Key Factors Affecting the Price of Airbnb Listings

13 Pages·2017·9.4 MB·English

sustainability
Article
Key Factors Affecting the Price of Airbnb Listings:
A Geographically Weighted Approach
ZhihuaZhang1,RachelJ.C.Chen2,*,LeeD.Han3,*andLuYang1
1 DepartmentofCivilandEnvironmentalEngineering,TheUniversityofTennessee,
Knoxville,TN37996-2313,USA;[email protected](Z.Z.);[email protected](L.Y.)
2 CenterforSustainableBusinessandDevelopment,TheUniversityofTennessee,
311ConferenceCenterBuilding,Knoxville,TN37996-4134,USA
3 DepartmentofCivilandEnvironmentalEngineering,319JohnD.TickleBuilding,
TheUniversityofTennessee,Knoxville,TN37996-2313,USA
* Correspondence:[email protected](R.J.C.C.);[email protected](L.D.H.);
Tel.:[phone](R.J.C.C.);[phone](L.D.H.)
Received:7August2017;Accepted:12September2017;Published:14September2017
Abstract: Airbnbhasbeenincreasinglygainingpopularitysince2008duetoitslowpricesanddirect
interactions with the local community. This paper employed a general linear model (GLM) and
a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb
listingpricesusingdatasetsof794samplesofAirbnblistingsofbusinessunitsinMetroNashville,
Tennessee. The results showed that the GWR model performs better than the GLM in terms of
accuracyandaffectedvariableselections. Statisticallysignificantdifferencesvariedacrossregions
inMetroNashville. Thecoefficientsillustrateadecreasingtrendwhilethereisanincreaseinthe
distancefromthelistedunitstotheconventioncenter, whichindicatesthatAirbnblistingprices
aremoresensitivetothedistancefromtheconventioncenterinthecentralareathaninotherareas.
ThesefindingscanalsoprovideimplicationsforstakeholderssuchasAirbnbhoststogainabetter
understandingofthemarketsituationandformulateasuitablepricingstrategy.
Keywords: Airbnb;sharingeconomy;price;GWR;factors
1. Introduction
Inrecentyears,thesharingeconomyhasgainedpopularityinmanybusinesssectors[1]suchas
transportation(e.g.,Uber,Lyft)andaccommodation(e.g.,Airbnb,HomeAway). Thesharingeconomy
can be defined as “peer-to-peer-based activity of obtaining, giving, or sharing the access to goods
andservices,coordinatedthroughcommunity-basedonlineservices”[2]. Inthepeer-to-peer(P2P)
accommodationindustry,Airbnbisaprominentexample,enablingownerstooffertheirunoccupied
housesorroomsforshort-termrental[3]. Foundedin2008,Airbnbhasbeenincreasingandgrowing
dramaticallyandhasservedmorethan150milliongueststhroughover3millionlistingsinmorethan
190countriesinlessthanadecade[4]. ThekeystothesuccessofAirbnbincludelowcostsanddirect
interactionswithlocalcommunitythatprovidesguestswithuniquestayexperiences[5].
Priceisoftenconsideredasoneofthekeyfactorsthatimpactsclients’selectionoflodgings. Past
studieshaveinvestigatednumerousfactorsaffectinghotelprice,identifyingasetofpricedeterminants
suchasbrandname,starrating,location,hotelage,numberofrooms,amenities,andsoforth[6–9].
Airbnbisarecentlodgingconceptthatdiffersfromtraditionalhotelsintermsofbookingsystems,
facilities,websitedesigns,andcustomerservice. BecauseofthegrowingdemandforAirbnb,several
studieshavefocusedonidentifyingpricedeterminantsofAirbnblistings[10,11]. Recently, global
regression models (GLM) such as OLS regression and quantile regression are the most commonly
adoptedmethodstoinvestigatefactorsaffectingAirbnblistingpricesincurrentstudies.
Sustainability2017,9,1635;doi:10.3390/su9091635 www.mdpi.com/journal/sustainability
Sustainability2017,9,1635 2of13
Duetotheirspatialautocorrelation,theglobalregressionmodelsexploreonlytherelationships
betweenanindependentvariableandasetofdependentvariables,maskingspatialheterogeneityinthe
relations[7,12]byignoringtheexistenceoflocalvariations. Tobetterunderstandthedeterminantsof
Airbnblistingspricesandhowtherelationshipscanbeaffectedbylocationdetails,thegeographically
weightedregression(GWR)modelisimplementedtoinvestigatetheeffectsoflocalfactorsonAirbnb
listingprices. TheGWRmodelallowsestimatedparameterstovaryacrossregionstoaccommodate
potential spatial dependencies [12]. Therefore, this study aims to adopt a GWR model to explore
thespatialvariationintherelationshipbetweenAirbnblistingpricesandtheirpricedeterminants,
andtocomparethemodelperformanceandestimationresultsbetweentheGLMandGWRmodelin
ordertocontributetotheliterature. Therestofthispaperisorganizedasfollows: Section2discusses
priorresearch,andSection3presentsthedataandmethodsusedinthispaper;next,inSection4we
presentthemainestimatedmodelresultsandtheirimplications,andconclusionsandsuggestionsare
presentedinSection5.
2. LiteratureReview
2.1. Airbnb
SincetheadventofAirbnbin2008,ithasgrownexponentially. ThisAirbnbphenomenonhas
attractedscholarlyattention,andmanystudieshaveexploredavarietyoftopicsrelatedtoAirbnb.
NumerousstudieshavefocusedonAirbnb’sadvantages,challenges[5,13,14],andlegalissues[15,16].
For example, Guttentag [5] analyzed the Airbnb issues through the lens of disruptive innovation
theory,andpointedoutthatthemainstrengthofAirbnbareitsrelativelylowcostandvariousbenefits
associated with staying in a local residence, while the potential threats to Airbnb’s future growth
arewidespreadillegality,potentialcrackdownfromlandlordsandcondoboards,andsafetyissues.
EdelmanandGeradin[16]reportedthatnewregulatoryframeworksshouldbeestablishedtoallow
software platforms such as Uber and Airbnb to operatelegally so that both service providers and
consumerscanenjoytheconvenienceandefficiencytheseplatformsprovide.
Several past studies have focused on Airbnb’s impacts on the hotel industry, including hotel
revenue[17,18]andtourismindustryemployment[3]. Forinstance,Zervas,ProserpioandByers[17]
found that the entry of Airbnb into the Texas market had a quantifiable negative impact on local
hotelrevenues,showingthata1%increaseinAirbnblistingsinTexasresultedina0.05%decrease
inquarterlyhotelrevenues. Usingthedataofatotalof657distinctlistingsforIdaho, USA,Fang,
YeandLaw[3]investigatedtheeffectofthesharingeconomy(Airbnb)onthetourismindustry,and
found that the sharing economy would generate new job positions and benefit the entire tourism
industry,butthemarginaleffectdecreasesasthesizeofthesharingeconomyincreases. Paststudies
haveexaminedtheimpactofAirbnbonhousingcost[15,19]. Barron,KungandProserpio[19]studied
theimpactofAirbnbbusinessesonhousepricesandrentalrates. Theirresultsshowedthata0.39%
increaseinrentalratesanda0.64%increaseinhousepricesresultedina10%businessincreasein
Airbnblistings.
ScholarsexploredhowguestswouldtrustandrateAirbnb,andhowanAirbnblistingachievesa
highreputation. TheirfindingsconcludedthatAirbnblisting’sreputationorguests’trustinAirbnb
listingcanbeaffectedby“Superhost”status[20],geolocatedAirbnbrentalimages[21],Airbnbhosts’
personalphotos[22],perceivedauthenticity,electronicword-of-mouth,pricesensitivity[23],and/or
perceivedbrandpersonality[24]. Severalstudieshaveinvestigatedhowcustomers’experienceand
servicequalityimpactedAirbnb’sbusinessreputation[25–29]thatpavedthewayforthesuccessof
Airbnb. Tohaveanin-depthunderstandingofcustomerexperienceswithAirbnb,Brochado,Troilo,
andShah[25]summarized1776reviewsfrom24Airbnblistingsacrossthreecountriesusingcontent
analysesandidentifiedeightthemesthatshowedaconvergenttrendofconsumerexperiencetowards
Airbnblistingusesandlodgingservices. Mody,SuessandLehto[26]developedamodeltoidentify
factorsaffectingcustomers’experienceinthehospitalityindustryandtestedtheirmodelinshared
Sustainability2017,9,1635 3of13
economysettings. Priporasetal.[28]investigatedtherelationshipsamongservicequality,satisfaction,
andconsumers’loyaltytowardsAirbnb,indicatingthatservicequalitywaspositivelycorrelatedwith
consumers’satisfactionandloyalty.
2.2. AirbnbListingPrice
Priceisavitaltopicinthehospitalityindustry[6],foraproperpricingstrategycanleadacompany
or enterprise to the next level of financial success [8]. Price plays an important role in the shared
economyinthehospitalityindustriessuchasAirbnbbecausepriceimpactsguests’lodgingselection
andalsosignificantlyimpactshosts’profits[13]. Thus,knowingfactorsaffectingthepriceisofgreat
value,andcanhelphostsaskareasonablepricesothatboththehostsandguestscanbenefitfrom
thesharingeconomy. Limitedstudieshavebeenconductedtoidentifywhatfactorsaffecttheprice
of Airbnb listings (Table 1). For example, Ikkala and Lampinen [10] reported that the reputation
of Airbnb listings is correlated with their price. Using Airbnb listings information from 33 cities,
WangandNicolau[11]identifiedfiveprice-determinantcategoriesofAirbnblistings,includinghost
attributes,siteandpropertyattributes,amenitiesandservices,rentalrules,andonlinereviewratings.
Lietal.[30]provedtheeffectsofdistancetothenearestlandmark,theimpactoffacility,andnearest
landmarkpopularityonAirbnblistingprices. Ahighratingscorecanbeconvertedtoapriceinthe
hospitalityindustry,andoneempiricalstudyreportedthatratingvisibilityleadstoanincreaseinprice
by2.69€[31].
Table1.OverviewofreviewedstudiesonAirbnb’spricedeterminants.
Literature Determinants Effects Details
Ikkalaand Qualitativestudy,11in-depth
Reputation(positivereviews) Positive
Lampinen[10] semi-structuredinterviewswithAirbnbhosts
Ratingvisibilities(morethan Ratingvisibilityleadstoanincreaseinprice
GuttandHerrmann[31] threereviews) Positive by2.69€
Multi-ScaleAffinityPropagation(MSAP)
Facility Positive methodtocluster;LinearRegressionmodel
Lietal.[30]
Distancetonearestlandmarks Positive withNormalNoise(LRNN)topredictthe
reasonableprice
Hostattributes
Siteandpropertyattributes OLSregressionandquantileregression
WangandNicolau[11] Amenitiesandservices Mixed DataofAirbnblistinginformationform
Rentalrules 33cities
Onlinereviewratings
Though few studies have been conducted on Airbnb listings prices, a series of studies have
exploredthepricedeterminantsofhotels. Servalimportantfactorsaffectingthehotelroompricehave
beenidentifiedsuchaslocation[6,7],hotelcategory[32,33],customerratings[34,35],hotelamenities
andservices[36,37],marketaccessibility[38],andproximityofcompetitors[33]. Previousempirical
studiesoftenemployedaglobalregressionmodel,consideringonlytherelationshipbetweenhotel
roompriceandexplanatoryvariablesonagloballevel. GWRhasbeenwidelyusedinmanyfields,
andhasproventobeausefultooltocapturethespatialvariationsinrelations[7,39,40].
Insummary,thereareonlyafewstudiesonpricedeterminantsofAirbnblistings,whichneed
to be extended. The purpose of this study is to identify the factors affecting Airbnb listing prices.
ConsideringthattherelationshipbetweenAirbnblistingpricesandtheirfactorscanvaryfromlocation
tolocation,theGWRmethodisselectedtoinvestigatethefactorsaffectingAirbnblistingpriceslocally
ratherthanglobally.
Sustainability 2017, 9, 1635 4 of 13
3. Data and Method
3.1. Study Area and Data
In this paper, Metro Nashville (Davidson County), Tennessee, was chosen as the study site. The
government of the City of Nashville and Davidson County merged in 1963. The county and city
government is known as the “Metropolitan Government of Nashville and Davidson County”, or
“Metro Nashville” for short. Metro Nashville was divided into 35 districts under the legislative
authority of the Metropolitan Government of Nashville and Davidson County, Metropolitan Council.
The shape format layer data of Metro Nashville, including road networks, council districts, and
boundaries, were obtained from the U.S. Census Bureau, and the Planning Department of
Metropolitan, Government of Nashville, and Davidson County.
SustainTahbieli tyd2a0t1a7 ,o9f, 1A63i5rbnb listings for Metro Nashville were collected and analyzed. The data 4woefr1e3
obtained from the website [4], which provides Airbnb listing information drawn from Airbnb.com.
Considering that some Airbnb listings did not have any transactions or were just listed but not well
3. DataandMethod
managed, we selected the listings with a super host status, namely, experienced hosts who are
passionate about making guests’ trips memorable. Finally, 974 samples of Airbnb listings from Metro
3.1. StudyAreaandData
Nashville were retrieved. Figure 1 shows the spatial distribution of Airbnb listings in Metro
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Figure1.SpatialdistributionofAirbnblistingsinMetroNashville.
Figure 1. Spatial distribution of Airbnb listings in Metro Nashville.
Table2depictsthevariablesselectedaspossiblepredictorsofAirbnblistingpricesbasedonthe
reviewofliteratureabove. LocationisacommonlyacceptedfactorproventoaffectAirbnblisting
prices. Locationcanberepresentedasdistancefromthecitycenter,highways,orlocalattractions[7].
SinceMetroNashvilleisnotahugecityandthemajorattractionsareinthedowntownarea,H-Distance
andC-DistancewereadoptedtoinvestigatetheeffectoflocationonAirbnblistingpriceinthisstudy.
ReviewsandratingsarealsoimportantfactorsinAirbnblistingprices. Bothhigherratingsandmore
numerousreviewscanleadtoahigherprice[10,31]. Thus,thenumberofreviews(Reviews)andthe
ratings(Ratings)ofAirbnblistingsarecategorizedasexplanatoryvariablesinthisstudy. “Age”can
partlyaccountforhotelpricingbehavior[6]. Inthisstudy,the“Age”variable,namely,thedurationof
timesincethelistingwasestablished,wasselectedasafactoraffectingAirbnblistingprices. Given
thatAirbnbisanewlyfoundedcompany,themonthisdesirableastheunitofagetobetterperceive
Sustainability2017,9,1635 5of13
theinfluenceofageonprice. Finally,thedependentvariable(Price)usedinthisstudyislistprices,
whichmaybeasuitablesubstituteduetolackofaccesstotheactualtransactionprice. Meanwhile,
thevariablepriceistheratiooflistedpricepernightoverthemaximumguests.
Table2.Variablesandbriefdescriptions.
Variable Description
Price Theratiooflistedpricepernightoverthemaximumguests(U.S.dollars).
Reviews NumberofreviewsofAirbnblistings.
H-Distance TheEuclideandistancetonearesthighway(km).
C-Distance TheEuclideandistancetotheNashvilleConventionCenter(km).
Age Monthssincethelistingwasestablished.
Rating TheoverallratingscoreofAirbnblistings.
3.2. GeneralLinearModel(GLM)
InaGLMmodel,thedependentvariableisestimatedwithasetofdependentvariablesglobally.
Themodelspecificationisexpressedas:
p
∑
y = β + β X +ε (1)
i 0 k ik i
k=1
where y denotes the ith observation of dependent variable, β = (β , β , β , ...... β ) are the
i 0 1 2 m
coefficientsofthepredictorsestimatedinthemodel,andε istherandomerror.
i
3.3. GeographicallyWeightedRegression(GWR)
IntheGWRmodel,thedependentvariableispredictedbyaseriesofexplanatoryvariablesin
whichtheestimatedparameterscanvaryspatially. ThebasicGWRmodelisshownbelow[40].
m
∑
y = β (u,v )+ β (u,v )X + ε (2)
i 0 i i k i i ik i
k=1
wherey denotesthedependentvariableatlocationi,X denotesthevalueofkthexplanatoryvariable
i ik
atlocationi, and β = (β , β , β , ...... β )arethecoefficientsofthepredictorsestimatedinthe
0 1 2 m
model. (u,v )denotesthecoordinatesoflocationi,andε istherandomerroratlocationi.
i i i
Theβatlocationiisestimatedusingweightedleastsquare,andcanbeexpressedas:
(cid:104) (cid:105)−1
βˆ(i) = XTW X XTW Y (3)
(i) (i)
whereW denotesannbyndiagonalspatialweightedmatrix. Xdenotestheexplanatoryvariables
(i)
datamatrix. Ydenotesthevectorofdependentvariable.
     
Wi1 0 ··· 0 1 x11 ··· xm1 y1
 0 Wi2 ··· 0   1 x12 ··· xm2   y2 
W(i) =  ... ... ... ...  X= ... ... ... ...  Y= ...  (4)
0 0 ··· Win 1 x1n ... xmn yn
ForspatialweightedmatrixW ,severalmethodsarecompiled. TheGaussianandbi-square
(i)
functionsarecommonlyadoptedtocalculatetheweightedmatrix. Inthisstudy,theGaussianfunction
isemployed,whichcanbeexpressedas:
(cid:32) (cid:18)d (cid:19)2(cid:33)
ij
W(i) =exp −0.5 (5)
j
b
Sustainability2017,9,1635 6of13
where d denotes the distance between location i and observed point j, b denotes the distance
ij
bandwidth. Agivendistanceorafixednumberofneighborhoodscanbeusedtodefinethebandwidth.
TheselectionofoptimalbandwidthcanbedeterminedbyselectingthemodelwithlowestAkaike
InformationCriterion(AIC)score;themodelwiththelowestAICistheoptimalmodel[39].
4. Results
4.1. GLMEstimation
Thispaperinvestigatedtherelationshipsbetweenthedependentvariable(Airbnblistingprice)
andtheindependentvariables(Reviews,Age,H-Distance,C-Distance,Ratings)usingtheGLM.Note
thatthisstudydidnotfocusoncomparingtheAirbnblistingswiththetypesoflistingpropertiessuch
asawholehouse,asharedroom,orasingleroom. Logically,thepriceofalistedwholehouseisalways
higherthanalistedsharedroomduetoitshighercapacityformorecustomers. Therefore,priceper
maximumnumberofguestswasutilizedasadependentvariableinthisstudy.Variousfactorsaffecting
theAirbnblistingpricehavebeenexploredandidentifiedintheliterature. Inthisstudy,wefocusedon
exploringspatialfactorsandexaminingrelationshipsbetweenidentifiedspatialfactors(forexample,
distancerelated)andprice. TheattributesofanAirbnbpropertyaffectlistingpriceandaretreated
asdummyvariablesthatcanbeeasilymodelledwithinalinearregressionmodel,butthosedummy
variablesmaynotbesuitableforinclusioninaGWRmodelduetopotentialmulticollinearityissues.
InaGWRmodel,weanalyzedtherelationshipbetweenpriceandlocationofeachAirbnblisting.
The price attribute of a specific Airbnb listing is treated as a dependent variable, not by using the
averagepriceofthosedistricts. The35districtsinMetroNashvillearenotcompletelyrelatedtothe
GWRmodellingprocedures;therefore,certaindistrictswereincludedselectivelyinthisstudy. The
resultsconcludedthattherelationshipbetweenpriceandratingisstatisticallysignificantindistricts5,
6,and19,whilethereisnosignificantrelationshipbetweenthespatialfactorandAirbnblistingsin
otherincludeddistricts. TheAirbnbconceptisstillatanearlystageinTennessee. Airbnb’slistings
inthestudiedcitypresentastrategybyofferingacomparativelylowerpricetoenticecustomersas
oneofAirbnb’sbusinessstrategiestopromoteitsservicesintheregion. ItisplausiblethatAirbnbin
Nashvilleisreceivinganaverageoraboveaverageratingduetoacomparativelowpriceofferedinthe
market,whichisappreciatedbythemajorityofcustomerswhostayedovernightinthatstudiedregion.
Table 3 shows the estimated results modeled by GLM. The Reviews and C-Distance are
significantlycorrelatedwithAirbnblistingprices,andthesetwovariablesaffectedAirbnblistingprices
negatively. Forexample,thecoefficientofReviewsis−0.051,whichmeansoneunitincreaseinthe
numberofreviewscanleadto5.1%decreaseinAirbnblistingprice. Also,thecoefficientofC-Distance
is −1.524, which means one unit increase in the distance to a convention center will decrease the
Airbnblistingpriceby152.4%. ThecoefficientofRatingis−0.073,andoneunitincreaseinRatingcan
decreasetheAirbnblistingpriceby7.3%. Insummary,thenumberofreviews,thereviewscores,and
thedistancetoconventioncenterarenegativelyrelatedtotheAirbnblistingprice,andtheinfluenceof
thedistancetotheconventioncenteronpriceisthebiggest. However,theAirbnbestablisheddateand
thedistancetothenearesthighwaydonotsignificantaffecttheAirbnblistingprice.
Table3.Summaryofestimationresultsofthegenerallinearmodel(GLM).
Variable Coefficient StandardError t-Value p-Value 95%ConfidenceLimits
Intercept 56.32561** 3.78106 14.90 <0.0001 48.90558 63.74563
H-Distance −0.36897 0.45514 −0.81 0.4178 −1.26215 0.52421
C-Distance −1.52435** 0.17030 −8.95 <0.0001 −1.85855 −1.19016
Reviews −0.05080** 0.01303 −3.90 0.0001 −0.07637 −0.02524
Age 0.02233 0.03803 0.59 0.5572 −0.05229 0.09695
Rating −0.07317* 0.03662 −2.00 0.0460 −0.14502 −0.00131
Notes:**=p<0.01;*=p<0.05
.
Sustainability 2017, 9, 1635 7 of 13
Sustainability2017,9,1635 7of13
4.2. GWR Estimation
4.2. GWREstimation
Summaries of parameter estimates in the GWR are shown in Table 4. The dataset used here is
the sSaumme masa rwieitsho tfhpea GraLmMe.t eCroensstiimdeartiensgi nthteh learGgWe sRamarpelesh soizwen, siinx Tdaebslceri4p.tiTvhee pdaaratamseetteursse edsthimeraeteiss
twheersea smuemamsawriizthedth ine tGhLe Mtab.Cleo, wnshidicehr ianrge tthhee mlaergane,s maminpimleusmiz,e l,oswixedr eqsucarriptitleiv, empeadriaamn, eutperpseer sqtuimarattieles,
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andmaximumvalues. Table4showsthattheestimatedparametercoefficientsvarysignificantly.
Table 4. Estimated geographically weighted regression (GWR) coefficients.
Table4.Estimatedgeographicallyweightedregression(GWR)coefficients.
Variable Mean Min First Quartile Median Third Quartile Max
Intercept 59.794 26.863 47.879 57.155 71.177 96.336
Variable Mean Min FirstQuartile Median ThirdQuartile Max
Reviews −0.063 −0.184 −0.128 −0.016 −0.003 0.021
IRntaetrincegp t −509..079964 −206..384653 −407..186779 −507..015515 7−10.1.07075 960.3.03564
ReAvgieew s −0.00.6046 3 −−00.1.11884 −−00.0.13298 −0.00.1031 6 −00..010638 0.00.23129
Rating −0.096 −0.345 −0.167 −0.051 −0.005 0.054
H-Distance 5.580 −1.079 2.292 4.964 7.425 18.403
Age 0.064 −0.118 −0.039 0.013 0.168 0.329
C-Distance −4.542 −16.479 −7.599 −2.536 −1.487 −0.330
H-Distance 5.580 −1.079 2.292 4.964 7.425 18.403
TCh-De iesftfaenccte of ex−pl4a.5n4a2tory v−a1r6i.a4b7l9es on Ai−rb7n.5b9 9listing pr−ic2e.s5 3a6re shown− in1. 4F8i7gures 2–6−. S0.i3g3n0ificant
spatial heterogeneity exists in the explanatory variables across the region, indicating that GWR may
be a Tbheetteefrf ecchtooifceex tpol ainnavteosrtyigvaateri atbhlee skoeyn Afaicrtbonrbs laisffteincgtinpgr ictehse aAreirsbhnobw lnisitninFgi gpurriecses2 –i6n.-dSeigpnthifi. cTahnet
sdpiasttaianlchee tteor othgee nceointyveenxitsiotsni ncetnheteerx apnladn tahtoer nyuvmarbiaebr leosf arcervoiseswtsh earree gnioeng,aitnivdeilcya trinelgattehda ttGo WthRe mAairybbneb
alisbteinttge rpcrhicoei,c ewthoilien vtheest digiasttaentchee tkoe tyhfea ncteoarrseastf fhecigtihnwgathy,e tAheir rbenvbielwist irnagtinpgrisc, easnidn -tdheep atghe. Tohf ethdei slitsatninceg
tcoanth beec eointhveern tpioonsitcievnetleyr oarn ndetghaetinvuelmy bceorrroeflaretevdie wwistha rAeinrbegnabt ilvisetliyngre plarticeeds.t otheAirbnblistingprice,
whileInth Feigduisrtea n2,c esitgontihfiecanneta nreesgtahtiivgeh wcoaryr,etlahteiornevs ibeewtwraeteinn gths,ea dnidsttahnecea gtoe othfet hceonlivsteinntgiocna ncebneteeri tahnedr
pAoisribtinvbe llyistoirngn epgraitciev eelxyiscto irnr emlaotsetd owf aitlhl rAegiribonnbs leixsctienpgt pforric tehse. small west central area (districts 18,24)
in MIentrFoi gNuarseh2v,isllieg.n Iitfi icsa unntdneergsattainvdeacbolrer etlhaattio tnhse bdeotwwneetonwthne adreisat aonfcfeertso mthuechco mnvoeren tcioonnvceenniteenrcaen idn
Aacircbenssbinligs ttirnagnspproicretaetxioisnt, ianttmraocstitoonfsa, lalnrdeg einotnesrteaxincempetnfot rfatchielitsimesa, lrlewsuelsttincge nintr aa lhairgehae(rd loisdtrgiicntsg 1p8r,i2c4e).
iInt cManet rboe Napasphliveidll eto. Itthies uAnirdbenrbst alinstdinabgl eprthicaet, tshpeecdiofiwcanlltyo,w thnea rceloasoerff ethrse mlisutcinhgm iso rteo ctohne vceonnivenencetioinn
accecnetsesri,n tghetr haingshpeorr ittast ipornic,ea.t tTrahcet icoonesf,fiacniedntesn itlelrutsatirnamtee an tdfeaccrieliatsieinsg,r terseunldti nwghiinlea thheigrhe eisr laond ignicnrgeapsrei cien.
Itthcea dnisbteanacpep flrieodmt othteh elisAteidrb unnbitlsis ttion gthpe rcicoen,vsepnetcioifinc acellnyt,etrh, ewchloicshe rinthdeiclaistetisn tghaist ttohet hAeircbonnbv elnisttiionng
cpernicteer ,ist hmeohrieg hseenrsititsivper itcoe .tTheh edcisoteafnficcei efnrotsmil ltuhset rcaotnevaedneticornea cseinngtetrr einn dthwe hcielnettrhael raeriesaa tnhainnc rinea ostehienr
tahreeadsi.s tancefromthelistedunitstotheconventioncenter,whichindicatesthattheAirbnblistingprice
ismoresensitivetothedistancefromtheconventioncenterinthecentralareathaninotherareas.

Figure2.Spatialdistributionofestimatedcoefficientsandtvaluesofthedistancetoconventioncenter.
Figure 2. Spatial distribution of estimated coefficients and t values of the distance to convention center.
FFiigguurree 33 sshhoowwss tthhee eeffffeeccttss ooff tthhee ddiissttaannccee ttoo tthhee nneeaarreesstt hhiigghhwwaayy oonn AAiirrbbnnbb lliissttiinngg pprriicceess.. TThhee
cceennttrraall MMeettrroo NNaasshhvviillllee ((ddiissttrriiccttss 55,,66,,1188,,1199)) sshhoowwss aa ssiiggnniifificcaanntt eeffffeecctt.. TThhee ddiissttaannccee iiss ppoossiittiivveellyy
aaffffeeccttiinngg tthhee pprriiccee;; tthhee ffaarrtthheerr tthhee ddiissttaannccee ttoo tthhee nneeaarreesstt hhiigghhwwaayy,, tthhee hhiigghheerr tthhee pprriiccee.. TThhee iinntteerrssttaattee
highways are away from the Nashville downtown, which means the listings away from the highway
Sustainability 2017, 9, 1635 8 of 13
Sustainability 2017, 9, 1635 8 of 13
Saurset aicnlaobsileitry 2t0o1 7d,o9,w16n3t5own in central Metro Nashville. Thus, it seems logical that the closer it 8iosf t1o3
downtown, the higher the Airbnb price will be listed.
are closer to downtown in central Metro Nashville. Thus, it seems logical that the closer it is to
In Figure 4, it clearly shows that the effects of the established date of a listing on Airbnb listing
hdiogwhwntaoywsna,r ethaew haigyhferor mtheth AeiNrbansbh vpirlileced wowilln btoew lisnt,ewd.h ichmeansthelistingsawayfromthehighway
prices are significant mainly in southern Metro Nashville (districts 16–18,25–27,30), and the effect on
are clIons Ferigtuored 4o,w itn ctolewarnlyi nshcoewntsr athlaMt tehtreo efNfeacsths voifl lteh.e Tehstuasb,liisthseede mdastelo ogfi caa llistthinatg tohne Acliorbsenrb iltisistintog
price is positive, indicating that the earlier the Airbnb listing is established, the higher the Airbnb
dporiwcenst oawren s,igthneifhiciagnhte mrtahienlAyi irnb nsbouptrhiecernw Millebtreo lNistaesdh.ville (districts 16–18,25–27,30), and the effect on
listing price. It seems that the older Airbnb listings tend to charge more partly because the lodging
priceI nisF pigousirteiv4e,, itincdleicaarltyinsgh othwast tthhea tetahrelieefrf ethctes Aofirtbhnebe slitsatbinligsh ies desdtaatbeliosfhaedli,s tthineg hoignhAerir tbhneb Aliisrtbinngb
places that have been listed through Airbnb with a longer period are more experienced, and those
plirsitcinesg aprericseig. nIti fisceaenmtsm thaiant lythien osloduetrh AerinrbMnbe tlriostNinagssh tveinllde t(od icshtraicrgtse 1m6–o1r8e, 2p5a–r2tl7y,3 b0e),caaundset htheee lfofedcgtionng
hosts are aware of why their guests are more likely to trust previous experiences in terms of safety
pprlaicceesis thpaots ihtiavvee, binedenic alitsitnegd tthhartotuhgehe Aarilribenrbth weiAthi rab nlobnlgisetri npgeriisoeds taarbe lmishoerde ,etxhpeerhiiegnhceerdt, haenAd itrhbonsbe
and service quality.
lhisotsitnsg aprer iacew.aIrtes eoefm wshtyh athtethire gouldesetrs Aarireb mnborleis ltiikneglsy tteon dtrutostc phraervgieomuso erxeppearrietlnycbese cianu tseermthse olof dsagfientgy
panladc essertvhiacteh qauvaelibtye.e nlistedthroughAirbnbwith alongerperiodaremoreexperienced,andthose
hostsareawareofwhytheirguestsaremorelikelytotrustpreviousexperiencesintermsofsafetyand

servicequality.
Figure 3. Spatial distribution of estimated coefficients and t values of the distance to the nearest highway.
Figure 3. Spatial distribution of estimated coefficients and t values of the distance to the
Fignuearere 3s.t Shpigahtiwala dyi.stribution of estimated coefficients and t values of the distance to the nearest highway.

Figure4.Spatialdistributionofestimatedcoefficientsandtvaluesofage.
Figure 4. Spatial distribution of estimated coefficients and t values of age.
InFigure5a,significantnegativecorrelationsbetweennumberofreviewsandpriceexistprimarily
Figure 4. Spatial distribution of estimated coefficients and t values of age.
inwestMetroNashville(districts18,20–25,34).Thenegativecorrelationsindicatethatthemorereviews
thelistinghas,thelowertheprice,whichisinconsistentwithpreviousstudies[11]. InMetroNashville,
gueststendtochooselowerpricelistings,andthenAirbnbhostswouldbemorelikelytolowertheir
priceinordertoattractmoreguests. ForMetroNashville,thereviewshavenotproventobecorrelated
totheprice.
Sustainability 2017, 9, 1635 9 of 13
Sustainability2017,9,1635 9of13
(a)
(a)
Figure5.SpatialdistributionofestimatedcoefficientsandtvaluesofReviews(a)andRating(b).
Figure 5. Spatial distribution of estimated coefficients and t values of Reviews (a) and Rating (b).
InFo FriFgiugruer e55ab, ,ssiiggnniiffiiccaanntt nneeggaattiivvee ccoorrrreellaattiioonnss bbeetwtweeeennr envuiemwbreart ionfg rsecvoireewssa nadndA irpbrnicbe liesxtiinstg
pprrimiceareilxyi sitn pwreimsta Mrileytroin Naasshmvailllle c(ednistrtrailctasr 1e8a,2o0f–2M5e,3t4ro). TNhaes hnveiglaletiv(de icsotrrircetlsat5i,o6n,1s9 i)n.diTchatee ntheagta tthivee
mcoorrree lraetvioienwsss uthgeg elisstttihnagt hthase, hthigeh leorwtheer trhaeti npgriscceo, rwe,htihche liosw inecrotnhseisptreincet ,wwithhi cphriesviinocuosn sstiustdeinets w[1i1th]. tIhne
Mpreetrvoio Nusasshtuvdillye,[ 1g0u,3e1st]s. Ttehnisd mtoa ychboeodseu elotwoethr eprpihcee nliosmtinegnso, nanthda tththene aAvierbrangbe hroastitns gwsoounldA ibreb nmboarree
lidkrealmy atoti cloawllyerm thoreeirp porsiicteiv ien tohradnetrh toos aetotrnacott hmeorrpel agtufoesrtms.s F[o1r7 ]M. Ientrtoe rNmassohfvliollwe,e trhpe rriecveiceowrrse hlaatveed ntoota
phriogvheenr tsoa tbisef accotriroenlartaetdin tgo, twhee pspriecceu. latethatitispartlybecauseguestsmighthavelowerexpectations
ofanFoAr iFrbignubreli s5tbin, sgigwniitfhicaanlot wnepgaritcive;e tchoernretlhaetiyoncas nbebteweeaesnil yresvaietiwsfi readtianngd scaorreems aonrde lAikierblynbto ligsitvinega
phriigceh reaxtiisntg p. Crimurarreinlytl yi,ns aat issfmacatlilo ncernattrinalg aoruetac oomf eMshetarvoe Nnoatsbheveilnler e(fldeiscttreidctisn 5t,h6e,1p9r).i cTehine tnheisgsattuivdey ,
caosrrtehleatoiountcso smuegsgoesftp trheavti othues shtiugdhieers ’thfien draintignsg tshcaotrhei, gthheer lpowriceer rtehseu pltrsicine, hwighhicehr sias tiinsfcaocntisoisnternatti nwgisth.
the previous study [10,31]. This may be due to the phenomenon that the average ratings on Airbnb
are dramatically more positive than those on other platforms [17]. In terms of lower price correlated
to a higher satisfaction rating, we speculate that it is partly because guests might have lower
Sustainability 2017, 9, 1635 10 of 13
expectations of an Airbnb listing with a low price; then they can be easily satisfied and are more likely
Sustainability2017,9,1635 10of13
to give a high rating. Currently, satisfaction rating outcomes have not been reflected in the price in
this study, as the outcomes of previous studies’ findings that higher price results in higher satisfaction
ratings.
4.3. ComparisonbetweenGWRModelandGLM
4.3. TChoemcpoamrispoanr biseotwnebeent wGWeeRn Mtheodeesl taimnda GteLdMre sultsoftheGWRmodelandtheGLMwasconducted.
Intheglobalmodel,somefactorsdidnotshowanysignificanteffectsonAirbnblistingprices,butdid
The comparison between the estimated results of the GWR model and the GLM was conducted.
significantlyrelatetothepriceinsomespecifiedareainGWRmodel,whichindicatesthattheGWR
In the global model, some factors did not show any significant effects on Airbnb listing prices, but
modelcanachievebetterperformance.
did significantly relate to the price in some specified area in GWR model, which indicates that the
Table 5 shows the comparison of the performance of the GWR model and the GLM. R2 and
GWR model can achieve better performance.
adjustedR2obtainedusingGWRweremuchhigherthanthatusingtheGLM;also,theAICrelatedto
Table 5 shows the comparison of the performance of the GWR model and the GLM. R2 and
theGWRmodelwerealsolower. TheseallsuggestconsiderableimprovementusingtheGWRmodel
adjusted R2 obtained using GWR were much higher than that using the GLM; also, the AIC related
comparedwiththeGLM.
to the GWR model were also lower. These all suggest considerable improvement using the GWR
Figure6showsthespatialvariationofthemodel’sexplanatorypower(LocalR2)producedby
model compared with the GLM.
GWRmodel. R2variedfrom0.086to0.376acrosstheregions,andthebiggerR2areprimarilylocated
incentFriagluarned 6n sohrothwesrn thMe estproatNiaal svhavriilalet.ioInt sohfo tuhled mbeondoetle’sd etxhpatlaanltahtoouryg hpothweeorv (eLroacllaRl 2Rw2) apsriomdpurcoevde bdy
bGyWtheR GmWodReml. oRd2 evla,rtiheedr efraormes 0ti.l0l8s6o mtoe 0l.o37ca6l aRc2rolossw tehret rheagnio0n.1s2, ,awndh itchhes bhiogugledr bRe2 eaxrep lporriemdatroiliym lporcoavteed
in central and northern Metro Nashville. It should be noted that although the overall R2 was
itsexplanatorypower.
improved by the GWR model, there are still some local R2 lower than 0.12, which should be explored
to improve its explanatory power.
Table5.ThecomparisonoftheperformanceoftheGWRmodelandtheGLM
Table 5. The comparison of the performance of the GWR model and the GLM
Performance GLMModel GWRModel
PerformancRe 2 GLM0 .M12odel 0.3G0WR Model
AdjustedR2 0.11 0.25
R2 0.12 0.30
AIC 8482.355540 8351.141298
Adjusted R2 0.11 0.25
Numberofparameters 6 6
ResAidIuCa lssumofsquares 384408,328.325.5545843024 272,217.9863051172.141298
Number of parameters 6 6
Residuals sum of squares 340,382.548324 272,217.960172

Figure6.Spatialdistributionofthepowerofexplanatoryvariables(LocalR2).
Figure 6. Spatial distribution of the power of explanatory variables (Local R2).