Electronic Electronic copy copy available available at at:: http http::////ssrn.com ssrn.com//abstract abstract==2457195 2457195

The Impact of Hazardous Industrial Facilities on Housing

Prices: A Comparison of Parametric and Semiparametric

Hedonic Price Models∗

Céline Grislain-Letrémy† Arthur Katossky‡

August 21, 2014

Abstract

The willingness of households to pay for prevention against industrial risks

can be revealed by real estate markets. By using very rich microdata, we

study housing prices in the vicinity of hazardous industries near three important French cities. We show that the impact of hazardous plants on the

housing values strongly differs among these three areas, even if the areas all

surround chemical and petrochemical industries. We compare the results from

both standard parametric and more flexible, semiparametric models of hedonic property. We show that the parametric model might structurally lead to

important biases in the estimated value of the impact of hazardous plants on

housing values.

Keywords: hedonic analysis, conditionally parametric regression, urban housing markets, industrial risk.

JEL classification: C21, Q51, R52, R21.

∗The conclusions and analysis set in this paper are those of the authors and do not indicate the views or

opinions of their institutions. The authors are grateful to Pierre-Philippe Combes, Éric Dubois, Laurent

Gobillon, Nicolas Grislain, Anne Lafferère, Claire Lelarge, David Martimort, Philippe Mongin,

Corinne Prost, Sandrine Spaeter, Corentin Trevien, Bertrand Villeneuve, and two anonymous referees for their reading and insightful comments. They also thank Pauline Charnoz, Amélie Mauroux,

Isabelle Méjean, and participants at the 28th annual congress of the European Economic Association,

the 12th International Workshop Spatial Econometrics and Statistics, the 2013 INSEE research seminar

on “Real estate in France, analyses and prospects,” the 2012 European Association of Environmental and

Resource Economists’, the European Regional Science Association’s, and Louis-André Gérard Varet’s annual conferences for their comments. The authors are grateful to the French Ministry of Ecology for

having funded the data collection when they were working there. Specifically, they thank Vincent Binet, Rémi Borel, Olivier Dupret, François Filior, Martine Giloppe, Jeanne-Marie Gouiffès, and

Brigitte Pouget from the Technical Studies Center of Public Works of Normandy and Centre, Nord and

Picardy, and South West France for their help with the data collection and standardization. The authors

thank Christophe Yon for creating some maps provided in this article. The Finance and Sustainable

Development Chair is thanked.

†

INSEE, CREST and University Paris-Dauphine. 15 boulevard Gabriel Péri, 92245 Malakoff cedex,

France. Phone: +33 615694058. Email: celine.grislain.letremy@ensae.fr.

‡Technical University of Denmark (DTU) and Copenhagen Business School (CBS). Building 324, DTU,

2800 Kgs. Lyngby, Denmark. Email: arka@dtu.dk. This work was started when both authors were working

at the French Ministry of Ecology and then at INSEE.
Electronic Electronic copy copy available available at at:: http http::////ssrn.com ssrn.com//abstract abstract==2457195 2457195

1 Introduction

Real estate markets can demonstrate their households’ willingness to pay to reduce their

exposure to hazardous industrial facilities. Indeed, hazardous industrial activities generate

strong negative externalities: neighboring populations partly bear the cost of a potential

industrial accident; and they can also endure day-to-day nuisances associated with the

ordinary course of these activities. However, these facilities also generate positive externalities (Greenstone et al., 2010): they provide, directly and indirectly, employment; and

through local taxes, they can contribute to the economic development of the municipalities.

Measuring the impact of the distance from the hazardous facilities on the housing values

reveals to what extent the facilities’ activities are perceived as disamenities.

We estimate the impact of the distance from the hazardous facilities on the housing values

by using hedonic price models. The first-order derivative of the hedonic price function

with respect to the distance from the hazardous plants provides an estimate of the buyers’

willingness to pay to live far from these plants. We study housing prices in the vicinity

of three important French cities (Bordeaux, Dunkirk, and Rouen). These three industrial

areas all have hazardous chemical and petrochemical industries in addition to other unhazardous industries. But, each has different socioeconomic characteristics and different

perceptions of industrial risk. This case study relies on a rich dwelling-level micro-database

in which each dwelling’s address is precisely geocoded. We collect detailed data relative

to the dwellings’ extrinsic characteristics such as their proximity to a central business district, shops, and public utilities; and their exposure to industrial risk and to other risks or

pollutions. The dwellings’ price and intrinsic characteristics come from notarial data.

Our first contribution is to study the impact of hazardous chemical and petrochemical industries on housing prices with rich microdata. Indeed, our unique database is much richer

than those used by studies on the impact of similar industrial risks on housing prices. The

impact of risk exposure on real estate prices is estimated for oil facilities by Boxall et al.

(2005) and Flower and Ragas (1994), for the chemical industry by Caroll et al. (1996),

and for industrial areas with chemical or petrochemical facilities by Sauvage (1997) and

Travers et al. (2009). These studies use data with few (or without) extrinsic characteristics

of the dwellings.1

1See Table 8 in Appendix A.1 for a review of all these studies and their data.

1
Our second contribution is to apply our database to two models to compare the estimated

impacts of the distance from hazardous plants on housing prices: a standard parametric

model and a semiparametric model. The semiparametric regression is a conditionally parametric regression based on a log-linear specification and that allows implicit prices to vary

with respect to space and time of sale, while keeping some smoothness in their distribution. This variation enables to relax and to test the assumption of the fixed parameters.

This assumption is tested by comparing the conditionally parametric regression with the

baseline log-linear model, which corresponds to a nested model under the null hypothesis

of fixed coefficients. We find that the fixed-parameter assumption is rejected, which confirms the need for flexible forms and the use of semiparametric models. This study is the

first to compare parametric and conditionally parametric regression models in the analysis of industrial risks, although this comparison has been used for other amenities, such

as light rail access (Redfearn, 2009). Other semiparametric models have been compared

with parametric models in the analysis of industrial facilities by McMillen and Thorsnes

(2003), or for agricultural pollution by Bontemps et al. (2008). Anglin and Gencay (1996)

also show that semiparametric hedonic models outperform parametric ones. McMillen

(2010), McMillen and Redfearn (2010a), Redfearn (2009), and Sunding (2010) specifically

compare parametric models with a conditionally parametric regression and recommend its

use. Redfearn (2009) shows that implicit prices vary spatially and temporally and that

assuming fixed implicit prices is a misspecification of the hedonic model. This misuse of

parametric models has important consequences in our case study. Even though the signs

and orders of the magnitude of the effects are similar in the two models for the very wide

majority of coefficients, the estimated impacts of the distance from highly hazardous plants

on housing prices significantly differs between the two models. The parametric model leads

to an important bias (here an overestimation) in the estimated value of this impact near

Bordeaux and Rouen.

Using the semiparametric model, we show that the impact of hazardous plants on housing

values strongly differs among the three industrial areas, even if they all surround chemical

and petrochemical industries. The gunpowder factory near Bordeaux is a former military

plant that was not necessarily perceived as hazardous by the neighboring population. We

find that its proximity is even valued, probably because its neighborhoods are green and

very quiet places. Thus, in this case, we capture the unobserved amenities that are spatially correlated with the distance from the plant. For Dunkirk and Rouen, chemical plants

2
are clearly identified as hazardous by local populations. However, for Dunkirk, we find no

significant impact of the distance from these plants on housing prices, likely because these

industrial risks are overshadowed by the nuclear plant in Gravelines (located 18 km from

Dunkirk). For Rouen, highly hazardous plants are perceived as disamenities: on average,

households are willing to pay around 1.2% of their dwelling price to move 100 more meters

away from these plants. This marginal willingness to pay decreases with respect to the

distance from the plants, because the marginal gains (in terms of exposure reduction) of

going further away from the hazard’s source are likely to decrease.

Our results have practical implications. They show that parametric hedonic models can

lead to an important bias in the estimated value of the marginal willingness to pay. Further, our results show that this estimated willingness to pay has a limited external validity:

it strongly differs among industrial areas, even among chemical and petrochemical industries; it also depends on the distance from these facilities. Thus, even when assuming that

households are aware of the full extent of the industrial risks,2 our findings call for the

careful use of a population’s willingness to pay for prevention against industrial risks in

the cost-benefit analyses of prevention measures. Until now, most cost-benefit analyses

have used the parameters taken from the estimation of parametric hedonic models and on

other study areas than the ones under consideration in these analyses. This use can lead

to a significant bias in the estimation of the efficient prevention level.

The paper is organized as follows. The parametric and semiparametric models are presented in Section 2. In Section 3, we describe the three industrial areas, the delimitation

of the study areas, and the data. Section 4 contains the results from the parametric and

semiparametric models. Section 5 concludes.

2 Model

Hedonic property models. Hedonic property models enable the estimation of the implicit price of the distance from highly hazardous plants (the first-order derivative of price

with respect to this distance). This implicit price is equal to the households’ (buyers’ or

sellers’) marginal willingness to move one more meter away from these plants. Indeed, in

the framework formalized by Rosen (1974), a dwelling is defined by its distance d from

2Currie et al. (2013)’s findings do not contradict perfect, or at least unbiased, information about hazardous industrial activities in the housing market, even in the presence of scientific uncertainty about

health risks.

3
a highly hazardous plant and several other characteristics X, which determine its price

P(d, X). When choosing their locations, households equalize their marginal willingness to

pay for increasing each characteristic by one unit with the marginal, or implicit, price of

this characteristic.3 Thus, the estimation of the hedonic price function provides an estimation of the implicit price of the distance d from highly hazardous plants ∂P(d, X)/∂d,

which can be interpreted as the households’ marginal willingness to pay to move one more

meter away from highly hazardous plants.

Unless making very specific assumptions, the hedonic price function is not linear and has

no known explicit form (see Freeman (2003) for a review of hedonic price methods). For

this reason, we first perform the following parametric models: linear, log-linear, log-log,

and linear with Box-Cox transformations of the price and continuous regressors while allowing these Box-Cox coefficients to be different (Grislain-Letremy and Katossky, 2013).

For each of these specifications, we add fixed effects (municipality dummies) and test for

different types of spatial dependency (spatial errors and/or spatial lag). A comparison

by likelihood ratio tests of these nested models shows that a more flexible form is better

(Grislain-Letremy and Katossky, 2013). These results illustrate the more general findings

established by Kuminoff et al. (2010): flexible Box-Cox models outperform the linear,

log-linear, and log-log specifications when spatial effects are used to control for omitted

variables.

In addition to the baseline log-linear model, we use two extensions of this model: a parametric and a semiparametric model.

2.1 Parametric model

The parametric model is a log-polynomial model with municipality and time dummies

based on an ordinary least squares. We estimate the logarithm of the price Pi of dwelling

i as a function of the distance from the plants di, the dwelling’s other characteristics Xi,

and the vector ηtiof time dummies (for month and year of sale). In Bordeaux, the square

3Specifically, a household of income y chooses its location by maximizing its utility U(z, d, X), where z

denotes the amount of composite consumer good (which comprises all consumer goods except land), under

its budget constraint y = z + P(d, X). The location choices by households maximize their utilities by

equalizing their marginal rates of substitution between each characteristic (d or xk) and money with the

implicit price of this characteristic (∂P(d, X)/∂d or ∂P(d, X)/∂xk):

Ud(z, d, X)

Uz(z, d, X)

=

∂P(d, X)

∂d , ∀k, Uxk

(z, d, X)

Uz(z, d, X)

=

∂P(d, X)

∂xk

. (1)

4
and the cube of the distance from highly hazardous plants are significant and are included

in the regression. For Rouen, only the square of the distance from highly hazardous plants

is significant and is included in the regression.4

OLS Bordeaux: ln Pi = α + β1di + β2d

2

i + β3d

3

i + γ

0Xi + τ0

ηti, (2)

OLS Dunkirk: ln Pi = α + β1di + γ

0Xi + τ0

ηti, (3)

OLS Rouen: ln Pi = α + β1di + β2d

2

i + γ

0Xi + τ0

ηti

. (4)

2.2 Semiparametric model

Motivation. The semiparametric model is also based on a log-linear specification. It

is a conditionally parametric regression (CPAR) that allows the marginal willingnesses to

pay for characteristics to vary with respect to space and time of sale, while keeping some

smoothness in their distribution.

Indeed, the marginal willingness to pay for each characteristic is a priori not uniform

over the study area (McMillen, 2010). One reason is that the marginal gains of risk exposure reduction by going further away from the hazard’s source are likely to decrease.

Gamper-Rabindran and Timmins (2003) show that the marginal benefits of risk reduction

(by cleaning up hazardous waste sites) are higher for the cheaper houses that are more

likely to be closer to a hazardous waste site. Another reason is that the variations over

the study areas might capture a preference-based sorting of buyers in the housing market:

households with a higher willingness to pay to live further away from the plants might intentionally choose a more distant location from these plants. Chay and Greenstone (2005)

find that the marginal benefit from air pollution reduction is lower in communities with

higher pollution levels, which is consistent with such a sorting.

The willingness can also vary after events that could have changed the risk perception, such

as local or national accidents or information policies. For example, Gayer et al. (2000) and

Kohlhase (1991) show that real estate prices can be significantly modified by information

policies on hazardous waste facilities. The willingness can also vary after changes in insur4More precisely, the squared and cubed terms of the distance from highly hazardous plants were left

out of the OLS regressions for Dunkirk because, when the three linear, squared, and cubed terms are all

included, none of them is significant. Similarly, the cubed term was left out of the OLS regressions for

Rouen because, when it is added in the parametric regression, the squared term becomes non significant.

This is very likely due to the inherent strong correlation of d, d

2

, and d

3

, as the ranges of the distance d

from highly hazardous plants are smaller near Rouen than near Bordeaux, and even smaller near Dunkirk

(Table 9 in Appendix A.2).

5
ance coverage. For example, flood insurance policies shape real estate prices (MacDonald

et al. (1990), Harrison et al. (2001), Morgan (2007), and Bin et al. (2008)).

McMillen (2010), McMillen and Redfearn (2010a), Redfearn (2009), and Sunding (2010)

specifically compare parametric models with a conditionally parametric regression and affirm the use of this semiparametric model. Redfearn (2009) provides two main reasons that

support its use instead of the standard hedonic models: the rejected standard assumption

of the fixed implicit prices and the presence of omitted local amenities. First, Redfearn

(2009)’s results show that the standard assumption of the fixed implicit prices is rejected

and suggest that imposing fixed parameters generates spatial patterns in the errors that

lead to parameter estimates highly sensitive to very small changes in the sample or in the

specification. Second, Redfearn (2009) compares the regressions with and without a known

local amenity and shows that the local regression analysis is more robust to omitted local

amenities.

Conditionally parametric regression: formalization. We estimate the logarithm of

the price Pi of dwelling i as a function of the distance from the plants di, the dwelling’s

other characteristics Xi, and the vector ηtiof the dummies for the time of sale. We

allow the coefficients to vary with respect to space (x and y coordinates, xci and yci) and

the time of sale (ti), while keeping some smoothness in their distribution. By denoting

Zi = (xci, yci, ti), we get

CPAR: ln Pi = α(Zi) + β(Zi)di + γ

0

(Zi)Xi + τ

0

(Zi)ηti

. (5)

More precisely, the CPAR is a set of weighted least square regressions with one regression

for each observation (see McMillen and Redfearn (2010a) for a review). Each regression

estimates the implicit prices at each observation by using a subsample of “close” observations. The proximity refers to spatial proximity and temporal proximity. The set of

observations used in each local regression corresponds to the observations within a window

around the considered observation j.

5 Formally, the objective function for the estimation

of the CPAR is for observation j:

CPAR: Xn

i=1

(ln Pi − α − βdi − γ

0Xi − τ0

ηti)

2Wij , (6)

5Estimating numerous separate regressions for each observation could imply a high degree of freedom.

This is not the case because of the smoothness implied by the overlapping samples (Redfearn (2009), see

McMillen and Redfearn (2010a) for more details).

6
where Wij = f(Zi

, Zj ) is the weight for each observation i; this weight is a decreasing

function of the spatial and temporal distance from observation i to the considered observation j.

Weights. First, we apply the Mahalanobis distance dM(Zi, Zj ) between Zi and Zj to

move from three dimensions to one dimension in our kernel:6

d

M(Zi

, Zj ) = q

(Zi − Zj )

T (cov(Z))−1(Zi − Zj ), (7)

where Z = (Z1, ..., Zn). Then, the weights are generated by a kernel weighting function,

the tri-cubic kernel weighting function, applied to this distance:

Wij =

 

1 −



dM(Zi, Zj )

dMmax(Zi, Zj )

3

!3

, (8)

where dM

max(Zi

, Zj ) is the largest Mahalanobis distance from the considered observation j

to any observation within the window.

Window. Whereas the choice of the kernel function has a limited impact on the estimation results (McMillen, 2010), the choice of the window is crucial. The size of the window

can be chosen by cross-validation; it corresponds to 50% of the initial sample for Bordeaux,

25% for Dunkirk, 80% for Rouen.7 However, larger windows are needed when the objective

is to measure the marginal effect of explanatory variables and not to predict the dependent

variable (McMillen, 2010). The larger windows are all the more needed that the interest

variable (the distance from highly hazardous plants) varies with some kernel variables (the

geographic coordinates): small windows can lead to imprecise estimates of the marginal

6Compared to the Euclidean distance, the Mahalanobis distance takes into account the correlations of

the data set and is scale-invariant.

7The CPAR model can be written under a linear form (McMillen and Redfearn, 2010a). By denoting

Yi = ln(Pi), we get

Yi = LYi + ui. (9)

Thus, a cross-validation estimate of h is a minimizer of the cross-validation measure CV (h):

CV (h) = 1

n

Xn

i=1 

Yi − Yˆh,i

1 − lii !2

, (10)

where Yˆh,i is the estimate based on the sample from which observation i is removed and using a window

of size h around i. Thus, CV (h) is the mean difference between the dependent variable and the estimated

value over all of the regressions when excluding the considered observation from the sample. As the initial

samples comprise 1,423 observations for Bordeaux, 1,032 for Dunkirk, and 584 for Rouen, the size of the

window estimated by cross-validation corresponds to 711 observations for Bordeaux, 258 for Dunkirk, and

467 for Rouen.

7
effect of the interest variable, as there may be no independent variation of the distance

from highly hazardous plants and the two geographic coordinates (McMillen, 2010). These

are the reasons why we use windows that correspond to 80% of the initial samples in the

three areas. The significance and the mean value of the impact of our interest variable

on the dwelling’s value remain unchanged when considering smaller windows (60%, 40%)

with either tri-cubic or gaussian kernels.

3 Industrial areas, study areas, and data

In this section, we detail the selection of the industrial plants, the delimitation of the areas,

and the collected variables.

3.1 Industrial areas

Many French hazardous industrial plants are surrounded by a high population density.

However, the important work required by data collection limits the number of sites that

could be studied. Thus only three sites are here studied. We choose these three industrial

areas because of the different socioeconomic characteristics of the surrounding neighborhoods and those neighborhoods’ different perceptions of industrial risks.

Socioeconomic characteristics. The three industrial areas studied are located near

important French cities: Bordeaux, Dunkirk, and Rouen (Figure 1). The three industrial

Figure 1: Location of Bordeaux, Dunkirk, and Rouen in France

Source: Base map from Académie d’Aix-Marseille, production by authors.

areas present different socioeconomic characteristics (Table 1). The neighborhoods of the

8
gunpowder industry near Bordeaux are the wealthiest; many of their inhabitants are executives in the aerospace industry. Dunkirk and its neighborhoods are the densest; many

households are workers. The neighborhoods near Rouen are in an intermediate position

between the first two areas: income is higher than near Dunkirk and lower than near

Bordeaux; many inhabitants are executives and work far from their homes. According to

the 2008 French annual declaration of social data, at best only 2% of the population in

each municipality in the three areas is employed by the highly hazardous plants that are

described below.

Table 1: Population’s socioeconomic characteristics in the three studied areas

Municipality INSEE

code

Population

(a)

Population

density (a)

(per sq km)

Constructed

area (b)

(%)

Average tax

revenue (a)

(euro)

Sports and

sociocultural

facilities (c)

Distance

from labor

pool (d)

Near Bordeaux (km)

Le Haillan 33200 8,378 904 8 26,817 14 5.9

Martignas-sur-Jalles 33273 6,633 251 2 29,005 27 9.2

Mérignac 33281 65,469 1,359 9 23,181 72 4.4

Saint-Aubin-de-Médoc 33376 5,550 159 1 41,577 12 11.1

Saint-Médard-en-Jalles 33449 26,984 315 3 27,599 48 4.8

Le Taillan-Médoc 33519 8,668 571 5 30,412 14 4.9

Near Dunkirk

Coudekerque-Branche 59155 22,994 2,515 11 18,641 23 4.3

Dunkirk 59183 69,274 1,855 12 20,218 18 4.1

Fort-Mardyck 59248 3,586 2,543 13 18,610 7 9

Saint-Pol-sur-Mer 59540 22,100 4,299 16 14,711 14 6.2

Near Rouen

Grand-Couronne 76319 9,346 552 5 20,083 18 20.5

Hautot-sur-Seine 76350 9,346 160 1 26,423 2 19.3

Moulineaux 76457 881 238 2 21,287 10 20.6

Petit-Couronne 76497 8,690 679 5 19,638 28 21.1

Sahurs 76550 1,310 117 1 27,017 5 26.8

Val-de-la-Haye 76717 751 74 1 23,673 5 20.5

Note: The municipalities of Fort-Mardyck and Saint-Pol-sur-Mer were attached to the

Dunkirk municipality in 2010.

Sources: (a) French National Institute for Statistics and Economic Studies (INSEE), (b)

building database of the Geographical National Institute, (c) topology database of the

Geographical National Institute, (d) Sitranet.

Risks and perceptions of industrial areas. The official classification of hazardous

plants is defined by legislation. The Seveso II Directive (Council directive 96/82/EC on the

control of major-accident hazards) defines hazardous industries according to the presence

and degree of hazardous substances or preparations. The legislation categorizes the plants

with respect to the nature and quantities of the hazardous substances as “upper tier” and

“lower tier” sites. The French legislation is actually harsher than the European one: the

French classification includes many other far less hazardous sites, called “authorized” plants.

9
The three industrial areas in this study include hazardous chemical and petrochemical industries. However, they present different industrial activities and very different perceptions

of the associated risks by the local populations. The gunpowder factory near Bordeaux was

settled in 1660. The municipalities in the neighborhoods were initially developed thanks to

this factory. Later, the urban development was explained by the attraction to Bordeaux’s

center. Today, the gunpowder comprises two “upper tier” sites as defined by the Seveso

II Directive. By manufacturing gunpowder and explosives, the factory mainly exposes

the local population to the risk of explosion. However, it is not necessarily perceived as

hazardous by the neighboring population because it was a former military plant. In many

parts of the neighborhoods only barbed wire can be seen. Furthermore, as the plant is large

(650 buildings over 350 hectares), the risk is relatively “contained” within the industrial

site. The only nuisance associated with the plant is the transportation of the hazardous

materials on a precise and limited route.

In contrast, the chemical activities near Dunkirk and Rouen are clearly identified as hazardous by the local populations. In Dunkirk, after World War II, urbanization was realized

around industrial areas that were not perceived as hazardous (shipyards, steel industry);

hazardous plants (chemistry, petrochemistry) appeared in Dunkirk harbor only in the

1970s. There are now 16 hazardous plants: 14 “upper tier” Seveso sites and two authorized

sites. Their activities consist of the storage and refining of oil products, metallurgy, the

manufacture of industrial gases and of chemical and pharmaceutical products, and waste

treatment. These industries expose the local population to risks of explosion, fire and toxic

pollution. The plants (either buildings, chimneys, or at least plumes of smoke) can be seen

from every neighborhood dwelling. However, the presence of a nuclear plant in Gravelines

(18 km from Dunkirk) might overshadow the exposure to these industrial risks.

Near Rouen, some plants were established in the 1920s and 1930s. However, the majority

of the plants progressively appeared between the 1960s and the 1990s, and urban areas

simultaneously extended while becoming closer and closer to these different plants. There

are now 13 hazardous plants: two “upper tier” Seveso sites, one “lower tier”, and ten authorized Seveso sites. Their activities are quite diverse: storage and refining of liquid

petroleum gas, production of Diester oil (biodiesel) and of liquid carbon dioxide, perfume

storage, paper manufacture, goods transportation, and warehouses. They expose the local

10
population to the same risks as in Dunkirk: explosion, fire, toxic pollution. Because of the

landscape, chimneys and silos cannot be seen from some neighborhood dwellings.

Although all these highly hazardous plants were established well before the study period,

several other events, either local or national, might have modified the risk perceptions

between 2000 and 2008. We detail all these local and national events in Table 2. Local

events are accidents and local policies. Only one accident happened near Dunkirk. On

January 12, 2007, the neighboring populations saw flames and plumes of smoke at the

Rubis Terminal (storage of liquid flammable and agrochemical products). The accident

triggered an emergency plan inside the plant and required the intervention of civil fire

brigades.8 Local policies included the distribution of information leaflets, the update of the

emergency plan for households, and the implementation of local committees for information

and consultation. The national events consist of the AZF accident in 2001 in Toulouse and

the implementation of a risk management law during the study period. The AZF accident,

which is the major industrial accident in France in the last decades, appears to have

left a particularly deep and long-lasting impression near Rouen because one of the plants

in this study, Grande Paroisse Normandy, has a very similar activity to the AZF plant

and belongs to the same company, Grande Paroisse (a subsidiary of Total group).9 This

accident precipitated the 2003 law that created in particular the technological disasters

insurance system, which improves the coverage of households, and the technological risk

prevention plans, which were implemented after the study period in each of the three areas.

3.2 Delimitation of study areas

Our data collection is based on a municipal basis: a whole municipality is either included

or excluded from our sample. In order to measure the impact of the distance from plants

on housing values, the study area has to be broad enough to include dwellings close to

and far from the plants. However, as Redfearn (2009) notes, “expanding the geographic

scope of the sample is likely to draw additional omitted amenities and/or more submarkets.” This is why we exclude from the scope of our data collection certain municipalities

in which the neighborhood quality is difficult to capture, as they would bring additional

8After the study period, other accidents occurred. In the gunpowder factory near Bordeaux, a fire

caused one death and seriously injured two people in December 2013 (“Gironde : un mort et deux blessés

dans un incendie sur un site classé Seveso” by Jean-Pierre Tamisier, December 6th, 2013, Sud-Ouest); near

Rouen, mercaptan gas escaped from one of the factories, the Lubrizol company, and caused a foul odor in

France and in England in January 2013 (“Foul-Smelling Cloud Drifts Over France, Alarming Residents”

by Scott Sayare, January 22nd, 2013, The New York Times).

9

“"Grande-Paroisse" à Rouen, un site Seveso hanté par le spectre d’AZF” by Audrey Garric, February

5, 2013. Le Monde.fr.

11
Table 2: Local and national events that may have modified industrial risk perception

during the study period (2000-2008)

Year Bordeaux Dunkirk Rouen National level

2001 - - - AZF accident

2002 - Information leaflets - -

2003 - - - Law

2004 - Update of the emergency- - plan for households

2005 - -

Creation of local

-committee for information

and consultation

2006 -

Creation of local

-

committee for information

and consultation

+ information leaflets

about the emergency plan

for households

2007 - Accident

Information leaflets

-about the emergency plan

for households (∗)

Note: (∗) In all municipalities except Moulineaux.

Source: Reports by the Technical Studies Center of Public Works of Normandy and Centre, Nord and

Picardy, and South West France.

omitted amenities. Municipalities such as the central business district and a few atypical

districts, corresponding to less than 5% of the samples, are excluded. For example, the

following is the selection of municipalities for Dunkirk, where this selection is the most

important. We exclude the select districts that are the Téteghem municipality (the select

residential suburb of Dunkirk), the historic center, and the sandbar in the east of the urban area (Dunkirk-Darses and Dunkirk-Malo-les-Bains). The unattractive districts such as

Mardyck village and Grande Synthe municipalities are also excluded. The Grande Synthe

municipality is an urban renewal zone where housing prices are relatively low because of

civil insecurity (petty crime and acts of violence), but this low neighborhood quality is

difficult to capture. Finally, the Leffrinckoucke municipality is excluded because of the

presence of a small hazardous plant that emits a black plume of smoke; this plant can

overshadow the other studied hazardous plants.

The chosen study areas include all highly hazardous plants here studied and dwellings very

close to these plants and as far away as 10 km for Bordeaux, 4 km for Dunkirk, and 5 km

for Rouen (Table 9 in Appendix A.2 and Figures 2, 3, and 4). This difference in size comes

from the heterogeneity in the average size of municipalities between the three sites.10 The

10The municipalities near Bordeaux are much wider than the ones near Dunkirk and Rouen. For example,

the area of Saint-Médard (near Bordeaux) is 85 sq km, whereas the one of Hautot-sur-Seine (near Rouen)

is 2 sq km. This is why the study area is much wider near Bordeaux.

12
majority of the literature that studies the impact of industrial risks on housing prices uses

similar study areas (Table 8 in Appendix A.1).

3.3 Data collection

Our analysis requires data on the price and characteristics of the dwellings (Table 3). The

price of the dwelling and its intrinsic characteristics (state, living space, number of rooms,

etc.) come from the PERVAL notarial data for the years 2000, 2002, 2004, 2006, and

2008. Thus, only the dwelling purchases (as opposed to rents) can be studied. We consider

transactions only between households. Furthermore, we restrict our analysis to houses (as

opposed to apartments), because they represent a homogeneous market.11 Each dwelling’s

address is precisely geocoded. Detailed data relative to the dwellings’ extrinsic characteristics are collected: commuting time (by car) from central business district, distance from

shops and public utilities, and exposure to industrial risk and to other risks or pollutions.

The database from the French solicitors also provides information about the buyers and

sellers: their gender, their age, their social and occupational group, their marital status,

and their municipality of origin.

Our database is unique and much richer compared to those in the literature that study

the impact of similar industrial risks on housing prices (Table 8 in Appendix A.1). These

studies use data with a similar number of observations but with few (or without) extrinsic

characteristics of the dwellings. Our samples comprise 1,423 observations for Bordeaux,

1,032 for Dunkirk, and 584 for Rouen. Further, in the chosen study areas, the transactions

within each jurisdiction are relatively uniformly distributed over the study period (Table

10 in Appendix A.2). The detailed descriptive statistics are provided in Appendix A.2

(Tables 9 and 11).

Our key variable of interest is the distance from the dwelling to the nearest highly hazardous

plant, highly hazardous plants being mainly Seveso sites, “upper tier” and “lower tier”

ones. We also consider the distance from the nearest “authorized plant” (subject to the

French regime of classified plants for the environment’s protection) and the locations in

risk management areas. The risk management areas are the emergency planning zones

for households and areas of control for future land use: the exclusion area, called “Z1

area”, corresponds to the area with lethal damages in case of an accident; “Z2 area” is the

11Houses make up the majority of dwellings in the cities and in our sample: collected data comprise 83%

of houses for Bordeaux, 87% for Dunkirk, and 72% for Rouen.

13
Figure 2: Study area near Bordeaux

Source: Base map from the topology database of National Geographic Institute, production by authors.

14
Figure 3: Study area near Dunkirk

Source: Base map from the topology database of National Geographic Institute, production by authors.

15
Figure 4: Study area near Rouen

Source: Base map from the topology database of National Geographic Institute, production by authors.

16
area where new buildings are allowed but limited and corresponds to irreversible damages.

Because these zones are very limited, they have a small number of transactions during the

study period (Table 9 in Appendix A.2). Finally, because the hazardous plants near Rouen

present the specificity of not being visible from some dwellings, we consider the dummy

for the view of plants from a dwelling for Rouen. This choice of variables to quantify

perceived exposure to industrial risk is consistent with other studies on the impact of similar

industrial risks on housing prices. All of these studies use the effective distance between

the dwelling and the plant (Table 8 in Appendix A.1). Some studies add other variables,

such as dummies for location in administrative areas for risk management (emergency

plans such as in Boxall et al. (2005) and Travers et al. (2009)) or variables traducing the

perception of pollution created by the plant (Boxall et al. (2005), Sauvage (1997)).

4 Results

We estimate the hedonic price function with three different models: the baseline parametric

OLS log-linear model and two extensions of this model, a parametric OLS log-polynomial

model, and a semiparametric CPAR log-linear model (Section 2). The results of these

hedonic regressions are presented for Bordeaux in Table 4, for Dunkirk in Table 5, and

for Rouen in Table 6.12 As expected, being recently built; in good condition; living space;

numbers of rooms, bathrooms, and parking spaces; and total acreage all increase the

dwelling’s value. The presences of outbuildings, a basement, and a swimming pool can also

increase the dwelling’s price. Because variations in commuting times are limited (Table 9

in Appendix A.2), and with the addition of municipality dummies in parametric models,

the commuting time from the central business district does not significantly modify the

dwelling’s value.13 In the three models, the signs are the same and the orders of magnitude

are similar.

4.1 Different impacts of the distance from highly hazardous plants between the studied areas

Even among the same category of industries (chemical and petrochemical industries), the

impact of the distance from highly hazardous plants on the dwelling’s value varies between

12Parametric regressions include municipality dummies and year and month dummies. For the sake of

readability, these estimated coefficients are not reported. Some of them are significant at the 1% or the

5% level.

13When excluding the municipality dummies, the commuting time is significant in all of the parametric

models. In the semiparametric ones, this effect is captured by the distribution of intercepts, as the intercept

is allowed to vary with respect to the kernel variables, in particular the geographic coordinates.

17
Table 3: Data

Dwellings’ characteristics

Intrinsic characteristics (a) Extrinsic characteristics

Price (including tax) Commuting time (by car) (b) ,(f) House or apartment from the central business district (∗)

Less than 5 years old Distance from the market square (c), (d)

Condition Distance from a drugstore (c), (d)

Living space Distance from a food shop (c),

(d), (e)

Number of rooms Distance from a bus stop (f)

Number of bathrooms Distance from a park (f)

Number of parking spaces Distance from a nursery or primary school (e), (g)

Presence of a terrace Distance from a high school (e), (g)

Presence of a balcony Distance from the nearest highly hazardous plants (†) (h)

Presence of an elevator Distance from the nearest authorized plants (‡) (h)

Presence of a swimming pool View of industrial plants (for Rouen) () (g)

Presence of a basement Location in a land use control area (Z1, Z2) (i)

Presence of a cellar Location in an area of emergency plan for households (j)

Presence of annexes Location in an area exposed to natural hazards (.) (i)

Presence of outbuildings Location in an area exposed to other hazards (.) (i)

Presence of an attic Location in a residual pollution area (k)

Total acreage Location in an environmental protection area (i)

Location in a conservation easement area (i)

Sound exposure to a land transport facility (l), (m)

Sound exposure to an air transport facility (l), (m)

Buyers’ and sellers’ characteristics (/)

Gender (a)

Age (a)

Social and occupational group (a)

Marital status (a)

Municipality of origin (a)

Sources: (a) PERVAL, (b) geographic directory of municipalities, (c) Chambers of Commerce and

Industry database, (d) municipal database, (e) phone book, (f) topology database of the National

Geographical Institute, (g) building database of the National Geographical Institute, (h) database for

classified plants per municipality, (i) land use plan, (j) prefecture, (k) Regional Office for Environment,

Planning and Housing, (l) sound map of the Departmental Office for Territories and Sea, (m) sound map

of the Technical Studies Center of Public Works.

Notes: Each distance from a facility is built as the distance from the closest facility.

(∗) Commuting time from the central business district is computed as the commuting time from the town

hall of Bordeaux, Dunkirk, or Rouen.

(†) Most hazardous plants are among the classified plants for the environment’s protection (mainly Seveso

sites).

(‡) Plants subject to the regime of classified plants for the environment’s protection.

() View from the dwelling of red and white Petroplus chimney or of Senalia silo.

(.) Area of servitude or notification.

(/) This information is relative to the individual buyer (or seller) or to the household reference person.

18
the three areas. For Bordeaux, in all of the models, the proximity to the gunpowder factory

is valued by the inhabitants in close vicinity (Table 4), probably because the neighborhoods

around the plant are green with many trees and are very quiet.14 Thus, we capture unobserved amenities that are spatially correlated with the distance from the plant. As the

parametric models, the conditionally parametric does not obviate problems with omitted

variables that may be correlated with the variable of interest.

For Dunkirk, the semiparametric model reveals that the distance from highly hazardous

plants does not significantly impact housing prices (Table 5). Indeed, the presence of a

nuclear plant in Gravelines (located 18 km from Dunkirk) likely overshadows the exposure

to other industrial risks. On the contrary, the parametric model indicates that the distance

from highly hazardous plants increases the housing prices in Dunkirk. This difference is

due to the size of the sample used by the regressions. In the semiparametric regression, the

extreme observations are rarely used and weigh less than the others; but in the parametric

regression, the whole sample is used and each observation has an equal weight. This is why

the semiparametric regression is more robust about outliers affecting the average values

in a standard regression model. Here, for Dunkirk, the main part of the sample is almost

uniformly concentrated between 700 m and 3 km from the highly hazardous plants. When

restricting the observations to between 700 m and 3 km, the parametric regression also

shows that the distance from the highly hazardous plants does not significantly impact

housing prices.

For Rouen, in all of the models, the distance from highly hazardous plants increases the

dwellings’ values (Table 6): the highly hazardous plants are perceived as disamenities.

The magnitude of this impact is important: on average, the households are willing to

pay around 1.2% of their dwelling’s price to move 100 more meters away from the highly

hazardous plants in Rouen (Table 7). This order of magnitude is consistent with other

studies relative to the impact of similar industrial risks on housing prices (Table 8 in Appendix A.1).

Reviewing other similar studies shows that the distance from hazardous plants can increase,

decrease, or have no significant impact on housing prices, depending on the studied case

14There are many scattered trees and lawns on the sidewalks in the neighborhoods around the gunpowder.

As these green areas do not belong to parks, their effect is not captured by the observed distance from

parks and is difficult to measure.

19
and in particular on the type of industries (Table 8 in Appendix A.1). Kiel and Williams

(2007) also find that some Superfund sites have a negative impact on housing values, while

other have either no impact or a positive one. Clark and Nieves (1994) also show that

the proximity to a petrochemical refinery weighs more on the housing prices than the

proximity to coal, gas, or oil-fired plants; to hazardous waste; or to a liquefied natural gas

site. Our results confirm this dependency on the studied case, which is here mainly due to

the differences in the perception of industrial activities and of their neighborhoods.

4.2 Testing the fixed-parameter assumption

When comparing the parametric model and the baseline log-linear model, the square or

the cube of the distance from highly hazardous plants are significant in the parametric

model for Bordeaux and Rouen (Tables 4 and 6). This significance suggests that the

fixed-parameter assumption does not hold. The fixed-parameter assumption is properly

tested by comparing the semiparametric model and the baseline log-linear model, which

corresponds to a nested model under the null hypothesis of fixed coefficients. Indeed the

OLS can be considered as a CPAR with a window size of 100% (the whole sample) and

an equal weight to each observation of the sample.15 The fixed-parameter hypothesis is

rejected at the 0.001% level, which supports both the use of the flexible forms and the

semiparametric model.

4.3 Comparing the impacts of the distance from plants estimated by

the parametric and semiparametric models

We compare the estimated impacts of the distance from the plants provided by the parametric log-polynomial model and by the semiparametric model.

Value. Parametric log-polynomial regressions lead to a bias in the estimated value of the

impact of the distance from highly hazardous plants on dwelling prices for Bordeaux and

Rouen. The implicit price of distance d from these plants equals the absolute marginal

willingness to pay AMW T P to move one more meter away (Section 2). To make comparisons between the dwellings or between the three areas easier, we also compute the relative

marginal willingness to pay RMW T P to move 100 more meters away from the plants

15See McMillen and Redfearn (2010a) for a summary of Cleveland and Devlin’s F-test and its adaptation

to the CPAR regression.

20
Table 4: For Bordeaux: parametric and semiparametric hedonic regressions

Model Log-linear, OLS Log-polynomial, OLS Log-linear, CPARExplained variable Dwelling price Dwelling price Dwelling priceExplanatory variable Estimate Std. error p-value Estimate Std. error p-value Mean Std. error p-value (∗)Intercept 11.4 0.080 < 2e-16 11.6 0.12 < 2e-16 11.3 0.32 <1e-11Less than 5 years 0.16 0.028 6.1e-09 0.16 0.028 9.1e-09 0.13 0.041 3.9e-05Average condition -0.015 0.014 0.29 -0.015 0.014 0.28 -0.030 0.030 0.14Poor condition -0.21 0.032 4.3e-11 -0.22 0.032 2.2e-11 -0.21 0.044 2.2e-06Living space 0.0020 2.3e-04 < 2e-16 0.0019 2.3e-04 < 2e-16 0.0020 3.5e-04 1.4e-104 rooms 0.25 0.027 < 2e-16 0.26 0.027 < 2e-16 0.24 0.055 <1e-115 rooms 0.34 0.027 < 2e-16 0.35 0.027 < 2e-16 0.32 0.048 <1e-116 rooms 0.37 0.030 < 2e-16 0.38 0.030 < 2e-16 0.36 0.066 <1e-117 rooms or more 0.40 0.036 < 2e-16 0.41 0.036 < 2e-16 0.41 0.057 <1e-11No bathroom -0.14 0.042 0.0013 -0.14 0.042 8.3e-04 -0.29 0.071 0.00132 bathrooms or more 0.13 0.016 7.6e-15 0.13 0.016 1.7e-14 0.14 0.022 4.4e-091 parking space 0.099 0.016 1.1e-09 0.097 0.016 1.7e-09 0.077 0.020 4.7e-042 parking spaces or more 0.15 0.025 1.0e-09 0.15 0.025 2.3e-09 0.13 0.031 1.2e-04Presence of outbuildings 0.052 0.021 0.013 0.051 0.021 0.015 -0.036 0.027 1.0Presence of a basement 0.12 0.030 2.7e-05 0.12 0.030 2.8e-05 0.072 0.030 0.036Presence of a swimming pool 0.13 0.019 1.4e-11 0.13 0.019 2.6e-11 0.21 0.030 2.0e-11Total acreage 1.6e-05 6.1e-06 0.0092 2.0e-05 6.5e-06 0.0016 4.7e-05 2.8e-05 1.2e-08Commuting time from CBD (†) 1.1e-04 6.6e-05 0.11 7.5e-05 6.7e-05 0.27 1.4e-04 1.7e-04 0.31Distance from plants (‡) -2.7e-05 8.9e-06 0.0025 -1.4e-04 5.4e-05 0.012 -1.8e-05 2.1e-05 0.057(Distance from plants)2 2.7e-08 1.2e-08 0.026(Distance from plants)3 -1.8e-12 7.8e-13 0.019

Notes: The parametric regressions include municipality dummies and year and month dummies. These estimated coefficients are not reported. Some of them are significantat the 1% or the 5% level.(†) CBD = Central Business District; (‡) plant denotes a highly hazardous plant.

(∗) See McMillen and Redfearn (2010a) for a summary of Cleveland and Devlin’s F-test and its adaptation to test the significance of the explanatory variables in the CPARregression.Sources: French solicitors - PERVAL, data collected and standardized by the Technical Studies Center of Public Works of South West France. 1,423 observations.

21
Table 5: For Dunkirk: parametric and semiparametric hedonic regressions

Model Log-linear, OLS Log-linear, CPARExplained variable Dwelling price Dwelling priceExplanatory variable Estimate Std. error p-value Mean Std. error p-value (∗)Intercept 10.8 0.075 < 2e-16 10.9 0.11 <1e-11Average condition -0.068 0.018 1.9e-04 -0.088 0.026 0.0015Poor condition -0.26 0.021 < 2e-16 -0.24 0.060 <1e-11Living space 0.0015 3.4e-04 7.4e-06 0.0016 8.8e-04 6.6e-064 rooms 0.17 0.031 1.2e-07 0.11 0.034 9.5e-055 rooms 0.25 0.030 < 2e-16 0.21 0.046 1.5e-106 rooms 0.31 0.035 < 2e-16 0.24 0.067 4e-117 rooms or more 0.32 0.044 1.1e-12 0.23 0.089 1.7e-07No bathroom -0.12 0.027 2.4e-05 -0.13 0.038 2.1e-042 bathrooms or more 0.13 0.035 2.2e-04 0.13 0.038 0.011Presence of outbuildings -0.037 0.022 0.086 -0.037 0.026 0.11Presence of a basement -0.010 0.017 0.55 0.015 0.016 0.97Total acreage 1.9e-04 3.1e-05 1.0e-09 5.0e-04 2.1e-04 <1e-11Commuting time from CBD (†) 9.8e-05 1.0e-04 0.34 2.5e-05 1.4e-04 1.0

Distance from plants (‡) 9.0e-05 2.1e-05 2.0e-05 7.8e-05 2.5e-05 1.0

Notes: The parametric regressions include municipality dummies and year and month dummies. These estimated coefficients are not reported. Some of them are significantat the 1% or the 5% level.(†) CBD = Central Business District; (‡) plant denotes a highly hazardous plant.

(∗) See McMillen and Redfearn (2010a) for a summary of Cleveland and Devlin’s F-test and its adaptation to test the significance of the explanatory variables in the CPARregression.Sources: French solicitors - PERVAL, data collected and standardized by the Technical Studies Center of Public Works of Nord and Picardy. 1,032 observations.

22
Table 6: For Rouen: parametric and semiparametric hedonic regressions

Model Log-linear, OLS Log-polynomial, OLS Log-linear, CPARExplained variable Dwelling price Dwelling price Dwelling priceExplanatory variable Estimate Std. error p-value Estimate Std. error p-value Mean Std. error p-value (∗)Intercept 11.0 0.22 < 2e-16 10.7 0.24 < 2e-16 10.8 0.31 <1e-11Average condition -0.077 0.027 0.0048 -0.074 0.027 0.0062 0.019 0.043 0.94Poor condition -0.31 0.042 6.0e-13 -0.31 0.042 4.2e-13 -0.28 0.057 2.8e-09Living space 0.0014 3.3e-04 2.1e-05 0.0013 3.2e-04 4.5e-05 0.0016 4.4e-04 4.2e-034 rooms 0.26 0.040 3.1e-10 0.25 0.040 1.3e-09 0.25 0.050 1.1e-065 rooms 0.37 0.040 < 2e-16 0.36 0.040 < 2e-16 0.39 0.065 <1e-116 rooms 0.44 0.044 < 2e-16 0.43 0.044 < 2e-16 0.37 0.059 <1e-117 rooms or more 0.50 0.049 < 2e-16 0.50 0.049 < 2e-16 0.47 0.038 <1e-11No bathroom -0.35 0.078 1.1e-05 -0.36 0.078 6.0e-06 -0.38 0.25 8.7e-092 bathrooms or more 0.13 0.031 3.4e-05 0.13 0.031 2.7e-05 0.088 0.061 7.4e-041 parking space 0.11 0.030 4.8e-04 0.094 0.030 0.0020 0.13 0.038 0.122 parking spaces or more 0.13 0.036 3.6e-04 0.12 0.036 0.0011 0.13 0.074 0.0086Presence of outbuildings 0.039 0.030 0.19 0.042 0.030 0.16 -0.0057 0.026 0.97Presence of a basement 0.098 0.024 6.3e-05 0.092 0.024 1.6e-04 0.085 0.024 0.0065Total acreage 3.1e-05 9.5e-06 0.0013 3.1e-05 9.5e-06 9.6e-04 8.5e-05 5.3e-05 5.5e-06Commuting time from CBD (†) 9.7e-05 1.0e-04 0.34 7.6e-05 1.5e-04 0.62 -1.6e-05 2.0e-04 1.0Distance from plants (‡) 1.3e-04 2.1e-05 3.7e-10 2.6e-04 5.0e-05 1.7e-07 1.2e-04 4.9e-05 8.9e-06(Distance from plants)2 -3.3e-08 1.1e-08 0.0037

Notes: The parametric regressions include municipality dummies and year and month dummies. These estimated coefficients are not reported. Some of them are significantat the 1% or the 5% level.(†) CBD = Central Business District; (‡) plant denotes a highly hazardous plant.

(∗) See McMillen and Redfearn (2010a) for a summary of Cleveland and Devlin’s F-test and its adaptation to test the significance of explanatory variables in the CPAR

regression.Sources: French solicitors - PERVAL, data collected and standardized by the Technical Studies Center of Public Works of Normandy and Centre. 584 observations.

23
(expressed as a percentage of the dwelling’s price).

AMW T P =

∂P(X)

∂d , (11)

RMW T P =

∂P(X)

∂d

∆d

P

with ∆d = 100. (12)

Table 7 presents the absolute and relative marginal willingnesses to pay, as estimated by

the parametric and semiparametric models.

Table 7: Households’ marginal willingness to pay to move one more meter away from the

highly hazardous plants

MWTP estimated by MWTP estimated by

log-polynomial, OLS log-linear, CPAR

Median Mean Std. Dev. Median Mean Std. Dev.

For Bordeaux

AMWTP (=C/m) -3.65 -5.32 5.24 -2.86 -2.75 3.83

RMWTP (%/100 m) -0.19 -0.25 0.21 -0.16 -0.18 0.21

For Dunkirk

AMWTP (=C/m) 8.91 9.35 3.72 - - -

RMWTP (%/100 m) 0.90 0.90 0 - - -

For Rouen

AMWTP (=C/m) 19.65 20.43 11.19 14.11 14.94 9.83

RMWTP (%/100 m) 1.90 1.70 0.67 1.22 1.18 0.49

Notes: For Dunkirk, the willingnesses to pay as estimated by the CPAR are not provided because the

distance from the nearest highly hazardous plant is not significant in this model.

In the parametric regression, the log of price is explained by the linear expression of the dwelling

characteristics and by a polynomial of the distance from the plants. The degree of this polynomial is 3

for Bordeaux, 1 for Dunkirk, and 2 for Rouen (Equations 2, 3, and 4). In the semiparametric regression,

the log of price is explained by the linear expression of the dwelling characteristics while allowing the

coefficients to vary with respect to the geographic coordinates and time (Equation 6).

Sources: French solicitors - PERVAL, data collected and standardized by the Technical Studies Center of

Public Works of Normandy and Centre. 1,423 observations for Bordeaux, 1,032 for Dunkirk, and 584 for

Rouen.

The parametric model leads to a significant bias in the estimation of the marginal willingness to pay, be it the absolute willingness or the relative one, for Bordeaux and Rouen.

When performing a Wilcoxon signed-rank test between the two distributions of the marginal

willingness to pay estimated by the parametric and semiparametric models, respectively,

the null hypothesis is rejected at the 0.001% level, that is the median difference is not zero.

Thus, the two distributions are significantly different.

This bias, which pleads for the use of the semiparametric model, comes from the different

sets of observations used by the two models. Indeed, the semiparametric model gives

a higher weight to sets of “close” observations (proximity being defined by the kernel in

24
terms of location and time), whereas the parametric model gives an equal weight to each

observation of the whole sample. This is why the parametric model’s bias could lead either

to an overestimation or to an underestimation of the average marginal willingness to pay.

If, as here for Rouen, these sets of “close” observations correspond to a lower marginal

willingness to pay than the average, the parametric model leads to an overestimation of

the average marginal willingness to pay (Table 7).

Variations. For Rouen, the parametric model also leads to a biased estimation (here

again an overestimation) of the variations in the absolute marginal willingness to pay with

respect to the distance from plants.

The households’ willingness to pay to move further away from highly hazardous plants

decreases with respect to the distance from plants over the study area for Rouen, be it

estimated by the log-polynomial parametric model or by the semiparametric model. Correlations between the marginal willingness to pay and the distance are significant at the

0.001% level and negative (-0.58 and -0.22 for the log-polynomial parametric and the semiparametric models, respectively).16 The decrease in the households’ willingness to pay

to move further away from plants is three times more important when estimated by the

parametric model (Figure 5).17

The decrease of the willingness to pay to move further away from highly hazardous plants

with respect to the distance from plants is due to the fact that the marginal gains (in terms

of exposure reduction) of moving further away from the hazard source are likely to decrease.

However, two other phenomena might explain the variations in the willingness to pay. First,

other spatially correlated amenities could bias these estimated variations. Second, there

16Only basic correlations are here presented because explaining the willingness to pay to move away

from the plants by the distance from these plants (and potentially demand shifters) raises an identification

problem due to a simultaneous choice. Indeed, households choose simultaneously the amount and the

marginal price for each characteristic, because the marginal price of each characteristic of the dwelling can

vary with the amount of this characteristic. A regression approach would implement the second step of

Rosen (1974)’s method. Bartik (1987) offers a method to implement this second-step regression by pooling

implicit prices estimated on each site (here Bordeaux, Dunkirk, or Rouen). However, we do not apply this

method because two of the three distributions of estimated coefficients by each first-step regression are

difficult to interpret as marginal willingnesses to pay to move further away from highly hazardous plants.

For Bordeaux, the implicit prices of the distance to highly hazardous plants are negative (Table 4); for

Dunkirk, the distance from highly hazardous plants does not significantly explain housing values (Table

5). Ekeland (2004) offers two other methods to implement this second-step regression by using data from a

single market, but our data do not enable us to apply them either because the variability of the dwelling’s

characteristics with respect to the buyer’s observed characteristics is required.

17Results are similar when using the relative marginal willingness to pay: correlations with the distance

are significant at the 0.001% level and negative; the decrease in the households’ willingness to pay to move

further away from plants is also three times more important when estimated by the parametric model.

25
might be sorting in the housing market: households with a higher willingness to pay to

live further away from the plants might intentionally choose a more distant location from

these plants. We observe the net result of all three effects; it appears that the first effect

dominates the last two.

Figure 5: Households’ absolute marginal willingness to pay to move one more meter away

from highly hazardous plants for Rouen

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OLS estimate (€/m)

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CPAR estimate (€/m)

Caption: The relationship between the marginal willingness to pay and the distance can

be depicted by an ordinary least-squares slope. This slope is three times steeper when the

marginal willingness to pay is estimated by the log-polynomial OLS model rather than by

the log-linear CPAR model.

Sources: French solicitors - PERVAL, data collected and standardized by the Technical

Studies Center of Public Works of Normandy and Centre. 584 observations.

5 Conclusion

Real estate markets can reveal their households’ willingness to pay to reduce their exposure

to hazardous industrial facilities. Using very rich microdata, we study housing prices in

the vicinity of hazardous industries located near three important French cities: Bordeaux,

Dunkirk, and Rouen. We estimate the impact of the distance from hazardous plants on

dwelling values by using hedonic price models.

We compare the results from standard parametric models of hedonic property and an alternative, more flexible, semiparametric model of hedonic property. This semiparametric

model is a conditionally parametric regression that allows the willingness to pay to vary

with respect to space and time of sale, while keeping some smoothness in its distribution.

26
Even though the signs and orders of magnitude of the effects are similar in the two models

for a very large majority of coefficients, the estimated impacts of the distance from highly

hazardous plants on housing prices significantly differs between the two models. The parametric model leads to an important bias in the estimated value of this impact for Bordeaux

and Rouen.

Using the semiparametric model, we show that this impact strongly differs among industrial

areas, even among chemical and petrochemical industries, likely because of the different

perceptions of industrial risks and dissimilar neighborhoods of these hazardous facilities.

We also show that this impact can vary within a study area. Indeed, the impact of the

distance from hazardous plants on housing prices can significantly decrease with respect to

the distance from the plants because the marginal gains in terms of the exposure reduction

of moving further away from the hazard source are likely to decrease.

There are two substantive lessons from our analysis. First, our results show that parametric

models can lead to an important bias in the estimated value of the marginal willingness to

pay. Second, our results show that the estimated willingness to pay for prevention strongly

differs among industrial areas, even among one category of industries, and depends on the

distance from these facilities. Thus, even when assuming that households are fully aware of

industrial risks, this inadequate estimation method and this limited external validity call

for careful use of the current estimations of the population’s willingness to pay for industrial

risk reduction in the cost-benefit analyses of prevention measures. Until now, most costbenefit analyses have used the parameters taken from the estimation of parametric hedonic

models on other study areas than the ones under consideration in these analyses.

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A Appendices

A.1 Other studies on the impact of hazardous chemical and petrochemical industries on housing prices

29
Table 8: Other studies on the impact of hazardous chemical and petrochemical industries on housing prices

Study Hazardous facilities Region Time Sample size Extrinsic characteristics (∗) Scope of the study area (†) Variable(s) for risk exposure Effect of risk exposureBoxall etal. (2005)

Oil and gasfacilities

Central Alberta(Canada) 1994 - 2001 532

Distance fromthe City, view ofmountains

17 km(mean), 7 km(std) (ruralarea)

Distance from the nearestplant, number of plants within4 km, number of emergencyplanning zones the property islocated in, sum of H2S releasewithin 4 km of property

Prices decrease forproperties located within 4km of facilities from150,000 to 450,000 $CDN2001, i.e. from -4% to -8%

Caroll etal. (1996)

PEPCONplant(chemistry)

Henderson,Nevada (UnitedStates)

1986 - 1990 7,780 No From 3 to 35 km Distance from the plant

Prior to explosion, pricesincrease by 4.2% at 3.2 kmfrom PEPCON, i.e. increase byaround $11 per meter. Impactof the explosion on implicitprice

Flowerand Ragas(1994)

Petrochemicalrefineries

St. BernardParish, Louisiana(United States)

1979 - 1991 1,999 No From 0 to 3.3 km Distances from the refineries or dummies for areas based on these distances

In one area prices decreasew.r.t distance. Elsewhere pricesincrease from $210 to $620 per100 m, i.e. from 1% to 3.4% per100 m, i.e. from $2 to $6 per m

Sauvage(1997)

Chemical andpetrochemicalfacilities

Waziers,Puget-sur-Argens,Carling, L’Hôpitaland St-Gaudens(France)

1988-1992

170, 64, 59,91, and 188

No

From 0 to 1.8km/2km/4 km

Distance from the plants

In Waziers and inPuget-sur-Argens, nosignificant impact. Elsewhere,prices increase w.r.t. distance.In L’Hôpital for example, pricesincrease by 110 F per meter,i.e. around 4% per 100 meter

Chemicalfacilities

Bradford (UnitedKingdom) 1987-1993 561 No From 0 to 2.5 km Distance from the plants, view of plants

Prices increase by £5.5 per m,i.e. around 2.6% per 100 meter.View over facilities decreasesprices by £960, i.e. by 4.6%

Travers etal. (2009)

Chemicalfacility

Port-Jérôme, SeineMaritime (France) 2001-2002 228

Distances fromroads, highway,Seine, play area,town hall

From 0 to 5km

Distance from the plants,location in emergency planningzone

Prices increase by

=C9.2 per m,

i.e. by 1.2% per 100 m

Notes: (∗) Other than variables for risk exposure and dummies for municipalities.(†) The scope of the study area is measured in terms of distance from the nearest plant.The impact on housing prices of other industrial activities has been studied, in particular the impact of power plants (Davis, 2011), copper smelter (McMillen and Thorsnes, 2003),waste treatment and storage (Greenstone and Gallagher (2008), see Farber (1998) for a review), and of natural gas (Boxall et al., 2005). Air pollution also significantly decreases housingprices (Chay and Greenstone (2005), see Maslianskaia-Pautrel (2008) for a review).
A.2 Descriptive statistics

Table 9: Extrinsic characteristics of the dwellings

Bordeaux Dunkirk Rouen

N 1, 423 1, 032 584

Commuting time

(by car) from city

center (min)

Min 16 4 15

Q1 24 7 19

Median 28 8 22

Q3 31 9 24

Max 38 14 31

Mean 27 8 22

Standard deviation 4 2 4

Located close to... ... the market square (<500m) 205 304 100

... a drugstore (<250m) 144 468 111

... a food shop (<250m) 198 696 219

... a bus stop (<250m) 676 892 459

... a park (<500m) 425 724 125

... a nursery or a primary school (<500m) 428 939 179

... a high school (<500m) 158 550 166

Distance from the

nearest highly

hazardous plant

(m)

Min 532 41 64

Q1 3, 333 830 702

Median 5, 141 1, 729 1, 137

Q3 6, 406 2, 449 2, 196

Max 10, 379 4, 084 5, 142

Mean 4, 935 1, 709 1, 450

Standard deviation 1, 863 951 1, 028

Distance from the

nearest

authorized plant

(m)

Min 89 41 64

Q1 1, 006 594 489

Median 1, 657 872 764

Q3 2, 663 1, 164 1, 179

Max 5, 279 1, 943 2, 884

Mean 1, 848 877 937

Standard deviation 1, 061 370 625

View of industrial plants n.s. n.s. 507

Located in an

administrative

area of...

... land use control (Z1) 0 0 35

... land use control (Z2) 0 0 57

... emergency plan 0 279 400

... natural hazard 33 0 0

... other hazard 0 0 58

... environmental protection 0 0 64

... conservation easement 0 39 208

Exposed to... ... land transport noise (?) 0 0 15

... air transport noise (?) 79 0 0

Notes: The unit is the number of dwellings, unless specified otherwise.

(?) A dwelling is considered as exposed to a land transport facility if the sound is above 60 dB and to an air

transport facility if the sound is above 50 dB.

Sources: French solicitors - PERVAL, data collected and standardized by the Technical Studies Center of

Public Works of Normandy and Centre, Nord and Picardy, and South West France.

31
Table 10: Transactions by municipality and by year

Area Municipality (INSEE code) 2000 2002 2004 2006 2008 All years

Bordeaux Le Haillan (33200) 36 31 34 38 24 163

Martignas-sur-Jalle (33273) 42 40 42 32 30 186

Mérignac (33281) 13 15 22 13 11 74

Saint-Aubin-de-Médoc (33376) 34 38 38 28 21 159

Saint-Médard-en-Jalles (33449) 48 157 155 149 84 593

Le Taillan-Médoc (33519) 52 50 55 49 42 248

All municipalities 225 331 346 309 212 1, 423

Dunkirk Coudekerque-Branche (59155) 82 80 88 86 85 421

Dunkirk (59183) 50 35 49 39 42 215

Fort-Mardyck (59248) 9 12 9 17 16 63

Saint-Pol-sur-Mer (59540) 52 68 86 58 69 333

All municipalities 193 195 232 200 212 1, 032

Rouen Grand-Couronne (76319) 49 60 58 42 51 260

Hautot-sur-Seine (76350) 4 5 1 0 2 12

Moulineaux (76457) 6 10 4 7 5 32

Petit-Couronne (76497) 45 46 30 28 39 188

Sahurs (76550) 11 16 10 9 5 51

Val-de-la-Haye (76717) 7 7 13 8 6 41

All municipalities 122 144 116 94 108 584

All areas 529 662 689 601 529 3, 039

Note: The unit is the number of transactions.

Sources: French solicitors - PERVAL, data collected and standardized by the Technical

Studies Center of Public Works of Normandy and Centre, Nord and Picardy, and South

West France.

32
Table 11: Intrinsic characteristics of the dwellings

Bordeaux Dunkirk Rouen

N 1, 423 1, 032 584

Price (current euro, tax. inc.) Min 22, 090 7, 622 9, 909

Q1 146, 428 74, 928 91, 469

Median 197, 000 99, 092 114, 700

Q3 256, 305 127, 000 153, 974

Max 800, 000 285, 300 430, 000

Mean 206, 887 104, 024 125, 282

Standard deviation 85, 661 41, 358 55, 146

Less than 5 years old 83 6 15

Condition Good 497 526 181

Average 857 298 346

Poor 70 208 57

Living space (sq m) (∗) Min 30 40 30

Q1 95 84 83

Median 110 95 100

Q3 134 110 120

Max 350 300 300

Mean 119 99 104

Standard deviation 36 25 34

Unspecified 260 210 275

Number of rooms 3 or less (†) 109 81 76

4 361 277 133

5 506 443 177

6 288 162 118

7 or more 159 69 80

Number of bathrooms 0 34 96 13

1 917 881 450

2 or more 472 55 121

Number of parking spaces 0 304 1032 134

1 971 0 305

2 or more 148 0 145

Holds... ... a terrace 83 9 56

... a swimming pool 207 0 0

... a basement 72 349 268

... a cellar 83 47 258

... outbuildings 161 157 123

... an attic 70 66 208

Total acreage (sq m) Min 30 0 30

Q1 606 155 310

Median 816 207 500

Q3 1, 052 305 852

Max 29, 597 6, 599 18, 724

Mean 967 267 784

Standard deviation 1, 115 264 1, 354

Notes: The unit is the number of dwellings, unless specified otherwise.

(∗) Living space is filled in for 18% of the observations for Bordeaux, 20% for Dunkirk, and

47% for Rouen. The imputed living space for missing values is randomly chosen among

the observed distribution of living spaces.

(†) Observations with zero room (stand-alone garages, garden sheds, or other outbuildings)

are excluded.

Sources: French solicitors - PERVAL, data collected and standardized by the Technical

Studies Center of Public Works of Normandy and Centre, Nord and Picardy, and South

West France. 33