The landscape from home: seeing andbeing seen.

A GIS-based hedonic price valuation

Jean Cavailhès∗,† Thierry Brossard‡ Mohamed Hilal∗

Daniel Joly‡ François-Pierre Tourneux‡ Céline Tritz‡

Pierre Wavresky∗

April 8, 2005

Abstract

Landscape attributes in the urban fringe of Dijon (France) are eval-uated by the hedonic pricing method based on an econometric model ofpanel data derived from the sales of 2520 houses. Landscapes as seen fromthe ground are analyzed from satellite images and from a digital elevationmodel using geographic methods. The results show that forests and farm-land in the immediate vicinity of houses have positive prices and roads anegative price when these features can be seen, while their prices are closeto zero when they cannot be seen. The arrangement of features in complexor fragmented landscapes commands a positive hedonic price. Being seenfrom other houses is a nuisance. Landscapes and visible features morethan 100—200 m away all have hedonic prices close to zero.

JEL Classification: Q2; Q3; R14; R21.Key words: landscape; amenity; hedonic price; urban fringe.

IntroductionMany people nowadays choose to work in the city but to live in the coun-try. Rural scenery, open spaces, forests, and farmland are all components of thelifestyle sought after by many households and therefore the subject of increasingattention from public authorities. Although this is nothing new, it is prompting∗CESAER, UMR INRA-ENESAD, 26 Bd Docteur Petitjean, BP 87999 F-21079 Dijon

cedex†corresponding author, e-mail: Jean.Cavailhes@enesad.inra.fr, telephone: 33380772580;

fax number: 33380772571‡ThéMA, UMR CNRS-Université de Franche-Comté, 32 rue Mégé van, F-25030 Besançon

cedex

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considerable interest today (Brueckner [1]). This can be explained by the out-ward spread of cities into the countryside which has characterized urbanizationin developed countries in recent decades (Caruso [2]). Thus at the November4, 2004 U.S. elections a total of $3.25 billion was voted for the conservation ofparkland and open spaces (Trust for Public Land) in 120 local ballots. In 1998the State of New Jersey opted to buy 1 million acres (20% of the state’s landarea) to safeguard that land from future development (Burchfield et al. [3]). InFrance the Conservatoire du littoral has purchased 73,200 ha along 861 km ofcoastline for the same purpose of preserving its natural character.All told, urbanization affects barely 1.9% of the land area of the United

States (Burchfield et al. [3]). Including transportation networks, airports,leisure and sports parks, etc., the total built environment came to 3.8% of theland area of France (source: our exploitation of Corine Land Cover). But therehas been a sharp upturn: built areas increased by 48% in the U.S. over a periodof just 16 years (1976—1992) (Burchfield et al. [3]) and by 60% in France overwhat was only a slightly longer period (1981—2000) (Cavailhès [4]). Moreover,this urbanization is occurring in the immediate vicinity of existing housing: inthe U.S. there are practically no new buildings more than 1 km from existingbuilt-up areas (Burchfield et al. [3]), which explains why people are so sensitiveto the urban sprawl they see close to home, eroding the surrounding green areas.Much economic work is being carried out on this issue. The restrictive hy-

potheses of the early urban economics models (Alonso [5]) have been slackened,replacing von Thünen’s homogeneous isotropic plain by a heterogeneous spacewith its nuisances and amenities (Brueckner et al. [6]). Attempts have beenmade to explain the spread of cities and the sprawl of metropolitan areas (Anaset al. [7], Brueckner [1]), to model these phenomena (Cavailhès et al. [8], [9],Marshall [10], Turner [11], Wu and Plantinga [12]), and to put a value on openspaces in or near to cities (Cheshire and Sheppard [13], Irwin [14], Roe et al.[15], Thorsnes [16], Wu et al. [17]) and on landscapes (Geoghegan et al. [18]).This paper concentrates on this last aspect of making an economic valuation ofresidential countryside around cities.Of the various methods of evaluating non-market goods that of hedonic prices

is adopted here for evaluating the price of rural scenery around Dijon (France).Within a radius of some 40 km around the city a few hundred villages and smalltowns are scattered over a predominantly agricultural plain and amid hills andvalleys covered by forests and farmland. This is commonplace rural scenery,which makes it more difficult to estimate its price than for sites of outstandingnatural beauty.The main difficulty lies in ‘measuring’ landscapes to obtain the quantitative

variables to enter into a hedonic price equation. However, this difficulty can nowbe overcome thanks to advances in satellite imagery and geographic informationsystems for processing satellite images. The novel feature of the method usedhere is that it analyzes a landscape as seen ‘from within’ instead of ‘from above’by taking account of objects and relief which may hide the view from the ground.In this way the view from a home can be reconstituted in a three-dimensionalspace. A panel data econometric estimation is then used to determine the within

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estimators of the model, with the instrumental variable method (because anexplanatory variable is endogenous). The georeferenced economic data relate to2520 houses sold between 1995 and 2002.The remainder of the paper is arranged into four parts. After a review of

relevant literature (section 1), the economic and geographic models are set outalong with the data sources (section 2). Then come the results (section 3) andthe conclusion (section 4).

1 Landscape valuationWhen evaluating non-market goods it is standard procedure to separate stated-preference methods (such as contingent valuation) from revealed-preference meth-ods (such as hedonic prices or transport costs). In applying these methods tolandscape, the question of the view has long been ignored because of inadequatedata. Basically landscape is what lies before one’s eyes, which is difficult to in-troduce in valuation models. To overcome the difficulty photographs have oftenbeen used when analyzing consumer preferences or assessing their willingnessto pay. Maps, aerial photographs, or satellite images are data sources where afictional observer views the landscape ‘from above’. A more recent source is theview from ground level or ‘from within’ which is used here.As this contribution uses the hedonic price method for evaluating landscapes

in the urban fringe, two restrictions are introduced so as to situate it within thevery extensive range of related literature. First, only research into ‘green’ ameni-ties or landscape externalities is analyzed, to the exclusion of analyses of urbanlandscapes, industrial sites, commercial amenities, etc. Second, precedence isgiven to valuation by the hedonic price method.

1.1 Preference analysis from photographs

Generally, the task is to use multiple regressions to analyze a mark awardedto landscape photographs by a panel. The explanatory variables are objectiveattributes (objects, land use, visual arrangement, etc.), subjective attributes(mystery, atmosphere, etc.), and sometimes personal characteristics (social cat-egory, sex, familiarity with the landscape). Much of this work is old. In 1989Gobster and Chenoweth [19] listed more than 80 references and recorded 1 194terms for describing esthetic preferences. For example, Kaplan et al. [20], whoare often cited among the pioneers in the field, explain the marks for photographsin the Great Lakes region (U.S.) by physical, ground cover, ‘informational’ (or-der, complexity, mystery, etc.) and perceptual (open, smooth, easy to cross)variables. Mystery is a good predictor of the mark, which, for those workers, isa classical result.Recent research has been conducted in the same vein such as that by Arriaza

et al. [21], Hammitt et al. [22], De Groot and van den Born [23], Johnston etal. [24], Kaplan and Austin [25], Kline and Wichelns [26], Meitner [27], andPalmer [28]. For example, Johnston et al. [24] use maps and photographs

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of alternative developments to show that households prefer fragmented ‘green’lots, bounded by hedges, not adjacent to built areas and main roads. They alsochoose fragmented, long and narrow housing subdivisions when density is low(which maintains the esthetic and ecological quality of the whole), but opt formore clustered forms for larger subdivisions.

1.2 Economic value of landscape attributes seen from above

Ground cover (agriculture, forest, ‘greenery’, etc.) within a perimeter arounda house (usually a circle or an administrative unit) is used to characterize the‘local living environment’. In most but not all cases positive hedonic pricesare obtained for wooded land cover (Kestens et al. [29]), particularly on theresidential lot itself (Des Rosiers et al. [30]) or on adjacent lots (Thornes [?])and for nearby recreational forests (Tyrväinen and Miettinen [31]) as well as, ofcourse, for parkland, golf courses or greenbelts. Farmland has a less clear-cutimpact with some studies concluding it has a positive effect on real-estate values(Roe et al. [15]), but others a negative effect (Smith et al. [32]).Beyond these results, which hold few surprises, some specific aspects have

been examined. The legal status of land influences the hedonic price of someattributes either because it affects expectations about subsequent conversionof open spaces to urban uses (Irwin [14]) or because the possibility of goinginto these areas for walks depends on that status (Cheshire and Sheppard [33]).The latter, through the second stage of the hedonic price method (Cheshireand Sheppard [34]), analyze the welfare afforded by green spaces and theirredistributive effects (Cheshire and Sheppard [13]).Landscape ecology provides variables for characterizing the shape or pattern

of patches formed by different types of land use: synthetic indexes (diversity,fragmentation, entropy, etc.), geometric variables (fractal dimension), or statis-tical summaries (mean, standard deviation). This approach is generally natu-ralistic as it assumes that the factors explaining a landscape’s ecological value(in terms of biodiversity, sought-after ecosystems, etc.) are also the basis ofits anthropic and in particular esthetic quality and finally of its economic valuewhich is capitalized on the land market. Geoghegan et al. [18], who present oneof the earliest examples of this approach, show that the presence of farmland-forest nearby (slightly) raises land values but has a negative effect when moreremote. The fragmentation and diversity of landscape around housing has anegative effect on real-estate values, except where very close and very far fromWashington. Acharya and Bennett [35] did very similar work on a Connecticutwatershed.The distance between housing and specific features generally has a positive

effect on real-estate values: proximity to a green area, a golf course, a forest park(Tyrväinen and Miettinen [31]), to a stretch of water (Mooney and Eisgruber[36], Spalatro and Provencher [37]) or to wetlands (Mahan et al. [38]). Thiseffect is sometimes non-linear, with housing that is very close to or very remotefrom the feature being worth less than housing at some intermediate distance(30—400 m for Bolitzer and Netusil [39]). In other instances contiguity has a

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positive value (Hobden et al. [40]). For example, Thorsnes [16] shows thathousing with direct access to forests is worth 20—25% more and that this extravalue vanishes if there is a road to cross to get to the forest. On the whole,research shows that when the proximity of open or green spaces has a significanteffect on prices (in the expected direction, which is not always the case), thateffect is substantial when the distance is zero or very short (a few tens of meters),and may be reversed because of congestion, and that it falls off rapidly withdistance, and is cancelled out beyond a few hundred meters at most.It should be recalled that research on landscapes seen from above often yields

intuitive results, but counterintuitive signs, values close to zero, or volatile val-ues are sometimes reported. It is hard to say whether such fragile results reflectreality or arise from the roughness of landscape variables: scenery viewed fromthe air is very different from scenery viewed from the ground (because of mask-ing by objects or because of relief which limits the field of view), the distancebetween two points is not in itself a landscape, and geographic resolution is of-ten coarse (several hundred meters). Economists depend on variables suppliedby geographers, which have become more precise and accurate in recent years.

1.3 Economic value of landscape attributes seen fromwithin

The view from the ground (‘from within’ as opposed to ‘from above’) has onlyrecently been introduced into economic valuation models. It entails integratingthe third dimension into the two-dimensional satellite image of land use. Withthis third dimension visual masks (relief and any tall objects) can be identifiedand an observer’s field of view defined.In this way Germino et al. [41] analyzed the landscape from satellite images

and a digital elevation model by using planimetric (‘map-like’) and panoramic(‘photograph-like’) simulations of a view. Bastian et al. [42] used such variablesto valuate the hedonic price of landscape and environmental attributes. Theyconcluded that in the Rocky Mountains (U.S.) elk habitats and trout rivers com-mand positive hedonic prices. Landscape diversity, the only landscape variablewith a non-zero price, is highly appreciated.To the best of our knowledge there are few other examples combining a three-

dimensional quantitative geographic model and a valuation of the hedonic priceof landscape seen from the ground. Among the exceptions, Paterson and Boyle[45] compare a rural region of Connecticut (U.S.) seen from within and fromabove. The sign of their results varies with the specification and in particularcontrasts the view from above and the view from within and the surface areaseen and the content of the field of view (built areas, forests). Lake et al. [46]calculate the price of road nuisances, noise, and the view in the urban area ofGlasgow (Scotland).

1.4 Economic methods of evaluating landscape

As said, this brief survey of the literature leaves aside work on valuation of land-scapes using stated preferences, whether contingent valuation or more recently

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the application of random utility models (Roe et al. [15]). It also omits researchcomparing the contingent valuation method to the hedonic price method suchas the pioneering work of Blomquist [47] on the view of Lake Michigan fromChicago.The main problems facing economists, other than the issue of landscape

variables just addressed, are the usual problems of spatial econometrics and thehedonic price method: spatial autocorrelated errors and endogenous variables.Endogeneity can be illustrated by two examples: in an area of high urban pres-sure, residential values are high and open spaces are scarce and small, with thesame urban pressure on land determining both the explained variable and theexplanatory variable; or easement contracts protecting farmland from develop-ment explain high real-estate values and the abundance of open spaces subjectedto such contracts. Spatial autocorrelations, which shall be looked at again, areinherent in the study of landscape which is spatial good par excellence.Some of the research presented above makes allowance for spatial autocorre-

lations, as in Paterson and Boyle [45] or Des Rosiers et al. [30], but few studiesaddress the issue of endogenous variables (Cheshire and Sheppard [33] are anexception) and, to the best of our knowledge, only Irwin [14] tackles both is-sues together. In analyzing urban nuisances, a methodologically similar issue tothat of landscape amenities, Brasington and Hite [48] and Kim et al. [49] makepainstaking studies of spatial autocorrelations.

** *

It should be remembered, then, that studies analyzing the economic value oflandscapes as seen from a house are both recent and few. We introduce severalnew features into this field. First, the study by Lake et al. [46] is the only one,to our knowledge, where the view may be blocked by tall objects (constructions,trees), such masks being identified by site visits, which excludes any automation,contrary to the procedure employed here. Second, image resolution is often ofabout 30 m (i.e. about 1000 m2), which means small objects cannot be madeout, whereas a resolution of 7 m is used here. Third, computation times are veryhigh if all the pixels of large images have to be scanned to check whether they canbe seen, which is why Bastian et al. [42] used only a sample of 158 observations;the image used here has 127.7 million pixels and is analyzed in full by multi-scalemodeling, which reduces computation time. Fourth, an econometric model ofpanel data with a control for endogeneity of explanatory variables is used here.

2 Models, sources, and dataThe conceptual framework employed here borrows from two strands of geogra-phy dealing with the study of landscape and with Lancaster’s consumer theory.A landscape is a combination of physical components (relief, ground cover) andan observer who perceives it through her own representations and practices.It might be said, in reference to Lancaster [43], that the view of a landscape

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is the output of a consumer’s auto-production using as its input an emittingsystem (physical components) to produce utility through sensorial and intellec-tual tools. It is proposed here to valuate the price of these attributes throughthe standard microeconomic program of maximization of utility under budgetconstraint.Readers will have recognized the foundations of Rosen’s hedonic price method

[44]. It is a framework which implies two major restrictions which are brieflyreviewed here before presenting the economic and geographic methods. First,this framework can be used for estimating residential-use values, i.e. the value ofnon-market attributes capitalized in a real-estate price, but not recreational-usevalues (the transport cost method would be more suitable) or existence values(valuated by stated-preference methods). Second, a house is a durable goodand the residential mobility of home-owners in France is low. Ideally, allowanceshould be made for agents’ expectations about how their landscape environmentwill evolve, which cannot be done here for want of suitable proxies (as Irwin [14]does by introducing the legal status of land). Hedonic price estimations here arebased on the state of the landscape as it was at the time the satellites passedoverhead (leading to the elimination of land for building because the landscapeenvironment changes quickly in a zone where building is underway).Moreover, the primary objective of this research is to determine whether

landscapes in the urban fringe of Dijon − commonplace landscapes that real-estate market experts and operators consider of too little values to be quantified− have a statistically non-zero price. This would go some way to explaining thespread of Dijon’s urban fringe, which is part of the expansion of cities towardthe surrounding countryside mentioned in the introduction. For the answer tobe relevant, precedence is given to variables and methods which underestimatethe absolute value of hedonic prices: landscape variables take account onlyof quantities of objects and not their esthetics, the econometric method used,which is the fixed-effect panel model, omits the ‘between regression’ component,and statistical bias (due to inevitable measurement errors) underestimates theparameters. In opting for systematic underestimation (in absolute value) robustresults are sought in qualitative terms (significance tests and parameter signsare essential) by accepting that the absolute value of estimated prices may beundervalued.

2.1 Economic and econometric model of hedonic prices

In evaluating the hedonic prices of the characteristics of houses, in particularthe price of landscape attributes, the first stage of the approach adopted byRosen [44] is used. Its microeconomic foundations can be reviewed succinctly.A household k, with socio-economic characteristics αk, maximizes a utility func-tion U = U(Z,H,αk) by consuming housing H(x1, ..., xh, ...), comprising a setof intrinsic (floor space, comfort, etc.) and extrinsic (accessibility, social or en-vironmental quality of the location, etc.) attributes xh and a composite goodZ, taken as the numéraire, under budget constraint Wk = P (H)+Z, where Wk

is income and P (H) the house price. The first order conditions of the standard

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microeconomic program give the hedonic price ph of characteristic xh, equalto the marginal rate of substitution of this characteristic and of the compositegood:

∂

∂xhP (H) =

∂U∂xh∂U∂Z

= ph (1)

The corresponding econometric model is:

E [P (H |x)] = Xβ (2)

giving bβ, from which bph can be deduced.The second stage in this approach, which is not used here, evaluates the

demand functions for attributes xh in accordance with the prices estimated inthe first stage, income Wk, and the characteristics αk of consumer k:

xh = fh

ÃXτ

bpτ ,Wk,αk

!(3)

This method raises four major econometric issues:

• First, the price of attributes does not vary linearly with their quantity: inequation (1) ph is not a constant because of fixed transaction or buildingcosts. The choice of the functional form for modeling this non-linearity ofbudget constraint is a first difficulty (Sheppard [50]). It is often resolvedby a logarithmic (as here) or Box-Cox transformation of the price P (H).

• Second, some explanatory variables may be endogenous because of thesimultaneous choice of the price of the house and of the quantities of itsattributes. This is usually a consequence of the non-linearity of budgetconstraint, which leads the consumer to choose both the price of housingand the quantity of certain attributes at the same time. This impliesrecourse to the instrumental variable method (Epple [51], Freeman [52]).The results show that the floor space of the housing is almost alwaysendogenous. But landscape variables also may be endogenous if the houseprice is determined simultaneously with some of its landscape attributes(Irwin [14]).

• It may also be that for a spatialized good such as housing there are spatialautocorrelations. This is the third difficulty and is emphasized here sincea seldom used solution is provided, which needs to be justified.

• Lastly, for the second stage of Rosen’s method, identification of the de-mand function, that is valuation of equations derived from (3), assumesthe provision of additional information and in particular of hedonic pricesevaluated on different markets (Brown and Rosen [53]). The first stagealone is used here because data is available for the Dijon study area only.

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To return to the third point, spatial correlation between variables may haveseveral sources:

1. direct influence of a transaction on its neighbors, for example when theprice at which a house is put on the market depends on prices observedin the neighborhood;

2. variables that have been omitted or poorly measured but which are com-mon to neighboring observations, concerning amenities or nuisances, socialor environmental externalities, accessibility to employment, or to markets,etc.;

3. dynamics that have not been introduced or poorly modeled, for exam-ple when the quality of a landscape shaped by the inhabitants attractsnew inhabitants with the same socio-demographic characteristics, therebyreinforcing that landscape quality;

4. overflow effects, for example when the public goods of one district areenjoyed by the inhabitants of neighboring districts.

Generally, such spatial correlations are controlled by introducing a spatialautoregressive parameter to be estimated or by modelling spatial autocorrela-tions between the disturbances of neighboring observations (depending on thedistance between them or the contiguity between the administrative entities towhich they belong). For example, Irwin [14] deals with the dual problem ofendogenous variables and of spatial autocorrelations in estimating the hedonicprice of green spaces by using the instrumental variable method and elimi-nating the closest neighboring observations. Brasington and Hite [48] use anauto-regressive term and a spatial lag of the explanatory variables in the caseof industrial nuisances.Here econometrics of panel data is used to solve the same problem. The

idea is that spatial correlations are due to omitted characteristics shared byobservations belonging to a single group. This model is adapted to the dataused for reasons illustrated by Figure 1. In Figure 1A, connections betweenobservation z and its neighbors are symbolized by lines (which become narrowerwith distance). If there is no sudden break (steady gradients in distance oromitted variables with regularly decreasing effects), a spatial autocorrelationmodel is suitable. However, group membership (Figure 1B, where observationshave the same spatial distribution as in 1A and where four groups are made outI, II, III, and IV) is better suited to situations where observations are closelyconnected within a group and where sudden breaks may occur between groups.

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zI

II

III

IV

B

z

A

Figure 1. Modeling spatial connections

Figure 1A models the link between the observation z and its neighbors in terms of distancebetween observations and Figure 1B in termes of membership of groups.

The lowest tier local government in France is the ‘commune’. It is the level ofgroup used in our model, which we call district or commune afterwards. Theyare not very populous (median 229 inhabitants), and not very extensive (median9 km2). They share characteristics which are not found in the data base suchas fiscal policy and local land policy (tax and zoning), enjoyment of local publicgoods (schools, etc.), and they have the same accessibility to markets for labor,goods and services. They also share miscellaneous amenities, nuisances, andexternalities. In addition, two neighboring districts often have very differentcharacteristics in these domains depending on their size, the composition oftheir population, local policies, or their geographic situation. This pattern ofhabitation and of administrative division of the study region prompts the useof a panel data econometric model rather than traditional modeling of spatialautocorrelation.Econometrics of panel data is used above all in individual-temporal models.

Take, for example, the model Ynt = b0 +Xntb +mn + εnt where n designatesan individual (e.g. a country), t the repetition of observations over time, andmn a variable characterizing the country. The model used here has the samestatistical form but is interpreted differently in economic terms. It is written:

lnPij = b0 +Xijb+mi + εij (4)

where Pij is the price of real-estate j in district i, Xij the matrix of explana-tory variables, b the vector of parameters to be evaluated, mi a variable specificto district i and εij an error term. Data is available for several years (1995—2002), but the time dimension is not of interest in itself here: our panel datamodeling is designed to check the unobservable or omitted characteristics of dis-trict i for the study of the hedonic price of the landscape, which is not thoughtto have varied over a fairly brief period. Obviously, it is an incomplete panel,since the number of observations varies by district (in addition, transactions arenot ordered as in an individual-temporal panel).Econometrics of panel data separates the fixed-effect model (mi is a dummy

variable) and the random-effect model (mi and εij are two random variables).

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We opted for the fixed-effect model, and so the within estimator (or LeastSquares Dummy Variable) is used, which is an intra-district estimator here.There are several reasons for this choice. First, our objective, it will be recalled,is to determine whether the commonplace landscapes of the study region havenon-zero prices. It is therefore preferable to take a model that underestimatesthe parameters like the within estimator. Second, with this estimator, there isless risk of ascribing the effect of omitted variables to landscape variables thanwith a random-effect model. There is a risk of correlating the variations of land-scape quality between communes with omitted variables of social quality of thecommune, municipal public goods, etc. Third, for variables used in regression,inter-district standard deviations (232 communes) are about half the strength ofintra-district standard deviations (10.9 observations per commune on average).Intra-district heterogeneity is therefore marked compared with the relative inter-district homogeneity, which suggests that the within estimator captures a largeshare of variance. Finally, the within estimator reduces the absolute value of pa-rameters in the event of measurement errors (which may be made for landscapevariables), introducing a possible bias in the desired direction.The fixed-effect model is evaluated on variables transformed into deviations

from group averages. The endogenous variable is the difference between thelogarithm of the price of a house j in district i, lnPij , and the mean of logarithmsof house prices in that district i, written lnPi. It is explained by the deviations ofexplanatory variables. More specifically, subtracting from (4) the equation equalto the sum of observations of a district divided by the number of observationsgives:

LnPij − lnPi =¡Xij −Xi

¢b+ υij (5)

where Xi is the matrix of means of explanatory variables in district i. The errorterm υij = εij − εi is heteroscedastic, but this is the case where the estimatorof generalized least squares (GLS) is the same as the estimator of ordinaryleast squares (OLS) (Sevestre [54]). Another source of heteroscedasticity is thenumber Ni of observations which differs from one commune to another. It iseasy to check it by multiplying all the terms of (5) by

pNi/ (Ni − 1).

Equation (5), after the foregoing transformation, is estimated without in-tercept. A Box-Cox transformation, made at an explanatory preliminary stageby the maximum likelihood method, shows that the transformation parameteris close to zero, which leads to adopting the logarithmic form for prices. Thestatistical tests are as follows:1 the Hausman test shows that the living space

1Hausman’s method based on ‘increased regression’ is used to test if a variable x is en-dogenous. This variable is first regressed on instruments Z. The residuals are written bη. Theequation of interest Y = Xβ+ ε is then modified by including bη: Y = Xβ+ bηb+ ε (increasedregression). A Student’s test is made of coefficient b and if this is statistically different from0, the null hypothesis is rejected: x is not exogenous. Once this hypothesis has been re-jected, Sargan’s method can be used to test the validity of the instruments: the residuals ofthe increased regression are regressed on instruments Z. Instruments whose coefficients arestatistically different from 0 are rejected (the hypothesis of the validity of these instrumentsis rejected). If there are non-valid instruments, the procedure is repeated.

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is endogenous (as is almost always the case with the hedonic method). Thisendogenous variable is replaced in (5) by its projection on instruments, whichare exogenous as the Sargan test shows, and two-stage least squares (2SLS) areused. The instruments used in the first regression are the exogenous explanatoryvariables of the second regression, the characteristics of the buyer and seller, andthe toponymy of the roadways (cf. infra, section 2.3.2). White’s test indicatesthat the residuals of the final estimation are homoscedastic. By design, thereare no spatial autocorrelations of the residuals, the means being zero for eachdistrict.

2.2 2.2. Geographic model of quantitative analysis of land-scape

A landscape, as said, is a portion of space before one’s eyes. The overall visi-ble area (or viewshed) is measured here by looking outward from a residentiallot through 360 degrees. This panorama of the landscape takes on aspects ofboth quantity and quality resulting from the objects at ground level. These ob-jects have a dual status: they open up or close down the view (masking effect,depending on their height) and they constitute the features of the landscape.Observation points are determined by their longitude and latitude, converted

into a system of Cartesian coordinates (‘Lambert’), which supposes, for local-izing real-estate transactions, that they are georeferenced in this system. Fromeach point in the study zone, rays are extended in all directions and tests areconducted along the rays for each pixel encountered (a pixel is the smallest geo-graphic object identified, here a square with 7 m sides) to determine the type ofobject occupying each of them. By allowing for the relief and the height of theobjects thus identified, the number of pixels visible along the ray is calculated.By summing these, one can determine the field of vision and the type of allpixels in it, which are the landscape features used in the economic model. Eachpixel l in the study region (which contains 127.7 million) is studied in this wayto make up maps, but the economic model concentrates only on the subset ofpoints where real-estate transactions are located.From the foregoing data, for each pixel, written l, three types of ‘view’ can

be modeled:

1. Land use seen from a satellite (from above) in concentric rings aroundl. This is the standard approach in geography (as in map making, forexample) or in landscape ecology (satellite image analysis). As said, this isthe approach used by the vast majority of economic studies that evaluateslandscape price.

2. The landscape as seen from the ground with a 360 degree view out from l(from within). Each pixel l is characterized by the panorama it providesan observer at l and at ground level (more exactly at a height of 1.80 m).The results show that in the study region a tiny fraction of pixels seen fromabove are seen from within, which justifies the use of this distinction.

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3. Exposure to being seen by others, which is the reverse of the precedingrelation, that is, all of the points from where the person at l can be seen.For instance, ‘exposure to view from roads’ designates all of the roadpixels from where a person at l can be seen. Seeing and being seen are notsymmetrical because of the interplay of the height and layout of objects.To the best of our knowledge, the two have never before been distinguishedin literature on the economic valuation of landscapes, while our resultsshow this is an important consideration.

Therefore, to analyze the ‘views’ of landscapes defined in this way, a landuse layer, which localizes and identifies objects, must be combined with a digitalelevation model which models the topography and architecture of the space.Data sources on land use, described by Joly et al. [55], are made up of

images from two satellites: Landstat 7 ETM (30 m and 15 m spatial resolution)and IRS 1 (Indian Remote Sensing, images at 5.6 m spatial resolution). Thesedata are corrected geometrically to allow for deformations induced by the moreor less oblique path of the satellite, and then combined and transformed in colorspaces to yield a spatial resolution of 7 m. The next stage is to classify the multi-channel satellite images to produce thematic information. The signal measuredis a composite one as it results from all of the objects composing a pixel; theclassification seeks to put each pixel in the most probable class. Ultimately,by giving precedence to objects which are liable to mask the view and to formimportant features of landscapes, 12 types of land use are identified: water,conifers, deciduous trees, bushes, crops (including bare plowed land), meadows,vineyards, roads, built areas, quarries, railroads, and trading estates.The digital elevation model is at 50 m resolution. It is dilated to superimpose

it on the 7 m resolution of the land use image, and then the altitude of eachpixel is reconstituted by interpolation.For each pixel in the study region, the total quantity of space seen and the

quantity of each type of land use seen in that area is calculated in accordancewith distance. To do this, it is theoretically necessary to test all the points ofthe study region (and beyond) to determine just how far the view extends. Thecomputation time required for such a procedure with our matrix of 127.7 millionpixels would be excessive. Two methods are employed to reduce computationtime.First, the 360 degree panorama is sampled by 120 rays spaced 3 degrees

apart, which does not detract from the precision of the econometric estimation(ray sampling is independent of error in this model). Next, calculations are madealong each ray up to a distance of 40 pixels (280 m). For each, a trigonometriccalculation can identify the pixels seen depending on the relief and the type ofobjects encountered. Some objects are ascribed a fixed height imposing a visualmask: 7 m from the ground for the roof of a house or other building, 15 m fordeciduous trees, 20 m for conifers, and 3 m for bushes. The other types (water,roads, railroads, fields) have zero height.Limitation of range to 40 pixels would be a problem if the 7 m resolution

were the only base used, as the maximum range would be a mere 280 m. To go

13

beyond this, a technique is used to cut computation time and which correspondsto the imperfection of the human eye. Indeed, the landscape corresponds to ascene where a scale of objects to be seen is superimposed on a second scalerelative to the distance at which they are seen. The closer an object is to thepoint of observation, the more of the field of view it occupies in proportion.This ‘visual impact’ falls in proportion to remoteness, until distant objects maybe incorporated in part or in full in the field of view and then change nature:it is no longer a tree, a house that is seen but a forest or a village, or even atract of farmland if the tree or house are small: in this case, only a farmed areais visible from afar. This property of human vision means the space beyond thefirst 280 m can be dealt with at a resolution of more than 7 m, which reconcilesshort calculation time and tests up to the bounds of the study area.More specifically, modeling up to 280 m from pixel l is based on a resolution

of 7 m. At this high resolution it is assumed that for each point of the studyregion the observer looks at each pixel to identify the space seen: pixels seenare counted and the type of land use identified for each. When the end ofthis first check is done (at 280 m from l), there is a shift to a base with 30m resolution and the same process is repeated (pixels previously tested areexcluded from the new computation). These are medium views (280—1200 m),where the images of objects become fuzzier until they merge into large units.By the same reasoning, a 150 m base is used for modeling objects located 2—6km away at a small scale, and finally a 1 km base is used for testing beyond thatup to 40 km away: any remote views are then taken into account. The 150 and1000 m resolution images come from a data source that is more compatible withsuch scales, Corine Land Cover, and as the resolution increases, the number ofland use categories falls. At 1000 m resolution, only farmland, forest, water,and towns remain. Objects such as bushes, roads, or quarries, which have onlya small footprint, are progressively abandoned as resolution changes, becausethe human eye cannot make them out a great distances.

2.3 Study region and variables

2.3.1 The study region

The study region is a belt around Dijon (France), made up of villages and markettowns scattered in a rural setting, which we term the ‘ periurban belt ’ of Dijon.It corresponds in the main to the ‘ periurban crown’ defined by the Institutnational de la statistique et des études économiques (INSEE) on the basis ofcommuting patterns (at least 40% of the working population in a communeis employed outside that commune, and generally within the conurbation ofDijon). The study region has been extended somewhat beyond this crown andbounded in the following way:

• Its inner bound is the Dijon city and its suburbs, which numbers 240,000inhabitants and which has been excluded from the zone for two reasons:first, it would be difficult to apply our method of landscape analysis be-cause of the very variable height of buildings; second, precedence is given

14

to the study of farmland, forest, and open spaces liable to explain thesettlement in the countryside around Dijon over the last 30 years.

• Its outer bound is given by access time to Dijon of less than 33 minutesor a distance by road of less than 42 km, which leads to the inclusion of afew communes beyond the periurban crown (it has been checked that theresults were not particularly sensitive to the bounds used).

This study region covers 3534 km2 and includes 140,703 inhabitants. It iscomposed of 266 communes with at least one transaction recorded in the database, with a mean population of 461 inhabitants (the median is 229 and thestandard deviation 733) and the average population density of 41 inhabitantsper km2 (median 26 and standard deviation 135). The hypotheses of both asingle labor market and a single real-estate market required for the hedonicprice method seem to hold up well: 74% of people in employment in this areacommute to work out of the commune (usually in Dijon and its suburbs); inaddition, from 1982 to 1999, 19,123 moved house out toward the periurban crownand 11,964 moved in the opposite direction.The study region covers four main geographic units differentiated by topog-

raphy and land use. North of Dijon are limestone plateaus with large cerealfarms employing little labor. South of Dijon lies a series of three strips: tothe west is the Auxois livestock farming region with its landscape of hedge-lined meadows in the valleys and woods on the higher land; then comes theArrière-Côte, a limestone plateau dissected by dry valleys with diversified farm-ing (fruit, cereals, livestock); to the east is the Saône River floodplain with itsforests and intensively farmed arable land (market gardening and arable crops).A sharp scarp separates the last two strips along which run the vineyards forwhich Burgundy is reputed.This is a region of clustered housing where views are rapidly masked by

neighboring houses. Land uses are, broadly (using data from Corine LandCover): built areas cover 2.4% of the land, farmland 59%, woodland and semi-natural formations 38%.

2.3.2 The data and economic variables

The economic data come from real-estate lawyers (‘notaires’), who are respon-sible for registering real-estate conveyances in France. The database is madeup of 2757 sales of detached houses in the study region between 1995 and 2002.These are sales between private individuals for which the lawyers record in thedatabase the price of the transaction and certain characteristics of the propertyand the economic agents involved. Some 237 observations were excluded for twokinds of reason: first some observations were atypical in terms of size or theirattributes and shortcomings of the data base (variables not completed or inputerrors) (N = 203), and second, districts with just a single observation (N = 34).Evaluations were made from 2520 observations.The available variables concerned first non-landscape attributes most of

which come from the notaires’ database and in some cases from our geographical

15

model or from the Institut géographique national (IGN). These variables were(the legend to Table 1 is in capitals in parentheses for the variables used there):

• Property characteristics:- living space in m2 (LSPACE),- lot size in m2 (LOTSIZE),- average room size, equal to living space dived by number of main rooms

(ROOMSIZE) also included in quadratic form (ROOMSIZE)2 ,- number of stories in the house (STORIES),- year or period of construction (AGE),- bathroom density, equal to number of bathrooms divided by living space

(BATH),- presence of an attic (ATTIC),- presence of basement (BASEMENT),- presence of swimming pool (POOL).

• Transaction characteristics:- date of conveyance (DATE),- property already occupied by buyer (BUYEROCC), the reference form

being unoccupied property,- form of transaction: between private individuals (PRIVATE) and of the

previous transaction: succession (SUCC) or division of estate (DIVISION), thereference being by mutual agreement.

• Location characteristics:- slope of the lot (in degrees),- distance to a road, i.e. less than 100 m: <100M_ROAD or 100—200 m:

100_200M_ROAD,- distance of less than 100 m from a railroad track: < 100M_RAIL,- land zoning of the district: mixed residential and business zones (MIXED-

ZONE),- location in floodable area (FLOOD).The variables correlated with the living space, such as the number of rooms

or of bathrooms were transformed to reduce this connection (mean room sizeand bathroom density per square meter of floor space). The parameter forbeing part of a floodable area is statistically not significant but the variable wasretained because of the counterintuitive sign obtained for the view over water.Some of the variables in the database were excluded from the regression.

For property characteristics, these were the variables whose hedonic price wasnot significantly different from zero (presence of outbuildings, parking spaces,cellars, lofts, terraces or balconies) and subjective appreciations of the lawyer(good or poor state of repair, type of house).This database also includes variables used as instruments. These are the

gender, occupation, age, marital status, and nationality of the buyer and seller,and the road toponomy (street, lane, avenue, cul-de-sac, hamlet, etc.) for thelocation.

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2.3.3 Landscape variables

The landscape variables cover the visible area, land use, landscape composition,topography, and the distance to objects. Visible areas are measured in squaremeters for six fields of view: less than 70 m, 70—140 m, 140—280 m, 280—1200m, 1.2—6 km, and more than 6 km. As explained, a distinction is made betweenthe view, exposure to the view of others, and the view from above.Pixels are classified into 12 categories (as before, the legend for Table 1 is in

capitals in parentheses for the variables used in the table):- view of built area (BUILT) and exposure to view from built area (EXP-

BUILT),- bush, quarry,- water (WATER),- forest (FOREST), a variable grouping deciduous trees and conifers (the

results are similar for both types of woodland and there are few conifers in thestudy region),- plow land (CROPS),- meadowland (MEADOW),- road (ROAD),- vineyard, railroad, trading estate.These variables were tested for the six ranges of view defined previously.

Belonging to the Val Suzon ‘Natural Area of Ecological, Floral, and FaunalInterest’ (VAL_SUZON) is also significant; this is included in the landscapevariables because these ecological characteristics may be related to landscapes.Landscape composition variables inspired by landscape ecology are also in-

troduced. They are calculated from the documentary base of Fragstat software[56] on land use image in 12 classes, for the view from above only (calculatingthem for the view from within would mix the landscape composition effect weseek to study and a quantity effect of the area viewed). Composition indexesand global variables and variables by land use class have been tested. Manyyield significant results, but with close correlations between them. The chosenglobal indexes are:- overall length of boundaries between patches of different types (BOUND-

ARY),- a compactness index (COMPACT), which is zero for compact forms and

one for very elongate forms,- the number of deciduous woodland patches (DECIDPATCH),- the length of woodland boundaries in this category (DECID_EDGE) in

meters.These landscape composition variables are statistically non different from

zero beyond the first 70 m around a house; to simplify results, the commentary<70M is not stated in these cases in Table 1.

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3 Results

3.1 Descriptive statistics

Table A-1 appended gives some descriptive statistics about the variables usedin the model. The 2520 transactions are divided among 232 districts, averaging10.9 (median 6).The narrowness of the view of the landscape should be emphasized. When

moving around a housing lot, the median area viewed is 1862 m2, which is barelythe average size of two residential lots. From the house at the third quartile ofthe distribution, one can see 22,020 m2. For 26.3% of the sample, the view isconfined to the adjacent pixels. It exceeds 1 ha in 31% of cases and is 1 km2

in 7.9%. For the top decile of the distribution, the view encompasses 36 ha ormore. The main reason for this restricted view is masking by buildings, whichare numerous because housing is clustered and residential lots are small (mean1030 m2 and median 800 m2). Built areas greatly reduce the field of view: theheight attributed to a house is 7 m and, in view of the median distance to thenearest house (also 7 m), the angle between the ground and the rooftop is 45◦

. This makes it difficult to see beyond. The inclusion in the geographic modelof the masking effect of houses is therefore decisive.In the immediate vicinity, that is less than 70 m from a house (we will see

that this is the most important field of vision), the observer located at one ofthese points almost always sees other buildings (500 m2 on average in this fieldof view), trees from 52% of these houses (average tree-covered area is 450 m2 ifnon zero), and open areas, fields or meadows, from 69% in the 70 m circle (2600m2 on average) and from about the same proportion in the 280 m circle (1.22ha on average). Roads are seen in the first 70 m from 37% of observations (900m2 on average).

3.2 Global results

Table 1 shows the results for the variables with parameters significantly differentfrom zero (the legend is explained in sections 2.3.2 and 2.3.3). A first regressionis made without landscape variables (column 1) and the other two columnsintegrate these without and with the landscape composition variables (columns2 and 3). In this final regression, R2 is 0.52 (R2 is defined as 1 — sum of squaresexplained / sum of squares total: R2 = 1− SSE/SST ).As expected, the living space is endogenous (Student’s t by the augmented

equation method is — 9.47). The Sargan test shows there is no other endogenousvariable. In particular, the absolute value of Student’s t-tests for all the land-scape variables is less than 0.3: choices of house price and landscape attributesare not affected by simultaneity. It is not surprising there is no simultaneity dueto the buyer’s behavior because, as shall be seen, landscape attributes play avery small part in determining real-estate prices, contrary to living space. Butit might have been thought, by the theoretical reasoning mentioned previously,that the same market forces would have weighed simultaneously on house prices

18

and on landscape variables (urban pressure, population density, etc.). It maybe that the choice of the within estimator explains this counterintuitive result.

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(1) (2) (3)Estimate t estimate t estimate t

LSPACE (ln m²) 1.399 16.4 1.417 16.4 1.419 16.5LOTSIZE (m²) 0.045 3.9 0.036 3.0 0.036 3.0ROOMSIZE (m²) -0.019 -4.4 -0.020 -4.5 -0.02 -4.6(ROOMSIZE)² (sq m²) 0.00012 1.7 0.000113 1.6 0.00012 1.7STORIES (number) -0.145 -7.7 -0.146 -7.7 -0.145 -7.7AGE (year) 0.00059 5.3 0.00055 4.9 0.00056 5.0BATH (number/ m² floor) 21.77 8.4 22.1 8.5 22.06 8.5ATTIC 0.089 3.7 0.093 3.9 0.092 3.8POOL 0.071 2.3 0.067 2.2 0.069 2.2BASEMENT 0.040 3.4 0.04 3.4 0.039 3.3DATE (year) 0.045 15.8 0.045 15.7 0.045 15.7PRIVATE -0.033 -2.7 -0.034 -2.8 -0.034 -2.8BUYEROCC -0.192 -5.6 -0.196 -5.7 -0.201 -5.9SUCC -0.046 -3.0 -0.046 -3.0 -0.457 -3,0DIVISON -0.062 -2.6 -0.065 -2.7 -0.065 -2.8SLOPE (degree) -0.0059 -1.5 -0.0079 -1.9 -0.0072 -1.7<100M_ROAD -0.057 -2.2 -0.041 -1.5 -0.033 -1.2100-200M_ROAD -0.080 -3.0 -0.083 -3.1 -0.076 -2.8<100M_RAIL -0.052 -1.6 -0.056 -1.7 -0.061 -1.8FLOOD -0.020 -0.8 -0.023 -1.0 -0.019 -0.8MIXEDZONE -0.045 -1.6 -0.047 -1.7 -0.047 -2.7FORET < 70M (pixel) 0.0024 2.7 0.0020 1.9ROAD < 70M (pixel) -0.00075 -1.9 -0.00073 -2.2MEADOW 140-280M (p ixel) 0.00009 1.5 0.00011 1.7CROPS< 140M (pixel) 0.0001 2.2 0.00012 2.6WATER 70-280M (pixel) -0.00071 -2.6 -0.00072 -2.7BUILT_SEEN < 70M (pixel) 0.0059 2.3 0.0053 2.1EXP_BUILT < 70M (pixel) -0.0045 -1.7 -0.0046 -1.7VAL_SUZON -0.099 2.0 -0.092 -1.9COMPACT 0.247 2.0BOUNDARY (m) 0.000035 1.8DECID_PATCH (number) 0.0102 3.1DECID_EDGE (m) -0.00024 -2.6R² 0.5113 0.5145 0.5180

Table 1. Global resultsUnit: a pixel = 49 m2 . t: Student’s t.

The parameters evaluated for non-landscape variables (column 1) are whatwould be expected by intuition and are consistent with studies for other partsof France. The introduction of landscape variables (columns 2 and 3) makeslittle change to the results of the first regression: variables characterizing thereal estate, the transaction, and the location are not closely connected to the

19

landscape framework. This result makes it possible to simplify Tables 2 and3: the first group of variables is present in the regressions for these tables, butthe estimated parameters are left blank so as not to overload the Tables withresults similar to those of Table 1.

3.3 Attributes of the property, transaction, and situation

The living space, which averages 111 m2 (median 100 m2) has a hedonic priceof 1374 euros per additional m2, which is 1.4% of the house price, a valuesimilar to values reported elsewhere in France (Cavailhèes [57], Gravel et al.[58], Kazmierczak-Cousin [59], Marchand and Skhiri [60]).2 One square meterof land is worth 6.1 euros on average.3

The hedonic price of a bathroom is about 21,100 euros, which is higher thanestimations for other studies (Cavailhès [57], Marchand and Skhiri [60]).4 Themean value of a swimming pool is 7280 euros (6.8% of the price), and for an attic9735 euros (9.2%). The date of construction has little effect on price, since ahouse built one year later than the average is worth only 60 euros more. This isa surprising result compared with other studies but did not surprise the marketoperators consulted (real estate lawyers, tax officers). A house on several storiesis worth less than one that is built on the level and the price of room size followsa bell-shaped curve.Sales negotiated between private individuals are 3580 euros less than sales

made by professionals (3.4% of the price), which is logical enough since trans-action fees are lower. When the sale comes after an inheritance or a division ofestate, the price is lower, which is probably because of the reduced bargainingpower when there are several sellers. Lastly, when the purchaser already occu-pies the property, she pays less for it (about 20%), which may well be becauseof the relative strengths of the bargaining positions.Localization attributes are important. Houses less than 100 m from a freeway

or a major road are worth 3500 euros less than the average, a mark-down whichreaches 8100 euros for the strip 100—200 m away. Railroad tracks also entaila devaluation of 6400 euros for transactions located less than 100 m away, butnot beyond that distance. Sloping sites are worth less: the hedonic price of onedegree of slope is −760 euros (0.7% of the price).Being part of a mixed land-use planning zone, that is, a zone for both housing

and economic activities results in a devaluation of 4900 euros (4.6% of the price).This result is consistent with the literature, which shows that a main objective of

2Prices reported in Section 3 are the means of the 2520 individual prices estimated fromthe Table 1 parameters.

3The price of land for agricultural use in the urban fringe of Dijon is 0.28 euros per m2 and0.95 euros when sold for urban use (Cavailhès and Wavresky [61]). The difference is due inpart to technical costs (provision of utility services) and economic costs (transaction fees), butit also shows the scale of the capital gains made by the landowner and urbanization operators.

4The number of toilets is not completed in the database and the price of an extra bathroomprobably encompasses that of a second toilet, which is generally goes thogether with thebathroom.

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land zoning is to divide land use into homogenous uses so as to reduce negativeexternalities (McDonald and McMillen [62]).Other localization variables have parameters which are not statistically dif-

ferent from zero: (i) global solar radiation, which is probably difficult for non-specialists to evaluate; (ii) topographic mean elevation (altitude of the houserelative to the mean altitude of the field of vision seen from it), whereas somelandscape architects claim that dominant positions command higher values; (iii)South facing lot (when sloping), a variable which is barely significant at the 15%level;5 (iv) localization in floodable zones, maybe because households are un-aware of this characteristic at the time of purchase (or because of flaws in theofficial zoning scheme?); (v) the distance from the point-shaped objects thatwe selected, such as potentially dangerous industrial sites (classified ‘Seveso’in French regulations), quarries, incinerators (possibly because households areinsufficiently aware of the risk?).

3.4 Parameter significances of the visible areas

Looking first at the immediate proximity of houses, that is, the first 70 maround them, columns (1) (area seen) and (2) (area from which one is exposedto view) of Table 2 show that the parameters are not significantly different fromzero. The results are modified by introducing the two variables simultaneously(column 3) or the difference between view and exposure to view (column 4): theparameters are significant, being positive for the view, negative for exposure toview, and positive for the difference.It is possible, as said, to see a house (which is 7 m high) or a tree (15 or 20

m high) without being seen from that point (by an observer 1.80 m tall): theview and exposure to view are not symmetrical because of the different heightsof objects. For 64% of houses, the area in view is greater than the area fromwhere one can be seen. In 37.7% of cases, the difference is of a single pixel(49 m2) and in 18% it exceeds two pixels. These differences, although small,are important: in the first 70 m around a house, the view is an amenity andexposure to view is a nuisance.Beyond the first 70 m the hedonic prices of areas seen or from where one can

be seen are not statistically different from zero (Table 2, columns 5−10) exceptfor the 70—140 m range where the view seems to have a negative price, whichwill be explained shortly when the content of this range is examined.It is as if households were short-sighted. This indifference to the view of

spaces beyond a few tens of meters, in particular to open views with distantranges, is a counterintuitive result. It can be explained by the characteristics ofthe study zone, which has been described as ordinary in terms of landscapes.In the Dijon region, distant horizons, when seen, are not formed by outstandingfeatures (emblematic buildings, sea, etc.) or lines of mountains against thehorizon (snow-capped mountains, etc.); on the contrary they are bluish-grayishin color, making them hard to distinguish against the skyline.

5That may be because of the limits of the digital elevation model at 50 m or because it ispossible to arrange the living rooms facing South even when the lot slopes northward.

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Shared attributes: property, transaction, and location attributes(1) (2) (3) (4) (5) (6) (7)

AREA SEEN < 70M 0.000209 0.002131** 0.002339** 0.002100** 0.004405**(1,1) (2,2) (2,3) (2,1) (2,6)

AREA EXP. TO VIEW < 70M 0.000071 -0.00097** -0.00095* -0.00091* -0.00194**(0.8) (-2,0) (-1,9) (-1,7) (-2,4)

AREA (SEEN - EXP. TO VIEW) < 70M 0,0094**(2,1)

AREA SEEN 70-140M -0.00013 -0.00108*(0.8) (-1,7)

AREA EXP. TO VIEW 70-140M 0.00005 0.001031(-0,3) (1,6)

(CONTINUED)Shared attributes: property, transaction, and location attributes

(8) (9) (10)AREA SEEN < 70M 0.004445** 0.004430** 0.004399**

(2,6) (2,6) (2,6)AREA EXP. TO VIEW < 70M -0.00182** -0.00183** -0.00184**

(-2,2) (-2,3) (-2,3)AREA SEEN 70-140M -0.00149** -0.00150** -0.00151**

(-2,0) (-2,0) (-2,0)AREA EXP. TO VIEW 70-140M 0.000901 0.000906 0.000921

(1,1) (1,1) (1,2)AREA SEEN 140-280M 0.000262 0.000258 0.000252

(1,0) (0,9) (0,9)AREA EXP. TO VIEW 140-280M 0.0000054 0.000004 0.000003

(-0,0) (-0,0) (-0,0)FIELD OF VISION 280-600M -0.003941

(0,2)FIELD OF VISION 280-INFINITE 0.012595

(0,8)

Table 2. Analysis of the visible areasThe table sets out the result of 10 regressions, which share the variables characterizing the

property, the location, and the transaction (i.e. the variables in Table 1, column 1) and differ in

the field of view variables: model (1) = area seen in the first 70 m; model (2) = area in the first

70 m from which the house occupier can be seen; model (3) = both these variables simultaneously;

model (4) = addition to (3) of the area seen in the 70—140 m range; etc. Model (9) = area seen and

area from which the house occupier can be seen in the first 70 m, ditto for 70—140 m and 140—280

m ranges and lastly existence of a field of view from 280 m to infinity. In parentheses: : Student’s

t. ** and * indicate significance at the 5 and 10 percent levels respectively.

However, it may be thought that these results are valid more widely thanfor just the Dijon area. First, ordinary agrarian landscapes are common in mostparts of France. In addition, the behavior of the inhabitants of periurban areasin France is consistent with the finding: often they enclose their gardens withboundary walls or hedges so they cannot be seen by their neighbors or passers-by, at the price of a loss of view of the landscape. Such enclosure is common,even prevalent, behavior contrasting French households with those in countriesor regions like the U.S. where residential lots are less commonly enclosed.These results cannot be compared with those in the literature because the

measurement of the area seen is seldom introduced into models and, as far aswe know, exposure to view never has been. Confining discussion to the view,Paterson and Boyle [45] report a negative hedonic price for the area seen in

22

Connecticut, which becomes close to zero when the type of land use is added.Bastian et al. [42] obtain no significant result for areas seen although they dealwith landscapes in the Rocky Mountains where the scenery is often outstanding.

3.5 Land use

Built areas. The opposition between the view and exposure to view has firstled to an examination of the role of built areas in this dialectic. Buildings arethe most common land use close to housing and a type for which exposure toview by others may be particularly sensitive. Table 1 (column 2) shows thatthe view of houses at less than 70 m has a positive hedonic price, which is 1140euros for an additional 100 m2 (1.1% of the house price) while the price of beingseen from 1 are of built area located in the same distance range is −990 euros(-0.9%). When these two variables are replaced by the difference between builtareas from where one is exposed to view and the built area seen, a difference of1 are has a hedonic price of −2265 euros (-2.1% of the house price). It is theprice of the inconvenience of having family privacy disturbed when one can beseen in one’s garden.Beyond 70 m built areas have a hedonic price no different from zero at

the 10% level of significance whatever the variable’s modality (view, exposure,difference between exposure and view). However, built areas seen at 70—140 mhave a slightly negative effect on price, which is significant at the 20% level.This explains why total areas seen in this range of view have a negative sign(Table 2): it is because of the extensiveness of the built areas and a slightlynegative price for this type of land use.

Forests. An additional tree-covered are in the first 70 m has a hedonic priceof 430 euros. Houses with a view of forests see on average 4.5 ares within the0—70 m range, meaning that a doubling of this quantity has a hedonic price of1950 euros, representing 1.8% of the price of a house.The shape of areas covered by deciduous trees also exerts significant effects

on house prices, which are added to the foregoing. The number of patches of thistype within a 70 m radius has a positive price (Table 1, column 3) of 1080 eurosper additional patch and conversely the price of the length of deciduous woodedges is negative: −25.5 euros per additional meter. The combination of thesetwo variables gives a precise indication of the shapes valued: numerous patcheswith short edges correspond to rounded copses and not to massed forests or longand thin formations. It can be seen that the geographic method used here, whileit does not grasp the esthetics of landscape, does nevertheless provide pointersabout the forms that are valued on the real-estate market.Lastly, another important result is that the actual view of tree-covered areas

counts, whereas the parameter of their mere presence within 70 m of a house isnot statistically different from zero when they are not visible (Table 3). Now, itmight have been thought that the presence of nearby, but unseen, forests wouldhave commanded a positive value for recreational (walking areas), protective(against noise), and ecological (air quality, fauna and flora, etc.) functions.

23

Shared attributes: property,transaction, and location attributes

Estimate t FOREST SEEN < 70M 0.002228 2,0ROAD SEEN < 70M -0.00077 -2.2MEADOW SEEN 140-280M 0.000108 1.6CROPS SEEN < 140M 0.00012 2.1WATER SEEN 70-280M -0.00076 -2.7BUILT SEEN < 70M 0.0052 2,0EXP. FROM BUILT < 70M -0.00469 -1.7COMPACT 0.275 2.1BOUNDARIES 0.000039 1.9DECID_PATCHES 0.0118 3.3DECID_EDGE -0.00038 -2.8FOREST UNSEEN < 70M 0.000489 1.3ROAD UNSEEN < 70M -0.00031 -1.1MEADOW UNSEEN 140-280M 0 -0.5CROPS UNSEEN < 140M -0.00004 -1,0WATER UNSEEN 70-280M 0 0.1BUILT UNSEEN < 70M -0.000032 0.2

Table 3. Analysis of seen and unseen areas

The regression includes the variables in column 1 of Table 1 (i.e. the property, transaction, and

location characteristics), not repeated in this table, the variables of columns 2 and 3 of Table 1 (i.e.

the land use variables seen from the ground and landscape composition variables) and the unseen

areas for the same land uses (equal to the difference between pixels seen from above and pixels seen

from within). Unit: pixel.

Bushes are not significantly different from zero in price, which can be ex-plained by the heterogeneity of this category (composed of hedges, fallow landbecoming overgrown with woody plants, thickets, etc.). Lastly, in fields of viewbeyond 70 m all tree-covered formations have prices that are statistically notdifferent from zero, which confirms the myopia of households.It is difficult to set these results against the literature, which is divided on

the hedonic price of wooded areas. Des Rosiers et al. [30] present referencesincluding many studies showing that the presence of trees on a residential lotis valued by the market and they conclude as for the Quebec region; Thorsnes[16] obtains a similar result in Michigan, as do Tyrväinen and Miettinen [31] forrecreational forests in the Helsinki region. Other workers, such as Garrod andWillis [63], Irwin [14] , Palmer [28], or Smith et al. [32] report opposing results.

Farmland. Crops seen at less than 140 m from houses have a positive hedonicprice (Table 1) of 27 euros per are more, so for houses from where they can beseen 900 euros when the quantity doubles. Meadows have significantly positiveprices only a little farther away, at 140—280 m: 23.40 euros per additional are,or 1290 euros for a doubling of the area seen (when not zero). Vineyards could

24

not be considered as they were too scarce. It transpires from comparison withforests that the hedonic price of farmland seen is positive at distances somewhatgreater than for trees, although it remains confined to a radius of 200—300 m orso.The literature concentrates on the legal status of farmland, which defines

whether or not there is access over land (Cheshire and Sheppard [33]) or whetherit is more or less protected from residential conversion (Bockstael and Irwin[64], Irwin [14] , Smith et al. [32]). The hedonic price includes expectationsabout this risk of conversion to the point that, for Smith et al. [32], householdspay less for housing located close to farmland, particularly when adjacent toit. Sullivan et al. [65] show the worth of tree- or grass-covered buffer zonesbetween farmland and housing, which confirms that very close proximity tofarmland lowers property values, a result which is consistent with that foundfor meadowland.

Water. Table 1 (column 2) shows that the view over water in the middledistance (70—280 m) has a negative hedonic price of about −160 euros for anadditional are. This result stems mostly from a part of the study region wherethere seems to be a high risk of flooding. However, this risk is controlled bya variable for being part of ‘floodable’ zones and water that is present but notseen commands a price close to zero (Table 3), whereas it poses the same risk offlooding as water that is in view. Paterson and Boyle [45] also report a negativeprice for the view over water, having checked that it is not due to the risk offlooding, but possibly to wetlands. However, most workers conclude that theview of water, or its proximity, has a positive price (e.g. Earnhart [66], Hammittet al. [22], Lansford and Jones [67], Mooney and Eisgruber [36], Spalatro andProvencher [37]). While not unique, the result here runs against the flow of theliterature for reasons which need to be investigated farther.

Roads. The hedonic price of the view of a road and of exposure to view froma road are almost equal and both variables are closely correlated. The firstmodality is used here therefore (Table 1, column 2). An additional are of roadin view at less than 70 m lowers the price of a house by 160 euros, or 1440 eurosfor doubling the number of ares of road in view. Lake et al. [46] also report asignificant result: for the urban area of Glasgow, the sight of a road lowers thereal-estate price by 2.5% (except in the backyard, which it is hard to explain).Roads within the radius of 70 m but not in view have a hedonic price which is

statistically not different from zero (Table 3). It is therefore not the presence ofthe road that is a nuisance when it is not seen (although it is a source of danger,air pollution, and noise) but the actual sight of it as it is a visual obstruction.This result is consistent with that for wooded areas: the presence of an objectwithin a certain radius around the house counts less than whether or not it canbe seen or whether one can be seen from it.Beyond the first 70 m, the sight of roads no longer significantly affects house

prices, indicating that such nuisances remain confined to a narrow strip. It

25

should be remembered that the location of a house within a strip of 200 mbeside a freeway or a major road is also a nuisance, which compounds that ofthe view. In this instance, the nuisance of traffic is reflected by an effect thatspreads beyond that of the sight of roads, but remains nonetheless limited inspace. Conversely, while being within a strip of a certain distance on eitherside of a railroad track is a significant nuisance, the view of railroad tracks doesnot entail any further decrease in value, which can be explained by the smallnumber of observations involved.

Zoning schemes and landscapes. It should be noted that the parametersobtained for land use variables are similar whether zoning schemes are taken intoaccount or not. However, several studies show that protection of open spacesby zoning enhances their value because of a lesser risk of urbanization withinthe foreseeable future (Irwin [14]). This is not the case here, possibly becauseof the flexibility of zoning ordinances in France: they can be altered by a singledecision of the municipal council, which leads to frequent revisions, meaninghouseholds cannot anticipate the probability of conversion of land use from thecurrent state of the zoning scheme.

Landscape composition. Two indexes of landscape composition are used(Table 1, column 3) in addition to those relative to the shape of deciduous wood-land set out above. These are: (i) a compactness index ranging from 0 (compactforms) to 1 (elongate forms), whose hedonic price is 260 euros for 1 percent ofadditional ‘elongation’; (ii) the total length of borders between patches of dif-ferent types, whose hedonic price is 3.70 euros per additional meter. Manyother indicators were tested, which when examined separately, have hedonicprices significantly different from zero (compactness indexes, mean patch sizeor patch density, aggregation or self-adjacency index, entropy, etc.), but theywere not used because they are correlated with the previous ones and with thevariables on deciduous woodland shapes. The results, for the combination usedhere as for other indicators taken separately, show that division, complexity,non-contiguity, landscape fragmentation, mosaic patterns, etc. have positivehedonic prices, which point to the conclusion that landscape esthetic matters.In addition, our variables ignore other characteristics of landscape form or es-thetics, which would alter the hedonic prices reported.

3.6 Landscape prices

By using the quality of life index method (QOL, Blomquist et al. [68]), hedonicprices evaluated for each landscape attribute can be used to calculate a globalprice of landscapes, which is the sum of the quantities weighted by prices.The mean price of the landscape environment viewed from the houses in the

sample in the urban fringe under study is 2510 euros and the median price 1350euros. Given that the mean price of a house in the sample is 105,500, 2.4%of the price corresponds on average to the price of the landscape in view, the

26

median being about 1.3%. Landscapes therefore represent only a small part ofthe real-estate value (although it should be borne in mind that price-reducingmethods have been employed here). This is not unexpected; but it is importantto emphasize that this value is significantly positive.Forests become more abundant with distance from Dijon as does farmland

(to a lesser extent) and the proportion of roads declines. It is therefore logicalthat the global price of landscapes should increase with distance, even if thewithin estimator is reflected by a unit price of each category independent ofdistance. An OLS regression of this global price of landscape over distance showsthat it increases by 44.70 euros when moving 1 km out from Dijon (Student’s ttest is 5.3).This result suggests that landscape amenities go some way to explaining

the settlement in the Dijon countryside. Landscape amenities are part of thetradeoff between land costs and commuting costs: when landscape amenitiesincrease with distance, the curve of land rent is flatter and the city is moreextended than in a homogeneous space, as Bruekner et al. [6] show:

p0(x) =1

q(x)

·−t+ u

a

uea0(x)

¸(6)

where p0(x) is the derivative of the residential bid rent, q(x) the housing con-sumption, u the utility (superscripts denote partial derivative), a and e are re-spectively the amenity and composite good consumptions. We show that a0(x),which is here the derivative of the landscape indexe (i.e. our landscape price),is positive, leading to a flatter rent than without amenities and, therefore, alarger spread of people in the countryside.However, no conclusion can be reached about the effect of landscape ameni-

ties on the location of rich and poor households. First, there are other amenities,nuisances, or externalities than landscapes which also influence the choice of lo-cation and second, contrary to what is sometimes claimed, wealthy people maymove nearer to green amenities or move away from them depending on theirdemand functions. Brueckner et al. [6] show, as did Diamont [69] in a seminalpaper, that all else being equal the rich move closer to an amenity if the incomeelasticity of their demand for it is greater than the income elasticity of theirdemand for residential goods and that otherwise they move away from it.6

4 Conclusions and prospectsA hedonic price model has been combined here with a GIS-based geographicmodel to evaluate the price of landscapes seen from houses in the urban fringeof Dijon (France). The economic model is the first stage of the hedonic pricemethod. The evaluations are made with the fixed-effect of the econometric panel

6The intuition is simple: proximity to an amenity increases land values, which reduces thesize of residential lots. If the rich are more elastic about the consumption of the residentiallot size than consumption of amenities, then all else being equal, they will increase the formerwhen their income increases by moving away from the amenity to find a lower land rent.

27

data method. The geographic model is used to identify, with a resolution of 7m, 12 types of land use from satellite images and to measure, by trigonometry,the viewshed taking into account the relief and obstacles that may block theview (houses, trees). The view of the landscape is quantified, in terms of visiblearea and of the type of objects seen, as is exposure to view, which is the reverserelation (areas from which an observer can be seen).Households are sensitive to their landscape environment. First, it is above

all the view of the tens of meters around a house which counts; beyond thatdistance, some attributes still have a hedonic price significantly different fromzero up to 200—300 m, but no farther: in this region, the existence and contentof remote views have no effect on house prices. Second, the view is an amenityand exposure to view a nuisance; in particular exposure to being seen fromother houses has a negative hedonic price. Thirdly, tree-covered formationshave positive hedonic prices, as does farmland, while roads have negative prices.Fourth, it is the view that influences the real-estate price and not the merepresence of certain types of land use: tree-covered areas or roads close to a housebut which are not visible from it have hedonic prices not significantly differentfrom zero. Fifth, landscape shape indexes, although defined in a coarsely mannerby our method, show that households prefer complex, fragmented shapes ofcompact copses that are interwoven or in mosaic patterns, rather than largemasses of forest or elongate patches.In short, the economic agent who appears from this study is shortsighted and

sensitive to a few characteristics of her immediate visual environment (trees,farmland, roads). She devalues exposure to being seen by others, is insensitiveto what is close by but cannot be seen, and sensitive to the form of landscapes.This sensitivity to landscapes may help explain migrations to mixed farmland-forest and residential spaces, which characterize the countryside close to Dijon.However, the results do not show whether it is well-off or poor households whichseek to locate close to landscape amenities (that would involve moving on tothe second stage of the hedonic price method).The economic importance of landscapes in the immediate vicinity of houses

raises the issue of their status as private, club, or public goods. A fraction of thisspace is made up of the residential plot which belongs to the house owner, whotherefore creates her own private landscape. Another fraction belongs to herneighbors, who, should the space be socially homogeneous, produce the sametype of landscapes, which are then, in a sense, the collective good of the group.But the social heterogeneity of the space may be a source of conflict if thelandscape development behaviors diverge. Lastly, the nearby space may includefarmland, private or public forests, landscape, or recreational spaces developedby public authorities. That raises the issues of externalities, interactions ofproximity among different economic agents, and issues of rent appropriation,which although rather low (the mean price found for the landscape seen from ahouse is 1350 euros) may not be negligible for some agents.There are few geographic-economic models of evaluation of landscape prices,

although the two disciplines are complementary when it comes to evaluating thisnon-market good which is hard to measure. One methodological conclusion of

28

our study is that landscapes must be analyzed as seen by an observer fromground level and not from maps or satellite images which have fictional ‘fromabove’ views. That implies having three-dimensional geographic models allow-ing for relief and objects that might conceal the view. Further developmentshall look deeper into this inter-disciplinary collaboration. In particular, somecurrently fragile or counterintuitive results need to be clarified (such as the viewover water). Comparison with other parts of France are underway. Data forlots, with ortho-photographs at 1 m resolution, may provide results that areboth precise and richer because it is then possible to make out the landscapedeveloped by the householder, the neighbors, and then other actors.However, even if it is refined and extended to other regions, this method is

reductive because it simplifies in the extreme what a landscape is and evaluatesonly use values related to residential consumption. The point that in spiteof these limitations it yields significant results is encouraging. However, we areaware that other methods are also required to enhance knowledge in this difficultdomain of the economic valuation of landscapes.

Acknowledgement 1 We are grateful for their many comments to partici-pants in several seminars — Econométrie des données de panel (ERUDITE,Université Paris XII), Economie spatiale et urbaine (GDR ASPE), Extensiondes villes de espace (ACI Espace et territoire) — and more specifically to PierreBlanchard, Pierre Donadieu, Julie le Gallo, Jean-Pierre Huiban, Hubert Jayet,Michel Mouchard, Virginie Piguet, Daniel Sevestre, and Jacques-François Thisse.

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