HOUSING AND THE MACROECONOMY: THE ITALIAN CASE Guido Bulligan

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HOUSING AND THE MACROECONOMYTHE ITALIAN
CASEGuido Bulligan
The Macroeconomics of housing markets
Session 1A Housing and the business cycle -
Domestic features

• Paris, 3-4 December 2009

Bank of Italy, Department of Economic Outlook
and Monetary Policy Studies
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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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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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The Housing market Cycle descriptive statistics
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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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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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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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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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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

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The Housing market SVAR analysis I

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The Housing market SVAR analysis I

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The Housing market SVAR analysis II

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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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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

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House price and residential investment (back)

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Cross-country heterogeneity (back)