Open AccessUNDERSTANDING SHORT-TERM RENTAL DATA SOURCES – A VARIETY OF SECOND-BEST SOLUTIONS
Author(s) -
Adam Pawlicz,
Catherine Prentice
Publication year - 2021
Publication title -
tourism in south east europe .../tourism in southern and eastern europe
Language(s) - English
Resource type - Conference proceedings
eISSN - 2706-2406
pISSN - 1848-4050
DOI - 10.20867/tosee.06.39
Subject(s) - renting , computer science , term (time) , variety (cybernetics) , data science , information source (mathematics) , data source , market research , world wide web , information retrieval , database , marketing , business , engineering , artificial intelligence , statistics , physics , civil engineering , mathematics , quantum mechanics
Purpose – This paper aims to identify major supply data sources for short-term rental market research and to provide their advantages and limitations. Methodology – In the paper a grounded approach was used based on a literature review. This review comprised two steps with the first being the query in major databases that was supplemented by academic search engine that resulted in 170 articles. The second step was to investigate the papers’ methodological sections to identify characteristics and limitations of all data sources. Findings – This study identifies three major data sources for the short-term rental market: web scraping with the use of self-made bots, Inside Airbnb and Airdna. A majority (e.g. 74% of papers using Airdna as a source) did not mention any limitations and provide no discussion about the data source, while the remainder gave only superfluous information about possible limitations of its use. Their characteristics and limitations are extensively discussed using a proposed framework that consists of three levels: intermediary, web scraping, and source-specific. Contribution – Very limited number of studies have focused on the short-term rental data sources and this is the first one that discusses advantages and limitation of their use. This paper may be of help to academics or professionals in identifying the right source of data to suit their technical knowledge, financial and technical resources and research areas.