The influence of Environmental policies on farmland Prices in the region Bretagne of France

Elodie Letort1 and Chalachew Temesgen2

Abstract

The region Bretagne is the top leading agricultural region in France in terms of agricultural annual turnover. In addition to urban pressure, the competition for farmland is enhanced by strong environmental regulations and incentives. In this paper, we study some determinants of farmland prices in Bretagne. Two particular points are stressed. The first one concerns the role of the different operators of the land market. The second one concerns the impacts of environmental policies and regulation on land market. For this, we estimate spatial hedonic pricing models based on individual level data. This data set gathers all transactions of farmland sales which are notified by notaries in Bretagne from 2007 to 2010. Estimation results show an increase in farmland prices in more constrained environmentally areas. It explains by the rising demand from farmers who have to meet the manure spreading regulations. Results also show the importance of spatial interaction on farmland market.

Key-words: environmental policies; hedonic price function; spatial econometric model.

1 ARAP (Association Régionale pour l’Agriculture Paysanne) – Research analyst associated with UMR SMART INRA, Rennes, France

2 UMR SMART INRA Rennes, France.

Introduction

The region Bretagne is a very agricultural area located at the north-west of France3.

Agriculture covers about 65 % of total land area. This share is higher than the French average

(53%). However the decline of farmland is faster in Bretagne, where almost 3% of the

regional farmland has been converted to non-agricultural activities between 2000 and 2010,

and only 1% in France (Agreste, 2011). The demands for land by agriculture and by non

agricultural businesses have both strong implications for the farmland sale market where both

farmers and non-farmers operate. French young farmers therefore face intense competition

from the farmers in place and non-farmers who live towns and urban areas (Levesque, Liorit,

and Pathier, 2012). Various studies report that farmers want to enlarge the operational area

size of their farm, with a farmers’ tendency to specialize their production system (Levesque et

al., 2012). Besides, non-farmers put a pressure on farmland so as to reap the prospect of

windfall gain due to future expected changes in land price over time. This capital gain is

mainly depending on the location and accessibility of land to the nearest urban growth pole

and communication center. The existing environmental regulation and agricultural zoning

policies is another factor that affects the structure of farmland demand within agriculture.

These double edge demands for farmland resource have two effects in the functioning of

farmland market in France. The supply effect refers to the declining availability of farmland

for agriculture production. The demand effect refers to the agricultural productivity of

farmland. The demand effect would naturally increase the market clearing price. However, the

trend of farmland prices in France has remained relatively stable over time as compared to

other European countries (Latruffe, 2008 and Swinnen, 2009)4. This is due to land market

regulations that involve the French government and farmers' organizations. Regional land

offices (Sociétés d’aménagement foncier et d’établissement rural or SAFER) operate on

farmland markets according to policy objectives concerning agriculture, environment or

infrastructure development. In addition, the farmland rental rates are constrained by

administrated bounds and the law limits the rights of landowners to protect farmers’ access to

3 According to the European nomenclature of regional levels (NUTS), Bretagne is one of the 22 NUTS2 regions of metropolitan France. There are 4 additional French oversea NUTS2 regions. The different levels of territorial units for the region Bretagne are presented in Annex A.

4 The historical price of farmland was increasing continuously during 1960s until 1980s and declines or stable from 1980s to 1996 and started to increases slightly the price from 1997 to 2010 ( Latruffe et al., 2008; Cavailhes et al., 2011).

farmland. These regulations often induce side payments in farmland transactions which are

not registered in their observed prices.

Principally, there are two main approaches to study the determinants of land prices. The first

approach is based on the actualized value of the farmland which is known as the Net Present

Value (NPV)5 model. The NPV model is based on theoretical and empirical developments of

the Ricardo capitalization formula. It sets the present value of the land as the discounted sum

of future expected revenues provided by farmland. This approach is often used to explain the

temporal evolution of the price of land in relation to macroeconomic variables. The NPV

farmland price model is also used to study the impact of agricultural support policies on the

farmland price. Several studies have shown that the policy agricultural supports, especially

direct payments to farmers, capitalize into the farmland price, either fully or partially,

depending on the modalities of their implementation (Floyd, 1965, Altson and James, 2001;

Guyomard et al., 2004, Cianian and Swinnen, 2006 and 2007 and Feichtinger and Salhofer

2011). The second approach relies on the construction of a hedonic price model. The hedonic

method helps to derive the marginal values of farmland characteristics which affect the

willingness to pay of buyers (Palmquist, 1989). The price of farmland should reflect its

characteristics, especially geographical location characteristics. This analysis deals with

individual plots of farmland over the defined geographical zones. The hedonic approach has

already been employed in a number of farmland market researches. For example, Miranwski

and Hammes (1984) determined the implicit price of for soil characteristics in Iowa (USA).

Palmquist and Danielson (1989) applied this technique to measure the impact of soil erosion

and drainage on farmland prices. Shi, Phipps and Colyer (1997) and Plantiga et al. (2002)

showed how parcel characteristics and urban factors influence farmland sales price in United

States. Cavailhés and Wavresky (2003) analyzed the urban influence in the southern part of

France. Attention has also been given to the buyers and sellers characteristics (King and

Sinden, 1994; Harding, Knight, and Sirman, 2003; Geerte Cotteleer, Corneils Gardbroek, and

Jan Lujit,2008). Empirically there are very few works dealing with environmental policies

and regulations.

The hedonic method is applied in this paper. Farmland characteristics include productive

values of farmland with the effects of environmental policies and regulations of farmland

market, taking into account the characteristics of buyers and sellers that may interfere with the

5 NPV models of farmland price are considered as theoretically sound and are the most cited model in farmland price literature (Alston 1986, Burt 1986, Featherstone et al.1987, Campbell and Schiller 1987 and Clark 1993).

valuation of the different farmland characteristics. We use a unique data gathering of the

individual transactions in Bretagne from 2007 to 2010.

Two particular points are stressed in this paper. The first one concerns the role of the different

operators of the land market. As farmland can be used for different purposes, we assumed that

a farmland will not be valued in the same way by all agents. For example, Facchini (1997)

defined three types of farmland values. These are the productive value, the investment value

and the consumption value. The production value of farmland is the profitability of

agricultural activities which usually depends on the marginal productivity of the land. The

investment value derives from the rental value of farmland. When an investor seeks to

enhance its savings by purchasing farmland, the farmland reflects both the current agricultural

rent and the potential future non-agricultural rental income if it is converted to urban

activities. The location and accessibility of the land will determine the non-agricultural rental

income and its probability. Finally, some individuals or agents would likely to buy farmland

only for personal recreational activities. They can use this farmland as garden or sport field

for example. This is defined as "the consumption value" of farmland that usually correspond

tonon-monetary benefits for the owner. The measurement of this consumption value is beyond

the scope of this paper, because it requires data which are not available. Therefore, this paper

only focuses on the production value and on the investment value of farmland.

The second important point of this paper concerns the environmental policies and regulation

according to the specificities of the studied region. Bretagne area is divided according to

several environmental zones. These zones are defined according to animal density and water

quality6. In these zones, farmers must respect restricted practices of fertilization and manure

management. In addition, the increase in animal density is limited or forbidden. There are

policy documents that outline the specifications of fertilization, manure spreading plan for

each farming type. For example the nitrate regulation limits the organic fertilization. Farmers

with manure excess must seek additional areas for manure spreading to maintain or increase

their herd size. This leads to increased competition between farmers which may increase the

price of the farmland. The effect of this particular environmental regulation on the price of

farmland is identified and measured in this paper.

The remainder of the paper is organized as follows. The second section briefly outlines the

specificities of the farmland market in Bretagne. The third section gives a brief description of

6 Following the Nitrate Directive (1991), various devices are implemented at the European level to reduce agricultural

pollution and improve water quality. These are mainly preventing nitrate content form farming practices and promoting environmentally friendly agricultural activities.

the hedonic approach and its specification is discussed. The fourth part provides the

description of data sets and variables and discusses the spatial autocorrelation problems and

related econometric solutions. We use a data set belonging to PERVAL. This data set gathers

all transactions of farmland sales which are notified by notaries. The results are presented and

discussed in the fifth section.

1. Characteristics of the farmland market in Bretagne

The region Bretagne is the top leading agricultural region in France in terms of agricultural

annual turnover. It is the first region for the production of milk, eggs, pork and poultry, and

several vegetables, such as cauliflower, artichokes and potatoes. All these various farms

compete for farmland in the region. Vegetable farmland is usually more expensive than crop

farmland and animal grazing farmland. Pigs and poultry require a minimal area for spreading

livestock manure. The region Bretagne is also a great region of mixed farming dominated by

milk production. Milk production is regulated by dairy quotas, introduced in 1994. These

quotas are not exchanged on a market, but their transfer is permitted with associated land.

Some studies found evidence that dairy quotas are capitalized into farmland prices

(Bartholomew and Boinon, 2001). This is also the case for the single farm payment, which

can be transferred between farmers since 2006 with farmland transactions.

On the other side, the competition for farmland is also intensified by strong urbanization

effects which are induced in part by the regional demographic dynamism. Non-agricultural

use of farmland is strong around major cities and peri-urban areas in Bretagne. Nearly half of

the municipalities of Bretagne belong to an urban area. Bretagne region is also bordered by

2800 km of coastline. On the Bretagne coast, the difference in land prices can sometimes vary

with a ratio of 1 to 200. These urban developments encourage investors to buy farmland in

the most coveted areas, anticipating a future conversion from agricultural to residential use. ,

Their willingness to pay is probably much higher than most farmers’ one. The impact of

competition between different land uses, particularly between agriculture and urbanization has

been studied recently by Lefebvre and Rouquette (2011) and Dachary et al. (2011). They

conducted a hedonic farmland price analysis for all regions of France between 1995 and 2010.

They showed that the demographic pressure and accessibility to urban centers are important

drivers of the farmland price.

In addition to urban pressure, the competition for farmland is enhanced by environmental

regulations and incentives in Bretagne. These policies mainly target and the water quality. In

1993, the regional authorities identified and classified “nitrate vulnerable zones” according to

the nitrate concentration of surface water. In 1996, additional measures have been

implemented in designated areas (French acronym ZES) with higher environmental pressure

from agriculture. These designated areas (ZES) have animal densities resulting in nitrogen

surplus that exceeds the ceiling of the nitrate directive (170kg of nitrogen per hectare). In

2001, the nitrate directive also motivated the creation of areas with complementary actions

(French acronym ZAC) in order to improve the quality of water used for the production of

drinkable water. Complementary actions are mainly winter coverage of arable land. Nearly

half of the NUTS47 regions in Bretagne were ZES in 2006 and nearly one-third of the water

basins are under the obligations of ZAC. Since 2002, a part of the region Bretagne is in

European litigation for non-respect of the Nitrates Directive of 1975 concerning the quality

required of surface water intended for the abstraction of drinking water. In these areas,

farmers are constraints by additional mandatory measures.

In 2009, a huge algal bloom was observed on the beaches of Bretagne. This phenomenon

enhanced the recurring debate on water quality in Bretagne and the poor effectiveness of

environmental policy measures in the agricultural sector. Nitrogen discharges associated with

animal effluent and fertilizer were reckoned as the main cause of the proliferation of the algal

bloom, the algae decomposition on beaches produces toxic gases. The death of wild boars in

one of the famous Bretagne Bay in July 2009 due to toxic gas further stimulates the public

debate and the tensions between agricultural and environmental lobbies. A national action

plan was prepared in 2010 for 8 designated water basins that correspond to the bays most

impacted by algal blooms in Côtes d'Armor and Finistère, two NUTS3 regions of Bretagne.

The action plan aims at reducing nitrate flows by 30% to 40% before 2015. This plan includes

curative and preventive measures. At the farm level, the plan requires the reporting of

nitrogen management, the shortening of manure spreading periods and fertilization limitations

according to the nitrogen balance. These measures are not more stringent than those existing

in ZES or in areas in European litigation. The plan also proposes voluntary measures,

encouraging the development of grassland based production systems.

7 The different levels of territorial units for the region Bretagne are presented in Annex A.

Few studies have examined the impact of environmental policies on the farmland price. Le

Goffe and Salanié (2005) analyzed the impact of the progressive implementation of the

"Nitrate Directive" in Bretagne from 1994 to 2000. This implementation consists in the

ceiling of the quantity of organic nitrogen per hectare. The theoretical approach of the paper

assumes that farms above the ceiling buy the right to spread manure from farms operating

below the ceiling, or buy additional land for the same purpose. Their analysis focused on in

house pig production. Their empirical investigation showed that in regions characterized by

high densities of pigs, the equivalent land rents increase by, 1 € per kg of nitrogen8. This cost

is higher than the rate of farm pollution tax (between 0.15 and 0.30 € / kg of excess nitrogen)

but much lower than the estimated cost of manure treatment in dedicated plants (€ 3 /kg of

nitrogen). They conclude that the regulation has some effect on farmland price, reflecting the

fact that pig farmers were forced to deviate from their unconstrained profit maximizing

behavior.

This kind of environmental policies and directives increases the competition between farmers

because of the limited availability of farmland. In one way or another, this would affect

prices. In addition the farmland market in France is governed by a set of laws and legal

institutions. SAFER is a public and non-profit agency which is composed of shareholders

(representatives of chambers of agriculture, trade unions, banks, representatives of councils,

management center ...). The main mission and responsibility of SAFER is to regulate the

farmland market in every French region. This agency has a mandate to improve the young

farmers’ accessibility to farmland, to help smallest farms to enlarge and to moderate land sale

price. SAFER directly operates on the farmland market either by agreement or by using its

pre-emption right9 given by law.

The status of farming tenancy is another specificity of the French farmland policy which may

go against free market mechanisms in many aspects. Tenant farmers are more protected by

law than the landlords. On the one hand, the tenant farmer has automatic right to purchase the

land he farms if his landowner wishes to sell. In our study, for example, tenant farmers are

buyers in 40% of the transactions from 1995 to 2010 in Bretagne. On the other hand, the

duration of a lease is usually nine years and automatically renewed as many times as the

8 This is considering organic nitrogen fertilization of 100 kg/ ha. 9 The friendly acquisition is where an individual decides to sell directly to the SAFER. The preemptive right is the right to

acquire property in priority to any other person when the owner expresses its willingness to sell.

tenant wants to remain on the farm. This is called "life time tenancy contract". In France, it is

very difficult for a landlord to retrieve the farmland from his tenant farmer10. This can explain

that higher sale prices are observed for "free farmland" (farmland without ongoing rental

contract at the time of sale) than for "leased farmland" (farmland with going rental contract at

the time of sale). A recent study by Lefebvre and Rouquette (2011) indicates that that this gap

has been steadily widening for the past twelve years.

2. The hedonic approach

Before discussing the specification of the empirical hedonic price model, a brief theoretical

recall is presented.

The theoretical foundation of the hedonic price method was developed by Lancaster in 1966.

In his seminal work, Lancaster reckons that consumer goods are quite heterogeneous and that

comparisons between each other are difficult. Lancaster makes the assumption that

consumer’s utility does not directly derives from the consumption good, but from its

characteristics or attributes. This decomposition of any heterogeneous consumption good into

homogenous attributes facilitates the comparison between two goods. The hedonic price

method estimates the implicit price of each attribute by regressing the good price over its

attributes. In 1974, Rosen used the theoretical framework of Lancaster to analyze the

functioning of the housing market and estimates a hedonic price function with the

characteristics of houses. The hedonic price function estimates was used to measure the

implicit price of each house characteristic and to calculate the willingness to pay for its

marginal change. Following this work, several problems were identified including the

potential simultaneous choice between the house price and the quantities of certain

characteristics, or the correlation between the explanatory variables and residuals (Epple,

1987).

This method was applied to the price of farmland by Palmquist (1989) who showed how to

derive the bid function for a plot of farmland. Different plots of farmland are endowed with

different characteristics in terms of soil quality, climate, irrigation potential and infrastructure.

We assume that a person buys a particular plot for its attributes and its location, and that price

of this land plot is determined by buyers’ willingness to pay for these specific characteristics.

It is assumed that no individual is able to influence the hedonic price equation as the market 10

Presently over two third of the current farmland is cultivated by non-owner farmers.

clearing price would eliminate the excess supply and demand for each type of farmland. This

approach has been widely used in the literature to study the agricultural land price in different

countries, such as Georgia (Elad, Clifton, and Epperson 1994), United States (Bastian et al.,

2002), Northern Ireland (Patton and McErlean, 2003), France (Le Goffe and Salanié, 2005)

and Finland (Pyykkönen, 2005).

We use different sets of explanatory variables to estimate the price of the land. Variables

describe the characteristics of the land, such as the size of the plot or the soil quality. These

factors affect the productivity of the land and therefore the expected income of farmland. All

policy supports that are bound to the agricultural area or production, like the manure

spreading rights and dairy quotas, can be capitalized into the land and are included in this

model. Variables, such as the coastline proximity or the location in urban-rural fringe area,

can represent the intensity of non-agricultural activity demand for land. Our model includes

two additional sets of variables: i) variables indicating the tenancy status of the land plot (land

under tenancy contract or farmland without on-going tenancy contract) and the direct or

indirect involvement of SAFER in the transaction process; ii) variables that represents the

environmental situation of the municipality of the transacted farmland. The simpler hedonic

price function applied to individual land price observations is linear and encompasses the

preceding sets of variables. It can be written as follows:

P X Z S F E= α + β + γ + ζ + δ + η + ε (1)

P is the vector of observed prices of transacted farmland plots (unit is euro per hectare,

excluding tax and notary fees) of farmland, X is the matrix of agricultural characteristics of

the plot, Z is the matrix of its non-agricultural characteristics, S is the array of policy

instruments related to the farmland, F and E describing the institutional and environmental

situation of the plots of farmland, respectively. The stochastic error term is represented by ε .

Several functional forms can be used in hedonic studies. The functional form of the hedonic

regression equation can either be linear, semi-log, or log-log. The most common specification

is the semi-logarithmic form. Each parameter estimate directly provides the percentage of

price that depends on the corresponding characteristic. The parameters measure the relative

change of the price following a unit change of respective characteristics. We also chose the

semi-log specification for this facility of implementation11. The econometric specification is

modified to take into account the spatial dependence and the spatial autocorrelation of our

observations of transacted farmland plots.

3. Problems of spatial autocorrelation

Where data have a spatial dimension, two specific issues must be considered. These are

spatial heterogeneity and spatial autocorrelation. Bretagne municipalities are highly

heterogeneous. A part of this heterogeneity is controlled by the inclusion of certain

municipality characteristics in the set of explanatory variables: population density, location in

suburban area, coastline proximity in our case study. If spatial unobserved heterogeneity

remains, we are confronted with a problem of heteroskedasticity and/or instability of the

model parameters that vary systematically with location (Le Gallo, 2000b). Taking account of

this unobserved heterogeneity can be done by correcting a possible heteroskedasticity and /or

using standard econometric methods (random model parameters ...).

Unlike the spatial heterogeneity, the treatment of spatial autocorrelation requires specific

econometric methods. Spatial autocorrelation is defined as the correlation of a variable with

itself according to the geographical pattern of the observations. This can be a spatial

dependence between the observations of the endogenous variable, a spatial dependence

between observations of exogenous variables or a spatial dependence between the error terms.

This problem is usually caused by omitted variables which have spatial dependence. In our

case, the sale price of farmland may be affected by the value given to surrounding farmland

and by the attributes of surrounding farmland. Location factors such as the demographic

pressure and the urban geographical structure of the area are the main factors that influence

the price of farmland, apart from its production values. Spatial autocorrelation destroys the

independence of observations which is assumed in usual econometric methods like ordinary

least squares (OLS). It is therefore necessary to detect their presence.

There are strong and complex links between spatial dependence and spatial heterogeneity.

Poor model specification or omission of explanatory variables can cause heteroskedasticity

11 The Box-Cox transformation is often used for its flexibility. Three reasons motivated the choice of the log-linear form: the interpretation of the results is simpler, easier to adapt to model spatial autocorrelation, and several studies have shown that the results changed little between the two models (Le Goffe and Salanié 2005).

and can also lead to spatial autocorrelation of the error terms (Le Gallo 2000a, 2000b, 2002).

It is therefore difficult to distinguish autocorrelation effects and heterogeneity effects between

each other. Similarly, the correction of a problem linked to the spatial dimension of the data is

likely to have side effects on other potential problems. For example, the inclusion of

explanatory variables in the model to control the spatial heterogeneity is likely to reduce or

eliminate the spatial autocorrelation of errors. In addition, the autoregressive model

specification with a spatially lagged endogenous variable probably captures the influence of

omitted variables on the dependent variable and reduces the presence of spatial

autocorrelation of error terms. It is therefore difficult to detect a specific dependence effect in

the presence of different forms of spatial dependence and heterogeneity.

Methods for testing and accounting for spatial autocorrelation were developed in the late

1970s. Since 2000, these methods have been improved and applied to various empirical

studies. In parallel new theoretical approaches, such as economic geography, have been

developed and the availability of spatial data has been increasing a lot. To test and capture the

spatial interdependence between observations, we must consider the geographical position of

the farmland. We have the municipalities in which sales occur but we do not know the exact

position of the transacted land in each municipality. So we start from the assumption that the

spatial interaction between two farmland sales depends on the distance between the

municipalities in which the farmland is located. The instrument used to represent this

interaction is the spatial weight matrix. The weight matrix enables the connection of each

observation with the others according to their relative geographical location. If y is a spatial

variable and W the weight matrix, we can measure the intensity of the overall effect of the ith

observation values in space by expression (2):

[ ]1

N

ij jij

Wy w y=

=∑ (2)

This notion of spatial lag is important because it allows us to introduce the effects of spatial

autocorrelation in the econometric models. The weight matrix can be written in different

ways. The technique often used in the literature (Patton and McEarlen 2002 Pyykkönen 2005)

consists of filling in the matrix with the inverse of the squared distance for each pair of

geographical locations to represent how municipalities are spatially connected. Without the

precise location of the farmland within its municipality, we use the municipality area to

calculate and distance between two hypothetical farmlands randomly located in the same

municipality. Above a certain distance between two municipalities we assume that the spatial

interaction is zero. The choice of this distance threshold depends on the size of the farmland

market in our studied area. By convention, the diagonal elements of the matrix are equal to 0.

These matrices are often normalized with the sum of each row set to 1.

The spatial lag autoregressive model (SAR model), characterized by the autocorrelation of the

endogenous variable is written as (3):

P WP Q= α + ρ + µ + ε

(3)

Where Q include all characteristics’ variables. This specification takes into account the

interactions that may exist between neighbors in determining the selling price of farmland.

The second term of the right hand member is the spatially lagged term. It should be treated as

an endogenous variable. OLS is not appropriate for this model since this estimator would be

biased and inefficient. The specification of the spatial error model (SEM model) with the

spatial autocorrelation of the error terms is written as (4):

W vε = λ ε + (4)

The error term is split into v which refers to the true independent homoskedastic residual

term which has mean zero and a constant variance. In this case, the OLS estimator is unbiased

but inefficient. The details of both models are developed in Lesage and Pace (2009). There is

another model that combines both a lagged endogenous variable and the spatial correlation of

error terms. It called spatial auto-correlation model (SAC model).

The spatial Durbin model (SDM model), characterized by a spatial lag of the dependent

variable and a spatial lag of the explanatory variables, is written as (5):

P WP WQ= α + ρ + µ + ε

(5)

Since the year 2000, the farmland market studies using the hedonic price approach have

focused on the potential spatial interactions between neighboring transactions. Elad et al.

(1994) segmented the land market into different local sub-markets to measure the spatial

heterogeneity. They estimated a specific hedonic price function for each sub-market. Patton

and McErlean (2003) introduced advanced spatial econometrics to estimate a hedonic price

model in Northern Ireland farmland. Their result showed that there are many spatial

interactions in this market: spatial heterogeneity and spatial dependence between the

observations of the endogenous variable. Ignoring these effects may lead to biased estimates.

These results suggest that it can be difficult for the owner to identify the value of his own

farmland characteristics and to set appropriate price. In this case, potential sellers of the

farmland set prices according to the historical sale price of nearby plots even if these plots

have different characteristics. This mimetic behavior introduced a direct influence of one’s

transaction on other neighborhood transactions.

There are various tests for spatial autocorrelation in the literature. These are based on the

Moran test and the statistical test of the Lagrange Multiplier (LM). These tests can detect the

presence of one or the other form of spatial dependence. The methodology and explanation of

these tests are largely presented in Le Gallo (2000) and Lesage and Pace (2009). In cases

where the both types of dependence exist, Anselin and Rey (1991) propose to retain the model

corresponding to the highest test statistical value. Pyykkönen (2005), Patton and McErlean

(2003) followed this rule and estimate a model with lagged endogenous variable to describe

farmland market.

Maximum likelihood (ML) is consistent for spatial models. The ML adapted approach is to

estimate part of the first-order conditions in a first step. In a second step the solutions of the

first step are introduced into the log-likelihood function. This log-likelihood function is said

"concentrated" since it depends on a fewer parameters. Much of the spatial econometrics

literature has focused on ways to avoid maximum likelihood estimation because of

computational difficulties. Patton and McErlean (2003) estimated this model using an

instrumental variable method based on the White estimator of the variance-covariance matrix

which is robust for any heteroskedasticity forms (Anselin and Bera 1998). However, it was

shown that tests for heteroskedasticity are not always reliable in the presence of spatial

autocorrelation of error terms (Anselin and Griffith, 1988). Lagged explanatory variables are

generally used as instruments (Kelejian and Robinson 1992). Pyykkönen (2005) compared an

adapted Maximum Likelihood (ML) estimator and with the preceding instrumental variable

(IV) method to estimate a model with lagged endogenous variable applied to the Finnish

farmland market. He finds that results of these two approaches are very similar.

Recently, some authors, like Lesage (2009), provide new approach to reduce computational

tasks and to construct maximum likelihood estimates in only few minutes. In this paper, we

use this approach to avoid choosing instrumental variables in case of lag dependent variable.

4. The empirical Model

All information of farmland transactions in Bretagne comes from the "PERVAL" data base.

We select the observations of farmlands with no buildings or forested land. After eliminating

farmland corresponding to the tails of the distribution of prices, a total of 5,700 observations

from 2007 to 2010 were used for the analysis. Additional variables describing the location

(the municipalities in this case) of the traded farmland are obtained from the agricultural

census of 1999 and 2008 of "INSEE" data base. The description and summary statistics of

these variables are presented in Table 1.

The price of farmland is defined in euro per hectare excluding transaction costs (trading costs)

and notary fees. The nominal price was deflated by the consumer price index (base 100 in

2005). Total size of traded farmland is used as an explanatory variable in the model. The

agronomic quality of soil is not directly observable in the database. However, "potentiality of

irrigation" was taken into account. Variables indicate whether the sold farmland have a

system of irrigation infrastructures, a drainage facility or a retention pond. The proportion of

vegetable farms in the Utilized Agricultural Area (UAA) of the municipality (where the

farmland sale is located) approximates the soil quality. An index of soil quality is build at the

NUTS5 level12. It indicates if the soil is more clay, silty clay or sandy. A dummy variable was

also built for each NUTS4. The average land rental rate is also including in the model. It

provides information on agronomic quality of soils and productivity of farmers because its

calculation is based on the average income of farmers in each NUTS3 region, and it can be

different from one area to another depending on agronomic capacity.

The geographic location of the farmland is an important factor for some buyers. We therefore

considered two variables: a variable that represents a farmland geographical proximity to the

coast and a variable that identifies whether a farmland is located in an urban area13. These

variables, associated with the population density, approximate the competition effect of

urbanization and tourism. The value of these farmland attributes would likely differ according

to the characteristics of the buyer. These variables were therefore crossed with the status of

the buyer (farmers, non-farmers, farming companies, local authorities and SAFER).

12

The different levels of territorial units for the region Bretagne are presented in Annex A.

13 A municipality is called in an urban area, if it belongs to an urban area (center offering at least 10,000 jobs and is not located in the crown of another urban center) or suburban (all municipalities in the urban area to the exclusion of the pole).

The role of the farmland market rules and policies is approximated by two variables in the

model: first, a variable represents whether a farmland was purchased by the farmer in place or

not and second, a variable indicates the involvement of local or regional associations or the

involvement of SAFER during the transaction process.

Table 2. Descriptive statistics of variables

Variables Unit Average Standard

devation

Data base

Prices of the sold farmland plot €/ha 4016 2974 Perval 2004-2010

Agricultural Factors

Area of the plot ha 7.21 11.40 Perval 2004-2010

Possibility of irrigation yes=1/no=0 0.01 - Perval 2004-2010

Vegetable share in UAA /ha SAU commune 0.05 0.14 RGA 2010

Soil quality - -

Clay soils yes=1/no=0 0.38 - Build variable

Silty clay soils yes=1/no=0 0.30 - Build variable

Sandy soils yes=1/no=0 0.01 - Build variable

Other soil quality yes=1/no=0 0.31 - Build variable

Non-Agricultural factors

Urban zone yes=1/no=0 0.52 -

Coastline proximity yes=1/no=0 0.24 -

Farmland policy factors

Land was leased by the purchaser yes=1/no=0 39.66 - Perval 2004-2010

Land acquired by a farmer yes=1/no=0 0.61 -

Land acquired by societies yes=1/no=0 0.09 -

Land acquired by non farmer individuals yes=1/no=0 0.26 - Perval 2004-2010

Land acquired by the SAFER yes=1/no=0 0.03 - Perval 2004-2010

Land acquired by local authorities yes=1/no=0 0.01 - Perval 2004-2010

Land rental rate €/ha 147.99 24.14 Build variable

Agricultural Policy factors

Density of dairy cows /ha SAU commune 0.46 0.16 RGA 2010

Single farm payments in Ille-et-Vilaine €/ha 379.82 55.79 Build variable

Single farm payments in Côtes d’Armor €/ha 375.36 31.75 Build variable

Environmental Policy factors

Porcine nitrogen load Kg N/ha SAU commune 115.53 43.29 RGA 2000

ZES yes=1/yes=0 0.60 - Build variable

Green algae areas (since 2010) yes=1/no=0 0.001 - Build variable

Contentious areas yes=1/no=0 0.10 - Build variable

Agricultural policy factors must be taken into account to explain farmland prices. Milk quotas

or payment entitlements transferred with farmland will likely have a positive impact on land

prices. The density of dairy cows at municipality’s level is included to approximate the

probability that the exchanged land is associated with a milk quota. The average single farm

payment entitlements at the NUTS4 regional level are available only for 2 NUTS3 (Ille-et-

Vilaine and Côtes d’Armor)14.

Concerning environmental policy factors, the farmland demand for manure spreading was

measured by the nitrogen pressure indicator at the municipality (NUTS5) level which was

designed by Le Goffe and Salanié (2005). Data on the nitrogen load due to total animal

production were directly calculated from the 2000 agricultural census of French Agriculture

Ministry data base15. We also crossed this variable with a variable indicating the location in

ZES, where total organic load exceeds the available land for manure spreading. The square of

this variable was included in the model as well to grasp any non linear effect. It was expected

that the demand for farmland differs in environmentally sensitive areas. Some dummies

variables (Green algae area) indicate the location in one of the bays targeted by the national

plan against algal blooms, and the location in one of the area concerned by environnemental

litigation.

The choice of the model is based on statistical tests. As a first step, the model is estimated by

OLS. From the results obtained, tests based on Lagrange multiplier and likelihood ratio, were

performed to detect the presence of autocorrelation. Another test is performed from the SAR

model to confirm the presence of spatial error dependence. All these statistical tests, presented

in Table 2, make evidence of spatial correlation in residuals and in dependent variable.

Table 2. Statistical tests for spatial auto-correlation

Test Model Value Probability Chi-squared

LR test OLS 347.129 0.000 6.635

LM test OLS 646.08 0.000 17.611

LM test SAR

They have different implications for coefficient estimates in case of misspecification. Table 3

describes properties of coefficient estimates obtained by the four models (SEM, SAR, SAC

and SDM model) in cases of misspecification. For cases where the true data generating

14

These informations are provided by local authorities of the NUTS3 regions of Ille-et-Vilaine and Côtes d’Armor.

15 Information available in the RGA 2010 does not allow us to calculate the animal nitrogen load; this is why we calculate this variable from the RGA 2000. In addition this 2000 variable can be assumed exogenous in our regression without any problem.

process (DGP) has spatial dependence in disturbances and includes spatial lag dependence,

the SAR, SAC and SDM models will produce unbiased coefficient estimates. The SDM

model is the only model that will produce unbiased coefficient estimates under all four data

generating process (James and Lesage 2009).

Table 3. Properties of coefficient estimates depending on the true DGP

True DGP SEM model SAR model SAC model SDM model

SEM - biased/ inefficient biased/inefficient unbiased/inefficient

SAR unbiased/inefficient - unbiased/inefficient unbiased/inefficient

SAC biased/inefficient unbiased/efficient - unbiased/inefficient

SDM biased/inefficient biased/ inefficient biased/ efficient -

Three estimates are then performed: the SAR model defined by equation 3, the SAC model

(without the specification of two different weight matrix), and the SDM model defined by

equation 5. All spatial models are estimated using the method of maximum likelihood and

codes developing by Lesage on Matlab and available on his website16.

We set to 0 the weighting matrix elements if the distance between the two corresponding

municipalities is greater than 10 km. Above this distance, we assume no spatial interaction

between the endogenous variables. Various estimates were performed using different

threshold distances. Our choice was based on R² and log-likelihood. However it must be

stressed that the results appear to be insensitive to the choice of this threshold distance

between 10 and 30 km.

The estimated coefficients from the three methods are shown in Table 4. Pace and Lesage

(2006) distinguish direct effects and indirect effects. In the cases where the model contain

spatial lags of explanatory or dependent variables, the interpretation of parameters are more

complicated. In fact, a change in the explanatory variable for an observation can potentially

affect the dependent variable in all other observations. The average direct impact represents

the average response of the dependent variable according to independent variables. This is

similar to typical regression coefficient interpretations. It measures the effects of change in

16

http://www.spatial-econometrics.com/

the ith observation of Q on iP . In contrast, the average indirect impacts measure the effects

of changes in the ith observation of Q on jP for j i≠ . The average total impacts measure

how changes in a single observation influence all observations.

For continuous variables, parameters were multiplied by 100 to provide the percentage change

in price due to a unit increase in the quantity of the attribute. Similarly, for binary variables,

parameters were also multiplied by 100 to provide the percentage change in price due to the

introduction of the characteristics relative to the baseline.

5. Results

To compare the three spatial models, we use some quality criteria. According to the log-

likelihood value in Table 4, the SDM model is the best one. Another way to compare models

is to consider R² criteria. The SAC model has the highest R² value and the largest number of

parameters statistically different from 0. Given the results, it is difficult to decide which is the

best model. Results will be based mainly on those obtained by SAC model which provide

more detailed information with 23 significant estimates. Table 5 presents average direct

effects estimated from the three spatial models. Table 6 presents average indirect (spillover)

effects. Average total effects are obtained by adding direct and indirect effects.

Table 4. Comparison of model selection criteria

SAR model SAC model SDM model

Log-likelihood -1830.19 -1820.5 -1786.4

R² criteria 0.272 0.326 0.300

Significant parameters 21 23 15

Parameter ρ 0.608*** 0.738*** 0.452***

Parameter λ - -0.462*** -

Coefficient estimates and inference of some parameters highly depend on model used. Results

obtained by SAC and SAR model are relatively similar. The main difference between these

two models is that the indirect effects are more important from the SAC model, because of the

higher parameter ρ (associated to the lag dependent variable). Results obtained by SDM

model are more different, especially concerning indirect effects. In this model, the indirect

effects are relied not only on lagged dependent variable but also on lagged explanatory

variables. Indirect effects show how changes in a single observation influence all

observations. The SDM model allows the spillover from a change in each explanatory

variable to differ, as opposed to the SAR and SAC cases which have a common multiplier for

each variable.

The potentiality of irrigation represents approximately 3% of the total transaction farmland

sales in the data. Le Goffe and Salanié (2005) showed that irrigated plot is 75% more

expensive than non-irrigated plot and a drained plot is 25% more expensive than non-drained

farmland. These results are closed to those obtained in American studies (Bastian et al, 2002).

Depending on the specifications, a farmland associated with irrigation or draining capacity is

42% to 58% more expensive than other farmland. The official rental rate, fixed by local

authorities, has a positive impact on farmland prices, indicating an increase of prices due to a

better agronomic quality of land. Similarly, clay and silty clay soils are a little more

expensive.

In our sample, only 50% of sold farmland was leased farmland i.e. there was ongoing tenant

contract during the time of sale of farmland. As expected, nearly 90% of farmland was

purchased by tenant farmers who rented the land before the sale. A French farmland market

study from 1997 to 2010 (Lefebvre and Rouquette 2011) showed that leased farmlands were

sold 15% cheaper than free farmland. This effect was partly due to the French legal status of

agricultural tenancy which gives an automatic priority to the tenant to buy the land he farms

and thus reduces the competitive mechanisms. Our results confirm that the land sold to its

former tenant farmer is cheaper (11% cheaper if we consider only directs effects, and between

28 and 40% cheaper with the spillover).

Lefebvre and Rouquette (2011) also showed that the price of free farmland is growing faster

than the leased farmland. They assume that a growing part of non-farmer individuals buy free

farmland at higher prices than farmers. A descriptive analysis of our data shows that about

60% of buyers were farmers. Almost 30% of buyers were individuals who did not declare

themselves as farmers. Surprisingly, about 95% of them live in the same municipality (or less

than 5km far) of the plot of farmland they purchased. Moreover, results show that non-

farmers buy at lower price than farmers (about 10% cheaper if we consider only directs

effects, and between 35 and 40% cheaper with the spillover), on average. Most of these

individual non-farmers buy farmland for rental income. The cash rental income of farmland is

usually considered as risk free and safe investment. The lower willingness to pay of non-

farmers reflects that the cash rental is set below the marginal value of the farmland in farming

activities (Dupraz and Temesgen 2012). Non-farmers can also purchase farmland for their

own use and to develop various personal activities. Home or shared gardens are in full growth

in the region Bretagne, mainly in the suburban areas. Finally, some individuals also bought

farmland near the coast or in urban areas at higher price for speculation, expecting a

conversion into residential or industrial use. On average, a non-farmer buy a land located in

an urban area about 15% more expensive than a farmer (and between 40 and 65% more

expensive if we consider indirect effect). According to the results obtained by SDM model, a

non-farmer is willing to pay this type of land 69% more than a farmer.

Local authorities and SAFER also intervene in farmland market. They purchased farmland

generally located in a suburban area to store and lend to farmers who wish to produce for

local markets of direct sales or to settle in organic farming. The price of this farmland is

generally high because of their suburban location. They are forced to pay higher price to

encourage the landowners to sell rather than waiting for a change of destination of their land.

Farmer societies operated 10% of transactions of total farmland sales in 2010 against only 5%

in 1995. Besides the division of labor, administrative facilities and diversification, farmers

create societies mainly for tax advantages and transmission facilities. They can transfer land

in form of society’s shares outside the usual regulated land market. They probably have a

strong interest (upgrading, isolation ...) to acquire additional farmland and they are able to pay

more (payment facility ...).

Concerning impacts of agricultural policies, it can be assumed that farmland values will

increase with the associated production entitlements. Bartholomew and Boinon (2001) show

that the dairy quota rent is twice the price of milk. The impact of dairy cows’ density on

prices differs greatly depending on the econometric specification. According to the SDM

model, the price of farmland increases by 1600 € per hectare with one additional dairy cow

per hectare. Assuming an average milk yield of 6000 kg per cow, this corresponds to an

average value of 0.46€ per kg of milk quota17. As expected, the CAP payment entitlements

have also positive and significant effects on farmland prices.

17 This calculation is realized in assuming an average density of 0.59 cows per hectare, which occur in 50% of transactions.

Table 5. Coefficient estimates by SAR, SAC and SDM model – Average direct effects

Variables SAR model SAC model SDM Model

Agricultural Factors Q Q Q WQ Area of the plot -0.0006 -0.0008 -0.0008 0.005

Possibility of irrigation 0.151*** 0.162*** 0.159*** -0.379***

Vegetable share in UAA 0.329*** 0.4619*** -0.152 0.877***

Soil quality (reference: other soil quality)

Clay soils 0.024 0.034** 0.022 0.027

Silty clay soils 0.031 0.037** 0.0049 0.057

Sandy soils -0.009 -0.0002 -0.037 -0.053

Land rental rate 0.0026*** 0.0029*** 0.0022 0.0034

Farmland Policy factors

Farmland’s buyer (reference: farmer)

Land was leased by the purchaser -0.110*** -0.107*** -0.105 -0.094

Land acquired by the SAFER 0.054 0.064 0.076*** 0.045

Land acquired by local authorities 0.645*** 0.634*** 0.629*** 0.100

Land acquired by non farmer individuals -0.107*** -0.104*** -0.101*** -0.126

Land acquired by societies 0.097** 0.095** 0.099 -0.014

Non-Agricultural factors

Urban zone

Constant (reference: farmer buyer) -0.029* -0.016 -0.023 -0.017

Land acquired by non farmer 0.148*** 0.152*** 0.155*** -0.077

Land acquired by societies 0.104 -0.050 -0.047 0.122

Land acquired by the SAFER 0.158** 0.154** 0.135*** -0.181

Land acquired by local authorities -0.050 0.09 0.095 0.629

Coastline proximity

Constant (reference: farmer buyer) 0.056** 0.077** -0.006 -0.0003

Land acquired by non farmer -0.012 -0.020 -0.036*** 0.424***

Land acquired by societies 0.051 0.064 0.067 -0.187

Land acquired by the SAFER -0.014 -0.034 -0.013 1.545***

Land acquired by local authorities -1.037*** -1.01*** -1.03*** -0.767

Agricultural Policy factors

Density of dairy cows 0.069 0.094* 0.059 0.166

Single farm payments in Ille-et-Vilaine 0.0012*** 0.0012*** <0.0001 0.003

Single farm payments in Côtes d’Armor 0.0007*** 0.0005*** 0.0003 0.0016**

Environmental Policy factors

Total nitrogen load

Constant -0.00039 -0.00037* -0.0007 -0.0001

Squared total nitrogen load 0.000002*** 0.000002*** <0.0001 <0.0001

ZES 0.00035*** 0.00035*** 0.0009*** -0.0007***

Green algae areas (since 2010)

GAA 1 -0.0079 0.0033 0.067*** -0.191***

GAA 2 -0.128** -0.142*** -0.176*** 0.067

Contentious areas (since 2010)

CA 1 0.148*** 0.189*** 0.273*** -0.052***

CA 2 -0.030 -0.043 -0.107 0.028

***, **, * implies that coefficient estimates are statistically significant at respectively 10%, 5% and 1% level.

Table 6. Coefficient estimates by SAR, SAC and SDM model – Average indirect effects

Variables SAR model SAC model SDM Model

Agricultural Factors WY WY WQ et WY Area of the plot -0.0013 -0.0017 0.0088*

Possibility of irrigation 0.256*** 0.416** -0.542

Vegetable share in UAA 0.704*** 0.891*** 1.465***

Soil quality (reference: other soil quality)

Clay soils 0.053** 0.066* 0.065

Silty clay soils 0.057** 0.084* 0.105

Sandy soils -0.0006 -0.024 -0.130

Land rental rate 0.0044*** 0.0070*** 0.0078**

Farmland Policy factors

Farmland’s buyer (reference: farmer)

Land was leased by the purchaser -0.166*** -0.305*** -0.251**

Land acquired by the SAFER 0.102 0.149 0.088

Land acquired by local authorities 0.978*** 1.782*** 0.724

Land acquired by non farmer individuals -0.159*** -0.296*** -0.289

Land acquired by societies 0.149** 0.269** 0.054

Non-Agricultural factors

Urban zone

Constant (reference: farmer buyer) -0.026 -0.083 -0.049

Land acquired by non farmer 0.235*** 0.411*** -0.026

Land acquired by societies -0.078 -0.137 0.181

Land acquired by the SAFER 0.237** 0.438** -0.125

Land acquired by local authorities 0.157 0.290 1.179

Coastline proximity

Constant (reference: farmer buyer) 0.118** 0.154** -0.002

Land acquired by non farmer -0.034 -0.035 0.723**

Land acquired by societies 0.099 0.14 -0.271

Land acquired by the SAFER -0.047 -0.045 2.810**

Land acquired by local authorities -1.56*** -2.868*** -2.182

Agricultural Policy factors

Density of dairy cows 0.145* 0.186 0.340*

Single farm payments in Ille-et-Vilaine 0.0019*** 0.0033*** 0.0064***

Single farm payments in Côtes d’Armor 0.0011** <0.0001 0.0032**

Environmental Policy factors

Total nitrogen load

Constant -0.0005 -0.0011 -0.0008

Squared total nitrogen load <0.0001 -0.354*** <0.0001

ZES 0.00076*** 0.00035*** -0.0005

Green algae areas (since 2010)

GAA 1 0.0047 -0.022 -0.296

GAA 2 -0.222*** -0.354*** -0.018

Contentious areas (since 2010)

CA 1 0.287*** 0.407*** 0.134

CA 2 -0.065 -0.084 -0.042

***, **, * implies that coefficient estimates are statistically significant at respectively 10%, 5% and 1% level.

The results for the organic nitrogen load were also very dependent on the type of model used

for estimation. Le Goffe and Salanié (2005)18 found an increase in the price of 4.4€ per kg of

nitrogen of porcine origin. They interpreted this increase in farmland prices by the rising

demand from farmers who have to meet the manure spreading regulations. This also shows

the intensification of pig production in Bretagne. In the regulation, municipalities were

categorized as highly loaded over 50 kg per hectare of organic nitrogen associated to pig

production. In sales data, 95% of transactions belong to a municipality with more than 50 kg

of nitrogen per hectare. The average amount of animal nitrogen is about 115 kg per hectare in

the region Bretagne. Our results obtained by SAC model show that farmland price increase by

2.48 € per kg of additional nitrogen per hectare. The inclusion of a quadratic term in our mode

allowed us to differentiate the impact of nitrogen loading according to its level. We noted that

when the nitrogen pressure increases, the price of farmland increases less quickly. Similar

result was also observed by Le Goffe and Salanié (2005). They interpreted this as the result of

regulatory measures implemented in ZES that make farmland available for manure spreading

specially in small farms (treatment obligation imposed on surpluses of larger farms and

limiting trade by establishing ceilings for manure spreading).

In 2010, 115 farmland plots were exchanged within 8 water basins concerned by a huge algal

bloom, mainly on the north coast of the region Bretagne. First, eight binary variables are

included in the model, equaling 1 when the land belongs to one of these water basins and 0

otherwise. We find that their effects on prices are different depending on the water basins. We

then grouped water basins with a positive impact on land price in a binary variable (called

GAA1), and grouped water basins with a negative impact on land prices in another one

(called GAA2). So we kept only two binary variables in models to analyze impacts of these

environmental areas. On the studied period, 584 farmland plots were exchanged within areas

concerned by an environmental litigation. In the same way as above, we kept only two binary

variables. The variable CA1 contains areas in litigation with a potential positive impact on

prices, and CA2 areas in litigation with a potential negative impact on prices. Most of

farmland located in bay concerned by green algae suffers a decline in prices (between 40%

and 48% taken into account spillovers). A farmer knows he will have to meet additional

constraints or can anticipate future constraints. In addition, the purchased farmland may not

18 We consider that the amount of nitrogen is different depending on the animal (pig, hog, sow), which has not been taken into account in the calculations made by Le Goffe and Salanié (2005). The calculation is made on all animals and not only on porks.

be directly usable because it requires environmental improvements. This may explain that

farmland price is lower. Moreover, farmers already located in these areas, have not yet been

forced to change their agricultural practices, in contrast to areas in litigation, where

environmental constraints are already imposed to farmers. In the most areas concerned by

European litigation, we observe a high increase in the farmland prices (between 15% and 27%

from direct effects and between 40% and 56% from total effects). This may explain by the

need for farmers to buy additional plots to spread the manure and thus comply with

environmental constraints.

Conclusion

Results and tests proved the existence of spatial interaction on farmland market in Bretagne.

Although it is difficult to interpret the effects due to the spatial data, there is likely a

"spillover" effect between farmland sales. Sellers are likely influenced by transactions that

have occurred in their neighborhood. This could be intensified by lack or asymmetry of

information. Consequently they rely on sales information available near to the location of

farmland. It would be good to better understand differences observed in results obtained by

the three models. This might help to better define spatial effects and to better analyze their

impact on the results, especially on environmental factors.

Nevertheless, we observe significant increase of farmland prices in more constrained

environmentally areas such as ZES and areas affected by European litigation. This may

indicate some effectiveness of environmental policies or at least the awareness of farmers to

environmental problems. In 2012, the European Commission has decided to refer France to

the European Court of Justice for failure to comply with the Nitrates Directive in 1991 that

involves 39 water basins. All these water basins are located in Bretagne.

This notion of effectiveness of environmental policies arises especially as government tends

towards simplification and relaxation of regulations and environmental constraints. Since

2011, a decree has extended the total farmland area taken into account for calculating the

surface usable for manure spreading. According to a French environmental association Eau et

Rivière, this will increase the amount of nitrogen applied to the soil by 20%. In addition, the

government planned to remove the ZES and ZAC in 2013. These recent evolutions, and the

future ones, are intended to less constrain farmers, in encouraging them to modernize and

better control themselves their nitrogen load.

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Annex A – Nomenclature of territorial units for statistics

Level of division Size of division

NUTS2 Region Bretagne

NUTS3 Departments 4 NUTS3 : Ille-et-Vilaine, Côtes d’Armor, Finistère, Morbihan

NUTS4 Cantons 171 NUTS4

NUTS5 Municipalities 1270 NUTS5