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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

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

slide2

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
slide3

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
slide5

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)
slide6

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
slide7

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
slide8

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
slide9

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
slide13

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

slide14

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