The impact of industrial risk regulation on local housing

markets: Evidence from a French policy change∗

Marianne Bléhaut†, Amélie Mauroux‡

January 17, 2017

PRELIMINARY. Please do not cite.

Abstract

In this paper, we assess the impact of a risk prevention policy based on information and land useplanning on housing markets. We focus on the impact of local regulation plans that were createdin 2003 in France in the wake of a massive industrial accident. This new policy impacts the hous-ing markets in the vicinity of industrial plants through many channels (risk perception, land useoffer, etc.). Hedonic price models and land use models predict both positive and negative effectson price and neighborhood composition. Assessing the global impact of such a change is thus afundamentally empirical question. We observe housing markets close to industrial plants beforeand after each local regulation is implemented. At each date, we can observe at-risk territoriesat different stage of the regulation process. Our identification strategy relies on these temporaland spatial variations and is similar to a difference-in-difference approach on panel data. Thismodel is estimated on real estate transactions data and on a high quality administrative data onthe housing stock to capture potential neighborhood changes. Our results suggest that additionalrisk regulation in France translated into a significant decrease in the price of housing in the neigh-borhood of industrial plants. Moreover, the policy change lead to a decrease in the number oftransactions. However, we find no strong evidence of spatial sorting induced by the policy, and∗We are deeply indebted to Laurent Gobillon, Miren Lafourcade and Bertrand Villeneuve for their support

and guidance of this work. We are also very grateful to Xavier d’Haultefoeuille for his patience and thoughtful

advice. We are very thankful to Kévin Beaubrun-Diant, Luc Behaghel, Clément Carbonnier, Sylvain Chabé-Ferret,

Gabrielle Fack, Patrice Geoffron, Florence Goffette-Nagot, Christian Hilber and Pierre Picard for their excellent

comments on this work, and to Gabriel Ahlfeldt, Giuseppe Arbia, Eric Gautier, Stephen Gibbons, Arthur Grimes,

Stephan Heblich, Camille Hémet, Alessandro Iaria, Manasa Patnam, Roland Rathelot, Olmo Silva, Yves Zénou,

Yanos Zylberberg for useful discussions and comments. I would also like to thank conference participants at the

Annual North American Meeting of the Regional Science Association International and the International Association

for Applied Econometrics Annual Conference for their comments.†Université Paris Saclay, Univ. Paris-Sud, RITM; CREST. Please address correspondance to: mari-

anne.blehaut@ensae.fr‡PSL - Université-Paris Dauphine; CREST-LEI

there is little short-term impact on the characteristics of residents. Our results are consistent withan ex ante imperfect information setting, in which sellers tend to use private information on risk toincrease their profit. In addition, we find no evidence of a positive impact of the land-use regulationmeasures, which is at odds with previous literature. We attribute this difference to the nature ofinformation revealed when land-use regulation is implemented.

Mots clés: risk prevention policy, land-use policy, real estate market, industrial hazard, publicpolicy evaluation, panel data.

JEL classification: R20, R21, R23, C21.

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1 IntroductionIn the last 50 years, a few massive industrial accidents have raised awareness on industrial risk indeveloped countries. Nuclear accidents have been particularly symbolic, but they do not accountfor all industrial accidents. In France, the chemical plant AZF exploded in Toulouse in 2001,killing 33 people and causing substantial urban damage. In particular, more than 26,000 dwellingsand about a hundred schools in Toulouse where damaged. This event lead to a massive policychange in industrial risk regulation. In 2003, a new law was passed to improve preventive informa-tion and explicitly plan land use and regulate real estate in the direct surroundings of hazardousplants. It aimed at solving existing situations inherited from past urbanization and to controlfuture urbanization to reduce population exposure to industrial hazards. This law created a newland-use regulation, called Industrial Risk Prevention Plan (the Plan de Prévention des RisquesTechnologiques, or PPRT).

The goal of this paper is to measure the impact of this policy change on local housing markets.In theory, such a policy change could have lead to both positive and negative impacts on housingprices. In particular, it increased information about objective industrial risks for local populations,created new real estate measures (expropriation, relinquishments) and restrictions on land use (banon new constructions) and construction (protection measures), and made some protection measureson existing homes compulsory. This policy thus likely impacted the housing market through variouschannels. It is impossible to know whether this should have a positive or negative impact withoutmore information about the prior beliefs of the residents, the elasticity of housing demand andsupply, the magnitude of the shock on the supply of residential land, etc. Assessing the impact ofsuch a change on local housing markets is thus a fundamentally empirical question.

Most empirical studies on industrial risk are based on a hedonic approach, which assumes thatpreference for a given amenity is revealed through the housing market and thus provides a wayto measure taste or distaste for a given characteristic. If industrial hazards are perceived as anegative amenity, then all other things being equal, housing prices in exposed neighborhoods arelower. Identifying empirically this relation has yet proven to be complex. Indeed, the location ofdangerous plants can hardly be thought of as random. The decision of building a new hazardousplant is often highly political, and they tend to be constructed in neighborhoods with ex antehousing prices lower than average and very specific socio-demographic characteristics (Davis, 2011).Moreover, these initial differences are reinforced over time after the plants opening.

In the literature, this endogeneity problem is often solved using quasi-experimental settingsin which a sudden unforeseen change modifies local amenities or their perception. The AmericanSuperfund clean-up program has for example been used in a number of publications (see for exampleGreenstone and Gallagher, 2008; Kiel and Williams, 2007; Viscusi and Hamilton, 1999). Anotherexample is provided by legal changes in pollution control, such as the Clean Air Acts passed inthe USA in the 1970’s (see for example Chay and Greenstone, 2005; Greenstone, 2002). Thesecontributions tend to show that the housing price-elasticity with respect to environmental quality isrelatively low. One major difference between this paper and the existing literature however is that inall the aforementioned papers, the shock allowing for causal identification strategies mostly changesthe objective risk exposure or environmental quality. Thus any subsequent change is attributed tothe value individuals place on the change in amenities, under the implicit assumption that theyhave a full knowledge of the before and after risk exposure. In contrast, PPRT does include riskreduction measures but never lead to shutting off the industrial plant. In addition, those measuresare taken on site and may not be visible. The source of hazard is thus still present after thepolicy and we argue that from the local residents’ point of view, PPRT mostly lead to successiveinformation shocks. Moreover, housing markets are not standard, and attempts at modeling andpredicting housing prices often lead to the conclusion that it is not an efficient market. For example,Case and Shiller, (1989) show that there is an important time persistence of real housing prices,

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and that real interest rates do not seem to be incorporated in prices. These counter-intuitiveresults can at least partially be explained by the specificities of the housing market such as hightransaction costs or tax considerations. They are also consistent with behaviors induced by the“disposition effect”. Shefrin and Statman, (1985) model this tendency to “sell winners too earlyand ride losers too long” on financial markets even when the contrary would be more efficient,and attribute it to loss aversion. Similar behaviors have been observed on the housing market.For example, Genesove and Mayer, (2001) analyze Boston housing market in the 1990s and findevidence of nominal loss aversion among sellers. As a result of these specificities, prices alone maynot be able to accurately reflect the neighborhood changes that occur when risk or risk perceptionshift in a given area, especially in the short term. Another major contribution of this paper is toanalyze how the housing stock adjusted, not only prices. We use high-quality administrative dataon the housing stock and evaluate the impact of the policy change on the volume of transactionsand vacant dwellings.

Finally, our paper is very much related to the literature on the urban causes and consequencesof land-use regulations. In particular, these regulations are often shown to amount to an increasein construction costs. As such, they lead to supply-side restrictions on the housing market, whichin turn cause prices to rise (Hilber and Robert-Nicoud, 2013; Saiz, 2010). Moreover, Hilber andRobert-Nicoud, (2013) show that land-use regulation is more likely to arise in places in which pricesare already high. As a consequence, there are again strong endogeneity concerns in the empiricalanalysis of the consequences of land-use on housing market characteristics, but most empiricalpapers on this matter use spatial heterogeneity of land-use regulation as a source of identification,and estimate ordinary least squares models for lack of better solutions (see for example Glaeser,Gyourko, and Saks, 2005; Glaeser and Ward, 2009; Ihlanfeldt, 2007; Kok, Monkkonen, and Quigley,2014). Our paper relies on a rather more convincing quasi-experiment, and shows that the potentialpositive effects of land-use regulations are offset by other negative impacts in the context of PPRT.

More precisely, this paper builds on a quasi-experimental setting in which a policy change isimplemented over a long period of time. The PPRT is a nationwide policy covering every Sevesoplant in France but its elaboration is a local process, so the timing of its implementation variesgreatly across industrial neighborhoods. We observe housing markets close to industrial plantsbefore and after the PPRT is implemented and, at each date, at-risk territories at different stageof the PRRT process. We take advantage of the temporal and spatial variation across PPRTperimeters to identify the impact of this risk regulation on housing markets. Our estimationstrategy is similar to a differences-in-differences approach with non-unique treatment date. Thismodel is estimated on notaries’ database on real estate transactions and high-quality administrativedata on housing stock. Compared to survey data, the use of real estate data guarantees a high levelof reliability and the use of actual transaction prices. The use of administrative data on housingstock guarantees exhaustiveness and allows us to study other housing market outcomes, such asthe vacancy rate or neighborhood composition. We geocoded both these datasets to compute thedistance to the closest dangerous plant.

Our results suggest that additional risk regulation in France translated into a significant de-crease in the price of housing units, and more specifically to a strong decrease in the price ofapartments at the each phase of the PPRT process. We show that in addition to this price effect,there is a significant increase in the number of transactions before risk disclosure becomes manda-tory, which is consistent with strategic behaviors played by homeowners. However, we find nostrong evidence of sorting as the PPRT implementation had little impact on local neighborhoods’characteristics, at least in the short run, and no evidence of displacement effects. Our results arerobust to a series of specification changes, and we perform heterogeneity analysis.

The following sections first present the policy change in more detail, then the data sources anddescriptive statistics, followed by the empirical strategy and finally the main results and discussion.

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2 Policy change2.1 ContextBefore the accident of the chemical plant AZF in 2001 (Toulouse, France), the main risk regulationin France was based on the European Council “Seveso directive”. The directive applies to morethan 10,000 industrial establishments in the European Union where dangerous substances are usedor stored in large quantities, mainly in the chemical, petrochemical, logistics and metal refiningsectors. Depending on the quantities and categories of dangerous substances present, the Sevesodirective defines two categories of risk, lower and upper-tier establishments. The French Ministryin charge of Sustainable Development estimates that in 2013 about 1.4 million people lived within1 kilometer of an upper-tier Seveso plant in France (SOeS, 2014). Industrial risk is thus far frombeing a marginal problem in the country. The AZF plant was an upper-tier Seveso establishment soit was known to be dangerous. Nevertheless, the scope and intensity of the damage and casualtiesfar exceeded what was considered possible in accident scenarios at the time. 33 peoples died, andmore than 2,400 were injured. Over 26,000 dwellings were damaged (more than 11,000 thousandseverely so), and local amenities were severely affected as several schools and roads were damaged.It is now considered as the worst industrial accident in France since the Second World War.

After the explosion, and in reaction to its catastrophic scope, the regulation around industrialhazardous plants was deeply changed in France. In July 2003, the Bachelot law on natural andindustrial risk was passed1 to complement the existing regulation on Seveso plants (Cahen, 2006).It created the Industrial Risk Prevention Plans (Plan de prévention des risques technologiques,PPRT in French), which aim at better protecting and informing the people living in the immediatesurroundings of upper-tier Seveso plants. 407 PPRT were to be instructed in 835 municipalities tocover over 600 existing French upper-tier Seveso sites. The 2003 law also strengthens regulationfor new upper-tier establishments, in order to avoid situations in which potential damage wouldbe too high.2 The objective of the new regulation set is thus twofold: solve difficult situationsinherited from past urbanization around existing industrial plants and control future urbanizationaccording to the level of risk exposure. In this paper, we focus on the analysis of the consequencesof the first part of the regulation, namely the PPRT for existing upper-tier Seveso plants.

2.2 ImplementationThe elaboration of a PPRT is conducted at a very local scale by the technical services of theState. One of the main features of the 2003 law is that all the stakeholders must be informed andinvolved in the process. The stakeholders include the plant operator, the employees of the plant,local administration and elected governments, and the local population. Thus it is likely that thePPRT will have an impact on the local housing market even before the new land-use regulationand construction rules become effective. The implementation of the PPRT can be decomposedinto three main phases, namely the “CLIC”, “prescription” and “approbation” phases, and we aimat measuring the impact each phase had on the local housing markets.

First, the 2003 law created Local Information and Dialogue Committees (Comité local d’informationet de concertation, CLIC), composed of representatives of the different stakeholders of the terri-

1Loi 2003-699 du 30 juillet 2003 relative à la prévention des risques naturels et technologiques et à la réparationdes dommages.

2The sites falling under the PPRT regulation are industrial establishments falling under the scope of the SevesoII regulation as upper-tier establishments and which were in activity in 2003. After July 2003, new upper-tier Sevesoplants can only open in zones with no proximity to residential areas. Land-use planning provisions and public utilityeasements (Servitudes d’Utilité Publiques) are imposed before the opening to prevent the city to get “too close” tothe new establishments.

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tory concerned by the existence of an upper-tier Seveso establishment.3 The CLIC discusses thepreventive measures and the risks the facility generates and passes information on to the generalpublic. Its members are early informed when a PPRT is to be launched and must be associated toits elaboration and consulted on the draft PPRT. By the end of 2003, 130 such committees existed,covering 70% of the upper-tier Seveso establishments (Cahen, 2006).

Second, the elaboration of an actual PPRT officially starts with its “prescription” by the Préfet,the prefect4 During this prescription phase, the local technical services of the State delimit theregulated zones and define the appropriate land-use and building norms. At first, the plant operatorprovides a safety report that lists all possible dangerous phenomena related to the activity ofthe plant and scenarios of accidents (for instance fire in gas tank 1, boil-over of tank 2, leak ofcarbon monoxyde, etc.). It defines the probability, intensity and kinetics of the thermal, toxic andoverpressure effects. Based on that report, the local technical services of the State simulate theintensity of hazards, from low (Fai) to very high (TF+) and draw a map of the simulated industrialhazards at each point of the territory.5 Then a second map is drawn to represent all constructions(housing, facilities, commercial) and public buildings in the direct vicinity of the industrial plant.The objective is to geolocate the population and buildings that would suffer majors damages incase of an accident. Finally, these two maps are superimposed to define a preliminary zoningmap. It provides a first spatial representation of the industrial risk on the territory. It delimitsthe areas which may be subject to regulation and the corresponding land-use scenario, dependingon the level of hazards (from low to very high) and type of damages. This first technical mapis then presented to the plant operator who can implement risk-reduction measures to lower theintensity and/or probability of the catastrophic event, and in the end, minimize the number ofexpropriations (Cahen, 2006).6 For instance, if a gas tank is buried, the impact distance of anexplosion will be much lower. Buildings that were at first in areas to be regulated may, in the end,be out of the PPRT perimeter. Due to this iterative process, the final maps all rely on differentrisk perimeters. Starting with the prescription, the sellers’ and renters’ disclosure on industrialrisk exposure (Information Acquéreur Locataire in French) is mandatory in the whole initial studyperimeter of the PPRT. This measure applies to dwellings being sold or rented in this perimeterand became effective in June the 1st, 2006. Information meetings can also be organized by thePréfet or the plant operator to describe the plant, to explain the possible dangerous phenomenonand to present land-use planning options. Finally, draft zoning maps and a regulation project aredistributed to the stakeholders and then submitted to a public inquiry.

Third, when consultation is closed and the zoning map is stabilized, the PPRT is approved bythe Préfet and attached to the local urban planning documents. This marks the beginning of the“approbation” phase. A PPRT is composed of three elements:

• zoning maps that represent for each point of the territory the level of exposure and delimitsthe different regulated zones accordingly;

3The CLIC is set up by a Prefect’s decree. It is composed of at most 30 members designated by the Prefect andsplit into five groups: administration (Préfets, DRIRE, DDE, SDIS, etc.), local authorities, operators, local residentsand employees. The CLIC is a place of information exchange and dialogue between the different stakeholders.

4The Préfets in France are representatives of the central State in the regions. They are in charge of the localimplementation of the public policies, in particular regarding territorial development and land settlement. They arealso responsible of the local public order and coordinate the emergency services in case of major catastrophes.

5Phenomenon with a fast kinetic are characterized by level of hazards while phenomenon with slow kinetic arecharacterized by boundary of irreversible impacts. The software SIGALEA is used to draw the map of simulatedhazard. It is an extension of Mapinfo that was specially designed by the Inspection des Installations Classées togeolocate industrial hazards caused by multiple phenomenon, and characterize for each point of the territory thecorresponding intensity and probability of accident, for thermal, toxic and overpressure effects.

6The Ministry in charge of Sustainable Development estimates that industrial operators invested between 200and 300 million euros per year so that the final PPRT perimeters could be reduced by 350 km2 compared with earlyestimates based on study perimeters.

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Table 1: PPRT land-use and real estate measures by zoningRegulated zones Future land-use planning Possible real-estate

and construction measures measuresDark red Ban on new construction Expropriation

Relinquishment

Light red Ban on new construction but possibility Relinquishmentto extent existing industrial buildingsif they are protected

Dark blue New construction possible but subject tolimitations on use or protection measures

Light blue New construction possible but subject tominor limitations

Source: General Directorate of Risk Prevention (DGPR), Ministry in charge of Sustainable Development (2006).

• a complete report of the risk assessment on the territory that explains and justifies the PPRTstrategy;

• the regulations that specify the measures for each zone defined by the regulatory zoning mapsfollowing the 4 levels of risk shown in Table 1.

As a result, the zoning and related regulations become effective easements (Servitude d’utilitépublique). In the most exposed area, the PPRT can impose expropriation or voluntary relinquish-ment of houses to suppress the risk (table 1). Some zonings impose bans on new buildings andother prescription on land use, or on new buildings. Some also impose adaptation works on exist-ing structures to reduce their vulnerability. A three-way financial agreement is signed between theState, the local authorities and the plant operator to implement expropriation and relinquishmentmeasures, and compensate the owners.

If the PPRT imposes protective works on existing residential buildings,7 they have to be realizedby the homeowners within a given period set by the Préfet. The cost cannot exceed 10% of theproperty value or 20,000 euros.8 A tax credit was created to help the households finance prescribedprotective works.9 Thanks to this tax credit, and up to the 20,000 euros ceiling, 90% of the financingof the protective works prescribed by a PPRT should be guaranteed to the households: 40% by thetax credit, 25% funded by the plant operator, and 25% by a participation of the local authorities.According to the French Ministry of Environment, PPRT approved as of 2014 have imposed real-estate measures to approximately 10,000 people, for a total cost close to two billion euros, andimposed protective works to 100,000 homes.

Figure 1 shows an example of final zoning map: the zoning map in Pointe d’Ambes Sud, closeto Bordeaux. The industrial plants are represented in white. The red zones correspond to medium(rc1 and rc2) to very high hazard levels (R1 to R3). In these areas, new residential constructions are

7Protective measures against thermal effects can be: adaptation of windows, protection of the facade with non-inflammable materials, protections of metal structures, etc. Measures to reinforce the structural shell of existingbuildings can be: anti-splinter films on windows, replacement of single glazed windows by laminate glass windows,strengthening of the anchoring of the openings frames, etc.

8For firms the costs cannot exceed 5% of their revenue, for local authorities 1% of their budget.9Article 200 quater A, Code général des impôts. It only applies to mandatory protective works, and not to

measures that were only recommended.

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Lecture: this map defines the regulated zones of the Pointes d’Ambes sud PPRT. Each color corresponds to a levelof risk hazards: red for very high, light red for high, dark blue for medium and light blue for low to medium.Zones are targeted by a code that refers to the PPRT land-use and building code rules. For instance, in zone B3new residential buildings are forbidden. Existing and new constructions must have a safety room resistant to toxiceffects. Protection works on existing buildings must be undertaken within 5 years after the PPRT approbation.

Figure 1: Zoning map of the Pointe d’Ambes sud PPRT6

banned, expropriation and relinquishment of existing dwellings are possible. They are decomposedin sub-zones in which the PPRT regulations are tailored to the exact type and intensity of effect.For instance, in zone R1, authorized buildings must resist overpressure up to 140 mbar and thermaleffect up to 8 kW/m2, whereas in zone R2 they must resist overpressure only up to 50 mbar. Thedark blue (B1 to B7) and light blue (bc1 to bc4) zones correspond respectively to a medium leveland a low to medium level of hazards. The PPRT regulation states these zones should not bedestined for new activities nor new residential construction. Adaptation works are mandatory forexisting buildings located in zones B1 to B8 (they must include a safety room resistant to toxicgas). As in red zones, the required protection level regarding high pressure and thermal effectsdepends on the distance to the plant, the type of hazards and the type of building. Their areprecisely defined in the PPRT rules.

2.3 Additional measuresThe 2003 law also created a new property insurance system to cover damages caused by an in-dustrial accident. The Technological Catastrophe insurance (Catastrophe Technologique, Cat Techin French) is mandatorily included in home insurance, regardless of the location and of the levelof exposure to industrial hazards. The plant operator is fully liable for any damages caused tothird parties,10 but compensation may be delayed and be partial if the operator is insolvent. Theobjective of the Cat Tech insurance is to guarantee a rapid and full indemnification of the victims.After a major technological accident,11 a Technological Catastrophe decree is issued by the Stateand the insurance companies advance repayment expenses. They then try to identify the liableparty to claim reimbursement. The risk of no responsible identification or its insolvency is coveredthanks to risk sharing. As 99% of households purchase a home insurance, the coverage of the CatTech insurance was very high since its creation.

2.4 What can we expect?A PPRT can impact residential choices and residential prices through many channels. Its impact onlocal residential market characteristics is thus ex ante unknown. In this subsection, we summarizethe main theoretical impacts PPRT measures can have on residential markets.

The CLIC stage of the policy is mainly characterized by private information. Indeed, currentowners or renters at the beginning of the CLIC process are likely to receive information on riskand to update their belief on risk even before the PPRT is prescribed. However at this stage, thereis no mandatory disclosure of the risk information to potential buyers or renters. This informationasymmetry between current and potential residents leaves room for strategic behaviors. Indeed, ifhomeowners anticipate that the price will go down as a result of the compulsory disclosure of riskexposure, they have an incentive to sell their house before the PPRT is prescribed and mandatorydisclosure applies. If this is the case, we will thus witness an increase in the number of transactionsbefore the PPRT is prescribed and potentially a decrease in the number of transactions after itis prescribed. The transitional increase in the supply-side of the market may also lead to a pricedecrease, depending on the elasticity of both demand and supply curves.

The prescription stage encompasses more numerous measures. First of all, risk reduction in theexisting plants is one of the main objectives of a PPRT. As a result, the objective probability of

10Industrial risks are covered by the Environment Code (art. L. 514-19), by third-party liability in the civil code(art. 1382) and by jurisprudence.

11A technological catastrophe is defined as an accident (except nuclear) causing damage to a high number ofhousing units (at least 500 dwellings) and caused by an industrial establishment falling under the ICPE authorizationand declaration regimes (art. L.128-1 and R. 128 of the Insurance Code). The ICPE regulation applies to allindustrial plants and, depending on the type and quantity of hazardous products present on site, they fall under aregistry, a declaration or an authorization regime. The upper-tier Seveso plants are all ICPE under authorization.

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an industrial accident decreases. If households are informed of the risk-reduction measures, theirrisk perception will be affected. Depending on their prior beliefs, this effect could go both ways.Risk reduction can also lead to a reduction in the negative externalities caused by the plant (suchas noise, air pollution, etc.). All else held equal, local housing prices in the at-risk zones are thuslikely to rise as a result of risk reduction.

In addition, there is also an information effect at this stage. The PPRT elaboration processincludes a public inquiry, consultation meetings and the publication of exposure maps. Disclosureon risk exposition becomes compulsory for dwellings that are being sold or rented in the studyperimeter. Thus both the local population and potential new residents are likely to receive a lotof information on the nature and scope of the industrial hazards, on accident scenario and on thepossible damage to the neighborhood. If the households had a biased perception, during the PPRTprocess and after the release of the zoning maps they may reassess their beliefs. If risk was initiallyunderestimated, housing prices are expected to fall in the perimeters exposed to the industrialhazards.

During the approbation phase, regulatory zoning maps may be seen as providing “objective”information on industrial hazards as the households are able to pin their dwelling on the mapand determine the level of exposure. There may thus be additional information effects. Moreimportantly, this stage is also characterized by restricting land use or imposing new regulations onexisting and future buildings. The latter aim at reducing the vulnerability of buildings and thusthe damage function, defined as the monetary cost of damage per intensity of hazards. All else heldequal, the anticipated damages will be lower for a building after it meets the new security standards.This “safety” attribute is expected to have a positive impact on housing prices. Nevertheless,complying with the prescriptions comes at a financial cost (material, labor, etc.) and non financialcosts (delay in construction, trouble caused by home works, etc.) that are expected to be integratedin the housing market price when works are done or to cause a decrease if they are to be done.

The PPRT regulation limits urban growth and controls the density around hazardous plants.It frequently implies expropriation and ban on new residential constructions. The direct effect isto reduce the total quantity of land available for residential use and to control the location of thisland. This scarcity effect on land will translate into a reduction in the supply of new housing units.All else held equal, this would imply an increase in price. On the other hand, regulatory zoningcan generate positive local externalities on the demand side (no new building in the neighborhood,reduction of density and of congestion in the access to public services, etc.). This amenity effectwill shift the demand upward. Pogodzinski and Sass, (1990) show that the global effect will dependnot only on the size of the supply and demand shock but also on the price elasticity of demand andsupply. Indeed, if demand is constant or inelastic, then housing price is expected to rise. If on thecontrary households are perfectly mobile and can move to other locations with no cost (demandperfectly elastic), then there will be no impact on housing price in the PPRT zone.

Finally, the PPRT impact may not be limited to price close to dangerous plants. In particular,if the total impact on prices is negative, homeowners may be more reluctant than potential buyersor renters to adjust their expected sale price at a lower level, as suggested by Genesove and Mayer,(2001). This would indeed lead to a transitory discrepancy between supply and demand, andthus to both a decrease of the average number of transactions and an increase of the numberof vacant dwellings. Moreover, if well-off households are more mobile than modest households,increased risk perception may have an impoverishment effect, even in the hypothetical case inwhich preferences and risk perception are homogeneous across households. There may also besome displacement effects, potential negative impacts in the direct vicinity of upper-tier Sevesoplants being compensated by positive effects further away from the plants. This is particularly trueif residential choices are constrained by the necessity to live relatively close to one’s work place forexample. In this case, people willing to relocate further away from Seveso plants may only move

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further enough to be safer, but stay relatively close to the plant. This effect will be reinforced if thenegative shock on the local supply of housings due to expropriation and relinquishment measuresis large.

Let us note that although the technological disasters insurance system improves the coverage ofdamages to housing in case of an accident, it is unlikely that this should impact the housing markets.Indeed, insurance premium are relatively low (a few euros per year) and, for the moment and tothe best of our knowledge, are not differentiated according to risk exposure.12 As a consequenceso far this system does not internalize risk exposure. Its creation in 2003 should have a positiveimpact on the price of housing in the direct vicinity of an industrial establishment.

3 Data3.1 Housing and sales dataData quality is one of the main assets of this paper. Our data include an exhaustive administrativedatabase on housing in France (Filocom), and data collected at the local level on real estatetransactions (Perval and Bien).

The Filocom database was created by the French tax administration using four different taxfiles on both housing units and households. As a result, this database includes households’ char-acteristics (for example age, earnings, family structure), housing characteristics (for example dateof construction, square footage, several quality measures) and landlords characteristics for all 30million housing units in France. The finest geographic scale that can be used to locate housingunits are cadastral plan sections, that roughly amount to blocks. In the remainder of this paper,we will thus refer to cadastral plan sections as blocks. They contain on average over 200 dwellingsbut this measure can vary greatly depending on how urban or rural the section is. In particular,it is much higher in the Paris region where housing is much denser than in other parts of France.Each housing stock variable is aggregated at the block level (we compute the mean of quantitativevariables such as earnings and the share of qualitative variables such as vacant housing).

This database can provide precise and detailed insight into the structure and characteristics ofa neighborhood, but it cannot account for housing prices. To study the impact of the PPRT onreal estate prices, we use the Perval and Bien notaries’ database as an alternative source. Shortof tax files, it is the most comprehensive source on real estate transactions in France. This data iscollected from notaries’ offices and contains a detailed description of the structural characteristicsof the housing units (number of room, surface, year of construction, bathroom, standing, parkinglot, etc.), the price in euro net of taxes, and some information about the seller and the buyer (age,nationality, zip code of home municipality, etc.). This data includes the precise cadastral planparcel of each transaction.

Empirical papers on the housing market typically rely on two types of data. On the onehand, papers focusing on prices usually rely on sales data, often obtained from a given website ofreal estate company. This type of data presents one major concern for researchers as it can lackrepresentativity. Moreover, it can only provide information on transactions and not on housingstock. On the other hand, papers trying to address this issue usually rely on surveys or censusthat are representative of people, but not necessarily of dwellings. In particular, it is often hardto get information on vacancy through these sources. We believe that our data addresses theseshortcomings. Moreover, geolocalizing both blocks and parcels is possible using the NationalGeographic Institute data on French cadastral plan. We have access to the data for even years

12The 2003 law tied the provision of the Cat Tech insurance to home insurance but, contrary to the NaturalCatastrophe insurance, imposed no rules on its price.

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only, between 2000 and 2012 for notaries datafiles, and between 1998 and 2012 for Filocom, andthus use these years only throughout this paper.

3.2 Industrial plants and PPRT censusWe use the same dataset on industrial plants as in Bléhaut (2015). It lists all upper-tier Sevesoplants active as of January 2014 with their geographical coordinates. These establishments mainlybelong to the chemical sector (36.3%), to the logistics sector (22.5%),13 or to the oil refining(3.4%) and metalworking sectors (2.4%, table 2). We merge this data with the listing of thePPRT that have already been prescribed or approved in January 2014 and are to recover geocodedupper-tier Seveso plants in our database of 374 PPRT (figure 2). In 2014, they were all prescribed,except two of them; 264 had been approved and 120 were still being drawn up.

Table 2: Main activity of Seveso plantsMain activity Number of plants

Haulage company 5 (1.0%)Waste collection management 6 (1.2%)Metalworking 12 (2.4%)Refinery 17 (3.4%)Stocks 113 (22.5%)Chemistry 182 (36.3%)Other 166 (33.1%)

Source: upper-tier Seveso plants under a PPRT in 2014, authors’ calculation.

Unfortunately, the real PPRT zone perimeters could not be recovered.14 In addition, theseperimeters would only be available for PPRT that are already prescribed. We thus recomposePPRT impact areas as 2,000 meter buffers around the upper-tier Seveso plants. In most cases,it is likely to be an upper bound to the real perimeters, so we will underestimate the impactof the PPRT on the local real estate market. Our estimates will thus be biased toward zero.Nevertheless, the initial “study perimeters” defined in the prescription decrees were likely largerthan the officially regulated zones after approbation. Consistent with the precautionary principle,the technical services in charge of these early drafts usually draw an upper envelop of the at-riskarea that is then refined and reduced after risk-reducing measures. Restricting to regulated zoneswould then under-estimate the information and anticipation effects in the study perimeter. Inpractice, we keep every housing unit sold in a 2000 meter radius around Seveso plants in thenotaries’ dataset in our main specification. Their main characteristics are presented in table 12 inappendix. We also conduct robustness checks within different radiuses. For the neighborhood andhouseholds characteristics, we observe the panel of blocks within the same perimeters.

The only two official dates in a PPRT implementation are the prescription and approbationdates, and only those could be recovered from the administrative PPRT listing we use. Thus wedo not know with certainty the creation date of the CLIC and cannot directly identify the impactof the CLIC phase. Nevertheless, we know that CLIC were created between the 2003 Bachelot lawand each PPRT prescription date. We can thus include lags to capture the impact of the privateinformation the landlords may have received from the CLIC before the prescription of the PPRT.

13Typical examples are storage facilities specializing in highly dangerous substances.14All PPRT documents (hazard maps, zoning maps, regulation, etc.) are uploaded on the prefectures’ websites,

most of the time only in pdf format. In 2014, the Ministry in charge of Sustainable Development launched a programto harmonize, centralize and make available all PPRT maps shapefiles in a single repository but this work is stillunder progress.

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Figure 2: Evolution of PPRT prescription and approbation over time

The study is restricted to housing markets between 2000 and 2012 in the zoning of the PPRTprescribed between 2007 and 2014. The long time period allows to include observations before theBachelot law was passed, as a strict “before policy” control. PPRT prescribed in 2006 or approvedin 2007 or 2008 amount to very few transactions or blocks (and very few PPRT, as seen in Figure2). For coherence reasons, we exclude them. At the time of the study, notaries’ files were notavailable for 2012 for the Paris area (Île-de-France) so we exclude this region from the analysis.Our final sample includes 440 upper-tier Seveso plants that amount to 346 PPRT. The number ofPPRT is smaller than the number of plants for two reasons. First, some plants listed as upper-tierSeveso plants in our 2014 census may not have been active in 2003. In this case, they are exemptof having a PPRT. Second, some PPRT include more than one plant, usually because they areclose to each other and they pose a joint risk on the neighborhood (an accident in one of themmay trigger chain reactions in the other ones).

3.3 Sample characteristicsFigure 2 summarizes the years of prescription and approbation for the the initial set of PPRT.The genral pattern displayed in this figure is valid for our estimation sample. The first PPRT wereprescribed as soon as 2006, but at first the Préfets’ decisions were scarce. The number of PPRTprescription started to rise in 2008, reaching a peak in 2009. Less than 20% of the PPRT wereprescribed before 2009. The negotiation process involved before a PPRT is approved can be quitelong (2.5 years in average, table 3), thus there is a significant delay before PPRT start reachingthis stage. About 30% of the PPRT are still in the prescription phase in 2014. Most of the PPRTcover a single upper-tier Seveso establishment, nevertheless some PPRT regulate several upper-tierneighboring establishments, up to 21 (table 3). This is not the general case, as in our estimationsample 87% of PPRT only apply to one upper-tier Seveso plant.

In practice, Filocom and notaries data was only available for even years. In consequence, wepool the PPRT that were prescribed or approved over a 2 year period. 4,255 blocks are locatedwithin a 2000 meter radius around a plant that is eventually under a PPRT (table 4). Amongthem, 62 had not been prescribed nor approved during our observation period. An additional 2,122are prescribed but not approved yet in 2012. In 2012, 6,317 housing transactions occurred in the2000 meter perimeter of an upper-tier Seveso under a prescribed or approved PPRT. 1,343 housing

11

Table 3: Characteristics of the PPRTMean Median Min Max Total

Time betweenprescription and 901 836 265 2225approbation (in days) (2.5 years) (2.3 years) (9 months) (6.1 years)

Number of Seveso 1.3 1 1 21 346by PPRT

Source: upper-tier Seveso plants under a PPRT in 2014, estimation sample, authors’ calculation.

units were sold in the neighborhood of a future PPRT.

Table 4: Housing transactions and blocks per PPRT status and year of observation

Blocks Housing transactions

Not prescribed Prescribed Approved Total Not prescribed Prescribed Approved Total

1998 4,255 0 0 4,255 – – – –2000 4,255 0 0 4,255 11,573 0 0 11,5732002 4,255 0 0 4,255 10,903 0 0 10,9032004 4,255 0 0 4,255 11,634 0 0 11,6342006 4,255 0 0 4,255 12,209 0 0 12,2092008 3,244 1,011 0 4,255 9,668 1,236 0 10,9042010 508 3,048 699 4,255 2,641 8,135 644 11,4202012 62 2,122 2,071 4,255 180 4,988 2,492 7660

Source: Filocom 1998-2012 and Perval 2000-2012, 2000m radius around upper-tier Seveso plants under a PPRT in2014, estimation sample, authors’ calculation.

Figure 3 represents the evolution of the average prices per square meter of homes locatedin a 2000 meter distance of an upper-tier Seveso before and after prescription (light lines) andapprobation (dark lines) of the PPRT (in 2000 euros). This figure suggests that the price of housesfollows a decreasing trend before prescription that continues during the prescription phase. Wealso observe a downward trend before and after approbation, but it is much lower. On the contrary,the prices of apartment tend to increase both before and after prescription and approbation. Onthis very descriptive figure, the effect of PPRT prescription seems to be higher for houses than theeffect of approbation and of the same order of magnitude for apartments.

4 Empirical strategyOur objective is to estimate the average effect of each stage of PPRT implementation on real estateprice and neighborhood characteristics in at-risk local housing markets. We will estimate a globalimpact of each stage of the policy, rather than an impact of each measure it encompasses.

4.1 Spatial and temporal variationTo assess the impact of a public policy, it is crucial to build a proper counterfactual of whatwould have been observed in the absence of the policy. This counterfactual cannot be directly

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Source: Perval 2000-2012, 2000m radius around upper-tier Seveso plants under a PPRT in 2014, estimationsample, authors’ calculation.

Figure 3: Annual average transaction prices (in 2000 euros per m2)

observed, so one has to build a “control group” based on the observation of units that were notimpacted by the policy. Typically, the literature relies on a differences-in-differences strategy,which compares the evolution of treated and control groups before and after the policy change.The control group then has to be as similar as possible to the observations impacted by the policybefore its implementation. In particular, the outcome trends before the policy should be the samein both groups. The 2003 law made compulsory PPRT around every upper-tier Seveso so there isno at-risk housing market that will remain untreated. Geographical areas outside the study zonesare not good candidates either. Those relatively close from the PPRT but outside the regulatedzones belong to the same local housing market so it is likely that they follow the same trends.Nevertheless, due to this spatial proximity, they may be contaminated by the policy (information,relocation of demand to safer areas, etc.) and a difference-in-differences estimator would thus bebiased. Areas located further away from the Seveso plant are not directly impacted by the PPRTbut are less comparable. Confounding factors due to the presence of a highly hazardous plant inthe treatment group (negative externalities due to air pollution, traffic, smell, noise, etc.) and itsabsence in the control group are likely to be high.

To avoid these sources of bias, we adopt an identification strategy that relies on temporalvariations inside the PPRT perimeters and on spatial variation across perimeters. The originality ofthe policy is that the process is completely local so neither the prescription date nor the approbationdate are nationwide. The launch of the PPRT is mandatory but left at the discretion of the Préfets.The advancement of PPRT prescription over time does not seems to follow a geographic pattern.Indeed, Figure 4 maps the evolution of PPRT prescription over time and does not display anyparticular pattern. The difference in prescription dates may be due to administrative constraints(number of plans to elaborate in relation to the expertise, training, availability of the staff) andlocal political strategies. However, Figure 5 (panel a) shows that the general pattern of PPRTprescription does not vary according to the number of PPRT in a given administrative region.There is no strong evidence of a selection into prescription by Préfets depending on the level ofexposure. The number of plants in a single PPRT can be considered as a measure of risk intensity,and figure 5 (panel b) shows that even if a slightly higher share of PPRT with only one plant wereprescribed in the early stages of the law, the difference with PPRT with more than one plant isrelatively low. The only exception is for 2012, when about 16% of PPRT with more than one plantwere prescribed, while this share is only 5% for PPRT with one plant.

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Figure 4: PPRT prescription over time

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Figure 5: Prescription

In our sample, each year between 2007 and 2012, new PPRT are prescribed (see Figure 2).As shown in figure 4, the expansion of PPRT prescription really started in 2008, and by 2010most of the Seveso plants that will in time have a PPRT have already prescribed it. Even so, in2012 some plants have yet to see their prevention plans prescribed. Using the timing of the policyimplementation as the source of identification thus seems to be a valid strategy.

The average duration between prescription and approbation is 2.5 years, but there is again agreat heterogeneity across PPRT, from 9 months to more than 6 years (table 3). As a consequence,approbation dates vary greatly and a significant share of PPRT are still under prescription at theend of the period of observation of our panel. One could worry that the delay between prescriptionand approbation is not exogenous. Nevertheless, figure 6 (panel a) shows that at each prescriptiondate, there was a relatively constant share of PPRT that would be approved fast (less than 2 yearsafter the prescription date). If anything, the share of PPRT being approved slowly diminishesover time, which may be a result of the technical staff improving their efficiency with experience.Again, there is very little evidence of a correlation between the length of the prescription phaseand the number of PPRT in the region, as shown in Figure 6 (panel b). In addition, panel c showsthe number of dwellings within 2000 meters of upper-tier Seveso plants does not tend to be higherfor PPRT that have not been approved yet as of 2014. The duration of the PPRT process thusdoes not seem to be directly driven by the number of households that would need to enforce theland-use restrictions and/or to be compensated following the approbation.

All in all, we thus consider that the timing of both the PPRT prescription and approbationdates are exogeneous. Each year, an upper-tier Seveso plant neighborhood falls into one of followingthree situations:

• the PPRT has not been prescribed yet,

• the PPRT is prescribed,

• the PPRT has been approved.

As long as the PPRT is not prescribed, we will observe the evolution of a housing market exposed

15

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48

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14

36

65

28

50

00

20

40

60

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140

2007 2008 2009 2010 2011 2012 2013

number of PPRT

Year of prescription

PPRT approved in less than two years after prescriptionPPRT approved after years or more after prescription

(a) By prescription date

0

5

10

15

20

25

30

35

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45

0 5 10 15 20 25 30 35 40 45 50

average duration in months

Number of PPRT in the region

(b) By number of PPRT in the region

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50

60

70

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0 5000 10000 15000 20000 25000 30000

average duration in months

Number of dwellings in 2002

Approved PPRT PPRT still not approved (lower bound of duration)

(c) By number of dwellings in 2000 m radiuses around theplants

Source: Perval 2000-2012, 2000m radius around upper-tier Seveso plants under a PPRT in 2014, authors’calculation.

Figure 6: Prescription length

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to industrial hazard but with no additional public information and no additional land-use planningmeasures. To further control for the potential private information impact of the CLIC, we canadd lags of the prescription variable. Conditional on these dummies, or in the absence of a CLICimpact, the areas where the PPRT is not prescribed yet are a natural counterfactual of the housingmarkets already under PPRT. We use this spatio-temporal variation in the policy implementationto identify the impact of the PPRT prescription and approbation on local housing markets. Thisapproach is similar to a difference-in-differences approach on panel data, with the difference thatthe treatment date is not unique and that the control group is composed of areas that will betreated in the future. Gibbons, (2015) implemented a similar identification to estimate the impactof wind turbines development on house prices in England and Wales.

To ensure identification of both the prescription and the approbation effect, we need to observeat each date PPRT perimeters in each of the three states (not prescribed yet, prescribed, approved).This is not a problem since our study stops in 2012 and some PPRT that we know are approvedin 2013 or 2014 are still un-prescribed and un-approved at this time. In addition, we includeobservations before the first PPRT was prescribed, that is to say before 2006, and before the lawcreating the PPRT was passed, that is to say before 2003. This allows us to identify both areafixed effects and temporal trends.

4.2 Measuring the impact of the policy change on transaction prices (atthe transaction level)

The transactions sample is not a repeated-sales sample, hence we use a repeated cross-sectionstrategy rather than actual panel data strategy. Denoting i a given transaction, p a PPRT areaand t a period, we estimate the following equation:

ln pipt =αt + αp +Xiγ + δ ln dip (1)+ βpresc-41{tp−4} + βpresc-21{tp−2} + βpresc1t>tp + βappro1t>ta + εipt,

where pipt is the price of the housing unit i sold in the year t in the area of PPRT p, in 2000 europer square meter. αt is a year fixed effect that controls for unobserved year specific events andαp is a PPRT fixed effect. Xi is the vector of dwelling characteristics for transaction i, dip is thedistance of i to the closest upper-tier Seveso plant, α is a constant and εipt an error term. 1{tp−2}takes the value one if the local PPRT is going to be prescribed in the next period (that is in thetwo following years), and 1{tp−4} takes the value one if the local PPRT is going to be prescribedin three or four years. These two dummies allow us to measure “anticipation” effects with respectto the prescription date, that is any effect that could have happened during the CLIC phase of thePPRT. 1t>tp

takes the value 1 if the transaction took place after the PPRT prescription date, 0otherwise. 1t>ta

takes the value 1 if the transaction took place after the PPRT approbation date,0 otherwise. The coefficients of interest are thus βpresc-4 and βpresc-2 (the average effects during theCLIC period), βpresc (the average effect of a PPRT being prescribed) and βapprob (the additionaleffect of the PPRT being approved, conditional on the PPRT being prescribed).15 The standarderrors are clustered at the municipality level.

15The total impact of the prescription and approbation phases is a weighted sum of βpresc and βapprob, as itdepends on the duration of the prescription phase. Given the multiple dates and duration in our data base, thecalculation and interpretation of such a global effect is not straightforward.

17

4.3 Measuring the impact of the policy change on neighborhood char-acteristics (at the block level)

We estimate a similar model for neighborhood characteristics on housing stock. The observationunit is a block, so this allows for a more traditional panel strategy. The equation we estimate isvery similar to equation 1. For a given neighborhood characteristic y, we estimate the followingfixed-effect model:

ybt =αt + αb (2)+ βpresc-41{tp−4} + βpresc-21{tp−2} + βpresc1t>tp

+ βappro1t>ta+ εbt,

where b denotes a block and t a year. αt is a time fixed effect and αb is a block fixed effect. εbt

is the error term. We cluster standard errors at the block level. As in the previous equation, theeffects of interest are βpresc-4, βpresc-2, βpresc and βapprob.

The block outcomes we are interested in are the following: selling rate (defined as the ratio ofhousing transactions over housing stock in the block), type of housing occupation (vacancy rate,principal vs. secondary housing rate) and socio-economic characteristics of the inhabitants (shareof live-in landlords and renters in the block, share of rented social housing, average size of thehouseholds, average number of children and average earnings per consumption units).

4.4 Identification assumptionOur strategy is similar to a differences-in-differences approach, with multiple treatment dates anda control group made of future treated areas. As it is the case in a typical differences-in-differencesapproach, our estimates will be valid under a “common trend” assumption. This assumption statesthat in the absence of treatment, the outcomes y should have evolved similarly in all at-risk housingmarkets. More precisely, there should not be any differences in linear trends between groups oftreatment, one group being all the housing markets treated in a given two-year period. In oursample, there are four groups: PPRT prescribed in 2007 or 2008, in 2009 or 2010, in 2011 or 2012,and in 2013 or later. As always in this kind of setting, we cannot fully test for this hypothesis, butwe can check whether a necessary condition is met. Namely, we verify whether the common trendassumption holds before the law is passed by estimating our model before 2003 with additionaltrends for each group (

∑tpαtp × t). A F-test should not reject the joint nullity of αtp .

Table 5: Common trend assumption test

Variable F-test p-value

Apartments log price per square meter (e/m2) 0.670Houses log price per square meter (e/m2) 0.253Log price per square meter (e/m2) 0.297Vacancy rate 0.136Selling rate 0.627

Source: Filocom 1998-2002 and Perval 2000-2002, 2000m radius around upper-tier Seveso plants under a PPRT in2014, authors’ calculation.

Table 5 shows the results of the test for each of our five main outcome variables. None of thesetests exhibit worrying significant pre-trends, since all the estimated p-value are well above the 10%threshold. However, we test the robustness of our main results to the introduction of

∑tpαtp

× t,and find that it does not affect our main conclusions.

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5 Results and discussion5.1 Baseline resultsTable 6 presents baseline results obtained by estimating equations 1 and 2. The first panel presentsestimates from the hedonic regression. Estimates of the PPRT impact before its prescription arenot significantly different from zero when considering all transactions, suggesting that the PPRTregulatory impact was not anticipated and not capitalized in the prices. Overall, prescription isassociated with a 3% decrease in transaction prices, and approbation with a 4% decrease. Thelatter is also significant at the 1% level, while the overall effect of prescription is significant at the5% level. The prescription and approbation effects seem to be stronger and more significant forapartment than for houses. In particular, the estimated price decrease is 4.4% after prescriptionand 5.1% after approbation for apartments (both these effects are significant at the 1% level). Bycontrast, our results suggest that the PPRT process had little significant impact on the price ofhouses located less than 2000 m away from an upper-tier Seveso plant. Only the prescription seemsto have lead to a significant price decrease for houses, and it is twice as small as for apartments.

Table 6: Estimated impact of the PPRT on housing prices and stock outcomes with anticipationeffects

Variable βpresc-4 βpresc-2 βpresc βapprob

Apartments log price (e/m2) -0.015 -0.025** -0.044*** -0.051***Nb obs =36,809 (0.011) (0.011) (0.016) (0.019)

Houses log price (e/m2) -0.001 -0.006 -0.022* -0.018Nb obs =39,050 (0.009) (0.009) (0.012) (0.012)

Log price (e/m2) -0.006 -0.015 -0.030** -0.041***Nb obs =75,859 (0.010) (0.010) (0.013) (0.012)Vacancy rate (pp) -0.019 0.203 0.471 -0.023Nb blocks =4,255 (0.162) (0.273) (0.369) (0.161)

Selling rate (pp) 0.182* 0.387*** 0.253* 0.046Nb blocks =4,255 (0.096) (0.133) (0.147) (0.094)

Source: Perval 2000-2012 and Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in2014, authors’ calculation.Note: *** significant at 1%, ** significant at 5%, * significant at 10%.Standard errors are reported below the coefficients between parenthesis and are clustered at the municipality levelfor transactions and block level for the selling rate. Sample size are reported below the coefficients between brackets.

In addition, the sub-sample of apartments also display some level of anticipation during theCLIC phase of the law. Indeed, the coefficient associated with 1{tp−2} suggests that there was a2.5% decrease in apartment prices in the period before the prescription, without further anticipation2 periods before prescription. Our results thus suggest that each wave of PPRT implementationtranslated into an additional negative information shock, at least partially capitalized into prices.This is at odds with Grislain-Letrémy and Katossky, (2013). They found that the housing prices inthe vicinity of three hazardous industrial plants (Rouen, Dunkirk, Bordeaux) reflect the negative

19

and positive amenities of the plants16 but that those price differences were not modified neitherby the vote of the 2003 law, nor by the creation of the CLIC. However, the scope of our study ismuch broader than theirs, and it is possible that the three sites they studied were very specificcompared to other upper-tier Seveso neighborhoods.

As a comparison, Bléhaut, (2015) finds that after the AZF explosion in 2001, the averagehousing prices located within a 2000 meter radius around upper-tier Seveso plants in France were1 to 2% lower than housing prices in control areas. Her estimations exclude the AZF area. Inthe other Seveso’s neighborhoods, this accident can been seen as a pure information shock on thenature and potential intensity of industrial risk. We find that the CLIC phase has an impact atleast as strong as this pure information shock, and prescription and approbation have a strongerimpact. It is not necessarily surprising, as information on local risk exposure is likely to have moreimpact on risk perception than information from a distant location.17 Moreover, the PPRT processprovides more detailed information and encompasses additional land-use regulation measures. Thisresult is also consistent with interviews by Osadtchy, (2014) of residents after the prescription ofa refinery’s PPRT in the south of France (Provence de Martigues, Bouches-du-Rhône). Before thePPRT, the awareness of the existence of an industrial risk was already pretty high: the plant isopen since 1930, several inhabitants work there and in 1992 six employees died after the breach ofa pipeline that caused an explosion and started a fire. Material damages to building occurred asfar as one kilometer away from the refinery. Nevertheless, she reports the PPRT first informationmeeting was a “huge blow” (“un coup de massue”) for the residents.18 According to her, until thePPRT was launched, they did not precisely apprehend the nature nor the intensity of the industrialhazard. The detailed cartography forced them to become aware of their vulnerability.

Similarly, the second panel in table 6 reports the estimated results with anticipation effectsfor the stock outcomes. Strikingly, there is no significant effect of any stage of the Bachelot lawon vacancies. There is however a positive effect on the selling rate both before and during theprescription stage. This shows that local housing markets are more active during the early stagesof the PPRT process, before the PPRT is approved and land-use regulations become mandatory.This result is consistent with some strategic behavior on the supply side during the CLIC and pre-scription phases of the process. During the first one, it is likely that sellers have more informationthan buyers, while during the second one disclosure of the risk status is already mandatory but thescope of the final perimeter and the extent of the land-use regulations are still unknown. It is thusrational for homeowners anticipating a decrease in prices to sell their dwellings as soon as possible.Let us note that in this context, a shift of supply may be sufficient to induce the price decreaseobserved in the first panel, depending on the elasticity of both supply and demand curves.

These conclusions are robust to the size of the buffer (see table 14). As expected, the estimatedimpacts increase as the perimeter we consider decreases. They are also consistent with controllingfor group-specific trends, by adding in our specification the following coefficients:

∑tpαtp

t (wheretp represents the date of prescription). Table 15 shows the estimated results with these additionaltrends. Overall, the quantitative and qualitative conclusions of this table are similar to the onesexposed in this paragraph are similar. There is one notable exception though: the apartment pricecoefficient βpresc−4 is slightly higher (2.4% instead of 1.5%) and becomes significant at the 5%level. In addition, price results are very robust to changes in the hedonic regression specification.In particular, table 16 (in appendix) present the main coefficients of an alternative specification in

16For instance the Seveso plant near Bordeaux is a former gunpowder factory, surrounded by a park and theneighborhoods around the plant are green and quiet, so contrary to other industrial areas there are high positiveamenities in this location.

17Bléhaut, (2015) indeed finds a stronger effect in the South-West quarter of France than in the rest of the country.18“We knew there was a danger (. . . ). Now, we know exactly how we can die from toxic gaz, high pressure and

thermal effects, we know exactly what impacts us!”, interview with an inhabitant (Osadtchy, (2014) – translationby the authors).

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which the dependent variable if the logarithm of the total price, as a function of a polynomial of thesurface and the average square footage of a room. This specification leads to remarkably similarestimates as our baseline one, with only very small quantitative differences and no qualitativedifference.

The results presented in table 6 are thus our reference throughout this article. In addition, forclarity purpose, we do not report additional specifications including the vacancy rate, as all resultsare small in magnitude and not statistically significant. We also do not report hedonic regressionresults for houses and apartments taken together as we find no substitution effects between thosetwo kinds of dwellings.19

5.2 Displacement effectsPlace-based policies often lead to displacement effects (see for example Givord, Rathelot, andSillard, 2013). In our case, one might for example anticipate positive impacts on prices furtheraway from dangerous plants, which would compensate the negative impact in their direct vicinity.To analyze such mechanisms, we estimate our two main equations of interest in successive 500mcircles beyond our initial 2000m cut-off. Table 7 shows the results of these estimations.

However, we do not find strong evidence of such displacement effects. The impact on apartmentprice seems to grow marginally stronger, albeit imprecise, as we go further away from the dangerousplants (up to 3000m). Between 3 and 4 km, there does not seem to be an effect on transactionprices, and the effect is difficult to interpret between 4 and 5 km as some coefficients are estimatedto be significantly negative but there is no strong coherence between different sets of estimations. Inany case, all point estimates are negative, and there is no indication that there could be a positiveimpact on transaction prices between 0 and 5 km away from Seveso plants after the implementationof the PPRT. Similarly, we do not find evidence of additional shocks on the volume of transactionsbetween 2 and 5 km away from the plants.

5.3 Heterogeneity of the effectWe test the heterogeneity of the PPRT impact with respect to the number of upper-tier Sevesoplants per PPRT, which can be seen as a proxy for risk intensity, and two qualitative measuresof the initial conditions of the at-risk housing markets: the urban density and the share of live-inlandlords.

19This conclusion holds for all the specifications we consider in this paper. Results are available upon request.

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Table 7: Estimated impact of PPRT on the housing prices and selling rate0− 2000m 2000− 2500m 2500− 3000m 3000− 3500m

βpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa

Apartments -0.015 -0.026** -0.043*** -0.051*** -0.024* -0.033** -0.024 -0.056* -0.022** -0.029** -0.064** -0.025 -0.021 -0.021 -0.031 -0.033(0.011) (0.011) (0.016) (0.019) (0.012) (0.015) (0.029) (0.030) (0.009) (0.013) (0.021) (0.017) (0.013) (0.016) (0.022) (0.026)

Nb obs 36,809 23,349 23,233 26,608

Houses -0.002 -0.007 -0.024* -0.018 -0.003 -0.008 0.001 -0.040** 0.030** 0.014 0.001 -0.012 0.006 -0.027** -0.024 -0.048***(0.009) (0.010) (0.012) (0.011) (0.011) (0.013) (0.016) (0.016) (0.015) (0.015) (0.015) (0.016) (0.012) (0.012) (0.017) (0.015)

Nb obs 39,050 19,532 21,239 21,909

All -0.006 -0.015 -0.030** -0.041*** -0.015 -0.019* 0.012 -0.060*** 0.006 -0.001 -0.028* -0.026* -0.011 -0.026** -0.041*** -0.040*(0.010) (0.010) (0.013) (0.012) (0.009) (0.010) (0.020) (0.019) (0.010) (0.010) (0.01) (0.01) (0.010) (0.011) (0.016) (0.021)

Nb obs 75,859 42,881 44,472 48,517

Selling rate (pp) 0.182* 0.387*** 0.253* 0.046 -0.121 0.068 0.128 -0.105 0.194 0.206 0.506** 0.008 0.058 0.051 0.089 -0.052(0.096) (0.133) (0.147) (0.094) (0.103) (0.173) (0.214) (0.124) (0.133) (0.175) (0.211) (0.121) (0.096) (0.153) (0.210) (0.088)4,255 1,974 2,240 2,431

3500− 4000m 4000− 4500m 4500− 5000mβpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa

Apartments -0.001 -0.006 0.050 -0.028 -0.015 -0.035** -0.038* -0.047 -0.011 -0.022* -0.026 -0.115***(0.010) (0.014) (0.020) (0.024) (0.010) (0.014) (0.021) (0.042) (0.014) (0.013) (0.022) (0.038)

Nb obs 29,839 28,210 23,662

Houses 0.001 0.002 -0.021 -0.011 -0.000 -0.012 -0.035** -0.018 0.012 -0.002 -0.039** 0.001(0.010) (0.011) (0.015) (0.015) (0.009) (0.011) (0.016) (0.013) (0.010) (0.011) (0.016) (0.014)

Nb obs 22,139 21,764 21,231

Selling rate (pp) 0.157 0.191 0.378* -0.032 0.097 0.317** 0.299 0.155** 0.157* 0.028 0.190 0.049(0.122) (0.164) (0.217) (0.086) (0.091) (0.140) (0.183) (0.078) (0.093) (0.126) (0.162) (0.075)2,611 2,632 2,647

Source: Perval 2000-2012 and Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in 2014, authors’ calculation.Note: *** significant at 1%, ** significant at 5%, * significant at 10%.Standard errors are reported below the coefficients between parenthesis and are clustered at the municipality level for transactions and block level forthe selling rate. Sample size are reported below the coefficients between brackets.

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Table 8: Estimated impact of PPRT on the housing prices and selling rate – number of Seveso plants in the PPRTAll PPRT with one plant PPRT with multiple plants

βpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa

Apartments -0.015 -0.026** -0.043*** -0.051** -0.005 -0.025 -0.052* -0.041 -0.025 -0.026 -0.033 -0.061*(0.011) (0.011) (0.016) (0.019) (0.010) (0.016) (0.027) (0.026) (0.016) (0.018) (0.026) (0.035)

Nb obs 36,809 20,790 16,019

Houses -0.002 -0.007 -0.024* -0.018 -0.006 -0.010 -0.018 -0.020 0.005 0.006 -0.026 -0.006(0.009) (0.010) (0.012) (0.015) (0.010) (0.012) (0.014) (0.013) (0.016) (0.016) (0.026) (0.027)

Nb obs 39,050 28,152 10,898

Selling rate (pp) 0.182* 0.387*** 0.253* 0.046 0.264** 0.581*** 0.376* 0.024 0.057 0.000 0.112 0.091(0.096) (0.133) (0.147) (0.094) (0.132) (0.191) (0.221) (0.101) (0.130) (0.185) (0.204) (0.334)

Nb. obs. 4,255 3,074 1,181

Source: Perval 2000-2012 and Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in 2014, authors’ calculation.Note: *** significant at 1%, ** significant at 5%, * significant at 10%.Standard errors are reported below the coefficients between parenthesis and are clustered at the municipality level for transactions and block level forthe selling rate. Sample size are reported below the coefficients between brackets.

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Table 9: Impact of PPRT on the housing prices – urban density

Low density Medium-range density High densityVariable βpresc-4 βpresc-2 βpresc βapprob βpresc-4 βpresc-2 βpresc βapprob βpresc-4 βpresc-2 βpresc βapprob

Apartments -0.009 -0.021** -0.034*** -0.047*** -0.089*** -0.025 -0.073** -0.053 -0.039 0.000 -0.056 -0.240**Nb obs = 36,809 (0.008) (0.010) (0.013) (0.017) (0.033) (0.032) (0.029) (0.137) (0.038) (0.096) (0.056) (0.098)

Houses -0.001 -0.006 -0.021* -0.026** 0.074** -0.015 -0.011 -0.134 0.052 0.039 -0,053 0,050Nb obs = 39,050 (0.009) (0.009) (0.012) (0.012) (0.034) (0.044) (0.033) (0.089) (0.070) (0.082) (0.076) (0.112)Selling rate (pp) 0.101 0.297* 0.235 -0.084 0.198 0.513*** 0.316** 0.037 0.244** 0.276** 0.140 0.177*Nb blocks = 4.255 (0.099) (0.159) (0.161) (0.191) (0.125) (0.158) (0.154) (0.134) (0.116) (0.116) (0.152) (0.103)

Source: Perval 2000-2012 and Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in 2014, authors’ calculation.Note: *** significant at 1%, ** significant at 5%, * significant at 10%.A transaction or block is considered to have a low density if in 1999 the municipality’s density is lower than the 90th percentile of French municipalities. A highdensity is higher than the 97th percentile, and a medium-range density lies between those two values. This ensures that the three categories are roughly equivalentin size.As both low, medium and high density municipalities can enter a given PPRT, these estimates are computed by interacting the dummies for low and high shareswith the successive treatment dummies. All the coefficients reported in a given line are thus computed through a single regression.Standard errors are reported below the coefficients between parenthesis and are clustered at the municipality level for transactions and block level for the sellingrate.

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Risk intensity

A given PPRT can include more than one plant if they are close to each other. The number ofupper-tier Seveso plants is a proxy of both the objective level of exposure to industrial hazardsand of its visibility in the neighborhood. Results are reported in table 8.

We find that the price and volume effects are stronger around sites with only one plant, whilethere does not seem to be a strong effect of the PPRT process on sites with more than one plant.There is one exception to this conclusion: the price effect of approbation on apartments in thevicinity of PPRT with multiple plants is stronger than in the vicinity of single plant PPRT.

These effects are rather puzzling at first sight. One should first note that the sample sizesare much smaller in the subgroups than in the main specifications, while there are still manyfixed effects to be estimated. Thus the power of these results is lower. That technicality aside,it is possible that in areas with more than one plant, the objective risks were already taken intoaccount rather well by residents. If this is the case, the additional information is likely to have noimpact, while residents may react to the regulation aspects of the new law. It is also possible thatthese sites employ more people, and thus that the population is more constrained to live in thevicinity of the plant. In areas with only one plant, the objective risk is smaller and might not havebeen taken into account before the policy implementation. There is thus more room for strategicbehaviors when information is revealed.

Initial urban density

The main results presented in table 6 show that the markets for houses and apartments displayvery different reactions to the PPRT implementation. In addition, these markets do not seem tointeract, in the sense that there is no evidence of substitution between houses and apartments.However, one potential explanation for these differences could be that the houses and apartmentsof our sample tend to be located in markets with different characteristics, in particular in termsof density. We explore this possibility here, and compare the effect of the policy for areas withdifferent levels of urban density.

We compute urban density at the municipality level, based on the 1999 census data (FrenchStatistical Institute, INSEE). Most of our data is located in urban municipalities, hence the den-sity is unusually high in our sample. To obtain balanced subsamples of high, medium and lowdensity, we thus use the 97th and 90th percentiles of the national municipality density. Sincethere can be transactions or block from different municipalities within a single PPRT, we interactthese categories with our treatment dummies in order to measure the effect of the policy for eachsubgroup.

Results are presented in table 9. In low density areas, there is a negative and statisticallysignificant impact of the prescription and approbation phases on houses prices, although it remainssmaller than the effect on apartments. In particular, for this category, prescription leads to a 3.4%decrease in apartment prices and a 2.1% decrease in houses prices, while approbation leads toa 4.7% decrease in apartment prices and a 2.6% decrease in houses prices. For this group, theeffect for apartments is very similar to the effect in the overall sample, and there is an additionalsignificant decrease in prices during the CLIC phase.

In medium-range density areas, the effect of the policy on transaction prices is more complex.For apartments, the estimated effects tend to be higher in magnitude than in the baseline results,but also more imprecise. As a result, only the early CLIC (βpresc−4) and prescription (βpresc)stages are associated with a statistically significant negative effect. Even more surprising, theearly CLIC effect is estimated to be positive and statistically significant for houses.

In high-density areas, there is no significant impact of the PPRT process on either houses orapartment prices, except for a negative impact on apartments prices at the approbation phase.

25

It is possible that in these areas housing prices are mostly driven by relatively high demands.In this case, the negative information shocks may be too marginal to have an impact on prices.The negative consequences of the approbation phase, such as mandatory works, may howeverhave a stronger impact since they amount to additional costs for potential buyers. All in all, thetransaction prices part of this table seems to be consistent with the hypothesis that with growingurban density, there is a decreasing impact of the information effects of the policy.

The effect on the selling rate display a very different pattern. Indeed, there seems to be mostlyan increasing number of transactions in high and medium-range density areas, while there is almostno significant impact in low density areas. This is consistent with the idea that in denser areas,there is more room for strategic behaviors since on the one hand the source of risk may be lessvisible, and on the other hand in the short term risk exposition may not be a first order decisioncriteria for buyers. In areas with a low or medium-range density, the results are consistent withthe fact that the market is not short on the supply side, but more probably on the demand side.Interestingly in these areas the effect on transaction rates is lower at the prescription stage, wheninforming buyers of the risk status becomes mandatory.

Initial share of live-in landlords

Another measure of the initial “quality” of the markets is the share of live-in landlords. Indeed,renters and live-in landlords tend to have very different characteristics (the latter have higherincome and are more likely to be families for example), and different behaviors on the housingmarkets. In particular, renters are likely to be more mobile than live-in landlords since they donot have to sell their dwelling before they can afford to move out (see for example Glaeser andGyourko, 2007; Grainger, 2012).

As in the previous paragraph, the share of live-in landlords is measured at the municipality levelwith the 1999 census. We decompose the sample between low and high shares of live-in landlordsbased on the median value of cadastral plan sections in 2000 meters radius around upper-tierSeveso plants that enter our sample.

Results are presented in table 10. As in the previous paragraph, there is evidence that theeffect on transaction prices and selling rate do not actually happen in the same areas. On the onehand, the negative impact of the policy on apartments prices is stronger and significant in the areaswith a high initial share if live-in landlords. In particular, the prescription is associated with a2.5% decrease, while the approbation leads to an 8.9% decrease of transaction prices. There is onestatistically significant coefficient associated with approbation for houses in areas characterized byan initially low share of live-in landlords. However, the point estimate is in fact smaller than itscounterpart for areas with high shares of live-in landlords so this does not seem to invalidate thegeneral idea that the impact of the policy on transaction prices in stronger in areas with a highshare of live-in landlords. On the other hand, the impact on the selling rate seems to be mostlydriven by the areas associated with a low share of live-in landlords. The estimates are both higherand more significant than for areas with a high share of live-in landlords.

All in all, the results thus display a stronger price decrease and lower effect on the volume oftransactions in areas with a high initial share of live-in landlords. The higher effect on prices couldresult from the fact that in areas with a high share of live-in landlords, buyers are likely to capitalizethe future value of the house as well as its current utility into their price (Grainger, 2012). Theymay also take into account the transitory burden of implementing the mandatory works on theirhome. In addition, there may be a quality component to this difference, since transactions in areaswith a low share of live-in landlords are more likely to be buy-to-rent investment. As pointed byGlaeser and Gyourko, (2007), rental and homeowners markets are characterized by different typesof dwellings.

26

Table 10: Impact of PPRT on the housing prices – share of live-in landlords

Low share of live-in landlords High share of live-in landlordsVariable βpresc-4 βpresc-2 βpresc βapprob βpresc-4 βpresc-2 βpresc βapprob

Apartments -0.008 -0.023** -0.031** -0.049*** -0.025** -0.010 -0.052*** -0.089**NB obs = 36,809 (0.010) (0.011) (0.015) (0.018) (0.012) (0.014) (0.016) (0.041)

Houses 0.001 -0.005 -0.020 -0.025* -0.003 -0.012 -0.026 -0.037NB obs = 39,050 (0.010) (0.010) (0.012) (0.013) (0.013) (0.014) (0.016) (0.025)Selling rate (pp) 0.230** 0.421*** 0.327** 0.147 0.132 0.347** 0.165 -0.077NB blocks = 4,255 (0.111) (0.131) (0.152) (0.101) (0.103) (0.155) (0.154) (0.139)

Source: Perval 2000-2012 and Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in2014, authors’ calculation.Note: *** significant at 1%, ** significant at 5%, * significant at 10%.A transaction or block is considered to have a low share of live-in landlords if in 1999 the municipality’s share islower than the median of municipalities entering our sample. Conversely, a high share of live-in landlords is higherthan this median value.As both low and high share municipalities can enter a given PPRT, these estimates are computed by interacting thedummies for low and high shares with the successive treatment dummies. All the coefficients reported in a givenline are thus computed through a single regression.Standard errors are reported below the coefficients between parenthesis and are clustered at the municipality levelfor transactions and block level for the selling rate.

5.4 Neighborhood characteristicsTable 11 reports the estimated impact of PPRT implementation on local neighborhood character-istics. The most important feature of this table is that there is no clear significant impact on mosthousehold and neighborhood characteristics. Not only are most estimates not significant, but theyare also very small in magnitude. This is not necessarily surprising. Indeed, residential mobility inFrance is rather low. The annual mobility rate is 7.3% and only 3% for homeowners (SOeS, 2015).A significant change in the neighborhood composition after the PPRT implementation could takemany years even if newcomers in the at-risk areas differ from the previous residents.

This phenomenon can also be due to a local job market effect. Industrial establishmentsare important job providers, especially in rural areas. Employees are likely to live close to theirworkplace or might even have to do so (positions implying on call periods, three-shift working, etc.),imposing rigidities in the local housing market. According to a survey on the feeling of exposure toenvironmental hazards (Enquête Sentiment d’Exposition aux Risques Environnementaux, Pautard,(2014)) 33.5% of the people living less than 1 km away from a Seveso plants declare they wereaware of the exposure but had no other alternative when they moved in. Only 18.3% of peopleliving in a location at risk of flooding respond the same way, suggesting that the constraints relatedto industrial hazards are much higher.

There is however some early evidence of slight changes in neighborhood composition and oc-cupation. In particular, the share of furnished housing seems to increase slightly during the CLICand prescription phases, which is consistent with a qualitative change of the neighborhoods. Inaddition, the size of households slightly but significantly decreases over each stage of the PPRTimplementation. The latter effect is hard to interpret since there is no significant effect of the lawon the number of children per household (except for the early CLIC stage). Given the size of the

27

Table 11: Estimated impact of the PPRT on neighborhood and household characteristics withanticipation effects

Variable βpresc-4 βpresc-2 βpresc βapprob

Vacancy rate (pp) -0.019 0.203 0.471 -0.023(0.162) (0.273) (0.369) (0.161)

Principal housing rate (pp) 0.085 -0.161 -0.503 -0.010(0.154) (0.261) (0.368) (0.178)

Secondary housing rate (pp) -0.106* -0.117 -0.069 -0.012(0.063) (0.107) (0.155) (0.079)

Furnished housing rate (pp) 0.040 0.074* 0.101** 0.045(0.026) (0.039) (0.047) (0.038)

Live-in landlord (pp) -0.024 -0.400 -0.769 -0.233(0.199) (0.348) (0.494) (0.246)

Rented (pp) -0.095 0.262 0.707 0.591**(0.192) (0.332) (0.468) (0.254)

Rented social housing (pp) 0.033 0.104 0.026 -0.238(0.131) (0.238) (0.331) (0.184)

Household size (cons. units) -0.007*** -0.013*** -0.014** -0.006*(0.003) (0.004) (0.006) (0.003)

Nb of children < 18 y.o. -0.009** -0.011 -0.006 -0.005(0.004) (0.007) (0.010) (0.005)

Nb of children < 6 y.o. 0.001 0.003 0.006 -0.003(0.002) (0.004) (0.005) (0.002)

Household earnings per consumption unit (e) 121.85* 151.078 157.659 -104.914(63.978) (97.183) (159.294) (98.458)

Nb blocks 4,255Block fixed effect x x x xYear dummy x x x xCluster Block Block Block Block

Source: Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in 2014, authors’ calcu-lation.Note: pp percentage points, *** significant at 1%, ** significant at 5%, * significant at 10%.Standard errors are reported below the coefficients between parenthesis and are clustered at the block level.

estimates, it is entirely possible that this is due to a relative lack of power.

6 ConclusionThis paper shows that PPRT implementation had a negative impact on transaction prices, aswell as a positive impact on the number of sales in the vicinity of hazardous plants. Each wave ofadditional information on objective risks translates into a small and robust decrease in transactionsprices, showing that both landlords and potential buyers take it into account in their decisions.This is consistent with the PPRT process translating into a negative stigma for the at-risk areas.These findings show that the negative effects of the risk regulation dominate the potential positive

28

effects of zoning in the direct vicinity of the plant: the negative informational impacts seem to offsetthe potential positive impacts of risk reduction and land-use restrictions. This results is at oddswith the usual findings of the literature on the impact of land-use regulation. We find evidenceof a land-use regulation policy leading to a price decrease due to information effects. Evaluationsof land-use policies should thus carefully take into account the context of the policy and check forthe nature of the information provided along with the the land use regulation, since there is likelya great heterogeneity in the type of land-use regulation and the reasons why municipalities choseto implement them.

We also find evidence of strategic behaviors and anticipation effects. Local homeowners mayhave an early access to additional information on risk and are likely to try and sell their propertybefore risk disclosure becomes compulsory. As prices keep falling following this change, this isa rational use of private information. Our results confirm that a comprehensive analysis of bothprices and stock outcomes is crucial when evaluating the impact of shocks on local housing markets,especially in the short run. Only relying on housing prices would lead to underestimating thePPRT’s impacts on local housing markets before the prescription stage and to get a poor pictureof the dynamic impact of the PPRT process.

This paper relies on high-quality administrative data to assess the impact of the policy changeon a number of other local housing market outcomes. We do not find strong evidence supportinga change in neighborhood composition. In particular, there is no change in household income inthe at-risk areas, but there is slight evidence of changes in household composition, as the averagenumber of persons per household tends to decrease. This limited impact is consistent with externalevidence of households facing high constraints in their housing decisions. Local job market effectsmay indeed impose strong constraints on the choice of residence that translate into rigidities in thehousing markets. However, these constraints do not appear to translate into local displacementeffects, as we find no evidence of reverse positive impact further away from the plant. The extent ofthese constraints remain an open question and could be addressed in further research. The limitedchange in neighborhood composition may also be due to the sequential nature of these two studies.Indeed, the PPRT implementation happens after the AZF accident, and the at-risk neighborhoodcomposition may have already adjusted by the time of the PPRT implementation.

This paper has several data shortcomings that may also mitigate our ability to detect smallereffects. Indeed, we do not observe the true perimeter for the informational effects, and our artificialones are likely to be upper bounds in most cases. This would bias the results towards zero.Moreover, we could only access the data for even years, and we thus have to pool PPRT stagesover two years. This leads to some imprecision in the measurement of the beginning of each phase.Finally, our analysis is of rather short term, since most PPRT entered the last stage in very recentyears.

29

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7 Appendix7.1 Characteristics of the housing units sold in the vicinity of Seveso

plants between 2000 and 2012

Table 12: Characteristics of the apartments and houses

Variable Apartments Variable HousesMean Standard Min Max Mean Standard Min Max

deviation deviationPrice euros/m2 1548 725 32 9291 Price euros/m2 1428 696 18 9978Living area in m2 66 25 11 306 Living area in m2 104 36 20 540

Construction year Construction yearBefore 1850 0.010 0.100 0 1 Before 1850 0.010 0.120 0 11850-1913 0.020 0.150 0 1 1850-1913 0.060 0.240 0 11914-1947 0.050 0.220 0 1 1914-1947 0.170 0.370 0 11948-1969 0.240 0.430 0 1 1948-1969 0.180 0.390 0 11970-1980 0.180 0.380 0 1 1970-1980 0.140 0.340 0 11981-1991 0.090 0.280 0 1 1981-1991 0.100 0.300 0 11992-2000 0.050 0.210 0 1 1992-2000 0.040 0.200 0 1After 2001 0.020 0.140 0 1 After 2001 0.020 0.130 0 1

Basement 0.020 0.150 0 1 BasementElevator 0.240 0.430 0 1 ElevatorOutbuilding Outbuilding 0.190 0.390 0 1Garden 0.020 0.150 0 1 Garden 1.000 0.040 0 1Parking lot (0/1) 0.440 0.500 0 1 Parking lot (0/1) 0.640 0.480 0 1

−1st 0.050 0.210 0 1 Single storey 0.230 0.420 0 11st 0.160 0.370 0 1 Two storeys 0.560 0.500 0 12nd floor 0.230 0.420 0 1 Three storeys or more 0.140 0.350 0 13rd floor 0.210 0.410 0 1 No information 0.070 0.260 0 14th floor 0.160 0.360 0 15th floor 0.090 0.290 0 16th floor or above 0.070 0.260 0 1

Number of rooms Number of roomsNumber of rooms 3.100 1.200 1 9 Number of rooms 4.760 1.450 1.0 13.0

One room 0.100 0.300 0 1 One room 0.000 0.070 0 1Two rooms 0.210 0.410 0 1 Two rooms 0.030 0.170 0 1Three rooms 0.310 0.460 0 1 Three rooms 0.130 0.340 0 1Four rooms 0.380 0.490 0 1 Four rooms 0.830 0.380 0 1

Number of bathrooms Number of bathroomsNumber of bathrooms 1.030 0.240 0.0 14.0 Number of bathrooms 1.190 0.510 0.0 25.0No bathroom 0.010 0.090 0 1 No bathroom 0.010 0.120 0 1One bathroom 0.920 0.270 0 1 One bathroom 0.750 0.430 0 1Two bathroom 0.030 0.180 0 1 Two bathroom 0.170 0.380 0 1No information 0.040 0.190 0 1 No information 0.060 0.230 0 1

Type of home Type of home

Standard 0.840 0.370 0 1 Detached house 0.460 0.500 0 1Studio 0.100 0.300 0 1 Cabin 0.000 0.050 0 1Duplex 0.060 0.230 0 1 Farm 0.000 0.050 0 1Triplex 0.000 0.070 0 1 Mansion 0.000 0.030 0 1

(continued next page)

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Table 12 – continuedVariable Apartments Variable Houses

Loft 0.000 0.020 0 1 Town house 0.190 0.390 0 1Villa 0.020 0.140 0 1No information 0.330 0.470 0 1

Dist. to the city center 1212 1054 6 8388 Dist.to the city center 1223 1048 6 9190Dist. to the closest Seveso plant 1442 388 138 2000 Dist. to the closest Seveso plant 1369 432 128 2000

Monthly trends Monthly trends

January 0.090 0.280 0 1 January 0.080 0.260 0 1February 0.080 0.270 0 1 February 0.070 0.260 0 1March 0.080 0.270 0 1 March 0.070 0.260 0 1April 0.080 0.270 0 1 April 0.080 0.260 0 1May 0.080 0.270 0 1 May 0.080 0.260 0 1June 0.100 0.300 0 1 June 0.100 0.300 0 1July 0.100 0.300 0 1 July 0.110 0.310 0 1August 0.070 0.260 0 1 August 0.080 0.280 0 1September 0.090 0.280 0 1 September 0.090 0.280 0 1October 0.080 0.270 0 1 October 0.080 0.280 0 1November 0.060 0.240 0 1 November 0.070 0.250 0 1December 0.100 0.290 0 1 December 0.100 0.290 0 1

2000 0.140 0.350 0 1 2000 0.160 0.370 0 12002 0.130 0.340 0 1 2002 0.150 0.360 0 12004 0.150 0.350 0 1 2004 0.160 0.360 0 12006 0.160 0.370 0 1 2006 0.160 0.370 0 12008 0.150 0.350 0 1 2008 0.140 0.350 0 12010 0.150 0.360 0 1 2010 0.150 0.350 0 12012 0.120 0.330 0 1 2012 0.080 0.270 0 1

Source: Perval 2000-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in 2014, authors’calculation.

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7.2 Results of the hedonic price model estimations

Table 13: Results of the Hedonic price model estimations

Variable Apartments Houses All

Intercept 7.176*** 6.775*** 6.71***(0.239) (0.114) (0.124)

Apartement (ref)

House 0.181***(0.018)

Living area in m2 -0.021*** -0.002 0.000(0.003) (0.001) (0.001)

Living area2 0.000*** 0.000* 0.000(0.000) (0.000) (0.000)

Living area3 0.000*** 0.000*** 0.000(0.000) (0.000) (0.000)

Living area4 0.000 -0.007*** -0.007***(0.001) (0.001) (0.001)

Construction yearBefore 1850 -0.027 -0.069*** -0.095***

(0.021) (0.021) (0.020)1850-1913 -0.017 -0.046*** -0.098***

(0.012) (0.012) (0.013)1914-1947 -0.020 -0.050*** -0.105***

(0.013) (0.008) (0.013)1948-1969 -0.029*** -0.028*** -0.039***

(0.008) (0.006) (0.006)1970-1980 (ref) (ref) (ref)

1981-1991 0.092*** 0.046*** 0.111***(0.009) (0.007) (0.006)

1992-2000 0.134*** 0.111*** 0.174***(0.013) (0.008) (0.009)

After 2001 0.162*** 0.151*** 0.203***(0.015) (0.012) (0.010)0.112** 0.076** 0.146***(0.049) (0.036) (0.045)

Elevator 0.030***(0.007)

Basement 0.004(0.008)

Outbuilding 0.029***(0.005)

Garden 0.105***(0.015)

Parking lot (0/1) 0.131*** 0.124***(0.008) (0.007)

Level−1th floor 0.020**

(0.010)1st -0.028***

(0.005)2nd floor (ref)

(continued next page)

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Table 13 – continued

Variable Apartments Houses All

3rd floor 0.011**(0.005)

4th floor 0.011(0.007)

5th floor -0.003(0.013)

6th floor or above -0.050***(0.016)

Single storey 0.054***(0.006)

Two storeys (ref)

Three storeys or more 0.010(0.008)

No information 0.035***(0.009)

Number of roomsOne -0.035 -0.047 0.071***

(0.043) (0.058) (0.018)Two (ref) (ref) (ref)

Three 0.040*** 0.048*** -0.036***(0.015) (0.018) (0.011)

Four 0.013 0.085*** -0.050***(0.026) (0.019) (0.018)

Number of bathroomsNo bathroom -0.152*** -0.355*** -0.320***

(0.045) (0.027) (0.027)One bathroom (ref) (ref) (ref)

Two bathroom 0.064*** 0.119*** 0.119***(0.014) (0.006) (0.006)

No information -0.110*** -0.221*** -0.200***(0.018) (0.017) (0.016)

Type of homeCabin -0.193***

(0.068)Villa 0.142***

(0.012)Farm 0.094*

(0.055)Mansion 0.333***

(0.056)Detached house (ref) (ref) (ref)

Town house -0.084***(0.007)

Other 0.769***(0.090)

No information -0.041***(0.006)

(continued next page)

35

Table 13 – continued

Variable Apartments Houses All

Standard (ref) (ref) (ref)

Studio -0.052(0.038)

Duplex 0.048***(0.011)

Triplex -0.003(0.030)

Loft 0.100*(0.059)

log-Distance to the city center the city center -0.037*** 0.011** -0.004(in m) (0.013) (0.004) (0.008)the closest Seveso plant 0.065*** 0.055*** 0.060***(in m) (0.023) (0.008) (0.014)

January (ref) (ref) (ref)February 0.000 0.003 -0.003

(0.007) (0.009) (0.006)March 0.001 0.004 0.001

(0.007) (0.011) (0.007)April 0.014* 0.004 0.008

(0.008) (0.009) (0.007)May 0.020*** 0.016* 0.017***

(0.007) (0.009) (0.006)June 0.038*** 0.034*** 0.038***

(0.007) (0.010) (0.007)July 0.050*** 0.053*** 0.054***

(0.006) (0.009) (0.005)August 0.060*** 0.066*** 0.065***

(0.007) (0.008) (0.006)September 0.054*** 0.058*** 0.059***

(0.007) (0.009) (0.006)October 0.057*** 0.050*** 0.053***

(0.007) (0.009) (0.006)November 0.053*** 0.051*** 0.054***

(0.008) (0.009) (0.006)December 0.050*** 0.043*** 0.045***

(0.008) (0.009) (0.006)2000 (ref) (ref) (ref)

2002 0.123*** 0.145*** 0.132***(0.014) (0.007) (0.009)

2004 0.387*** 0.345*** 0.363***(0.019) (0.009) (0.012)

2006 0.698*** 0.601*** 0.642***(0.018) (0.012) (0.013)

2008 0.768*** 0.674*** 0.716***(0.020) (0.013) (0.013)

2010 0.755*** 0.645*** 0.694***(0.020) (0.013) (0.014)

2012 0.799*** 0.701*** 0.744***(0.024) (0.016) (0.017)

Nb of Clusters 343 784 787Nb of Observations 36809 39050 75859R2 0.675 0.638 0.613

36

Source: Perval 2000-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in 2014,authors’ calculation.Note: *** significant at 1%, ** significant at 5%, *significant at 10%.Standard errors are reported below the coefficients between parentheses and are clustered at themunicipality level.

7.3 Additional results

37

Table 14: Impact of PPRT on the housing prices – distance

1800m 2000m 2200mβpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa βpresc-4 βpresc-2 βp βa

Apartments -0.017 -0.030*** -0.046*** -0.049*** -0.015 -0.026** -0.043*** -0.051*** -0.016* -0.025*** -0.043*** -0.049**(0.010) (0.011) (0.015) (0.018) (0.011) (0.011) (0.016) (0.019) (0.009) (0.010) (0.015) (0.020)

Nb obs 28,972 36,809 46,101

Houses -0.001 -0.006 -0.027** -0.013 -0.002 -0.007 -0.024* -0.018 -0.004 -0.008 -0.019* -0.020*(0.009) (0.010) (0.013) (0.012) (0.009) (0.010) (0.012) (0.011) (0.008) (0.009) (0.012) (0.011)

Nb obs 31,691 39,050 46,457

All -0.007 -0.019* -0.026** -0.028** -0.006 -0.015* -0.030** -0.041** -0.008 -0.016** -0.029*** -0.043**(0.010) (0.010) (0.01) (0.01) (0.009) (0.009) (0.012) (0.012) (0.008) (0.008) (0.011) (0.011)

Nb obs 60,663 75,859 92,558Vacancy rate (pp) 0.064 0.389 0.701 0.023 -0.019 0.203 0.471 -0.023 -0.001 0.198 0.531 0.019

(0.190) (0.190) (0.438) (0.185) (0.162) (0.273) (0.369) (0.161) (0.142) (0.408) (0.326) (0.154)Nb obs 3504 4255 5012

Selling rate (pp) 0.270** 0.515*** 0.379** 0.062 0.182* 0.387*** 0.253* 0.046 0.133 0.329 0.239* 0.046(0.115) (0.115) (0.172) (0.107) (0.096) (0.133) (0.147) (0.094) (0.087) (0.006) (0.135) (0.087)

Nb obs 3504 4255 5012

Source: Perval 2000-2012 and Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in 2014, authors’ calculation.Note: *** significant at 1%, ** significant at 5%, * significant at 10%.Standard errors are reported below the coefficients between parenthesis and are clustered at the municipality level for transactions andblock level for the selling rate.

Table 15: Estimated impact of the PPRT on housing prices and selling rate – controlling fortime-specific trends

Variable βpresc-4 βpresc-2 βpresc βapprob

Apartments log price (e/m2) -0.024** -0.033** -0.040*** -0.049**Nb obs =36,809 (0.012) (0.013) (0.015) (0.019)

Houses log price (e/m2) -0.005 -0.006 -0.004 -0.005Nb obs =39,050 (0.009) (0.009) (0.013) (0.012)

Log price (e/m2) -0.013 -0.021** -0.020 -0.031**Nb obs =75,859 (0.010) (0.010) (0.013) (0.013)Vacancy rate (pp) -0.185 -0.174 -0.188 -0.222Nb blocks =4,255 (0.144) (0.243) (0.331) (0.168)

Selling rate (pp) 0.206** 0.438*** 0.339* 0.053Nb blocks =4,255 (0.105) (0.162) (0.196) (0.103)

Source: Perval 2000-2012 and Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in2014, authors’ calculation.Note: *** significant at 1%, ** significant at 5%, * significant at 10%.Standard errors are reported below the coefficients between parenthesis and are clustered at the municipality levelfor transactions and block level for the vacancy and selling rate. Sample size are reported below the coefficientsbetween brackets.

Table 16: Estimated impact of the PPRT on housing prices – robustness to the hedonic regressionspecification

Variable βpresc-4 βpresc-2 βpresc βapprob

Apartments log price (e/m2) -0.015 -0.024** -0.043*** -0.051***Nb obs =36,809 (0.011) (0.011) (0.016) (0.019)

Houses log price (e/m2) -0.001 -0.006 -0.023* -0.018Nb obs =39,050 (0.008) (0.010) (0.012) (0.012)

Log price (e/m2) -0.006 -0.015 -0.030** -0.041***Nb obs =75,859 (0.010) (0.010) (0.013) (0.012)

Source: Perval 2000-2012 and Filocom 1998-2012, 2000 m radius around upper-tier Seveso plants under a PPRT in2014, authors’ calculation.Note: *** significant at 1%, ** significant at 5%, * significant at 10%.Standard errors are reported below the coefficients between parenthesis and are clustered at the municipality level.

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