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Abstract
How much should you charge someone to live in your house? Or how much would you pay to live in someone else???s house? Would you pay more or less for a planned vacation or for a spur-of-the-moment getaway? ??? Answering these questions isn???t easy. And the struggle to do so, my colleagues and I discovered, was preventing potential rentals from getting listed on our site???Airbnb, the company that matches available rooms, apartments, and houses with people who want to book them. ??? In focus groups, we watched people go through the process of listing their properties on our site???and get stumped when they came to the price field. Many would take a look at what their neighbors were charging and pick a comparable price; this involved opening a lot of tabs in their browsers and figuring out which listings were similar to theirs. Some people had a goal in mind before they signed up, maybe to make a little extra money to help pay the mortgage or defray the costs of a vacation. So they set a price that would help them meet that goal without considering the real market value of their listing. And some people, unfortunately, just gave up. ??? Clearly, Airbnb needed to offer people a better way???an automated source of pricing information to help hosts come to a decision. That???s why we started building pricing tools in 2012 and have been working to make them better ever since. This June, we released our latest improvements. We started doing dynamic pricing??? that is, offering new price tips daily based on changing market conditions. We tweaked our general pricing algorithms to consider some unusual, even surprising characteristics of listings. And we???ve added what we think is a unique approach to machine learning that lets our system not only learn from its own experience but also take advantage of a little human intuition when necessary.
... With the rapid growth of short-term rentals over the last several years, scholars have begun to examine price determinants of Airbnb listings (Gibbs et al., 2018;Hill, 2015;Lee et al., 2015;Li, Moreno, & Zhang, 2015;Teubner et al., 2017;Wang & Nicolau, 2017). Airbnb has its own hedonic pricing algorithm, the development of which is described by Hill (2015). ...
... With the rapid growth of short-term rentals over the last several years, scholars have begun to examine price determinants of Airbnb listings (Gibbs et al., 2018;Hill, 2015;Lee et al., 2015;Li, Moreno, & Zhang, 2015;Teubner et al., 2017;Wang & Nicolau, 2017). Airbnb has its own hedonic pricing algorithm, the development of which is described by Hill (2015). It uses three major elements in suggesting a listing price: similarity, recency and location. ...
... The recency element adjusts projected listing prices for seasonality and non-cyclical pricing changes. Finally, the location element predicts the impact of location on pricing, given that Airbnb listings are more broadly distributed than hotels, and given the importance of neighbourhood amenities which cannot be determined by a simple distance from the city centre (Hill, 2015). ...
Hedonic modelling techniques have frequently been used to examine real estate valuation, and they have recently started to be applied to short-term rental valuation. Relying on a web-scraped data set of all Airbnb transactions in New York City (NYC) between August 2014 and September 2016, this paper presents the first hedonic regression model of Airbnb to take into account neighbourhood effects and to predict both average price per night and revenue generated by each listing. The model demonstrates that locational factors – above all, transit accessibility to jobs – and neighbourhood variation have a large impact on both price per night and monthly revenue, and further reveals how professionalization of the short-term rental market is driving more revenue to a narrower segment of hosts. Further, the findings suggest that Airbnb hosts earn a significant premium by converting long-term housing in accessible residential neighbourhoods into de facto Airbnb hotels. This premium incentivizes landlords and hosts with properties in accessible neighbourhoods to replace long-term tenants with short-term guests, forcing those in search of housing to less accessible neighbourhoods.
... 2015). According to focus groups conducted by Airbnb, hosts become confused when trying to set their prices (Hill, 2015). Unlike the hotel business, which has trained professionals, industry benchmarking reports and technical tools to help set pricing for rooms, Airbnb units are generally managed by regular people with very limited support of pricing tools. ...
... Although Airbnb has been building pricing tools for hosts since 2012, these tools have been very basic and only focused on simple factors related to characteristics of the property, such as number of rooms, neighboring properties and amenities like parking (Hill, 2015). After years of working on a pricing algorithm, Airbnb recently selectively released a new pricing tool that takes both property characteristics and demand into account (Hill, 2015). ...
... Although Airbnb has been building pricing tools for hosts since 2012, these tools have been very basic and only focused on simple factors related to characteristics of the property, such as number of rooms, neighboring properties and amenities like parking (Hill, 2015). After years of working on a pricing algorithm, Airbnb recently selectively released a new pricing tool that takes both property characteristics and demand into account (Hill, 2015). The pricing of Airbnb listings is unusually complex because, in addition to traditional demand factors such as seasonal changes, local events and location, each Airbnb listing exhibits unique characteristics and hosts often adopt extra roles, such as concierge, cook and tour guide (Hill, 2015). ...
Purpose
The purpose of this paper is to provide a comprehensive analysis of dynamic pricing by Airbnb hosts.
Design/methodology/approach
This study uses attribute and sales information from 39,837 Airbnb listings and hotel data from 1,025 hotels across five markets to test different hypotheses which explore the extent to which Airbnb hosts use dynamic pricing and how their pricing strategies compare to those of hotels.
Findings
Airbnb is a unique and complex platform in terms of dynamic pricing where hosts make limited use of dynamic pricing strategies, especially as compared to hotels. Notwithstanding their limited use, hosts who own listings in high-demand leisure markets, manage entire places, manage more listings and have more experience vary prices the most.
Practical implications
This study identified a great need for Airbnb to encourage dynamic pricing among its hosts, but also warned of the potential perils of dynamic pricing in the sharing economy context. The findings also demonstrated challenges for hotel managers interested in actionable information related to Airbnb as a competitor.
Originality/value
This is the first Airbnb study to use a comprehensive set of data over a continuous period in multiple markets to look at a number of listing and host factors and determine their relation with dynamic pricing strategies.
... However, the uniqueness of the accommodation services offered on Airbnb makes it very difficult to set prices optimally. Hill (2015) described hosts as being confounded when prompted by the platform to set a price and not being able to determine the real market value of their offerings. Airbnb has recognized the significant problem it faces due to inefficient pricing by hosts and has consequently introduced a "price tips" feature in specific markets to support hosts in their pricing decisions (Airbnb, 2015). ...
... Ikkala and Lampinen (2014) used qualitative methods to identify how hosts may price their listings below market in order to be able to choose their exchange partners, but the research does not address the factors that affect price. Hill (2015) discussed the pricing tip tool offered by Airbnb, but did not provide a comprehensive list of the factors that affect price. Ert, Fleischer, and Magen (2016) looked at factors affecting Airbnb prices, but only considered a small number of independent variables related to trust. ...
... They thus elect to charge higher prices, more carefully vet guests and probably are more likely to reject reservation requests. Nonetheless, Airbnb claims that the number of reviews has a huge impact on price (Hill, 2015) and Lee et al. (2015) also found positive impacts of review count on sales but no influence of overall review scores. Together these findings suggest that more research is needed to better understand the influence of reviews in the context of Airbnb. ...
This paper examines the impact of a variety of variables on the rates published for Airbnb listings in five large metropolitan areas in Canada. The researchers applied a hedonic pricing model to 15,716 Airbnb listings. As expected, the results show that physical characteristics, location, and host characteristics significantly impact price. Interestingly, more reviews are associated with a drop in price. This information is useful to hosts who are forming a pricing strategy for their listings as well as for Airbnb, who needs to support them. The paper raises important questions about pricing in the sharing economy and suggests avenues for future research in this area.
... Compared to a wide range of sophisticated revenue management tools adopted by hotels in hotel pricing (Abrate et al., 2012;Chen and Schwartz, 2008a, b;Hung et al., 2010;Schwartz, 2000Schwartz, , 2006Schwartz, , 2008, the price of Airbnb listings is largely affected by hosts' unprofessional pricing behavior, which has led to lower operational and financial performances (Hill, 2015;Li et al., 2016). For instance, Li et al. (2016) argue that hosts are less likely to change room rates when the demand suddenly changes during major holidays and conventions, resulting in lower daily revenues and occupancy rates as well as a higher chance of exiting the market. ...
... For instance, Li et al. (2016) argue that hosts are less likely to change room rates when the demand suddenly changes during major holidays and conventions, resulting in lower daily revenues and occupancy rates as well as a higher chance of exiting the market. This argument was supported by Hill's (2015) focus group observation, where hosts normally were stumped when setting a price for their listings due to a lack of relevant knowledge and expertise. ...
... Many hosts, as observed by Hill (2015), simply picked a comparable price by looking at the price charged by their neighbors, without considering the uniqueness of their listings. Some hosts set a price purposefully to reflect their particular goals in mind which, nevertheless, have little to do with the market value of their listings (Hill, 2015). ...
Purpose
This study aims to identify a wide array of utility-based attributes of Airbnb listings and measures the effects of these attributes on consumers’ valuation of Airbnb listings.
Design/methodology/approach
A hedonic price model was developed to test the effects of a group of utility-based attributes on the price of Airbnb listings, including the characteristics of Airbnb listings, attributes of hosts, reputation of listings, and market competition. We examined attributes as they relate to the price of Airbnb listings, and therefore estimated consumers’ willingness to pay for the specific attributes. The model was tested by using a dataset of 5,779 Airbnb listings managed by 4,602 hosts in 41 census tracts of Austin, Texas in the United States over a period from Airbnb’s launch in Texas up until November 2015.
Findings
We found that the functional characteristics of Airbnb listings were significantly associated to the price of the listings. We also found that three out of five behavioral attributes of hosts were statistically significant. However, the effect of reputation of listings on the price of Airbnb listings was weak.
Originality/value
This study inspires what we call a factor-endowment valuation of Airbnb listings. It shows that the intrinsic attributes that an Airbnb listing endows are the primary source of consumer utilities, and thus consumer valuation of the listing is grounded on its functionality as an accommodation. This conclusion can shed light on the examination of competition between Airbnb and hotel accommodations that are built on the same or similar intrinsic attributes.
... Airbnb has made some attempt to develop pricing tools that the hosts can use to set their listings' prices. However, the first tool launched in 2012 was quite basic, as it only focused on simple factors including, among others, the number of rooms, neighbouring properties and amenities such as parking (Gibbs et al. 2018;Hill 2015). ...
... A second, more elaborate pricing tool, Smart Pricing, was released a few years later, which takes both property characteristics and demand into account. The tool uses machine learning to provide hosts with a suggested price for a specific date that hosts may accept or change according to their perception (Gibbs et al. 2018;Hill 2015). Smart Pricing thus has a purely advisory function, so it may have no real influence on holiday rentals' price because most hosts do not use the tool (Tong and Gunter 2020). ...
Multiple variables determine holiday rentals' price composition in cultural tourism destinations. This study sought, first, to test a model including the variables with the greatest impact on tourism accommodations' prices in these destinations and, second, to demonstrate the proposed model's applicability to cultural city destinations by identifying the adaptations needed to apply it to different contexts. Two cities were selected for the model application-Seville in Spain and Porto in Portugal-both of which are located in different countries and are well-known cultural tourism destinations. The data were extracted from Booking.com because this accommodations platform has adapted its offer to the sharing economy, becoming one of the most important players in the market, and because research on holiday rentals using data from Booking.com is scarce. The results show that the variables used are relevant and highlight the adaptations necessary for specific cultural tourism destinations, thereby indicating that the model can be applied to all cultural tourism destinations. The proposed approach can help holiday rental managers select the correct tools for determining their accommodation units' daily rates according to their product and marketing context's characteristics.
... 10 ingredients of great games: Airbnb has been known to struggle with the optimization of the prizing on the platform and is termed as the biggest challenge faced by Airbnb (Hill 2015). As per calculations, Airbnb hosts miss up to 46% of additional income generation because of inefficient pricing for the property (Gibbs et al. 2018). ...
... The Airbnb hosts tend to get confused when setting the prices for the apartments. (Hill 2015) To tackle the problem, Airbnb, with its "Using data to help set your price," (2015) article, has launched a feature termed as 'Price Tips' (Dara, 2015). ...
Peer-to-peer sharing platforms or multisided platforms (MSPs) like Airbnb, eBay, Uber, YouTube are the new status-quos. The internet era and the wide-scale adaptation of computers and mobile devices can be seen as a vital reason for this advancement in the market for MSPs. This step-up was followed by a surge in the number of MSPs competing in the same market segments and hence, keeping market dominance in respective segments became vital. A key solution to which is, keeping the users involved on the platform. By, giving them intrinsic and extrinsic motivators to look forward to and by creating a sense of belongingness. These motivators are pursued using ‘gamification’ i.e. using game-like elements on the platform. For example, ratings, reputation levels, earnings, benefits which reflect in the virtual world and later in the physical world in the form of monetary perks or benefits. The Superhost status (SHT) used by Airbnb.com is one such motivator. This paper will explore the effect of the SHT of Airbnb on neighbourhoods in Berlin using data acquired from insideairbnb.com by comparing it with the regular host status (SHF), by analysing the statistical differences between the occupancy rates and the revenue per available listing (RevPAR) for SHTs and regular hosts (SHF) with the help of exploratory data analysis. After analysis, the results concluded that the Occupancy and Revenue earned by the superhosts (SHT) are significantly higher compared to the regular hosts. The results prove that potential guests are more receptive towards the superhosts compared to regular hosts.
Keywords: Airbnb, Occupancy Rate, RevPAR, Gamification, Superhost, Regular host, Revenue, multi-sided platforms, Peer-to-peer platform
... For example: Ert et al. (2016) investigated the factors (e.g. competitors' prices, hosts' reviews and trust) affecting the prices of shared accommodation promoted in Airbnb; Hill (2015) analysed how Airbnb hosts can use the pricing tip tool offered by Airbnb for setting competitive prices. Overall, an increasing number of studies stresses that if Airbnb hosts wish to succeed and to be selected by Airbnb users, then the former have to adopt an operational mindset and management practices (e.g. ...
... Managers in the hotel industry do not face a real problem when establishing the price, since they have available technical tools for revenue management and different industry benchmarking reports, but Airbnb hosts do not have sufficient revenue management training and support of pricing resources (Gibbs et al., 2018b). Airbnb hosts are confused when trying to set the price to maximize a listing's revenue performance (Hill, 2015), so different tools can help them setting the right listing price, although they take the final decisions of how much hosts want to charge a listing against others in the same neighborhood. ...
Airbnb hosts’ pricing decisions are the choices hosts make when setting the daily rate for their listing of properties. Some Airbnb hosts have a long run approach, charging lower prices to attract more customers and achieve higher occupancy rates, while others have a short-run approach, charging higher prices to maximize the short-run opportunities from the market. The pricing strategy of Airbnb hosts is the key to their eventual success and plays an important role, since it influences their relationship with customers. Airbnb listings can be hard to price. Therefore, each Airbnb host faces an important decision when entering the market: What is the perfect daily rate to charge such that to achieve their goal? Major aspects, such as property location, type (private room, entire home, etc.) and amenities, target customers, other Airbnb competitors, thinking like a guest, the safety and beauty of the neighborhood, seasonality, etc., must be considered. Considering Airbnb’s exponential growth since it started in 2007, it is obvious that establishing a suitable pricing strategy is vital for any new host. The present paper uses information from the new listings in 2020 to investigate different hypotheses that explore the pricing strategies of Airbnb hosts for their new listings on the market. This study highlights, on one hand, the great need for Airbnb to encourage dynamic pricing among its new hosts and, on the other hand, the challenges faced by these hosts when they establish the price. An important characteristic of this article is the set of theoretical and methodological implications for the pricing strategy for the new Airbnb new hosts. Furthermore, this document reinforces the idea that the pricing strategy differs between cities and countries, emphasizing the strategy in the case of the new Airbnb new hosts.
... Researchers also suggest that the distance to the city center represents a major location factor (Gutiérrez et al., 2017;Wegmann and Jiao, 2017). However, Hill (2015) thought that determining the location element by a simple distance from the city center would mask the heterogeneity of neighborhoods and neighborhood amenities. The accessibility to transportation services (e.g., main road, subway, airport, train station) has been mentioned by many researchers in relevant studies on hotels (Adamiak et al., 2019;Yang et al., 2014;Zhang et al., 2017). ...
Since entering the Chinese market in 2015, Airbnb has become a major player in the Chinese home-sharing arena. This article uses data from 8012 active Airbnb listings in Shanghai and presents three models (linear regression, geographically weighted regression, and random forest) to study the determinants of Airbnb listing prices and incorporate geographic variation in price modeling. Results show that property quality plays a key role in shaping listing prices. Due to Airbnb’s distinctions from traditional lodging in both features and business models, Airbnb pricing determinants differ accordingly. For example, location conditions were found to have a limited impact in regions with established transportation networks. Among the three models, random forest performed best in terms of prediction accuracy. Lastly, practical implications are discussed.
... Finally, the fifth topic is about the role of new technologies on the development of the sharing economy. The sharing economy, in fact, was born before the spread of the internet, but the web, the digitization, and the social media have changed the tourism industry and have contributed to the spread of the sharing economy in tourism, with both positive and negative effects (Molz, 2013;Hill, 2015;Harting et al., 2017;Edelman & Luca, 2014;Cheng & Foley, 2018). ...
The sharing economy is at the centre of current debates involving new technologies, sustainability, big data and stakeholder engagement. This edited volume encourages new theoretical and empirical development on sharing economy studies in the service industries field.
... Demand is frequently reported as one of the most important price determinants in most fields [16,17]. For listings in the accommodation-sharing market, price determinants are more complex [9,18,19]. Prices in Airbnbs are demonstrably impacted by the learned experience of the individual hosts: Wu [20] (2016) suggests that hosts with less experience are at a disadvantage in terms of knowledge and dynamic-pricing skills. In addition to those studies, much evidence suggests that, compared to professional hosts, nonprofessional hosts use potentially ineffective pricing systems, including insufficient price adjustments and inadequate responses to well-known demand shocks [3,21] that determine pricing choices for tourist rental accommodations. ...
Nonprofessional hosts in the P2P accommodation-sharing markets have been demonstrated to be inferior in pricing. The sharing market is increasingly recruiting more professional hosts but is bothered by the disharmony from nonprofessionals’ feelings of being cast aside in this drive. To respond to this practice and disharmony, we develop a modeling framework with price-sensitive customers and earning-based hosts to investigate how varying ratios of professional and nonprofessional hosts affect pricing and impact sharing-market outcomes according to contemporary and long-term success indicators. This study is one of the first attempts to examine whether more professional hosts as supply decision makers is more beneficial to the sharing market. Numerical experiments for model analysis led to two primary managerial implications. A high ratio of professional hosts does not necessarily maximize indicators of hosts’ earnings, platform’s profit, or supply size, indicators that measure the accommodation-sharing market’s contemporary and long-term success. In addition, the market improves with magnified differences in the unique features of two types of hosts and they can cater to customers’ experiences and expectations, differentiating the market positioning of the two types of hosts.
... Therefore, conditional on the hedonic attributes, there might be a spatial price formation process by which hosts mimic the prices of their neighbours. Indeed, Airbnb offers a pricing algorithm that suggests prices to hosts based on location and similarity (Hill, 2015). ...
This paper studies the existence of two different supply operators in the peer-to-peer accommodation rental market for the city of Madrid. We specifically analyse spatial dependencies in price formation and whether the so-called professional hosts (i.e. those who have several Airbnb listings) set prices differently from single-property hosts. To this end, hedonic price models are estimated with and without spatial price dependence. Listings' structural characteristics and accessibility measures to transportation hubs and sightseeing spots are considered in the regressions. Our results provide clear evidence that price mimicking is higher among non-professional hosts whereas professional hosts set prices more independently.
... For more information about the pricing algorithm, seeHill (2015) andYee and Ifrach (2015).2 Berlin, Munich, Hamburg, Cologne, Frankfurt a.M., Stuttgart, Dresden.3 ...
... 2015). The main idea behind this result is that hosts are confused when setting their prices (Hill 2015). In order to overcome this situation, Airbnb has created tools for hosts to improve their pricing process (Airbnb 2019). ...
The purpose of our study is to analyse whether consumers’ preferences – evaluated
through social media –to different tourist sites have a significant impact on Airbnb’s
prices. With this purpose, we develop an empirical application in the city of Barcelona
where we evaluate the impact of Instagram – identifying the main points of interest in
this city- on listings’ prices. We estimate a micro-territorial hedonic model on Airbnb’s
prices against different subsamples established according to listings’ characteristics.
Our results show a negative and significant effect for the representative variable of the
geographic distance of Airbnb’s listings to the tourist sites on Instagram. In particular,
each additional 10% increase in the distance from Instagram tourist spots to Airbnb’s
listings resulted in a 2.7% decrease in Airbnb’s listing price in Barcelona. This study
provides additional evidence about the relevant role of social networks when
accommodation offerings are examined, even when we consider accommodations
included in the sharing economy.
... The sizes of Airbnb listings are normally larger than hotel rooms, thus accommodating a larger number of people (Gibbs et al., 2018a(Gibbs et al., , 2018b. Concerning managerial competences, Airbnb providers are described as "amateur innkeepers" (Kreeger and Smith, 2017), "irrational" (Cai et al., 2019), "inefficient" and "confused" (Hill, 2015). Hosts are micro-entrepreneurs (Stabrowski, 2017) with minimal business experience (Chen and Xie, 2017), not usually supported by benchmarking reports (Gibbs et al., 2018b). ...
Purpose
This paper aims to examine the question of whether commercial, peer-to-peer accommodation platforms (Airbnb, in particular) and hotels are in fierce competition with each other with the possible presence of substitution threats, and compares the time series of the occupancy values across two supplier types.
Design/methodology/approach
The cities of Milan and Rome are used as case studies for this analysis. To assess the extent of synchronization, the series of Airbnb and hotels are transformed into a series of symbols that render their rhythmic behavior, and a mutual information metric is used to measure the effect.
Findings
The results show that Airbnb hosts and hotels have different seasonal patterns. The diverse occupancy trends support the absence of direct competition between Airbnb and hotels. The findings are consistent in the two analyzed cities (Milan and Rome). Interestingly, there are higher similarities between seasonal occupancy series of Airbnb listings in Milan and Rome, on one side, and hotels in Milan and Rome, on the other, than between Airbnb and hotels in the same city.
Research limitations/implications
The findings show a progressive de-synchronization (within mutual information) among the five groups of Airbnb hosts triggered by the rising professionalization degree. This result suggests the existence of a partial different business model for multi-listing hosts.
Practical implications
The study illustrates an absence of any substitution threat between Airbnb and hotels in both cities. This could have important consequences, especially for the pricing and revenue management policy. In fact, the higher the substitution threat, the higher the attention that Airbnb entrepreneurs should pay to the pricing strategy implemented by hotels, and vice versa.
Originality/value
This study sheds new light on the competition threat between Airbnb and hotels. In this study, hotels and Airbnb hosts appear as two very separate markets.
... Airbnb providers are free to accept them or change the price (Moreno-Izquierdo et al., 2018). Therefore, the research of pricing determinants and more generally, the study of performance antecedents is particularly complex in the field of Airbnb, considering the overlapping (and the interaction) between the following variables: listing characteristics (Perez-Sanchez et al., 2018;Xie et al., 2019a), host attributes (Moreno-Izquierdo et al., 2019;Xie et al., 2019b), the machine learning approach (Chattopadhyay and Mitra, 2019;Gibbs et al., 2018b;Hill, 2015), and relationships within the local context (Falk et al., 2019;Tang et al., 2019). A limited number of studies makes the current knowledge fragmented and partially controversial (Kwok and Xie, 2019), and not surprisingly, many authors require new empirical studies (Gibbs et al., 2018b;Oskam et al., 2018;Wang and Nicolau, 2017). ...
This paper explores the performance determinants of Airbnb listings, analyzing three research questions. First, the study investigates the different effects generated by the antecedents on price and revenue; second, it ranks different groups of variables; third, it distinguishes between private rooms and entire homes or apartments. These research questions are addressed by analyzing Airbnb listings in Milan, a business city where the sharing economy is growing fast. In particular, the study will use the monthly data of all Airbnb listings in Milan recorded by AirDNA during the period from November 2014 to June 2019, which consists of 323,184 total observations. Some hedonic price models are calculated, adding the Shapley value approach. Empirical findings show some important differences between price and revenue determinants. Furthermore, listing type and size, along with location and seasonality, are by far the most important factors that explain performance differentials among Airbnb properties.
... In this regard, although a pricing policy for Airbnb hosts entails the assessment of utilitybearing attributes or quality signs, and to what extent they matter to guests to gain value for their expenditure, research has detected inefficient pricing by Airbnb hosts due to: (1) the uniqueness of the rental services offered on Airbnb (Gibbs et al., 2018) and (2) the emotional drivers applied by non-professional hosts (cf. Ikkala & Lampinen, 2014; see also Hill, 2015;Li, Moreno, et al., 2016, among others). Li, Pan, et al. (2016) find that Airbnb non-professional hosts are apparently unable to optimally set prices. ...
Our research aims to address the following research questions: (a) to identify guests’ hidden experiences in a distribution of terms over a fixed vocabulary by analysing a bulk set of online reviews through the process of text mining, and in particular, (b) to assess if the Airbnb guest experience represented in them can be used to enhance Airbnb services. On the other hand, our study analyses the relationship between the topics identified and Airbnb pricing, and mainly measures the influence of gender as a moderating cue. In this regard, a growing body of research has emerged to examine gender differences in leisure participation. In particular, our study concludes how the guests’ gender affects the contributions of listings’ features in price prediction. Females are more intrinsically motivated and preferentially mention, for instance, the Airbnb accommodation’s location and the gratifying (local) experiences in their narratives. On the contrary, male guests highlight hygiene and apartment facilities. To sum up, our research provides design guidelines to reflect the willingness to hire an apartment, offering insights for research and practice, and allowing the layout of pricing-recommendation systems.
... We tweaked our general pricing algorithms to consider some unusual, even surprising characteristics of listings. And we've added what we think is a unique approach to machine learning that lets our system not only learn from its own experience but also take advantage of a little human intuition when necessary" (Hill 2015). ...
METHODOLOGY It is a conceptual paper based not on the results of particular field research. Its methodology is grounded on desk research, which contained the review of the scientific literature and observations of research institutions analyzing the practical development going on nowadays in the different fields of the economy. MOST IMPORTANT RESULTS The study proves that as a result of the fourth industrial revolution, the elbowroom of pricing is growing. The growing zone for price-setting makes it necessary for companies to use new and more sophisticated methods among them price personalization. Personalization requires lots of information about the individual behavior of customers. The advanced analytics of Big Data can deliver real-time information; however, if they are used without control, the privacy of customers may become hurt. The paper discusses the effects of the General Data Protection Rules (GDPR) on companies and customers. RECOMMENDATIONS The paper concludes that price personalization, which is a kind of price discrimination, should be used by sellers with care. The effects of GDPR will be not limited to the European Union; it will have worldwide consequences.
... O primeiro fator, nomeado 'pricing e impacto econômico' (alfa de Cronbach = 0,912), é composto por seis artigos que trabalham especificamente temáticas financeiras. Abordando questões como a precificação de quartos de hotéis (Zhang, Ye, & Law, 2011), de 'BnBs' (Monty e Skidmore, 2003), de serviços de hospedagem vinculados à economia do compartilhamento (Wang & Nicolau, 2017) ou ainda discutindo o algoritmo de precificação da Airbnb (Hill, 2015), contribuem para reforçar a discussão acerca do impacto financeiro promovido pela Airbnb na indústria hoteleira (Zervas, Proserpio, & Byers, 2017). Aqui, o estudo de Rosen (1974) cria um ponto comum ao contribuir para a discussão sobre aspectos hedônicos e seus impactos na disposição do quanto pagar. ...
Objetivo do estudo: O objetivo desta pesquisa é apresentar as características dos artigos que analisam a Airbnb publicados nos principais periódicos científicos internacionais e nos periódicos científicos nacionais pertencentes ao campo do Turismo e ao campo da Administração entre 2008 e 2018.Metodologia/abordagem: Este estudo fundamenta-se em uma abordagem de método misto, empregando uma revisão sistemática seguida uma análise bibliométrica realizada por meio de um estudo de co-citações a partir de uma análise fatorial exploratória.Originalidade/Relevância: Até o momento não foi identificada nenhuma revisão sistemática sobre a Airbnb que apresentasse as características das produções apontando as similaridades e diferenças entre a produção internacional publicada nos principais periódicos científicos e a produção científica nacional, nem uma análise bibliométrica que indicasse a constituição teórica que sustenta as investigações sobre Airbnb.Principais resultados: Os artigos da amostra concentram-se em alguns periódicos, apresentando um crescimento exponencial ao longo dos anos, sendo majoritariamente não baseados em teoria, utilizando abordagem quantitativa, empregando como principais técnicas estatísticas regressões e SEM, focando em efeitos econômicos e empregando como principal unidade de análise dados secundários. O autor com mais publicações é Daniel Guttentag. As citações feitas pela amostra fundamentam-se em três fatores, nomeados (1) ‘Pricing e Impacto Econômico’, (2) ‘Sustentáculo Teórico’ e (3) ‘Estado da Arte e Estudos Futuros’.Contribuições teóricas/metodológicas: Este artigo agrega a literatura sobre tema ao avançar os achados feitos por Humes e Freire (2018) e Dann, Teubner e Weinhardt (2019) por meio da realização de um estudo bibliométrico.
... in Figure 3.2a), emphasising that hosts take full control of their listing in terms of availability, prices, rules and how much interaction there need be between hosts and guests. Airbnb has been developing their pricing tools, Smart Pricing, over the years, and their calculation is based on a machine-learning algorithm considering the type of listing, locations, seasons, demands, etc. (Hill, 2015). Unlike other platforms who control their price algorithms, such as Uber, Airbnb only suggests a price to hosts, but the final decision on the price depends on each host (Gibbs et al., 2018b). ...
This research contributes to advancing our knowledge on the topical issue of the proliferating use of digital platforms, specifically the home-sharing platform, Airbnb. The aim is to answer the research question of how to measure possible impacts stemming from the adaptation of Airbnb as a form of digital platform economy. A set of various spatial analysis methods and a predictive model were constructed utilising novel datasets such as Airbnb accommodation data and Zoopla rental price data, as well as open government datasets such as dwelling types, housing tenure, and points of interest. This gave rise to a set of methods and results that are four-fold. On the one hand, the spatial distribution and temporal trends, is analysed using the space time cube. These findings show that for London and San Francisco, Airbnb tend to be centrally located, favouring residential areas. In addition, this method also shows the seasonality of the use of the platform. Secondly, using Geographically Weighted Regression (GWR) and the multi-scale form of GWR, it is possible to look at the local scale and the influencing factors for Airbnb locations, and the thesis shows that these are related to functional elements such as hotels, food and beverages availability, and access to public transport links. Thirdly, how Airbnb may be disrupting the housing system by exacerbating the already problematic condition posed by the housing crisis in London is explored. To do this, the focus is shifted onto Airbnb misuse, defined from entire property listings that do not conform with the local regulation, and we look at the relation between those listings and residential areas that are experiencing rapid rental price changes. These changes are measured by extracting the difference in rents for certain years based on the Zoopla longitudinal data. The results conclude that indeed, there is a linear relationship between them, indicating that Airbnb might be putting pressure on housing provision. Lastly, a gravity model is constructed to forecast possible future locations for Airbnb. These are determined in terms of proximity to touristic locations, the historical Airbnb supply, and rental prices. The estimated Airbnb rental distribution based on the model follows a similar distribution to the actual rent derived from Zoopla rental price data. This last outcome suggests that prime Airbnb locations are often located in highly-priced residential neighbourhoods. These often are prime areas for residential location, that now have competing interests with Airbnb conversion. Overall, this thesis provides an analytical perspective that can prompt a conversation on best practices which mitigate the adverse impact of over-saturated short-term rental adaptation in urban settings. Keywords: Platform economy, Airbnb, Zoopla, tourism, housing, gravity model.
... Ikkala and Lampinen (2014) used qualitative methods to recognize how hosts may price their listing below the market price to be able to select their exchange partners, but their research did not report the factors that influence the price. Hill (2015) studied Airbnb's pricing tip tool; however, the study did not offer a wide-ranging list of the features that influence the price. Other research has explored pricing in Airbnb listings as a strategic marketing decision but not factors influencing pricing (Amaro et al., 2017;Tussyadiah & Sigala, 2018). ...
Recognizing that the pricing strategy of the newly emerging online shared accommodation industry would be different from that of the traditional hotel industry, this study attempted to identify the variables that are the main determinants of the peer-to-peer tourist accommodation price. Using a data set of Airbnb accommodation listings for Toronto, the study established a relationship between room pricing and various listing variables and identified a reduced number of listing attributes that influence the room price significantly. Focusing on a reduced number of important variables, Airbnb hosts can not only increase average profit but would also give tourists a better rental experience. Along with traditional multiple regressions approach, the study also applied two different approaches and found that the analysis of hedonic pricing using nonlinear and nonparametric approaches is quite promising.
... Some hosts list properties at reduced prices to solicit a higher number of requests and more options for guest selection (Ikkala and Lampinen, 2014), but many hosts cannot generate maximum possible revenue due to ineffective pricing (Gibbs et al., 2018a). Dynamic strategies also depend on the hosts' motivation, professionalism, and access to pricing tools (Hill, 2015). ...
... Some hosts list properties at reduced prices to solicit a higher number of requests and more options for guest selection (Ikkala and Lampinen, 2014), but many hosts cannot generate maximum possible revenue due to ineffective pricing (Gibbs et al., 2018a). Dynamic strategies also depend on the hosts' motivation, professionalism, and access to pricing tools (Hill, 2015). ...
The objective of this study is to review the extant sharing economy (SE) literature. Applying a systematic literature review approach, this study thematically synthesizes the findings of 219 articles on sharing economy. It explores the definitional dilemma, sharing economy as a phenomenon and key theories used in the literature. It analyses the stakeholders and their motivations for participating in SE, which is mainly present in the accommodation and transportation sectors. We discuss various facets of these two sectors. The study shows how SE firms operate with novel business models with unique revenue streams. It synthesizes the challenges that SE faces. This study points out SE's economic, social, and environmental impacts. It highlights the lack of regulations and policies for SE around the world. Finally, we provide the implications of this work and suggest future research avenues.
... In 2015, Airbnb introduced a dynamic pricing feature known as 'Smart pricing'. If a host turns on the feature, Airbnb suggests a price and a host can choose to accept the suggestion or alter it (Hill, 2015). Gibbs, Guttentag, Gretzel, Yao, and Morton (2018) found that dynamic pricing was not uniformly used by hosts in five cities in Canada. ...
The sharing economy has allowed people from all over the world to more effectively utilize their assets. Owners or controllers of assets in the sharing economy are free to set any price they want subject to prevailing market demand as long as they operate in an imperfectly competitive market environment. This paper examines how various characteristics of an Airbnb listing (size, number of photos, ratings, host responsiveness, superhost status, distance from city centre, etc.) affect the prices of accommodation and determines which factors strongly affect price using weighted least squares (WLS) and quantile regression. A hedonic pricing model was developed and applied to data from the cities of Barcelona, Madrid, and Seville to determine how the different characteristics of an Airbnb listing affect the price of accommodation in these major three Spanish tourist cities. The estimation results, which are resilient to various robustness checks, indicate that overall rating as well as characteristics indicative of the size of the accommodation have the strongest positive influence on price, while the number of reviews and distance from the city centre have the strongest negative influence on price.
... Others have a set target they wish to make per month and price accordingly, especially hosts who are students or free-lancers whose economic goals may change from month to month (Tomalty, 2014). Some hosts and owners use the pricing algorithms available on platform sites or revenue management sites, which include making pricing suggestions based on similar properties nearby and market trends (Hill, 2015). A comparison of P2P accommodation rates on Airbnb and Homeaway in the same market found significant differences in many markets, such as Austin where Airbnb's room rates were typically higher than Homeaway (Peltier, 2015). ...
The sharing economy in general and peer-to-peer (P2P) accommodations have attracted the attention of academic researchers; research in this area has exponentially increased over the past few years. These researchers have come from a variety of disciplines including tourism and hospitality, business, psychology, law, and cultural studies. The P2P accommodation segment has several aspects which make it unique, even in the realm of the sharing economy. This critical review examined 107 peer-reviewed articles regarding P2P accommodations published between 2010 and 2017. Topics explored have been: consumer behavior, legal issues, conceptualizing P2P accommodations, in the sharing economy, revenue management, trust and mistrust, P2P accommodations and hotels, owner motivations, affordable housing concerns, and emerging fields. Gaps in the literature and future research topics are explored.
... To assist in pricing decisions, Airbnb employs AI and ML to help hosts make pricing decisions about their property. Pricing is a complex process for hosts given traditional demand factors, such as seasonal changes, local events, and location, as well as the fact that each listing exhibits unique property characteristics (Hill, 2015). Airbnb provides pricing assistance with an ML algorithm that makes pricing suggestions for each date that a host makes a property available. ...
Artificial intelligence (AI) is at the forefront of a revolution in business and society. AI affords companies a host of ways to better understand, predict, and engage customers. Within marketing, AI’s adoption is increasing year-on-year and in varied contexts, from providing service assistance during customer interactions to assisting in the identification of optimal promotions. But just as questions about AI remain with regard to job automation, ethics, and corporate responsibility, the marketing domain faces its own concerns about AI. With this article, we seek to consolidate the growing body of knowledge about AI in marketing. We explain how AI can enhance the marketing function across nine stages of the marketing planning process. We also provide examples of current applications of AI in marketing.
... Sharing economy platforms in other industries have price recommendation tools to support the participants on the platform. Airbnb has developed a data mining tool (Hill, 2015) which uses features of a listing (among other parameters such as future demand) such as location, amenities, guest reviews to predict a price range of a listing and associated probabilities of success for a sale. Tang and Sangani (2015) predict price category and neighborhood category for Airbnb listings in San Francisco. ...
This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders, and scale of operations among others to estimate a price range for suppliers' services. Data was gathered from existing marketplace websites, which was then used to train and test the model. The model demonstrates an accuracy of 65% for US based suppliers and 59% for Europe based suppliers to classify a supplier's 3D Printer listing in one of the seven price categories. The improvement over baseline accuracy of 25% demonstrates that machine learning based methods are promising for network based pricing in manufacturing marketplaces. Conventional methodologies for pricing services through activity based costing are inefficient in strategically pricing 3D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.
... Sharing economy platforms in other industries have price recommendation tools to support the participants on the platform. Airbnb has developed a data mining tool (Hill, 2015) which uses features of a listing (among other parameters such as future demand) such as location, amenities, guest reviews to predict a price range of a listing and associated probabilities of success for a sale. Tang and Sangani (2015) predict price category and neighborhood category for Airbnb listings in San Francisco. ...
Purpose:
This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices.
Design/methodology/approach:
A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders, and scale of operations among others to estimate a price range for suppliers’ services. Data was gathered from existing marketplace websites, which was then used to train and test the model.
Findings:
The model demonstrates an accuracy of 65% for US based suppliers and 59% for Europe based suppliers to classify a supplier’s 3D Printer listing in one of the seven price categories. The improvement over baseline accuracy of 25% demonstrates that machine learning based methods are promising for network based pricing in manufacturing marketplaces.
Originality/value:
Conventional methodologies for pricing services through activity based costing are inefficient in strategically pricing 3D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.
... Therefore, various studies have explored the room pricing determinants with respect to the demand side (Becerra et al., 2013;Chen and Rothschild, 2010;Espinet et al., 2003;Lee and Jang, 2012;Schamel, 2012;Thrane, 2007;Yang et al., 2016;Guttentag et al., 2018) and supply side (Heo and Hyun, 2015;Lee, 2011;Masiero et al., 2015) in the accommodation industry. Hosts from a peer-to-peer rental platform like Airbnb are often unable to understand the actual room pricing to be influenced by their listing offerings during the setting of their room price on the rental platform (Hill, 2015). A comprehensive literature review of the influence of particular listing attributes on room price during 2016-18, encompassing a sample dataset of Airbnb listings in various countries, methodologies applied, and the dimension of explanatory variables (for exploring room pricing determinants), is presented in Table 1. ...
This research primarily contributes to the identification of the important variables that significantly influence room pricing on the Airbnb rental platform. The study adopted a comparative approach by using three different methods—OLS, random forest, and decision tree—and applied it to a vast amount of data from the Airbnb listing dataset of 11 US cities. Each individual amenity mentioned in the listing in the textual format was used as an independent variable. We also added six other common listing variables to obtain interesting insights into the influence of these variables from the perspective of the host, guest, traveler, and tourist. Apart from identifying city-specific variable importance using different models, we estimated a composite score of variable importance that may be helpful to generalize the influence of amenities and other explanatory variables in the presence of any city-specific regional heterogeneity on the shared rental platform.
... Airbnb hosts have repeatedly been reported that they are inefficient in setting the "right" listing price (Learnairbnb.com, 2015) or confused when trying to set up the price to maximize a listing's revenue performance (Hill, 2015). ...
This study examined the effects of pricing strategies, including price positioning and dynamic pricing, on an Airbnb listing's revenue with a particular interest on the performance difference between multi-unit and single-unit hosts. A series of econometric analyses were performed using a dataset of 320,243 listings managed by 216,058 hosts in 10 major U.S. markets across a longitudinal period from October 2014 to July 2017. The results suggest while price positioning and dynamic pricing have positive impacts on an Airbnb listing's revenue performance, a multi-listing host performs better than a single-listing host in driving a listing's revenue, through (a) positioning a listing at a higher price than the average listing price in a neighborhood and (b) adopting less dynamic pricing strategies. Our study fills the void of pricing research in room-sharing economy literature and generates important insights about the pricing strategies and the consequent performance outcome between two different host types.
... Beyond these currently discussed topics, several other, less obvious research gaps come to mind. First, two of the most recent developments include experiences and the platform's automated pricing algorithm (Hill, 2015;Gibbs et al., 2017b). With the introduction of experiences in late 2016, Airbnb attempts to tap into an additional business potential, where locals offer (i.e. ...
Purpose: A growing body of research from various domains has investigated Airbnb, a two-sided market platform for peer-based accommodation sharing. We suggest that it is due time to take a step back and assess the current state of affairs. In this paper, we hence conflate and synthesize research on Airbnb.
Design/methodology/approach: To facilitate research on Airbnb and its underlying principles in electronic commerce, we hence present a structured literature review on Airbnb.
Findings: Our findings are based on 118 articles from the fields of Tourism, Information and Management, Law, and Economics between 2013 and 2018. Based on this broad basis, we find that a) research on Airbnb is highly diverse in terms of domains, methods, and scope, b) motives for using Airbnb are manifold (e.g., financial, social, environmental), c) trust and reputation are considered crucial by almost all scholars, d) the platform’s variety is reflected in prices, and e) the majority of work is based on surveys and empirical data while experiments are scarce.
Originality/value: Our study provides a comprehensive overview of work on the accommodation sharing platform Airbnb, to the best of our knowledge, representing the first systematic literature review. We hope that researchers and practitioners alike will find this review useful as a reference for future research on Airbnb and as a guide for the development of innovative applications based on the platform’s peculiarities and paradigms in electronic commerce practice. From a practical perspective, the general tenor suggests that hotel and tourism operators may benefit from a) focusing on their core advantages over Airbnb and differentiating features and b) aligning their marketing communication with their users’ aspirations.
Implications: Based on the present assessment of studied topics, domains, methods, and combinations thereof, we suggest that research should move towards building atop of a common ground of data structures and vocabulary and that attention should focus on the identified gaps and hitherto scarcely employed combinations. The set of under-represented areas includes cross-cultural investigations, field experiments and audit studies, the consideration of dynamic processes (e.g., based on panel data), Airbnb’s “experiences” and automated pricing algorithms, as well as the rating distribution’s skewness.
... Concerning Airbnb fees, it is worth mentioning the existence of a tool for the dynamic pricing, offered and developed by the platform itself, which gives the tenants specific quantitative suggestions about the prices to be applied. This tool, starting from an automatized research of comparables, calibrates fees on the basis of peculiar characteristics of the asset, on the attractiveness of the area (with distinctions on a seasonal basis) and on the intensity of the demand (Hill 2015). Therefore, it is a service that facilitates the entrepreneurial action for those who, under normal circumstances, would not be able to operate in such a dynamic, diverse and non-transparent market not having even the basic knowledge required. ...
During the past decade, new forms of the sharing economy have been developed as an alternative tool for the satisfaction of heterogeneous needs. From both short- and long-distance transportation to the rental of apartments, this expanding economy has created a new private supply of those services that, traditionally, were only provided by professionals. In parallel with its unexpected development, the size of its impact on relatively traditional economic sectors has grown too. This fact has determined the need, an ever more one, for studies on the dynamics of this phenomenon. The short-term housing-rental sector plays a central role within the universe of the sharing economy. In fact, it has spawned a new rental market, parallel to the traditional one, that is characterized by short and very-short-term contracts and by the immediate and easily accessed encounter between demand and supply, made possible through the use of digital platforms. Airbnb plays, without any doubt, a leading role in this phenomenon. Born in the US, it has had an astonishing global expansion in just a few years. In fact, currently, it operates in 191 countries, homogeneously, that is to say, without trying to match the enormous differences between local legislation on tourism and real estate to major procedural and systemic differences. This service was born in 2007, but it is only since 2013 that, in Italy, its presence has become massive; until then, only a few hundred listings were published for the whole nation. Its development has thus been exponential, and as the projections confirm, in all likelihood, in the next decade it will maintain the same rate of growth. By now, it has already been several years that researchers and operators have highlighted the influence of Airbnb in the market of tourist accommodations. What is still poorly detailed, perhaps because of its low visibility and immediacy, is the relationship that this kind of short-term rental contract has with the traditional real estate market. Therefore, the intent of this study is to take the first step towards the comprehension of the impact that the uncontrolled growth of the sharing economy has, specifically, on the Italian real estate market. Logically, the rental submarket is the one that is affected the most by the growth of the sharing economy. In fact, compared to traditional rentals, contracts that are stipulated with Airbnb provide lessors with much higher revenues and much lower restrictions. That is one of the reasons that the Airbnb’s user base has experienced such an enormous expansion. However, this, has also led to the fear that these new dynamics are liable to distort the traditional real estate market.
The economy has suffered unprecedentedly during the COVID-19 pandemic, including the shared accommodation sector. This study aims to discover the pandemic consumer behavior model for the recovery of the sector as well as investigate the economic resilience of tourists’ behavior to prevent and control the normalized pandemic. Most of the resilience literature discussed the level of economic and industry revitalization. There are relatively few studies on the individual level of tourists’ resilience. Therefore, we applied the adjusted theory of planned behavior with pandemic-related intrinsic factors to construct the research model, which is analyzed by the SEM approach. The results show that perceived risk affects tourists’ perceived value, trust, and behavioral attitude when repurchasing shared accommodation during the pandemic. The repurchase intention is indirectly affected by the behavioral attitude and perceived value. We concluded that the perceived risk of the pandemic could be resilient with respect to the perceived value, trust, and behavioral attitude for the repurchase intention of the shared accommodation for the sector to recover.
Technological advancement has led to the emergence of online platforms fueled by the sharing economy across various industries. This study focuses on Airbnb - a specific asset-based sharing platform in the hospitality industry. Applying the theory of attribute substitution, we explore the wisdom of the crowd manifested in online reviews, in impacting pricing. We found that online review valence and volume have a positive association with room price. Depending on the crowdedness of the location this association is stronger or weaker. Customers care more about room popularity (volume) in a certain area when the fast system of decision-making is triggered. When, however, the slow system is triggered, customers consider the crime rate of a location (valence). Findings show how environmental stimuli and customer reviews decide room price - a variable that was decided traditionally by companies (e.g., hotels). The research furthers our understanding on asset-based platforms in the sharing economy.
Purpose
Short-term rental option enabled via accommodation sharing platforms is an attractive alternative to conventional long-term rental. The purpose of this study is to compare rental strategies (short-term vs long-term) and explore the main determinants for strategy selection.
Design/methodology/approach
Using logistic regression, this study predicts the rental strategy with the highest rate of return for a given property in the City of Philadelphia. The modeling result is then compared with the applied machine learning methods, including random forest, k-nearest neighbor, support vector machine, naïve Bayes and neural networks. The best model is finally selected based on different performance metrics that determine the prediction strength of underlying models.
Findings
By analyzing 2,163 properties, the results show that properties with more bedrooms, closer to the historic attractions, in neighborhoods with lower minority rates and higher nightlife vibe are more likely to have a higher return if they are rented out through short-term rental contract. Additionally, the property location is found out to have a significant impact on the selection of the rental strategy, which emphasizes the widely known term of “location, location, location” in the real estate market.
Originality/value
The findings of this study contribute to the literature by determining the neighborhood and property characteristics that make a property more suitable for the short-term rental vs the long-term one. This contribution is extremely important as it facilitates differentiating the short-term rentals from the long-term rentals and would help better understanding the supply-side in the sharing economy-based accommodation market.
A significant reason for the concentration of demand in a subset of the supply in the peer-to-peer market for tourist accommodation is herding behavior, by which the decisions of the first guests are imitated by those who follow. This article proposes a profit- and utility-maximization microeconomic model and implements it with data of Airbnb listings corresponding to 10 European cities. Results show that the influence of each additional review is positive but decreasing, inducing a more balanced distribution of demand among offered accommodation and thus dampening the herding effect. Moreover, reservation policy—specifically, enabling the instant booking option—is a key to explain the initial push that accommodations need to be demanded now and, hence, to increase their possibilities of being demanded in the future.
Essential resources, like electricity and water, can experience rapidly changing demand or supply while the other side of the market is unchanged. Short-run price variation could efficiently allocate resources at these critical times but only if consumers exhibit short-run demand elasticity. The question for firms in these markets has always been how to enable this response. Randomized control trials are increasingly used to test dynamic pricing and technologies that can assist in response by providing information and/or automated response. However, the trials typically do not randomize short-run prices. This paper illustrates how demand from a randomly assigned control group can be used to test the effectiveness of different technologies in increasing short-term price elasticity. To do so, we use a nonparametric control function approach that eliminates the bias inherent in estimating short-term price response using only household random assignment. We find that only automation technology leads to the short-term price elasticity needed to justify real-time pricing.
This paper was accepted by Matthew Shum, marketing.
Purpose:
Despite the importance of hosts who contribute to the success of accommodation sharing through sharing underutilized space with guests, current literature sheds little light on what exactly incentivizes hosts to grow their properties. This study investigates the effects of multifaceted motivations including financial benefits, online social interaction, and membership seniority, and their interplay on hosts’ multiple listing behavior.
Design/methodology/approach:
The study is instantiated on real-world business data collected from an accommodation-sharing platform in China. The dataset includes 3,199 observations of 252 multi-listing hosts in Beijing who managed 815 properties from September 2012 to October 2016.
Findings:
The study discloses that financial benefits, online social interaction, and membership seniority significantly incentivize hosts to list multiple properties on the accommodation-sharing platform. In particular, the social incentive is the most important driver among the three. With a 1% increase in online social interactions, the number of properties operated by a host would increase by 13.5%. While the financial benefits and online social interaction motivate hosts to engage in the multi-listing behavior, such effects are significantly mitigated as the membership seniority increases.
Research limitations/implications:
This study adds to the extant literature a unique yet less researched perspective of supply expansion driven by hosts. It also provides important practical implications for managing multiple properties for a healthy and viable accommodation-sharing community.
Originality/value:
While a majority of the extant research on the sharing economy primarily takes a consumer-related perspective, this study addresses a different and original topic about hosts’ multiple-listing behavior that drives the supply of accommodation sharing. It is a first empirical investigation of the increase of accommodation sharing supply with host motivations explained.
Keywords: accommodation-sharing; sharing economy; multiple listing; host incentives; data analytics
Research in the sharing economy predominately focuses on issues related to the exchange parties and the sharing platforms, ignoring the secondary market of the numerous entrepreneurs emerging around sharing ecosystems. By conducting an exploratory study, this chapter first identified the secondary market entrepreneurs supporting the Airbnb ecosystem and then, it investigated how they impact the sharing accommodation experiences by categorising their services based on the Porter's value chain model. The study also investigated the ability of these entrepreneurs to shape and form new ‘hospitality’ markets by categorising their market forming capabilities according to the “learning with the market” framework. Findings reveal that the services provided by these entrepreneurs: are similar to the accommodation services provided in the commercialised hospitality context; and they influence the market practices of the ‘trading’ actors participating in the Airbnb ecosystem. Consequently, the sub-economies created by the secondary market of these entrepreneurs are shaping and evolving the sharing accommodation market to a commercialised ‘authentic’ hospitality experience.
ResearchGate has not been able to resolve any references for this publication.