Part of the M&G Group

Introducing the M&G Real EstateEuropean Connectivity Rankings

June

201

7

For investment professionals only

2

Introduction 3

Mega trends 3

Connectivity 3

Urbanisation 4

How Big Data can help 4

Measuring urban connectivity 4

Connectivity scores and rankings 5

Top city performers 5

Top performers by enablers 6

Top performers by effects 6

Where is the value? 8

Analysis 8

Implications for real estate investors 10

Helsinki: Potential connectivity climber 10

Appendices

City connectivity rankings 11

Methodology 13

Connectivity metrics: enablers 14

Connectivity metrics: effects 15

City density classification 16

Connectivity scores vs yields 18

Contents

3

1. Introduction

Connectivity is the smart physical and digital

infrastructure that make our cities tick and helps their

economies to grow. It keeps the wheels of our transport

systems turning smoothly. And crucially, it brings

together residents, visitors, businesses and public and

private institutions.

The power of connectivity doesn’t stop there. At M&G

Real Estate, we believe that well-serviced connectivity

infrastructures help to underpin a city’s property

fundamentals too. We also believe that a better

understanding of connectivity can help investors to

identify relative value opportunities in real estate and to

future-proof their investments more effectively.

That’s why we developed the M&G Real Estate European

Connectivity Rankings to grade 64 European cities1

based on their capacity to improve physical and digital

urban connectivity in the face of the growing density

pressures that face Europe’s cities today.

The table below highlights a snapshot of our top-10

city performers based on their final connectivity scores.

In this paper, we outline the methodology behind our

rankings, with the complete rankings appearing on page

11 of the Appendix.

Mega trendsConnectivity

‘Good density’, according to the Urban Land Institute (ULI), is defined by four drivers: technology, capital, urban form

and design, and infrastructure and connectivity. And the results of good urban density include elements of mixed-use

design, connectivity, green spaces, cohesiveness and liveability (Figure 1.2).

Source: Density: drivers, dividends and debates, June 2015, Urban Land Institute.

Outcomes of Bad Density

Mixed Use Connected Planned Spacious

Incremental Designed Green Appropriate

Liveable Cohesive

Monotonous Isolated Unmanaged Unliveable

Segregated Inflexible Ugly Polluting

Crowded ConspicuousOutcomes of Good Density

Densification

Technology CapitalUrban Form and

DesignInfrastructure and

Connectivity

Enablers and Secondary Drivers

Fig 1.2: Densification: Drivers, dividends and debates

Fig 1.1 Top 10 city performers

Total Connectivity Rank City Density* Total Enabler Rank Total Effects Rank1 Paris High 1 56

2 Berlin Medium 2 31

3 Stuttgart Medium 3 28

4 Zurich Medium 5 14

5 Stockholm Medium 8 15

6 Amsterdam Medium 7 36

7 Munich Medium 6 39

8 London High 4 62

9 Bremen Low 11 5

10 Luxembourg Low 18 4

Source: M&G Real Estate, December 2016. *Please see Appendix 5 for the definition of city density.

1Our analysis utilises annual PMA prime rent and yield data for these 64 European cities.

4

Urbanisation

Over 70% of Europeans live in cities and the urban areas

around them, with that figure expected to reach 80% by

2050. And with around 85% of European GDP generated

in cities, this backbone of the region’s economy is only

set to grow in importance.

However, the physical infrastructures needed to support

growing cities are struggling to cope and housing demand

is outpacing supply. This kind of densification does not fit

the model envisaged by the ULI and is evident across

the EU where the overcrowding rate of households, as

measured by Eurostat, is higher in cities (c.18%) than in

towns and suburbs (c.14%) or rural areas (c.7%).

Demand for better connectivity is also being boosted

by an unprecedented wave of migration as more people

move into European cities to live and work. Business and

tourist travel is also on the rise as travel becomes more

affordable, new routes become available, the stock of

short-stay accommodation improves, and income levels

rise in Asia and elsewhere.

How Big Data can help

Big Data could help to plug the funding gap and break

the link between economic growth and congestion with

cheap data-based solutions. The range, volume and

frequency of data are growing rapidly as cities start

to install sensors into everything from streetlights to

parking bays. By next year already, some 80% of cars in

Western Europe will be able to receive and generate real-

time traffic data, according to INRIX. In addition, some

cities like Gothenburg in Sweden are pressing ahead with

autonomous car technology, aiming to introduce 100

driverless cars on their roads by end-2017.

Taken together, that suggests congestion looks set

to fall as more travellers are diverted at peak hours

from crowded routes to less-packed ones. Indeed, there

is already some evidence of that: despite London’s

relatively poor overall showing, research by INRIX shows

that traffic signal optimisation and smart motorways

are proving successful at reducing traffic congestion in

the UK capital and on its principal motorway, the M252.

Emissions of pollutants should also fall, because fewer

vehicles would be idling in traffic jams and there would

be fewer cars on the street.

Applying data analytics to create intelligent

transportation systems will play a key role in improving

urban connectivity, in line with predictions that Big Data

will boost European GDP by 1.9% by 20202.

Measuring urban connectivity

In this study, we measure how the physical and digital

infrastructures, both public and private, performed in 64

European cities. Our objective is to identify real estate

value in dense, sustainable and well-connected urban

centres from a connectivity perspective.

Our assumption is that relatively high scores support

strong real estate fundamentals. Cities with the most

visionary connectivity policies are more likely to continue

to attract talented people despite rising density pressures

like traffic congestion and carbon emissions.

We split the connectivity metrics we track into two

categories: ‘enablers’ and ‘effects’. Enablers can be

considered as urban connectivity ‘inputs’ (e.g. city

mobility strategy, Wi-fi speed, the coverage of Real

Time Passenger Information apps), while effects can

be considered urban connectivity ‘outputs’ (e.g. network

affordability, transport carbon emissions, safety). A more

detailed breakdown of evaluation indicators is outlined in

the Methodology section at the end of this paper.

% T

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30

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0

19501960

19701990

19802010

2000

80

90

100

F2030

F2020

Urban Rural

F2050

F2040

Source: United Nations, 2015.

Fig 1.3: Europe urban and rural population (% total 1950-2050)

% o

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16

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*The overcrowding rate describes the proportion of people living in an overcrowded dwelling, as defined by the number of rooms available to the household, the household’s size, as well as its members’ ages and their family situation. Source: Eurostat.

Fig 1.4: EU27: Overcrowding rate by degree of urbanisation (2015)*

2 Source: Inrix.

5

2. Connectivity scores and rankings

Fig 1.1 on page 3 shows our top-10 city performers

based on their connectivity scores and the graphic

below maps the 64 reviewed cities across Europe: the

larger the bubble the higher the connectivity score.

Top city performers

Paris, Berlin and Stuttgart top the total connectivity

rankings. Paris performs particularly well in Real-Time

Passenger Information (RTPI) coverage and green

transport modes, with 83% of journeys to work made

using public transport, cycling or walking.

German, French and Swiss cities dominate the top 10

positions. The common denominator is the relatively

higher levels of investment per capita on transport

infrastructure and maintenance.

Conversely, peripheral European cities, such as Naples,

Arhus, Montpellier and Seville, appear at the bottom

of the rankings. To dig deeper into these results, we

plotted enabler scores against effect scores by density

category (Fig 2.2).

Fig 2.1: Total connectivity score map

Paris

Barcelona

London

Rome

Manchester

Leipzig

BrusselsBristol

Edinburgh

Madrid

Cologne

Stuttgart

Amsterdam

BerlinHamburg

Munich

Dublin

Leeds

Naples

AntwerpGuildfordThe Hague

Lisbon

Sevilla

Glasgow

Milan

Cambridge

Marseille

Düsseldorf

Århus

Oslo

Nice

Bordeaux

Oxford

Hanover

Frankfurt

Mannheim

WarsawDortmund

Luxembourg

Vienna

Bremen

Copenhagen

Stockholm

Lyon

Helsinki

Lille

Urtrecht

Gothenburg

Zurich

Malmo

Toulouse

Bologna

Aberdeen

Dresden

Espoo

Prague

BirminghamRotterdam

Nantes

Eindhoven

Valencia

Montpellier

Nürnberg

Source: M&G Real Estate, December 2016.

6

We found that high-density cities generally score relatively

better when it comes to enabler indicators, while low-

density cities have higher effect scores. Medium-density

cities tend to fall somewhere in between.

However, there are notable exceptions in each density

category. In our view, it is these cities that, from a

connectivity perspective, hold the most promise for real

estate investors beyond Europe’s more liquid gateway

markets because they could represent a source of

latent value.

Enablers: Top city performers

Paris has the highest enabler score, followed by Berlin

and Stuttgart. Conversely, Montpellier, Prague and

Nuremberg have the lowest scores.

Among the high-density cities, London and Barcelona

are particularly strong in the areas of RTPI app coverage,

green transport mode provision and mobility strategy.

Barcelona’s score is high because it was one of the first

European Demonstration Cities to adhere to CIVITAS

objectives when the initiative was first launched in 2002.

The Catalan city’s Municipal Council for the Environment

and Sustainability also launched its ‘Agenda 21’ agenda

that year, whose commitments were renewed in 2013

and include plans to ‘improve mobility and make

pedestrian life a welcoming setting’.

Among medium-density cities, Stuttgart, Zurich and

Amsterdam also achieve high enabler scores. With

an average broadband speed of 85Mbps, Stuttgart

provides the fastest downloading speed by far across

the reviewed cities, boosting its Wi-Fi speed score.

Amsterdam ranks second with a Wi-Fi speed score of

80Mbps, which supports the Dutch capital’s portfolio of

smart city projects, including a public city dashboard that

displays updated statistics on transport, environment,

community, culture and security every 10 seconds3.

Zurich’s enabler score is boosted by its RTPI app

coverage indicator. Typically, tourist destinations such

as Berlin, Lisbon and London score relatively well on this

measure. Digital connectivity is also recognised as critical

infrastructure in the Mayor of London’s 2050 Plan, which

aims to improve connectivity in underserved areas.

Effects: Top city performers

Espoo, Nantes and Luxembourg – all low-density cities

– occupy the top-three positions in the effects rankings.

Bremen

Bremen, a top-10 ranked low-density city, scores relatively

highly in terms of average commute time (25 minutes),

safety and security (0.11 road deaths per 10,000

population), as well as low carbon emissions (25% due

Fig 2.2: Enabler vs effects scores by density category

Source: M&G Real Estate, December 2016.

100

90

80

70

60

50

40

30

20

10

00 40302010 50 60 70 80 90 100

Effects score

En

ab

lers

sco

re

High Density Medium Density Low Density

Paris

Barcelona

London

Rome

ManchesterLeipzig

BrusselsBristol

Valencia

Edinburgh

Madrid

Cologne

Stuttgart

Amsterdam

Berlin

Hamburg

Munich

Dublin

LeedsNaples

Antwerp

Guildford

Montpellier

The Hague

Lisbon

SevillaGlasgow

Milan

Cambridge

Marseille

Düsseldorf

Århus

Oslo

Nice

Bordeaux

Oxford

Hanover

Frankfurt

Mannheim

Warsaw

Dortmund

LuxembourgVienna

Bremen

Copenhagen

Stockholm

Rotterdam

Lyon

Helsinki

Lille Urtrecht

Gothenburg

Zurich

MalmoToulouse

BolognaAberdeen

Dresden

EspooPrague

Eindhoven

Nantes

Fig 2.3: Average city connectivity score by density category

Density Category

Total Connectivity

Enablers Effects

High 59 67 25

Medium 52 52 52

Low 44 40 58

Source: M&G Real Estate, December 2016

3Source: http://citydashboard.waag.org/

7

to transport). Bremen’s efforts in the field of promoting

cleaner and more sustainable public transport were

recognised when the city received a CIVITAS award in

2014 for “Urban mobility and social inclusion – Planning

accessibility for more sustainable cities”. A number of

transport-related engagement tools introduced in the

city, including an online geo-referenced consultation

platform, also helped Bremen to win the accolade.

In common with all the German cities we reviewed,

Bremen performs consistently well on transport emissions.

This is driven by the Low Emissions Zones (LEZs) it

introduced in 2008 with the aim of improving air quality

by restricting polluting vehicles within defined areas.

Aberdeen

Among the UK’s low-density cities, Aberdeen achieves

the highest score in terms of effects, boosted by

affordability (the cost of a monthly travel card on

public transport equates to 1.5% of monthly GDP per

capita) carbon emission (13% due to transport) and

award indicators.

In 2014, the European Commission awarded the city a

European Mobility Week award. Aberdeen’s Sustainable

Urban Mobility Plan was recognised for addressing

social and economic objectives including a focus on

sustainable transport. The plan was developed in close

consultation with citizens and stakeholders, with almost

500 people completing online surveys or providing

feedback through social media.

Vienna

Among the top-10 ranked medium-density cities, Vienna

scores relatively high on affordability (1.4% of GDP per

capita spent on public transport) and safety and security

(0.10 road deaths per 10,000 population). We understand

Vienna is in the process of creating a living lab that will

test designs and systems to implement intelligent traffic

solutions, green buildings, water management and a

smart grid infrastructure across the city. In the long

term, this initiative will serve to boost its score both in

terms of enabler as well as effect indicators, and could

see it climb our rankings when next updated.

8

3. Where is the value?

The following section builds on the previous analysis

by linking total connectivity scores to market yields by

density category, as shown in Fig 3.1 below. The  aim

here is to support the investment decision-making

process by identifying relative pricing opportunities from

a connectivity perspective.

Analysis

The relationship between scores and market yields shows

that the more connected the city, the lower the yield.

Many of the cities that achieve high connectivity scores

are also among the most liquid investment markets.

High-density cities like Paris, London and Barcelona, for

example, achieve top-quartile total connectivity scores.

They also offer keen property yields, well below 5%.

Paris and London are the most liquid European markets,

attracting average annual investment volumes in excess

of €6 billion per annum since 20014.

Some medium-density cities, namely Stockholm, Munich

and Berlin also offer investors high connectivity scores at

a relatively keen price.

For the same top-quartile connectivity score, select

medium- and low-density cities offer higher property

yields. These include Amsterdam, Lyon and Rotterdam,

as well as Bremen, Luxembourg and Malmö. While

these are relatively less liquid markets, the maturity and

performance of their connectivity infrastructures can

offer yield discounts above 200 basis points compared

with high-density cities.

Among high-density cities, Naples is a clear loser in the

connectivity rankings and this is reflected in the higher

yield. Relatively higher yields are also reflected in the

bottom quartile connectivity scores of medium- and

low-density cities such as Antwerp, Montpellier and The

Hague. Nuremberg, Birmingham and Cologne are also

low-quartile performers but offer keen yields below 5%.

Clearly, while the relationship between connectivity and

yields is broadly downward trending, the scores are wide

ranging, highlighting the fact that a number of other

urbanisation and real estate drivers also contribute to

market pricing. These could include liquidity, a tight

supply-demand balance, or alternative density enablers

such as innovation.

When seeking value beyond traditional gateway markets,

we believe that investors should target cities that achieve

a combination of high enabler and effect scores (ideally

above 50). We believe this optimal balance of density

drivers is more likely to efficiently service densification

pressures and support strong property fundamentals.

Of the reviewed cities, 18 fall into this category of

relatively high enabler and effect scores. They comprise

a range of high-, medium- and low-density cities and are

shown in Figure 3.2.

At 6.0%, Toulouse offers the highest yield. It is also easily

the most southerly city in the list, followed by Lyon. Five

of the cities are in the Nordic region, with another five in

Germany, including relatively high-yielding Bremen. The

UK has only one entry in the shape of Bristol.

Fig 3.1: Yield vs connectivity scores by density category

Source: PMA, M&G Real Estate, December 2016.

8.0

7.0

6.0

5.0

4.0

3.00 40302010 50 60 70 80 90 100

Weighted connectivity score

Pri

me

offi

ce y

ield

(e

nd

20

15

)

High Density Medium Density Low Density

Paris

Barcelona

London

Manchester

LeipzigBrussels

Bristol

Edinburgh

Madrid

Cologne

StuttgartAmsterdam

BerlinHamburg

Munich

Dublin

Leeds

Naples

Antwerp

Guildford

Montpellier

The Hague

Lisbon

Glasgow

MilanCambridge

Marseille

Düsseldorf Oslo

Bordeaux

Hanover

Frankfurt

Mannheim WarsawDortmund

Luxembourg

Vienna

Bremen

Copenhagen

Helsinki

Stockholm

RotterdamLyonLille

Urtrecht

Gothenburg

Zurich

Malmo

Toulouse

Bologna

Aberdeen

Dresden

Espoo

Prague

Nantes

NurembergBirmingham Rome

4 Source: CBRE, DTZ, Eurostat, M&G Real Estate.

9

Fig 3.2: All round connectivity winners

Enabler score: 51 to 100

Effect score: 51 to 100

City Market yield (%) City Market yield (%)

Toulouse 6.0 Helsinki 4.9

Warsaw 5.7 Copenhagen 4.5

Bremen 5.6 Stuttgart 4.5

Rotterdam 5.5 Gothenburg 4.5

Dresden 5.4 Vienna 4.4

Lyon 5.3 Berlin 4.3

Luxembourg 5.3 Frankfurt 4.2

Malmo 5.3 Stockholm 3.8

Bristol 5.0 Zurich 3.3

Key: High Density, Medium Density, Low Density

Fig 3.3: How the other cities fare further down the ranking

Enabler score: 0 to 50

Effect score: 51 to 100

Enabler score: 51 to 100

Effect score: 0 to 50

Enabler score: 0 to 50

Effect score: 0 to 50

City Market yield (%) City Market yield (%) City Market yield (%)

Bologna 6.8 Lisbon 5.9 Naples 8.0

Utrecht 6.5 Brussels 5.2 Antwerp 6.8

Nantes 6.4 Hannover 5.2 Montpellier 6.5

Espoo 6.4 Amsterdam 5.1 The Hague 6.3

Aberdeen 6.0 Rome 4.8 Leeds 5.8

Bordeaux 6.0 Milan 4.5 Dortmund 5.5

Marseille 5.8 Dublin 4.4 Edinburgh 5.3

Mannheim 5.4 Barcelona 4.3 Glasgow 5.3

Prague 5.4 Hamburg 4.2 Leipzig 5.2

Lille 5.3 Madrid 4.0 Guildford 5.0

Cambridge 4.8 Munich 3.8 Nuremberg 5.0

Düsseldorf 4.4 Paris 3.5 Manchester 4.8

Oslo 4.3 London 3.5 Birmingham 4.8

Cologne 4.5

Key: High Density, Medium Density, Low Density

Source: M&G Real Estate December 2016, PMA March 2016.

10

HELSINKI: Potential connectivity climber

Helsinki comes in at number 21 in our rankings but has the potential to climb higher in the years ahead due to

connectivity-friendly initiatives. By 2025 it hopes to make its centre car-free—not by banning cars but by building

a transport system that renders them redundant.

Finland’s sense of shared national endeavour is important here. To help roll out an ambitious new app that aims

to provide the end-user with a bespoke and seamless solution in real time to all its transport problems, the city

government is rewriting legislation to bring the laws covering different modes of transport into harmony. The app,

which has been developed by MaaS Global (short for Mobility as a Service), is called Whim and the residents of

Helsinki will soon be able to use it to travel across the city from early 2017.

Whim mixes and matches a variety of participating public and private transport services. For example, Whim could

suggest a bicycle from the city’s bike-share scheme (if one is near your front door), followed by a train and then a

taxi; an on-demand bus (“hail” it on the app and it will come and pick you up); or a one-way car-share to a tram and

a rented “e-bike” with a small electric motor. Once a route has been chosen it will make all the bookings needed,

as well as ensuring that hire vehicles are available and public-transport sections are running on time. Costs will be

displayed for every option, making clear the trade-offs between speed, comfort, and price.

Customers will be able to buy one-off journeys or ‘bundles’ modelled on mobile-phone contracts, allowing for a

certain amount of travel each month. For perhaps €95 a month it might offer free city-wide public transport, 100km

of local taxi use, 500km of car rental, and 1,500km on national public transport.

Whim is precisely the type of technology that is changing the way we interact with the urban landscape, with

implications for the real estate investment community. This technology could soon be replicated in England with

suggestions of Birmingham as the next possible test case.

Source: MIT Technology Review, The Economist, September 2016.

Implications for real estate investment

For real estate investors seeking value, factors such as

liquidity and supply-demand imbalances always need to

be evaluated. But a city’s connectivity performance is an

increasingly important factor too.

Access to cheap live transit data looks set to dramatically

improve the way residents travel across urban centres. At

a city level, low-cost intelligent transport solutions should

radically improve digital and physical infrastructures,

contributing to economic growth. As such, the scores of

more peripheral European markets have the potential to

improve substantially.

Technological advances can make smaller capital cities

like Copenhagen and Helsinki and regional cities like

Bremen and Malmö more attractive places to work and

live. The analysis developed here aims to identify those

emerging locations that, from a connectivity perspective,

are best-placed to deliver sustainable property

fundamentals and superior pricing opportunities.

The M&G Real Estate Connectivity Rankings will likely

see risers and fallers in the years ahead as cities adopt

smart technologies to withstand the evolving challenges

that come with city living and working, and – of course

– urban real estate investment. We highlight one such

potential climber below.

11

Appendix 1. City performance table

Country City DensityCategory

TotalRank

EnablerRank

EffectRank

TotalScore

Enabler Score

EffectScore

France Paris High 1 1 56 100 100 13

Germany Berlin Medium 2 2 31 98 98 52

Germany Stuttgart Medium 3 3 28 97 97 57

Switzerland Zurich Medium 4 5 14 95 94 79

Sweden Stockholm Medium 5 8 15 94 89 78

Netherlands Amsterdam Medium 6 7 36 92 90 44

Germany Munich Medium 7 6 39 90 92 40

UK London High 8 4 62 89 95 3

Germany Bremen Low 9 11 5 87 84 94

Luxembourg Luxembourg Low 10 18 4 86 73 95

Germany Hamburg Medium 11 10 37 84 86 43

France Lyon Medium 12 13 12 83 81 83

Austria Vienna Medium 13 15 8 81 78 89

Sweden Malmo Low 14 24 3 79 63 97

Spain Barcelona High 15 9 59 78 87 8

Ireland Dublin Low 16 12 42 76 83 35

Netherlands Rotterdam Medium 17 17 17 75 75 75

Germany Frankfurt Medium 18 14 30 73 79 54

Denmark Copenhagen High 19 21 16 71 68 76

Sweden Gothenburg Low 20 20 22 70 70 67

Finland Helsinki Low 21 28 7 68 57 90

UK Oxford Medium 22 19 34 67 71 48

France Nantes Low 23 41 2 65 37 98

Portugal Lisbon High 24 16 47 63 76 27

Poland Warsaw Medium 25 27 26 62 59 60

France Toulouse Low 26 30 18 60 54 73

Germany Hannover Medium 27 25 35 59 62 46

Netherlands Utrecht Medium 28 39 9 57 40 87

Germany Dresden Low 29 31 19 56 52 71

Spain Madrid High 30 22 44 54 67 32

Italy Bologna Medium 31 44 6 52 32 92

Italy Milan High 32 23 52 51 65 19

France Lille Low 33 36 21 49 44 68

UK Aberdeen Low 34 45 10 48 30 86

UK Cambridge Low 35 37 25 46 43 62

Norway Oslo Low 36 43 20 44 33 70

Belgium Brussels High 37 29 48 43 56 25

Finland Espoo Low 38 61 1 41 5 100

Germany Düsseldorf Medium 39 38 32 40 41 51

Netherlands Eindhoven Medium 40 54 11 38 16 84

Connectivity rankings

12

Country City DensityCategory

TotalRank

EnablerRank

EffectRank

TotalScore

Enabler Score

EffectScore

Germany Leipzig Low 41 33 49 37 49 24

Italy Rome Medium 42 26 63 35 60 2

Germany Mannheim Medium 43 46 27 33 29 59

Spain Valencia High 44 34 54 32 48 16

UK Bristol Low 45 32 55 30 51 14

Germany Dortmund Medium 46 47 33 29 27 49

UK Manchester Medium 47 35 57 27 46 11

France Marseille Low 48 49 29 25 24 56

France Nice Low 49 52 24 24 19 63

France Bordeaux Medium 50 55 23 22 14 65

UK Edinburgh Low 51 42 50 21 35 22

UK Guildford Low 52 53 38 19 17 41

Germany Cologne Medium 53 48 51 17 25 21

Netherlands The Hague Low 54 51 43 16 21 33

UK Birmingham Medium 55 40 64 14 38 0

Belgium Antwerp Medium 56 50 53 13 22 17

Czech Republic Prague Medium 57 63 13 11 2 81

UK Glasgow Medium 58 57 41 10 11 37

Italy Naples High 59 58 45 8 10 30

Denmark Århus Low 60 60 46 6 6 29

UK Leeds Low 61 56 61 5 13 5

Spain Sevilla Medium 62 59 60 3 8 6

France Montpellier Low 63 62 58 2 3 10

Germany Nuremberg Medium 64 64 40 0 0 38

Source: M&G Real Estate, December 2016.

Connectivity rankings (continued)

13

Appendix 2. Methodology

City classification

The reviewed cities are classified into density categories,

as outlined below, and the full list appears at the end of

this section.

For the purpose of density classification, the population

and city area indicators were calculated within the

Local Administrative Unit (LAU) as defined by Eurostat

in 2016. A city is an LAU when the majority of the

population lives in an urban centre of at least 50,000

inhabitants.

City ranking and scores

For the ranking exercise, connectivity indicators for

each city were measured, which were further split into

enablers and effects as outlined in Appendices 3 and 4.

Please refer to Appendix 8 for references to data sources.

Enablers

The enabler indicators measure connectivity maturity

including Wi-Fi speed, vision and strategy for future

mobility, RTPI app coverage (e.g. Citymapper, Ally,

Moovit), share of journeys to work using green transport

modes, length of dedicated cycle paths per square

kilometre, electric vehicle chargers within a 10km radius,

car and ride sharing schemes.

Effects

The effect indicators measure connectivity performance,

i.e. the degree to which connectivity-related goals

are fulfilled in an effective and efficient manner. This

includes affordability in terms of price of a monthly

public transport ticket, share of transport-related carbon

emissions, passenger satisfaction, average commute

time to work, number of hours wasted in traffic per

annum, public transport speed and number of road

fatalities per inhabitant.

Rank and score

For each indicator we defined a point scale, with the

maximum and minimum end of the scale being defined

by the best and worst performance of the 64 cities. For

each indicator, best performing cities were allocated up

to a maximum of 100 and a minimum of 0 points.

These scores were further weighted as outlined in

Appendixes 3 and 4. Among the enabler indicators, we

favoured provision of digital infrastructure such as Wi-Fi

speed, transport app coverage as well as availability of

green transport modes. Among the effect indicators,

we favoured cities that favoured inclusivity of transport

through high affordability scores as well as strong safety

and environmental performance.

The city that achieved the top score on all the weighted

enabler and effect indicators achieved a score of 100.

The cities were also ranked from 1 to 64, corresponding

to the best and worst performer.

Yields

Prime office yields were used as a proxy for All Property

yields as these sector yields were available for all the

reviewed cities.

Average city connectivity score by density category

Density Category

Density criteria

High Above 5,000 residents per square km

Medium Between 2,001 and 4,999 residents per square km

Low Up to 2,000 residents per square km

14

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me

tric

s

16

Appendix 5: Density categories

Country City Density (LAU population/LAU)* Density categorySpain Barcelona 16,316 High

France Paris 8,800 High

Italy Naples 8,434 High

Denmark Copenhagen 7,664 High

Belgium Brussels 7,279 High

Italy Milan 7,273 High

Portugal Lisbon 6,172 High

Spain Valencia 5,841 High

Spain Madrid 5,225 High

UK London 5,177 High

Spain Sevilla 4,930 Medium

Netherlands Amsterdam 4,817 Medium

Sweden Stockholm 4,596 Medium

Germany Munich 4,531 Medium

Switzerland Zurich 4,377 Medium

Austria Vienna 4,196 Medium

UK Birmingham 4,066 Medium

Germany Berlin 3,837 Medium

UK Glasgow 3,405 Medium

Netherlands Utrecht 3,401 Medium

UK Oxford 3,370 Medium

Poland Warsaw 3,334 Medium

Netherlands Rotterdam 2,951 Medium

Germany Stuttgart 2,914 Medium

Germany Frankfurt 2,824 Medium

Germany Düsseldorf 2,754 Medium

Italy Bologna 2,730 Medium

Germany Nuremberg 2,677 Medium

France Lyon 2,604 Medium

Germany Cologne 2,552 Medium

Germany Hannover 2,539 Medium

Czech Republic Prague 2,513 Medium

Belgium Antwerp 2,505 Medium

Netherlands Eindhoven 2,491 Medium

Germany Hamburg 2,312 Medium

Italy Rome 2,190 Medium

UK Manchester 2,123 Medium

France Bordeaux 2,078 Medium

Germany Dortmund 2,052 Medium

Germany Mannheim 2,047 Medium

City density classification

17

Country City Density (LAU population/LAU)* Density categorySweden Malmo 1,947 Low

Luxembourg Luxembourg 1,940 Low

France Lille 1,895 Low

UK Bristol 1,848 Low

UK Edinburgh 1,838 Low

Germany Leipzig 1,788 Low

France Marseille 1,729 Low

Germany Bremen 1,686 Low

Germany Dresden 1,617 Low

France Toulouse 1,583 Low

UK Leeds 1,377 Low

Norway Oslo 1,374 Low

Ireland Dublin 1,370 Low

UK Aberdeen 1,216 Low

France Nantes 1,183 Low

France Nice 1,175 Low

Sweden Gothenburg 1,155 Low

France Montpellier 1,029 Low

Finland Helsinki 856 Low

Netherlands The Hague 826 Low

Denmark Århus 682 Low

UK Guildford 518 Low

Finland Espoo 494 Low

UK Cambridge 309 Low

* Local Administrative Unit (LAU) as defined by Eurostat. A city is an LAU when the majority of the population lives in an urban centre

of at least 50 000 inhabitants.

Source: Eurostat 2016, M&G Real Estate.

City density classification (continued)

18

Appendix 6: Connectivity scores vs yields

Total Connectivity Score Yield (%, end 2015)

Paris 100 3.5

Berlin 98 4.3

Stuttgart 97 4.5

Zurich 95 3.3

Stockholm 94 3.8

Amsterdam 92 5.1

Munich 90 3.8

London 89 3.5

Bremen 87 5.6

Luxembourg 86 5.3

Hamburg 84 4.2

Lyon 83 5.3

Vienna 81 4.4

Malmo 79 5.3

Barcelona 78 4.3

Dublin 76 4.4

Rotterdam 75 5.5

Frankfurt 73 4.2

Copenhagen 71 4.5

Gothenburg 70 4.5

Helsinki 68 4.9

Nantes 65 6.4

Lisbon 63 5.9

Warsaw 62 5.7

Toulouse 60 6.0

Hannover 59 5.2

Utrecht 57 6.5

Dresden 56 5.4

Madrid 54 4.0

Bologna 52 6.8

Milan 51 4.5

Lille 49 5.3

Aberdeen 48 6.0

Cambridge 46 4.8

Oslo 44 4.3

Brussels 43 5.2

Espoo 41 6.4

Düsseldorf 40 4.4

Leipzig 37 5.2

Rome 35 4.8

Mannheim 33 5.4

Bristol 30 5.0

19

Total Connectivity Score Yield (%, end 2015)

Dortmund 29 5.5

Manchester 27 4.8

Marseille 25 5.8

Bordeaux 22 6.0

Edinburgh 21 5.3

Guildford 19 5.0

Cologne 17 4.5

The Hague 16 6.3

Birmingham 14 4.8

Antwerp 13 6.8

Prague 11 5.4

Glasgow 10 5.3

Naples 8 8.0

Leeds 5 5.8

Montpellier 2 6.5

Nuremberg  0 5.0

Source: M&G Real Estate, December 2016, PMA March 2016.

20

For Investment Professionals only. This document is for investment professionals only and should not be passed to anyone else as further distribution might be restricted or illegal in certain jurisdictions. The distribution of this document does not constitute an offer or solicitation. Past performance is not a guide to future performance. The value of investments can fall as well as rise. There is no guarantee that these investment strategies will work under all market conditions or are suitable for all investors and you should ensure you understand the risk profile of the products or services you plan to purchase. This document is issued by M&G Investment Management Limited (except if noted otherwise below). The services and products provided by M&G Investment Management Limited are available only to investors who come within the category of the Professional Client as defined in the Financial Conduct Authority’s Handbook. They are not available to individual investors, who should not rely on this communication. Information given in this document has been obtained from, or based upon, sources believed by us to be reliable and accurate although M&G does not accept liability for the accuracy of the contents. M&G does not offer investment advice or make recommendations regarding investments. Opinions are subject to change without notice. M&G Investments and M&G Real Estate are business names of M&G Investment Management Limited and are used by other companies within the Prudential Group. M&G Investment Management Limited is registered in England and Wales under numbers 936683 with its registered office at Laurence Pountney Hill, London EC4R 0HH. M&G Investment Management Limited is authorised and regulated by the Financial Conduct Authority. M&G Real Estate Limited is registered in England and Wales under number 3852763 with its registered office at Laurence Pountney Hill, London EC4R 0HH. M&G Real Estate Limited forms part of the M&G Group of companies. M&G Investment Management Limited and M&G Real Estate Limited are indirect subsidiaries of Prudential plc of the United Kingdom. Prudential plc and its affiliated companies constitute one of the world’s leading financial services groups and is not affiliated in any manner with Prudential Financial, Inc, a company whose principal place of business is in the United States of America.  DEC 17 / W252007

Vanessa Muscarà Senior Research Analyst

+44 (0)20 7548 6714

vanessa.muscara@mandg.com

Richard Gwilliam Head of Property Research

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richard.gwilliam@mandg.com

Christopher Andrews, CFA Head of Client Relationships and Marketing, Real Estate

+(65) 6436 5331

chris.j.andrews@mandg.com

For more informationLucy Williams Director, Institutional Business UK and Europe, Real Estate

+44 (0)20 7548 6585

lucy.williams@mandg.com

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+31 (0)20 799 7680

stefan.cornelissen@mandg.co.uk

www.mandg.com/realestate