Received: 20 November 2015 Revised: 29 March 2017
Business, housing, and credit cycles
European Central Bank, Frankfurt am
CPB Netherlands Bureau for Economic
Policy Analysis, The Hague, The
Gerhard Rünstler, European Central
Bank, Sonnemannstrasse 20, D-60314
Frankfurt am Main, Germany.
We use multivariate unobserved components models to estimate trend and cycli-
cal components in gross domestic product (GDP), credit volumes, and house
prices for the USA 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 prop-
erties 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.
The role of the financial sector in the propagation of economic fluctuations is at the heart of both macroeconomic research
and of considerations about the redesign of economic policy after the financial crisis. One element in these discussions
is macro-prudential policies aimed at dampening cyclical fluctuations in credit volumes and residential property prices.
The implementation of such policies requires forming a view on the cyclical stance of these two financial series.
In this paper we apply an extended version of the multivariate structural time series model (STSM), as introduced
by Harvey and Koopman (1997), to estimate trend and cyclical components in real gross domestic product (GDP), real
total credit to the private nonfinancial sector, and real residential property prices. We use quarterly data from 1973:Q1 to
2014:Q4 for the USA and the five largest European economies. We are interested in three particular questions that are
relevant to macro-prudential policies: first, what are the defining characteristics of house price and credit cycles? Second,
how do they relate to GDP cycles? And third, how reliable are real-time estimates of the cycles?
Recent economic research documents the important role of boom–bust cycles in credit volumes and residential property
prices in the buildup of financial instability and subsequent financial crises (e.g., Mian & Sufi, 2010; Schularick & Taylor,
2012; Jordà, Schularick, & Taylor, 2015, 2016). One important mechanism underlying these so-called leverage cycles is
a mutually reinforcing interaction between mortgage supply and the value of housing (Geanakoplos, 2009). An increase
in house prices implies a higher value of collateral available to back mortgages. Under certain conditions, this would
fuel mortgages volumes and housing sales and thereby drive up prices further. Conversely, during busts the low value
of collateral may severely constrain banks' balance sheets and dampen credit supply. Kiyotaki and Moore (1997) and
Iacoviello (2005) show that embodying collateral constraints in dynamic stochastic general equilibrium (DSGE) models
generates highly persistent responses in both output and leverage. Subsequent studies point to the aggravating effects of
liquidity risk, fire sales and house price fads (Burnside, Eichenbaum, & Rebelo, 2016; Shleifer & Vishny, 2011).
In order to contain cyclical fluctuations in credit and housing, financial supervisory authorities have started imple-
menting various macro-prudential policy measures, the most important being countercyclical capital buffers as foreseen
by the Basel III regulations (BIS, 2010). The regulations explicitly refer to a measure of the credit cycle, suggesting that
the buffers should be released once the credit-to-GDP ratio exceeds its long-run trend by 2%. Giese et al. (2014) argue
The author was affiliated with the ECB while this paper was written.
212 Copyright © 2017 John Wiley & Sons, Ltd. wileyonlinelibrary.com/journal/jae J Appl Econ. 2018;33:212–226.