Global Cities and Local Housing Market Cycles

  • Alessandra CanepaEmail author
  • Emilio Zanetti Chini
  • Huthaifa Alqaralleh


In this paper, we consider the dynamic features of house price in metropolises that are characterised by a high degree of internationalisation. Using a generalised smooth transition (GSTAR) model we show that the dynamic symmetry in house price cycles is strongly rejected for the housing markets considered in this paper. Further, we conduct an out-of-sample forecast comparison of the GSTAR with a linear AR model for the metropolises under consideration. We find that the use of nonlinear models to forecast house prices, in most cases, generate improvements in forecast performance.


House price cycles Dynamic asymmetries Nonlinear models 

JEL Classification

C10 C31 C33 



The authors appreciate comments and suggestions from two anonymous referees. We also thank Rickard Sandberg, Jan G. de Gooijer, Yongmiao Hong, Takashi Yamagata for their useful comments. Thorough and insightful remarks from the participants of the 10th Nordic Econometrics Meeting (May 2019, Stockholm, Sweden) and the Asian Meeting of the Econometric Society (June 2019, Xiamen, China) are also gratefully acknowledged.


  1. Abelson, P., Joyeux, R., Milunovich, G., Chung, D. (2005). Explaining house prices in Australia: 1970 to 2003. Economic Record, 81, 96–103.CrossRefGoogle Scholar
  2. Alqaralleh, H., & Canepa, A. (2019). Dynamic asymmetries of housing market cycles in large urban areas EST Working Papers 03/19. Italy: University of Turin.Google Scholar
  3. Abrahm, J.M., & Hendershott, P.M. (1993). Patterns and Determinants of Metropolitan House Prices, 1977-91. In Browne, & Rosengren (Eds.) Proceedings of the 25th Annual Boston Fed Conference. Real Estate and the Credit Crunch, (Vol. 18 p. 56). Boston.Google Scholar
  4. Abraham, J., & Hendershott, P. (1996). Bubbles in metropolitan housing markets. Journal of Housing Research, 7, 191–207.Google Scholar
  5. André, C. (2010). A bird’s eye view of OECD housing markets. OECD Economics Department Working Paper No. 746.Google Scholar
  6. André, C. (2015). Housing cycles: stylised facts and policy challenges. In: Proceedings of OENB Workshops No 19. Oesterreichische Nationalbank, Vienna.Google Scholar
  7. Balcilar, M, Gupta, R., Miller, S.M. (2015). The out-of-sample forecasting performance of non-linear models of regional housing prices in the U.S. Applied Economics, 47, 2259–2277.CrossRefGoogle Scholar
  8. Bao, T., Hommes, C.H., Makarewicz, T.A. (2017). Bubble formation and (In)efficient markets in learning-to-forecast and optimize experiments. Economic Journal, 127, 581–609.CrossRefGoogle Scholar
  9. Badarinza, C., & Ramadorai, T. (2018). Home away from home? Foreign demand and London house prices. Journal of Financial Economics, 130, 532–555.CrossRefGoogle Scholar
  10. Bernanke, B., Gertler, M., Gilchrist, S. (1996). The financial accelerator and the flight to quality. Review of Economics and Statistics, 78, 1–15.CrossRefGoogle Scholar
  11. Blatt, J.M. (1980). On the Frisch model of business cycles. Oxford Economic Papers, 32, 467–79.CrossRefGoogle Scholar
  12. Bolt, W., Demertzis, D., Diks, C.G.H., Van der Leij, M.J. (2014). Identifying booms and busts in house prices under heterogeneous expectations CeNDEF Working Papers 14-13. Universiteit van Amsterdam: Center for Nonlinear Dynamics in Economics and Finance.Google Scholar
  13. Borio, C. (2014). The financial cycle and macroeconomics: what have we learnt?. Journal of Banking and Finance, 45, 182–98.CrossRefGoogle Scholar
  14. Cabrera, J.F., Wang, T., Yang, J. (2011). Linear and nonlinear predictability of international securitized real estate returns: a reality check. Journal of Real Estate Research, 33, 565–594.Google Scholar
  15. Canepa, A., & Zanetti Chini, E. (2016). Dynamic asymmetries in house price cycles: a generalized smooth transition model. Journal of Empirical Finance, 37, 91–103.CrossRefGoogle Scholar
  16. Canepa, A., & Zanetti Chini, E. (2019). Housing market cycles in London. ESTWorking Papers, University of Turin.Google Scholar
  17. Chan, K., & Tong, H. (1986). On estimating thresholds in autoregressive models. Journal of Time Series Analysis, 7, 178–190.CrossRefGoogle Scholar
  18. Capozza, D.R., & Seguin, P.J. (1996). Expectations, efficiency, and euphoria in the housing market. Regional Science and Urban Economics, 26, 369–386.CrossRefGoogle Scholar
  19. Capozza, D.R., Hendershott, P.H., Mack, C. (2004). An anatomy of price dynamics in illiquid markets: analysis and evidence from local housing markets. Real Estate Economics, 32, 1–32.CrossRefGoogle Scholar
  20. Christiano, L.J., & Fitzgerald, T.J. (2003). The band pass filter. International Economic Review, 44, 435–465.CrossRefGoogle Scholar
  21. Clapp, J.M., Dolde, W., Tirtiroglu, D. (1995). Imperfect information and investor inferences from housing price dynamics. Real Estate Economics, 23, 239–270.CrossRefGoogle Scholar
  22. Case, K.E., & Shiller, R.J. (1989). The efficiency of the market for single-family homes. American Economic Review, 79, 125–37.Google Scholar
  23. Case, K.E., & Shiller, R.J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity, 2, 299–362.CrossRefGoogle Scholar
  24. Cook, S. (2006). A non-parametric examination of asymmetrical behaviour in the UK housing market. Urban Studies, 11, 2067–2074.CrossRefGoogle Scholar
  25. Cook, S., & Watson, D. (2017). Asymmetric price adjustment in the London housing market: a disaggregated analysis. Research Journal of Economics, 1, 1–7.CrossRefGoogle Scholar
  26. Cook, S., & Holly, S. (2000). Statistical properties of UK house prices: an analysis of disaggregated vintages. Urban Studies, 37, 2045–2055.CrossRefGoogle Scholar
  27. Crawford, G., & Fratantoni, M. (2003). Assessing the forecasting performance of regime-switching, ARIMA and GARCH models of house prices. Real Estate Economics, 31, 223–243.CrossRefGoogle Scholar
  28. Cutler, D.M., Poterba, J.M., Summers, L.H. (1991). Speculative dynamics. Review of Economic Studies, 58, 529–546.CrossRefGoogle Scholar
  29. Davis, P.E., & Zhu, A. (2005). Commercial Property Prices and Bank Performance. BIS Working Paper n. 175.Google Scholar
  30. Dusansky, R., & Koç, .̧C. (2007). The capital gains effect in the demand for housing. Journal of Urban Economics, 61, 287–298.CrossRefGoogle Scholar
  31. Eubank, R.L., LaRiccia, V.N., Rosenstein, R.B. (1992). Testing symmetry about an unknown median via linear rank procedures. Nonparametric Statistics, 1, 301–311.CrossRefGoogle Scholar
  32. Favilukis, J., Kohn, D., Ludvigson, S.C., Van Nieuwerburgh, S. (2013). International capital flows and house prices: theory and evidence, chapter in NBER book: housing and the financial crisis. In Glaeser, E.L., & Sinai, T. (Eds.)Google Scholar
  33. Glaeser, E.L., Gyourko, J., Saiz, A. (2008). Housing supply and housing bubbles. Journal of Urban Economics, 64, 198–217.CrossRefGoogle Scholar
  34. Glaeser, E.L., & Nathanson, C.G. (2015). Housing bubbles. Handbook of Regional and Urban Economics, 5, 701–751.CrossRefGoogle Scholar
  35. Glaeser, E.L., & Gyourko, J. (2018). The economic implications of housing supply. The Journal of Economic Perspectives, 32, 3–30.CrossRefGoogle Scholar
  36. Global Power City Index Yearbook 2018. (2018). MMF institutes for urban strategies. The Mori Memorial Foundation.Google Scholar
  37. Granger, C.W.J., & Terasvirta, T. (1993). Modelling Nonlinear Economic Relationships. Oxford: Oxford University Press.Google Scholar
  38. Gyourko, J., Mayer, C., Sinai, T. (2013). Superstar cities. American Economic Journal, 5, 167–99.Google Scholar
  39. Hamilton, J.D. (2018). Why you should never use the Hodrick-Prescott filter? Review of Economics and Statistics, 100, 831–843.CrossRefGoogle Scholar
  40. Hirshleifer, D.A., Hsu, P.H., Li, D. (2013). Innovative efficiency and stock returns. Journal of Financial Economics, 107, 632–54.CrossRefGoogle Scholar
  41. Hodrick, R.J., & Prescott, E.C. (1997). Postwar U.S. business cycles: an empirical investigation. Journal of Money, Credit, and Banking, 29, 1–16.CrossRefGoogle Scholar
  42. Holly, S., & Jones, N. (1997). House prices since the 1940s: cointegration, demography and asymmetries. Economic Modelling, 14, 549–565.CrossRefGoogle Scholar
  43. Kim, S., & Bhattacharya, R. (2009). Regional housing prices in the USA: an empirical investigation of nonlinearity. Journal of Real Estate Finance and Economics, 38, 443–460.CrossRefGoogle Scholar
  44. Kiyotaki, N., & Moore, J. (1997). Credit cycles. Journal of Political Economy, 105, 211–248.CrossRefGoogle Scholar
  45. Kumar, A. (2009). Hard-to-value stocks, behavioral biases, and informed trading. Journal of Financial and Quantitative Analysis, 44, 1375–1401.CrossRefGoogle Scholar
  46. Iacoviello, M., & Neri, S. (2010). Housing market spillovers: evidence from an estimated DSGE model. American Economic Journal: Macroeconomics, 2, 25–64.Google Scholar
  47. Lundbergh, S., & Terasvirta, T. (2004). Forecasting with smooth transition autoregressive models. In Clements, M.P., & Hendry, D.F. (Eds.) A companion to economic forecasting: Blackwell Publishing.Google Scholar
  48. Mayer, C.J., & Somerville, C.T. (2000). Residential construction: using the urban growth model to estimate housing supply. Journal of Urban Economics, 48, 85–109.CrossRefGoogle Scholar
  49. Malpezzi, S. (1999). A simple error correction model of housing prices. Journal of Housing Economics, 8, 27–62.CrossRefGoogle Scholar
  50. Meen, G. (2002). The time-series behavior of house prices: a transatlantic divide? Journal of Housing Economics, 11, 1–23.CrossRefGoogle Scholar
  51. Miles, W. (2008). Boom-bust cycles and the forecasting performance of linear and nonlinear models of house prices. Journal of Real Estate Finance and Economics, 36, 249–264.CrossRefGoogle Scholar
  52. Muellbauer, J., & Murphy, A. (1997). Booms and busts in the U.K. housing market. Economic Journal, 107, 1701–1727.CrossRefGoogle Scholar
  53. Randles, R.H., Fligner, M.A., Policello, G.E., Wolfe, D.A. (1980). An asymptotically distribution-free test for symmetry versus asymmetry. Journal of the American Statistical Association, 75, 168–172.CrossRefGoogle Scholar
  54. Saiz, A. (2010). The geographic determinants of housing supply. Quarterly Journal of Economics, 125, 1253–96.CrossRefGoogle Scholar
  55. Sassen, S. (2003) In Borsdorf, A, & Parnreiter, C (Eds.), The global city: strategic site/new frontier. Wien: Verlag der Österreichischen Akademie der Wissenschaften.Google Scholar
  56. Seslen, T.N. (2004). Housing Price dynamics and household mobility decisions. USC LUSK/FBE Real Estate Seminar, 9, 1–42.Google Scholar
  57. Sichel, D. (1993). Business cycle asymmetry: a deeper look. Economic Inquiry, 31, 224–236.CrossRefGoogle Scholar
  58. Sollis, R., Leybourne, S., Newbold, P. (1999). Unit roots and asymmetric smooth transitions. Journal of Time Series Analysis, 20, 671–677.CrossRefGoogle Scholar
  59. Sollis, R., Leybourne, S., Newbold, P. (2002). Tests for symmetric and asymmetric nonlinear mean reversion in real exchange rates. Journal of Money, Credit and Banking, 34, 686–700.CrossRefGoogle Scholar
  60. Teräsvirta, T. (1994). Specification, estimation and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89, 208–218.Google Scholar
  61. Tong, H. (1983). Threshold Models in Non-Linear Time Series Analysis. No 21 in Lecture Notes in Statistics. New York: Springer.CrossRefGoogle Scholar
  62. Shiller, R. (1990). Market volatility and investor behavior. The American Economic Review, 80, 58–62.Google Scholar
  63. UBS UBS Global Real Estate Bubble Index. (2018). UBS Group AG, Zuric.Google Scholar
  64. Zanetti Chini, E. (2018). Forecasting dynamically asymmetric fluctuations of the U.S. business cycle. International Journal of Forecasting, 34, 711–732.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Alessandra Canepa
    • 1
    • 2
    Email author
  • Emilio Zanetti Chini
    • 3
  • Huthaifa Alqaralleh
    • 4
  1. 1.Department of Economic and Statistics Cognetti De MartiisUniversity of TurinTurinItaly
  2. 2.Department of Economics and FinanceBrunel University LondonUxbridgeUK
  3. 3.Department of Economics and LawSapienza University of RomeRomeItaly
  4. 4.Department of Economics, Business and FinanceMutah UniversityKarakJordan

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