Cavailhes J. GIS Based Hedonic Pricing of Landscape 2009

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Environ Resource Econ (2009) 44:571590DOI 10.1007/s10640-009-9302-8

GIS-Based Hedonic Pricing of Landscape

Jean Cavailhs Thierry Brossard

Jean-Christophe Foltte Mohamed Hilal

Daniel Joly Franois-Pierre Tourneux

Cline Tritz Pierre Wavresky

Accepted: 16 June 2009 / Published online: 27 June 2009 Springer Science+Business Media B.V. 2009

Abstract Hedonic prices of landscape are estimated in the urban fringe of Dijon (France).

Viewshed and its content as perceived at ground level are analyzed from satellite images

supplemented by a digital elevation model. Landscape attributes are then fed into economet-

ric models (based on 2,667 house sales) that allows for endogeneity, multicollinearity, and

spatial correlations. Results show that when in the line of sight, trees and farmland in the

immediate vicinity of houses command positive prices and roads negative prices; if out of

sight, their prices are markedly lower or insignificant: the view itself matters. The layoutof features in fragmented landscapes commands positive hedonic prices. Landscapes and

features in sight but more than 100300 m away all have insignificant prices.

Keywords Amenity Hedonic pricing Landscape View

1 Introduction

Rural scenery, open spaces, woodland, and farmland are green landscapes sought after by

many households in most developed countries. This paper focuses on the valuation of the

viewshed and its contents, as seen by residents from their homes, in a French leafy periur-

ban belt. This is an important issue because public authorities are wary of urban sprawl and

careful in the management of open spaces and green areas in and around cities.

This research was financed by Burgundy Regional Council, Cte-dOr Departmental Council and Dijon

Conurbation Joint Councils. It uses data on real-estate transactions from the PERVAL Corporation.

J. Cavailhs (B)

CESAER-INRA, 26 Bd Docteur Petitjean, BP 87999, 21079 Dijon Cedex, Francee-mail: [email protected]

T. Brossard J.-C. Foltte D. Joly F.-P. Tourneux C. Tritz

CNRS-ThMA, 32 rue Megevand, 25030 Besanon, France

M. Hilal P. Wavresky

INRA-CESAER, 26 Boulevard Petitjean, BP 87999, 21079 Dijon Cedex, France

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572 J. Cavailhs et al.

Hedonic pricing is employed here to value landscapes in a periurban belt around Dijon, the

main city in Burgundy (France). These are commonplace rural landscapes, with villages and

small towns scattered over plains, hills, and valleys covered by farmland and woodland. We

analyze a landscape as seen from within instead of from above by allowing for objects

and relief that may block out the view. The view from home can thus be reconstituted in athree-dimensional space, allowing us to identify both landscape objects (trees, fields, roads,

etc.) present in the viewshed, and the same objects that are present in the surroundings but

hidden by masks. Hedonic prices of these seen and unseen objects are then derived from

data for 2,667 house sales using either a fixed-effects model estimated by the instrumental

variable method or a random-effects model.

The remainder of the paper is arranged into four parts. After a brief review of the literature

(Sect. 2), the economic and geographic models are set out along with the data (Sect. 3); then

come the results (Sect. 4). Section 5 presents the discussion and conclusions.

2 Landscape Valuation

Econometric landscape valuation presupposes that quantitative landscape variables are intro-

duced into econometric models. Different methods or models such as they are developed by

geographers for characterizing landscape are appropriate and can be used to this end. We

present some examples here arranged according to the type of material: ground-level photo-

graphs to mark the esthetic value of landscape, maps to measure distances between objects

(1 dimensional approach), aerial photographs or satellite images to classify the land cover

or calculate landscape indices (2 dimensional approach), virtual landscapes reconstructed in

three dimensions, as is done here, by combining satellite images and digital elevation models.Photographs have long been used to analyze the esthetic value of landscapes by regression

methods. A score given by a panel is explained by objective attributes (land cover, visual

arrangement, etc.), subjective attributes (mystery, atmosphere, etc.), and sometimes personal

characteristics (gender, age, etc.). Much of this work was done in the 1980s. Gobster and

Chenoweth (1989) listed more than 80 references and recorded 1194 terms for describing

esthetic preferences. For example, marks for photographs in the Great Lakes region (US)

are explained by physical, ground-cover, informational (order, complexity, mystery), and

perceptual (open, smooth, easy to cross) variables (Kaplan et al. 1989). Recent research has

followed similar lines; for example, Johnston et al. (2002) use maps and photographs to show

that households choose fragmented, long and narrow housing subdivisions when density islow, but opt for more clustered forms for denser subdivisions. Ground-level photographs are

also used to estimate the economic value of landscapes by contingent evaluation (e.g. Willis

and Garrod 1993) or by the choice-experiment method (Hanley et al. 1998).

Distance between an observer and an object is used as a landscape variable. Real-estate

values generally decrease with distance to green areas, golf courses, forest parks ( Tyrvinen

and Miettinen 2000), stretches of water(Spalatro and Provencher 2001)ortowetlands(Mahan

et al. 2000). This effect is sometimes non-linear. For example, Bolitzer and Netusil (2000)

show that the proximity of open or green spaces affects house prices when the distance is

very short (a few tens of meters), but the effect falls off rapidly with distance, and disap-pears beyond a few hundred meters at most. Thorsnes (2002) shows that housing with direct

access to forests is worth 2025% more, but that this extra value vanishes if there is a road

to cross to get to the forest. Therefore, researchers must take into account the exact locations

of observers and objects alike.

The land cover within a radius around a house can be analyzed from aerial photographs

or satellite images. The findings are used for landscape valuation, mostly by the hedonic

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GIS-Based Hedonic Pricing of Landscape 573

method. In most although not all cases positive hedonic prices are reported for trees (Kestens

et al. 2004), particularly on land adjacent to the residential lot (Thorsnes 2002), and for

nearby recreational woods (Tyrvinen and Miettinen 2000) as well as for parkland, golf

courses, or greenbelts. Farmland has a less clear-cut impact with some studies concluding it

has a positive effect on real-estate values (Roe et al. 2004, who use the choice-experimentmethod) and others reporting contrary effects (Garrod and Willis 1992). The legal status of

land is sometimes included in the hedonic equation either because it affects expectations

about development (Irwin 2002) or because access rights to parcels affect their recreational

value (Cheshire and Sheppard 1995).

Landscape ecology provides variables for characterizing the shape of patches formed

by the land cover: diversity, fragmentation, entropy, fractal dimension, or other statistical

summaries. For example, Geoghegan et al. (1997) show that landscape fragmentation and

diversity have negative effects on real-estate values, except where very close to and very far

from Washington DC.

The view from the ground entails integrating the third dimension (i.e. relief and any tall

objects) into 2D satellite images. It has only recently been introduced into hedonic-valuation

models: to the best of our knowledge, there are just a few examples to date. Germino et al.

(2001) analyze a landscape from satellite images and a digital elevation model to simulate a

view, and Bastian et al. (2002) use such variables for the hedonic pricing of landscape; they

conclude that in the Rocky Mountains (US) landscape diversity, the only landscape variable

that is significant, is highly appreciated. Paterson and Boyle (2002), using precise satellite

imagery information, compare the land cover and the view from the ground in a rural region

of Connecticut (US). The sign of their results varies with the specification, showing that

the visibility measures are important determinants of prices and that their exclusion maylead to incorrect conclusions regarding the significance and signs of other environmental

variables (Paterson and Boyle 2002: 417). Here, we extend and enhance this conclusion by

distinguishing between objects in view and objects hidden by relief or masks that block the

view. Lake et al. (1998) estimate the price of road noise and view in Glasgow (Scotland);

the viewshed is identified by systematic visits (to measure building heights), and the findings

show that the view of a road reduces the real-estate price. In the same way, we distinguish

seen from unseen roads.

In short, most studies use data on distance (1D), and maps, aerial photographs, or satellite

images (2D). Very few reconstruct 3D landscapes as is done here by taking account of relief

and tall objects that block the view. Our method allows us to evaluate the hedonic price ofobjects whether in or out of sight, by using hedonic pricing models. We take into account

both endogeneity of covariates and spatial autocorrelation by using a fixed-effects model

estimated by the instrumental method, and a random-effects model.

3 Study Region, Geographical and Econometric Models, Data

3.1 The Study Region

The study region is a belt around Dijon (France). Its inner bound is the city of Dijon and its

suburbs, which are excluded from the study. Its outer bound is given by access time to Dijon

of less than 33 min or a distance by road of less than 42 km.1 The region covers 3,534 km2

1 These limits were determined by first setting a threshold of 40% of commuters, and then rounding by

including some interspersed communes.

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Fig. 1 South-eastern sector of the study region

and has 140,703 inhabitants. It is composed of 266 communes (a commune is the lowest tier

of local government in France), with a mean population of 461 inhabitants (median: 229,

standard deviation: 733). Land cover is 2.4% built areas, 59% farmland, and 38% forests and

natural formations.

Figure 1 shows the settlement pattern in the south-eastern sector of the study region (other

quadrants are similar). This region is made up of many villages and small towns forming

densely populated clusters isolated from their neighbors by broad expanses of farmland,

woods, and forests. The average population density of villages is 1700 inhabitants per square

kilometer when population is divided by the area of the village polygon (composed of build-

ings, streets and roads, and open and green spaces whether private or public); but the mean

population density of the study region is only 41 inhabitants per square kilometer. Clearly,

two different scales co-exist: dwellings are tightly clustered (just a few tens of meters apart)

within villages, while villages lie several kilometers apart. Moreover, from one commune to

the next there are often stark variations in population, household income, local public policy

(tax, land zoning), quality of schools, etc.

3.2 A GIS-Based Geographic Model of Quantitative Analysis of Landscape

A landscape can be quantified in terms of its extent and its content, which are analyzed here

using a GIS-based model (see a survey in Bateman et al. 2002). Its extent varies with both

relief and the objects that may block the view. Its content is a matter of the type of objects

visible. The viewshed is measured by simulating the view of an observer whose eyes are 1.8 m

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Fig. 2 Viewshed without and with objects blocking the view. (A) There is an uninterrupted view from 0 to155m from the observer located at cell I; between 155 and 325 m the view is blocked by the hill-crest. Thesecond hill is visibile between 325 and 385 m. (B) The tree 65m from the observer blocks out the view beyond

above ground level. This simulation of view is made everywhere, all around each observation

point of the study region. Each place in the surroundings is visible or not, depending upon

topography and land-use structure (Fig. 2). This process operates using a cellular represen-

tation of space: a squared grid divides the study area into regular cells (7 7 m = 49 m2),

which are the smallest spatial units for identification of geographical objects.

The distance from the observer to the seen objects is measured by distinguishing six radius

areas to take into account the depth of the viewshed: 070, 70140, 140280, 2801200 m,

1.26, and 640 km. Figure 3 shows this process applied to a flat area: Fig. 3a illustrates the

land use and 3-B shows the viewshed from the central point, containing different land-use

types located at different distances. On average, only 18% of the land cover can be seen from

the ground (the median is 8.9%).

To analyze views in this way, a land-cover layer that localizes and identifies objects is

combined with a digital elevation model that processes topography (see Joly et al. 2009).

Land-cover data are derived from two satellites: Landstat 7 ETM (Enhanced Thematic Map-

per; 30 m and 15 m spatial resolution) and IRS 1 (Indian Remote Sensing; 5.6 m spatial

resolution). The model is based on the state of the landscape at the time the satellites passedoverhead (June and September 2000). The economic data cover the period 19952002. The

landscapes changed little over this period, so satellite images from 2000 can be used.2

2 The European database Corine Land Cover (CLC) provides two satellite images in 1990 and 2000, from

which the land use change between the two dates can be calculated. The resolution of CLC is too coarse to

be used in our study but it shows that the change in land use in the study region has been slow. Moreover, the

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Fig. 3 Land cover (a) and view at ground level (b)

Figure 3a illustrates the land cover in three rings around a transaction point. This point

is located in a village where two roads intersect and around which the built environment

is relatively tight-knit, even if some open spaces form gaps. Outside the village, the area is

covered by crops alone. The entire space is taken into account. Figure 3b shows the viewshed.

The space is subdivided into seen or masked sectors, where only the cells actually seen by

the observer are filled out (in grey or black). They make up just 12% of the area of the 280 mradius ring. A substantial difference arises between the area of the ring and the area seen,

because of topographical masks and land cover that hide more of the view the closer they

are to the observer. We term unseen object the difference between the total number of land

cover cells and the total number of cells seen.

Images are then processed by standard remote sensing procedures to correct their geom-

etry, merge the two satellite images, and classify the pixels, which correspond to the cells.

Twelve types of land cover are identified: conifers and deciduous trees (merged as trees);

crops, meadows and vineyards (merged as agriculture); bushes; roads and railroads (merged

as networks); built cells; water; quarries; and trading estates. Some objects are ascribed afixed height imposing a visual mask: 15 m for deciduous trees, 20 m for conifers, 3 m for

bushes, 1 m for vineyards and 7 m for houses.3 The others land uses (water, roads, railroads,

fields) have zero height.

3.3 Econometric Model

We begin with the usual hedonic price equation: ln Pi = Xi b + i , where Pi is the price

of real-estate i , Xi the matrix of explanatory variables (including an intercept), b the vector

Footnote 2 continuedeconometric model estimated for 20002001 yields results that are statistically similar to those obtained over

the whole period.

3 The model may be sensitive to the height of the houses, which are the most common type of object blocking

the view. They are mainly detached houses without upper storeys. We tested the effect of the chosen height

(from 5 to 9 m) on the econometric results; they are not statistically different between 6 and 9 m. The height of

constructions is very variable in the city of Dijon and its suburbs, where there are many apartment buildings;

for this reason the city was excluded from the study region.

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of parameters to be evaluated, and i an error term.4 We examine in turn the questions of

endogeneity, spatial correlation, and multicollinearity (see a detailed discussion related to

these questions in Irwin 2002).

First, covariate endogeneity may have several causes: when the consumer chooses simul-

taneously the price of housing and the quantity of an attribute (e.g. the living space); whenthe market determines both the l h.s. and some r.h.s. variables of the equation (e.g. if urban

pressure is high, residential values are high and open spaces are scarce; conversely, the scar-

city of open spaces influences residential prices; Irwin 2002); when omitted variables are

correlated with variables present in the equation. Thus, the instrumental variable method

(IV) is employed here. We use as instruments either personal features of the agents (Epple

1985; Sheppard 1999) or other instruments for projecting endogenous landscape variables

(See Sect. 3.4). If endogeneity occurs, the main equation is then estimated by the 2SLS.

Second, for a located good such as housing, spatial dependency is often present because

nearby observations share more similarities than observations which are far apart. Moreover,

located data are often spatially heterogeneous, which entails spatial heterogeneity of the esti-

mators for different zones. These two aspects may be addressed by means of spatial fixed

effects. This rests on the assumption that the spatial range of the unobserved heterogeneity/

dependence is specific to each spatially delineated unit (Anselin and Lozano-Garcia 2008).

Following this method, we introduce into the equation a variable m j characterizing the

commune j : ln Pi j = Xi j b+bj m j + i j that captures the effects of attributes whose values

are shared by observations located in this commune, including badly measured or omitted

variables, to the extent that the effect of these covariates is identical for each house within

the commune, and may be appropriately modeled by a linear shift in the model intercept.

Thus, there are no inter-commune correlations between the residuals.5

The m j s are eitherfixed-intercept shifters in the fixed-effects model (m j = Ij ), or random-intercept shift-

ers in the random-effects model (m j = j ). The fixed-effects model is better at handling

omitted or poorly measured variables, but it fails to take account of inter-commune effects.

The random-effects model allows us to introduce additional explanatory variables (e.g. inter-

commune differences between landscape variables), but it involves a risk of bias if some

inter-commune variables are badly measured, and some Xi j s may be correlated with the j s.

Therefore, we prefer the fixed-effects model. Even so, the random-effects model is also used

to check effects of inter-commune landscape variables and to compare the results obtained

by the two approaches.

Spatial autocorrelation may also occur because of the location of the houses in a commune.A Morans index between the neighbors i j s is computed and its significance is tested.

6

Thirdly, multicollinearity between landscape variables is an important issue, because the

land-cover types may be correlated for several reasons: complementarity, such as between

roads and houses, dominant uses (e.g.: farmland occupying the main part of an alluvial plain

and limiting the space available for other uses), the same land-cover should be present on

both sides of two adjacent rings. Fortunately, as Pearsons correlation coefficients show, the

view from the ground reduces these spatial links, because high objects block the view in a

quasi-random way, and break the regular pattern of land uses. We chose the view from the

ground because it is the actual view, and this choice entails the statistical advantage of greatly

4 The result of a Box-Cox test supports the use of the log-linear form.5 A Morans index test for observations belonging to neighboring communes allowed us to check this is indeed

the case.6 We use a contiguity matrix where observations less than 200 m apart are neighbors. This distance is the

threshold used in France to define urban morphology (distance cut-offs of 50 and 100 m were also tested).

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reducing multicollinearity. Nevertheless, multicollinearity may subsist, and is managed by

standard methods: merging of adjacent rings when a landscape variable exhibits a high corre-

lation and yields similar parameters on both sides; transformation of other correlated variables

(variables introduced as a percentage of a viewshed, etc.).

Finally, the statistical tests are carried out as follows: Hausmans method is used to testwhether variables are endogenous (by the increased regression method); Sargans method

is used to test the validity of the instruments; two Morans indexes between neighboring

residuals are calculated (houses less than 200 m apart and houses belonging to neighboring

communes) and their significance is tested; the homoscedasticity of the residuals is submitted

to Whites test.7

3.4 Data and Variables

Data were collected from real-estate lawyers (notaires), who are responsible for registering

real-estate conveyances in France. The database is made up of 2757 sales of detached housesbetween 1995 and 2002, and records the price of the transaction and certain characteristics

of the property and the economic agents involved.8 Each observation is also characterized

by its longitude and latitude in a French system of Cartesian coordinates (the Lambert

system), allowing a link with the geographical data. Some 90 observations were excluded

(atypical observations, shortcomings of the data base, etc.): evaluations were made from

2,667 observations. The variables used in the regressions are defined in Table 1.

Three variables, closely correlated with the living space (lot size, number of rooms and of

bathrooms), were transformed into lot size/living space, average room size (also included in

quadratic form), and number of bathrooms/living space. New houses resold within 5 years

have specific characteristics, which are captured by a dummy variable. Some of the vari-

ables in the database were excluded because either of insignificant parameters (presence of

outbuildings, parking spaces, cellars, lofts, terraces or balconies) or subjective appreciation

by the notaire (quality of the structure, etc.). Other variables characterize the transaction

(operator, previous transaction, house occupied or not, remoteness of the buyers previous

residence), the location (proximity to a highway, location both in the zoning scheme and a

floodable zone, distance from the town hall), the topography of the parcel (slope, orientation,

steep-sidedness), and the year of the transaction (dummy variables that take into account

inflation, interest rate, tax policy, etc.). The database also includes variables used as instru-

ments to project characteristics of the house that may be endogenous: the gender, occupation,age, marital status, and nationality of the buyer and the seller. Other instruments were used

to project landscape attributes that may be endogenous: Percentage of Like-Adjacence, Con-

tagion Index, Interspection and Juxtaposition Index, Division Index, Perimeter-Area Ratio

Distribution, Simpsons Evenness Index, and Patch area mean (McGarigal et al. 2002).

The landscape variables are made up of the number of cells seen and unseen (i.e. the dif-

ference between the land cover and the seen cells) arranged in the six rings (some variables in

adjacent rings are merged). They are computed for an observation point located at the center

of the residential lot. However, the view may change within the size of the parcel; therefore

we have checked that the econometric results are not influenced by the lot size.9 We tested

7 Other problems occur in the second stage of the Rosen (1974) method (Brown and Rosen 1982; Day et al.

2007), which we do not examine because this second stage cannot be made here.

8 This data base contains only houses that were sold, with no telling whether or not they are representative of

the housing stock as a whole.9 We estimate the econometric model by calculating the average view over a square around every observation

point with sides of 3, 5 or 9 cells, depending on whether the area of the residential lot, recorded in the data base,

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Table 1 Variables

Abbreviation Definition

LSPACE Living space (m2) (logarithm)

LOT/LSPACE Lot size (m2)/living space (m2)

ROOMSIZE Average room size = living space/number of main rooms

(ROOMSIZE)2 Average room size: square form

STORIES Number of stories in the house (included habitable attic or basement)

BATHROOMS Number of bathrooms/living space

ATTIC Presence of an attic

PERIOD OF Period of construction: before 1850; 18501916; 19171949;

CONSTRUCTION 19501969 (reference); 19701980; 19811991; 19922002; unknown

LESS 5 YEARS Building constructed since less than 5 years, and reselled

BASEMENT Presence of a basement

AN1995 to AN2002 Date of conveyance: dummies from 1995 to 2001 (2002 = reference)

PRIVATE Transaction without real estate offce (directly between private individuals)

SALE OFFICE Transaction by a real estate office

LAWYER OFFICE Transaction by a real estate lawyer office

BUYER OCC Property already occupied by the buyer

SELLER OCC Property already occupied by the seller

DIST BUYER Distance between the house and the buyers location (logarithm)

FRENCH Buyer of French nationality

SUCC Previous transaction = succession

DIVISON Previous transaction = division of estate

NORMAL SALE Previous transaction = normal sale

100_200_ROAD 100200 m from a major road

POS-UD Zone UD of the zoning scheme, i.e. located on periphery of the village

MIXED ZONE Mixed zone of the zoning scheme: residential and business zone

DIST TOWN HALL Distance to the town hall from a transaction point

SOUTH South orientation of the parcel

FLOODING Liable to flooding

STEEP Steep sidedness

POPULATION Population of the commune

DISTANCE DIJON Distance to Dijon from the town hall of a commune

(DISTANCE DIJON)2 Distance to Dijon from the town hall of a commune: square form

INCOME Mean income of the commune households

TREE Number of tree-covered cells (R_TREE: rate of these cells)

TREE LOT/LSPACE Number of tree-covered cells LOT/LSPACE

AGRI Number of cells of agriculture (R_AGRI: rate of these cells)

AGRI LOT/LSPACE Number of cells of agriculture LOT/LSPACE

AGRI POSUD Number of cells of agriculture class UD of the zoning scheme

NETWORK TRANSPORT Number of cells of road/railroad (R_NETWORKS: rate of these cells)

BUILT Number of built cells (R_BUILT: rate of these cells)

BUSH Number of cells of bush (R_BUSH: rate of these cells)

WATER Number of cells of water

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Table 1 continued

Abbreviation Definition

DECID_PACHES Number of patches of deciduous trees within a 70 m radius

DECID_EDGE Length of deciduous wood edges within a 70 m radius (m)AGRI_PACHES Number of patches of crops between 70 and 140 m

COMPACT Compactess index (0 = compact forms; 1 = elongate forms),


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In the fixed-effects model, the adjusted R2 is 0.70; the 2 Log Likelihood is 671.4 in

the random-effects model. Some 35% of the intercept shifters are significant at the 5% level

in the fixed-effects model, and the random intercepts are significant at the 1% level in the

random-effects model (z-value equals 5.34). The living space is endogenous (Students t

in the increased regression is 13.7) and Sargans test shows that the characteristics of theagents used as instruments are exogenous. Thus, the main equation is estimated by the 2SLS,

using as covariate the projection of the living space on the instruments. Whites test shows

that the residuals are homoscedastic. Morans index between residuals of houses less than

200 m is 0.015, and between residuals of houses pertaining to neighboring communes is

equal to 0.008661. These values are insignificant, which suggests statistically insignificant

effects of spatial autocorrelation, both at the inter-commune and the intra-commune levels.

Regarding the landscape variables, the first finding is that, unlikein other studies (e.g. Irwin

2002; Irwin and Bockstael 2001), landscape attributes are not endogenous.10 The difference

probably arises from stringent public control of land cover in France that limits the market

forces. Moreover, in the absence of spatial autocorrelations and with landscape covariates

being exogenous, the tests do not allow us to conclude that landscape estimates are biased

by omitted variables.

The significance, sign, and magnitude of the parameters estimated by the fixed-effects

model using the 2SLS and by the random-effects model are different regarding some char-

acteristics of the house and of the transaction (area of the rooms, date of construction, etc.).

Signs for landscape variables are always the same whatever the model, and the significance

at the 5% level is slightly different for two variables only (trees seen in the 140280 m range,

proportion of bushes seen in the 70140 m range).

A large number of inter-commune effects were tested with the random-effects model.They are significant in two cases only: transport networks seen less than 280 m away and

trees seen less than 70 m away. As discussed in Sect. 3.3, the random-effects model presents

drawbacks in comparison with the fixed-effects model estimated by the IV method. Thus,

we comment below mainly on the results of the latter model.

The parameters evaluated for non-landscape variables (property, transaction and location

attributes) are consistent with other French studies (e.g. Cavailhs 2005). Interestingly, two

land zoning variables are significant: house prices are lower for locations both in mixed

residential and business zones (such mixed land use often entails nuisances for inhabitants),

and on the periphery of the villages (i.e. zones UD of the zoning scheme): prices are lower

on the periphery of towns or villages than close to the town hall.For landscape attributes, Table 2 shows that most objects located more than 70 m away

have insignificant hedonic prices. Exceptions are farmland, where it is the view between 70

and 280 m that matters and transport networks in sight, which are significant up to 280 m

away. Water seen is also significant whatever the distance (with a surprising negative param-

eter). The hedonic price of other types of land cover is insignificant beyond 70 m. Other

variables were tested (dummies or quantitative variables for the rings beyond 280 m), which

are all insignificant. It is as if households were short-sighted. This indifference to the view

beyond a few tens of meters, or a few hundreds of meters, can be explained by the character-

istics of the study zone, where distant horizons, when seen, are not formed by outstandingfeatures, sea, or snow-capped lines of mountains, etc.; on the contrary they are bluish-grayish

in color, making them hard to distinguish against the skyline.

10 In the first step (projection of the landscape variables on the instruments), the partial R2 is contained

between 0.1 and 0.3, according to the model; the instruments are exogenous (Sargans statistic is superior

to 0.20); finally Hausmans test rejects the endogeneity of the landscape variables (Students t values in the

augmented equation are between 1.6 and +1.2).

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Table 2 Results

(1) (2)

Fixed-effects, 2SLS Random-effects

INTERCEPT 11.89 12.50

LSPACE 0.0126 0.0069

LOT/LSPACE 0.0169 0.0167

ROOMSIZE 0.0175 0.0012

(ROOMSIZE)2 3.4E-5 7.0E-5

STORIES 0.1349 0.0159

BATHROOMS 18.508 2.639

ATTIC 0.1108 0.0526

BASEMENT 0.0428 0.0690

PERIOD CONSTR.BEFORE 1850 0.0948 0.0832

18501916 0.0580 0.0628

19171949 0.05288 0.0875

19501969 Reference Reference

19701980 0.017 0.0523

19811991 0.0546 0.0712

19922002 0.0104 0.0565

UNKNOWN 0.0229 0.0204

LESS5 YEARS

0.0451

0.0613

AN1995 0.2540 0.2694

AN1996 0.1936 0.2158

AN1997 0.2069 0.2305

AN1998 0.1723 0.1956

AN1999 0.1212 0.1326

AN2000 0.0369 0.0410

AN2001 0.0118 0.00639

AN2002 Reference Reference

SELLER OCC 0.0443

0.0740

BUYER OCC 0.1653 0.1688

DIST BUYER 0.0064 0.00764

FRENCH 0.0997 0.0366

PRIVATE 0.0114 0.0088

SALE OFFICE 0.0256 0.0353

LAWYER OFFICE Reference Reference

SUCC 0.0391 0.0589

DIVISION 0.0583 0.0509

NORMAL SALE Reference Reference100_200_ROAD 0.0735 0.0430

POS-UD 0.0398 0.0230

MIXED ZONE 0.0642 0.0331

DIST TOWN HALL 4.0E-5 .0E-52

SOUTH 0.00042 4.5E-5

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Table 2 continued

(1) (2)

Fixed-effects, 2SLS Random-effects

FLOODING 0.0208 0.0223

STEEP 7.E-5 2.0E-5

Ring Location from Dijon

TREES SEEN


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4.3 Land Uses

At the mean point of the residential lot, trees seen in the first 70 m have a significant positive

hedonic price: the price of a house increases by 3% per additional standard deviation. More-

over, the actual view of trees is valued more highly than their mere presence: the parameterof trees unseen is three times smaller. The latter is the value of nearby trees for recreational

(walking areas), protective (against noise), and ecological (air quality, fauna and flora, etc.)

functions, but not for scenery seen from home, which is higher by far.

The difference between the two parameters may be attributed to the view sensu stricto,

disregarding the other functions of tree-covered land uses. For a variation of one standard

deviation of tree-covered area, the view represents therefore some 2% of the price of a house

and the other functions (recreation, protection, ecology) about 1%. When the distinction is

no longer made between seen and unseen tree-covered cells, as when the view from above is

analyzed, a parameter of 0.0027 is obtained for a variation of one cell of those within 70 m,

that is an intermediate value between cover actually seen in the ring (0.0057) and cover not

seen (0.0017). The 3D geographic model therefore provides greater precision than the 2D

model.

The shape of areas covered by deciduous trees within a 70 m radius (landscape ecology

indices were not calculated for conifers, which are rare) also exerts significant effects on

house prices, compounding the foregoing: an additional patch has a positive contribution

(+1.4% of the house price) and conversely 100 additional meters of boundary have a neg-

ative effect (0.5%). The combination of these two variables provides an indication of the

shapes valued: numerous patches with short edges correspond to rounded copses and not to

massed forests or narrow, elongated formations.Surprisingly, the random effects model shows that trees seen less than 70 m away have a

parameter higher on the periphery of the study area than close to Dijon. One might expect

their price to be higher in this inner belt, due to their scarcity close to the city. Nevertheless,

when trees are present but unseen their value is higher close to Dijon: wooded surround-

ings are dearer close to the city than on the periphery of the zone, where the parameter is

barely significant at the 10% level. Lastly, when seen more than 70 m away, trees command

insignificant prices, confirming the myopia of households.

Farmland seen at less than 70 m has an insignificant parameter, but crops and meadows

seen between 70 and 280 m have a positive effect on house prices: +6.6% per standard devi-

ation.11 It transpires from comparison with trees that the hedonic price of farmland seen ispositive at distances somewhat greater than for trees, although it remains confined to a radius

of 300 m or so. This is consistent with other results (Johnston et al. 2002; Smith et al. 2002).

Two contradictory effects may be combined in the 070 m range: the view of fields (positive

effect) and nuisances (noise, smells, etc.), leading to an insignificant overall effect. Farmland

that is present but not seen within the 70280 m radius commands a positive price, but only

a fifth of that of farmland that is seen, confirming the importance of the view itself. The

conclusions are similar, then, to those just presented for tree-covered cells.

In view of these findings, it must be asked whether public support for farming and forestry

is adequate in respect of one of its objectives which is to help maintain landscapes. For one

thing, the hedonic price of farmland in view is far less than that of tree-covered land uses

11 Farmland seen between 70 and 280 m makes up 56% of the area of the viewshed. Farmland is flat (it does

not hide the view) and occupies extensive areas in the study region. It is to be expected then that abundant

farmland is related to a wide viewshed and scarce farmland to a more restricted viewshed (because the land

is then occupied by tall objects such as buildings or trees). The parameter estimated for the 70280 m ring

therefore corresponds to a wide viewshed largely occupied by farmland.

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in view, whereas public support is in inverse proportions; for another thing, such support is

unrelated to the location of farmland relative to housing while households place a positive

value on farmland only when it is very close to housing.

The interaction parameters between lot size and the area of both farmland seen and trees

seen are negative: the larger the lot, the lower the marginal price of visible farmland or trees.There may be a substitution relationship between green landscape and lot size.

In contrast to tree-covered and farmland cells just examined, roads (and railroad tracks) in

view at less than 280 m lower the price of a house by 1.3% per standard deviation. Networks

within this radius but not in view command an insignificant price: it is less the presence

of roads that is a nuisance when they are not seen (although they are source of danger, air

pollution, and noise) than the actual sight of them, as they are a visual obstruction. This

result is consistent with that for trees and agriculture: the presence of an object counts less

than whether or not it can be seen. Beyond the first 280 m, the sight of roads no longer

significantly affects house prices, indicating that such nuisances remain confined to a narrow

strip.12 Transport networks seen in the 280 m circle have a clearly more negative parameter

close to Dijon, where these networks are dense and crowded, than at the periphery of the

region, where unseen roads in this circle have a positive sign (probably because they are

correlated with omitted variables: local public goods, etc.).

Among other types of objects, buildings are the most common land cover close to housing.

Their hedonic price is insignificant whatever the distance. Two opposite effects might explain

this finding: on the one hand, nearby houses allow social relations with neighbors, and on

the other hand the view of these structures may be less appreciated than green land cover.

The parameter of bushes seen is insignificant (except in the 70280 m range, with a positive

sign), which may be explained by the heterogeneity of this type of object (coppices, fallowland, groves, recent plantations, etc.). Finally, the sight of rivers or lakes has a significant

negative sign, which is not due to flooding risk (zones liable to flooding are controlled in the

equation). This result is contrary to the usual findings of the literature; however it is based on

a small number of observations (only 69 houses have viewsheds with 5% or more of water

in the 0280 m ring).

Lastly, landscape composition variables were introduced into the regression by a step-

wise method, and four indices were kept: the number of patches of deciduous trees and their

lengths within a 70 m radius (as said), a compactness index ranging from 0 (compact forms)

to 1 (elongate forms), and the number of patches of farmland located in the 70280 m range.

For 1% of additional elongation, price rises by 0.23%, and by 0.2% per additional patch offarmland. The results, for the combination used here as for other indicators taken separately,

show that division, complexity, non-contiguity, landscape fragmentation, mosaic patterns,

etc., command positive hedonic prices.

Note that over several decades, the re-parceling of farmland has formed large plots with

simple geometric shapes to facilitate work with farm machinery, hedges have been torn up

and tracks plowed up to enlarge production areas while crop rotations have been simplified.

Forests have undergone comparable, although less extensive, change with the same objec-

tive of increasing productivity. There is a clear contrast between landscapes arising from

the productive function of farming (and forestry) and landscapes valued for the non-marketfunctions of these activities.

12 Note that a location at less than 200 m from a freeway or a major road reduces the price by 7.8% (see the

100_200_ROAD parameter in Table 2).

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5 Discussion and Conclusion

Hedonic price models have been combined here with a GIS-based geographic model to eval-

uate the price of landscapes seen from houses in the urban fringe of Dijon (France). The

geographic model is used to identify, with a resolution of 7 m, 12 types of objects fromsatellite images and to measure the viewshed, by trigonometry, taking into account relief and

obstacles that may block the view. The landscape is quantified in terms of viewshed and of

the type of objects seen and unseen. The econometric models are the first stage of Rosens

approach, estimated from 2,667 house sales, which allows for endogeneity by the instru-

mental method and spatial correlations by either a fixed-effects model or a random-effects

model.

The main advantage of our geographic model is that it can be used to calculate landscape

variables from any of the 144 million cells of the study region. Estimations can thus be

extended to new transactions if the economic data base is broadened. Results can be mapped

too, as the following example shows. The price of a marginal loss of viewshed due, say, to

new building blocking out 10% of the view can be calculated at any point. Hedonic prices are

used to calculate the predicted price of this marginal loss of landscape, which is equal to the

sum of the quantity of each hidden object weighted by its price. Figure 4 shows the result for

one town, Genlis, and the surrounding villages. Obstruction of 10% of the viewshed entails

a loss of value on the outskirts of villages, where the view is primarily of fields and trees:

sometimese2000 or more (1.52% of the house price). It has a positive price where the new

buildings mask roads.

This example shows that the pairing of the geographic model (allowing the landscape

to be measured from any point) and the econometric model (allowing hedonic landscapeprices to be predicted for marginal variations in its attributes) opens up new perspectives.

Given the current state of research it is not yet possible to use such models for prescriptive

purposes, say for selecting the location of a new building by reducing its monetary impact

on the value of the view for its neighbors. But this might be a possible future use. The geo-

graphical model presented here has been used by Electricit de France (EDF), the French

public-sector power company, to route its high-voltage power lines where they are least

visible.

The main shortcoming of this geographic model is that it yields results which are approxi-

mation of the actual situations and which may be biased if certain assumptions are inaccurate.

In particular, a comparison with orthophotographs shows that the present model may under-estimate the viewshed by exaggerating the amount blocked out by buildings.

The great advantage of the fixed-effects econometric model is that it takes into account all

the factors depending on distance from Dijon. Almost all the covariates, including those for

landscapes, vary with this urbanrural gradient and the co-variations are almost impossible to

account for without the fixed-effects model. The main drawback of this model is that it allows

for intra-communal variations of landscape variables only, and ignores inter-communal vari-

ations. Moreover, whatever the precautions taken to avoid the effects of omitted variables,

the method cannot guarantee freedom from bias related to this problem. The method also

allows us to test for endogeneity of explanatory variables (including landscape attributes) byusing the instrumental method.

The results are consistent with the literature on several points. They show, first, that it is

above all the view of the tens of meters around a house that counts; beyond a hundred meters

or so, a few attributes remain significant up to 150300 m, but no farther. Second, the results

confirm that land cover around houses has a significant effect on housing prices, generally

with the expected signs: trees have positive hedonic prices, as does farmland, while roads

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Fig. 4 The price of an obstruction of 10% of the view in and around Genlis. Note: For cells located morethan 200 m for built polygons, the price of obstructed view is not calculated as it would be absurd to calculate

the price of loss of view from a house located in the middle of a field or a forest. These cells are light grey in

the Figure (not calculated box). For a cell belonging to or close to a built polygon, the blocking of the view

generally entails a loss of value, which loss is greater when the cell is located on the edge of the village (everdarker greys). In some instances (in white in the Figure), the blocking of the view is reflected by an increased

value when it is roads that are masked by new buildings.

have negative hedonic prices. In some instances the signs are counterintuitive (water), which

is not uncommon in the literature and shows that further research is required.

We also show, which is new in the literature, that it is the view that influences the real-

estate price and not the mere land cover: trees or farmland close to a house but not visible

from it command far lower hedonic prices than when they are seen. Trees close to houses

but out of sight contribute to the residential setting by providing amenities (peace and quiet,fresh air, etc.) but their hedonic price is a third of that of trees in view. Unseen farmland is

worth just one-fifth of the hedonic price of farmland in sight and unseen nearby roads have an

insignificant hedonic price, while they are a source of nuisances (noise, danger, etc.). These

results about the importance of the actual view are confirmed by the results about landscape

shapes: landscape shape indexes show that households prefer complex, fragmented shapes

and mosaic patterns of scenery.

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However, our method is reductive because it simplifies in the extreme what a landscape

is and evaluates only use values related to residential consumption. Moreover, the hedonic

method used does not ensure full compliance with the all-else-being-equal requirement. The

point that in spite of these limitations on the whole it yields significant results is encouraging.

However, we are aware that other methods are also required to enhance knowledge in thedomain of the economic valuation of landscapes.

Appendix: Descriptive Statistics (Landscape Variables)

See Table 3

Table 3

Variable Ring Number of

houses with

the attribute

Value for houses with the attribute

Mean Total SD Intra-SD Inter-SD

Trees seen


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Cavailhes J. GIS Based Hedonic Pricing of Landscape 2009