Conference on Macroeconomics of Housing Markets

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Conference on Macroeconomics of Housing Markets

• Comments on the Papers of session 1 A
• By Gilles Dufrénot (University of Aix-Marseille
  and Banque de France)

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Plan of the discussion

• Le cours sarticule autour de trois points
• I.-GENERAL COMMENTS
• ?The questions, the methodology and the main
  results
• Still unsolved puzzles (!)
• II.-SPECIFIC COMMENTS (methodological aspects)
• Paper by Bulligan
• Paper by Alvarez and Cabrero
• Paper by Ferrara and Vigna

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GENERAL COMMENTS

• Le cours sarticule autour de trois points
• Why the  agnostic  approach leaves us with
  unsolved puzzles

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What are we interested in?

• Le cours sarticule autour de trois points
• Three important goals
• Assess the vulnerability of the economy to
  Housing markets (Hence the importance of finding
  cross correlations)
• Try to identify the main channels of transmission
  of the shocks from the housing sector to the
  economy (hence the importance of using several
  indicators of the housing activity)
• Prospective approach find leads and lags
  dynamics to say whether housing variables can be
  chosen as advanced indicators of the GDP

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How can we do that?

• Theoretical models typically Dynamic
  Stochastic General Equilibrium models ? growing
  literature (appropriate to study how productivity
  shocks, technological shocks, monetary shocks,
  etc affect the agents decisions of investment
  in the housing sectors and the impact on the GDP
• Statistical models ( agnostic  about the main
  transmission channels) ? only want to detect
  correlations, leads and lags dynamics, common
  factors in the series

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The three papers adopt the second approach (1)

• 1.- Country-specific studies based on time series
• The paper by Bulligan deals with the case of
  Italy and use a structural VAR framework to study
  the effects of monetary policy shocks ? Whats
  new the sign restriction approach in the
  long-run matrix to define the sequance of shocks
• The paper by Alvarez and Cabreo deals with the
  case of Spain ? Whats new butterwoth filters
  to study the cross correlation between variables
  of the housing sector and macroeconomic variables
  

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The three papers adopt the second approach (2)

• 1.- Country-specific studies based on time series
• The paper by Ferrara and Vigna deals with the
  case of France.
• Whats new The second part of the paper (!)
  that discusses the long-run cross correlations
  betwwen the housing markets and the economic
  variables this allows them to explain why France
  was less affected by the sharp decrease in the
  housing prices

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The three papers adopt the second approach (3)

• 2.- All three papers yields similar conclusions
• The find strong correlations between the
  business cycle and the cycle of the housing
  activity (whichever variables are used to capture
  the dynamics in either sector or the other)
• They conclude that the activity in the housing
  sector can be considered as a leading indicator
  of the business cycles.

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First unsolved puzzle (1)

• 1.- What about the house price gap ?
• Which variables were at play during the last 10
  years to explain the booming in the housing
  investment and the upward price trend?
• Fundamental variables growth of disposable
  income per capita, long-term and short-term
  interest rates, inward immigration flows (Spain),
  credit growth, changes in equity prices, etc..
• Non-fundamental variables ( of increase in the
  housing prices , not explained by the
  fundamentals)

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First unsolved puzzle (2)
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First unsolved puzzle (3)

• If non-fundamentals are at play during the booms,
  then we can expect a sharp correction when the
  prices and activity in the housing sector
  decline.
• So, during upwards, when the house price are
  over-valued, we expect to find a weaker
  correlation with the GDP (because you have a
  bubble)

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First unsolved puzzle (4)

• Now, when the bubbles burst, there are two
  indications
• Firstly, they do so when some macroeconomic
  fundamentals begin to deteriorate (income,
  unemployment, credit conditions, etc. ? In this
  case the GDP growth may be a leading indicator of
  the activity in the housing sector (!)
• Secondly, deteriorating macroeconomic conditions
  are the source of downward revisions in
  expectations ? Stronger correlations betweeen the
  GDP and the activity in the housing sector

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First unsolved puzzle (5)

• Implications for the time series-based approaches
  
• Selection bias if the period includes episodes of
  huge price decrease (may explain the strong
  positive correlations that are found)
• Concerning the lead and lags effects the
  approaches do not handle the house price gap

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Second unsolved puzzle (1)

• Spurious short-term cross-correlation ?
• Except in Spain and Ireland, the residential
  investment does not account for a large share of
  the economies.
• Ratio of housing construction in of GDP 6,5
  for the advanced economies and over the past 3
  decades 5.
• Accordingly, for the correlation between the
  housing sector and the GDP to be strong, there
  must be large corrections in the housing
  construction!!

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Second unsolved puzzle (2)

• Two consequences for the time series models
• Again a problem of selection bias the
  correlation found include periods of huge
  -downward - corrections in the samples
• Choice of the variables even if we accept the
  idea that the housing sector activity leads the
  GDP, the variable of interest should be the
  investment rate in this sector (real residential
  investment to GDP)

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Third unsolved puzzle

• The papers focus too much on the short-term, but
  the long-run correlation may also be important
• The papers argue that cyclical componenst of the
  housing variables affect the cyclical upturns and
  downturns of the GDP.
• However, for downturns in the GDP, it is known
  that they occur in the industrialized countries
  when the ratio of housing investment to GDP
  evolve below its historical trend !
• ? This implies that the trend components of the
  housing variables affect the cyclical components
  of the GDP

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SPECIFIC COMMENTS

• Le cours sarticule autour de trois points
• Paper by Guido Bulligan

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Cyclical andtrend growth analyses

• Business approach
• The old methodology by Burns and Mitchell has
  been updated by Harding and Pagan (2002), Journal
  of Monetary Economics ? link between the turning
  points and the moments of the series cycles are
  obtained as regards their contribution to
  volatility, trend growth, correlation and
  non-linear effects.
• Missing nonlinearities in the series structural
  breaks
• ? Use non-parametric filters such as polyspectra
  (bispectraum, trispectrum) evolutionary
  sprectrum

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VAR models (1)

• VAR analysis to study the implication of monetary
  policy shocks
• Isnt there a problem of  multiscale  housing
  markets are characterized by long cycles with a
  persitent dynamics, as compared with the other
  macroeconomic variables in the VAR? Is it
  possible to estimate the effects of a shock by
  considering a VECM?
• There is a similar study as yours done by Carlos
  Vargas-Silva (2009) for the US (forthcoming in
  the Journal of Macroeconomics), showing that

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VAR models (2)

• VAR analysis to study the implication of monetary
  policy shocks
• 1/ the magnitude of monetary shocks on the
  housing markets is very dependent on the
  selection of the horizon for which the
  restrictions hold in the VAR
• 2/ as compared with classical choleschi
  decomposition ,, the impact of monetary policy on
  the housing market is much less certain with the
  sign restriction approach.
• Do you find similar things for Italy?

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SPECIFIC COMMENTS

• Le cours sarticule autour de trois points
• Paper by Luis Alvarez and Alberto Cabrero

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Filters(1)

• There is one filter that may overperform the ones
  described wavelet for several reasons
  (simultaneous description of the high frequency
  and low-frequency components)
• Compared with the butterworth filter, you do not
  need to eliminate some  high  or  low 
  frequencies, because the filter is  multiscale 
• Compared with the Kernel regression, Wavelet
  decomposition is also non-parametric, but the
  analysis is done in the  frequency domain 
  which is more appropriate for the study of
  business cycles then time domain methodologies

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Comparison with DSGE models (1)

• One problem your empirical results sometimes
  contradict the theoretical findings.
• But, it does not mean that the DSGE models are
  wrong!
• The negative or positive response to shocks
  depends upon
• 1/ the interval of variation of the parameters
  that serve to calivrate the models and
• 2/ upon the nature of the technological,
  productivity, monetary shocks.
• How can you use your time-series based filters to
  see whether your findings do indeed contradict
  the conclusions of the models?

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Comparison with DSGE models (2)

• Three steps (Monte Carlo)
• 1/ obtain simulated series from the DSGE models,
  for a given set of calibrated parameters
• 2/ Apply the Butterworth and Kernel filters . Do
  this a number of times (example 1000 times)
  because, the effects of shocks may be nonlinear,
  you must look at the Generalized impulse response
  functions (GIRF)
• 3/ Compare the population of cross-correlations
  between the housing/residential investment and
  GDP with the cross-correlation you find when
  using the statistical data.

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Alternative methodologies for asymmetry

• Problem with the measures of brevity, violence,
  steepness you do not know which variables are
  at play to account for the observed assymetries
  (credit constraints? Capacity constraints, labour
  markets ?).
• Alternative models including transition variables
• Deterministic models such as TAR or STAR models
• Stochastic models such as Markov Switching models
  with endogenous probability of transition.

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SPECIFIC COMMENTS

• Le cours sarticule autour de trois points
• Paper by Laurent Ferrara and Olivier Vigna

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Choice of the 2-step version of the HP filter

• Motivation why using this filter if other
  filters yield similar turning points? Are there
  any robustness study elsewhere in the literature?
  
• One question to which extend can we say that
  turning point in the housing sector are causing
  those observed in the GDP? May be we are simply
  detecting common factors
• Something original the use of Confidence
  indicator in the building sector (perception of
  the activity by the housing industrials) ?
  capture the supply side of the housing market

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Long-run analysis (1)

• Most interesting part of the paper, but no
  rigourous statistical analysis to test the
  arguments. This seems a promising area of
  research because it relies on the fundamentals of
  then housing markets
• The conclusions challenges those of the IMF.
  While the authors argue that the movements in the
  housing prices and investment were smaller in
  magnitude as compared with the other european
  countries (Spain, the UK, Ireland), the IMF finds
  that France was among the countries with highest
  overvalued house price (20) and a housing ratio
  investment significantly above the historical
  trend.

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Long-run analysis (2)

• The IMF concludes that France was among the
  countries that should experience the largest
  decrease in the housing prices due to the the
   greatest exuberance  in the house price.
• It would be interesting to see how the authors
  arguments can be corroborate by a statistical
  analysis and why their depart from the IMF
  findings.

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• Thanks for your attention