Understanding House Price Index Revisions

Elul, Ronel; Silverstein, Joseph; Stark, Tom

doi:10.2139/ssrn.2541692

Cited by 1 publication

(1 citation statement)

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“…This exercise ignores the role of data revisions. However, these have been found to be of secondary importance for explaining revisions to real‐time estimates of GDP and credit cycles (Edge & Meisenzahl, ; Orphanides & Van Norden, ), while revisions to house price data are generally rather small (Elul, Silverstein, & Stark, ). The subsample parameter estimates are shown in Table C.1.…”

Section: Resultsmentioning

confidence: 99%

Business, housing, and credit cycles

Rünstler

Vlekke

2017

J of Applied Econometrics

120

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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. Note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. Terms of use: Documents in AbstractWe use multivariate unobserved components models to estimate trend and cyclical components in GDP, credit volumes and house prices for the U.S. and the five largest European economies. With the exception of Germany, we find large and long cycles in credit and house prices, which are highly correlated with a medium-term component in GDP cycles. Differences across countries in the length and size of cycles appear to be related to the properties of national housing markets. The precision of pseudo real-time estimates of credit and house price cycles is roughly comparable to that of GDP cycles. Studies have so far mostly used univariate non-parametric methods, i.e. band-pass filters and turning point analysis, to extract financial cycles. These methods require a priori assumptions on cyclical characteristics and are applied separately to each series. They are therefore not particularly suitable for assessing cyclical co-movements and evaluating the cyclical stance in real-time.Multivariate STSMs do not suffer from these shortcomings. We propose various modifications of the standard STSM to jointly model the cyclical dynamics in GDP and the financial series.In line with earlier studies, we find pronounced cycles in credit and house prices with a length of 12 to 18 years in most cases. There also arise important differences across countries. We find cycles to be particularly long and large for the U.K. and Spain, of more moderate length and size for the U.S., Italy, and France, and comparatively short and small for Germany. These differences turn out to be related to a specific structural characteristic of national housing markets: cycles are longer and larger for countries with high rates of private home ownership.Moreover, financial cycles are closely related to a medium-term component in the GDP cycle.More precisely, the multivariate estimates emphasize the presence of medium-term fluctuations in GDP, which are longer than the traditional business cycle (3 to 8 years). We find high correlations between the financial and GDP cycles in the medium term, but more moderate correlations over the business cycle. While previous studies have documented that peaks and troughs in financial cycles coincide with major turning points in ...

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Section: Resultsmentioning

confidence: 99%