Airbnb in tourist cities: comparing spatial patterns of ... · Airbnb in tourist cities: comparing...

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1 Airbnb in tourist cities: comparing spatial patterns of hotels and peer-to-peer accommodation Journal: arXiv preprint | Submitted on 22 Jun 2016 JAVIER GUTIÉRREZ Departamento de Geografía Humana. Universidad Complutense de Madrid [email protected] JUAN CARLOS GARCÍA-PALOMARES Departamento de Geografía Humana. Universidad Complutense de Madrid [email protected] GUSTAVO ROMANILLOS Departamento de Geografía Humana. Universidad Complutense de Madrid [email protected] MARÍA HENAR SALAS-OLMEDO Departamento de Geografía Humana. Universidad Complutense de Madrid [email protected] Abstract: In recent years, what has become known as collaborative consumption has undergone rapid expansion through peer-to-peer (P2P) platforms. In the field of tourism, a particularly notable example is that of Airbnb, a service that puts travellers in contact with hosts for the purposes of renting accommodation, either rooms or entire homes/apartments. Although Airbnb may bring benefits to cities in that it increases tourist numbers, its concentration in certain areas of heritage cities can lead to serious conflict with the local population, as a result of rising rents and processes of gentrification. This article analyses the patterns of spatial distribution of Airbnb accommodation in Barcelona, one of Europe’s major tourist cities, and compares them with the accommodation offered by hotels and the places most visited by tourists. The study makes use of new sources of geolocated Big Data, such as Airbnb listings and geolocated photographs on Panoramio. Analysis of bivariate spatial autocorrelation reveals a close spatial relationship between the accommodation offered by Airbnb and the one offered by hotels, with a marked centre-periphery pattern, although Airbnb predominates over hotels around the city’s main hotel axis and hotels predominate over Airbnb in some peripheral areas of the city. Another interesting finding is that Airbnb capitalises more on the advantages of proximity to the city’s main tourist attractions than does the hotel sector. Finally, it was possible to detect those parts of the city that have seen the greatest increase in pressure from tourism related to Airbnb’s recent expansion. Keywords: Collaborative consumption, P2P platforms, Airbnb, mass tourism, spatial analysis, Barcelona, sharing economy 1. Introduction The last few years have seen the emergence of the so-called sharing economy (also known as collaborative consumption), within the framework of a lifestyle in which more importance is attached to sharing goods than to owning them (“using rather than owning”). With this system, consumers benefit from lower costs for using goods and services at the same time as they avoid wasting resources (Leismann et al., 2013). Collaborative consumption has been driven by the development of Internet platforms that facilitate peer-to-peer relations. The Internet and especially Web 2.0 has brought about many new ways of sharing as well as facilitating older forms of sharing on a larger scale (Belk, 2014; Botsman y Rogers, 2011). Collaborative consumption could therefore be broadly defined nowadays as peer-to-peer-based activity for obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services (Hamari et al., 2015). One of the fields in which collaborative consumption has burst onto the scene with greater intensity is that of tourism, both in the travel sector (car-sharing) and that of accommodation (home exchanges and room/apartment rentals), the best-known platforms being BlaBlaCar and Airbnb, respectively. The exchange of

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Airbnbintouristcities:comparingspatialpatternsofhotelsandpeer-to-peeraccommodation

Journal:arXivpreprint|Submittedon22Jun2016

JAVIERGUTIÉRREZDepartamentodeGeografíaHumana.UniversidadComplutensedeMadrid

[email protected]

JUANCARLOSGARCÍA-PALOMARESDepartamentodeGeografíaHumana.UniversidadComplutensedeMadrid

[email protected]

GUSTAVOROMANILLOSDepartamentodeGeografíaHumana.UniversidadComplutensedeMadrid

[email protected]

MARÍAHENARSALAS-OLMEDODepartamentodeGeografíaHumana.UniversidadComplutensedeMadrid

[email protected]

Abstract:

Inrecentyears,whathasbecomeknownascollaborativeconsumptionhasundergonerapidexpansionthroughpeer-to-peer(P2P)platforms.Inthefieldoftourism,aparticularlynotableexampleisthatofAirbnb,aservicethat puts travellers in contactwith hosts for the purposes of renting accommodation, either rooms or entirehomes/apartments. Although Airbnb may bring benefits to cities in that it increases tourist numbers, itsconcentrationincertainareasofheritagecitiescanleadtoseriousconflictwiththelocalpopulation,asaresultofrisingrentsandprocessesofgentrification.ThisarticleanalysesthepatternsofspatialdistributionofAirbnbaccommodationinBarcelona,oneofEurope’smajortouristcities,andcomparesthemwiththeaccommodationofferedbyhotelsandtheplacesmostvisitedbytourists.ThestudymakesuseofnewsourcesofgeolocatedBigData, such as Airbnb listings and geolocated photographs on Panoramio. Analysis of bivariate spatialautocorrelationrevealsaclosespatialrelationshipbetweentheaccommodationofferedbyAirbnbandtheoneofferedbyhotels,withamarkedcentre-peripherypattern,althoughAirbnbpredominatesoverhotelsaroundthecity’s main hotel axis and hotels predominate over Airbnb in some peripheral areas of the city. Anotherinteresting finding is that Airbnb capitalises more on the advantages of proximity to the city’s main touristattractionsthandoesthehotelsector.Finally,itwaspossibletodetectthosepartsofthecitythathaveseenthegreatestincreaseinpressurefromtourismrelatedtoAirbnb’srecentexpansion.

Keywords: Collaborative consumption, P2P platforms, Airbnb, mass tourism, spatial analysis, Barcelona,sharingeconomy

1. Introduction

The last few years have seen the emergence of the so-called sharing economy (also known as collaborativeconsumption),withintheframeworkofalifestyleinwhichmoreimportanceisattachedtosharinggoodsthantoowning them (“using rather than owning”). With this system, consumers benefit from lower costs for usinggoods and services at the same time as they avoid wasting resources (Leismann et al., 2013). Collaborativeconsumption has been driven by the development of Internet platforms that facilitate peer-to-peer relations.The InternetandespeciallyWeb2.0hasbroughtaboutmanynewwaysofsharingaswellas facilitatingolderforms of sharing on a larger scale (Belk, 2014; Botsman y Rogers, 2011). Collaborative consumption couldthereforebebroadlydefinednowadaysaspeer-to-peer-basedactivityforobtaining,giving,orsharingtheaccesstogoodsandservices,coordinatedthroughcommunity-basedonlineservices(Hamarietal.,2015).

Oneof the fields inwhichcollaborativeconsumptionhasburstonto thescenewithgreater intensity is thatoftourism, both in the travel sector (car-sharing) and that of accommodation (home exchanges androom/apartmentrentals),thebest-knownplatformsbeingBlaBlaCarandAirbnb,respectively.Theexchangeof

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accommodationsbetweenprivateindividualshashistoricallydevelopedinformally,buttheInternet,andmorespecificallyWeb2.0,hasallowedittogrowexponentiallyandtakeonnewcharacteristics(RussoandQuaglieri,2014). Peer-to-peer platforms in the field of accommodation go well beyond marketing and advertising theproperties. They screen both parties, have access to the owners’ inventories,manage rental bookings, collectpayments and provide some form of insurance coverage for damages caused by the renters (Pizam, 2014).Compared to business models that connect the business with the consumer (B2C), such as Expedia orBooking.com forhotelbookings, thebusinessmodel for thesealternativeplatforms isbasedondirect contactbetweenindividuals(persontopersonorP2P),whichmayinvolvehostsandtravellers(Airbnb)orpeoplewhowish to exchange their accommodation free of charge (Couchsurfing). Renters can obtain accommodations atlowerpricesfromAirbnbthanfromhotelsinmostcities

Airbnb is the most successful P2P platform in the field of accommodation and defines itself on its website(www.airbnb.com) as “a trusted community marketplace for people to list, discover, and book uniqueaccommodations around theworld—onlineor fromamobilephoneor tablet”. It connectspeoplewhohavespace to spare (hosts) with those who are looking for a place to stay (guests). Airbnb reaches more than2,000,000listingsin190countries,mainlyentireapartmentsandhomes(57%)andprivaterooms(41%).TheAirbnbofferinghasgrownexponentially.Accordingtothecompany’sowndata,thenumberoflistingsrosefrom50,000 in late2010to200,000bymid-2012,300,000byearly2013,morethanonemillionworldwidebytheendof2014andovertwomillionbytheendof2015.MostofthesepropertiesarelocatedinEuropeandtheUSA.Airbnb’s headquarter is located in San Francisco, where the company was founded. As a result of its rapidworldwideexpansion,internationalofficeshavebeenprogressivelyopenedinsomeoftheworld’smajorcities:London, Paris, Berlin, Milan, Barcelona, Copenhagen, Dublin, Moscow, São Paulo, Sydney and Singapore.AlthoughAirbnbremainsprivatelyheld, itsvaluationofover$10billionnowexceeds thatofwell-establishedglobalhotelchainslikeHyatt(Zervasetal.,2014).Airbnbrevenuescomefromchargingafeetoguests(between10%and12%)andhosts(3%).

As a disruptive innovation in the field of tourism accommodation1, Airbnb proposed a novel businessmodel,built around modern internet technologies and Airbnb's distinct appeal, centred on cost-savings, householdamenitiesand thepotential formoreauthentic localexperiences (Guttentag,2013).Takingpart inAirbnbhasbeen defined as significantly different to ‘mainstream’ consumption in terms of meaningful life enrichment,humancontact,accessandauthenticity.ExperiencingacityandlivinglikealocalareaspectsthatarevaluedandsoughtafterbyAirbnbusers(Yannopoulouetal.,2013).Mostimportantly,Airbnb’srelativelylowcostsappeartobeamajordraw.Airbnbhostsareable toprice their spacesverycompetitivelybecause thehosts’primaryfixedcosts(e.g.rentandelectricity)arealreadycovered; hostsgenerallyhaveminimalornolabourcosts, donotusuallydependsolelyontheirAirbnbrevenueandgenerallydonotchargetaxes(Guttentag,2013).

Airbnbisatacleardisadvantageforcompetingwithaccommodationofferedbytheformaleconomyintermsofquality of service, staff professionalism, brand reputation and security for both hosts and guests. In order tooffset some of these disadvantages Airbnb provides various services, such as a 24-hour customer telephoneserviceandthepublicationofguests’reviewsandratingsinordertoimprovethequalityofserviceandcreatetrustamongusers.Trustisalsofosteredthroughcommunicationbetweenhostsandguestsbydirectmessagingand through users’ profiles, which may display a photograph and include descriptive personal information(Guttentag, 2013). Nevertheless, these practices may determine the trustworthy perceived by hosts (Ert,Fleischer, & Magen, 2016) and also facilitate discrimination based on the seller’s race, gender, age, or otheraspectsofappearance(EdelmanandLuca,2014). Inthewakeofactsofvandalisminsomeapartments,whichhaveincurredheavylossesforthehosts,Airbnbhasreactedbycontractinganinsurancepolicy(HostGuarantee)thatprovidesprotectionforupto$1,000,000worthofdamage(www.airbnb.com).

ThepotentialimpactsofAirbnbonlocaleconomiesarecomplexanddifficulttomeasure.Theresultsofthestudyby Fang et al. (2006) suggest that the entry of sharing economy benefits the entire tourism industry bygeneratingnewjobpositionsasmoretouristswouldcomeduetotheloweraccommodationcost.However,sincelow-endhotelsarebeingshockedandreplacedbyAirbnb, themarginaleffectdecreasesas thesizeof sharingeconomyincreases.Zervasetal.(2014)havestudiedtheimpactofAirbnb’sentryintotheTexasmarketonhotel

1Thedisruptiveinnovationtheorydescribeshowproductsthatlackintraditionallyfavouredattributesbutofferalternativebenefitscan,overtime,transformamarketandcapturemainstreamconsumers(Guttentag,2013).

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roomrevenue, estimating that inAustin,whereAirbnb supply ishighest, the impacton revenue is roughly8-10%. This impact is non-uniformly distributed, with lower-priced hotels, and hotels not catering to businesstravelbeingthemostaffectedsegments.TheseresultsareconsistentwiththetypicalAirbnbclientprofile,thatis,young,adventurousandbudget-conscioustourists,whoarefamiliarwiththeuseoftheInternet(Guttentag,2013;RussoandQuaglieri,2014).AccordingtotheestimationsofZervasetal.(2014),inTexaseachadditional10%increaseinthesizeoftheAirbnbmarketresultedina0.37%decreaseinhotelroomrevenue.Ontheotherhand,incountrieswhereAirbnbislesswell-established,suchasKorea,itseffectsonhotelrevenuehavesofarbeennegligible(Choietal.,2015).

FromtheperspectiveofthespatialdistributionoftheAirbnbimpactswithinthecities,ithasbeenarguedthatAirbnb listings are more scattered than hotels, so Airbnb guests may be especially likely to disperse theirspendinginneighbourhoodsthatdonottypicallyreceivemanytourists(seeGuttentag,2014).EconomicimpactstudiescarriedoutbyAirbnbshowthatmostAirbnbproperties(74%)areoutsidethemainhoteldistricts,79%of travellerswant to explore a specific neighbourhood, and 42% of guest spending is in the neighbourhoodswheretravellersstayed(https://www.airbnb.co.uk/economic-impact).

Nevertheless,thispossibledispersionmaybecompatiblewithaparticularconcentrationoflistingsinthecentralareasofthecities,includingareasnotcoveredbyhotels.AsZervasetal.(2014)pointout,Airbnbcanpotentiallyexpandsupplywhereverhousesandapartmentbuildingsalreadyexist,incontrasttohotels,whichmustbebuiltat locations in accordancewith local zoning requirements. Therefore, expanding in historic centreswould beeasierforAirbnbthanforhotels,whichnotonlyrequireswholebuildingstobeavailablebutalsotherelevantpermits from the authorities. It is precisely these central areas of the city that register the greatest touristconcentrations, as confirmed by analyses carried out from geolocated photographs taken by tourists (García-Palomaresetal.,2015).IfAirbnbshowsacleartendencytowardsexpansioninhistoriccentres,thenthiscouldaggravate the problems of crowding2 and tourism gentrification3 that some of these areas have to support incertainheritage cities (Russo, 2002;Neuts andNijkamp, 2012).This is not only a questionof coexistingwithever-growingmass tourism, but homeswould also suffer from rising rents brought about by the progressiveexpansionofAirbnbandotherapartmentrentalplatforms.Theproblemwouldnotsomuchderive(atleastforthetimebeing)fromthequantityofaccommodationssuppliedbyAirbnbineachcity,butfromitsconcentrationin areas that are already subjected to strong pressure from tourism and the associated processes ofgentrification.

The sharingeconomyhas transformedmanyaspectsof the tourismsector.Nevertheless, academic studiesonAirbnbanditseffectsonthetraditionaltouristsectorandcitiesareparticularlyscant.Guttentag(2013)studiedAirbnb as a disruptive innovation in the accommodation sector. Zebras et al. (2014) and Choi et al. (2015)focusedtheirattentiononcompetitionfromAirbnbwiththetraditionalaccommodationsector.Yannopouluetal.(2013)analysedtheconstructionofuser-generatedbrands(UGBs),usingdiscursiveandvisualanalysisofUGBs’socialmediamaterial,takingAirbnbandCouchSurfingasexamples.NoneofthesestudiesexaminedthespatialdistributionpatternsofAirbnblistings.ThepresentstudyattemptstoidentifythedensityofAirbnblistingsinBarcelona,oneofEurope’stoptouristcities,andcomparethiswiththedensityofhotelaccommodationonoffer.These results are subsequently correlated with the distribution of the main sightseeing spots in the city,identifiedfromgeolocatedphotographsfromthePanoramiodatasource(seeGarcía-Palomaresetal.,2015).GIStools and spatial statistics were used to carry out the analysis, which specifically examines univariate andbivariatespatialautocorrelation.

2Crowdingislinkedwiththeconceptsofcarryingcapacityandsustainability.AccordingtoTheWorldTourismOrganization, carrying capacity is themaximumnumber of people thatmay visit a tourismdestination at thesame time without causing destruction of the physical, economic and socio-cultural environment and anunacceptable decrease in the quality of visitors’ satisfaction. Carrying capacity is essentially a threshold thatindicates the point at which a situation becomes unsustainable. Crowding is thus specifically seen as theviolationofthesocioculturalcarryingcapacity(NeutsandNijkamp,2012).3Tourismgentrificationhasbeendefinedasthetransformationofamiddle-classneighbourhoodintoarelativelyaffluent and exclusive enclave marked by a proliferation of corporate entertainment and tourism venues(Gotham, 2005). In this study the term tourism gentrification has a wider meaning, referring to thetransformationsinresidentialareasbroughtaboutbytourismwithoutnecessarilyresultingintheformationofaffluentandexclusiveenclaves.

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Thearticleisstructuredasfollows.Aftertheintroduction,Section2outlinestheareaofstudy,Sections3and4describethedataandthemethodology,respectively,Section5showsthemainresultsandSection6presentstheconclusionsandfinalremarks.

2. StudyArea:Barcelona

TheareaselectedforstudywasthecityofBarcelona.BarcelonaisahistoriccitythatsuffersseriousproblemsfrommasstourismandhasalargeconcentrationofaccommodationonofferontheAirbnbwebsite.Delimitationofthestudyareawasbasedontheadministrativereferenceunit:themunicipalityofBarcelona(Figure1).Thisisa relatively compact area of 18,017 hectares around the traditional city. With 1,612,000 inhabitants, thepopulationdensityofthemunicipalityofBarcelonaishigh,withmorethan89.5inhabitantsperhectare.

Figure1:MunicipalityofBarcelona

In 2014, Barcelonawas the fifth city in Europe in terms of the number of international tourists, behind onlyLondon,Paris,BerlinandRome(EuropeanCitiesMarketing,20154).Onaglobalscale,itisamongthetwenty-fivefavourite city destinations for international tourism (Top Cities Destination Ranking 2013, EuromonitorInternational).Itspopularityroseconsiderablyafterithostedthe1992OlympicGames.In1990thenumberofovernightstaystotalled3.8million,involving1.7milliontourists.In2000,therewere7.9millionovernightstaysand 3.1 million tourists. By 2014 this total had reached almost 17 million overnight stays, with 7.8 milliontourists(79.5%oftheminternationaltourists).Thishugeinfluxofvisitorshasanenormouseconomicandsocialimpact on the city, generating more than 26 million euros a day and more than 120,000 jobs in tourism(BarcelonaTurisme:BarcelonaTourismAnnualReport,20145),butalsoproducingahighpressureon thecitycentrethatledtoasignificantgentrificationprocess(Cócola,2015).

4http://www.cvent.com/events/ecm-benchmarking-report-2013-2014/event-summary-9dc7593d7ca947d995cd7ca658a0777d.aspx5http://professional.barcelonaturisme.com/imgfiles/estad/Est2014b.pdf

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3. Data

This research ismainlybasedon the analysis of theAirbnbgeolocateddataobtained from the InsideAirbnbwebsite http://insideairbnb.com/. Inside Airbnb is an independent initiative and the data made availablethrough its website are “not associated with or endorsed by Airbnb or any of Airbnb's competitors”. The datautilises public information compiled from the Airbnb web-site, including not only the location of all Airbnbaccommodationsbutalsotheavailabilitycalendarfor365daysinthefuture,andthereviewsforeachlisting6.These data are available for more than 30 cities, including the main cities in Europe (London, Paris, Berlin,Madrid, etc.), the United States and Canada (New York City, San Francisco, Los Angeles, Washington D.C.,Montreal,Vancouver,Toronto,etc.)andAustralia(SydneyandMelbourne).

Data compiled for Barcelona refers to October 2015. Two files called listings.csv were downloaded. Thesecontainedample informationoneach listingwith respect toRoomType (entirehomes/apartments;privatevsshared rooms; number of bedrooms and beds), Activity (estimated nights/year; reviews/listings/month;reviews;estimatedoccupancy;price/night; estimated income/month),Availability,ListingsperHost, etc.Fromthex, y coordinates stored in each recordapoint layermapwas created in ageodatabase inArcGISwith thelocationandfeaturesofeachaccommodation(Figure2a).Themapshowsaclearconcentrationofpointsinthecitycentre.Thisspatialpatternistheresultofanexplosivegrowth,asillustratedinthesupplementaryvideoontheevolutionofAirbnbaccommodationsinBarcelona(seevideo).Byusingthenumberofreviewspermonthasaproxyforthelevelofoccupationoftheaccommodations,anaveragevalueof1.48wasobtainedforthecentreof the city 7and 1.14 for the rest, data that also expresses the higher pressure exerted by Airbnb on the citycentre. With regard to the type of accommodation, 54% were entire homes/apartments, 45% were privateroomsandonly1%weresharedrooms,withpricesaveragingabout35euros/bed.Thedatabasealsorevealsthat this platform is not only used by private individuals but also by professionals. The proportion of Airbnbhostswhorentoutmorethanoneroomorapartment isabout27%and22%of theroomsorapartmentsarerentedoutbyhostswhooffermorethan5accommodations.

AccommodationofferedbyAirbnbwascomparedwiththatofthecity’shotels(Figure2a).Dataonhotelsweretaken from the Catalonia Tourism Registry8, compiled and updated weekly by the regional government(GeneralitatdeCatalunya).Therecordsforeachhotelcontaindataonthenumberofroomsandbedsavailableaswell as the corresponding postal address. Geolocation of these data was carried out using ArcGIS addressmatchingtools.Thereare670hotels inBarcelonaofferingmorethan70,000places. In thecaseofAirbnb, thenumberofaccommodationsis14,500,withanofferingofapproximately51,000places(Table1).However,theAirbnbdata shouldbeput into context.While thehoteloffering covers365daysof theyear,Airbnbbedsareavailableoverlesstime.InBarcelona,theaverageavailabilityofthelistingsisfor280daysayear(Table2).Oftheselistings,1,706(12%)areavailableforfewerthan90daysayear.

Inorderto identifysightseeinghotspots,geolocatedphotographsfromthePanoramiodatasourcewereused.ThedataweredownloadedthroughthePanoramiowebsiteAPI9toobtainsamplesofallthephotographsstored,andcontainedinformationaboutthegeographiccoordinates,theIDoftheownerofthephotograph,aurllinktothephotographandthedateonwhichitwasuploaded.Downloadinggenerated".csv"files,whichcontainedthegeographical coordinates of the locationof eachof thephotographs. These coordinateswereused to create alayer of points for each of the locations in a GIS. Geolocated photographs were differentiated according towhethertheyhadbeentakenbytouristsorresidents.WeusedthesamecriterionasFischerforhisGeotaggers'WorldAtlasandGarcía-Palomaresetal.(2015):ifthisperiodexceededonemonth,thenthephotographswereattributedtoresidents;iftheperiodwaslessthanonemonth,thentheywereattributedtotourists.ThenumberofphotographstakeninBarcelonawasmorethan92,000,ofwhich28.5%weretakenbytourists(Table3).Thespatial distribution of photographs taken by tourists ismuchmore concentrated than that of those taken byresidents,reflectingthelocationofthecity’smaintouristattractions(Figure2b).

6Allinformationonthissourcecanbeconsultedathttp://insideairbnb.com/about.html7CiutatVellaandEixampledistricts.8http://empresaiocupacio.gencat.cat/es/treb_ambits_actuacio/emo_turisme/emo_empreses_establiments_turistics/emo_registre_turisme_catalunya/emo_llistat_establiments_turistics/index.html9http://www.panoramio.com/api/data/api.html

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Table1.DataontouristaccommodationsinBarcelona:hotelsversusAirbnb

Hotels/Airbnblistings Bedrooms Beds PlacesHotels 670 37,405 70,465 73,158Airbnb 14,539 22,059 33,167 50,969

Source:InsideAirbnbandGeneralitatdeCatalunya.

Table2.BasicdataonlistingsofferedbyAirbnbinBarcelona Roomtype Total

Entirehome/apt Privateroom SharedroomListings 7,816 6,566 157 14,539Price Mean 111.8 39.3 27.3 78.2

SD 140.2 26.8 21.5 110.5Availability Mean 276.1 285.8 305.8 280.1

SD 103.8 111.0 107.0 107.3Beds Mean 3.1 1.3 3.6 2.3

SD 2.0 1.0 3.4 1.8Reviews/listing/month

Mean 1.28 1.41 1.18 1.34SD 1.31 1.56 1.51 1.43

Source:InsideAirbnb.Table3:Photographstatistics(Photographs/hectare) Tourists'photographs Locals'photographs Allphotographs

Total Density* Total Density* Total Density*

Barcelonamunicipality 26,361 1.50 66,114 3.75 92,475 5.25

Finally,populationdatafromsectionsoftheBarcelonamunicipalcensuswereused.Thepopulationdatawereobtained from the official registry of inhabitants (Padrón del Instituto Nacional de Estadística:http://www.ine.es/),taking2013asthedateofanalysis.

Figure2:LocationofhotelandAirbnboffers(a)anddensityofphotographstakenbytouristsandresidents(b)

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4. Methodology

The following methodology was employed to analyse the spatial distribution of Airbnb and Hotelaccommodationsandphotographs:

a) Dataaggregatedbycensustractwereusedtoproducedensitymapsanddescriptivestatistics,andthusdetermine the intensity and degree of concentration of the accommodations (differentiated by hotelsandAirbnb)andtouristphotographs.

b) DistributiondataonhotelandAirbnbaccommodationswasnormalisedinordertoeliminatetheeffectofdifferentrangesinthevariablessothatdistributionscouldbecomparedwitheachother.

c) Univariatespatialautocorrelationtoolswereusedtoidentifythelocationandextentofspatialclustersoftypesofaccommodationsandtouristphotographs.

d) Bivariate spatial autocorrelation tools were used to analyse spatial autocorrelation betweenaccommodationtypes(hotelsandAirbnb)andtouristphotographs.

e) Finally,theratesofhotelandAirbnbaccommodationsper1,000inhabitantswereobtainedinordertoanalysepressurefromtourismontheresidentpopulation.

Aggregateddataatcensussectionlevelanddensitymappingareaninitialvisualapproachtodistributionoftheaccommodation offering, allowing the spatial patterns between Airbnb and hotel accommodation to becompared.Thedescriptivestatisticsenablemeasurementstobetakentodeterminethedegreeofconcentrationor dispersion of the types of accommodations. Data normalisation was used in order to map the degree ofrelative predominance of one type of offering over another in each of the census sections. To do this, thenormaliseddensitiesoftheAirbnbofferingweredeductedfromthenormaliseddensitiesofthehotelsofferedineachsection.

Basedonaggregateddatabycensussections, the locationpatternswerethenanalysedusingspatialstatisticalindicators.GlobalMoran'sIstatisticwascalculatedtomeasurespatialautocorrelationbasedonfeaturelocationsandattributevalues.AnselinLocalMoran'sI(LISAstatistic)wasusedtoidentifylocaltendenciesinthelocationofthedifferenttypesofaccommodation.WithLISAanalysisitwaspossibletodistinguishHigh-Highclusters(ahighvaluesurroundedprimarilybyhighvalues),Low-Lowclusters (a lowvaluesurroundedprimarilyby lowvalues), and spatial outliers, either High-Low (high values surrounded primarily by low values) or Low-High(low values surrounded primarily by high values) (Anselin, 1995). Global and Local BivariateMoran’s Iwereusedinordertomeasurespatialautocorrelationbetweenvariablesandtoidentifyspatialclustersinwhichthehigh values of one variable were surrounded by high values of the second (i.e. lagged) variable (high-highclusters)andsoon.

Selection of the spatial interactionmethod requires special attentionwhen computing spatial autocorrelationstatistics. In this casewe chose to consider the spatial interactionbetweenobservationswithin a1kilometreradius,thatis,atypical15-minutewalk.Anyobservationwithinthisradiuswouldthereforebeconsideredintheanalysiswithaweightinverselyproportionaltothedistanceseparatingtheoriginandthedestination.

GeoDawasusedtocomputebothunivariateandbivariateGlobalandAnselinLocalMoran’sI.GeoDaisanopensource package developed in the GeoDa Center for Geospatial Analysis and Computation 10. This softwareprovides all the necessary tools for performing spatial analysis in a simple way, even for non-experts oracademics, at the same time as it generates high value and easy to understand outputs. As with other GISsoftware,GeoDaoffersspecifictoolsforunivariateexploratoryanalysis.ThemainadvantageisthatitalsoallowsthecomputationofabivariatespatialMoran’sIautocorrelationindex(Anselinetal.,2006).

5. Results

5.1.Distributionoftouristaccommodations

MapsanddescriptivestatisticsofthedistributionofaccommodationsbycensussectionsareshowninFigure3andTable 4. The averagenumber ofAirbnb accommodations by census sections is 48, comparedwith69 for

10GeoDaCenter&AffiliatedSoftware:https://geodacenter.asu.edu/software.Lastvisitedon03February2016.

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hotels,withmaximumvaluesofaround600accommodationsforAirbnbandmorethan2000forhotelsinsomesectionsofthecentre.Thenumberofcensussectionswithmorethan200accommodationsisalsomuchgreaterinthecaseofhotels(Table4).ThehotelsarehighlyconcentratedinthecensussectionscomprisingtheRamblas-PaseodeGraciaaxis,certainareasdedicatedtobusinessandfinance,liketheDiagonalmainstreet,orthecoastalaxisfromtheBarcelonetabeachtotheForum.Outsidetheseareastheavailabilityismuchlower.ThedifferencesinthedistributionofAirbnbaccommodationsarenotsomarked,asshownbythecoefficientofvariation,whichhasmuchlowervaluesforAirbnbthanforhotels.

Table4:StatisticsonthedistributionofaccommodationofferedbyhotelsandAirbnbaccordingtocensusareas.

Airbnb HotelsTotal AccommodationsAccommodations/haTotal AccommodationsAccommodations/ha

Count: 1061 1061 1061 1061 1061 1061Minimum: 0 0 0 0 0 0Maximum: 175 616 64.0 48 2368 255.4Sum: 14515 50969 6586.9 712 73158 6089.5Mean: 13.7 48.0 6.2 0.7 69.0 5.7StandardDeviation: 21.3 76.7 8.8 3.0 233.5 18.5CV: 155.6 159.7 141.9 443.8 338.7 322.8NºCensussections>100Accommodations 153(14.4%) 153(14.4%)NºCensussections>200Accommodations 50(4.7%) 90(8.5%)

Figure3:Totalnumberanddensityofplaces,accordingtocensussections:a)hotels,andb)Airbnb

InordertocomparethedistributionofhotelandAirbnbaccommodationbycensussections,Figures4aand4bshowthedensityofaccommodationplaceswithnormaliseddata.Thiscancelsoutdifferences in therangesofthe two variables, thus making them comparable. In contrast to the marked concentration of hotels on theRamblas-PaseodeGraciaaxis,Airbnbaccommodationisfoundinaconcentricringaroundthecentralhubofthecity,thePlazadeCataluña.Theareaitcoversismuchmoreextensivethanthatofthehotelsandisoccupiedbytraditionalcitycentreresidentialdistricts,suchasElRaval,LaBarceloneta,LaRibera, theGothicQuarter,andtheareaaroundtheSagradaFamiliaChurch.Inallthesezones,thepresenceofAirbnbisgreaterinrelativetermsthan that of hotels (Figure 4c). As a result, the Airbnb accommodation contributes to increasing tourismpressureonthecentre.

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Figure4:Densityofnormaliseddistributions:a)hotels;b)Airbnb;c)differences

Spatial statistical analysis confirms the statistical significanceof these locationpatterns.GlobalMoran’s Indexshowsastrongpositivespatialcorrelationinbothcases(positiveMoran’sIndexandp-value=0.00000)(Table5), but it is higher for Airbnb than for hotels. Using Anselin Local Moran’s I statistic, the spatial clusterdistributioncanbeidentified(Figure5).Asexpected,bothcasesshowaclearconcentrationofHHclustersinthecitycentreandLLontheperiphery.Inthecaseofhotelaccommodation,theHHclustersarelocatedalongthemain Ramblas-Paseo de Gracia axis, around which LH outliers appear. These are central areas that havetraditionally had a marked residential character but no hotel accommodation. In the case of Airbnb, the HHclustersextendthroughallthecensussectionsinthecitycentre,includingthosethatareresidentialinnature.Inbothcases, towards theouteredgeof thecentralarea isabeltofsectionswithvalues thatarenotsignificant,which wouldmark the limit of tourist accommodation in the central area. In the case of Airbnb, this belt isnarrowandveryclearlydefinedandissurroundedbyLLcensussectionsinalltheperipheryofthemunicipality.Incontrast,inthecaseofhotels,thebeltwithnotsignificantvaluesismuchmorediffuseandextensivebecauseofthepresenceofhotelsontheaxisoftheDiagonalandinsomeperipheralcensussections(withnotsignificantvaluesorwithHLoutliers,whichreducestheextensionofLLclusters)ThenumberofcensussectionsaccordingtotypesofclusterconfirmsthatthedistributionofAirbnbgivesagreaterpositivespatialcorrelationthanthatofhotels,withmorecensussectionsmadeupoftypesHHandLL,andfeweroutliers(LHandHL)(Table6).

Figure5:AnselinLocalMoran'sIstatistic(LISA):a)accommodationsinhotels;b)accommodationsinAirbnb

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Table5:GlobalMoran’sIstatistics

Hotels Airbnb PanoramioGlobalMoran’sIndex 0,23 0,70 0,18z-score 27,91 78,55 25,84p-value 0,01 0,01 0,01

Table6:Numberofcensussectionsaccordingtotypeofspatialcluster(LISA)

Hotels Airbnb PanoramioTotal Percentage Total Percentage Total Percentage

High-High(HH) 81 7.6 258 24.3 64 6.0Low-Low(LL) 392 36.9 575 54.2 422 39.8Low-High(LH) 77 7.3 33 3.1 62 5.8High-Low(HL) 15 1.4 13 1.2 17 1.6NotSignificant 496 46.7 182 17.2 496 46.7Total 1061 100.0 1061 100.0 1061 100.0

5.2. Areasvisitedbytourists

Inordertoanalysethemaintouristareas,wemappedthedensityofphotographstakenbytouristsintermsofbothabsolutevalues(photographs/ha)(Figure6)andnormalisedvalues(Figure6b) to facilitatecomparisonswith the density of hotel andAirbnb accommodations. The photographs reflect the spatial distribution of thecity’s main sightseeing spots . The most photographed places, and consequently the most visited, are theBarcelonaofGaudi(theSagradaFamiliaChurch,CasaBatlló,CasaMilà,theGüellPark,etc.),theGothicQuarterinthehistoric centre, theport area and thebeach, togetherwithother tourist spots, such asBarcelonaFootballClub’sNouCampstadium,theTorreAgbarandForumbuildings,orgreenspaceswithscenicviewslikeMontjuicandTibidabo.Thisall showsadistributionofcensussections thataredispersedthroughout thecityandhaveveryhighintensities,inwhichvaluesexceedmorethan2000andeven3000photos,comparedtoanaverageof25photographspersection(Figure6aandTable7).

Figure6:Photographstakenbytourists:a)totalnumberanddensity;b)normalizeddata

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Table7:Basicstatisticsonthedistributionofphotographstakenbytouristsaccordingtocensussections

Totalphotographs Photographs/haCount: 1061 1061Minimum: 0 0Maximum: 3827 70.9Sum: 26649 1176.8Mean: 25.1 1.1StandardDeviation: 161.2 3.3CV: 641.8 300.8

Moran’s Index indicates a strong positive spatial autocorrelation in the distribution of areas, althoughwith alower value than in the case of Airbnb and hotels (Table 5). Calculation of Anselin Local Moran’s I statistic(Figure7)showstwozonesinwhichHHclustersarelocated,oneinthearearoundLasRamblasandtheGothicQuarterinthecity’shistoriccentre,andtheothertothenorthofthis,centredaroundtheSagradaFamiliaandincluding thePaseodeGracia axis. Logically, in themoreperipheral census sectionsaway from the coast, thepresenceoftouristsisdilutedandLLclusterspredominate.

Figure7:AnselinLocalMoran'sIstatistic(LISA)forthedistributionofphotographstakenbytourists

5.3. Relationsbetweenthelocationsofdifferenttypesoflodgings:hotelsvsAirbnb

Relations between the location patterns of hotel and Airbnb accommodation can be analysed using bivariateautocorrelationindicators,boththeMoranIndexandLISA.ThebivariateMoranIndexshowsaveryhighpositivespatial autocorrelation between location of the hotels and accommodations offered by Airbnb (Table 8). Themappedclustersareshown inFigure8and thenumberof censussectionsby typeare inTable9.TypeHH islocatedalongtheRamblas-PlazadeCataluña-PaseodeGraciaaxis.ThesecensussectionshavealargenumberofhotelplacesandaresurroundedbysectionswithahighsupplyofAirbnbaccommodation.Censussectionsofthistypealsoappear in traditional residentialdistricts in thecentre,wheresomehotelsare found inareaswithaverystrongAirbnbpresence.Nevertheless,inthesecentralresidentialdistrictsLHcensussectionspredominate,with a low number of hotel places and a high level of Airbnb accommodation. On the periphery, LL clustersprevail, that is, census sectionswith a lownumber of hotel places surroundedby sectionswith a lowAirbnb

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presence.ThereareonlyafewcasesofHLtypecensussections(highconcentrationofhotelsandlowpresenceofAirbnb),oneexamplebeingtheDiagonalaxis.

Tosummarise, thedistributionofclustersshowsaclearcentre-peripherypattern.After thecentre(HH)comesuccessivebeltsofLH,not significantandLLvalues.ThehighnumberofHHandLLclustersproves theclosespatialassociationbetweenthetwotypesofaccommodationoffers.Thedistributionofoutliersisverytypical:LHoutliersinthecitycentrearoundthebighotelaxis(Airbnbexpansion)andHLoutliersintheLLbelt(hotelsdispersedontheperiphery).

Figure8:BivariateAnselinLocalMoran’sIstatisticbetweenoffersofhotelandAirbnbaccommodations.

Table8:BivariateGlobalMoran’sIstatistics(*Significantatthe0.01level)

Hotels-Airbnb Hotels-Panoramio Airbnb-PanoramioGlobalMoran’sIndex 0.32 0.08 0.18z-score 57.70* 15.48* 25.,84*

Table9:NumberofcensussectionsaccordingtotypesofclusterwithrespecttothelocationofhotelandAirbnb

RelationHotels–AirbnbTotal Percentage

High-High(HH) 106 10.0Low-Low(LL) 557 52.5Low-High(LH) 191 18.0High-Low(HL) 32 3.0NotSignificant 175 16.5Total 1061 100.0

5.4. Sightseeingspotsandaccommodation

Tourists tend to stay inplacesclose toareaswhere themainsightsandother touristattractionsaresituated.Therefore, it is only to be expected that there is a strong spatial association between location of theaccommodation(inhotelsandwithAirbnb)andtheareasofthecitythatareofinteresttotourists(photographstakenby tourists). Although the bivariateMoran’s I confirms a very strongpositive spatial autocorrelation in

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bothcases,thisisgreaterforAirbnb(Table8),whichsuggestsabetterlocationofthistypeofaccommodationwithrespecttothecity’stouristattractions.

ThebivariateLISA(Figure9andTable10)showsthattheHHcensussectionsaremorenumerousintheAirbnb-Panoramio relation than in the Hotel-Panoramio relation. These are census sections with a high density ofaccommodationssurroundedbysectionswithahighdensityofphotographs.Inthecaseofhotels,HHareinthecensussectionsoftheRamblas-GothicQuarter,andaroundthePaseodeGracia(withtouristsitesatCasaMilà-LaPedreraandCasaBatlló).WithrespecttoAirbnb,HHarelocatedalongthesesameaxes,buttheyalsoextendinparticular to theresidentialdistrictof theEnsanchearound theSagradaFamilia.Withrespect tohotels, thesesamecensussectionsformpartoftypeHL(lownumberofhotelsinareaswithahighnumberofphotographs).ThereisamarkedcontrastbetweenthoseresultsobtainedforhotelsandthoseforAirbnbinthecensussectionscomprising the port, La Barceloneta and the beach.With respect to the relation between the hotels and thephotographs, theappearanceofHHcensussections in theportandbeachareas isdue to thepresence in thiszone of hotels that are surrounded by much-photographed sites, with the exception of the traditional LaBarcelonetadistrict,whichhasnohotelsandappearsasHL.TheoppositeoccursinthecaseofAirbnb,sincetheLHcensussectionspredominateintheportandbeachzones,whileLaBarcelonetaisHH.Aroundthesecentralareasarecensussectionswithvaluesthatarenotsignificant.LLpredominateinperipherallocationstowardstheouter edge,particularly in the caseofAirbnb,while in thatofhotels there is a greaterpresenceofHL censussectionsduetohotelsscatteredinareaswherefewphotographsaretaken.

Figure9:BivariateAnselinLocalMoran’sIstatisticforhotels-PanoramioandAirbnb-Panoramio

Table10:BivariateLISA.Numberofcensussectionsaccordingtotypesofclusterinrelationtohotel-PanoramiolocationandAirbnb-Panoramiolocation.

Hotels-Panoramio Airbnb-PanoramioTotal Percentage Total Percentage

High-High(HH) 50 4.7 102 9.6Low-Low(LL) 430 40.5 438 41.3Low-High(LH) 78 7.4 24 2.3High-Low(HL) 13 1.2 8 0.8NotSignificant 481 45.3 480 45.2Others* 9 0.9 9 0.9Total 1061 100.0 1061 100.0*Censussectionslargerthan2kmwide(withoutneighbourswithin1kmfromthecentroid)

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5.5. Pressurefromtourismonresidentialareas

Inordertoanalysetourismpressureonresidentialareas, thenumberofplacesofaccommodation(hotelsandAirbnb) per 1,000 inhabitants has been calculated according to census sections, excluding those sections thathavehardlyanypopulation(<5inhabitants/ha),suchasgreenspacesorindustrialzones(Figure10).

Figure11ahighlights the tourismpressure exertedbyhotels along themainhotel axis,where several censussections exceed 500 accommodations per 1000 inhabitants. Between this axis and the periphery there is anabruptdropinpressureonresidentialareasfromtouristaccommodation.ThisdropismuchmoregradualinthecaseofAirbnb(Figure11b).The lodgingsavailablethroughAirbnbextendoverresidentialareas inthecentrewhichhavenohotelsandwheretherewereformerlynoaccommodations.Somecensussectionshavemorethan100Airbnbplacesper1000 inhabitants, reachingamaximumofalmost400placesper1000 inhabitants.ThepressurefromthistypeofaccommodationonthecentreisintensifiedbythefactthatAirbnboccupationlevelsaregreater in the centre thanon theperiphery (seeSection3).Finally,Figure10c shows the totalnumberofplacesforbothtypesofaccommodation(hotelsandAirbnb).Thismapshouldbeanalysedwithcaution,sincethelevelofoccupationishigherforhotelsthanforAirbnb,butitillustratesthepressureexertedbytourismonthecity due to Airbnb lodgings, with several census sections in which the number of places exceeds and evendoublesthenumberofinhabitants(seealsoTable11).

Figure10:Populationdensityaccordingtocensussections(inhabitants/ha).

Figure 11: Tourismpressure on residential areas: a)Hotel places per 1000 inhabitants; b) Airbnb places per1000inhabitants;c)Hotelplaces+Airbnbplacesper1000inhabitants.

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Table 11: Basic statistics on the distribution of the accommodations offered by hotels and Airbnb per 1000inhabitantsaccordingtocensussections. Hotel places / 1000

inhabitantsAirbnb places /1000inhabitants

Hotel + Airbnb places/1000inhabitants

Count 1061 1061 1061

Minimum 0 0 0Maximum 1796.1 391.7 2148Sum 46079.3 31941.6 78021Mean 43.4 30.1 73.5StandardDeviation 150.0 45.9 180.6CV 345.4 152.5 245.6

6. Conclusions

The irruption of P2P accommodation platforms in tourist cities has received very little attention fromresearchers,particularlyinrelationtothelocationofitsaccommodationsandtheirpossibleimpactonthecity.ThepresentstudyaimstoclosethisgapwithacontributiononthelocationofAirbnbaccommodationofferedincities with mass tourism, related to hotel location and the most visited tourist attractions (locationaladvantages), and the resident population (tourism pressure), using Barcelona, a city with one of the highestnumbersoftouristsinEurope,asacasestudy.

The results of the study show that the distribution of theAirbnb accommodations offered inBarcelonahas aclearcentre-peripherypattern.Itslistingstendtobeconcentratedinthecitycentre,wheretheycoverawiderareathanthemainhotelaxisastheyalsoextendtoverycentralresidentialdistrictsthatarenotcoveredbyhotellodgings. Spatial autocorrelation analysis shows that the spatial distribution ofAirbnb accommodations has astatisticallysignificantgreaterpositivespatialautocorrelationthanthatofhotels.ThedistributionofAirbnbismuch simpler and more regular, from the HH clusters in the centre to the LL clusters at the periphery,incorporatinganarrowbandofnotsignificantcensussections,andwithascarcityofoutliers.Incontrast,hotelsshowmorecomplexpatterns,withlessextensionofHHandLLclustersandagreaterextensionofnotsignificantcensussectionsandoutliers.

BivariatespatialautocorrelationanalysisrevealsaclosespatialassociationbetweentheAirbnbaccommodationsofferedandthatofhotels.Thecentre-peripherypatternsshownbytheresultsofthisanalysisareveryclear.Theaxisofhotels in thecentre (clusterHH)givesway toabanddominatedbyAirbnb(LH),a secondbandofnotsignificantvaluesand finallyaperipheral areadominatedbyLL clusters,with someHLoutliers (areaswithaconcentrationofhotelsbutnotAirbnb).Inshort,thecensussectionswithHHandLLclustersandnotsignificantvalues indicate a similar behaviour for both types of accommodation inmuch of the city. It is in the outlierswheredifferencesarefound,withcentrallocationswhereAirbnbprevails(LH)andperipheraloneswherethereisapredominanceofhotels(HL).

Analysis of the bivariate autocorrelation between the accommodations and the sightseeing spots (tourists’photographs geolocated on Panoramio) confirms a close spatial association between both variables(accommodationandplacesvisited),withverysimilardistributionsinthetwotypesofaccommodation.Inbothcases, two areas of concentration of HH clusters are identified which basically coincide with the medievalhistoriccentreinthesouthandtheBarcelonaofGaudiinthenorth.Thedifferencesbetweenthetwomapsaregenerally to Airbnb’s advantage. Airbnb has a greater number of HH census sections (high concentration ofaccommodationplacesinsectionssurroundedbyheavilyvisitedareas),whilehotelsshowagreaternumberofoutliers, revealing a worse location in relation to the most visited tourist attractions, since places areconcentrated in the census sections of areas where few photographs are taken (HL) or in those with a lowdensity of places in highly-photographed surroundings (LH). The first occurs particularly in hotels on theperipheryandcanbeexplainedbythefactthatthesehotelsaregenerallyorientedtowardsbusinesstravelandthereforehaveotherlocationalrequirements.Thesecondrevealstheexistenceofcensussectionsinwhichthehotelsectordoesnottakesufficientadvantageofproximitytotheplacesmostvisited.Inshort,theresultsabovesuggestthatAirbnbbenefitsingreatermeasurethanhotelsfromproximitytothemostvisitedplacesinthecity

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(greaternumberofHHclustersandfewerHLoutliers),probablybecauseofitsgreaterfacilityforexpansioninalreadybuilt-upareas.

Finally, the relation between accommodation places and the resident population shows that new residentialareasarebeingaddedtothetraditionalareasofstrongpressurefromtourismalongthecity’smaintouristaxis,and Airbnb clearly contributes to that pressure. It is in these census sections where problems have arisen,involvingthecoexistenceofthenewAirbnblodgingsandtheresidentpopulation,particularlyincertaincensussectionsofLaBarceloneta,ElRaval,theGothicQuarterandLaRibera.ThereasonfortheseconflictsisthattheexpansionofAirbnbhasledtoordinaryrentalflatsbeingremovedfromthemarket,resultinginincreasedrentsandprocesses thatdriveout the localpopulation(more thanhalf theAirbnb lodgings inBarcelonaconsistsofentirehomes/apartments).Airbnb isalso transforming thebusinessstructureof theseareas,as in thecaseofshopsandrestaurants,whichareincreasinglygearedtotourists.

Airbnbischangingthetouristaccommodationmodelinawaythat,currently,createsconflictincitieswithmasstourism. Barcelona is trying to control expansion of this type of rental through inspections to ensure thatapartmentsarenotfunctioningillegallyandthattaxesarepaid,withfinesofupto90,000eurosimposed.Inthisway,notonlyaremore taxes collectedbutAirbnb’s competitiveadvantageover traditional accommodation isreduced,therebyreducingitsprospectsforfurtherexpansion.

Acknowledgments

TheauthorsgratefullyacknowledgefundingfromtheICTThemeoftheEuropeanUnion'sSeventhFrameworkProgram(INSIGHTproject-InnovativePolicyModelingandGovernanceToolsforSustainablePost-CrisisUrbanDevelopment, GA 611307), from the Madrid Regional Government (S2015/HUM-3427) and a post-doctoralfellowshipfromMinisteriodeEconomíayCompetitividadofSpain(FPDI2013/17001).

References

Anselin,Luc.1995.“LocalIndicatorsofSpatialAssociation—LISA.”GeographicalAnalysis27(2):93–115.doi:10.1111/j.1538-4632.1995.tb00338.x.

Anselin,Luc,IbnuSyabri,andYoungihnKho.2006.“GeoDa:AnIntroductiontoSpatialDataAnalysis.”GeographicalAnalysis.doi:10.1111/j.0016-7363.2005.00671.x.

Belk,R.(2014).Youarewhatyoucanaccess:Sharingandcollaborativeconsumptiononline.JournalofBusinessResearch,67(8),1595-1600.

Botsman,R;Rogers,R(2010):What'sMineIsYours:TheRiseofCollaborativeConsumption,HarperBusiness,NewYork

Belk,R.(2014).Youarewhatyoucanaccess:Sharingandcollaborativeconsumptiononline.JournalofBusinessResearch,67(8),1595-1600.

Cócola,A.(2015).Tourismandcommercialgentrification.RC21InternationalConferenceon“TheIdealCity:betweenmythandreality.Representations,policies,contradictionsandchallengesfortomorrow'surbanlife”.Urbino(Italy),27-29.August2015.http://www.rc21.org/en/wp-content/uploads/2014/12/E4-C%C3%B3cola-Gant.pdf

Choi,K.H.,Jung,J.H.,Ryu,S.Y.,DoKim,S.,&Yoon,S.M.(2015).TheRelationshipbetweenAirbnbandtheHotelRevenue:IntheCaseofKorea.IndianJournalofScienceandTechnology,8(26).

Edelman,B.G.andLuca,M.(2014).Digitaldiscriminaton:Thecaseofairbnb.com.HarvardBusinessSchoolNOMUnitWorkingPaper,(14-054).

Ert,E.,Fleischer,A.,&Magen,N.(2016).Trustandreputationinthesharingeconomy:TheroleofpersonalphotosinAirbnb.TourismManagement,55,62–73.http://doi.org/10.1016/j.tourman.2016.01.013

Page 17: Airbnb in tourist cities: comparing spatial patterns of ... · Airbnb in tourist cities: comparing spatial patterns of hotels and ... , the business model for these alternative platforms

17

García-Palomares,J.C.,Gutiérrez,J.andMínguez,C.(2015).Identificationoftouristhotspotsbasedonsocialnetworks:acomparativeanalysisofEuropeanmetropolisesusingphoto-sharingservicesandGIS.AppliedGeography,63,408–417.

Gotham,K.(2005).Tourismgentrification:thecaseofNewOrleans’VieuxCarre(FrenchQuarter).UrbanStudies,42,7,1099–1121.

Guttentag,D.(2013).Airbnb:disruptiveinnovationandtheriseofaninformaltourismaccommodationsector.CurrentIssuesinTourism,18,1192-1217.

Hamari,J.,Sjöklint,M.,&Ukkonen,A.(2015).Thesharingeconomy:Whypeopleparticipateincollaborativeconsumption.AvailableatSSRN2271971.

Leismann,K.,Schmitt,M.,Rohn,H.,&Baedeker,C.(2013).Collaborativeconsumption:towardsaresource-savingconsumptionculture.Resources,2(3),184-203.

Neuts,B.,&Nijkamp,P.(2012).Touristcrowdingperceptionandacceptabilityincities:AnappliedmodellingstudyonBruges.AnnalsofTourismResearch,39(4),2133-2153.

Russo,A.P.(2002).The“viciouscircle”oftourismdevelopmentinheritagecities.Annalsoftourismresearch,29(1),165-182.

Russo,A.PyQuaglieri,A.(2014):“Lalógicaespacialdelintercambiodecasas:unaaproximaciónalasnuevasgeografíasdelocotidianoenelturismocontemporáneo”.ScriptaNova.RevistaElectrónicadeGeografíayCienciasSociales.17(483).http://www.ub.es/geocrit/sn/sn-483.htm

Yannopoulou,N.,Moufahim,M.,&Bian,X.(2013).User-GeneratedBrandsandSocialMedia:CouchsurfingandAirBnb.ContemporaryManagementResearch,9(1).

Zervas,G.,Proserpio,D.,&Byers,J.(2014).Theriseofthesharingeconomy:EstimatingtheimpactofAirbnbonthehotelindustry.BostonU.SchoolofManagementResearchPaper,(2013-16).