# RECORD eb2663068d73d32e83378e9ddd14f5caa58f11e6
# TITLE: Location, location and professionalization: a multilevel hedonic analysis of Airbnb listing prices and revenue
# YEAR: 2019.0  VENUE: Regional Studies, Regional Science  DOI: 10.1080/21681376.2019.1592699
# AUTHORS: Robbin Deboosere; D. Kerrigan; D. Wachsmuth; A. El-geneidy
# PAGES: 15  CANDIDATE TABLE PAGES (1-based): [6, 9]
# ABSTRACT: ABSTRACT Hedonic modelling techniques have frequently been used to examine real estate valuation, and they have recently started to be applied to short-term rental valuation. Relying on a web-scraped data set of all Airbnb transactions in New York City (NYC) between August 2014 and September 2016, this paper presents the first hedonic regression model of Airbnb to take into account neighbourhood effects and to predict both average price per night and revenue generated by each listing. The model demonstrates that locational factors – above all, transit accessibility to jobs – and neighbourhood variation have a large impact on both price per night and monthly revenue, and further reveals how professionalization of the short-term rental market is driving more revenue to a narrower segment of hosts. Further, the findings suggest that Airbnb hosts earn a significant premium by converting long-term housing in accessible residential neighbourhoods into de facto Airbnb hotels. This premium incentivizes landlords and hosts with properties in accessible neighbourhoods to replace long-term tenants with short-term guests, forcing those in search of housing to less accessible neighbourhoods.


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Location, location and professionalization:
a multilevel hedonic analysis of Airbnb listing
prices and revenue
Robbin Deboosere, Danielle Jane Kerrigan, David Wachsmuth & Ahmed El-
Geneidy
To cite this article: Robbin Deboosere, Danielle Jane Kerrigan, David Wachsmuth & Ahmed
El-Geneidy (2019) Location, location and professionalization: a multilevel hedonic analysis
of Airbnb listing prices and revenue, Regional Studies, Regional Science, 6:1, 143-156, DOI:
10.1080/21681376.2019.1592699
To link to this article:  https://doi.org/10.1080/21681376.2019.1592699
© 2019 The Author(s). Published by Informa
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Group
Published online: 03 Apr 2019.
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generated revenue in at least two separate months allows this model to be more accurate. Finally,
to ensure the analysis only includes listings actively reserved during this period, listings with no
user reviews were discarded. The resulting population for this model was 386,153. This popu-
lation is equal to the number of unique listings multiplied by the number of months that the list-
ing had revenue. Using a geographic information system (GIS) the x,y-coordinates of each listing
were mapped for further spatial analysis.
The model has two separate dependent variables and 20 independent variables, presented in
Table 1. The ﬁrst dependent variable is the average price per night, which for the study popu-
lation was US$153.16. This is the same dependent variable used by Gibbs et al. (2018),
Wang and Nicolau (2017) and Teubner et al. (2017). The second dependent variable is the aver-
age revenue per month, which was US$2287.19. There are three categories of independent vari-
ables: structural, host, and location and neighbourhood. All data on an individual listing’s
structural and host variables come from the AirDNA data set. Structural variables included
are the number of bedrooms (mean = 1.1), the number of bathrooms (mean = 1.1), the maximum
number of guests (mean = 3.0), a dummy variable that equals 1 if the listing is an entire
Table 1. Model variables.
Variable
Description
Meana
SD
Price
Average price per night (US$)
153.16
113.25
Revenue
Revenue per month (US$)
2287.19
2352.45
Structural variables
Entire apartment
Dummy variable if the entire apartment is rented
0.6109
0.4875
Shared room
Dummy variable if the room is shared
0.0274
0.1633
Bedrooms
Number of bedrooms
1.1112
0.6606
Bathrooms
Number of bathrooms
1.0850
0.3262
Guests
Maximum number of guests
2.9785
1.7281
Host variables
Superhost
Dummy variable if the host is a superhost
0.0868
0.2816
Multihost – 2–10
Dummy variable if the host has between two and 10
listings
0.3655
0.4816
Multihost – >10
Dummy variable if the host has more than 10 listings
0.0165
0.1272
Number of photos
Number of photographs for a listing
14.2015
10.8734
Instant booking
Dummy variable for is an instant booking is available
0.1292
0.3355
Reviews
Number of reviews
31.5926
36.1101
Overall rating
Overall star rating of listing
4.5738
0.3934
Location and neighbourhood variables
Subway
Dummy variable if the listing is within 800 m of a
subway stop
0.9038
0.2948
Transit accessibility
Accessibility to jobs by transit in 30 min (100,000
jobs)
13.1812
8.1446
Dist Times Square
Network distance to Times Square (km)
6.4343
3.9046
Diversity
Land-use diversity, measured by an entropy index
0.9557
0.2867
Population density
Population density in the census tract (1000 residents/
km2)
29.1037
15.4984
Median household
income
Median household income in the census tract (US
$1000)
74.1427
36.4930
Percentage white
Percentage of residents who identify as white in the
census tract
47.5045
26.6700
Listing density
Number of Airbnb listings in the census tract
322.2075
261.7637
Note: aThe mean of a dummy variable represents the percentage of all listings that have a value of 1 for that
dummy variable.
Location, location and professionalization: a multilevel hedonic analysis of Airbnb listing prices and revenue
147
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apartment, and a dummy variable that equals 1 if the listing is a shared room. These structural
variables were included based on previous studies (Gibbs et al., 2018; Hill, 2015; Lee et al.,
2015; Teubner et al., 2017; Wang & Nicolau, 2017).
The host variables used were: a dummy variable equal to 1 if the host has superhost status; a
dummy variable equal to 1 if the host has between two and 10 listings; a dummy variable equal to
1 if the host has more than 10 listings; and a dummy variable equal to 1 if the instant book feature
was available. Additionally, the model used variables for the number of photographs included
with each listing (mean = 14.2), the number of reviews (mean = 31.6) and the overall rating of
the listing (mean = 4.6). While Teubner et al. (2017) looked at details such as the host’s gender,
age and if they were smiling in their proﬁle photograph, they found no signiﬁcant price effects
based on these details. Accordingly, such variables were not included in this study, and host vari-
ables, in general, were chosen based on previous studies (Gibbs et al., 2018; Hill, 2015; Lee et al.,
2015; Wang & Nicolau, 2017).
Multiple location and neighbourhood variables were used. These included: a dummy vari-
able equal to 1 if a listing is located within 800 m of a subway station (approximately a 10-min
walk); the number of jobs that can be reached within 30 min by transit from each listing (‘job
accessibility’ per 100,000 jobs; mean = 13.2); the network distance to Times Square (mean =
6.4 km); the land-use diversity measured by an entropy index (mean = 0.96); the population
density per census tract (1000 residents/km2; mean = 29.1); the median household income
(in US$1000s; mean = 74.1); and the percentage of residents who identify as non-Hispanic
White (mean = 47.5). Accessibility to jobs is used as a proxy for accessibility to tourist ame-
nities based on the idea that the majority of Airbnb guests are tourists. Ideally there would
be a direct variable for accessibility to tourist amenities such as restaurants, entertainment
venues, local landmarks and museums. As this information is not widely available, accessibility
to jobs is a proxy as most typical tourist venues and attractions are employers and contribute to
jobs. Further, Ashworth and Page (2011) note the fact that tourist landscapes coexist with
employment, dispersed and grouped into zones such as the ‘entertainment district’. This
‘employment-based attraction’ is especially true as tourism in major urban centres tends to
be based around attractions such as cultural venues, including museums and theatres, as well
as shopping streets and restaurant and bar districts (Bull & Church, 2001). In NYC, Midtown
Manhattan is one of the preeminent centres of employment, and also the location of many
tourist amenities. Therefore, high access to jobs in NYC reﬂects a high degree of access to
Midtown Manhattan and other tourist amenities, but also reﬂects other parts of the city
that would be attractive to tourists. Transit accessibility is used as tourists are less likely to
have access to the use of a personal vehicle. Cervero and Kockelman (1997) argue that
land-use diversity is likely representative of other attributes including density and walkability.
Studies have shown that higher degrees of walkability (albeit based on ‘walk score’ data) are
related to higher property values (Pivo & Fisher, 2011; Rauterkus & Miller, 2011).
All the distances involved were calculated as network distances, based on the nearest census
block group centroid. The distance from the closest block group centroid was used instead of the
distances from the listing x,y-coordinates to reduce computational complexity. The relevant sha-
peﬁles used in the calculations were provided by the City of New York’s Planning Department.
Data regarding accessibility to jobs were gathered from the Access Across America project. This
data set measures the number of jobs accessible within 30 min per block and was aggregated to
the census block group to be on the same scale as the other location and neighbourhood variables.
Owing to the overwhelming number of jobs in Midtown Manhattan, this measure acts as an
approximate proxy of the centrality of any individual block group. Land-use diversity – a
proxy for neighbourhood walkability – was measured through an entropy index based on the
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Extra bathrooms command a larger price and revenue premium than extra bedrooms. One
additional bedroom is associated with a price increase of 13.2% and a 11.0% increase in monthly
revenue. By contrast, each additional bathroom is associated with a 14.2% increase in listing price
per night and 14.8% higher revenue per month. The higher performance associated with
additional bathrooms could reﬂect the extent to which they indicate more luxurious listings or
serve as a proxy for a larger home at a given number of bedrooms. An extra place for a guest pre-
dicts a 6.4% higher listing price and a 7.7% increase in monthly revenue.
Table 2. Model results.
Price
Revenue
Coefﬁcient
p
Conﬁdence
intervala
Coefﬁcient
p
Conﬁdence
intervala
Time variables
January
–0.1063
***
–0.1085
–0.1040
–0.5841
***
–0.5958
–0.5725
February
–0.1406
***
–0.1431
–0.1380
–0.3920
***
–0.4049
–0.3790
March
–0.0874
***
–0.0898
–0.0850
–0.0919
***
–0.1041
–0.0797
April
–0.0296
***
–0.0319
–0.0273
0.0879
***
0.0762
0.0997
May
0.0019
–0.0003
0.0042
0.2917
***
0.2803
0.3031
June
0.0402
***
0.0379
0.0424
0.2706
***
0.2591
0.2822
July
0.0215
***
0.0193
0.0238
0.1971
***
0.1856
0.2086
August
0.0121
***
0.0099
0.0142
0.2288
***
0.2180
0.2396
September
0.0413
***
0.0392
0.0434
0.3691
***
0.3585
0.3797
October
0.0346
***
0.0322
0.0369
0.3041
***
0.2923
0.3159
November
–0.0069
***
–0.0093
–0.0046
–0.0621
***
–0.0742
–0.0501
Structural variables
Entire apartment
0.5115
***
0.5052
0.5178
0.5121
***
0.4985
0.5256
Shared room
–0.3670
***
–0.3815
–0.3525
–0.4235
***
–0.4549
–0.3922
Bedrooms
0.1321
***
0.1270
0.1372
0.1097
***
0.0988
0.1206
Bathrooms
0.1421
***
0.1340
0.1502
0.1481
***
0.1307
0.1656
Guests
0.0637
***
0.0615
0.0659
0.0768
***
0.0720
0.0815
Host variables
Superhost
0.0565
***
0.0460
0.0670
0.1507
***
0.1289
0.1726
Multihost – 2–10
–0.0037
–0.0092
0.0018
0.0660
***
0.0542
0.0778
Multihost – > 10
–0.0924
***
–0.1135
–0.0713
0.0887
***
0.0446
0.1328
Number of photos
0.0036
***
0.0033
0.0038
0.0019
***
0.0013
0.0024
Instant booking
–0.0177
***
–0.0256
–0.0099
0.1424
***
0.1256
0.1591
Reviews
–0.0005
***
–0.0006
–0.0004
0.0084
***
0.0082
0.0086
Overall rating
0.0826
***
0.0771
0.0880
0.0433
***
0.0311
0.0555
Location and neighbourhood variables
Subway
–0.0201
***
–0.0338
–0.0063
–0.0350
***
–0.0593
–0.0107
Transit accessibility
0.1220
***
0.1090
0.1350
0.1320
***
0.1120
0.1520
Dist Times Square
–0.0147
***
–0.0168
–0.0126
–0.0245
***
–0.0277
–0.0213
Diversity
0.0093
–0.0055
0.0240
0.0065
–0.0211
0.0341
Population density
–0.0011
***
–0.0016
–0.0006
–0.0011
***
–0.0017
–0.0005
Median household
income
0.0012
***
0.0009
0.0015
0.0009
***
0.0006
0.0013
Percentage white
0.0019
***
0.0015
0.0022
0.0010
***
0.0005
0.0014
Listing density
0.0001
***
0.0001
0.0002
0.0001
***
0.0001
0.0002
Constant
3.3377
***
–0.0338
–0.0063
5.6364
***
5.5323
5.7404
Notes: Dependent variables: ln(price per night) and ln(monthly revenue).
a95% conﬁdence interval.
*95% signiﬁcance level; **99% signiﬁcance level; ***99.9% signiﬁcance level.
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A set of characteristics that collectively suggest a listing is a commercial operation, as opposed
to a part-time rental, are associated with substantially higher monthly revenue than the baseline,
but not consistently higher nightly rates. Commercial Airbnb operators, in other words, appear to
set pricing more aggressively in order to maximize occupancy and, hence, revenue. The ﬁrst of
these characteristics is ‘superhost’ status, which Airbnb confers on hosts based on a high fre-
quency of hosting, positive reviews from guests and a low cancellation rate. According to the
model, this superhost status allows hosts to charge a slight premium (5.6% more per night)
and results in a substantial increase of 15.1% in monthly revenue. The differential between
these two ﬁgures implies that superhosts conduct considerably more bookings per month than
other hosts. The signiﬁcant impact of superhost status on both price and revenue supports a simi-
lar ﬁnding by Wang and Nicolau (2017), but contradicts Teubner et al. (2017) who did not
identify superhost status as having statistical signiﬁcance.
Airbnb allows hosts to activate an instant booking feature, which allows prospective guests to
bypass the usual host approval process and have their requested booking conﬁrmed immediately.
Listings with the instant booking feature activated have 1.8% lower average nightly prices, but
have much larger monthly average revenue of 14.2%. Much like superhost status, instant booking
is characteristic of high-volume listings, which provide a more hotel-like experience than less
commercial listings whose hosts prefer to approve booking requests manually.
The ﬁnal factor associated with commercial short-term rental operations that predicts higher
revenue is the number of listings a given host controls. Hosts with between two and 10 listings do
not charge a signiﬁcantly different price per night from hosts with a single listing, but they earn
6.6% more in monthly revenue, reﬂecting higher occupancy rates. By contrast, hosts managing
more than 10 properties charge a substantially lower price per night than single-listing hosts
(9.2% less), but earn substantially more revenue (8.9%). Noting the same result, Li et al.
(2015) suggest that this pattern could reﬂect learning effects among high-volume hosts, especially
responses to change in demand (either seasonal or event based).
Listings with more photographs charge marginally higher prices and earn marginally more
revenue. One additional review is associated with a 0.05% decrease in price, but a 0.8% increase
in revenue. This additional revenue is likely due to the fact that lower priced listings are booked
more frequently and therefore they will have more reviews (Gibbs et al., 2018). One additional
star in average rating is associated with a price increase of 8.3% and a revenue increase of 4.3%.
Proximity to mass transit is a strong predictor of listing price and revenue, but only when it
facilitates access to the central city. In fact, controlling for accessibility, listings in proximity to
subway stations charge less per night (2.0% less) and have less monthly revenue (3.5% less).
However, listings with high accessibility (deﬁned here as access to jobs via transit) charge an aver-
age of 12.2% more per night and earn an average of 13.2% more monthly revenue. This ﬁnding
Table 2. Continued.
Random-effects parameters
Price
Revenue
Estimate
Conﬁdence
intervala
Estimate
Conﬁdence
intervala
Borough level
sd(Constant)
0.0632
0.0332
0.1205
0.0695
0.0334
0.1450
Census tract level
sd(Constant)
0.0891
0.0826
0.0960
0.0775
0.0693
0.0868
Listing level
sd(Constant)
0.2641
0.2624
0.2659
0.4958
0.4913
0.5002
sd(Residual)
0.1436
0.1433
0.1440
0.7373
0.7355
0.7390
Note: a95% conﬁdence interval.
Location, location and professionalization: a multilevel hedonic analysis of Airbnb listing prices and revenue
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