Three essays on peer-to-peer markets: the case of Airbnb

(1)

“THREE ESSAYS ON PEER-TO-PEER MARKETS:

THE CASE OF AIRBNB.”

Doctoral thesis

Doctoral Programme of Applied Economics 2020

Veronica Leoni, PhD Candidate

(2)

“THREE ESSAYS ON PEER-TO-PEER MARKETS:

THE CASE OF AIRBNB.”

Doctoral thesis

Doctoral Programme of Applied Economics 2020

Veronica Leoni

Thesis Supervisor: Jan Olof William Nilsson, PhD Thesis Supervisor: Paolo Figini, PhD

Thesis tutor: Antoni Riera Font, PhD

Doctor by the Universitat de les Illes Balears

(3)

(4)

Acknowledgments

First and foremost, I would like to express my most sincere gratitude to my advisors Dr Paolo Figini and Dr William Nilsson for the continuous support of my Ph.D study and related research, for their patience, motivation, and immense knowledge. Their guidance helped me in all the time of research and writing of this thesis. I could not have imagined having better advisors and mentors and it is, indeed, a source of proud for me to have worked with you.

Besides my advisors, I would like to thank my tutor, Dr Antoni Riera Font for his precious help and guidance during these years.

My sincere thank goes to the reviewers for their insightful comments and encouragement, but also for the hard questions which incited me to widen my research from various perspectives.

I thank my beloved colleagues for the stimulating discussions, for all the fun we have had in the four years and all the unconditional support during this very intense last academic year. I am truly grateful for your friendship!

Last but not the least, I would like to thank my family and my partner for supporting me spiritually throughout writing this thesis and my life in general.

(5)

Figure 3. 1 Price Surge (Wi) ... 151 Figure 3. 2 Price surge (Wi) over lead-time (local polynomial) ... 152 Figure 3. 3. Distributions of Listings’ log-revenue: July and August 2016 ... 154 Figure 3. 4. Distribution of the heterogeneous treatment effect (out-of-bag) July and August 2016 ... 167 Figure 3. 5. List of variables that maximize the variance in the treatment effects. ... 168 Figure 3. 6.a Treatment effects predictions ... 170 Figure 3. 6.b Treatment effects predictions ... Error! Bookmark not defined.

(10)

List of Abbreviations

Abbreviation Meaning

ADR average daily rate

ATE average treatment effect CV coefficient of variation

EEV endogenous explanatory variable

GPS Global Positioning System

HTE heterogeneous treatment effects

ICT Information and communications technology IPD intertemporal price discrimination

OLS ordinary least squares

OR occupancy rate

P2P peer-to-peer

REVPAR revenue per available room

SE sharing economy

ZOIB zero-inflated beta model

(11)

Resumen

Esta tesis tiene como objetivo explorar los mecanismos económicos y los fundamentos de los mercados “peer to peer” en el sector turístico. En este trabajo analizamos la entidad más representativa de la economía colaborativa -Airbnb- en uno de los destinos turísticos más populares del Mediterráneo, las Islas Baleares. Este trabajo está compuesto por tres artículos o secciones, cada uno de los cuales responde a una pregunta de investigación específica, adoptando el modelo econométrico más adecuado con el fin de proporcionar los resultados más precisos.

El primer artículo, con título “It’s not all about the price. The determinants of occupancy rates in p2p accommodation: a methodological contribution”, tiene como objetivo identificar los determinantes clave de las tasas de ocupación de alojamiento, en mercados

“peer to peer”.

El segundo artículo, “Stars vs lemons. Survival analysis of peer-to peer marketplaces: the case of Airbnb”, explora los determinantes de supervivencia de los servicios ofertados en la plataforma Airbnb, y el papel que juega la reputación online para reducir la asimetría informativa en este mercado emergente.

El tercer artículo, “Dynamic pricing and revenues of Airbnb listings: estimating heterogeneous causal effects”, analiza en qué medida la implementación de precios dinámicos, concretamente una discriminación de precios intertemporal, afecta a los ingresos de los anfitriones de Airbnb.

Vemos que la contribución de esta tesis es triple: (i) contribuye conceptualmente, al abordar algunos de los temas que anteriormente se obviaban en la literatura propia de los

(12)

mercados “peer-to-peer”; (ii) contribuye metodológicamente, pues supera los modelos adoptados en estudios previos; (iii) contribuye empíricamente, pues estudia uno de los destinos turísticos más populares del Mediterráneo -las Islas Baleares- a la par que arroja luz sobre el alcance del fenómeno del alojamiento en mercados “peer-to-peer” y sus implicaciones para la gestión pública y empresarial.

Resum

Aquesta tesi té com a objectiu explorar els mecanismes econòmics i els fonaments dels mercats "peer to peer" en el sector turístic. En aquest treball analitzem l'entitat més representativa de l'economia col·laborativa -Airbnb- en una de les destinacions turístiques més populars de la Mediterrània, les Illes Balears. Aquest treball està compost per tres articles o seccions, cadascun dels quals respon a una pregunta d'investigació específica, adoptant el model economètric més adequat per tal de proporcionar els resultats més precisos.

El primer article, amb títol "It's not all about the price. The determinants of occupancy rates in p2p accommodation: a Methodological contribution", té com a objectiu identificar els determinants clau de les taxes d'ocupació d'allotjament, en mercats" peer to peer ".

El segon article, "Stars vs lemons. Survival analysis of peer to peer marketplaces: the case of Airbnb", explora els determinants de supervivència dels serveis oferts a la plataforma Airbnb, i el paper que juga la reputació “online” per reduir l'asimetria informativa en aquest mercat emergent.

El tercer article, "Dynamic pricing and Revenues of Airbnb listings: estimating heterogeneous causal effects", analitza en quina mesura la implementació de preus

(13)

dinàmics, concretament una discriminació de preus intertemporal, afecta els ingressos dels amfitrions d'Airbnb.

Veiem que la contribució d'aquesta tesi és triple: (i) contribueix conceptualment, a l'abordar alguns dels temes que anteriorment s'obviaven en la literatura pròpia dels mercats "peer-to-peer"; (ii) contribueix metodològicament, ja que supera els models adoptats en estudis previs; (iii) contribueix empíricament, ja que estudia una de les destinacions turístiques més populars de la Mediterrània -les Illes Balears- al mateix temps que dona llum sobre l'abast del fenomen de l'allotjament en mercats "peer-to-peer"

i les seves implicacions per a la gestió pública i empresaria.

Summary

This thesis is aimed at exploring the economic mechanisms and rationale of peer-to peer- marketplaces in the tourism sector. In this work we investigate the poster child of sharing economy, Airbnb, in one of the most popular Mediterranean tourism destinations, the Balearic Islands. The current work is a collection of three essays, each one answering a specific research question and adopting the most suitable econometric model in order to provide accurate results.

The first essay, “It’s not all about the price. The determinants of occupancy rates in p2p accommodation: a methodological contribution”, is aimed at identifying the key drivers of occupancy rates in peer-to-peer accommodation.

The second essay, “Stars vs lemons. Survival analysis of peer-to peer marketplaces: the case of Airbnb”, explores the determinants of listings’ survival on Airbnb platform and the role of online reputation in reducing the asymmetry of information in this emerging market.

(14)

The third essay, “Dynamic pricing and revenues of Airbnb listings: estimating heterogeneous causal effects”, investigates at which extent the implementation of dynamic pricing, in specific, inter-temporal price discrimination, affects Airbnb listings’

revenue.

The contribution of this thesis is threefold: (i) it contributes conceptually by covering some topics which were previously overlooked in peer-to-peer literature; (ii) it contributes methodologically by refining the models adopted in previous studies; (iii) it contributes empirically by studying one of the most popular Mediterranean tourism destinations, the Balearic Islands, and shedding light on the extent of the p2p accommodation phenomenon and its managerial and policy implications.

(15)

Introduction

The booming of Sharing Economy.

In recent years an increasing interest for what is world widely known as “sharing economy” (from now referred as SE) has been registered, gaining importance in academic research, media and everyday public discourse.

The multitude of definitions and terms used to refer to this phenomenon may cause confusion regarding what is properly defined as SE and what resides outside of this complex framework. Such confusion does not simply link to a semantic turmoil, due to the maze of definitions and terms, such as Collaborative Consumption (Botsman and Rogers, 2010), Mesh Economy (Gansky, 2010), Crown based capitalism (Arun Sundararajan, 2016), but also to a debate on what is proper sharing and what Belk defines as “pseudo-sharing”(Belk, 2014). As the author states, it is sometimes difficult to discern where “sharing” ends and “commerce” begins. Coming up with a solid and coherent definition of SE is quite challenging and we lack a way to harmonize these diverse systems under a single term because of its extreme heterogeneity (Lamberton and Rose, 2012).

Botsman, and Rogers the famous authors of What’s Mine is Yours: How Collaborative Consumption is Changing the Way We Live (Botsman and Rogers, 2010), defined sharing economy as “an economic system based on sharing underused assets or services, for free or for a fee, directly from individuals”.

Hamari et al. (2016), suggests that it consists in a peer-to-peer based activity of obtaining, giving, or sharing access to goods and services, coordinated through community based

(16)

online platforms. These online communities, known as peer-to-peer platforms, are the virtual markets where the transactions take place.

Graph 1, built upon Rachel Botsman (2013) presentation, displays the relationship between the most used terms. According to Botsman, who points out that such terms are erroneously used as synonyms, the collaborative economy (hereafter CE) is the general container within which the other practices fall. The CE includes the collaborative production, consumption, finance, and learning.

Always according to Botsman, the sharing economy is a specific subset of the CE, concerning a more efficient use of idling capacities. Finally, the peer-to-peer economy would represent the most authentic part of the SE (the “true” sharing) i.e. markets based on mutual trust (among peers), facilitating the sharing and direct exchange of goods or services.

Graph 1. Relationship between the most used terms.

Sharing is nothing new, but the Internet is accelerating its penetration into daily life and the huge interest on the topic, which could date back these last 8-10 years, is the result of important structural and social changes in modern markets. Informal exchanges within consumers-owners have long existed but they usually concerned luxury goods for longer

Collaborative Economy

Sharing Economy

Peer-to-peer economy

(17)

periods of time e.g. leisure boats or tourism properties (Horton and Zeckhauser., 2016).

The activity of sharing was conventional among friends or within the family and generally did not include an explicit payment. The novelty of these internet-based marketplaces is that they are now more “open”, allowing exchange among strangers at a very large geographical scale.

Before going to the catalysts of SE success, it is important to shed some light on the different terms used and provide a general taxonomy.

First of all, we should distinguish the term Sharing Economy from the wider term Collaborative Economy defined as “an economy built on distributed networks of connected individuals and communities versus centralized institutions, transforming how we can produce, consume, finance, and learn” (Bootsman and Rogers, 2010). Always following their theory, Collaborative consumption is the economic model based on sharing, swapping, trading, or renting products and services, enabling access over ownership. It is about reinventing not just what we consume but how we consume.

With the term Peer Economy, we place emphasis on the actors involved in the transaction.

“Peer-to-peer” platforms connect buyers and sellers facilitating the exchange of assets directly between individuals (peers), rather than between an individual and a professional provider (Fraiberg and Sundararajan, 2015).

While the above-mentioned definitions do not include reference to the nature of the transaction, the idea of Gift Economy clearly refers to exchanges without any immediate payment or expectation of future quid pro quo.

The term Gig Economy is related to the employment field and to the breakup of the traditional labour system in favour of independent jobs (gigs) (Friedman, G, 2014).

(18)

Lastly, Arun Sundararajan (2016) coined the interesting term of Crowd-based Capitalism, highlighting the crucial element characterizing this phenomenon i.e. the centrality of people and a new way of organizing the economic activity supplanting the traditional corporate-centred model.

The reason why people participate in the sharing market is still under study and due to the heterogeneity of the activities involved, we probably do not need to obtain a final and univocal answer. In their study, Butcher et al. (2016) suggests sharing attitudes to be firstly driven by social-hedonic motives linked to the excitement of meeting new people.

Secondly, moral motives such as the willingness to help other people and act more sustainable (particularly from an ecological point of view) and lastly driven by monetary motives. Save money and gaining extra income from sharing, has also impact on people attitudes towards this practice. Hamari et al., (2016) confirms that the “economic gains”

is an understandable motivator for many people to involve in this alternative market. P2p platforms have introduced products and services which are often cheaper than the traditional ones (Guttentag, 2015).

The recent economic and financial crisis¹ has been indicated as one of the main catalysis of this new type of economy (amongst other: Heinrichs, 2013; Sundararajan, 2015;

Hamari et al., 2016). The expansion of p2p markets could possibly be a response to the recession and the overconsumption experienced before the financial crisis. People with scarcity of monetary resources can now obtain temporary access to assets which otherwise they could not afford to buy. Internet mediation leads to lower transaction costs (Horton and Zeckhauser, 2016) and allow people to obtain cheaper and faster knowledge

1 The global financial crisis 2007-2008

(19)

about the overall available offer. This has reduced the amount of time spent for information seeking, bargaining and policy enforcement costs (Arribas et al., 2016).

Sundararajan (2015) justifies SE flourishing also by the increase in digital trust and the consumerization of the market. Digital trust could be considered the centrepiece and not only concerning the trust we actually have about the Internet space itself but more specifically regarding the growth of mutual trust among strangers. We assist to gradual changeover from institutional to peer trust where reputation is becoming the new currency (Boostman, 2010).

The increase of mutual trust among strangers is the result of a bottom-up reputational system (Gebbia, 2016). Each person who is active on the internet leaves its footprints.

Those get recorded and become part of what is generally called the online reputation. Our actions are under the eyes of the web community, hence reputation results as the sum of what the others (peers) think about us. In online communities this reputational capital is more spendable than financial situation and represent a calling card. Mutual trust is therefore the bearing wall of the collaborative system as well as the most significant breakaway between traditional market and the peer alternative. Reputation mechanism includes rating (for instance star rating), written reviews and pictures (photographs). This is particularly relevant when the transactions involve services provision with an increase of the so called ‘visual-based trust’ (Ert et al., 2016).

Consumerization of the market is identified as another key driver for the SE. We assist to the “reengineering of production /consumption” where the role of the consumer in the economic system is taking more and more relevance leading to what we previously called

“consumer empowerment”. The informal character of sharing platforms and the possibility for everyone to access the market, makes borders between professional and

(20)

non-professional offer blur (Sundararajan, 2014) with the consequent appearance of the

´prosumer²´ figure. Customer “supremacy” leads to the co-existence of two lines of supply, the traditional one, provided by professional agents, and the p2p offer, where the producers, consumers, funders and distributors often overlap.

Not surprisingly, sharing economy raised doubts and concerns amongst the stakeholders.

Several debates and controversies are arising against the sharing behaviour and the main reason is its legitimacy (EY España, 2015; Hajibaba and Dolnicar, 2018). Traditional businesses felt threatened by these new actors and called for more regulation. Academic community in the law field and legal authorities are still looking for possible solutions to interpret the existing law in the context of p2p markets and deciding whether new regulation is required. Discontent stems from the fact that platforms seem to have much more freedom compared to traditional suppliers and this generates issues concerning consumer protection, data protection, labour laws, discriminations, law relating to payment, taxation, security, liability and insurance (Arribas et al., 2016) and competition fairness (Hajibaba and Dolnicar, 2018).

Even the positive social impact of sharing economy has been questioned. This market, generating global annual revenues for $15bn (with an expected increase to $335bn by 2025³), raises doubts even concerning the goodness of its intrinsic social values. While Botsman and Rogers (2010) focus on the potential of disrupting hyper-consumption in favour of more sustainable and efficient use of assets, with consequent benefits for the environment, Martin (2016) wonders about the real social consequences of sharing.

Opponents see SE more as a reinforcement of neoliberal economic paradigm than a

2 George Ritzer, Prosumer Capitalism, 56 SOC. Q. 413 (2015) (Analyzing the relationship between presumption and capitalism).

3 PWC, 2014. The sharing economy: how will it disrupt your business? Megatrends: the collisions.

Available: http://pwc.blogs.com/files/sharing-economy-final_0814.pdf [Accessed 22nd April 2015]

(21)

sustainable alternative. Critiques attack the lack of concern with issue of environment, casualization of labour and the exclusivity of sharing i.e. “only those who already own, can share assets” advocating for the necessity of a real and equalitarian sharing paradigm.

This is the idea of “pseudo-sharing” occurring when a business relationship masquerading as communal sharing (Belk, 2014).

Maselli et al. (2016) pointed out the scarcity of the existing quantitative research with a consequent difficulty, for policy makers, to implement adequate measures. Be it an occasional or frequent practice, offer assets (services) on platforms, generate supplementary income which should be taken into consideration. Different theories have been mentioned. Someone agrees with a “laissez-faire” policy while on the contrary some cities- citing Maselli et al. (2016)-are considering placing a cap on the income that any single worker can earn from the sector.

The unprecedented scale of the sharing phenomenon, its potential disruptive power on traditional markets and the substantial lack of quantitative studies, justify the necessity of further research on this topic. The vastness of activities included in the “sharing”

panorama and the heterogeneity in such activities, makes impossible to treat them in a single research project. Therefore, in the current thesis, I will focus on the sharing economy in the tourism accommodation sector. Specifically, I will study the posterchild of peer-to-peer platforms, Airbnb, in one of the most popular Mediterranean tourism destinations, the Balearic Islands.

(22)

Peer-to-peer economy: a disruptive innovation

The growth of sharing economy has been certainly fuelled by the rapid evolution of the internet and mobile technologies. The introduction of a new platform business model has led to a disintermediation of traditional industries (Wirtz et al., 2019). People can now interact, and trade, directly with one another at unprecedent scale. While early platforms were devoted to file sharing (like Napster) or the trade of physical goods (like Ebay), current platforms are more related with service provision or temporal-access to underused items or spare capacities (Frenken et al., 2015).

This emerging paradigm is disruptive to traditional markets in two main senses: i. it leads to consumption “reengineering” by allowing consumer to switch from ownership to temporary access (Sundararajan. 2013); ii. from the supply side, it departs from the conventional company-driven paradigm. New entries have proved to be able to capture impressive market shares in a variety of different sectors ranging from transportation and hospitality to banking and risk capital (Avital et al., 2014).

In this second sense, peer-to-peer providers should be considered “disruptors” if they are able to successfully challenge incumbents (Christensen et al., 2015). According to Schumpeter (1947), an innovation consists of an invention that is new to the market, while adopting of something that already exists, it is simply is an imitation.

Despite the negative effects that the arrival of new actors, could have on incumbents performance (Blal et al., 2018; Drahokoupil,2016; Sarlay and Neuhofer, 2020), the disruptive force of peer-to-peer offer and its impact of canonical markets could also be interpreted in Schumpeterian key. New entries could foster the so-called creative destruction (Schumpeter, 1942) i.e. the introduction of new technologies of production lead to an incessant restructuring of traditional schemes.

(23)

Incumbents can respond to successful disruptors by strengthening their current business model or adopting the new existing one (Osiyevskyy, 2015). For disruption triggered by information technology, existing literature found that incumbent firms should redefine their identity to incorporate changing beliefs in the market place and among stakeholders (Tripsas, 2009).

Peer-to-peer economy in the tourism sector: the case of Airbnb

Online platforms currently offer any kind of product and service which could be sorted into five main macro-sectors: lodging and meals, transportation, finance, provision of good/services and employment.

Peer-to-peer marketplaces have flourished particularly within the field of travel and tourism, in which locals supply services to tourists (Ert et al., 2016). The disruptive power of platform mediated transactions in the tourism system, is becoming more and more popular in academic environment.

Tourism is undoubtfully one the sector most affected by the introduction on ICT and it is in continuous adaptation with the new trends. The technological progress and tourism have been going hand in hand for years (Buhalis and Law, 2008); the constant renewing and the change in the social paradigm encourage people to choose alternative types of tourism far away from the traditional schemes.

Inside what is usually called collaborative tourism, or shareable tourism (Tussyadiah and Sigala, 2018), we have different kinds of procedures such as house swapping, ridesharing, couch surfing, dinner hosting and similar innovations epitomize the role of sharing during the tourism experience.

Airbnb has been recognised as the posterchild of p2p economy. Since its foundation in 2008, the platform has experienced an exponential growth, turning to the leading provider

(24)

of travel accommodation within the sharing economy. In its launch, Airbnb, originally called “Air Bed and Breakfast”, offered overnight stays on air mattresses during a San Francisco conference in 2007⁴.

Recently valued at US$38 billion⁵ and operating on a global scale, this commission-based web-platform for room sharers and travellers, is now surpassing the major hotel chains in term of beds availability. Airbnb currently offers private accommodation listings in over 220 countries and regions worldwide (Airbnb, 2019).

Its spectacular growth is still too recent to be thoroughly reflected in academic literature (Oskam and Boswijk, 2016). However, in the last few years we recorded a booming of studies focused on Airbnb from various domains and adopting interdisciplinary approaches.

Based on an accurate recent review of the literature made by Dann et al., (2019), there would be around 120⁶ scientific papers completely focused on Airbnb. Building upon the authors, we identify five main macro-themes.

i. User motives

More than forty papers explore the consumers (guests) and suppliers (hosts) motives to involve in p2p transactions. Most of these studies concerns the reason for the guests to stay overnight in a strangers’ house, rather than opting for canonical supply (among the many, Tussyadiah, 2015; Möhlmann, 2015; Hamari et al., 2016; Hawlitscheck et al.,

4 https://news.airbnb.com/about-us/, accessed on July 31^st, 2020

5 Forbes: https://www.forbes.com/sites/greatspeculations/2018/05/11/as-a-rare-profitable-unicorn-airbnb- appears-to-be-worth-at-least-38-billion/

6 Including also the papers published after Dann et al., (2019) the number increases by more than 50 papers.

(25)

2016a; Guttentag, 2018). So far, less interest has been paid to the reasons for the hosts to enter in this market (Ikkala and Lampinen, 2016; Earnest, 2017).

ii. Reputation systems and trusts

Airbnb describes itself as a platform “promoting people-to-people connection, community and trust around the world”⁷. The reputation systems and the role of trust is undoubtfully one of the hot topics in the sharing economy literature. People choosing peer accommodation needs quality and safety signals as well as hosts need to manage their own reputation to attract guests.

On Airbnb, there are several tools enabling trust among users and the academic literature has mostly focused on three of them. i. the star rating (among the many: Slee, 2013;

Zervas et al., 2015; Ert et al., 2016; Ke, 2017; Teubner and Glaser; 2018; Bridges and Vásquez, 2018); ii. text-reviews and self-descriptions (among the many: Tussyadiah, 2016b; Ma et al., 2017; Bae et al., 2017); iii. host and guest profiles’ pictures (Ert et al., 2016; Deng and Ravichandran, 2017; Ert and Fleischer, 2019); iv. “superhost “status (Liang et al., 2017; Teubner et.al, 2017; Gunter, 2018); v. listings’ pictures. (Xie and Mao, 2017; Chattopadhyay and Mitra, 2019; Teubner et.al, 2017).

iii. Price and Pricing

Most of this branch of literature is based on empirical analysis and most of the data used come from two providers: InsideAirbnb and Airdna. The first, being an independent and non-commercial webpage⁸ and the latter being a private business, supplying data against payment⁹.

7 https://news.airbnb.com/about-us/, accessed on the 07/01/2020

8 http://insideairbnb.com/about.html, accesed on the 01/01/2020

9 https://www.airdna.co/about, accesed on the 01/01/2020

(26)

The literature on Airbnb pricing, could be divided in two subgroups. The first is about the determinants of listings’ price and adopting the hedonic model perspective (among the many: Li et al., 2015; Chen and Xie, 2017; Wang and Nicolau, 2017; Teubner et al., 2017;

Drogu and Pekin, 2017; Gibbs et al., 2018). The second subgroup of studies, investigates the implementation of pricing strategies by Airbnb hosts (Gibbs et al., 2018; Kwock and Xie, 2018; Oskam et al., 2018; Magno et al., 2018).

iv. Economic and social impacts

A branch of literature investigates the economic impact of this new market on the traditional lodging industry (Zervas et al., 2015; Zervas et al., 2017; Xie and Kwok, 2017;

Bla et al., 2017; Benner, 2017; Benítez-Aurioles, 2019; McGowan and Mahon, 2018;

Heo et al., 2019) and on the local housing market (Schäfer and Braun, 2016; Gurran and Phibbs, 2017; Horn and Merante, 2017; Barron et al., 2018; Farronato and Fradkin, 2018;

Heo et al., 2019; Garcia-López et al., 2019). Concerning the latter, one of the key topic is effect of tourism growth, led by the expansion of tourism demand and new tourism offer, on urban urbanism (Freytag and Bouder, 2018; del Romero Renau, 2018; Ioannides et al., 2019; Gonzalez-Perez,2019). With the term “touristification”, we refer to how tourism transform urban spaces into tourist areas that are produced by visitors and their practices (Freytag and Bouder, 2018). Touristification of urban areas is clearly linked to the process of tourism-driven gentrification i.e. the residential displacement of host communities, as a result of overlapping of tourism and residential spaces (Cocola-Gant, 2018).

v. Legal concerns

The rise of Airbnb and its disruptive power in the tourism market, raised doubts on its legality and on the necessity to adapt the existing law to such new business model. The

(27)

existing research, mostly from business and legal scholars, focuses on taxation, both tourism and income taxation (Lee 2016; Cleveland, 2016), consumer protection (Strabowski, 2017) and discrimination (Cui and Zhang, 2016; Kakar et al., 2016;

Eldeman et al., 2017). Another important topic, not largely disucssed in academic literature, is the implication of this informal economy on employee protection and a critical comparison with the employment in the canonical lodging industry. The rise of p2p platforms, has fuelled contingent employment also in tourism and hospitality (Forgacs and Dolnicar, 2017) which is a highly labour-intensive sector. On the one hand, these platforms allow earning extra income without high entry barriers, on the other hand the rise of gigs might lead to higher job precarity (Schor and Attwood-Charles, 2017).

vi. Demand Modelling

Finally, two research streams consider modelling Airbnb demand. The first one concerns the determinants of Airbnb demand, using a causal approach (Gunter and Onder, 2018;

Gunter et al., 2020) while the second is focused on predicting the demand using different methodological approach (Ifrach et al., 2020; Volgger et al., 2019).

For a complete review of all the methodologies implemented and the results obtained, we refer to Dann et al., (2019).

(28)

Thesis structure

Despite its novelty, the peer-to-peer economy has fuelled an intense academic research over the last few years, turning in one of the hot topics in the field of tourism economics and tourism management. Although the on-growing interest for this topic, the variety of studies and approaches used, there is still a severe lack of knowledge on different aspects of the sharing economy.

In the current work, we decided to focus on Airbnb, symbol of the sharing economy in the accommodation sector, and becoming increasingly popular among travellers across the world.

The dynamics triggering both guests and hosts behaviours, deserve far more attention and one way to ensure an adequate comprehension surely requires the access to reliable data.

For these reasons, in the current work, we decided to focus on three key topics which are not yet widely explored in the existing literature and we opted for a more quantitative approach.

The main goal of this thesis is deepening the knowledge on peer-to-peer marketplaces and disclosing mechanisms and behaviours characterizing its participants. The contribution of this work is threefold: (i). conceptually, by covering some specific topics that were not so far considered in the existing literature; (ii). methodologically, by offering statistically sound analysis, thus improving the accuracy of results obtained in previous studies; (iii). empirically, studying one of the most popular Mediterranean tourism destinations, the Balearic Islands and one of the most popular p2p platforms:

Airbnb.

Thanks to a grant agreement between the University of the Balearic Islands and the local Tourism Agency (ATB), we obtained the access to a rich dataset of properties listed on

(29)

Airbnb platform. The dataset, provided by Airdna company, covers geographically the Balearic Archipelago (Mallorca, Menorca, Ibiza and Formentera) during the time span between July 2015 and September 2016. The dataset is composed of two subsets: one, a panel dataset including all the different daily prices set by each host for a specific listing;

two, a cross-sectional dataset including all the features and characteristics of the different properties and hosts.

The body of the work is structured into three main essays, each one developed as a self- contained piece of research, and answering a specific research question. Each chapter could be read independently of the others and has the format of a publishable academic paper.

In chapter one¹⁰, we investigate the key drivers of listings’ demand. From a theoretical perspective, we consider the monopolistic competition as the relevant market regime for accommodation in private houses. Building on Salop model (Salop, 1979), we empirically study the demand for p2p accommodation, proxied by the listings’ occupancy rate. In order to provide reliable estimates, we propose an ad-hoc mixed model, tailored on the characteristics of the occupancy rate distribution i.e. a proportion with a high share of zeros. Our model takes into account the crucial issue of endogeneity between the quantity and the price, often overlooked in similar studies. One of the main results of this first essay is that the demand for Airbnb listings tends to be inelastic, while location and listings’ reputation are within the main drivers of listings’ occupancy rare. In addition, methodologically, this essay highlights the importance of choosing the correct model, since coefficients are highly sensitive to misspecifications.

10 Leoni, V., Figini, P., and Nilsson, J. O. W. (2020). It’s not all about price. The determinants of occupancy rates in peer-to-peer accommodation. International Journal of Contemporary Hospitality

(30)

Chapter two¹¹ investigates the determinants of listings’ survival on the platform. In a market characterized by asymmetry of information between guests and hosts, online reputation and quality signals play a fundamental role. The rationale of this chapter is twofold. First, we estimate the effect of listings’ features, market characteristics and host managerial choices on the survivorship rate. Second, we strive at understanding the rationale of the exit pattern to dispel the replica of a “cyber” market for lemon (Akerlof, 1978). Our results accredit that the customers’ value perception is a crucial factor and a strong determinant of the survival chance. The peer-to-peer offer appears to be subject to a “natural selection” mechanism that dispels the spectre of a cyber “market for lemon”.

Last chapter, chapter three¹², studies the use of intertemporal price discrimination by Airbnb hosts’ and the effect of pricing strategies on the listings’ performance. Adopting the potential outcome framework, often referred as the Rubin causal model, we estimate the causal effect of a price surge (increasing the price as we approach to the service consumption date) on the listings’ revenue. Apart from estimating the average effect, we investigate the difference in such effect across subjects (treatment heterogeneity) and the features that make a pricing strategy particularly favourable or detrimental. In this chapter we opt for a recent machine learning technique known as Causal Forest. Our analysis shows that, on average, price surges have a negative effect on revenues. However, the magnitude of such effect depends on some specific characteristics that affect customers’

sensitivity to price changes.

11 Leoni, V. (2020). Stars vs lemons. Survival analysis of peer-to peer marketplaces: the case of Airbnb. Tourism Management, 79, 104091.

12 Leoni, V., & Nilsson, J. O. W. (2020). Dynamic pricing and revenues of Airbnb listings: estimating heterogeneous causal effects (No. 92). Universitat de les Illes Balears, Departament d'Economía Aplicada.

(31)

Finally, the last part of the thesis is devoted to a wide discussion of key findings and the implications.

Due the nature of the available data and the research questions that we strive to answer, the current work is mostly empirical but not overlooking at the economic theory that indeed guides our intuition and hypothesis.

For calculation purposes and the creation of graph and tables, we employed Stata (v.16) and R studio.

(32)

References

Airbnb (2019), “Fast facts”, available at: https://press.airbnb.com/fast-facts/ (accessed 21 July 2020).

Arribas, G. V., Steible, B., & De Bondt, A. (2016). Cost of non-Europe in the sharing economy: legal aspects. Barcelona: European Institute of Public Administration.

Pobrane z: http://www. europarl. europa. eu/RegData/etudes/STUD/…/EPRS_STU (2016) 558777_EN. pdf (30.10. 2016).

Avital, M., Andersson, M., Nickerson, J., Sundararajan, A., Van Alstyne, M., &

Verhoeven, D. (2014, January). The collaborative economy: a disruptive innovation or much ado about nothing?. In Proceedings of the 35th International Conference on Information Systems; ICIS 2014 (pp. 1-7). Association for Information Systems. AIS Electronic Library (AISeL).

Bae, S. J., Lee, H., Suh, E. K., and Suh, K. S. (2017). Shared experience in pretrip and experience sharing in posttrip: A survey of Airbnb users. Information and Management, 54(6), 714-727.

Barron, K., Kung, E., and Proserpio, D. (2018). The sharing economy and housing affordability: Evidence from Airbnb.

Belk, R. (2014). Sharing versus pseudo-sharing in Web 2.0. The Anthropologist, 18(1), 7-23.

Benítez-Aurioles, B. (2019). Is Airbnb bad for hotels? Current Issues in Tourism, 1-4.

Benner, K. (2016). Airbnb in disputes with New York and San Francisco. New York Times.

(33)

Blal, I., Singal, M., & Templin, J. (2018). Airbnb’s effect on hotel sales growth. International Journal of Hospitality Management, 73, 85-92.

Blal, I., Singal, M., and Templin, J. (2018). Airbnb’s eﬀect on hotel sales growth.

Botsman, R. (2013). The sharing economy lacks a shared definition. Fast Company, 21, 2013.

Botsman, R., and Rogers, R. (2010). What’s mine is yours. The rise of collaborative consumption.

Bridges, J., and Vásquez, C. (2018). If nearly all Airbnb reviews are positive, does that make them meaningless? Current Issues in Tourism, 21(18), 2057-2075.

Bucher, E., Fieseler, C., & Lutz, C. (2016). What's mine is yours (for a nominal fee)–

Exploring the spectrum of utilitarian to altruistic motives for Internet-mediated sharing. Computers in Human Behavior, 62, 316-326.

Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research. Tourism management, 29(4), 609-623.

Chattopadhyay, M., & Mitra, S. K. (2019). Do airbnb host listing attributes influence room pricing homogenously?. International Journal of Hospitality Management, 81, 54- 64.

Chen, Y., and Xie, K. (2017). Consumer valuation of Airbnb listings: a hedonic pricing approach. International journal of contemporary hospitality management, 29(9), 2405- 2424.

(34)

Chen, Y., Mak, B., and Li, Z. (2013). Quality deterioration in package tours: The interplay of asymmetric information and reputation. Tourism Management, 38, 43-54.

Chhabra, D., Healy, R., and Sills, E. (2003). Staged authenticity and heritage tourism.

Annals of tourism research, 30(3), 702-719.

Cocola-Gant, A. (2018). Tourism gentrification. In Handbook of gentrification studies.

Edward Elgar Publishing.

Codagnone, C., Biagi, F., & Abadie, F. (2016). The passions and the interests: Unpacking the'sharing economy'. Institute for Prospective Technological Studies, JRC Science for Policy Report.

Cui, R., Li, J., and Zhang, D. (2016). Discrimination with incomplete information in the sharing economy: Evidence from field experiments on Airbnb. available on SSRN.

Dann, D., Teubner, T., and Weinhardt, C. (2019). Poster child and guinea pig–insights from a structured literature review on Airbnb. International Journal of Contemporary Hospitality Management, 31(1), 427-473.

del Romero Renau, L. (2018). Touristification, sharing economies and the new geography of urban conflicts. Urban Science, 2(4), 104.

Deng, C., and Ravichandran, T. (2017). How Consumers Perceive Trustworthiness of Providers in Sharing Economy: Effects of Photos and Comments on Demand at Airbnb.

Dogru, T., and Pekin, O. (2017). What do guests value most in Airbnb accommodations?

An application of the hedonic pricing approach.

Drahokoupil, J., & Fabo, B. (2016). The platform economy and the disruption of the employment relationship. ETUI Research Paper-Policy Brief, 5.

(35)

Edelman, B., Luca, M., and Svirsky, D. (2017). Racial discrimination in the sharing economy: Evidence from a field experiment. American Economic Journal: Applied Economics, 9(2), 1-22.

Ert, E., and Fleischer, A. (2019). What do Airbnb hosts reveal by posting photographs online and how does it affect their perceived trustworthiness? Psychology and Marketing.

Ert, E., Fleischer, A., and Magen, N. (2016). Trust and reputation in the sharing economy:

The role of personal photos in Airbnb. Tourism Management, 55, 62-73.

España, E. Y. (2015). Impactos derivados del exponencial crecimiento de los alojamientos turísticos en viviendas de alquiler en España, impulsado por los modelos y plataformas ce comercialización P2P. Exceltur, Madrid.

Farronato, C., and Fradkin, A. (2018). The welfare effects of peer entry in the accommodation market: The case of airbnb (No. w24361). National Bureau of Economic Research.

Forgacs, Gabor, and Sara Dolnicar. "14 The Impact on Employment." Peer-to-Peer Accommodation Networks (2018).

Fraiberger, S. P., and Sundararajan, A. (2015). Peer-to-peer rental markets in the sharing economy. NYU Stern School of Business research paper, 6.

Frenken, K., Meelen, T., Arets, M., & Van de Glind, P. (2015). Smarter regulation for the sharing economy. The Guardian, 20(5), 2015.

Freytag, T., & Bauder, M. (2018). Bottom-up touristification and urban transformations in Paris. Tourism Geographies, 20(3), 443-460.

(36)

Friedman, G. (2014). Workers without employers: shadow corporations and the rise of the gig economy. Review of Keynesian Economics, 2(2), 171-188.

Gansky, L. (2010). The mesh: Why the future of business is sharing. Penguin.

Garcia-López, M. À., Jofre-Monseny, J., Martínez Mazza, R., and Segú, M. (2019). Do short-term rental platforms affect housing markets? Evidence from Airbnb in Barcelona.

Gebbia, J. (2016). How Airbnb designs for trust. TED. com.

Gibbs, C., Guttentag, D., Gretzel, U., Morton, J., and Goodwill, A. (2018). Pricing in the sharing economy: a hedonic pricing model applied to Airbnb listings. Journal of Travel and Tourism Marketing, 35(1), 46-56.

Gibbs, C., Guttentag, D., Gretzel, U., Yao, L., and Morton, J. (2018). Use of dynamic pricing strategies by Airbnb hosts. International Journal of Contemporary Hospitality Management, 30(1), 2-20.

González-Pérez, J. M. (2019). The dispute over tourist cities. Tourism gentrification in the historic Centre of Palma (Majorca, Spain). Tourism Geographies.

Gunter, U. (2018). What makes an Airbnb host a superhost? Empirical evidence from San Francisco and the Bay Area. Tourism Management, 66, 26-37.

Gunter, U., Önder, I., and Zekan, B. (2020). Modeling Airbnb demand to New York City while employing spatial panel data at the listing level. Tourism Management, 77, 104000.

Gurran, N., and Phibbs, P. (2017). When tourists move in: how should urban planners respond to Airbnb? Journal of the American planning association, 83(1), 80-92.

(37)

Guttentag, D., Smith, S., Potwarka, L., and Havitz, M. (2018). Why tourists choose Airbnb: A motivation-based segmentation study. Journal of Travel Research, 57(3), 342- 359.

Hajibaba, H., and Dolnicar, S. (2018). Airbnb and its competitors. Peer-to-peer accommodation networks: Pushing the boundaries, 63-76.

Hamari, J., Sjöklint, M., and Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the association for information science and technology, 67(9), 2047-2059.

Hawlitschek, F., Teubner, T., and Gimpel, H. (2016, January). Understanding the sharing economy--Drivers and impediments for participation in peer-to-peer rental. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 4782-4791). IEEE.

Heinrichs, H. (2013). Sharing economy: a potential new pathway to sustainability. GAIA- Ecological Perspectives for Science and Society, 22(4), 228-231.

Heo, C. Y., Blal, I., and Choi, M. (2019). What is happening in Paris? Airbnb, hotels, and the Parisian market: a case study. Tourism Management, 70, 78-88.

Horn, K., and Merante, M. (2017). Is home sharing driving up rents? Evidence from Airbnb in Boston. Journal of Housing Economics, 38, 14-24.

Horton, J. J. and Zeckhauser, R. J. (2016), “Owning, using and renting: some simple economics of the ‘sharing economy’”, NBER working paper No. 22029.

Hughes, G. (1995). Authenticity in tourism. Annals of tourism Research, 22(4), 781-803.

Ifrach, B., Holtz, D. M., Yee, Y. H., and Zhang, L. (2020). U.S. Patent No. 10,664,855.

Washington, DC: U.S. Patent and Trademark Office.

(38)

Ioannides, D., Röslmaier, M., & Van Der Zee, E. (2019). Airbnb as an instigator of

‘tourism bubble’expansion in Utrecht's Lombok neighbourhood. Tourism Geographies, 21(5), 822-840.

Kakar, V., Franco, J., Voelz, J., and Wu, J. (2016). Effects of host race information on Airbnb listing prices in San Francisco.

Ke, Q. (2017, June). Sharing means renting? An entire-marketplace analysis of airbnb. In Proceedings of the 2017 ACM on Web Science Conference (pp. 131-139). ACM.

Kwok, L., and Xie, K. L. (2019). Pricing strategies on Airbnb: Are multi-unit hosts revenue pros? International Journal of Hospitality Management, 82, 252-259.

Lamberton, C. P., and Rose, R. L. (2012). When is ours better than mine? A framework for understanding and altering participation in commercial sharing systems. Journal of Marketing, 76(4), 109-125.

Lampinen, A., and Cheshire, C. (2016, May). Hosting via Airbnb: Motivations and financial assurances in monetized network hospitality. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 1669-1680). ACM.

Lee, D. (2016). How Airbnb short-term rentals exacerbate Los Angeles's affordable housing crisis: Analysis and policy recommendations. Harv. L. and Pol'y Rev., 10, 229.

Li, J., Moreno, A., and Zhang, D. J. (2015). Agent behavior in the sharing economy:

Evidence from Airbnb. Ross School of Business Working Paper Series, 1298, 2015.

Liang, S., Schuckert, M., Law, R., and Chen, C. C. (2017). Be a “Superhost”: The importance of badge systems for peer-to-peer rental accommodations. Tourism management, 60, 454-465.

(39)

Ma, X., Neeraj, T., and Naaman, M. (2017, May). A computational approach to perceived trustworthiness of airbnb host profiles. In Eleventh International AAAI Conference on Web and Social Media.

MacCannell, D. (1973). Staged authenticity: Arrangements of social space in tourist settings. American journal of Sociology, 589-603.

Magno, F., Cassia, F., and Ugolini, M. M. (2018). Accommodation prices on Airbnb:

effects of host experience and market demand. The TQM Journal, 30(5), 608-620.

Martin, C. J. (2016). The sharing economy: A pathway to sustainability or a nightmarish form of neoliberal capitalism?. Ecological economics, 121, 149-159.

Maselli, I., Lenaerts, K., & Beblavy, M. (2016). Five things we need to know about the on-demand economy. CEPS essay, (21/8).

McGowan, R., and Mahon, J. (2018). David versus Goliath: Airbnb and the New York hotel industry. Archives of Business Research, 6(4).

Möhlmann, M. (2015). Collaborative consumption: Reasons for choosing a sharing economy option. In Academy of Management Proceedings (Vol. 2015, No. 1, p. 12728).

Briarcliff Manor, NY 10510: Academy of Management.

Osiyevskyy, O., & Dewald, J. (2015). Explorative versus exploitative business model change: the cognitive antecedents of firm‐level responses to disruptive innovation. Strategic Entrepreneurship Journal, 9(1), 58-78.

Oskam, J., and Boswijk, A. (2016). Airbnb: the future of networked hospitality businesses. Journal of Tourism Futures, 2(1), 22-42.

(40)

Oskam, J., van der Rest, J. P., and Telkamp, B. (2018). What’s mine is yours—but at what price? Dynamic pricing behavior as an indicator of Airbnb host professionalization.

Journal of Revenue and Pricing Management, 17(5), 311-328.

Poon, A. (1993). Tourism, technology and competitive strategies. CAB international.

Sarlay, S., & Neuhofer, B. (2020). Sharing economy disrupting aviation: travelers’

willingness to pay. Tourism Review.

Schäfer, P., and Braun, N. (2016). Misuse through short-term rentals on the Berlin housing market. International journal of housing markets and analysis, 9(2), 287-311.

Schumpeter, J. (1942). Creative destruction. Capitalism, socialism and democracy, 825, 82-85.

Schumpeter, J. A. (1947). The creative response in economic history. The journal of economic history, 7(2), 149-159.

Schor, J. B., and Attwood‐Charles, W. (2017). The “sharing” economy: labor, inequality, and social connection on for‐profit platforms. Sociology Compass, 11(8), e12493.

Slee, T. (2013). Some obvious things about internet reputation systems. Retrieved Oct, 6, 2015.

Stabrowski, F. (2017). ‘People as businesses’: Airbnb and urban micro- entrepreneurialism in New York City. Cambridge Journal of Regions, Economy and Society, 10(2), 327-347.

Stiglitz, J. E. (1989). Imperfect information in the product market. Handbook of industrial organization, 1, 769-847.

(41)

Sundararajan, A. (2013). From Zipcar to the sharing economy. Harvard business review, 1(1), 1-2.

Sundararajan, A. (2016). The sharing economy: The end of employment and the rise of crowd-based capitalism. Mit Press.

Teubner, T., and Glaser, F. (2018). Up or out-The dynamics of star rating scores on Airbnb. In ECIS (p. 96).

Teubner, T., Hawlitschek, F., and Dann, D. (2017). Price determinants on AirBnB: How reputation pays off in the sharing economy. Journal of Self-Governance & Management Economics, 5(4).

Teubner, T., Hawlitschek, F., and Dann, D. (2017). Price determinants on AirBnB: How reputation pays off in the sharing economy. Journal of Self-Governance and Management Economics, 5(4).

Tussyadiah and Marianna Sigala (2018) Shareable tourism: tourism marketing in the sharing economy, Journal of Travel and Tourism Marketing, 35:1, 1-4, DOI:

10.1080/10548408.2018.1410938

Tussyadiah, I. P. (2015). An exploratory study on drivers and deterrents of collaborative consumption in travel. In Information and communication technologies in tourism 2015 (pp. 817-830). Springer, Cham.

Tussyadiah, I. P. (2016). Strategic self-presentation in the sharing economy: Implications for host branding. In Information and Communication Technologies in Tourism 2016 (pp.

695-708). Springer, Cham.

(42)

Tussyadiah, I. P., & Sigala, M. (2018). Shareable tourism: tourism marketing in the sharing economy.

Varma, A., Jukic, N., Pestek, A., Shultz, C. J., and Nestorov, S. (2016). Airbnb: Exciting innovation or passing fad? Tourism Management Perspectives, 20, 228-237.

Volgger, M., Taplin, R., and Pforr, C. (2019). The evolution of ‘Airbnb-tourism’:

Demand-side dynamics around international use of peer-to-peer accommodation in Australia. Annals of Tourism Research, 75, 322-337.

Wang, D., and Nicolau, J. L. (2017). Price determinants of sharing economy-based accommodation rental: A study of listings from 33 cities on Airbnb. com. International Journal of Hospitality Management, 62, 120-131.

Wirtz, J., So, K. K. F., Mody, M. A., Liu, S. Q., & Chun, H. H. (2019). Platforms in the peer-to-peer sharing economy. Journal of Service Management.

Xie, K. L., and Kwok, L. (2017). The effects of Airbnb’s price positioning on hotel performance. International Journal of Hospitality Management, 67, 174-184.

Xie, K., and Mao, Z. (2017). The impacts of quality and quantity attributes of Airbnb hosts on listing performance. International Journal of Contemporary Hospitality Management.

Zervas, G., Proserpio, D., & Byers, J. W. (2017). The rise of the sharing economy:

Estimating the impact of Airbnb on the hotel industry. Journal of marketing research, 54(5), 687-705.

(43)

Zervas, G., Proserpio, D., and Byers, J. (2015). A first look at online reputation on Airbnb, where every stay is above average. Where Every Stay is Above Average (January 28, 2015).

Zervas, G., Proserpio, D., and Byers, J. W. (2015, June). The impact of the sharing economy on the hotel industry: Evidence from Airbnb's entry into the Texas market. In Proceedings of the Sixteenth ACM Conference on Economics and Computation (pp. 637- 637). ACM.

Zervas, G., Proserpio, D., and Byers, J. W. (2017). The rise of the sharing economy:

Estimating the impact of Airbnb on the hotel industry. Journal of marketing research, 54(5), 687-705.

(44)

Chapter 1¹³

It’s not all about price. The determinants of occupancy rates in p2p accommodation:

a methodological contribution.

1.1 Abstract

This chapter identifies the key drivers of occupancy rates in peer-to-peer accommodation.

The applied methodology fits the specific characteristics of this market segment: (i) the peculiar distribution of the occupancy rate (a ratio characterised by a large share of zeros) requires the adoption of a mixed discrete-continuous model; (ii) the insidious issue of price endogeneity is dealt with a control function approach; (iii) the econometric specification takes into account the monopolistic competition, the relevant market regime in the hospitality industry. The model is tested on Airbnb listings in the Balearic Islands (Spain). The occupancy rate of peer to peer properties in the Balearic Islands strongly depends on their geographical location and online reputation. There is a qualitative difference between two groups: listings with positive occupancy rates, which demand tends to be inelastic, and listings with zero occupancy. We found that the price is a not a statistically significant determinant of the latter group membership. This work applies a zero-inflated beta model, never used in previous analyses of occupancy rates, to provide a benchmark for future studies. This procedure allows the estimation of unbiased marginal effects. It thus offers important technical and managerial implications, as a wrong understanding of how occupancy depends on price would deliver ineffective managerial decisions. This paper highlights the importance of methodological choices, since coefficients are highly sensitive to misspecifications of the model.

13 Leoni, V., Figini, P., Nilsson, J.O.W., 2020. It’s not all about price. The determinants of occupancy rates in p2p accommodation: a methodological contribution, International Journal of Contemporary Hospitality Management, forthcoming: https://doi.org/10.1108/IJCHM-05-2019-0464

(45)

1.2 Introduction

A growing interest in the ‘Sharing Economy’ (SE), also known as peer-to-peer (p2p) or collaborative economy, has been documented in academic research, media and public discourse. Although sharing is not new, what is new is the scale of its penetration into daily life and its impact on markets and social change. In this respect, the tourism and travel sector is at the forefront, with relevant experiences such as Blabla Car in road transport, TripAdvisor in information disclosure and quality assessment, and Airbnb in accommodation services.

Most of the recent literature, which is recalled and analysed in the next section, focuses on Airbnb, the posterchild of the success of p2p platforms worldwide and recently valued at US$38 billion¹⁴: this platform is now surpassing the major hotel chains in the number of available beds. The relevance of Airbnb, its presumed disruptive effect on the hospitality market and the worldwide public debate regarding its legality have fomented a widespread academic interest and triggered investigation, among other things, into the motivations behind people’s participation in sharing activities, their impact on traditional service suppliers, and the analysis of revenue performance, price dynamics and occupancy rates (OR).

This essay contributes to the last aforementioned topic: the determinants of demand in the SE. First, it tackles some of the data and methodological issues at stake when working with Airbnb (and similar marketplaces). In fact, it would be erroneous to adopt the same approach used to study OR in traditional accommodation because Airbnb data have three main peculiarities: (i) most of the listings are not constantly on the market and have instead an on/off nature since they might be used by the owner for personal reasons or

14Forbes: https://www.forbes.com/sites/greatspeculations/2018/05/11/as-a-rare-profitable-unicorn-airbnb- appears-to-be-worth-at-least-38-billion/

(46)

rented on different platforms. The listing can be ‘off’ (unavailable) if the host has

‘blocked’ it (using Airbnb terminology) on purpose in order to avoid bookings; (ii) most of the listings are composed of only one unit (unlike multi-room hotels) and hence, in a single day, the OR can only be 0 or 1. Therefore, occupancy rates can only be computed over a meaningful aggregate (either over time or geographically); (iii) because of its fractional nature and the massive presence of zeros (that is, listings that are never booked during the period under investigation), the distribution of OR is peculiar, thus generating critical concerns if basic linear or pure binary models are implemented in the estimation.

To tackle the three above-mentioned features, we apply a mixed discrete-continuous model to the data.

The occupancy rate is one of the main indicators of performance in the hospitality industry, together with the average daily rate (ADR) and the revenue per available room (REVPAR). While it is likely that p2p hosts aim at maximizing REVPAR more than occupancy, investigating REVPAR would require special care when dealing with prices as a determinant factor. However, OR measures the physical quantity sold and is a direct indicator of demand, which can be explained by price, among other things. The focus of this study is hence on the determinants of demand (measured through OR) in the SE, not on performance.

When investigating demand through OR, the endogeneity with price is one of the most important estimation issues to address and a well-discussed topic in tourism economics and other fields. Many studies have shown that the estimation of price elasticity is biased if endogeneity is not accounted for (Kim and Uysal, 1997; Pekgün et al., 2013;

Mumbower et al., 2014; Lurkin et al., 2017). If one assumes that the price is predetermined and exogenous, inaccurate conclusions and policy implications would follow. For instance, a wrong understanding of how occupancy depends on price would

Three essays on peer-to-peer markets: the case of Airbnb

“THREE ESSAYS ON PEER-TO-PEER MARKETS:

THE CASE OF AIRBNB.”

Doctoral thesis

Doctoral Programme of Applied Economics 2020

Veronica Leoni, PhD Candidate

“THREE ESSAYS ON PEER-TO-PEER MARKETS:

THE CASE OF AIRBNB.”

Doctoral thesis

Doctoral Programme of Applied Economics 2020

Veronica Leoni

Thesis Supervisor: Jan Olof William Nilsson, PhD Thesis Supervisor: Paolo Figini, PhD

Thesis tutor: Antoni Riera Font, PhD

Doctor by the Universitat de les Illes Balears

Acknowledgments

Table of Contents