what about the spatial dimension of subsidiarity in housing policy?

W02 – Migration, Residential Mobility, and Housing 

Policy 

WHAT ABOUT THE SPATIAL DIMENSION OF 

SUBSIDIARITY IN HOUSING POLICY? 

Roland Goetgeluk 

R.W.Goetgeluk@tudelft.nl 

Tom de Jong 

T.deJong@geo.uu.nl

What about the spatial dimension of subsidiarity in housing policy? 

ENHR 2007 International Conference on ‘Sustainable Urban Areas’ 


Roland Goetgeluk 

OTB Research Institute for Housing, Urban and Mobility Studies 

Delft University of Technology, r.w.goetgeluk@tudelft.nl 

+31 15 278 2876, +31 15 278 4422 

Tom de Jong 

Faculty Geosciences 

Utrecht University, t.dejong@geo.uu.nl 

+31 30 253 1393, +31 30 253 2037 

Keywords: housing policy, town & country planning, migration patterns, spatial clustering, 

intramax, modifiable area problem 

Abstract: This paper addresses two related questions. Do our statistical spatial data suit our 

theoretical and policy models? Do our present spatial organizations of municipalities and 

provinces match the principle of subsidiarity in Dutch housing policy and Town & Country 

Planning? Subsidiarity refers to the concept that any central authority should only performed 

tasks which cannot be performed effectively at a more immediate or local level. Using a valid 

spatial level given the goal (function) is at stake. For nearly 30 years the Dutch Ministry of 

Housing, Spatial Planning & the Environment uses a fixed classification of housing market 

regions. They are used the calculate indexes on migration, affordability, home ownership, 

housing shortages et cetera over time. For other policy goals, such as Urban Restructuring, 

data collected at ZIP-codes of administrative areas like neighborhoods, are used to rank 

neighborhoods. The results are used to define new policy goals and/or instruments. However, 

are the spatial level of the analyses and the policy measures justified? Last years, we proved 

that the spatial dimension of housing markets regions and neighborhoods change significantly 

over time. Last year we used the official definition of housing market regions to define a 

simple goal function, to select valid data and an INTRAMAX cluster technique to trace 

spatial patterns in inter-municipal migration 1990 en 2000. Now, we have extended the 

analysis referring to subsidiarity in housing policy. We compare the official administrative 

level of the municipality and province with our functional INTRAMAX regions and the EU 

NUTS 1. We conclude that subsidiarity implies scaling up municipalities and provinces. 

Therefore all kinds of indexes become less informative. Unfortunately the impact on the 

indexes can not be shown because the official report uses the criticized official classification 

and the report has not been published. 

Workshop: W02: Migration, Residential Mobility and Housing Policy 

Authors: Roland Goetgeluk & Tom de Jong 

1


What about the spatial dimension of subsidiarity in housing 

policy? 

1. Introduction 

This contribution is the fourth in a row at the ENHR. It will be the last. They deal with the 

‘modifiable area problem’ (Davies 2003, Isaaks & Mohan 1989) or the ecological fallacy 

problem (Blalock 1984). It means that every spatial classification must be directly linked to 

its goal since a false classification under or overestimates a process or pattern. Do our 

statistical spatial data suit our theoretical and policy models? 

This paper addresses two related questions. Do our statistical spatial data suit our theoretical 

and policy models? Do our present spatial organizations of municipalities and provinces 

match the principle of subsidiarity in Dutch housing policy and Town & Country Planning? 

Subsidiarity refers to the concept that any central authority should only performed tasks 

which cannot be performed effectively at a more immediate or local level. Using a valid 

spatial level given the goal (function) is at stake. 

For nearly 30 years the Dutch Ministry of Housing, Spatial Planning & the Environment uses 

a fixed classification of housing market regions. They are used the calculate indexes on 

migration, affordability, home ownership, housing shortages et cetera over time. For other 

policy goals, such as Urban Restructuring, data collected at ZIP-codes of administrative areas 

like neighborhoods, are used to rank neighborhoods. The results are used to define new policy 

goals and/or instruments. However, are the spatial level of the analyses and the policy 

measures justified? Last years, we proved that the spatial dimension of housing markets 

regions and neighborhoods change significantly over time. 

Last year we used the official definition of housing market regions to define a simple goal 

function, to select valid data and an INTRAMAX cluster technique to trace spatial patterns in 

inter-municipal migration 1990 en 2000. Now, we have extended the analysis referring to 

subsidiarity in housing policy. We compare the official administrative level of the 

municipality and province with our functional INTRAMAX regions and the EU NUTS1. 

We organized the contribution as follows. First we present the background on subsidiarity and 

the modifiable area problem. Second we deal with the methodology. Third, we show the 

results. We include a summary of the results of 2006 and it serves as the context. The analysis 

is descriptive. We end with the conclusions. Unfortunately we were not allowed yet to present 

how the indexes on migration, affordability, home ownership, housing shortages change if the 

official housing market classification and our INTRAMAX classification are used. The final 

reports on affordability, home ownership, housing shortages for the Ministry of Housing, 

Spatial Planning & the Environment are not published yet. A problem is that some studies use 

the INTRAMAX while others use the official classification. 

2. Subsidiarity 

Subsidiarity means that any central authority should only perform tasks, which cannot be 

performed effectively at a more immediate or local level 1 . The managerial and the spatial 

scale of these immediate and local levels should match to tackle problems. This means that a 

1 The concept has become widespread since European Charter of Local Self-Government (1985), Treaty of 

Maastricht (1992) and of course the Treaty establishing a Constitution for Europe (2004). 



2


regularly the existing administrative regionalization must be tested. We must try to falsify 

present classifications of immediate and local levels function instead of confirming. 

Figure 1 

Borders of 12 Provinces, 370 municipalities and the municipal population size for 

2007 

Sources: Central Bureau of Statistic 2007 

Four democratic administrative levels play an important role housing policy, housing 

construction plan/zoning schemes and Town & Country Planning: Europe, the national 

government, the province and the municipality. Figure 1 shows the spatial dimensions of the 

last three. The Provincial States, directly elected, elect themselves the First Chamber. This 

Chamber resembles the House of Lords in the UK or the French Sénate. The Second Chamber 

of the National Parliament is elected directly. Before the rise of the Kingdom of the 

Netherlands (1813 first Dutch King, 1814 first Constitution), the national parliament was 

based on representatives of the provinces. The province is the oldest administrative level and 

therefore still strongly embedded in national law (New Constitution 1848). The provinces 

have not changed in shape and size. The municipalities have existed since 1830 and have 

direct democratic chosen parliament. Before the rise of the Kingdom of the Netherlands, they 

did not exit. The cities boards ruled and took part in the Staten (Staten) of the provinces. The 

state does exist a long time, but that nation-state exists since the late 19 th century. The 

development of Europe leads to an erosion of the state in favor of Europe and the immediate 

and local level. However, is local the level of the municipality and immediate the provincial 

level? Back to the future? 

Is there need to ask ourselves if administrative levels and it spatial dimension function well? 

Yes! Many studies show mismatches between on one hand linking goals of housing, labor 

market, transport, nature in Town & Countryside planning and on the other hand the 



3


decentralization processes. Van der Wouden et al. (2006a) argue that these mismatches have 

increased: deregulation and decentralization to the commercial parties (project developers, 

housing corporations), lower level public organizations (provinces, municipalities) and 

centralization to the EU, have led failing supra local and supra provincial organization of 

planning. Subsidiarity paradoxically demands up scaling of the municipality (mergers) and 

provinces from a functional perspective. 

To solve the problem all kinds of proposals have been made. The merging of municipalities is 

popular. There number of municipalities has decreased from 1430 (1830) to less than 400 in 

2007 due to mergers. However often they are not based on a functional nodal perspective. 

Mergers within a conurbation (core city and the surrounding suburbs) are rather limited. 

Before 1813 the opposite would have been the case since the countryside was free for 

annexation. The introduction of the municipal level has eroded this functional clustering. 

Some legal form of cooperation between municipalities in large conurbations exits (WGR+, 

Law on Common Regulations Plus). However, they lack democratic control and of course 

negotiations are difficult especially related to housing allowances, the spatial distribution of 

new construction in the rental and owner-occupying sector. At the provincial level all kinds 

of proposals have been made as well. A proposal is a collaboration of provinces (NUTS 2 2) 

into 4 regions (NUTS 1). The advantage is that Dutch law with respect the First Chamber 

remains the same. A commission chaired by former Prime-Minister Kok advocated a 

Randstad-Holland Province. However Ritsema Van Eck et al. (2006b) proved that the 

Randstad-Holland is not a unity at all. Based on analysis of specialty, integration and 

complementary of flow data (working, leisure, shopping, financial flow and firm linkages), 

the Randstad-Holland still consists of heavenly specialized conurbations and does not 

function as a network city. Strange enough this study does not include migration flows as we 

have done so far. 

Many studies show that one size fits all does not exist and surely not over time. Logically 

changing the spatial dimensions of administrative layers, let alone abolishing them, is difficult 

for legal arguments. But is this true for scientific and policy related research? We think not. 

At the present a number of studies for the Ministry of Housing, Spatial Planning & the 

Environment are nearly ready. They deal with the development in time and space of indexes 

on migration, affordability, home ownership, housing shortages et cetera. Most of these 

studies use a fixed classification of housing market regions. The housing market region is the 

area in which those on the demand side of the housing market are generally prepared to move 

house without this causing any unacceptable loss of social or cultural contacts or a change of 

work” (DGVH 1982). In the housing market region most people move for housing related 

motives. Motives related to the household (cohabitation, divorce) and work/study careers are 

excluded. It makes sense since housing related moves are explained by peoples’ preferences 

and perceptions of social (social norms) and contextual (supply and regulation) structures 

(Azjen & Madden 1986). The other moves are necessary to realize non housing goals like 

cohabition, divorce or accepting a job far away. So, only if the spatial classification is timeinvariant 

than the indexes make sense. 

In the past we have shown that this classification is invalid. In this contribution we will show 

this as well. Unfortunately, we can not show how the official classification leads to 

sometimes completely different results. Why? The final reports on affordability, home 

ownership, housing shortages for the Ministry of Housing, Spatial Planning & the 

Environment are not published yet. This argument makes sense. The problem is that some of 

these studies use the INTRAMAX while others use the official classification. So, in this 

2 Nomenclature des Unités Territoriales Statistiques 



4


contribution we will predict what immediate size is needed from the perspective of the 

provincial tasks. At the provincial level matching demand and supply on the housing market 

for various motives for moving is at stake. 

3. Methodology 

Many studies on housing, migration, segregation, use statistical data collected at various 

spatial (administrative) scales. For instance, segregation and concentration are measured by 

means of Gini coefficients or multilevel regressions parameters. However, good models and 

good variables means nothing if the spatial scale is fraud. In simple words: is a bad 

neighborhood everywhere bad and is a good neighborhood everywhere good? Goetgeluk & 

Wassenberg (2005) showed the logic behind the geo statistics. They test the assumption of 

intra-class homogeneity and interclass heterogeneity for neighborhoods. Figure 2 shows how 

averages hide reality and how standard deviations hide the spatial patterns. However, in 

combination with Rook’s reality things become clear. The cluster results of figure 2 would be 

different. Neighborhood A indeed would have been a merger of the four sub-areas. For the 

neighborhoods B and C two pairs of clusters would emerge although they would be different 

in shape: horizontal and diagonal. Unfortunately of geo statistics are rather unknown under 

social (spatial) scientists, except for those involved in land use modeling and spatial economic 

modeling (Visser 2005, Wadell et al 2004). Some statistical basic knowledge might improve 

many studies since it would reveal if ‘people suit our models’ (after Bio 2000). If the 

‘rooksflight’ shows complexity, why not model the complexity? 

Figure 2 

How statistics may trigger our mind 

Neighborhood A B C 

5 5 10 10 10 0 

5 5 0 0 0 10 

Mean 5,0 5,0 5,0 

Standarddeviation 0,0 5,0 5,0 

Rook's (spatial statistic) 1,0 0,5 0,0 

Source: Goetgeluk & Wassenberg, 2005 

Goetgeluk & Wassenberg (2005) also showed that the goal of a research is important for the 

goal-function. Again we use the neighborhood. According to most people the neighborhood is 

often a priori the same as some administrative area. If one is interested in ‘Neighborhood 

satisfaction’ on should at least define what the neighborhood is. Figure 3 is revealing. The 

direct living environment, which is based on the aggregate activity patterns of respondents, 

resembled the neighborhood, which was based on the aggregation of the individual polygons 

drawn by the respondents. This was nice result since it showed that for people living in a 

neighborhood the spatial scale is an expression of activities, networks and of course 

information people collect. Figure 3 shows how the patterns cross the administrative boarders. 

In many municipal monitoring systems ‘Neighborhood satisfaction’ is presented at this 

administrative level. 



5


Figure 3 The heat maps for the direct living environment (left) and the neighborhood (right) 

Utrecht overplayed by the zip code 4 (blue). 

Direct living environment 

Neighborhood 

Source: Goetgeluk & Wassenberg, 2005 

Above examples can be generalized. We argue that in most cases any goal (function) 

demands for data collected at a lower spatial level and for different periods. The goal-function 

searches for clusters of these lower level data. The resulting classification minimizes the 

summed ‘within class’ variances and maximizes the ‘between class” variances for a specific 

goal. So, different policy goals demand for different goal functions and data in time and/or 

space. For statistical modeling a wrong classification leads to large standard errors of the 

parameters estimates and bad model performance. 

For nearly 30 years Dutch Housing policy and Town & Country Planning use fixed 

classifications of housing market regions to compare indexes on migration, affordability, 

home ownership, housing shortages over time. The housing market region is the area in which 

those on the demand side of the housing market are generally prepared to move house without 

this causing any unacceptable loss of social or cultural contacts or a change of work” (DGVH 

1982). In the housing market region most people move for housing related motives. Motives 

related to the household (cohabitation, divorce) and work/study careers are excluded. It makes 

sense since housing related moves are explained by peoples’ preferences and perceptions of 

social (social norms) and contextual (supply and regulation) structures (Azjen & Madden 

1986). The other moves are necessary to realize non housing goals like cohabition, divorce or 

accepting a job far away. So, only if the spatial classification is time-invariant than the 

indexes make sense. We showed that they are time-variant. 

So, the interaction between municipalities is a measure for the functional distance. High 

levels of interaction indicate a short functional distance. Hence, the clustering of 

municipalities that are close in terms of functional distance will lead to the creation of 

functional regions. If this clustering is based on residential movement flows, it will lead to the 

demarcation of housing market regions, as they exist in practice. Of course the clusters reflect 

revealed choices. This explain why such functional clusters are so vital for monitoring and 

calculation of the indexes of affordability, home ownership and housing shortages for the 

Ministry of Housing, Spatial Planning & The Environment. Space moulds. Four steps are 

necessary. Since we use the same data as last year we 



6


1. Estimate the share of moves related to the three motives for moving by distance 

We used the WBO’s 1989/90 and 2002. The WBO started in the late seventies as a substitute 

for the National Population and Housing Census. Since 1977, every four years between the 

50.000 and 70.000 persons answers questions of their past, present and intended housing 

situation. Approximately 20 percent of the household moves per two year or 1,2 million 

households of 2 million person 18 years or older. We used the weight factor ‘Person’ of the 

WBO since the migration matrix (step 2) refers to people. For the descriptive analysis we 

used the weight factor ‘households’. We estimated the relationship between distance, age and 

the motive for moving. Distance in km is a straight line between the geographical center 

points (x, y) of municipalities. If people move within a municipality the distance is zero. We 

recoded this distances into 5 Km classes (weighted average). We used the categories 18-34, 

34-54, 55 and older. Age is a proxy for the combined effect of events in the related 

study/work, household and housing careers. We have four motives for moving: ‘Housing’, 

‘Household’ and ‘Work/Study’ and ‘Other reasons’. This last motive shows a distance decay 

that resembles, which is similar to one of housing and household. We distributed ‘Other 

reasons’ over these two classes. 

2. Prepare the inter-municipal person matrix 

The migration matrixes are for free and handled by the national Central Bureau Statistics. The 

migration periods are 1988-1990 and 2000-2002. These periods were chosen to link them to 

the WBO’s 89/90 and 2002. We took the average flow to account for yearly fluctuations. The 

migration flow is one-year age group specific. We made four separate matrices: total, 18-34, 

35-54, 55+. The square migration matrix consists of a diagonal, which reflect the intramunicipal 

migration, while the two triangles above and below the diagonal reflect the in- an 

out migration. The diagonal, intra municipal moves, is empty. This may have an impact (step 

3). For each pair of municipalities the crow-flight distance is calculated and classified into 5 

km classes. Finally we splitted each of the four matrices up according the three motives for 

moving (step 1). For he analyses of 2007 we only used the ‘total’ matrices for each motive for 

moving in a cumulative way: housing motive (base), base + household motive (base X) and 

base X + study/work (total). 

3. Determine spatial patterns for each inter-municipal migration matrix (2006 & 2007) 

We used one of several well-known methods for clustering on functional distance: Intramax- 

Analysis (Masser & Scheurwater, 1977). It is a well-tested method (see for overview 

flowmap.geog.uu.nl). The proportional amount of interaction within a group is maximized in 

each step of the procedure. In our case, at each step of the merger process between all the 

remaining separate areas, the two areas are merged that have the strongest relative 

relationship in terms of people moving house. This means that two areas are grouped on the 

basis of the maximization of the following target function: 

Imax=Tij / (Oi * Dj) + Tij / (Dj * Oi) 

In which: 

Tij = interaction between origin area i and destination area j 

Oi = Σj Tij 

Dj = Σi Tij 

The interaction between each pair of municipalities (T) is divided by the product of the sum 

per row and column. The formula reveals a strong familiarity to chi-square based measures. 

The Intramax is a stepwise hierarchical procedure: N areas are grouped in N-1 steps. The total 

intrazonal flow is zero at N areas, and 100% at 1 (N-(N-1)) areas. In each step inter-zonal 

flows (in and out) become intra-zonal. Based on the definition of the housing market region 



7


the stop-criterion is 75% intra-zonal migration. For the comparison for 1990 and 2002 we 

used this criterion. For the present analyses we apply also an new criterion: 12 provinces. We 

deal with spatial data and we cluster spatially. We applied Intramax in the free GIS-package 

FlOWMAP. This GIS is Flowmap(flowmap.geog.uu.nl). 

4. Compare the maps and conclude 

By means of overlay in GIS the rather simple maps can be compared by sight instead of all 

kinds of sophisticated statistics (Visser 2005). 

The final step is to use the new classification to calculate the indexes of affordability, home 

ownership and housing shortages. 

4. Does the official housing market classification suit our 

housing policy indicator efforts? 

The age group 18-34 is twice as large (650,000 persons) as the next age group (3.40,000 

persons) and more than three time as large as the 55+ (180.000 persons). Most people move 

until 10 km. The average ‘search area’ is 80 km 2 or 0.23% of the whole of Dutch area. 

Figure 4 shows the relative flows per distance class per motive for moving in 1989/90 en 

200/2002. The distance decay has changed: long distance moves occur more often in 2002. 

This distance decay pattern is different for the various age groups. The age group 18-34 

moves more often for household and work/study related motives. The general conclusion is 

that most age groups just move over longer distances than in the past. 

Our findings are supported by other studies: Eastern, Northern and Southern regions become 

more attractive. Partially the housing prices and shortages in the West explain this. For 

teleworkers, consultants and professional commuters, such locations are suitable. We know 

that people accept longer commuting times (Geurs 2006, Hilbers et al. 2004, Van Ham 2002). 

Return migration of elderly might be an explanation for the age-group 55+. At this very 

moment a specific Housing Need Survey by the Ministry of Housing, Spatial Planning & the 

Environment (WoON Ouderen Module) is focusing on this age group. The age-specific, 

motive specific distance decay functions reveal themselves also in the INTRAMAX clusters 

(figure 5). 

Figure 5 shows that the official classification really makes no sense even without the 

INTRAMAX. Its internal logic fails. For instance the smallest region is Groningen 

(Groningen is yellow in the North). This region equals the municipality and the city of 

Groningen. Groningen is a primate city in the North-east. Although it has many relationships 

with it surrounding municipalities in the province of Groningen and Drenthe, the relationships 

between small remote municipalities dominate in the North. The Rotterdam region consists of 

many more cities and villages. In the analysis of indexes Groningen will boost earlier than 

Rotterdam only because of its size. The INTRAMAX shows the opposite. Indeed the number 

of moves in the North demands for a larger area than in the Rotterdam area. The city of 

Groningen functions indeed as one of the few cores where people work, bit not housed. 



8


Figure 4 Distance decay by motive in relative numbers for 1989/90 and 2002 

Sources: Goetgeluk 1997/adapted, WBO 2002/OTB 

The region Rotterdam is interesting in comparison with the Amsterdam region. Rotterdam is 

more or less comparable in all three classifications. The reason is that Rotterdam and Den 

Haag (The Hague) are part of the southern wing of the Randstad-Holland. This wing is not 

economically booming at all. The northern part is. This results in housing shortages, sky-high 

prices and therefore forced moves. Hence, the Amsterdam functional region has changed 

between 1990 and 2000. 

We draw the conclusion that the official classification may not be used for research activities 

since it from the perspective of subsidiarity it is wrong. Further we argue that the local level is 

not the municipality, but at least a level higher. Especially in the west this is true. 

Figure 5 

Comparison of the Intramax housing market regions for 1990 and 2000 and the 

official 31 and the urban structure in the Netherlands 

1990 Functional 2002 Functional 



9


Official 31 

Urban pattern 

Sources: WBO 1989/90 and 2002/OTB 

5. Searching for the right size 

If the local level must resemble the housing market region, than the provincial level might be 

suboptimal too. Figure 6 shows the match between the INTRAMAX housing market regions 

and the provinces. Different patterns occur. For instance the functional housing market region 

of Utrecht (14) nearly consists of the whole province of Utrecht. Also other patterns exist: 

housing market 1 (Groningen) consist of nearly two provinces. We conclude that the province 

will face more and more opposition from larger conurbations, which have more power either 

because of mergers or special legal power like the WGR+ (Law on Common Regulations 



10


Plus.) This will become even more problematic since the new law on Town and Country 

Planning (WRO) will reduce the controlling power of the province more. 

Figure 6 

Comparison of the Intramax housing market regions 2002 with the official provincial 

classification and the urban pattern 

a: Color: provincial borders 

b: Black/numbers: Intramax housing market regions 2002 

Sources: WBO 2002/OTB 

If it does not fit, what size would be useful? In our first test the number of 12 provinces is 

fixed. Since changing the constitution is not so easy, it makes sense to have 12 provinces. 

Only if that does not work, we may question if twelve provinces are needed. Some legal 

collaboration like the WGR+ might be useful and practical for legal reasons. Another option 

is changing the constitution. 

Figure 7 shows that 12 provinces do not suit our ideas behind subsidiarity. The Netherlands is 

a country of conurbations as figure 1 and 5 already showed. In many instances the provinces 

are equal to the housing market regions. More important is that the maps show large 

differences in the way people are distributed over space for the three motives for moving. 

Most Dutchmen will recognize the spatial patterns immediately. In accordance to the study of 

Ritsema Van Eck et al. (2006b) all functional maps show that the Randstad-Holland is not a 

unity at all. Often the Randstad-Holland is split up into the North Wing (Haarlem- 

Amsterdam-Utrecht) and the South Wing (Den Haag-Rotterdam). We agree that the 

Randstad-Holland sounds nice, but in reality does not exist at all. So, former prime-minister 

Kok may be wrong (see section 2). 

Figure 7 

Comparison of the 12 intramax and the official provincial classification on basis of 

various flows of migration 



11


Only housing related motives 

Housing + household 

Housing + household + works/study 


Provincial classification 



12


Figure 8 

Comparison of the 3 intramax ‘housing market regions’, the NUTS 1 classification on 

basis of various flows of migration 

Only housing related motives 

Housing + household 

Housing + household + works/study 


NUTS 1 classification 



13


Interesting is the recruiting area for household formation is somewhat different that the areas 

for housing and labor. Romance has its own space, but in the end all romance need is a roof 

and food. The labor and housing market even structure were we love. 

Figure 8 shows the alternative stop-criterion: 75% intrazonal. We added the NUT1 

classification of the EU. For those who are bit familiar with the logics behind the geography 

of our history the outcome is striking. Figure 9 shows the Netherlands around 1500 and 

around 1648 when the Republic of the Seven Provinces was formally accepted (Treaty of 

Munster). The second map shows the origin of our provinces, which were of Feudal (Holland) 

or the Diocese of Utrecht. The last had many economic relations with the Northern provinces. 

This old clustering has had a decisive impact on the economic, social and spatial organization 

of the Netherlands. In the 12 th and the 13 th the present urban structure was carefully planned 

for economic, political and military reasons (Rutte 2002). Only in the late 19 th and 20 th new 

cities and the nation-state were carefully planned. Old structures do no vanish that easy. 

Figure 9 The organization of the Netherlands between 1380 and 1648 

The Republic of & Provinces and Spanish Flanders at 

Between 1400-1500, before Carolus V 

the Treaty of Munster 1648 

Source: Vermaseren Beknopte Atlas der algemene en vaderlandse geschiedenis. 

This implies that a provincial classification may be old, but that the larger clusters are much 

more older. Given the development of the EU (ACED 2007), in which the subsidiarity 

process results in less power for the nation states in favor for on one hand Europe, the Council 

and so on and the other hand the regions, looking back to the future makes sense for the 

provinces. 

Of course our INTRAMAX map based on al migration flows the first map will never be 

accepted. The province of Utrecht would never accept a separation form the Randstad- 

Holland and especially Amsterdam. Political reasons have their functionality as well. But at 

least we show that collaboration between provinces makes sense. 



14


6. Back to the future? 

The modifiable areas problem has a direct relationship between functional regions and the 

concept of subsidiarity. In studies that deal with place and space researchers and policymakers 

should be aware of the risk of ecological fallacies. One risk is using spatial data that is 

more and more available. However, is this data suitable for our modeling? One basic rule is 

that the spatial level op the input must be much lower than the spatial level we want to 

explain. Further we argued that the spatial level must be linked to the objective of a research 

or policy question. Showing the risk of this kind of fallacy was the goal of this contribution. 

We used an example linked to migration, residential mobility and housing policy. We showed 

that the present classification would result in invalid indicators. In a more general sense many 

policy papers and research unjustly assume that the data used as a specific spatial scale is 

valid. This is a mistake. The core of research dealing with migration, housing choice and 

housing policy should always test if the spatial scale fits the objective and therefore the goals 

function. Neighborhood may even not exist if the spatial scale is fraud. The message is: One 

Size Fits Nothing or many Data do not suit our models. 

The final reports on affordability, home ownership, housing shortages for the Ministry of 

Housing, Spatial Planning & the Environment are not published yet. A problem is that some 

studies use the INTRAMAX while others use the official classification. After their 

publication we will extend this contribution and publish it. Sometimes researchers involved in 

policy research sometimes have a split personality. 

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what about the spatial dimension of subsidiarity in housing policy?

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