Download Real Estate Investment in British Provincial Cities: Too Much or Too

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
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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2
Spatial Diversification
Capital Value (£
millions)
City/Mid Town
West End
Rest of London
South East
South West
Eastern
East Midlands
West Midlands
North West
Yorks & Humber
North East
Scotland
Wales
Northern Ireland
All
Source: IPD (2007)
3
Offices
Retail
19,496
13,327
11,230
9,412
1,681
2,758
392
1,871
1,771
1,436
314
2,249
448
..
66,384
496
3,229
11,316
15,319
6,572
9,625
4,498
7,861
8,338
7,852
4,176
7,783
3,180
298
90,543
Industrial
5,234
7,294
1,672
3,041
1,980
3,236
2,235
1,556
368
1,132
572
..
28,318
All
20,299
17,173
28,813
32,990
10,279
15,959
7,235
13,452
11,292
12,859
5,082
11,574
4,452
307
191,767
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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4
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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5
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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6
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.
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Provincial Cities
Examining Birmingham, Edinburgh, Glasgow, Leeds, and
Manchester
Analysis of each city in terms of:
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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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GDP
9
Employment
10
Consumer Spending
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Means and Standard Deviations of Rental
Growth 1981-2008
Standard Retail
Retail Warehouse
Office
Industrial
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
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Correlation Analysis
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Correlations over time across cities for rents
Most combinations of cities and sectors show statistically
significant correlations except for:
◦
◦
◦
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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
Direction of Causality
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Granger-causality tests identify lagged relationships and
assess the direction of such relationships across sectors
and between cities over time
n
n
i 1
i 1
rtcj , sk  f {  i rtcji1, sk 1    i rtcji, sk }


Where cj and sk are city j and sector k respectively
Run for all possible combinations of city and sector
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Inter City and Inter Sector Comparisons
London
Retail
London
Birmingham
Edinburgh
Glasgow
Leeds
Manchester
Retail
Office
Industrial
Retail
Office
Industrial
Retail
Office
Industrial
Retail
Office
Industrial
Retail
Office
Industrial
Retail
Office
Industrial
Office
Birmingham
Indust
rial
Retail
1,2
1
1,2
1,2
Office
2
1,2
Edinburgh
Indust
rial
1
1,2
1
Retail
Office
1,2
1
1
2
1,2
1
1
Glasgow
Indust
rial
Retail
2
2
2
2
2
2
2
1
1
2
1
1
1
1
1
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.
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Office
1,2
1
1
1,2
1
Leeds
Indust
rial
2
1
1,2
2
1
1,2
Retail
1,2
1
1,2
1
Manchester
Office
Indust
rial
Retail
Office
Indust
rial
1,2
1
1
1,2
1
1
2
1,2
1
2
1,2
1
2
2
1,2
2
1
1,2
1
1
2
2
1,2
2
1
1,2
2
1
2
1
2
1
2
1,2
2
1
2
2
1
1,2
2
1,2
1,2
1,2
1
2
1
2
2
2
2
1,2
2
1
1
2
1
1
1
Overview
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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?
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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
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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.
p
q
i 1
i 1
xt  i xt i  i t i

If there is volatility clustering the error variance is timevarying and can be written as a function of its lagged
values
q
 t2   0   i t2i  t
i 1
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GARCH
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A generalised autoregressive conditional heteroscedasticity
(GARCH) model begins by estimating conditional variance
from an ARMA model and then is specified as:
q
p
i 1
i 1
 t   0   i t2i    i t i

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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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Testing for Volatility Clustering and Shock
Persistence
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ARCH processes are found only in Birmingham and
the City of London.
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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
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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.
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