Predicting AIRBNB Sales with Google Searches in a Customer Journey Context

Mads Zacho Krarup, Niels Buus Lassen, Rene Madsen

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Abstract

This paper presents a predictive model of Airbnb sales in Copenhagen, build on a dataset with over a 33-months of Airbnb bookings in Copenhagen and related searches on Google. Moreover, geospatial patterns of the AIRBNB listings are detected by constructing a 100 m x 100 m grid cells in UTM 32 coordinates of the city of Copenhagen. The predictive models built are a stepwise regression model, which is compared with a neural network model, both performing well on training as well as on k fold cross validation data with an R2 value on 86%-96%.
Original languageEnglish
Title of host publicationSymposium i anvendt statistik : 22.-24. januar 2018
EditorsPeter Linde
Number of pages18
Place of PublicationKøbenhavn
PublisherInstitut for Fødevare- og Ressourceøkonomi, Københavns Universitet og Det Nationale Forskningscenter for Arbejdsmiljø
Publication date2018
Pages32-49
ISBN (Print)9788779043336
Publication statusPublished - 2018
Event40. Symposium i Anvendt Statistik - Københavns Universitet, København, Denmark
Duration: 22 Jan 201824 Jan 2018
Conference number: 40
http://www.statistiksymposium.dk/

Conference

Conference40. Symposium i Anvendt Statistik
Number40
LocationKøbenhavns Universitet
CountryDenmark
CityKøbenhavn
Period22/01/201824/01/2018
Internet address

Cite this

Krarup, M. Z., Buus Lassen, N., & Madsen, R. (2018). Predicting AIRBNB Sales with Google Searches in a Customer Journey Context. In P. Linde (Ed.), Symposium i anvendt statistik: 22.-24. januar 2018 (pp. 32-49). Institut for Fødevare- og Ressourceøkonomi, Københavns Universitet og Det Nationale Forskningscenter for Arbejdsmiljø. http://nfa.dk/api/PdfRelay/Get?id=http://pure.ami.dk/ws/files/5005424/Linde_P_Symposium_i_anvendt_statistik_2018.pdf