Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-03T09:33:20.161Z Has data issue: false hasContentIssue false
  • English
  • Français

ECONOMIC ACTIVITY, CREDIT MARKET CONDITIONS, AND THE HOUSING MARKET

Published online by Cambridge University Press:  03 July 2017

Luca Agnello ,
Vitor Castro  and
Ricardo M. Sousa
Luca Agnello
Affiliation:
University of Palermo
Vitor Castro
Affiliation:
University of Coimbra and NIPE
Ricardo M. Sousa*
Affiliation:
University of Minho, NIPE, and London School of Economics
*
Address correspondence to: Ricardo M. Sousa, University of Minho, Department of Economics and Economic Policies Research Unit (NIPE), Campus of Gualtar, 4710-057 Braga, Portugal; e-mail: rjsousa@eeg.uminho.pt.
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, we assess the characteristics of the housing market and its main determinants. Using data for 20 industrial countries over the period 1970Q1–2012Q2 and a discrete-time Weibull duration model, we find that the likelihood of the end of a housing boom or a housing bust increases over time. Additionally, we show that the different phases of the housing market cycle are strongly dependent on the economic activity, but credit market conditions are particularly important in the case of housing booms. The empirical findings also indicate that although housing booms have similar lengths in European and non-European countries, housing busts are typically shorter in European countries. The use of a more flexible specification for the hazard function that is based on cubic splines suggests that it evolves in a nonlinear way. From a policy perspective, our study can be useful for predicting the timing and the length of housing boom–bust cycles. Moreover, it highlights the importance of monetary policy by influencing lending rates and affecting the likelihood of occurrence of housing booms.

Keywords

Housing Booms and BustsDuration AnalysisWeibull ModelDuration DependenceCubic Splines
Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Special Issue 7: Recent Insights into Financial, Housing, and Monetary Markets , October 2018 , pp. 1769 - 1789
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We are grateful to participants to the 2nd International Workshop on “Financial Markets and Nonlinear Dynamics” (FMND), organized in Paris on June 4–5, 2015 (http://www.fmnd.fr), the Guest Editor, Fredj Jawadi, and Philip Rothman for their constructive comments and suggestions that considerably improved this paper. Castro and Sousa acknowledge that this work has been financed by Operational Programme for Competitiveness Factors–COMPETE and by National Funds through the FCT–Portuguese Foundation for Science and Technology within the remit of the project “FCOMP-01-0124-FEDER-037268 (PEst-C/EGE/UI3182/2013)”. Castro also wishes to thank the financial support provided by FCT–Portuguese Foundation for Science and Technology under the research grant SFRH/BSAB/113588/2015 (partially funded by COMPTE, QREN and FEDER).

References

REFERENCES

Agnello, L., Castro, V., and Sousa, R. M. (2012) How does fiscal policy react to wealth composition and asset prices? Journal of Macroeconomics 34 (3), 874–890.Google Scholar
Agnello, L., Castro, V., and Sousa, R. M. (2013) What determines the duration of a fiscal consolidation program? Journal of International Money and Finance 37, 113–134.Google Scholar
Agnello, L., Castro, V., and Sousa, R. M. (2015) Booms, busts and normal times in the housing market. Journal of Business & Economic Statistics 33 (1), 25–45.Google Scholar
Agnello, L. and Schuknecht, L. (2011) Booms and busts in housing markets: Determinants and implications. Journal of Housing Economics 20 (3), 171–190.Google Scholar
Allison, P. (1982) Discrete-time methods for the analysis of event histories. Sociological Methodology 13, 61–98.Google Scholar
Arghyrou, M. G. (2009) Monetary policy before and after the euro: Evidence from Greece. Empirical Economics 36 (3), 621–643.Google Scholar
Arghyrou, M. G. and Tsoukalas, J. D. (2011) The Greek debt crisis: Likely causes, mechanics and outcomes. World Economy 34 (2), 173–191.Google Scholar
Beck, N., Katz, J., and Tucker, R. (1998) Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science 42, 1260–1288.Google Scholar
Berben, R.-P., Locarno, A., Morgan, J., and Valles, J. (2004) Cross-Country Differences in Monetary Policy Transmission. European Central Bank, ECB working paper no. 400.Google Scholar
Castro, V. (2010) The duration of economic expansions and recessions: More than duration dependence. Journal of Macroeconomics 32, 347–365.Google Scholar
Castro, V. (2011) Can central banks' monetary policy be described by a linear (augmented) Taylor rule or by a nonlinear rule? Journal of Financial Stability 27 (4), 228–246.Google Scholar
Castro, V. (2013) The duration of business cycle expansions and contractions: Are there change-points in duration dependence? Empirical Economics 44 (2), 511–544.Google Scholar
Castro, V. and Sousa, R. M. (2012) How do central banks react to wealth composition and asset prices? Economic Modelling 29 (3), 641–653.Google Scholar
Favara, G. and Imbs, J. (2015) Credit supply and the price of housing. American Economic Review 105 (3), 958–992.Google Scholar
Georgiadis, G. (2012) Towards an Explanation of Cross-Country Asymmetries in Monetary Transmission. Deutsche Bundesbank working paper no. 7.Google Scholar
Glaeser, E. L., Gottlieb, J. D., and Gyourko, J. (2010) Can Cheap Credit Explain the Housing Bubble? National Bureau of Economic Research, NBER working paper no. 16230.Google Scholar
Hwang, M. and Quigley, J. (2006) Economic fundamentals in local housing markets: Evidence from US metropolitan regions. Journal of Regional Science 46 (3), 425–453.Google Scholar
Jawadi, F. (2008) Does nonlinear econometrics confirm the macroeconomic models of consumption? Economics Bulletin 5 (17), 1–11.Google Scholar
Jawadi, F. (2012) Introduction to time-varying modeling with macroeconomic and financial data. Macroeconomic Dynamics 16 (S2), 167–175.Google Scholar
Jawadi, F. and Léoni, P. (2012) Nonlinearity, cyclicity and persistence in consumption and income relationships: Research in honor of Melvin J. Hinich. Macroeconomic Dynamics 16 (S3), 376–393.Google Scholar
Jawadi, F., Sousa, R. M., and Traverso, R. (forthcoming) On the macroeconomic impact and the wealth effects of unconventional monetary policy, Macroeconomic Dynamics. doi:10.1017/S1365100515000292.Google Scholar
Jenkins, S. (1995) Easy estimation methods for discrete-time duration models. Oxford Bulletin of Economics and Statistics 57, 129–138.Google Scholar
Justiniano, A., Primiceri, G. E., and Tambalotti, A. (2015) Credit Supply and the Housing Boom. National Bureau of Economic Research, NBER working paper no. 20874.Google Scholar
Leamer, E. L. (2007) Housing is the business cycle. In Proceedings–Economic Policy Symposium–Jackson Hole, pp. 149–233. Kansas City, MI: Federal Reserve Bank of Kansas City.Google Scholar
Mallick, S. K. and Mohsin, M. (2010) On the real effects of inflation in open economies: theory and empirics. Empirical Economics 39 (3), 643–673.Google Scholar
Mallick, S. K. and Sousa, R. M. (2012) Real effects of monetary policy in large emerging economies. Macroeconomic Dynamics 16 (S2), 190–212.Google Scholar
Payne, J. E. and Zuehlke, T. W. (2006) Duration dependence in real estate investment trusts. Applied Financial Economics 16 (5), 413–423.Google Scholar
Prentice, R. and Gloeckler, L. (1978) Regression analysis of grouped survival data with application to the breast cancer data. Biometrics 34, 57–67.Google Scholar
Poterba, J. (1984) Tax subsidies to owner-occupied housing: An asset-market approach. Quarterly Journal of Economics 99 (4), 729–752.Google Scholar
Rothman, P. (1996) International evidence on business-cycle nonlinearity. In Barnett, W. A., Kirman, A. P., and Salmon, M. (eds.), Nonlinear Dynamics and Econometrics. Proceedings of the 10th International Symposium in Economic Theory and Economerics, pp. 333–341. Cambridge, U.K.: Cambridge University Press.Google Scholar
Rothman, P. (1999) Nonlinear Time Series Analysis of Economic and Financial Data. Norwell, MA: Kluwer Academic Press.Google Scholar
Rothman, P. (2006) Comments on ‘structural change in macroeconomic time series’. Journal of Macroeconomics 28, 151–153.Google Scholar
Sousa, R. M. (2010a) Housing wealth, financial wealth, money demand and policy rule: Evidence from the euro area. North American Journal of Economics and Finance 21 (1), 88–105.Google Scholar
Sousa, R. M. (2010b) Consumption, (dis)aggregate wealth and asset returns. Journal of Empirical Finance 17 (4), 606–622.Google Scholar
Taylor, J. B. (2007) Housing and monetary policy. Housing, housing finance and monetary policy. In Jackson Hole Symposium, pp. 463–476. Kansas City, MI: Federal Reserve Bank of Kansas City.Google Scholar
Taylor, J. B. (2009) Economic policy and the financial crisis: An empirical analysis of what went wrong. Critical Review: A Journal of Politics and Society 21 (2–3), 341–364.Google Scholar
Zuehlke, T. W. (1987) Duration dependence in the housing market. Review of Economics and Statistics 69 (4), 701–709.Google Scholar
Zuehlke, T. W. (2003) Business cycle duration dependence reconsidered. Journal of Business and Economic Statistics 21 (4), 564–569.Google Scholar
Zuehlke, T. W. (2013) Estimation and testing of nonproportional Weibull hazard models. Applied Economics 45 (15), 2059–2066.Google Scholar