Housing and macroeconomic cycles in the major euro

Housing and macroeconomic cycles in the major euro area countries Álvarez, L. J. , G. Bulligan, A. Cabrero, L. Ferrara and H. Stahl Conference on Macroeconomics of Housing Markets Banque de France, 3 -4 December 2009

Outline 1. Motivation 2. Methodologies for cycle estimation 3. Cross country comovements 1. Correlation analysis 2. Turning points 4. 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 onesided form (Butterworth(1930)) § Can be seen as a generalization of the HP filter (The HP filter is a particular lowpass Butterworth filter of the sine) § Highly flexible. Can be given a modelbased interpretation § Filters are very close to the ideal bandpass 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 (1980 Q 1 -2008 Q 4) § 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 Residential investment (Average contemp. CC: 0. 29) Spanish residential investment leads that of the other countries Country in a row leads/lags country in a column Nominal house prices (Average contemp. CC: 0. 09) French nominal house prices tend to lead and Spanish house prices to lag Real house prices (Average contemp. CC: 0. 33) Spanish real house prices tend to lag, but not conclusive leading in other countries 11

Changes in synchronization since EMU Simple 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 EMU Peña and Rodríguez measure (2003) Roughly speaking, Peña&Rodriguez measure is similar to a multivariate R 2 § 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 1. Country comovements are higher for GDP than for residential investment (trade flows) • Predominant role of local factors in housing markets 2. Comovements in the housing sector are much weaker for prices than for real variables 3. Comovements are considerably stronger in the EMU period 4. No German locomotive! 19

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