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.
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Note the city of London was at the top of the GPCI index in 2018. However, an extensive investigation on housing market cycles in London is considered in Canepa and Zanetti Chini (2019). Note also that the GPCI index is published yearly, therefore the ranking of the cities changes over time. However, the cities under consideration have been ranking in the top twenty for at least the last five years.
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Note that according to the United States Office of Management and Budget an MSA has an urbanized core of minimally 50,000 population and includes outlying areas determined by commuting measures.
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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.
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Canepa, A., Chini, E.Z. & Alqaralleh, H. Global Cities and Local Housing Market Cycles. J Real Estate Finan Econ 61, 671–697 (2020). https://doi.org/10.1007/s11146-019-09734-8
- House price cycles
- Dynamic asymmetries
- Nonlinear models