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.     

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.     

Asymmetric price responses to hotel competition caused by heterogeneous customers’ willingness to pay

Heterogeneous customers’ willingness to pay affects hotel competition and results in competitors’ asymmetric price responses. To study this lopsided effect, we construct a game framework featuring an upscale hotel and an upper-midscale hotel under a series of assumptions. We first analyze hotel pricing and competitor price responses and then compare the competitors’ responding adjustments. Primary findings show that (1) the price response expressed as an amount by the upscale hotel is more than that of the upper-midscale hotel, and that (2) the price response expressed as a percentage by the upscale hotel is less than that of the upper-midscale hotel. Finally, we present reasons for our findings and offer suggestions to hotel revenue managers.     

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.     

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.     

Improving revenue performance with third-degree price discrimination in the cruise industry

The leisure cruise industry has enjoyed high levels of growth for nearly five decades due in part to traveler interest in the cruise experience, but also to relatively lower pricing. Although revenue management of cruise fares is now standard practice, there are untapped opportunities to improve yields through data-driven market segmentation and third-degree price discrimination. This paper uses a finite mixture modeling approach to develop, empirically validate, and compare pricing models. By unveiling segments of travelers based on individual attributes, third-degree price discrimination can improve target marketing, the timing and appeal of price discounts, and the matching of variable demand with fixed, though differentiated, room supply. Empirical results from running thousands of simulations with pricing data from one of the world’s largest cruise lines show that the segmentation analysis using third-degree price discrimination can increase fare revenue more than four percent. The modeling approach used in this research extends the emerging literature on revenue management in the cruise industry and offers meaningful managerial implications for advanced pricing tactics and revenue management.     

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.     

Exploring effective price presentation format to reduce decision difficulty and increase decision satisfaction

Variable pricing is frequently employed by service firms that adopt revenue management practices. This strategy is effective in stimulating and increasing revenue by appealing to customers with different levels of price sensitivity. However, by providing excessive price options, a company may cause choice overload for customers. Within the framework of behavioral economics, this study explores an effective price presentation strategy to mitigate choice overload due to a large assortment size of price options in the context of hotels. The findings of the experimental design study suggest that the number of categories that distinguish and organize price options positively affects consumers' perceived decision difficulty and consequent decision satisfaction when a large assortment size of price options is provided. This research extends the understanding of the effect of variable pricing on consumers' responses and provides marketers with guidance on how to manage variable pricing and its price presentation format.     

Let your algorithm shine: The impact of algorithmic cues on consumer perceptions of price discrimination

How can hospitality and tourism firms reduce consumers’ perceptions of unfairness regarding their price discrimination practices? We propose that pricing algorithms, as advanced revenue management tools, can play a role. We demonstrated the effects of an algorithmic cue through a tweet analysis and four experiments. We found that informing consumers that the offered price is determined by algorithms mitigated their negative responses in the disadvantaged price discrimination context. Also, such algorithmic cue effects only occur when the algorithm is less anthropomorphic (vs. highly anthropomorphic or human). The positive impact of an algorithmic cue was identified not only when customers directly experienced disadvantaged price discrimination but also when they obtained the information from different channels. The results suggest hospitality and tourism firms that have implemented pricing algorithms in their revenue management can benefit from disclosing an algorithmic cue to customers.     

An analysis of asymmetry in dynamic pricing of hospitality industry

The paper analyzes the influence of changing tourist arrival on various hotel performance measures related to prices, such as price charges per room, revenue per available room, occupancy rate, and total revenue using price dynamics of Oslo as a case. It also measured whether there is an asymmetric relation between the above performance measures in response to the increase and decrease of tourist arrival. While the presence of a significant long-run relationship between tourist arrival and revenue per available room, capacity utilization, and total revenue has been confirmed, it was found that the price per room does not have such a relationship, and it suggests that the price adjustments are made only on short-run considerations. Another interesting observation is that the magnitude of asymmetric influence is high for revenue per available room and occupancy rate when tourist arrival declines, compared to the corresponding magnitude when tourist arrival increases. These findings indicate that there lies a scope to improve the dynamic pricing model currently being followed.     

Theme park revenue management with a dual admission policy

There are several efforts to increase the revenue of theme parks. One of them is to divide admission policy into normal and express admission. If a certain customer buys express admission ticket with more expensive price, it is possible to reduce the excessive waiting time by using the exclusive waiting line for some or all the attraction’s rides. In this study, decision-making elements such as the price of the express admission ticket, maximum allowed number of express admission ticket per day, and the type of rides which can apply the express admission ticket are investigated to maximize the revenue.     

Competitive revenue management for fixing quota and price of hotel commodities under uncertainty

This article develops an artificial neural network (ANN) based forecasting model using the past profit records of hotel commodities. Based on forecasting, hotel commodities are categorised into two kinds: ones that push up the revenue, and others, which pull it down. Thereafter, long and short term goals are formulated for fixing quota and proper revenue management under uncertainty. For long term goal, analytical network process (ANP) framework is adopted to establish interrelationships among the factors using DEMATEL methodology. Risk adjusted maximum expected profit is employed for short term goal. Subsequently optimal numbers of commodities are obtained using a fuzzy goal programming approach, and favourable price of the individual commodities is determined keeping the price elasticity as one. Finally, a comparison is made between the respective revenue generated with the new quota and price from the proposed revenue maximization model, and that of the old practised price and quota. The paper demonstrates the superiority of the proposed approach. A case study of a hotel has been taken up to demonstrate the model.     

How do advertising expenditures influence hotels’ performance?

Existing studies have shown a positive relationship between advertising and hotel room revenue. However, it is not clear through which channel advertising affects hotel revenue. We contribute to the literature by showing that advertising has a significant positive impact on hotel room price, but not on room occupancy. Thus, advertising affects room revenues through room price, not quantity demanded.     

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).     

Differences in perceptions about food delivery apps between single-person and multi-person households

The growth in mobile communications is a significant reason attributed to the O2O commerce explosion in China. Mobile applications have seized this opportunity adapting it to restaurants and food delivery services, recognized as an alternative strategy to increase sales revenue and for consumers to conveniently receive products and services. This study was designed to specifically explore five salient quality attributes representing convenience, design, trustworthiness, price, and various food choices associated with food delivery apps in consideration of their impacts upon user-perceived value, attitudes and intention to continuously use. Results determined that user trustworthiness was the most important quality attribute of the food delivery app business. Additionally, this study found that single-person households placed most importance upon quality attributes for ‘various food choices,’ ‘price’ and ‘trustworthiness.’ Comparatively, multi-person households placed most importance upon ‘design,’ ‘convenience,’ and ‘trustworthiness.’ Managerial implications, limitations and recommendations for future research are provided.     

Do price promotions drive consumer spending on luxury hotel services? The moderating roles of room price and user-generated content

Price promotion, as price information, and user-generated content (UGC), as non-price information, play an important role in generating luxury hotel revenue. This study empirically investigates how price promotion influences actual consumer spending on luxury hotel services except room price, by considering the contingency role of room price and volume and valence of UGC. Combined data of daily settlements and Tripadvisor customer reviews of a regional luxury hotel chain are used for the analyses. The results indicate that, overall, price promotion negatively influences consumer spending on luxury hotel services and its negative effect is strengthened when the room is higher priced or the valence of UGC is high. Furthermore, a larger volume of intrinsic attribute-related UGC–amenity and location–with price promotion leads to more consumer spending than a larger volume of extrinsic attribute-related UGC–food and staff. The findings provide hotel managers with important insights into pricing and UGC management.     

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.     

Determining guests’ willingness to pay for hotel room attributes with a discrete choice model

Hotel managers need to understand the marginal utility customers associate with a specific attribute of a hotel in order to effectively set up rate fences and to price their rooms accordingly. This study adopted a stated choice experiment and discrete choice modeling method to obtain hotel guests’ willingness to pay (WTP) for a specific set of room attributes within a single hotel property. The attributes include room views, hotel floor, club access, free mini-bar items, smartphone service, and cancellation policy. The study discovered that leisure travelers versus business travelers, and first-time visitors versus repeat visitors, perceive different WTP values for various attributes. These findings provide valuable information for hotel managers to segment their market and conduct revenue management practices in order to maximize revenue and profit. The results also demonstrate the value of discrete choice modeling in obtaining WTP for hotel room attributes.     

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.