Real Estate & Planning: 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

Real Estate & Planning: 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

Real Estate & Planning: 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

Real Estate & Planning: 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

Real Estate & Planning: McGough & Tsolacos (2001) IPD property yields cointegrate with gilt and dividend yields, but not rent growth, whilst ECM part doesnt 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

Real Estate & Planning: 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

Real Estate & Planning: 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

Real Estate & Planning: 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 terms b.Constant in real terms c.Risk premium is constant d.Linked to returns on other risky assets Our approach

Real Estate & Planning: We examine –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

Real Estate & Planning: Sources: Office initial yields – Scott (1996), CBRE. Gilt yields and dividend yields – Barclays Capital (2013)

Real Estate & Planning: Case ACase BCase CCase D Lag length1111 St. dev. ratio (m/m*) Multiplier correlation LR test statistic p-value Results High p-values mean that efficiency cannot be rejected However, graphs reveal sustained differences between simulated and actual multipliers Simulated vs. actual multiplier

Real Estate & Planning: Required return assumptions: A = constant in nominal terms, B = constant in real terms, C = constant risk premium, D = varies with equity returns

Real Estate & Planning: 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

Real Estate & Planning: SVAR output

Real Estate & Planning: 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

Real Estate & Planning: Sources: Rent – Devaney (2010), CBRE. Stock – our estimates, City of London local authority.

Real Estate & Planning: Required return assumptions: A = constant in nominal terms, B = constant in real terms, C = constant risk premium, D = varies with equity returns

Real Estate & Planning: 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 An earlier version of the paper can be obtained from Contact : Dr Steven Devaney 18