# More volatile, less equal. Housing and welfare in 21st century France

Renaud Le Goix, William Kutz, Ronan Ysebaert

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Renaud Le Goix, William Kutz, Ronan Ysebaert. More volatile, less equal. Housing and welfare in 21st century France. Housing Studies, 2025, pp.1-27. 10.1080/02673037.2025.2565248. hal-05311249

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# More volatile, less equal housing and welfare in 21st century France

Renaud Le Goix

Université Paris Cité, UMR Géographie-cités CNRS - 75013 Paris, France.

renaud.legoix@u-paris.fr

William Kutz

Centre for Öresund Region studies, Lund University, Lund, Sweden, william.kutz@cors.lu.se

Ronan Ysebaert

Université Paris Cité, UAR RІАТЕ CNRS - UPC - 75013, Paris, [ronan.ysebaert@cnrs.fr](mailto:ronan.ysebaert@cnrs.fr)

**Author's manuscript, as accepted in Housing Studies. This pre-print should be cited as:**
Le Goix, R., Kutz, W., & Ysebaert, R. (2025). More volatile, less equal housing and welfare in 21st century France. Housing Studies, 1–27.
[https://doi.org/10.1080/02673037.2025.2565248](https://doi.org/10.1080/02673037.2025.2565248)

## ARTICLE HISTORY

Received 30 July 2024. Accepted 17 September 2025

Copyright : CC-BY (original publication is open access on the journal website)
[https://doi.org/10.1080/02673037.2025.2565248](https://doi.org/10.1080/02673037.2025.2565248)

## Abstract

Contributing to analyses of housing welfare and inequality, this article examines how the territorial circumscription of asset-based welfare policies impact the pattern and trajectory of residential inequality in situ. We look to France, which since the 2000s has used asset-based welfare policies as a tool for rebalancing local territorial cohesion throughout the country. These policies, which once had broad nationwide eligibility, have progressively fragmented into a patchwork of geographically-defined benefits and incentives that seek to catalyze buying and selling in certain places over others. Drawing on aggregated datasets of individual housing
transactions, we map how asset-based welfare policies have redrawn the geography of residential inequality in recent decades. We find a growing bifurcation arising from the reforms: trending at once towards increasingly volatile and unequal outcomes. As this has happened amid explicit governmental efforts to reduce socio-spatial imbalances in the country, we question whether asset-based welfare can meaningfully provide the security and well-being it purports to realize.

## Résumé

Cet article contribue à l'analyse de l'abordabilité de l'accès à la propriété et des inégalités. Il examine comment la délimitation territoriale des politiques sociales fondées sur les actifs (asset-based welfare) influe sur le modèle et la trajectoire des inégalités résidentielles sur place. L'analyse porte sur la France, qui depuis les années 2000, a explicitement lié la politique de logement et la construction d'un régime basé sur l'accumulation d'actifs, dans trois régions urbaines (Paris, Lyon, Avignon). A partir de données désagrégées décrivant les achats-ventes de logements (transactions individuelles, liant vendeurs, acheteurs, prix et crédit), nous utilisons des techniques de classification pour analyser et cartographier les caractéristiques spatiales et temporelles des inégalités résidentielles entre 2002 et 2018. Nous identifions une bifurcation significative dans l'articulation territoriale d'asset-based welfare, tendant vers des dynamiques plus volatiles et inégales. Dans le contexte des efforts explicites des autorités locales et de l'État pour réduire les déséquilibres socio-spatiaux, nous nous interrogeons l'équilibre et l'équité d'un tel régime assurantiel liant la propriété d'un logement à l'accumulation patrimoniale.

**Keywords:** Asset-based welfare; homeownership; public policy; France; wealth; vulnerability; inequalities
Introduction

Anxiety over the impacts of aging populations on state pension and social security systems, coupled with the continued restructuring of public welfare programs, has led both policymakers and the housing industry to encourage individuals to take responsibility to protect themselves against financial uncertainty by becoming more self-sufficient economic subjects (Doling and Ronald, 2010). Housing investment, supported by a general increase in ownership rates and property values is one area where long-term security and well-being has been constituted to either reorient or preserve welfare systems (Van Gent, 2010).

In France, unlike other parts of Europe and North America, asset-based welfare policies have taken on an explicitly territorial and temporal dimension. Since the 2010s the State has actively sought to modulate the type, duration, and geographical eligibility of incentives and subsidies for homebuyers and builders alike, in order to facilitate and constrain the flow of real estate investments into different areas of the country over others (Le Brun, 2022). So important has this shift become that in 2022 alone housing incentives totaled 41.5 billion euros of national spending – equivalent to the national defense budget that same year (Driant, 2024). The purpose of this article is to contextualize and assess the outcomes that these policy decisions have produced in addressing residential inequality in terms of its geographical distribution and temporal progression since the early 2000s.

Contributing to debates on housing welfare and inequality, we argue that, far from encouraging a more secure, long-term investment climate, the French State's adoption of a spatially assertive form of “proactive asset-based welfare” (Wind et al., 2020) has fundamentally aggravated market polarization and instability. Geographically, the problem is
most visible in increasing segregation within the Paris region since the mid-2000s, as well as
between Greater Paris and other French cities. As we elaborate below, some markets have
trended towards increasingly *risky* investment climates, others towards the consolidation and
*reproduction* of residential wealth, and a third group marked by the uncertain accrual and
*renewal* of residential property values. Temporally, our sample reveals significant fluctuations
in the character of buying and selling across housing submarkets: shifting from trajectories of
investment *risk-* to *renewal-* or *reproduction* (or vice-versa), with only a small number of
markets stabilizing since the early 2000s. In short: housing and welfare in 21st century France
is more volatile and less equal than has been seen in decades.

Our argument is organized into five parts. The first contextualizes the development of
asset-based welfare efforts in France and its progressive integration with national territorial
development objectives. Part two outlines the data and methods employed to derive a spatial
and temporal understanding of homebuying in the country. We use the resulting typology of
acquisition characteristics to formulate a theoretical framework to explain changes in
residential submarkets since the early 2000s. The remainder of the paper operationalizes this
framework. Part three maps where distributions of residential risk, renewal, and reproduction
prevail across the more than 700 municipalities included in our sample. Part four, explains how
these patterns of buying and selling fluctuate over time, such that some places are consistently
stuck in one wealth trajectory while others exhibit frequent and pronounced changes within
them. Finally, we assess the extent to which different forms of market stability and volatility
are statistically linked (or not) to the type of housing incentives available in each location at a
given point in time.

Theoretically, our analysis responds to a taken-for-granted view that the national scale
is the most appropriate level at which to study asset-based welfare policies (Wijburg, 2019).
Residential wealth trajectories and their durability over time are fundamentally tied to place-
specific circumstances. The shape and persistence of inequality arising from asset-based welfare policies therefore cannot be adequately understood without accounting for local market conditions and their evolution (Driant, 2024). France provides an ideal case to study this problem due to the State's mandate linking asset-based welfare to territorial cohesion objectives in the country. The inequities arising from these actions are occurring not because the State has relinquished its role in fostering societal security and well-being, but rather in redefining how, where and when that regulation takes place. Therefore, our analysis offers a theoretical counterpoint to depictions of asset-based welfare as a straightforward manifestation of the neoliberal state abandoning its responsibility to the market or even a post-neoliberal alternative to the housing crisis. To appreciate this, we look to the spatial and temporal differentiation of asset-based welfare in a single state and how these policies have helped to redefine the nature of residential inequality evolve over time.

## Contextualizing asset-based welfare in France

Like in many western European countries since the 1970s, rhetoric linking crises of the welfare state to persistent unemployment, weak pension savings, declining standards of living and the increasing value of real estate, have made owning a home “almost an imperative to sustain a decent lifestyle” in France (Benites-Gambirazio and Bonneval, 2022, 1928). Today, French households own 60% of the available stock of apartments and 92% of houses, of which homeowners are mainly found in cities and suburbs, while landlords and tenants are concentrated in larger cities (Casanova Enault et al., 2023). According to INSEE, the National Institute of Statistics and Economic Studies, homeownership in the country increased from 51.2% in 1983 to a peak of 58% in 2013, with only marginal fluctuations since then (57.2% in 2023). This low ownership rate over the past decade underscores the significance of France's historically well-protected rental sector where over a quarter of households continue to live in
social housing. Yet, the restructuring of rental supply and investment channels have pushed
many into outright ownership or the private rental market (Gimat et al., 2022). This trend is
not accidental as the development of a homeowner society has historically enjoyed broad
political support in the country (Bonvalet and Bringé, 2013).

The origins of asset-based welfare in France can be traced to the year 1976, which
marks the end of the postwar construction boom that produced the now infamous suburban
public housing estates, or *grands ensembles*. With the publication of the 1977 Barre Report,
public assistance began to turn away from direct subsidies for the construction industry to build
new homes, towards incentivizing households to access property through individual financing
options (Bourdieu and Christin, 1990).<fnref n="1" /> The policies remained largely unchanged until 1993
when a housing shortage prompted policymakers to reform the historic means-tested allocation
of residential assistance towards market-based provisioning and tax breaks to spur private
industry to build more housing. The most important intervention came in 1995 with the
introduction of a zero-interest loan scheme (*Prêt à taux zéro*) for first-time buyers to replace
the subsidized loan programs from 1977. The goal was to encourage middle-income tenants to
become homeowners so as to free up public housing units for those in greater need but with
fewer means. Further legislation (Loi Périssol) was introduced in 1996 to provide additional
incentives and tax exemptions for private investment in rental properties; both schemes have
since undergone revisions over the years, particularly since the Great Recession (see below).

A critical, albeit overlooked, aspect of asset-based welfare in France is the temporal
and territorial dimension that the reforms have acquired since the mid-2000s. Just in the way
that not every buyer is eligible for tax exemptions or subsidies, so too does legislation
circumscribe where support is available to prospective homeowners in the country. Every
French municipality has a zoning designation (A, B, or C) that reflects the measure of

<footnote n="1">¹ Aide Personnalisée au Logement, Prêt Accession à la Propriété, and Prêt Conventionné.</footnote>
disequilibrium between housing supply and demand at a given place and time. Zone A / Abis refers to areas with the most acute distortions, and Zone C the least. Each zone determines the type and number of incentives that builders and buyers might obtain.<fnref n="2" /> As Le Brun (2022) shows, this distribution of benefits and incentives is spatially selective in addressing the main problems affecting housing affordability, and therefore changes as the conditions of the market evolve over time. In Paris, for example, tax incentives for investment in affordable rental properties were initially accessible to landlords throughout the entire region, but after 2009 they became progressively restricted to denser, more centrally-located zones and suburbs of the Île-de-France (Figure 1). By contrast, zero-interest loan schemes were obtainable nationwide in their first iteration (2005-2011), but since reactivation in 2012, their territorial eligibility became increasingly constrained such that by 2018 they were only applicable in B2 and C-designated zones.

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<footnote n="2">² For example zero-interest loans or social lending opportunities; tax reductions (landlords) and rent ceilings (tenants) as well as other deductions on property income and rent. See: https://www.ecologie.gouv.fr/politiques-publiques/zonage-b-c-version-jour-revision-du-zonage-issue-larrete-du-02-octobre-2023</footnote>
<caption>Figure 1. Geographical eligibility of State incentives for potential homeowners and landlords, the example of Paris (simplified). Source: Adapted with permission from Le Brun (2022)</caption>

Though beyond the scope of our analysis, it is important to note that asset-based welfare
reforms have also incentivized residential construction through the proactive assetization of
second home ownership (Wind et al., 2020), primarily through buy-to-let and build-to-let
mechanisms like the now defunct Scellier (2008-2012), Duflot (2013-14), and Pinel (2015-
2024) programs. These reforms have also been geographically delimited by ABC Zoning so as
to encourage construction in specific cities and regions of the country over others. In this
regard, supply- and demand-side housing legislation are relationally, if not inversely,
constituted. Both play an unambiguous role in structuring the trajectories of people and capital
in the development of asset-based welfare across the French urban system (Peris and Casanova
Enault, 2023). Yet, given the lack of an established precedent linking spatial and temporal
applications of asset-based welfare to the trajectories residential inequality, our first challenge
is to map the geographical differentiation of local housing markets since these policies were
first integrated into statewide territorial cohesion efforts.

Locating housing wealth through transaction data

Local approaches to the study of residential inequality fall into three main categories, which we use as the starting point for our analytical framework. The first focuses on categorizing households, tenure types, and individual life-course trajectories through a combination of cluster analyses, longitudinal sequence analysis, and regression techniques. Fine-grained analyses of individual life-course trajectories from Sweden are a good example of this scholarship due to the quality and breadth of available data there (Borg et al., 2022; Wind and Hedman, 2018). Other countries such as the Netherlands provide full-population registers that combine tax records and housing registries (Hochstenbach et al., 2024). While such studies tend to reduce spatial stratification to the interpretation of stylized facts and categories (urban
centers vs. suburbs, or city size, class structure, generational age groupings, etc.) their overarching value is in the way they consider inequality as more of a process rather than a position that evolves through acts of buying and selling property (Bryant et al., 2024).

A second, related strand of research emphasizes the locational facets of housing wealth flows as a means to chart the spatial stratification of asset-based welfare and homeownership more broadly. Hochstenbach (2023), for example, links data on landlords' place of residence to the location of their rental properties in order to show how proximity informs property acquisition strategies. Likewise, Peris and Casanova Enault (2023) integrate data from tax registers and property deeds to highlight the “home-bias” guiding investment flows within and between cities. Both variants demonstrate that although a prevailing current in housing studies points to the deterritorialization of real estate investment arising from the neoliberalization and financialization of property markets, “place” continues to be a key factor shaping homeowner investment strategies. It informs how individuals perceive and calculate risks and benefits to buying and selling, how their class-based relationships are spatially articulated, and the evolution of these circumstances across space and over time.

A final strand of scholarship focuses on place-level aggregations of transaction data to explain the character and unevenness of local “housing regimes” (Le Corre, 2021; Tammaru et al., 2016). As Wind and Hedman (2018, p. 2738) argue, the simple fact of “moving into the right/wrong area at the right/wrong moment in time can generate windfall profits/losses for individual homeowners...”; therefore, transaction data can be instrumental in exploring the geography of inequality that other types of district-level studies are unable to measure. Aggregated transaction data has previously been used to classify housing markets in French cities (Le Corre, 2021; Le Goix et al., 2019), as well as to compare affordability indices among European cities (Le Goix et al., 2021b).
Building on the aforementioned approaches, our analysis examines the spatial and temporal pattern of asset-based welfare policies as a crucial factor shaping residential inequality. We specifically draw on municipal aggregations of individual housing transactions as a way to identify the geographical differentiation of residential sub-markets (snapshots of market activity at a given point in time); we then chronologically order different timestamped maps to observe the trajectory of residential inequality in a certain location over time. To this end, we look to the city-regions of Paris, Lyon and Avignon for the years 2002-2018.<fnref n="3" /> Encompassing 708 municipalities, the cases reflect different positions, market characteristics, and residential ownership types within the French urban system (Casanova Enault et al., 2023). Each city-region is marked by high levels of social polarization, sprawling suburbs, residential gentrification and urban redevelopment, while also differing in important ways. Paris, the French capital and a global city, has an economy characterized by a concentration of governmental agencies, global cultural industries, and international financial institutions. Lyon, a European center, has a strong presence of banking, chemical, pharmaceutical and biotech industries and hosts the second largest university population in France. Avignon, although having important tourism and cultural industries, and being a major logistical hub both for agricultural markets and high-speed rail links in southern France, has a fragile, if not declining, economy with an urban structure marked by inner-city depopulation and sprawling suburbs typical of many medium-sized cities in the country (Chouraqui, 2021). Together, each case provides sufficiently distinct yet related groupings of residential activity for comparison.

<footnote n="3">³ Specifically, the Île-de-France (Paris) and the Functional Urban Areas (according to the European standard definition) of Lyon and Avignon.</footnote>
## Research design and methodology

To begin to analyze residential inequality arising from asset-based welfare policies requires a base-level understanding of the characteristics that circumscribe the various housing markets of our sample. We do so by means of a multivariate analysis (cluster analysis) applied to residential transactions (i.e. their affordability, debt levels, ownership and seller characteristics, and property types). The remainder of this section describes the composition of these data and how the analysis was operationalized to map and measure housing inequality in the country.

### Data: transaction records and variables

Our data comprise individual sales records aggregated at the municipal level – the most common scale to capture the structure of local residential markets. In France, the municipality is the lowest administrative level at which housing and zoning policies are enacted, and is the arena in which key actors, especially real-estate agents, structure information about housing markets. The scale also provides minimal statistical variance in prices, which allows us to better control for potential outliers (Josselin et al., 2024). These records are collected by the French Chamber of Notaries and are stored on two different databases: “BIEN” for transactions made in the Paris region and “PERVAL” for those made in the rest of the country.<fnref n="4" /> They are proprietary databases that are commonly used in housing research, pricing and hedonic modeling (Géniaux et al., 2015), and provide for the ability to link fluctuations in property prices to neighborhood change (Guérois and Le Goix, 2009). Both have limitations that are well-documented and easily resolved (Casanova et al., 2017). While transaction data are susceptible to fluctuations in business cycles that can influence the extent to which different households acquire property, and thereby the trends we wish to study, the advantage of these data, in comparison to census data or property-tax assessments, is that they provide a more

<footnote n="4">⁴ See: https://www.basebien.com/ and https://www.perval.fr/.</footnote>
comprehensive picture of market dynamics in *situ*. This includes information on the age and
location of property, its price, the type of financing and amount of debt contracted, the socio-
professional categories of buyers and sellers. The marshalling of this complex basket of data is
important. Studies of asset-based welfare variously refer to three main types of property
ownership: households who purchase for their own occupation as well as households who
purchase as a rental investment or as a second home. If we are to meaningfully assess how
housing policies impinge upon patterns and temporalities of residential inequality, it is crucial
be able to examine the relationships between ownership categories together rather than
individually.

In this regard, the variables we include in the analysis describe the financial,
demographic and material aspects of each acquisition (Table 1). All the variables originate
from the transaction data, except income, which derives from the INSEE database. The
financial variables we examine are comprised of (i) transaction prices, (ii) purchasing power,
and (iii) indebtedness. We measure transaction prices as the mean cost per square meter of the
properties included in the sample. We deliberately use nominal prices, because, following
Maher (1994), we find inflation-adjusted prices to be problematic for assessing the imbalances
we want to observe across residential markets. Housing is a highly differentiated commodity
and asset whose value is adjusted by age, condition, location, and the variation of societal
standards over time (Christophers, 2016), which means that our ability to compare housing
prices to other indices is of limited value than might first appear. Furthermore, we do not use
price as an input variable in the analysis precisely because price inflation in Greater Paris is so
significant that it would distort the cluster classifications derived from the remaining sample.
We do, however, refer to price as a supplemental variable to help contextualize our findings
where necessary.
To work around the use of constant and nominal prices, we relying on two synthetic measures relative to income and debt. One is purchasing power, calculated as a price-to-income ratio – specifically, the number of months it would take an average buyer to purchase just one square meter of property (months/m²). This is measured as the average price of apartments and houses over local median income, according to data provided by the INSEE. The other is a debt-to-price ratio (%) to measure the debt burden contracted to invest in a property. Together we can say the average price of a property in the sample is 2,460 euros/m², which would take an average buyer 1.65 months/m² to acquire, leveraged at 65% of the nominal purchase price.

Alongside financial considerations, we include the socio-professional status of buyers and sellers and the material aspects of the property to characterize the acquisitions. This choice allows us to consider how different acquisitions are not simply variations of abstract investment strategies, but are structured by different types of properties, acquisition incentives and ownership structures (owner-occupied, buy-to-let, second home) prevailing in a given location – which is again why transaction data is useful for this type of analysis. In France, socio-professional variables are usually broken-down into 8 categories, but we chose to focus on only the main groupings that contribute to changes in residential markets (Pinçon, et al, 1986) in order to define the average profile of buyers and sellers in our sample. Blue-collar workers and employees represent 28% of buyers and 20% of sellers in the sample, while executives represent 23% of buyers and 18% of sellers – the two groups who tend to be the most polarizing of market actors and whose spatial clustering typically indicates the presence of segregated or gentrifying neighborhoods (Clerval, 2020). Likewise, retirement status is an important indicator to characterize investment dynamics, such as multi-property ownership (e.g. buy-to-let for revenue in retirement); selling and refinancing strategies to improve household equity (e.g. reverse mortgages, viager); and intergenerational wealth transfers, among others. Retirees represent 24% of sellers, but only 7% of buyers in the sample.
Following Le Corre’s (2021) study of residential submarkets, we also include the material aspects of the buildings in our analysis, such as the age and size of properties, their proximity to central neighborhoods, and links to urbanization cycles. This allows us to identify an over- or underrepresentation of certain housing types traded in an area over time. A few observations stand out: family homes (those with more than 5 rooms) comprise 29.4% of acquisitions, while suburban homes built in the 70s and 80s account for 21% and 20% of transactions, respectively. Furthermore, we found a preponderance of small houses (2 rooms or less) in older, former blue-collar inner suburbs, as well as small apartments (1 bedroom and studios) located in central neighborhoods. Small houses only represent 2.5% of transactions and small apartments constituted 11% of acquisitions. New single-family homes and apartments (those built within five years of the acquisition), represent 4% and 14% of the sample, respectively.

Finally, the longitudinal aspect of the analysis, covering the years 2002-2018 is deliberate. The 15-year period and corresponding timestamps (2002-2008-2012-2018) allow us to account for key events occurring in the development of asset-based welfare in the country. The first period (2002-2008) bookends the onset of the housing bubble in Europe; the second (2008-2012) delimits the expansion of tax-deductions for first-time homebuyers to offset the effects of the Great Recession; the third (2012-2018) marks the expansion of fiscal incentives for private, multi-ownership investment to expand the stock of rental housing thereafter.

## Cluster analysis

Our analysis follows a long-established tradition of using multivariate statistical analysis to describe the character and evolution of residential inequality (Reibel, 2011). Tracing residential change requires capturing a series of permutations between variables whose thresholds between categories are not *a priori* determined or even apparent. This exploratory technique, known as
cluster analysis, is instrumental in explaining change within socio-economic categories,
housing types and neighborhoods. Our approach draws on Delmelle (2015) and Mikelbank
(2011) to map spatial and temporal trajectories, using a hierarchical cluster analysis (HCA)
built on the factors derived from a prior principal component analysis (PCA). The PCA
summarizes the main components of our normalized input variables (an option matching the
requirements of continuous numerical variables) in order to reduce the level of complexity of
the data to a small number of factors; in our case, 4 factors. The HCA then allows us to measure
the spatial composition of those factors, and thereby improve the share of total inertia (the sum
of squared Euclidean distances to the nearest cluster center) explained by the clustering.<fnref n="5" /> The
solutions are then hierarchically ordered in a classification tree, or *dendrogram*, according to
the degree of proximity of observations for a given number of (k) clusters.<fnref n="6" /> Using the HCA as
an exploratory method, we analyzed the statistical gaps between each node of the dendrogram
and found that 10 clusters were a good balance between the distinctiveness of each cluster
(inertia gap) and their analytical variety (k=10, share of total inertia= 62.3%). Each cluster was
then compared to the average value of the input variables comprising our sample (Table 1) in
terms of its over- or under- representation, measured in standard deviations from the mean (or
"z-values", see Figure 3). Together, this forms the basis of a typology of residential
acquisitions, with each cluster characterizing its own distinct profile of market activity.

Nevertheless, if our goal, following Bryant *et al.* (2024, p.476), is to see “wealth
through the lens of accumulation, rather than static distribution[s],” as “trajectories rather than
positions,” then we also need to account for how residential inequality changes over time. In
fact, what is perhaps most striking about the 10 clusters in Figure 3 is the way that they readily

<footnote n="5">⁵ See supplemental material for procedural notes and reproducible R code: [URL will be made available after peer review].</footnote>

<footnote n="6">⁶ We usually arbitrate for a cluster solution when the increase of between-cluster inertia is greater between *k*-1 and *k*, than between *k* and *k*+1 (Husson *et al.* 2010), which means that the inertia gap can be visually determined by the relative height between nodes on the hierarchical tree (Figure 2), or by means of the share of total statistical inertia.</footnote>
exemplify three well-established trajectories identified in housing scholarship: one trending towards increasing residential vulnerability or **risk** (Dwyer and Lassus, 2015; Flynn and Schwartz, 2017), another towards the consolidation and **reproduction** of residential wealth (Pinçon and Pinçon-Charlot, 1989; Méndez and Gayo, 2019), and a third dynamic, evoking a mode of uncertain value accrual and **renewal** (Badcock, 1989; Clerval, 2020) – trends that map closely onto our statistical findings when we cut the dendrogram at 3 cluster partitions (k=3, share of total inertia= 41.6%), which in principle would be the ideal number of clusters to study in terms of inertia gap. For this reason, we use these conceptual frames to inform our interpretation of the processes and patterns observed in the transaction data. We discuss each cluster profile in the next section.

<table><caption>Table 1. Selected variables</caption><thead><tr><th>Structure</th><th>Variable</th><th>Code</th><th>Description</th><th>mean</th><th>sd</th><th>Q0.5</th></tr></thead><tbody><tr><td rowspan="3">Financial</td><td>Debt/Price</td><td>Debt2Price</td><td>Share (%) of debt incurred in the acquisition</td><td>62.31</td><td>31.14</td><td>76.34</td></tr><tr><td>Price/m2</td><td>Price_m2</td><td>Purchase price (euros) per square meter</td><td>2860.01</td><td>1243.32</td><td>2685.61</td></tr><tr><td>Price/Income</td><td>PricelncomeRatio</td><td>Duration (months) needed to buy 1m² of property on an average income</td><td>1.65</td><td>0.72</td><td>1.50</td></tr><tr><td rowspan="6">Demographic</td><td>Buyers Executives</td><td>BuyersExecutives</td><td>Share (%) of buyers who are executives and upper managers</td><td>22.86</td><td>14.20</td><td>20.00</td></tr><tr><td>Buyers Retired</td><td>BuyersRetired</td><td>Share (%) of buyers who are retired.</td><td>7.00</td><td>6.15</td><td>6.12</td></tr><tr><td>Buyers Workers & BuyersWorkersEn</td><td>Share (%) of buyers who are ordinary workers and employees</td><td>Share (%) of buyers who are ordinary workers and employees</td><td>27.83</td><td>14.92</td><td>27.27</td></tr><tr><td>Sellers Executives</td><td>SellersExecutives</td><td>Share (%) of sellers who are executives and upper managers</td><td>17.84</td><td>11.55</td><td>16.67</td></tr><tr><td>Sellers Workers & SellersWorkersEn</td><td>Share (%) of sellers who are ordinary workers and employees</td><td>Share (%) of sellers who are ordinary workers and employees</td><td>19.86</td><td>11.66</td><td>19.05</td></tr><tr><td>Sellers Retired</td><td>SellersRetired</td><td>Share (%) of sellers who are retired.</td><td>23.63</td><td>11.16</td><td>23.53</td></tr><tr><td rowspan="7">Material</td><td>1970s SFH</td><td>Pct1970Homes</td><td>Share (%) of single family homes built in the 1970s</td><td>21.05</td><td>20.00</td><td>16.67</td></tr><tr><td>1980s SFH</td><td>Pct1980Homes</td><td>Share (%) of single family homes built in the 1980s</td><td>16.25</td><td>17.62</td><td>12.50</td></tr><tr><td>New SFH</td><td>PctNewHomes</td><td>Share (%) of single family homes built within 5 years of the transaction</td><td>4.34</td><td>11.30</td><td>0.00</td></tr><tr><td>New Apartments</td><td>PctNewApartment</td><td>Share (%) of apartments built within 5 years of the transaction</td><td>14.05</td><td>22.08</td><td>2.44</td></tr><tr><td>Large Homes</td><td>PctFamilyHomes</td><td>Share (%) of single family homes with more than 5 rooms</td><td>29.48</td><td>18.94</td><td>28.57</td></tr><tr><td>Small Houses</td><td>PctSmallHouses</td><td>Share (%) of single family homes with 2 rooms or less</td><td>2.52</td><td>6.72</td><td>0.00</td></tr><tr><td>Small Apartments</td><td>PctSmallAparnter</td><td>Share (%) of apartments with 1 room / studios</td><td>11.31</td><td>13.76</td><td>8.48</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>Figure 2. Classification tree (dendrogram), 10 cluster typology, and analytical framework: risk, renew, reproduce.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 3. Hierarchical Cluster Analysis, *z* values.</figcaption></figure>

Cluster: 1. Risk. Moderate risk socially-mixed mature suburbs; 2. Renew. Suburban single-family homes; 3. Renew. New-build suburbs; 4. Risk. Middle-class suburban single-family homes; 5. Risk. Working-class suburban single-family homes; 6. Risk.
# Deriving residential trajectories: Risk, renew, reproduce

## Risk

Represented in reddish colors (Figures 3 to 5), risk trajectories predominate in markets distinguished by affordable properties, and low, if not stagnant, price inflation since 2006. Risk is therefore found where affordability (lower price-to-income ratios) is tethered to high levels of indebtedness. The exception to this trend occurred in the years immediately following the Great Recession (2008-2012), but corrected again soon after. **Cluster 4**, exemplifies risk in more affordable (1.38 months/m²) single-family homes in middle-class suburbs built in the 1970s. These acquisitions are highly leveraged (78.9%) and correlate to an over-representation of retirees selling *en masse* (32% of sellers; 8% of buyers) to blue-collar workers and employees (35% buyers). A variation of this profile, **Cluster 5**, has an over-representation of employees and workers (36%), and buyers investing in small apartments, and suburban homes from the 1980s. **Cluster 6**, consists of highly leveraged (>80%) transactions made in affordable inner-suburbs (2,300 euro/m² or 1.43 months/m²). Workers and employees are significantly more active in this segment, both as buyers (46%) and sellers (32%). **Cluster 1** is arguably the most central profile around which the 10 groupings vary in our analysis. The profile correlates to investment in socio-economically diverse middle- and working-class suburbs (more retirees, employees and workers as sellers and buyers *vis-à-vis* executives), mixed apartment types, and “mature” properties built before the 1980s, with moderate risk in affordable neighborhoods.

Together, the four variants indicate that risk, as a market trajectory, is characterized by entry-level buying strategies, higher than average indebtedness and – crucially – significant uncertainty regarding future gains (Figure 4).
<figure><img src="image_6.png" /><figcaption>Figure 4. Financial parameters structuring trajectories of residential inequality.</figcaption></figure>

Reproduce

At the other end of the spectrum is a pathway characterized by the consolidation of household
wealth (purple to bluish colors, Figures 3 to 5). Absolute unaffordability, steady price inflation
and a disproportionate presence of executives and managers as both buyers and sellers, are
paramount to understanding this trajectory. Acquisitions are distinguished by exceptionally
high price-to-income and debt-to-income ratios, which suggests that buyers cannot afford to
purchase property in this segment without recourse to personal wealth recycled through the
sale of other assets or intergenerational wealth transfers (Simon and Essafi, 2017) – a tendency
that has also been instrumental in driving price inflation since the Great Recession.
Three cluster variations comprise the trajectory. The first, **Cluster 10**, typifies high-end central neighborhoods, essentially Paris' most centrally-located markets. Small apartments are over-represented in this category at an average price exceeding 8,200 euros/m² (4.87 months/m²) and a debt ratio of 63%. Executives are the main buyers (51 %). As is typical of high-end real estate, workers and employees are fairly absent from the market (6.7%), and retirees tend to sell more than they buy.<fnref n="7" /> A second variant, **Cluster 8**, exhibits similar traits. While also displaying a strong presence of executives and managers, unaffordability (3,734 euros/m²) and debt burden (65%), the acquisitions are found in both central areas and wealthy suburban markets for single-family homes. Finally, **Cluster 9**, describes neighborhoods in the late stages of gentrification, having witnessed such socio-economic transformation since at least the 1990s, and are now mainly accessible to only high-end buyers. The market correlates to high inflation, higher prices (3,548 euros/m²) and high debt-to-price ratios (64%). Acquisitions likewise target small single-family homes in former working-class inner suburbs where renovations are common. Buyers and sellers in these markets are overrepresented by executive professionals (30%) and some categories of workers and employees (21%), who have also sought to gain from these market dynamics. In this sense, Cluster 9 is likely a “trajectory-spanning” profile that does not characterize (any longer) residential “renewal” but also does not straightforwardly exemplify high-end, exclusive wealth reproduction.

<footnote n="7">⁷ The centrality of Paris’ inner markets is a good example of why the metric (months/m²) proves especially useful. Because this metric takes the average price of apartments and houses in a given municipality and then relates that to the local median income of individuals already in that municipality, the calculation could depart quite significantly from the price-to-income ratio experienced by a buyer investing in the municipality from outside, which is key feature of gentrification and ultra-wealthy foreign property investors in Global Cities like Paris. Because the variable assumes that all buyers in a given municipality have the same median income as those who currently live there, it is all the more important that we consider the other socio-professional and material characteristics of the transactions to explain why, for example, buyers with much higher incomes than the local average are not the ones buying into ‘risk’ areas. We wish to thank the anonymous reviewers for this help addition.</footnote>
Renew

Our third trajectory pertains to a set of residential valorization strategies by middle- and upper-
middle class buyers and sellers in areas with an over-representation of new single-family homes
and small properties. Activity in this segment, marked by a combination of lower indebtedness,
lower prices, and higher affordability, indicates that buyers rely on larger down payments either
from savings, from the resale of other property, or from intergenerational wealth transfers to
make acquisitions. In this sense, we use the term *renewal* to convey a variety of investment
patterns that display to varying degrees the ideal-typical pattern asset-based welfare
valorization, but also processes of gentrification, build-to-let investment, and petty rentierism
without necessarily a clear distinction between them.

**Cluster 2**, for example, is strongly associated with a mix of middle- (18%) and upper-class buyers (21%), retired buyers (8.2 %) and newly built apartments (27%). Evidencing a mixture of classic features of asset-based valorization *and* rentierism, the profile correlates to low indebtedness (32%), entry-level investments (2600 euro/m²) and mixed suburbs, which typify areas benefiting from tax-exemptions and investment schemes promoted by the French government to increase the stock of private rental properties (Le Brun, 2022). By contrast, **Cluster 3**, exhibits the ideal-typical trajectory of asset-based valorization. This profile is characterized by newly built apartments and homes (56% and 25% of transactions respectively) in suburban subdivisions with an average distribution of buyers and sellers (22% workers and employees; 15% executives, 8% retired), in affordable neighborhoods (2,382 euros/m², 1.42 months/m²) and moderate indebtedness (27%). It clearly structures some of the most preeminent suburban markets in Eastern Lyon and in Avignon, where new built multi-family complexes and single-family subdivisions have attracted first time homeowners and private landlords. These markets have been promoted by public authorities, not only through national incentives for homeownership, but also local pathways and financing instruments for middle-
classes to buy 20% below market value (in Lyon, see Berté et al., 2022). Such conditions have
helped foster the perception of homeownership as a superior form of tenure and financial
security, fueled in part by lower interest rates between 2010 and 2022, and an influx of first-
time buyers benefitting from investment incentives after 2008. **Cluster 7** is illustrative of early-
stage gentrifying markets comprised of acquisitions made with lower debt-ratios (31%), across
more affordable (1.4 months/m²) suburban markets. This profile correlates to buyers who rely
on investment schemes and personal savings for the acquisition and retrofitting of older homes
and apartments – a trend often witnessed in markets South of Paris, but is especially in older
residential suburbs around Lyon and Avignon. The occupational categories of buyers and
sellers in this segment are close to the average. Retirees and executives are more likely to buy
in these areas, while retirees are more common among sellers (31%), which indicates a pattern
of outward migration for retirees and an influx of second-time buyers looking to ascend the so-
called property ladder.

A framework to analyze neighborhood dynamics

Now that we have outlined the characteristics of each cluster profile and the three trajectories
into which they are subsumed, we can now examine how these markets evolve in relation to
the geographical patterning of asset-based welfare policies. Therefore, the first half of our
remaining analysis maps the spatial distribution of residential inequality among the sampled
municipalities; the second half examines how these markets change vis-à-vis the geographical
delimitation of the policies themselves.

Mapping residential wealth patterns

The map of residential acquisitions in France exhibits a striking bifurcation in wealth
trajectories over the 2000s: one that shows a significant polarization between risk and
reproduction profiles around Greater Paris, and increasing renewal profiles across Lyon and
Avignon (Figure 5).

In Paris, the fractures of residential inequality follow the capital’s long-standing
geographical divisions between wealthy and working-class neighborhoods. Starting in 2002,
the city-region evidenced an over-representation of **reproduced** wealth in the majority of its
central *arrondissements* and western suburbs where middle- and upper-class districts are
located. These areas are almost exclusively characterized by high-end buying and selling
among executives and managers (Clusters 8 and 10) and previously gentrified neighborhoods
(Cluster 9). All residential areas *intra-muros*, including historically working-class and
immigrant neighborhoods, are comprised of prohibitively expensive, highly-leveraged
acquisitions. The same holds for many of Paris’ western suburbs beyond the well-known, old-
money locales like Neuilly-sur-Seine and Versailles, and late-gentrified inner-suburbs to the
east like Montreuil and Bagnolet.

Surrounding this field of concentrated wealth, are the vast majority of municipalities in
the Île-de-France region that typify **risk-laden** markets of all types. Some of the most well-
known of these struggling areas are unsurprisingly found in working-class, inner-suburban
neighborhoods (Clusters 5 and 6), like those in the northern Department of Seine-Saint-Denis,
where widespread clichés about gang violence, cultural disintegration, and *la banlieue* have
become synonymous with public housing estates (*grands ensembles*) in municipalities like
Clichy-sous-Bois, Aulnay-sous-Bois and Drancy (Dikec, 2011). Yet, many suburban towns
just to the east of these, such as Le Blanc-Mesnil and Bobigny have actually transitioned up
towards more “moderate” risk (Cluster 1), with the latter transitioning in 2018 into a fully-
gentrified inner-suburb (Cluster 9).

Indeed, market risk in Greater Paris has been caused as much by territorial stigma as by
banal forms of economic insecurity. Often, the roll out of large-scale, monoculture housing
developments have contributed to overly-uniform cycles of residential inflation and depreciation across the region. In the master-planned area of Marne-la-Vallée (wedged equidistant between central Paris to the west, and Euro Disney to the east), part of the government's New Town program, the municipality of Lognes has remained a risk-laden "affordable" suburb with a mixture of dense, semi-detached homes and small multi-unit apartments built in the 1970s and 1980s (Cluster 5). Although well-connected to downtown Paris by the regional transit system, for many buyers, sluggish property prices have not offset debt burdens over time. Only recently has the municipality benefited from urban regeneration programs that have helped transition into a renewal market (Cluster 2), underscoring the "trajectory-spanning" character of Cluster 5.

In fact, between patterns of accumulated wealth and vulnerability lies only a scattering of Parisian markets distinguished by **renewal** dynamics, particularly in inner-suburbs. As will be shown, this subset of clusters rarely stabilized over the 2000s, to the extent that by 2018 the segment effectively disappears from Greater Paris as new modes of investment risk and reproduction predominate. For example, Magny-le-Hongre is a planned community near Disneyland Paris where international homebuilders such as K&B Homes have been active since the early 2000s. The municipality followed a strong upward trajectory marked initially by mixed, middle-class investment in new single-family homes (Cluster 2), then suburban asset-valorization dynamics (Cluster 3) before market conditions become more selective for buyers as it transitioned from renew-laden activities into a reproduce trajectory by 2018 (Cluster 9). By contrast, Lieusaint, a small municipality in an industrial-commercial corridor southeast of Paris, exemplifies similar renewal dynamics prior to the onset of the financial crisis of 2008 (Cluster 2 then 3), after which market-risk predominates (Cluster 6). Although a new phase of investment in single-family homes started to reverse this trend, since 2015, risk profiles again characterized the municipality (Clusters 1 and 4).
Overall, the weak durability of renewal dynamics in Greater Paris is partly linked to a lack of land to build on and declining profitability in the rental market (Sénat, 2024), which has reduced demand for build-to-let properties. This observation is supported by trends in Lyon, where municipalities that benefited from large-scale national urban renewal programs,<fnref n="8" /> (e.g. Vaulx-en-Velin, Vénissieux) evidence positive valorization dynamics in newly built suburbs (Cluster 3). Others, such as Villeurbanne, have transitioned from a renew profiles into a high-end gentrified market indicative of reproduced residential wealth (Cluster 9).

Data from both Lyon and Avignon consistently evidenced renewal-based activity of all cluster types.<fnref n="9" /> For example, the municipalities of Villeneuve-lès-Avignon (west of Avignon) and Flerieu-sur-l'Arbresle (west of Lyon) both had in 2002 markets characterized by median price-to-income ratios and low indebtedness (Cluster 2), but over time became characterized by new-build construction and price inflation that favored executives and retirees (Clusters 3 and 7). While in Villeneuve-lès-Avignon renewal activity is characterized by the steady growth of higher-end multi-family apartments, further afield, it has been driven by densification, as seen in places like Rochefort-du-Gard (13 km west of Avignon) and L'Isle-d'Abeau (40 km southeast of Lyon). All of this is to say that, although an overarching pattern of renewal activity is observed in both city-regions, there is considerable variability as to what market drivers predominates in a given profile and thereby the wider trajectory itself. As will be shown, none of our sampled municipalities evidenced a consistent pattern of residential valorization over time, which makes it difficult to unambiguously to disassociate ideal-typical patterns of asset-based welfare from other economic phenomena like gentrification or petty rentierism,

<footnote n="8">⁸ Notably, the Programme National de Rénovation Urbaine of 2003.</footnote>

<footnote n="9">⁹ The rare exceptions to this trend are central districts of Lyon (constituting the city's most exclusive neighborhoods - Cluster 9), but part of city's historic core (Arrondissements 5, 8 & 9) are made up of more affordable properties and less-leveraged acquisitions (Cluster 2).</footnote>
<figure><img src="image_7.png" /></figure>

Source: Base PERVAL / BIEN - Chambre des Notaires, INSEE, IGN, 2021
Credits: Authors, 2024

<caption>**Figure 5.** Mapping the typology residential inequality across Paris, Lyon and Avignon (HCA, ward, 10 clusters, inertia 60 %).</caption>

Overall, it is worth asking to what extent the trends identified in Lyon and Avignon correspond to those identified in the Greater Paris. With high-speed rail connectivity expanding throughout the country over the 2000s, it is conceivable that second-tier cities might be attracting buyers outpriced or unwilling to access the Parisian property ladder (Kutz, 2016; 2018). Our findings
suggest that as residential polarization increased in the Île-de-France over the 2000s, renewal-
based activity further concentrated in Lyon and Avignon – particularly when accounting for
the fact that renewal markets are distinguished by the presence of lower indebtedness made
possible by the sale of equity or intergenerational wealth transfers, whether from the more
expensive markets of Île-de-France or elsewhere (Vermeersch et al., 2018). Recent studies by
the INSEE underscore the importance of such residential moves, with 283,000 persons leaving
the Paris region for second-tier cities in 2021 alone (+25% compared to 2019 and pre-pandemic
activity - Brutel, 2023). But this is only part of the picture. Linked to these diverging trends in
housing wealth and vulnerability is the sheer volatility of buying and selling taking place over
the same time period.

***Uneven and combined volatility***

As far back as Kemeny’s (1991) earliest formulation, asset-based welfare hinges on a
continuous trajectory of equity appreciation that homeowners must follow in order to sustain
fiscal autonomy from public subsidies. While the initial cost of saving for a deposit and credit
repayments are high relative to income, they should reduce over time to the point of full
amortization before the homeowner enters into retirement. This inverse relationship between
asset valorization and depreciating expenditures, though an ideal-type model, is a
straightforward benchmark to compare contrasting pathways of residential well-being:
trending from markets characterized by residential vulnerability to those exemplifying asset-
capitalization or even stabilized wealth accumulation. Arundel and Ronald (2020) however
indicate that *too much* change in the market can have the opposite effect of undermining the
promise of asset-based welfare. Therefore, if we observe wealth trajectories breaking from this
ideal-typical model of valorization, it tells us something about how and to what extent
distortions have emerged in the construction of residential welfare over time. In this regard, we
are talking about two related phenomena: the frequency of change in the profile of residential
markets *within* a given trajectory, and; second, the degree to which such profiles switch *across*
trajectories of risk, renewal and reproduction.

To begin to grasp such dynamism, it is useful to consider the most extreme cases of
market stability. Out of the 708 municipalities we sampled, only 19 maintained the same cluster
profile between 2002-2018. Of these, three are in Cluster 4 (risk – *middle class single-family
suburbs*) and 6 (risk – *working class inner-suburbs*) all in Greater Paris. The other 16 are found
in the core areas of Lyon and Paris, stabilized in Cluster 8 (reproduce – *exclusive central and
suburban markets*). These trends evidence the increasing polarization of the residential
landscape that parallel wider socio-economic fractures occurring since the 1980s (Tammaru *et
al.*, 2016), which have been well-documented in France in terms of class dynamics (Pinçon, *et
al*, 1986), demographic change (Clerval, 2020), ethnic discrimination (Bonnet *et al.*, 2015) and
household structures (Rhein, 1998). Our contention, however, is that this contrasting pattern of
risk-laden and wealth-reproducing activity is linked to instability over when and where
acquisitions actually took place.
<figure><img src="image_8.png" /><figcaption>Figure 6. Stability and change across residential markets, 2002–2018.</figcaption></figure>

The remaining 689 municipalities in our sample are where the majority of instability is located: shifting from *risk-* to *renew-* or *reproduce* trajectories (and vice-versa) as others hardened over time. To get a sense of this volatility we synthesized changes among the 708 municipalities into a flow chart (Figure 6) matching the timestamps of the previous map (Figure 5). Our findings indicate that the presumed ideal-typical trajectory of asset valorization is far more the exception than the norm for most of the markets in our sample. The few cases that actually follow this pattern were all located in the Paris region, switching from investment *risk* (Cluster 1, 4 5 or 6) to *reproduction* over time. Examples include places like Saint-Maurice (wedged between the Seine and Paris’ Bois de Vincennes), Villejuif (a suburb at the southernmost terminus of the Paris Metro system), Maison-Alfort (an eastern suburban corridor, 8 km from Paris), and Pontoise (a New Town development northwest of Paris). Acquisitions made in these areas at the start of the 2000s would have occurred in the context of the most average profile
of buying and selling in the country, moving from risk profiles into markets evoking attractive, in-demand destinations for buyers and sellers alike. Outside Paris, however, Lyon and Avignon showed a consistent pattern of renew-based market activity; changes between trajectories tended to oscillate between risk and renew clusters with almost no change into or out of reproduced wealth profiles.

Overall, we can say that as opposed to consistency and stability that is in many ways so crucial to the construction of asset-based welfare, residential markets in France display a considerable amount of diverse and contrasting valorization pathways that are geographically clustered by region. The share of municipalities characterized by risk represent the lion's share of our sample (Figure 6): this proportion fell from 58.3% to 33.5% of residential activity between 2002 and 2012, but increased again to 40.8% by 2018. Municipalities characterized by the consolidation and reproduction of residential wealth progressively became pivotal, increasing from 21% of the sample in 2002 to 29.9 in 2012 and 35.1% by 2018. Renewal, for its part, appears instrumental in transitioning into and out of risk-laden markets (20.6% in 2002, 36.5% in 2012, and 24.9% in 2018). Such trajectories of wealth and vulnerability are all the more striking in that they occurred precisely at a time when legislation was expanding to improve the quality and affordability of housing throughout the country. Wealth deterioration has been especially acute in suburbs built between the 1970s and 1990s, where aging, working-class populations are concentrated (Lévy, 2005). Again, although such municipalities may offer more affordable entry-level options for homeowners, buying in these areas typically implies a heavier debt burden with low, if not declining, property values vis-à-vis other markets.

## Linking wealth trajectories to asset-based policies

To bring the analysis full circle, we now need to consider how patterns of residential inequality intersect with the territorial delimitation of asset-based welfare policies in situ. To do this, we
performed a cross-tabulation correlating the relationships between ABC Zoning and residential trajectories over time (Figure 7 and Table 2), and found that housing inequality is indeed highly structured by the spatial and temporal ordering of zoning legislation.

Starting in 2003, fiscal incentives for private landlords were available in *all* zoning categories (albeit with different thresholds) nationwide. Over this initial period, we see that reproduce-, and to a lesser extent, risk-laden trajectories are over-represented in central areas with significant market disequilibrium (Zone A), while renewal dynamics have a stronger association with areas exhibiting less disequilibrium (Zones B and C). Between 2005 and 2011, a new zero-interest loan scheme tied to ABC Zoning conditionalities was implemented for first-time homebuyers, which appeared to have had little effect on the existing pattern of residential trajectories in the sample. However, when the policies were selectively reimplemented in 2012 with a new, spatially targeted zoning designation made between central Paris and its inner suburbs (now “Zone A bis”) and markets located along Paris’ outer ring of suburbs (now “Zone A”), a bifurcation in wealth trajectories emerged. Investment risk became more strongly associated with the outer suburbs of Paris (Zone A), and reproduced wealth with the capital’s “core” markets (Zone A bis). From 2012 to 2018, the distinction grew in step with the increasing selectivity of residential benefits and incentives. As shown in Table 2, incentives to private landlords applied to Zones A and B after 2012 (markets with higher disequilibrium), and excluded Zones B2 and C (markets with moderate disequilibrium). Conversely, by 2018, zero-interest loans were only available in Zones B and C, and excluded Zones A and A bis. These changes are observable in the mosaic plots: Risk trajectories continue to prevail in A-listed zones, while reproduce trajectories concentrate in “A bis” zones. Renew trajectories are over-represented in areas with moderate market disequilibrium (B1 and C Zones), especially in new build-to-rent markets in Lyon and in Avignon, as well as in some outer suburban markets of the Ile-de-France. The fact that these areas consistently evidence an over-
representation of renewal trajectories, despite the many housing reforms enacted over this
period, suggests that the association between residential trajectories and zoning is strongest in
areas where market imbalances are the most acute. Zoning-based policies have the greatest
effect on *accentuating* inequalities where disequilibrium is already the strongest. Where
imbalances are less acute, their impact is harder to disassociate from other market forces
impinging on the character of buying and selling therein.

<figure><img src="image_9.png" /><figcaption>Figure 7. Mosaic plots: links between ABC Zoning and Risk / Renew / Reproduce</figcaption></figure>

<footnote>*Data is only available since ABC zoning records began in 2003. Mosaic plots display contingency tables as rectangles, which represent a breakdown of the risk/renew/reproduce typology according to ABC zoning designations. Shades display Pearson residuals, a standardized measure of how far the observed frequency deviates from the expected frequency. As such, in 2008, there is a strong overrepresentation of A zones in the Reproduce category; in 2012, there is a strong underrepresentation of A areas in the Risk category.</footnote>
<table><caption>Table 2. Changes to market incentives and ABC Zoning over time.</caption><thead><tr><th rowspan="2"></th><th colspan="4">Zero interest loans (2)</th><th colspan="4">Private landlords fiscal incentive</th></tr><tr><th>2003</th><th>2008</th><th>2012</th><th>2018</th><th>2003</th><th>2008</th><th>2012</th><th>2018</th></tr></thead><tbody><tr><td>A & Abis (1)</td><td>o</td><td>x</td><td>o</td><td>o</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>B</td><td>o</td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td></tr><tr><td>B1</td><td></td><td>x</td><td>o</td><td>x</td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td>B2</td><td></td><td>x</td><td>o</td><td>x</td><td></td><td>x</td><td>*(3)</td><td>o</td></tr><tr><td>C</td><td>o</td><td>x</td><td>o</td><td>x</td><td>x</td><td>x</td><td>*(3)</td><td>o</td></tr></tbody></table>

(1) Abis zoning has been introduced with the 2010 rezoning, and describes very central and expensive markets in Paris and inner suburbs

(2) Zero intrest loans: Created in 2005, abolished in 2011 and reinstated under more restrictive conditions in 2012, the PTZ for existing property has been definitively withdrawn from zones A and B1 since January 2018.

(3) Optional local agreements in Scellier 2009-2012 (zone C), and Pinel 2015-2021
Detailed maps for each year available here :
https://analytics.huma-

Such observations raise the fundamental question as to what is actually driving volatility in these markets. For theorists like Glaeser et al. (2008), instability is linked to the elasticity of land and housing prices. Likewise, Fingleton et al. (2018) argue, we cannot assume that the problem is tied to distortions in housing supply and demand alone. Our contention is that in France, territorial development and cohesion policies have had a remarkable effect on residential market volatility. Specifically, how development occurs in a given place can have significant impacts not just on the character of housing inequality but the tools available to buyers and owners to respond to it. Infrastructure projects like the new Grand Paris Express have helped incite the speculative retention of real estate, asset inflation, and the outpricing of lower-income groups from affected areas (Guibard and Le Goix, 2024). These are not one-off events, but evolve alongside ongoing modifications to territorial indicators that modulate the residential zoning designations that underpin asset-based welfare policies, and thereby the character of inequality on the ground (Clerval, 2020; Ryan-Collins et al., 2017). Given that housing markets are relationally constituted through various forms of investment arbitrage within and between cities and regions, instabilities arising in one market can have knock-on
effects in others as buyers and sellers trade up and across property ladders in an effort to build
household equity over time (Kutz and Lenhardt 2016) – an acute problem witnessed in smaller
municipalities surrounding Greater Paris and Avignon. In short: territory as much as policy
drives the trajectories of residential inequality *in situ*.

Conclusion: More Volatile, Less Equal

Demsas (2022), reflecting on housing policy in the United States, contends that “the
homeownership society was a mistake.” She writes:

> “Homeownership works for some because it cannot work for all... And if we want housing to act as a wealth-building vehicle, home values have to increase significantly over time... *A home is bound to a specific geographic location, vulnerable to local economic and environmental shocks that could wipe out the value of the land or the structure itself right when you need it...* [Home values] are predicated not on sweat equity or hard work but on luck... is this kind of random luck, which compounds over time, the best way to organize society?” (our emphasis).

Demsas' assertion can be extended to the crisis in France, where efforts to promote asset-based
welfare have led to strikingly problematic outcomes. As Aalbers (2016), Fingleton et al.
(2018), and others have shown: the housing crisis is not going to be resolved by producing
more housing nor more homeowners. Our contention is that to effectively understand the
challenges of asset-based welfare and inequality today, we need to look beyond ownership,
supply and price inflation in isolation and consider how residential welfare policies territorially
differentiate and regulate market outcomes. This means explaining how the value of residential
property is materially, financially, and socially embedded within local policy contexts, and the durability of those circumstances in constituting household security and well-being over time.

The development of asset-based welfare in France, driven by benefits and incentives for homeowners and investors alike, are directly tied to territorial cohesion imperatives that have driven increasingly unequal and volatile outcomes in residential well-being over the past two decades. With few exceptions, these trends significantly depart from the ideal-typical model of valorization upon which asset-based welfare policies depends. So striking are these contradictions that it is again worth reiterating Demsas question: *Is this the best way to organize society?*

Of course, our conclusions are not necessarily applicable to other contexts within or outside France, but they are a useful source of comparison with other cases of asset-based welfare wherever similar data are available and wherever other examples of “an ambivalent and evolving relationship with wealth inequality and household risk” exists (Bryant *et al.*, 2024, 475). As such, our analysis may be instructive for developing a more general explanation about the constitution and evolution of residential wealth and vulnerability for the future.

## Acknowledgements

We wish to thank the reviewers, Sophie Oldfield and the Menthonnex writing collective for their constructive comments on previous iterations of the manuscript. The usual disclaimers apply.

## Funding

This work was supported by the ANR Agence Nationale de la Recherche WIsDHoM project – Wealth Inequalities and the Dynamics of Housing Market [grant number ANR-18-CE41-0004], and the Institut Universitaire de France.
## Bibliography

Aalbers, M. (2016) *The Financialization of Housing : A Political Economy Approach*. London: Routledge.

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), pp. 176-200.

Arundel, R. & Ronald, R. (2020) The false promise of homeownership: Homeowner societies in an era of declining access and rising inequality, *Urban Studies*, 58 pp.1120-1140.

Badcock, B. A. (1989) Homeownership and the accumulation of real wealth, *Environment and Planning D*, 7(1), pp.69-91.

Benites-Gambirazio, E. & Bonneval, L. (2022) Housing as asset-based welfare. The case of France, *Housing Studies*, pp.1-15.

Berté, C., Boulay, G., Benites-Gambirazio, E. & Bonneval, L. (2022) « 20 % de plus, 20 % moins cher ». Le plan 3A de la Métropole de Lyon ou les ambiguïtés de l'accession abordable, *Géographie, économie, société*, 24(1), pp. 103-123

Bonnet, F., Lalé, E., Safi, M. & Wasmer, E. (2015) Better residential than ethnic discrimination! Reconciling audit and interview findings in the Parisian housing market, *Urban Studies*, 53(13), pp.2815-2833.

Bonvalet, C. & Bringé, A. (2013) Les effets de la politique du logement sur l'évolution du taux de propriétaires en France., *Revue européenne des sciences sociales*, 51(1), pp.153-177.

Borg, I., Kawalerowicz, J. & Andersson, E. K. (2022) Socio-spatial stratification of housing tenure trajectories in Sweden: A longitudinal cohort study, *Advances in Life Course Research*, 52(100467).

Bourdieu, P. & Christin, R. (1990) La construction du marché, *Actes de la Recherche en Sciences Sociales*, (81-82), pp.65-85.

Brutel, C. (2023) En 2021, des déménagements un peu plus nombreux qu'avant la crise sanitaire depuis l'Île-de-France vers les autres régions. Available at https://www.insee.fr/fr/statistiques/7637352 (accessed 27 June 2023).

Bryant, G., Spies-Butcher, B. & Stebbing, A. (2024) Comparing asset-based welfare capitalism: wealth inequality, housing finance and household risk, *Housing Studies*, 39(2), pp.459-480.

Casanova Enault, L., Bocquet, M. & Boulay, G. (2023) Who owns France? Uncovering the structure of property ownership for a better understanding of the socio-spatial distribution of wealth, *Journal of Urban Affairs*, pp.1-18.

Casanova, L., Boulay, G., Gérard, Y. & Yahi, L. (2017) Deux bases de données, aucune référence de prix. Comment observer les prix immobiliers en France avec Dvf et Perval ?, *Revue d'Économie Régionale et Urbaine*, Octobre(4), pp.711-732.

Chouraqui, J. (2021) Medium-sized cities in decline in France: between urban shrinkage and city centre devitalisation, *Raumforschung und Raumordnung | Spatial Research and Planning*, 79(1), pp.3-20.

Christophers, B. (2016) For real: land as capital and commodity, *Transactions of the Institute of British Geographers*, 41(2), pp. 134–148

Clerval, A. (2020) Gentrification and social classes in Paris, 1982-2008, *Urban Geography*, pp.1-25.
Delmelle, E. C. (2015) Five decades of neighborhood classifications and their transitions: A comparison of four US cities, 1970–2010, *Applied Geography*, 57(February), pp.1-11.

Demsas, J. (2022) The homeownership society was a mistake. *The Atlantic*. (December 20) https://www.theatlantic.com/newsletters/archive/2022/12/homeownership-real-estate-investment-renting/672511/.

Dikec, M. (2011) *Badlands of the Republic: Space, Politics and Urban Policy*. Hoboken: Wiley-Blackwell.

Doling, J. & Ronald, R. (2010) Home ownership and asset-based welfare, *Journal of Housing and the Built Environment*, 25 pp. 165-173.

Driant, J.-C. (2009) *Les politiques du logement en France*. Paris: La Documentation française.

Driant, J.-C. (2024) *Les métropoles et les marchés du logement*. Paris: POPSU / Autrement.

Dwyer, R. E. & Lassus, L. a. P. (2015) The great risk shift and precarity in the U.S. housing market, *The Annals of the American Academy of Political and Social Science*, 660, pp.199-216.

Fingleton, B., Fuerst, F. & Szumilo, N. (2018) Housing affordability: Is new local supply the key?, *Environment and Planning A*, 51(1), pp. 25-50.

Flynn, L. B. & Schwartz, H. M. (2017) No exit: Social reproduction in an era of rising Income inequality, *Politics & Society*, 45(4), pp. 471-503.

Geniaux, G., Napoléone, C. & Leroux, B. (2015) Les effets prix de l'offre foncière, *Revue d'Economie Régionale et Urbaine*, 05(1-2), pp. 273-320.

Gimat, M., Guironnet, A. & Halbert, L. (2022) *La financiarisation à petits pas du logement social et intermédiaire en France. Signaux faibles, controverses et perspectives, in: Working paper, Chaire Villes, logement, immobilier*. Paris: SciencesPo.

Glaeser, E. L., Gyourko, J. & Saiz, A. (2008) Housing supply and housing bubbles, *Journal of Urban Economics*, 64(2), pp.198-217.

Guérois, M. & Le Goix, R. (2009) La dynamique spatio-temporelle des prix immobiliers à différentes échelles : le cas des appartements anciens à Paris (1990-2003), *Cybergeo : European Journal of Geography*. 470, http://www.cybergeo.eu/index22644.html

Guibard, L. & Le Goix, R. (2024) Those who leave: Out-migration and decentralisation of welfare beneficiaries in gentrified Paris, *Urban Studies*, 61(10), pp.1990-2010.

Hochstenbach, C. (2023) Networked geographies of private landlordism: mapping flows of capital accumulation and rent extraction, *Housing Studies*, pp.1-26.

Hochstenbach, C. & Aalbers, M. B. (2023) The uncoupling of house prices and mortgage debt: towards wealth-driven housing market dynamics, *International Journal of Housing Policy*, pp.1-29.

Hochstenbach, C., Howard, A. & Arundel, R. (2024) Increasing social and spatial inequalities in parental co-residence, *Tijdschrift voor Economische en Sociale Geografie*, Early View.

Husson, É., Josse, J. & Pagès, J. (2010) Principal component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data ? *Technical Report*, Accessed: http://factominer.free.fr/more/HCPC_husson_josse.pdf.

Josselin, D., Blanke, D., Coulon, M., Boulay, G., Casanova Enault, L., Peris, A., Le Brun, P. & Lecourt, T. (2024) Uncertainties Related to Real Estate Price Estimation Scales. In: P. François, B.-H. Mireille and D. Eric (eds.) *Geographical Data Imperfection 2: Use Cases*, pp.127-163. London: Wiley.

Kemeny, J. (1991) *Housing and Social Theory*. London: Routledge.

Kutz, W. (2018). Financialization interrupted: Unwilling subjects of housing reform in Morocco. *City, 22*(4), 568-583.
Kutz, W. & Lenhardt, J. (2016) "Where to put the spare cash?" Subprime urbanization and the geographies of the financial crisis in the Global South., *Urban Geography*, 37(6), pp.926-948.

Le Brun, P. (2022) Un soutien géographiquement inégal : la sélectivité spatiale des aides publiques à l’investissement immobilier résidentiel des ménages en France, *Géographie, économie, société*, 24(1), pp.43-68.

Le Corre, T. (2021) Une base de données pour étudier vingt années de dynamiques du marché immobilier résidentiel en Île-de-France, *Cybergeo: European Journal of Geography*, 992, https://doi.org/10.4000/cybergeo.37430.

Le Goix, R., Casanova Enault, L., Bonneval, L., Le Corre, T., Benites, E., Boulay, G., Kutz, W., Aveline, N., Migozzi, J. & Ysebaert, R. (2021a) Housing (In)equity and the Spatial Dynamics of Homeownership in France: A Research Agenda, *Tijdschrift voor Economische en Sociale Geografie*, 112(1), pp.62-80.

Le Goix, R., Giraud, T., Cura, R., Le Corre, T. & Migozzi, J. (2019) Who sells to whom in the suburbs? Home price inflation and the dynamics of sellers and buyers in the metropolitan region of Paris, 1996-2012, *PLoS ONE*, 14(3), e0213169, https://doi.org/10.1371/journal.pone.0213169.

Le Goix, R., Ysebaert, R., Giraud, T., Lieury, M., Boulay, G., Coulon, M., Rey-Coyrehourcq, S., Lemoy, R., Ramasco, J. J., Mazzoli, M., Colet, P., Theurillat, T., Segessemann, A., Marcinczak, S. & Bartosiewicz, B. (2021b) Unequal housing affordability across European cities. The ESPON Housing Database, Insights on Affordability in Selected Cities in Europe *Cybergeo : European Journal of Geography*, 974, https://doi.org/10.4000/cybergeo.36478.

Lévy, J.-P. (2005) Change in the social hierarchy of French urban housing between 1978 and 1996, *International Journal of Urban and Regional Research*, 29(3), pp.581-607.

Maher, C. (1994) Housing Prices and Geographical Scale: Australian Cities in the 1980s, Urban Studies, 31(1), pp. 5-27.

Méndez, M. L. & Gayo, M. (2019) *Upper Middle Class Social Reproduction: Wealth, Schooling, and Residential Choice in Chile*. Cham: Palgrave.

Mikelbank, B. (2011) Neighborhood déjà vu: Classification in metropolitan cleveland, 1970-2000, *Urban Geography*, 32(3), pp.317-333.

Peris, A. & Casanova Enault, L. (2023) Proximity or opportunity? Spatial and market determinants of private individuals' buy-to-let investments, *Environment & Planning B*, 51(7), pp.1478-1495

Piketty, T. (2013) *Le capital au XXIe siècle*. Paris: Seuil.

Pinçon, M., & Pinçon-Charlot, M. (1989). *Dans les beaux quartiers*. Paris: Seuil.

Pinçon, M., E. Préteceille, & P. Rendu (1986). *Ségrégation urbaine: classes sociales et équipements collectifs en région parisienne*. Paris: Anthropos.

Reibel, M. (2011) Classification approaches in neighborhood research: Introduction and review, *Urban Geography*, 32(3), pp.305-316.

Rhein, C. (1998) Globalisation, social change and minorities in metropolitan Paris: The emergence of new class patterns, *Urban Studies*, 35(3), pp.429-447.

Ryan-Collins, J., Lloyd, T. & Macfarlane, L. (2017) *Rethinking the Economics of Land and Housing*. London: Zed Books.

Sénat (2024) Rapport d'information fait au nom de la commission des affaires économiques (1) sur la crise du logement, Paris: Sénat. https://www.senat.fr/rap/r23-567/r23-5671.pdf

Simon, A., & Essafi, Y. (2017). Concurrence générationnelle et prix immobiliers. *Économie Régionale et Urbaine*, (1), 109-140.
Tammaru, T., Musterd, S., Van Ham, M. & Marcińczak, S. (2016) A multi-factor approach to understanding socio-economic segregation in European capital cities. In: T. Tammaru, M. Van Ham, S. Marcińczak and S. Musterd (Eds) *Socio-economic segregation in European capital cities : East meets West*, London: Taylor and Francis.

Van Gent, W.P.C. (2010) Housing policy as a lever for change? The politics of welfare, assets and tenure, *Housing Studies*, 25(5), pp.735-753

Vermeersch, S., Launay, L. and Charmes, E. (2018) *Quitter Paris ? Les classes moyennes entre centres et périphéries*. Grane: Créaphis.

Wijburg, G. (2019) Privatised Keynesianism and the state-enhanced diversification of credit: the case of the French housing market, *International Journal of Housing Policy*, 19(2), pp.143-164.

Wind, B., Dewilde, C. & Doling, J. (2020) Secondary property ownership in Europe: contributing to asset-based welfare strategies and the ‘really big trade-off’, *International Journal of Housing Policy*, 20(1), pp. 25-52.

Wind, B. & Hedman, L. (2018) The uneven distribution of capital gains in times of socio-spatial inequality: Evidence from Swedish housing pathways between 1995 and 2010, *Urban Studies*, 55(12), pp. 2721-2742.