Konut Piyasalarında Makroekonomik Faktörlerin Etkisi: ABD Örneği

Abstract

Bu makale makroekonomik, finansal ve emtia piyasaları göstergelerinin konut piyasaları üzerindeki etkisini analiz etmektedir. Genelleştirilmiş Doğrusal Modeller (GDM) ve Çok Değişkenli Uyarlanabilir Regresyon Eğrileri (ÇDRE) tarafından üretilen modellerin yeterliliğini konut piyasası eğilimini tahmin etmek için bağımsız ölçüm yöntemlerine göre karşılaştırıyoruz. Araştırmalarımıza göre bu modeller ilk kez makroekonomik göstergelerin konut piyasaları üzerindeki etkisini ve konut piyasalarındaki eğilimine yönelik tahmini belirlemekte kullanılmaktadır. Ampirik çalışmalar, ABD konut piyasalarına odaklanmakta ve önerilen modellerin gösterimi Ocak 1999-Haziran 2018 periyodu arasında gözlemlenen aylık S\&P/Case-Shiller Ulusal Konut Fiyat İndeksine ve ABD macroeconomic faktörlerine uygulanmaktadır. Bu çalışma makro ekonomik göstergeler ve konut piyasaları arasındaki etkileşimi vurgulayarak ve konut piyasalarının mekanizmasını analiz ederek literatüre katkıda bulunmaktadır. Bulguları, konut fiyat eğilimlerinin daha doğru bir şekilde tahmin edildiğini ve bu modellerin açıklayıcı değişkenlerin ortak etkisini yakaladığını göstermektedir. Ayrıca, ÇDRE yönteminin tahmin ve geleceğe yönelik tahmin gücüne kıyasla GDM'den daha iyi performans gösterdiğini ortaya koymuştur.

Keywords

• Konut Piyasası
• Genelleştirilmiş Doğrusal Modeller
• Çok Değişkenli Uyarlanabilir Regresyon Eğrileri

The Impact of Macro-Economic Drivers in Housing Markets: The US Case

Abstract

This paper analyzes the effect of macro-economic, financial and commodity market indicators on housing markets. We compare the efficiency of the models generated by Generalized Linear Models (GLM) and Multivariate Adaptive Regression Splines (MARS) according to method free measures for estimating the housing market trend. These models are used for the first time to identify the influence of macro-economic indicators on housing markets and the estimation of the trend in housing markets to our best knowledge. The empirical analysis focuses on the US housing market, and the illustration of the proposed models is done through the monthly historical realizations of S\&P/Case-Shiller National Home Price Index (HPI) and the US macro-economic indicators over the period from 1999-January to 2018-June. It contributes to the literature by highlighting the interaction between macro-economic indicators and housing markets and analyzing the mechanism of housing markets. The findings indicate that the house price trends are estimated with more accuracy and these models capture the joint influence of explanatory variables. Further, the MARS method is shown to outperform GLM compared to the prediction and forecasting power.

Keywords

• Housing Market
• Generalized Linear Models
• Multivariate Adaptive Regression Splines

References

1. Adams, Z. and Füss, R. (2010). Macroeconomic determinants of international housing markets. Journal of Housing Economics, 19(1):38–50.
2. Agnello, L. and Schuknecht, L. (2011). Booms and busts in housing markets: determinants and implications. Journal of Housing Economics, 20(3):171–190.
3. Andrews, D. (2010). Real house prices in oecd countries: The role of demand shocks and structural and policy factors. OECD Economic Department Working Papers, 1(831):0–34
4. Arestis, P., Mooslechner, P., and Wagner, K. (2009). Housing Market Challenges in Europe and the United States. Palgrave Macmillan UK.
5. Aspachs-Bracons, O. and Rabanal, P. (2010). The drivers of housing cycles in Spain. SERIEs, 1(1):101–130.
6. Barlevy, G. and Fisher, J. (1993). Fast mars, technical report 110. Technical report, Stanford Linear Accelerator Center.
7. Blanchflower, D. G. and Oswald, A. J. (2013). Does high home-ownership impair the labor market? Technical report, National Bureau of Economic Research.
8. Bojan, G. and Darja, K. G. (2016). Influence of macroeconomic factors on prices of real estate in various cultural environments: Case of Slovenia, Greece, France, Poland and Norway. Procedia Economics and Finance, 39:597 – 604. 3rd Global Conference on Business, Economics, Management and Tourism.

9. Branch, W. A., Petrosky-Nadeau, N., and Rocheteau, G. (2016). Financial frictions, the housing market, and unemployment. Journal of Economic Theory, 164:101–135.
10. Carson, R. T. and Dastrup, S. R. (2013). After the fall: An ex post characterization of housing price declines across metropolitan areas. Contemporary Economic Policy, 31(1):22–43.
11. Chauvet, M., Gabriel, S., and Lutz, C. (2016). Mortgage default risk: New evidence from internet search queries. Journal of Urban Economics, 96:91–111.
12. Diewert, W. E., Nakamura, A. O., and Nakamura, L. I. (2009). The housing bubble and a new approach to accounting for housing in a cpi. Journal of Housing Economics, 18(3):156–171.
13. Dokko, J. K., Keys, B. J., and Relihan, L. E. (2015). Affordability, financial innovation, and the start of the housing boom. Working paper.
14. Englund, P. and Ioannides, Y. M. (1997). House price dynamics: an international empirical perspective. Journal of Housing Economics, 6(2):119–136.
15. Follain, J. R. (1982). Does inflation affect real behavior: The case of housing. Southern Economic Journal, 48(3):570–582.
16. Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1):1–67.
17. Gadi Barlevy, J. D. M. F. (2011). Mortgage choices and housing speculation. Available at SSRN, 5(1):1–63.
18. Glaeser, E. L. and Parker, J. A. (2000). Comments and discussion. Brookings Papers on Economic Activity, 2000(2):146–159.
19. Green, R. K. and Hendershott, P. H. (2001). Home-ownership and unemployment in the US. Urban Studies, 38(9):1509–1520.
20. Guirguis, H. S., Giannikos, C. I., and Anderson, R. I. (2005). The us housing market: Asset pricing forecasts using time varying coefficients. The Journal of Real Estate Finance and Economics, 30(1):33–53.
21. Hastie, T. (1989). Flexible parsimonious smoothing and additive modeling: Discussion. Technometrics, 31(1):23–29.
22. Iacoviello, M. (2005). House prices, borrowing constraints, and monetary policy in the business cycle. American Economic Review, 95(3):739–764.
23. Johnes, G. and Hyclak, T. (1999). House prices and regional labor markets. The Annals of Regional Science, 33(1):33–49.
24. Lambertini, L., Mendicino, C., and Punzi, M. T. (2017). Expectations-driven cycles in the housing market. Economic Modelling, 60:297–312.
25. Leamer, E. (2013). Chapter 41 - housing is the business cycle. In Caprio, G., Beck, T., Claessens, S., and Schmukler, S. L., editors, The Evidence and Impact of Financial Globalization, pages 589 – 643. Academic Press, San Diego.
26. Lerbs, O. (2011). Is there a link between homeownership and unemployment?: Evidence from German regional data. International Economics and Economic Policy, 8(4):407–426.
27. Malpezzi, S. (1999). A simple error correction model of house prices. Journal of Housing Economics, 8(1):27–62.
28. Mankiw, N. G. and Weil, D. N. (1989). The baby boom, the baby bust, and the housing market. Regional Science and Urban Economics, 19(2):235–258.
29. Nelder, J. A. and Baker, R. J. (1972). Generalized Linear Models. Wiley Online Library.
30. Ortalo-Magne, F. and Rady, S. (2006). Housing market dynamics: On the contribution of income shocks and credit constraints. The Review of Economic Studies, 73(2):459–485.
31. Oswald, A. J. (1999). The housing market and Europe’s unemployment: A non-technical paper. Technical report, Department of Economics University of Warwick.
32. Poterba, J. (1991). House price dynamics: The role of tax policy. Brookings Papers on Economic Activity, 22(2):143–204.
33. Rapach, D. E. and Strauss, J. K. (2009). Differences in housing price forecastability across US States. International Journal of Forecasting, 25(2):351 – 372.
34. Schnure, C. (2005). Boom-bust cycles in housing: The changing role of financial structure. IMF Working Paper, 200(5):1–27.
35. Singell, L. D. and Lillydahl, J. H. (1990). An empirical examination of the effect of impact fees on the housing market. Land Economics, 66(1):82–92.
36. Valentini, P., Ippoliti, L., and Fontanella, L. (2013). Modeling US housing prices by spatial dynamic structural equation models. The Annals of Applied Statistics, 7(2):763–798.