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Hot or not? Räumliche Analyse von Airbnb-Listings in Deutschland, Berlin, Hamburg, München und Köln

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  • Reif, Julian

Abstract

Die Buchungsplattform Airbnb ist zu einem relevanten Buchungskanal im Tourismus weltweit geworden. Mit Blick auf die vielfältigen sozio-ökonomischen Auswirkungen der Plattform ist die Kenntnis über die räumliche Lage des Airbnb-Angebots von hoher Bedeutung. Für Deutschland gibt es bisher keine umfassende, auf die räumliche Verteilung des Airbnb-Angebotes abzielende, Analyse. Der vorliegende Artikel untersucht daher die räumliche Konzentration von Airbnb-Angeboten in Deutschland und den vier einwohnerstärkstem Städten Deutschlands. Erstmals werden neben visuellen Analysen auch mit Hilfe von räumlicher Statistik Maßzahlen zur Airbnb-Konzentration vorgelegt. Dabei wird die Methodik der räumlichen Autokorrelation verwendet. Die Ergebnisse zeigen, dass es eine starke positive räumliche Konzentration des Airbnb-Angebots gibt. Bundesweit zeigen sich über 2.200 statistisch signifikante Airbnb-Hotspots, die sich i. d. R. an der Verteilung der deutschen Großstädte orientieren. Der Blick auf die Millionenstädte ergibt ebenfalls eine starkes räumliches Konzentrationsmaß. In Berlin sind die Airbnb-Listings am stärksten konzentriert, gefolgt von Köln, Hamburg und München. Die Ergebnisse dienen als Grundlage für weitere Forschung und als Ansatz für planerische Fragestellungen.

Suggested Citation

  • Reif, Julian, 2022. "Hot or not? Räumliche Analyse von Airbnb-Listings in Deutschland, Berlin, Hamburg, München und Köln," Working Paper Series 3, Deutsches Institut für Tourismusforschung, Fachhochschule Westküste.
    • Handle: RePEc:zbw:ditfwp:280143
    DOI: 10.48590/t0pr-5t03
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    References listed on IDEAS

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    Keywords

    Räumliche Autokorrelation; Moran's I; Airbnb-Listings; Big Data; Städtetourismus; Spatial Autocorrelation; Moran's I; Airbnb-Listings; Big Data; Urban Tourism;
    All these keywords.

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