Real Estate Investment in British Provincial Cities: Too

Much or Too Little?

Neil Dunse, Colin Jones and Michael White

 Heriot-Watt University

Edinburgh

Research funded by Scottish Widows Investment Partnership

2

Introduction Institutional investment in the UK is focused in London

and the South East region. The City of London office market exhibits the most

variable returns of any local property market in the UK and hence is amongst the riskiest (Dunse et al, 2010).

The City of London has a strong inter-linkage between occupational and investment markets that means that its office market is vulnerable to exogenous cycles in financial services as well as the endogenous property market cycle (Lizieri, 2008).

This suggests there is a case for more investment in provincial cities not least because of the portfolio diversification benefits.

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Spatial Diversification Capital Value (£ millions)

Offices Retail Industrial All

City/Mid Town 19,496 496 20,299 West End 13,327 3,229 5,234 17,173 Rest of London 11,230 11,316 28,813 South East 9,412 15,319 7,294 32,990 South West 1,681 6,572 1,672 10,279 Eastern 2,758 9,625 3,041 15,959 East Midlands 392 4,498 1,980 7,235 West Midlands 1,871 7,861 3,236 13,452 North West 1,771 8,338 2,235 11,292 Yorks & Humber 1,436 7,852 1,556 12,859 North East 314 4,176 368 5,082 Scotland 2,249 7,783 1,132 11,574 Wales 448 3,180 572 4,452 Northern Ireland .. 298 .. 307 All 66,384 90,543 28,318 191,767 Source: IPD (2007)

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Spatial Disaggregation Byrne and Lee (2009, 2010) examine local

authority level data and reveal even greater concentration of institutional investment within the 376 local authority districts in England and Wales.

Just over half and 84% of the value of retail and office investment respectively in only 30 local authorities (not necessarily same areas).

The authors report increasing spatial concentration of institutional investment in offices and shops between 1998 and 2003.

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Property Grouping Hoesli et al (1997) analyse the extent to which property

returns can be grouped by area or by property type. The analysis is based on the premise that if the urban

area is appropriate for diversification, clusters of similar locations would be expected.

The research results are dominated by the importance of property type/sector as a diversifier rather than area.

Hamelink et al (2000) find that London office markets behave distinctly and there is a broad split between office and industrial markets in the immediate fringe of London and all other ‘peripheral’ markets.

But all major provincial city office markets are included in the same cluster suggesting limited diversification potential

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Rents or Yields A range of studies have drawn attention to the

role of national capital markets in influencing local yields (capitalisation rates) and hence returns.

MacGregor and Schwann (2003) argue that this may occur because a London-based real estate perspective is imposed on peripheral regions.

Henneberry (1999) similarly concludes that imperfections in the property capital market are driven by national factors with local rent trends/cycles ignored.

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Analysis Cities are the focus of analysis rather than regions

or local authority districts. Spatial property markets are taken to be essentially

urban as differences in the structures of local economies, and hence economic growth combined with variations in local supply responses lead to individual city rent trends (Jones and Orr, 1999; Orr and Jones, 2003).

National influences would be present in the form of monetary and fiscal policies, macroeconomic cycles.

Given the existence of local cycles/trends it is possible to postulate that there is scope for diversification.

Examining Birmingham, Edinburgh, Glasgow, Leeds, and Manchester

Analysis of each city in terms of: Economic background (GDP, Employment,

consumer spending) Property market – rental growth

Identification of patterns of correlation, causation in movements in rental change across cities and sectors

Identification of direction of relationships – which cities/sectors lead/lag

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Provincial Cities

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GDP

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Employment

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Consumer Spending

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Means and Standard Deviations of Rental Growth 1981-2008

Mean SD Mean SD Mean SD Mean SD

Birmingham 4.624 4.305 3.963 3.111 5.049 10.027 3.279 5.688

Edinburgh 4.640 4.460 4.619 2.397 4.709 11.390 4.336 6.265

Glasgow 4.416 5.120 4.248 3.007 3.576 5.902 3.364 4.954

Leeds 5.000 5.123 4.754 3.320 3.835 7.364 3.277 7.231

Manchester 5.982 5.730 2.783 2.270 4.521 6.905 3.177 5.191

All London 5.260 7.970 4.199 2.773 3.626 13.180 3.674 7.512

South East 4.470 6.254 4.979 2.801 2.043 7.513 3.271 7.101

All UK 4.724 5.926 4.396 2.115 3.336 10.589 3.063 6.714

Standard Retail Retail Warehouse Office Industrial

Correlations over time across cities for rents Most combinations of cities and sectors show

statistically significant correlations except for:◦ between South East offices and Leeds offices◦ between South East offices and industrials in

Birmingham, Edinburgh, Glasgow, Leeds, Manchester

◦ South East retail had insignificant correlations with offices in Leeds and Manchester, and with industrials in Birmingham, Edinburgh, Glasgow, Leeds, Manchester, London

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Correlation Analysis

Granger-causality tests identify lagged relationships and assess the direction of such relationships across sectors and between cities over time

Where cj and sk are city j and sector k respectively

Run for all possible combinations of city and sector

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Direction of Causality

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Inter City and Inter Sector Comparisons

London Birmingham Edinburgh Glasgow Leeds Manchester

Retail Office Industrial

Retail Office Industrial

Retail Office Industrial

Retail Office Industrial

Retail Office Industrial

Retail Office Industrial

London Retail 1,2 1,2 2 2 2 1,2 2 2 2 2 Office 1 1,2 1 2 1 1 1,2 1 1,2 1,2 1 Industrial 1,2 2 1 1 2 1 1,2 1 1 1 1 1,2

Birmingham Retail 1,2 1,2 1,2 2 2 2 1,2 2 2 2 1 Office 1 1 1,2 1 1,2 1 1,2 1,2 Industrial 1 1 2 1 1 1,2 1 1 1 1 1,2

Edinburgh Retail 2 1 1 Office 2 1 1,2 2 1,2 2 Industrial 1 1 1 1 1 1

Glasgow Retail 1 1,2 2 2 1,2 2 2 1,2 Office 2 2 2 2 2 Industrial 1,2 1 1 1

Leeds Retail 2 1,2 2 1 Office 2 2 Industrial 2 1

Manchester Retail 1 Office 1 Industrial

Where 1 implies Granger-Causality runs from the city and sector on the top row to the city and sector in the left hand column. 2 implies Granger-causality running from the left hand column city and sector to the top row city and sector.

London, the South East and Edinburgh experienced faster term long term economic growth

Not translated into equivalent rental growth and returns because of the role of supply

In most sectors the highest average annual rental growth and returns occurred in provincial cities – with less volatility

Potential to diversify between regional cities But is this happening to the extent justified by

risk and expected return performance?16

Overview

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Risk and Return Plots: Offices

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Risk and Return Plots: Retail

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Risk and Return Plots: Industrial

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ARCH Processes Risk may be an inaccurate estimate of

volatility particularly if they are correlated over time

An ARCH model can be written as an ARMA (p, q) process.

  If there is volatility clustering the error

variance is time-varying and can be written as a function of its lagged values

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q

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GARCH A generalised autoregressive conditional

heteroscedasticity (GARCH) model begins by estimating conditional variance from an ARMA model and then is specified as:

The coefficient α (commonly known as the ARCH effect) captures the tendency for the conditional variance to cluster while the γ (commonly known as the GARCH effect) captures the tendency for shocks to have a persistent influence on the conditional variance.

We test ARMA models and associated ARCH/GARCH models as appropriate for total returns, income returns and rents.

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p

ii

q

iitit

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Testing for Volatility Clustering and Shock Persistence

ARCH processes are found only in Birmingham and the City of London.

While the GARCH process is not found for Birmingham, it is statistically significant in the City of London market implying that exogenous events and shocks will cause persistence in conditional volatility.

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Conclusions

If trends in GDP, employment, and consumer spending continue after recession in the same way as they had before, London and Edinburgh might be expected to outperform the UK average.

However new supply in these locations may weaken rental growth and capital value increases.

Volatility clustering in both Birmingham and the City of London may affect investor behaviour.

The volatility of the market and local economic performance both impact on investment strategy.