1. HOUSING AND THE MACROECONOMY: THE ITALIAN CASE Guido Bulligan*
“The Macroeconomics of housing markets”
Session 1A: Housing and the business cycle - Domestic features
HOUSING AND THE MACROECONOMY: THE ITALIAN CASE Guido Bulligan*
Paris, 3-4 December 2009
* Bank of Italy, Department of Economic Outlook and Monetary Policy Studies 1

2. Motivations
Rising housing prices, indebtedness and imbalances (graphs)
Real house prices in Italy have increased by 40% since last cyclical trough
Households debt (as % of GDP) has doubled in the last 10 years
Local nature of housing markets, cross-country heterogeneity (graphs)
High home-ownership rate (72%)
Low (but increasing) level of indebtedness of Italian households
Incomplete housing finance markets (products variety, transaction costs)
Empirical evidence
Existing empirical evidence mainly focused on Anglo-saxon countries
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3. Aim of the paper
Searching for stylized facts of the Italian housing market
Over the cycle: growth cycle approach
In reaction to a monetary policy shock: SVAR analysis
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4. The Housing market Cycle: descriptive statistics
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5. The Housing market Cycle: GC approach
Focus on cycles as deviationd from trend:
Cycle defined as fluctions with period between 3 and 10 years as shortest cycle is 26 quarters and longest is 46 quarters
Use of band pass filter (Baxter and King)
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6. The Housing market Cycle: Synchronization I
Lead/lag relationships analyzed through cross-correlations
Residential investment leads real house price by 3 quarters
GDP and demand components (C and I not shown) lead house price by 7 quarters
Inflation and policy rate lead house price by 1 and 3 quarters
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7. The Housing market Cycle: Synchronization I
Lead/lag relationships analyzed through cross-correlations
GDP and demand components (C and I not shown) lead res. inv. by 2 quarters
Inflation and policy rate lag house res. inv. By 1 and 4 quarters
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8. The Housing market Cycle: Synchronization II
Lead/lag relationships
analyzed through cross-
concordance
Residential investment lead real house price by 5 quarters
GDP and demand components (C and I not shown) lead house price by 7 quarters
Inflation and policy rate lead house price by 5 and 1 quarters
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9. The Housing market: SVAR analysis I
VARIABLES:
Endogenous: CPI, GDP, Nominal House price index (PH)*, real residential investment (INV), Policy rate (P.rate)**
Exogenous: dummy variables, World commodity price index
DATA
Quarterly data
All variables (except policy rate) in log-levels
IDENTIFICATION
Recursive (Cholesky)
Sign restrictions on impulse-reponses
Notes:
* In the following graphs, the response of real house price is obtained by construction
** P. rate is the bank of Italy repo rate until 1999 and rate on main refinancing operation of ECB from 1999
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10. The Housing market: SVAR analysis I
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11. The Housing market: SVAR analysis I
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12. The Housing market: SVAR analysis II
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13. The Housing market: SVAR analysis
At business cycle horizons, monetary policy shocks account between 10 and
15% of variance of residential investment and around 10% of variance of
nomina house price but less then 10% of variance of real house prices
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14. Conclusions To summarize:
Housing cycles are longer than cycles in macro variables
Housing cycles are asymmetric in terms of duration, intensity and price adjustment
GDP and components lead housing cycle
Inflation and interest rates are more coincident
Monetary policy shocks have modest but significant and long-lasting effects on housing variables (maximum impact between 0.2% and 1% for res. investment and between 0.1% and 0.5% for real house price)
Monetary policy shocks explains around percent of variability of housing variables at the 2-5 years horizons their role is insignificant at shorter horizons
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15. House price and residential investment (back)
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16. Cross-country heterogeneity (back)
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