1. Page 0 Modelling Effective Office Rents by Matt Hall DTZ, 125 Old Broad Street, London, EC2N 2BQ Tel: +44 (0)20 3296 3011 Email: matthew.hall@dtz.commatthew.hall@dtz.com & Tony McGough DTZ, 125 Old Broad Street, London, EC2N 2BQ Tel: +44 (0)20 3296 2314 Email: tony.mcgough@dtz.comtony.mcgough@dtz.com

2. Page 1 Introduction Data calculation Models Results

3. Page 2 Introduction Prime office rents are often questioned for lack of responsiveness to market pressures Issue

4. Page 3 Introduction Prime office rents

5. Page 4 Introduction Prime office rents are often questioned for lack of responsiveness to market pressures Some reasons Smaller markets struggle to get evidence of declines Less active market especially in quiet times But see some example of this even in London Asymmetric Properties of rental movement (Hendershott et al)2008 Definitely something there but a quantifiable known is effective rents Issue

6. Page 5 Introduction Incentives are used to hold up rents in downturns It is in landlord’s interests to maintain headline rents Supports capital value and provides a market floor Provides a lock in value for developers Market evidence used in rent reviews Especially with upward only rent reviews Impact and how to treat incentives for investment returns well documented (Brown) 1995 Effective rents

7. Page 6 Introduction Prime and effective office rents in London City Source: DTZ Research

8. Page 7 Data Collection Need to standardise approach Take account of standards – UK 3 month always free for fit out – Other markets in ‘turn key’ condition – No rent frees beyond first break (5 years in UK 3 years in Italy) Standardise lease lengths at 10 years – No rent frees beyond first break (5 years in UK 3 Calculate effective rent (((Lease length – (rent free period-fit out allowance))/lease length)*prime rent

9. Page 8 Models From this we calculate an implicit rent series of rent free months and model this Other variables used Availability Stock Main driver of prime rent – not the rent itself Lagged incentive

10. Page 9 Models Source :DTZ Research CoefficientStd. Errort-StatisticProb. C0.8832350.3856582.2902030.045 DLOG(UKLOOGVAJKL)-4.7252.098057-2.2520810.048 UKLOCOAV/UKLOCOST8.1697062.1974123.7178770.004 LOG(UKLOCORF(-1))0.4878660.1365373.5731430.0051 R-squared0.857329 Mean dependent var2.6595 Adjusted R-squared0.814528 S.D. dependent var0.576503 S.E. of regression0.248279 Akaike info criterion0.286433 Sum squared resid0.616427 Schwarz criterion0.46902 Log likelihood1.994971 Hannan-Quinn criter.0.269531 F-statistic20.03045 Durbin-Watson stat2.160189 Prob(F-statistic)0.00015 London City

11. Page 10 Source :DTZ Research Results Paris IDF Office markets

12. Page 11 Source :DTZ Research Results Brussels Office markets

13. Page 12 Conclusions Rents are similarly volatile at effective rents level Some markets (UK) have used incentives a long time but other markets (Belgium) are using them more – particularly in difficult conditions Some markets (Madrid) do not use incentives and thus appear more volatile

14. Page 13 Source :DTZ Research Conclusions Madrid Office markets

15. Page 14 Source :DTZ Research Conclusions Manchester Office markets

16. Page 15 Source :DTZ Research Conclusions Manchester Office markets

17. Page 16 Conclusions Rents are similarly volatile at effective rents level Some markets (UK) have used incentives a long time but other markets (Belgium) are using them more – particularly in difficult conditions Some markets (Madrid) do not use incentives and thus appear more volatile Incentives are being increasingly used and this trend will continue