Valuing Spatially Integrated Amenities and Risks in Coastal Housing Markets

29 Pages·2011·0.91 MB·English

The Center for Natural Hazards Research
Thomas HarriotCollege ofArts and Sciences
East Carolina University
A 116 Brewster Bldg. @Greenville. NC 27858
Tel: [phone]. Fax: [phone]'143
wlVw.ecu.edu/hazards • [email protected]
Valuing Spatially Integrated Amenities and Risks in
Coastal Housing Markets
January 2006
Okmyung Bln*
Assistant Professor, DepartmentofEconomics, EastCarolinaUniversity
Tom Crawford
Assistant r>rofossor, DepartmentofGeography, EastCarolina University
Jamie Brown Kruse
ProfessorofEconomicsand Director, Natural HazardsMitigation Research Center, EastCarolina
University
Craig Landry
Assistant Professor, Dcpartmcnl ofEconomics, EastCarolina University
'Correspondlng authormay be reached at: Voice: 252-328-6820; Fax: [phone]; Email:
[email protected]. The authors thank Glenn Blomquist, and seminarparticipantsat theAssociation of
Environmentaland Resource EconomistsSession at the Southern EconomicAssociation 2005Annual
Meeting, fortheirhelpful comments. The authors also thank HankMontgomeryfor providing New
HanoverCountyreal estate data and Paul Hindsleyforproviding valuable research assistance. This
researchwas supported byEastCarolina University2005 Research DevelopmentGranl.
Valuing Spatially Integrated Amenities and Risks in Coastal
Housing Markets
ABSTRACT
Hallstrom and Smith (2005) argued thatcoastal amenities and risk arc so highlycorrelated thatconventional
hedonic methods cannotidentifyseparateeffects. In this study,we construct a thrce-dirnensional measure of
view, nccounting for natural topographyand builtobstructions, thatvaries independentofthe risk to
disentangle these spatiall}fintegmtc(1 housing characteristics. A spatial hedonic model is developed to provide
consistent estimates ofthewillingness to pay for coastalamenities and risk. Our findings suggestan
alternative to the J-Iallstrom and Smithapproach thatincorporates the multipledimensions ofspatial
attributes can besuccessful in isolatingrisk from amenities on thecoast.
Keywords: coastal amenities, coastal hazards, hedonic prices,spatial regression
JELClassifications:D12,Q24, Q26, R21.
Valuing Spatially Integrated Amenities and Risks in Coastal
Housing Markets
I. Introduction
Coastalareas in the U.S. have beenwitness togrowingpopulation and increased economicactivityin recent
years. Population in thecoastal zonegrew37.20/0between 1970and 2000,andwhile thisgrowth is roughly
proportional to the averagegrowth seen in interiol'parts ofthecountryover the same time pcdod,
population densityin the coastal zone is two to three times higher (Colgan 2004). The contributionofcoastal
areas toeconomic activityis ratherlargegiven the limited land area: whereas the coastal zone contains only
40/0 ofU.S. land area, economicacti.yityin the coastal zone contributed 11% to the U.S. economyin 2000
(Colgan 2(04).1 The relativelydensepopulation and abundant economic act.ivityin the coastal zoneis
cxposed to vagades stemmingfrom the interactionofland,sea,and climate,placingcoastal developmentat
considerable risk. Natural hazard risks in the coastal zone include storms Olllrrie~nesand nor'eastcrs on the
castcoast),with associated flooding and wind damage, as weU as ewsion stemming fWI11 stonns and sca level
rise. Recentpredictionssuggest thatwe are entering aperiod ofincreased stann activity (Goldenbergetal.
2001;\X1cbsteret al. 2005),and sea level is projected to rise 9 to 88centimetersovcr the nextcentury
(Intergovernmental Panel on ClimateChange (IPCC) 2(01) creatingpotential problems for the coastal
economy.
Meanwhile, coastalpropertyvalues continue to soarand construction projects in the coastal zone expand.2
Do buyersand builders in the coastalzonecomprehend thediverse risks that characterize this landscape?
Oneway to address this question iswith data on housingsales. The hedonic property model provides an
intuitive 2n21ytical tool th2t can be used to exatnine theeffects ofhousingcharacteristicson prices. Ifcoastal
properties in asingle housing marketvary in theirdegree ofriskofstorm/flood damage and buyers and
sellers observe and react to thesevariations, then the hedonic price function will reflectvariations in propetty
risk. From the perspectiveofthe household, marginal implicit prices from the estimated hedonic price model
can beinterpreted as marginalwillingness to pay for housingattributes (Rosen 1974). A nUl11berofpapers
have utilized this approach tovalue risk (Beron etal. 1997;Gayer, I-Iarnilton, and Viscusi 2000;Simmons,
Kruse, and Smith 2000; Landry, Keeler, and Kriesel 2003; Binand Polasky2(04).
1ContributiontoeconomicactivityIsmeasuredbyemploymentandvalueadded(Colgan2004).
2Forexample,anarticlebyFrankNortonIntheRaleighNews&Observerreportsthataverageseilingpricesforresidential
propertiesInWrightsvilleBeach,NorthCarolinahaveIncreased420percentsince2001,andthenumberofbuildingpermitsIn
CarolinaBeachovertheprevious24monthperiodexceedIhenumberofpermitsIssuedovorthepast20years("BeachPricesRide
Crest"29May2005). WrightsvilleBeachandCarolinaBeacharetwoofourstudyslles.
2
In coastal zones, however, riskisoften highlycorrelatedwith coastal amenities, such as water-frontage,
distance to beach, and ocean view..Hallstrom and Smith (2005) claim that this correlation is perfect(orclose
toperfect) and make usc ofa repeatsales model and adifference-in-difference framework tovalue changes in
subjective risk assessmentdue toa near miss" hurricane event-which theyidentif}'as achange in
u
infonnaUon.3 In essence, both risk and amenities at<e spatially-delineated,and thedegreeofcorrelation
between these spatialattributes can be high in the coastal setting. Nonetheless, risks and amenities maynot
be perfectlyconelated-itdependson how the researcherdescribes and models agent perceptionsand thus
how housingattributes are measured. Our findings :'luggestan altemative to the Hallstrom and Smith
approach that incorporates the rnultipledirnensionsofspatial attributes can besuccessful in isolating risk
from amenities on thecoast.
This stud}' uses spatialeconometric methods and parcel measuresderived usinggeographic information
systems (GIS) to provideseparateestimatesofthe marginal valueofboth coastal risks and amenities in the
hedonic framework using the conventional modelingapproach-a parametric regression model utili:dngdata
on housingattributes and asinglesales price for each propeny. Ourstudyarea iscomprisedofbeach
communities in New I-hnoverCounty,North Carolina. The selectedcomrnunit.iesare among the most
highlydeveloped areas in the region and Me at riskofflooding and shoreline erosion dueprimaril)' to severe
weathereventssuch as tropical storms and nor'easters.
\Y/e use Federal Emergency ManagementAgency (FEMA) flood maps to identifySpecial Flood I-fazarel Areas
(1 chanceofflooding peryear),and use adichotomous classification for aU properties in outsample
%
accordingto this distiuction. The North Carolina DepartmentofEnvironmentand Natural Resources
provided information on historical emsion rates (though for outsample there is novariation in this risk
measure). Coastal atnenities that vary by p<lrccllocationare measured using spatialdata processing
techniques implementedwithin GIS. \Y./e utilizeLIDAR (Light Detectionand Ranging) dataand temporal
information on parcel development histories to createaquantitative measureofoceanview for each parcel.
Ourviewmeasure is both spatiall}'and temporallyexplicit in thatitaccounts for the fact thatviews can
change overspace:md t.ime as buildings and infrasu'ucturesurroundingapropenyare altered,4 Wle also
quantify for each parcel the Euclidean distance to the beach/oceanshoreline. Given that the ocean view and
distancemeasures need notbe perfectlycorrelated with risk rneasures, ourspatialeconometril;:analysis allows
for identification ofthesedifferential effects.
JAhighdegreeofcorreIalionbetweenrlsk'andcoastalamenitiesIsalsonotedIn81n,Kruse,andLandry(2005). Ifamenltlesare
nollncludedIntheirmodel,themarginalvalueofrisktakesonthe~wrong~signduetothecorrelationwithamenllfes. However,
IncludIngamenitymeasuresIntheirmodelfixestheproblem,suggesUn'gthaicorrelationamongriskandamenlllesIsnotperfect.
4OurdatasupporttheclaimthatoceanvIewscanchange. forexample,ofthe69housesthatsoldIn2001havinganoceanview,
18(25%)experiencedadecreaseintheamountvIewfrom2000to2001 duetonewlybuildstructures. ThemeandecreaseIn
angularfieldofviewwas14.4degreeswithamaximumdecreaseof36.4degrees.
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II. A Simple Behavioral Framework
We take an approach similar to Hallstrom andSmith (2005) (hereafter HS) by consideringasimpleexpected
utility maximization problem with only twooutcomes, hunicane (H) and no hurricane (NH),S Out"primary
modifi~ationto their model is thatwe do not restrictamenities and risk to be represented bya onc
dimensional attribute-distance from the beach, r..........<ls do HS. Flood and erosion risk, in fact, varyncmss
multjplc physical dimensions, as doesview amenity.6 Distance frorn thebeach is included in our model as
onlya measure ofpr<?ximity, presumably reflecting recreationalvalue (to theextent thatonecan control for
variation in erosion risk along theshore). The household's expected utility function is:
v
=P(l~E)UII{tI,l;b,1IJ- /((tJ,l;b,I~E,io)- L(b,F,E,io}j (1)
+(I -P(F,E))UNH{II,I;b,1IJ- /((v,r,b,F,E,io)),
where the utility function is statedependent)p(r~E)is the subjective probabilityofadamage-producingevent,
F represents location in a flood zone)E represents location in an erosion hazal'd zone (historical erosion rate
as proxy)) fJrepresentsview amenity,his avectorofhousing characteristics,11/is income) H(.)is housingsales
price (expressed in annual terms ifincome is expressed in annual terms), L(.)is the size ofthe monetaryloss
due to storm netofinsurancecoverage, and iois the insurance rateperdollarofcoverage. Note thathazard
risks and amenities arc represented bydiffel·entcovadates in (I). This specification differs ftom thatofHS)
in which I~distance from the shore, represents both hazards and amenities. Generally)weassume thatFand
E varyindependentlyofVand 1:7 In addition,weassume thathazard information is constantover the period
ofouranalysis and therefore suppress the I-IS infonnation parameter,I.
Undercompetitive marketconditions, buyers take the hedonic pricescheduleasgivcn and optimizeexpectcd
utility through the choiceofhousingcharacteristics,with residual incorneleftover for consumptionofa
compositecommodity. Assumingcontinuous fisk and amenity measures, in equilib~·illm we have the
following:
(2)
(3)
f>WeretaInthehurricane/nohurricane&tatesofnatureInkeepIngwiththeHSframeworkbutusehurricanetorepresentanyflood
anderosIonproduclngweatherevent.
I>Floodriskdependsuponlocalionwithinthefloodplain,whichvarieswithlatitude,longllude,andelevallon. ErosIonriskIncreases
withproximitytothewaterfront,buttypicallyvariesalongthecoastline(asevidencedbyvariationInhistoricerosionrates).
likewise,viewsvarywithlatitude,10ngllude,andelevation,andchangeoverlimeassurroundingdevelopmentevolves.
lOurdataonlypartiallysupportthIscontention. WeareunabletoexamineerosionriskbecausethereIsnovariationInthe
hIstoricalerosionrateestimatesforNewHanoverCounty. Assuch,distancefromtheshorewillbecorrelatedwitherosIonrisk
becausethereIsnotadditionalvariationInerosionriskacrosstheshoreline. ThecaseofNewHanoverCountyIspeculiarand
ratheruniqueIntheregard,asmostcoastalcountiesexhIbitvarlallonInthehistoricalerosionrate.
4
for fl::: lV',md j::: F,E. Equadol1 (2) indkates that the marginal implicit hedonic pricc fot" arnenities rcflects
the expected amcnityvalue. Equation (3) indicates that the marginn} implicit price for risk attributes refl<;cts:
(i) the perceived marginal change in theprobabilityofstol'ln damage,pfi (ii) theincremental utilitydifference
across states, UH- UNH;and (iii) the marginal impactofrisk on the mngnitudeofloss,pUl/"J-j. Dividingby
the expected value ofthe marginal utilityofincome produces measures ofwillingness to pay. Ifone is willing
to assume that risk attributes affectonly the pt'Obabilityofloss and not the magnitudeofloss, orviceversa,
equation (3) sin1plifiessomewhat.' We use the framework of(1) and the results in (2) and (3) toguideour
formulation ofthe hedonic price model and interpret the parameterestimates.
III. Study Area and Data
Ourstudyarea encompasses fouf primarybeach communities- Carolina Beach,Figure Eight Island, Kure
Beach,and \'hightsville Beach- along theAtlantic CoastofNew HanoverCounty, Nonh CaroHna (Figure
1). Ongoingdeveloprncnt has increased overallexposure ofprivateand social capitnlassets to natural
disasters such as stormsurge flooding and shorelineet'Osion caused bysevereweatherevents. Over the
period ofouranalysis a numberofhurricanes made landfall along the Nonh Carolinacoastline. Between
1995 and 2002,six named storms affected the southeasterncoastofNorth Carolina~Arthur,Berthaand
Fmnin 1996, Bonniein 1998,and Dennis and Floydin 1999. Despite t~chigh level ofhurricaneactivity,
structumllossesin the study area were relativelyrninor. Fotexample, duringhurricanes Dennis and Floyd
(Floyd being the strongeststorm d~ringthis period atcategory 4) onlyone buildingwas destroyed in
\X1rightsville Beach,while a more significantportion were threatened byerosion in otherareas~63properties
on Figure Eight Island and 5each in Carolina Beachand Kure Beach (Rogers 2000). Given the relatively
high level ofhurricane activity,we feel itis reasonable to assume thatbuyers and sellers wetccognizantof
natural hazard risks when engaging in the housing marketduring the period ofOul' analysis. W/e assume the
level ofinformation and pcrception.sofrisk arc constantover this time period. Moreovet, since direct
impacts on the studyareawere relatively minorwe can be relativelyconfident that exogenousshiftsin
housingsupplyare notlikely to playa role,and thus the hedonic propertyprice function should be telatively
stable.
\V./e utilizea rich spatial dataset thnt includes property parcel boundaries and agridded elevationsurfaceall
stored within aGIS using the North CarolinaState Planecoordinatesystern (with horizontal and vertical
units in feet and referencing the 1983Nonh American datum). Elevation surfaces weredcrived from
LIDAR source data obtained from thc National Occnnicand Atmospherici\dministtation. Parcel data were
8Forexample,IfJaffectstheprobabilityofloss,bulnotthemagnItude-selfprotectionIntheparlanceofEhrlichandBecker
(1972}-ltem(HI)willnotappearInequallon(3). Onthehand,Ifj affectsthemagnitudeofloss,butnot theprobablHly-what
EhrllchandBeckerrefertoasselfInsurance-Items(I)and(II)willnotappearInequation(3).
5
obtained frorn the New I-hooverCountyTax Office aod contained information on sale yeal', sale price,and
various structuralattributes. W/c selected a total of1,075single-family residential homes thatwere sold in our
foul' studycommunities between 1995and 2002. I-lousesales prices are adjusted to 2002prices usinga
conSUmel"price index fOl' housing. Theaverage sellingpricewas $297,968with aminimum sales priee of
$30,298and a maximum of$3,575,000. The first and third (]uartileofthe sales priceis $110,000and
$295,000, respectively.
An innovation ofthis shH..Iyis our amenit-y measure ofocean viewcalculated usingGIS techniques. fI.'lost
shtdies measurewaterview usingbinaryorordinal values (Le. total, partial,or noview) obtained from site
visits, tax assessor mass appraisal data, or map-based interpretationofrelative location (Le.waterfront;::: total
view,interior;::: partialornoview) (Benson etai. 1998; Fraserand Spencer 1998;Rinehartand Pornpe 1999;
Seileretal. 2001; Bateman etal. 2002; Bourassaetal. 2004).9 Advances in geospatial technolo!:,')'and data
availability haveenabled objective,quantitative measures ofspatialenvirol1lnentalattributes (Geogheganet aI.
1997;Batemanetal. 2002) includingviews. Research ingeographic informationscience, urban planning, and
landscapearchitecture has developed novel approaches to representation and rneasurenlentofview (Fisher
1995,1996;Kidneret al. 2001;O'SullivanandTurner2001;Turner et al. 2001;Con and Goodchild 2002;
Fisher-Gewittzmanetal. 2003; Llobera 2003;Jiang2005; KUlllsap etai. 2005), Behavioral research,aided in
somecases by the use ofGIS, has related view characteristics to human behavioral patterns (Bishop 2003;
Turner2003;Wienerand Franz 2004;Stamps 2005a, 2005b). Propcrtyvaluation studies have begun to adopt
GIS-based approaches to view (Lakeet al. 2000;WingandJohnson 2003; Paterson and Boyle2002;Bishopet
a1. 2004). Gurmeasure ofocean view,which we denoteas JiieJJJ,was created using acarefullyconstructed
database subjected to a multi-step processingsequenceinvolvingcustomized programs. All processingwas
implemented usingArc/Info GIS software. Next section describes in detail ourstratem' to quantifyocean
vicw, About 32%ofthe properties (346 out ofthe 1075properties in ourdata) have apositivevalueofJiiew,
and an average ofoceanvicwis about 18.37degrees in one miledistance.
Anotherimportantcoastal amenity measure is the distance to nearest beach, Earlierstudies have shown that
proximity to shoreline is highl}fdcsimblc in housingmarkets (Shabman and Bertelson 1979;fI.,mon, Gressel,
and Mulkey 1984; Earnhart2001). The averagedistance to the ncarestbcach is 1,743 feet for tbc selccted
properties. In addition,we utilho:ed aerial photographs tovisually interpret and constructdummyvariables for
the existenceofpierand sound frontage. About 16% ofthe properties haveasound frontage and about two
thirds ofthose homes have,a pier.
9ArecentartIclebyDavidA. FahrentholdInWaslllngfonPostreportsIhatNewHampshirehasembarkedonacontroversialquest
toquantifyandthentaxthebeautyoflheirresidents'vistas. ThearUclereportsthatcurrentlylhereIsnoscIentificwaytovalueview
("N.H.PuisaPriceonPanoramas:PropertyTaxesSoarBasedonScenery~14November2005),
6
Ourstudy focuses on the flood hazard risks measured b}'location within SpecialFlood Hazard Area (SFHA).
TheSFHA denotes the 100-yeal'floodplains (onepercentannual chanceofflooding). Flood risk areas are
identified using the Flood Insurance Rate i\-1aps fmm FEMA. \Y/ecreate adummyvariable thatdenotes
parcels located within the SFHA. R.isk measuresother than flood do notvarywithin the sample and are
excluded from this study. Although ourbehavioral model in~ludeserosian risk) there is novariation in the
erosion risk far the studyarea, and thuswedrop erosion risk in ourempirical model.lOSincewe cannot
control for erosion risk,we mustinterpretourresults regardingproximity to the beach with caution, as
proximitywill reflect thevalue ofaccess for recreation and leisure activities butalso exposme to risk from
coastalerosion.
In addition,we calculate thedistance ta nearesthurricane evacuation mute and also thedistance to nearest
central business district (downtown.\X!ilmington). Distancesare measured as the Euclidean distance from the
centroid ofthe property to the nearestedge ofa feature. Othet·variables used in this studyincludestructural
characteristicsofpmpe1'tiessuch as age ofthe stt1.1ctU1'e, numberofbathrooms)sizeoflot)etc. A typical
home is about22}'earsold and has 9,235square feet oflotsize. Approximatel}'90percentofthe homes
have airconditioning,and 49 percent havea fit-eplace. About45 pel·centofthe homes are multistoryunits)
and 8percent have hardwood floo1'~. Less than 5% ofthe homes haveadetachedgarageOfaswimming
pool. Property tax appraisers rated about 150/0ofthe homes in"goodcondition". \Y/e use dumm}'variables
to control for unobservable differences across townships. Alarge fraction ofhouse sales occurred in
CaroHna Beach (40%) and Kure Beach (34%). Table Idefines thevariables usedin the hedonic price
function and provides summarystatistics.
Table 2displays pairwise correlationcoefficients for the amenity and risk variables included in out·anal)'sis.
The estimated correlation between flood hazard (SFHA) and distance to the beach (BEACH) is not
statisticallydifferent from zero in ourdataset. This empirical result is atodds with the theoretical rnodel of
HaJlstrorn and Smith (2005), inwhich distance to the shore is assumed toc!osc!}' reflect flood risk. Since
flood hazard varies across multipledimensions) the correlationwith distance to t.he shore is notstatisticall)'
significantin ourdata. The correlationcoefficient forviewal11enity (VIE\XI) and distance frorn the shore is
negatjve and different from zero) butisalsodifferent from negative one at p <0.0001. Thus,view amenity
and proximity in the shore are notlinearlydependent. Likewise, and moreimportantly,viewand flood
hazard, while positivelycorrelated,are not perfectlycorrelatedeither (p <0.0001). Ollrdata do notappear to
be ill-conditioned with respect to flooding hazards and viewarnenities. \Y/eagain notc thatwcdo not
considererosion risk in our analysis, as there is notvariation in·estimatesofthe historical erosion rate in New
HanoverCounty.
11)NorthCarolinaDivisIonofCoastalManagementreportsthatthelongternerosionrateforlhestudyareaIsabouttwofeetperyear
thatdonotvaryacrosssites(htlp:lldcm2.enr.slate.nc.usfMaps!chdownload.htm).
7
IV. Measuring the View
Viewis an intuitive concept f01" virtuaUyaU sighted personsdue to everydaylived experience. Despite its
simplicity,constructinga measure ofview requires definingaspecific viewattribute to measure among the
many possiblecandidates. Viewarea is onesuch measureobtained [mm aJ!ieJIIshedwhich is defined as the
total areavisible from an observation pointataspecified location. A tlieJlJShedneed notbespatially
contiguous; total visiblearea maybe fragmented into multiple patches. An alternative 2nd ordct"measureis
the afca ofthe largestsingle JJicJlJshedpatch, orsome measureofthedistribution oftJieJ1fsheripatch sizes.
Descriptive measures ofpatch spatial configuration usingconcepts and techniques borrowed from. landscape
ecology havealsobeen developed. Further,stratification ofJJie1JJShedarea by distance bands from the
observation pointyields further area ordistributional measures thatdifferentiate between neat"and distant
views. These and otherviewmeasures aredescribed in Batty(200I), Germino ctaJ. (200I), L10bera (2003),
and Stamps (2005b).
W/e selecteda faid}' straightforwardand intuith'e attribute to measure view. OurJJieJJJrneasure represents the
scopeofaparcel's oceanview. Itidentifies all possible fields ofview from acentral pointand sums their
angularwidths toyield the summed total field ofviewin degrees (00-3600). Priorvaluation studies have
utiJjzed asubjectivescope mcaSUl"e as theviewattribute ofinterest. Forcoastal comrnunities, these measures
arc typicallycontained in mass tax appraisal databases with values having been determined in the field bylocal
tax asses~ors and l"epotted usingordinal codes (i.e" full, partial, or noview). \Y/e are unawareofanyvaluation
studies thathave used quantitative measures ofscopesuch as ours. :Moreover,despite the abilityto create
GIS-basedviewsheds nnd various 2nd orderviewmeasures,qunntitativc measures ofscope are largelyabsent
in the GIS Iiteratul"e.
Creationof?urviewmeasure required the constructionofannual topographic surfaces from LIDARdata.
LIDAR instrumentation mounted on an airplane emits adense arrayoflaser pulses thatstrikesurface
features and reflect back to the recording LIDAR sensor\vhich contains aglobal positioningsystem that
corrects foraircraftpitch and roll. The time it takes for emitted pulses to retum to the sensor is used to
determine surfnce elevations. Ourbase elevation layerwas agridded surface ata5-foot pixel resolution flown
inSeptember2000 (Figure 2). This'topographic surface represents built structures,vegetation, and bare
earth. This isespcciall}fimportant to realistically measure vicwsince these features act to obstructocean
views. A recent hedonic studyby Paterson and Boyle (2002) utilizes an aerialviewshed measure createdvia
GIS techniques; howevel", theirlneasure relied on aHbareearth" topography that does not realistically
account for the presenceofobstructjng builtorvegetation features. To construct annual elevation sUffaces,
separate layers were built for each yearusingairphotos and buildingheightinformation from tax assessor
8
data to add 01'subtractelevation fol' digitized buildingfootprints as necessal'Y, Atotal ofeightelevation
surfaces were aeated-one fol' each yeaL11
POl"simplicity, we distill the methods used to create lJie1l'into two steps that involved:.(1)aeatinga parcel's
viewshed (i.e. viewarea) at a I-miledistance from the parcel, and (2) calculationoftheviewshed's sumrned
field ofview (Le. degrees). Creating thevie\Vshed requires positioningaviuual "observer"somewhere on the
landscape. FOl"each parcel)we establishedan observerpoint at the centel'point ofthebuildingfootprint at
an elevation 10 feet below the estimated maximum heightofabuildingwhich was obtained from the
e1C'~'ation surfaces or from tax assessordata that reported the building's numberofstories,12 An ocean zone
wasdemarcated usingavectOl"shm'e1inelayer that repl"esented theocean/beach interface. The targetareaof
potentialviewwas limited to thearea ofoceanwithin one mile from the observerlocation. To derive
viewshed, the "lineofsight" mytracing technique comrnonlyavailable in GIS softwarewas used toidentify
every5-footpixel in the targetocean areavisible from the obset\'el'location (Figure 2).
To create oUl' final tJieJl!measure (i.e;) the summed field ofviewin degrees), we constructed circulararc
segmentsat theouter I-miledistance oftheviewshed,calculated theirindividual lengths, and summed the
individual lengths. A hypothetical maxirnum summed arc length was the circumferenceofthe I-milecircle,
orapproximately6.28 miles sincecircumference = 2 X pi X mdius. VieJ!Jis thereforederived as: tJielJl=
(arclengtllmib / 6.28milcs) X 360". POl'example)an arclength of3.14miles translates to 1800. Given the linear
natureofourstudyarea's shoreline;an approximate maximum possible value for lJieJJIwould be 180".D Figure
3iHusttates viewsheds and reports values for the 14m'measure for two cxmnplc properties.
V. Methods
Since the pioneeringwotkofRosen (1974), the hedonic price Inodel has been used extensively in
environmental and natural resource economics as atool for non-marketvaluation. The model assurnes that
values ofheterogeneousgoods are reflected in pl'iccdifferentials. Residential propertycan bedistinguished
based upon structural) neighborhood, and envil'Onrnentalcharacteristics,and one can assume that utility
IIAsanexample,tocalculateviewfortransacllonsIn1997,weusedthe1997elevatlonsurfaceforwhIchelevationsforbuild!ng
footprintsofbuild!ngsbuiltIn1998,1999,and2000weresubtractedfromtheorigInalyear2000surface. TransactionsIn2002used
Ihe2002surfaceforwhichelevaUonsforfootprintsofnewbulfdlngsbuiltIn2001 and2002wereaddedtotheorigInalyear2000
surface.
12WhilesomebuildIngsmayhavevlew!ngdecksontheroof,IhlsconventlonwasusedunderIheassumptlonIhatmostpeople
wouldviewfromavantagepointabout10feetbelowthemaximumbuildIngheight.
13AnImportantconsIderationwiththIsmeasureIsthatItIspossibleforsomeoftheoceanareaInteriortoatermlnal1¥mllearc
segmenttonotbepartoftheviewshed. Forexample,anobserverata3-storyresIdencelocatedone+blockfromtheoceanfrontrow
mayhaveto"lookover~a2-sloryoceanfrontresidenceora15-ftsanddunetoseetheocean. ThiswouldvisuallyobstructIhenear
shoreoceanareasothatviewableoceanareaonlybecomesvisibleatsomedistancefromtheshoreHne. ThIsunderscoresthefact
thatourviewmeasureIsquaUtativelydifferentfromvlewshed. However,forourdatathetwomeasuresarehIghlycorrelatedtothe
a
extentthattheyareeffectively!denticalln quantitativesense. Th!swillgenerallybethecaseforothersellingswithlinear
shorelines. WeelectedtouseviewduetoiteaseofInterpretation.
9