dataesr/scientific-paragraphs-categorization huggingface dataset:
Before going any further to understand it, it is crucial to ensure that it is not the result of an artefact. In this respect, valuable information can be found in the study by Robert J. Hill, Michael Scholz, Chihiro Shimizu, and Miriam Steurer, who compare the theoretical properties of several hedonic methods and other methods used in various European Union countries, and test them on price data from two large non-European cities, Sydney and Tokyo. In addition to recommending the use of hedonic methods, over that of appraisals, the method developed by Insee and used as a calculation in the Notaires-Insee index is recommended because it is simple to use and gives more stable results for smaller samples. Beyond the legitimate pride of having a method created by the Insee teams validated, this result is important for two reasons.
Against this background, the OECD has developed the Affordable Housing Database (AHD), which brings together cross-national information from OECD and EU member states on housing market context, housing conditions, and public policies, to help governments monitor access to good-quality affordable housing and strengthen the knowledge base for policy evaluation. The effectiveness and efficiency of housing policy measures depends on the context in which they are implemented and on interactions with broader social and economic structures and policies. Hence, assessing the outcomes and potential impact of housing policies requires a system-level approach. Housing systems vary widely across OECD and EU countries, making international comparisons and benchmarking challenging. Identifying groups of countries with broadly similar housing systems allows both assessing the relative performance of different systems in terms of housing outcomes and comparing countries with their most relevant peers.
While quantitative information on housing market context, housing conditions and household indebtedness is available for most countries in the database, variable definitions often differ across countries and information on policies is often qualitative and patchy, which restrains the scope for systematic data analysis. Nevertheless, examining the information on policies within and across the country groups previously determined allows us to identify some similarities and differences in housing policies. The rest of the article is organised as follows: the next section briefly reviews the literature on housing systems; the third section describes the new OECD Affordable Housing Database; the fourth section derives the typology of housing systems through PCA and cluster analysis and describes the main characteristics of the groups of countries obtained in terms of housing outcomes and policies; the fifth section discusses the results and concludes.
Dewilde and De  Decker (2016)  extend the analysis to the evolution of housing outcome inequalities. They find that in countries with highly "commodified" housing regimes low income households experience more affordability problems but better housing conditions and that over time affordability has declined for low-income households and tenants in the private rental sector relative to middle-income households in Western Europe, a trend which can be explained by increased "financialisation" of housing and declining supply of private rental housing 3. The new OECD Affordable Housing Database 2 3   Across the OECD, low-income households are increasingly struggling with high housing costs and poor housing quality, in terms of living space available, adequacy of sanitary conditions and neighbourhood quality  (Salvi del Pero et al.
, 2016). Therefore, the OECD was mandated by its member countries to develop new tools to assess the effectiveness and efficiency of different approaches to affordable housing. The first phase of the project identified the main challenges households are facing to access good-quality affordable housing, the main housing policy instruments put in place by OECD countries and the degree to which they overlap with social policies. In a second phase, the OECD developed, with support from the European Commission, a new on-line database, the OECD Affordable Housing Database (AHD), which was released in early 2017 4. This new tool aims at helping countries measure access to good quality affordable housing and at strengthening the knowledge base needed for policy evaluation, by providing cross-country comparable indicators on housing outcomes and housing policy practices.
The AHD includes 39 OECD and EU countries, but in many cases the information is incomplete. Limited data coverage is a particular issue for non-European countries, which restricts the possibilities for comparative analysis across continents. While the use of broad policy instruments is documented for most countries, details of policies, which are essential to ensure comparability, are often only available for a limited set of countries. Hence, different country samples are used in this paper, according to data availability (see  Box). Information in the AHD was drawn from different OECD sources, other readily available international and national databases, and, for some topics, a specific questionnaire. 2. Hoekstra (2009)  does not include France in the analysis. France would belong to the dual rental system, with a clear distinction between social housing and the private rental market.
3. There is no obvious single definition of commodification in housing  (Doling, 1999; Dewilde & De Decker, 2016). A possible characterisation of "commodified" housing is a regime where housing is mainly allocated through the market and access is related to ability to pay. "Financialisation" refers to the increased dependence of housing markets on globalised financial markets through mortgage finance  (Aalbers, 2008). 4. http://www. oecd. org/social/affordable-housing-database. htm. This webpage contains information about data sources, cross-country comparability, and, where relevant, raw data or descriptive information. This is particularly important as data collected at the national level may rely on definitions that are not harmonised across countries. Building a typology of housing systems to inform policies in OECD and EU member States.
Box -Coverage of the OECD Affordable Housing Database and samples in the analyses The OECD Affordable Housing Database includes 39 countries. However, for some the information is sparse. Hence, various samples are used across this paper, reflecting data availability and trade-offs between the number of countries included in the analyses and the richness of the set of variables taken into account. Table  A  summarises the samples used in figures, tables and statistical analyses. More precisely, data were extracted from the OECD National Accounts Database, the Housing Prices Database, the Social Expenditure Database (SOCX) and Tax-Benefit models. In addition, indicators on housing tenure, affordability and quality were derived from micro-data available from the European Union Statistics on Income and Living Conditions survey (EU-SILC) and national-level household surveys.
In countries with integrated rental systems and a large housing stock owned by housing associations, like Denmark and the Netherlands, all tenants are classified as renting in the private sector, because the data from EU-SILC do not allow a distinction by type of tenant. Similarly, the large Swedish stock of municipal rental housing is not classified as social housing, as it is generally allocated on the basis of waiting lists rather than according to needs-related criteria, even though it plays an important role in housing low-income households  (Pittini & Laino, 2012). Regulations and the dominance of non-profit landlords in these integrated rental systems tend to lower rent levels and increase security of tenure compared to markets dominated by private providers. Data by income quintiles are also included in the database.
Household debt-to-income ratios derived from the OECD National Accounts Database complement housing cost measures, which for homeowners are affected by differences in mortgage repayment structures 7. Indicators of housing quality in the AHD include dwellings physical characteristics, amenities (e. g. flushing toilet), living space available (e. g. number of rooms) and housing deprivation 8. These measures help identify the incidence of the poorest housing conditions, but more indicators would be needed to compare housing quality more widely in advanced economies, for example along the dimensions of building quality and maintenance, energy efficiency, noise insulation, neighbourhood quality and distance to public amenities. Housing affordability and quality indicators require not only detailed information regarding the dwelling but also household income and composition (such as the age and number of household members).
Similarly, the large Swedish stock of municipal rental housing is not classified as social housing. For details, see http://www. oecd. org/social/affordable-housing-database. htm. Reading note: In Switzerland, the housing tenure distribution is 5% of outright owners, 35% of owners with mortgage, 55% of private tenants, 3% of subsidised tenants and 2% of other categories. Coverage: 37 countries (cf. Box). Sources: OECD, Affordable Housing Database (AHD).
The correlations between the factors obtained and the principal components derived from housing market features and housing condition indicators will be subsequently examined to assess relationships between housing policies and outcomes. The first factor, which explains nearly 30% of the variance of the dataset, is associated with the variety of policy instruments used. Countries using the most policy instruments feature on the left side of the axis, while those using few instruments stand on the right side. The second factor, which explains about 18% of the variance, is more difficult to interpret. 9 The top part is associated with the presence of mortgage relief for over-indebted homeowners and the absence of subsidies for the development of affordable homeownership and of subsidised mortgages and guarantees to homebuyers.
The bottom part is associated with the absence of social housing, tax relief for access to homeownership and mortgage relief for over-indebted homeowners, as well as with the presence of subsidies for the development of affordable homeownership. Hence, the countries at the upper end of the chart tend to provide tax relief and a safety net to homeowners, but no subsidies. Those at the bottom 9. For details, see http://www. oecd. org/social/affordable-housing-database. htm; Dewilde (2015)  provides a useful assessment of strengths and weaknesses of EU-SILC data for housing research. Building a typology of housing systems to inform policies in OECD and EU member States end tend to be characterised by the absence of social housing, but use subsidies to promote affordable homeownership. Altogether, associations between housing policy instruments do not show clear patterns.
A typology of housing systems To reveal the main features of housing systems, a PCA is performed on variables representing housing market context, housing conditions and household indebtedness. To avoid scalerelated distortions, the variables are standardised to zero mean and unit variance. Next, cluster analysis is used to group countries with similar profiles. Finally, in order to shed light Reading note: (0) and (1) refer to the absence/existence of the policy in the country. For example, ha(0) means that housing allowances are absent. The percentage indicated on each axis refers to the percentage of variance explained by the axis. Coverage: 35 countries (cf. Box). Sources: OECD, Affordable Housing Database (AHD); authors' calculations. on the main characteristics of housing systems, we test for differences in the means of the indicators included in the analysis across country groups.
We also look at the values of policy indicators across country groups, albeit in a less systematic way, as the information is less comprehensive than for the variables included in the PCA. Our baseline analysis includes 25 countries, for which 34 variables, covering the most important characteristics of a housing system, are available. Further analysis, based on a narrower set of variables, allows us to assign, albeit more tentatively, seven countries to the groups identified in our baseline analysis. Seven of the 39 countries included in the AHD are discarded, as data are too patchy to allow meaningful analysis (cf. Box). The PCA reveals a very strong first principal component (horizontal axis), which explains nearly 60% of the variance of the dataset and opposes high outright homeownership, but with relatively poor housing conditions, to more indebtedness and private rental, but with overall better housing conditions.
Coverage: 25 countries (cf. Box). Sources: OECD, Affordable Housing Database (AHD); authors' calculations. Building a typology of housing systems to inform policies in OECD and EU member States shows the test results for each variable and pair of country groups. The "Western" and "Northern" groups, which include the most advanced OECD economies, are clearly differentiated by tenure structure and level of household indebtedness, but differences in housing conditions are generally insignificant. Only the number of rooms for owners with mortgages is significantly higher in the "Northern" than in the "Western" group, although only at the 10% confidence level. Conversely, differences in tenure variables are generally significant at least at the 5% level and often at the 1% level. The "Northern" group has more owners with mortgages, higher household debt levels and more private sector tenants than the "Western" group, which has more outright owners and social sector tenants.
1 2   The binary nature of availability indicators for the eight types of housing policy instruments included in the MCA is a strong limitation, as the extent and amount spent on similar policy measures can vary greatly across countries. Unfortunately, most quantitative indicators included in the OECD Affordable Housing Database are only available for a relatively small set of countries, the only exception being variables related to housing allowances. This precludes a systematic quantitative analysis. Therefore, we proceed in a more qualitative manner, trying to analyse policy features across the country groups identified above, even if the small sample size makes statistical comparisons of proportions of countries implementing each housing policy measure irrelevant 13. On the four housing policy instruments used in the vast majority of the countries in the sample, we note some differences across groups.
Other avenues for further research include studying links between the dimensions included in the AHD and mortgage market structures, as well as factors affecting housing supply, especially land-use planning. (2000 -2013 ), the Netherlands, New Zealand (2000 -2013)  and Norway. In half of these countries, the share of social rental housing still declined as a share of the total housing stock. Increases were strong in the Czech Republic and Estonia, but from very low starting levels (OECD Affordable Housing Database). Belgium, Canada, Denmark, France, Germany, Ireland, Netherlands, New Zealand, Switzerland, United Kingdom and United States. 26. Australia, Canada, Denmark, Finland, France, Germany, Italy, Ireland, Japan, Netherlands, New Zealand, Norway, Spain, Sweden, Switzerland, United Kingdom and United States.
The research The starting point for "Building a typology of housing systems to inform policies in OECD and EU member states" is the publication in 2017 of the OECD's Affordable Housing Database (AHD) 1 which has been developed to help countries monitor access to good-quality affordable housing and strengthen the knowledge base for policy evaluation. The database includes indicators from OECD countries and EU member states grouped along three main dimensions: housing market context, housing conditions, and public policies towards affordable housing. The article provides a second stage in making this database operational: the construction of a typology of housing systems using principal component analysis and cluster analysis based on housing market features and housing condition indicators included in the database supplemented by other OECD data, notably on household indebtedness.
The researchers here however step back from these debates and look only to identify patterns and the attributes of the groupings that emerge. As such it is very much a tool to be used by others rather than at this stage drawing implications for instance about such issues as path dependency or the relative success of different types of policy between the identified categories. The methodology used for developing the typology is straightforward and well understood but it is used in a particularly careful and comprehensive fashion. The data used come mainly from the new database which concentrates on three main topic areas: the housing market context (an interesting use of the term market?); housing conditions including affordability, quality and to a limited degree accessibility but concentrating on homelessness; and public policies notably government financial support by tenure, social housing provision and rent and security legislation.
Using the Affordable Housing Database The Affordable Housing Database showed that some 33 OECD countries, of which 25 are in Europe, had some forms of housing allowances in place for low income households in the rented sector. However, only half of these countries had in place housing allowances for low income owner-occupiers 2 (including 12 of the 25 European countries). One additional country (Denmark) had them only for the elderly; another, Switzerland, had allowances only in some cantons.
Relevance to the typology There are probably three main messages in relation to the André-Chalaux typology: • The example reinforces the relative importance of economic pressures and macroeconomic policy as compared to housing policies in determining housing outcomes; • The example also suggests that if the typology is based mainly on housing market and housing policy variables it is likely to be fairly unstable in the face of macro-economic change. So for instance, in the housing finance context examined here the USA and Germany would be extremely unlikely to be in the same group; while France and Austria look like the odd ones out in the "Western" group; • Third, the database itself cannot -and is not meant to -provide the level of detail that can enable analysis of specific housing issues, but it can be a useful starting point as it was in this piece of research.
Access to homeownership among younger households It might have been expected that another project undertaken for the OECD in 2017, on whether changes in mortgage regulation has impacted on young people's capacity to access owneroccupation (Whitehead & Williams, 2017), would have benefitted from the Affordable Housing Database. In practice, however, the most relevant OECD data came from Society at a Glance (OECD, 2016 and earlier years) which includes information on the proportion of younger people under 30 living with parents and on the employment rates of this group. The evidence showed that in twenty-three countries the proportion of younger people under thirty was above the OECD average of just under 60% living with parents in 2014. It also showed that seven of the top nine countries lay within groups "South Central" and "Eastern" of the André-Chalaux typology, the other two being Spain and Portugal.
Importantly, the proportion of younger people living with parents across the OECD had risen since 2007 with Italy, Hungary, Greece and France among those with the biggest increases. Evidence on employment rates among younger people suggests a similar picture -with the number of jobs taken by this age group falling by 8% on average since 2007; but by a quarter or more in seven countries all but one of which (Ireland) correspond to the countries with the highest increases in those living with parents and are included in groups "South Central" and "Eastern" of the André-Chalaux typology. These data were undoubtedly part of the reason for commissioning the research. In practice however the Affordable Housing Database included little of relevance to this particular project except for the data on home ownership policies which identified some policies supporting access to homeownership.
Conclusions "Building a typology of housing systems to inform policies in OECD and EU member states" has many valuable attributes. One of the most immediately relevant is that it helps to introduce a wider audience to the new OECD Affordable Housing Database -an important addition to the OECD's growing databank. Even though there are clearly gaps, its publication will of itself almost certainly provide an incentive to member countries to improve their own data. The analysis presented -based on principle components and cluster analysis techniquesidentifies four groups of OECD countries. However perhaps the most immediately important result is a simple correlation analysis which suggests that market factors are far more closely correlated with outcomes than housing policies. This may in part be a result of data deficiencies -in particular there is no evidence on the scale of the interventions and the data available suggest that by no means all interventions were captured.
Our empirical approach focuses on the market for land intended for the construction of individual houses. We use the Sit@del2 databases  for building permits issued, and the EPTB survey  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014)  on land prices, together with data on soils, topography and agricultural opportunity costs (the value of agricultural production that is lost by assigning land to housing), as well as the presence of former industrial sites. We econometrically estimate an inverse demand equation for land, where constructed quantities are instrumented by exogenous supply variations. Permits and prices result both from supply effects and demand effects, that we aim to distinguish here. Economic theory considers the price elasticity of demand to be negative as, for a given demand function, increasing the quantity of land offered should lead to a decrease in its price.
Number of old industrial sites (INDUS) Like agriculture, industry is facing national and international shocks that affect its profitability regardless of the local context, in particular the land market. Industries are facing technological shocks that lead to cessation of business, so freeing up construction land (gas plants, printing plants, etc. ). Former industrial activities and service activities have been systematically inventoried since 1994. The data collected for these inventories are archived in a national database, Basias (Base des Anciens Sites Industriels et Activités de Service) (b). We can use the number of old industrial sites as an instrument at the municipal level. Due to effects of externality and the local labour market, the presence of a former industrial site can have a negative effect on construction and housing prices.
Reminder: The opinions and analyses in this article are those of the author(s) and do not necessarily reflect their institution's or Insee's views. To cite this article: Balcone, T. & Laferrère, A. (  2018 ). New or old, why would housing price indices differ? An analysis for France. Economie et Statistique / Economics and Statistics, 500-501-502, 69-95. * Insee at the time this research began (thomas. balcone@enseignementsup. gouv. fr; anne. laferrere@dauphine. fr). We are grateful to the Scientific Board of the Indices Notaires-Insee, to Ronan Le Saout and Benjamin Vignolles for helpful discussions. The latter also provided his computation of the distance variable in EPTB. We thank Cédric Cailly for his help with the EPTB data and Insee and SDES (Service de la donnée et des études statistiques, of the ministère de la Transition écologique et solidaire) for allowing access to restricted data (convention n° 2014065NF).