Page 1

COMPARISON OF PRIME LOCATIONS FOR

EUROPEAN DISTRIBUTION AND LOGISTICS 2009

Abridged edition

-0-


Comparison of prime locations for European logistics and Distribution 2009

This report has been produced by Cushman & Wakefield LLP, in collaboration with Logistics in Wallonia and AWEX, for information purposes. It is not intended to be a complete description of the markets or developments to which it refers. The report uses information obtained from public sources which Cushman & Wakefield LLP believe to be reliable, but we have not verified such information and cannot guarantee that it is accurate and complete. The report also refers to these economic sources: Eurostat, Consensus Economics Inc.; The Economist; Reuters; Experian Business Strategies; Centre for Business & Economic Research. No warranty or representation, express or implied, is made as to the accuracy or completeness of any of the information contained herein and Cushman & Wakefield LLP shall not be liable to any reader of this report or any third party in any way whatsoever. All expressions of opinion are subject to change. The prior written consent of Cushman & Wakefield LLP, Logistics in Wallonia or AWEX is required before this report can be reproduced in whole or in part.

-1-


Comparison of prime locations for European logistics and Distribution 2009

EXECUTIVE SUMMARY ........................................................................... 3 INTRODUCTION........................................................................................ 5 METHODOLOGY....................................................................................... 6 ELEMENTS OF THE ‘RANKED-MATRIX’. ............................................... 6 NUTS-1 IS BUILT UP FROM NUTS-2 DATA ............................................ 6 WEIGHTS, SOURCE MATERIAL AND SENSITIVITY .............................. 7 EXAMPLE OF A MATRIX-ELEMENT ....................................................... 8 DOMAIN: COSTS ...................................................................................... 8 OVERALL RESULTS .............................................................................. 12 The choice of the regions. ................................................................................................................................................. 12 Ranking byNUTS-2 region.................................................................................................................................................. 13 Ranking by NUTS-1 region................................................................................................................................................. 15

A LOOK AT THE FUTURE...................................................................... 16 Forecasted Matrix 2020 by NUTS-2 region. ...................................................................................................................... 21 Forecasted Ranking by NUTS-1 region. ........................................................................................................................... 24

ATTACHMENTS ...................................................................................... 25 ATTACHMENT A: GLOSSARY .............................................................. 25 ATTACHMENT B: MATRIX RANKING BY NUTS-2 REGION. ............... 27 ATTACHMENT C: MATRIX RANKING BY NUTS-1 REGION. ............... 29 ATTACHMENT D: FORECASTED MATRIX NUTS-2 REGIONS 2020... 30 ATTACHMENT E: FORECASTED MATRIX NUTS-1 REGIO’ S 2020. .. 32 ATTACHMENT G: CALCULATION TABLE BUYING POWER IN THE 3HOUR DRIVETIME PERIMETER.............................................................. 37 ATTACHMENT H: THEMATIC MAPS OF THE DOMAINS AND THE TOTAL SCORES, BY NUTS-2 REGION .................................................. 40


Executive Summary This report makes a ranking of top locations for distribution and logistics in Europe, based upon macroeconomic parameters. This is done by using a ranked-matrix method for 61 European regions and for the optimal location of a European Distribution Centre ( EDC, not a regional logistics centre). As in previous reports, published in 2004 and 2006 by the Flanders Institute for Logistics, several Belgian provinces come in the top of this ranking. Liège comes out as the number 1 location, closely followed by Limburg (B), Hainaut, and Nord-Pas-de-Calais. The main reasons for this top-ranking are : -

excellent access to the main European markets ( the core West-German markets like the

Rurhgebiet, the in the Netherlands, the core Benelux markets like Randstad/Antwerp/Brussels , Paris/Ilede-France and the greater London area) -

a central geographic location that is optimal to cover a wide range of European markets

-

top transport infrastructure and volume, close to main ports or with good multimodal links to

these ports -

low costs for land, warehouses and labour

-

labour force that is available, highly productive, skilled for supply chain jobs and with a good

language knowledge The top-15 consists of regions from Belgium, Northern France (Nord-Pas-de-Calais and Alsace) and Western Germany ( DĂźsseldorf, Koblenz, KĂśln and Arnsberg). Regions from the Netherlands are, given their history as important logistics locations, often perceived as top regions for distribution; in this macro-economic ranking they score relatively average ( Limburg (NL)/ Venlo comes on 23rd place, NoordBrabant/Eindhoven 31st place, Zuid-Holland/ Rotterdam 37th place ). The main reasons for this relatively low score of the Dutch regions are : -

relatively high costs for land and warehouses, combined with a severe urban planning system

that makes it difficult to guarantee the required space for future logistics property development -

road congestion problems, especially in the Randstad areas

-

labourforce availability has proven to be relatively limited in periods of strong economic activity

The matrix used in this study is based upon the quantifiable variables that play a role in the decision to locate an EDC; the relative weight of the variables in the matrix is based upon surveys amongst decision makers, like the European Cities Monitor survey that Cushman & Wakefield publishes on a yearly basis. Over the years these weights have shifted a little: the relative importance of Labour ( Available Labourforce / Labour productivity ) was given more weight ( 9%) than in the 2004 and 2006 studies, especially because available labourforce was a growing problem over 2007 and 2008.

-3-


4

Comparison of prime locations for European logistics and distribution 2009

Ranking Nuts-2 regions 2009 Weight % LIEGE LIMBURG -B (Genk-Hasselt) HAINAUT (Charleroi) NORD - PAS-DE-CALAIS (Lille) NAMUR Luxembourg - B( Arlon) ALSACE (Strasbourg) OOST-VLAANDEREN (Gent) ANTWERPEN ARNSBERG KÖLN KOBLENZ …. MAZOWIECKIE (Warszawa) TIROL (Innsbruck) GREATER LONDON SW SCOTLAND (Glasgow) SYDSVERIGE (Malmö)/Öresund VASTSVERIGE (Göteborg) CATALUNA (Barcelona) COM. DE MADRID LISBOA VALE DO TEJO median score

Costs 21% 4.1 3.6 3.2 2.8 3.7 3.3 3.7 5.8 7.4 4.7 8.6 6.0

Transport system 29% 1.5 2.0 2.2 2.5 2.4 3.4 2.8 2.0 1.5 3.6 1.8 3.1

Accessibility 29% 1.0 1.2 1.7 3.0 2.0 1.5 2.1 2.0 2.0 1.5 0.7 1.0

Supply 9% 2.4 1.2 1.0 2.1 4.3 2.3 2.8 2.5 2.4 2.0 3.0 2.8

Labour 9% 2.6 2.9 2.1 2.5 2.1 3.6 3.8 3.2 2.0 4.0 3.4 4.5

4.7 9.0 12.1 8.8 9.0 8.4 10.4 10.7 6.8 6.7

5.2 4.6 2.6 4.1 4.8 5.5 3.9 4.8 4.9 3.3

8.2 5.1 3.9 7.1 6.8 7.3 7.8 10.0 12.0 3.0

1.8 5.0 6.0 3.5 4.0 4.3 4.6 3.8 3.5 2.8

4.5 4.4 7.6 4.5 4.3 4.1 3.2 2.6 5.0 3.9

Know-how SCORE 3% Total 2.5 2.1 1.7 2.1 3.3 2.2 3.8 2.7 3.5 2.7 4.0 2.7 3.8 2.9 2.0 2.9 1.0 3.0 3.3 3.1 2.5 3.1 3.0 3.2 6.0 4.3 2.0 3.0 3.0 3.0 4.0 5.0 5.5 3.0

5.6 5.7 5.7 5.9 6.1 6.3 6.4 7.3 7.3 4.1

Ranking 2009 1 2 3 4 5 6 7 8 9 10 11 12 53 54 55 56 57 58 59 60 61

For a location decision, the actual situation is important, but it is even more important to know what the future will bring in terms of infrastructure development, land supply, expected warehouse rent evolution etc. That is why a forecast of the matrix data was developed for the time horizon of 2020. According to this forecast Liège will not be able to hold its nr 1 position : it is extremely well located, but the limited availability of land give this region a slight disadvantage versus Hainaut who will be nr1 in our view. This reflects the growing importance of good transport infrastructure towards markets south of the actual core European logistics regions; the Seine-Nord Europe canal junction that will upgrade the inland waterway between the Paris region and the North of France and Belgium also increases the score of Nord-Pas-deCalais and Hainaut. The gradual shift of the centre of gravity of the European markets towards central Europe will result in a rise of German regions like Köln and Düsseldorf; the good geographical position towards main German and central-European markets will keep Limburg (B) and Liège in a strong 2 and 3 position:

Forecast 2020 NUTS-2 regions Weight % HAINAUT (Charleroi) LIMBURG -B (Genk-Hasselt) LIEGE NORD - PAS-DE-CALAIS (Lille) DÜSSELDORF KÖLN ALSACE (Strasbourg) ARNSBERG VLAAMS BRABANT (Vilvoorde) SAARLAND

Cushman & Wakefield 2009

Costs 19% 5.9 5.9 6.7 6.6 9.3 10.3 6.1 7.3 10.1 6.7

Transport system 27% 2.4 2.2 1.9 2.3 1.8 1.7 2.8 3.5 2.1 3.6

Accessibility 27% 1.8 1.7 1.2 2.5 0.9 0.8 2.1 1.4 1.5 2.1

Supply Labour 8% 15% 1.0 2.3 1.3 3.3 2.3 3.3 1.3 2.2 3.3 2.3 3.3 2.3 2.0 4.3 2.0 3.0 2.8 1.7 1.7 2.8

Forecasted Knowhow SCORE Ranking 2020 3% Totaal 3.0 2.8 1 1.7 2.9 2 3 2.5 2.9 4 3.5 3.1 5 2.5 3.2 6 2.5 3.3 7 3.5 3.4 8 3.3 3.5 9 1.8 3.5 3.3 3.5 10


Comparison of prime locations for European logistics and distribution 2009

5

Introduction

This report gives a comparison of European top-regions for logistics, based on macro-economic factors with an impact on distribution and logistics. The Ranked Matrix methodology ( see further ‘Methodology’) enables a quantitative comparison of strengths and weaknesses of each region. Every two years, Cushman & Wakefield (C&W) publish the European Distribution Report, which maps the different European countries in terms of logistics and distribution. In the perspective of advising players of the logistics sector in their localisation strategy, the data matrix used in the present study is based upon regional data (Eurostat NUTS-1 and NUTS-2). In Belgium, regions in NUTS-1 are concordant with the actual Belgian regions, and NUTS-2 regions are concordant with the province level. NUTS-3 regions, which in Belgium are concordant with “arrondissements”, are not used in this study. This study uses matrix data from both NUTS-1 and NUTS-2 regional levels in order to produce an analysis of Wallonia’s strengths and weaknesses towards competing regions such as the Netherlands, Brussels and Flanders, Northern France, Germany’s Länder, and other additional European top-regions for logistics. The present study is an actualisation of previous studies delivered in the frame of missions for the Flanders Institute for Logistics in 2004 and 2006.

Cushman & Wakefield 2009


6

Comparison of prime locations for European logistics and distribution 2009

Methodology Elements of the ‘ranked-matrix’. The following domains are being accounted through the use of a Ranked Matrix (see also Attachment A, Glossary): •

Costs of warehouse spaces, land for professional use and labour (weights 21% in the

overall matrix) •

Transportation system: characteristics of the different transportation modes (weights

29% in the overall matrix) •

Accessibility to markets (weights 29% in the overall matrix)

Offer in terms of logistics property and land provision (weights 8% in the overall matrix)

Labour: offer in terms of workforce and productivity (weights 9% in the overall matrix).

Note: oppositely to previous similar studies (for the Flanders Institute for Logistics in 2004 and 2006), the weight of this domain has been increased, considering the lack of workforce which under the current critical economic conditions has become an issue in several regions. •

Know-How: education and trainings in logistics, and language knowledge (weights 3%

in the overall matrix. The matrix uses weights concordant with the ones used by C&W in any average EDC demand. The weights reflect the importance given by decision makers to the various location factors. These are followed and updated according to yearly surveys led by C&W, such as the European Cities Monitor. Like in 2006, the matrix-element “Population proximity” was replaced with “Spending power within a 180 min. drive-time”. Not only does this element give a better measure of the local markets, but it also makes the matrix a better index for more regionaldriven distribution centres. In Central Europe, this factor shows a double leverage-effect, as the spending power increases more proportionally then in Western Europe, and as the roadnetwork develops so fast that more people are being covered by the considered 3 hour perimeter.

NUTS-1 is built up from NUTS-2 data The NUTS-1 matrix is calculated according to a bottom-up method: scores of lower level (NUTS-2) are calculated first. For the constitution of the NUTS-1 matrix, the arithmetical average of NUTS-2 regions in the NUTS-1 territory gets calculated. Like in the 2006 study, this study was broadened to the NUTS-2 level (oppositely to the 2004 study), adding 23 extra regions from the EU-27 to the corpus. These are isolated NUTS-2 regions, in the sense that surrounding regions have not been calculated into the matrix, with the consequence that we were not able to calculate an overall average of the concordant NUTS-1 regions for

Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

7

these regions. The 23 extra regions are located lower in the NUTS-2 ranking, Prague having the best score among them, yet reaching the 29th place only (see below). Hence, this is not an obstacle to the constitution of a ranking of NUTS-1 regions.

Weights, Source material and Sensitivity Attachment F describes the domains, and matrix-elements of these domains with their respective weights. It also gives a view on the source material used in the study, as well as an indication of their sensitivity. As far as sensitivity of an element is concerned, one can only give an indication of it, since this is about a ranking of data which vary from region to region in a non linear way. Wherever a satisfying level of linearity was found among data, these were translated into points and added to the score. For some of the matrix elements, different data were put into balance. This is the case of “Available workforce”, where both unemployment figures (as an indication of immediate availability) and the percentage of younger people were taken into account in order to calculate the future availability of workforce. Yet such elements cannot be assigned with univocal indicators of sensitivity. As this analysis of strengths and weaknesses uses a Ranked Matrix, whenever possible, privilege was given to: •

quantifiable elements

elements which allow cross-region and cross-border comparison.

Domains which, although important in logistics and distribution, could not be taken into account for the reasons explained here above, were: •

quantity and fluidity in the deliverance of permits and administrative procedures (such as toll

procedures) •

taxation, rulings, etc.

Since figures on permits and administrative procedure are hardly available, not only is their actual situation here at stake, but also the perception players and potential players may have of them.

Cushman & Wakefield 2009


8

Comparison of prime locations for European logistics and distribution 2009

Example of a matrix-element In this chapter we show an example of how the matrix elements are being calculated. In this abridged edition of the study we only explain the details of this matrix-element. The detailed scores for each region can be found in Attachments B to E for all matrix elements. The detailed explanation on the matrix-elements can be found in the full version of this study.

Domain: Costs Matrix-element: Rents

The matrix-element “Rents” gives a ranking of the rental values for warehouse spaces in the considered regions. Theses values were calculated on the basis of a standard warehouse space of approximately 10,000 sq.m., and according to the current norms in logistics property (minimum 10.5 meter free height, 6ton tolerance floor, minimum one loading dock per 1,000 sq.m., sprinklers, partition, etc.). Values used in the matrix were taken from the C&W Industrial Space Across the World 2009 study. One notes that Ile-deFrance barely reaches a rental value of 51€/sq.m./year, which translates in a score of 7.4 for his matrixelement. These rents for semi-industrial property in the broader region of Paris remain exceptionally low, especially when compared with prices in the residential or office property in that region. This trend was already showing in the 2004 and 2006 editions of our study. This can only be explained by the active policies led by the French government regarding the creation of a sufficient offer (sufficient building land, commercialisation through governmental institutions, which can be compared with “intercommunales” in Wallonia). Yet, such policies are not tenable on the longer term in the immediate surroundings of a major city like Paris. This region will inevitably achieve a higher score in the years to come (see below). In the matrix, scores gain one point for every average increase 5€/sq.m./year in the rental value. This indicator is called “Sensitivity indicator” of the matrix-element (see also Attachment F for an overview). The sensitivity is provided whenever there is sufficient linearity in the data, and whenever one indicator is taken into account. This sensitivity is a useful tool, which in the future will allow a monitoring of what will or could be the effects of an increase of rental values in Wallonia property market on its current position in the competition (see also chapter “A Look at the Future”, which provides a prognosis of each matrixelement for 2020). Weight: within the “Costs” domain, this element was given a 38% weight. Pondered with the 21% of the overall domain, this means the matrix-element “Rents” has a 7.7% weight in the overall matrix. Regions achieving the best scores for this element are: •

Several Belgian regions such as Hainaut, Liège, Luxembourg and Limburg

Zeeland and other provinces of Northern Netherlands

Regions in Northern France achieve scores going from average to good (Champagne-Ardenne

and Picardie have a better score than Nord-Pas-de-Calais, Provence and Alsace).

Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

9

Regions presenting worse scores for this element include: •

Several English regions (especially the Greater London area)

•

Top-regions in Spain and Italy. The overall relatively high rental values in these regions,

combined with a strong demand for warehouse and local distribution solutions explains this phenomenon. •

Many Dutch regions find themselves in the lower section of the ranking regarding this element.

Some exceptional values (outliers) like Greater London were assigned with a weakened score in order to maintain them below the 12.5 limit. The following calculation table gives an example of a full translation of rents into matrix scores:

Cushman & Wakefield 2009


10

NUTS code AT13 AT32 AT33 BE10 BE21 BE22 BE23 BE24 BE25 BE31 BE32 BE33 BE34 BE35 CZ01 DE21 DE3 DE6 DE71 DEA1 DEA2 DEA3 DEA5 DEB1 DEB2 DEB3 DEC0 ES3 ES51 FR10 FR21 FR22 FR30 FR41 FR42 FR71 FR82 HU01 IT2 IT6 LU00 NL11 NL12 NL13 NL21 NL22 NL23 NL31 NL32 NL33 NL34 NL41 NL42 PL07 PT13 SE04+Cop.DK SE05 SK01 UK73 UKI1&2 UKM3

Comparison of prime locations for European logistics and distribution 2009

NUTS2 REGION

WIEN SALZBURG TIROL (Innsbruck) BRUSSELS CAP.REGION ANTWERPEN LIMBURG (B) OOST-VLAANDEREN VLAAMS BRABANT WEST-VLAANDEREN BRABANT WALLON HAINAUT LIEGE LUXEMBOURG (B) NAMUR PRAHA OBERBAYERN (München) BERLIN HAMBURG DARMSTADT (Frankfurt) DÜSSELDORF KÖLN MUNSTER ARNSBERG KOBLENZ TRIER RHEINHESSEN-PFALZ (Kaiserslautern) SAARLAND COM. DE MADRID CATALUNA (Barcelona) ILE DE France ( Paris) CHAMP.-ARDENNE (Reims) PICARDIE (Amiens) NORD - PAS-DE-CALAIS (Lille) LORRAINE ( Nancy ) ALSACE (Strasbourg) RHONE-ALPES (Lyon) PROVENCE-ALPES COTE D'AZUR ( Marseille) KOZEP-MAGYAR.(Budapest) LOMBARDIA (Milano) LAZIO (Roma) LUXEMBOURG (GRAND DUCHE) GRONINGEN REGION FRIESLAND (Leeuwarden) DRENTHE (Emmen) OVERIJSSEL ( Enschede) GELDERLAND (Nijmegen) FLEVOLAND (Lelystad) UTRECHT REGION NOORD-HOLLAND (Amsterdam) ZUID-HOLLAND (Rotterdam) ZEELAND (Terneuzen) NOORD-BRABANT (Eindhoven) LIMBURG -NL ( Venlo) MAZOWIECKIE (Warszawa) LISBOA VALE DO TEJO SYDSVERIGE (Malmö)/Öresund VASTSVERIGE (Göteborg) BRATISLAVSKY KRAJ WEST MIDLANDS (Birmingham) GREATER LONDON SW SCOTLAND (Glasgow)

Cushman & Wakefield 2009

Rent 2009 Score Rent 54 7.8 46 4.6 48 5.4 47 4.8 43 3.4 36 0.6 38 1.0 55 8.0 42 3.0 48 5.4 35 0.2 36 0.6 35 0.2 38 1.0 45 4.0 78 11.6 60 8.2 72 11.0 72 11.0 66 10.4 60 8.2 48 5.4 47 4.8 49 6.6 52 7.6 50 6.8 48 5.4 87 12.0 81 11.8 51 7.4 38 1.0 40 1.6 41 2.8 48 5.4 45 4.0 47 4.8 44 3.8 45 4.0 64 9.8 62 9.6 60 8.2 40 1.6 40 1.6 40 1.6 40 1.6 50 6.8 40 1.6 65 10.2 60 8.2 60 8.2 43 3.4 60 8.2 50 6.8 60 8.2 48 5.4 73 11.4 64 9.8 42 3.0 70 10.6 99 12.2 70 10.6


Comparison of prime locations for European logistics and distribution 2009

Cushman & Wakefield 2009

11


12

Comparison of prime locations for European logistics and distribution 2009

Overall Results In the total score of the matrix, all elements with teir respective score are brought together in one figure. It is not a measure for the direct local logistics activity but a measurement of the potential attractiveness of a region for the location ofan EDC, according to measurable macro-economic criteria.

The choice of the regions. Cushman & Wakefield have published the European Disitribution Report on a regular basis; this reports traditionally gave a ranking of countries in and around the so called “Blue Banana” area: the spine of European economic activity and logistics. Over the years a number of ‘Key European Hubs’ have been added to this report and into the regional data matrix of Cushman & Wakefield, more specifically with the development of Central Europe. Since 2006 Cushman & Wakefield has expanded its regional reports to 61 NUTS-2 regions, covering most of the ‘Key European Hubs’ of following map:

Source: Cushman & Wakefield, European Distribution Report 2006 & 2008

Cushman & Wakefield 2009


13

Comparison of prime locations for European logistics and distribution 2009

Ranking byNUTS-2 region. The full matrix by NUTS-2 region can be found in Attachment B. The summary of gives following table:

Ranking Nuts-2 regions 2009 Weight % LIEGE LIMBURG -B (Genk-Hasselt) HAINAUT (Charleroi) NORD - PAS-DE-CALAIS (Lille) NAMUR Luxembourg - B( Arlon) ALSACE (Strasbourg) OOST-VLAANDEREN (Gent) ANTWERPEN ARNSBERG KÖLN KOBLENZ …. MAZOWIECKIE (Warszawa) TIROL (Innsbruck) GREATER LONDON SW SCOTLAND (Glasgow) SYDSVERIGE (Malmö)/Öresund VASTSVERIGE (Göteborg) CATALUNA (Barcelona) COM. DE MADRID LISBOA VALE DO TEJO median score

Costs 21% 4.1 3.6 3.2 2.8 3.7 3.3 3.7 5.8 7.4 4.7 8.6 6.0

Transport system 29% 1.5 2.0 2.2 2.5 2.4 3.4 2.8 2.0 1.5 3.6 1.8 3.1

Accessibility 29% 1.0 1.2 1.7 3.0 2.0 1.5 2.1 2.0 2.0 1.5 0.7 1.0

Supply 9% 2.4 1.2 1.0 2.1 4.3 2.3 2.8 2.5 2.4 2.0 3.0 2.8

Labour 9% 2.6 2.9 2.1 2.5 2.1 3.6 3.8 3.2 2.0 4.0 3.4 4.5

4.7 9.0 12.1 8.8 9.0 8.4 10.4 10.7 6.8 6.7

5.2 4.6 2.6 4.1 4.8 5.5 3.9 4.8 4.9 3.3

8.2 5.1 3.9 7.1 6.8 7.3 7.8 10.0 12.0 3.0

1.8 5.0 6.0 3.5 4.0 4.3 4.6 3.8 3.5 2.8

4.5 4.4 7.6 4.5 4.3 4.1 3.2 2.6 5.0 3.9

Know-how SCORE 3% Total 2.5 2.1 1.7 2.1 3.3 2.2 3.8 2.7 3.5 2.7 4.0 2.7 3.8 2.9 2.0 2.9 1.0 3.0 3.3 3.1 2.5 3.1 3.0 3.2 6.0 4.3 2.0 3.0 3.0 3.0 4.0 5.0 5.5 3.0

Ranking 2009

5.6 5.7 5.7 5.9 6.1 6.3 6.4 7.3 7.3 4.1

1 2 3 4 5 6 7 8 9 10 11 12 53 54 55 56 57 58 59 60 61

As in previous reports, published in 2004 and 2006 by the Flanders Institute for Logistics, several Belgian provinces come in the top of this ranking. Liège comes out as the number 1 location, closely followed by Limburg (B), Hainaut, and Nord-Pas-de-Calais. The main reasons for this top-ranking are : -

excellent access to the main European markets ( the core West-German markets like the

Rurhgebiet, the in the Netherlands, the core Benelux markets like Randstad/Antwerp/Brussels , Paris/Ilede-France and the greater London area) -

a central geographic location that is optimal to cover a wide range of European markets

-

top transport infrastructure and volume, close to main ports or with good multimodal links to

these ports -

low costs for land, warehouses and labour

-

labour force that is available, highly productive, skilled for supply chain jobs and with a good

language knowledge The top-15 consists of regions from Belgium, Northern France (Nord-Pas-de-Calais and Alsace) and Western Germany ( Düsseldorf, Koblenz, Köln and Arnsberg). Regions from the Netherlands are, given their history as important logistics locations, often perceived as top regions for distribution; in this macro-economic ranking they score relatively average ( Limburg (NL)/ Venlo comes on 23rd place, NoordBrabant/Eindhoven 31st place, Zuid-Holland/ Rotterdam 37th place ). The main reasons for this relatively low score of the Dutch regions are : -

relatively high costs for land and warehouses, combined with a severe urban planning system

that makes it difficult to guarantee the required space for future logistics property development -

road congestion problems, especially in the Randstad areas

-

labourforce availability has proven to be relatively limited in periods of strong economic activity

The matrix used in this study is based upon the quantifiable variables that play a role in the decision to locate an EDC; the relative weight of the variables in the matrix is based upon surveys amongst decision makers, like the European Cities Monitor survey that Cushman & Wakefield publishes on a yearly basis.

Cushman & Wakefield 2009


14

Comparison of prime locations for European logistics and distribution 2009

Over the years these weights have shifted a little: the relative importance of Labour ( Available Labourforce / Labour productivity ) was given more weight ( 9%) than in the 2004 and 2006 studies, especially because available labourforce was a growing problem over 2007 and 2008. For the analysis of strengths and weaknesses of each region one can consult the matrix in Attachment B with all the detail of the each score of the domain sub-elements. In this matrix relative strengths and weaknesses are visually represented in green and red: - good scores have a green background ( if < than 50% of the median score ); - bad scores have a red background ( if > than 150% of the median score ); The total score and the 6 big â&#x20AC;&#x153;Domainsâ&#x20AC;? of this matrix , are also translated into thematical maps in Attachment H.

Cushman & Wakefield 2009


15

Comparison of prime locations for European logistics and distribution 2009

Ranking by NUTS-1 region. A NUTS-1 region is an aggregation of one or more NUTS-2 region into a higher geographical territory. The ranking by NUTS-1 region is calculated by means of the average of the scores of the underlying NUTS-2 regions. We only do this calculation for the core European logistics area : the Benelux, western German and northern French NUTS-1 regions. The full NUTS-1 ranking can be found in Attachment C with the same visual representation as for the NUTS-2 matrix ( strengths in green, weaknesses in red). The summary gives following result:

Costs Weight % WALLONIE (B) NORD - PAS-DE-CALAIS (F) VLAANDEREN (B) EST (F) BRUSSELS HOOFDST. GEWEST (B) NORDRHEIN-WESTFALEN (DL) SAARLAND (DL) RHEINLAND-PFALZ (DL) ILE DE France (F) BASSIN PARISIEN (F) ZUID-NEDERLAND (NL) OOST-NEDERLAND (NL) WEST-NEDERLAND (NL) LUXEMBOURG (GR. DUCHE) NOORD-NEDERLAND (NL)

21% 4.4 2.8 6.7 3.9 8.7 7.4 5.6 6.0 6.4 3.2 8.8 6.6 9.0 10.2 5.3

Transport system

Accessibility

Supply

Labour

Know-how

29% 2.4 2.5 1.8 3.2 1.8 2.7 3.5 3.5 1.9 4.3 2.3 3.3 2.2 3.4 4.5

29% 1.6 3.0 1.8 2.3 1.6 1.4 2.6 1.3 4.0 3.9 1.5 2.5 2.9 1.8 3.9

9% 2.5 2.1 2.2 2.6 3.6 2.4 1.5 2.3 3.3 2.7 2.3 2.5 3.1 3.2 2.2

9% 2.4 2.5 3.2 3.5 0.8 3.5 2.5 4.7 1.5 2.8 7.4 7.4 6.8 3.5 5.8

3% 3.3 3.8 1.6 3.8 2.3 2.9 3.3 3.2 2.8 4.0 2.0 2.8 2.1 2.5 2.7

SCORE

2.58 2.69 2.97 3.07 3.22 3.32 3.37 3.38 3.54 3.67 3.83 4.03 4.30 4.31 4.33

Ranking 2009

Ranking 2006

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2 3 1 4 5 6 8 9 10 12 7 11 13 15 14

The rise of Wallonia and Nord-Pas-de-Calais was already clear in the 2006 report published by the Flanders Instiute for Logistics, but is now translated into a number 1 and 2 position for Wallonia and rd Nord-Pas-de-Calais; this means that Flanders steps back to 3 position, reflecting the fact that some of

its former top-regions like Antwerp suffer of lack of space and road congestion. Densely populated top regions for logistics tend to become victim of their own success somehow. This is also the case for the Dutch regions who historically played a major role in European logistics and distribution. NUTS-1 region Zuid-Nederland, that consists of the NUTS-2 regions Noord-Brabant and Limburg-NL, drops further from th th the 7 place in 2006 to 11 place now in 2009; the major reason for this drop are the real estate costs that

have risen proportionally more than in surrounding regions. France has two NUTS-1 regions in the top-5: Nord-Pas-de-Calais and Est. Amongst the German “Länder” th has Nordrhein-Westfalen the best score and 6 place, closely followed by two other German “Länder”:

Saarland and Rheinland-Pfalz. In terms of local buying power, the German regions have the highest score of all regions studied.

Cushman & Wakefield 2009


16

Comparison of prime locations for European logistics and distribution 2009

A look at the future A way of analysing the strengths and weaknesses, is to look at the way factors which have been taken into account will evolve in the future, according to the prognosis we can establish today. The indicators mentioned here above have to be actualised on a regular basis. The indicators which are most likely to change, and which in consequence have to be carefully followed, are: •

Costs and offer in warehouse space and industrial land. The Flemish regions have some of the

best scores in these categories, but the limited provision in some territories can make these prices go up in a relatively short period of time. If the governmental instances do not free enough land to be put on the market, a reverse effect might be generated. •

The Transportation infrastructure and the accessibility to markets, and more specifically the

following aspects: -

road congestion

-

evolution in the transportation infrastructure and freight figures

-

accessibility to markets. Schürmann, Spiekermann and Wegener (University of Dortmund)

specialised in that area, and have built several scenarios on the subject (Trans-European Transport Networks and Regional Economic Development 2002, http://ww.raumplanung.unidortmund.de/rwp/ersa2002/cd-rom/papers/174.pdf) for the period 1996-2016 (see below, loss and competitive advantage in terms of accessibility to markets). -

The development of spending power, in Western Europe, but even more in the former countries

of the Eastern block. Since these present a relatively important population with a still limited spending power, one should keep an eye on them as well. Once the spending power and the transport infrastructure in these region will have developed, the gravity point of the European market will shift towards to East. Ports like Hamburg, which are closer to the Eastern European population concentrations (like Poland), could take advantage of this situation. The evolution of economies such as Portugal, and the pace at which it reached the European standards can teach us much on how the former countries of the Eastern block are likely to evolve. According to Experian forecasts (http://www.businessstrategies.co.uk/content.asp?ArticleID=603), one can expect that consumption per inhabitant in the Central European countries will reach Portugal’s current level in 2016. These countries will have then enjoyed the same evolution as Portugal did after its entry in the EU in 1986. The expected evolutions of data regarding population, employment and spending power are based on regional forecasts from Experian (http://www.business-strategies.co.uk/Content.asp?ArticleID=603). On this basis, and in combination with Schürmann, Spiekermann and Wegener’s scenarios regarding accessibility mentioned here above, matrixes were developed which give an idea of how ours will look like in 2020. Further on, in these forecasts, models developed by the EU ESPON “Transport services and networks” workgroup were widely taken into account. The following map gives an example of what the tonnages transported between the new EU countries and the EU-15 will look like in 2019:

Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

17

Source: ESPON (European Spatial Observation Network) Project 1.2.1., Transport services and networks: territorial trends and supply, Final report, September 2004, available electronically only at http://www.espon.eu/mmp/online/website/content/projects/259/652/file_2202/fr-1.2.2.-full.pdf

Cushman & Wakefield 2009


18

Comparison of prime locations for European logistics and distribution 2009

Of course, the increasing aging of the European population mentioned here above (in regions like Northern Italy, Northern Spain, Eastern Germany and Eastern Europe in general) has been taken into account. South-Eastern England will be relatively less impacted by the aging of population, as a consequence of its constant level of economic migration, which secures a younger population structure than in most of the other European countries for several years. Expected increase of population 2000-2030:

Source: ESPON (European Spatial Observation Network) Project 1.2.1., Transport services and networks: territorial trends and supply, Final report, September 2004, available electronically only at http://www.espon.eu/mmp/online/website/content/projects/259/652/file_2202/fr-1.2.2.-full.pdf

Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

19

Expected increase of population 200-2030:

Source: ESPON (European Spatial Observation Network) Project 1.2.1., Transport services and networks: territorial trends and supply, Final Report, September 2004, alleen electronisch verkrijgbaar via http://www.espon.eu/mmp/online/website/content/projects/259/652/file_2202/fr-1.2.1-full.pdf

Cushman & Wakefield 2009


20

Comparison of prime locations for European logistics and distribution 2009

In the forecast regarding the crucial element of “Available workforce”, the Experian forecasts on employment, and the latest current unemployment figures per NUTS-2 region published by Eurostat in early 2008 (2006 figures) were essentially taken into account. Unemployment figures per NUTS-2 region, 2006:

Source: Eurostat. It is also important to know that further prescriptions were used in these forecasts: •

Offer of industrial land. Historically, and for decades, Flanders has presented lower real estate

prices than most of the surrounding regions. The relatively wide offer in terms of land prescriptions is crucial for this matter. In Flanders, this subject is studied in the frame of the “Strategisch Plan Ruimtelijke Economie”. Results of the forecasts presented in this study show that a large portion of the future planning rounds will be devoted to the needs related to industrial land as assessed by the “Strategisch

Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

21

Plan Ruimtelijke Economie” for Flanders. A sufficient offer in terms of industrial land is necessary in order to control the evolution of rental and capital values. It is also important that the different players are given sufficient legal securities, also regarding permits and concessions. •

Labour costs. On term, the current big differences between labour costs will widely decrease,

under the influence of the EU development. Of course, differences will always remain between more and less urbanised regions. •

Transportation system. Important works on capacity and infrastructure will be led according to

the currently known scenarios, like TEN. This will be a priority in the process of removing the few flaws in the transportation system. •

The further development of inter-modality will be of prior importance as well in order to meet the

needs of the future transportation fluxes. It seems the currently planned concrete improvements regarding rail, air and ship transportation will be carried out within the foreseen timeframe.

Forecasted Matrix 2020 by NUTS-2 region. The full forecasted matrix 2020 can be found in Attachment D. The summary underneath shows Liège is rd forecasted to lose its number 1 position to Hainaut. Liège stays extremely well ranked on 3 place but the

limited availability of land give this region a slight disadvantage versus Hainaut who will be nr1 in our view. This reflects the growing importance of good transport infrastructure towards markets south of the actual core European logistics regions; the Seine-Nord Europe canal junction that will upgrade the inland waterway between the Paris region and the North of France and Belgium also increases the score of Nord-Pas-de-Calais and Hainaut. The gradual shift of the centre of gravity of the European markets towards central Europe will result in a rise of German regions like Köln and Düsseldorf; the good geographical position towards main German and central-European markets will keep Limburg (B) and Liège in a strong 2 and 3 position:

Cushman & Wakefield 2009


22

Comparison of prime locations for European logistics and distribution 2009

Summary:

Forecast 2020 NUTS-2 regions Weight % HAINAUT (Charleroi) LIMBURG -B (Genk-Hasselt) LIEGE NORD - PAS-DE-CALAIS (Lille) DÜSSELDORF KÖLN ALSACE (Strasbourg) ARNSBERG VLAAMS BRABANT (Vilvoorde) SAARLAND RHEINHESSEN-PFALZ (Kaiserslautern) ANTWERPEN NAMUR BRUSSELS CAP.REGION OOST-VLAANDEREN (Gent) BRABANT WALLON (Wavre) KOBLENZ PICARDIE (Péronne) Luxembourg - B( Arlon) LORRAINE ( Nancy ) WEST-VLAANDEREN LIMBURG -NL ( Venlo) TRIER MUNSTER OVERIJSSEL ( Enschede) ZEELAND (Terneuzen) HAMBURG ILE DE France ( Paris) FLEVOLAND (Lelystad) CHAMP.-ARDENNE (Reims) DRENTHE (Emmen) BERLIN GELDERLAND (Nijmegen) ZUID-HOLLAND (Rotterdam) NOORD-BRABANT (Eindhoven) PRAHA (Prague) DARMSTADT (Frankfurt) GRONINGEN REGION RHONE-ALPES (Lyon) PROVENCE-ALPES COTE D'AZUR ( Marseille) FRIESLAND (Leeuwarden) WIEN LUXEMBOURG (GRAND DUCHE) UTRECHT REGION NOORD-HOLLAND (Amsterdam) BRATISLAVSKY KRAJ WEST MIDLANDS (Birmingham) OBERBAYERN (München) MAZOWIECKIE (Warszawa) KOZEP-MAGYAR.(Budapest) SALZBURG TIROL (Innsbruck) LOMBARDIA (Milano) LAZIO (Roma) SYDSVERIGE (Malmö)/Öresund SW SCOTLAND (Glasgow) GREATER LONDON VASTSVERIGE (Göteborg) CATALUNA (Barcelona) LISBOA VALE DO TEJO COM. DE MADRID median score

Costs 19% 5.9 5.9 6.7 6.6 9.3 10.3 6.1 7.3 10.1 6.7 7.3 7.9 7.1 10.5 6.8 9.6 8.3 6.1 6.5 5.4 7.3 9.2 7.9 8.3 6.9 7.1 10.9 10.6 7.6 5.4 6.2 9.6 7.8 10.9 9.9 6.8 13.3 6.5 7.2 6.7 6.3 9.8 13.3 11.8 11.8 5.9 11.2 11.6 6.5 7.6 8.4 9.3 11.0 9.8 10.0 10.0 16.4 10.0 12.2 8.4 12.1 8.3

Transport system 27% 2.4 2.2 1.9 2.3 1.8 1.7 2.8 3.5 2.1 3.6 3.8 1.8 2.8 1.9 2.3 2.5 2.9 2.8 3.5 3.6 2.0 2.2 3.6 3.6 3.2 2.3 1.7 2.0 3.2 4.6 3.7 2.7 3.3 2.0 2.5 3.0 2.3 4.6 3.1 3.7 4.7 2.7 3.3 2.6 2.3 3.8 2.9 3.1 4.5 3.9 4.6 4.5 4.1 4.1 4.7 3.9 2.4 5.3 3.5 4.7 4.6 3.1

Accessibility 27% 1.8 1.7 1.2 2.5 0.9 0.8 2.1 1.4 1.5 2.1 1.7 2.4 2.1 1.7 2.5 1.6 0.9 3.2 1.6 2.9 2.9 1.4 1.4 2.3 2.4 3.1 4.2 3.1 3.3 3.0 3.7 4.4 2.3 2.8 2.1 4.6 1.7 3.8 5.8 6.3 4.3 4.9 1.9 2.7 3.4 7.1 4.0 4.7 7.9 6.5 4.9 4.8 4.1 5.6 6.8 7.2 4.1 7.3 7.4 11.8 9.7 3.0

Supply Labour 8% 15% 1.0 2.3 1.3 3.3 2.3 3.3 1.3 2.2 3.3 2.3 3.3 2.3 2.0 4.3 2.0 3.0 2.8 1.7 1.7 2.8 1.5 2.7 2.8 3.8 2.8 3.3 4.0 1.0 2.5 4.5 2.8 1.8 2.5 4.3 1.3 3.7 1.8 5.3 2.8 3.7 2.4 5.3 3.0 5.3 2.8 4.7 2.3 3.0 2.0 5.7 3.8 5.7 3.0 0.8 3.8 2.3 2.0 4.8 2.5 5.3 2.5 5.7 3.3 1.7 2.5 7.0 2.8 4.8 2.5 6.7 2.4 5.3 5.6 2.1 2.0 5.3 2.8 3.3 4.8 1.7 2.0 5.7 2.9 3.6 3.5 3.7 3.5 5.8 3.3 5.3 2.0 5.3 3.4 5.7 4.0 3.4 1.9 3.0 2.8 5.2 3.8 5.7 5.0 5.8 4.0 6.3 4.0 5.2 4.0 3.8 3.8 5.5 6.5 5.0 4.3 5.8 5.0 6.0 3.5 5.0 4.0 5.4 2.8 4.5

Forecasted Knowhow SCORE Ranking 2020 3% Totaal 1 3.0 2.8 1.7 2.9 2 2.5 2.9 3 4 3.5 3.1 5 2.5 3.2 6 2.5 3.3 7 3.5 3.4 8 3.3 3.5 9 1.8 3.5 10 3.3 3.5 3.3 3.5 11 1.0 3.5 12 13 3.5 3.5 14 2.3 3.5 15 2.0 3.5 16 3.3 3.5 17 3.0 3.6 18 4.0 3.6 19 3.5 3.7 3.8 3.7 20 1.1 3.8 21 2.0 3.9 22 23 3.3 3.9 24 3.3 3.9 25 2.8 4.0 26 2.3 4.1 27 2.3 4.1 28 2.8 4.2 29 2.8 4.2 4.0 4.2 30 2.8 4.4 31 32 3.0 4.4 33 2.8 4.4 34 2.0 4.4 35 2.0 4.4 36 2.8 4.4 37 2.8 4.5 38 2.5 4.6 3.3 4.6 39 3.8 4.7 40 41 2.8 4.8 42 3.8 4.8 43 2.5 4.9 44 2.0 4.9 45 2.0 5.0 46 4.3 5.2 47 2.5 5.2 2.8 5.3 48 4.5 5.4 49 4.5 5.4 50 51 4.3 5.5 52 4.3 5.7 53 4.0 5.8 54 5.0 5.8 55 3.0 6.0 56 3.0 6.2 57 1.8 6.3 3.0 6.7 58 3.8 6.8 59 60 5.0 7.3 61 4.8 7.5 3.0 4.4

Ranking 2009 3 2 1 4 14 11 7 10 25 18 19 9 5 15 8 13 12 20 6 16 17 23 21 27 26 24 41 22 30 29 33 42 35 37 31 28 34 38 32 39 40 47 36 44 43 45 55 51 53 46 50 54 49 52 58 61 56 48 59 57 60

One of the major shifts in the 2020 forecasted matrix versus the actual situation is that most European markets will better accessible from a larger area. If we compare the Accessibility indixes of 2016, developed by Schürmann, Spiekermann en Wegener (Trans–European Transport Networks and Regional Economic Development, 2002, see above) with the actual ones, it strikes that several rather peripheral locations like Acquitaine/France or Schleswig-Holstein Northern Germany have accessibility scores that rank above the European average. This is due to the gradual development of the transport infrastructure towards regions that are remotely located versus the core EU; the EU supports this development very

Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

23

actively. The map ‘Accessibilty by road & rail in 2016’, following an average devopment scenario (TEN Scenario 10) will look as follows according to Schürmann, Spiekermann en Wegener in 2016: Accessibilty by road & rail in 2016:

Source: Trans –European Transport Networks and Regional Economic Development, 2002, http://www.raumplanung.uni-dortmund.de/rwp/ersa2002/cd-rom/papers/174.pdf The map above indeed shows a much less concentrated picture than than the actual Accessibility maps. ( see ESPON,TRANSPORT SERVICES AND NETWORKS: TERRITORIAL TRENDS AND BASIC SUPPLY OF INFRASTRUCTURE FOR TERRITORIAL COHESION (2002-04), Third Interim Report, http://www.espon.lu/online/documentation/projects/thematic/1197/3.ir-1.2.1.pdf, pagina 166 and following)

Cushman & Wakefield 2009


24

Comparison of prime locations for European logistics and distribution 2009

Regions like the Ruhrgebiet, North-East Belgium and the Southern part of the Netherlands will in part lose the competitive advantage of their good accessibility to major markets. On the other hand this means that this enlarged accessibility gives even better and quicker access to larger markets from the central locations, which might lead to more and bigger central EDCâ&#x20AC;&#x2122;s and proportionally less regional distribution centres.

Forecasted Ranking by NUTS-1 region. Like for the 2009 matrix we calculate the NUTS-1 2020 forecasted ranking by means of the average scores of the underlying NUTS-2 regions. The full detail of the NUTS-1 2020 forecasted ranking can be found in Attachment E. The averages for Wallonia and Flanders are slightly lower than the score of Nord-Pas-de-Calais. Nordrhein-Westfalen would rise from 6th to 4th place by 2020 because of its more and more central location and real estate prices that are expected to stay relatively stable. Northern French regions like Bassin Parisien , Picardie and to a lesser extent, Champagne-Ardenne are also expected to develop gradually as mature logistics locations. Summary: Weight % NORD - PAS-DE-CALAIS (F) WALLONIE (B) VLAANDEREN (B) NORDRHEIN-WESTFALEN (DL) SAARLAND (DL) BRUSSELS HOOFDST. GEWEST (B) EST (F) RHEINLAND-PFALZ (DL) BASSIN PARISIEN (F) ILE DE France (F) ZUID-NEDERLAND (NL) OOST-NEDERLAND (NL) NOORD-NEDERLAND (NL) WEST-NEDERLAND (NL) LUXEMBOURG (GR. DUCHE)

Costs 19% 6.6 7.0 7.6 8.8 6.7 10.5 5.8 7.5 5.7 10.6 9.6 7.6 6.3 10.5 13.3

Cushman & Wakefield 2009

Transport system 27% 2.3 2.6 2.1 2.6 3.6 1.9 3.2 3.4 3.7 2.0 2.4 3.2 4.3 2.3 3.3

Accessibility 27% 2.5 1.6 2.2 1.3 2.1 1.7 2.5 1.3 3.1 3.1 1.8 2.6 3.9 3.0 1.9

Supply 8% 1.3 2.1 2.3 2.7 1.7 4.0 2.4 2.3 2.1 3.8 2.8 2.2 2.2 3.3 3.5

Labour 15% 2.2 3.2 3.7 2.7 2.8 1.0 4.0 3.9 4.5 2.3 6.0 5.8 5.6 5.4 3.7

Know-how 3% 3.5 3.2 1.5 2.9 3.3 2.3 3.6 3.2 4.0 2.8 2.0 2.8 2.7 2.1 2.5

SCORE

Ranking 2020

Ranking 2009

Ranking 2006

3.09 3.26 3.41 3.46 3.48 3.52 3.56 3.60 3.93 4.16 4.16 4.19 4.57 4.61 4.87

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2 1 3 6 7 5 4 8 10 9 11 12 15 13 14

3 2 1 6 8 5 4 10 12 9 7 11 14 13 15


Comparison of prime locations for European logistics and distribution 2009

25

Attachments Attachment A: Glossary Ranked Matrix A table where geographical regions are measured in a few domains built under a certain amount of elements (columns). Each cell in the table is concordant with a geographical region and a certain element. The score is assigned according to the C&W methodology, depending on size and value. The total of the scores for a region allows a ranking of these regions. The Ranked Matrix is an excellent instrument when it comes to analysing the strengths and weaknesses of a region. Domain A category of macro-economic data which are used in the Ranked Matrix. Costs, Transportation system, Accessibility, Supply, Labour, Know-How. It gathers all the subjects which concern the sectors of logistics and distribution, and which can be quantified. Yet there are other interesting subjects which simply cannot be quantified, like the quantity and fluidity of permit deliverance, and other aspects of governmental administration (like tolls), taxation, rulings… Matrix-elements Specific macro-economic factors allowing the quantification of a specific domain. The absolute value of a region for a matrix-element is assigned according the C&W methodology. This allows the study of several regions, and their comparison with other EU regions following an identical methodology. In this study, 19 matrix-elements were retained, grouped under 6 different domains. Weight In the Ranked Matrix, weights which are used in order to assign a certain degree of importance to specific domains and matrix-elements. The total of weight percentages of all the matrix-elements of a domain is 100%, and allows the calculation of a subtotal of the scores per domain. The total weight percentages of all the domains is 100%, and allows the calculation of a total score per region. The corrected weights in this study are based on the experience of C&W in the field of location studies for logistics and distribution. Sensitivity An indication of the relation between the absolute data for a matrix-element, and the adapted score. This sensitivity gives the degree to which the absolute value must vary before the score does. The sensitivity is only rendered when the relation between absolute data and the score is sufficiently linear, and when the indicator is made of only one component. This sensitivity is also important when it comes to assess the stability of a specific ranking, for instance by observing how much the value of a region must drop before this region goes down in the Ranked Matrix. NUTS Means “Nomenclature d’Unités Territoriales Statistiques” carried out by Eurostat. This allows the definition of comparable regions across the borders of the EU countries. NUTS levels retained in this

Cushman & Wakefield 2009


26

Comparison of prime locations for European logistics and distribution 2009

study are: NUTS-1 (region), NUTS-2 (province). The NUTS-0 (country) and NUTS-3 (arrondissement) levels were not retained. The following site maps the different NUTS regions: http://ec.Europa.eu/comm/eurostat/ramon/NUTS/maps_searchpage_en.cfm).

Cushman & Wakefield 2009


Attachment B: Matrix Ranking by NUTS-2 region.

-27-


28

Costs Weight Weight %

LIEGE BE33 LIMBURG -B (Genk-Hasselt) BE22 HAINAUT (Charleroi) BE32 FR30 NORD - PAS-DE-CALAIS (Lille) NAMUR BE35 Luxembourg - B( Arlon) BE34 FR42 ALSACE (Strasbourg) OOST-VLAANDEREN (Gent) BE23 ANTWERPEN BE21 ARNSBERG DEA5 KÖLN DEA2 KOBLENZ DEB1 BRABANT WALLON (Wavre) BE31 DÜSSELDORF DEA1 BRUSSELS CAP.REGION BE10 FR41 LORRAINE ( Nancy ) WEST-VLAANDEREN BE25 DEC0 SAARLAND DEB3 RHEINHESSEN-PFALZ (Kaiserslautern) FR22 PICARDIE (Amiens) DEB2 TRIER FR10 ILE DE France ( Paris) NL42 LIMBURG -NL ( Venlo) NL34 ZEELAND (Terneuzen) VLAAMS BRABANT (Vilvoorde) BE24 NL21 OVERIJSSEL ( Enschede) MUNSTER DEA3 FR21 CHAMP.-ARDENNE (Reims) CZ01 PRAHA (Prague) NL23 FLEVOLAND (Lelystad) NL41 NOORD-BRABANT (Eindhoven) FR71 RHONE-ALPES (Lyon) NL13 DRENTHE (Emmen) DARMSTADT (Frankfurt) DE71 NL22 GELDERLAND (Nijmegen) LU00 LUXEMBOURG (GRAND DUCHE) NL33 ZUID-HOLLAND (Rotterdam) NL11 GRONINGEN REGION FR82 PROVENCE-ALPES COTE D'AZUR ( Marseille) NL12 FRIESLAND (Leeuwarden) HAMBURG DE6 BERLIN DE3 NL32 NOORD-HOLLAND (Amsterdam) NL31 UTRECHT REGION UK73 WEST MIDLANDS (Birmingham) HU01 KOZEP-MAGYAR.(Budapest) AT13 WIEN SK01 BRATISLAVSKY KRAJ IT2 LOMBARDIA (Milano) AT32 SALZBURG OBERBAYERN (München) DE21 IT6 LAZIO (Roma) PL07 MAZOWIECKIE (Warszawa) AT33 TIROL (Innsbruck) UKI1&2 GREATER LONDON UKM3 SW SCOTLAND (Glasgow) E04+Cop.D SYDSVERIGE (Malmö)/Öresund SE05 VASTSVERIGE (Göteborg) ES51 CATALUNA (Barcelona) ES3 COM. DE MADRID PT13 LISBOA VALE DO TEJO median score

Cushman & Wakefield 2009

Supply

SCO R

E

Rank ing 2 009

wledge tal Subto

cation

age kn o

ics E du

Labour

Langu

tal Subto

Logist

Labo ur pro ducti vity

Availa ble L abourf orce

Subto tal

Supp ly

,000

Accessibility

Land

Availa ble U nits > sq.m 10

U Co re

entral Eur tal Subto

Acces s to C

g Pow er

tal

Transport system

Acces s to E

Buyin

Shipp ing F reigh t

Subto

Air F

reigh t

reigh t Rail F

Freig ht Road

Rail D ensity

Conge st Road

Road

Densi ty

tal Subto

Labo ur Co sts

Price s Land

Rents

(€)

ion

ope

NUTS2 REGION (Major city)

NUTS code

Comparison of prime locations for European logistics and distribution 2009

Know-how

3.0 38%

3.0 38%

2.0 25%

3.5 21%

4.0 27%

1.0 7%

1.0 7%

3.0 20%

2.0 13%

1.0 7%

3.0 20%

5.0 29%

1.5 25%

3.5 58%

1.0 17%

5.0 29%

1.0 50%

1.0 50%

1.5 9%

2.0 67%

1.0 33%

1.5 9%

1.0 50%

1.0 50%

0.5 3%

0.6 0.6 0.2 2.8 1.0 0.2 4.0 1.0 3.4 4.8 8.2 6.6 5.4 10.4 4.8 5.4 3.0 5.4 6.8 1.6 7.6 7.4 6.8 3.4 8.0 1.6 5.4 1.0 4.0 1.6 8.2 4.8 1.6 11.0 6.8 8.2 8.2 1.6 3.8 1.6 11.0 8.2 8.2 10.2 10.6 4.0 7.8 3.0 9.8 4.6 11.6 9.6 8.2 5.4 12.2 10.6 11.4 9.8 11.8 12.0 5.4 5.4

2.5 1.6 1.0 0.2 1.0 0.6 1.6 6.4 7.6 2.8 7.8 3.8 6.4 8.4 9.8 1.0 7.0 4.8 4.0 2.8 2.0 4.6 8.0 2.4 9.4 6.8 6.0 2.8 2.8 6.2 8.6 0.8 5.8 9.8 7.4 10.4 9.8 5.2 0.4 4.8 9.0 7.2 10.6 11.4 8.8 5.2 11.2 2.8 9.6 10.6 11.0 8.2 2.2 12.2 11.8 5.2 4.0 4.0 11.6 12.0 9.0 6.2

11.8 11.0 11.0 6.5 11.8 11.8 6.5 12.0 13.0 7.5 10.5 8.5 13.5 10.5 13.0 6.5 11.5 7.0 7.8 6.8 9.5 7.5 11.5 11.0 13.5 11.0 9.5 6.5 5.5 11.5 11.5 6.8 11.0 11.0 11.5 13.0 12.5 11.0 6.8 11.0 10.5 10.5 12.5 12.0 12.0 3.5 14.0 3.8 11.0 9.5 10.5 10.5 3.0 9.5 12.5 11.5 13.0 13.0 6.5 6.8 5.7 11.0

4.1 3.6 3.2 2.8 3.7 3.3 3.7 5.8 7.4 4.7 8.6 6.0 7.8 9.7 8.7 4.0 6.6 5.6 6.0 3.4 6.0 6.4 8.4 4.9 9.9 5.9 6.7 3.1 3.9 5.8 9.2 3.8 5.5 10.6 8.2 10.2 9.9 5.3 3.3 5.2 10.1 8.4 10.2 11.1 10.3 4.3 10.6 3.1 10.0 8.1 11.1 9.3 4.7 9.0 12.1 8.8 9.0 8.4 10.4 10.7 6.8 6.7

1.0 1.0 1.2 2.2 1.2 1.5 3.0 1.2 1.0 3.0 1.0 3.0 1.0 0.5 0.5 3.0 1.0 1.5 2.5 5.0 3.0 0.5 2.5 2.5 1.0 5.0 3.0 6.0 4.0 5.0 3.0 4.0 5.0 1.5 4.5 3.0 2.5 7.0 6.0 5.0 1.0 2.5 2.0 3.0 2.0 6.5 1.5 7.0 6.0 3.5 2.5 5.0 7.0 3.0 3.0 3.5 8.0 8.5 4.0 3.5 6.5 3.0

2.0 2.0 2.0 4.0 2.0 2.0 2.5 2.0 6.0 2.0 5.5 3.0 4.0 6.5 7.0 3.0 2.0 3.0 3.0 3.0 2.0 9.5 2.5 3.0 6.5 2.5 2.0 1.0 2.5 2.0 3.0 5.0 2.5 6.0 3.0 3.5 6.0 2.0 4.0 2.5 6.5 4.5 7.0 5.5 4.5 2.5 4.5 1.5 5.5 2.5 6.5 5.0 3.0 3.0 11.0 2.5 2.0 2.0 5.5 4.0 4.0 3.0

0.8 2.0 1.0 1.0 1.5 2.0 1.7 1.0 0.8 4.0 2.0 3.0 1.0 2.0 1.0 2.0 2.5 3.0 3.0 3.0 4.0 1.0 1.9 3.0 0.9 4.0 4.0 3.2 3.0 3.0 2.1 2.0 4.0 1.5 3.0 3.0 1.6 4.0 4.0 6.0 2.0 1.5 2.0 2.0 2.0 3.5 2.0 5.9 3.0 4.0 3.5 3.5 3.9 4.0 1.3 5.5 5.0 4.0 5.5 3.8 6.0 3.0

1.5 2.0 2.0 2.5 3.0 3.9 3.0 1.9 1.0 3.0 1.5 3.0 1.5 1.5 1.5 3.0 1.8 4.0 4.0 2.9 4.0 1.0 1.4 2.0 1.5 2.0 3.0 3.9 4.4 2.0 1.4 2.5 3.0 1.5 2.0 3.0 0.9 4.0 3.9 5.0 1.5 2.5 1.5 1.5 2.5 4.6 3.5 6.0 2.0 4.5 2.5 2.5 4.4 4.5 1.5 4.3 4.5 5.5 3.5 4.5 4.2 2.5

1.5 2.0 2.0 1.8 3.0 3.0 2.0 1.9 1.0 4.0 1.0 3.0 2.0 1.0 2.0 3.0 2.0 3.0 5.0 4.0 5.0 1.5 1.4 2.0 2.0 2.5 4.0 4.0 3.8 3.0 1.4 3.0 4.0 2.5 2.5 3.0 1.0 4.0 4.0 5.0 1.5 3.0 2.0 2.0 2.0 4.8 4.0 5.5 3.0 5.0 3.0 4.5 4.2 5.0 1.4 5.3 3.5 5.5 4.8 5.0 5.8 3.0

2.0 3.5 3.8 4.5 4.0 3.0 4.0 3.0 3.0 4.0 3.0 4.0 3.0 2.0 1.8 6.0 4.5 4.0 4.0 3.0 4.0 1.0 3.0 2.0 2.0 5.0 4.0 5.0 3.8 3.0 3.0 3.0 5.0 0.5 4.0 3.0 2.0 6.0 3.0 6.0 3.0 3.0 1.3 2.0 3.0 3.5 2.7 5.0 2.5 5.0 2.5 3.0 3.5 5.0 1.0 4.5 3.5 5.5 3.5 3.0 4.0 3.0

2.0 3.0 4.0 2.5 3.0 7.0 3.0 3.0 1.0 5.0 2.0 3.0 4.0 2.0 2.0 5.0 1.5 6.0 5.0 4.0 4.0 3.0 2.5 2.0 2.0 3.0 5.0 6.0 4.0 3.0 3.0 3.5 3.0 4.0 3.0 5.0 1.0 4.0 2.0 4.0 1.0 3.5 2.0 3.0 5.0 4.0 3.5 7.0 5.5 7.0 4.5 5.0 6.0 7.0 2.0 4.0 3.0 3.0 2.5 8.0 3.0 3.0

1.5 2.0 2.2 2.5 2.4 3.4 2.8 2.0 1.5 3.6 1.8 3.1 2.2 1.7 1.8 3.5 1.8 3.5 3.8 3.8 3.7 1.9 2.1 2.3 1.9 3.4 3.6 4.7 3.9 3.3 2.4 3.3 3.8 2.4 3.2 3.4 1.8 4.8 4.0 4.8 1.7 2.9 2.2 2.6 2.9 4.7 2.9 6.0 4.2 4.7 3.3 4.2 5.2 4.6 2.6 4.1 4.8 5.5 3.9 4.8 4.9 3.3

1.2 1.8 2.2 5.8 3.4 1.4 5.4 3.2 4.4 3.0 0.2 0.8 1.6 0.4 2.8 7.2 5.0 4.2 3.8 7.4 2.4 8.2 0.6 6.0 2.0 4.8 7.8 7.0 10.6 5.6 3.6 9.6 6.6 1.0 2.6 4.0 5.2 6.8 10.0 7.6 8.8 9.4 6.2 4.6 6.4 11.6 10.2 10.4 8.4 9.2 8.6 9.8 12.2 9.0 8.0 11.4 11.0 11.8 11.2 10.8 12.0 6.2

0.4 0.5 1.0 1.6 1.0 1.0 0.5 1.0 0.6 0.5 0.3 0.5 1.0 0.3 0.5 0.5 1.2 1.8 0.5 2.0 0.5 2.0 0.3 1.5 0.5 1.0 0.5 3.0 3.5 2.0 1.0 4.5 2.5 1.8 1.5 0.5 1.5 2.5 5.0 3.0 3.2 3.5 2.0 1.5 2.5 6.5 4.5 8.0 2.7 4.5 4.0 4.7 8.5 4.5 2.0 5.0 6.0 6.5 6.5 10.0 12.0 1.8

3.0 3.0 3.5 3.8 3.5 3.3 2.8 3.6 3.5 2.5 2.8 2.8 3.5 2.8 3.5 3.0 3.8 2.8 2.8 4.3 3.0 4.5 3.5 3.8 3.5 3.0 2.8 3.5 1.0 3.8 3.5 4.5 3.0 2.5 3.3 3.0 3.5 3.5 5.0 3.5 2.0 1.5 3.8 3.3 5.0 1.0 1.2 1.0 2.9 1.4 1.7 3.5 1.0 1.5 4.5 8.0 3.0 3.5 7.0 9.0 12.0 3.3

1.0 1.2 1.7 3.0 2.0 1.5 2.1 2.0 2.0 1.5 0.7 1.0 1.6 0.7 1.6 2.6 2.6 2.6 1.7 3.7 1.4 4.0 0.9 3.0 1.4 2.3 2.7 4.1 4.9 3.2 2.1 5.8 3.6 1.7 2.1 1.8 2.8 3.7 6.3 4.2 4.4 4.6 3.3 2.6 3.9 6.9 5.4 7.4 4.2 5.2 4.8 5.8 8.2 5.1 3.9 7.1 6.8 7.3 7.8 10.0 12.0 3.0

1.7 1.5 1.0 3.0 6.0 2.5 3.5 2.2 2.2 2.0 3.0 2.5 2.1 3.0 3.2 3.0 1.9 1.5 1.5 2.0 2.5 2.2 1.8 3.0 2.1 2.0 2.5 3.5 2.0 3.0 1.5 2.0 4.0 4.8 2.0 3.0 1.0 3.0 3.5 3.0 2.5 3.3 2.0 2.0 3.0 1.5 2.5 2.0 3.0 3.5 3.5 3.0 1.8 5.0 5.0 3.0 3.5 4.0 4.2 3.5 3.0 2.5

3.0 0.8 0.9 1.2 2.5 2.0 2.0 2.7 2.5 2.0 3.0 3.0 3.0 2.0 3.9 2.0 2.4 1.5 1.5 3.1 3.0 4.3 3.0 3.0 3.2 2.5 2.0 2.0 2.0 2.5 3.0 2.8 1.0 5.0 3.0 3.4 5.0 1.2 4.8 1.1 2.7 3.0 5.0 4.0 3.0 2.5 3.0 1.5 4.0 4.0 3.4 4.5 1.8 5.0 7.0 4.0 4.5 4.5 5.0 4.0 4.0 3.0

2.4 1.2 1.0 2.1 4.3 2.3 2.8 2.5 2.4 2.0 3.0 2.8 2.6 2.5 3.6 2.5 2.2 1.5 1.5 2.6 2.8 3.3 2.4 3.0 2.7 2.3 2.3 2.8 2.0 2.8 2.3 2.4 2.5 4.9 2.5 3.2 3.0 2.1 4.2 2.1 2.6 3.2 3.5 3.0 3.0 2.0 2.8 1.8 3.5 3.8 3.5 3.8 1.8 5.0 6.0 3.5 4.0 4.3 4.6 3.8 3.5 2.8

1.4 2.0 0.5 0.6 0.8 1.8 3.8 3.0 2.4 4.5 4.8 5.6 1.5 3.7 0.9 2.2 4.9 2.5 6.1 0.9 5.7 2.0 6.5 6.1 4.8 6.6 4.2 1.5 1.6 7.5 7.0 4.1 4.8 5.8 7.5 5.2 7.1 3.8 1.1 3.1 4.3 1.4 6.8 7.9 4.2 1.1 3.7 0.9 4.1 4.8 6.8 1.3 0.8 4.9 8.6 3.3 2.4 2.3 0.4 0.4 1.8 3.7

4.9 4.6 5.3 6.3 4.7 7.1 3.8 3.5 1.2 2.9 0.5 2.4 1.5 0.7 0.6 5.1 4.0 2.5 1.4 5.8 3.3 0.4 9.3 10.0 0.8 9.5 3.1 6.4 4.2 5.4 8.3 4.3 10.5 0.2 8.8 0.1 6.8 8.2 3.6 9.7 0.3 3.3 4.3 4.1 6.0 9.4 0.8 12.0 5.2 3.0 0.3 6.1 12.0 3.4 5.5 7.0 8.2 7.8 8.7 6.9 11.5 4.7

2.6 2.9 2.1 2.5 2.1 3.6 3.8 3.2 2.0 4.0 3.4 4.5 1.5 2.7 0.8 3.2 4.6 2.5 4.5 2.5 4.9 1.5 7.4 7.4 3.5 7.6 3.8 3.1 2.5 6.8 7.4 4.2 6.7 3.9 7.9 3.5 7.0 5.3 1.9 5.3 3.0 2.0 6.0 6.6 4.8 3.9 2.7 4.6 4.5 4.2 4.6 2.9 4.5 4.4 7.6 4.5 4.3 4.1 3.2 2.6 5.0 3.9

2.5 2.5 3.0 2.5 3.0 4.0 2.5 3.0 1.0 3.0 1.5 2.5 3.5 1.5 1.5 2.5 1.2 3.0 3.0 3.0 3.0 1.5 1.5 2.0 3.5 3.0 3.0 3.0 4.0 3.0 1.5 2.0 3.0 2.0 3.0 3.0 1.5 2.5 3.0 3.0 1.0 2.5 1.5 1.5 2.0 5.0 3.0 5.0 3.0 3.5 2.0 4.0 5.0 3.5 1.5 3.0 3.5 3.5 3.0 4.0 5.0 3.0

2.5 0.9 3.5 5.0 4.0 4.0 5.0 1.0 1.0 3.5 3.5 3.5 3.0 3.5 3.0 5.0 0.9 3.5 3.5 5.0 3.5 4.0 2.5 2.5 1.0 2.5 3.5 5.0 3.0 2.5 2.5 5.0 2.5 3.5 2.5 2.0 2.5 2.5 5.0 2.5 3.5 3.5 2.5 2.5 3.0 6.0 4.5 6.0 5.0 5.0 3.5 6.0 7.0 5.0 2.5 3.0 2.5 2.5 5.0 6.0 6.0 3.5

2.5 1.7 3.3 3.8 3.5 4.0 3.8 2.0 1.0 3.3 2.5 3.0 3.3 2.5 2.3 3.8 1.1 3.3 3.3 4.0 3.3 2.8 2.0 2.3 2.3 2.8 3.3 4.0 3.5 2.8 2.0 3.5 2.8 2.8 2.8 2.5 2.0 2.5 4.0 2.8 2.3 3.0 2.0 2.0 2.5 5.5 3.8 5.5 4.0 4.3 2.8 5.0 6.0 4.3 2.0 3.0 3.0 3.0 4.0 5.0 5.5 3.0

Total

2.10 2.10 2.19 2.69 2.73 2.75 2.90 2.90 2.96 3.08 3.13 3.15 3.16 3.22 3.22 3.24 3.28 3.37 3.48 3.48 3.51 3.54 3.54 3.55 3.60 3.84 3.85 3.86 3.87 4.02 4.12 4.14 4.22 4.23 4.24 4.31 4.32 4.33 4.36 4.44 4.44 4.48 4.61 4.71 4.88 4.98 5.23 5.32 5.35 5.38 5.45 5.58 5.63 5.65 5.66 5.91 6.08 6.32 6.36 7.28 7.28 4.12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61


know ledge

Logis tics E ducati on

y Labo ur pro ducti vit

Avail able L abou rforce

Avail able U nits > sq.m

10,00 0

Centr al Eu rope

EU C ore

5.0 29% 1.6

1.0 50% 2.7

1.0 50% 2.3

1.5 9% 2.5

2.0 67% 1.2

1.0 33% 4.7

1.5 9% 2.4

1.0 50% 3.2

1.0 50% 3.4

0.5 3% 3.3

2.58

1

NORD - PAS-DE-CALAIS (F)

2.8

0.2

6.5

2.8

2.2

4.0

1.0

2.5

1.8

4.5

2.5

2.5

5.8

1.6

3.8

3.0

3.0

1.2

2.1

0.6

6.3

2.5

2.5

5.0

3.8

2.69

2

Labour

Rank ing 2

SCO RE

Subto tal

Buyin

Rail F

Road

Rail D

Road

Subto tal

Labo ur Co s

Price s Land

(€) Rents

Land

Supply

009

1.0 17% 3.4

Lang uage

3.5 58% 0.9

Accessibility

Subto tal

1.5 25% 2.0

Subto tal

5.0 29% 2.4

Supp ly

3.0 20% 4.0

Subto tal

1.0 7% 3.2

Acce ss to

2.0 13% 2.3

Transport system

Acce ss to

3.0 20% 2.4

g Pow er

1.0 7% 1.3

Subto tal

Shipp ing F reigh t

1.0 7% 2.4

reigh t

4.0 27% 1.2

Costs Weight Weight %

Freig ht

3.5 21% 4.4

ensity

2.0 25% 12.0

Road

3.0 38% 2.3

Dens ity

WALLONIE (B)

3.0 38% 1.5

ts

Air F reight

Cong estion

NUTS1 REGION 2009

Attachment C: Matrix Ranking by NUTS-1 region.

Know-how Totaal

VLAANDEREN (B)

3.2

6.4

12.2

6.7

1.0

3.7

1.4

1.6

1.8

3.2

2.1

1.8

3.3

0.8

3.5

1.8

2.0

2.3

2.2

3.4

2.8

3.2

2.2

1.0

1.6

2.97

3

EST (F)

4.7

1.3

6.5

3.9

3.0

2.8

1.9

3.0

2.5

5.0

4.0

3.2

6.3

0.5

2.9

2.3

3.3

2.0

2.6

3.0

4.4

3.5

2.5

5.0

3.8

3.07

4

BRUSSELS HOOFDST. GEWEST (B)

4.8

9.8

13.0

8.7

0.5

7.0

1.0

1.5

2.0

1.8

2.0

1.8

2.8

0.5

3.5

1.6

3.2

3.9

3.6

0.9

0.6

0.8

1.5

3.0

2.3

3.22

5

NORDRHEIN-WESTFALEN (DL)

7.2

6.3

9.5

7.4

1.9

4.0

3.0

2.3

2.5

3.3

3.5

2.7

2.9

0.4

2.7

1.4

2.6

2.3

2.4

4.3

1.8

3.5

2.3

3.5

2.9

3.32

6

SAARLAND (DL)

5.4

4.8

7.0

5.6

1.5

3.0

3.0

4.0

3.0

4.0

6.0

3.5

4.2

1.8

2.8

2.6

1.5

1.5

1.5

2.5

2.5

2.5

3.0

3.5

3.3

3.37

7 8

RHEINLAND-PFALZ (DL)

7.0

3.3

8.6

6.0

2.8

2.7

3.3

3.7

4.3

4.0

4.0

3.5

2.3

0.5

2.8

1.3

2.2

2.5

2.3

5.8

2.4

4.7

2.8

3.5

3.2

3.38

ILE DE France (F)

7.4

4.6

7.5

6.4

0.5

9.5

1.0

1.0

1.5

1.0

3.0

1.9

8.2

2.0

4.5

4.0

2.2

4.3

3.3

2.0

0.4

1.5

1.5

4.0

2.8

3.54

9

BASSIN PARISIEN (F)

1.3

2.8

6.7

3.2

5.5

2.0

3.1

3.4

4.0

4.0

5.0

4.3

7.2

2.5

3.9

3.9

2.8

2.6

2.7

1.2

6.1

2.8

3.0

5.0

4.0

3.67

10

ZUID-NEDERLAND (NL)

7.5

8.3

11.5

8.8

2.8

2.8

2.0

1.4

1.4

3.0

2.8

2.3

2.1

0.6

3.5

1.5

1.7

3.0

2.3

6.8

8.8

7.4

1.5

2.5

2.0

3.83

11

OOST-NEDERLAND (NL)

3.3

6.8

11.3

6.6

4.8

2.5

3.3

2.0

2.7

4.0

3.0

3.3

4.3

1.5

3.3

2.5

2.3

2.7

2.5

7.2

7.9

7.4

3.0

2.5

2.8

4.03

12 13

WEST-NEDERLAND (NL)

7.5

8.6

12.0

9.0

2.5

5.4

2.2

1.5

1.8

1.8

2.0

2.2

5.5

1.6

3.6

2.9

2.0

4.3

3.1

7.0

6.3

6.8

1.6

2.5

2.1

4.30

LUXEMBOURG (GR. DUCHE)

8.2

10.4

13.0

10.2

3.0

3.5

3.0

3.0

3.0

3.0

5.0

3.4

4.0

0.5

3.0

1.8

3.0

3.4

3.2

5.2

0.1

3.5

3.0

2.0

2.5

4.31

14

NOORD-NEDERLAND (NL)

1.6 4.8

5.3 5.3

11.0 11.0

5.3

5.7 2.5

2.3 3.0

4.7 2.2

4.0 2.4

4.3 2.5

5.7 3.3

3.7 3.5

4.5

7.0 4.2

2.7 0.9

3.3 3.4

3.9

3.3 2.6

1.1 2.5

2.2

3.9 3.4

9.5 4.4

5.8

2.8 2.5

2.5 3.4

2.7

4.33

15

2.8

3.38

mediaan

-29-

6.4

2.7

2.3

2.5

3.5


30

Comparison of prime locations for European logistics and distribution 2009

Attachment D: Forecasted Matrix NUTS-2 regions 2020.

Cushman & Wakefield 2009


BE32 HAINAUT (Charleroi) BE22 LIMBURG -B (Genk-Hasselt) BE33 LIEGE FR30 NORD - PAS-DE-CALAIS (Lille) DEA1 DÜSSELDORF DEA2 KÖLN ALSACE (Strasbourg) FR42 DEA5 ARNSBERG BE24 VLAAMS BRABANT (Vilvoorde) SAARLAND DEC0 RHEINHESSEN-PFALZ (Kaiserslautern) DEB3 BE21 ANTWERPEN BE35 NAMUR BE10 BRUSSELS CAP.REGION BE23 OOST-VLAANDEREN (Gent) BE31 BRABANT WALLON (Wavre) DEB1 KOBLENZ FR22 PICARDIE (Péronne) BE34 Luxembourg - B( Arlon) LORRAINE ( Nancy ) FR41 BE25 WEST-VLAANDEREN LIMBURG -NL ( Venlo) NL42 TRIER DEB2 DEA3 MUNSTER OVERIJSSEL ( Enschede) NL21 ZEELAND (Terneuzen) NL34 DE6 HAMBURG ILE DE France ( Paris) FR10 FLEVOLAND (Lelystad) NL23 CHAMP.-ARDENNE (Reims) FR21 NL13 DRENTHE (Emmen) DE3 BERLIN GELDERLAND (Nijmegen) NL22 ZUID-HOLLAND (Rotterdam) NL33 NOORD-BRABANT (Eindhoven) NL41 PRAHA (Prague) CZ01 DE71 DARMSTADT (Frankfurt) GRONINGEN REGION NL11 RHONE-ALPES (Lyon) FR71 FR82 PROVENCE-ALPES COTE D'AZUR ( Marsei FRIESLAND (Leeuwarden) NL12 WIEN AT13 LUXEMBOURG (GRAND DUCHE) LU00 UTRECHT REGION NL31 NOORD-HOLLAND (Amsterdam) NL32 BRATISLAVSKY KRAJ SK01 WEST MIDLANDS (Birmingham) UK73 DE21 OBERBAYERN (München) MAZOWIECKIE (Warszawa) PL07 KOZEP-MAGYAR.(Budapest) HU01 SALZBURG AT32 TIROL (Innsbruck) AT33 LOMBARDIA (Milano) IT2 LAZIO (Roma) IT6 SYDSVERIGE (Malmö)/Öresund E04+Cop.D SW SCOTLAND (Glasgow) UKM3 GREATER LONDON UKI1&2 VASTSVERIGE (Göteborg) SE05 CATALUNA (Barcelona) ES51 LISBOA VALE DO TEJO PT13 COM. DE MADRID ES3 median score

Cushman & Wakefield 2009

Transport system

Accessibility

Rank ing 2 009

nkin g Fore ca

SCO RE

sted Ra

ge know led tal Subto

tics E

Labour

Lang uage

Logis

Subto

tal

ducati on

ity

2020

Supply

Labo ur pro ductiv

tal

Availa ble L abourf orce

Subto

al Eu

EU C ore

Centr tal Subto

Acces s to

Acces s to

tal

g Pow er Buyin

Subto

Rail F

Air F reigh t

reigh t

ht Freig

Shipp ing F reight

rope

Costs Weight Weight %

Road

ensity

ion Rail D

Conge st Road

Densi ty Road

Subto

tal

Labo ur Co sts

Price s

REGION 2020 FORECAST

Land

Rents

NUTS code

Availa ble U nits > sq.m 10,00 0 Land Supp ly

31

Comparison of prime locations for European logistics and distribution 2009

Know-how

3.0 38%

3.0 38%

2.0 25%

3.5 19%

4.0 27%

1.0 7%

1.0 7%

3.0 20%

2.0 13%

1.0 7%

3.0 20%

5.0 27%

1.5 25%

3.5 58%

1.0 17%

5.0 27%

1.0 50%

1.0 50%

1.5 8%

2.0 67%

1.0 33%

2.8 15%

1.0 50%

1.0 50%

0.5 3%

2.0 2.0 2.5 4.0 8.0 9.0 3.5 6.5 8.0 6.5 7.0 4.0 3.5 8.0 3.2 7.0 7.0 3.5 3.0 3.0 3.5 8.5 7.5 7.0 3.5 4.0 11.0 9.5 4.5 3.0 2.5 9.0 5.0 10.0 9.0 5.5 11.5 2.5 5.0 4.5 2.0 7.0 12.0 11.0 11.0 5.0 12.0 11.5 6.0 8.0 5.0 5.5 10.0 10.0 11.0 12.0 15.0 11.0 12.5 7.3 11.5 7.0

7.0 7.0 8.0 7.5 10.0 11.0 7.5 7.0 11.0 6.0 7.0 9.0 8.0 12.0 7.5 10.5 9.0 7.0 7.0 6.0 8.5 9.0 7.0 9.0 8.0 8.0 11.0 12.0 8.0 6.0 7.0 9.5 8.5 11.0 10.0 7.5 17.0 7.5 8.5 7.0 7.5 12.5 14.0 12.5 12.5 7.0 9.9 12.5 7.0 8.0 11.0 13.0 12.0 9.0 6.7 7.0 19.0 6.7 15.0 11.0 16.0 8.5

10.0 10.0 11.0 9.0 10.0 11.0 8.0 9.0 12.0 8.0 8.0 12.0 11.0 12.0 11.0 12.0 9.0 8.5 11.0 8.0 11.0 10.5 10.0 9.0 10.5 10.5 10.5 10.0 11.5 8.0 10.5 10.5 11.0 12.0 11.0 7.5 10.5 11.0 8.5 9.5 11.0 10.0 14.0 12.0 12.0 5.5 12.0 10.5 6.5 6.5 9.5 9.5 11.0 10.5 13.5 11.5 14.5 13.5 7.5 6.2 7.0 10.5

5.9 5.9 6.7 6.6 9.3 10.3 6.1 7.3 10.1 6.7 7.3 7.9 7.1 10.5 6.8 9.6 8.3 6.1 6.5 5.4 7.3 9.2 7.9 8.3 6.9 7.1 10.9 10.6 7.6 5.4 6.2 9.6 7.8 10.9 9.9 6.8 13.3 6.5 7.2 6.7 6.3 9.8 13.3 11.8 11.8 5.9 11.2 11.6 6.5 7.6 8.4 9.3 11.0 9.8 10.0 10.0 16.4 10.0 12.2 8.4 12.1 8.3

2.0 2.0 2.0 2.5 1.0 1.0 3.0 3.0 1.5 2.0 2.5 1.9 2.0 1.0 2.0 1.5 3.0 3.0 2.0 3.0 2.0 2.5 2.5 3.0 4.0 2.5 1.0 1.0 4.5 4.8 4.5 2.1 4.5 2.5 3.0 2.8 1.5 6.0 3.6 5.5 4.5 1.5 2.5 3.0 2.0 4.5 2.0 2.3 5.0 5.5 3.5 3.0 5.8 4.5 8.0 3.5 2.5 8.5 3.5 6.0 3.0 2.8

2.5 2.5 3.0 4.0 8.0 7.0 3.0 2.0 8.0 3.0 3.5 8.0 2.5 8.0 2.5 6.5 4.0 3.0 3.0 4.5 2.5 4.0 3.0 3.0 3.0 3.5 8.0 11.0 2.5 1.5 3.0 6.0 4.0 8.0 3.5 3.5 7.5 2.5 6.5 5.0 3.0 5.0 5.5 6.0 9.0 3.0 5.0 6.8 4.5 3.4 3.0 3.5 6.5 6.0 2.0 2.5 12.5 2.0 6.5 4.8 5.5 4.0

1.0 1.5 1.0 1.0 1.5 1.0 2.0 3.0 1.0 3.0 2.5 1.0 1.5 1.0 1.5 1.0 2.5 2.5 2.0 1.5 2.0 1.5 4.0 4.0 4.0 3.0 1.5 1.0 3.0 4.0 4.0 1.5 3.0 1.5 2.5 2.5 1.3 4.0 1.5 3.0 6.0 2.0 2.5 2.0 1.5 4.0 2.0 3.0 3.5 3.0 3.5 3.5 2.5 3.5 5.0 4.5 1.0 4.0 5.0 5.5 4.0 2.5

2.0 2.0 1.5 2.0 1.0 1.0 2.5 3.0 1.5 4.0 4.0 1.0 3.0 1.5 2.0 1.5 2.5 2.0 3.0 3.0 1.8 1.5 3.5 2.5 2.0 2.0 1.3 1.0 2.0 5.0 3.0 2.0 2.0 1.0 1.5 3.0 1.2 4.0 1.9 3.0 5.0 2.9 3.0 1.5 1.5 3.5 2.2 2.0 3.7 3.2 4.3 4.2 1.8 2.4 4.2 4.2 1.2 5.0 3.1 4.0 3.8 2.2

2.0 1.5 1.5 1.5 1.0 1.0 2.0 4.0 1.5 2.5 5.0 1.0 3.0 1.5 2.0 2.0 2.0 3.5 3.0 3.0 2.0 1.5 5.0 4.0 2.5 2.0 1.3 1.2 3.0 3.5 4.0 2.8 2.5 1.0 1.5 3.0 2.4 4.0 2.8 3.6 5.0 3.3 2.5 2.0 2.0 3.2 2.0 2.8 3.7 3.4 4.7 4.7 2.8 4.3 3.5 5.2 1.3 5.3 4.0 6.0 5.0 2.8

3.0 3.5 2.5 4.0 2.0 2.0 4.0 4.0 2.0 5.0 4.0 3.0 4.0 2.0 3.0 3.0 4.0 3.0 3.0 6.0 4.0 3.0 4.0 4.0 5.0 2.0 2.8 1.0 3.0 5.0 5.0 2.8 4.0 2.0 3.0 2.6 0.5 6.0 2.7 2.7 6.0 2.7 3.0 2.0 1.5 4.0 2.8 2.5 3.0 3.1 5.0 5.0 2.5 3.0 3.5 4.5 0.8 5.5 3.2 3.6 2.8 3.0

4.0 3.0 2.0 2.3 2.0 2.0 3.0 5.0 2.5 6.0 5.0 1.0 3.5 2.0 3.0 4.0 3.0 3.0 7.0 5.0 1.5 2.5 4.0 5.0 3.0 2.0 1.0 2.5 3.0 6.0 3.0 3.4 3.0 1.0 3.0 3.5 3.5 4.0 3.3 2.0 4.0 3.0 5.0 3.0 2.0 3.6 5.0 4.5 6.0 3.5 7.0 7.0 5.5 5.0 2.8 3.5 2.0 3.0 2.3 3.0 8.0 3.0

2.4 2.2 1.9 2.3 1.8 1.7 2.8 3.5 2.1 3.6 3.8 1.8 2.8 1.9 2.3 2.5 2.9 2.8 3.5 3.6 2.0 2.2 3.6 3.6 3.2 2.3 1.7 2.0 3.2 4.6 3.7 2.7 3.3 2.0 2.5 3.0 2.3 4.6 3.1 3.7 4.7 2.7 3.3 2.6 2.3 3.8 2.9 3.1 4.5 3.9 4.6 4.5 4.1 4.1 4.7 3.9 2.4 5.3 3.5 4.7 4.6 3.1

2.6 2.5 1.5 6.2 0.4 0.2 5.4 3.0 2.5 4.2 3.8 4.9 3.6 3.1 3.9 1.9 0.8 7.6 1.7 7.3 5.6 1.1 2.6 6.4 5.1 6.4 8.1 8.4 6.0 7.3 6.8 8.5 3.5 5.5 3.6 9.9 0.8 7.2 9.5 10.0 7.8 8.5 4.3 5.0 6.5 9.0 6.8 8.2 11.0 10.3 8.0 7.6 8.3 9.2 11.0 11.8 8.6 11.8 9.9 11.0 9.5 6.4

1.0 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.0 0.5 1.0 1.0 0.5 1.5 1.0 0.5 1.0 1.0 1.0 1.5 1.0 0.5 0.5 1.0 1.5 3.2 0.5 2.0 1.0 2.5 3.5 1.5 1.5 1.0 3.5 1.8 2.5 4.5 5.0 3.0 4.5 0.5 1.5 2.0 8.0 2.5 4.0 8.5 6.5 4.5 4.5 2.7 4.7 6.0 5.0 2.0 6.5 6.5 12.0 10.0 1.5

3.5 3.0 3.0 3.8 2.8 2.5 2.8 2.0 3.5 2.5 2.5 3.5 3.5 3.5 3.6 3.5 2.5 4.3 3.3 3.0 3.8 3.5 3.0 2.5 3.0 3.8 1.8 4.5 3.8 3.5 3.0 1.2 3.3 3.5 3.5 0.5 2.4 3.5 4.5 5.0 3.5 0.8 3.0 3.3 3.8 1.0 5.0 1.7 1.0 0.5 1.4 1.5 2.8 3.5 3.0 8.0 4.5 3.5 7.0 12.0 9.0 3.3

1.8 1.7 1.2 2.5 0.9 0.8 2.1 1.4 1.5 2.1 1.7 2.4 2.1 1.7 2.5 1.6 0.9 3.2 1.6 2.9 2.9 1.4 1.4 2.3 2.4 3.1 4.2 3.1 3.3 3.0 3.7 4.4 2.3 2.8 2.1 4.6 1.7 3.8 5.8 6.3 4.3 4.9 1.9 2.7 3.4 7.1 4.0 4.7 7.9 6.5 4.9 4.8 4.1 5.6 6.8 7.2 4.1 7.3 7.4 11.8 9.7 3.0

1.0 1.5 2.0 1.0 3.0 3.0 2.0 2.0 2.0 1.5 1.5 2.0 3.0 3.0 2.0 2.0 2.0 1.5 2.5 3.6 1.8 2.0 2.5 2.5 2.0 4.5 2.5 2.0 3.0 3.0 4.0 3.3 2.0 1.0 1.5 2.0 5.2 3.0 2.0 3.5 3.0 2.5 3.0 2.0 2.0 2.0 3.0 3.5 2.0 2.0 3.5 5.0 3.0 3.0 3.5 3.5 5.0 4.0 4.5 3.0 3.5 2.5

1.0 1.0 2.5 1.5 3.5 3.5 2.0 2.0 3.5 1.8 1.5 3.5 2.5 5.0 3.0 3.5 3.0 1.0 1.0 2.0 3.0 4.0 3.0 2.0 2.0 3.0 3.5 5.5 1.0 2.0 1.0 3.2 3.0 4.5 3.5 2.7 6.0 1.0 3.5 6.0 1.0 3.3 4.0 5.0 4.5 1.9 3.7 4.4 1.8 3.5 4.0 5.0 5.0 5.0 4.5 4.0 8.0 4.5 5.5 4.0 4.5 3.5

1.0 1.3 2.3 1.3 3.3 3.3 2.0 2.0 2.8 1.7 1.5 2.8 2.8 4.0 2.5 2.8 2.5 1.3 1.8 2.8 2.4 3.0 2.8 2.3 2.0 3.8 3.0 3.8 2.0 2.5 2.5 3.3 2.5 2.8 2.5 2.4 5.6 2.0 2.8 4.8 2.0 2.9 3.5 3.5 3.3 2.0 3.4 4.0 1.9 2.8 3.8 5.0 4.0 4.0 4.0 3.8 6.5 4.3 5.0 3.5 4.0 2.8

1.0 3.0 3.0 1.0 3.0 3.0 5.0 3.0 2.0 3.0 3.0 5.0 3.0 1.0 5.0 2.0 5.0 3.0 5.0 3.0 6.0 5.0 5.0 3.0 5.0 5.0 1.0 3.0 5.0 5.0 5.0 1.0 7.0 5.0 7.0 6.0 3.0 5.0 3.0 1.0 5.0 5.0 5.0 7.0 7.0 4.0 6.0 5.0 0.5 4.0 7.0 7.0 7.0 5.0 2.0 5.0 5.0 5.0 5.0 3.0 5.0 5.0

5.0 4.0 4.0 4.5 1.0 1.0 3.0 3.0 1.0 2.5 2.0 1.5 4.0 1.0 3.5 1.5 3.0 5.0 6.0 5.0 4.0 6.0 4.0 3.0 7.0 7.0 0.3 1.0 4.3 6.0 7.0 3.0 7.0 4.5 6.0 3.8 0.2 6.0 4.0 3.2 7.0 0.8 1.0 3.5 1.9 8.0 5.0 0.3 8.0 7.5 3.0 3.4 5.0 5.5 7.5 6.5 5.0 7.5 7.9 9.0 6.2 4.0

2.3 3.3 3.3 2.2 2.3 2.3 4.3 3.0 1.7 2.8 2.7 3.8 3.3 1.0 4.5 1.8 4.3 3.7 5.3 3.7 5.3 5.3 4.7 3.0 5.7 5.7 0.8 2.3 4.8 5.3 5.7 1.7 7.0 4.8 6.7 5.3 2.1 5.3 3.3 1.7 5.7 3.6 3.7 5.8 5.3 5.3 5.7 3.4 3.0 5.2 5.7 5.8 6.3 5.2 3.8 5.5 5.0 5.8 6.0 5.0 5.4 4.5

2.5 2.5 2.5 2.0 1.5 1.5 2.0 3.0 2.5 3.0 3.0 1.0 3.0 1.5 3.0 3.5 2.5 3.0 3.0 2.5 1.2 1.5 3.0 3.0 3.0 2.0 1.0 1.5 3.0 3.0 3.0 2.5 3.0 1.5 1.5 3.0 2.0 2.5 2.0 3.0 3.0 3.0 3.0 1.5 1.5 4.0 2.0 2.0 4.0 4.0 3.5 3.5 3.0 4.0 3.5 3.0 1.5 3.5 3.0 5.0 4.0 3.0

3.5 0.8 2.5 5.0 3.5 3.5 5.0 3.5 1.0 3.5 3.5 1.0 4.0 3.0 1.0 3.0 3.5 5.0 4.0 5.0 0.9 2.5 3.5 3.5 2.5 2.5 3.5 4.0 2.5 5.0 2.5 3.5 2.5 2.5 2.5 2.5 3.5 2.5 4.5 4.5 2.5 4.5 2.0 2.5 2.5 4.5 3.0 3.5 5.0 5.0 5.0 5.0 5.0 6.0 2.5 3.0 2.0 2.5 4.5 5.0 5.5 3.5

3.0 1.7 2.5 3.5 2.5 2.5 3.5 3.3 1.8 3.3 3.3 1.0 3.5 2.3 2.0 3.3 3.0 4.0 3.5 3.8 1.1 2.0 3.3 3.3 2.8 2.3 2.3 2.8 2.8 4.0 2.8 3.0 2.8 2.0 2.0 2.8 2.8 2.5 3.3 3.8 2.8 3.8 2.5 2.0 2.0 4.3 2.5 2.8 4.5 4.5 4.3 4.3 4.0 5.0 3.0 3.0 1.8 3.0 3.8 5.0 4.8 3.0

Totaal

2.81 2.86 2.87 3.09 3.18 3.31 3.42 3.45 3.46 3.48 3.50 3.50 3.50 3.52 3.53 3.55 3.56 3.58 3.67 3.70 3.77 3.88 3.91 3.92 3.95 4.07 4.12 4.16 4.18 4.25 4.35 4.36 4.36 4.41 4.42 4.45 4.47 4.59 4.61 4.74 4.76 4.83 4.87 4.94 4.95 5.18 5.24 5.27 5.36 5.42 5.47 5.72 5.77 5.77 6.04 6.19 6.25 6.70 6.75 7.30 7.51 4.35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

3 2 1 4 14 11 7 10 25 18 19 9 5 15 8 13 12 20 6 16 17 23 21 27 26 24 41 22 30 29 33 42 35 37 31 28 34 38 32 39 40 47 36 44 43 45 55 51 53 46 50 54 49 52 58 61 56 48 59 57 60


Costs Weight Weight % NORD - PAS-DE-CALAIS (F)

3.0 38% 4.0

3.0 38% 7.5

2.0 25% 9.0

3.5 19% 6.6

4.0 27% 2.5

1.0 7% 4.0

1.0 7% 1.0

1.0 7% 4.0

3.0 20% 2.3

5.0 27% 2.3

1.5 25% 6.2

5.0 27% 2.5

1.0 50% 1.0

Supply 1.0 50% 1.5

2.0 67% 1.0

Labour 1.0 33% 4.5

2.8 15% 2.2

1.0 50% 2.0

Know-how 1.0 0.5 50% 3% 5.0 3.5

Rank ing 2 009

SCO R

E

Rank ing 2 020

wledge e kno

Subto tal

ducati on Logist ics E

1.5 8% 1.3

Langu ag

ctivit y produ

Subto tal

Labo ur

Availa ble L abou rforce

Subto tal

Land Supp ly

Availa ble U nits > sq.m 10,00 0

entral Eu

Accessibility 3.5 1.0 58% 17% 0.5 3.8

Subto tal

Acces s to C

Power

2.0 13% 1.5

Acces s to E U Co re

Buyin g

Subto tal

Shipp ing F re

ight

Transport system 3.0 20% 2.0

Air F

Rail F re

reigh t

ight

ight Road Fre

Rail D ensity

ngest ion Road Co

ensity Road D

Subto tal

Labo u

Land P

NUTS1 2020 FORECAST

Rents

rices

r Cost s

rope

Attachment E: Forecasted Matrix NUTS-1 regio’ s 2020.

Total 3.09

1

2

WALLONIE (B)

3.2

8.1

11.0

7.0

1.9

3.5

1.3

2.4

2.3

3.1

4.0

2.6

2.3

0.9

3.4

1.6

2.1

2.1

2.1

2.8

4.1

3.2

2.9

3.4

3.2

3.26

2

1

VLAANDEREN (B)

4.1

8.6

11.2

7.6

1.9

4.7

1.4

1.7

1.6

3.1

2.1

2.1

3.9

1.1

3.5

2.2

1.8

2.8

2.3

4.2

2.8

3.7

2.0

0.9

1.5

3.41

3

3

NORDRHEIN-WESTFALEN (DL)

7.6

9.3

9.8

8.8

2.0

5.0

2.4

1.9

2.5

3.0

3.5

2.6

2.5

0.5

2.4

1.3

2.6

2.8

2.7

3.0

2.0

2.7

2.3

3.5

2.9

3.46

4

6

SAARLAND (DL)

6.5

6.0

8.0

6.7

2.0

3.0

3.0

4.0

2.5

5.0

6.0

BRUSSELS HOOFDST. GEWEST (B)

8.0

12.0

12.0

10.5

1.0

8.0

1.0

1.5

1.5

2.0

2.0

1.9

3.1

0.5

3.5

1.7

3.0

5.0

4.0

1.0

1.0

1.0

1.5

3.0

2.3

3.52

6

5

EST (F)

3.3

6.8

8.0

5.8

3.0

3.8

1.8

2.8

2.5

5.0

4.0

3.2

6.4

0.8

2.9

2.5

2.8

2.0

2.4

4.0

4.0

4.0

2.3

5.0

3.6

3.56

7

4

7.7

9.0

3.5

3.6

4.2

1.0

2.5

2.1

1.5

1.8

1.7

3.0

2.5

2.8

3.0

3.5

3.3

3.48

5

7

RHEINLAND-PFALZ (DL)

6.3

7.5

2.7

3.0

3.3

4.0

4.0

4.0

3.4

2.4

0.5

2.7

1.3

2.0

2.5

2.3

4.3

3.0

3.9

2.8

3.5

3.2

3.60

8

8

BASSIN PARISIEN (F)

3.3

6.5

8.3

5.7

3.9

2.3

3.3

3.5

3.5

4.0

4.5

3.7

7.5

1.0

3.9

3.1

2.8

1.5

2.1

4.0

5.5

4.5

3.0

5.0

4.0

3.93

9

10

ILE DE France (F)

9.5

12.0

10.0

10.6

1.0

11.0

1.0

1.0

1.2

1.0

2.5

2.0

8.4

0.5

4.5

3.1

2.0

5.5

3.8

3.0

1.0

2.3

1.5

4.0

2.8

4.16

10

9

ZUID-NEDERLAND (NL)

9.0

9.5

10.8

9.6

2.8

3.8

2.0

1.5

1.5

3.0

2.8

2.4

2.4

1.0

3.5

1.8

1.8

3.8

2.8

6.0

6.0

6.0

1.5

2.5

2.0

4.16

11

11

OOST-NEDERLAND (NL)

4.7

8.2

11.0

7.6

4.3

3.2

3.3

2.0

2.7

4.0

3.0

3.2

4.9

1.5

3.3

2.6

2.3

2.0

2.2

5.7

6.1

5.8

3.0

2.5

2.8

4.19

12

12

NOORD-NEDERLAND (NL)

2.3

7.3

10.8

6.3

5.0

2.8

4.7

4.0

4.3

5.7

3.7

4.3

7.3

2.7

3.3

3.9

3.3

1.0

2.2

5.0

6.7

5.6

2.8

2.5

2.7

4.57

13

15

WEST-NEDERLAND (NL)

9.3

11.0

11.6

10.5

2.5

6.6

2.0

1.5

1.8

1.9

2.0

2.3

5.9

1.6

3.6

3.0

2.4

4.3

3.3

6.0

4.2

5.4

1.6

2.5

2.1

4.61

14

13

LUXEMBOURG (GR. DUCHE)

12.0 6.3

14.0 8.2

14.0 10.8

13.3

2.5 2.5

5.5 3.8

2.5 2.0

3.0 2.0

2.5 2.5

3.0 3.1

5.0 3.5

3.3

4.3 4.3

0.5 0.9

3.0 3.4

1.9

3.0 2.3

4.0 2.5

3.5

5.0 4.0

1.0 4.0

3.7

3.0 2.3

2.0 3.4

2.5

4.87

15

14

2.8

3.60

mediaan

-32-

7.6

2.6

2.2

2.3

3.7


Comparison of prime locations for European logistics and distribution 2009

33

Attachment F: Explanation of the matrix-elementen.

Domain (weight in matrix) Costs (21%)

Matrixelement (weight in domain) Rents (38%) Land prices (38%) Labour costs (25%)

Transport System (29%)

Road density (29%)

Indicator

Sensitivity

Source material

Rents for logistical warehouses (current norms, 10.000 sq.m.) ; weighted average of the transactions of the last 3 years Prices for industrial land (well equipped and well located); weighted of the transactions of the last 3 years Salary costs per employee. Corrections for outliers, strong rural territories, different method of calculation per country, differences between countries in terms of salaries in the transportation sector. Proximity of highway network and 4-line roads

+5 €/sq.m./year → +1 point in Ranked Matrix score

C&W research

+30 €/sq.m. → +1 point in Ranked Matrix score

C&W research

+2600 €/year/employee → +1 point in Ranked Matrix score (this is indicative, and non linear).

Countries: International Labour Organisation and www.ggdc.net Regions: Eurostat Region database + Prices and Earnings, 2009 Edition, UBS

+20 km highway or 4-line roads/1000 sq.km. → -0.2 point in Ranked Matrix score (linear relation, yet exponential relation at the extremities). + 30 minutes extra congestion on average/day → +1 point in Ranked Matrix score

Eurostat Region database Michelin roadmaps

Road congestion (7%)

Average length of congestion on the most jammed locations in each region.

Rail density (7%)

Proximity of rail networks (amount of rail line per 1000 sq.km. and per 1000 inhabitants). Volumes transported + amount of trips from/to the region + average time-distance to transport terminals + accessibility to European markets by road.

Road freight (20%)

Cushman & Wakefield 2009

Transport & Mobility Leuven for Belgian regions ; Indicatorenboek Duurzaam Goederenvervoer Vlaanderen, 2008; C&W research for all the other regions Eurostat Region database Eurostat Region database Connectivity to transport terminals (ICON 2001) “Accessibility index via road” of S&W, published by ESPON


34

Comparison of prime locations for European logistics and distribution 2009

Rail freight (13%)

Airfreight (7%) Shipping freight (20%)

Cushman & Wakefield 2009

Volumes transported + amount of trips from/to the region + average time-distance to transport terminals + accessibility to European markets by rail. Volumes transported + average timedistance to cargo airports + accessibility to European markets by air. Volumes transported through large seaports (amount of containers when available) + statistics on inland navigation (like IWT)

Eurostat Region database Connectivity to transport terminals (ICON 2001) “Accessibility index via road” of S&W, published by ESPON Eurostat Region database Cargo data on airports (C&W) “Accessibility index via road” of S&W, published by ESPON Eurostat Region database Statistics seaports and inland navigation “Access-time to seaports” (ESPON)


Comparison of prime locations for European logistics and distribution 2009

Domain (weight in matrix) Accessibili ty (29%)

Supply (9%)

Labour (9%)

KnowHow (3%)

35

Matrixelement (weight in domain) Spending power (25%) Access to EU core markets (58%) Access to Eastern Europe (17%) New buildings >10000 sq.m. (50%)

Indicator

Sensitivity

Source material

Spending power within a 180 minute drive time (in million €) Accessibility to the EU-27 countries (+Norway + Swiss), on basis of a gravity model (population and spending power). Time-distance to important population concentrations in Eastern Europe

+ 100 million € → -1 point in Ranked Matrix score +20 points in this index → -0.5 point in Ranked Matrix score

C&W calculation through use of GIS system (CACI), and GfK data. “Accessibility index multimodal/by road” 2001 of S&W, published by ESPON

+45 min → +0.5 point in Ranked Matrix score

European road network; time-distance calculated with GIS software (Navteq)

Supply in new logistical warehouse locations (> 10,000m²)

C&W research

Land supply (50%)

Supply of land for logistical purposes + available provision

1 = immediate availability. 2 = potential availability on the short term, etc., till 5 = no offer. 1 = immediate availability. 2 = potential availability on the short term, etc., till 5 = no offer.

Available workforce (67%)

General unemployment figures + unemployment of the younger people (<24)+percentage of younger people (as an indicator of future entry of new workforce on the labour market) Added-value per employee in the services sector

Labour productivity (33%) Logistics education (50%)

Cushman & Wakefield 2009

Quantification of the Logistics education and trainings by level and amount of attendances.

C&W research + SPRE (Strat. Plan Ruimtelijke Economie, Vlaand..) + ETIN advisers (www.werklocaties.nl) Eurostat Region database

+1000 € added-value per employee in the services sector → -0.3 point in Ranked Matrix score +1000 points logistics education → -0.2 point in Ranked Matrix score

Eurostat Region database FIL intern list of logistics education, local internet lists of trainings.


36

Comparison of prime locations for European logistics and distribution 2009

Language knowledge (50%)

Cushman & Wakefield 2009

Knowledge of important European languages. English : Test of English as a Foreign Language (TOEFL). Other languages: estimates per region and country.

+10 on the TOEFL CBT Total Mean score â&#x2020;&#x2019; -1 point in Ranked Matrix score

TOEFL-test data; data on knowledge of French and German per country, corrected with regional presence of migrants.


Attachment G: Calculation table Buying Power in the 3-hour drivetime perimeter Following table gives for each region the population, the buying power and the ranking score applied in the matrix. The GIS-system that was used for this is CACI.

-37-


Rank Spending NUTS 3 hour drivetime Total Spending Spending code round Population Power ( milj. EUR) Power PowerScore REGION KÖLN DEA2 KÖLN 67,445,618 1,241,549 1 0.2 DÜSSELDORF DEA1 DÜSSELDORF 67,148,318 1,233,210 2 0.4 0.6 NL42 LIMBURG (NL) Venlo 66,685,919 1,218,905 3 KOBLENZ DEB1 KOBLENZ 62,006,170 1,157,073 4 0.8 DARMSTADT (Frankfurt) DE71 Frankfurt 58,504,078 1,104,560 5 1.0 LIEGE 1.2 BE33 LIEGE 60,579,596 1,104,147 6 LUXEMBOURG (B) BE34 Neufchateau 60,334,292 1,101,403 7 1.4 BRABANT WALLON BE31 Wavre 59,391,812 1,100,764 8 1.6 LIMBURG (B) 1.8 BE22 Genk 60,163,600 1,097,316 9 VLAAMS BRABANT BE24 Vilvoorde 58,464,183 1,079,513 10 2.0 HAINAUT BE32 Charleroi 57,155,906 1,069,111 11 2.2 2.4 DEB2 TRIER TRIER 55,643,932 1,039,368 12 NL22 GELDERLAND Arnhem 56,463,183 1,033,985 13 2.6 BRUSSELS CAP.REGION BE10 BRUSSELS 55,527,678 1,032,979 14 2.8 ARNSBERG 3.0 DEA5 ARNSBERG 55,852,544 1,030,959 15 OOST-VLAANDEREN BE23 Gent 54,637,681 1,022,314 16 3.2 NAMUR BE35 NAMUR 54,081,941 972,950 17 3.4 3.6 NL41 NOORD-BRABANT Tilburg 53,558,138 963,806 18 DEB3 RHEINHESSEN-PFALZ Kaiserslautern 50,297,168 959,584 19 3.8 51,156,601 956,560 20 4.0 LU00 LUXEMBOURG (GRAND DUCHE) LUXEMBOURG 4.2 DEC0 SAARLAND Saarbrücken 48,907,543 935,748 21 ANTWERPEN BE21 ANTWERPEN 51,101,230 915,684 22 4.4 NL31 UTRECHT REGION UTRECHT 50,158,342 902,925 23 4.6 4.8 NL21 OVERIJSSEL Zwolle 48,019,330 874,629 24 WEST-VLAANDEREN BE25 Brugge 46,526,800 869,150 25 5.0 NL33 ZUID-HOLLAND (Rotterdam) Rotterdam 46,872,025 840,368 26 5.2 5.4 FR42 ALSACE Strasbourg 41,058,601 835,666 27 Lelystad NL23 FLEVOLAND 45,667,554 828,519 28 5.6 FR30 NORD - PAS-DE-CALAIS Lille 44,178,566 819,588 29 5.8 6.0 NL34 ZEELAND Middelburg 44,973,126 810,100 30 NL32 NOORD-HOLLAND (Amsterdam) Amsterdam 44,137,075 800,160 31 6.2 UK73 WEST MIDLANDS (Birmingham) Birmingham 41,367,737 791,780 32 6.4 6.6 NL13 DRENTHE Assen 41,836,241 770,515 33 NL11 GRONINGEN REGION GRONINGEN 41,331,543 763,479 34 6.8 FR21 CHAMP.-ARDENNE Reims 38,674,817 717,913 35 7.0 7.2 FR41 LORRAINE Nancy 34,447,083 680,672 36 FR22 PICARDIE Amiens 33,638,737 627,041 37 7.4 NL12 FRIESLAND Leeuwarden 33,517,133 608,879 38 7.6 MUNSTER 7.8 DEA3 MUNSTER 28,251,879 592,646 39 29,777,598 586,368 40 8.0 UKI1&2 GREATER LONDON GREATER LONDON FR10 ILE DE FRANCE Paris 31,005,247 576,138 41 8.2 8.4 IT2 LOMBARDIA (Milano) Milano 28,581,481 570,870 42 OBERBAYERN (München) DE21 München 28,411,565 568,619 43 8.6 HAMBURG DE6 HAMBURG 30,470,406 539,926 44 8.8 AT33 TIROL (Innsbruck) Innsbruck 21,562,308 458,054 45 9.0 AT32 SALZBURG SALZBURG 20,197,282 392,610 46 9.2 BERLIN DE3 BERLIN 24,729,226 382,900 47 9.4 FR71 RHONE-ALPES (Lyon) Lyon 17,258,345 320,612 48 9.6 IT6 LAZIO (Roma) Roma 16,148,489 246,256 49 9.8 11,150,818 194,708 50 10.0 FR82 PROVENCE-ALPES COTE D'AZURMarseille AT13 WIEN Wien 14,063,705 173,405 51 10.2 SK01 BRATISLAVSKY KRAJ BRATISLAVA 13,630,577 148,225 52 10.4 CZ01 PRAHA PRAHA 13,471,122 136,482 53 10.6 ES3 COM. DE MADRID MADRID 9,249,878 126,238 54 10.8 E04+Cop.D SYDSVERIGE (Malmö)/Öresund Malmö 6,084,276 117,218 55 11.0 ES51 CATALUNA (Barcelona) Barcelona 7,549,757 111,316 56 11.2 UKM3 SW SCOTLAND (Glasgow) Glasgow 5,499,395 102,658 57 11.4 HU01 KOZEP-MAGYAR.(Budapest) Budapest 9,375,108 80,146 58 11.6 SE05 VASTSVERIGE (Göteborg) Göteborg 4,113,083 69,964 59 11.8 PT13 LISBOA VALE DO TEJO LISBOA 5,733,147 57,885 60 12.0 PL07 MAZOWIECKIE (Warszawa) Warszawa 10,459,765 45,886 61 12.2

Cushman & Wakefield 2009


39

Comparison of prime locations for European logistics and distribution 2009

By using a ranking method and score one does not get full linearity in the score; following graph gives a view on the linearity of this matrix-element

Buying Power Score vs. Buying Power 14.0

12.0

10.0

8.0 Spending PowerScore 6.0

4.0

2.0

0.0 0

500,000

Cushman & Wakefield 2009

1,000,000

1,500,000


Attachment H: Thematic maps of the Domains and the total scores, by NUTS-2 region

Cushman & Wakefield 2009


-41-


Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

Cushman & Wakefield 2009

43


Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

Cushman & Wakefield 2009

45


Cushman & Wakefield 2009


Comparison of prime locations for European logistics and distribution 2009

Cushman & Wakefield 2009

47

/Comparison-20  

http://investinwallonia.nessa.globulebleu.com/wp-content/uploads/PUBLICATIONS/EXTERNES/Comparison-2009-Prime-Logistics-Locations-abridged-ed...

Read more
Read more
Similar to
Popular now
Just for you