Performance Drivers of UK Unlisted Real Estate Funds

Transcription

1 Performance Drivers of UK Unlisted Real Estate Funds Kieran Farrelly The Townsend Group George Matysiak Master Management Group, Warsaw Presentation at the ERES Conference, Vienna, July 2013,

2 Research Questions Generally the property investment industry has been unable to quantify robustly the key sources of risk and return in property portfolios/funds 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 What are the key factors that drive real estate fund performance? Market, stock, leverage... How does financial leverage affect fund performance? Is it a positive contributor? Is the impact of leverage uniform or asymmetric?

3 Sources of Risk and Return In Real Estate Funds Market risk: Allocations to more volatile sectors Macro / supply risks Transparency, property rights Stock risk: Asset level (operating) leverage Risk continuum from ground rents to speculative developments Age, structure Income quality Diversification Fund structure: Financial leverage: floating rate/fixed rate debt, collateralization Vehicle characteristics: age, structure, fees/costs (alignment), fiscal efficiency Public market volatility if listed Portfolio Structure / Market Risk Stock Risk Real Estate Fund Risk & Return Fund Structure Accounting Policy Accounting policy: Treatment of items e.g. mark-tomarket valuations of interest hedging instruments, costs incurred

4 Relevant Prior Studies 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 market 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 (13), 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 Lagged performance: Fuerst & Matysiak (13), Marcato & Tira (10), IPF (11)

5 Fund Data Sample Sample of 75 UK institutional funds with measured performance from 2003 Q Q4 Able to test significance of market, stock and fund structure factors Panel modelling framework employed to make best use of the available data Unbalanced panel and excluded funds with < one year data history and winsorized 0.5% from tails Sample Fund Style Exposure Quarterly Sample Total Returns Value Added 14.1% Opportunity 7.0% 30% 20% 10% Core Specialist 22.5% Core Balanced 56.3% 0% -10% -20% -30% -40% 10/1/2003 4/1/ /1/2004 4/1/ /1/2005 4/1/ /1/2006 4/1/ /1/2007 4/1/ /1/2008 4/1/ /1/2009 4/1/ /1/2010 4/1/ /1/2011

6 Variables and Statistics Std. Variable Mean Median Maximum Minimum Dev. Skewness Kurtosis Fund Total Return (%) 0.80% 2.31% % % 9.72% Fund Total Return (%) - Reduced Sample 0.79% 2.31% 24.24% % 7.10% Market Exposure Total Return (%) 1.23% 2.52% 15.64% % 4.85% Market Concentration 38.96% 16.91% % 1.48% 38.21% Net Initial Yield (%) 5.88% 5.82% 13.78% 1.58% 1.25% Net Initial Yield Less Time Period Average (%) 0.03% 0.06% 7.27% -3.87% 0.92% Number of Real estate Assets Gross Asset Value , Reversionary Potential Reversionary Potential of Passing Rent Total Void Rate (% Market Rental Value) 8.30% 6.79% % 0.00% 7.95% % Leases With Term Greater Than 10 Years 37.12% 34.30% % -9.10% 22.17% % Leases With Term Less Than 5 Years 35.23% 33.24% % 0.00% 22.52% % Income from Top 10 Tenants 39.16% 36.80% % 8.80% 17.44% Loan To Value Ratio 19.48% 7.40% % 0.00% 23.74% Net Loan To Value Ratio 12.70% 4.15% 97.89% % 27.75%

7 Panel Regression Approach Firstly panel unit root tests were conducted Guided by Hausman test on whether to employ fixed or random effects regression models effects regression specification, including lagged performance is as follows: Presence of lagged term violates strict erogeneity assumption inclusion of lag is potentially correlated with future disturbance Typically panel IV/GMM estimators are employed but not suitable given sample size Employ Bruno (2005) estimator to account for a potential bias in fixed effects estimates

8 Panel Unit Root Tests Fisher-Type Panel Unit Root Tests Pesaran Panel Unit Root Test Variable Inverse Chi-Sq Inverse normal Inverse Logit Fund Total Return (%) Market Exposure Total Return (%) Market Concentration Net Initial Yield (%) Net Initial Yield Less Time Period Average (%) Number of Real Estate Assets Gross Asset Value Reversionary Potential Reversionary Potential of Passing Rent Total Void Rate (% Market Rental Value) % Leases With Term Greater Than 10 Years % Leases With Term Less Than 5 Years % Income from Top 10 Tenants Loan To Value Ratio Net Loan To Value Ratio

9 Panel Regression Multi Factor Pooled OLS Bias Corrected Pooled OLS Bias Corrected Strong 1:1 relationship with market returns Lag Total Return 0.391** (0.023) Market Exposure Total Return 0.984** (0.109) Lag Net Loan to Value Ratio ** (0.004) Lag Excess Initial Yield 0.222* (0.115) Lag Total Void (% ERV) (0.013) 0.356** (0.042) 0.968** (0.229) 0.024** (0.011) 0.857** (0.289) 0.043** (0.022) 0.396** (0.027) 0.925** (0.161) (0.014) 0.795** (0.295) 0.043* (0.022) 0.376** (0.024) 1.033** (0.104) ** (0.003) (0.091) (0.012) 0.336** (0.046) 1.021** (0.215) 0.020* (0.011) 0.768** (0.281) 0.040** (0.015) 0.378** (0.028) 0.980** (0.132) (0.013) 0.699** (0.245) 0.040** (0.020) Void and initial yield spread the most significant stock variable 10% increase in net leverage equates to 0.8% increase in annual returns Lag Top Ten Tenant Exposure (% Rent) (0.006) Lag Passing Rental Reversion (0.015) 0.043* (0.022) (0.021) 0.042** (0.021) (0.024) Generally expected riskreturn relationships are found to hold Lag % Lease Length > Ten Years (0.005) Lag Market Concentration 0.008** (0.003) (0.011) (0.022) (0.014) (0.031) R Squared Small number of factors explain a significant proportion of fund performance No Cross Sections No Observations ** 1% Sig, * 5% Sig

10 Panel Regressions Asymmetric Leverage Impact Pooled OLS Lag Total Return 0.213** (0.022) 0.168** (0.043) Bias Corrected 0.205** (0.026) Pooled OLS 0.107** (0.015) 0.090** (0.031) Bias Corrected 0.107** (0.019) Isolated the impact of financial leverage upon fund performance in positive and negative market conditions Market Exposure Total Return 1.300** (0.095) Lag Excess Initial Yield (0.080) 1.278** (0.183) 0.502** (0.217) 1.242** (0.113) 0.439** (0.212) 1.037** (0.021) (0.082) 1.049** (0.040) 0.447** (0.214) 1.035** (0.025) 0.415* (0.218) Significant asymmetric impact greater downside than upside Lag Net Loan to Value Ratio x Negative Market Dummy Lag Net Loan to Value Ratio x Positive Market Dummy ** (0.006) 0.022** (0.004) ** (0.017) 0.028** (0.012) ** (0.014) 0.027** (0.013) ** (0.006) 0.020** (0.004) ** (0.016) 0.030** (0.012) ** (0.013) 0.031** (0.012) Period Included? Yes Yes Yes No No No R Squared Total No Cross Sections No Observations % increase in net leverage equates to % increase in annual returns when market returns are positive, but leads to a c. 4% decrease in returns when market performance declines Leverage appears to exacerbate the nonnormality of real estate returns ** 1% Sig, * 5% Sig

11 Panel Regressions LTV Thresholds 20% Net Loan To Value Threshold Lag Total Return 0.085** (0.031) Market Exposure Total Return 1.032** (0.041) Lag Excess Initial Yield (0.267) Lag Net Loan to Value Ratio x Positive Market Dummy x Below Net Loan to Value Threshold Dummy Lag Net Loan to Value Ratio x Positive Market Dummy x Above Net Loan to Value Threshold Dummy Lag Net Loan to Value Ratio x Negative Market Dummy x Below Net Loan to Value Threshold Dummy Lag Net Loan to Value Ratio x Negative Market Dummy x Above Net Loan to Value Threshold Dummy (0.012) (0.023) ** (0.018) ** (0.025) 30% Net Loan To Value Threshold 0.087** (0.031) 1.050** (0.041) 0.439* (0.267) 0.025** (0.010) 0.034* (0.020) ** (0.015) ** (0.022) 40% Net Loan To Value Threshold 0.087** (0.031) 1.047** (0.041) 0.442* (0.261) 0.022* (0.013) 0.034* (0.019) ** (0.016) ** (0.023) 45% Net Loan To Value Threshold 0.064** (0.028) 1.062** (0.040) 0.480* (0.257) 0.036** (0.014) (0.021) ** (0.016) ** (0.023) 50% Net Loan To Value Threshold 0.049* (0.028) 1.077** (0.040) 0.516** (0.255) 0.040** (0.013) (0.021) ** (0.016) ** (0.025) Explored the leverage impact further 40% appears to be a material threshold point only 22% of net LTV sample data points were above this level Regression standard errors calculated via bootstrap procedure Minor reduction in statistical validity but remain robust models R Squared Overall No Cross Sections No Observations ** 1% Sig, * 5% Sig

12 Conclusions Sample permitted the testing of a rich set of real estate fund factors Panel modelling approach applied Relatively small number of variables or factors explain the time series and cross section properties of real estate fund performance well Market performance a key driver Found significant asymmetric performance effects of employing financial leverage. Leverage appears to exacerbate the non-normal characteristics of fund returns i.e. in positive markets it contributes positively but in negative markets it has a much larger negative contribution Found evidence that this asymmetric effect varies with level of leverage employed Study limits focussed entirely on UK, largely core fund sample and consequently limited sample of higher LTV observations