Cornell Hospitality Quarterly
2019, Vol. 60(3) 193 –199
© The Author(s) 2018
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The increasing success of Airbnb, an international, Internet-
based firm that connects owners of rental properties with
short-term renters, has attracted increasing attention since
its inception in 2008. Airbnb is now active in more than
65,000 cities and 191 countries and has facilitated more
than 160 million stays (Airbnb, 2017). By mid-2017, Airbnb
claimed to have more than 3 million rental listings, raising
concerns of disruption of the traditional lodging sector and,
more locally, negative externalities such as loss of tax rev-
enue, traffic congestion, noise, and disruptive behavior
(Edelman & Geradin, 2016; Guttentag, 2015). Proponents,
on the other hand, note the increased income accruing to
property owners, improvements in economic efficiency,
and the emergence of the “gig” economy for cities and
municipalities (Koopman, Mitchell, & Thierer, 2015;
Quattrone, Proserpio, Quercia, Capra, & Musolesi, 2016).
Although Airbnb recently launched a public Application
Programming Interface (API), this API is not sufficiently
open for researchers and firms to access individual listing
data across the Airbnb platform.1 Hence, even though
Airbnb’s impact is of great interest to many different indi-
viduals, assessing that impact may require the use of data
that present a skewed picture of Airbnb’s activities.
Finding useful data on Airbnb and similar entities is
increasingly important given the significant challenges these
firms pose to the operations of the traditional lodging sector.
Home-rental market revenue is only about one fifth the size
of the traditional lodging sector but is growing at a faster
pace than the hotel industry (Kirkham, 2017). Emerging evi-
dence suggests that Airbnb and similar products not only
expand the supply of available rooms but also reduce con-
ventional hotel revenue on high demand dates by up to 40%
(Jordan, 2015). The greater the market penetration of Airbnb,
the lower the average price for conventional hotel rooms
(Neeser, Peitz, & Stuhler, 2015). The economic impact of
Airbnb is not uniform, however, with lower-priced hotels
and those catering to nonbusiness travelers being most
affected over time (Zervas, Proserpio, & Byers, 2017).
Recent survey evidence of Airbnb users indicates that nearly
two thirds used Airbnb as a hotel substitute (Guttentag &
Similar evidence suggests that Airbnb benefits the tour-
ism industry by lowering tourism costs, leading to increased
spending on nonlodging services and goods. Lower-end
hotels, however, are displaced by Airbnb, suggesting a trad-
eoff between gains for the nonlodging sector and the lower
end of the lodging sector (Fang, Ye, & Law, 2016). Finding
useful data with regard to Airbnb rentals, however, is often
an arduous task in that researchers must either manually
sample the data (which is likely to introduce errors), scrape
the data from Airbnb (which is often not replicable as the
777218CQXXXX10.1177/1938965518777218Cornell Hospitality QuarterlyAgarwal et al.
1Old Dominion University, Norfolk, VA, USA
Vinod Agarwal, Department of Economics, Strome College of Business,
Old Dominion University, Norfolk, VA 23529, USA.
Differing Views of Lodging Reality: Airdna,
STR, and Airbnb
Vinod Agarwal1, James V. Koch1, and Robert M. McNab1
Airbnb is an Internet-based firm that connects potential short-term renters with hosts who own or control rental
properties. Its rapidly expanding activities are tracked by Airdna, an independent firm that generates seemingly conventional
performance metrics describing Airbnb. These metrics include occupancy rates, average daily rates, and revenue per
available room. However, Airdna does not adhere to long-established STR definitions for these variables. Using data from
Virginia Beach, Virginia, we demonstrate that Airdna’s performance metrics exhibit notable upward biases vis-á-vis STR’s
metrics. Potential rental hosts, hoteliers, tax collectors, and investors are at risk if they act on the assumption that Airdna’s
metrics are comparable with widely understood measures used by STR and tourism experts.
Airbnb, Airdna, occupancy rate, average daily rate, revenue per available room, Smith Travel Research, hotel industry,
194 Cornell Hospitality Quarterly 60(3)
information on the site changes daily), or rely on others to
produce information on Airbnb hosts and guests.
Airdna is a firm that generates data and analytics focus-
ing on short-term rental entrepreneurs and investors. Airdna
sells Airbnb performance data and is the dominant supplier
of data concerning Airbnb’s performance. Airdna’s unique
position is based in part on a proprietary algorithm that dis-
cerns whether specific Airbnb rental properties are avail-
able, reserved for occupancy, or blocked by the owner and
unavailable for rental.2 A large (and growing) number of
public and private decision makers utilize Airdna’s data to
explore the impact of Airbnb on the traditional lodging sec-
tor, renter behavior, rental pricing, and investment profit-
ability. Comparing Airdna data with seemingly comparable
data from STR (formerly Smith Travel Research) concern-
ing the performance of the traditional lodging sector is not
only a “selling point” of Airdna but also of practical impor-
tance to prospective rental hosts, governments anxious to
understand the Airbnb phenomenon, and investors.3 An
increasing number of articles and studies rely on Airdna’s
measures of average daily rate (ADR), occupancy, and rev-
enue per available room (RevPAR).
This article examines whether Airdna’s performance
metrics are comparable with the norms of the traditional
lodging sector by examining the performance of Airbnb
rentals in Virginia Beach, Virginia. We compare the Airdna
metrics with those of STR and inquire whether Airdna
overstates the performance of the short-term lodging sec-
tor. If, as we argue, Airdna’s underlying definitions of
occupancy and RevPAR are biased upward, then Airbnb’s
impact is overstated—a conclusion of significant interest
to hoteliers, renters, investors, tax collectors, and policy-
The remainder of the article is structured as follows. In
the following section, we briefly review the extant literature
on the use of Airdna data to examine the performance of
Airbnb. We then discuss the potential flaws in the Airdna
methodology, with specific attention to the difference
between listing nights and room nights. We illustrate the
potential biases using Airdna data for the City of Virginia
Beach, Virginia. We then discuss Airdna’s response to our
queries and findings. The concluding section places our
findings in context and offers suggestions on how to correct
for these biases.
The Use of Airdna Data by
Researchers and Decision Makers
A growing number of popular press articles, consulting
studies, and peer-reviewed manuscripts rely on Airdna data
to estimate Airbnb activities. In some cases, Airdna data are
used to examine host characteristics, for example, whether
Airbnb hosts are comparable with those of free alternatives
such as “couch surfing” (Jung et al., 2016). Airdna data are
used to examine whether obtaining and maintaining repu-
table ratings is important for “superhosts” (Gunter, 2018).
In these cases, Airdna data may prove useful and reasonably
reliable as these studies rely more on characteristics of list-
ings rather than the performance of listings.
HVS Consulting and Valuation (2015) employed Airdna
data to estimate the impact of Airbnb on the New York City
lodging market and found that Airbnb poses a significant
threat to hoteliers’ revenues. CBRE Hotels’ Americas
Research (CBRE) found that Airbnb is not only growing
rapidly in major metropolitan areas but also that, in some
localities, Airbnb ADR exceeds hotel ADR (Lane &
Woodworth, 2016). CBRE also noted that in almost every
major market, the share of hosts with multiple units mark-
edly increased in 2016 and that revenue from hosts with
multiple units is growing faster than single unit hosts
(CBRE Hotel Americas Research, 2017). These, and other
consulting studies, rely extensively on Airdna data.
Airdna data have been used to examine the development
of Airbnb in the United States and other countries, to
include Austria (Gunter & Önder, 2017), Canada (Sovani
& Jayawardena, 2017), the Netherlands (Boswijk, 2017),
and South Africa (Visser, Erasmus, & Miller, 2017). Gibbs,
Guttentag, Gretzel, Yao, and Morton (2017), for example,
use Airdna listing data and STR data to argue that while
hotels employ dynamic pricing strategies to maximize rev-
enues, many Airbnb hosts in Canada do not employ a simi-
Not surprisingly, listings for entire homes and private
rooms, listings with pictures, and host responsiveness com-
mand higher prices on Airbnb (Dogru & Pekin, 2017; Gunter
& Önder, 2017). Airdna occupancy data have been used as
the basis for an estimate that Asian and Hispanic hosts charge
8% to 10% lower prices in San Francisco (controlling for
rental unit characteristics, as well as additional information
on neighborhood property values and demographics; Kakar,
Voelz, Wu, & Franco, 2017). For Boston, Airdna data suggest
that Airbnb listings earn a significant price premium relative
to the long-term rental market (Horn & Merante, 2017).
A number of new studies directly compare Airdna and
STR performance data such as supply, ADR, and RevPAR
for Airbnb and hotels in Boston (Mody, Suess, & Dogru,
2017). For Los Angeles, Airbnb hosts’ ADR is higher than
the traditional lodging sector; however, occupancy and
RevPAR are lower than in the traditional sector (Dell,
Doby, Tillipman, & Zhuplev, 2017). The same authors
found that ADR and RevPAR are lower for Barcelona.
These studies employ Airdna’s performance metrics with-
out any adjustment.
Thus, Airdna data are being widely used for a variety of
purposes by many different parties. To the extent these data
do not impart the information their users believe to be the
case, the usefulness of studies and decisions based on these
data can be called into question.
Agarwal et al. 195
Are the Airdna Performance Measures
A central question relating to Airdna’s data is whether its
performance metrics are comparable with STR’s data,
which have constituted the industry standard for many
years. To understand the difference between the two sets of
data, we draw on the example of Virginia Beach. With
almost 450,000 residents, Virginia Beach is the most popu-
lous city in Virginia. The city is a well-known resort loca-
tion that annually attracts hundreds of thousands of visitors
to its beaches and other attractions. These visitors anchor
the city’s large conventional hotel and tourism sector. The
U.S. Travel Association estimated in 2016 that tourism was
responsible for US$1.494 billion of economic activity in
Virginia Beach (U.S. Travel Association, 2017). The perfor-
mance of the traditional lodging sector is of understandable
importance to hoteliers, tax collectors, and others associ-
ated with the tourist industry in Virginia Beach.
We purchased data from Airdna for the Virginia Beach
for the period of October 2014 through August 2017. The
data contained individual listings aggregated by month as
well as summary statistical information on various mea-
sures of performance by month. In January 2017, for
example, the data contained information on 421 listings
(the majority were residential homes) for Virginia Beach.
The individual listings varied from single private rooms to
significantly larger properties with as many as 10 bed-
rooms. The Airdna data contained a variety of market per-
formance measures that, at first glance, correspond to
published STR data.
Monthly Airdna data constitute a “list of listings.” Airbnb
classifies properties currently being offered for rent to
guests as available listings.4 Properties that are available
and have at least one reserved night in a month are consid-
ered booked listings. Unlike STR data that aggregate the
actual supply and bookings data of respondents, Airdna data
are estimates generated by a proprietary algorithm.
In the case of Virginia Beach, we agreed with Airdna’s
count of total available listings and total booked listings
when we examined listings of entire homes, private rooms,
and shared rooms.5 However, the first problem we noted
was that all properties that were available were not included
in the calculation of certain Airdna performance metrics.
This is a centerpiece of our concern. Simply being offered
for rent is not sufficient for a host’s property to be consid-
ered “available” by Airdna when it calculates occupancy
rates, ADRs, and RevPARs. Airdna includes only “booked
listings” in the calculation of these variables. This is equiva-
lent to a hotelier claiming that a room not rented during a
month should be excluded from the calculation of occu-
pancy rates and other measures.
In general, we found that the number of monthly avail-
able listings for one-bedroom homes exceeded the number
of actual booked listings. Thus, in August 2016, Virginia
Beach had 72 available listings for one-bedroom homes,
and of those, 64 were booked listings. This is a not a large
difference. During the offseason, however, the numbers
were markedly different. In February 2017, Virginia Beach
had 60 available listings for one-bedroom homes, but only
28 booked listings. On average, during the sample period in
question, there were 52 available listings for one-bedroom
homes compared to 37 booked listings each month. The
exclusion of properties without reservations clearly under-
states the available supply of Airbnb listings.6
Depending on the characteristics of the market in question,
the actual supply of Airbnb listings may be significantly higher.
In Virginia Beach, for example, the actual supply of Airbnb
listings was approximately 50% higher in some months when
all listings were included in the measure of supply. This is not
a trivial difference and constitutes significant bias.
A second problem emerged because of the way Airdna
populates the denominators of its major activity measures.
It employs total available nights as the denominator.
However, its total available nights are not, in many cases,
equal to the total number of available room nights, a subtle
but very important distinction. For listings that consist of
one bedroom (including shared rooms, private rooms, stu-
dios, and one-room entire houses), there is no distinction
between total available nights and total available room
rights. However, when the listing contains more than one
bedroom, then they are not identical. One listing may con-
tain multiple rooms.
With respect to Virginia Beach, we determined that the
ratio of booked room nights to booked nights was 2.11 for
the 35-month sample period. This average, however, con-
cealed a rapid increase in the ratio of actual room nights to
booked nights in late 2016 and into 2017, reaching a high of
2.88 rooms per listing in January 2017. The number of
available listings of properties with four or more bedrooms
increased significantly between 2015 and 2017.
We believe this reflects the increasing corporatization of
Airbnb properties. Anecdotal evidence from Virginia Beach
points to increasingly larger properties that are investor-
owned with the specific intent of using these properties for
short-term vacation rentals.7 The evolution of the ratio
strongly suggests that the trend in Virginia Beach is away
from single-bedroom properties. Given that listings of mul-
tiroom properties are proliferating on Airbnb, this consti-
tutes a bias that understates the number of available rooms
and, thus, Airbnb supply. This phenomenon is not limited to
Virginia Beach because investor-owned properties appear
to be rising as share of Airbnb properties nationwide.
Airdna data for Virginia Beach reveal that approximately
87% of Airbnb-derived revenues during our sample period
originated from apartments, condominiums, and full houses
rather than from properties that sought to rent out a single room
as part of a larger property. Of the revenues derived from
196 Cornell Hospitality Quarterly 60(3)
“whole property” rentals, approximately 68% originated from
properties with more than two bedrooms. We argue that this is
evidence of the increasing proliferation of multiroom proper-
ties and underscores the need to use available room nights as
opposed to Airdna’s current practice of available listing nights.
It could be, however, that some hotels have suites with
multiple rooms, and this would skew STR data in a similar
fashion. Our discussions with hoteliers revealed that hotel
suites generally are considered as one room, per industry
standard. We do not have information on the national distri-
bution of two-or-more bedroom suites to one-bedroom
suites or standard rooms, but we surveyed hotel owners in
the Hampton Roads region (officially the Virginia Beach–
Norfolk–Newport News Metropolitan Statistical Area) to
determine their numbers of multiroom suites. Our survey
included 69 hotels representing 7,782 rooms, or approxi-
mately 21% of all hotel rooms in the region. We found that
two-bedroom suites accounted for only 4.2% of all hotel
rooms among these surveyed hotels. There were zero two-
plus bedroom suites among the surveyed hotels.8
In sum, we conclude that Airdna’s performance mea-
sures are biased upward for two reasons. First, Airdna’s
metrics utilize booked listings. If a property is available
for rent during a given month, yet does not have any actual
reservations, it is excluded from Airdna’s measures of per-
formance. This methodology biases occupancy and
RevPAR upward. Second, even if the property in question
is available and rented for one (or more days), it is only
one listing by Airdna, regardless of the number of avail-
able bedrooms in the property in question. Our conclusion
is straightforward: Airdna’s estimates of ADR, occupancy,
and RevPAR for Airbnb are exaggerated. The actual num-
bers (i.e., the numbers based on standard STR definitions)
Hence, Airbnb and Airbnb properties appear to be more
effective than they are in practice. The equivalent practice
for the traditional lodging sector would be to exclude rooms
that did not rent during a month from the calculation of
occupancy rates and RevPARs. This would have a signifi-
cant (and misleading) impact on these performance metrics,
especially in tourist-centric areas that typically operate
much slower during off seasons. These methodological
flaws mean that it is difficult to make meaningful compari-
sons between Airdna and STR data. The two firms are not
measuring the same things—it is a classic “apples and
This makes it much more difficult to assess the relative
importance and performance of Airbnb properties versus those
of hotels and motels. Comparable metrics are required to do so.
The publicly available Airdna market performance data are not
comparable with STR data. For those interested in precision
and accuracy, their only current recourse is to use Airdna indi-
vidual listing data to calculate performance metrics that are
comparable with the industry standard. Otherwise, they may
rely on performance data that are biased upward and draw spu-
rious conclusions as to the health of the Airbnb marketplace.
Discussions With Airdna
There is nothing dishonest about the way Airdna chooses to
compute occupancy rates, ADRs, and RevPARs. Nevertheless,
Airdna has chosen not to employ commonly accepted defini-
tions of these performance metrics, and thus, its data will be
misleading when unknowing individuals attempt to compare
the Airdna performance information with that of STR.
Given our concerns regarding the methodological foun-
dations of the Airdna performance metrics, we initiated a
conversation with Airdna regarding the calculation of the
performance metrics. The first response from Airdna was,
Can you elaborate on your concerns for these metrics? We have
been covering data on these locations for a significant period of
time (30 months and counting in the US) and provided data to
almost 20,000 customers from individual hosts/investors
through to multinational organizations and over fifty globally
recognized research institutions such as Harvard, Oxford,
Princeton and National University of Singapore so our data has
been thoroughly carved up and crunched. To date, we have not
had any wide-ranging concerns on our methodology from any
of these sources so we would be keen to hear your feedback in
On receipt of this communication, we asked two specific
questions. Our first question highlighted the first potential
source of bias, that is, the difference between total booked
nights and total booked room nights. We wrote,
. . . when Airdna reports ADR for the entire place, you are
using as denominator your measure of total booked nights
(regardless of the size of the property). The denominator is not
the total number of booked room nights. This is a meaningless
distinction where one bedroom homes are concerned because
nights equal rooms, but makes a significant difference for the
entire place when one recognizes that many Airbnb hosts rent
homes with multiple bedrooms.
We received the following email response:
We considered different ways to report ADR and we found
using “Entire Place” to be the most helpful not looking at the
ADR per room within an “entire home listing.” If you wish to
calculate this ADR you have the possibility doing so by using
the property file.
Our second question was,
In calculating available nights or available room nights, Airdna
excludes from consideration any property that might have been
available for rent, but had no reservations in the month in
Agarwal et al. 197
question. This exclusion means Airdna is underestimating
available nights or available room nights resulting in
overestimation of occupancy rates and RevPAR.
Airdna’s response was,
About our occupancy rate calculation, when we designed our
reports we had a lengthy internal discussion about this. Our
CEO had many Airbnb properties and knows that if someone is
actively managing their property they should be able to have a
reservation every month. We decided on the annual occupancy
rate calculation not to take into consideration months with no
booking as throwing lots of un-managed, zero occupancy,
properties into the calculation doesn’t give a good picture of
In effect, Airdna’s collective responses were, “Lots of
people are using our data. They aren’t complaining.
What’s the problem?” Nevertheless, we suspect that firms,
governments, and researchers would view Airdna data dif-
ferently if they were aware of the measurement differ-
ences we have highlighted here. We appreciate Airdna
providing us with insight into its methodological decisions
underlying its performance metrics because the firm did
not have to do so. Even so, its practices have made its
performance metrics incompatible with industry stan-
dards. Airdna metrics are biased upward and provide a
rosier picture of the health (and potential profitability) of
Airbnb properties than would otherwise be provided if
Airdna chose to adapt their methodology to widely
The Bureau of Economic Analysis (BEA) of the U.S.
Department of Commerce defines and computes Gross
Domestic Product (GDP). Using the same broad defini-
tion—the total value of the goods and services produced
in each jurisdiction during a given period, usually a
year—the BEA also computes GDP at the state and met-
ropolitan area level. Suppose now that another organiza-
tion began to produce estimates of GDP for specific cities,
but in doing so decided to alter the BEA definition by
adding the value of leisure time. Would we consider these
new numbers to be comparable with the widely accepted
BEA estimates? We are confident the answer is no.
The same reasoning applies to Airdna’s performance
measures. Life would be easier for the hotel and motel
industry, cities and counties, elected officials, Airbnb
hosts, and researchers if Airdna adhered to standard,
widely accepted definitions. We recommend that Airdna
change its methodology in two specific ways. When
determining market supply, Airdna should employ avail-
able listings rather than booked listings. If a property is
available for rent, then it should not be excluded from
analysis merely because it failed to secure a rental during
a given month. Given that Airbnb supply was underesti-
mated by approximately 50% in Virginia Beach in some
months, this is an important adjustment to make to ensure
that consumers of Airdna data have an accurate measure
of Airbnb supply.
Second, Airdna should use available room nights rather
than available nights when calculating ADR and RevPAR.
As the number of multiroom properties increases, the bias
of calculating based on listings will undoubtedly increase.
While this adjustment lowers the estimated performance of
Airbnb rentals as we found that there was an average of two
rooms per listing in Virginia Beach, it is necessary if one
desires to compare and contrast the performance of the
short-term rental sector with the traditional lodging sector.
In the meantime, we recommend that individuals inter-
ested in using the Airbnb data use the individual listing data
to make the adjustments that we have discussed in this arti-
cle. In the end, we should neither formulate public policies
nor make private investment decisions on the basis of apples
and oranges comparisons.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, or publication of this article.
The author(s) received no financial support for the research,
authorship, or publication of this article.
1. Airbnb launched an official Application Programming
Interface (API) in October 2017. The API is not open as
interested parties must apply for access, and it appears to be
focused on property owners.
2. Note that Airdna currently captures and sells data for Airbnb
but does not offer similar data for other vacation rental web-
sites such as Flipkey, Homeaway, or Vacation Rentals By
Owner (VRBO) .
3. According to Airdna, the market summary reports “provide a
high level overview of Airbnb rentals in major markets around
the world. With revenue per available room (RevPAR), average
daily rate (ADR), Occupancy, and monthly supply & demand
trends.” Airdna claims these reports “bring hotel-style perfor-
mance metrics to the vacation rental industry” (Airdna, 2017).
4. Each individual observation contains a listing status identifier
that categorizes the listing status as either “true” or “false.” A
listing property’s status is false if all the days in a month are
blocked days (i.e., unavailable to guests) or if the Airbnb cal-
endar for the property in question was not visible on Airbnb for
that month. A listing’s property status is true if there is at least
one day a month that is not blocked, or if the Airbnb calendar
for the property is visible and the owner is accepting reserva-
tions. A status of true implies that a listing is currently being
198 Cornell Hospitality Quarterly 60(3)
offered for a guest to rent or that it has been rented by a guest in
5. Subsequently, we chose to exclude shared rooms because
the total number of such listings was very small; the number
never exceeded nine, and the single room share of total avail-
able listings never exceeded 2.44%.
6. Smith Travel Research, “A Guide to Our Terminology,” www.
strglobal.com/resources/glossary/en-gb. For example, Smith
Travel defined occupancy (OCC) as the “percentage of available
rooms sold during a specified time period. Occupancy is calcu-
lated by dividing the number of rooms sold by rooms available.”
7. In our discussions with representatives of the Virginia Beach
Commissioner of Revenue and that city’s Convention and
Visitors Bureau, it became apparent that several new devel-
opments of apartments and condominiums were investor-
owned and targeted for short-term rentals.
8. Survey questions and aggregated responses are available on
9. Email received from Adam Alexander of Airdna on May 10,
10. Email received from My Larson of Airdna on June 16, 2017.
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Vinod Agarwal is a Professor of Economics in the Deparmtent of
Economics in the Strome College of Business at Old Dominion
University. He also serves as the Director of the Economic
Forecasting Project and as deputy Director of the Dragas Center
for Economic Analysis and Policy.
James V. Koch is an Emeritus Professor of Economics at Old
Robert M. McNab is a Professor of Economics in the
Department of Economics in the Strome College of Business at
Old Dominion University. He also serves as the Director of the
Dragas Center for Economic Analysis and Policy.