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.     

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.     

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.     

The seven lives of Airbnb. The role of accommodation types

This paper investigates how COVID-19 is impacting different accommodation types, and whether travellers' choices regarding accommodation type are affected by the need for physical distance. Study 1 shows that travellers are very reluctant to book shared flats on Airbnb during the pandemic. However, full flats – controlling for price levels - are preferred to hotel rooms. Study 2 clarifies the process behind the increased choice for full flats, i.e., the need for physical distance. In Study 3, we actively manipulate physical distance and show that assuring physical distance will reduce the concerns towards hotel and shared flat options. Apart from enlightening the psychological process behind accommodation choice, the study offers operators actionable suggestions on how to maximise bookings despite the pandemic.     

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).     

Methodological discussion of Airbnb's hedonic study: A review of the problems and some proposals tested on Bordeaux City data

Sharing economy-based accommodations has grown dramatically worldwide. Given the crucial role of pricing, academics have used hedonic methods to estimate a large number of implicit prices and accommodation characteristics. It is now time to question our estimation methods. Based on the hedonic methodological literature, this article outlines five proposals to improve model performance: extended specification of attributes, flexible specification of functional forms, market segmentation, and treatment of time and spatial heterogeneities. Each proposal is tested on 13,991 Airbnb listings in Bordeaux, one of the world's top urban tourism destinations. Findings offer significant guidelines for future studies and more relevant information to market players but also suggest that low performance on specific segments is not due to technical problems.     

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 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.     

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.     

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.     

Threat of infectious disease during an outbreak: Influence on tourists' emotional responses to disadvantaged price inequality

The ongoing COVID-19 pandemic has negatively influenced the global tourism industry. Despite the documented negative impacts of diseases on tourism demand and people's perceived health risk, researchers have seldom examined the psychological responses of tourists travelling during an infectious disease outbreak. We therefore conducted three studies to examine this key aspect, and our findings indicate that tourists have a strong negative emotional reaction towards disadvantaged tourism-related prices in response to a high (vs low) infectious disease threat. Furthermore, risk aversion acts as an underlying mechanism driving this effect: tourists are more risk aversive under the threat of an infectious disease, which consequently magnifies their negative emotional reaction. At last, theoretical and practical implications of these findings for tourism 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.     

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.     

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.     

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.     

Understanding the impact of host facial characteristics on Airbnb pricing: Integrating facial image analytics into tourism research

People rapidly and subconsciously process information from facial images. On sharing economy platforms, facial cues can provide a useful supplement to other information provided by reputation systems. Previous small-scale, rater-informed studies examining trust and attractiveness based on facial features on Airbnb found mixed support for impacts on pricing. We re-examine their impact using deep learning to classify host faces for an extensive data set of Airbnb accommodation in 10 US cities (n = 78,215). Together, trust and attractiveness contribute to almost a 5% increase in prices for Airbnb accommodation. We also test Gray's theory of motivation via the examination of pricing for different types of accommodation, finding that trust is more important in situations of smaller accommodation shared with strangers. The paper concludes with limitations and implications for research and practice.     

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.     

Forecasting tourism recovery amid COVID-19

The profound impact of the coronavirus disease 2019 (COVID-19) pandemic on global tourism activity has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to seek the best methods to predict the recovery of tourism from the devastating effects of COVID-19. In this study, econometric and judgmental methods were combined to forecast the possible paths to tourism recovery in Hong Kong. The autoregressive distributed lag-error correction model was used to generate baseline forecasts, and Delphi adjustments based on different recovery scenarios were performed to reflect different levels of severity in terms of the pandemic's influence. These forecasts were also used to evaluate the economic effects of the COVID-19 pandemic on the tourism industry in Hong Kong.     

Light at the end of the tunnel: Visitors' virtual reality (versus in-person) attraction site tour-related behavioral intentions during and post-COVID-19

Consumer behavior is changing as a result of the COVID-19 pandemic, thus compelling attraction sites to find new ways of offering safe tours to visitors. Based on protection motivation theory, we develop and test a model that examines key drivers of visitors' COVID-19-induced social distancing behavior and its effect on their intent to use virtual reality-based (vs. in-person) attraction site tours during and post-COVID-19. Our analyses demonstrate that visitor-perceived threat severity, response efficacy, and self-efficacy raise social distancing behavior. In turn, social distancing increases (decreases) visitors' intent to use virtual reality (in-person) tours during the pandemic. We find social distancing to boost visitors' demand for advanced virtual tours and to raise their advocacy intentions. Our results also reveal that social distancing has no effect on potential visitors' intent to use virtual reality vs. in-person tours post-the pandemic. We conclude by discussing vital implications that stem from our analyses.     

新冠肺炎疫情对中国旅游业的影响及其应对探讨

新冠肺炎疫情给全球旅游业带来了巨大的冲击和挑战,深入探讨疫情对旅游业的影响及应对成为各界关注的重点。本文从居民出游意愿、场所空间容量、市场经营主体、旅游政策等供需关系方面分析了新冠肺炎疫情对中国旅游业的影响。研究表明：（1）疫情对居民出游消费信心、意愿和能力造成较大影响,但潜在出游需求仍然存在。（2）疫情对旅游地空间环境造成较大物理和心理压缩,与旅游关联紧密的文化产业、娱乐业的生产空间容量也受到了间接影响。（3）疫情对旅游产业链、旅游市场主体经营等方面造成了全面且深远的影响。（4）疫情防控常态化下旅游政策供给以“流动管制”和“行业纾困”并重为主。面对疫情的持续影响,建议着重从组织响应、空间响应和企业韧性3个方面进一步强化旅游业应对能力,即：完善业外支撑、业内驱动、业界保障的三位一体组织响应体系;构建旅游目的地（点）-连结（线）-网络结构（网络）的三级协同空间响应机制;从企业组织、产品服务、管理和营销、市场品牌、员工心理等5个方面加强旅游企业韧性建设,以增强中国旅游业恢复发展能力。