Study of Price Determinants of Sharing Economy-Based Accommodation Services: Evidence from Airbnb.com

Chang, Chun‐Wei; Li, Shengli

doi:10.3390/jtaer16040035

Cited by 10 publications

(10 citation statements)

References 32 publications

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“…According to a news report, some reasons include inaccurate descriptions of properties, prices being not cheaper than hotels, and long response time when communicating with hosts [1]. The annual booking times have a decreasing growth rate since 2018, the accumulated listing shows a decreasing growth rate since 2017, and the closest competitor Vrbo has started to occupy the market shares that originally belong to Airbnb [2].…”

Section: Introductionmentioning

confidence: 99%

Use Python Data Analysis to Gain Insights from Airbnb Hosts

Tian

2021

Advances in Mathematical Physics

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This paper uses Python and its external data processing package to conduct an in-depth analysis machine study of Airbnb review data. Increasingly, travelers are now using Airbnb instead of staying in traditional hotels. However, in such a growing and competitive Airbnb market, many hosts may find it difficult to make their listings attractive among the many. With the development of data science, the author can now analyse large amounts of data to obtain compelling evidence that helps Airbnb hosts find certain patterns in some popular properties. By learning and emulating these patterns, many hosts may be able to increase the popularity of their properties. By using Python to analyse all data from all aspects of Airbnb listings, the author proposes to test and find correlations between certain variables and popular listings. To ensure that the results are representative and general, the author used a database containing many multidimensional details and information about Airbnb listings to date. To obtain the desired results, the author uses the Pandas, NLTK, and matplotlib packages to better process and visualize the data. Finally, the author will make some recommendations to Airbnb hosts based on the evidence generated from the data in many ways. In the future, the author will build on this to further optimize the design.

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Section: Introductionmentioning

confidence: 99%

Use Python Data Analysis to Gain Insights from Airbnb Hosts

Tian

2021

Advances in Mathematical Physics

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“…The literature on pricing and the identification of price determinants for virtual 3D assets is sparse. However, previous work emphasizes approaches to examine price determinants based on ML for other fields of application, ranging from accommodation [49][50][51] and the stock market [52,53], to e-commerce [54,55], cryptocurrencies [56][57][58], and energy prices [59]. Apart from dynamic pricing approaches based on reinforcement learning [60,61], most publications have focused on the application of supervised learning algorithms to identify price determinants.…”

Section: Related Studiesmentioning

confidence: 99%

Towards Virtual 3D Asset Price Prediction Based on Machine Learning

Korbel

Siddiq

Zarnekow

2022

JTAER

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Although 3D models are today indispensable in various industries, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies relevant price determinants of virtual 3D assets through the analysis of a dataset containing the characteristics of 135.384 3D models. Machine learning algorithms were applied to derive a virtual 3D asset price prediction tool based on the analysis results. The evaluation revealed that the random forest regression model is the most promising model to predict virtual 3D asset prices. Furthermore, the findings imply that the geometry and number of material files, as well as the quality of textures, are the most relevant price determinants, whereas animations and file formats play a minor role. However, the analysis also showed that the pricing behavior is still substantially influenced by the subjective assessment of virtual 3D asset creators.

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“…Activities aimed at producing cultural value were encouraged in Helsinki, Finland, through smallscale entrepreneurship, facilitating Airbnb's growing popularity [34]. In Shanghai, China, tourists are beginning to prefer Roomorama and the Airbnb platform to traditional hotels: the total sales and user base of these accommodation-sharing platforms has increased significantly, reaching 16.5 billion and 147 million, respectively, in 2018 [35,36].…”

Section: Literature Reviewmentioning

confidence: 99%

The Circular Economy: A Study on the Use of Airbnb for Sustainable Coastal Development in the Vietnam Mekong Delta

He¹

2021

Sustainability

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The concept of the circular economy has become well known for its solution-oriented approach to transforming available resources into a closed-loop resource system. However, in the context of coastal areas, coastal resources seem to be ignored in the tourism production and consumption process. In relation to the 2030 Agenda for Sustainable Development Goals (SDGs), this article discusses how sharing economy practices may sustain coastal resources through ecotourism, applying a sharing-economy theory that emphasizes changes in the new form—a circular economy—rather than a single traditional Airbnb model or ecotourism model. This study proposes a coastal sustainable development structure model based on the integration between the sharing economy and ecotourism with three modes—positive economic effects, positive economic pressures, and sustainable coastal development—and uses coastal residents’ expectations of their living conditions as moderating factors to investigate the impact of the circular economy on coastal sustainability. We developed a survey-based model that included 303 samples from the indigenous residents of 13 provinces throughout the Vietnam Mekong Delta. The results show that the integration of ecotourism with the Airbnb model has a positive effect on residents’ living conditions, supporting sustainable local development. However, the advancement of technology and residents’ awareness involves barriers to coastal development because the process of modernization is still limited in coastal areas. More specifically, in the case of the Vietnam Mekong Delta, our results suggest that limited technical knowledge and language ability stand as barriers to coastal businesses, showing that the lack of inter-regional connectivity limits the magnitude of local tourism in coastal areas. These findings are useful for assessing residents’ living conditions so that coastal development can work towards poverty reduction. Finally, the establishment and expansion of policies by local authorities can be an indispensable part of coastal economic development by limiting the negative effects of the abuse of natural resources and facilitating family businesses in coastal zones in an effort towards the integration of economic development and social and environmental responsibility.

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Study of Price Determinants of Sharing Economy-Based Accommodation Services: Evidence from Airbnb.com

Cited by 10 publications

References 32 publications

Use Python Data Analysis to Gain Insights from Airbnb Hosts

Use Python Data Analysis to Gain Insights from Airbnb Hosts

Towards Virtual 3D Asset Price Prediction Based on Machine Learning

The Circular Economy: A Study on the Use of Airbnb for Sustainable Coastal Development in the Vietnam Mekong Delta

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