014). Factors Affecting Motives for Housing Demand: The Case of a Turkish Province. Theoretical and Empirical Researches in Urban Management, 9(3), 70-86. Retrieved May 10, 2020, from www.jstor.org/stable/24873513
• Amundsen, E. S. (1985). Moving costs and the microeconomics of intra-urban mobility. Regional Science and Urban Economics, 15, pp. 573-583.
• Badurlar, İ. Ö. (2008). Investigation of the relationship between house prices and macroeconomic variables in Turkey, Anadolu University Journal of Social Sciences, 8(1), 223-238.
• Bajari, P., Chan, P., Krueger, D., & Miller. D. (2013). A Dynamic Model of Housing Demand: Estimation and Policy Implications. International Economic Review, 54(2), 409-442.
• Beamish, J., Goss, J. & Emmel, J. (2015). Lifestyle Influences on Housing Preferences, Housing and Society, 28, 1-28.
• Bekmez, S. & Özpolat, A. (2014). A comparative regional analysis for housing demand in Turkey. Journal of Business and Economics, 5(10): 1854-1866.
• Cameron, A. C., &Trivedi P. K. (2005). Microeconometrics: Methods and Applications. 1st Edition, Cambridge University Press, New York.
• Collinson, R. (2011). Rental Housing Affordability Dynamics, 1990—2009. Cityscape, 13(2), 71-103. Retrieved May 16, 2020, from www.jstor.org/stable/41426486.
• Çamoğlu, S. M., & Çakır, E. (2020), Housing demand of household by market value and property: the case of Ordu, Turkey. Suleyman Demirel University, The Journal of Faulty of Economics and Administrative Sciences, 25(1), 110-123.
• DeFusco, A. A., Nathanson, C. G., & Zwick, E. (2017), Speculative dynamics of prices and volume, working paper 23449, NBER. 4, 25, 40.
• Dewilde, C., & Lancee, B. (2013). Income Inequality and Access to Housing in Europe. European Sociological Review, 29(6), 1189-1200. Retrieved May 16, 2020, from www.jstor.org/stable/24480015.
• Dilek, S., Küçük, O., Gümüş, N. & Amini, R. (2018). How do we make our housing decisions? A research in Kastamonu, Research of Financial Eonomic and Social Studies, 3(3): 576-589.
• Edin, A., & P. Englund. (2002). Moving costs and housing demand: Are recent movers really in equilibrium? Journal of Public Economics. 44(3), 299-320.
• Fadiga, M. L & Wang, Y. (2009). A multivariate unobserved component analysis of US housing market, Journal of Economics and Finance, 33, 13-26.
• Garson, D. (2014). Logistic Regression: Binary and Multinomial. G. David Garson and Statistical Associates Publishing, USA.
• Goodman, A. C. (1995). Housing demand with transaction costs. Journal of Housing Economics, 4, pp. 307-32.
• Goodman, A., & Thibodeau, G. T. (2003). Housing Market Segmentation and Hedonic Prediction Accuracy, Journal of Housing Economics, 12(3), 181-201.
• Gujarati, D., N. (2004). Basic Econometrics, Fourth Edition, Tata McGraw Hill, USA.
• Hoffmann, M., & Kremer, P. (1986). Zahlentafeln für den Baubetrieb, 9th Edition, Stuttgart, Germany.
• Holmqvist, E., & Turner, L. (2014). The Swedish welfare state and housing markets: Under economic and political pressure. Journal of Housing and the Built Environment, 29(2), 237-254. Retrieved May 16, 2020, from www.jstor.org/stable/43907269.
• Hosmer, D.W., Lemeshow, S., & Sturdivant, R.X. (2013). Applied Logistic Regression. 3rd Edition, John Wiley & Sons, Hoboken, NJ.
• INTES (2019). The Report on the Construction Sector in Turkey. Retrieved January 3,2020, from https://intes.org.tr/wp-content/uploads/2019/01/%C4%B0N%C5%9EAAT-EKT%C3%96R%C3%9C-RAPORU.pdf
• Ioannides, Y. M., & Zabel, J. E. (2003). Neighbourhood Effects and Housing Demand. Journal of Applied Econometrics, 18: 563-584.
• Karahan, E, E. (2009). Housing career concept for explaining housing demand, Istanbul Ticaret University Journal of Sciences, 1(15), 79-105.
• Lebe, F. & Akbaş, Y. E. (2014). Analysis of housing demand in Turkey: 1970-2011. Atatürk University Journal of Economics and Administrative Sciences, 28(1): 57-83.
• Lee, T. H., & Kong, C. M. (1977). Elasticities of Housing Demand. Southern Economic Journal, 44(2), 298-305.
• Lindh, T., & Malmberg, B. (2008). Demography and Housing Demand—What Can We Learn from Residential Construction Data? Journal of Population Economics, 21(3), 521-539. Retrieved May 10, 2020, from www.jstor.org/stable/40344688.
• Linlin, Z., Xiuting, L., & Jichang, D. (2016). The Impact of Aging on Urban Housing Demand Based on CGE. Filomat, 30(15), 4151-4171. Retrieved May 10, 2020, from www.jstor.org/stable/24899497.
• Nordvik, V. (2001). Moving costs and the dynamics of housing demand. Urban Studies, 38(3), 519-533.
• Ong, T.S. (2013). Factors Affecting the Price of Housing in Malaysia, Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB), 1(5), 414-429.
• Öztürk, N., & Fitöz, E. (2009). The determinants of the housing sector in Turkey: an empirical analysis, ZKU Journal of Social Sciences, 5(10), 21–46.
• Press, J., & Wilson, S. (1978). Choosing between logistic regression and discriminant analysis. Journal of the American Statistical Association, 73(364), 699–705.
• Rosen, S. (1974). Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition, Journal of Political Economy, 82(1), 34-55.
• Sinai, T., & Souleles, N. S. (2005). Owner-Occupied Housing as a Hedge Against Rent Risk, The Quarterly Journal of Economics, 120(2), 763–789.
• Skaburskis, A. (1997). Gender Differences in Housing Demand. Urban Studies, 34(2), pp. 275-320.
• The World Bank (2020). Turkey Economic Monitor 4: Adjusting the sails.
• Tse, C. B, Rodgers, T. & Niklewski, J. (2007). Financial crisis and the UK residential housing market: Did the relationship between interest rates and house prices change?, Economic Modelling, 29, 684-690.
• Uysal, D., & Yiğit, M. (2016). Determinants of housing demand in Turkey (1970-2015): an empirical study, Selçuk University Journal of Vocational School of Social Sciences, 1(19), 185-209.
• Uysal, D., & Yiğit, M. (2016). Determinants of housing demand in Turkey (1970-2015): an empirical study, Selçuk University Journal of Vocational School of Social Sciences, 1(19), 185-209.
• Yang, Z., Yi, C., Zhang, W., & Zhang, C. (2014). Affordability of housing and accessibility of public services: Evaluation of housing programs in Beijing. Journal of Housing and the Built Environment, 29(3), 521-540. Retrieved May 16, 2020, from www.jstor.org/stable/43907288.
• Yang, Z., Yi, C., Zhang, W., & Zhang, C. (2014). Affordability of housing and accessibility of public services: Evaluation of housing programs in Beijing. Journal of Housing and the Built Environment, 29(3), 521-540. Retrieved May 16, 2020, from www.jstor.org/stable/43907288.
• Yayar, R., & Bursal, M. (2019). Türkiye’de Konut Kira Fiyatlarının Hedonik Tahmini. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 8 (3), 2010-2026. Retrieved from http://www.itobiad.com/tr/issue/47378/597554
• Zheng, X., Xia, Y., Hui, E. & Zheng, L. (2018). Urban housing demand, permanent income and uncertainty: Microdata analysis of Hong Kong's rental market. Habitat International. 74. 10.1016/j.habitatint.2018.02.004.
• Zheng, X., Xia, Y., Hui, E. & Zheng, L. (2018). Urban housing demand, permanent income and uncertainty: Microdata analysis of Hong Kong's rental market. Habitat International. 74. 10.1016/j.habitatint.2018.02.004.