efficiency in the city of london office market : a supply perspective
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Efficiency in the City of London office market : A supply perspective. Steven Devaney (University of Reading), Oliver Holtemöller (Halle Institut for Macroeconomics) and Rainer Schulz (University of Aberdeen). Informational efficiency. Why does it matter? - PowerPoint PPT PresentationTRANSCRIPT
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Steven Devaney (University of Reading), Oliver Holtemöller (Halle Institut for Macroeconomics) and Rainer Schulz (University of Aberdeen)
Efficiency in the City of London office market: A supply perspective
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• Why does it matter?– Land use allocation within property market– Resources allocated to property in the
economy– Investment flows of financial institutions
• Our objectives:– Test the informational efficiency of prices
(yields) in the City of London office market– Explore whether mispricing affects office
development decisions
Informational efficiency
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• Analyses of prices or yields might– Test whether they react as expected to
changes in fundamental drivers– Estimate ‘rational’ prices or yields and examine
how these differ, e.g. Hendershott (1996, 2000)
• Findings are often against efficiency, but common issues are– Quality of data and appraisal basis of data– Role of expectations
Previous literature
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Sivitanides et al. (2001)
• Regress NCREIF cap rates onto a priori determinants
• Movements not rational given mean reversion in rents
Chen et al. (2004)
• Regress spread over bond rate onto a priori determinants
• Movements also not rational, though authors try and justify
Hendershott& MacGregor (2005a)
• Extensive cleaning of NCREIF data before modelling
• Still found irrational relationships with income growth proxies
Previous studies – US
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McGough & Tsolacos (2001)
• IPD property yields cointegrate with gilt and dividend yields, but not rent growth, whilst ECM part doesn’t work
Hendershott& MacGregor (2005b)
• Property yields cointegrate with proxies for cash flow growth and equity market variables
• Results suggest that UK cap rates have been rational
Clayton et al. (2009)
• Use survey data on risk premiums and expected rent, plus sentiment indicators
• Argued that fundamentals are the main driver of US cap rates over time
Previous studies – UK & US
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• We construct ‘rational’ multipliers (1 / yield) and compare these with actual multipliers
• Based on well known approach of Campbell & Shiller (1988) for equity market
• Start with expression for present value:
Our approach
1j1t
ktj1k
jtt Ω
)H(1ΠD
EP
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• Expressed in terms of multipliers:
• We model what the income multiplier rationally should be given information on key inputs
• But expectations and required return rates are not observed directly
Our approach
1j1t
ktj1k
itj1i
t Ω)H(1Π)G(1ΠEM
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• Use VAR to forecast inputs given information on their past values and those of related variables
• Use four different assumptions on how required returns are set:a. Constant in nominal termsb. Constant in real termsc. Risk premium is constantd. Linked to returns on other risky assets
Our approach
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• We examine 1952-2012– Office market data: rents and yields (Devaney,
2010; Scott, 1996; CBRE)– Financial data: equity returns and yields, gilt
returns and yields (Barclays Capital, 2013)– Economic data: GDP growth and inflation (ONS)– Development data: stock and completions
(Smyth, 1985; Barras, 1979; City of London), construction costs (BCIS, ONS)
Dataset
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Sources: Office initial yields – Scott (1996), CBRE. Gilt yields and dividend yields – Barclays Capital (2013)
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Case A Case B Case C Case DLag length 1 1 1 1St. dev. ratio (m/m*)
1.03 0.53 0.41 0.22
Multiplier correlation
-0.07 -0.20 -0.29 -0.25
LR test statistic 4.344 4.928 5.484 27.133p-value 0.36 0.29 0.24 0.00
Results
• High p-values mean that efficiency cannot be rejected
• However, graphs reveal sustained differences between simulated and actual multipliers
Simulated vs. actual multiplier
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1952195419561958196019621964196619681970197219741976197819801982198419861988199019921994199619982000200220042006200820102012
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50Case A Case B Case C
Obse
rved
– S
imul
ated
log
mul
tiplie
r
Required return assumptions: A = constant in nominal terms, B = constant in real terms, C = constant risk premium, D = varies with equity returns
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• Second (structural) VAR to explore this aspect
• Inputs: completions, costs, simulated multiplier and estimated mispricing term
• Impulse response functions indicate if shocks in one variable (e.g. mispricing term) subsequently affect others (e.g. completions)
• Potential interpretations of responses are strategic behaviour or shared (wrong) perceptions
Developer response
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SVAR output
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• Initial finding: informational efficiency cannot be rejected, but sensitive to model and lags
• Work is in progress to check the stability and the sensitivity of models and results
• SVAR results are suggestive of developer response to instances of mispricing
• Related work is in progress with regard to pricing of real estate equities and manager responses
Conclusions and issues
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Sources: Rent – Devaney (2010), CBRE.Stock – our estimates, City of London local authority.
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1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4Case A Case C Actual
Log
Multi
plie
rs (n
orm
alise
d)
Required return assumptions: A = constant in nominal terms, B = constant in real terms, C = constant risk premium, D = varies with equity returns
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The authors are grateful for permission from CBRE to use their unpublished historical rent and yield series in the analysis and to Barclays Capital for permission to use data from the Equity Gilt Study 2013.
An earlier version of the paper can be obtained fromhttp://www.iwh-halle.de/e/publik/disc/15-12.pdf
Contact : Dr Steven Devaney ([email protected])
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