june 2010 office risk premia eres 2010 milan page 1 eres 2010 conference milan property and...
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page 1JUNE 2010OFFICE RISK PREMIA
ERES 2010MILAN
ERES 2010 Conference
Milan
PROPERTY AND PORTFOLIO RESEARCH NORTH AMERICA EUROPE ASIA-PACIFIC
Panel Estimates of Office Risk Premia in Europe
24 June 2010Dr Sotiris TsolacosProperty & Portfolio Research
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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
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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
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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
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BASICS IN YIELD DETERMINATION
• yield = R – g• R is required total return
• g is expected net income growth
• yield = (RF + RP) - g
• RF 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
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Accepting defensive and diversification qualities of property especially in periods of overall financial stress
YIELD IMPACTBEHAVIOURAL 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
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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.
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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 (+)
jtjjtjt
jtjtjjt
Xy
Xy
a
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PANEL MODEL RESULTS
Sample 1991-2009 (448 obs) Sample 2002-2009 (200 obs)
Coefficient (p-value) Coefficient (p-value)
Real rent growth -0.02 (0.00) -0.01 (0.02)
Spread 0.18 (0.00) 0.18 (0.28)
Adj. R2 0.76 0.77
DW 0.34 0.79
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PREMIA APPLIED (FIXED EFFECTS MODELS)
-140-120-100
-80-60-40-20
020406080
100120140
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Full Sample Restricted Sample (02-09)(bps)
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PREMIA APPLIED (RANDOM EFFECTS MODELS)
-140-120-100
-80-60-40-20
020406080
100120140
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Full Sample Restricted Sample (02-09)(bps)
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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
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