The effects of Airbnb’s price positioning on hotel performance

This study examined the relationship between the price positioning of Airbnb listings, measured in price difference between a hotel property and the nearby Airbnb listings as well as price dispersion among these Airbnb listings, and the performance of nearby hotels. An exploratory analysis using field data points collected from the Airbnb listings and their hotel counterparts in the metropolitan area of Austin, Texas between Quarter 3, 2008 (debut of Airbnb in Austin) and Quarter 2, 2011 reveals intriguing findings. The entry of Airbnb listings was penetrative to local hotels. However, the price positioning of Airbnb, manifested in higher average price as compared to nearby hotels, as well as larger price dispersion among individual listings, significantly mitigated such penetration. Important theoretical contributions and practical implications for hotels are discussed.     

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.     

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.     

Substitution threat between Airbnb and hotels: Myth or reality?

The rise of peer-to-peer accommodation platforms in the tourism and hospitality industry has created an interesting and growing debate around the threats of substitution between them and traditional hotels. Previous studies have provided contradictory findings. Here we address the issue by analyzing the degree of synchronization between the daily occupancy of hotels and that of Airbnb listings in Milan, Italy, over a period of four years. The findings show that the two series are widely desynchronized during the week, on workdays and trade-fair days, when hotels work prevalently within the business segment, and when Airbnb listings mainly accommodate leisure guests. By contrast, a partial synchronization (and therefore a potential substitution threat) is revealed during weekends and holidays.     

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.     

Does Airbnb have a homogenous impact? Examining Airbnb’s effect on hotels with different organizational structures

Although a number of studies have examined Airbnb’s impact on hotels, previous studies have yielded mixed results and are limited in their geographical scope. Additionally, the impact of Airbnb on hotels with different organizational structures has not been previously analyzed. Accordingly, the purpose of this study is threefold: 1) to add to our understanding of the impact of an increase of Airbnb inventory by clarifying previously inconclusive results; 2) to examine the extent to which Airbnb listings affect hotel performance measures in the overall U.S. hotel market; and 3) to investigate the influence of Airbnb on key hotel metrics by elaborating the effect of Airbnb on hotels operated under different organizational forms- chain-managed, franchised, and independent. Our results show an adverse impact of Airbnb on hotel RevPAR and ADR metrics across different organizational structures. However, interestingly, Airbnb listings did not negatively affect occupancy numbers. Theoretical and practical implications are discussed.     

The pricing of European airbnb listings during the pandemic: A difference-in-differences approach employing COVID-19 response strategies as a continuous treatment

The COVID-19 pandemic has been a major shock to the global tourism industry. Given its peculiarity, this paper analyzes one of the most intriguing questions in the Airbnb literature – the pricing of Airbnb listings – by taking advantage of a difference-in-differences methodology that largely draws on variations in country-level policy responses to the pandemic. Relying on a dataset containing weekly information from 130,999 continuously active listings across 27 European countries from 2019 to 2020, this study first investigates the exogenous impact of response policies (proxied by the COVID-19 Stringency Index) on demand. Secondly, accounting for the endogeneity of both demand and prices, this research analyzes pricing responses to demand variations. Results show that: i) increases in the COVID-19 Stringency Index cause significant declines in Airbnb demand; ii) increases in demand cause, on average, increases in Airbnb prices; and iii) pricing strategies between commercial and private hosts differ substantially.     

Advance booking and hotel price variability online: Any opportunity for business customers?

Is it possible for business customers to effectively adjust their purchasing strategies, as a response to revenue management? We consider daily online best available rates for a panel of 357 hotels in Milan and Rome, up to an advance booking of 29 days.We analyse price trajectories, finding that dynamic pricing strategies with no established trend towards the arrival date are prevalent, with a predominance of decreasing trajectories for lower-scale hotels in Milan during fairs. We show that price levels are explained by a variety of structural determinants. We quantify the effects of advance booking, room quality, services, competition, seasonality and fairs, underlining their different importance on leisure and business destinations. Other features, such as breakfast and refunding options, appear to be used as marketing tools to differentiate rooms, keeping a low pace in price adjustment. Managerial implications are discussed, with reference to both corporate travel departments and hôteliers.     

Tourism clusters and peer-to-peer accommodation

This study examines the importance of tourism clusters in peer-to-peer accommodation. Based on a rich dataset of 112,748 Airbnb listings in Florida, one of the top U.S. tourism destinations, this study uses geographically weighted regression to explore the spatially heterogeneous effects of tourism clusters on Airbnb performance across individual counties (intraregional clusters) and neighboring counties (interregional clusters). The results indicate that overall tourism clusters, especially in the industries of accommodation and food services, lead to superior Airbnb performance, but the tourism clusters-Airbnb performance relationship varies across industry and region, confirming the existence of intraregional and interregional clusters. These findings can help Airbnb hosts and tourism policymakers in other regions implement localized tourism industry strategies for maximizing Airbnb performance.     

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).     

Location advantages of lodging properties: A comparison between hotels and Airbnb units in an urban environment

This study aims to investigate various types of location advantages that contribute to lodging property performance. Using monthly revenue data for individual urban hotels and Airbnb units in Houston, Texas from 2014 to 2016, we apply the Hausman-Taylor model to estimate the effects of location factors. Several factors are confirmed, including accessibility to points of interest, transport convenience, the surrounding environment, and market conditions. The overall effect of location advantage is more substantial for urban hotels than Airbnb units. Findings do not reveal a sizable competition effect between urban hotels and Airbnb units. Furthermore, we unveil factors associated with location advantages for different hotel classes and Airbnb types by estimating the model using different sub-samples.     

Sentiment and guest satisfaction with peer-to-peer accommodation: When are online ratings more trustworthy?

This study aims to decode guest satisfaction with peer-to-peer accommodations by analyzing the relationship between guests’ sentiment and online ratings and examining how analytical thinking and authenticity influence this relationship. Based on reviews of 4602 Airbnb listings in San Francisco, we empirically find that positive (negative) sentiment is linked to a high (low) rating. We further show that this link is stronger when guests manifest a higher extent of analytical thinking and authenticity. Both Tobit and ordered logit models yield consistent estimation results, showing the robustness of our findings. Our study contributes to the tourism and hospitality literature by theoretically explaining the association between sentiment and ratings. In addition, this paper enriches our knowledge regarding the trustworthiness of Airbnb ratings.     

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.     

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.     

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.     

Lord,please save me from my sins! Can CSR mitigate the negative impacts of sharing economy on consumer trust and corporate reputation?

The explosive growth of Airbnb not only provides travelers with novel accommodation experiences at prices that suit their budget, but also challenges the existing regulatory and market structures. While Airbnb with its distinct peer-to-peer (P2P) accommodation business model is considered a disruptive innovation in the hospitality industry, little is known about its negative side. This gap in extant literature has motivated the present study, aiming to establish whether the negative aspects of Airbnb undermine consumers' overall trust in the company and its corporate reputation, and whether this link is moderated by corporate social responsibility (CSR). Data required to answer these questions was collected via a survey in which 348 potential Airbnb users in Taiwan, selected using a nonprobability purposive sampling technique, took part. Partial least squares-structural equation modeling (PLS-SEM) was employed to analyze the data. The results indicate that consumers' overall trust in Airbnb is negatively affected by various factors, ranging from legal, regulatory, and taxation issues to fake reviews/listings. The moderation analysis findings further reveal that, while the overall trust−corporate reputation link is strengthened by the environmental and philanthropic CSR, it is weakened by economic CSR, which can lead to unfavorable consumer attitudes and behavioral intentions.     

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。     

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.     

An agent-based modelling approach to housing market regulations and Airbnb-induced tourism

Airbnb has shown constant growth and it provides income and taxes to tourist destinations. However, the prevalence of a substantial number of Airbnb providers in tourist destinations may lead to bottlenecks in rental housing markets. Regional planners and policy-makers across the world are therefore imposing restrictions to regulate this hitherto unregulated business model. The present paper sheds light on the link between housing-market regulation and the growth of Airbnb, based upon Norwegian Airbnb listings and agent-based modelling. The simulation results suggest that Airbnb's current growth will not simply flatten out when the supply matches the demand, but will be followed by a series of sudden crises and subsequent quick recoveries. These instabilities will put stress on local rental markets and threaten both the local tourism industry and rental housing markets. Moderate taxation may contribute to a more even distribution of Airbnb listings in Norway, notably across the urban space.