Trends, Cycles and Convergence in U.S. Regional House Prices

Clark, Steven P.; Coggin, T. Daniel

doi:10.1007/s11146-009-9183-1

Cited by 80 publications

(54 citation statements)

References 33 publications

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“…15 For instance, the Morlet wavelet has an optimal joint time-frequency concentration in the sense that the Heisenberg uncertainty attains the minimum possible value, so that this complexvalued wavelet is quite well localized in both time and frequency space. 16 Moreover, the Morlet wavelet consists of a complex exponential modulated by a Gaussian, where we set the central angular frequency ω 0 equal to 6, so that the terms scale and period can be used interchangeably. 17 To test for significance, a Monte Carlo simulation with 10,000 draws of white noise time series was used.…”

Section: Some Basic Ingredientsmentioning

confidence: 99%

On the cyclicity of regional house prices: New evidence for U.S. metropolitan statistical areas

Flor¹,

Klarl

2017

Journal of Economic Dynamics and Control

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This paper is mainly concerned with the analysis of regional house price cycles. Based on a newly available data set consisting of the 40 largest U.S. Metropolitan Statistical Areas (MSAs), we introduce a wavelet transform based metric to study the housing cycle synchronization across MSAs. We derive several conclusions: (i) We show that regional housing cycle dissimilarities are significantly and strongly connected to geography. Terms of use: Documents in(ii) We show that U.S. regional housing cycles are considerably shorter compared to business cycles. (iii) By employing statistical methods, we also show that regional housing prices significantly converge after the bursting of the bubble only at higher business cycle frequencies, whereas for lower business cycle frequencies this is not the case. This is a notable result, because it directly implies that housing cycles behavior is different even at the business cycle.JEL-Code: C320, C330, E320, R110, R310.

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Section: Some Basic Ingredientsmentioning

confidence: 99%

On the cyclicity of regional house prices: New evidence for U.S. metropolitan statistical areas

Flor¹,

Klarl

2017

Journal of Economic Dynamics and Control

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“…In growth literature, assuming that income per worker has a unit root, the stochastic convergence is defined as cointegration between the incomes (Bernard and Durlauf, 1995;Campbell and Mankiw, 1989). A number of empirical studies on house price convergence, such as MacDonald and Taylor (1993); Meen (2002); Cook (2005); and Clark and Coggin (2009), have focused on this stochastic convergence to examine whether there exists a stable long-run relationship of housing prices between regions or between regions and housing prices nationally. 6 The implication of cointegration on the convergence is as follows.…”

Section: Conventional Convergence Testsmentioning

confidence: 99%

House price convergence: Evidence from US state and metropolitan area panels

Kim

Rous

2012

Journal of Housing Economics

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“…The first category is unit root test and its various extended forms including panel data unit root testing and unit root testing with structure breaks (Clark and Coggin, 2009), etc. The disadvantage of unit root tests is that it does not necessarily imply the existence of convergence when the unit root test cannot be rejected (Montañés and Olmos, 2013).The second category is Granger causality test combined with VAR models, cointegration test and impulse response, etc.…”

Section: Introductionmentioning

confidence: 99%