European Real Estate Society Annual Conference

Vienna 2013

 The City of London Office Bias

 by

Stephen Lee 

Cass Business School, City University London

Introduction

It is well known that UK institutional investors have a bias towards the City of London office market. Yet studies show that investment outside the City of London offers higher returns, lower risks and larger rental growth. In addition, the that initial yields are lower in the City of London than in almost every other market, i.e. institutional investors over-price City offices and under-price almost all other office markets. An economically sensible way to measure this bias is to calculate the additional required return on offices outside the City of London necessary to tilt the intuition’s allocation away from that observed.

DataSummary Statistics: Quarterly Data 2001-2012

Office Av Ret SD Av Av Av SD Market % % Rent Yield Cap% % City 1.41 4.30 -0.27 5.80 19.75 3.56 Mid-Town 1.93 4.43 0.05 5.74 7.13 1.38 West End 2.30 4.58 0.27 5.22 21.93 5.33 Inner London 1.81 3.65 -0.13 6.14 3.19 0.33 Outer London 1.02 3.42 -0.31 6.92 7.05 1.37 South East 0.83 3.34 -0.48 7.09 17.40 3.28 South West 1.20 3.55 -0.05 7.14 2.99 0.48 Eastern 1.20 3.25 -0.03 7.01 5.24 0.92 East Midlands 1.56 3.33 0.04 7.48 0.85 0.15 West Midlands 0.99 3.63 -0.17 6.95 3.35 0.42 North West 1.07 3.61 0.11 6.80 3.75 0.65 Yorks & Humberside 1.14 3.67 0.07 6.75 2.36 0.40 North East 1.48 3.75 0.15 7.08 0.55 0.17 Scotland 1.22 3.45 -0.08 6.36 3.95 0.38 Wales 1.23 3.37 0.10 6.91 0.51 0.16

MethodologyThe usual asset allocation model is as follows:  11 )/(*w

An alternative approach, and the one adopted here, is to calculate the expected returns implied by the observed weights wobs and derive the expected return vector , given the historic variance-covariance matrix but allow the relative risk parameter λ to be estimated by the optimisation process such that the expected return of the City equals that calculated from historical data.

obsw

Data and MethodologyAnnualised City of London Office Bias: Bps

Office Market

Quarterly Actual %

Quarterly Implied %

Annualised Diff Bp

City 1.41 1.41 --- Mid-Town 1.93 1.47 -184 West End 2.30 1.52 -312 Inner London 1.81 1.19 -247 Outer London 1.02 1.14 47 South East 0.83 1.10 110 South West 1.20 1.12 -33 Eastern 1.20 1.07 -50 East Midlands 1.56 1.06 -200 West Midlands 0.99 1.14 58 North West & Merseyside 1.07 1.10 12 Yorks & Humberside 1.14 1.13 -4 North East 1.48 1.12 -144 Scotland 1.22 1.11 -42 Wales 1.23 0.00 -493

Possible Explanations for the City Bias

1. Liquidity

2. Quality

3. Lot Size

4. Familiarity

5. Norms

Liquidity?

According to Key et al (1998) “… ‘illiquidity’ tops the list of things property investors dislike about property ...”. Indeed, Lizieri (2009) postulates that the observed holdings in property markets maybe driven “by need for liquidity … over and above optimal risk-adjusted returns”. That is, investors who value liquidity may be willing to deviate from portfolio allocations derived from MPT. In other words, while liquidity risk may be low for investors who have already “taken the plunge” into markets outside the City issues with illiquidity may prevent a subset of investors from ever investing in such markets.

So is that it?

Liquidity?

Average Quarterly Transaction Rate 2001-2012

Overall Boom Bust Market Number Value Number Value Number Value City 5.9% 7.6% 6.6% 8.7% 3.9% 5.1% Mid Town 5.7% 6.4% 6.3% 6.8% 3.9% 5.1% West End 5.0% 5.6% 5.3% 6.0% 4.4% 4.7% Inner London 5.8% 7.0% 6.4% 7.2% 4.7% 6.6% Outer London 4.4% 4.7% 4.7% 5.2% 3.7% 3.3% South East 4.3% 5.1% 4.8% 5.7% 3.0% 3.6% South West 4.8% 5.3% 5.1% 5.6% 4.4% 4.8% Eastern 4.7% 5.3% 5.0% 5.6% 4.3% 5.0% East Midlands 5.0% 4.5% 5.9% 5.1% 2.9% 3.3% West Midlands 4.7% 6.0% 4.7% 5.9% 4.8% 6.5% North West 4.7% 6.0% 4.6% 6.1% 4.8% 5.6% Yorks & Humber 5.4% 6.3% 5.6% 6.8% 4.7% 5.1% North East 7.8% 9.0% 8.6% 10.7% 5.4% 4.2% Scotland 6.3% 7.0% 6.6% 7.0% 5.7% 7.1% Wales 7.0% 7.3% 8.0% 8.1% 5.5% 6.2%

Liquidity?

Average Purchases and Sales: 2001-2012

Number Value

Overall Boom Bust Overall Boom Bust Market Purch % Purch % Purch % Sales % Sales % Sales % City 5.7 12% 7.1 12% 1.0 5% 6.8 11% 6.4 11% 8.0 11% Mid Town 3.2 6% 3.9 7% 0.8 4% 3.7 6% 3.7 6% 4.0 6% West End 7.4 15% 8.8 15% 2.8 13% 9.0 15% 8.4 14% 11.1 16% Inner London 1.8 4% 2.2 4% 1.0 5% 1.9 3% 1.8 3% 2.3 3% Outer London 3.5 7% 4.1 7% 1.5 7% 3.9 6% 3.8 6% 4.6 7% South East 10.3 21% 12.1 21% 4.8 23% 13.6 22% 14.0 24% 12.4 18% South West 2.4 5% 2.8 5% 1.4 7% 3.1 5% 3.0 5% 3.8 5% Eastern 3.0 6% 3.6 6% 1.7 8% 4.4 7% 4.2 7% 5.4 8% East Midlands 1.2 2% 1.4 2% 0.5 2% 1.1 2% 1.3 2% 0.9 1% West Midlands 2.1 4% 2.4 4% 1.4 7% 2.6 4% 2.3 4% 3.8 5% North West 2.1 4% 2.3 4% 1.4 7% 3.0 5% 2.7 5% 3.9 6% Yorks & Humber 2.2 4% 2.5 4% 1.1 5% 2.3 4% 2.0 3% 3.1 4% North East 0.8 2% 0.9 2% 0.3 1% 0.7 1% 0.7 1% 0.8 1% Scotland 3.1 6% 3.6 6% 1.2 6% 3.8 6% 3.5 6% 5.0 7% Wales 0.6 1% 0.8 1% 0.3 2% 0.6 1% 0.6 1% 0.8 1%

Liquidity?

Average Time to Transact

Marketing Period Due Location/type Mean SD Diligence Settlement Total Cathedral City retail 4.2 1.5 5 1 10.2 Large town retail 4.7 1.7 4 1 9.7 Small town retail 5.8 2.6 6 1 12.8 Major city shopping centre 8.6 4.5 12 1 21.6 Large city shopping centre 7.7 3.4 12 1 20.7 Small city shopping centre 7.6 3.6 12 1 20.6 Retail warehouse 5.0 2.7 4 1 10.0 Retail warehouse park 5.2 3.0 6 1 12.2 City offices 7.4 3.1 8 1 16.4 WE offices 6.2 2.2 8 1 15.2 Provincial offices 6.8 2.9 6 1 13.8 Business park 6.0 3.2 6 1 13.0 Standard industrial shed 5.4 2.5 6 1 12.4 Distribution warehouse 5.7 2.1 6 1 12.7

McNamara (1998)

Quality?

A prerequisite for institutional investment in standing property is for stock to be available and of “investible” quality (Key and Law, 2005).

Indeed, Malpezzi and Shilling (2000) find that real estate investors tilt their real estate holdings towards quality.

While, Henneberry et al (2004) argue that the “stock of office properties in London is likely to be of significantly better quality than elsewhere”

So is that it?

Quality?

More tenants, Bigger size

Wiak and Key (2009) Real World Conference,

End-2008Tenants per Prop

Total FS m sf

Total Rent £m

Total ERV £m

Average FS 000 sf

Average Rent £000

Average OMRV £000

City Offices 3.95 26 552 565 16 327 334West End Offices 3.36 23 721 804 7 116 252Big 6 Offices -- 18 316 321 10 180 182M25 Offices -- 17 343 316 16 335 308Rest UK Offices -- 62 781 778 16 197 196Percent of total % % % % % %City Offices -- 18 20 20 25 28 26West End Offices -- 16 27 29 11 10 20Big 6 Offices -- 12 12 12 15 16 14M25 Offices -- 12 13 11 25 29 24Rest UK Offices -- 43 29 28 25 17 15

Number of tenants, Property Size: End 2008

Quality?

Fewer break clauses

Offices by Region: leases with break clause as a percent of total sample

Region 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Central London 27.5 42.3 41.5 45.6 43.5 27.9 33.5 36.2 47.4 46.0 Rest of South East 44.9 49.1 43.5 45.7 38.1 27.4 52.1 55.0 64.2 57.8 Rest of UK 36.3 40.9 43.8 47.5 37.1 39.8 51.9 49.5 54.0 53.0

Office by age: leases with break clause as a percent of total sample

Age 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Pre 1980 31.4 37.6 37.1 49.2 45.1 35.9 49.1 52.3 36.8 43.1 1980-1989 42.3 58.3 51.5 48.1 48.6 36.4 39.7 45.8 58.8 59.1 Post 1990 34.1 44.6 44.1 48.3 44.8 28.5 36.5 29.0 54.5 47.6

Lot Size?One important quality investment characteristic is lot size for a number of reasons.

1. Economies of scale: i.e. the management costs of one property with a value of £50m are considerably less than the management costs of 10 properties of £5m

2. Larger properties can have a greater number of tenants, which lessens the impact if one leaves.

This suggests that institutions would benefit from holding fewer but larger properties. Indeed, the average number of properties in institutional portfolios has declined over time. From 93 in 1981 to 40 by 2010.

So is that it?

Size and Age?

Number 95-11 80-94 70-79 40-69 Pre 40 City 18% 38% 10% 12% 22% West End 17% 15% 5% 14% 50% Rest SE 28% 55% 7% 6% 4% Rest UK 24% 37% 35% 10% 8% Cap value 95-11 80-94 70-79 40-69 Pre 40 City 34% 47% 6% 6% 6% West End 32% 20% 9% 15% 25% Rest SE 41% 49% 5% 3% 2% Rest UK 25% 44% 31% 9% 9% Av Size 95-11 80-94 70-79 40-69 Pre 40 City 48.4 31.1 15.6 13.5 7.4 West End 29.0 19.9 28.5 16.1 7.5 Rest SE 15.9 9.4 8.1 6.2 4.8 Rest UK 10.9 7.9 7.3 10.0 6.9

Source: IPD Annual Index 2011

Average data 2001-2011

City Office Portfolios are Younger and Bigger

81% by Value post 80 buildings and Average size >£30m

Familiarity?

A number of researchers report that familiarity is particular important to investors.

Hence another reason for the City office bias is the relative optimism investors have towards a familiar market.

In other words, investor’s have limited information about markets they are unfamiliar with and so display a preference for assets with which they are more familiar, despite the gains from diversification into the “unknown.”

So is that it?

Familiarity?

A main criticism of the limited information explanation is that it only fits the data when investors forecast higher returns for the City offices than the rest of the office markets in the UK.

But there must be times in which City investors actually forecast lower returns for the City of London office market than the rest of the UK.

During these times, the City office portfolio should be tilted toward the other markets.

However, as shown in Table 1 the bias towards City offices has remained stable and persists over time.

Norms/Benchmarks?

Tversky and Kahneman (1974) suggest that investors may determine future allocation by initially anchoring on their current allocation. Thus, if the initial allocation were 3%, then a 6% allocation would be regarded as a fairly extreme deviation from policy, i.e. a doubling of their current exposure. Such conservatism is based on norms, traditions and habits indoctrinated by years of customs and so there is a “tendency of groups to stick to established patterns” (Thaler and Sunstein, 2009). This also resonates with Lizieri’s (2009) “spatial prism”.

So is that it?

Norms?

In the context of a City of London bias this would mean that investors are City biased because their peer group is City biased. In other words, managers take a high risk if they deviation for benchmark weights and so will have a distinct prejudice against certain regions and so there will be a very low exposure to such markets.

Graff and Young (1996) support of this view in the US.

Conclusions

This paper seeks to test the hypothesis that institutional investment in the City of London office market does not conform to the assumption of economic rationality by using a simple asset allocation model.We find that the additional return required for office markets outside the City necessary to tilt the intuition’s allocation away from that observed is in excess of 300 bps per annum in a number of markets. These implied shadow or management costs seem well above any reasonable estimates that would justify investment in such markets. In other words, it appears to provide evidence of irrationality (in the strict traditional economic sense) in the behaviour of institutional investors.

Conclusions

However, the literature suggests that a wider spectrum of factors need to be examined to explain the asset allocation decisions of fund managers, beyond traditional proxy for risk (SD).

Therefore we looked at 5 factors

1. Liquidity2. Quality3. Lot Size4. Familiarity5. Norms

but find none that seems to provide a satisfactory explanation

European Real Estate Society Annual Conference

Vienna 2013

 The City of London Office Bias

 Any Questions?