Efficiency in the City of London office market : A supply perspective

1. 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)

2. Informational efficiency • 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

3. Previous literature • 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

4. Previous studies – US

5. Previous studies – UK & US

6. Our approach • 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:

7. Our approach • 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

8. Our approach • 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: • Constant in nominal terms • Constant in real terms • Risk premium is constant • Linked to returns on other risky assets

9. Dataset • 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)

10. Sources: Office initial yields – Scott (1996), CBRE. Gilt yields and dividend yields – Barclays Capital (2013)

11. Results Simulated vs. actual multiplier • High p-values mean that efficiency cannot be rejected • However, graphs reveal sustained differences between simulated and actual multipliers

12. Required return assumptions: A = constant in nominal terms, B = constant in real terms, C = constant risk premium, D = varies with equity returns

13. Developer response • 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

14. SVAR output

15. Conclusions and issues • 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

16. Sources: Rent – Devaney (2010), CBRE. Stock – our estimates, City of London local authority.

17. Required return assumptions: A = constant in nominal terms, B = constant in real terms, C = constant risk premium, D = varies with equity returns

18. 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 from http://www.iwh-halle.de/e/publik/disc/15-12.pdf Contact : Dr Steven Devaney (s.devaney@reading.ac.uk)