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HOUSING AND THE MACROECONOMY: THE ITALIAN CASE Guido Bulligan*

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

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HOUSING AND THE MACROECONOMY: THE ITALIAN CASE Guido Bulligan*

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

  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

  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

  4. The Housing market Cycle: descriptive statistics

  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)

  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

  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

  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

  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 1990-2008 • 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

  10. The Housing market: SVAR analysis I

  11. The Housing market: SVAR analysis I

  12. The Housing market: SVAR analysis II

  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

  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 10-20 percent of variability of housing variables at the 2-5 years horizons their role is insignificant at shorter horizons

  15. House price and residential investment (back)

  16. Cross-country heterogeneity (back)

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