Quality signals on Airbnb: a hedonic regression approach
This study aims at identifying guests’ willingness to pay for characteristics of listings on Airbnb, putting a particular emphasis on trust-building information provided by the platform. In order to do so, a hedonic regression model is applied to an extensive dataset that was gathered in 2017 from A...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hamburg
Helmut Schmidt Universität Hamburg, Department of Economics
May 2021
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| Schriftenreihe: | Diskussionspapierreihe / Helmut Schmidt Universität Hamburg, Fächergruppe Volkswirtschaftslehre
Nr./No. 189 |
| Schlagworte: | |
| Zusammenfassung: | This study aims at identifying guests’ willingness to pay for characteristics of listings on Airbnb, putting a particular emphasis on trust-building information provided by the platform. In order to do so, a hedonic regression model is applied to an extensive dataset that was gathered in 2017 from Airbnb’s website and encompasses listings from seven major German cities, namely Berlin, Munich, Hamburg, Cologne, Dresden, Stuttgart and Frankfurt am Main. Our results regarding tangible characteristics are mostly in line with expectations: Additional space and certain amenities increase the value of a listing. The same holds true for an accommodation’s distance to the city center, although we find proof for a non-linear relationship. Results for trust-building factors on the other hand are mixed. While favorable review scores and membership duration have a positive effect on prices, we cannot establish such a relationship for "superhost" and "verified ID" badges. In contrast to other studies, which are, however, focused on the US, we cannot find price differences linked to hosts’ gender or ethnicity. Using an extended data set that encompasses listings from 2007 to 2008, we furthermore construct hedonic price indices for all seven cities, which suggest supply shifts due to regulatory pressure |
| Beschreibung: | 42 Seiten Illustrationen, Diagramme, Karten |
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| 520 | 3 | |a This study aims at identifying guests’ willingness to pay for characteristics of listings on Airbnb, putting a particular emphasis on trust-building information provided by the platform. In order to do so, a hedonic regression model is applied to an extensive dataset that was gathered in 2017 from Airbnb’s website and encompasses listings from seven major German cities, namely Berlin, Munich, Hamburg, Cologne, Dresden, Stuttgart and Frankfurt am Main. Our results regarding tangible characteristics are mostly in line with expectations: Additional space and certain amenities increase the value of a listing. The same holds true for an accommodation’s distance to the city center, although we find proof for a non-linear relationship. Results for trust-building factors on the other hand are mixed. While favorable review scores and membership duration have a positive effect on prices, we cannot establish such a relationship for "superhost" and "verified ID" badges. In contrast to other studies, which are, however, focused on the US, we cannot find price differences linked to hosts’ gender or ethnicity. Using an extended data set that encompasses listings from 2007 to 2008, we furthermore construct hedonic price indices for all seven cities, which suggest supply shifts due to regulatory pressure | |
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| spelling | Löw, Franziska Verfasser (DE-588)1081682019 aut Quality signals on Airbnb a hedonic regression approach Franziska Löw, Hendrik Lüth Hamburg Helmut Schmidt Universität Hamburg, Department of Economics May 2021 42 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Diskussionspapierreihe / Helmut Schmidt Universität Hamburg, Fächergruppe Volkswirtschaftslehre Nr./No. 189 This study aims at identifying guests’ willingness to pay for characteristics of listings on Airbnb, putting a particular emphasis on trust-building information provided by the platform. In order to do so, a hedonic regression model is applied to an extensive dataset that was gathered in 2017 from Airbnb’s website and encompasses listings from seven major German cities, namely Berlin, Munich, Hamburg, Cologne, Dresden, Stuttgart and Frankfurt am Main. Our results regarding tangible characteristics are mostly in line with expectations: Additional space and certain amenities increase the value of a listing. The same holds true for an accommodation’s distance to the city center, although we find proof for a non-linear relationship. Results for trust-building factors on the other hand are mixed. While favorable review scores and membership duration have a positive effect on prices, we cannot establish such a relationship for "superhost" and "verified ID" badges. In contrast to other studies, which are, however, focused on the US, we cannot find price differences linked to hosts’ gender or ethnicity. Using an extended data set that encompasses listings from 2007 to 2008, we furthermore construct hedonic price indices for all seven cities, which suggest supply shifts due to regulatory pressure Airbnb (DE-588)1077226985 gnd rswk-swf Immobilienmarkt (DE-588)4123416-9 gnd rswk-swf airbnb sharing economy hedonic models GLM rental markets price indices real estate Airbnb (DE-588)1077226985 b Immobilienmarkt (DE-588)4123416-9 s DE-604 Lüth, Hendrik Verfasser (DE-588)1111566895 aut Helmut Schmidt Universität Hamburg, Fächergruppe Volkswirtschaftslehre Diskussionspapierreihe Nr./No. 189 (DE-604)BV019390486 189 |
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| title | Quality signals on Airbnb a hedonic regression approach |
| title_auth | Quality signals on Airbnb a hedonic regression approach |
| title_exact_search | Quality signals on Airbnb a hedonic regression approach |
| title_exact_search_txtP | Quality signals on Airbnb a hedonic regression approach |
| title_full | Quality signals on Airbnb a hedonic regression approach Franziska Löw, Hendrik Lüth |
| title_fullStr | Quality signals on Airbnb a hedonic regression approach Franziska Löw, Hendrik Lüth |
| title_full_unstemmed | Quality signals on Airbnb a hedonic regression approach Franziska Löw, Hendrik Lüth |
| title_short | Quality signals on Airbnb |
| title_sort | quality signals on airbnb a hedonic regression approach |
| title_sub | a hedonic regression approach |
| topic | Airbnb (DE-588)1077226985 gnd Immobilienmarkt (DE-588)4123416-9 gnd |
| topic_facet | Airbnb Immobilienmarkt |
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