Predicting AIRBNB Sales with Google Searches in a Customer Journey Context

Mads Zacho Krarup; Niels Buus Lassen; Rene Madsen

Predicting AIRBNB Sales with Google Searches in a Customer Journey Context

Mads Zacho Krarup, Niels Buus Lassen, Rene Madsen

Institut for Digitalisering

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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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%.

Originalsprog	Engelsk
Titel	Symposium i anvendt statistik : 22.-24. januar 2018
Redaktører	Peter Linde
Antal sider	18
Udgivelsessted	København
Forlag	Institut for Fødevare- og Ressourceøkonomi, Københavns Universitet og Det Nationale Forskningscenter for Arbejdsmiljø
Publikationsdato	2018
Sider	32-49
ISBN (Trykt)	9788779043336
Status	Udgivet - 2018
Begivenhed	40. Symposium i Anvendt Statistik - Københavns Universitet, København, Danmark Varighed: 22 jan. 2018 → 24 jan. 2018 Konferencens nummer: 40 http://www.statistiksymposium.dk/

Konference

Konference	40. Symposium i Anvendt Statistik
Nummer	40
Lokation	Københavns Universitet
Land/Område	Danmark
By	København
Periode	22/01/2018 → 24/01/2018
Internetadresse	http://www.statistiksymposium.dk/

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Citationsformater

Krarup, M. Z., Buus Lassen, N., & Madsen, R. (2018). Predicting AIRBNB Sales with Google Searches in a Customer Journey Context. I P. Linde (red.), Symposium i anvendt statistik: 22.-24. januar 2018 (s. 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

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title = "Predicting AIRBNB Sales with Google Searches in a Customer Journey Context",

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%. ",

author = "Krarup, {Mads Zacho} and {Buus Lassen}, Niels and Rene Madsen",

year = "2018",

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editor = "Peter Linde",

booktitle = "Symposium i anvendt statistik",

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Krarup, MZ, Buus Lassen, N & Madsen, R 2018, Predicting AIRBNB Sales with Google Searches in a Customer Journey Context. i P Linde (red.), Symposium i anvendt statistik: 22.-24. januar 2018. Institut for Fødevare- og Ressourceøkonomi, Københavns Universitet og Det Nationale Forskningscenter for Arbejdsmiljø, København, s. 32-49, 40. Symposium i Anvendt Statistik, København, Danmark, 22/01/2018. <http://nfa.dk/api/PdfRelay/Get?id=http://pure.ami.dk/ws/files/5005424/Linde_P_Symposium_i_anvendt_statistik_2018.pdf>

Predicting AIRBNB Sales with Google Searches in a Customer Journey Context. / Krarup, Mads Zacho; Buus Lassen, Niels; Madsen, Rene.
Symposium i anvendt statistik: 22.-24. januar 2018. red. / Peter Linde. København: Institut for Fødevare- og Ressourceøkonomi, Københavns Universitet og Det Nationale Forskningscenter for Arbejdsmiljø, 2018. s. 32-49.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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PY - 2018

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N2 - 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%.

AB - 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%.

M3 - Article in proceedings

SN - 9788779043336

SP - 32

EP - 49

BT - Symposium i anvendt statistik

A2 - Linde, Peter

PB - Institut for Fødevare- og Ressourceøkonomi, Københavns Universitet og Det Nationale Forskningscenter for Arbejdsmiljø

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T2 - 40. Symposium i Anvendt Statistik

Y2 - 22 January 2018 through 24 January 2018

ER -