The spatial polarization of housing markets and wealth ...

Centre for Urban StudiesWorking Paper SeriesAugust 2018urbanstudies.uva.nl/workingpapers

The spatial polarization of housing markets and wealth accumulation

Rowan Arundel & Cody Hochstenbach

CUS Working Paper No.32©Rowan Arundel & Cody Hochstenbach

http://urbanstudies.uva.nl/workingpapers

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CUS Working Paper Series – WPS-No. 32 Centre for Urban Studies, University of Amsterdam

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The spatial polarization of housing markets and wealth accumulation

Rowan Arundel & Cody Hochstenbach

Abstract

Access to homeownership has always been uneven, but recent developments point to increasing inequalities. Labour markets

show divergence in terms of income and contract security, with a growing share of precarious outsiders. Intergenerational

inequalities are on the rise, amplifying the importance of parental resources in assisting adult children in entering homeownership.

Added to this, is a crucial spatial dimension to housing market polarization with divergence between ‘hotspot’ high-gain areas

versus low-gain ‘coldspots.’ The paper tackles the question of how where one buys has gained crucial importance in structuring

future housing-wealth accumulation with highly uneven access to high-gain areas. The research turns to the salient case of the

Netherlands, drawing on full-population register data. First, spatial analyses expose increasing spatial polarization in house-

value development. Second, household-level analyses demonstrate divided housing market access, revealing the combined impact

of income, employment status and different forms of parental wealth in shaping spatially variegated access. Taken together,

these dynamics in spatial polarization, labour market inequality and intergenerational support may determine opportunities

for housing wealth accumulation fundamentally exacerbating societal inequalities.

Key words: homeownership, housing wealth, spatial inequality, uneven development, dualization, intergenerational support

1. Introduction Housing matters. Housing is fundamental to shaping individual life course trajectories, being crucial

to the attainment of independence and starting a family (Clark & Dieleman 1996). It is also central

– and increasingly so – towards household economic security and wealth strategies (Doling &

Ronald 2010; Ansell 2014; Aalbers & Christophers 2014). On a societal level, housing is further

key in producing and reproducing various dimensions of inequality (Desmond 2016). Particularly,

differentiated access to homeownership and the potential to accumulate property assets appear

central to shaping societal inequalities.

Across many countries, housing assets currently represent the largest financial holding for most

households (Rowlingson & McKay 2012; Arundel 2017). Housing and property wealth also reflect

other entrenched societal divisions. While housing policy may act to attenuate social divisions, for

example through non-market housing provision and housing subsidies, over the past several

decades and especially since the Global Financial Crisis (GFC), developments have pointed

towards starkly increasing housing inequalities (Forrest & Hirayama 2015). In turn, the role of

housing itself in amplifying other dimensions of inequality has intensified (Allegré & Timbeau,


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2015; Arundel, 2017; Dorling, 2014). The gradual and uneven erosion of welfare-state safety nets

(Brenner et al. 2010) alongside labour market restructuring (Bell & Blanchflower 2011;

Stockhammer 2013) have fundamentally increased the role of housing assets towards household

economic security.

It is thus crucial to understand how uneven housing market access shapes inequalities. In

addressing the question of who is able to buy, recent scholarly work has singled out emerging

generational divides (McKee 2012; Forrest & Hirayama 2015). For numerous reasons, younger

generations struggle to buy, particularly when they can’t rely on parental financial support.

However, as we argue in the paper, it is not just a matter of who is able to buy. In a context of

spatial polarization, where booming and struggling property markets coexist, where you buy may

profoundly affect asset accumulation.

Extending previous work on the topic, this paper therefore sets out to unravel the factors that

structure young-adult households’ likelihood to buy property in ‘high-gain’ locations – that is, areas

where the potential for housing asset accumulation is higher. To do so, the paper employs an

integrated perspective tackling three interrelated dynamics that structure unequal housing access:

spatial inequality in house-value developments, labour market divides, and the role of parental

resources in shaping young adults’ housing access. In doing so, we argue for an essential spatial

understanding of housing’s role in inequality trends. Spatial uneven development interacts with

various socio-economic household dimensions in shaping housing wealth inequalities. The

research empirically investigates these processes through an analysis of housing market

polarization in the Netherlands between 2006 and 2016 and current dynamics of inequality in

access.

The Dutch case is particularly salient, as it is characterized by relatively modest income inequality,

but strong wealth inequality (Reuten 2018). The highly financialized housing market serves as a

key explanation for wealth disparities (Van Bavel & Salverda 2014; Fernandez & Aalbers 2016).

Around 60% of the Dutch housing stock is owner occupied and 30% social rent. Traditionally,

social rent accommodated large population segments, but the tenure is declining in size and access

increasingly restricted to low-income populations (Musterd 2014).

The paper begins by outlining contemporary developments that have exacerbated uneven housing

dynamics. The sections that follow introduce two further key dimensions: labour market divides,

and the growing role of parental resources in housing market entry. The empirical approach

examines the interconnection between these three dynamics – spatial polarization in housing


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markets, labour market inequalities, and parental support – within the case of the Netherlands.

The research focuses on two primary components: (a) an examination of nation-wide trends of

increasing spatial polarization in neighbourhood-level housing value1 developments, and (b)

modeling how labour market position alongside parental resources structure entry into divergent

asset-accumulation neighbourhoods. To do so, it draws on longitudinal and individual-level

register data from Statistics Netherlands. The discussion of the results brings attention to the way

in which these interacting dynamics may contribute to fundamental divides in wealth accumulation.

2. Theory

Housing market context A variety of forces over the past several decades have, by and large, increased housing’s role in

deepening societal inequalities (Forrest & Hirayama 2015). Across countries, housing

transformations have triggered growing divides in property values. Two key factors are increased

but uneven flows of capital into housing and mortgage system changes. These dynamics are linked

to the marketization and ‘financialisation’ of housing (Aalbers, 2016; Aalbers & Christophers 2014;

Arundel 2017). Financialisation has promoted increased investment in property alongside

expanded mortgage credit that has allowed greater leverage for housing purchase and an

embedding of real estate into global circuits of capital (Aalbers 2008; Schwartz & Seabrooke 2008;

Aalbers & Christophers 2014).

On a macro-level, financialization and expanded access to (cheap) credit essentially inflated the

values of collateral assets pushing up property values (Stiglitz 2012). Property markets became

increasingly an absorber of a global glut of capital, or ‘wall of money’ in pursuit of investment

opportunities (Fernandez & Aalbers 2016). Lower returns on bonds, bank deposits and pension

annuities, increased the appeal of real estate for those with capital (Green & Bentley 2014). As

Aalbers (2016:139) argues, “house prices did not skyrocket because demand increased faster than

supply (although this can be a contributing factor) but because the supply of money directed

towards housing went up.” Such house-value inflation was fundamental to growing divides over

the long-term, where growing wealth for market insiders contrasts with rising affordability barriers

for many others (Allegre & Timbeau 2015).

The spatial inequality of housing market developments Location plays an essential role in housing asset accumulation. While this may be a self-evident

real estate trope, spatial divisions in housing markets are seemingly on the rise. Financialization

1 The terms ‘housing values,’ ‘house values’ and ‘property values’ are used interchangeably in the paper.


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has arguably intensified the capitalist logic of uneven development (Smith 1984). Increasing spatial

polarization plays out on various scales, for example between regions, between cities but also at

the very local level of neighbourhoods. At higher spatial scales trends point towards the channeling

of capital into first and second-tier global cities (Sassen 1991; 2014), which are considered the loci

of future economic growth. Property markets in such cities are targeted for both asset returns and

a relatively secure store of capital (Fernandez et al., 2016). Conversely, peripheral (shrinking)

regions are confronted with disinvestment and capital shifting away (Martinez-Fernandez et al.

2012).

The changing geography of capital flows, has gone hand in hand with state restructuring. Many

states have shifted from a form of “spatial Keynesianism” – with policies dampening spatial

inequalities by providing a counterweight to market forces – towards market-enabling

entrepreneurial policies that privilege winner areas (Brenner 2004). National policies increasingly

focus on successful city regions, under the presumption that this will boost national economic

growth. This is evidenced in contexts such as the Netherlands where more centralized power

facilitated national policy towards central-city growth (Terhorst & Van de Ven 1995). In addition,

local governments develop policies to outcompete other cities in capturing capital flows (Harvey

1989). The privileging of already strong and burgeoning locales, and direction of private and state

resources to such areas, may aggravate spatial polarization on different scales (Etherington & Jones

2009).

This does not only pertain to inter-regional differentiation, but also to divides at smaller scales

such as the neighbourhood. Selective residential mobility and social mobility tend to reproduce

concentrated affluence and disadvantage, as reflected by persistent patterns of socio-economic and

ethnic segregation (Sampson & Sharkey 2008; Sampson 2012). Recent studies point to an increase

in socio-spatial polarization, with income segregation on the rise in cities across Europe and the

United States (Bischoff & Reardon 2014; Tammaru et al. 2016). Such studies focus on population

distributions but similar patterns likely apply to neighbourhood housing market divisions as

measured, for example, by changing property values.

Existing research has pointed to associations between higher social class and increased housing

capital gains (Hamnett 1999) alongside recognition that such patterns can be geographic in nature

(Hamnett 1992; Arundel & Ronald forthcoming). However, direct research into spatial disparities

in housing wealth accumulation has been limited. A recent Swedish study concludes that higher-

income individuals were able to achieve greater capital gains, with residential trajectories in terms

of where they live over their life course serving as an important explanation (Wind & Hedman


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2017). Similarly, a US study shows that low-income and migrant groups in poor neighbourhoods

suffered the worst capital losses after the real-estate bubble burst in 2008 (Newman & Holupka

2016).

Uneven developments exacerbate socio-spatial inequalities and house-value polarization, making

it more important to buy into the ‘right’ location. While the persistence of neighbourhood

inequality is important, neighbourhood change, notably gentrification, may also exacerbate

housing wealth divides. Especially more affluent population groups are able to capitalize on rent

gaps (Smith 1979), using their capital and access to mortgage credit to close such gaps (Wyly &

Hammel 1999; Aalbers 2007; Hochstenbach & Boterman 2017). Higher-income newcomers may

thus be able to buy into the most profitable niches. While long-term homeowners may also benefit

from rising house values, low-income tenants may struggle to stay put (Newman & Wyly 2006).

Labour market context Understanding housing inequality cannot be separated from the underlying factors that structure

unequal access to the housing market. Clearly, labour market position fundamentally shapes both

the ability to access homeownership and the type of local submarket households are able to invest

in. Of course, housing preferences and decisions are also influenced by a host of related social and

cultural factors (Ley 1996; Bourdieu 2005). Nevertheless, a sufficiently high income and degree of

economic security appear necessary to enter homeownership in the first place, while a particularly

strong economic position is needed to enter high-demand submarkets.

Contemporary labour market developments point towards increasing income and wealth

disparities across countries over the course of recent decades (Arundel & Doling, 2017; Saez and

Zucman 2016; Piketty 2014; Nolan et al. 2014). Economic restructuring has undermined greater

economic certainty that existed under previous Fordist conditions (Beck 1992; Giddens 1999), as

evidenced by the rise of contract precarity and underemployment (Standing 2011; Buchman and

Kriesi 2011; Nolan et al. 2014). While unemployment rates fluctuate over time, new positions have

been less likely to be full-time or stable, with steady long-term increases in the shares of involuntary

part-time and temporary contracts (Arundel & Doling 2017; OECD 2016). These trends

contribute to a hollowing out of the traditional middle classes across advanced economies

(Milanovic 2016).

Various interrelated dynamics have driven labour market restructuring. Developments in

mechanisation have led to employment displacement especially among lower and middle incomes.

Globalization and neoliberal reforms have increased the mobility of capital and work, undermining

union bargaining power (Brown et al., 2011; OECD, 2011). Labour reforms have tended to further


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reduce workers’ protection while privileging shareholders and short-term profit interests. The

consequences are a concentration of economic returns among holders of capital, with declining

labour income shares (Stockhammer 2013; Piketty 2014; Wood, 2017). Finally, the turn towards a

service-oriented economy across many countries is associated with stronger labour and socio-

economic polarization (Sassen 1991; Oesch 2006).

These drivers promote a growing ‘dualization’ between insiders and outsiders on the labour

markets (Davidsson & Naczyk 2009; Emmenegger, 2012). Dualization emphasizes that rather than

universal labour flexibilisation, reforms have tended to (relatively) spare a core group of ‘insiders’

thereby widening the gap with ‘outsiders’ facing the brunt share of worsening labour market

conditions (Biegert, 2014). Broadly-speaking, those already in more vulnerable circumstances

appear worst hit (Hills et al. 2013).

Taken together, labour market trends have had very differentiated outcomes across groups and

across time, with significant evidence of rising intergenerational inequality. Older cohorts – albeit

by no means a uniform group – were more likely to benefit from favourable past labour conditions

and be more shielded from market reforms (Hausermann and Schwander 2012; Biegert, 2014).

These advantages intersected in many contexts with easier homeownership access and subsequent

house-value increases, resulting in significant shares of older cohorts with (substantial)

accumulated property wealth (Forrest & Hirayama 2015). On the other hand, younger cohorts

have faced labour market deterioration alongside growing housing unaffordability and, post-crisis,

more restricted access to credit. Data from the US and Europe have shown long-term decreases

in relative incomes for young adults (Bell & Blanchflower 2011; Arundel & Doling 2017), while

crisis outcomes have most impacted younger generations (Hills et al. 2013; Clapham et al. 2014;

McKee 2012).

Intergenerational family support Labour and housing restructuring have particular generational consequences on the housing

market. Homeownership rates among young adults are in decline across many countries (Lennartz

et al., 2016), with young adults more often staying in, or returning to the parental home (Arundel

& Ronald, 2016; Stone et al. 2014). They also have to rely to a greater extent on private rental and

insecure housing options (Clapham et al., 2014; Lennartz et al., 2016; Hochstenbach & Boterman

2015). The concentration of housing wealth among older generations ostensibly enhances the role

of intergenerational support in young adult housing trajectories, especially with regards to

purchasing a home (Forrest & Hirayama, 2015). Intergenerational transfers may take many forms:

from direct financial assistance, acting as guarantors in enabling credit access, to in-kind support


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through childcare or co-residence that may allow increased savings towards home purchase

(Helderman and Mulder, 2007; Forrest and Hirayama, 2015; Öst 2012). Beyond financial

assistance, parents may lend other support, such as resourceful social networks and specific

housing knowledge (Hochstenbach & Boterman 2017). Finally, the intergenerational transmission

of homeownership is more broadly related to general social-class reproduction (Friedman et al.

2015), socialization (Gurney & Rowlands 2000), and normative (classed) expectations of

transitions to adulthood (Druta & Ronald, 2017).

Beyond simply access to homeownership, support from parents can also be fundamental in

structuring what housing is purchased. Parental resources may allow young adults to enter more

expensive housing segments (Spilerman & Wolff 2012). This also influences where young adults are

able to buy. Previous work shows how parental background plays an important role in the

residential location of young adults (Van Ham et al. 2014; Hochstenbach & Boterman 2017;

Coulter 2017), for example forging residential segregation based on parental resources

(Hochstenbach 2018).

Many young adults have an urban orientation, and increasingly so (Ley 1996; Boterman 2012).

Education institutions, facilities and labour market opportunities concentrate in larger cities.

Within these cities, middle-class young adults typically prefer centrally-located gentrifying

neighbourhoods. These areas are also commonly housing submarkets of higher capital gains. In

addition to intergenerational solidarity, parental assistance in buying into gentrifying

neighbourhoods can therefore also represent a strategic financial investment. Parental financial

support does not just flow randomly into the housing market, but is channeled into specific urban

neighbourhoods (Hochstenbach & Boterman 2017). Such support further drives up

neighbourhood house prices, contributing to the exclusion or displacement of lower-income

groups and stronger spatial polarization.

Taken together, dynamics of spatial inequality, divides in labour market position, and differentiated

access to family resources may structure both entry to homeownership and access to more or less

profitable submarkets. Given the spatial fixity of real estate markets, such dynamics may intensify

existing segregation and forge new spatial divides of uneven housing capital accumulation.

3. Data and analytical strategy The empirical research address two primary foci: (a) spatial polarization in housing values across

the Netherlands, and (b) modeling how labour market position alongside parental resources

structure entry into different asset-accumulation neighbourhoods. In other words, the paper


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investigates polarization between ‘hot’ and ‘cold’ property markets as well as how new home buyers

sort into these different submarkets.

This paper draws on data from the System of Social-statistical Datasets (SSD) of Statistics

Netherlands, which allows users to combine different register datasets. It contains data on the full

population registered in the Netherlands, as well as dwelling-level information for the total housing

stock. All data are geocoded and longitudinal, making it possible to follow people over space and

time.

Spatial analysis of housing value dynamics In order to assess spatial housing market polarization, it is necessary to measure house-value

developments at low spatial scales. We draw on dwelling-level registry data to determine property

values for each year in the 2006-2016 period. Information on dwelling size is not available, so we

rely on total values. Only those dwellings that have real estate information available for all years

are included in our analyses. We exclude demolished and newly-built dwellings, as well as those

with missing data in any of the years. This leaves a ‘stable’ housing stock, which we use to

determine average neighbourhood-level property values. Property values (Dutch: Waardering

Onroerende Zaken [WOZ]) are based on building characteristics, official valuations, and recent selling

prices of nearby properties (Rijskoverheid, 2018). There is a strong correlation between house

values and house prices, but differences exist: rental units also get a value assigned, and a time lag

exists as changing sale prices take one or two years to translate into changes in assessed values.

We follow the 2016 Statistics Netherlands country-wide neighbourhood classification (Dutch:

buurten), typically delineated by natural boundaries, waterways and major infrastructure.

Neighbourhood boundaries are stable over time. To comply with SSD privacy requirements, we

exclude all neighbourhoods where less than ten (stable) dwellings had property values assigned.

We exclude a total of 1,621 neighbourhoods, mostly scarcely populated rural, industrial, or newly

developed areas, and a very small number due to missing data.2 This leaves a total of 11,202

neighbourhoods with 5,949,451 stable dwellings in our analyses.

We measure (inflation corrected) house-value development at the neighbourhood level in

percentage change over different periods, including 2006-2016 (the full data range) and 2014-2016

(early post-crisis ‘recovery’). GIS analysis is used to spatially map uneven developments in detail.

The resulting maps are presented as cartograms with neighbourhoods distorted proportional to

2 i.e. land-registry data is missing for the municipality of Steenbergen (10,006 dwellings distributed over 18 neighbourhoods in 2016). Due to the municipality’s small size this omission does not influence our findings.


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dwelling numbers using the Gastner-Newman diffusion algorithm (Gastner & Newman 2004), as

this graphically indicates the relative size of property markets.

Subsequently, we analyze trends in housing market polarization by comparing low-value

neighbourhoods with high-value ones. We calculate house-value ratios, comparing the most

expensive neighbourhoods (i.e. top 10%) with the least expensive (i.e. bottom 10%). Increasing

ratios over time indicate a stronger house-value polarization. A Gini coefficient for average

neighbourhood values over the years is further measured to capture spatial inequality trends. The

Gini is a common measure to capture inequality, ranging from 0 (full equality) to 100 (complete

inequality).

Regression modelling The second empirical part of this paper looks into the question of where – in which

neighbourhood property markets – young adult households buy a home. The key question is to

what extent young adults of different socio-economic and parental backgrounds sort into hotter

or colder property markets with varied asset accumulation potential.

We first answer this question through descriptive analyses, focusing on the characteristics of young

adults moving into the different neighbourhoods. We do so by constructing neighbourhood decile

groups based on the 2014-2016 house-value development. To account for differences in

neighbourhood size, decile breaks are weighted by number of dwellings. The top decile thus

includes the 10% dwellings with the strongest neighbourhood house-value developments: the

‘hottest’ property markets nationally. Subsequently, we estimate regression models with the 2014-

2016 neioghbourhood-level percentage change in property values as dependent variable. Key

predictor variables concern labour market position and parental wealth background, as described

below.

Research population Our analyses focus on young-adult households that we classify as ‘new’ entrants having entered

homeownership at any point in 2015. All analyses are at the household level, because this is where

economic resources are bundled and translate into housing market purchasing power. We only

include households with one or two adult members, where the oldest household member is aged

between 16 and 39. Only households where all members moved residence are included to ensure

we are dealing with a new purchase, rather than people moving in together. Our selection only

includes young adults who were previously tenants or living in the parental but who are owner-

occupiers after moving. In most cases these young adults will be first-time buyers, although we

cannot rule out that this includes returns into owner occupancy. As we are interested in the role


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of parental resources, only households where the wealth position of at least one parent is known

are included. After narrowing down our population, and excluding cases with missing data, our

population consists of 87,014 households (see Table 1 for descriptive statistics).

Table 1: Descriptive data. Household population in the multivariate analysis N=87,014. Data source: SSD, Statistics Netherlands, own calculations. % Mean

Gross household income (in Euros) 57,861 Source of income (main earner)

Employment - temporary contract 53.6 Employment - permanent contract 28.7 Employment - unknown/not applicable 1.0 Self employment 5.2 Benefits 2.1 Student bursary 9.2 Other 0.2

Age (oldest household member) 28.5 Household type

Single person 40.7 Couple without children 39.5 Couple with children 17.2 Single parent 1.6 Other 1.0

Ethnicity (main earner) Native Dutch 94.0 Non-western non-native 3.8 Western non-native 2.2

Gender (main earner) Female 33.0 Male 67.0

Highest followed education level (main earner) Unknown 5.0 Low 2.5 Mid 35.3 High 57.2

Parental housing wealth (in Euros) 171,347 Parental other wealth (in Euros) 274,418 Number of siblings 2.68

Parents together 54.99 Average neighbourhood house value in 2014 (in Euros) 211,066 Neighbourhood house value change 2014-2016 (in %) -0.85


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Labour market indicators We include various labour-market indicators in our analyses, considering both gross household

income and employment situation in 2015. Employment situation is assigned for the household’s

main earner and is derived from tax registers. We distinguish five employment situations, based

on highest source of income over the year: employed, self-employed, receiving benefits, receiving

student bursaries, and a small other/unknown category. We subsequently draw on insurance

registers to determine whether employed main earners are on a permanent or temporary contract.3

In a small number of cases, employment status is categorized unknown, or not applicable (i.e. for

of major shareholders, or company directors).

It should be acknowledged that our analyses focus on a relatively well-off group of home buyers.

Those in rent or in the parental home – skewing lower on socio-economic status – are not part

of the research scope.

Parental resources Our analyses also focus on parental resources. Although we don’t have data available on actual

intergenerational financial transfers, we are able to include parental wealth positions. We

distinguish between net parental housing wealth and net other non-housing forms of parental

wealth. These net wealth measures subtract all outstanding debts, including mortgages. We

distinguish between the types of wealth because non-housing wealth such as savings may be more

easily transferable than (illiquid) housing wealth.

Parental wealth is summed for all parents of a household to account for the fact that couples with

affluent parents on both sides may receive more support than couples with affluent parents only

on one side. In other words, we measure the total wealth (minus debts) of all parents of all adult

household members.4 In cases where parents are separated, parental wealth is summed for both

parents. When wealth is missing for a parent (i.e. due to death or a parent living abroad), only the

assets of the other parent are used.

Young-adult households’ own wealth position may also play a role, but unfortunately our data only

allows us to include household wealth post-moving. At this point first-time buyers may have eaten

up their savings to buy and maximized their mortgage capacity. Including pre-moving wealth is

3 Very minor discrepancies between the tax and insurance datasets exist, though they are unlikely to skew results. 4 In the multivariate analyses we control for the total number of siblings as this may influence how much financial support one child may receive.


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not possible, as we cannot distinguish personal wealth from parental wealth for young adults that

were living in the parental home.

4. Results

Spatial polarization of housing wealth The spatial analysis examines the uneven development of housing values across the Netherlands over the 2006-2016 period. This period includes rising average values until about 2010 with a subsequent stagnation then decline in the aftermath of the GFC. Only after 2015, average house values start increasing again (see Table 2). However, this national trend masks strong spatial variations in property value dynamics.

GIS analyses reveal stark spatial inequality in house-value development (Figure 1, see Appendix 1 for additional maps). National trends over the 2006-2016 period show a decline in house values to a point just below their 2006 values, but this pattern is strongly differentiated across the country (Figure 1a). Most areas show stronger declines while a few specific property markets have, on the contrary, displayed significant property value appreciation. Rising property values – i.e. hot markets – are mostly concentrated in a limited number of larger cities, namely Amsterdam, Utrecht, and Groningen.

The 2014-2016 period (Figure 1b) shows a striking pattern of divergent ‘recovery.’ Despite national indications of property value increases, a spatial understanding of these developments reveals instead a story of broad stagnation nationally. The glaring exception here is Amsterdam, where substantial property value gains dominate. Notwithstanding differences in absolute property values, the spatial analysis uncovers the stark geographic heterogeneity in real estate developments, and suggests a growing differentiation in potential asset accumulation among homeowners depending on where households buy property.

Composite indicators measuring inequality in housing values, broadly confirm increasing house-value polarization between neighbourhoods across the country (Table 2). A pattern of hot spots becoming hotter and cool spots cooler emerges. The 90:10 ratio shows that neighbourhoods in the 90th percentile had housing values that were 2.79 times higher than those in the 10th percentile in 2006. By 2016, this ratio had gone up to 3.04 – indicating increasing spatial inequality. Annual figures show a clear and consistent trend upwards, excluding a dip between 2013 and 2014. A very similar story is apparent looking at the 80:20 ratio (where values of the 80th percentile are divided by those of the 20th percentile), although the multiples are lower and the slope is more gradual as would be expected. Looking at the weighted 90:10 neighbourhood decile breaks (where divisions between deciles account for the number of dwellings), we see a longer and more gradual reversal as an effect of the crisis but a similar pattern of overall increase and especially in recent years. The weighted 80:20 ratio, while showing the same trend is much more stable.

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Figure 1: Change in property values per neighbourhood across the Netherlands

Cartogram distortion based on Gastner-Newman diffusion algorithm.

Data source: SSD, Statistics Netherlands, own calculations. Values adjusted for inflation using the Consumer Price Index, Statistics Netherlands.

Tilburg

Amsterdam

The Hague

Almere

Utrecht

Rotterdam

Groningen

Eindhoven

Rotterdam

Almere

Tilburg

Amsterdam

Utrecht

Groningen

The Hague

Eindhoven

0 50 km

Cartogram proportional to number of dwellingsa) 2006-2016

> 40%30% to 40%20% to 30%10% to 20%2% to 10%-2% to 2%-10% to -2%-20% to -10%-30% to -20%- 40% to -30%< -40% Excluded (<10 dwellings)Eight largest municipalities

b) 2014-2016Cartogram proportional to number of dwellings

$

Eight largest municipalities


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Table 2: Inequality measures between neighbourhoods based on average property values.

Note: analyses based on a stable housing stock over time. Data source: SSD, Statistics

Netherlands, own calculations.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

unweighted 90:10 2.79 2.80 2.83 2.87 2.91 2.92 2.94 2.98 2.94 2.96 3.04

unweighted 80:20 2.01 2.02 2.02 2.04 2.07 2.08 2.09 2.12 2.08 2.08 2.14

weighted 90:10a 2.25 2.24 2.26 2.29 2.32 2.30 2.30 2.29 2.28 2.31 2.35

weighted 80:20a 1.70 1.70 1.70 1.72 1.73 1.71 1.71 1.71 1.72 1.72 1.73

GINI 22.89 22.95 23.13 23.42 23.68 23.67 23.81 24.06 23.76 23.87 24.49

Mean neighbourhood property valuesb €297,907 €318,169 €331,340 €339,018 €337,296 €321,614 €307,688 €337,296 €268,340 €259,104 €265,485

Mean property valuesb €173,871 €190,215 €207,847 €217,309 €220,705 €219,914 €220,239 €216,983 €206,013 €201,492 €205,767

Number of neighbourhoods 11,202 11,202 11,202 11,202 11,202 11,202 11,202 11,202 11,202 11,202 11,202

Number of dwellings 5,949,451 5,949,451 5,949,451 5,949,451 5,949,451 5,949,451 5,949,451 5,949,451 5,949,451 5,949,451 5,949,451

a Weighted for number of dwellings per neighbourhood (percentile divisions based on dwelling counts). b Adjusted for inflation. Values in 2015 euros.

Finally, the Gini captures overall inequality in the distribution of average neighbourhood-level

housing values. Increasing Gini-coefficients (from 22.9 to 24.5) confirm a clear and substantial

pattern of spatial house-value polarization: nationwide, the distribution of housing values across

neighbourhoods has become more unequal. Between 2015 and 2016, the Gini shows a rapid

increase, suggesting accelerating polarization in the most recent years.

Given the time lag and more tempered nature of assessed property values in comparison with

actual sale prices, it is expected that the results would show even higher levels of inequality

measured by sales prices. Recent data on prices, only available for the largest municipalities, show

an intensified dominance of Amsterdam compared to the national average (Hochstenbach 2017).

This indicates that as the city captures an increasingly disproportionate share of property capital

flows, spatial polarization may accelerate.

Divided access We are subsequently interested in where young adults entering homeownership buy their home.

More specifically, we aim to unravel to what extent various socio-economic characteristics shape

access to high-gain versus low-gain submarkets for the 2014-2016 period, as already unraveled

above.

Descriptive findings Descriptive results (Table 3) reveal that, as may be expected, gross household income is positively

associated with the ability to buy in high-gain neighbourhoods. Households buying into the top


15

20% of neighbourhoods earn 2 to 6 percent more than the average young-adult household in our

population, while those moving into the lowest-gain neighbourhoods, earn 8 percent below

average. The relatively muted relationship can partly be explained by the fact only buyers are

included who would need a more substantial income to buy a home at all.

Table 3: Variation from mean for predictor variables across neighbourhood deciles

(mean=100). Data source: SSD, Statistics Netherlands, own calculations

<< lowest gain neighbourhoods highest-gain neighbourhoods >>

Deciles of neighbourhood house value change 2014-2016 1 2 3 4 5 6 7 8 9 10

Gross income 92 101 97 99 101 100 101 102 106 102

Parental (total) wealth 88 99 93 95 102 97 93 95 110 126

Parental housing wealth 90 105 94 97 101 95 94 96 108 116

Parental other wealth 82 96 89 94 103 97 92 95 115 141

Low education 128 115 108 116 112 100 98 101 74 63

Mid education 118 112 116 117 109 104 101 96 85 59

High education 88 91 89 88 93 98 99 103 111 129

Permanent contract 98 102 100 103 105 100 101 101 103 91

Temporary contract 104 102 101 100 97 100 101 101 94 101

Self employed 92 114 96 90 96 92 102 91 98 122

Unemployed 130 89 112 106 96 113 101 94 97 72

Student 99 79 98 87 84 105 95 96 104 140

Legend > 110 < 98 > 102 < 90

Looking at education reveals stronger differentiation. While overall, our population is a relatively

highly educated group,5 there are nonetheless clear divergences in neighbourhood outcomes.

Those with a low or middle education buy into low-gain areas relatively often, while those with a

high education concentrate in high-gain areas.

Employment status of principal household earner reveals some interesting findings. Both positions

of permanent and temporary contracts display relatively even distributions. Those on a permanent

contract do not buy into high-gain areas more often, neither does there appear to be a substantial

penalty for those on a temporary contract. Surprisingly, young adults buying into the highest-gain

5 This is partly because we focus on young adults and homebuyers – both groups with higher education levels than the total population.


16

areas are relatively often self-employed or students – a point we will elaborate upon in the

subsequent section.

Interestingly, parental wealth shows stronger divergences in neighbourhood outcomes than

household income. On average, the parents of young adult households moving into the highest-

gain neighbourhoods (top decile) hold 26% more wealth than average. Conversely, parental wealth

of young adults buying into the lowest-gain neighbourhoods is 12% below average. Distinguishing

between parental housing wealth and other non-housing parental wealth (i.e. savings), we find the

latter show an even stronger correlation with neighbourhood outcomes. This indicates that other

forms of wealth may be more liquid and easily transferable between generations than housing

wealth.

Multivariate analyses We estimated OLS regression models with the 2014-2016 neighbourhood property value change

as the dependent variable, to assess the isolated impact of labour market position and parental

wealth background (Table 4). We construct a two-step model, adding key parental variables in the

second step.

We first see a significant positive association between gross household income and buying into

higher-gain property markets. This is an important initial indication of an intensification of socio-

economic inequality through unequal asset accumulation potential. Education measures further

corroborate such reproduction of socio-economic divisions with highly educated households

being significantly more likely to move into high-gain neighbourhoods than their peers with a low

or middle education level.

Even after controlling for other factors, employment position measures reveal similarly

unexpected results as apparent in the descriptive findings. We find, surprisingly, that young adults

on a temporary contract, in self-employment, or in education are more likely to move into high-

gain areas than young adults on a permanent contract. One could argue that these groups have a

more urban orientation – where the strongest increases in housing values are also recorded. We

therefore estimated additional models (available upon request) focusing only on young adults that

bought in urban areas (models across the eight largest cities, the four largest cities, Amsterdam and

the Amsterdam metropolitan region). Even when narrowing our population, a similar effect for

those self-employed and temporarily employed is found. In other words, even within cities, these

young-adult households appear to sort into relatively higher gain areas. One explanation would be

that these represent ‘marginal gentrifiers’ – with lower economic but higher cultural capital (Rose

1984). They may lack the economic position to buy into established areas, seeking out niche

markets of incipient gentrification instead.


17

Table 4: Multivariate models (OLS regression). Dependent variable = percentage change

in house values 2014-2016 of the destination neighbourhood. Data source: SSD, Statistics

Netherlands, own calculations. Note: *p<0.05 **p<0.01 ***p<0.001.

Model 1 Model 2

Beta p Beta p Gross household income (in Euros) 0.083 *** 0.000 0.076 *** 0.000

Source of income (main earner)

Employment - temporary contract (ref) (ref) Employment - permanent contract 0.031 *** 0.000 0.030 *** 0.000 Employment - unknown/not applicable 0.016 *** 0.000 0.014 *** 0.000 Self employment 0.032 *** 0.000 0.031 *** 0.000 Benefits 0.007 * 0.039 0.006 0.087 Student bursary 0.058 *** 0.000 0.541 *** 0.000 Other 0.020 *** 0.000 0.019 *** 0.000

Age (oldest member) 0.023 *** 0.000 0.018 *** 0.000

Household type

Single person (ref) (ref) Couple without children -0.084 *** 0.000 -0.085 *** 0.000 Couple with children -0.094 *** 0.000 -0.092 *** 0.000 Single parent -0.017 *** 0.000 -0.016 *** 0.000 Other 0.016 *** 0.000 0.017 *** 0.000

Ethnicity (main earner)

Native Dutch (ref) (ref) Non-western non-native 0.014 *** 0.000 0.017 *** 0.000 Western non-native 0.029 *** 0.000 0.027 *** 0.000

Gender (main earner)

Female (ref) (ref) Male -0.038 *** 0.000 -0.036 *** 0.000

Highest followed education level (main earner)

Unknown 0.008 * 0.016 0.008 * 0.026 Low -0.002 0.522 -0.003 0.461 Mid (ref) (ref) High 0.124 *** 0.000 0.120 *** 0.000

Parental housing wealth 0.036 *** 0.000 Parental other wealth 0.042 *** 0.000

Number of siblings -0.030 *** 0.000 Parents together -0.036 *** 0.000

Average neighbourhood house value in 2014 0.091 *** 0.000 0.085 *** 0.000

Constant *** 0.000 *** 0.000 Number of cases 87,014 87,014 Adjusted R2 0.0478 0.0525


18

Looking at parental background variables, the subsequent model confirms a significant

independent ‘effect’ of parental housing and non-housing wealth on buying in higher-gain

neighbourhoods. These results clearly underscore the importance of intergenerational support in

accessing prime housing markets. Given that we are not able to look directly at financial transfers,

we acknowledge that parental wealth may also capture the broader social-class background of

young adults, although we do control for other key socio-economic indicators. Furthermore, the

‘number of siblings’ shows a significant negative effect indicating that potentially sharing parental

wealth with more siblings reduces the odds to move into high-gain areas. This reinforces the

likelihood of parental resources being important in direct support beyond broader class position

and associated preferences.

Among our control variables, the models return interesting results for ethnicity. We find that non-

western non-natives are significantly more likely to buy in higher-gain neighbourhoods compared

to their native Dutch counterparts. While this is initially an unexpected result, when we run a model

focusing only on the four largest cities or on Amsterdam, we instead find this group instead to be

significantly less likely to buy into high-gain areas. This therefore would seem to be explained by

their urban focus. In other words, non-western non-natives homebuyers tend to buy more often

in urban areas and especially within the largest cities where we also find many high-gain

neighbourhoods. Within these cities, however, they are more likely to buy into neighbourhoods

showing a relatively weak development in housing values.

It is furthermore important to keep in mind that we focus on a specific subset of homebuyers,

with non-western non-natives much more likely to depend on rental housing. Hence, urban house-

value increases may indeed benefit a small group of ethnic minorities – enabling those that bought

to accumulate substantial housing assets – but a larger share among these minority groups may in

fact find themselves on the losing end where house-value increases translate into declining rental

housing affordability.

Robustness checks Alongside the robustness checks for the subsample of larger cities and the Amsterdam region

discussed above, we carried out a series of additional models over different combinations of time

periods for property value change (including the full period 2006-2016, 2010-2016, 2011-2016, and

so forth). We also estimated regressions using larger borough districts (Dutch: wijken) as spatial

units. These robustness checks returned highly similar results across all variables, indicating that

our findings are not tied to a specific period or spatial scale.


19

5. Conclusion Through an analysis of detailed longitudinal register data for the Netherlands, we reveal patterns

of increasing spatial polarization in house values. Differences between ‘hot’ property markets –

where property values show the strongest increases – versus ‘cold’ markets are growing. Most

recent years indicate an acceleration in such spatial polarization. In other words, where one buys

has gained importance in securing future housing wealth accumulation. Investigating the flow of

young adults into the housing market, we further uncover sharply divided access to neighbourhood

property markets. Other than an orientation of self and temporary employed households towards

higher-gain gentrifying areas, the results revealed significant sorting of homebuyers into high or

low gain neighbourhoods across socio-economic lines. On top of household resources, the

findings expose how parental wealth structures access to high-gain areas, particularly where

parental non-housing wealth is concerned.

On the basis of these empirical findings we can make various broader, analytical contributions.

First, our paper demonstrates the importance of taking into account multiple dimensions of socio-

economic status in assessing divided housing access. Income and employment position remain

crucial, but socio-economic inequalities also depend on multiple interacting factors. Increasing

labour market precarity makes it more important to differentiate between temporary and

permanent contracts. Deepening generational divides amplify the importance of parental

resources. While we looked specifically at new buyers, such divisions among homeowners only

supplement further divides between homeowners and renters entirely excluded from property

wealth accumulation. These dynamics are often reinforced over time, as homeowners who

accumulate substantial assets are again more able to move into high-gain areas. Our findings thus

point to the existence of multiple, often overlapping and mutually-reinforcing, socio-economic

cleavages between housing insiders and outsiders.

Second, our spatial analyses speak to broader literatures on uneven development and growing

spatial divides. We find clear and consistent increases in spatial inequalities in terms of house

values, with capital gains increasingly concentrating in (certain) major cities. Uneven house-value

developments likely reflect but also reinforce broader spatial inequalities, for example with regard

to social segregation and regional divergences in economic performance. Current trends of spatial

polarization do not come out of nowhere, but are linked to decades of welfare-state restructuring

(Brenner 2004; Brenner et al. 2010), as well as the liberalization and financialization of housing

(Aalbers 2016).


20

Finally, our paper promotes a spatial understanding of wealth accumulation – one that may also

be relevant to interpret other societal inequalities. It is not just a question of owner occupiers

versus renters, as our findings point to notable segmentation between buyers. Buying in the right

place may enable some low-status households to make windfall gains, but polarization suggests

benefits mostly concentrated among already-privileged households, and increasingly so. Wealth

inequalities are not only mapped onto space, but space itself figures prominently in reproducing

and amplifying such inequalities. In other words, it matters where you buy: shaping both individual

life-courses as well as societal inequalities. This only becomes more critical as spatial inequalities

intensify.


21

References Aalbers, M.B. (2007). Geographies of housing finance: The mortgage market in Milan, Italy.

Growth and Change, 38(2), 174-199.

Aalbers, M.B. (2008). The Financialisation of Home and the Mortgage Market Crisis. Competition

& Change, 12(2), 148–166.

Aalbers, M.B. (2016). The Financialisation of Housing: A Political Economy Approach. New York:

Routledge.

Aalbers, M.B., & Christophers, B. (2014). Centring Housing in Political Economy. Housing, Theory

and Society, 31(4), 373–394.

Allegré, G., & Timbeau, X. (2015). Does housing wealth contribute to wealth inequality? A tale of two New

Yorks. Paris: Sciences Po.

Ansell, B. (2014). The political economy of ownership: Housing markets and the welfare state.

American Political Science Review, 108(2), 383-402.

Arundel, R., & Ronald, R. (2016). Parental co-residence, shared living and emerging adulthood in

Europe: semi-dependent housing across welfare regime and housing system contexts.

Journal of Youth Studies, 19(7), 885–905.

Arundel, R., & Ronald, R. (forthcoming). The false promise of homeownership. (HOUWEL

Working Paper Series). University of Amsterdam.

Arundel, R. (2017). Equity Inequity: Housing Wealth Inequality, Inter and Intra-generational

Divergences, and the Rise of Private Landlordism. Housing, Theory and Society, 34(2), 176–

200.

Arundel, R., & Doling, J. (2017). The end of mass homeownership? Changes in labour markets

and housing tenure opportunities across Europe. Journal of Housing and the Built

Environment, 1–24.

Beck, U. (1992). Risk society: Towards a new modernity (Vol. 17). London: Sage.

Bell, D.N.F., & Blanchflower, D. G. (2011). Young people and the Great Recession. Oxford

Review of Economic Policy, 27(2), 241–267.


22

Biegert, T. (2014). On the outside looking in? Transitions out of non-employment in the United

Kingdom and Germany. Journal of European Social Policy, 24(1), 3–18.

Bischoff, K., & Reardon, S.F. (2014). Residential segregation by income, 1970-2009. In: Diversity

and disparities: America enters a new century, Logan JR (ed) 208–33. New York, NY: Russell

Sage Foundation.

Boterman, W.R. (2012). Residential practices of middle classes in the field of parenthood. PhD thesis

University of Amsterdam.

Bourdieu, P. (2005). The social structures of the economy. Polity.

Brenner, N. (2004). New state spaces: Urban restructuring and state rescaling in Western Europe. Oxford:

Oxford University Press.

Brenner, N., Peck, J., & Theodore, N. (2010). Variegated neoliberalization: geographies,

modalities, pathways. Global networks, 10(2), 182-222.

Brown, P., Lauder, H., & Ashton, D. (2011). Digital Taylorism. In P. Brown, H. Lauder, & D.

Ashton (Eds.), The global auction: The broken promises of education, jobs and incomes. Oxford:

Oxford University Press.

Buchmann, M.C., & Kriesi, I. (2011). Transition to Adulthood in Europe. Annual Review of

Sociology, 37(1), 481–503.

Clapham, D., Mackie, P., Orford, S., Thomas, I., & Buckley, K. (2014). The Housing Pathways

of Young People in the UK. Environment and Planning A, 46(8), 2016–2031.

Clark, W. & Dieleman, F.M. (1996). Households and Housing: Choice and Out- comes in the Housing

Market. New Brunswick, NJ: Rutgers University.

Coulter, R. (2017). Local house prices, parental background and young adults’ homeownership in

England and Wales. Urban Studies, 54(14), 3360-3379.

Davidsson, J., & Naczyk, M. (2009). The Ins and Outs of Dualisation: A Literature Review (SSRN

Scholarly Paper No. ID 1489906). Rochester, NY: Social Science Research Network.

Desmond, M. (2016). Evicted: Poverty and Profit in the American City, New York: Broadway.

Doling, J., & Ronald, R. (2010). Home ownership and asset-based welfare. Journal of Housing and

the Built Environment, 25(2), 165–173.


23

Dorling, D. (2014). All That Is Solid: How the Great Housing Disaster Defines Our Times, and What We

Can Do About It. London: Penguin UK.

Druta, O., & Ronald, R. (2017). Young adults’ pathways into homeownership and the

negotiation of intra-family support: A home, the ideal gift. Sociology, 51(4), 783-799.

Emmeneger, P. (2012). The age of dualization: The changing face of inequality in deindustrializing societies.

Oxford: Oxford University Press.

Etherington, D., & Jones, M. (2009). City-regions: new geographies of uneven development and

inequality. Regional Studies, 43(2), 247-265.

Fernandez, R., & Aalbers, M. (2016). Financialization and Housing: Between Globalization and

Varieties of Capitalism. Competition & Change, 20(2).

Fernandez, R., Hofman, A., & Aalbers, M. B. (2016). London and New York as a safe deposit

box for the transnational wealth elite. Environment and Planning A,

Forrest, R., & Hirayama, Y. (2015). The financialisation of the social project: Embedded

liberalism, neoliberalism and home ownership. Urban Studies, 52(2), 233–244.

Friedman, S., Laurison, D., & Miles, A. (2015). Breaking the ‘class’ ceiling? Social mobility into

Britain's elite occupations. The Sociological Review, 63(2), 259-289.

Gastner, M. T., & Newman, M. E. J. (2004). Diffusion-based method for producing density-

equalizing maps. Proceedings of the National Academy of Sciences of the United States of America,

101(20), 7499–7504.

Giddens, A. (1999). Risk and Responsibility. The Modern Law Review, 62(1), 1–10.

Green, D. G., & Bentley, D. (2014). Finding Shelter: Overseas Investment in the UK Housing Market.

London: Civitas.

Hamnett, C. (1999). Winners and Losers: Home Ownership in Modern Britain. London: UCL.

Hamnett, C. (1992). The geography of housing wealth and inheritance in Britain. Geographical

Journal, 158(3), 307-321.

Harvey, D. (1989). From managerialism to entrepreneurialism. Geografiska Annaler B, 71, 3–18.


24

Häusermann, S., & Schwander, H. (2012). Varieties of dualization? Labor market segmentation

and insider outsider divides across regimes. In P. Emmeneger (Ed.), The age of dualization:

The changing face of inequality in deindustrializing societies (pp. 27–51). Oxford: OUP.

Helderman, A., & Mulder, C. (2007). Intergenerational Transmission of Homeownership: The

Roles of Gifts and Continuities in Housing Market Characteristics. Urban Studies, 44(2),

231–247.

Hills, J., Cunliffe, J., Gambaro, L., & Obolenskaya, P. (2013). Winners and losers in the crisis: the

changing anatomy of economic inequality in the UK 2007-2010 (Social Policy in a Cold Climate,

Research Report 2). London: Centre for Analysis of Social Exclusion, LSE.

Hochstenbach, C. (2017). Inequality in the gentrifying European city. PhD thesis University of

Amsterdam.

Hochstenbach, C. (2018). Spatializing the intergenerational transmission of inequalities: Parental

wealth, residential segregation, and urban inequality. Environment and Planning A: Economy

and Space, 50(3), 689-708.

Hochstenbach, C., & Boterman, W.R. (2015). Navigating the field of housing: Housing pathways

of young people in Amsterdam. Journal of Housing and the Built Environment, 30(2), 257-274.

Hochstenbach, C., & Boterman, W.R. (2017). Intergenerational support shaping residential

trajectories: Young people leaving home in a gentrifying city. Urban Studies, 54(2), 399-

420.

Lennartz, C., Arundel, R., & Ronald, R. (2016). Younger Adults and Homeownership in Europe

Through the Global Financial Crisis. Population, Space and Place, 22(8), 823–835.

Ley, D. (1996). The New Middle Class and the Remaking of the Central City. Oxford: Oxford

University Press.

Martinez-Fernandez, C., Audirac, I., Fol, S., & Cunningham-Sabot, E. (2012). Shrinking cities:

Urban challenges of globalization. International Journal of Urban and Regional Research, 36(2),

213-225.

McKee, K. (2012). Young People, Homeownership and Future Welfare. Housing Studies, 27(6),

853–862


25

Milanovic, B. (2016). Global inequality: A new approach for the age of globalization. Harvard University

Press.

Musterd, S. (2014). Public housing for whom? Experiences in an era of mature neo-liberalism:

The Netherlands and Amsterdam. Housing Studies, 29(4), 467-484.

Newman, S.J., & Holupka, C.S. (2016). Is timing everything? Race, homeownership and net

worth in the tumultuous 2000s. Real Estate Economics, 44(2), 307-354.

Newman, K., & Wyly, E.K. (2006). The right to stay put, revisited: gentrification and resistance

to displacement in New York City. Urban Studies, 43(1), 23-57.

Nolan, B., Salverda, W., Checchi, D., Marx, I., McKnight, A., & Tóth, I.G. (2014). Changing

Inequalities and Societal Impacts in Rich Countries: Thirty Countries’ Experiences.Oxford: OUP.

Organisation for Economic Cooperation and Development (OECD). (2016). Divided we stand:

Why inequality keeps rising (OECD Publications). Retrieved from

http://dx.doi.org/10.1787/9789264119536-en

Organisation for Economic Cooperation and Development (OECD). (2011). Employment

Database (OECD Publications). Retrieved from

http://www.oecd.org/employment/labour-stats

Oesch, D. (2006). Coming to Grips with a Changing Class Structure: An Analysis of

Employment Stratification in Britain, Germany, Sweden and Switzerland. International

Sociology, 21(2), 263–288.

Öst, C. E. (2012). Parental wealth and first-time homeownership: A cohort study of family

background and young adults’ housing situation in Sweden. Urban Studies, 49(10), 2137-

2152.

Piketty, T. (2014). Capital in the 21st Century. Cambridge: Harvard University Press.

Reuten, G. (2018). De Nederlandse vermogensverdeling in internationaal perspectief. Een

vergelijking met 26 andere OECD-landen. TPEdigitaal, 12(2), 1-8.

Rijskoverheid (2018). Hoe bepalen gemeenten de WOZ-waarde? Retrieved from

https://www.rijksoverheid.nl/onderwerpen/waardering-onroerende-zaken-woz/vraag-

en-antwoord/woz-waarde-bepalen


26

Rose, D. (1984). Rethinking gentrification: beyond the uneven development of Marxist urban

theory. Environment and planning D: Society and Space, 2(1), 47-74.

Rowlands, R., & Gurney, C. M. (2000). Young peoples? Perceptions of housing tenure: a case

study in the socialization of tenure prejudice. Housing, theory and society, 17(3), 121-130.

Rowlingson, K., & McKay, S. (2012). Wealth and the Wealthy: Exploring and Tackling Inequalities

Between Rich and Poor. Bristol: Policy Press.

Saez, E., & Zucman, G. (2016). Wealth inequality in the United States since 1913: Evidence from

capitalized income tax data. The Quarterly Journal of Economics, 131(2), 519-578.

Sampson, R.J., & Sharkey, P. (2008). Neighborhood selection and the social reproduction of

concentrated racial inequality. Demography, 45(1), 1-29.

Sampson, R. J. (2012). Great American city: Chicago and the Enduring Neighborhood Effect. Chicago:

University of Chicago Press.

Sassen, S. (1991). The Global City: New York, London, Tokyo. Princeton: Princeton University Press.

Sassen, S. (2014). Expulsions. Brutality and Complexity in the Global Economy. Cambridge/ London:

The Belknap Press of Harvard University Press.

Schwartz, H., & Seabrooke, L. (2008). Varieties of Residential Capitalism in the International

Political Economy: Old Welfare States and the New Politics of Housing - Springer.

Comparative European Politics, 6, 237–261.

Smith, N. (1979). Toward a theory of gentrification a back to the city movement by capital, not

people. Journal of the American Planning Association, 45(4), 538-548.

Smith, N. (1984). Uneven Development: Nature, Capital, and the Production of Space. Athens, Georgia:

University of Georgia Press.

Spilerman, S., & Wolff, F. C. (2012). Parental wealth and resource transfers: How they matter in

France for home ownership and living standards. Social Science Research, 41(2), 207-223.

Standing, G. (2011). The Precariat: The New Dangerous Class. London: Bloomsbury.

Stiglitz, J. (2012). The Price of Inequality. London: Penguin.


27

Stockhammer, E. (2013). Why have wage shares fallen? An analysis of the determinants of

functional income distribution. In Wage-led growth (pp. 40-70). Palgrave Macmillan,

London.

Stone, J., Berrington, A., & Falkingham, J. (2014). Gender, Turning Points, and Boomerangs:

Returning Home in Young Adulthood in Great Britain. Demography, 51(1), 257–276.

Tammaru, T, Marcińczak, S, Van Ham, M. (eds) (2016) Socio-economic Segregation in European Capital

Cities: East Meets West. New York/London: Routledge.

Terhorst, P., & Van de Ven, J. (1995). The national urban growth coalition in The

Netherlands. Political Geography, 14(4), 343-361.

Van Bavel, B. & Salverda, W. (2014). Vermogensongelijkheid in Nederland. Economisch Statistische

Berichten 99(4688): 392–395.

Van Ham, M., Hedman, L., Manley, D., Coulter, R., & Östh, J. (2014). Intergenerational

transmission of neighbourhood poverty: an analysis of neighbourhood histories of

individuals. Transactions of the Institute of British Geographers, 39(3), 402-417.

Wind, B., & Hedman, L. (2017). The uneven distribution of capital gains in times of socio-spatial

inequality: Evidence from Swedish housing pathways between 1995 and 2010. Urban

Studies, Online First. DOI:0042098017730520.

Wood, J.D.G. (2017). The effects of the distribution of mortgage credit on the wage share:

Varieties of residential capitalism compared. Comparative European Politics, 15(6), 819-847.

Wyly, E. K., & Hammel, D. J. (1999). Islands of decay in seas of renewal: Housing policy and the

resurgence of gentrification. Housing Policy Debate, 10(4), 711-771


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Appendix 1 Change in property values per neighbourhood across the Netherlands over 2 year intervals.

1a) 2006-2008

Data source: SSD, Statistics Netherlands, own calculations. Values adjusted for inflation using the Consumer Price Index.Cartogram distortion based on Gastner-Newman diffusion.$ 0 40 KM

> 40%30% to 40%20% to 30%10% to 20%2% to 10%-2% to 2%-10% to -2%-20% to -10%-30% to -20%- 40% to -30%< -40%

Excluded (<10 dwellings)


Cartogram proportional to number of dwellings


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Change in property values per neighbourhood across the Netherlands over 2 year intervals.

1b) 2008-2010







30


1c) 2010-2012







31


1d) 2012-2014







32


1e) 2014-2016






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