Investigating the whole picture: Comparing the effects of Airbnb supply and hotel supply on hotel performance across the United States

This study examines and compares the extent to which Airbnb and hotel supply affect key hotel performance measures in the United States. The results show that although both Airbnb and hotel supply adversely affect hotel revenues (i.e., RevPAR), the magnitude of the impact of hotel supply on RevPAR is much larger than that of Airbnb. Airbnb adversely affects hotel room prices (i.e., ADR), however; it does not affect occupancy rates (i.e., OCC). Yet, increasing hotel supply negatively affects OCC but not ADR. The results from the state-level analyses further showed that the negative effects of Airbnb and hotel supply on RevPAR, ADR and OCC persist only in states with high hotel supply. Analyzing the joint effects of Airbnb and hotel supply on hotel performance in a manner that is both geographically-comprehensive and spatially-meaningful, this study provides a more complete and nuanced understanding on the economic dynamics of the accommodation industry.     

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 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.     

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.     

What caused the rise of Airbnb? An examination of key macroeconomic factors

The social and economic implications of the Airbnb phenomenon have been the subject of much research. Yet, the academic literature on Airbnb is nascent. Specifically, the issue of whether major macroeconomic conditions affect the supply of Airbnb has not been investigated. To address this gap, we propose a conceptual model that explains the determinants of Airbnb supply and examine the extent to which major macroeconomic factors affect the supply of Airbnb. Specifically, we analyze the effects of hotel room rates (ADR), hotel demand, tourism demand, house prices, gross domestic product (GDP), wages and unemployment on the supply of Airbnb in 50 U.S. states. Results show that increases in hotel ADR, house prices, and GDP have contributed to an increase in the supply of Airbnb, whereas increases in unemployment rates and wages have adverse effects on Airbnb supply. Theoretical and policy implications are discussed within realms of macroeconomic theory.     

Quantifying the economic impact of COVID-19 on the U.S. hotel industry: Examination of hotel segments and operational structures

The COVID-19 pandemic has had substantial negative impacts on the global economy. While all sectors of the economy are expected to be adversely affected, the economic implications of this pandemic for the hotel industry have not yet been widely investigated. The purpose of this study is to examine the extent to which the COVID-19 pandemic has affected the U.S. hotel industry. The results showed that daily room OCC, ADR and RevPAR have plunged about 74%, 47% and 86%, respectively. Although the impact is observed across all hotel segments, economy-scale hotels were more resilient, whereas luxury-scale hotels experienced the largest decline. Also, chain-managed hotels are the most affected by the COVID-19 pandemic as compared to franchise and independent hotels. Quantifying the magnitude of this impact, we found that the U.S. hotel industry's revenue losses accumulated to over $30 billion between March-2020 and May-2020. Implications for practitioners, policy-makers, and researchers are discussed.     

‘Pop-up’ tourism or ‘invasion’? Airbnb in coastal Australia

The global rise of peer to peer ‘home-sharing’ platforms such as Airbnb has accelerated negative forms of ‘touristification’ in major cities, often defying local regulation. But less is known about how such platforms affect regions beyond or peripheral to major population centres. This article addresses this question, examining 12 case-study communities in coastal Australia. Through in-depth interviews and focus groups with local stakeholders and planners, as well as analysis of Airbnb listings, tourism and housing data, we find that Airbnb style platforms intersect with, and impact, local governance, neighbourhoods and housing markets in different ways. We conceptualise these differences as a place-based taxonomy ranging from ‘pop-up’ to ‘invasive’ tourism, and highlight implications for research, policy and practice.     

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.     

Price and RevPAR determinants of Airbnb listings: Convergent and divergent evidence

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.     

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.     

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.     

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.     

How do dynamic capabilities explain hotel performance?

This research study explores the relationship between three dynamic capabilities and their impact on hotel performance. Specifically, we examine the relationship between human resource management (HRM), quality management (QM) and sustainability. In addition, we analyse how QM and sustainability explain hotel performance measured by occupancy rate, average daily rate (ADR) and revenues per available room (RevPAR). These capabilities can generate income, enabling hotels to adapt as quickly as possible to the changing environment. Findings show a significant relationship between HRM, QM and sustainability. The relationship between QM and hotel performance and between sustainability and hotel performance is fully mediated by the differentiation competitive advantage. Our results represent an advance in hotel theory and management because they integrate HRM, QM and sustainability, and show their ability to be a source of competitive advantage and profitability.     

The 7 Ps marketing mix of home-sharing services: Mining travelers’ online reviews on Airbnb

The 7 Ps model is a very useful tool in helping service firms solve managerial issues in marketing. Guided by the 7 Ps marketing mix framework, a big-data, supervised machine learning analysis was performed with 1,148,062 English reviews of 37,092 Airbnb listings in San Francisco and New York City. The results disclose similar patterns in both markets, where travelers shared their experience about Service Product and Physical Evidence most often; Price and Promotion were the least mentioned elements. Furthermore, through a series of comparisons of Airbnb’s 7 Ps marketing mix among the listings managed by different types of hosts, multi-unit and single-unit hosts seem to offer similar services with a small observable difference; whereas superhosts and the ordinary hosts deliver different services. This study makes valuable methodological contributions and provides practical marketing insights for hoteliers and the hosts and webmasters on home-sharing websites. Policymakers should pay special attention to multi-unit hosts.     

RevPAR vs. GOPPAR: Property- and firm-level analysis

Revenue per Available Room (RevPAR) has been widely used as the standard performance measure for the hotel industry. However, hoteliers have also recognized limitations of RevPAR and developed other alternative performance measures. Gross Operating Profit per Available Room (GOPPAR) has become popular as an important alternative performance measure because it resolves limitations of RevPAR. Nevertheless, practical implications of the comparative validity and usefulness between the two measures have not been fully and empirically investigated in the past literature. Therefore, the current study seeks to fill that gap by comparing RevPAR and GOPPAR at the property- and firm-level. This study found that RevPAR is, in general, a more useful measure than GOPPAR at the firm-level and mixed results at the property-level.     

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.     

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.     

Do regional factors matter? Determinants of hotel industry performance in China

The hotel industry is deeply embedded in local regional conditions, though the influence of regional factors is seldom investigated. Three key hotel industry metrics, Revenue per Available Room (RevPAR), Average Room Rate (ARR), and rooms rented, were examined for their determinants using ordinary least squares and negative binomial regression models on key endogenous and exogenous (regional) factors, including Destination Attractiveness and Capacity, Business and Commercial Environment, Image and Openness, and Tourism Professional Specialization. Using China as a case study, and at different hotel star designations (2- to 5-star hotels), the results show that RevPAR is determined mainly by labor quality and Tourism Professional Specialization. Particularly, regional factors significantly affect ARR and rooms rented. The influence and sophistication of regional factors increases with higher star designations. This paper highlights links between the hospitality industry and regional economic development, which should be considered by both the hotel industry and tourism policy makers.     

Core and supplemental elements of hospitality in the sharing economy: Insights from semantic and tonal cues in Airbnb property listings

Advances in peer-to-peer sharing, made popular by platforms like Airbnb, have altered previous conceptualizations of the lodging hospitality product. This study performs semantic and tonal analyses on a large-scale dataset collected from Airbnb. Our results support a concept of lodging hospitality that comprises core products and services, supplemental customer care, and a third factor we term ‘host sharing.’ Furthermore, the study offers insight into the topics and rhetorical tactics currently defining lodging hospitality marketing on the Airbnb platform. These findings can be used to provide guidance for Airbnb hosts to provide suitable information in their listings.     

Using segmentation to compete in the age of the sharing economy: Testing a core-periphery framework

Airbnb has emerged as a credible competitive threat to the hotel industry. Consequently, hotel brands are having to rethink the experiences they provide to customer in an increasingly competitive environment. Despite these trends in the industry, experience-related research that examines and informs these developments remains under-represented in the hospitality and tourism literature. The present study offers a systematic approach to examine the potential differences in experiential consumption in the accommodations industry. Using a multiple-group analysis approach, it examines the moderating effects of individual characteristics and situational factors on the nature and dynamics of experiential consumption in the accommodations industry. The findings of the study culminate in the core-periphery framework of the hospitality consumption experience that can provide a relevant theoretical lens for future research into the different sectors and types of experiences within the hospitality and tourism industry. The study also outlines important implications for the hotel industry’s strategic experience design initiatives, from the standpoint of product development, the segmentation, targeting and positioning (STP) process, and marketing communications.