1. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 1 ERES 2010 Conference Milan PROPERTY AND PORTFOLIO RESEARCHNORTH AMERICAEUROPEASIA-PACIFIC Panel Estimates of Office Risk Premia in Europe 24 June 2010Dr Sotiris Tsolacos Property & Portfolio Research

2. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 2 BACKGROUND ► Yields (office) difficult to predict due to swings in investment sentiment ► The impact of liquidity and of capital inflows and outflows has not fully been factored in yield forecasting ► Are general (more global or pan-European) influences responsible for the variation in yields? ► How quantifiable are these influences? ► Measuring sentiment and risk premia key to study the future trajectory of yields

3. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 3 OBJECTIVES ► Provide empirical evidence on the presence of pan-European factors affecting office yields ► Attempt to quantify the impact of pan-European factors on yields through time ► Highlight methodological issues in estimating such influences

4. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 4 THE LITERATURE RISK PREMIA IN REAL ESTATE ► Various ways to define and estimate risk premia ► Excess return on a market index ► Gap over the risk free rate ► CAPM framewords ► Other econometric models ► Attempts to establish fundamental or implied yields ► A comparison of property yields with government bond yields or a rolling average for government bond yields will provide an estimation of implied yields. ► Significant work on REITs and explaining excess returns ► A less amount of similar work on direct real estate ► Credit risk, unexpected inflation and spread between government and commercial bonds were significantly priced in the securitized real estate market, whereas real T-bill yields and unexpected inflation were the two risk factors affecting the excess returns of direct real estate (Sing 2004). ► Premia are assigned to various risks (political, regulatory, market transparency, etc); levels of arbitrariness could be significant ► Risk premia are time varying

5. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 5 BASICS IN YIELD DETERMINATION yield = R – g R is required total return g is expected net income growth yield = (R F + RP) - g R F is the return on government bonds RP is the real estate risk premium g is expected net income growth RP reflects both capital and real estate market influences Liquidity Access to debt Returns on other asset classes Over-reaction Confidence

6. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 6  Accepting defensive and diversification qualities of property especially in periods of overall financial stress YIELD IMPACT BEHAVIOURAL INFLUENCE  Improved databases, information, analytics and new investment products  Beauty contest  Better understanding of property risk and the relationship between the economy and property market LIKELY INFLUENCES ON SENTIMENT AND PREMIA

7. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 7 DATA & METHODOLOGY ► Data from 25 Western European and core CEE centres enter the analysis ► The sample is 1990 to 2009 although for some locations the sample is a little shorter ► The data are pooled and panel yield models are specified ► Different panel specifications with fixed and random effects are estimated  Panel data may have cross section effects, time effects, or both.  A fixed effect model assumes differences in intercepts across markets or years, whereas the random effects appear in error variances. The Hausman test informs the choice of the model.  Since the slopes are constant in fixed and random effects models the common poolability F-tests are carried out.

8. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 8 THE MODELS Where: - the dependent variable y is the prime office yield - α is the mean intercept and λ represents location or time effects - u is the error with zero mean and constant variance and γ is the dummy variable which is part of the error in the random effect model - β is the matrix of parameters to be estimated - X is the matrix of explanatory variables - Three variables enter the matrix X : - (I) real rent office growth (-) - (ii) the long-term government bond yield (+) - (iii) the spread between the BAA corporate bond and the long-term government bond yield (+)

9. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 9 PANEL MODEL RESULTS Sample 1991-2009 (448 obs)Sample 2002-2009 (200 obs) Coefficient (p-value) Real rent growth-0.02 (0.00)-0.01 (0.02) Spread0.18 (0.00)0.18 (0.28) Adj. R 2 0.760.77 DW0.340.79

10. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 10 PREMIA APPLIED (FIXED EFFECTS MODELS) (bps)

11. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 11 PREMIA APPLIED (RANDOM EFFECTS MODELS) (bps)

12. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 12 IMPLICATIONS ► Estimates for premia applied depend on specification of panel model and time period. ► On the basis of these results the methodology should be used for direction purposes and not for point estimates of premia ► The premia applied do not necessarily point to mispricing ► Panel models are not fully elaborated: liquidity measures should be included ► The methodology should be used in conjunction with a yield forecast model and assist in assigning balanced risks ► Other issues ► Serial correlation ► Adjustment to group of cities to make data poolability stronger

13. JUNE 2010 OFFICE RISK PREMIA ERES 2010 MILAN page 13 sotiris.tsolacos@pprglobal.com These materials contain information from a variety of public and proprietary sources. Property & Portfolio Research, Inc. (“PPR”) has not reviewed and does not warrant or guarantee the completeness, accuracy, timeliness, or authenticity of such information in preparing these materials Any commentary, analysis, opinions, advice, recommendations and / or forecasts in these materials represent the personal and subjective views of PPR, and are subject to change at any time without notice. Any forecasts contained in these materials are based on data (including third party data), models, and experience of various professionals, and are based on various assumptions, all of which are subject to change without notice. Additionally, PPR reserves the right to make changes at any time, without notice, to any modeling used in the forecasts in these materials. As with all models, the degree in making any forecasts is unproven, and no guarantee or estimate of actual performance is given or warranted by PPR. PPR does not warrant, assure, represent or guarantee any decision made by the reader of these materials using any of the information, commentary, analysis, opinions, advice, recommendations or forecasts contained herein. To the maximum extent permitted by law, PPR disclaims any and all liability in the event any information, commentary, analysis, opinions, advice, recommendations or forecasts in this material prove to be inaccurate, incomplete or unreliable, or result in any investment or other losses. Entire contents copyright 2010 by Property & Portfolio Research, Inc. (“PPR”). All rights reserved. Reproduction in any media or format, in whole or in part, of any report or data of PPR or its affiliates, is prohibited without prior written permission.