Long-run equilibrium for the

Greater Paris Office Market ; Rental and Demand adjustments

European Real Estate Society Annual ConferenceBucharest, 2014

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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• 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

Literature and methodology

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Rent modeling

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Demand modeling

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• Long-run model:

• Short-run model:

THE GREATER PARIS OFFICE MARKET

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• 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

The Greater Paris office market

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Vacancy and rental movements

40

50

60

70

80

90

100

110

120

130

2

3

4

5

6

7

8

9

10

11

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Real effective rent (new leases)Vacancy rate

%Index

Figure 2: Real rent and vacancy rate in the Greater Paris office market (1991-2012)

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Movements in office demand fundamentals

Figure 2: Occupied stock and office employment in the Greater Paris office market (1991-2012)

Figure 3: Occupied stock and real rents in the Greater Paris office market (1991-2012)

-2

-1

0

1

2

3

4

5

6

-2

-1

0

1

2

3

4

5

6

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Occupied stock (annual variation)Office employment (annual variation)

% %

50

60

70

80

90

100

110

-1

0

1

2

3

4

5

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Occupied space (annual variation)Real effective rent (new leases)

Index %

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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 breaksGraphical 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

Notes: Dependent variable = ln(Real Rent). *, **, *** indicate significant at 10%, 5% and 1% respectively.

• 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:

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 t-Statistic

       

Vacancy rate(-2) -0.04 *** -6.0

ln(Rent(-3)) -0.32 *** 3.4

Error correction term -0.29 *** -4.2

Intercept -0.00   -0.9

      

      

N 84 Adjusted R² 0.55

Durbin-Watson 2.16    

       

Notes: Dependent variable = ln(Real Rent). *, **, *** indicate significant at 10%, 5% and 1% respectively.

• 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 Shorter lease lenght Higher market turnover

• Autoregressive structure of the rents

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Demand modellingTable 3: Long-run equilibrium demand model

Notes: Dependent variable = ln(Occupied Space). *, **, *** indicate significant at 10%, 5% and 1% respectively.

• Coefficients for employment and rents: significant and expected signs

• All breaks can be interpreted and the related coefficients have the expected signs

• For example:

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

Notes: Dependent variable = ln(Occupied Space). *, **, *** indicate significant at 10%, 5% and 1% respectively.

Variable Coefficient t-Statistic

       

Occupied Space(-1) 0.31*** 3.3

Occupied Space(-2) 0.28*** 2.7

Error correction term -0.09* -1.8

Intercept 0.00*** 2.7

      

      

N 85 Adjusted R² 0.23

Durbin-Watson 1.93    

       

• Error correction term is significant

• Slow speed of space adjustement = 9%

• Space absorption is sticky due to rigidities

Long-term term leases Costs associated with lease

termination

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International comparison

Notes: Cautious is required when comparing between studies with different specifications, sample periods and proxies.

Market

Authors

Price

elasticity

Income

elasticity

Sample period and frequency

Specification and estimation

Economic activity proxy

Paris

-0.12

1.1

1991-2012 quarterly

𝑙𝑛𝑂𝑆𝑡∗ = 𝛼0 +𝛼1𝑙𝑛𝐸𝐴𝑡 + 𝛼2𝑙𝑛𝑅𝑡 +𝑖𝜎𝑖𝐷𝑈𝑀𝑖 + εt

ECM

Office employment

12 European markets

Mouzakis and Richards (2007)

-0.15

0.63

1980-2001 annual panel data

𝑙𝑛𝑅𝑡∗ = 0 +1𝑙𝑛𝐸𝐴𝑡 + 2(𝑙𝑛𝑆𝑡 −𝑣) + εt

ECM

Local level gross value added

Paris

Malle (2010)

-0.19

0.97

1996-2008 quarterly

𝑙𝑛𝑂𝑆𝑡∗ = 𝛼0 +𝛼1𝑙𝑛𝐸𝑡 + 𝛼2𝑙𝑛𝑅𝑡 + εt OLS

Office employment

London

Hendershott et al. (2010)

-0.20

0.38

1977-2006 annual

𝑙𝑛𝑅𝑡∗ = 0[lnሺ1 −𝑣∗ሻ− 𝛽0] +1𝑙𝑛𝐸𝐴𝑡 − 2𝑙𝑛𝑆𝑡 +εt

ECM

Financial and business services employment

Dublin

McCartney (2012)

-1.13

1.70

1978-2010 annual

𝑙𝑛𝑅𝑡∗ = 0 +1𝑙𝑛𝐸𝐴𝑡 − 2𝑙𝑛𝑆𝑡 +εt

ECM

Gross National Product

5 secondary European markets

Brounen and Jennen (2009)

-1.94

6.04

1990-2006 annual panel data

𝑙𝑛𝑅𝑡∗ = 0 +1𝑙𝑛𝐸𝐴𝑡 +2ln [ሺ1− 𝑣ො��ሻ∗𝑂𝑆𝑡]+ εt

ECM

National employment

Table 4: Elasticity estimates for selected European office markets

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Conclusion and future work

• Results summary: Justified structural breaks allow us to capture

cointegrating relationships

Error correction approach confirmed as relevant to model space market 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– Adam, Z. and Füss, R. 2012. Distangling the short and long-run effects of

occupied stock in the rental adjustment process. Journal of Real Estate Finance and Economics 44: 570-590.

– 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 40: 317-350.

– 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 in models with regime shifts. Journal of Econometrics 70: 99-126.

– Hendershott, P. H., Jennen, M., & MacGregor, B. D. (2013). Modeling space market dynamics: an illustration using panel data for US retail. The Journal of Real Estate Finance and Economics, 47(4), 659-687.

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– Malle R. 2010. Un modèle à équations simultanées du cycle des bureaux en région parisienne. Economie et Prévision 3: 93-108.

– McCartney, J. 2012. Short and long-run rent adjustment in the Dublin office market. Journal of Property Research 29: 201-226.

– Perron, P. 1989. The Great Crash, the oil price shock and the unit root hypothesis. Econometrica 57: 1361-401.

– Rosen, K. 1984. Toward a Model of the Office Building Sector. Real Estate Economics 12: 261–269.

– Wheaton, W.C., R.G. Torto and P. Evans. 1997. The Cyclic Behavior of the London Office Market. The Journal of Real Estate Finance and Economics 15: 77-92.

– Wheaton W. 1987. The Cycle Behavior of the National Office Market. Journal of the American Real Estate and Urban Economics Association 15: 281-299.

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References