IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/55694.html
   My bibliography  Save this paper

Konut Piyasası ve Ekonomik Büyüme İlişkisi: Türkiye Üzerine Zaman Serileri Analizi (2000-2012)
[Housing market and economic growth relation: time series analysis over Turkey (2000-2012)]

Author

Listed:
  • KARGI, Bilal

Abstract

In this study, certain selected factors about growth data and housing market within last decade are examined. Housing is a human’s physiological need and economic development can be carried out by means of extending the possibility of satisfying this high valuable need or there can be shown a linkage between economic growth and processes at market. For this reason when we handle 2000-2012 period, the indicators about economic growth and housing acquisition are being searched to explain this main hypothesis. From the obtained quarter data about concerning period, correlation relations, augmented Dickey-Fuller Unit Root Test, Granger Causality Analysis and multiple regression models are researched. Housing is a basic need but has a high cost to be met by intense savings and high burrowing. For this reason; 1- consumers’ incomes increase, 2-credit opportunities widen and 3-continuity macroeconomic performance which is giving impression of being trustworthy of a long term expenditure item. This study assumes that the most important debility of Turkey’s economy is the sensitivity of political events but especially after 2002 it’s relatively stabilized. Under his two hypothesis, we can reach that the housing market is widened. The other factors that determine housing demand (as a result of macroeconomic stability) low housing interest rates, low increase of housing prices because of low inflation and extension of banking system credit capacity. The study result in under this fundamental frame as follows. Extension of credit capacity and sensitivity of housing expenditure to gross national product (GDP) is the sign that in Turkey’s economy there is no housing balloon. Especially extension of credit capacity and housing expenditures which are acting together with GDP until 3rd quarter of 2008 is keeping it’s sensitivity to the decrease of GDP after this period but not to the extension of banking system credit capacity. According to the intense of demand according to the GDP increase and economic stability, there can be price inflation by means of probable supply constriction in the market which is arranged by interest. Although it’s not mentioned in this study, housing prices are not increased very much and it shows that supply is enough. Housing interests are drop down according to the general interest drop down related to international liquidity wideness. For this reason quantity of housing credits usage is increased. But also the other reason of increase is the extend of fixed period of time. The source of the extend of fixed period of time is the arrangements made by the law 5582. But in spite of the highness of the housing purchase cost and long term credit sources, in conclusion we can say that the consumers are following the general statement of economy and sensitive to the usage of burrowing opportunities. Likewise, after the 3rd quarter of 2008 refraction period, according to the GDP fluctuate the credit usage level is not advance as the preceding period on the contrary has a horizontal movement. The most important conclusion from the number 5 equation of regression models is the finding the change of 1 unit housing interest in GDP housing(gsyihknt) variable shows the decrease of housing expenditure at -288,35 unit. But also at %5 meaning level, from kkfo to tufeknt Granger causality relation (0.02059) and the strong (0.720992) correlation relation between this two variables indicates that housing credits are effected from inflation before all else. Decrease of inflation is the reason of Granger (0.02975) regarding to increase at GDP and also found the negative correlation (-0.641993) between this two variables. At the other regression model number (6) equity explains the housing credits of banking system. According to this model also it has not an effective rank (-0.000474), the 1 unit increase of banking system capacity decrease (-0.000474) the housing credits. The main reason is (as shown in first chapter) excessive usage of credit by both households and private sector could be said. In the other hand increase at the inflation, decreases the banking system credit capacity (-1079.328) is observed. In addition to this explanations, the increase at the banking system credit capacity is effected very much from the decreasing of interest (kkfo=3.602.661). According to the number (7) equity, the (tufeknt) variable which is used as an indicator for the housing prices is explained by GDP (5.18) and the increase at GDP also rises housing prices. Related to this, the increase at banking system credit capacity has a positive (1.41) effect on housing interest. Especially at the relation between housing prices and housing credits, the increase at housing prices has a decreasing effect (-5.64) in housing credit capacity. Also the correlation level between prices and credits supports reverse aspect relation (-0.351545). It must be mentioned that no Granger causality determined between this two variables. But there is in model’s finding about Granger causality at a level of %5 that (0.00185) designation of housing prices by GDP. Finally the increase at housing prices, increases housing interests (0.207636) unit is observed. The findings about last regression model number (8) equity is as follows. At the designation of housing credit interest, the general trend of GDP increase (-4.97), housing expenditures (-1.11), banking system housing credits (-3.02) have negative effects. Hence explanation of the decrease of housing credits is this three basic variables increase. The widening at banking system credit capacity has an increasing effect (5.88) on housing credit interests. Inflation and housing prices (tufeknt) are the two variables increasing according to the housing credit interests. In the same way, correlation relation between housing credits and inflation (0.749064) and housing prices (0.720992) is strong. Within the Granger causality tests, from housing credits to inflation (0.00068) and housing prices (0.02059) Granger causality is found. Finally in the light of this findings in Turkey’s economy at the mentioned time period a unbalanced situation and balloon formation in housing market couldn't be found. Although there is a strong bound between housing expenditures and GDP but also has a correct way and weak relation with banking system credit capacity increasing rates. In the other words, households housing demand is effected and determined by decreasing credit interest rates and inflation with GDP not the credit expansion. Thus, this shows the households sensitivity to general economic trends and acting cautiously for a long term saving and borrowing coast as in housing expenditures.

Suggested Citation

  • KARGI, Bilal, 2013. "Konut Piyasası ve Ekonomik Büyüme İlişkisi: Türkiye Üzerine Zaman Serileri Analizi (2000-2012) [Housing market and economic growth relation: time series analysis over Turkey (2000-2012)]," MPRA Paper 55694, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:55694
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/55694/1/MPRA_paper_55694.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edward E. Leamer, 2007. "Housing is the business cycle," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 149-233.
    2. Matteo Iacoviello & Stefano Neri, 2010. "Housing Market Spillovers: Evidence from an Estimated DSGE Model," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(2), pages 125-164, April.
    3. Iacoviello, Matteo & Minetti, Raoul, 2008. "The credit channel of monetary policy: Evidence from the housing market," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 69-96, March.
    4. Baffoe-Bonnie, John, 1998. "The Dynamic Impact of Macroeconomic Aggregates on Housing Prices and Stock of Houses: A National and Regional Analysis," The Journal of Real Estate Finance and Economics, Springer, vol. 17(2), pages 179-197, September.
    5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    6. Eloisa T. Glindro & Tientip Subhanij & Jessica Szeto & Haibin Zhu, 2011. "Determinants of House Prices in Nine Asia-Pacific Economies," International Journal of Central Banking, International Journal of Central Banking, vol. 7(3), pages 163-204, September.
    7. Sophocles Brissimis & Thomas Vlassopoulos, 2009. "The Interaction between Mortgage Financing and Housing Prices in Greece," The Journal of Real Estate Finance and Economics, Springer, vol. 39(2), pages 146-164, August.
    8. John Muellbauer & Anthony Murphy, 2008. "Housing markets and the economy: the assessment," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 24(1), pages 1-33, spring.
    9. Mr. Vladimir Klyuev, 2008. "What Goes Up Must Come Down? House Price Dynamics in the United States," IMF Working Papers 2008/187, International Monetary Fund.
    10. Agnello, Luca & Schuknecht, Ludger, 2011. "Booms and busts in housing markets: Determinants and implications," Journal of Housing Economics, Elsevier, vol. 20(3), pages 171-190, September.
    11. Andreas Hornstein, 2009. "Problems for a fundamental theory of house prices," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 95(Win), pages 1-24.
    12. Ercan UYGUR, 2004. "Cari Açık Tartışmaları," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 19(222), pages 5-20.
    13. Nicholas Apergis, 2003. "Housing Prices and Macroeconomic Factors: Prospects within the European Monetary Union," International Real Estate Review, Global Social Science Institute, vol. 6(1), pages 63-74.
    14. Nicholas Apergis, 2003. "Housing Prices and Macroeconomic Factors: Prospects within the European Monetary Union," International Real Estate Review, Asian Real Estate Society, vol. 6(1), pages 63-47.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yetkiner, Hakan & Nazlioglu, Saban, 2018. "Is there an optimal level of housing wealth in the long-run? Theory and evidence," Research in International Business and Finance, Elsevier, vol. 46(C), pages 257-267.
    2. Tafirenyika Sunde & Paul-Francois Muzindutsi, 2017. "Determinants of house prices and new construction activity: An empirical investigation of the Namibian housing market," Journal of Developing Areas, Tennessee State University, College of Business, vol. 51(3), pages 389-407, July-Sept.
    3. Musso, Alberto & Neri, Stefano & Stracca, Livio, 2011. "Housing, consumption and monetary policy: How different are the US and the euro area?," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3019-3041, November.
    4. Katrakilidis, Constantinos & Trachanas, Emmanouil, 2012. "What drives housing price dynamics in Greece: New evidence from asymmetric ARDL cointegration," Economic Modelling, Elsevier, vol. 29(4), pages 1064-1069.
    5. Todd Kuethe & Valerien Pede, 2011. "Regional Housing Price Cycles: A Spatio-temporal Analysis Using US State-level Data," Regional Studies, Taylor & Francis Journals, vol. 45(5), pages 563-574.
    6. T. Thanh-Binh Nguyen & Kuan-Min Wang, 2010. "Causality between housing returns, inflation and economic growth with endogenous breaks," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 8(1), pages 95-115.
    7. Nyakabawo, Wendy & Miller, Stephen M. & Balcilar, Mehmet & Das, Sonali & Gupta, Rangan, 2015. "Temporal causality between house prices and output in the US: A bootstrap rolling-window approach," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 55-73.
    8. Theodore Panagiotidis & Panagiotis Printzis, 2016. "On the macroeconomic determinants of the housing market in Greece: a VECM approach," International Economics and Economic Policy, Springer, vol. 13(3), pages 387-409, July.
    9. Fatnassi, Ibrahim & Slim, Chaouachi & Ftiti, Zied & Ben Maatoug, Abderrazek, 2014. "Effects of monetary policy on the REIT returns: Evidence from the United Kingdom," Research in International Business and Finance, Elsevier, vol. 32(C), pages 15-26.
    10. Bouchouicha, Ranoua & Ftiti, Zied, 2012. "Real estate markets and the macroeconomy: A dynamic coherence framework," Economic Modelling, Elsevier, vol. 29(5), pages 1820-1829.
    11. Rangan Gupta & Christophe André & Luis Gil-Alana, 2015. "Comovement in Euro area housing prices: A fractional cointegration approach," Urban Studies, Urban Studies Journal Limited, vol. 52(16), pages 3123-3143, December.
    12. Vinci, Sabato & Bartolacci, Francesca & Salvia, Rosanna & Salvati, Luca, 2022. "Housing markets, the great crisis, and metropolitan gradients: Insights from Greece, 2000–2014," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    13. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    14. repec:zbw:bofitp:2010_019 is not listed on IDEAS
    15. Gan-Ochir Doojav & Davaasukh Damdinjav, 2021. "Policy-Driven Boom and Bust in the Housing Market: Evidence from Mongolia," Asian Development Review (ADR), World Scientific Publishing Co. Pte. Ltd., vol. 38(02), pages 279-317, September.
    16. Deng, Yongheng & Girardin, Eric & Joyeux, Roselyne, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," China Economic Review, Elsevier, vol. 48(C), pages 205-222.
    17. Luca GATTINI & Paul HIEBERT, 2010. "Forecasting and Assessing Euro Area House Prices Through the Lens of Key Fundamentals," EcoMod2010 259600061, EcoMod.
    18. Goodness C. Aye & Rangan Gupta, 2013. "Forecasting Real House Price of the U.S.: An Analysis Covering 1890 to 2012," Working Papers 201362, University of Pretoria, Department of Economics.
    19. Njindan Iyke, Bernard, 2015. "Assessing the Effects of Housing Market Shocks on Output: The Case of South Africa," MPRA Paper 69610, University Library of Munich, Germany, revised 01 Feb 2016.
    20. Christian Dreger & Konstantin A. Kholodilin, 2011. "An Early Warning System to Predict the House Price Bubbles," Discussion Papers of DIW Berlin 1142, DIW Berlin, German Institute for Economic Research.
    21. Christophe Andre & Rangan Gupta & Patrick T. Kanda, 2012. "Do House Prices Impact Consumption and Interest Rate? Evidence from OECD Countries using an Agnostic Identification Procedure," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 58(1), pages 19-70.

    More about this item

    Keywords

    Economic Growth; Housing Market; Housing Expenditures; Housing Interests; Turkish Economy.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:55694. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.