Ekonomi Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/410

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Browsing Ekonomi Bölümü Koleksiyonu by Subject "Airbnb"

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    Article
    Airbnb and COVID-19: SPACE-TIME vulnerability effects in six world-cities
    (Elsevier, 2022) Kourtit, Karima; Nijkamp, Peter; Östh, John; Türk, Umut; 0000-0002-8440-7048; 0000-0002-4068-8132; 0000-0002-7171-994X; AGÜ, Yönetim Bilimleri Fakültesi, Ekonomi Bölümü
    This study examines the COVID-19 vulnerability and subsequent market dynamics in the volatile hospitality market worldwide, by focusing in particular on individual Airbnb bookings-data for six world-cities in various continents over the period January 2020–August 2021. This research was done by: (i) looking into factual survival rates of Airbnb accommodations in the period concerned; (ii) examining place-based impacts of intracity location on the economic performance of Airbnb facilities; (iii) estimating the price responses to the pandemic by means of a hedonic price model. In our statistical analyses based on large volumes of time- and space-varying data, multilevel logistic regression models are used to trace ‘corona survivability footprints’ and to estimate a hedonic price-elasticity-of-demand model. The results reveal hardships for the Airbnb market as a whole as well as a high volatility in prices in most cities. Our study highlights the vulnerability and ‘corona echoeffects’ on Airbnb markets for specific accommodation segments in several large cities in the world. It adds to the tourism literature by testing the geographic distributional impacts of the corona pandemic on customers’ choices regarding type and intra-urban location of Airbnb accommodations.
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    Article
    The effect of the COVID-19 on sharing economy: survival analysis of Airbnb listings
    (2021) Umut TÜRK; Serap SAP; AGÜ, Yönetim Bilimleri Fakültesi, Ekonomi Bölümü; TÜRK, Umut; SAP, Serap
    This research investigates the survival probability of listings in the Airbnb platform during theCOVID-19 period between January-October 2020 in Istanbul. In line with the research aim, Cox'sProportional Hazard Model is adopted to conduct survival analysis, where the physical and spatialattributes of Airbnb listings are used as predictors. Our findings show that while physical attributesshow similarity to previous findings, spatial attributes show substantial differences in the Pre-COVIDand Post-COVID comparison. The contributions of the study have two facets. Theoretically, thisresearch's findings contribute to the current literature by understanding the changing consumerpreferences and identifying the factors that affect Airbnb listings' survival rates during the COVID-19pandemic. The findings may also help practitioners understand changing customers' preferencesduring COVID, especially in terms of locational choices. Moreover, customer feedback's quality andquantity might help the Airbnb hosts to improve their service quality, attract more customers, and bemore resilient under the changing conditions.
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    Article
    The path of least resistance explaining tourist mobility patterns in destination areas using Airbnb data
    (ELSEVIER SCI LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND, 2021) Turk, Umut; Osth, John; Kourtit, Karima; Nijkamp, Peter; 0000-0002-8440-7048; AGÜ, Yönetim Bilimleri Fakültesi, Ekonomi Bölümü; Turk, Umut
    Destination attractiveness research has become an important research domain in leisure and tourism economics. But the mobility behaviour of visitors in relation to local public transport access in tourist places is not yet well understood. The present paper seeks to fill this research gap by studying the attractiveness profile of 25 major tourist destination places in the world by means of a 'big data' analysis of the drivers of visitors' mobility behaviour and the use of public transport in these tourist places. We introduce the principle of 'the path of least resistance' to explain and model the spatial behaviour of visitors in these 25 global destination cities. We combine a spatial hedonic price model with geoscience techniques to better understand the place-based drivers of mobility patterns of tourists. In our empirical analysis, we use an extensive and rich database combining millions of Airbnb listings originating from the Airbnb platform, and complemented with TripAdvisor platform data and OpenStreetMap data. We first estimate the effect of the quality of the Airbnb listings, the surrounding tourist amenities, and the distance to specific urban amenities on the listed Airbnb prices. In a second step of the multilevel modelling procedure, we estimate the differential impact of accessibility to public transport on the quoted Airbnb prices of the tourist accommodations. The findings confirm the validity of our conceptual framework on 'the path of least resistance' for the spatial behaviour of tourists in destination places.