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This paper 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.

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. The authors 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 USA over a period from Airbnb’s launch in Texas up until November 2015.

The authors found that the functional characteristics of Airbnb listings were significantly associated to the price of the listings, and that three of five behavioral attributes of hosts were statistically significant. However, the effect of reputation of listings on the price of Airbnb listings was weak.

This study inspires what they 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.

The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.

The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.

The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.

Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.

With the prevalence of the sharing economy phenomenon, there are an increasing number of hosts on Airbnb who manage more than one listing. Managing more listings likely makes hosts more seasoned in terms of serving guests, but it may undermine host quality due to hosts’ constrained capability. This paper aims to examine the effects of host quality attributes and the number of listings per host on the reservation performance of these listings.

Using a large-scale but granular data set of 5,805 active listings of 4,608 Airbnb hosts in Austin, Texas, this study estimates the effects of host attributes (host quality and listing quantity) on the performance of the hosts’ Airbnb listings through a blend of regression models.

This study evidences that host quality attributes significantly influence listing performance through cue-based trust. In addition, this study finds a “trade-off” between host quality and the quantity of their listings. As the number of listings managed by a host increases, the performance effects of host quality diminish.

The business implications of this study include the suggestion that sharing economy businesses such as Airbnb should sustain service quality through incentivizing hosts to improve host quality while balancing the quantity of listings managed.

This study contributes to the literature through its meaningful theoretical extension in the sharing economy context and unique data-driven insights enabled by an analytical approach. It addresses the critical but less researched topic of host quality and listing quantity and generates important practical business and policy implications.

Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers’ perspective, this study aims to apply the signaling theory to the booking of Airbnb listings and explore the influence of quality signals on the odds of an Airbnb listing being booked.

A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt.

Results show that, in addition to host-specific information such as “Superhost” and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments.

This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.

By building on cooperation–competition theory, this study aims to investigate the multidimensional flipped effects of neighborhood hotels on Airbnb listings’ popularity, examining the degree to which such impacts are influenced by hotel types and geographical areas.

This study explores the interdependent and competitive relationship between neighborhood hotels and Airbnb from the perspective of effects on Airbnb listings’ popularity by exploring a data set covering 10,492 Airbnb listings and 2,691 hotels from Ctrip.

Results reveal that neighborhood hotels’ number of reviews, review ratings and prices each have positive spillover effects on Airbnb listings’ popularity, while quality assurance labels and negative review topic sentiments exert competitive effects on Airbnb popularity. Moreover, the number of budget chain hotels and high-star hotels have positive and negative effects on Airbnb popularity, respectively. Geographical areas also have a moderating effect on the relationship between various hotel-related influencing factors and Airbnb.

This study can offer hotel managers and Airbnb operators a clearer understanding of these businesses’ coexisting relationship. Findings can also provide Airbnb-specific guidelines for practitioners in terms of site selection, promotional features and development strategies for Airbnb listings.

This study establishes a cooperation–competition relationship model between hotels and Airbnb and considers the flipped effects of hotels on Airbnb for the first time. It expands previous studies by considering the multidimensional effects of hotels on Airbnb listings’ popularity and by examining the influences of hotel types and geographical areas on hotels’ impacts on Airbnb.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to examine the impact of the COVID-19 pandemic on the traditional hotel industry and Airbnb in nine major European cities. The author examines differences between the two business models and analyses various strategies of Airbnb hosts to cope with the crisis.

A detailed empirical analysis is presented based on data from STR and Inside Airbnb for the period January 2018–September 2020. To assess the impact of the pandemic on the hotel industry, year-to-year changes in various performance metrics are presented. The author also investigates the impact of the pandemic on Airbnb prices with panel data regression analysis. Using text-mining methods, signs for new use-cases are explored, including renting flats for home-office or quarantine.

The results support that Airbnb supply is more flexible. While hotel supply quickly returned to a level close to 2019, the average number of Airbnb listings was lower by more than 15%. Furthermore, the price analysis showed that Airbnb rates decreased more moderately than hotel prices. These findings suggest that a significant share of hosts pivoted from short-term accommodation provision and used their property differently, e.g. rented on a long-term basis. The analysis of listing characteristics revealed that the role of longer stays increased; however, the results do not support a shift towards advertising listings for home-office or quarantine purposes.

This paper presents the impact of the pandemic on the hospitality sector in a wide sample of European cities, explores the adjustment of hotels and Airbnb and provides new evidence on the differences between the business models.

The purpose of this paper is to review the extant literature on Airbnb – one of the most significant recent innovations in the tourism sector – to assess the research progress that has been accomplished to date.

Numerous journal databases were searched, and 132 peer-reviewed journal articles from various disciplines were reviewed. Key attributes of each paper were recorded, and a content analysis was undertaken.

A survey of the literature found that the majority of Airbnb research has been published quite recently, often in hospitality/tourism journals, and the research has been conducted primarily by researchers in the USA/Canada and Europe. Based on the content analysis, the papers were divided into six thematic categories – Airbnb guests, Airbnb hosts, Airbnb supply and its impacts on destinations, Airbnb regulation, Airbnb’s impacts on the tourism sector and the Airbnb company. Consistent findings have begun to emerge on several important topics, including guests’ motivations and the geographical dispersion of listings. However, many research gaps remain, so numerous suggestions for future research are provided.

By reviewing a large body of literature on a fairly novel and timely topic, this research provides a concise summary of Airbnb knowledge that will assist industry practitioners as they adapt to the recent rapid emergence of Airbnb.

This is the first paper to review the extant literature specifically about Airbnb.

本论文旨在审视过去文献对Airbnb的研究-旅游业中最显著发明之一-以衡量迄今为止的研究发展历程。

经过大量文献搜索，共132份同行评审型期刊文章，来自不同研究领域，被作者审阅。每个文章的关键词被摘抄出来，本论文采用内容分析方法来分析文本。

经过文献综述，作者发现大多数Airbnb研究都发表在近几年，往往发表在酒店/旅游期刊。期刊文章作者集中在美国/加拿大和欧洲。基于内容分析结果，发表的期刊文章被分类在六个主题-Airbnb顾客、Airbnb服务提供主、Airbnb供应商、以及其对旅游目的地的影响，Airbnb规范、Airbnb对旅游行业的影响、以及Airbnb公司。研究结果还归纳出几项重要的话题，包括顾客动机和民宿地理分布。然而，大多数研究空缺仍然存在，因此，本论文总结出多项未来研究方向。

本论文通过审阅大量较新和及时的文献，对Airbnb的相关知识进行了精准梳理，这个研究结果对从业者适应Airbnb较新较快发展的现象，有着实践意义。

本论文是首篇审阅有关Aribnb文献的文章。

Airbnb、文献综述、共享经济、P2P、短期出租

Listing popularity indicates the public’s interest in a listing on peer-to-peer (P2P) accommodation platforms. Although listing popularity is crucial to the survival and development of the P2P accommodation platform, this issue has received limited attention in the tourism management discipline. Drawing upon the heuristic-systematic model and uncertainty reduction theory, this study aims to examine the impacts of host and property attributes on listing popularity.

The model was empirically validated using a data set of 6,828 listings on a popular P2P accommodation platform called Airbnb. This study chooses a hierarchical regression analysis to perform the model validation.

The findings reveal that host self-disclosure, host reputation and host identity verification are key host attributes in promoting listing popularity. Meanwhile, property visual description, property photo verification and property visual appeal are important property attributes in facilitating listing popularity.

The study adds useful insights on understanding on determinants of listing popularity. Future researchers are recommended to empirically verify the underlying psychological mechanism by which host attributes and property attributes influence listing popularity.

The P2P accommodation platform should promote the listing popularity by taking advantage of the host attributes and providing property attributes.

First, to the best of the authors’ knowledge, this study is one of the few studies to explore the formation of the listing popularity. Second, this study examines how the host and property attributes promote the listing popularity through the heuristic and systematic information processing modes.

This study aims to identify the key determinants of Airbnb rating scores.

This study is based on a sample of 127,257 listings across 43 cities. A total of 24 explanatory variables were identified, and they were further grouped into host verification information, communication, policy of renting, space, information about environment, price and experience of hosting. Both Tobit and ordered logit models were used to perform the analysis.

The results indicate that good communication, large space and provision of information about the listings’ environment have a positive effect on users’ satisfaction, whereas experience of hosting negatively influences users’ satisfaction.

This study contributes to the peer-to-peer accommodation literature by affording a more complete understanding about guest satisfaction and its determinants.