Henrik Andersson
email: henrik.andersson@tse-fr.eu

Emmanuelle Lavaine
email: emmanuelle.lavaine@univ-smb.fr

Nitrates and People Perception: a Hedonic Application

Keywords: Hedonic Price Analysis, difference in difference, water pollution

This paper examines the effect of living in vulnerable zone in term of nitrates on property prices using a change in the geographical delimitation of the zone in France. Using an identification strategy based on a spatial difference-in-differences specification, we show that the 2012 revision policy decreases significantly property prices not only in areas that becomes vulnerable at the time of the policy but also in areas that were already considered as vulnerable before the policy change.

Introduction

In Europe, Council Directive 91/676/EEC has been implemented to protect waters against pollution caused by nitrates from agricultural sources through a number of measures incumbent on Member States. These measures concern monitoring surface waters and groundwater, designating vulnerable zones, introducing codes of good agricultural practice, adopting action programs, and evaluating the actions implemented. In this project, we are concerned with vulnerable zones which are all known areas of land which drain into surface waters and groundwater which are affected by pollution or at risk of being so. In fact, nitrates in drinking water, which may come from nitrogen fertilizers applied to crops, are a potential health risk [START_REF] Crutchfield | Benefits of safer drinking water: the value of nitrate reduction[END_REF]. Vulnerable zones have been revised in 2012 in France due to pollution concentration from surface waters and groundwater observed in 2010-2011.

Today, nearby 55 % of France is classified as vulnerable zones, which corresponds to regions where agricultural land is the most important. This project aims to examine the impact of a potential change in the environment linked with the change of vulnerable zones in 2012 on property prices. In fact, water quality can have a significant effect on property values [START_REF] Leggett | Evidence of the effects of water quality on residential land prices[END_REF].

The literature underlines the need to develop novel methods to improve our ability to estimate the marginal willingness to pay for environmental improvements [START_REF] Bento | Equity impacts of environmental policy[END_REF], [START_REF] Lavaine | The capacity of hedonic pricing analysis to reflect the economic benefits of air quality[END_REF]. A wide range of studies on this topic use quasi-experimental technique. [START_REF] Davis | The effect of health risk on housing values: Evidence from a cancer cluster[END_REF] measures the effect of health risk on housing values by exploiting a severe increase in pediatric leukemia in an isolated county in Nevada as a natural experiment.

In addition, [START_REF] Currie | Environmental health risks and housing values: Evidence from 1,600 toxic plant openings and closings[END_REF] measure recently the housing market and health impacts of 1,600 openings and closings of industrial plants that emit toxic pollutants. [START_REF] Chay | Does air quality matter? evidence from the housing market[END_REF] exploit the quasi-random assignment of air pollution changes across counties induced by federally mandated air pollution regulations to identify the impact of particulate matter on property values. [START_REF] Greenstone | Does hazardous waste matter? evidence from the housing market and the superfund program[END_REF] use the Superfund cleanups to analyze its local welfare impacts. In the same vein, to mitigate selection and endogeneity problems and to infer the impact of a new directive on property prices, we use the 2012 boundary revision as a quasi-experimental approach. While this is not the first difference in difference study to use the effect of an environmental policy on hedonic prices as a natural experiment, this study aims to analyze the consequences of a shift in risk perception on housing markets not only in areas directly concerned by a policy change, but also in neighborhood areas indirectly concerned by this policy change. It aims to shed light on people's willingness to pay for perceived differences in nitrates risk. The analysis of this paper focuses on municipalities in the Centre and Poitou Charentes region, where residents recently were considered to be living in a sensitive geographical area in terms of nitrates, though this is not the only place in France. The geographic region for the control group is then represented by all towns not classified as vulnerable zones. House prices in new sensitive areas are compared before and after the 2012 designation of vulnerable zones with the nearby municipalities. The conditions of supply and demand for housing are relatively similar in both groups, so that we can expect a similar set of implicit prices in vulnerable zones and in its surroundings.

Data and empirical strategy

Data sources

Property prices are drawn from a unique and detailed dataset -PERVAL-compiled by the Chambre of Notaires (who records property transactions) from 2011 to 2014. Table 1 presents a summary statistics about property transaction over 4 years in the Centre and Poitou Charentes Region. We have precisely obtained information about property transactions for 4 departments1 namely Vienne (86) and Charente Maritime (17) for the Poitou Charente region; Loiret (45) and Indre et Loire (37) for the Centre region. We have classified municipalities with respect to the date they have been considered as vulnerable.

Out of 676 municipalities, 135 have never been classified as nitrates sensitive, 456 have been classified as nitrates sensitive before 2012 and 85 have been classified as nitrates sensitive in 2012. Figure 1 provides a geographical representation of this delimitation. The key variables of the PERVAL dataset are the property prices, the number of floors, the number of rooms, the type of flat or house, the property surface, the presence of a terrace, an attic, a parking space, a balcony, a pool or a terrace and a variable which indicates if the property is less than 5 years old. Panel A of table 1 shows the average price by property and year.

We observe that property prices are higher before the 2012 boundary than after 2012 for all municipalities of the study, hinting at potential effects from the regulation. Also shown is the number of transactions per municipalities by year. One concern with our analysis is that a change of a sensitive area can affect the number of property transactions, potentially leading to sample selection bias. As shown in Table 1, the number of transactions is higher across the nitrates areas than in non-nitrates areas. Further, when we run regressions using the number of transactions as the dependent variable, we do not find a statistically significant relationship between the number of transactions and the geographical boundary, suggesting a change in the number of transactions, if it arises, is unlikely to bias our estimates.

Empirical Methodology

Our goal is to assess the impact of vulnerable zone boundary on property prices. We estimate difference in difference models to exploit the change in the boundary in 2012. Housing prices are compared before and after the geographical boundary change with nearby municipalities acting as a control group. We implement this by estimating the following equation:

Y imy = βnitrates im * policy y + δX imy + σ y + α m + imy (1)
where Y is the log of property prices of the transaction i in municipality m at year y2 .

Turning to the independent variables, 'policy' is an indicator variable for the year of the new boundary of vulnerable area that corresponds to 2012, and 'nitrate' is an indicator variable for whether the municipality is classified as vulnerable. β is the difference-in-difference parameter. X imy is a vector of properties controls that includes year of construction, the surface of the property, the surface of the parking, the surface of the main room, the number of rooms, the presence of an attic and the presence of a balcony. It also differentiates specific attributes for house and flat such as the type of house and the presence of a pool and a terrace or the type of flat and the number of level. We control for temporal patterns by including year dummies in σ y . Because many properties were sold repeatedly during the sample period it is possible to control for unobserved municipalities-characteristics. Thus, we include municipalities fixed effects (α m ) to control for time-invariant characteristics of the municipality. imy represents the error term, which consists of an idiosynchratic component and a term clustered on the municipality and year3 . [START_REF] Kuminoff | Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities[END_REF] suggest that large gains in accuracy can be realized by moving from the standard linear specifications for the price function to a more flexible framework that would use a combination of spatial fixed effects, quasi-experimental identifications, and temporal controls for housing market adjustment.

As with any difference in difference design, the key underlying assumption for identification is that the control group serves as a valid counterfactual for the treatment group with parallel trends. Although we cannot explicitly verify this assumption, we feel this threat is limited in this setting for several reasons. First, figure 2 shows a similar evolution of prices in both group before the 2012 revision of boundary areas. Furthermore, we vary the definition of treatment to test the robustness of our results in the next section. Because the boundary of a sensitive zone could be anticipated, we also could expect sorting of households according to preferences prior to the boundary. Using a really rich and exhaustive dataset for property transactions, we are able to distinguish municipalities that were already considered as vulnerable in 2012 to municipalities that will become vulnerable in 2012. The issue of sorting, following the 2012 policy, is limited in municipalities considered vulnerable before 2012 as households may have already sort themselves at the time of the old geographical boundary.

Results

Figure This is because housing markets are not uniform [START_REF] Adair | Investment and private residential development in inner city dublin[END_REF], [START_REF] Fletcher | Heteroscedasticity in hedonic house price models[END_REF].

Hence, literature highlights it is unrealistic to treat the housing market in any geographical location as a single entity. Unfortunately, the definition, composition, and structure of submarkets have not been given much attention in hedonic price literature, although this is an important empirical issue [START_REF] Fletcher | Heteroscedasticity in hedonic house price models[END_REF]. Thus, the market segmentation has been done in this study dividing the market with respect to house and flat. The lack of significant results in table 2 compared to the following tables is in line with this literature highlighting the importance of market segmentation in empirical work. In the same vein, the literature highlights that examining the full housing value distribution can have important policy implications for the results of a hedonic analysis (Gamper-Rabindran and Timmins, 2013).

Gamper-Rabindran and Timmins (2013)'s valuation of the Superfund cleanup shows that a focus on the median housing value would have understated the larger effects at the lower tails of the housing value distribution. In this paper, the distinction between house and flat acts as a proxy of the property value distribution. In general, results highlight a decrease in property prices in municipalities classified as vulnerable (the treatment group) compared to municipalities classified as not vulnerable (the control group). Even if we consider people are informed about the change of vulnerable zone boundary, the demand for property may not decrease in municipalities that were already considered as sensitive in term of nitrates due to an absence of newer risk. Unless it is assumed that owners of properties, located in municipalities classified as sensitive before 2012, are informed about a larger nitrate risk in 2012, then the perception of a risk derived the hedonic pricing analysis does not explain a decrease in property prices in municipalities that were already at risk. The perception of the increase in health risks seem to be stronger than the real increase in health risks in municipalities that were at risk before 2012. The 2012 boundary revision seems to act as a reminder about the potential nitrate danger faced by municipalities.

Conclusion

The goal of this paper was to examine the population perception surrounding nitrates risk.

To account for the endogeneity of nitrate exposure, we exploit the change in boundary areas in term of nitrates that occurred in 2012 in France. To do so, the empirical model compares property prices in municipalities considered as vulnerable (the treatment group) to municipalities considered as not vulnerable (the control group). Results are not significant for the overall market and highlight the importance of considering different market segment such as house and flat. In line with literature, results highlight that examining the full property distribution leads to an absence of significativity. Considering house and flat as a sub-market, results show a significant and negative impact of the 2012 boundary definition on property values. Regression coefficients underlines that property prices decrease for house and flat after the new geographical boundary of 2012 in municipalities classified as vulnerable compared to counterparts. These results provide an answer of the impact of geographical boundary due to environmental risks on property prices.

Implementing boundaries could increase property prices via additional measures. In fact, setting up additional measures of risk reduction in these geographical boundaries by the state could reduce households' exposure and so increase the price of their dwellings. Thus, the net impact of risk prevention boundaries on housing prices could theoretically be an increase or a decrease of prices in the vicinity of the hazardous plants. Nevertheless, these new boundaries, above all, can reveal risk, and results from this paper suggest that the net impact on housing prices would be negative.

Figure 1 :

 1 Figure 1: Geographical boundary of nitrates sensitive areas.

Figure 2 :

 2 Figure 2: Adjusted yearly price level by nitrate sensitive municipalities

  2 provides a yearly graph of adjusted prices levels from 2011 to 2014 for the treatment and control groups, with prices levels adjusted by δX imy , σ y and α In fact, it is interesting to wonder what could be the impact of the 2012 revision on municipalities that were already considered as vulnerable. To do so, we choose to vary the treatment group ('nitrates') to distinguish the impact of the 2012 revision on municipalities that were already classified as vulnerable in 2012 and municipalities that will only become vulnerable in 2012. Column 1 uses a difference-in-differences model without property characteristics. Column 2 takes full advantage of control variables. Property characteristics are added to control for any differences in the evolution of property characteristics between the treatment and control group. The estimates are negative but not significant in the three different treatment as shown in every columns of table 2. This absence of significant results for the overall sample leads us to wonder about the existence of potential heterogeneity among houses and flats in this market.Thus, the following tables, table 3, table 4 and table 5 divide the dataset between flats and houses due to potential differences in the evolution of their markets and prices. The first two columns of each table present the results for flat whereas the last two columns of each table focus on house results. Treatments in table 2 are the same ones as used in tables 3, 4 and 5. While the treatment group in table 3 corresponds to municipalities that were considered as vulnerable before 2012 or in 2012, the control group corresponds to municipalities that are not vulnerable. The difference-in-differences estimator from table 3 is negative and significant for both house and flat. Adding control variables in column 2 and 4, decrease the size of the coefficient. Results show property prices decrease by 4% for house and 5% for flat after the 2012 revision in municipalities that were considered as vulnerable before 2012 or in 2012. We repeat the same exercise in table 4 restricting the treatment group to municipalities that are only considered as vulnerable in 2012. The difference-in-differences estimator from table 4 is negative and significant for flat in both specifications. The coefficient, representing the impact of the revision on house prices located in a vulnerable zone since 2012, is not significant when adding control variables in column 4. Results show property prices decrease nearby 6% for flat after the 2012 revision in municipalities that were considered as vulnerable in 2012. We finally repeat once again the exercise restricting now the treatment group in table 5 to municipalities that were considered as vulnerable before 2012 only. The difference-in-differences estimator from table5

	is negative and significant for house in both specifications. However, the coefficient repre-
	senting the impact of the revision on flat prices, located in a vulnerable zone before 2012,
	is not significant without considering control variables in column 1. Results show property
	prices decrease nearby 4% for house and flat after the 2012 revision in municipalities that
	were considered as vulnerable before 2012. First, using alternatively the same definition of

m . Prior to the 2012 regulation, prices levels are higher in municipalities with classification. Immediately after the regulation, prices levels in vulnerable areas decrease whereas prices levels in nonvulnerable areas increase. After the 2012 revision, prices level in municipalities that are not classified as nitrates sensitive exceed those of nitrates sensitive areas. This visual display clearly demonstrates a strong, temporal effect of the 2012 regulation on prices levels. The following tables provide regression estimates of equation (

1

), which are largely analogous to this figure. First, table 2 presents the effect of the regulation on the overall property market. Fixed effects are included in all of the columns. While the first block of table

2

looks at the entire dataset, the last two blocks restrict the sample with respect to the treatment group. In fact, each block corresponds to a different treatment. Hence, We test for the heterogeneity of the treatment results by interacting the 'policy' variable with different definition of treatment. treatment, it is interesting to note the difference of results between table

2 and table 3, 4 and 5

[START_REF] Feitelson | The impact of airport noise on willingness to pay for residences[END_REF]

noted that in theory, hedonic price studies do not require the segmentation of housing markets if all attributes are adequately taken into account. However, in practice, several types of market segmentation are likely to exist in most markets.

Table 1 :

 1 Summary Statistics Mean(SE) Reported values are means with standard deviations in brackets. The number of observations is 13840. The time period covered in the analysis is 2011-2014, and the unit of analysis is the transaction -year. 'Treated' is defined as municipalities in a sensitive area, and 'control' are municipalities in nonsensitive area in term of nitrates.

	Variables	All time period	Before 2012	After 2012
		N= 13840	N=7477	N=6363
			A. Dependent variables
	Average Prices (Euros) 159774.8 (124793.6) 166764.7 (130183.4) 151561.3 (117626.8)
			B Property characteristics
			B.1 House characteristics
	typ house	.30 (.45)	.32 (.46)	.28 (.44)
	pool	.02 (.16)	.03 (.17)	.02 (.15)
	terrace	.10 (.31)	.09 (.29)	.11 (.32)
	attic	.15 (.36)	.15 (.36)	.15 (.36)
	balcony	.12 (.33)	.10 (.30)	.15 (.35)
	parking surface	.55 (3.82)	.20 (2.03)	.97 (5.17)
	main room surface	89.01 (55.30)	94.39 (57.42)	82.67 (52.01)
	less5years	.11 (.31)	.08 (.28)	.13 (.34)
	room nb	3.90 (1.92)	4.10 (1.83)	3.67 (2.01)
			B.1 Flat characteristics
	typ flat	.76 (.42)	.80 (.39)	.71 (.44)
	level nb	2.39 (1.81)	2.51 (1.94)	2.30 (1.70)
	attic	.15 (.36)	.15 (.36)	.15 (.36)
	balcony	.12 (.33)	.10 (.30)	.15 (.35)
	parking surface	.55 (3.82)	.20 (2.03)	.97 (5.17)
	main room surface	89.01 (55.30)	94.39 57.42)	82.67 (52.01)
	less5years	.11 (.31)	.08 (.28)	.13 (.34)
	room nb	3.91 (1.92)	4.1 (1.83)	3.67 (2.01)

Note:

Table 2 :

 2 The impact of vulnerable zones revision on property prices with respect to treatment This table presents the coefficient estimates of the reduced form estimate of the effect of vulnerable zone boundary on the log of property prices. The treatment group represents both properties that were vulnerable before 2012 and properties that will become vulnerable in 2012. All regressions are estimated using municipalities fixed effect, with standard errors clustered at the year and municipality level. Robust standard errors in parentheses. Statistical significance is denoted by: *** p<0.01, ** p<0.05, * p<0.1

		(1)	(2)	(3)	(4)	(5)	(6)
	VARIABLES	treatment 1	treatment 2	treatment 3
	nitrates*policy	-0.0250	-0.0168	-0.00851	-0.0163	-0.0318	-0.0151
		(0.0211)	(0.0153)	(0.0227)	(0.0217)	(0.0235)	(0.0163)
	attic		-0.0540***		-0.0142		-0.0461***
			(0.0155)		(0.0266)		(0.0145)
	balcony		0.119***		0.126***		0.126***
			(0.0153)		(0.0196)		(0.0156)
	parking surface		0.00625***		0.00657***		0.00579***
			(0.00103)		(0.00115)		(0.00109)
	main room surface		0.00348***		0.00274**		0.00332***
			(0.00111)		(0.00136)		(0.00118)
	less5years		0.323***		0.325***		0.335***
			(0.0217)		(0.0237)		(0.0248)
	room nb		0.124***		0.140***		0.128***
			(0.0227)		(0.0277)		(0.0239)
	house		0.263***		0.258***		0.285***
			(0.0231)		(0.0328)		(0.0239)
	Municipalities FE	Yes	Yes	Yes	Yes	Yes	Yes
	Year FE	Yes	Yes	Yes	Yes
	Observations	13,787	13,787	6,803	6,803	11,387	11,387
	R-squared	0.266	0.655	0.229	0.622	0.279	0.677

Note:

Table 3 :

 3 The impact of vulnerable zones revision on property prices for property classified as vulnerable in 2012 and before 2012 (treatment 1) This table presents the coefficient estimates of the reduced form estimate of the effect of vulnerable zone revision on the log of property prices. The treatment group represents both properties that were vulnerable before 2012 and properties that will become vulnerable in 2012. All regressions are estimated using municipalities fixed effect, with standard errors clustered at the year and municipality level. Robust standard errors in parentheses. Statistical significance is denoted by: *** p<0.01, ** p<0.05, * p<0.1

		(1)	(2)	(3)	(4)
	VARIABLES	Flat	House
	nitrates*policy	-0.0758** -0.0513** -0.0704*** -0.0424***
		(0.0367)	(0.0209)	(0.0221)	(0.0162)
	Property characteristics	No	Yes	No	Yes
	Municipalities FE	Yes	Yes	Yes	Yes
	Year FE	Yes	Yes	Yes	Yes
	Observations	4,777	4,777	9,010	9,010
	R-squared	0.375	0.669	0.392	0.671

Note:

Table 4 :

 4 The impact of vulnerable zones revision on property prices for property classified as vulnerable in 2012 (treatment 2) : This table presents the coefficient estimates of the reduced form estimate of the effect of the revision on the log of property prices. The treatment group represents only properties that will become vulnerable in 2012. All regressions are estimated using municipalities fixed effect, with standard errors clustered at the year and municipality level. Robust standard errors in parentheses. Statistical significance is denoted by: *** p<0.01, ** p<0.05, * p<0.1

		(1)	(2)	(3)	(4)
	VARIABLES	Flat		House
	nitrates*policy	-0.115** -0.0612* -0.0533* -0.0333
		(0.0445) (0.0352) (0.0288) (0.0215)
	Property characteristics	No	Yes	No	Yes
	Municipalities FE	Yes	Yes	Yes	Yes
	Year FE	Yes	Yes	Yes	Yes
	Observations	3,122	3,122	3,681	3,681
	R-squared	0.229	0.548	0.386	0.671

Note

Table 5 :

 5 The impact of vulnerable zones revision on property prices for property classified as vulnerable before 2012 (treatment 3) This table presents the coefficient estimates of the reduced form estimate of the effect of the revision on the log of property prices. The treatment group represents only properties that were vulnerable before 2012. All regressions are estimated using municipalities fixed effect, with standard errors clustered at the year and municipality level. Robust standard errors in parentheses. Statistical significance is denoted by: *** p<0.01, ** p<0.05, * p<0.1

		(1)	(2)	(3)	(4)
	VARIABLES	Flat		House
	nitrates*policy	-0.0532 -0.0403** -0.0755*** -0.0431**
		(0.0402) (0.0182)	(0.0236)	(0.0170)
	Property characteristics	No	Yes	No	Yes
	Municipalities FE	Yes	Yes	Yes	Yes
	Year FE	Yes	Yes	Yes	Yes
	Observations	3,907	3,907	7,480	7,480
	R-squared	0.407	0.721	0.404	0.675

Note:

Department is a French geographical level below the regional level.

The unit of observation is the transaction-year. We have14348, 13376, 16159 and 13624 transactions in 2011, 2012, 2013 and 2014 respectively.

We cluster on the municipality because this is the unit of boundary for vulnerable zones, and on the year since this is the unit of measurement for the price. This yields a total of 2704 clusters (676 municipalities*4 years). By clustering on the municipality we are also accounting for potential serial correlation within a municipality.


Henrik Andersson
email: henrik.andersson@tse-fr.eu

Emmanuelle Lavaine
email: emmanuelle.lavaine@umontpellier.fr

Nitrates and Property Values: Evidence from a French Market Intervention

Keywords: Hedonic Price Analysis, difference in difference, water pollution

This paper examines the effect of properties being located in vulnerable zones in term of nitrates on the property prices using a change in the classification of vulnerable zones in France in 2012. Using an identification strategy based on a spatial differencein-differences specification, we show that the revision of the classification significantly decreased not only property prices in zones that became classified as vulnerable after the revision, but also those of properties already classified as vulnerable. However, the effect was stronger for the former, 10% vs. 5%, and this differences may reflect a difference in how zones are classified. The risks covered in the 2012 classification cover a broader range of risks, and hence the larger price effect may reflect this additional perceived risk exposure.

Introduction

Nitrates in drinking water, which may come from nitrogen fertilizers applied to crops, are a potential health risk [START_REF] Crutchfield | Benefits of safer drinking water: the value of nitrate reduction[END_REF]. In Europe, Council Directive 91/676/EEC (EEC, 1991) has been implemented to protect water sources against pollution caused by nitrates from agricultural sources through a number of measures incumbent on the European Union (EU) member states. These measures concern monitoring surface water and groundwater, designating vulnerable zones, introducing codes of good agricultural practice, adopting action programs, and evaluating the actions implemented. The measures provide benefits in the form of reducing the potential health risk, but since they are costly to implement the evaluation of them is important. Benefit-cost analysis (BCA) provides a powerful, and transparent, tool for policy evaluations, but it requires that both benefits and costs are measured in a common metric. The standard approach is to use money as this common metric and since health effects do not have easily available prices it means that non-market valuation techniques are required to monetize the social value of reducing the health risks reductions from measures like the ones above.

Broadly speaking there exists two approaches to monetize health risk reductions, as well as other "non-market goods", i.e. stated-(SP) and revealed-preference (RP) methods (see, e.g., [START_REF] Freeman | The Measurement of Environmental and Resource Values: Theory and Methods[END_REF]. The former approach, i.e. SP methods, is based on respondents' choices in hypothetical situations, whereas the latter, i.e. RP methods, is based on actual decisions in markets related to the non-market good of interests. Both approaches have their strengths and weaknesses, but economists have historically preferred the RP approach since it is based on actual decisions. Regarding water quality it has been shown with market data that it can have a significant effect on property values [START_REF] Leggett | Evidence of the effects of water quality on residential land prices[END_REF].

Hence, market data can be used to estimate the marginal willingness to pay (WTP) for access to clean and safe water. However, the literature underlines the need to improve current and to develop novel methods to improve our ability to estimate the marginal WTP for environmental improvements with market data (e.g., [START_REF] Bento | Equity impacts of environmental policy[END_REF][START_REF] Guignet | Contamination and incomplete information: Bounding implicit prices using high-profile leaks[END_REF].

A wide range of studies on this topic use quasi-experimental techniques. For instance, [START_REF] Davis | The effect of health risk on housing values: Evidence from a cancer cluster[END_REF] measures the effect of health risk on housing values by exploiting a severe increase in pediatric leukemia in an isolated county in Nevada as a natural experiment.

In addition, [START_REF] Currie | Environmental health risks and housing values: Evidence from 1,600 toxic plant openings and closings[END_REF] recently examined the effect of health impacts of 1,600 openings and closings of industrial plants that emit toxic pollutants on the housing market. [START_REF] Chay | Does air quality matter? evidence from the housing market[END_REF] exploit the quasi-random assignment of air pollution changes across counties induced by federally mandated air pollution regulations to identify the impact of particulate matter on property values. [START_REF] Greenstone | Does hazardous waste matter? evidence from the housing market and the superfund program[END_REF] use the Superfund cleanups to analyze its local welfare impacts.

In this study we are interested in the effect on property prices from exposure to nitrates. We are concerned with examining the impact on property prices from being located in vulnerable zones, which are all known areas of land which drain into surface waters and groundwater which are affected by pollution or at risk of being so. Zones classified as vulnerable zones were revised in December 2012 in France due to pollution concentration from surface waters and groundwater observed in 2010-2011. Before 2012, areas of land were classified as being at risk if the population was exposed to contaminated drinking water. The first delimitation was completed in July 1997 and was revised several times since then in 2000, 2003, 2007, 2012, and most recently in 2014. 1 The 2012 boundary revision was mainly concerned with eutrophication risk, and was due to a litigation initiated by the European Commission (EC) against France for misapplication of the Nitrates Directive (EEC, 1991). France was sentenced by the European Court of Justice (ECJ) on June 13, 2013, because of the 2007 boundary revision. The ECJ considered that a wider classification would have been justified given the excessive levels of nitrates or eutrophication in surface and groundwater bodies. In short, the EC considered that France did not take into account eutrophication risk into zoning and did not include enough areas as vulnerable located in the four following basins: Adour-Garonne, Loire-Bretagne, Rhin-Meuse et Rhône-Méditerranée. 2 The nitrates threshold for safe drinking water that is frequently used in regulatory texts is 50mg/l 3 , even if this level is not enough to protect from eutrophication. 4 A difference between the EC and France was that the EC used the maximum value relative to nitrates concentration to define contaminated water, whereas France used the mean of the concentration. The French Circular related to the 2012 reclassification instead use the 90th percentile resulting from the 2010-2011 monitoring campaign to evaluate nitrate pollution. 5 When its value exceeds 40mg/l, the Circular states it is necessary to 1 Due to data limitation our analysis do not cover the 2014 delimitation. 2 http://www.donnees.centre.developpement-durable.gouv.fr/zv/comite bassin/Rapport Comite bassin.pdf 3 An area is vulnerable if surface and groundwater, particularly for drinking water supply, have or may have a nitrate content greater than 50 mg/l and/or if the waters of estuaries, coastal or marine waters and surface freshwaters show a tendency to eutrophication that can be reduced by decreasing nitrogen inputs. In the same vein as [START_REF] Chay | Does air quality matter? evidence from the housing market[END_REF], to mitigate selection and endogeneity problems and to infer the impact of a new directive on property prices, we use the 2012 boundary revision of vulnerable zones as a quasi-experimental approach. While this is not the first difference in difference (DiD) study to use the effect of an environmental policy on hedonic prices as a natural experiment, this study aims to analyze the consequences of new information that may have altered the risk perception. That is, the study examines the effects on housing markets from the new information, not only in areas directly concerned by a policy change, but also in areas indirectly concerned by this policy change. In fact, the study focus, in addition to areas never classified as vulnerable, on two types of areas:

(i) areas of land that have been classified as being sensitive in term of nitrates before 2012, and (ii) areas of land considered as sensitive from December 2012. Thus, the paper aims to shed light on people's WTP for perceived differences in nitrates risk on health and eutrophication. The analysis of this paper focuses on municipalities in the Centre and Poitou Charentes region, where residents recently were considered to be living in a sensitive geographical area in terms of nitrates, though this is not the only place in France. The geographic region for the control group is then represented by all towns not classified as vulnerable zones. House prices in new sensitive areas are compared before and after the 2012 designation of vulnerable zones with the nearby municipalities. The conditions of supply and demand for housing are relatively similar in both groups, so that we can expect a similar set of implicit prices in vulnerable zones and in its surroundings.

The article is structured as follows. In section 2 we first describe the data used and then we outline the empirical strategy. We then in section 3 present the findings from the regression results with a focus on the impact from vulnerable zones on the property prices.

Section 4 provides a discussion of our findings and some concluding comments.

2 Data and empirical strategy The classification of whether an area is a nitrate sensitive, or a vulnerable, area is at the municipality level. Information on which municipality that is considered to be vulnerable is provided by the DRIEA, subordinated to the Ministry for an Ecological and Inclusive Transition, and responsible for the environment, town planning and housing at the regional level in France. 7 The matching of the data from PERVAL and the info on vulnerable zones was done using GPS coordinates for the municipalities of our four French departments, i.e. Loiret, Indre et Loire, Vienne and Charentes Maritime. Hence, each property is defined as vulnerable, and if so at what point in time, or not at the municipality level. This is illustrated Figure 1 that shows the selected areas for our analysis. We have classified municipalities at three levels with respect to the date they have been considered

as vulnerable. The first year that municipalities were considered as vulnerable was in 2007 and then in December of 2012 there was the reclassification mentioned above. Thus, municipalities have therefore been classified as: (i) never being nitrates sensitive, shown as 6 Department is a French geographical level below the regional level. There exist 96 departments in France, and the numbers in the parentheses refer to the official "department INSEE code". For our classification, see below, we will use municipalities which is a French geographical level below the department level.

7 For info (in French only), http://www.driea.ile-de-france.developpement-durable.gouv.fr/.

nitrate sensitive since December 2012.

[Figure 1 about here.]

Table 1 presents summary statistics of the property transactions that took place between 2011-2014 in the Centre and Poitou Charentes Region. The unit of analysis is the transaction -month -year and Panel A of Table 1 shows the average price. Transactions occurred between 2011 and 2014, but all property price have been converted to the 2014 level using the INSEE property price index.8 Also shown is the number of transactions per municipalities by year. The number of observations is 15,390.9 One concern with our analysis is that a change of a sensitive area can affect the number of property transactions, potentially leading to sample selection bias.

[Table 1 about here.]

The key variables of the PERVAL data set are, in addition to the property prices, the number of rooms, the type of apartment or house, the property surface, the presence of an attic, a parking space, a balcony, and a variable which indicates if the property is less than 5 years old. We add the floor level for apartments while we add the presence of a pool or a terrace for houses. We also include specific variables to distinguish houses and apartments. We construct type of house as a dummy which is coded as 1 if it is a standard house (detached house), considered as the most popular type of houses in the data set (it represents more than 50% of all transactions) versus a farmhouse, a chalet, a city house, a rural house or a villa. We construct type of apartment as a dummy which is coded as 1 if it is a standard apartment (two rooms in one floor with possible mezzanine), considered as the most popular type of apartment in the data set (it represents nearby 78% of all transactions), versus a studio or a duplex, or apartments with more than two rooms. All these dwelling characteristics will appear as control variables in the regression analyzes.

Empirical Methodology

Our goal is to assess the impact of vulnerable zone boundaries on property prices. We estimate DiD models to exploit the change in the boundaries in December 2012. Hous-ing prices are compared before and after the geographical boundary change with nearby municipalities acting as a control group. We implement this by estimating the following equation:

Y imt = βnitrates im * policy t + δX imt + σ t + α m + imt (1)
where Y is the log of property price with subscripts i, m, and t, defining the individual transaction, the municipality, and the time period (month-year), respectively. Turning to the independent variables, policy is an indicator variable for the month and year of the new boundary of vulnerable areas that corresponds to December 2012, and nitrate is an indicator variable for whether the municipality is classified as vulnerable. The coefficient for interaction variable β is the DiD parameter. The property controls are represented by the vector X imt that includes year of construction, the surface of the property, the surface of the parking, the surface of the main room, the number of rooms, the presence of an attic and the presence of a balcony. It also differentiates specific attributes for house and apartment such as the type of house and the presence of a pool and a terrace or the type of apartment and the floor level. Property characteristics are added to control for any differences in the evolution of property characteristics between the treatment and control group. We also control for temporal patterns by including year and month dummies in σ t . Because many properties were sold repeatedly during the sample period it is possible to control for unobserved municipalities-characteristics. Thus, we include municipalities fixed effects (α m ) to control for time-invariant characteristics of the municipality. Finally, imy represents the error term, which consists of an idiosynchratic component and a term clustered on the municipality and year.10 This approach is in line with the suggestion in [START_REF] Kuminoff | Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities[END_REF] that large gains in accuracy can be realized by moving from the standard linear specifications for the price function to a more flexible framework that would use a combination of spatial fixed effects, quasi-experimental identifications, and temporal controls for housing market adjustment.

The different treatments with their treated and control observations are described in Table 2. As with any DiD design, the key underlying assumption for identification is that the control group serves as a valid counterfactual for the treatment group with parallel trends. Although we cannot explicitly verify this assumption, we feel this threat is limited in this setting for several reasons. Firstly, we vary the definition of treatment to test the robustness of our results in the next section. Because the boundary of a sensitive zone could be anticipated, we also could expect sorting of households according to preferences prior to the boundary. Using our really rich and exhaustive data set for property transactions, we are able to distinguish municipalities that were already considered as vulnerable in 2012 to municipalities that will become vulnerable in 2012. The issue of sorting, following the 2012 policy, is limited in municipalities considered vulnerable before 2012 as households may have already sorted themselves at the time of the old geographical boundary. Secondly, as is illustrated in Figure 2 in the result section, prices in both groups reveal a similar pattern before the 2012 revision of boundary areas.

[Table 2 about here.]

Results

In this section we provide the results from the hedonic DiD models. Since the main focus is on the effect on property prices from the boundary classifications the tables with the regression output are available in the appendix, and in this section we instead only present a summary of the effects from the reclassification. However, the regression output is an important validity test of the results and we therefore briefly discuss them here.

Table 4 shows the results from the regressions on Treatment 1 and 2. Regarding the other property characteristics, i.e. all characteristics expect nitrates * policy, overall we find the expected effects on the property prices. That is, properties with more of attractive characteristics are prized higher in the market. For instance, we find that the size of surface of the house or the apartment and access to a parking space have a positive effect on the property price. There are also findings that are different for houses and apartments, but in line with our expectations. For instance, having access to a balcony has a positive effect on the price of an apartment, but not on a house price, whereas the number of rooms has a positive effect on the house, but not on the apartment price. Regarding the former it is reasonable that having access to a balcony in an apartment is more important than a house that usually provides some outdoor area even without a balcony, and hence the different findings are in line with our expectations. The latter finding could be a result of that once surface is reflected in the apartment price, then the number of rooms has no statistically significant effect on the apartment price. The results are similar between treatments. The exception is Attic that is positive and statistically significant in the apartment subsample in Treatment 2, which is against our expectations.

The equivalent regression results for Treatment 3 and 4 are shown in Table 5. Results

are similar to the ones in Table 4 for Treatments 1 and 2. Again, the only finding that is not in line with our expectations is Attic, which is positive and statistically significant in the apartment subsample in Treatment 3. Based on the available information on this variable, it is not possible for us to explain why it has a positive effect on the apartment prices in two of our treatments. However, there are no other findings that are contrary to the expectations, and hence the standard validity requirements of a hedonic model are fulfilled. An interesting finding from the regressions in Table 4 and 5 is the importance of taking into account the heterogeneity that exists in a sample consisting of both houses and apartments. Hence, when examining the effect on the property prices from the classification of vulnerable zones we will use the results from all regressions including when they are run separately for apartments and houses.

Figure 2 provides a graph of monthly adjusted prices levels from 2011 to 2014 for the treated and control group in Treatment 2, with prices levels adjusted by δX imt , σ t and α m using the results from Table 4. It can be seen that prior to the December 2012 reclassification, the prices follow a similar pattern, with levels slightly higher in the municipalities that were going to become classified as vulnerable. Immediately after the reclassification, prices levels in vulnerable areas decrease whereas prices levels in non-vulnerable areas stay constant. Thus, after the 2012 revision, prices level in municipalities that are not classified as nitrates sensitive exceed those of nitrate sensitive areas. Hence, these visual illustration clearly demonstrates that prices follow the same pattern before the reclassification, and a strong, temporal effect on the prices levels after the reclassification.

[Figure 2 about here.]

Table 3 summarizes the effect on the property price from being located in a municipality classified as nitrate vulnerable by focusing on the DiD coefficient, i.e. β, from Eq. ( 1), which empirical estimates are found in Table 4 and5. The three columns with results show the findings with the first one providing the results from the overall sample, and column 2 and 3 showing the results for apartments and houses separately. By first examining the effect in Treatment 1, i.e. vulnerable before or after 2012 compared to never vulnerable, on the overall property market we find that there is no effect on property prices from the classification of being located in a vulnerable zone. This absence of significant results for the overall sample suggests, as for the other property characteristics, that taking into account a potential heterogeneity among houses and apartments in the effect from vulnerable zones on the prices may be important. Indeed, when we split the market according to whether the properties are houses or apartments we find a statistically significant effect on the house prices. For houses we find a 6.7% decrease in the price from being in a nitrate sensitive area.

[Table 3 about here.]

As shown in Table 3 the results for Treatment 2 differ from those of Treatment 1.

In Treatment 2 the treated group consists of those properties than became classified as vulnerable in December 2012, and the control group is those properties that have never been classified as located in vulnerable zones. The second block of Table 3 reveals a negative and statistically relationship for both the overall sample and when it has been separated into houses and apartments (for apartments at the 10% level). The decrease in the property price from the December 2012 revision of municipalities is about 10% in the three different samples. We further extended our analysis by examining the effect of the 2012 revision on prices of properties already classified as vulnerable before 2012, by using never vulnerable as the control group, i.e. Treatment 3. Results from Treatment 3 show no significant effect in the overall market and for apartments whereas house prices decrease by 5.2% after the December 2012 reclassification. We finally repeat once again the exercise using the group that became classified as vulnerable in December 2012 as treated, and restricting the control group to properties that were considered as vulnerable before 2012

only. The results are shown in the last block of Table 3 and the DiD estimator is again negative and significant for the overall sample and for house prices. Again, the effect on apartment prices is not statistically significant. Results show that house prices for those properties that became classified as vulnerable in 2012 decrease by 5.7% compared to those that were classified as vulnerable already in 2007.

To summarize, the findings that being located in a vulnerable zone is robust for houses, but not for apartments. The effect is negative and statistically significant in all 4 regressions for houses, but only in the regression for Treatment 2 for apartments. The monetary value can be obtained by using the mean of the house prices from

Discussion

The goal of this paper was to examine the effect on property prices from nitrate exposure.

To account for the endogeneity of nitrate exposure, we exploit the change in boundary areas in term of nitrates that occurred in 2012 in France. To do so, the empirical model compares property prices in municipalities considered as vulnerable (the treatment group) to municipalities considered as not vulnerable (the control group).

Overall we find a decrease in property prices in municipalities classified as vulnerable (the treatment group) compared to municipalities classified as not vulnerable (the control group). The findings differ, though, depending on the definition of the treatment and the control groups. As expected we find a negative effect from the classification in Treatment 2, i.e. treated = Vulnerable 2012 and control = Never Vulnerable. More surprisingly, we also find a negative and significant effect of the policy on house prices that are not concerned by the new zone boundary, i.e. Treatment 3 where already classified as sensitive were treated. One potential explanation for this effect is that the reclassification acts as a reminder about the potential nitrate risk faced by municipalities also in those areas that were already classified as vulnerable. The effect on the property prices is, however, larger in the areas classified as vulnerable in 2012 compared with those that were classified in 2007.

This relationship corresponds to the more comprehensive risk classification in 2012. Hence, the findings could be interpreted as a combination of a risk reminder and a understanding that the new classification is more comprehensive in the sense that it covers more risks.

That is, if property owners/buyers are well informed about the risk classification then the larger effect from the new classification could be a measure of the risk premium of the additional risks covered in the new classification. [START_REF] Feitelson | The impact of airport noise on willingness to pay for residences[END_REF] noted that in theory, hedonic price studies do not require the segmentation of housing markets if all attributes are adequately taken into account. However, in practice, several types of market segmentation are likely to exist in most markets. This is because housing markets are not uniform [START_REF] Adair | Investment and private residential development in inner city dublin[END_REF][START_REF] Fletcher | Heteroscedasticity in hedonic house price models[END_REF].

Hence, the literature highlights that it is unrealistic to treat the housing market in any geographical location as a single entity. Unfortunately, the definition, composition, and structure of sub-markets have not been given much attention in hedonic price literature, although this is an important empirical issue [START_REF] Fletcher | Heteroscedasticity in hedonic house price models[END_REF]. This study examines the effect from taking into account the heterogeneity in the market. The lack of significant results in Table 3 compared to the following tables is in line with the literature highlighting the importance of market segmentation in empirical work. In the same vein, the literature highlights that examining the full housing value distribution can have important policy implications for the results of a hedonic analysis (Gamper-Rabindran and Timmins, 2013).

Gamper-Rabindran and Timmins (2013)'s valuation of superfund cleanups shows that a focus on the median housing value would have understated the larger effects at the lower tails of the housing value distribution. In this paper, the distinction between houses and apartments acts as a proxy of the property value distribution.

This study provides empirical estimates of relevance for policy use in France. Moreover, it confirms the importance of taking into account heterogeneity in the property market when using the hedonic regression technique. Further, an important contribution is the finding that not only properties that were reclassified due to the new policy were affected by it, but also those already classified. The data in this study does not allow for examining whether individuals are not well-informed about the risks when acting in the property markets, or if risk perception may change of over time, i.e. the perception declines with time and a reminder is necessary to keep it accurate. We believe this to be an interesting and important line of research to better understand decisions when using the revealed preference approach.

Figures and tables

  white in Figure 1, (ii) nitrate sensitive before December 2012, shown as grey in Figure 1, and (iii) nitrate sensitive from December 2012, shown as black in Figure 1. Hence, the different colors indicate whether and when municipalities were considered as vulnerable, and out of 676 municipalities, 135 have never been classified as nitrate sensitive, 456 have been classified as nitrate sensitive since before December 2012 and 85 are classified as

Figure 1 :

 1 Figure 1: Geographical boundaries of nitrate sensitive areas

Figure 2 :

 2 Figure 2: Adjusted yearly price level by nitrate sensitive municipalities

  -annual evolution at least since 2004-2005 to define its evolution; if increasing, the zone has to be considered as vulnerable, whereas if stable or decreasing, nearby measurement points should be checked (points not included in the surveillance network)

	to confirm the non-classification or to invalidate it. This 2012 reclassification partly meets
	the delimitation deficiencies recognized by the ECJ in the context of the litigation. To-
	day, nearly 55% of France is classified as vulnerable, which corresponds to regions where
	agricultural land is dominating.

4 http://www.cnrs.fr/inee/communication/breves/docs/Eutrophisation resume.pdf 5 http://www.centre.developpement-durable.gouv.fr/IMG/pdf/Circulaire ZoneVulnerable 191211 cle7f77b8.pdf analyze its inter

  Table 1, i.e. 183,278 e. This results in a price reduction of ca. 10,000 e for properties classified as in vulnerable zones in 2007 compared to those never classified as vulnerable (Treatment 3), and a reduction of ca. 20,000 e for those classified as vulnerable in 2012 compared to never vulnerable. As mentioned previously, before 2012 areas of land classified as vulnerable were concerned with contaminated drinking water risk, whereas the ones classified as vulnerable in December 2012 were concerned with eutrophication risk. One interpretation of the results is therefore that, if the December 2012 reclassification reminded the market of the previous classification, the difference between the two would reflect the WTP to reduce eutrophication risk, and hence, the marginal WTP to reduce health risk due to contaminated drinking water is close to equal to the marginal WTP to reduce eutrophication risk.

Table 1 :

 1 Summary Statistics

	All time period Before 2012 After 2012

Notes: Means with standard deviations in brackets. Let mean= x, Std.dev.(dummy)= x(1 -x The time period covered is 2011-2014, and the unit of analysis is the transaction -month -year. The dataset has 6,390 apartments and 9,010 houses. a: Attic = 1 if the attic has not or cannot be converted to to a room.

Table 2 :

 2 Treatment classification

	Treatment Treated	Control
	1	Vulnerable 2007 and/or 2012 Never vulnerable
	2	Vulnerable 2012	Never vulnerable
	3	Vulnerable 2007	Never vulnerable
	4	Vulnerable 2012	Vulnerable 2007

Table 3 :

 3 The impact of vulnerable zones revision on property prices with respect to treatment This table provides a summary of the reduced form coefficient estimates of the effect of vulnerable zone boundary on the log of property prices from Table4 and 5.

	Description	Overall	Apartment	House
			Treatment 1
	nitrates*policy	-0.027	-0.050	-0.067***
		(0.024)	(0.032)	(0.023)
	N	15,390	4,777	9,010
	R 2	0.652	0.362	0.651
			Treatment 2
	nitrates*policy	-0.095***	-0.096*	-0.109***
		(0.030)	(0.052)	(0.030)
	N	7,772	3,122	3,681
	R 2	0.620	0.543	0.653
			Treatment 3
	nitrates*policy	0.003	-0.017	-0.052**
		(0.022)	(0.029)	(0.024)
	N	12,618	3,907	7,480
	R 2	0.672	0.716	0.651
			Treatment 4
	nitrates*policy	-0.087***	-0.079	-0.057**
		(0.028)	(0.051)	(0.028)
	N	10,390	2,525	6,859
	R 2	0.672	0.707	0.633
	House Characteristics	Yes	Yes	Yes
	Municipalities FE	Yes	Yes	Yes
	Year FE	Yes	Yes	Yes
	Month FE	Yes	Yes	Yes
	Notes:

Robust standard errors in parentheses. Standard errors clustered at the year and municipality level. Statistical significance is denoted by: *** p<0.01. ** p<0.05. * p<0.1 See Table

2

for a description of the treatments.

Table 4 :

 4 Regression results treatments 1 and 2: Dependent variable log of the price : Robust standard errors in parentheses. Standard errors clustered at the year and municipality level. Statistical significance is denoted by: *** p<0.01, ** p<0.05, * p<0.1 See Table2for a description of the treatments.

			Treatment 1			Treatment 2
	Variables	Overall	Apartment	House	Overall	Apartment	House
	nitrates*policy	-0.027	-0.050	-0.067*** -0.095***	-0.096*	-0.109***
		(0.024)	(0.032)	(0.023)	(0.030)	(0.052)	(0.030)
	Attic	-0.055***	-0.078	-0.046***	-0.021	0.062***	-0.010
		(0.016)	(0.076)	(0.012)	(0.027)	(0.014)	(0.020)
	Balcony	0.109***	0.060***	0.004	0.113***	0.006***	0.033
		(0.012)	(0.010)	(0.032)	(0.016)	(0.002)	(0.033)
	Parking	0.005***	0.006***	0.002*	0.006***	0.014***	0.002*
		(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
	Surface	0.004***	0.014***	0.003***	0.003**	0.456***	0.002**
		(0.001)	(0.001)	(0.001)	(0.001)	(0.048)	(0.001)
	Less-5-years	0.342***	0.459***	0.179*** 0.331***	-0.002	0.166***
		(0.023)	(0.042)	(0.014)	(0.023)	(0.025)	(0.020)
	Rooms	0.127***	-0.014	0.101*** 0.145***	-0.002	0.108***
		(0.023)	(0.018)	(0.020)	(0.028)	(0.025)	(0.023)
	House	0.240***			0.230***
		(0.020)			(0.029)
	2-room-flat		-0.185***			-0.014***
			(0.023)			(0.004)
	Floor		-0.014***			-0.075
			(0.003)			(0.104)
	Detached			0.093***			0.047***
				(0.013)			(0.017)
	Pool			0.246***			0.254***
				(0.031)			(0.041)
	Terrace			0.095***			0.097***
				(0.013)			(0.019)
	Municipalities FE	Yes	Yes	Yes	Yes	Yes	Yes
	Year FE	Yes	Yes	Yes	Yes	Yes	Yes
	Month FE	Yes	Yes	Yes	Yes	Yes	Yes
	N	15,390	4,777	9,010	7,772	3,122	3,681
	R 2	0.652	0.362	0.651	0.620	0.543	0.653

Notes

Table 5 :

 5 Regression results treatments 3 and 4: Dependent variable log of the price : Robust standard errors in parentheses. Standard errors clustered at the year and municipality level. Statistical significance is denoted by: *** p<0.01, ** p<0.05, * p<0.1 See Table2for a description of the treatments.

			Treatment 3			Treatment 4
	Variables	Overall	Apartment	House	Overall	Apartment	House
	nitrates*policy	0.003	-0.017	-0.052** -0.087***	-0.079	-0.057**
		(0.022)	(0.029)	(0.024)	(0.028)	(0.051)	(0.028)
	Attic	-0.046***	0.050***	-0.045*** -0.090***	-0.234	-0.064***
		(0.015)	(0.010)	(0.013)	(0.017)	(0.178)	(0.013)
	Balcony	0.110***	0.004***	-0.014	0.105***	0.073***	0.005
		(0.014)	(0.001)	(0.037)	(0.016)	(0.017)	(0.042)
	Parking	0.004***	0.014***	0.002*	0.004***	0.007***	0.002
		(0.001)	(0.001)	(0.001)	(0.001)	(0.002)	(0.002)
	Surface	0.004***	0.492***	0.003*** 0.005***	0.015***	0.004***
		(0.001)	(0.045)	(0.001)	(0.000)	(0.001)	(0.000)
	Less-5-years	0.358***	-0.015	0.188*** 0.335***	0.387***	0.181***
		(0.026)	(0.020)	(0.016)	(0.034)	(0.048)	(0.017)
	Rooms	0.131***	-0.015	0.103*** 0.098***	-0.028	0.080***
		(0.025)	(0.020)	(0.021)	(0.007)	(0.022)	(0.006)
	House	0.259***			0.218***
		(0.022)			(0.023)
	2-room-flat		-0.017***			-0.194***
			(0.003)			(0.034)
	Floor		-0.014			-0.005
			(0.030)			(0.005)
	Detached			0.092***			0.125***
				(0.014)			(0.011)
	Pool			0.247***			0.222***
				(0.033)			(0.023)
	Terrace			0.102***			0.082***
				(0.015)			(0.013)
	Municipalities FE	Yes	Yes	Yes	Yes	Yes	Yes
	Year FE	Yes	Yes	Yes	Yes	Yes	Yes
	Month FE	Yes	Yes	Yes	Yes	Yes	Yes
	N	12 618	3 907	7 480	10390	2525	6859
	R 2	0.672	0.716	0.651	0.672	0.707	0.633

Notes

Annual price index calculated as mean of year (quarter info) from INSEE, https://www.insee.fr/fr/statistiques/series/102770558?INDICATEUR=2770594.

The unit of observation is the transaction-month-year. We have8138, 4139, 2614 and 499 transactions in 2011, 2012, 2013 and 2014, respectively. The number of transactions in 2014 is less as we do not have observations for the entire year.

We cluster on the municipality because this is the unit of boundary for vulnerable zones, and on the year since this is the unit of measurement for the price. This yields a total of 2704 clusters (676 municipalities*4 years). By clustering on the municipality we are also accounting for potential serial correlation within a municipality.

Appendix -Regression output

[Table 4