1. Long-run equilibrium for the Greater Paris Office Market ; Rental and Demand adjustments European Real Estate Society Annual Conference Bucharest, 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

2. INTRODUCTION 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 2

3. 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 3

4. MODELING STRATEGY 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 4

5. 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) 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 5

6. 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 6 Literature and methodology

7. 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 7

8. Rent modeling 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 8 (1) (2)

9. Demand modeling 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 9 (3) Long-run model: Short-run model: (4)

10. THE GREATER PARIS OFFICE MARKET 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 10

11. 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 11

12. Vacancy and rental movements Figure 2: Real rent and vacancy rate in the Greater Paris office market (1991-2012) 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 12

13. 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) 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 13

14. EMPIRICAL RESULTS 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 14

15. 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 15

16. Rent modelling Table 1: Long-run equilibrium rent model VariableCoefficientt-Statistic Vacancy rate-0.08***-16.3 -0.26***-10.9 -0.15***-4.9 0.27***12.5 -0.13***-6.5 0.06***3.0 Intercept5.07***109.9 N88Adjusted R²0.93 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 16

17. Rent modelling Table 2: Short-run adjustement model VariableCoefficientt-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 N84Adjusted R²0.55 Durbin-Watson2.16 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 17

18. Demand modelling Table 3: Long-run equilibrium demand model Notes: Dependent variable = ln(Occupied Space). *, **, *** indicate significant at 10%, 5% and 1% respectively. VariableCoefficientt-Statistic ln(Office employment)1.10***23.1 ln(Real rents)-0.12***-11.2 -0.05***-7.3 0.02***4.3 -0.02***-4.7 0.01***3.5 -0.02***-3.6 0.02***5.0 Intercept12.87***70.0 N87Adjusted R²0.99 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 18

19. Demand modelling Table 5: Absorption adjustement model Notes: Dependent variable =  ln(Occupied Space). *, **, *** indicate significant at 10%, 5% and 1% respectively. VariableCoefficientt-Statistic  Occupied Space(-1) 0.31***3.3  Occupied Space(-2) 0.28***2.7 Error correction term-0.09*-1.8 Intercept0.00***2.7 N85Adjusted R²0.23 Durbin-Watson1.93 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 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 19

20. International comparison Notes: Cautious is required when comparing between studies with different specifications, sample periods and proxies. Table 4: Elasticity estimates for selected European office markets 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 20

21. 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)) 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 21

22. 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. 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 22

23. – 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. 03/07/2015 ERES 2014 - Bruneau C. and Cherfouh S. 23 References