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In the past decade, urban tourism has increased worldwide as a result of the development of peer-to-peer (P2P) accommodation platforms such as Airbnb, causing a major disruption to the tourism industry and urban space. The expansion of tourist accommodation in cities has motivated many governments to act, to control its effects and reduce conflicts between tourists and residents. The purpose of this paper is to identify the attractions that have motivated the concentration of P2P accommodation and its effects in specific areas of Valencia different from the historical centre: the Russafa and El Cabanyal-Canyamelar neighbourhoods.

The methodology used includes fieldwork and spatial analysis of factors such as the housing market, tourist attractions, local businesses and urban renewal policies.

The current spatial distribution pattern of tourist housing in Valencia is the result of the convergence of various factors: the initial presence of tourists in some areas; the evolution of certain aspects of the neighbourhood due to urban renewal; the concentration of tourist and leisure activities; the effects of the legal framework

Many researchers have addressed the effects of rising short-term rentals (STRs) in cities, but the causes of their concentration in specific neighbourhoods different from historical centres have not yet been sufficiently investigated. This research looks in depth at the urban causes and effects of the spatial distribution of tourist housing in Valencia, to anticipate possible future concentrations of STRs in other areas and to avoid gentrification. The methodology and results could be applied to other cities. The research implies a detailed and analysis of different aspects that act simultaneously such as the housing market, the evolution of the population and changes in the business.

The purpose of this paper is to examine the impacts of Airbnb on hotel employment.

A triple-difference regression framework was used to compare changes in hotel employment in three cities in South Africa where Airbnb started operating relative to cities without this home-sharing platform. Data on hotel employment was drawn from the survey on hotel service conducted by Smith Travel Research whereas the data on Airbnb’s listings was drawn from the Airbnb’s homepage for the period between July 2015 and December 2018. The analysis was restricted to 792 hotels.

Although prior studies have showed that Airbnb has an adverse impact on hotel performances, the findings show that total hotel employment expanded after the entry of Airbnb, accompanied by a marked relative shift towards self-employment. Airbnb supply negatively affected permanent employment, which was compensated by increase in temporary employment and self-employment. While the paper finds no evidence of adverse employment impacts, the hourly earnings declined for hotel employees were compensated by increase in income among self-employed Airbnb hosts. Although these findings might be contradictory to the general conjecture, such evidence calls for a comprehensive investigation of Airbnb’s overall economic impact.

First, the study was based on the impact of Airbnb on hotel employment in South Africa. Caution is therefore required when generalising the findings of this study in other geographic areas. Second, some variables that may affect employment have not been included in this study because of the unavailability of data.

The results show that the sharing economy plays an important role in solving the unemployment problem, particularly in South Africa, where there is a high rate of unemployment. More practical implications are further discussed.

To the best of author’s knowledge, this paper provides the first systematic evidence of the impact of Airbnb on labour market outcomes in the hotel industry in South Africa.

本论文研究Airbnb对酒店就业的影响。

研究采用三重差分回归模型, 对南非三个有Airbnb经营的城市与其他没有Airbnb经营的城市做酒店就业情况的对比分析。酒店就业数据来自于Smith Travel Research进行的Hotel Service问卷。Airbnb数据来自于2015年七月至2018年十二月之间的Airbnb主页上的租赁房屋数据。共792家酒店参与了数据分析。

尽管之前的研究表明Airbnb对酒店绩效有着负面影响, 但是本论文研究表明, 在Airbnb进驻城市后, 该城市的酒店就业提升了, 并伴随着显著的自营就业。然而, Airbnb最终还是对长期的就业有着负面影响, 只是短期就业和自营就业的比重增大而已。虽然本论文未发现Airbnb对就业有负面影响, 但是酒店员工的时薪降低了, 随之弥补的是自营就业的收入增加。尽管本研究结果对整体的文献有着矛盾的地方, 但是仍需针对Airbnb经济影响的全面调研。

首先, 本论文仅研究了南非Airbnb对酒店就业情况的影响。其研究结果在其他地区的推广性则有待考察。第二, 受到数据限制, 一些可能会影响就业率的因素未被收录在本研究样本内。

研究结果表明共享经济对解决就业问题有着重要影响, 尤其是在失业率较高的南非地区。更多的实际意义可以被深入讨论。

本论文是首篇系统分析了Airbnb对南非酒店业劳动市场影响的文章。

The purpose of this paper is to explore the intricate relationship between the flagship of the sharing economy, Airbnb and hotel revenue per available room (RevPAR) in South Africa.

To identify the impact of Airbnb on hotel RevPAR, the paper used a triple difference-in-differences framework that compares changes in cities in South Africa where Airbnb started operating relative to areas without Airbnb. A total of 569 hotels were analysed.

While the study finds no evidence of adverse impacts of Airbnb on hotel RevPAR, the findings show that the entry of Airbnb led to a decrease in RevPAR of budget hotels. However, its impact is more pronounced during periods of peak demand, consequently, disrupting the pricing power of hotels.

The research was based on the impact of Airbnb on hotel RevPAR in hotels situated in specific cities in South Africa. Caution is therefore required when generalising the findings of this study to other hotels in other geographic areas. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel RevPAR. However, the findings underscore the notion that innovations are not intrinsically disruptive but only relative to another product. In so doing, the study adds to the limited body of work in the field on disruptive innovation and to the academic discourse on innovation in tourism more broadly.

First, the findings suggest the impact on hotels tends towards Airbnb generally playing a largely complementary role rather than a diversionary one. However, to increase RevPAR, hotels should systematically change their pricing models to account for flexible capacity by rethinking the wisdom of seasonal pricing and reduce prices during peak seasons to avoid inviting more competition from Airbnb.

To the best of the author’s knowledge, this paper is the first to explore the relationship between Airbnb and hotel markets using a triple difference methodology.

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.

This study aims to investigate how the COVID-19 pandemic has impacted and changed Airbnb market in the Greater Melbourne area in terms of its temporal and spatial patterns and identify possible shifts in underlying trends in travel activities.

A panel data set of Airbnb listings in Melbourne is analysed to compare temporal patterns, spatial distribution and lengths of stay of Airbnb users before and after the COVID outbreak.

This study found that the COVID disruption did not fundamentally change the temporal cycle of the Airbnb market. Month-to-month fluctuations peaked at different levels from pre-pandemic times mainly because of lockdowns and other restrictive measures. The impact of COVID-19 disruptions on neighbourhood-level Airbnb revenues is associated with distance to CBD rather than number of COVID cases. Inner city suburbs suffered major loss during the pandemic, whereas outer suburbs gained popularity due to increased domestic travel and long stays. Long stays (28 days or more, as defined by Airbnb) were the fastest growing segment during the pandemic, which indicates the Airbnb market was adapting to increasing demand for purposes like remote working or lifestyle change. After easing of COVID-related restrictions, demand for short-term accommodation quickly recovered, but supply has not shown signs of strong recovery. Spatial distribution of post-pandemic supply recovery shows a similar spatial variation. Neighbourhoods in the inner city have not shown signs of significant recovery, whereas those in the middle and outer rings are either slowly recovering or approaching their pre-COVID levels.

The COVID-19 pandemic has significantly impacted short-term rental markets and in particular the Airbnb sector during the phase of its rapid development. This paper helps inform in- and post-pandemic housing policy, market opportunity and investment decision.

To the best of the authors’ knowledge, this is one of the first attempts to empirically examine both temporal and spatial patterns of the COVID-19 impact on Airbnb market in one of the most severely impacted major cities. It is one of the first attempts to identify shifts in underlying trends in travel based on Airbnb data.

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 sharing economy has caught great attention from researchers and policymakers. However, due to the dearth of available data, not much empirical evidence has been provided. This paper aims to empirically assess the impacts of Airbnb on hotel performances in South Africa.

Using South Africa as a case study, the study measures the impacts of Airbnb on hotel performances on three key metrics, namely, room prices, occupancy and Revenue per available room (RevPAR). A difference-in-difference model is estimated using a population-based data set of 809 hotels from 2016 to 2018.

The results reveal that despite Airbnb significantly and negatively impacting on hotel occupancies it has a non-significant effect on hotel prices and RevPAR. Although from the theoretical perspective a disruptive innovation business model such as Airbnb can possibly have a negligible effect on hotel performances because it may attract a different group of customers and create a new market, the empirical findings of this study fail to support this theoretical hypothesis. Consequently, the findings diverge with newly developed knowledge in other markets and point to nuanced and contextual complementary effects.

Although some interesting findings are revealed into his study, some caveats remain. For instance, the study relied on data from hotels not from Airbnb. If the data of Airbnb can become available, it would be interesting to further examine whether the aggregated RevPAR of Airbnb can compensate for the aggregated loss of hotel RevPAR. This type of analysis could provide a broader evaluation scope regarding the overall effect of Airbnb on hotel performances. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel performances.

While hotels have launched a campaign to portray Airbnb as being commercial operators looking to compete illegally with hotels for the same segment of customers, this study shows that the rhetoric has been exaggerated. Airbnb, and more broadly, vacation rentals do not represent a war with hotels. They represent an answer to a different need. Indeed, the study reveals that Airbnb’s offer is a mere supplement to the market contrary to media rhetoric that it is meant to substitute hotels. The study has several implications for practitioners. First, these results are important because they serve as evidence against news articles that claim Airbnb is driving hotels out of business. They also show that if current trends continue, employees in the hotel industry in South Africa do not need to be concerned about losing their jobs because of Airbnb’s emergence. It is also important information for investors who may be concerned that Airbnb is hurting the hotel industry’s bottom line. Second, as the share of Airbnb listings on the accommodation market varies dramatically between cities, it is likely that eventual regulations/restrictions should be introduced in the provincial levels, while most of the cities continue benefiting from the increasing number of Airbnb visitors.

To the best of the author’s knowledge, this study is the first in South Africa to provide empirical evidence that Airbnb is significantly changing consumption patterns in the hotel industry, as opposed to generating purely incremental economic activity.

The purpose of this study is to investigate the effect of the COVID-19 pandemic on key performance metrics of accommodation properties by elaborating on the roles of business models (i.e. franchised, chain-managed and independent hotels, and the sharing economy) and state-level restrictions in the US.

The pandemic is considered a variable interference against the average daily rate, occupancy and revenue per available room, which permits the examination of the before and after effects of the pandemic. The panel data model is used to examine the effect of the recent pandemic on the accommodation sector in the USA.

The results showed that chain-managed hotels were the most adversely impacted by the COVID-19 pandemic, while independent hotels were the least adversely impacted. Interestingly, and consistent with emerging consumer needs suggested by spatial distance theory, the pandemic does not have significant negative effects on Airbnb. The adverse impact of the pandemic on hotels was exacerbated in more restrictive states, while Airbnb remained immune to regulatory differences.

This study addresses the dearth of research on the types, roles and efficacy of business models in the accommodation industry and makes important theoretical contributions to the study of business model resilience in the accommodation industry, leveraging the resource-based theory of the firm and spatial distance theory.

The findings of this study make a significant contribution to the extant literature on the resilience of business models in the accommodation industry and have important implications for hotels, Airbnb owners, accommodation brands and destination and health policymakers. They demonstrate that a lower level of corporate control and greater flexibility in brand and operational standards allow for a more effective response to business disruptions such as a global pandemic.

This paper explores the role of Airbnb listings on hotel revenues in certain popular Greek tourist destinations.

This paper explores the role of Airbnb listings on hotel revenues in certain popular Greek tourist destinations. The analysis makes use of the panel GMM method, while the findings document that the Airbnb listings exert a negative impact on hotel revenues.

The results indicate that it is primarily non-business and low-price hotels that are being influenced, while the hotel industry responds to the competition through lower room prices and not through occupancy rates. The paper documents that although the Airbnb is a new factor in the Greek tourism industry, it has turned into a significant competitor against hotels.

This is the first paper which documents that although Airbnb is a new factor in the Greek tourism industry, it has turned into a significant competitor against hotels. The findings are expected to provide further insights into the workings of the sector and the potential regulated policies needed to be adopted.

The purpose of this study is to examine Airbnb research using bibliometric methods. Using research performance analysis, this study highlights and provides an updated overview of Airbnb research by revealing patterns in journals, papers and most influential authors and countries. Furthermore, it graphically illustrates how research themes have evolved by mapping a co-word analysis and points out potential trends for future research.

The methodological design for this study involves three phases: the document source selection, the definition of the variables to be analyzed and the bibliometric analysis. A statistical multivariate analysis of all the documents’ characteristics was performed with R software. Furthermore, natural language processing techniques were used to analyze all the abstracts and keywords specified in the 129 selected documents.

Results show the genesis and evolution of publications on Airbnb research, scatter of journals and journals’ characteristics, author and productivity characteristics, geographical distribution of the research and content analysis using keywords.

Despite Airbnb having a history of 10 years, research publications only started in 2015. Therefore, the bibliometric study includes papers from 2015 to 2019. One of the main limitations is that papers were selected in October of 2019, before the year was over. However, the latest academic publications (in press and earlycite) were included in the analysis.

This study analyzed bibliometric set of laws (Price’s, Lotka’s and Bradford’s) to better understand the patterns of the most relevant scientific production regarding Airbnb in tourism and hospitality journals. Using natural language processing techniques, this study analyzes all the abstracts and keywords specified in the selected documents. Results show the evolution of research topics in four periods: 2015-2016, 2017, 2018 and 2019.