Environ Resource Econ (2009) 44:571–590DOI 10.1007/s10640-009-9302-8

GIS-Based Hedonic Pricing of Landscape

Jean Cavailhès · Thierry Brossard ·Jean-Christophe Foltête · Mohamed Hilal ·Daniel Joly · François-Pierre Tourneux ·Céline 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 imagessupplemented 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, andspatial correlations. Results show that when in the line of sight, trees and farmland in theimmediate vicinity of houses command positive prices and roads negative prices; if out ofsight, their prices are markedly lower or insignificant: the view itself matters. The layoutof features in fragmented landscapes commands positive hedonic prices. Landscapes andfeatures in sight but more than 100–300 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 bymany households in most developed countries. This paper focuses on the valuation of theviewshed 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 andcareful in the management of open spaces and green areas in and around cities.

This research was financed by Burgundy Regional Council, Côte-d’Or Departmental Council and DijonConurbation Joint Councils. It uses data on real-estate transactions from the PERVAL Corporation.

J. Cavailhès (B)CESAER-INRA, 26 Bd Docteur Petitjean, BP 87999, 21079 Dijon Cedex, Francee-mail: Jean.Cavailhes@enesad.inra.fr

T. Brossard · J.-C. Foltête · D. Joly · F.-P. Tourneux · C. TritzCNRS-ThéMA, 32 rue Megevand, 25030 Besançon, France

M. Hilal · P. WavreskyINRA-CESAER, 26 Boulevard Petitjean, BP 87999, 21079 Dijon Cedex, France

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Hedonic pricing is employed here to value landscapes in a periurban belt around Dijon, themain city in Burgundy (France). These are commonplace rural landscapes, with villages andsmall towns scattered over plains, hills, and valleys covered by farmland and woodland. Weanalyze a landscape as seen “from within” instead of “from above” by allowing for objectsand 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 buthidden by masks. Hedonic prices of these seen and unseen objects are then derived fromdata for 2,667 house sales using either a fixed-effects model estimated by the instrumentalvariable 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); thencome 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 bygeographers for characterizing landscape are appropriate and can be used to this end. Wepresent 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 coveror calculate landscape indices (2 dimensional approach), virtual landscapes reconstructed inthree 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 regressionmethods. A score given by a panel is explained by objective attributes (land cover, visualarrangement, etc.), subjective attributes (mystery, atmosphere, etc.), and sometimes personalcharacteristics (gender, age, etc.). Much of this work was done in the 1980s. Gobster andChenoweth (1989) listed more than 80 references and recorded 1194 terms for describingesthetic preferences. For example, marks for photographs in the Great Lakes region (US)are explained by physical, ground-cover, “informational” (order, complexity, mystery), andperceptual (open, smooth, easy to cross) variables (Kaplan et al. 1989). Recent research hasfollowed similar lines; for example, Johnston et al. (2002) use maps and photographs to showthat households choose fragmented, long and narrow housing subdivisions when density islow, but opt for more clustered forms for denser subdivisions. Ground-level photographs arealso used to estimate the economic value of landscapes by contingent evaluation (e.g. Willisand 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-estatevalues generally decrease with distance to green areas, golf courses, forest parks (Tyrväinenand Miettinen 2000), stretches of water (Spalatro and Provencher 2001) or to wetlands (Mahanet 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 isvery 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 directaccess to forests is worth 20–25% more, but that this extra value vanishes if there is a roadto cross to get to the forest. Therefore, researchers must take into account the exact locationsof observers and objects alike.

The land cover within a radius around a house can be analyzed from aerial photographsor satellite images. The findings are used for landscape valuation, mostly by the hedonic

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method. In most although not all cases positive hedonic prices are reported for trees (Kestenset al. 2004), particularly on land adjacent to the residential lot (Thorsnes 2002), and fornearby recreational woods (Tyrväinen and Miettinen 2000) as well as for parkland, golfcourses, or greenbelts. Farmland has a less clear-cut impact with some studies concluding ithas 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 ofland is sometimes included in the hedonic equation either because it affects expectationsabout development (Irwin 2002) or because access rights to parcels affect their recreationalvalue (Cheshire and Sheppard 1995).

Landscape ecology provides variables for characterizing the shape of patches formedby the land cover: diversity, fragmentation, entropy, fractal dimension, or other statisticalsummaries. For example, Geoghegan et al. (1997) show that landscape fragmentation anddiversity have negative effects on real-estate values, except where very close to and very farfrom Washington DC.

The view from the ground entails integrating the third dimension (i.e. relief and any tallobjects) into 2D satellite images. It has only recently been introduced into hedonic-valuationmodels: 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 aview, and Bastian et al. (2002) use such variables for the hedonic pricing of landscape; theyconclude that in the Rocky Mountains (US) landscape diversity, the only landscape variablethat is significant, is highly appreciated. Paterson and Boyle (2002), using precise satelliteimagery information, compare the land cover and the view from the ground in a rural regionof 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 environmentalvariables” (Paterson and Boyle 2002: 417). Here, we extend and enhance this conclusion bydistinguishing between objects in view and objects hidden by relief or masks that block theview. 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 findingsshow that the view of a road reduces the real-estate price. In the same way, we distinguishseen from unseen roads.

In short, most studies use data on distance (1D), and maps, aerial photographs, or satelliteimages (2D). Very few reconstruct 3D landscapes as is done here by taking account of reliefand 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 accountboth endogeneity of covariates and spatial autocorrelation by using a fixed-effects modelestimated 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 itssuburbs, which are excluded from the study. Its outer bound is given by access time to Dijonof 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 byincluding 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 tierof 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 andnatural formations.

Figure 1 shows the settlement pattern in the south-eastern sector of the study region (otherquadrants are similar). This region is made up of many villages and small towns formingdensely populated clusters isolated from their neighbors by broad expanses of farmland,woods, and forests. The average population density of villages is 1700 inhabitants per squarekilometer 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 meanpopulation 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 tothe 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 hereusing a GIS-based model (see a survey in Bateman et al. 2002). Its extent varies with bothrelief and the objects that may block the view. Its content is a matter of the type of objectsvisible. 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 to155 m 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 65 m from the observer blocks out the view beyond

above ground level. This simulation of view is made everywhere, all around each observationpoint of the study region. Each place in the surroundings is visible or not, depending upontopography 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 radiusareas to take into account the depth of the viewshed: 0–70, 70–140, 140–280, 280–1200 m,1.2–6, and 6–40 km. Figure 3 shows this process applied to a flat area: Fig. 3a illustrates theland use and 3-B shows the viewshed from the central point, containing different land-usetypes located at different distances. On average, only 18% of the land cover can be seen fromthe ground (the median is 8.9%).

To analyze “views” in this way, a land-cover layer that localizes and identifies objects iscombined 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 spatialresolution). 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 1995–2002. Thelandscapes 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, fromwhich the land use change between the two dates can be calculated. The resolution of CLC is too coarse tobe 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 pointis located in a village where two roads intersect and around which the built environmentis relatively tight-knit, even if some open spaces form gaps. Outside the village, the area iscovered 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 bythe 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 theyare to the observer. We term ‘unseen object’ the difference between the total number of landcover 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 (mergedas ‘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 forbushes, 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 + εi , where Pi is the priceof real-estate i , Xi the matrix of explanatory variables (including an intercept), b the vector

Footnote 2 continuedeconometric model estimated for 2000–2001 yields results that are statistically similar to those obtained overthe whole period.3 The model may be sensitive to the height of the houses, which are the most common type of object blockingthe 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 ofconstructions 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 εi an error term.4 We examine in turn the questions ofendogeneity, spatial correlation, and multicollinearity (see a detailed discussion related tothese 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 urbanpressure 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 arecorrelated 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 (Epple1985; 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 becausenearby 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 fixedeffects. 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 thecommune j : ln Pi j = Xi j b +b j m j + εi j that captures the effects of attributes whose valuesare shared by observations located in this commune, including badly measured or omittedvariables, to the extent that the effect of these covariates is identical for each house withinthe 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 = I j ), or random-intercept shift-ers in the random-effects model (m j = ε j ). The fixed-effects model is better at handlingomitted 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 someinter-commune variables are badly measured, and some Xi j s may be correlated with the ε j s.Therefore, we prefer the fixed-effects model. Even so, the random-effects model is also usedto check effects of inter-commune landscape variables and to compare the results obtainedby the two approaches.

Spatial autocorrelation may also occur because of the location of the houses in a commune.A Moran’s index between the neighbors’ εi j s is computed and its significance is tested.6

Thirdly, multicollinearity between landscape variables is an important issue, because theland-cover types may be correlated for several reasons: complementarity, such as betweenroads and houses, dominant uses (e.g.: farmland occupying the main part of an alluvial plainand limiting the space available for other uses), the same land-cover should be present onboth sides of two adjacent rings. Fortunately, as Pearson’s correlation coefficients show, theview from the ground reduces these spatial links, because high objects block the view in aquasi-random way, and break the regular pattern of land uses. We chose the view from theground 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 Moran’s index test for observations belonging to neighboring communes allowed us to check this is indeedthe case.6 We use a contiguity matrix where observations less than 200 m apart are neighbors. This distance is thethreshold 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 bystandard 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: Hausman’s method is used to testwhether variables are endogenous (by the increased regression method); Sargan’s methodis used to test the validity of the instruments; two Moran’s indexes between neighboringresiduals are calculated (houses less than 200 m apart and houses belonging to neighboringcommunes) and their significance is tested; the homoscedasticity of the residuals is submittedto White’s test.7

3.4 Data and Variables

Data were collected from real-estate lawyers (notaires), who are responsible for registeringreal-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 characteristicsof the property and the economic agents involved.8 Each observation is also characterizedby 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 from2,667 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 ofbathrooms), were transformed into lot size/living space, average room size (also included inquadratic form), and number of bathrooms/living space. New houses resold within 5 yearshave 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 ofoutbuildings, parking spaces, cellars, lofts, terraces or balconies) or subjective appreciationby the notaire (quality of the structure, etc.). Other variables characterize the transaction(operator, previous transaction, house occupied or not, remoteness of the buyer’s previousresidence), the location (proximity to a highway, location both in the zoning scheme and afloodable 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 accountinflation, 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 usedto project landscape attributes that may be endogenous: Percentage of Like-Adjacence, Con-tagion Index, Interspection and Juxtaposition Index, Division Index, Perimeter-Area RatioDistribution, Simpson’s 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 inadjacent rings are merged). They are computed for an observation point located at the centerof the residential lot. However, the view may change within the size of the parcel; thereforewe 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 ofthe housing stock as a whole.9 We estimate the econometric model by calculating the average view over a square around every observationpoint 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; 1850–1916; 1917–1949;

CONSTRUCTION 1950–1969 (reference); 1970–1980; 1981–1991; 1992–2002; 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 buyer’s 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 100–200 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), <70 m

other landscape variables: the surface area of the viewshed, the share of each object in theviewshed, non linear forms (logarithm, square form), etc. The number of cells is the mostsignificant variable, which we introduce into the regressions, except in some cases to reducemulticollinearity. Interaction variables are introduced between lot size and the number of bothtree-covered and farmland cells (descriptive statistics show correlations between these landuses and lot size) and between farmland and developable areas of the land zoning scheme(to take account of households’ expectations about development). Unseen cells that are notintroduced into the equations are the reference for the landscape variables. They are mainlycells located more than 1.2 km from the observer.

Lastly, landscape indices, currently used in landscape ecology, provide information aboutlandscape composition and shape. They were calculated on land cover images in 12 classesin a 70 m radius circle. Computations were applied in the same way that Fragstats software(McGarigal and Marks 1995; McGarigal et al. 2002) with a new programming routine focusedon transaction points to save calculation time. We selected the most significant indices by aforward stepwise method.

4 Results

4.1 Descriptive Statistics

Table 3 (Appendix) gives some descriptive statistics about the landscape variables used inthe model. The 2,667 transactions are divided among 235 communes, averaging 11.3 trans-actions per commune. The narrowness of the viewshed should be emphasized. The medianarea visible from the cell of observation is 1813 m2. For 26.7% of the sample, the view isconfined to the adjacent cells; from the cell at the third quartile of the distribution, one cansee 21,420 m2; it exceeds 1 ha in 31.2% of cases and is 1 km2 in 7.8%. The main reasonfor this restricted view is masking by buildings that are almost invariably only a few tens ofmeters apart. As said, the density of the inhabited polygons is 1700 inhabitants per squarekilometer. This is due to the land-use regulation that forces clustered settlement in the studyregion.

4.2 Overall Results

Table 2 shows the results (see legend in Table 1) estimated by the fixed-effects model (usingthe 2SLS) (column 1), and by the random-effects model (column 2).

Footnote 9 continuedis (respectively) closer to 411 m2 (3 × 3 × 49 m2), 1225 m2 (5 × 5 × 49 m2) or 2401 m2 (9 × 9 × 49 m2).The results are not statistically different from those obtained by measuring from a single cell.

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In the fixed-effects model, the adjusted R2 is 0.70; the −2 Log Likelihood is −671.4 inthe random-effects model. Some 35% of the intercept shifters are significant at the 5% levelin the fixed-effects model, and the random intercepts are significant at the 1% level in therandom-effects model (z-value equals 5.34). The living space is endogenous (Student’s tin the increased regression is −13.7) and Sargan’s 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. White’s test showsthat the residuals are homoscedastic. Moran’s index between residuals of houses less than200 m is −0.015, and between residuals of houses pertaining to neighboring communes isequal to −0.008661. These values are insignificant, which suggests statistically insignificanteffects of spatial autocorrelation, both at the inter-commune and the intra-commune levels.

Regarding the landscape variables, the first finding is that, unlike in other studies (e.g. Irwin2002; Irwin and Bockstael 2001), landscape attributes are not endogenous.10 The differenceprobably arises from stringent public control of land cover in France that limits the marketforces. Moreover, in the absence of spatial autocorrelations and with landscape covariatesbeing exogenous, the tests do not allow us to conclude that landscape estimates are biasedby omitted variables.

The significance, sign, and magnitude of the parameters estimated by the fixed-effectsmodel 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 significanceat the 5% level is slightly different for two variables only (trees seen in the 140–280 m range,proportion of bushes seen in the 70–140 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 andtrees seen less than 70 m away. As discussed in Sect. 3.3, the random-effects model presentsdrawbacks 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 locationattributes) are consistent with other French studies (e.g. Cavailhès 2005). Interestingly, twoland zoning variables are significant: house prices are lower for locations both in mixedresidential 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 loweron 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 awayhave insignificant hedonic prices. Exceptions are farmland, where it is the view between 70and 280 m that matters and transport networks in sight, which are significant up to 280 maway. 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. Othervariables were tested (dummies or quantitative variables for the rings beyond 280 m), whichare all insignificant. It is as if households were short-sighted. This indifference to the viewbeyond 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-grayishin 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 containedbetween 0.1 and 0.3, according to the model; the instruments are exogenous (Sargan’s statistic is superiorto 0.20); finally Hausman’s test rejects the endogeneity of the landscape variables (Student’s t values in theaugmented 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∗∗∗1850–1916 −0.0580∗∗∗ −0.0628∗∗∗1917–1949 −0.05288∗∗ −0.0875∗∗∗1950–1969 Reference Reference

1970–1980 0.017 0.0523∗∗∗1981–1991 0.0546∗∗∗ 0.0712∗∗∗1992–2002 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 Reference

100_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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GIS-Based Hedonic Pricing of Landscape 583

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 <70 m 0.0057∗∗∗ CLOSE 0.0031

FAR 0.0073∗∗∗TREES SEEN × LOT/LSPACE <70 m −1.7E-4 −1.5E-4∗TREES UNSEEN <70 m 0.0017∗∗∗ CLOSE 0.0015∗∗∗

FAR 0.0008∗TREES UNSEEN × LOT/LSPACE −6.0E-5∗∗∗ −4.0E-5∗∗∗R_TREES SEEN 70–140 m 0.0010 0.00815

TREES SEEN 140–280 m −0.0007 −0.0011∗∗R-BUSHES SEEN <70 m 0.0264 0.040

R-BUSHES SEEN 70–140 m 0.2448∗∗ 0.1351∗R-BUSHES SEEN 140–280 m 0.0854 −0.0122

R_AGRI SEEN <70 m −0.0130 0.0131

R_AGRI UNSEEN <70 m 0.00043 9.4E-4

AGRI SEEN 70–280 m 1.7E-4∗∗∗ CLOSE 0.0001∗∗∗FAR 0.00012∗∗∗

AGRI SEEN × LOT/LSPACE 70–280 m −0.0064∗∗∗ −0.0057∗∗∗AGRI SEEN × POSUD 70–280 m −5.0E-5∗ −3.0E-5

AGRI UNSEEN 70–280 m 3.6E-5∗∗∗ CLOSE 3.5E-5∗∗∗FAR 3.5E-5∗∗∗

AGRI UNSEEN × LOT/LSPACE 70–280 m −0.0020∗∗ −0.0023∗∗∗AGRI + TREES SEEN 0.28–40 km 2.5E-5 5.1E-5

BUILT SEEN <70 m 0.00206 0.00128

R_BUILT SEN 70–280 m −0.0018 0.00126

R_BUILT SEEN 0.28–1.2 km 0.00471 0.0356

NETWORKS SEEN 0–280 m −0.0003∗∗ CLOSE −0.0004∗∗FAR −0.0003∗

NETWORKS UNSEEN 0–280 m 4.5E-5 CLOSE 2.0E-5

FAR 0.00011∗∗R_NETWORKS SEEN 0.28–1.2 km −0.2478 −0.159

WATER SEEN 0–40 km −0.0417∗∗ −0.0324∗∗DECID_EDGE −0.0005∗∗∗ −0.0004∗∗∗DECID_PACHES 0.0109∗∗∗ 0.0118∗∗∗AGRI_PACHES 0.0025∗∗∗ 0.00172∗∗∗COMPACT 0.2313∗ 0.1507

POPULATION 2.6E-5∗∗∗DISTANCE FROM DIJON −0.0551∗∗∗

(DISTANCE FROM DIJON)2 0.00086∗∗∗INCOME 0.00002∗∗∗

Level of significance : *** 1%; ** 5%; * 10%

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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 positivehedonic 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 standarddeviation of tree-covered area, the view represents therefore some 2% of the price of a houseand the other functions (recreation, protection, ecology) about 1%. When the distinction isno longer made between seen and unseen tree-covered cells, as when the view from above isanalyzed, 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 notseen (0.0017). The 3D geographic model therefore provides greater precision than the 2Dmodel.

The shape of areas covered by deciduous trees within a 70 m radius (landscape ecologyindices were not calculated for conifers, which are rare) also exerts significant effects onhouse 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 theshapes valued: numerous patches with short edges correspond to rounded copses and not tomassed forests or narrow, elongated formations.

Surprisingly, the random effects model shows that trees seen less than 70 m away have aparameter higher on the periphery of the study area than close to Dijon. One might expecttheir 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 isbarely significant at the 10% level. Lastly, when seen more than 70 m away, trees commandinsignificant prices, confirming the myopia of households.

Farmland seen at less than 70 m has an insignificant parameter, but crops and meadowsseen 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 radiusof 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 0–70 m range: the view of fields (positiveeffect) and nuisances (noise, smells, etc.), leading to an insignificant overall effect. Farmlandthat is present but not seen within the 70–280 m radius commands a positive price, but onlya fifth of that of farmland that is seen, confirming the importance of the view itself. Theconclusions 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 forestryis adequate in respect of one of its objectives which is to help maintain landscapes. For onething, 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 doesnot hide the view) and occupies extensive areas in the study region. It is to be expected then that abundantfarmland is related to a wide viewshed and scarce farmland to a more restricted viewshed (because the landis then occupied by tall objects such as buildings or trees). The parameter estimated for the 70–280 m ringtherefore 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 isunrelated to the location of farmland relative to housing while households place a positivevalue on farmland only when it is very close to housing.

The interaction parameters between lot size and the area of both farmland seen and treesseen 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) inview at less than 280 m lower the price of a house by 1.3% per standard deviation. Networkswithin this radius but not in view command an insignificant price: it is less the presenceof roads that is a nuisance when they are not seen (although they are source of danger, airpollution, and noise) than the actual sight of them, as they are a visual obstruction. Thisresult is consistent with that for trees and agriculture: the presence of an object counts lessthan whether or not it can be seen. Beyond the first 280 m, the sight of roads no longersignificantly affects house prices, indicating that such nuisances remain confined to a narrowstrip.12 Transport networks seen in the 280 m circle have a clearly more negative parameterclose to Dijon, where these networks are dense and crowded, than at the periphery of theregion, where unseen roads in this circle have a positive sign (probably because they arecorrelated 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 explainthis finding: on the one hand, nearby houses allow social relations with neighbors, and onthe 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 70–280 m range, with a positivesign), 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 significantnegative sign, which is not due to flooding risk (zones liable to flooding are controlled in theequation). This result is contrary to the usual findings of the literature; however it is based ona small number of observations (only 69 houses have viewsheds with 5% or more of waterin the 0–280 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 theirlengths 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 70–280 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 withsimple geometric shapes to facilitate work with farm machinery, hedges have been torn upand 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 fromthe 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 the100_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). Thegeographic 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 andobstacles that may block the view. The landscape is quantified in terms of viewshed and ofthe type of objects seen and unseen. The econometric models are the first stage of Rosen’sapproach, 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-effectsmodel.

The main advantage of our geographic model is that it can be used to calculate landscapevariables from any of the 144 million cells of the study region. Estimations can thus beextended to new transactions if the economic data base is broadened. Results can be mappedtoo, as the following example shows. The price of a marginal loss of viewshed due, say, tonew building blocking out 10% of the view can be calculated at any point. Hedonic prices areused to calculate the predicted price of this marginal loss of landscape, which is equal to thesum of the quantity of each hidden object weighted by its price. Figure 4 shows the result forone town, Genlis, and the surrounding villages. Obstruction of 10% of the viewshed entailsa loss of value on the outskirts of villages, where the view is primarily of fields and trees:sometimese2000 or more (1.5–2% of the house price). It has a positive price where the newbuildings mask roads.

This example shows that the pairing of the geographic model (allowing the landscapeto 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 prescriptivepurposes, say for selecting the location of a new building by reducing its monetary impacton 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 Electricité de France (EDF), the Frenchpublic-sector power company, to route its high-voltage power lines where they are leastvisible.

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 allthe factors depending on distance from Dijon. Almost all the covariates, including those forlandscapes, vary with this urban–rural gradient and the co-variations are almost impossible toaccount for without the fixed-effects model. The main drawback of this model is that it allowsfor 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 alsoallows 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 isabove all the view of the tens of meters around a house that counts; beyond a hundred metersor so, a few attributes remain significant up to 150–300 m, but no farther. Second, the resultsconfirm that land cover around houses has a significant effect on housing prices, generallywith 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 calculatethe price of loss of view from a house located in the middle of a field or a forest. These cells are light grey inthe Figure (‘not calculated’ box). For a cell belonging to or close to a built polygon, the blocking of the viewgenerally 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 increasedvalue when it is roads that are masked by new buildings.

have negative hedonic prices. In some instances the signs are counterintuitive (water), whichis 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 visiblefrom it command far lower hedonic prices than when they are seen. Trees close to housesbut 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 isworth just one-fifth of the hedonic price of farmland in sight and unseen nearby roads have aninsignificant hedonic price, while they are a source of nuisances (noise, danger, etc.). Theseresults about the importance of the actual view are confirmed by the results about landscapeshapes: landscape shape indexes show that households prefer complex, fragmented shapesand mosaic patterns of scenery.

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However, our method is reductive because it simplifies in the extreme what a landscapeis and evaluates only use values related to residential consumption. Moreover, the hedonicmethod used does not ensure full compliance with the all-else-being-equal requirement. Thepoint 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 ofhouses withthe attribute

Value for houses with the attribute

Mean Total SD Intra-SD Inter-SD

Trees seen <70 m 953 7.52 5.79 5.15 3.39

Trees seen*lot size <70 m 953 98.68 154.0 131.2 64.25

Trees unseen <70 m 1,447 23.01 32.26 27.74 17.72

Trees unseen*lot size <70 m 1,447 299.8 914.5 819.2 389.7

Rate of trees seen 70–140 m 891 0.3279 0.3758 0.3335 0.1472

Trees seen 140–280 m 841 11.17 17.34 13.45 8.925

Rate of bushes seen <70 m 1176 0.1079 0.1727 0.1598 0.0434

Rate of bushes seen 70–140 m 627 0.0571 0.0904 0.0832 0.0222

Rate of bushes seen 140–280 m 461 0.0461 0.0814 0.0709 0.0277

Rate of farmland seen <70 m 1,839 0.5073 0.2807 0.2485 0.1722

Rate of farmland unseen <70 m 2,667 0.2740 0.1496 0.1267 0.0796

Farmland seen 70–280 m 1,160 322.6 441.0 387.5 195.7

Farmland seen*lot size 70–280 m 1,160 5.133 12.792 11.480 4.7102

Farmland seen*posU 70–280 m 603 293.7 403.8 358.9 161.4

Farmland unseen 70–280 m 2,667 2614.8 780.32 550.6 552.9

Farmland unseen*lot size 70–280 m 2,667 26.19 27.60 22.99 15.27

Farmland + woodland seen >280 m 814 96.35 161.93 146.9 53.04

Built seen <70 m 2,494 10.87 5.059 4.606 2.446

Rate of built seen 70–140 m 1,000 0.1233 0.161 0.1502 0.0414

Rate of built seen 140–280 m 231 0.1525 0.2219 0.2024 0.0349

Network transport seen 0–280 m 1,164 35.597 62.98 55.59 22.89

Network transport unseen 0–280 m 2,657 257.8 168.3 125.9 111.7

Rate of network transport seen >280 m 212 0.0390 0.0550 0.0509 0.0099

Water seen 0–40 km 267 1 0 0.1842 0.1842

Decid_edge <70 m 1,499 139.3 126.6 100.8 91.37

Decid_paches <70 m 1,509 4.120 3.412 2.446 2.784

Agri_paches <70 m 2,667 19.48 9.647 6.794 6.849

Compact <70 m 2,667 0.6105 0.0408 0.0377 0.0158

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