Appendix D Selected Feature Map Layers of Amsterdam Housing Markets, KWB/1999 Data [ 132 ]

Home , Buikslotermeer, Geuzenveld, Oostzanerwerf, Willemspark (Amsterdam), Zuideramstel

[ 131 ]

Appendix D Selected feature map layers of Amsterdam housing markets, KWB/1999 data [ 132 ]

Legend Feature map layer D1 Assessed property value 1 Westelijk havengebied 2 Oostzanerwerf 1 2 3 4 5 6 3 IJplein 4 Spaarndam- merbuurt 7 8 9 10 11 12 5 Landlust 6 Indische buurt 7 IJ-eiland 13 14 15 16 17 18 8 BanneBuik- sloot 9 Volewijck 19 20 21 22 23 24 dark = cheap; 10 Oostelijke light = Eilanden expensive 11 Westindische buurt 12 Staatslieden- Water coverage indicator buurt 13 Sloterdijk 0.6 0.8 0.6 0.6 0.5 0.0 14 De Punt 15 Buikslotermeer 16 Oostelijk 1.0 0.5 0.6 1.0 0.0 0.0 havengebied 17 Nieuwmarkt 18 Jordaan 19 Houthavens 0.5 0.9 0.6 1.9 0.5 0.0 20 Nellestein 21 Middenmeer 22 Willemspark 1.8 0.7 0.7 0.6 0.9 0.9 23 Oude Burg- dark = large; wallen light = small 24 Nieuwe Burg- wallen

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

dark = 19 20 21 22 23 24 sparse areas; light = dense areas [ 133 ]

Feature map layer D3 Extent of urbanisation Legend 1 Westelijk havengebied 1 2 3 4 5 6 2 Oostzanerwerf 3 IJplein 4 Spaarndam- 7 8 9 10 11 12 merbuurt 5 Landlust 6 Indische buurt 13 14 15 16 17 18 7 IJ-eiland 8 BanneBuik- dark = most sloot 9 Volewijck 19 20 21 22 23 24 urban areas; light = least 10 Oostelijke urban areas Eilanden 11 Westindische buurt 12 Staatslieden- buurt Feature map layer D4 Population density 13 Sloterdijk 14 De Punt 15 Buikslotermeer 1 2 3 4 5 6 16 Oostelijk havengebied 17 Nieuwmarkt 7 8 9 10 11 12 18 Jordaan 19 Houthavens 20 Nellestein 13 14 15 16 17 18 dark = least 21 Middenmeer inhabitants 22 Willemspark per sq. km.; 23 Oude Burg- wallen 19 20 21 22 23 24 light = most inhabitants 24 Nieuwe Burg- per sq. km.) wallen

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

dark = lowest 19 20 21 22 23 24 percentage; light = highest percentage [ 134 ]

Legend Feature map layer D6 Percentage of one-person households 1 Westelijk havengebied 2 Oostzanerwerf 1 2 3 4 5 6 3 IJplein 4 Spaarndam- merbuurt 7 8 9 10 11 12 5 Landlust 6 Indische buurt 7 IJ-eiland 13 14 15 16 17 18 8 BanneBuik- sloot dark = lowest 9 Volewijck 19 20 21 22 23 24 percentage; 10 Oostelijke light = highest Eilanden percentage 11 Westindische buurt 12 Staatslieden- buurt 13 Sloterdijk Feature map layer D7 Average net income including subsidies per resident 14 De Punt 15 Buikslotermeer 16 Oostelijk 1 2 3 4 5 6 havengebied 17 Nieuwmarkt 18 Jordaan 7 8 9 10 11 12 19 Houthavens 20 Nellestein 21 Middenmeer 13 14 15 16 17 18 22 Willemspark 23 Oude Burg- dark = wallen 19 20 21 22 23 24 low income; 24 Nieuwe Burg- light = wallen high income

Feature map layer D8 Percentage of 15-24 years old

1 2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

dark = lowest 19 20 21 22 23 24 percentage; light = highest percentage [ 135 ]

Feature map layer D9 Percentage of 25-44 years old Legend 1 Westelijk havengebied 1 2 3 4 5 6 2 Oostzanerwerf 3 IJplein 4 Spaarndam- 7 8 9 10 11 12 merbuurt 5 Landlust 6 Indische buurt 13 14 15 16 17 18 7 IJ-eiland 8 BanneBuik- dark = lowest sloot 9 Volewijck 19 20 21 22 23 24 percentage; light = highest 10 Oostelijke percentage Eilanden 11 Westindische buurt 12 Staatslieden- buurt Feature map layer D10 Percentage of 45-64 years old 13 Sloterdijk 14 De Punt 15 Buikslotermeer 1 2 3 4 5 6 16 Oostelijk havengebied 17 Nieuwmarkt 7 8 9 10 11 12 18 Jordaan 19 Houthavens 20 Nellestein 13 14 15 16 17 18 21 Middenmeer 22 Willemspark dark = lowest 23 Oude Burg- wallen 19 20 21 22 23 24 percentage; light = highest 24 Nieuwe Burg- percentage wallen

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

dark = lowest 19 20 21 22 23 24 number; light = highest number [ 136 ]

Legend Feature map layer D12 Percentage of low income takers 1 Westelijk havengebied 2 Oostzanerwerf 1 2 3 4 5 6 3 IJplein 4 Spaarndam- merbuurt 7 8 9 10 11 12 5 Landlust 6 Indische buurt 7 IJ-eiland 13 14 15 16 17 18 8 BanneBuik- sloot dark = lowest 9 Volewijck 19 20 21 22 23 24 percentage; 10 Oostelijke light = highest Eilanden percentage 11 Westindische buurt 12 Staatslieden- buurt 13 Sloterdijk Feature map layer D13 Percentage of high income takers 14 De Punt 15 Buikslotermeer 16 Oostelijk 1 2 3 4 5 6 havengebied 17 Nieuwmarkt 18 Jordaan 7 8 9 10 11 12 19 Houthavens 20 Nellestein 21 Middenmeer 13 14 15 16 17 18 22 Willemspark 23 Oude Burg- dark = lowest wallen 19 20 21 22 23 24 percentage; 24 Nieuwe Burg- light = highest wallen percentage

Feature map layer D14 Percentage of 15-65 years old with unemployment benefit as primary source of income

1 2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

dark = lowest 19 20 21 22 23 24 percentage; light = highest percentage [ 137 ]

Feature map layer D15 Percentage of industrial enterprises (including construction) Legend 1 Westelijk havengebied 1 2 3 4 5 6 2 Oostzanerwerf 3 IJplein 4 Spaarndam- 7 8 9 10 11 12 merbuurt 5 Landlust 6 Indische buurt 13 14 15 16 17 18 7 IJ-eiland 8 BanneBuik- dark = lowest sloot 9 Volewijck 19 20 21 22 23 24 percentage; light = highest 10 Oostelijke percentage Eilanden 11 Westindische buurt 12 Staatslieden- buurt Feature map layer D16 Percentage of commercial enterprises 13 Sloterdijk 14 De Punt 15 Buikslotermeer 1 2 3 4 5 6 16 Oostelijk havengebied 17 Nieuwmarkt 7 8 9 10 11 12 18 Jordaan 19 Houthavens 20 Nellestein 13 14 15 16 17 18 21 Middenmeer 22 Willemspark dark = lowest 23 Oude Burg- wallen 19 20 21 22 23 24 percentage; light = highest 24 Nieuwe Burg- percentage wallen

Feature map layer D17 Percentage of non-commercial enterprises

1 2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

dark = lowest 19 20 21 22 23 24 percentage; light = highest percentage

[ 139 ]

Appendix E Feature map layers of Amsterdam housing markets, taxation panel data

Feature map layer E1 Total price levels

dark = low price; light = high price [ 140 ]

Feature map layer E2 Per sq. m. price levels

dark = low prices; light = high prices

Feature map layer E3 Year of construction

dark = old buildings; light = new buildings [ 141 ]

Feature map layer E4 Dwelling type

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

Feature map layer E5 Plot size

dark = small plots; light = large plots [ 142 ]

Feature map layer E6 Dwelling size

dark = small dwellings; light = large dwellings

Feature map layer E7 Dwelling quality

dark = low rank; light = high rank [ 143 ]

Feature map layer E8 Maintenance of the dwelling

dark = low rank; light = high rank

Feature map layer E9 Situation of the dwelling

dark = low rank; light = high rank [ 144 ]

Feature map layer E10 Situation by a canal

dark = no; light = yes

Feature map layer E11 Date of transaction

dark = sales in the 80s and early 90s; light = recent sales [ 145 ]

Appendix F Feature map layers of Amsterdam housing markets, taxation/1992-1993 data

Feature map layer F1 Transaction price (total) Legend A Binnenstad T A AN D Oud-West E, K, L Amsterdam LD Oud-zuid G AGA PK Zeeburg N Amsterdam- AK K E Noord P K Geuzenveld/ Slotermeer P S S Zuideramstel T dark = cheap; light = expensive Zuidoost

Feature map layer F2 Year of construction

A ANT

AGA PK

AK K E

dark = old buildings; light = new buildings [ 146 ]

Legend Feature map layer F3 Dwelling type A Binnenstad D A ANT Oud-West E, K, L Amsterdam LD Oud-zuid G AGA PK Zeeburg N Amsterdam- Noord AK K E P Geuzenveld/ K Slotermeer S PS Zuideramstel T Zuidoost dark = single-family houses; light = multi-storey buildings

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

A ANT

L D

AGA P K

AK K E

dark = small dwellings; light = large dwellings [ 147 ]

Feature map layer F5 Size of the garden (multi-storey) or plot (single-family) (sq. m.) Legend A Binnenstad T A AN D Oud-West E, K, L Amsterdam LD Oud-zuid G AGA PK Zeeburg N Amsterdam- AK K E Noord P K Geuzenveld/ Slotermeer P S S Zuideramstel T dark = small gardens or plots; light = large gardens or plots Zuidoost

Feature map layer F6 Dwelling quality

A ANT

A G A PK

A KK E

PS dark = low rank; light = high rank [ 148 ]

Legend Feature map layer F7 Quality of the situation A Binnenstad D A ANT Oud-West E, K, L Amsterdam LD Oud-zuid G AGA PK Zeeburg N Amsterdam- Noord AK K E P Geuzenveld/ K Slotermeer S PS Zuideramstel T Zuidoost dark = low rank; light = high rank

Feature map layer F8 Maintenance of the dwelling

A ANT

AGA PK

A KK E

P S

dark = low rank; light = high rank [ 149 ]

Feature map layer F9 Situation by a canal Legend A Binnenstad A A NTD Oud-West E, K, L Amsterdam L D Oud-zuid G AGA PK Zeeburg N Amsterdam- AK K E Noord P K Geuzenveld/ Slotermeer PSS Zuideramstel T dark = no; light = yes Zuidoost

Feature map layer F10 Land lease ‘Erfpacht’

A ANT

L D

A G A P K

AK K E

P S

dark = no ‘Erfpacht’; light = ‘Erfpacht’

[ 151 ]

Appendix G Feature map layers of Amsterdam housing markets, taxation/2000-01 data

Feature map layer G1 Transaction price (total) Legend A Stadsdeel VPK Binnenstad D Stadsdeel Oud-West GD G NNPGAA Stadsdeel Zeeburg K, V Stadsdeel G Amsterdam Oud-Zuid N M Stadsdeel QQK M N Oost/Water- graafsmeer N dark = low prices; light = high prices Stadsdeel Amsterdam- Noord P Feature map layer G2 Year of construction Stadsdeel Geuzenveld/ Slotermeer V PK Q Stadsdeel Osdorp

NNPGAA

QQK M N

dark = old buildings; light = new buildings [ 152 ]

Legend Feature map layer G3 Dwelling type A Stadsdeel VPK Binnenstad D Stadsdeel Oud-West G D G Stadsdeel N NPGAA Zeeburg K, V Stadsdeel Amsterdam G Oud-Zuid M N Stadsdeel Oost/Water- QQKM N graafsmeer N Stadsdeel dark = single-family houses; light = multi-storey buildings Amsterdam- Noord P Stadsdeel Feature map layer G4 Dwelling size (sq. m.) Geuzenveld/ Slotermeer Q VPK Stadsdeel Osdorp

NNPGAA

QQK M N

dark = small dwellings; light = large dwellings [ 153 ]

Feature map layer G5 Size of the garden (multi-storey) or plot (singel-family) (sq. m.) Legend A Stadsdeel VPK Binnenstad D Stadsdeel Oud-West GD G NNPGAAStadsdeel Zeeburg K, V Stadsdeel G Amsterdam Oud-Zuid N M Stadsdeel QQK M N Oost/Water- graafsmeer N dark = small gardens or plots; light = large gardens or plots Stadsdeel Amsterdam- Noord P Feature map layer G6 Dwelling quality Stadsdeel Geuzenveld/ Slotermeer VPK Q Stadsdeel Osdorp

N NPGAA

Q QK M N

dark = low rank; light = high rank [ 154 ]

Legend Feature map layer G7 Quality of the situation A Stadsdeel VPK Binnenstad D Stadsdeel Oud-West G D G Stadsdeel NNPG AA Zeeburg K, V Stadsdeel Amsterdam G Oud-Zuid M N Stadsdeel Oost/Water- QQK M N graafsmeer N Stadsdeel dark = low rank; light = high rank Amsterdam- Noord P Stadsdeel Feature map layer G8 Maintenance of the dwelling Geuzenveld/ Slotermeer Q V P K Stadsdeel Osdorp

NNPGAA

QQK M N

dark = low rank; light = high rank [ 155 ]

Feature map layer G9 Situation by a canal Legend A Stadsdeel VPK Binnenstad D Stadsdeel Oud-West GD G NNPGAA Stadsdeel Zeeburg K, V Stadsdeel G Amsterdam Oud-Zuid N M Stadsdeel QQKM NOost/Water- graafsmeer N dark = no; light = yes Stadsdeel Amsterdam- Noord P Stadsdeel Geuzenveld/ Slotermeer Q Stadsdeel Osdorp

[ 157 ]

Appendix H The disaggregated AHP models for the Dutch Randstad H.1 Urban Amsterdam

Figure H1.1 Dis-aggregated model I

Supply-side friction 0.549 Social factors 0.187 Service infrastructure 0.124

Physical environment 0.073 Accessibility 0.067

Figure H1.2 Dis-aggregated model II

Social factors 0.388 Supply-side friction 0.220 Service infrastructure 0.148

Accessibility 0.123 Physical environment 0.121

Figure H1.3 Dis-aggregated model III

Physical environment 0.247 Social factors 0.237 Service infrastructure 0.225

Accessibility 0.212 Supply-side friction 0.079 [ 158 ]

H.2 Urban Randstad

Figure H2.1 Dis-aggregated model I

Supply-side friction 0.422 Social factors 0.179 Service infrastructure 0.132

Physical environment 0.095 Municipality 0.093 Accessibility 0.079

Figure H2.2 Dis-aggregated model II

Social factors 0.377 Service infrastructure 0.163 Accessibility 0.148

Supply-side friction 0.133 Physical environment 0.102 Municipality 0.077

Figure H2.3 Dis-aggregated model III

Service infrastructure 0.264 Accessibility 0.243 Social factors 0.209

Physical environment 0.173 Supply-side friction 0.064 Municipality 0.047 [ 159 ]

H.3 VINEX

Figure H3.1 Dis-aggregated model I

Supply-side friction 0.306 Social factors 0.208 Service infrastructure 0.185

Physical environment 0.134 Accessibility 0.117 Municipality 0.050

Figure H3.2 Dis-aggregated model II

Physical environment 0.297 Service infrastructure 0.223 Accessibility 0.209

Social factors 0.132 Supply-side friction 0.072 Municipality 0.066

[ 161 ]

Appendix I The variables for the aggregated housing market dataset [ 162 ]

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

Variables collected from WBO1) 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) 2) KWB = Kerncijfers wijken en buurten (1999) (Core figures of districts and neighbourhoods) [ 163 ]

Variables collected from KWB2) 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

Sustainable Urban Areas

1. Beerepoot, Milou, Renewable energy in energy performance regulations. A challenge for European member states in implementing the Energy Performance Building Directive 2004/202 pages/ISBN 90-407-2534-9

2. Boon, Claudia and Minna Sunikka, Introduction to sustain-

able urban renewal. CO2 reduction and the use of performance 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 and building 2005/100 pages/ISBN 90-407-2595-0

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 www.library.tudelft.nl/ned/publiceren.

Delft Centre for Sustainable Urban Areas carries out research in the field of the built environment and is one of the multidisciplinary research centres at TU Delft. 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 of Architecture, Technology, Policy and Management and Civil Engineering and Geosciences participate in this Delft Research Centre.

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.

Delft University Press