Determinants of Airbnb Bookings: A Sequential Bayesian Update Approach, Study notes of Business

Download Determinants of Airbnb Bookings: A Sequential Bayesian Update Approach and more Study notes Business in PDF only on Docsity! Exploring the Booking Determinants of the Airbnb Properties: An Example of the Listings of London Abstract: The aim of this paper is to investigate the factors which influence the probability of an Airbnb property being booked using the properties in London as an example. A binomial logistic model is estimated by sequential Bayesian updating due to the large volume of the data. The results show that, in spite of the market factors revealing great influence, the attributes of the properties play a more important role in influencing the booking probability of the properties. These research findings are potentially beneficial to both the Airbnb practitioners and the industrial organizers. Keywords: Airbnb, Booking Determinants, Binomial Logistic Model, Sequential Bayesian Updating, Big Data, Sharing Economy 1 Introduction As a pioneer and symbol of the sharing economy in the hospitality industry, Airbnb has enjoyed impressive and sustained growth in the last decade. Airbnb is an online community marketplace for renting accommodations from private individuals. Now it lists more than three million properties covering 65 thousand cities in 191 countries (Airbnb, 2017). Benefitted from the sharing economy based business model, Airbnb enables small businesses and entrepreneurs to compete with traditional “brick and mortar” businesses (Morgan & Kuch, 2015; Orsi, 2013). However, due to the exponential expansion, it is more and more challenging for the hosts to stand out and be selected by customers among the numerous competitors on the same platform. The aim of this paper is to investigate the factors which influence the probability of a property being booked using the Airbnb properties in London as an example. Due to the large volume of sample size, sequential Bayesian updating approach is introduced to tourism and hospitality field for the first time. The findings of this study would be valuable and helpful for the current and potential hosts to improve the booking rates and revenue which would be beneficial to the development of Airbnb industry. 2 Literature Review 2.1 Airbnb: An emerging platform of sharing economy based accommodation As the rapidly emerging and the global expansion of Airbnb, a plenty of research, especially since 2014, was conducted to explore this phenomenon and the mechanism beyond. As a new accommodation rental platform in the market, some studies sought to provide an evaluation of Airbnb. By applying descriptive statistics, Zekanović-Korona and Grzunov (2014) explored the structure of Airbnb users and the advantages and disadvantages of the platform. Similarly, a SWOT analysis was conducted with Airbnb to assess different dimensions of this business model, including the investment and financial strategies, legal issues on safety and tax, and its impacts on the other accommodation providers (Meleo, Romolini, & De Marco, 2016). As the rapid development of Airbnb, its impacts on traditional hotels and online travel agents were identified. In particular, some studies investigated the competitive and substitute roles of Airbnb to the traditional hotels (Varma, Jukic, Pestek, Shultz, & Nestorov, 2016; Guttentag & Smith, 2017). Choi, Jung, Ryu, Do Kim, and Yoon (2015) found no impact of Airbnb on hotels’ revenue performance with Korea as the context. However, Aznar, Sayeras, Rocafort, and Galiana (2017) argued that there was a positive relationship between the presence of Airbnb apartments and hotels’ return on equity with Barcelona’s hotel sector as the research target. In addition, the spatial patterns of hotels and Airbnb apartments were identified in a city destination (Gutiérrez, García-Palomares, Romanillos, & Salas-Olmedo, 2017). Not limited to the economic and geographical impacts, the social, cultural and environmental impacts brought by Airbnb were also discussed by both academics and the public (Haines, 2016; Gurran & Phibbs, 2017). Focusing on the Airbnb platform, some studies were conducted to explore the characteristics of this platform and consumers’ behavioral patterns on this platform. It the odds of the property being booked (-8.34%), whereas the more introductions of the property by texts (in hundred words) and pictures may increase the odds by 5.45% and 20.08%, respectively. Particularly, if the host mentions the space of the property, it would significantly improve the odds by 11.93%. Holding all other factors fixed, being a Superhost in London will increase the odds of the listings being booked by 28.5%. Furthermore, the disclosure of hosts’ profile would improve the odds by 45.58%, the verification of hosts’ identity would increase the odds by 19.64%. In general, guests prefer large properties with more bedrooms and amenities. In particular, the offer of internet connection and kitchen would increase the odds of the property being booked by 35.29% and 26.76%, respectively. The guests are sensitive about private space while booking Airbnb properties. Shared bedroom is less preferred (-59.63%) while independent bathrooms are favored (21.40%). In the case that the entire property is available as a whole, the odds or the property being booked would increase dramatically by 290.84%. Extra charges, such as security deposit, cleaning fees, and the fees for extra person, would keep guests from booking (-0.02%, -0.03%, and -0.93%, respectively). In contrast, discounts on weekly or monthly bases or flexible refund policy would be more than welcome (9.37%, 8.82%, and 5.34%, respectively). If a property can be booked instantly without contacting the host, its odds of being booked is increased by 33.23%. In the case that guests’ identity documents are requested by the host, the odds of the property being booked would decrease by 13.91%. Online reviews are also crucial for guests’ decision. One additional piece of review would increase the odds of the property being booked by 0.69%. Geographical convenience of the properties also plays an important role in attracting guests. The properties that are closer to Tube stations and city center are more popular in the market. In the current case of London, being away from the Tube station and the city center by 1km will decrease the odds of the listings being booked by 7.62% and 6.71%, respectively. The “theme” provided by the properties is also influential. In comparison with the plain properties without a “theme”, the most popular “theme” is romantic (12.89%) followed by family gathering (10.96%). In contrast, the properties that are set for business or social occasions are less popular (- 6.37% and -3.55%, respectively). In terms of the property type, house or townhouse are preferable by the guests (9.89%), whereas bed & breakfast are not (-17.35%). Regarding the bed type, in comparison with other types, having pull-out sofas or real beds are considered as a desirable attribute (28.55%) while having merely couches are not (-27.99%). Table 1 Summary of selected parameters Description Mean ΔOdds Price per capita -0.0116 -1.16% Number of neighboring listings 0.0001 0.01% Available neighboring listings -0.0002 -0.02% Number of characters in rules -0.0871 -8.34% Property Description 0.0530 5.45% Description of space (Dummy) 0.1127 11.93% Number of listing pictures 0.1830 20.08% Super host (Dummy) 0.2511 28.54% Host profile (Dummy) 0.3756 45.58% Host verified ID (Dummy) 0.1793 19.64% Number of Bedrooms 0.0147 1.48% Number of Amenities 0.0187 1.89% Number of bed per bedroom -0.0984 -9.37% Bathroom per capita 0.1940 21.40% Internet (Dummy) 0.3023 35.29% Kitchen (Dummy) 0.2371 26.76% Security deposit -0.0002 -0.02% Cleaning fee -0.0003 -0.03% Fee for extra person -0.0094 -0.93% Weekly discount (Dummy) 0.0896 9.37% Monthly discount (Dummy) 0.0846 8.82% Instant reservation (Dummy) 0.2869 33.23% Refund (Dummy) 0.0520 5.34% Guest verification Required (Dummy) -0.1497 -13.91% Number of Reviews 0.0069 0.69% Distance to the nearest tube station -0.0792 -7.62% Distance to city center -0.0694 -6.71% Property function (Group of dummies) None Benchmark Romantic 0.1214 12.89% Family 0.1040 10.96% Business -0.0659 -6.37% Social -0.0361 -3.55% Property type (Group of dummies) Others Benchmark Apartment 0.0175 1.77% House & Townhouse 0.0943 9.89% Bed & Breakfast -0.1905 -17.35% Room Type (Group of dummies) Shared room Benchmark Private room 0.4677 59.63% Entire property 1.3632 290.84% Bed Type (Group of dummies) Others Benchmark Couch -0.3284 -27.99% Pull-out Sofa/Real bed 0.2512 28.55% 5 Conclusion A binomial logistic model is estimated by sequential Bayesian updating method in the current study to investigate the determinants of the booking probabilities of Airbnb properties in London. The results show that in addition to the market factors such as the price and the spillover effects of neighboring properties, the attributes of the property play a very important role in influencing the probability of the property being booked. These research findings would be beneficial for the existing and potential Airbnb hosts in their decision-making process. Based on the current situation of the markets and the properties, the hosts can reallocate limited resources from less important factors to more important factors in order to achieve a better booking status. The results of the current study can also be used by industrial management organizations to predict potential occupancy rate as well as potential revenue for regulation purposes.