The eruption of Airbnb in tourist cities: Comparing spatial patterns of hotels and peer-to-peer accommodation in Barcelona

In recent years, what has become known as collaborative consumption has undergone rapid expansion through peer-to-peer (P2P) platforms. In the field of tourism, a particularly notable example is that of Airbnb. This article analyses the spatial patterns of Airbnb in Barcelona and compares them with hotels and sightseeing spots. New sources of data, such as Airbnb listings and geolocated photographs are used. Analysis of bivariate spatial autocorrelation reveals a close spatial relationship between Airbnb and hotels, with a marked centre-periphery pattern, although Airbnb predominates around the city's main hotel axis and hotels predominate in some peripheral areas of the city. Another interesting finding is that Airbnb capitalises more on the advantages of proximity to the city's main tourist attractions than does the hotel sector. Multiple regression analysis shows that the factors explaining location are also different for hotels and Airbnb. Finally, it was possible to detect those parts of the city that have seen the greatest increase in pressure from tourism related to Airbnb's recent expansion.     

Spatial price mimicking on Airbnb: Multi-host vs single-host

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. Results provide clear evidence that price mimicking is higher among non-professional hosts whereas professional hosts set prices more independently.     

The Airbnb paradox: Positive employment effects in the hospitality industry

The purpose of this study is to examine the extent to which Airbnb supply affects employment in the hospitality, tourism, and leisure industries. Accordingly, we analyzed the effects of Airbnb supply on various sectors in the hospitality, tourism, and leisure industries in 12 major metropolitan statistical areas in the United States for the period between July-2008 and February-2018. The results showed that Airbnb supply positively affects employment in all sectors of the hospitality, tourism, and leisure industries. The magnitudes of these effects are not only statistically but also economically significant. Although prior studies have showed that Airbnb has an adverse impact on hotels' financial performance measures, our results show that employment in the hotel sector increases with increased Airbnb listings. While this outcome might be contradictory to the general conjecture, such evidence calls for a comprehensive investigation of Airbnb's overall economic impact. Research and practical implications are further discussed.     

Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com

The advent of the “sharing economy” challenges not only the business of hotel industry but also the theories and models based on the conventional hotel industry. A key dimension of the hospitality industry is pricing. The aim of this study is to identify the price determinants of sharing economy based accommodation offers in the digital marketplace. Specifically, a sample of 180,533 accommodation rental offers in 33 cities listed on Airbnb.com is investigated using ordinary least squares and quantile regression analysis. Twenty-five explanatory variables in five categories (host attributes, site and property attributes, amenities and services, rental rules, and online review ratings) are explored for the intricacies of the relationships between pricing and its determinants.     

The effects of Airbnb on hotel performance: Evidence from cities beyond the United States

Although Airbnb's impact on hotels has been quantified for major hotel markets in the United States, these effects have not been quantified in international hotel markets. Accordingly, the purpose of this study is to examine the effects of Airbnb listings on key hotel performance metrics in an international context. In particular, we examine the effects of Airbnb listings on hotel revenue per available room (RevPAR), average daily rate (ADR), and occupancy rate (OCC) in major international hotel markets, namely London, Paris, Sydney and Tokyo. The results show that Airbnb listings in these major cities have been increasing more than 100% year over year and that the effect of Airbnb on hotel RevPAR and OCC is negative and statistically significant. In particular, a 1% increase in Airbnb listings decreases hotel RevPAR by between 0.016% and 0.031% in these hotel markets. The implications of these findings for destinations and hoteliers are discussed.     

Modeling Airbnb demand to New York City while employing spatial panel data at the listing level

Using spatial panel data comprising a cross section of 1,461 continuously active Airbnb listings obtained from AirDNA, as well as time series data from NYC and Company and the OECD covering the time period September 2014 to June 2016, the present study quantifies own price, cross price, and income elasticities of Airbnb demand to New York City within an empirical tourism demand framework. The particular goal of the study is to establish whether the relationship between Airbnb and the traditional accommodation industry is of a substitutional or of a complementary nature. Employing a one-way fixed-effects spatial Durbin model, it can be concluded that demand is price-inelastic for Airbnb accommodation in New York City, which is a luxury good, and that the city's traditional accommodation industry as well as neighboring Airbnb listings are substitutes for the investigated Airbnb listings. The estimation results are robust against several alternative specifications of the regression equation.     

Spatial spillovers of agglomeration economies and productivity in the tourism industry: The case of the UK

This research investigates the direct and (indirect) spatial spillover effects of agglomeration economies on the productivity of the tourism industry. With increasing concerns about the persistence of low (labour) productivity in tourism across many developed economies, there is an urgent need to address this productivity challenge. Using major under-exploited UK microeconomic panel data, spatial econometric modelling is employed to estimate the effects of agglomeration economies on productivity. Findings reveal the significant effects of agglomeration economies on productivity within a specific region, but also significant spatial spillover effects across neighbouring regions, suggesting the possibility of productivity convergences. Competitive and complementary effects of agglomeration economies on productivity are identified.     

Power dynamics in peer-to-peer accommodation: Insights from Airbnb hosts

This study explores power dynamics in peer-to-peer (P2P) accommodation by examining how they manifest in the host-guest relationship. In so doing, it advances understanding on the role of hosts in the P2P practice and contributes insights on the factors conditioning the reciprocity of the P2P exchange. Drawing from power theories, the study uncovers how the interdependencies among hosts, guests and platforms influence power constellations emanating from P2P accommodation growth and reflexively redefine host practice. Specifically, the study illustrates how hosts are attempting to resist power imbalances characterising the exchange and how the dynamic environment of P2P accommodation leads to distinct representations of host types. The study makes explicit a conceptual framework that captures the power shifts noticeable in P2P accommodation that may be of theoretical and practical value to academics and policymakers alike.     

Brand personality of Airbnb: application of user involvement and gender differences

ABSTRACT

In this study we utilized the concept of brand personality to determine how travelers perceived Airbnb as a lodging brand and investigated the effect of involvement level (low versus high) on view of brand personality. As a viable segmentation variable, this study also utilized the interaction effect to examine how gender affects consumers’ involvement level. Significant differences were observed between travelers with high and low involvement in terms of the dimensions of sincerity, excitement, competence, and ruggedness. In addition, we found the interaction effect to be significant, indicating that level of involvement is higher in female travelers than in male travelers. This research will provide meaningful marketing insights for target marketing and positioning based on consumer involvement and gender within the context of Airbnb.     

Peer-to-peer interactions: Perspectives of Airbnb guests and hosts

In peer-to-peer (P2P) accommodation settings such as Airbnb, social interactions between peers are distinctively different from those between guests and employees in conventional lodging establishments. This study focuses on a reciprocal aspect of social interactions in P2P accommodations and aims to: 1) explore how guests and hosts perceive online and face-to-face interactions between them and 2) investigate the association between P2P interactions and some outcome variables (encounter satisfaction, word-of-mouth intention, and continuous intention to use) based on the roles of guest and host. A total of 503 responses from an online research panel were analyzed. The results show that guests perceive overall interaction experiences more positively than do hosts. Moreover, guests and hosts place weight on different interaction factors that contribute to their satisfaction and behavioral intentions. This study sheds light on the reciprocal interactions between guests and hosts, providing important theoretical and practical implications for the P2P lodging experience.     

Dynamic pricing and revenues of Airbnb listings: Estimating heterogeneous causal effects

This paper investigates the extent to which the implementation of intertemporal price discrimination affects Airbnb listings’ revenue. We found that on average, a price surge (i.e., increasing the price as we approach the date of service consumption) has an adverse effect on revenue. However, the magnitude of such effect exhibits significant heterogeneity among listings. Through the application of generalized random forests, a causal machine learning technique, we identify exacerbating and moderating treatment modifiers and shed light on the listing dimensions that cause price surges to be particularly detrimental for hosts’ revenues.     

Spanning across the boundary of Airbnb host community: a network perspective

The success of Airbnb has not only provided people with an alternative way of managing their living spaces but also created a rapidly growing number of Airbnb host communities. Guided by boundary spanning theory, this study examines how Airbnb host online communities evolve through novel organizational forms. Data for this study were collected from an online Airbnb host community from which two stages of analysis were performed. These included social network analysis and content analysis. The findings indicate that the online community functions as an informal specialized sub-unit of Airbnb and that hosts’ roles in the organization involve a number of boundary spanning aspects. The research contributes to an evolving understanding of hosts in the peer-to-peer (P2P) accommodation literature as well as extending the theory of boundary spanning in its application to the sharing economy context.     

The construction of home feeling by Airbnb guests in the sharing economy: A semantics perspective

Although the literature of peer-to-peer accommodation is increasingly recognizing the importance of home feeling, little has been done to theorize the construction of home feeling and its impacts on Airbnb guests’ future intention. To fill in this gap, this study systematically unpacked how Airbnb guests construct the feeling of home. A total of 42,085 review comments containing the feeling of home from three major cities in the USA were analyzed from a semantics perspective. Based on our findings we conceptualize home feeling as a PASS_h process, which contains multiple dimensions: physical and spatial, social, and affective, coupled with hospitality. Essentially, the home feeling with the interaction of these dimensions fulfills the daily hybrid needs of the guests.This article also launches the Annals of Tourism Research Curated Collection on Peer-to-peer accommodation networks, a special selection of research in this field.     

Pricing in the sharing economy: a hedonic pricing model applied to Airbnb listings

ABSTRACT

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.     

Exit and transition: Exploring the survival status of Airbnb listings in a time of professionalization

This study analyzes the survival status of shared and non-shared listings in the peer-to-peer accommodation market. Using a large data set from Airbnb in Beijing, we identify 8640 shared listings and 50,741 non-shared listings. We then investigate the exit event and the identity transition event for both types of listings by applying a discrete-time hazard model. Our results suggest that, for the exit event, the two types of listings show significant differences in terms of survival determinants, including response time, tourism specialization, market volume, professionalization, and Covid-19. For the identity transition event, we find that internal flow exists in the market, mainly from shared listings to non-shared listings, and this flow is influenced by certain factors (i.e., capacity, facility, rating, reviews, minimum stay, service quality, tourism specialization, market volume, platform professionalization, and Covid-19).     

共享民宿空间集聚特征及其影响机制研究——以北京市Airbnb为例

在构建“双循环”新发展格局的时代背景下,共享民宿的科学布局对有效释放旅游市场需求潜力,促进国内大循环的畅通和发展,具有积极的意义。本文以北京市Airbnb为例,综合运用空间分析和地理探测器等方法探究了共享民宿的空间集聚特征及其影响机制。结果表明：（1） Airbnb在空间上呈显著的集聚分布,整体上表现出“大集聚、小分散”的空间形态,形成4个明显的高密度核心区;（2） 置信度高于99 %的热点区所占比重最大,主要集聚在市中心周围至东四环附近地区,且Airbnb与其他相关地理要素存在不同程度的空间集聚;（3） 休闲娱乐设施数量、距市中心距离和公共服务设施数量等因素的解释力较强,双因子交互作用的解释力均强于单因子,休闲娱乐设施数量对因子交互作用的影响最大;（4） 在影响机制中,房东作为供给者,其选址行为和主观意愿为基础因素;房客作为需求方,其多样化的需求成为主导因素;政府作为监管者,其宏观调控举措是调节因素;平台作为管理者,其战略决策和市场推广是引导因素。不同利益主体之间的耦合交互与权衡制约,各类要素在不同发展阶段的综合作用,最终形成了影响共享民宿空间集聚特征的合力。     

Trust and reputation in the sharing economy: The role of personal photos in Airbnb

‘Sharing economy’ platforms such as Airbnb have recently flourished in the tourism industry. The prominent appearance of sellers' photos on these platforms motivated our study. We suggest that the presence of these photos can have a significant impact on guests' decision making. Specifically, we contend that guests infer the host's trustworthiness from these photos, and that their choice is affected by this inference. In an empirical analysis of Airbnb's data and a controlled experiment, we found that the more trustworthy the host is perceived to be from her photo, the higher the price of the listing and the probability of its being chosen. We also find that a host's reputation, communicated by her online review scores, has no effect on listing price or likelihood of consumer booking. We further demonstrate that if review scores are varied experimentally, they affect guests' decisions, but the role of the host's photo remains significant.     

Stars vs lemons. Survival analysis of peer-to peer marketplaces: the case of Airbnb

This paper analyses the determinants of listings' survival on peer-to-peer marketplaces. Working on a dataset of Airbnb listings in Ibiza, we implement survival analysis to estimate the relationship between listings' key attributes and the probability to leave the platform. In addition, we highlight the importance of user-generated content to reduce the asymmetry of information and prevent adverse selection. Results confirm that listings' characteristics, location, degree of local competition and hosts' managerial skills, significantly affect the survival chance. Moreover, we found that low quality listings (proxied by the customer rating) are intended to disappear: the reviewing system successfully signals the quality on this market and drive the market selection process.     

Attractiveness of cultural activities in European cities: A latent class approach

Cultural tourism behaviour and destination preference was analysed for 19 European capital cities, utilising the level of participation in cultural activities (participation) and the level of enjoyment of those cultural activities (attractiveness). A latent class model with three classes described the association in the data satisfactorily. Class 1 was labeled “low participation and high attractiveness”, Class 2 was labeled “high participation and high attractiveness”, and Class 3 was labeled “high participation and low attractiveness”. The Class 2 respondents with high participation and attractiveness had the highest cultural capital, and could be considered ‘specific cultural tourists’, whereas the Class 3 respondents could be considered ‘general cultural tourists’. Class 1 respondents, with relatively infrequent participation but high enjoyment, are potentially most interesting in marketing terms.