Comparing spatial features of urban housing markets: Recent evidence of submarket formation in metropolitan Helsinki and Amsterdam II

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Appendix D

Selected feature map layers

of Amsterdam housing

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Feature map layer D2 Density, addresses/neighbourhoods

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 dark = sparse areas; light = dense areas dark = cheap; light = expensive 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0.6 0.8 0.6 0.6 0.5 0.0 1.0 0.5 0.6 1.0 0.0 0.0 0.5 0.9 0.6 1.9 0.5 0.0 1.8 0.7 0.7 0.6 0.9 0.9 dark = large; light = small

Feature map layer D1 Assessed property value

Water coverage indicator

Legend 1 Westelijk havengebied 2 Oostzanerwerf 3 IJplein 4 merbuurt 5 Landlust 6 Indische buurt 7 IJ-eiland 8 sloot 9 Volewijck 10 Oostelijke Eilanden 11 Westindische buurt 12 buurt 13 Sloterdijk 14 De Punt 15 Buikslotermeer 16 Oostelijk havengebied 17 Nieuwmarkt 18 Jordaan 19 Houthavens 20 Nellestein 21 Middenmeer 22 Willemspark 23 Oude wallen 24 Nieuwe wallen

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Feature map layer D3 Extent of urbanisation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 dark = most urban areas; light = least urban areas

Feature map layer D4 Population density

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 dark = least inhabitants per sq. km.; light = most inhabitants per sq. km.)

Feature map layer D5 Percentage of non-westerners

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 dark = lowest percentage; light = highest percentage

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Feature map layer D6 Percentage of one-person households

Feature map layer D7 Average net income including subsidies per resident

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 dark = low income; light = high income

Feature map layer D8 Percentage of 15-24 years old

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Feature map layer D9 Percentage of 25-44 years old

Feature map layer D10 Percentage of 45-64 years old

Feature map layer D11 Number of families

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 dark = lowest number; light = highest number

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Feature map layer D12 Percentage of low income takers

Feature map layer D13 Percentage of high income takers

Feature map layer D14 Percentage of 15-65 years old with unemployment benefit

as primary source of income

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Feature map layer D15 Percentage of industrial enterprises (including construction)

Feature map layer D16 Percentage of commercial enterprises

Feature map layer D17 Percentage of non-commercial enterprises

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Appendix E

Feature map layers of

Amsterdam housing markets,

taxation panel data

Feature map layer E1 Total price levels

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Feature map layer E2 Per sq. m. price levels

dark = low prices; light = high prices

Feature map layer E3 Year of construction

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Feature map layer E4 Dwelling type

dark = single-family houses; light = multi-storey buildings

Feature map layer E5 Plot size

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Feature map layer E6 Dwelling size

dark = small dwellings; light = large dwellings

Feature map layer E7 Dwelling quality

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Feature map layer E8 Maintenance of the dwelling

dark = low rank; light = high rank

Feature map layer E9 Situation of the dwelling

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Feature map layer E10 Situation by a canal

dark = no; light = yes

Feature map layer E11 Date of transaction

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Appendix F

Feature map layers of

Amsterdam housing markets,

taxation/1992-1993 data

dark = cheap; light = expensive

Feature map layer F1 Transaction price (total)

A K A A N T L D A G P K A K K E P S

Feature map layer F2 Year of construction

dark = old buildings; light = new buildings

Legend A Binnenstad D Oud-West E, K, L Amsterdam Oud-zuid G Zeeburg N Amsterdam-Noord P Geuzenveld/ Slotermeer S Zuideramstel T Zuidoost

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Feature map layer F3 Dwelling type

Feature map layer F4 Dwelling size (sq. m.)

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Feature map layer F5 Size of the garden (multi-storey) or plot (single-family) (sq. m.)

dark = small gardens or plots; light = large gardens or plots

Feature map layer F6 Dwelling quality

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Feature map layer F7 Quality of the situation

Feature map layer F8 Maintenance of the dwelling

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Feature map layer F9 Situation by a canal

Feature map layer F10 Land lease ‘Erfpacht’

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Appendix G

Feature map layers of

Amsterdam housing markets,

taxation/2000-01 data

V P K G D N N P G A A G N Q Q K M N

Feature map layer G1 Transaction price (total)

dark = low prices; light = high prices

Feature map layer G2 Year of construction

dark = old buildings; light = new buildings

Legend A Stadsdeel Binnenstad D Stadsdeel Oud-West G Stadsdeel Zeeburg K, V Stadsdeel Amsterdam Oud-Zuid M Stadsdeel Oost/Water-graafsmeer N Stadsdeel Amsterdam-Noord P Stadsdeel Geuzenveld/ Slotermeer Q Stadsdeel Osdorp

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Feature map layer G3 Dwelling type

Feature map layer G4 Dwelling size (sq. m.)

dark = small dwellings; light = large dwellings Legend A Stadsdeel Binnenstad D Stadsdeel Oud-West G Stadsdeel Zeeburg K, V Stadsdeel Amsterdam Oud-Zuid M Stadsdeel Oost/Water-graafsmeer N Stadsdeel Amsterdam-Noord P Stadsdeel Geuzenveld/ Slotermeer Q Stadsdeel Osdorp

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Feature map layer G5 Size of the garden (multi-storey) or plot (singel-family) (sq. m.)

dark = small gardens or plots; light = large gardens or plots

Feature map layer G6 Dwelling quality

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Feature map layer G7 Quality of the situation

Feature map layer G8 Maintenance of the dwelling

dark = low rank; light = high rank Legend A Stadsdeel Binnenstad D Stadsdeel Oud-West G Stadsdeel Zeeburg K, V Stadsdeel Amsterdam Oud-Zuid M Stadsdeel Oost/Water-graafsmeer N Stadsdeel Amsterdam-Noord P Stadsdeel Geuzenveld/ Slotermeer Q Stadsdeel Osdorp

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Feature map layer G9 Situation by a canal

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Appendix H

The disaggregated AHP

models for the Dutch

Randstad

Figure H1.1 Dis-aggregated model I

0.549 0.187 0.124 0.073 0.067 Supply-side friction Social factors Service infrastructure Physical environment Accessibility

Figure H1.2 Dis-aggregated model II

0.388 0.220 0.148 0.123 0.121 Social factors Supply-side friction Service infrastructure Accessibility Physical environment

Figure H1.3 Dis-aggregated model III

0.247 0.237 0.225 0.212 0.079 Physical environment Social factors Service infrastructure Accessibility Supply-side friction

H.1 Urban

Amsterdam

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Figure H2.1 Dis-aggregated model I

0.422 0.179 0.132 0.095 0.093 0.079 Supply-side friction Social factors Service infrastructure Physical environment Municipality Accessibility

Figure H2.2 Dis-aggregated model II

0.377 0.163 0.148 0.133 0.102 0.077 Social factors Service infrastructure Accessibility Supply-side friction Physical environment Municipality

Figure H2.3 Dis-aggregated model III

0.264 0.243 0.209 0.173 0.064 0.047 Service infrastructure Accessibility Social factors Physical environment Supply-side friction Municipality

H.2 Urban

Randstad

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H.3 VINEX

Figure H3.1 Dis-aggregated model I

0.306 0.208 0.185 0.134 0.117 0.050 Supply-side friction Social factors Service infrastructure Physical environment Accessibility Municipality

Figure H3.2 Dis-aggregated model II

0.297 0.223 0.209 0.132 0.072 0.066 Physical environment Service infrastructure Accessibility Social factors Supply-side friction Municipality

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Appendix I

The variables for the

aggregated housing

market dataset

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The variables for the aggregated housing market dataset of Amsterdam, Rotterdam and The Hague

V a r i a b l e s c o l l e c t e d f r o m W B O1)

The weight variable: a measure of reliability of the data the lower the weight, the closer the sample is to the total population of that area

CBD distance 1: close (within 15 min to CBD); 3: at the edge of the locality Satisfaction with quality of vicinity 1: best; 5: worst

Attractiveness of build environment 1: best; 5: worst

Annoyance of neighbourhood 1: most unpleasant; 5: most pleasant (Lack of ) ties to the neighbourhood 1: strong ties; 5: no ties

Lack of identity 1: strong identity; 5: no identity Daily shopping services 1: perfect availability; 3: no availability Parking space 1: perfect availability; 3: no availability Medical services 1: perfect availability; 3: no availability Public transport stops 1: perfect availability; 3: no availability Greenery 1: perfect availability; 3: no availability Facilities for 12-18 years old 1: perfect availability; 3: no availability Comprehensive schools 1: perfect availability; 3: no availability Young children’s playing-grounds 1: perfect availability; 3: no availability Presence of negative externalities (noise and air pollution) 1: no or little; 3: plenty

Presence of graffiti 1: no or little; 3: plenty Disturbance of direct neighbours 1: no or little; 3: plenty Disturbance of other neighbours 1: no or little; 3: plenty Amount of traffic in the area 1: no or little; 3: plenty

Feeling of responsibility/commitment in the community 1: most committed; 5; not committed, instead ‘individualised’ Satisfaction with socioeconomic mix of residents 1: satisfied; 5: unsatisfied

Traffic safety 1: safe; 5: unsafe

Security against burglary/robbery 1: most afraid; 5: least afraid (obs: 5 is the best value here)

Safety 1: safe; 2: not safe

1) WBO = Woningbehoefteonderzoek (1998-2000) (Housing need research)

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V a r i a b l e s c o l l e c t e d f r o m K W B2)

Name of the subdistrict

Addresses per neighbourhood (density-proxy) 1–11856

Extent of urbanisation Classification 1: highly urban; 5: least urban Sq. m. of area including water 2–76,539

Sq. m. of land area 2–46,492

Population density (inhabitants per sq. km.) 0–31,001

Total population 0–727,050

Population of males 0–357,110

Population of females 0–369,950

Percentage of 0-14 years old children 1–65 Percentage of 15-24 years old 2–96 Percentage of 25-44 years old 3–76 Percentage of 45-64 years old 1–71 Percentage of 65+ years old 1–98 Percentage of non-westerners (first and second generation 0–89 immigrants)

Percentage of one person households 3–99

Number of families 0–141,280

Percentage of families with kids 7–93

Average family size 2.1–4.6

Average net income including subsidies per resident 5,000–68,600 Average net income including subsidies per income taker 8,900–118,500 Percentage of low income takers 12–95 Percentage of high income takers 4–74 Percentage of 15-65 years old with unemployment benefit as the primary source of income 0–95

Number of dwellings 0–369,070

Assessed market value of dwelling (total price, 1000 NLG) 43–1,170

Number of (urban) firms in the neighbourhood Classification: 1 (0–10) – 9 (2,000+) (9 categories)

Percentage of industrial enterprises(including construction) 0–63% Percentage of commercial enterprises 24–99% Percentage of non-commercial enterprises 0–61% Label: Subdistrict code (Buurtcode) 8-digit id. code Label: name of the subdistrict

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2. Boon, Claudia and Minna Sunikka, Introduction to

sustain-able urban renewal. CO₂ reduction and the use of perfor-mance agreements: experience from The Netherlands

2004/153 pages/ISBN 90-407-2535-7

3. De Jonge, Tim, Cost effectiveness of sustainable housing investments

2005/196 pages/ISBN 90-407-2578-0

4. Klunder, Gerda, Sustainable solutions for Dutch housing. Reducing the environmental impact of new and existing houses

2005/163 pages/ISBN 90-407-2584-5

5. Bots, Pieter, Ellen van Bueren, Ernst ten Heuvelhof and Igor Mayer, Communicative tools in sustainable urban planning

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6. Kleinhans, R.J., Sociale implicaties van herstructurering en herhuisvesting

2005/371 pages/ISBN 90-407-2598-5

7. Kauko, Tom, Comparing spatial features of urban housing markets. Recent evidence of submarket formation in metro-politan Helsinki and Amsterdam

2005/163 pages/ISBN 90-407-2618-3

Copies can be ordered at

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Various location specific attributes contribute to the spatial dynamics of housing markets. This effect may partly be of a qualitative and discontinuous nature, which

causes market segmentation into submarkets. The question however is, whether the most relevant partitioning criteria is directly related to the transaction price or

to other, socioeconomic, demographic and physical features of the location. Two neural network techniques are used for analysing statistical house price data from Amsterdam and Helsinki. The analytic hierarchy process is used as a support-ing technique. With these techniques it is possible to analyse various dimensions of housing submarket formation. The findings show that, while the price and demand factors have increased in importance, supply factors still prevail as key criteria in

both cases. The outcome also indicates that the housing market structure of Amsterdam is more fragmented than that of Helsinki, and that the main discriminating housing market features, and the ways they have changed in time,

are somewhat different.

The Delft Research Centres bundle TU Delft’s excellent research and provide integrated solutions for today’s and tomorrow’s problems in society. OTB Research Institute for Housing, Urban and Mobility Studies and the Faculties

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