Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban ...

13 Pages·2007·0.65 MB·English

LandscapeandUrbanPlanning79(2007)240–252
Using GIS and landscape metrics in the hedonic price modeling of the
amenity value of urban green space: A case study in Jinan City, China
Fanhua Konga,∗, Haiwei Yinb, Nobukazu Nakagoshia
aGraduateSchoolforInternationalDevelopmentandCooperation,HiroshimaUniversity,Kagamiyama1-5-1,Higashi-Hiroshima739-8529,Japan
bDepartmentofUrbanandResourcesSciences,NanjingUniversity,Nanjing210093,China
Received7November2005;receivedinrevisedform11January2006;accepted14February2006
Availableonline2May2006
Abstract
Urbangreenspaceshaveimportantamenityvaluesthatincludeprovisionofleisureopportunitiesandaestheticenjoyment.However,mostof
thesevalueslackamarketprice.Consequently,theyareusuallyignoredorunderestimatedbyurbanplanningpolicy-makers,withtheresultthat
remnanturbangreenspacesarebeinggraduallyencroacheduponbyurbansprawl.Asaresult,quantitativeinformationregardingtheimplicit,
non-marketpricebenefitsfromurbangreenspaceisurgentlyrequired.Propertiesboughtandsoldonthemarketarecompoundcommoditiesthat
embodyamenityvaluesandpeoplearewillingtopaytoliveintheproximityoflocalamenityenvironment.Thushedonicmodels,whichuse
suchpropertiesasproxies,canoftenbeemployedtoquantifyenvironmentalamenities.InChina,residentialhousingreform(inplacesince1998)
hasterminatedthetraditionalresidentialwelfaresystem,andmadeitpossibletoquantifythemonetaryvalueofgreenspaceamenitiesbasedon
hedonicpricingmodels.ThisstudywasconductedinJinanCity,andwillhelpaddressthepreviousabsenceoftheapplicationofhedonicprice
modelstothevaluationofurbangreenspaceamenitiesinmainlandChina.GISandlandscapemetricswereusedindetermininghedonicprice
modelvariables.Asexpected,theresultsprovedthatthehedonicpricingmodelperformedwellusingthisapproach,andaccordinglyitwasfurther
improved.Resultsalsoconfirmedthepositiveamenityimpactofproximateurbangreenspacesonhouseprices,andhighlightedthepreferences
ofhomeownersinJinanCity.Greenspaceamenityvariablesthatwerestatisticallysignificantatthe5%levelincludedthesize–distanceindexof
sceneryforest,accessibilitytoparkandplazagreenspacetypes,andthepercentageofurbangreenspace.Inaddition,land-usepatchrichness,the
locationsectorandtheeducationenvironmentalsoprovedtobehighlysignificantvariables.Theresultsofthestudyshouldprovideinsightsto
policy-makersinvolvedinurbanplanning.
©2006ElsevierB.V.Allrightsreserved.
Keywords: Hedonicpriceanalysis;Greenspaceamenity;Housing;Geographicalinformationsystem;Landscapemetrics
1. Introduction andOsgood,2003).Intheabsenceofanexplicitmarketpricefor
aunitofenvironmentalamenity,thebenefitsareusuallyignored
The development of environmental awareness has resulted or underestimated by urban planning policy-makers, with the
inastrongdemandbyurbanresidentsforgreenspaceforvar- consequence that remnant urban green spaces have shrunk in
ious purposes, including aesthetic enjoyment, recreation, and sizeandhavebeengraduallyencroacheduponbyurbandevel-
access to clean air or a relatively quiet environment (Miller, opmentandsprawl(Lockeretz,1989;Englishetal.,1990;Leiva
1997; Tyrva¨inen and Miettinen, 2000). However, amenity val- andPage,2000).Suchconflictingtrendsraisetheneedforgreen
uesattachedtourbangreenspacesarenon-marketprice,envi- spaceprotectionandallocation,whichinturnrequiresestimates
ronmental benefits (Robinette, 1972; Grey and Deneke, 1978; oftherecreationalvalueofgreenspaces.Consequently,proxy
Miller, 1997; Tyrva¨inen and Va¨a¨na¨nen, 1998) that cannot be measuresforhowtheamenitiesareperceivedmustbedeveloped.
directlytradedonanopenmarket(Moreetal.,1988;Sengupta Thedeterminationofthevaluetosocietyofsuchnon-market
pricedrecreationresourcesisnotanewconcepttoeconomists
(Price, 2000). Several methods such as travel cost models, the
∗ Correspondingauthor.Tel.:[phone];fax:[phone]. contingentvaluationmethod,andhedonicpricingmodelshave
E-mailaddress:[email protected](F.Kong). beendeveloped,andwereimprovedinrecentdecades.
0169-2046/$–seefrontmatter©2006ElsevierB.V.Allrightsreserved.
doi:10.1016/j.landurbplan.2006.02.013
F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252 241
Travelcostmodelshavebeenwidelyusedtodeterminethe realestatevaluationcanonlybefullytakenintoaccountbyusing
environmentalvalueofrecreationresources.Thebasicpremise thedescriptiveframeworkofaGIS.Oneofthemostbasicadvan-
ofthetravelcostmethodisthatthetimeandtravelexpensesthat tagesofaGISistopositionpropertiesonalocalmapinterms
peoplespendtovisitasiterepresentthe“price”ofaccesstothe oftheirgeographiccoordinates(Dinetal.,2001).Spatialstatis-
site.Travelcostmodelsareappropriatewhenpeopletravelvari- ticswithinaGIS,basedondigitizedremotesensingdata,have
ousdistancestoreacharecreationresource.Althoughtheyhave madepossiblethedevelopmentofaccurate,consistent,andunbi-
beenusedinurbansituations(Dwyeretal.,1983;Petersonetal., asedexplanatoryvariables,suchasaccessibilitytopublicgreen
1983),theyaregenerallynotthoughttoworkwellforneighbor- spaces,inafastandefficientmanner.Thesecanthenbeusedto
hoodrecreationresourcesbecauseoftheverysmalldifferences measure the environmental characteristics of properties better,
betweenusersindistancetraveled(Moreetal.,1988). increasingtheunderstandingofhousepricingvariationsascom-
Contingent valuation method is the most prevalent method pared to previous studies (Wyatt, 1996; Can and Megbolugbe,
usedtoestimateaneconomicvalueforenvironmentalgoods.By 1997;Geogheganetal.,1997;Lakeetal.,2000;Brasingtonand
creatingahypotheticalmarketinwhichindividualscanexpress Hite, 2005). Supported by GIS, landscape ecology has expe-
their willingness to pay for an environmental good, contin- riencedunprecedentedrapiddevelopmentsincethemid-1980s
gentvaluationmethodemployssurveytechniquestoaskpeople (Wu et al., 2000). The creation and development of landscape
about the amount they would pay for non-market commodi- metricsbylandscapeecologistshavemadequantificationofspa-
tiesifmarketsdidexist(MitchellandCarson,1989;Tyrva¨inen tialstructurepossible.Jointecologicalandeconomicsmodeling
and Va¨a¨na¨nen, 1998; Xu et al., 2003). However, because the based on GIS will potentially make a large contribution to the
responses do not involve actual market purchases, a distinct valuationofenvironmentalamenitiesandtheunderstandingof
drawback of this method is that responses are based on hypo- humanimpactsonurbanecosystems(Geogheganetal.,1997).
thetical rather than actual behavior (Tyrva¨inen and Va¨a¨na¨nen, Todate,hedonicpricingmodelshavenotbeenusedtoeval-
1998). uate implicit prices for the amenity of urban green spaces in
Since the application of hedonic pricing model to environ- mainland China, nor for any other non-priced environmental
mental economics found a statistically significant relationship resources associated with house prices. This was mainly the
between property values and air quality (Ridker and Henning, resultofthepreviousrealestatemarketsystem.Inthetraditional
1967),economistshavepaidincreasingattentiontotheinforma- residentialwelfaresystem,thestate,ratherthantheindividual,
tionalcontentofhouseprices(McLeod,1984;Geogheganetal., heldrightstohouses.Theoldsystemrestrictedthesaleofres-
1997).Propertyisacomplicatedcommoditywithmanydimen- idential houses in the market as real goods. It was impossible
sions,anddifferencesinthesellingpricesofhouseswillbedic- to access and verify house transaction prices. Thus far, quan-
tatedbyanumberoffactors,includingthequalityofthehousing titative research regarding urban green space amenities based
structure,neighborhoodcharacteristics,accessibilitytothecen- onhedonicpricingmodelshasbeenlimited(WangandHuang,
tralbusinessdistrict,aswellastheenvironmental(dis)amenities 2005).However,thenatureofwelfarehousing(FuLiFang)has
associatedwiththeproperty.Hedonicpricingmodelsuseamar- beenfundamentallychangedsinceeconomicreformunderthe
ketgood(thehouse)asaproxy,withinwhichanon-marketgood, policy of “housing monetarization” (Shang Pin Fang), which
suchasanenvironmental(dis)amenity,isimplicitlytraded.They was promulgated in 1998 (Zhu and Guo, 2004). Since then,
have been frequently applied in America after being devel- housing has been sold under free market conditions with con-
opedbyRosen(1974)(McLeod,1984;CheshireandSheppard, sumersgiventherighttoentertheresidentialmarketandchoose
1995;Tyrva¨inen,1997).Otherempiricalstudieshavealsobeen housingattributesdependingontheirtaste(Yang,2001;Wang
conducted in European countries (Garrod and Willis, 1992; and Huang, 2005). This provides an opportunity to conduct a
Tyrva¨inen,1997;Tyrva¨inenandMiettinen,2000).Urbangreen study and fill the gap in this research field in mainland China
spacesproducebenefitsthatpresumablymakeaneighborhooda by developing and performing a hedonic pricing estimation of
niceplaceinwhichtolive,andthesebenefitsarereflectedinthe the benefits deriving from urban green space amenities. How-
pricesofsurroundingproperties.Consequently,theamenityof ever,theChineseresidentialmarketisinembryonicform,and
urbangreenspacescouldbevaluedinmonetarytermsaccord- needs to be further improved. For example, work units (Dan
ingtohowmuchpeoplepayforsuchbenefitsintheirhousing. Wei) bought commodity housing for subsequent allocation to
Byusinghedonicpricingmodel,thevalueofanenvironmental their workers still existed. In 2000, only 87.29% of total was
amenitycanbeestimatedfromthepricesofrelatedactualmar- soldtotheendusers(ChinaStatisticsBureau,2000).Inaddition,
kethousetransactions.Themethodisusuallytermeda“revealed housing prices are a business secret for real estate developers,
preferencemethod”inordertodistinguishitfromthestatedpref- whodonotwanttomakepublicdetailsofhousingtransactions.
erencemethodssuchascontingentvaluationmethod,whichare Hence,datacollectionregardinghousepricesremainsdifficult.
basedonintendedratherthanactualbehavior(Moreetal.,1988; In this paper, real estate transaction prices were captured only
Tyrva¨inenandMiettinen,2000). for the year 2004 in Jinan City. Based on the hedonic pric-
In recent decades, the development of geographic informa- ing model, we attempted to indirectly evaluate the amenity of
tion systems (GIS) has gradually made hedonic pricing model urbangreenspaces.Atthesametime,implicitpricesforother
apowerfultool,butatpresent,itisstillunderutilizedinurban environmentalcharacteristicsrelatedtohousing,suchastheedu-
andenvironmentaleconomics(BrasingtonandHite,2005).The cationalenvironmentandenvironmentaldisamenities,werealso
commondictumthatlocationisthemostimportantparameterfor estimated.
242 F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252
This paper is structured as follows: in Section 2 theoreti- andPollakowski(1979)recommendedtheuseoftheBox–Cox
calmodelsofhedonicpricingmethodaredeveloped,including flexiblefunctionalforminhedonicanalysis,whichwouldmea-
linear and semi-log models; Section 3 describes the data and sure best performance with goodness-of-fit. This functional
explanatory variables; in Section 4 the valuation technique is form is commonly applied in the hedonic literatures (Kau and
applied to the amenity of urban green spaces and the benefits Sirmans, 1979; Shultz, 2001; Smith et al., 2002). However,
are estimated; finally some conclusions are drawn in Section CasselandMendelsohn(1985)havepointedoutadrawbackof
5. The main objectives of this study were to: (1) attempt to thismethodology:thatusingabest-fitcriteriontochoosefunc-
apply hedonic pricing models to the valuation of urban green tionalformsdoesnotnecessarilyleadtomoreaccurateestimates
spaceamenitiesinJinanCity;(2)searchforsuitablevariablesin ofcharacteristicprices.Followingpreviousstudies(Gretherand
hedonicmodelsusingGISandlandscapemetrics;(3)presentthe Mieszkowski,1980;AcharyaandBennett,2001;DesRosierset
monetaryvalueofurbangreenspaceamenitiestourbanplanners al., 2002), this paper employs common functional forms (lin-
anddecision-makersforoptimizingurbandevelopmentprojects ear and semi-log linear hedonic price functions) with multiple
and greening planning. The results of this paper constitute the regressionanalysis.
firstempiricalstudyinmainlandChinaaboutquantitativevalua-
tionofurbangreenspaceamenitiesbasedonthehedonicpricing
2.1. Linearmodel
models.
Thelinearhedonicmodelestimatedinthispaperisasfollows:
2. Thehedonicpricingmodel
P =α+Eβ+Gγ +Lη+ε (2)
Beforeobtainingestimatesusingthehedonicpricingmodel,
where P is an (n×1) vector of mean housing cluster prices;
theformforthehedonicpricefunctionmustbechosen.Hous-
E is an (n×1) matrix of neighborhood environmental charac-
ingisnotahomogeneouscommodity(McLeod,1984).Ahouse
teristics; G is an (n×1) matrix of accessibility characteristics
buyer purchases both a dwelling and a set of site character-
(spatial character measured by GIS); L is an (n×1) matrix of
istics. The price at which a house is traded is determined by
landscapeindices(spatialcharactermeasuredbylandscapemet-
a host of factors: housing structure, location, neighborhood,
rics);α,β,γ,andηareassociatedparametersvectors;andεisan
and environmental characteristics. The household must find
(n×1)vectorofrandomerrorterms.Thenotationintheabove
the dwelling with the best combination of features at the best
equation is slightly different from that in Eq. (1), with inde-
price (O’Sullivan, 2002). The implicit prices of spillover ben-
pendentvariablesgroupedintothreetypes.Table1providesa
efits from the neighborhood or environmental characteristics
detaileddescriptionofeachvariableusedinthehedonicpricing
canbeinferredbasedonthehedonichypothesis(Rosen,1974).
model.Themodelingprocedureinvolvedenteringallvariables
Consequently, the traditional hedonic pricing model takes the
of a specific group into an ordinary least squares regression,
followingform:
andexaminingtheadjustedR2.Theanalyzedresultsareshown
P =f(x1,x2,...,xn) (1) in Table 2. However, as Rosen (1974) points out, there is no
reasontoexpecttherelationshipbetweenthepriceandtheenvi-
where P is the market price of the housing and x , x , ..., x ronmental variable to be linear. In fact, non-linearity is to be
1 2 n
are the characteristics contained in the property. These char- expected, because purchasers cannot treat individual housing
acteristics can be categorized into three broad groups of vari- attributes as discrete items from which they can pick and mix
ables.Structuralvariablesrelatetothedirectcharacteristicsof untilthedesiredcombinationofcharacteristicsisfound(Garrod
theproperty;accessibilityvariablesdefinetheeasewithwhich and Willis, 1992; Morancho, 2003). Thus, semi- or double-
amenitiescanbereachedfromtheproperty;andneighborhood logarithmic specifications are frequently formulated, although
environmentalcharacteristicsvariablesdescribethequalityofits linearmodelsarestillinusebecauseoftheeasewithwhichthe
surroundings,suchasindustrialpollutionandindicatorsregard- parameterscanbeinterpreted(AcharyaandBennett,2001;Des
ingthetypeandstructureofland-use(Freeman,1993;Lakeet Rosiersetal.,2002;Huntetal.,2005).
al., 2000). In this paper, 17 variables were chosen in the con-
structionofhedonicpriceindices.Thevariables,withdetailed
2.2. Semi-logmodel
descriptions,canbefoundinTable1.
The hedonic pricing model employed in the evaluation of
Inthispaper,thesemi-logmodelwasspecifiedbyanatural
recreation resources typically uses multiple regression tech-
log transformation of the housing price in the hedonic regres-
niquestorelatepropertypricedetailstodiversecharacteristics
sions.Themodelisasfollows:
ofdifferingproperties,andtosortoutthedifferentcontributions.
Manyeconomistshavestressedthateconomictheorydoesnot ln P =α+Eβ+Gγ +Lη+ε (3)
suggestanappropriatefunctionalformforhedonicpriceequa-
tions(Rosen,1974;Freeman,1979;HalvorsenandPollakowski, Inadditiontothedirectlinearandsemi-logregressionmodel-
1979; Cassel and Mendelsohn, 1985). Consequently, it is rea- ing(Table2),anotheranalysiswasperformedusingastepwise
sonable to try several functional forms and utilize a multiple regressiontechniqueintheSPSSsoftwaretoeliminatecollinear
regressionequation(CasselandMendelsohn,1985).Halvorsen variables(Table3).
F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252 243
Table1
Variabledescriptionsandtheirexpectedeffectsonhousetransactionprices
Independentvariable Description Expectedsigna
S-DPARK Ameasureoftheintegratedimpactofthenearestparksize(Spa)andstraight-linedistancefromthepropertyto +
theboundaryofthenearestpark(Dpa),S-DPARK=ln(Spa/Dpa)
S-DPLAZA Ameasureoftheintegratedimpactofthenearestplazasize(Spl)andstraight-linedistancetotheboundaryofthe +
nearestplaza(Dpl),S-DPLAZA=ln(Spl/Dpl)
S-DSCEN Ameasureoftheintegratedimpactofthenearestsceneryforestsize(Ssc)andstraight-linedistancetothe +
boundaryofthenearestsceneryforest(Dsc),S-DSCEN=ln(Ssc/Dsc)
TYPEGR Typeofthenearesturbanpublicgreenspace.Plaza,park,andsceneryforestareweightedwith1,2,and3, +
respectively
ACPARK Ameasureoftheeaseofaccessibilitytoapark;accumulatedtimecostfromthecentroidofthehousingcluster −
tothenearestpark(inmin)
ACPLAZA Ameasureoftheeaseofaccessibilitytoaplaza;accumulatedtimecostfromthecentroidofthehousingcluster −
tothenearestplaza(inmin)
ACSCE Ameasureoftheeaseofaccessibilitytoasceneryforest;accumulatedtimecostfromthecentroidofthehousing −
clustertothenearestsceneryforest(inmin)
ACCBD AmeasureoftheeaseofaccessibilitytotheCBD;accumulatedtimecostfromthecentroidofthehousing −
clustertothecentralbusinessdistrict(inmin)
PLGR Ameasureofpercentageofthelandscapethatisurba(cid:2)n(cid:3)greenspace(cid:4)ina300mradiuswindow,indicatingthe +
greeningratioofthehousingcluster,PLGR=Pg= nj=1agj/A ×100,whereagjisthegreenspaceareaof
patchj,andAisthewindowsize
NPGR Ameasureofthefragmentationofurbanpublicgreenspacesina500mradiuswindow.NPGR=Ng,whereNgis ?
thetotalnumberofgreenspacepatchesinonewindow
PRGR Patchrichnessoftheneighboringurbanpublicgreenspacesina500mradiuswindow,PRGR=m,wheremis ?
thenumberofurbangreenspaces(classes)presentinonewindow
AIGR Ameasure(in%)ofaggregationofpublicgreenspacesina500mradiuswindow,AIGR=(gii/maxgii)×100, ?
wheregiiisthenumberoflikeadjacencies(joins)betweenpixelsofpublicgreenspaces(i);maxgiiisthe
maximumnumberoflikeadjacencies(joins)betweenpixelsofpublicgreenspaces(i),basedonthesingle-count
method
NPLA Ameasureofpatchdensityoftheneighboringland-useina500mradiuswindow,NP=Nl,whereNlisthetotal ?
numberofland-usepatchesinonewindow
PRLA Patchrichnessoftheneighboringland-useina500mradiuswindow,PRLA=m,wheremisthenumberof ?
land-usepatchtypes(classes)presentinonewindow (cid:2) (cid:3) (cid:4)
EDM Ameasureofenvironmentdisamenity,EDM=(n/d)= n2/ ni=1di ,wherenisthenumberoffactories −
withina1000mbuffer,diisthedistancefromthecentroidofthehousingclustertofactoryi;orEDM=1/D,
whereDisthenearestdistancefromthecentroidofthehousingclustertoafactorywhenthereisnofactory
withinthe1000mbuffer (cid:2) (cid:3) (cid:4)
EE Ameasureofeducationenvironment,EE=(n/d)= n2/ ni=1di ,wherenisthenumberofuniversities +
withina500mbuffer,anddiisthedistancefromthecentroidofthehousingclustertouniversityi;orEE=1/D,
whereDisthenearestdistancefromthecentroidofthehousingclustertoauniversitywhenthereisno
universitywithinthe500mbuffer
DLOC Ameasureofthelocationsectorofhousingcluster.Thesamplesaredividedintofourparts:north,west,east, +
andsouthandweightedwith1,2,3,and4,respectively
a +and−representincreasinganddecreasingeffects(respectively)onthehousetransactionprice;?indicatesanaprioriundeterminedsign.Definitionsoflandscape
metricsarebasedonMcGarigaletal.(2002).
3. Dataanddefiningexplanatoryvariables up areas have expanded from 24.6km2 in 1949 to more than
190km2in2003(JinanStatisticsBureau,2003).JinanCityowns
The characteristics or variables to be included in the esti- atotalofapproximately105.6km2 publicgreenspaces(classi-
matesmustbedeterminedbeforeobtainingsuchestimatesusing fiedassceneryforests,plazas,andparks)(KongandNakagoshi,
hedonic pricing model. This decision may involve recognition 2005),amountingtoabout19.62%ofthetotalstudyarea.There
ofrelevantvariablesthatareomittedfromtheanalysisanddeter- arecomparativelyfewpublicgreenspacesinthebuilt-uparea,
minationofhowtocapturethechosenvariables(Cheshireand and the majority (91.1%) are found in the urban fringe, espe-
Sheppard,1995). cially on the south and southeast hills. Jinan City is known as
“CityofSprings”forthereareatleast72famousspringsinthe
3.1. Housepriceandstructurecharacteristicsdata city. The sources of these springs are the southern and south-
easternsceneryforestareas(Fig.1).Hence,conservationofthe
ThisstudywasconductedinJinanCity,thecapitalofShan- southern and southeastern scenery forest is a key issue (Kong
dong Province in China. Jinan City has experienced dramatic andNakagoshi,2005).However,greenspaceamenitiesattract
population growth (from 3.19million in 1952 to 5.75million migrants, and the rapid urban development has encroached on
in 2002) and has sprawled greatly in the last 50 years. Built- muchoftheurbangreenspaceinrecentdecades,especiallyinthe
244 F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252
Table2
Regressionresultsoflinearmodelandsemi-logmodel,dependentvariable:inprice
Independentvariable Linearmodel(R2=0.691,adjusted Independentvariable Semi-log model (R2=0.698,
R2=0.642) adjustedR2=0.650)
Coefficients t-Ratio Coefficients t-Ratio
Constant 4205.716 3.602 Constant 8.436 24.365
S-DPARK 43.309 0.753 S-DPARK 1.837E−03 0.108
S-DPLAZA 213.253 1.894 S-DPLAZA 4.612E−02 1.382
S-DSCEN 86.160 1.479 S-DSCEN 1.873E−02 1.084
TYPEGR 280.159 1.562 TYPEGR 1.539E−02 0.289
ACCBD −22.226 −1.222 ACCBD −6.023E−03 −1.117
ACPARK −11.873 −0.368 ACPARK −1.137E−02 −1.189
ACPLAZA −37.478 −1.074 ACPLAZA −9.529E−03 −0.921
ACSCE −15.937 −0.622 ACSCE −7.304E−03 −0.961
PLGR 62.326 4.537 PLGR 2.030E−02 4.983
AIGR 5.403 1.356 AIGR 1.876E−03 1.588
NPGR 429.778 2.971 NPGR 0.138 3.228
PRGR −1130.393 −2.922 PRGR −0.351 −3.061
PRLA −377.081 −4.088 PRLA −0.118 −4.317
NPLA −4.129 −0.662 NPLA −2.720E−03 −1.471
DLOC 194.435 2.078 DLOC 7.476E−02 2.695
EE 69.402 2.640 EE 1.722E−02 2.208
EDM −18.518 −0.796 EDM −4.246E−03 −0.616
southern scenery forest area. Proper management is needed to ture and public services (Ministry of Construction, PR China,
preventencroachmentontogreenspacesandtoreallocatethem, 1998).
butoptimizingthespatialpatternofurbangreenspacerequires Atthestartofthestudy,variablesrelatingtostructuralchar-
evaluationoftheeconomicvaluesofurbangreenspaces.There- acteristics were designed, including the age of the house, and
fore, quantitative information in monetary terms concerning whetherithadgas,internet,bathroom,orheating,ashasbeen
greenspacebenefitsisurgentlyneededforfutureurbanplanning donebymostpreviousresearchers(McLeod,1984;DesRosiers
inJinanCity. etal.,2002).However,aparticularweaknessofthehouseprice
Housing prices have fluctuated considerably since reform surveywasthatonlylimitedinformationwasavailableregard-
of the real estate market in 1998, so the data collection sur- ing differences between property structures. These properties
vey was only carried out in August, 2004. All the surveyed wereconstructedinthelasttwoyears,andthosefromthesame
houses were built between 2003 and 2004, and sold in 2004, housingclusterwerebuiltbythesamerealestatedeveloper.Con-
at a time when local house prices were most stable. A single sequently,thesurveyedpropertiesarelikelytoexhibitstructural
year of data was used to avoid problems with time variations, similarities, not only in terms of their age but also in terms of
but this resulted in a relatively small sample. In the end, 124 theirlayoutandinteriorandexteriordesignfeatures.Ingeneral,
housingclusterswereselected.Thehousingclustersarelocated property structure characteristics appear to differ mostly with
withintheurbanareaandcomparedbyroads,andareoftenbuilt size.Somerelatedpapershavedemonstratedthatpropertysize
byasinglerealestatedeveloper,withvaryingbasicinfrastruc- has a positive relationship with selling price (McLeod, 1984;
Table3
Regressionresultsaftereliminatingcollinearvariables,dependentvariable:inprice
Independentvariable Linearmodel(R2=0.648, Independentvariable Semi-log model (R2=0.673,
adjustedR2=0.620) adjustedR2=0.647)
Coefficients t-Ratio P-value Coefficients t-Ratio P-value
Constant 3735.850 4.666 0.000 Constant 8.172 37.838 0.000
S-DSCEN** 141.094 3.275 0.001 S-DSCEN** 4.919E−02 4.094 0.000
PRLA** −417.878 −5.319 0.000 PRLA** −0.123 −5.084 0.000
PLGR** 63.546 4.997 0.000 PLGR** 2.055E−02 5.702 0.000
ACPLAZA* −51.631 −1.737 0.085 ACPLAZA* −1.849E−02 −2.571 0.011
EE** 71.813 2.964 0.004 DLOC* 6.295E−02 2.529 0.013
DLOC* 155.359 1.746 0.084 ACPARK −1.642E−02 −2.405 0.018
ACPARK −32.087 −1.379 0.171 EE** 1.841E−02 2.648 0.009
TYPEGR** 370.239 2.313 0.022 NPGR** 0.139 3.283 0.001
S-DPLAZA* 188.579 1.760 0.081 PRGR** −0.192 −2.780 0.006
* Indicatesstatisticalsignificanceatthe10%level.
** Indicatesstatisticalsignificanceatthe5%level.
F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252 245
Fig.1. Thestudyareaandgeographicdistributionofthe124sampleproperties.
Tyrva¨inen and Miettinen, 2000; Morancho, 2003). The major categories(plaza,park,andsceneryforest)andweighted(with
focus of this investigation was not to search for a comprehen- 1, 2, and 3, respectively) according to their expected amenity
siveexplanatorymodelofhousepricedetermination,butrather effect.Thatis,morehighlyweightedgreenspaceisexpectedto
todemonstratethecontributionofurbangreenspacevariables. haveapositiveandhigherimpactonhousingsaleprice.When
Therefore,variablesrelatingtohousestructuralcharacteristics asizevariableforthenearesturbangreenspacewaschosen,the
were dropped from the final analysis, and the real transaction question of spatial pattern (with the exception of green space
property price per square meter of each housing cluster was type), was usually ignored. However, particularly uneven dis-
chosentobethedependentvariableinthefinalmodel.Thereby, tribution patterns of property and green space may influence
the property location in this paper was the geometrical center the analysis results. For example, as shown in Fig. 2(a), the
of each housing group and was determined by GIS. A map of nearesturbangreenspacetopropertiesH andH isthesame
1 2
the study area including the location of the sample properties (S). If the other variables effecting housing price are ignored,
shows that most properties are located within the contiguous the price of H would be the same as that of H . But in fact
1 2
built-upareaofJinanCity,withrelativelyfewinitsperipheral H has a higher price than H , because the effects of amenity
2 1
area(Fig.1). onthehousesalepriceareinaccordancewiththegeneraldis-
tancedecaytheory,asshowninFig.2(b)(CanandMegbolugbe,
3.2. Neighborhoodenvironmentalcharacteristicsdata 1997; Donnelly, 2005). Including both the distance to and the
sizeofthenearestgreenspaceasindependentvariablesinthe
Thesetofexplanatoryvariablesrelatestotheneighborhood hedonic model would result in a biased estimation. Suitable
environmentalattributesoftheproperties,includingurbangreen transformationofthetwovariableswasthereforeneededbefore
space,educationenvironment,andenvironmentaldisamenities conductingtheregressionmodeling.Consequently,aninterac-
(e.g.,industrialpollution). tionterm,the“size–distanceindex”,wasdevelopedasln(S/D),
To evaluate the effect of an amenity on the selling price of where S is the size of the nearest green space and D is the
a given house, previous studies generally have regressed vari- distance to the nearest green space. This index was incorpo-
ablesthatdefineurbangreenspacecharacteristics,suchastype ratedinandaddedtothehedonicpricingmodeltoestimatethe
of green space, the distance from the property to the nearest amenityeffectforproximitytoacertainsizegreenspace,allow-
greenspace,andthesizeofthenearestgreenspace(Tyrva¨inen ing the effect of distance to vary with the size of the relevant
andMiettinen,2000;Morancho,2003).Differenttypesofurban green space. The size–distance index was classified into three
greenspacemayinfluencehousingprices,whichshouldbetaken types: S-DSCEN, S-DPARK, and S-DPLAZA, and they were
intoconsiderationinmodels(Tyrva¨inenandMiettinen,2000). allassumedtohaveapositiveimpactonthehousingsaleprice
Here,themainurbangreenspacetypesweredividedintothree (Table1).
246 F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252
Fig.2. (a)Descriptionofthepossibleinfluenceofdistancetothenearesturbangreenspaceonthehousingprice,causedbythespatialpatternofgreenspacesand
properties;(b)distance–pricerelationshipinaccordancewiththegeneraldistancedecaytheory.
Schools are an important local public service that affects survey of households in Jinan City, conducted by Zhang and
house prices (Goodman and Thibodeau, 1998; Haurin and Wang(2003)andSun(2005)foundthatmostconsumerswould
Brasington, 1996). It is a common belief that people consider prefer residences in the south and east rather than in the west
theeducationenvironmentwhenbuyinganewhouse,andmany andnorth.Thefoursectors(north,west,east,andsouth)were
researchershavefoundthatpropertyvaluesarehigherwherethe givenweightsof1,2,3,and4,respectively.Iftheseweightsare
education environment is better (Hanushek, 1986; Brasington accurate,positiveregressioncoefficientscanbeexpected.
and Hite, 2005). In this study, universities were chosen as a
proxy to perceive the effect of the education environment. It 3.3. Spatialcharacteristicsdata
wasfocusedontheamenityeffectrelatedtodistancefromthe
nearestuniversitytoagivenpropertyandtheuniversitydensity 3.3.1. Accessibilityvariables
(numberofuniversities)withinaspecifieddistancesurrounding Accessibility variables define the ease with which local
theproperty.Thus,thereisapremiseinthispartoftheanaly- amenities can be reached from a property (Lake et al., 2000).
sisthatalluniversitiesareofthesamequality.Theinteraction In this study, the measure of accessibility was the calculated
proxyvariableofeducationenvironment,calculatedusingspa- accumulatedtraveltime(min)fromeachpropertytoeachofthe
tialstatistics,wasadaptedtomeasuretheimpactoftheeducation public green spaces and the central business district by public
environment.Educationenvironmentwasdefinedasthenumber bus or on foot along various road types. A GIS-based spatial
ofuniversitieswithin500mbufferaroundeachhousinggroup, analysisprovidesapowerfulmeansofdeterminingtheaccessi-
dividedbythemeandistance.Iftherewasnouniversitywithin bilityvariables.
a500mbuffer,thereciprocalofthenearestdistancewaschosen Thefirststageincalculatingaccessibilitywastogeneratea
(Table1). land-usemapofthestudyareafromremotesensingdata(2004
Incontrasttothepositiveeffectofamenities,pollutionsuchas SpotImages,10m,4bands),whichincludedtheroad(Fig.1)
the“threewastes”(wastegas,wastewater,andwasteresidues) and walking networks; a vector topographic map (1:10000)
dischargedbyindustrywouldimpingedirectlyonthesurround- created in 2000 was used as a reference. Road network land-
inginhabitantsandresultindisamenityeffectstotheproximate use types were defined as arterial road, secondary truck road,
properties(Adleretal.,1982;Schulzeetal.,1986;Brasington branchroad,andpath;andwalkingnetworkland-usetypeswere
and Hite, 2005). Previous research has generally found a sta- definedasriver/water,mountains,andothers(Table4).Thenext
tistically significant price–distance relationship regarding the stage involved transforming the land-use map to grid data of
disamenity effects of these sites (Michaels and Smith, 1990). 10mresolutionandassigningdifferentimpedancevaluestothe
However, usually not only the distance but also the density of gridcells(namedcost-distance)todepictthetimecosttotravel
theindustrysurroundingapropertywillaffectthehousingprice. 10km along different land-use types by public bus or on foot
Consequently,aproxyvariablefortheenvironmentaldisamenity (Table 4). For the road network, the cost-distance was defined
ofindustrialpollutionwasdevelopedthathasananalogywith as the time (min) it would take a public bus to travel between
the education environment, except that the window size was a each property and the nearest public green spaces or central
1000mradius(Table1). businessdistrict.However,thewalkingnetworkdiffersfromthe
Locationsectordifferencesalsoappeartohaveanimpacton roadnetwork,becausetherearepathsalongwhichonlywalkers
residentialprices.Thestudyareawasdividedintofoursectors can travel or roads unsuitable for pedestrians. Thus, the walk-
based on their different development histories, land-use com- ingnetworkcost-distancewasestimatedbasedonthepossible
positions,andurbanfunctions,asdesignatedintheJinanCity walkingspeed.Thedistance/costweightedsamplingtoolinthe
masterplanning2004–2020(JinanPlanningBureau,2004).A GIS spatial analysis module was then utilized to calculate the
F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252 247
Table4 state,theintegrationofeconomicsandecologyishamperedby
Definitionsoftravelspeedandcost-distance(min/10km)alongdifferentland- thefailuretoincludespaceineconomictheoriesandmodels.The
usetypes
joiningoflandscapeecologyandeconomicswouldcontributeto
Land-usetype Travelspeed Cost-distance the promotion of improved modeling approaches and relevant
(km/h) (min) techniques in the field of ecological economics. In this study,
Roadnetwork Arterialroad 30 20 in an attempt to capture the impact of green space and land-
Secondarytruckroad 25 24 usespatialpatternonthesurroundingproperties,sixlandscape
Branchroad 20 30 spatial indices (percentage of urban green space-PLGR, patch
Path 15 40
richness of urban green space-PRGR, number of green space
Walkingnetwork River/water 0.75 800 patches-NPGR,aggregationofurbangreenspace-AIGR,patch
Mountains 1.2 500 richness of land-use-PRLA, and number of patches of land-
Others 4 150
use-NPLA)wereaddedtothehedonicpricingmodel.Allwere
selectedaccordingtotheirpresumedeconomicrelevance.Itwas
timeitwouldtakeapublicbusorawalkertogettothenearest hopedthatthespatiallandscapemetrics,whichwerecharacter-
amenities(greenspacesorthecentralbusinessdistrict). ized by differing richness, density, or aggregation, might add
explanatory power to the hedonic function, and consequently
3.3.2. Landscapemetrics enrichthemodel.Tocapturetheselandscapemetricsinalocal
Beyond the preceding traditional variables used to explain area,a“movingwindow”analysiswasconductedsupportedby
propertyvalue,itwasassumedthatthespatialpattern(composi- FRAGSTATS(version3.3),withwindowsizesof300and500m
tionandconfiguration)ofsurroundinggreenspaceandland-uses radius(McGarigaletal.,2002).Thus,thecompositionindices
wouldalsoaffectthehousingsaleprice.Inthepasttwodecades, (percentage of green spaces, patch richness of green spaces,
landscape ecology has become one of the most rapidly devel- numberofpatchesofgreenspaces,patchrichnessofland-use,
opingecologicalfieldsworldwide.Onereasonisthatitenables andnumberofpatchesofland-use;seeTable1fordescriptions)
spatial pattern analysis through its extensive use of landscape werespatiallyreferencedwhichallowedtheirspatialvariability
metrics(O’Neilletal.,1988;Gustafson,1998;LiandWu,2004). tobevisualized(Fig.3)(KongandNakagoshi,inpress).There-
The spatial pattern quantified by landscape indices is related after, a sampling tool in the GIS spatial analysis module was
not only to ecological functions but can also reflect the socio- usedtocapturequantitativeinformationatawindow-sizescale
economicfunctionsofthelandscape.AsCostanzaetal.(1997) aroundtheproperty.
Fig.3. Greenspacerichnessaroundthepropertywith500mradiuswindowsizewasquantifiedbypatchrichnessmetricsandobtainedbythe“movingwindow”
method.
248 F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252
Landscapepatternsarescale-dependent(Wuetal.,2002),as of green space patches (NPGR) are both positive. This result
are the amenity impacts of urban green spaces or other land- is somewhat surprising because aggregation of green spaces
use types on housing prices (Geoghegan et al., 1997; Mitsch (AIGR) presents the degree of aggregation, whereas number
and Gosselink, 2000). However, most landscape indices are of green space patches (NPGR) indicates the fragmentation
sensitive to changes in scale (resolution or extent) (Turner et of public green spaces. This result can be attributed to the
al., 1989; Wu et al., 2000; Li and Wu, 2004). Consequently, scale chosen for this study. The 500m radius window around
in this paper all indices (with one exception) were calculated the property is sufficiently small such that all public green
fromafixed500mradiuswindowtoavoidscaleeffectsinthe spaces,whetheraggregatedorfragmented,servetoincreasethe
analysis. The sole exception is the percentage of urban green valueofneighboringproperties.Thusitcanbeconcludedthat,
spaces (PLGR), which was captured in a 300m radius win- given the shortage of and resultant pressure on land in urban
dow. The percentage of urban green spaces includes not only areas, the insertion of green spaces (even if fragmented) and
thethreetypesofpublicgreenspacebutalsotheattachedgreen anincreaseinthepatchnumberwillproducepositiveamenity
spaces,suchasresidentialgreenspace,roadsidegreenspaces, valuesandbefavoredbypeople,indicatingthatthisisaprefer-
andripariangreenspace(KongandNakagoshi,inpress).With able approach for increasing green space in urban settings. In
a 300m radius window surrounding a property, percentage of additiontoaggregationofgreenspaces(AIGR),percentageof
urbangreenspaces(PLGR)couldindicatethegreeningratioin greenspaces(PLGR)exhibitedapowerfulpositiveinfluenceon
thehousinggroup.Severaltrialsweremadetotesttheimpactof housingprices,andthevarioussize–distanceindicesalsohada
changesinwindowsizeontheestimatedresults,anditproved positiveimpactonhousesaleprices.Ingeneral,thedecreased
that the 500m radius window (300m for percentage of urban accessibilityofpublicgreenspacesandcentralbusinessdistrict
greenspaces)wasappropriatetocapturetheamenityeffect,as all have a relatively weak, but significant negative impact on
expected. the property values. The positive price impact of green space
Theland-usepatternisamajorcontributortoqualityoflife types (TYPEGR) indicates that various types of public green
andthusinfluencestheneighborhoodhousingprice(Geoghegan space with varying attributes differ in their impact on housing
etal.,1997;AcharyaandBennett,2001).Thenumberofpatches price. The spatial landscape metrics for land-use, patch rich-
and richness, indicative of the fragmentation and diversity of ness of land-use (PRLA) and number of patches of land-use
land-useatacertainscale(500mradiuswindow),wereadded (NPLA), both demonstrate a negative impact on neighboring
tothehedonicpricingmodel.Theland-usedatasetusedinthe houseprices.Theenvironmentaldisamenity(EDM)appearsto
studyareawasreclassifiedintoeighttypes(residential,industry, haveaweakbutstatisticallysignificantrelationshipwithhouse
publicfacility,trafficandroad,greenspace,agriculture,water, prices,whichconfirmsthatnearbyindustrialpollutantsdepress
and others) from the standard for classification of urban land- houseprices.
useinChina(MinistryofConstruction,PRChina,1990),based Beforemakingadetailinterpretationoftheestimatedcoef-
onthefunctions,land-usecover,andownership.Theimpactof ficients in Table 2, the model was improved by eliminat-
land-usepatternsonhousingpriceisdeterminedbyhowpeople ing collinear variables and non-significant variables through
perceiveaparticularlocation,aswellasbythescalechosenfor a stepwise approach. The results of this estimation show that
theanalysis(Geogheganetal.,1997).Higherdiversityandfrag- size–distance index of scenery forest (S-DSCEN), patch rich-
mentationoflandproximatetoresidentialhousingmayproduce nessofland-use(PRLA),percentageofgreenspaces(PLGR),
negativeeffectsonpropertyvaluesbyintroducingnoiseorlight greenspacetypes(TYPEGR),andeducationenvironment(EE)
pollutionandnegativevisualimpacts.Ontheotherhand,these are all statistically significant at the 5% level (Table 3). The
mayhaveapositiveimpactbyprovidingconvenience,suchas natural logarithm of the selling price yielded a slightly better
closeproximitytoshopping,work,andrecreation.Conversely, functionalformforthedependentvariable,whichwasslightly
a simple district marked mainly by residential uses may result improved for two variables, that is, number of green space
inaquietandcomfortableenvironment.Thus,duetothecon- patches (NPGR) and patch richness of green spaces (PRGR),
flictingeffects,thesign(+or−)ofpatchrichnessofland-use enteringthemodel,butontheotherhand,thenaturallogarithm
(PRLA) and number of patches of land-use (NPLA) remains of the selling price prevented the variables: green space types
ambiguous (indicated with the sign “?” in Table 1) before the (TYPEGR)andsize–distanceindexofplaza(S-DPLAZA),from
hedonicestimationisperformed. beingincludedinthemodel.Thesemi-logformisexpressedas
relativeratherthanabsoluteimplicitprices;consequently,itcan
4. Results beusedtogiveaflexibleinterpretationoftheestimatedcoeffi-
cients(DesRosiersetal.,2002).Thefindingscanbesummarized
The results of the parameter estimates are presented in asfollows:
Table 2. The estimated coefficients generated using both the
linearandsemi-logmodelsexhibitedsignsinaccordancewith (1) Theconstantispositiveduetothebasevalueoftheproper-
expectations. They provide a clear indication that the features ties,regardlessofadditionalfeatures.However,comparison
ofurbangreenspacedoexertasignificantimpactonproperty ofthevalueoftheconstant(transformedtoChinesemone-
prices. taryunit-RMB3539.4)withtheaveragehousingpriceofthe
A particularly noteworthy result of the analysis is that the 124samples(RMB3465.3),indicatesthatthechosenvari-
impact of aggregation of green spaces (AIGR) and number ablesinthehedonicpricingmodelyieldedgoodexplanatory
F.Kongetal./LandscapeandUrbanPlanning79(2007)240–252 249
power and also reflect the preferences of homeowners in (8) Finally, the positive price impact of the education envi-
JinanCity; ronment suggests people tend to purchase houses in good
(2) The size–distance index of scenery forest (S-DSCEN) university districts. The good education environment is a
revealed that proximity to scenery forest had a pos- type of substitute for urban green space amenities, imply-
itive amenity impact, raising the house value by 5% ing that homeowners may trade off amenity to get a good
for each percentage increase in the index. However, the educationenvironment.Onaverage,a1%increaseinedu-
othertwosize–distanceindices(size–distanceofparkand cationenvironmentisassociatedwithaconsistent1.9%rise
size–distanceofplaza,S-DPARKandS-DPLAZA,respec- inhouseprice(persquaremeter).
tively) did not show significance even at the 10% level.
The higher attractiveness of scenery forest is not without 5. Discussionandconclusions
its drawbacks, as 90.5% of scenery forests are located in
thesouthandsoutheastofJinanCity,whichisthesourceof This paper reports a first attempt to apply hedonic pricing
thespringssuchasBaotuQuanspring,andnownegatively models to evaluation of the amenity of urban green spaces in
impactedbyencroachmentanddevelopment; Jinan City, China, and highlights the fact that hedonic pricing
(3) Thepatchrichnessofland-usevariable(PRLA)ina500m modelswouldbeimprovedbyembodyingspatialcharacteristic
radiuswasfoundtohaveapowerfulandnegativeinfluence variables captured by GIS and landscape metrics; it thus con-
on housing prices, indicating that people prefer to live in tributes to the further joining of economics and ecology, and
a quiet and comfortable environment, with homogeneous promotesdevelopmentofecologicaleconomics.
land-useintheimmediatevicinityoftheirhouses;
(4) Properties with a higher percentage of green space area 5.1. Specificationandcaptureofcharacteristicvariablesin
(PLGR) within a 300m radius have higher house values, thehedonicpricingmodel
with each percentage point of green space adding about
2.1%tothepricepersquaremeter; Thisstudyinvestigatesonlythemeanvaluepersquaremeter
(5) Accessibility to scenery forest (ACSCEN) was not sig- of a housing cluster and focuses solely on the neighborhood
nificant at the 10% level, whereas accessibility to plazas environment,especiallyspatialconcerns,whereashousestruc-
(ACPLAZA)andparks(ACPARK)weresignificantat5% turalcharacteristicsandtimeseriesproblemswerebothavoided.
level.Accessibilitytoplazasdisplayedslightlyhighersig- The omission of variables relating to structural characteristics
nificance than accessibility to parks. A 1% improvement of housing was rarely found in previous research that applied
inaccessibilitytoplazasandparkswillleadtoanincrease hedonic models. Thereby, it is possible that the results of this
inpricepersquaremeterof1.8and1.6%,respectively.In analysismightsufferfromsampleselectionbias.However,the
Jinan City, 93.5% of plazas and 70.2% of parks, but only resultswereconsistentwithexpectations,suggestingthatsam-
0.9%ofsceneryforestareasarelocatedwithinthebuilt-up pleselectionbiasisnotanissue.
area;mostofthelatterarelocatedinthesouthandsoutheast Thecreationoftheinteractiontermseducationenvironment
urban peripheral area (Fig. 1). The convenient transporta- and environmental disamenity and the size–distance indices
tionwithinthebuilt-upareamakesiteasiertoaccessparks allowed refined specification variables to be used in the hedo-
andplazasthantoaccesssceneryforests.But,comparison nicpricingmodel,whichdifferentiatesthisfromsomeprevious
ofparksandplazasrevealedtheydifferedmainlybecauseof studies.Asisusualwithhedonicpricingmodels,manyfactors
thesurroundingland-usetype.Theplazadesignationdefines werelistedthatmayinfluencepropertyvalues,anditisconse-
green spaces of an open type, most of which are located quentlyoftendifficulttosortouttheeffectsofspecificvariables
nearmainroads(Fig.1),whereasgreenspacesdesignated withgreataccuracy.
asparksarealwayssurroundedbywallsorwater,whichwill Theintegrationofeconomyandlandscapeecologyisawor-
form a serious barrier in terms of accessibility and reduce thygoalthatwouldbenefitgreatlyfromsystematicanalysisthat
theiramenityeffect; includesspatialscale,ratherthanusingonlyaggregatedstatis-
(6) The significant and positive impact of the location sector tical data (Vermaat et al., 2005). In this paper, six landscape
(DLOC)provedthatpeoplewouldprefertoliveinthesouth metrics(percentageofurbangreenspace-PLGR,patchrichness
andeastratherthanthewestandnorth; of urban green space-PRGR, number of green space patches-
(7) The positive significant impact of number of green space NPGR,aggregationofurbangreenspace-AIGR,patchrichness
patches(NPGR)furtherconfirmedthatincreasingthepatch ofland-use-PRLA,andnumberofpatchesofland-use-NPLA)
numberofpublicgreenspacesraisesthehousevalue.How- wereaddedtothehedonicpricingmodel,whichallowedamuch
ever, the negative sign of patch richness of green space more complete explanation of urban green spaces and land-
(PRGR)indicatesthatincreasingthistypeofpublicgreen use. Moreover, the inclusion of the “moving window” method
spacewithina500mradiusaroundapropertywilldepress (supported by FRAGSTATS) allowed the composition indices
thehouseprice.Greaterrichnessofurbangreenspacetypes (percentage of green spaces, patch richness of green spaces,
willbringmoreopportunityforrecreation,butatthesame numberofpatchesofgreenspaces,patchrichnessofland-use,
timewillhavenegativeeffectsontheresidentslivingnearby, and number of patches of land-use) to be spatially referenced,
suchasnoise,andneonlightsatnight.Therichnesseffect and allowed their spatial variability to be visualized. Clearly,
mayalsoberelatedtotheresearchscale; thedevelopmentofGIShasbeenamajordrivingforcehere,for