1. Factor Modelling of UK Unlisted Funds: Panel Data Analysis of Performance Drivers Kieran Farrelly CBRE Investors & Henley Business School, University of Reading & George Matysiak Henley Business School, University of Reading JUNE 2011

2. Page 1 CB Richard Ellis Investors Global Multi Manager Table of Contents Research questions and objectives Sources of risk and return in unlisted funds Prior literature Data Panel unit root testing Panel regression analysis Conclusions and next steps

3. Page 2 CB Richard Ellis Investors Global Multi Manager Research Questions & Objectives  CAPM (market model) is based on the assumption that there are no additional factors present which are correlated with the market return  Inclusion of other factors has been found to better explain the cross section of asset returns  Ross (76): macroeconomic factors – Arbitrage Pricing Theory  Fama & French (92), Jegadeesh & Titman (1993), Carhart (97) : fundamental factors – value/growth/momentum  Multifactor models employed extensively in equities for risk management and performance attribution purposes  Generally the property investment industry has been unable to quantify well the key sources of risk in property portfolios  Unlisted property funds have become a significant conduit in the real estate investment landscape  Purpose of this study is to identify which direct property portfolio and unlisted fund ‘structure’ characteristics/factors explain the cross section performance of unlisted property funds  End goal is to develop a multifactor model and subsequent portfolio management tool for understanding portfolio risk of both property funds/ and funds-of-funds

4. Page 3 CB Richard Ellis Investors Global Multi Manager Sources of Risk and Return in Property Funds Property Fund Risk & Return Portfolio Structure / Market Risk Stock RiskFund Structure  Structure (market risk):  Allocations to more volatile sectors  Macro risks  Stock risk:  Asset level (operating) leverage  Risk continuum from ground rents to speculative developments  Age, structure  Fund Structure:  Financial leverage risk where used  Vehicle characteristics: age, structure, fees/costs

5. Page 4 CB Richard Ellis Investors Global Multi Manager Prior Studies: Multifactor Modelling of Property Market/Portfolio/Fund Returns  Market Risk  Macroeconomic factors (APT):  Ling & Naranjano (90,97) – per cap consumption, real govt bond yields, term structure, unexpected inflation  Liow (94) – industrial production, unexpected inflation significant predictors of expected risk premia  Marcato & Tira (10) – GDP, stock market  Property markets  Pai & Geltner (07) location (Tier I & III location performance differential), Fuerst & Matysiak (08 ) - weighted direct market return, IPF (11) – UK region exposure, property type tracking error/concentration  Stock risk – direct portfolio assets’ characteristics  Yield – Fuerst & Marcato (09) high/low yield return differential, Bond & Mitchell (09) equivalent yield, IPF (11) relative equivalent yield  Size – Zieiring & McIntosh (99) – size positively related to risk and return, Pai & Geltner (07) + Fuerst & Marcato (09) - performance differential between asset sizes, IPF (11) – average lot size, asset concentration  Income: Pai & Geltner (07) - performance differential between assets with short/long ease lengths, IPF (11) - void rate, covenant strength, % income from top 10 tenants  Development/Vacancy: IPF (11)  Fund structure  Financial leverage: Fuerst & Matysiak (08), Marcato & Tira (10), IPF (11) all found financial leverage to be significant  Liquidity: Lee (00) found no evidence, Marcato & Tira (10) found evidence  Cash exposure : Marcato & Tira (10)  Style: Fuerst & Matysiak (08) – core/value added/opportunisitc styles impacted performance  Performance Persistence: Fuerst & Matysiak (08), Marcato & Tira (10), IPF (11)

6. Page 5 CB Richard Ellis Investors Global Multi Manager Dataset  Unique sample of UK unlisted funds  Quarterly returns from 2003:Q4-2010:Q4  Good depth in terms of fund/portfolio characteristics (x variables)  Data runs over what we’d consider to be a full cycle  Sources:  CBRE Investors database 2003:Q4 – 2004 Q3 – collated by HSBC/IPD  IPD UK Property Funds Vision data 2004:Q4 to 2010:Q4  Consistently collected data via quarterly questionnaire  Unbalanced panel with sample of funds with sufficient data points growing through time  Commences with data on 28 funds  Maximum of 75 funds in any given period  Large proportion of the sample are open-ended funds and would be considered as having a core risk profile –Both balanced/diversified and sector specialist vehicles Source: IPD UK Pooled Property Fund Indices Performance 2003:Q4 = 100

7. Page 6 CB Richard Ellis Investors Global Multi Manager Sample Statistics Mean Median Maximum Minimum Std. Dev Skew Kurtosis 3 Month Excess Returns-3.1%-1.3%52.5%-61.3%8.9%-0.869.41 Cash % Assets5.4%3.0%45.7%-0.4%6.4%1.786.44 % Development (%ERV)1.5%0.0%45.3%0.0%4.2%5.2338.39 Lease Length Conc17.3%12.8%100.0%0.4%14.7%2.4411.37 Initial Yield5.8%5.7%10.2%1.6%1.3%0.213.06 Number of Assets50.2735.00397.001.0058.173.3317.07 LTV18.7%8.0%98.8%-0.3%22.5%1.042.89 Reversionary Yield6.6%6.4%11.9%2.6%1.5%0.503.65 OFFICE Exposure26.6%25.6%100.0%0%27.5%1.294.31 IPD PAS Concentration36.0%11.6%100.0%1.5%36.9%0.771.99 Rental Reversion1.191.152.960.890.204.2029.38 Void Rate7.0%5.9%37.5%0.1%5.1%1.627.42 % Top 10 Tenants38.3%36.0%100.0%8.8%16.7%0.693.25 Histogram – 3 Month Excess Total Returns Histogram – Initial Yield Histogram – Loan to Value Ratio

8. Page 7 CB Richard Ellis Investors Global Multi Manager Identifying Factors: Panel Approach  First stage of multifactor modelling is the identification of statistically significant factors  We have employed a panel data approach to do this  This approach allows us to identify and test parameters without restrictive assumptions  e.g. do investment styles have differential impacts ?  Firstly we used a number of panel unit root tests to assess whether the variables are trend stationary  We then tested for the presence of fixed and/or random effects  Fixed effects: used when we want to control from omitted /unobserved variables whose impact will differ between cases  Random effects: used when we want to control from omitted /unobserved variables whose impact will have the same constant impact but vary randomly between cases. Hausman test used to assess whether random effects are present

9. Page 8 CB Richard Ellis Investors Global Multi Manager Panel Unit Root Tests Summary  Panel unit root tests are statistically more powerful than individual unit root tests  Panel unit root tests show both yield variables and ‘number of assets’ are I(1)  Otherwise other variables can be deemed I(0) Method 3 Month Excess Returns Cash % Assets Lease Length Conc Initial Yield Reversionary Yield Number of AssetsLTV Office Exposure IPD PAS Concentration Reversionary Potential Void Rate % Top 10 Tenants Null: Unit root (assumes common unit root process) Levin, Lin & Chu t*-2.66-14.09-2.57-2.79-2.200.04-4.18-3.90-8.99-1.59-2.87-2.56 Prob0.00 0.520.00 0.060.00 Null: Unit root (assumes individual unit root process) Im, Pesaran and Shin W-stat-2.60-8.69-2.280.400.012.22-3.66-2.13-7.57-1.19-3.39-2.97 Prob0.00 0.010.650.510.990.000.020.000.120.00 ADF - Fisher Chi-square203.52322.31192.67123.54140.66125.97203.28137.14287.20178.62211.96231.35 Prob0.030.000.010.930.610.910.00 0.060.00 PP - Fisher Chi-square232.05506.31291.3788.90124.49164.80198.49186.35324.68212.84263.59392.51 Prob0.00 1.000.900.160.00 ConclusionI(0) I(1) I(0)

10. Page 9 CB Richard Ellis Investors Global Multi Manager Fixed Effects Regression – 2004:Q1 to 2010:Q4 Dependent Variable: _3MTREXCCoefficientStd. Errort-StatisticProb. C0.050.031.760.08 3MTREXC(-1)0.710.0235.070.00 LOAN TO VALUE(-1)0.210.028.620.00 % OFFICE EXPOSURE (-1)-0.130.04-3.490.00 REVERSIONARY POTENTIAL (-1)-0.080.02-4.300.00 % TOP 10 TENANTS (-1)0.060.031.770.08 R-squared0.55 Mean dependent var-0.03 Adjusted R-squared0.53 S.D. dependent var0.09 S.E. of regression0.06 Akaike info criterion-2.64 Sum squared resid5.68 Schwarz criterion-2.36 Log likelihood2077.72 Hannan-Quinn criter.-2.54 F-statistic22.17 Durbin-Watson stat1.89 Prob(F-statistic)0.00  Fixed effects regression was found to be the appropriate – model has good explanatory power  Thus there are significant differences between funds and over time periods  Not surprising given there are a range of fund structures and styles Distribution of Cross Section Fixed Effects

11. Page 10 CB Richard Ellis Investors Global Multi Manager Panel GMM Regression Dependent Variable: _3MTREXC Panel Fixed Effects CoefficientProb. Panel GMM Coefficients 1Prob. Panel GMM Coefficients 2Prob. C0.050.08 3MTREXC(-1)0.710.00 0.67 0.00000.690.0000 LOAN TO VALUE(-1)0.210.00 0.26 0.00050.250.0002 % OFFICE EXPOSURE (-1)-0.130.00 -0.22 0.0177-0.240.0076 REVERSIONARY POTENTIAL (- 1)-0.080.00 -0.10 0.0061-0.070.0273 % TOP 10 TENANTS (-1)0.060.08 0.04 0.5550 Mean dependent var-0.03-0.00 S.D. Dependent var0.090.05 S.E. of regression0.060.08 Sum squared resid5.689.8410.17 As there is a lagged dependent variable (momentum) in the preferred specification we have used the Panel GMM estimator Coefficients magnitude have changed though signs and significance remain for 4 of the variables - but %top ten tenants variable is no longer significant (but note that Arellano-Bond standard errors can be very unreliable!) Second GMM discards this variable and significant variables remain similar

12. Page 11 CB Richard Ellis Investors Global Multi Manager Provisional Conclusions  Identified the key fundamental factors which best determine the cross section of unlisted property funds over time  Factors found amongst what we consider to be the three key sources of risk-returns in funds  Presence of fixed effects points to differences across funds and over time Next steps:  Continue to test additional factors  Creation of ‘factor returns’ via cross section regressions  Use these as a basis for estimating a factor covariance matrix which can then be used to create portfolio construction/optimisation tools  Risk budgeting via factors  These will also be used for performance attribution purposes  Estimate asymmetric impacts of factors upon performance