European Real Estate Society Annual Conference
Bucharest, 2014
Long-run equilibrium for the
Greater Paris Office Market ;
Rental and Demand adjustments
Catherine Bruneau* and Souad Cherfouh**
* Professor, University Paris I Pantheon Sorbonne and Paris School of
Economics
** PhD student, University Paris I Pantheon Sorbonne and BNP Paribas Real
Estate
INTRODUCTION
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Motivation for the research
•
Despite its leading position in Europe, limited
research on the Greater Paris rental office market
•
Analysis of the Parisian market dynamics (focus on
the rental and demand equations)
•
Extend the existing office market research to the
French case to the really recent past
•
Combine cointegration techniques to a multiple
structural break approach
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MODELING STRATEGY
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Literature and methodology
•
Theoretical background  multi-equation framework
[Rosen, 1984]; [Wheaton, 1987];
[Wheaton et al. 1997]; [Malle 2010] etc
•
Objective: provide a full comprehension of the main
underpinning market dynamics
•
3 behavioural equations:



Demand:(economic activity and rents)
Supply:(economic activity, vacancy rate, construction
costs, interest rates)
Rents:(vacancy rates)
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Literature and methodology
•
Econometric methodology  cointegration approach
[Hendershott et al. 2002]; [Brounen and Jennen, 2009];
[McCartney, 2012]; [Hendershott et al., 2013] etc
Reduced-form equations
•
Justification: most economic, financial and real estate
series are non stationary
•
Objective: provide a relevant framework able to
account for specific characteristics of space markets
 well-known slow adjustment
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Literature and methodology
•
Econometric methodology  multiple structural break
approach
[Perron, 1989]; [Banerjee and Urga,1995]; [Gregory and Watt,
1996]; [Gregory and Hansen, 1996]
•
Justification: no cointegration found between the
variables of interest
•
Objective: account for the structural changes that may
affect the long run equilibria through shifts in the mean
Nonlinear approach in the space market literature
[Englund et al. 2008]; [Brounen and Jennen, 2009]; [Hendershott
et al., 2010]; etc
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Rent modeling
•
(1)
(2)
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Demand modeling
• Long-run model:
(3)
• Short-run model:
(4)
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THE GREATER PARIS
OFFICE MARKET
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The Greater Paris office market
•
Largest office market in Europe in terms of stock (53 m sm) and
in terms of turnovers (over 2 m sm take-up/annum since 2000)
•
Second largest market in terms of investment after London
(€ 9.6 b/annum since 2000 vs 11.8 for London)
•
Major market with specific characteristics compared to the
London market:
 Higher level of office space centralization in France
 Economic growth prospects strongly reliant on a relatively

stable private and public demand
Shorter terms of rental agreements
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Vacancy and rental movements
Figure 2: Real rent and vacancy rate in the
Greater Paris office market (1991-2012)
%
Index
130
11
120
10
110
9
100
8
90
7
80
6
70
5
60
4
50
3
40
2
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Real effective rent (new leases)
Vacancy rate
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Movements in office demand
fundamentals
Figure 3: Occupied stock and
real rents in the Greater Paris
office market (1991-2012)
Figure 2: Occupied stock and
office
employment
in
the
Greater Paris office market
(1991-2012)
%
%
6
6
5
5
4
4
3
3
2
2
1
1
0
0
-1
-1
-2
-2
1992
1994
1996
1998
2000
2002
2004
2006
Occupied stock (annual variation)
Office employment (annual variation)
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2008
2010
2012
%
Index
110
5
100
4
90
3
80
2
70
1
60
0
50
-1
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Occupied space (annual variation)
Real effective rent (new leases)
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EMPIRICAL RESULTS
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Estimation procedure
 Three-step procedure:
1. Structural break identification  pragmatic approach:
 No closed-form solutions for the limiting distribution of the unit root test


statistics in the case of multiple breaks
Graphical examination of the long-run residuals  choice of the dates
that best corresponds to changes in the mean of the residual.
Critical values obtained from Monte Carlo simulations.
2. FMOLS estimation of the cointegrating relationships for both
sub-systems with their respective breaks.
3. OLS estimations of the ECM within each sub-system
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Rent modelling
Table 1: Long-run equilibrium
rent model
Variable
Vacancy rate
Coefficient
Coefficient for vacancy rate:
significant and expected
sign
•
All breaks are justified by
referring
to
recent
developments of the parisian
market
;
the
related
coefficients
have
the
expected signs.
•
For example:
t-Statistic
-0.08***
-0.26***
-16.3
-10.9
-0.15***
-4.9
0.27***
12.5
-0.13***
-6.5
0.06***
3.0
Intercept
5.07***
109.9
N
88
Adjusted R²
0.93
Notes: Dependent variable = ln(Real Rent).
*, **, *** indicate significant at 10%, 5% and
1% respectively.
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•
 1994Q4
→ sharp rent correction
consecutive to the bubble burst
in the early 1990’s
 2000Q4
→ demand crisis: 2%
vacancy rate for « relatively low »
rental values → correction in the
distorsion
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Rent modelling
Table 2: Short-run adjustement
model
Variable
Coefficient
-0.04 ***
-6.0
ln(Rent(-3))
-0.32 ***
3.4
Error correction term
-0.29 ***
-4.2
Intercept
-0.00
-0.9
Durbin-Watson
84
Adjusted R²
Error correction term is
significant ; long run causality
from the vacancy rate
towards the rent (Granger,
1988)
•
Speed of rental adjustement
= 29%
•
Relatively high speed of
adjustement compared to
other European markets
t-Statistic
Vacancy rate(-2)
N
•
0.55
2.16


Notes: Dependent variable = ln(Real Rent).
*, **, *** indicate significant at 10%, 5% and
1% respectively.
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Shorter lease lenght
Higher market turnover
Autoregressive structure of
the rents
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Demand modelling
Table 3: Long-run
demand model
Variable
ln(Office employment)
ln(Real rents)
Intercept
N
equilibrium
Coefficient
23.1
-11.2
-7.3
0.02***
4.3
-0.02***
-4.7
0.01***
3.5
-0.02***
-3.6
0.02***
5.0
12.87***
70.0
Adjusted R²
0.99
Notes: Dependent variable = ln(Occupied
Space). *, **, *** indicate significant at 10%,
5% and 1% respectively.
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Coefficients for employment and
rents: significant and expected
signs
•
All breaks can be interpreted
and the related coefficients
have the expected signs
•
For example:
t-Statistic
1.10***
-0.12***
-0.05***
87
•
 1993Q3 → downwards correction
in the supply crisis context
 2001Q3 → downwards demand
correction relative to changing
economic conditions
 2007Q4 → financial crisis impact
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Demand modelling
Table 5: Absorption adjustement
model
Variable
Coefficient
t-Statistic
Occupied Space(-1)
0.31***
3.3
Occupied Space(-2)
0.28***
2.7
Error correction term
Intercept
N
Durbin-Watson
-0.09*
•
Error correction
significant
•
Slow
speed
of
adjustement = 9%
•
Space absorption is sticky
due to rigidities
-1.8
0.00***
85 Adjusted R²
term
is
space
2.7
0.23
1.93


Long-term term leases
Costs
associated
with
termination
lease
Notes: Dependent variable = ln(Occupied
Space). *, **, *** indicate significant at 10%,
5% and 1% respectively.
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International comparison
Table 4: Elasticity estimates for selected European office markets
Market
Authors
Paris
Price
elasticity
Income
elasticity
Sample
period and
frequency
Specification
estimation
-0.12
1.1
1991-2012
𝑙𝑛𝑂𝑆𝑡∗ = 𝛼0 +
𝛼1 𝑙𝑛𝐸𝐴𝑡 + 𝛼2 𝑙𝑛𝑅𝑡 +
𝑖 𝜎𝑖 𝐷𝑈𝑀𝑖 + εt
ECM
Office
employment
quarterly
and
Economic
activity proxy
12
European
markets
Mouzakis
and
Richards
(2007)
-0.15
0.63
1980-2001
annual
panel data
𝑙𝑛𝑅𝑡∗ = 0 +
1 𝑙𝑛𝐸𝐴𝑡 + 2 (𝑙𝑛𝑆𝑡 −
𝑣) + εt
ECM
Local
gross
added
Paris
Malle
(2010)
-0.19
0.97
1996-2008
quarterly
𝑙𝑛𝑂𝑆𝑡∗ = 𝛼0 +
𝛼1 𝑙𝑛𝐸𝑡 + 𝛼2 𝑙𝑛𝑅𝑡 + εt
OLS
Office
employment
London
Hendershott
et al. (2010)
-0.20
0.38
1977-2006
annual
𝑙𝑛𝑅𝑡∗ = 0 [ln 1 −
𝑣 ∗ − 𝛽0 ] +
1 𝑙𝑛𝐸𝐴𝑡 − 2 𝑙𝑛𝑆𝑡 +
εt
ECM
Financial and
business
services
employment
Dublin
McCartney
(2012)
-1.13
1.70
1978-2010
annual
𝑙𝑛𝑅𝑡∗ = 0 +
1 𝑙𝑛𝐸𝐴𝑡 − 2 𝑙𝑛𝑆𝑡 +
εt
ECM
Gross National
Product
5
secondary
European
markets
Brounen
and Jennen
(2009)
-1.94
6.04
1990-2006
annual
panel data
𝑙𝑛𝑅𝑡∗ = 0 +
1 𝑙𝑛𝐸𝐴𝑡 +
2 ln⁡
[ 1−𝑣 ∗
𝑂𝑆𝑡 ] + εt
National
employment
level
value
ECM
Notes: Cautious is required when comparing between studies with different
specifications, sample periods and proxies.
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Conclusion and future work
•
Results summary:
 Justified structural breaks
relationships

Error correction approach confirmed as relevant to model
space market


•
allow us to capture cointegrating
Settles the traditional drivers of rents and demand in the long-run
Characterizes their role in the short-run  outlines the rigidities
specific to the market structure
On going work:
 Submarket analysis
 dynamics and interactions between
Parisian submarkets (Stevenson (2007), Ke and White (2014))
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References
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–
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–
–
–
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occupied stock in the rental adjustment process. Journal of Real Estate
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Banerjee, A. and Urga, J. 1995. Modelling structural breaks, long memory
and stock market volatility: an overview. Journal of Econometrics 119: 1-34.
Brounen, D. and Jennen, M. 2009. Local office rent dynamics. Journal of
Real Estate Finance and Economics 39: 385-402.
Füss, R., Stein, M. and Zietz, J. 2012. A Regime-Switching Approach to
Modeling Rental Prices of U.K. Real Estate Sectors. Real Estate Economics
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Gregory, A.W., Nason, J.M. and Watt, D.G. 1996. Testing for structural breaks
in cointegrated relationships. Journal of Econometrics 71: 321-42.
Gregory, A.W. and Hansen, B.E. 1996. Residual-based tests for cointegration
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Hendershott, P. H., Jennen, M., & MacGregor, B. D. (2013). Modeling space
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Real Estate Finance and Economics, 47(4), 659-687.
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References
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Malle R. 2010. Un modèle à équations simultanées du cycle des bureaux en
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McCartney, J. 2012. Short and long-run rent adjustment in the Dublin office
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Rosen, K. 1984. Toward a Model of the Office Building Sector. Real Estate
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Wheaton, W.C., R.G. Torto and P. Evans. 1997. The Cyclic Behavior of the
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Wheaton W. 1987. The Cycle Behavior of the National Office Market.
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