Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com

Wang, Dan; Nicolau, Juan Luis

doi:10.1016/j.ijhm.2016.12.007

Cited by 428 publications

(476 citation statements)

References 53 publications

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“…In Figure 5a, significant negative correlations between number of reviews and price exist primarily in west Metro Nashville (districts 18,[20][21][22][23][24][25]34). The negative correlations indicate that the more reviews the listing has, the lower the price, which is inconsistent with previous studies [11]. In Metro Nashville, guests tend to choose lower price listings, and then Airbnb hosts would be more likely to lower their price in order to attract more guests.…”

Section: Gwr Estimationmentioning

confidence: 69%

“…For example, Ikkala and Lampinen [10] reported that the reputation of Airbnb listings is correlated with their price. Using Airbnb listings information from 33 cities, Wang and Nicolau [11] identified five price-determinant categories of Airbnb listings, including host attributes, site and property attributes, amenities and services, rental rules, and online review ratings. Li et al [30] proved the effects of distance to the nearest landmark, the impact of facility, and nearest landmark popularity on Airbnb listing prices.…”

Section: Airbnb Listing Pricementioning

confidence: 99%

“…Airbnb is a recent lodging concept that differs from traditional hotels in terms of booking systems, facilities, website designs, and customer service. Because of the growing demand for Airbnb, several studies have focused on identifying price determinants of Airbnb listings [10,11]. Recently, global regression models (GLM) such as OLS regression and quantile regression are the most commonly adopted methods to investigate factors affecting Airbnb listing prices in current studies.…”

Section: Introductionmentioning

confidence: 99%

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Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

et al. 2017

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Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM) and a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The results showed that the GWR model performs better than the GLM in terms of accuracy and affected variable selections. Statistically significant differences varied across regions in Metro Nashville. The coefficients illustrate a decreasing trend while there is an increase in the distance from the listed units to the convention center, which indicates that Airbnb listing prices are more sensitive to the distance from the convention center in the central area than in other areas. These findings can also provide implications for stakeholders such as Airbnb hosts to gain a better understanding of the market situation and formulate a suitable pricing strategy.

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Section: Gwr Estimationmentioning

confidence: 69%

Section: Airbnb Listing Pricementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

et al. 2017

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“…In addition, the determinants of prices on Airbnb are discussed to some extent. Factors such as host attributes, site and property attributes, amenities and services, rental rules, and online review ratings are stated to have a significant relationship with listings' prices on Airbnb platform (Li, Pan, Yang, & Guo, 2016;Wang & Nicolau, 2017).…”

Section: Airbnb: An Emerging Platform Of Sharing Economy Based Accommmentioning

confidence: 99%

Exploring the Booking Determinants of the Airbnb Properties: An Example of the Listings of London

Qiu

Fan

Liu

2017

Information and Communication Technologies in Tourism 2018

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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.

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“…For example, Airbnb and Uber have catalyzed a vibrant SE discourse, participated in by the media, information communication technology industries, venture capitalists, entrepreneurs and grassroots activists [4]. Although with a common expectation that the sharing economy niche will disrupt multiple regimes by radically decentralizing economic activity, in the five years since the emergence of a sharing economy concept rooted in contrasting and contradictory ways.…”

Section: Introductionmentioning

confidence: 99%

Sharing Economy Research

Bing¹,

Zhang²

2017

dtssehs

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Abstract. With the rapid growth of Sharing Economy, this article focuses on a review of Sharing Economy research based on web of science database. Altogether there are 65 records tilted as "sharing economy" or "sharing economies" in recent 10 years, excluding conference papers. Analysis results of relating literatures indicate the following results. (1) Concerning document types, article, editorial material and meeting abstract ranked in Top 3; (2) In view of countries/territories, USA, England, and Switzerland ranked in Top 3; (3) Two funding agencies, i.e., ESRC Management and Business Development Fellowship Grant and Hong Kong Polytechnic University, ranked in Top 1, accounting for 3.077% respectively; (4) As for organizations, Boston College and Massachusetts Institute of Technology MIT ranked in Top 1. Then it was followed by Boston University and Open University UK, accounting for 4.615%; (5) The year of published articles mainly distributed between 2014 and 2017, accounting for 97%; (6) 47.692% of the research focused in Business Economics, then 15.385% in Geography and 13.846% in Government Law; (7) Considering source titles, Cambridge Journal of Regions Economy and Society ranked in Top 1, accounting for 18.182%. Overall, the related research topics can be classified into two aspects of sociology and information technology. In the end, conclusions were made with a summary of key results and new opportunities for future research.

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Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com

Cited by 428 publications

References 53 publications

Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

Exploring the Booking Determinants of the Airbnb Properties: An Example of the Listings of London

Sharing Economy Research

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