The making of the new institutional regime is not only national but also local. This contributes to the reinforcement of a new institutional regime where residential markets are seen as growth drivers with inflationary effects [ 3 ]. Public and private actors have converging interests in housing market expansion fiscal resources, economic opportunities and produce a specific representation of the housing demand [ 42 , 43 ].
This characterization of a new institutional regime leads to the question of whether it accentuates socio-spatial inequalities and segregation or not. Since the institutional regime relies on the mobilization of households, the financial effort price-to-income ratio appears as a key variable, as well as the socioeconomic origins of prospective buyers i. Not only the outcomes of changes in the suburban housing production regime have not been well characterized in France yet, but the theoretical significance of the Paris-region suburban case study, its historical and interpretative bias, are also to be discussed before we proceed further.
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The city of Paris and its metropolitan region are often cited as a model, and as an exception, but are rarely actually included in comparative research. New York, London, Tokyo, Chicago, Los Angeles, Hong-Kong and Shanghai , the immense body of literature discussing urban production, housing and segregation in Paris has been mostly published in the French language, and gives to Paris a relatively and regrettably exceptional standing in the field, starting with the setting up of the post-Second Empire Paris as a unique model of urban segregation, planning and public spaces.
Often, Paris is compared to its European counterpart, London [ 45 ]. Another explanation derives from of a certain idiosyncrasy of French language human geography itself, i.
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Although scholarly works in the French language thoroughly analyze segregation in Paris and its suburbs [ 52 — 56 ], segregation in the Paris metropolitan region, outside the limits of the inner city, is still scarcely documented in the English language: the recent Socioeconomic segregation in European capital cities excludes Paris [ 13 ], the second largest European capital.
Recent work describing socioeconomic segregation and ethnic segregation also often elaborate on this relative exceptionalism of the Paris case study [ 57 , 58 ]. To frame the research question, he insists on how the construction of debates on socioeconomic segregation in Paris has focused on explanations, with a mix of fascination and dramatization of segregation in the banlieues , with a US bias. His approach covers socioeconomic segregation professionalization, mass unemployment and growing casualization of labor and ethnic segregation by the means of census data, but his research does not cover how the transformation of the production of housing systematically contributes to inequalities, especially the contribution of suburban and mass homeownership to the process [ 58 ].
When segregation in France is however quantitatively analyzed in the English language, this is for instance to be compared with US socioeconomic segregation patterns, the theoretical focus being on cross-national variations on segregation, e. Some focus has also been on categories of neighborhoods to compare, for instance revitalization in distressed areas, segregation and social mixing, comparing between Paris, and cities in North American and in the UK [ 60 ].
It is a corollary that the study of French and Parisian suburbs suffer from an American bias, suburban dynamics being easily matched to phenomena loosely compared with US suburban dynamics, despite the proclaimed exceptionality of the Parisian case study. Therefore, b a common oversimplification is to analyze the Paris region as a counter model to American cities, and a model of the supposed hallmarks of European cities: post-industrial transition, high unemployment, high level of skilled workers, higher densities, lower levels of social segregation, lower spatial mismatch, better public transportation [ 64 ].
In the same movement, Paris has also been compared to other metropolitan areas, focusing on two major standpoints: on the one hand similar trends towards gentrification and suburbanization; and on the other poor job accessibility, exposure to the negative externalities of concentrated poverty in deprived neighborhoods. Interestingly, recent work however pushes for more nuanced analysis of middle-class diversity and social mixing in segregation patterns [ 57 , 60 ]. Meaning, Paris as a case-study is simultaneously and ambiguously used to both confirm and contradict some general hypothesis about global urbanization.
This is even more true when discussing the typologies of suburbanization. In different comparative analyses, the Paris region is used to document variegated categories of suburbanism:. In short, an overflow of empirical analysis describes the suburbs of Paris as a unique case study, and this paper may well be just another one.
But this highlights the requirement to engage in a critical analysis of suburbs as both a products, economical and social processes. Characterizing social change and the effect of the production regime on the property market and inequalities can be one way to do so.
Taking transactions seriously is a means to study suburbs as an owner-occupied housing market, under the assumption that purchasing a property is a total social act [ 77 ]. To do so, we argue it is critical to get a better understanding of the shape and dynamics of inequalities in the outer-suburbs of Paris, by the means of exploring the actual role that typical suburban single family homes, residential estates and subdivisions, have in property ownership, for instance on upward or downward mobilities [ 78 ], for two main reasons:.
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.
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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.
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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.
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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.
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.
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.
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. 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: 1 where A i the potential of i , O i the stock of population at j , f d ij a negative function of distance, generally a power or exponential curve. Function parameters have been estimated by the means of semi-variograms, i.
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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.