Economics Essay

Economics Essay Thomas Tynan

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Individual Coursework Submission Form

Specialist Masters Programme

Surname: Tynan First Name: Thomas

MSc in: Real Estate Investment Student ID number: 130003447

Module Code: SMM174

Module Title: Real Estate Economics – Individual Coursework Essay

Lecturer: Tony Key

Submission Date: 02/12/2013

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Real Estate Economics – Individual Coursework Essay

Question: Draw on theories of rent and urban economics to analyse and

explain the patterns in office rents shown in the Colliers UK Map and

Central London data table.

Monday 2nd

December 2013

Thomas E. Tynan

Student No. 130003447

Word Count: 3,043

(excluding figures & reference list)


3

Contents

1. Introduction 1

2. Background

2.1 Central Place Theory & Urban Hierarchy 2

2.2 Residual Theory of Land & Bid-Rent Theory 3

3. Analysis

3.1 Data Collection 5

3.2 Population Distribution & Rent 6

3.3 Distance, Accessibility & Rent 8

3.4 Floorspace & Rent 11

3.5 A Closer Look at London 12

4. Conclusion 15

5. References 16


1

Section 1 – Introduction

Commercial real estate rental values can dramatically vary across a city, region or

country. When examining the rental value per sq.ft. of grade A office units (prime rents)

across the UK as depicted by the 2012 Colliers International research and forecasting report

(Colliers International Rental Map, 2012) we can immediately see the magnitude of variance.

The difference in rent for physically similar pieces of land is due to space market

segmentation, which is fuelled by supply and demand for a specific location and the type of

usage that location supports (Geltner & Miller, 2007). Additionally, how the urban area

changes over time, or is expected to change will drive business decisions. These changes in

rent can be viewed and analysed at a national level as well as at a more local scale when

examining changes across an individual city.

This study examines and quantitatively analyses the factors affecting changes in

prime office rents at a national level (excluding Ireland). Further investigation then focuses on

the possible cause of local rent variation within a select city (London). For the purpose of the

study I have outlined the main theories of rental and urban economics and then drawn upon

the three variables highlighted within these theories to examine the allocated dataset.


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Section 2 – Urban and Rental Economic Theories

2.1 Central Place Theory & Urban Hierarchy:

In general, larger more rapidly growing cities will be able to sustain higher rents

(Geltner & Miller, 2007). Therefore the distribution of larger cities at a national level will

inherently determine and explain the spatial variation in values. Furthermore, the location of

cities is not a random phenomenon, and each city resembles a working cog within a national

framework or system of cities. As cities within a system develop and evolve over time

through centralising forces they increase in size (hierarchy) and decrease in frequency

(Beckmann, 1958). Centralising forces revolve around economies of scale and economies of

agglomeration. The production of a good or service is deemed to be more cost effective and

time efficient if completed within a single large-scale environment assuming fixed production

costs. Therefore an increase in the output quantity can be achieved for every unit of required

input (Geltner & Miller, 2007). Economies of agglomeration provide a platform for reduced

cost and or productivity advantages by the physical clustering of companies and businesses

(Catherine & Donald, 1999). Companies normally maximise benefits from economies of

agglomeration by locating near industry similar firms, therefore increasing the free flow of

relevant profitable information. As economies of scale and agglomeration begin to form they

cause cumulative causation and a momentum in growth. Consequently the reverse can happen

when central areas begin to decline. This is normally caused when the cost of locating within

a city exceeds the gains mainly through factors such as congestion, pollution, urban

transportation costs and crime. The centralising forces for companies that inhabit densely

populated cities must be of sufficient strength to overcome high rental value as well as the

factors mentioned above.

Transportation costs underline central place theory and the later extension of urban

hierarchy. The higher the transportation costs, the frequency of cities will increase and

therefore physical distance between them reduces (Geltner & Miller, 2007). In theory, cities

and their hinterlands or service areas will assume hexagonal shapes in order to minimise the

average distance from the city centre to any point within its service area. Higher order cities

producing more specialised goods and services will locate centrally within a group of lower

order cities to enable equal access to the these products on a transportation cost basis.

Therefore the key concept is that cities will form equidistant apart and higher order cities will

be fewer and further apart than lower order cities (Bertaud, 2004).


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2.2 Residual Theory of Land & Bid-Rent Theory:

The bid-rent model (Alonso, 1964) looks to further explain how land values, and

therefore rent, varies in relation to accessibility (Geltner & Miller, 2007). Land is one of the

main factors of production and this alone underlines its ability to obtain a value, which varies

depending on the derived demand for any given area. The attraction of renting or owning land

is the occupier’s ability to produce goods or services, which in-themselves demand a value.

Location is the driving force behind the variation in price of one area of land in comparison to

another due to factors such as access to additional areas of attraction and minimised

transportation costs (Geltner & Miller, 2007).

When considering all four factors of production; land, labour, capital and materials,

land is the only factor which is not mobile and is unable to be moved. Therefore the

remaining more mobile factors of production are always paid first to prevent them going

elsewhere. The residual income is then available for the landowner; this is the known as the

residual theory of land (Geltner & Miller, 2007). Considering this simplistic theory you can

assume that areas which are required to pay a premium for the mobile factors of production

due to poor location will have less income remaining to pay landowners therefore rents will

be less. Vis versa occurs when the mobile factors of production are cheap to source due to the

ease of access. Therefore assuming all income is split between the factors of production, not

one company should be able to outperform another. Cities have a greater residual value so are

able to sustain and afford higher rents.

The bid-rent curve focuses on transportation costs (the moving of inputs and outputs)

as the determining factor of production which alters rent. These costs are not solely borne by

the land occupier and seller but indirectly by the purchaser of the good or service.

Transportation costs in the eyes of the producer resembles a monetary value whereas for the

consumer it is resembled mainly by the time taken to travel to acquire the product. The bid-

rent is defined as the maximum rent a potential land user would be willing to pay (‘bid’) to

acquire a specific piece of land and / or location (Geltner & Miller, 2007).


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Figure 1 displays a bid-rent curve where the city centre minimises costs and therefore

the rent required is maximised. As transportation costs increase with distance from the city

rental values decrease as there is reduced income to service rent costs (Capozza & Helsley,

1987). The steeper the bid-rent line the greater the users sensitivity to accessibility and

transportation costs. Line A (figure 1) indicates occupancy by the highest bidder and could

resemble a financial company’s headquarters where production is maximised by low

transportation costs. Line B (figure 1) could resemble smaller less important offices which

serve the headquarters, and finally line C (figure 1) indicates minimal requirement for

accessibility such as a data centre.

The residual land theory, the bid-rent theory and the central place theory are

explained based upon the simple monocentric city model and therefore does not truly

resemble the complexities of modern city structure. These theories do however present the

basic economic principles and factors concerning the spatial pattern of cities and in turn land

value, which is largely a function of transport costs and therefore location.

Distance from Centre

Land Rent

Centre

A

B

C

Figure 1 Bid-rent modelled from Geltner & Miller (2007). Displays the bid-rent curve for 3 different potential

users (A, B & C) and how their willingness to pay, or bid-rent, changes as a function of distance from the centre.


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Section 3 – Analysis

3.1 Data Collection:

The following analysis is based upon the extraction of rental values for prime (grade

A) office space from the Colliers Rent distribution map (Colliers International Rental Map,

2012). The reason I have selected this data is because it represents solely prime office space

and therefore is unbiased towards variation in rental values due to the lesser quality of

buildings. The prime rent data acquired represents England, Wales and Scotland. Ireland has

been excluded from the analysis as the additional cost of travelling across water will distort

the results. This represented a sample size of 129 cities.

Population data was extracted from the Office for National Statistics (ONS, 2011). To

accurately account for the true working population of the larger urban areas and not just the

central core the entire conurbation was included This therefore accounted for commuters

travelling from smaller nearby towns into the CBD.

Distance as a second variable was measured based upon the distance from the centre

of a city or town to any other city or town as depicted by Google maps (Google Directions,

2013). This was deemed an appropriate measure as it calculates the shortest distance (miles)

along the UK road network (not ‘as the crow flies’) and therefore accurately represents travel

time and costs supporting the main economic theory driving land value as discussed

previously. The UK was then segmented in terms of regions and the distance of lower order

cities within the region to the dominant city centre was measured. The dominant city of each

region was selected based up its population size. Lower order towns and cities were assigned

to a neighbouring regions dominant city if they lay along the border of their region and were

geographically closer to the neighbouring dominant city as opposed to their own.

The final factor examined is the total floorspace of each city. This was acquired from

the Valuation Office Agency (VOA, 2012) and represented the total floorspace (m2) of all

grades of office space in the UK for 2011. The data was broken down at various levels from

national to regional and onto county and city.

It is important to note that when quoting coefficient and R² values throughout the

analysis, only limited diagnostic testing has taken place. All three variables have however,

passed the t-test at the 5% percent significance level.


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3.2 Population Distribution & Rent:

The theory discussed earlier identifies the fact that the larger the city, the greater the

ability of the city to sustain higher rental values. The scatter plot below represents how rent

changes as a function of population. Only the largest twenty UK cities have been selected for

this section of analysis.

Figure 2 demonstrates a positive relationship between rent and population. The

analysis has excluded London as one of the samples due to the sheer magnitude of its

population and the fact it therefore visually distorts the graph. It does however support the

trend that as population increases so does rent. When examining figure 1 it is clear that

Bristol and possibly Cardiff are outliers and do not fit the trend. This could be explained by

the fact that both cities lack nearby surrounding towns feeding the working population whilst

still maintaining their status as high order cities resulting in high rental values. Manchester,

Birmingham and Leeds for example benefit from local commuter towns falling within their

catchment and boosting population. Additionally both Bristol and Cardiff are not situated

centrally within the UK and instead in very close proximity to the coast. This further explains

the lack of ability for a greater number of people to access the cities. The high level of prime

Birmingham

Manchester

Leeds

Liverpool

Sheffield

Newcastle upon Tyne

Bradford

Bristol

Cardiff

Wakefield

Coventry

Nottingham

Leicester

Sunderland Hull

Brighton

Plymouth

Stoke-on-Trent

Wolverhampton

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000

Prim

e R

ent per

sq.f

t.

Population

Displays the relationship between prime rent and population of the 19 largest English and Welsh cities (excluding London)

Figure 2 Scatter plot displaying rent as a function of population size for the twenty largest cities

after London in the UK (excluding Ireland).


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rents in Bristol will also be caused by the status of Bristol as a port city and rents will be

driven by demand for space from companies dependent on shipping as a method of transport.

Overall the results support the theories of central place and urban hierarchy in

explaining rental pattern and city size. Furthermore, when examining the distribution of the

four largest UK cities at a national scale (figure 3) we can see that in general the larger cities

are not located within close proximity to one another. This supports the theory that cities

service their surrounding area and the larger the city the larger the surrounding area (Geltner

& Miller, 2007). The theory is slightly constrained as the UK does not assume a featureless

plain and this could explain the collection of large cities in the thin central UK midland area.

London

Birmingham

Leeds Manchester

Figure 3 Displays the spatial distribution of the four largest UK cities (column indicates

population size), which additionally supports high rental values (Note: not to scale).

Variable Coefficient C 16.06614

POPULATION 3.34E-06 Adjusted R-squared 0.719253

When running a simple regression

(Table 1) we can see that as population

increases by 1 person, rent increases by

£0.000334. When considering the population

increasing in increments of 10,000 the rent

increases by £3.34. Additionally we are able to

state that the population explains 72% of the

change in rental values

Table 1 Regression of figure 2.


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3.3 Distance, Accessibility & Rent:

Urban theory maintains that the variation in rent is largely due to location and

specifically the accessibility and transport costs involved in getting to and from that location.

When modelling rent as a function of distance away from any given dominant centre as

depicted by figure 4 we can see there is a slight trend. Figure 4 indicates a slight negative

correlation between rent and distance from the city centre. This supports the bid-rent theory

discussed earlier as well as the theoretical model displayed in figure 1. The scatter plot

displays the distance of every city away from its dominant centre within that region and the

prime rent achieved within that city.

Whilst a strong correlation is not witnessed, the results still support the fact that

residual theory of land determines the rental value of an area. The further away from the

dominant centre the greater the transportation costs and therefore the rent is reduced. More

expensive regions within the UK have the potential to distort the results when plotted together

with less wealthy areas. Further extensive analysis of each individual regions relationship to

their dominant centre could provide stronger correlations. Figure 5a and 5b display an

example of two regions selected (Birmingham & Manchester – the second largest cities after

London).

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0 20 40 60 80 100 120 140

Prim

e R

ent per

sq.f

t.

Distance from nearest dominant centre (miles)

Displays the relationship between rental value and disatnce from the nearest dominant centre

Figure 4 Scatter plot displaying rent as a function of distance from the nearest dominant centre.


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Figure 5a and 5b demonstrate a much stronger correlation between rent and distance

from the dominant centre when analysed at an individual regional level. This provides greater

support for the bid-rent and residual land theories as determining factors of the pattern and

distribution of rents particularly within each UK region.

0.00

5.00

10.00

15.00

20.00

25.00

30.00

0.00 10.00 20.00 30.00 40.00 50.00

Prim

e R

ent per

sq.f

t.

Distance from Birmingham Centre (miles)

a) Displays the relationship between rental value and distance from Birmingham for the West Midlands Region

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0.00 10.00 20.00 30.00 40.00 50.00

Prim

e R

ent per

sq.f

t.

Distance from Manchester Centre (miles)

b) Displays the relationship between rental value and distance from Manchester for the North West Region

Figure 5a & 5b Scatter plots displaying rent as a function of distance from Birmingham and Manchester

City Centres respectively.


DISTANCE -0.045886 Adjusted R-squared 0.047884


Further regression analysis shows that

as distance increases by a mile away from the

dominant centre rents will decrease by £0.046.

This variable (distance) can explain 4.79% of

the change in rental value away from the centre.

This value is lower than predicted as the results

could be skewed by more wealthy regions as

mentioned previously.

Variable Coefficient

C 24.87270

DISTANCE -0.304039

Adjusted R-squared 0.605627

Variable Coefficient

C 23.20754

DISTANCE -0.301494

Adjusted R-squared 0.725921

Table 3a Regression of figure 5a. Table 3b Regression of figure 5b.

Birmingham (3a) and Manchester (3b) individually are better explanatory

variables of rent as a function of distance. They explain 72.6% and 60.56% of rental

value changes within each region respectively. They additionally both show a decrease

in rents of £0.30 for every mile away from the dominant centre.


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Additional analysis examined the centralising forces of London as the highest order

city of the UK. Rents were modelled as a function of the distance to London from the top 19

largest cities after London (figure 6).

Figure 6 shows a reasonable negative correlation between distance from London and

prime rent. This indicates London exhibits a certain degree of centralising forces that can be

felt throughout the country.

Birmingham Leeds

Sheffield

Bradford

Manchester

Liverpool

Bristol

Cardiff

Wakefield Coventry

Nottingham

Leicester Sunderland

Newcastle upon Tyne

Hull

Brighton

Plymouth

Stoke-on-Trent

Wolverhampton Derby

London

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

0.00 50.00 100.00 150.00 200.00 250.00 300.00

Prim

e R

ent per

sq.f

t.

Distance (miles)

Displays the relationship between rental value and distance from London of the top 20 largest UK Cities after London

Figure 6 Scatter plot displaying the rent as a function of distance from London for the 19 largest

cities of the UK (excluding London).


DISTANCE -0.038261 Adjusted R-squared 0.153132


As we can see from figure 6,

London to a certain extent determines rental

values across the country. As distance from

London increases per mile, rents decrease

by £0.038. Furthermore the centralising

forces of London can explain 15.3% of the

pattern of rents across the UK.


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3.4 Floorspace & Rent:

The final variable that is analysed in terms of its ability to determine the spatial

pattern of rents is the total floor space within any given location. Office floorspace resembles

a proxy for economies of agglomeration. The scatter plot below displays rent as a function of

total floor space for any given city.

Simply demand and supply economics would suggest that the greater the amount of

floor space, the lower the rental value due to competition between landlords driving down

prices. The theory of positive economies of agglomeration counteracts this and demand

increases by drawing businesses to one central point (CBD) as it provides a platform for

reduced cost and productivity advantages. The demand for office space increases and exceeds

the supply forcing rents up. This additionally supports the residual land theory whereby

reduced costs for other factors of production increase the residual income available for the

landlord. Whilst differing grades of office floor space can cause bias results it is assumed that

the percentage of each quality type of office floor space is consistent throughout each city.

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0 500 1000 1500 2000 2500

Prim

e R

ent per

sq.f

t.

Floorspace (Thousands m2 )

Displays the relationship between total office floor space of a city and prime rent

Figure 7 Scatter Plot displaying rent as a function of total office space for a given location


SPACE 0.006273 Adjusted R-squared 0.281675

From the strong relationship

witnessed in figure 7 we can state that

total floorspace explains 28.17% of the

change in rental values. As floorspace

increase by 1m² rent values increase by

£0.006. Table 5 Regression of figure 7.


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3.5 A Closer Look at London:

London shows no evidence of a monocentric city pattern and instead exhibits

variations in land use that depict a polycentric city. At the local level, London displays

extreme variations in rent depending on location within the city (figure 8). Figure 9 on the

following page shows the spatial distribution of prime rents across central and west London

(Adapted from Colliers Research & Forecasting Report, 2012). We can immediately see

maximum rents of £100 per sq.ft. in Mayfair (West End) reducing all the way down to a

minimum rent observed in Hounslow of £14.50 per sq.ft. This 85.5% reduction in prime rent

occurs over a distance of 10.9 miles.

The West End as an area of London demonstrates the highest average rent with The

City of London following closely. There is a strong correlation between distance from

Mayfair and the reduction in rental values (figure 8). The samples selected for the analysis

were the main areas found along a transect originating from East London and travelling WSW

(see figure 9 for transect).

Mayfair

Victoria

Hammersmith

Brentford Hounslow

Heathrow

Ealing

Soho

City Core City Midtown

Chiswick

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14 16 18 20

Prim

e R

ent per

sq.f

t.

Distance from Mayfair (miles)

Displays the relationship between distance and prime rent when moving SW away from Mayfair along a transect

Figure 8 Displays rent as a function of distance away from Mayfair (area of highest prime office rent).

The data points are colour coded to represent the area (mini CBD) they belong to, see figure 10 for key.


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City Midtown - £52.50

Figure 9 Displays the spatial pattern of prime rents in Central & West London taken from the Colliers 2012 Research & Forecasting Report for London Offices.

Key: West End

The City

Other

West London

Belgravia - £55.00

Covent Garden - £60.00

Euston - £45.00

St James - £90.00

Soho - £82.50

Paddington - £52.50

Mayfair - £100.00

Marylebone - £65.00

Victoria - £62.50

City Core - £57.50

N. City - £22.50

E. City - £57.50

Docklands - £25.00

Canary Wharf - £35.00

Southbank - -£45.00

Hammersmith - £34.50

Chiswick - £45.00

Uxbridge - £27.50

Brentford - £18.00

Ealing - £29.50

Harrow - £17.00

Richmond - £35.00

Twickenham - £17.00

Hounslow - £14.50 Heathrow

- £21.50

Heathrow S. Park - £25.00

Wimbledon - £29.00

Transect


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The pattern and distribution of these small scale, high functioning centres within

London, notably the West End and The City, does not support Burgess’s (Burgess, 1925)

concentric ring model. Instead it bears a closer resemblance to Harris and Ullman’s multiple

nuclei model (Harris & Ullman, 1945). This model envisages separate urban zones with

differentiated functions and supporting differing agglomeration factors forming mini-CBD’s

(Harris & Ullman, 1945). In the case of London, The City better represents a financial hub in

terms of the headquarters of large global banks and law firms whilst the West End represents

more client focused businesses such as real estate firms and advertising agencies.

Figure 10 displays a theoretical diagram of a polycentric city that best explains the

pattern of varied prime office rents around London. In general, change in rent follows the

monocentric bid-rent curve and exhibits a negative relationship with increasing distance from

the CBD. In the case of the polycentric city, Harris and Ullman’s multiple nuclei theory

displays how rent may then increase again within the same city as you enter different areas

which are defined by occupation land use and type of economy of agglomeration. Figure 8

displays a slight resemblance to figure 10 when examining the maximum rents for each of the

selected areas (mini CBD’s).

Figure 10 Displays a combined theoretical model of Harris and Ullman’s multiple nuclei theory and the

bid-rent theory.

Distance from the CBD

Primary CBD – West End

Secondary CBD – The City

Sub-Urban CBD – South West (Chiswick)

Airport Zone – Heathrow

Monocentric Bid-Rent Curve

Rent


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Section 4 – Conclusion:

The urban theories examined in the beginning of this study were used to identify

economic factors that define and determine the spatial pattern of land values and in turn rental

values at a national and then at an individual local city level.

The results from the analysis on rent as a function of population supports the

theory that in general, the larger and higher order cities can sustain higher rental values.

Furthermore, central place theory and urban hierarchy explain the spatial distribution of cities

across the UK. By understanding rent as a function of city size and the principle that cities are

not positioned randomly we can begin to explain why rents vary across the UK.

When examining rent as a function of distance away from a dominant centre there

is a clear negative relationship. As distance from the centre increases, rental values decrease.

This is largely explained by William Alonso’s bid-rent model, which identifies the fact that

demand for space increases in the city centre due to reduced transport costs and higher

profitability.

The final section of analysis investigates rent as a function of office floor space,

which has been utilised as a proxy for economies of agglomeration. A strong correlation was

observed between rent and office floor space. We can therefore state that economies of

agglomeration produce high levels of demand within city centres, which drives up rental

values. Following on from this, when examining London at a local scale we can see that it

adheres to a polycentric, multiple nuclei pattern. It contains a number of smaller economies of

agglomeration within the main city thus explaining the variation in rents over shorter

distances.


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Section 5 – References

Alonso, William. 1964. Location and Land Use. Cambridge, Mass.: Harvard University

Press.

Beckmann, J., Martin, 1958. City Hierarchies and the Distribution of City Size. Economic

Development and Cultural Change, Vol. 6, No. 3 (Apr., 1958), pp. 243-248 Published

by: The University of Chicago Press.

Bertaud, Alain. 2004. The Spatial Organization of Cities; Deliberate Outcome or Unforeseen

Consequence? IURD Working Paper Series, Institute of Urban and Regional

Development, UC Berkeley.

Burgess, W., Ernest, 1928. Residential Segregation in American Cities. Annals of the

American Academy of Political and Social Science, Vol. 140, The American Negro

(Nov., 1928), pp. 105-115

Catherine J. Morrison Paul & Donald S. Siegel, 1999. Scale Economies and Industry

Agglomeration Externalities: A Dynamic Cost Function Approach. The American

Economic Review, Vol. 89, No. 1 (Mar., 1999), pp. 272-290.

Capozza, D. & Helsley, R., 1987. The Fundamentals of Land Prices and Urban Growth.

Journal of Urban Economics 26, 295-306. Faculty of Commerce and Business

Administration, University of British Columbia.

Colliers International Rental Map, 2012. National Office Rents Map 2012. Available Online

at: http://www.colliers.com/en-gb/uk/insights/offices-rents-map (Accessed between

10th November & 29

th November 2013).

Colliers International Research & Forecasting UK Report Q2, 2012. Available Online at:

http://www.colliers.com/~/media/Files/EMEA/UK/research/offices/201207-central-

london-offices.pdf (Accessed between 10th November & 29th November 2013).

Geltner, D. M., Miller, N. G., Clayton, J. & Eichholtz, P., 2007. Commercial Real Estate:

Analysis & Investments. 2nd

Edition. Mason: South-Western Cengage Learning.

Google Maps Driving Directions, 2013. Available Online at: https://www.maps.google.co.uk/

(Accessed on the 16rd

of November 2013).

Harris, D., Chauncy, & Ullman, L., Edward, 1945. The Nature of Cities. Annals of the

American Academy of Political and Social Science, Vol. 242, Building the Future

City (Nov.,1945), pp. 7-17.

Office for National Statistics (ONS) Population, Mid 2011. Available Online at:

http://www.ons.gov.uk/ons/taxonomy/index.html?nscl=Population#tab-data-tables

(Accessed on the 15rd

November 2013).

http://www.colliers.com/en-gb/uk/insights/offices-rents-map

http://www.colliers.com/~/media/Files/EMEA/UK/research/offices/201207-central-london-offices.pdf

http://www.colliers.com/~/media/Files/EMEA/UK/research/offices/201207-central-london-offices.pdf

https://www.maps.google.co.uk/

http://www.ons.gov.uk/ons/taxonomy/index.html?nscl=Population#tab-data-tables


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Valuation Office Agency (VOA), Business Floorspace (Experimental Values), 2012.

Available online at:

http://www.voa.gov.uk/corporate/statisticalReleases/120517_CRLFloorspace.html

(Accessed on the 16th

November 2013).

http://www.voa.gov.uk/corporate/statisticalReleases/120517_CRLFloorspace.html

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