Laction publique face à la mobilité (Logiques sociales) (French Edition)

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We assume hypothesis 1 that professionalization a rise of executives, intermediate occupation and salaried employees, as defined in public statistics categories well describes the dynamics of property ownership in suburbs, and that local trajectories in price appreciation are critical in understanding the different regimes of wealth accumulation for suburban households. Property prices, therefore, directly affect social and spatial inequalities of residents and buyers in a stratified market, and also intensify inequalities through asset-capitalization hypothesis 2.

Second, a strong public policy emphasis has been set on integrating city centers and suburban areas, under a reincorporation of metropolitan governmental bodies after , but this reform explicitly excluded outer suburbs, because of their specific spatial and socioeconomic patterns, set aside from the rest of the core agglomeration [ 87 — 89 ]. This theoretical problem of the explicit linkages and dependance between outer-suburbs and the central agglomeration of Paris has however been preeminent in the literature.

Suburbs have long been regarded as the locus for the lower middle class [ 90 ], excluded from more central locations by the high cost of housing. However, some recent studies have sought to show the greatest diversity of population dynamics, in terms of age, class, [ 22 , 83 , 91 ], or origin [ 92 ], as well as the variegated forms of political and social engagement [ 93 ]. To some extent, suburbanization in France is mostly seen as a movement of residential loosening that cannot be easily compared to the edgeless city [ 94 ].

In the early s, the average size of suburban municipalities was inhabitants according to E. This growing discrepancy between place of residence and place of work feeds an intense growth of commuting trips between centers and peripheries. A certain level of job diversification has been seen beyond residential services [ 95 ], as well as a growing trend towards sub-centering in connection with the emergence of secondary job centers [ 96 , 97 ]. Data show that the suburbs of Paris indeed face similar problems as other large metropolitan areas. Distances separating jobs from housing have been increasing, public transit systems are efficient within the very heart of the city but most trips connecting suburbs to suburbs are difficult to achieve within a reasonable amount of time, this having detrimental effect especially on low-skilled workers with poor job accessibility [ 64 ], although car-dependance effects on segregation are found to be mitigated by residential mobility [ 95 ].

Residential mobility is however impaired by the lack of affordable housing [ 98 ]. To some extent, this paper remains in a tradition that consists in exclusively focusing on the outer ring of suburbs. By doing so, we aim at clarifying the effects of a tension between two interpretative trends, either of suburbs as a market being fueled by sprawl and social homogenization; or whether the maturation of suburbs yields a stronger diversification of socioeconomic profiles due to commuting patterns, sub-centering and locational strategies or constraints.

From this second standpoint, we assume that sub-centering and maturity of suburbanization should result in a structured and diversified pattern of homeownership segregation between the different social classes hypothesis 3. Housing has been characterized since the s by continuous tensions on housing markets: according to public data from INSEE [ 98 ], The price index has been multiplied by a factor two for apartments between and ; and by 3,5 for homes.

We analyzed in this research the submarket of single-family detached suburban homes, i. To explicitly link the dynamics of property prices and the dynamics of inequalities on the market, we analyzed transactions using the characterization of sellers and buyers by socio-occupational categories in an owner-driven market. This strategy stems from the limits of the commonly stylized relationships between inequalities and housing prices. Many analyses of economic inequalities are based on the fact that inequalities and asset capitalization between households depend on housing value inflation within a crisis of affordability.

However, from a methodological point of view, most of the standard approaches to housing markets assume that large parts of the differences between real estate values depend upon social and urban parameters and the socioeconomic composition of the neighborhood, i. Economic geographers mainly approach the issue of price through the hegemonic framework of econometrics, narrowing down the issue to control dependent and independent variables in modeling housing market segments.

The immense body of work from spatial econometrics and housing segmentation derive from neo-classical models, which tend to explain property valuations through the mixed effects of fixed characteristics and spatial attributes [ ]. Indeed, standard econometrics assign a value to a typical good, according to the hedonic attributes of the property consent to pay for each of the attributes , under the hypothesis that sellers and buyers agree on a market price for the attributes.

This is usually performed by the means of a regression model, explanatory variables derived from the attributes of the properties, characteristics of the surroundings i. Classical approaches focus on price formation more than on a detailed geography of property price and the actual mapping and representation of market dynamics. Information on the dynamic geography and socioeconomic profiles of sellers and buyers has therefore been overlooked in the literature. Consequently, the explicit understanding of space is not always properly handled by the models: much of the research effort has been directed to the definition of submarket segments described by typical goods, and correcting or controlling for spatial autocorrelation problems in price determination, for which many modeling implementation have been tested [ , ].

The need for better spatial analysis in hedonic price modeling in contextualizing the housing markets and its spatial interactions is often acknowledged [ ]. But some scholars argue that space and distance are inadequate explanatory variable, some exploring the multilevel interactions of amenities with price according to distance in order to better account for geographically nested effects and scalar interactions [ , ]; while others contend the need to radically over-simplify the problem of autocorrelation and consider spatial coordinates x and y as explanatory variables [ ].

Sophisticated econometric approaches have also been tested to better include the variegated specifications of externalities resulting from neighborhood effects, street effects and locational effects. These are functions implemented with discrete variables constructed on distance thresholds from schools, parks [ ], light-rail and trams [ ] or urban renewal districts [ ].

Such models also handle externalities e. This research have contributed to contextualize the effect of distance on property pricing. Elaborating a theory of price and spatial interaction however requires a better and explicit understanding of exogenous and relational socio-spatial interactions that interfere at many nested scalar effects. Hedonic pricing focus on explaining price formation rather than on a detailed geography of property price and its mapping and representation. Though now implementing many refinements, this approach relies mostly upon the concept of equilibrium and pays little attention to prices dynamics.

Price dynamics are indeed a geographical problem because of the remarkable catching up and convergence processes that occur between neighborhoods explained by spatially displaced demand, a dynamic observed for instance in London [ 85 ], and Paris [ ], that strongly supports rent gap theory [ ]. The rents extracted from urban locations convey the assumption that house price inflation could not be reduced to the invisible hand of supply and demand governing price dynamics. For instance a study examines the dynamics of price due to market leading market lagging phenomenon, by the means of a cross-correlation matrix representing price linkage between different areas in the UK housing-market spatially indexed time-series [ ].

Hence, well-known phenomena related to the dynamic of property appreciation or decay have to be studied as spatial dynamics. Furthermore, it has been shown that housing price inflation does not necessarily involve a contraction of demand [ ], nor does increased supply imply depreciation [ ]. The reasons for this is that urban land is embedded in a system of value production and capture through which social, political and property relations of capitalism are intermediated [ ], that also disconnects value from the market theoretical equilibrium.

Some scholars therefore conceptualized real estate as other forms of capital and commodity [ ], a means to advance the uncoupling of housing rent from land rent. Such uncoupling matters for the geography of real estate markets, as stock properties do not have to include increased costs of production i. This problem highlights the needs for a more spatial approach [ 9 ], as well as the development of analytical tools to model, visualize, and explain the evolution and distribution of transactions, prices and accumulation across the differentiated spaces of the urban fabric.

To do so, we test interpolation and visualization methods that rely on an explicit function of distance travel-time , within suburban homogeneous single-family homes in subdivisions , although spatially fragmented submarkets. Individual transactions for the whole metropolitan region of Paris were obtained from the Chamber of the Notaries—Paris Notaire Service PNS , a commercial provider for details on data provision, see S1 Methodological Appendix.

This database contains a sample of transactions for the region and its suburbs, within the administrative limits of Ile-de-France 1 million rows , covering a 16 year timeframe: , , , then every year from to All transactions are geographically indexed, with the address, the parcel number, latitude and longitude.

All records contain information on property amenities and pricing Fig 1 , but also a series of understudied variables on sellers and buyers, such as age, sex, socio-economic status, national origin, place of residence, and mortgage. The main variables used in this study are property price and occupation of sellers and buyers when the property is sold or purchased by an individual Fig 2 , set aside are real-estate investment trusts REITS and real-estate professionals.

Residential markets in France, especially in suburbs, have not been structured yet by institutional investors and investment funds [ ]. Regarding the price, the aim being to distinguish between the various local patterns of appreciation and depreciation, we adopt the nominal price of properties as an indicator of housing price inflation. In a national context where price inflation is decoupled from macro-economic fundamentals slow economic growth and low inflation in France during the last decades [ 1 ], we are more interested in the unequal geography of nominal price dynamics, from which stems affordability issues for households and increased price to income ratios.

Violin plots represent kernel density estimates. Thresholds defined as 1st decile, first quartile, median, third quartiles and 9th decile; price scale, log For breakdown of price trends by socio-occupational categories, cf.

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S1 Fig. Other categories e. Although they are public records, transaction data are considered in France a proprietary database, distributed to researchers by PNS as a commercial product and subject to restrictions for the dissemination of results. Given the cost of the database for public research institutions, only a limited number of years and a sample of transaction have been acquired, considering the scholarly work that has already been published using datasets covering the period for the Paris region [ 11 , ], and some exploratory work recently conducted on suburban market data [ 12 ].

Data flow is described in Fig 3. Detailed methodology regarding data selection and sampling is provided in S1 Methodological Appendix. For spatial analysis purpose, two final issues had to be dealt with: the weakness of samples when matched with small local geographies, and the fragmented structure of the built environment, made of subdivisions, large tract housing development, but also detached houses scattered in semi-rural landscapes.


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To offset these limitations, a combination of a suitable grid and techniques of interpolation of point data was used. Because of requirements regarding the confidentiality of individual transactions, it was impossible to use aggregates of less than 5 transactions. Given the spatial distribution of transactions, it is challenging to find any suitable geography that will allow us to render finer grain local dynamics and maintain the requirement for aggregating transactions. Given the spatial fragmentation of transactions in the outer peripheries, the problem remains when using larger spatial units, such as municipalities.

The main analysis was conducted at a 1km-cell grid level as provided by the French census institute INSEE for local analysis.

Argumentaire

Fig 4 also highlights why the geography of municipal boundaries usually used to map property prices are inadequate in many cases in suburbia as it does not fit the actual geography of suburbanization and neighborhoods made of a mix of close-knit subdivisions and scattered countryside homes. As discussed in the literature, amenities, exclusivity, club realm and locational rent strongly interact in producing socioeconomic homogeneity at the neighborhood or subdivision level [ — ]. This grid combines three main advantages for a study of suburban areas details provided in S1 Methodological Appendix :.

Disneyland Paris is located north of this map. This is an appropriate spatial proxy for homogeneous areas matching the fragmented suburban built environment, as secondary street segments generally define local submarkets [ — ]. The grid is an appropriate proxy for homogeneous areas matching the fragmented suburban built environment.

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It fits the spatial patterns of urbanization, and does not impute a value to areas that have no values, or no potential buyers or sellers. It is also consistent with regulations on data and statistical secrecy. The computed values will be therefore imputed to the closest cells, making two nearby properties more likely to be priced equally spatial interaction hypothesis if cells are connected by local streets. Interpolation is a classical problem: in many cases, the problem consists in mapping or visualizing a continuous surface temperature, wind where the phenomenon can be accurately estimated in all points, with a small number of actual measures.

But the usual methods of spatial interpolation e. Some solutions have been implemented on real-estate advertising websites, that deal with the problem of generalizing the information from transactions in a neighborhood. We propose an alternative approach that computes a synthetic value based on distance and weight of the observed population, as initially proposed by Stewart [ ] for an analysis of the distribution of student population and catchment areas of American Universities, and more recently applied for socioeconomic phenomena [ ].

The potential of population is generally defined as a stock of population weighted by distance:. Function parameters have been estimated by the means of semi-variograms, i. The computation of the price potential follows a two step procedure: first, the potential for price is computed as the potential total value in a cell Pp ; then, the potential for the number of transactions Pt in a given cell is computed.

To characterize change and local patterns of inflation, we apply a cluster analysis based on property prices. Selected years. Author: R. With gridded interpolated variables, we perform a multivariate analysis to cluster neighborhood change, as outlined in Fig 3. The same interpolation technique is also applied to determine the potential number of transactions within a 10 min.

We apply the following steps to analyze changing balances between sellers and buyers for each category, and to compare the transitions and sequences of neighborhoods, revealing the differences in the dynamics of neighborhoods. The data do not allow us to analyze upward or downward mobility of households per se : the dataset describes the socio-occupational category of households Fig 2.


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To analyze social change, we focus on the local balance between sellers and buyers in each grid cell, for each category, for each year. By doing so, we adopt a design that assumes that segregation stems from the balance between groups moving from one place to another. As an example, Fig 6 shows the dynamics for two main categories of actors. Intermediary occupations are more active as buyers than sellers in the inner part of the region, whereas retirees show an exact opposite trend: in , they are more likely to move and buy properties in the outskirts and exurbs, leaving the mature suburbs of the first rings.

We also analyzed the matrix of correlations between the proportion of buyers and sellers of each category, showing the main effect of inertia cf. Because of the structure of exclusive trends executives selling to executives; workers and employees more likely to sell to workers and employees , we deem that the balance between transactions are good indicators of local change. A: Intermediary occupations.

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B: retirees. Second, using sellers-buyers balance by categories as input variables describing each cell, a cluster analysis ward method, euclidian distance describes the categories for each neighborhood at each given date, assuming the different socio-occupational net balances are significant in analyzing local trends. We finally elaborated on a sequence analysis method to sort out neighborhood change in US metropolitan areas [ , ]. We analyzed the longitudinal categorical sequences of neighborhoods, applying sequential pattern mining procedures. For this we used the R TraMiner package algorithms [ ] to sort out and describe the successive sequences of states for each neighborhoods, for each given year.

The method sorts out the most frequent permutations found in the sequence of states for each neighborhood, so as to analyze local change as sequences and permutation between different states: for instance when a new suburban neighborhoods evolves and become an aging declining suburb.

A clustering method was applied to aggregate the sequences into a number of groups. For that purpose we used the agnes function Cluster library of the TraMiner package. First, we present and discuss the results for the dynamics of property values over time, in order to better contextualize the unequal home value inflation in which transactions are taking place.

Second, the main trajectories of property value growth and decline are summarized by the means of an exploratory cluster analysis.

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Third, we classify the cells describing neighborhoods by sellers-buyers balance for each category at each date, and analyze the longitudinal categorical sequences of neighborhoods between and We finally analyze the correlation between price dynamics and longitudinal sequences of neighborhoods. When appropriate, results are discussed with the literature within this section.

Regarding home price inflation between and , data show an interesting combination of two trends: a constant appreciation in the entire region, and an apparent homogenization of prices towards the higher brackets of property values above , Euros Fig 5. For an animated visualization cf. S3 Fig. The spatial patterns of price inflation showed mixed and heterogeneous tendencies. Cluster profiles of price inflation are reported in Fig 7 : blueish colors describe the trends of highest price brackets above , euros , green colors corresponds to median price brackets, and reddish colors describes lowest price brackets.

The general trend is that price inflation has affected almost every suburban context, but it has also increased spatial heterogeneity between neighborhoods. Inflation has also strengthened the unequal spatial structure of prices and the hierarchy of neighborhoods. The spatial structure of unequal price growth over time follows 7 distinct trajectories:. A: Map of clusters. B: Profiles of average home values by clusters. First, the hierarchy of neighborhood price brackets was maintained and strengthened between and In cluster 3 more expensive high-end neighborhoods near Versailles, Saint-Germain-en-Laye, Fontainebleau, Coupvray better recover from the crisis, compared to furthest neighborhoods similarly priced in , but experiencing a substantial value gap after cluster 4.

Such neighborhoods are those that better control their local environment, land availability, and develop local strategies of territorial control, exclusion, and club economy, in order to protect local tidiness and social homogeneity, as discussed by Charmes in his essay on the clubbization of French Periurban municipalities [ 67 ]. In the French context, it derives from the governance structure of small municipalities, local bodies of government whose principles perfectly match those of the club economy, as a local organization managing the interest of its members, most notability by means of slow-growth policies and control of land use [ 67 ].

We also notice a slower price appreciation in average price contexts, in clusters 2, 5, 6, 7 and 8. All have similar prices between and , then each of these clusters follow disctinct trajectories after Cluster 8 for instance describes areas of mature subdivisions in the outer-suburbs built between and , such as Cergy, Les Mureaux on the West-side; Goussainville, Meaux on the North-East side; Lagny, Coulommiers on the East part of the region; Cesson, Melun to the South: although properties lost an average of 25, Euros during the crisis, the recovery was complete in , up to , euros for a typical suburban tract home.

This trend is almost similar to trajectories followed by neighborhoods in furthest location like in Thoiry cluster 6 , but the depreciation of values started earlier. In remote areas with a mix of exurban and rural settlement, as well as in small peripheral towns such as Provins and Houdan clusters 5 and 7 , very slowly but not entirely recover after Neighborhoods described by cluster 2 Nangis, Nemours, Magny, Etampes however show an incomplete recovery of residential markets.

Lower price brackets also tend to depreciate after data show a slow depreciation in lower priced neighborhoods as in clusters 1, 7, 9, 10 , see the purple, red, orange lines after Cluster 1 shows a common pattern of absolute continuous depreciation starting with the global real-estate and financial crisis as soon as , mostly in the far-eastern part of the region Jouy-sur-Morin.

In other cases, such as cluster 10 , the depreciation started only after , which describes remote exburban areas e. This depreciation of lower-end neighborhoods even yields very volatile trends in the furthest peripheries in the lowest brackets of property prices after as in cluster 9 , although outliers, rare and exceptional transactions may produce such volatile local trends. Such decreasing trends, although sometimes interpreted as a long awaited stabilization of markets and good news for affordability, are however inherently burdens that put indebted households at risk: their financial vulnerability will link the depreciation of assets and negative equity.

Many local contexts follow such unsustainable patterns. These declining tendencies highlight the lack of sustainability of some remote suburban neighborhoods and subdivisions. Such trends inform how households in maturing and lower-end subdivisions may be trapped in place [ ] by decreasing property values, which compromise their capacity for reinvestment because of devaluation and even negative equity; whereas other comment the depreciation and decline of furthest away subdivisions, rendered obsolete by increased energy costs that impacts the burden of daily commuting and shopping trips by car [ ].

These first series of results show common grounds with trends identified in more central places: where generalized inflation has strengthened the unequal spatial structure of price and the hierarchy of neighborhoods. Suburban housing however shows a greater tendency to follow heterogeneous trends, compared to more central locations. The center of Paris and its inner suburbs are more likely to show more homogeneous prices because of inflation [ 11 , ], as well as in Marseille [ 9 ]. We now examine the results of the typology of sellers-buyers balance to analyze neighborhood change in connection with the variegated local trajectories of inflation.

The main goal of this typology derives from the overarching hypothesis of the research: rather than dualization executives vs. Each cluster is described according to the z-scores Fig 8 and mapped in grid cells Fig 9. For an animated visualization of the typology, cf. S4 Fig. The eleven clusters describe different stages of stability, maturity, or rapid change in neighborhoods.

Each cluster, for any given year, describes the significant socio-occupational categories balance between sellers and buyers; clusters are interpreted as a momentum between sellers and buyers, i. A first series of clusters is overrepresented, as on the dendrogram on Fig 9 , and are variations close to the average profiles. Cluster 1 describes an average profile of neighborhoods, characterized by a complete stability of socioeconomic profiles across all socio-occupational categories: z-scores indicate a light trend of maturing population with more retirees moving in neighborhoods that can be described as stable and maturing exurbs, villages and rural areas with residential settlements made of scattered single family homes.

This is the only spatial context in which retirees showed a positive balance: as in Fig 2 , retirees were moving out of the Paris region, selling more than they bought. In exurban subdivisions and suburban areas, Clusters 2 and 3 are variations in trends and magnitude of this average stable profile, characterized by the average dynamics described on Fig 2 , i.

As on the maps, cluster 2 best describes stable exurban markets , whereas cluster 3 clearly delineates an average suburban profile where intermediate occupations and executives were leading actors on both selling and purchasing markets. In Cluster 4 , executives moving in the areas are however overrepresented, all other variables remaining around the average profile. Cluster 7 describes inner suburban neighborhoods with a steady influx of the middle-class intermediary occupation and employees , exclusive of other categories.

Finally, in some areas, very rapid change has been produced by an overwhelming influx of some socio-occupational categories, mutually exclusive of others. Cluster 8 is clearly characterized as the last refuge of blue collar workers in heavily deindustrialized region: these were the last neighborhoods in which the dynamics of workers as buyers superseded the dynamics of workers selling properties. Cluster 9 is clearly defining places where social change was produced by a strong and significant balance favoring the arrival of professionals, executives, academic and engineers.

It better defines places in which urban renewal, gentrification, and also the patterns of employment and transportation has made the housing stock more attractive to this social group. Often spatially associated with cluster 4 , cluster 9 is an avatar of the suburban golden ghetto. Cluster 10 describes a very dynamic very mixed market: a common trend characterized by an overrepresentation of all categories: workers, salaried employees, intermediary occupations, professionals and executives, all with strong positive balances, against retirees, massively selling in this diverse active market.

Such trends are likely to be found in areas of rapid change in the built environment renewal and infill development. Cluster 11 is a variation of cluster 10 , with an overrepresentation of salaried employees and intermediate occupations as buyers more than as sellers. This partition demonstrates the validity of hypothesis 1 professionalization as a driver of change in socioeconomic segregation , as increased numbers of executives, intermediate occupation and salaried employees are predominant actors on the markets, as sellers, and also as buyers, the differentiated impact of these three categories being well circumscribed by the cluster analysis.

Hypothesis 3 is also well supported by the analysis: sub-centering, deindustrialization and the maturity of suburbanization yield a very structured and highly diversified pattern of segregation. It is also clear from the comparison between prices Fig 7 and the balance between sellers and buyers Fig 9 that not only changes in socio-professional structure explain segregation, but households are sorted out between neighborhoods according to the variation of prices, the variegated patterns of asset capitalization in real estate value Hypothesis 2. This critique is also to be extended to the methodology employed in research on socioeconomic change, using classical segregation indices.

Such indices were initially developed to study segregation patterns between racialized groups [ ]. This method can be to some extent misleading when referring to dual patterns of socio-economic segregation, applied to a commonly used divide in the analysis of socioeconomic segregation patterns [ 13 ]: such research is classically constructed to juxtapose opposite groups on the socio-professional spectrum e.

Given the aforementioned sellers-buyers typology, described for each given year, the last series of results derive from the sequencing of consecutive states for each neighborhood. We analyze how the socio-economic patterns of a neighborhood change over time with the succession of sellers moving out and buyers moving in.

To do so, the R TraMiner package algorithms [ ] were designed to sort out and describe in sequences the successive states of neighborhoods: this allows us to analyze local change as sequences and permutations between different states. To better map the trajectories of the sellers-buyers balance, we use this as an exploratory tool, adding values to the more static cartography Fig 9 and animated version in Fig S4 Fig. The main results are described on Fig 10 with descriptive statistics on modal categories: data show that mostly rural and exurban categories clusters 1 and 2 are overrepresented, with an increased share over time of cluster 3 average suburban profile , cluster 4 upper segments in the inner suburbs and cluster 5 mature stable middle class suburbs.

We note that between and , there was a decrease in diversity in the typology, followed after when the market recovered by a significant increase in diversity of state permutations. This can be interpreted as a result of market adaptations to the global real-estate and financial crisis.

The crisis had lesser ramifications in France compared to other OECD countries, however, the results on the distribution of sellers and buyers has been a stabilization of the market with a lower level of variety in transactions in suburbs. The dynamics of the market, in terms of diversity of socioeconomic profiles and potential for local change, increased when the market recovered after This increased diversified pattern of segregation supports hypothesis 3. Prepared with R package TraMiner 2. Finally, a last exploratory hierarchical clustering was also applied to the neighborhood sequencing.

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For exploratory analysis, the goal was to summarize the results of the sequence analysis and to aggregate the sequences into a reduced number of groups. First, many transitioned from type 3 to type 2 in the early s, or during the pre-crisis years in most of these neighborhoods progressively stabilized after sequence type 3. Second, their dynamics were characterized by transitions towards cluster 5 mature middle-class suburbs and type 7 intermediary markets in sequence type 4 , in what can be described as dynamic markets.

The majority of transitions occurred at two periods of time: between and ; and after As described by the overall entropy index between and , these neighborhoods stabilized with a much lower probability of transitioning Fig Starting with a diversity of characteristics, e. They occasionally transitioned downward, coming to more stable profiles—type 3, in green in —. These results and successive typologies allows us to better map unequal patterns of pricing, price dynamics, and neighborhood sequences.

But prices not only indicate a market value, but are also instrumental in wealth accumulation, between spatial contexts in which price are likely to go up where households are likely to capitalize on assets, and neighborhoods in which price are likely to go down, riskier in terms of investment. In short, lower prices mean a gain in affordability for the lower middle class; but in terms of wealth accumulation, there are winners and loosers.

And the probability to pertain to one group or another correlates with the social segregation patterns in the region. We found a strong support for hypothesis 2: the local trajectories of price appreciation and depreciation correlate with the successive sequences of neighborhoods. This analysis shows interesting and contrasted evidences Fig The normalized graphs shows price trend compared to the annual average: sequence type 1 as well as sequence type 2 , areas with a constant influx of retired persons and an average positive balance of intermediate occupations yellow and light green colors on Fig 2 followed a clearly depreciative trend, compared to the average values: the lower the values, the less likely price went up compared to the rest of the market.

For the households owning a property in such contexts and staying in this property, this means less wealth accumulation in real estate, and also the risk of a depreciation of values over time: data clearly show that after , values tended to slightly decrease, in a context of a relatively atone market. By contrast, neighborhoods that followed sequence type 6 in the upper market segments blue on Fig 2 concentrated the highest values, and prices have remained at the highest level from to the highest the value, the highest the probability to protect the investment over time, the highest the probability to accumulate assets, an analysis that has been well framed in how residential markets are structured and protected in suburbia [ 67 ].

In the mid-market segments, distinct patterns are less clear, because prices belonged to very similar brackets. Data on Fig 12 show however two neighborhood sequences with clearly negative trends. Sequence type 3 characterized transitions towards mature middle-class suburbs: prices used to be above average in , then went down, with a strong depreciative trend after the crisis in and Prices in sequence type 4 intermediary markets also did not appreciate as fast as other contexts, and show stable relative trends.

By contrast, owners in sequence type 5 , dynamic mixed markets with a net inflow of executives, salaried employees and intermediate occupations. This paper highlights one of the main characteristics of French social field experiments: a triangular game involving the State, local partners and experts. This difficulty constitutes a recurring impediment for partners to setting the goals of field experiments. Then, the paper points out a dual tendency of the FEJ the national experimental fund for youth. The first one is a thematic shift from youth to issues that only concern educational problems: even though the FEJ originally emphasized the importance of promoting bottom-up innovative actions that try to cope with numerous social issues connected with youth, it ended up dealing with educational issues.

Finally, this article examines the core of the triangular game, namely the replication on a larger scale of the field experiments. It shows that this nodal point remains a blind spot for most of all these embedded partners. Ce que M. On observe alors que la tension entre logiques politique et scientifique reste forte. Nous attachons beaucoup d'importance, dans les milieux juridiques,. Y a-t-il mutation des fondements de l'action publique? Les politiques publiques "Y a-t-il mutation des fondements de l'action publique? Il y a des exemples d'entreprises publiques qui sont dans des situations de concurrence ; il y a des exemples "Y a-t-il mutation des fondements de l'action publique?

Dans "Y a-t-il mutation des fondements de l'action publique?