dataesr/scientific-paragraphs-categorization huggingface dataset:
The different location of new construction explains only part of the difference To correct as much as possible for the different locations of new versus second-hand dwellings, when the precise locations (for instance the distance to the city center) are not known, the second-hand transactions observed were re-sampled to mimic the locations of new dwellings. More precisely "geographical clones" of the new dwellings were created by drawing a sub-sample of second-hand transactions in the same municipalities as new constructions. The only difference between such "clones" and the original database is then the municipality distribution of dwellings. Thus, indices calculations carried out with the same methodology (the adjacent two-period time dummy hedonic model) on these two samples would allow assessing if the difference in the municipality location of new and second-hand dwellings can explain the differences between their price evolutions.
New dwellings index (IPLN) New dwellings of the ECLN database We conclude that over the 2006-2010 period, the differences in municipality location have a negligible influence in 2007 and 2010 and that, even in 2008 and 2009, they do not fully explain the difference in the evolution of the two indices. It must be underlined that the correction for municipality location ignores the differences of locational distribution within a municipality. This difference is likely to be important because of the location of available land. If this could have been taken it into account, the correction might have been more effective. This leads us to the next step of our investigation.
Evidence from Nantes Métropole, France on the potential influence of air pollution or noise exposure on the price of dwellings (see the following Literature review section). Even scarcer are the studies where corresponding data could be calculated for every dwelling transaction. Investigating such original variables, the present article provides new insights into the effects of greater accessibility and environmental quality on apartment values in a local context: the conurbation of Nantes Métropole. To implement the hedonic model, we rely on an original cross-sectional database partially obtained from numerical simulations. These were carried out as part of a wide multidisciplinary research project, using a chain of physically-based models. The starting point of these was traffic data in Nantes Métropole  (Mestayer et al.
, 2012) , and noise exposure and air quality around dwellings were calculated. These environmental data were matched to our geo-referenced database, which includes data on apartment transactions in the 24 communes of Nantes  Métropole in 2002 Métropole in , 2006 Métropole in and 2008 , and distances to a set of reference points. As housing observations constitute a type of data characterized by location attributes, we apply spatial econometrics in relation to hedonic price modeling. The spatial dependence between observations in our sample is then taken into account at various points in space. In order to deal with spatial autocorrelation and spatial heterogeneity, we use the instrumental variables and Generalized Method of Moments approaches (GMM) proposed by  Kelejian and Prucha (2010)  to estimate annual spatial autoregressive models with unknown heteroscedasticity in the disturbances.
Description of the data The determinants of Nantes Métropole apartment prices are analyzed using cross-sectional data. Nantes Métropole is an urban community bringing together 24 communes of the Loire-Atlantique département in the Pays de la Loire région. It is located in the West of France, 380 km from Paris, and covers over 523 km 2. It is crossed by one major river (the Loire), and two other rivers (the Erdre and the Sèvre). It counts 600,000 inhabitants, half of whom live in the central commune of Nantes. There were over 2. 3 million daily trips in 2015 in this territory, 55% of which were by car (both drivers and passengers) and 15% by public transport. The total traveled distances were 21 km a day, corresponding to a total travel time of 67 minutes 3. The database allows us to link the prices of apartments that were sold in Nantes Métropole to their intrinsic and extrinsic attributes (accessibility, geographical and socio-economic environment and environmental quality).
All of the descriptive statistics appear in Table  1. The data come from the notaries' Perval database, providing information on the 25,000 transactions of apartments and houses in Nantes Métropole in 2002, 2006 and 2008 4. It is worth noting that all housing transactions in any part of France are covered by two notarial databases: Perval and by the BIEN database for Paris  (Gouriéroux & Laferrère, 2009). The data were geo-referenced as part of the multidisciplinary research project. We use here data on the 5,590 apartment transactions, after cleaning the data from missing information. As real-estate transaction data cannot be treated as continuous over time, the three years are considered separately. The data provide information on the transaction (date, price, nature of the transfer, etc.
Apartments are well-served by public transport: 46% are within 2 km of a railway station, 25% have a bus stop less than 100 meters away, and 48% have a tram stop less than 500 meters away 4 5 6. 5. The French decree of 2002, 30 th January specifies the minimal surface area that a dwelling owner is allowed to rent to be qualified as decent: this minimal surface area is 9 m². Moreover, the Loi Carrez (Carrez law) aims at certifying the surface area of dwellings that are sold in France (to be occupied by the owner or not): this certification is mandatory from 8 m². This is thus not a surprise if the Perval database contains 95 observations less than 20 m², as Nantes is an attractive city for students studying in the large university of Nantes and other institutions of higher education. This situation is comparable in France not only in Paris, but also in other large metropolises throughout the territory (Lyon, Toulouse, Montpellier, Rennes, Lille, etc.
When all these forms of financial support are considered, nearly four out of ten recent first-time homeowners say they were helped by their family at the time of purchase. This proportion remains stable between 2002 and 2013, with the higher frequency of gift assistance in 2013 being offset by the decline in other forms of aid (cf. Figure  V ). The proportion of first-time homeowners reporting having received financial assistance for their purchase by a recent inheritance remained stable at 6% over the entire period. 12. The drop in the last quartile of living standards in 2013 is linked to the fall in the number of buyers not offset by the rise in the average price of housing. 13. See for example  Piketty (2011) ,  Alvaredo et al. (2017). 14. As the questionnaire allows multiple answers to these different questions on family assistance, we present here a variable constructed so that the answers to the different modalities are exclusive.
000 €200. 000 -€300. 000 > €300. 000 but there are now also many more individualised trajectories. For instance,  Clark et al. (2003) , using detailed longitudinal datasets for the USA, found that about 75% of all housing careers fall within 11 (standard) trajectories, while the other 25% are much more diverse. For the Dutch situation, such studies have not been performed, due to lack of long time longitudinal data, but there is a substantial body of literature on cross sectional data and this clearly shows that propensities to move vary by household characteristics. In case the demographic make-up, i. e. household characteristics of the Dutch owner-occupiers change, this may partly explain changes in residential mobility. In other words, are changes in the overall propensity of home owners mobility explained by "compositional effects"? For the Dutch (and other) context, the main findings are that age is negatively related to the propensity to move.
Over the past four decades, statistical agencies in several countries have accumulated The dynamisation and subsequent vulnerability of the Dutch owner-occupied sector a series of cross section micro databases on housing and residential mobility. This now offers the possibility to perform an analysis over a longer period of time. Examples are an analysis of long distance migration trends in the USA  (Cooke, 2011)  and the tenure shift in New Zealand  (Bourassa & Shi, 2017) , but such studies are still rare. However, one drawback of the Oaxaca-Blinder method is that it is only fit for using micro level data and it cannot include external effects such as fluctuations in the GDP and or interest rates 2. Changes in external effects when comparing data from different years may affect the behavioural component, then making it difficult to characterize long-term trends of residential mobility (cleared of external factors).
Therefore, we need to carefully select two databases in times that compare in terms of overall economic development  (Box). As mentioned earlier, the second objective of this study is to explore in more detail the vulnerability of the contemporary (particular) Dutch owner-occupied sector to economic crisis (  Van der Heijden et al. , 2011). Here we just compare a cross section from around 2005 with the era shortly after the start of the crisis. The composition of the population will not have changed much in such a short time, so comparing two separate regression models in order to investigate which household types were most affected in terms of residential mobility can be regarded as sufficient. 2   We now turn to the presentation of the Oaxaca-Blinder method. As indicated we will investigate the entire proportion of owneroccupiers that moved in the past two years.
Box -Data and variables The Dutch national institution responsible for statistics (Statistics Netherlands) has been conducting a housing survey every three to four years since 1981 (National Housing Demand survey, WBO, before 2006, then Netherlands' Housing survey, WoON). The data include detailed information on housing and household characteristics. It also provides information on the previous dwellings of recent movers, whether the household intends to move, and housing preferences. In the article, residential mobility refers to "recent moves", that is, residential moves occurred within the two past years for households who are owner-occupiers at the time of survey. The data are based on household interviews, but since 2006, more information has been added from register data. This has improved the information on household income but also created some problems for comparisons with previous datasets.
The first part of the analysis aims to disentangle compositional and behavioural effects of the change in residential mobility of home owners in the long run. Here we need to find two years that are comparable in terms of economic circumstances (see the section on methodology). We are aware that it is impossible to find a perfect match in terms of economic situation. However, we assess that the economic background is quite comparable between the data of 1986 and 2006. In both periods the Dutch economy (and house prices) was recovering after an economic slowdown and GDP growth reached levels between 2% and 3%. The notion of "recent moves" referring to moves occurred in the past two years, so the 1986 data refer to residential moves occurred in 1984-1985; for the 2006 dataset, it refers to moves occurred in 2003-2004 because the year 2005 was not fully covered.
This also avoids to have to adjust for inflation. Furthermore, the 1986 database only provides net household incomes, while the later databases contain disposable incomes. Some caution is thus needed, but using quintiles improves the comparability to a great extent. characteristics (structure by age group, household type and income), β is the vector of the estimated coefficients, that is, the effect of given characteristics on having moved, analysed as related to behaviour. X β β   � The first part corresponds to the compositional effect, where the effects of changes in the population structure (composition) are calculated keeping behaviours (the β-parameters) constant. The second part represents the behavioural effect, that is, the effect of changes in the β-parameters, calculated keeping the population structure constant.
Results As mentioned earlier in the introduction, the percentage of owner-occupiers increased rapidly from the 1980s and onwards (Table  1 ). The rental sector declined in relative terms but remained stable in absolute terms at 3. 0 million dwellings. The main factors behind the increase of owner-occupation were a change in the policy focus from widespread support of the (social) rental sector with large scale building programs, towards the owner-occupation sector. Whereas "brick & mortar" and operations subsidies for social rental dwellings 3. In fact, the database already includes a weight factor. The dynamisation and subsequent vulnerability of the Dutch owner-occupied sector were virtually abolished in the 1990s, owner-occupiers were able to benefit from a very generous tax relief on paid mortgage interest.
7* ad4lrhp(-4)+ (0. 7**2)* ad4lrhp(-8) + (0. 7**3)*ad4lrhp(-12))/(1 + 0. 7 +(0. 7**2) + (0. 7**3)) where ad4lrhp is the absolute value of the annual change in log real house prices, instrumented during the pre-1996 period of interpolated annual house price data. Demog: Demographic data are annual data each 1 st January (source: Insee). They were interpolated and lagged accordingly. In the permanent income model, demography affects the ratio of the working age population for all ages, measured as an 8-quarter moving average, to the total population.
H: The housing stock has been recursively computed on the principal of perpetual inventory using data from the housing stock in constant prices (source: National accounts, Insee). The level is set by the value of stock in 2010. Gross fixed capital formation is housing GFC in volume and the deterioration rate is that of national accounts. θ: Income uncertainty is proxied by the 4-quarter change in the unemployment rate in the consumption equation, and 2-quarter changes in the other equations. Trans: Transaction costs come from "valeur-immobilier-france" (source: Ministry of Housing). The impact of car scrapping subsidies is computed following  Adda and Cooper (2000)  for the first wave and extrapolated according to the link with car registration for the second wave. 17. Like consumption and income, all nominal balance sheet data are deflated by the consumer price deflator and population.
ESTIMATES FOR HOUSE PRICES AND MORTGAGE STOCK EQUATIONS We warmly thank Jacques Friggit and Gérard Forgeot for their help on the datasets, Stephen Bazen, Pierre Cahuc, Habiba Djebbari, Emmanuel Flachaire, Xavier Joutard, Barbara Petrongolo, Marc Sangnier, Patrick Sevestre and two referees for their helpful comments, Pierre-Yves Cusset for his support, and Gustave Kenedi for his corrections. All remaining errors are our own. T he 2014 reform of the French real estate transfer taxes (RETT), allowing an increase in départements' 1 tax rate of 0. 7 percentage point (from 3. 80% to 4. 50% of the tax base), aimed to raise their tax revenue, in a context of state grants reductions and increasing social spending. Not all départements implemented the RETT increase, and not at the same time, which is the starting point for a natural experiment.
Data To undertake this evaluation, we use two main variables, which are the number of transactions and the tax bases, both by département  Following this, we applied some correction to these raw datasets, in order to make them match to the months when the bill of sale is signed (and not to the months of tax revenue collection). Our control variables include the unemployment rates, the number of new residential construction, population, property tax rates, and three local variables on the départements' finances. Data on the unemployment rates come from Insee 11 and are quarterly data by département for metropolitan France, and yearly data for the overseas départements, both seasonally adjusted. In order to estimate monthly data, we made linear interpolation. Data on the new residential construction (monthly building permits by département) come from the database Sit@del2, and are compiled by Insee.
42, meaning that there is a loss of 42% in the tax revenues with respect to a situation of no behavioral response, the first quarter after the reform. Computing the Laffer tax rate, we conclude that départements' tax revenues are still on the increasing side of the Laffer curve. Note that our results are valid for partial equilibrium. We do not estimate the possible other general equilibrium aspects resulting from the distortion of the housing market, such as changes in investment from the local governments or impact in the labor market. Applied to national transactions data, our estimate means that around 15,000 transactions were missing because of the transfer tax increase. This estimate is likely a lower bound of the true impact. This evaluation can be extended in three ways: using the non-cumulative monthly data on the number of transactions; doing a precise estimation of the price effect through hedonic model (using the notarial databases BIEN -Base d'informations économiques notariales -and Perval); implementing a regression discontinuity design to estimate the possibility that buyers could have "voted with their feet" (i.
Data This study uses original data spatially matching notarial databases covering real estate transactions in 2006 with the regulated flood risk prevention plans (PPRi) official zoning and municipal data on land use and past natural disaster records. The data on real estate transactions are taken from notarial databases for the year 2006 (the BIEN -notarial economic database -for Île-de-France, and the Perval database for other départements 15 ). These very rich databases provide a detailed set of information on each transaction: sales price (exclusive of notarial and negotiating fees), address, plot surface area, number of rooms, type of housing unit, period of construction, with a garage or not, car parking, cellar, etc. ). It also provides some information on the seller and buyer (age, nationality, place of residence, etc.
). The study is limited to private sales completed in 2006, involving private individuals and vacant homes (excluding unusual properties such as mills, former railway stations, etc. , and excluding life leases), where the price, excluding notarial and agency fees, exceeded €1,500. We only retained standard apartments (with a maximum of nine rooms and a living area of at least 10 m 2 ) and individual houses for which the land surface is indicated (with a maximum of twelve rooms and a living area of at least 20 m 2 ) 16. The notarial data were spatially matched with maps of the regulated PPRi official zoning available on the Cartorisque 1 5 1 6 website 17 and the information taken from the Gaspar 18 database on PPRs. To identify the units located within the regulated zones subject to the IAL requirement, the housing transactions in the notarial databases were geopositioned to the cadastral parcel using the Parcellaire® database from the IGN.
The 1 Zrisque indicator results from the matching of the coordinates obtained with the PPRi maps. This unique database is used to accurately determine whether or not the property is situated in a regulated PPRi zone and, for transactions after 1 st June 2006; whether the housing transaction is subject to the IAL requirement. At the time of matching, the Cartorisque GIS did not contain all the regulatory PPRi maps, which led to some "false zero" results (properties actually within a PPRi zone but appearing outside the PPRi in the dataset) during matching if the map wasn't available. To overcome biases in the estimates, the database was restricted to municipalities in which at least one recorded transaction within a PPRi zone in 2006, which made sure that the PPRi map for the municipality was available at the time of matching.
Municipal data from Insee and the Corine Land Cover geographic database have also been exploited to control the extrinsic attributes of the housing units and characterise the attractiveness of their immediate surroundings (municipal population, relative share of natural spaces in the municipality, etc. ). The "as the crow flies" distance of the properties from the centre of the municipality is also included as a proxy of distance and accessibility of the town centre. Does information to buyers affect the sales price of a property? Natural Risk Observatory (ONRN) on the per municipality average cost of flood damage claims paid out under the natural disaster ("Cat Nat") insurance scheme between 1995 and 2010 19. These indicators are a proxy of the expected compensation for damage cost by households in the event of a flood.
The repricing method, when updated at least every five years, performs quite well on our datasets. The biggest surprise is that the Paasche and Laspeyres versions of the hedonic double imputation method exhibit substantial drift in the Sydney apartment dataset. Some drift is also observed in the Tokyo apartment dataset. Fortunately, no NSI in Europe is using either of these methods. The Tӧrnqvist version of the hedonic double imputation method, used by Germany, is not affected by drift. Eurostat recommends that each NSI compute separate hedonic indices for houses and apartments. We are able to do this for Sydney, but not for Tokyo, since almost all the transactions in the latter are for apartments. Furthermore, indices specifically for new housing are needed for the owner occupied housing price index (OOHI) which is being used on an experimental basis in the Harmonized Index of Consumer Prices (HICP) (see  Eurostat, 2017).
The age of dwellings is included as a characteristic in the Tokyo data set but not in the Sydney data set. Hence we are able to compute an HPI for new dwellings for Tokyo, but not for Sydney. Our findings for Tokyo in this regard have important implications for HPIs, OOHIs, and the HICP in Europe. The remainder of the paper is structured as follows. The next section explains the theoretical properties of the hedonic methods used by NSIs in Europe to compute their HPIs. After that the hedonic methods are compared empirically using data for Sydney and Tokyo. Our main findings are then summarized in the conclusion.
When data points are scarce, RTD4Q and RTD5Q (i. e. , 4 or 5 quarter windows) are recommended over RTD2Q (i. e. , a 2 quarter window). NSIs in Europe using the RTD method have selected the following window lengths: France = 2, Cyprus = 4, Ireland = 5, Portugal = 2, Croatia = 4. An important feature of the RTD method is that once a price change P t+k /P t+k-1 has been computed it is never revised. Hence when data for a new period t+k+1 becomes available, the price indices P t , P t+1 ,. , P t+k are already fixed. The sole objective when estimating the hedonic model inclusive of data from period t+k+1 is to compute P t+k+1 , irrespective of how many periods are included in the hedonic model. More generally, this property of never being revised is recommended by Eurostat (2017) and is shared by all the hedonic price indices considered here.
Evaluations of the different methods for Sydney (2003-2014) The Sydney data set The functional form for our hedonic models is semilog. The explanatory characteristics for houses are as follows: the actual sale price; time of sale; property type (i. e. , detached or semi); number of bedrooms; number of bathrooms; land area; postcode (there are 202 postcodes in the data set). For apartments we have the same set of characteristics. However, we drop the land area characteristic for apartments in our hedonic An evaluation of the methods used by European countries analysis since it refers to the whole strata, and we do not have any information on the number of apartments in the building. For a robust analysis it was necessary to remove some outliers. This is because there is a concentration of data entry errors in the tails, caused for example by the inclusion of erroneous extra zeroes.