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Housing Cycles and Cross-Country Comovements: An Analysis

This study examines the housing cycles within the Euro area and its impact on the degree of cyclical comovement in the whole region. The methodology involves the use of ideal band-pass and Butterworth filters to estimate the cycles, as well as correlation analysis and turning point analysis.

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Housing Cycles and Cross-Country Comovements: An Analysis

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  1. Outline • Motivation • Methodologies for cycle estimation • Cross country comovements • Correlation analysis • Turning points • Conclusions

  2. Motivation • Housing offers the best warning sign of recessions • Leamer (2007) • Recent sharp house price falls are having far reaching effects on real variables • Wealth effects on consumption • Labour intensive sector • Property as collateral (credit constraints) • Housing markets are idiosyncratic (nontradability) • Regulation • Land availability • Do different housing cycles within the euro area affect the degree of cyclical comovement in the whole area?

  3. The Methodology (I):The ideal band pass filter • We define the business cycle as the outcome of an ideal band-pass filter • The filter fully removes high-frequency fluctuations (e.g. those with a period of less than 6 quarters (1.5 years)) • … and also long-run movements (e.g. over 32 quarters (8 years)) • The gain function of a filter indicates the extent to which it affects the series • A gain over 1 indicates that those fluctuations are amplified • A gain of 1 implies no effect • A gain of 0 shows that fluctuations are fully suppressed

  4. The methodology (II): Butterworth filters • Widely used in engineering in their one-sided form (Butterworth(1930)) • Can be seen as a generalization of the HP filter (The HP filter is a particular low-pass Butterworth filter of the sine) • Highly flexible. Can be given a model-based interpretation • Filters are very close to the ideal band-pass filter • These filters are able to remove satisfactorily short-run and medium run fluctuations • This the method we use

  5. The Methodology (III): Alternative estimation procedures • Hodrick- Prescott and linear kernels do not approximate well the ideal filter • Short cycles are almost fully passed through and cyclical fluctuations with long periods are only partially removed. Linear kernels have an oscillatory gain • Baxter and King (1999) band-pass filter involves losing k observations at the end (the most interesting period for policy-makers!) and beginning of the series. It is less satisfactory than the Butterworth filter

  6. Are there comovements in the business cycle and housing variables ?

  7. Does housing lead the business cycle? Correlation of cyclical components with GDP cycle • Residential investment leads GDP in Spain and Germany, as in Leamer (2007), but not in France and Italy (but leading in Ferrara and Vigna 2009) • Housing starts, as in Leamer (2007), and building permits provide, as expected, earlier warning signals

  8. Cross country correlation analysis. Full sample (1980Q1-2008Q4) • Cross country correlations are higher for GDP than for residential investment (trade flows) • Cross country correlations are higher for housing investment than for non residential investment (public and business investment in construction are hardly synchronized) • Nominal house prices are almost orthogonal • Moderate synchronization in real house prices mostly reflects comovements in consumer prices

  9. Which are the leading countries?Cross correlations Country in a row leads/lags country in a column • Spanish GDP and, less clearly, French GDP lead those of the other countries • No German locomotive! • The results broadly hold with y-o-y rates

  10. Lead/lag analysis cross correlations of housing variables In domestic housing markets, country-specific factors tend to play a stronger role • Spanish residential investment leads that of the other countries • French nominal house prices tend to lead and Spanish house prices to lag • Spanish real house prices tend to lag, but not conclusive leading in other countries Residential investment (Average contemp. CC: 0.29) Country in a row leads/lags country in a column Nominal house prices (Average contemp. CC: 0.09) Real house prices (Average contemp. CC: 0.33)

  11. Changes in synchronization since EMUSimple measure • Comovements in GDP are considerably stronger in the EMU period (external trade) • Comovements in residential investment and in the construction sector as a whole are also much stronger in the EMU period • Comovements in nominal house prices have increased, but remain low • In contrast, real house prices comovements are even lower in the EMU period

  12. Changes in synchronization since EMUPeña and Rodríguez measure (2003) Roughly speaking, Peña&Rodriguez measure is similar to a multivariate R2 • For real variables, provides the same message than the average cross country correlation • Comovements in GDP are considerably stronger in the EMU period (external trade) • Comovements in residential investment and in the construction sector as a whole are also much stronger in the EMU period • Comovements in nominal house prices remain low • But real house prices comovements have increased in the EMU period

  13. Turning Point (TP) Analysis • Identification of TPs using BBQ algorithm Peak at t: { y(t) > y(t-k) , y(t) > y(t+k) , k=1,…, K } Trough at t: { y(t) < y(t-k) , y(t) < y(t+k) , k=1,…, K }, Alternative: use a Markov switching model • Computation of binary variables ( Sit ) for a country i • Sit = 1 during a descending phase of the cycle • Sit = 0 during an ascending phase of the cycle

  14. Measure of synchronisation based on concordance indices (CI) between 2 countries i and j : • CI=1 full synchronization; CI=0 no synchronization • Cross-concordance indices (CCI) to take leads and lags k into account (k = -4, -2, -1, 0, 1, 2, 4) • To assess leads and lags, we choose k that maximizes CCI • Also a lead-lag analysis is made in case of strong cross concordance

  15. Concordance analysis of GDP: Lead/lag assessment • Evidence of synchronisation among the 4 countries (CIs> 0.7) • The French and Spanish cycle tend to lead the German one • Confirms cross-correlation analysis Evidence of common behaviour in GDP cycles among the four countries, Germany being slightly lagging (No German locomotive!)

  16. Concordance indices: Household investment • Household investment • Synchronisation between France and Spain (CI=0.72) • Cross concordance between Germany and Italy (CCI=0.69), Italy lags by 2 quarters • Housing cycles are quite heterogeneous, especially the German one • But evidence of relationship between France and Spain for housing activity cycles

  17. Household investment: House prices • House prices • Strong relationship between Spain and Italy (CCI=0.78), Spain is leading (4 Q) • Spain with Germany (CCI=0.69) and with France (CCI=0.63) • Less evidence for other countries • Less integration between countries regarding house prices

  18. Conclusions • Country comovements are higher for GDP than for residential investment (trade flows) • Predominant role of local factors in housing markets • Comovements in the housing sector are much weaker for prices than for real variables • Comovements are considerably stronger in the EMU period • No German locomotive!

  19. VIELEN DANK FÜR IHRE AUFMERKSAMKEIT! ¡GRACIAS POR SU ATENCION! MERCI DE VOTRE ATTENTION! GRAZIE PER L'ATTENZIONE! THANKS FOR YOUR ATTENTION!

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