ENTER2018 eTourism Conference presentation

1. Price determinants
of Airbnb rentals
– the case of Denmark
Carl Marcussen, senior researcher, PhD
Center for Regional and Tourism Research,
Bornholm, Denmark, www.crt.dk
ENTER 2018, Jönköping, Sweden, 25.01.2018

2. Overview
• Web scraping: An option for harvesting data – in this case about Airbnb
• The current status of Airbnb in major cities
• Airbnb in Denmark – Copenhagen / major cities – and coastal areas
• Price determinants of Airbnb rentals – the case of Denmark
• Discussion: Web scraping – is it useful? --- Price determinants.

3. Web scraping – of Airbnb.com
• Two passionate web scrapers in the context of Airbnb
• Murray Cox: 45 cities - Many variables - http://insideairbnb.com
Europe: 20 North America: 21 Rest of World: 4
• Tom Slee: 118 cities… – Fewer variables - http://tomslee.net
Europe: 56 North America: 45 Rest of World: 17
11
8
7
5
3 3
2 2 2 2 2
9
0
2
4
6
8
10
12
Tom Slee
Europe
56 desti-
nations

4. Listings in selected major cities, Sept. 2017
Sources: www.statista.com/statistics/752498/airbnb-number-of-listings-in-major-cities-worldwide/, visited Jan. 2018 + Wikipedia
Rank City Listings Mill. inhab. Per 1000 inhab.
1 London 62.141 8,8 7,1
2 Paris 57.051 10,4 5,5
3 New York 38.745 8,5 4,5
4 Rio de Janeiro 33.705 6,5 5,2
5 Rome 25.635 2,9 8,9
6 Barcelona 23.174 1,6 14,4
7 Tokyo 19.649 13,5 1,5
8 Sydney 18.690 4,8 3,9
9 Berlin 18.599 3,6 5,2
10 Los Angeles 17.358 4,0 4,4
x1 Copenhagen Greater 17.289 1,3 13,2
x2 Copenhagen Province 15.273 0,8 20,0
x3 Copenhagen Municip. 13.177 0,6 21,9
Top 10 314.747 64,6 4,9
E.
E.
E.
E.
E.
E.

5. 20 European cities –
Airbnb web scrapes by Murray Cox
Amsterdam (TS)
Antwerp
Athens
Barcelona (TS)
Berlin (TS)
Brussels
Copenhagen (TS)
Dublin (TS)
Edinburgh (TS)
Geneva (TS)
London (TS)
Madrid (TS)
Malaga
Mallorca * ((TS))
Manchester
Paris (TS)
Rome (TS)
Trentino **
Venice (TS)
Vienna (TS)
Notes:
* Island
** Region
TS= 13 of 20
Tom Slee too

6. 56 European destinations –
Airbnb web scrapes by Tom Slee
Alpes Maritime
Amsterdam (MC)
Asturias
Barcelona (MC)
* Belgium
Berlin (MC)
Bologna
Bordeaux
Brighton
Bristol
Brno
Clichy sous Bois
Copenhagen (MC)
* Denmark
Dublin (MC)
Edinburgh (MC)
* Estonia
Florence
Frankfurt
Geneva (MC)
Girona
Granada
Groningen
Helsinki
* Hungary
* Iceland
Linköping
Lisbon
London (MC)
Lyon
Madrid (MC)
Mallorca (MC)
Melun
Menorca
Nantes
Nice
Palermo
Paris (MC)
Porto
Reykjavik
Rome (MC)
Saint Denis
Saint Malo
* Switzerland
Tallinn
Turin
Tuscany
Veneto
Venice (MC)
Versailles
Vienna (MC)
Warsaw
York
Zagreb
Zurich
Aarhus
Notes:
* 6 countries
MC=
Murray Cox too
(14 of 56)
Unfortunately,
Tom Slee will
discontinue his
philanthropic
scraping activities.
None after July 2017.
Reference: http://tomslee.net/airbnb-data-collection-get-the-data

7. Airbnb listings per 1000 inhabitants
- European cities (and areas) only
1 Versailles 84,0
2 Nice 33,8
3 Alpes Maritime 29,9
4 Reykjavik 29,6
5 Florence 24,5
6 Venice 24,2
7 Lyon 20,6
8 Granada 15,1
9 Copenhagen Greater 13,2
10 Tuscany 13,1
11 Barcelona 11,8
12 Aarhus 11,7
13 Rome 9,2
14 Amsterdam 7,7
15 London 7,3
16 Edinburgh 7,0
17 Paris 6,8
18 Bordeaux 6,3
19 Berlin 6,1
20 Dublin 5,9
Note: Airbnb listings (min. 3000) according to Tom Slee, ~July 2017.
Population (metropols) according to Wikipedia. - Selected cities

8. Airbnb listings per capita, municipalities DK, July 2017
Source: Airbnb listings July 2017, Tom Slee. – Population 1.1.2017, Stat.DK
Rank Municipality Listings Inhabitants Per 1000 inhab.
1 Copenhagen 13.117 602.481 21,8
2 Fanø (island) 62 3.345 18,5
3 Frederiksberg 1.858 105.037 17,7
4 Samsø (island) 64 3.724 17,2
5 Gribskov 590 41.213 14,3
6 Bornholm (island) 537 39.695 13,5
7 Ærø (island) 82 6.177 13,3
8 Odsherred 371 33.023 11,2
9 Læsø 19 1.793 10,6
10 Århus 3.434 335.684 10,2
Top 10 by listings/population 20.134 1.172.172 13,3

9. Airbnb listings per province in Denmark, 2017
Source: Airbnb listings July 2017, Tom Slee. – Population 1.1.2017, Stat.DK
Province i Denmark Listings pop 1/12017 Per 1000 inhab.
01 Copenhagen City 15273 764816 20,0
02 Copenhagen Sub. 2016 542601 3,7
03 North Sealand 2015 460214 4,4
04 Bornholm 537 39695 13,5
05 East Sealand 501 246594 2,0
06 W & S Sealand 1348 585959 2,3
07 Funen 1550 494049 3,1
08 South Jutland 1453 723175 2,0
09 East Jutland 4655 875084 5,3
10 West Jutland 749 429169 1,7
11 North Jutland 1969 587335 3,4
Total, Denmark 32066 5748691 5,6
Greater CPH (1+2) 17289 1307417 13,2

10. Previous studies on Airbnb
re. price determinants

11. Variables included in Airbnb
web scrapes (by Tom Slee)
1. room_id
2. host_id
3. room_type
4. borough
5. neighbourhood *
6. reviews
7. overall_satisfaction
8. accommodates
9. bedrooms
10. price (in $US)
11. minstay *
12. latitude and longitude
13. last_modified
14. name: catch phrase
Note:
* Not included
in the scrapes
available for
Denmark
Reference: http://tomslee.net/airbnb-data-collection-get-the-data
Possible to add, e.g.:
Population density in
the municipality of each
listing.

12. Some factors affecting the price per night
No. of
reviews
Superhost
badge
Population density
Satisfaction
score
Location

13. Some factors affecting the price per night
No. of
reviews
Superhost
badge
Price per m2 of housing
Satisfaction
score
Location

14. Included variables:
Lowest=0. Highest=1.
Not dummy variable.
Per renting unit, not per B&B
Dummy variable, 0 or 1.
Lowest (~3)=0. Highest(5)=1.
Not dummy variable.
Percentage – here 2%
Percentage – here 19%
In English or .. 3/4
Max # persons/bedroom.
With 12% fee. 1 EUR~7.46 DKK.

15. Results – model 1
Model 2 (weighted by reviews): R2 = 0.500. Model 3 (w., log of Y): R3 = 0.554.
Note: Dependent variable: Listprice_DKK_incl_12_pct. – 1 EUR ~ 7.46 DKK

16. Thank you for your attention
Questions - Comments - Discussion
• Is web scraping useful? – Is it easy to make web scrapes?
• How to explain (or set) list prices of accommodations?
• Which variables to explain – by which determinants?
Acknowledgement:
This work is part of CRT’s own contribution to the project
“Innovation in coastal areas” – WP about “sharing economy”. -
Supported by the “Danish Innovation Research Foundation”.

17. Acknowledgement:
Acknowledgement:
This work is part of CRT’s own contribution to the project
“Innovation in coastal areas” – WP about “sharing economy”. -
Supported by the “Danish Innovation Research Foundation”.