Market Structure with the Entry of Peer-to-Peer Platforms: The Case of Hotels and Airbnb

41 Pages·2016·2.84 MB·English

Market Structure with the Entry of Peer-to-Peer
Platforms: The Case of Hotels and Airbnb
Chiara Farronato,1 Andrey Fradkin 2
January 2016
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Abstract
Online peer-to-peer marketplaces have reduced entry costs across a variety of in-
dustries. These marketplaces allow small and part-time service providers (peers)
to participate in economic exchange, often in competition with more traditional
suppliers. For example, Airbnb and Uber allow almost anyone to become a hote-
lier or a cab driver. We use the market for short-term accommodation to study
the determinants of Airbnb growth and its effects on the industry. We find that,
across major US cities, a larger Airbnb presence is associated with low opportu-
nity costs of renting out spare rooms, high investment costs of building hotels,
and high demand volatility. Furthermore, we show hosts who are active on the
site are highly responsive to changes in market prices when choosing wether to
host for a given set of dates. A 1% increase in average hotel prices results in a
2.2% increase in Airbnb bookings. We then estimate the effects of Airbnb entry
on hotel revenue. On average, we find that a 10% increase in the size of Airbnb
reduces hotel revenue by 0.6%. We also show that the effect of Airbnb varies
across cities and hotel scales, with larger effects in cities with constrained hotel
supply and larger effects caused by the entry private rooms (as opposed to entire
properties). Lastly, we discuss preliminary work in estimating consumer welfare
and evaluating how it changes with the entry of Airbnb.
1Harvard Business School, [email protected]
2MIT Sloan School of Management and Airbnb Inc., [email protected]
1 Introduction
The internet has greatly reduced entry and advertising costs across a variety of industries.
In particular, peer-to-peer marketplaces such as Airbnb, Uber, and Etsy, have enabled small
and part-time service providers (peers) to broadly participate in economic exchange. Several
of these companies have grown exponentially and have matched millions of consumers over
the past decade. For example, since its founding in 2008, Airbnb has grown to list more
rooms than any hotel group in the world and has connected over 60 million guests to hosts
as of December 2015. In this paper we empirically and theoretically investigate how this
growth has affected peers, traditional firms (hotels), and consumers.
The key source of variation in our study is the differing growth rates of Airbnb across
US cities. Therefore, we begin by studying the predictors of Airbnb growth. We show that
peer participation depends on the attractivess of the market for short-term accommodation
and other factors affecting the difficulty of hosting. These results motivate an empirical
specification which allows us to study the effects of peer production on traditional firms
(hotels). We use this specification to show that peer entry affects hotel revenue and that
this effect varies across cities. Cities with a constrained hotel supply, due to the difficulty
of building new hotels and relatively high levels of demand, experience greater entry of
peers, and a greater reduction in hotel revenues. Lastly, we estimate a model of equilibrium
in the accommodation industry and use it to quantify welfare, and its distribution across
consumers, hotels, and peer producers. (NOTE, THIS PART IS NOT YET DONE).
To study these questions, we combine proprietary data on Airbnb for 50 US cities dur-
ing the period between 2011 and 2014 with data on hotel revenues, prices, and sales. We
first document the heterogeneity of growth of Airbnb listings across cities, and show how
population demographics, hotel supply constraints, and demand volatility help explain this
heterogeneity. Hosts typically participate in the Airbnb platform by renting out spare bed-
rooms, apartments, or other properties. The returns from hosting depend on the costs of
renting out a spare room, the market price of accommodations, and awareness of the plat-
form. On the cost side, hosts must have unused space, list it on Airbnb, and prepare it for
each stay. Furthermore, hosts often spend time interacting with guests both before and dur-
ing the trip, and this interaction is often perceived as risky. We show that the penetration
of Airbnb listings (as a fraction of total housing supply) is correlated with the share of city’s
residents who are single and/or childless. These residents likely have lower costs of hosting
because they tend to have more vacancies (due to travel and empty nests) and lower risks
(because they have no children).
The benefits to hosts are determined by the equilibrium price that they can charge in
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the market. One factor determining this price is the supply of hotels, which is fixed in the
short run. In turn, hotel supply is determined by the difficulty of hotel entry. We show that
in cities where it is harder to build hotels, because of regulatory or geographic constraints,
there are higher hotel prices and a larger share of the market supplied by peers.
Another factor affecting Airbnb size is the volatility of demand. A given city can ex-
perience periods of high demand and low demand due to seasonality, festivals, or sporting
events. When the difference in peaks and lows is large, the provision of accommodation
exclusively by hotels can be inefficient: if hotel supply is large, there will be many periods
with high vacancy rates, and if the hotel supply is low, there will be many periods of high
prices. Precisely in these cities peer entry is profitable, and indeed the size of Airbnb is larger
in cities with high demand volatility. Lastly, we show that these demand fluctuations affect
the number of bookings conditional on a given level of available Airbnb listings. We find
that a one percent increase in the price of accommodations increases the number of booked
listings by 2.2%, demonstrating that peers hosting choices on a day to day level are highly
responsive to market incentives.
Next we turn to quantifying the effect of Airbnb on hotels. There are two main challenges
in conducting this exercise: appropriately measuring the size of Airbnb and the endogeneity
of Airbnb supply. With regards to measurement, we demonstrate that both active and
available listings on Airbnb are not appropriate measures of Airbnb size: the first suffers
from attrition problems, and the second turns out to be couter-cyclical (low in periods of
high demand, and high otherwise) due to the fact that hosts are more likely to keep accurate
calendars in high demand periods. Therefore, there are relatively more “stale vacancies”
(see: Fradkin [3]) in low demand periods. Instead we devise an adjusted measure of available
listings which takes calendar updates into account, and which we use for our entire analysis.
With regard to endogeneity, the size of Airbnb in each market depends on demand trends
and fluctuations in that market.1 Our identification strategy addresses both of these con-
cerns. We use two plausibly exogenous demand trend proxies, the number of passengers
flying into a city and Google searches for hotels in a city, as well as linear city-level time
trends to control for market specific trends. Furthermore, because the number of available
listing at a given time period is correlated with idiosyncratic demand shocks, we instrument
for the number of Airbnb listings with a lagged value, which is exogenous to concurrent
shocks in demand.
Our baseline estimate of Airbnb’s effect on hotels is that a 10 percent increase in the
number of available listings on Airbnb reduces hotel revenues by .6 percent. This effect
1We cannot use the empirical approach of Greenwood and Wattal [4] to study the effects of Airbnb
because, unlike Uber, Airbnb did not discretely choose to enter each market.
3
is due to both a reduction in hotel prices and a reduction in room occupancy rates. The
effect of Airbnb on hotel revenue is heterogeneous across cities. The effect is larger in
cities characterized by high hotel entry costs where a doubling of Airbnb size decreases hotel
revenuesby15percent. Intheothercities, thereductionisonly3percent. Theheterogeneity
in estimates is due to differences in both the size of Airbnb and the effects of Airbnb across
markets conditional on that size.
Next, we explore heterogeneity across hotel types and Airbnb listings. Airbnb has sim-
ilarly sized effects on the revenues of economy to upper-scale hotels. However, when we
break down Airbnb supply by room type, this relationship is more nuanced. We find that
private rooms have larger competitive effects on hotels and that those competitive effects
are concentrated on lower end and independent hotels.
Lastly, we describe a model of industry equilibrium and an estimation strategy for the
model. (NOTE: we are still estimating this model but we will be done by the time of the
presentation). In the model, hotel types and Airbnb represent differentiated goods, whose
demand varies over time due to market wide demand fluctuations, such as seasonality, and
idiosyncratic demand shocks (e.g. Berry et al. [1]). On the supply side, hotels compete
in a game of differentiated Bertrand competition subject to capacity constraints. Airbnb
producers take prices as given and host travelers if the market clearing price on the platform
isgreaterthanthecostofhosting. Weintendtousethismodeltocomputeconsumersurplus,
producer surplus, and optimal market structure across the different cities.
2 Data on Airbnb and the Accommodation Market
This paper uses data on Airbnb and hotels. Our proprietary Airbnb data consists of infor-
mation aggregated at the listing type – private rooms or entire apartments – city, and time
level.2 The variables we observe include the number of stays, active and available listings,
as well as average listed and transacted prices. An available listing is defined as one that
is either booked through Airbnb or is open to be booked on the date in question according
to a host’s calendar. An active room is defined as a listing that is available to be booked
(according to the calendar) or is booked for at least one date in the future. For each aggre-
gation of date, market, listing type, and outcome, we observe the distribution of transaction
prices, the mean number of guests, and other characteristics.
The hotel data comes from Smith Travel Research (STR), an accommodations industry
data provider that tracks over 161,000 hotels. Our sample contains daily prices and occu-
2Shared rooms, a third type of listing offered on Airbnb, are very rare.
4
pancy rates for the 50 largest US cities for the period between January 2011 and May 2014.3
STR obtains its information by running a periodic survey of hotels. For the 50 largest mar-
kets, an average of 68 percent of properties are surveyed, covering 81 percent of available
rooms. STRthenusessupplementarydatasuchasdataonsimilarhotelstoimputeoutcomes
for the remaining hotels which are in their census but do not participate in the survey.4 The
data is then aggregated to seven hotel scales, which indicate the quality and amenities of the
hotels. For the rest of the analysis we aggregate the data to five categories: luxury, upscale,
midscale, economy, and independent.
3 The Growth of Peer Production
Airbnb describes itself as a trusted community marketplace for people to list, discover,
and book unique accommodations around the world online or from a mobile phone. The
marketplace was founded in 2008 and in every year since then it has more than doubled
the number of guests accommodated. Airbnb has created a market for a previously rare
transaction: the short-term rental to strangers of an apartment or part of an apartment. In
the past, these transactions were not commonly handled by single individuals because there
were large costs to securely exchanging money, communicating with strangers and ensuring
trust. While Airbnb is not the only company serving this market, it is the dominant platform
in most US cities.5 Therefore, we use Airbnb data to study the overall effect of the entry of
a peer-to-peer platform in the market for short-term accommodation.
The demand and supply for Airbnb clears at a local level. A traveler typically looks for
accommodation in a particular city and a host can typically host only in her city of residence.
So a platform can be successful in one city but fail to spread in another, and its success is
affected by the market conditions of the city. In this section we show that the diffusion of
Airbnb is heterogeneous across locations and explain that heterogeneity.
Table 1 shows city-level descriptive statistics regarding hotels and Airbnb. In the average
city, hotels charge $107 per room and their occupancy rate is 66 percent. Airbnb has both
lower prices ($82) and occupancy rates (17 percent). The within-city standard deviation
of these outcomes varies greatly across cities. For example, the city at the 25th percentile
3The cities are ranked based on the absolute number of hotel rooms in 2014.
4See Census Database: http://www.str.com/products/census-database and STR Trend Reports:
http://www.str.com/products/trend-reports
4http://www.transtats.bts.gov/Tables.asp?DB_ID=125&DB_Name=Airline%20Origin%20and%
20Destination%20Survey%20%28DB1B%29&DB_Short_Name=Origin%20and%20Destination%20Survey
5The most prominent competitor is Homeaway/VRBO, a public company whose business has histori-
cally been concentrated in rentals of entire homes in traditional vacation markets such as beach and skiing
destinations.
5
has a standard deviation of hotel prices of $9 ($15 for Airbnb prices), while the city at the
75th percentile has a standard deviation of $19 ($24 for Airbnb prices). This indicates that
markets differ not only in levels but in the extent to which conditions fluctuate within a year
and over time.
During the period which we study, Airbnb represents a small share of the overall market
as a percentage of total rooms available for short-term accommodation. The overall share of
available rooms as of May 2014 is on average 2 percent, and in most cities it is between 0.4
and 3 percent (25th and 75th percentiles). Two other normalizations confirm that Airbnb
was still small in most US cities as of May 2014. Across all cities, Airbnb bookings represent
3 percent of all potential guests, and they represent less than 1 percent of total housing units
within an MSA.
Figure 1a plots the size of Airbnb over time for the top 10 cities among the 50 markets
in our data. The y-axis is the number of available listings.6 Airbnb supply has grown across
all markets in our sample. This general growth is specific to the peer-to-peer sector and
does not represent a broader growth of the supply of short-term accommodations (see figure
A1). However, not all cities grow at the same rate. Even among the top 10 cities, there are
fast growing markets like San Francisco and New York, as well as slow growing markets like
Chicago. Across all 50 markets, there is even larger variation in the growth patterns.
In figure 1b the size of Airbnb is measured as the monthly average share of Airbnb
listings out of all rooms available for short-term accommodation.7 Airbnb’s penetration
greatly varies across US cities even when normalizing by total accommodation supply. On
average across the 50 cities, 1.9% of accommodation supply is composed of Airbnb listings
as of May 2014, but in the bottom 25 percent of cities less than 0.4 percent of rooms are
Airbnb listings, and in the top 25 percent more than 3 percent are Airbnb listings. Because
Airbnb listings can host more people than a typical hotel room, Airbnb constitutes a larger
share of potential travelers, but at 3 percent on average this share is still small.
3.1 Predictors of Peer-to-Peer Growth
Einav et al. [2] present a model of the equilibrium composition of supply with peer-to-peer
platforms and traditional firms. The main trade-off between the two forms of supply is
in the cost structure: traditional firms have dedicated capacity, for which they face high
investment costs, but low marginal costs of providing each unit of service. On the other
6The absolute number is not shown to protect company’s information.
7ThetotalnumberofavailableroomsisthesumofavailablehotelroomsandlistingsavailableonAirbnb.
Thesameheterogeneityisapparentifweadjustforcapacity,orifwenormalizethenumberofAirbnblistings
by the number of total housing units within an MSA.
6
hand, peer (or flexible) suppliers have low investment costs, but face higher marginal costs.
Entry of dedicated and flexible capacity will then depend on the relative costs. In particular,
the authors show that peer production is favored by high investment costs for dedicated
producers, and by low marginal costs of providing services as peers. We empirically verify
thatthecitieswhereAirbnbismostsuccessfularealsothosewiththehighestaccommodation
prices, which are in turn caused by high costs of building hotel capacity, and low marginal
costs of providing peer accommodation.
We first demonstrate the relationship between Airbnb penetration and hotel prices. Fig-
ure 2 shows that Airbnb share of the market is postively correlated with the average revenue
per room in a market, with New York being both the most expensive city and the one with
the highest peer-to-peer penetration.
One determinant of hotel prices is high investment costs, which lead to low hotel sup-
ply relative to demand. We use two proxies for hotel fixed costs of entry. The Wharton
Residential Land Use Regulation Index (WRLURI) measures the amount of regulation in
each metropolitan area and is based on a nationwide survey described in Gyourko et al. [5].
The share of a metropolitan area which is undevelopable is based on Saiz [7], who uses this
measure as well as the WRLURI to calculate the housing supply elasticity at the metropoli-
tan area level. Figures 3a and 3b confirm that both measures are positively correlated with
Airbnb penetration in a city.
Another cost factor affecting the viability of peer production is the marginal cost of peers.
Different types of individuals vary in their propensities to host strangers in their homes. For
example, an unmarried 30-year-old professional will likely be more open to hosting strangers
than a family with children. This is due to several reasons. First, children increase the
host’s perceived risk to the transaction. Second, unmarried professionals are more likely to
travel and leave their rooms or properties vacant, creating space to be rented on Airbnb.
Figure 4a plots the number of available Airbnb listings per person in May 2014 against the
percentage of unmarried adults while Figure 4b plots it against the percentage of children
in the population. The two figures verify that cities where more unmarried adults and less
children live are those where Airbnb has indeed spread more.
The cost of housing can also affect the propensity of peers to supply short-term accom-
modation. In particular, when the cost of housing is a larger share of household income,
there are greater incentives to monetize a spare bedroom. Figure A2 confirms a positive re-
lationship between the share of household income used to pay rent in 2010 and the Airbnb’s
size in 2014.
On the demand side, Einav et al. [2] show that volatility in demand makes a supply mix
withpeerproductionmoreefficient. Thisisbecauseinmarketswithvariabledemand, having
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a fixed hotel capacity implies either high prices in periods of high demand - if fixed capacity
is small - or low occupancy in periods of low demand - if fixed capacity is large. We use data
from airline flights and Google searches to proxy for city specific accommodation demand
trends and fluctuations. Our data on air travel comes from surveys conducted by the Bureau
of Transportation Statistics.8 We use this data to separately measure trips originating in
a city as part of a round trip as well as trips entering a city as part of a round trip from
another city. Our data on search comes from Google Trends, which provides a normalized
measure of search volume for a given query on Google. Our query of interest is “hotel(s)
c”, where c is the name of a US city available through STR. We obtain this data for our 50
cities at a weekly level for the period between January 2011 and March 2015. We normalize
all of the trends series by the search rate in New York during the second week of 2011.
Table 2 displays the summary statistics for these two measures of demand. There is a
fivefold difference between the standard deviation of travelers between the 75th and 25th
percentiles of cities. Figures 5a and 5b show that the Airbnb penetration at a city level is
correlated with these measures of demand volatility.
To conclude this section, we combine all the descriptive results into a regression. Table
3 displays results from a regression where the dependent variable is the size of Airbnb as of
May 2014 and the explanatory variables are combinations of the measures of relative costs
and demand variability described above. Despite the small sample size, all three factors
- hotel investment costs, peers’ marginal costs, and demand volatility - have statistically
significant explanatory power in predicting Airbnb’s penetration.
4 The Peer Supply Function
In the previous section we looked at the growth of Airbnb across the top US cities. In this
section we study how hosts respond to fluctuations in market-level demand over time. This
analysis allows us to estimate the elasticity of supply for hosting with respect to the price.
Note that, a priori, we expect that peers’ opportunity costs of hosting will be larger than
those of hotels. Hotels are designed with efficiency in mind, wheareas the typical host has
an alternative primary job, is not trained in the hospitality business, and does not have the
benefit of scale economies. Given these high costs, hosts should be more likely than hotels
to be on the margin of hosting and not-hosting, depending on the equilibrium price they can
charge.
Suppose there is a distribution of marginal costs of hosting among otherwise identical
hosts in the population c ∼ F , where i references an individual host, m indexes the city,
imt mt
and t the day. These costs may be determined by factors such as how busy hosts are at a
8
given time period and whether the hosts are traveling at the time (and therefore have vacant
space). Hosts choose to host only if the market clearing price is greater than their cost,
p ≥ c . Therefore, the total number of bookings will be determined by the following
mt imt
equation: B = Aχ F (p ) In this equation, A refers to the number of Airbnb listings
mt mt mt mt mt
which may potentially choose to host on a given night. We can then linearize the above
equation in the following manner, where κ is the elasticity of supply with respect to price.
log(B ) = χlog(A )+κlog(p )+µ +(cid:15) (1)
mt mt mt mt mt
The above equation suffers from the standard simultaneity bias because the price of ac-
commodations is correlated with time-varying fluctuations in hosting costs. Furthermore,
the number of listings is itself endogenous because hosts may enter during particularly at-
tractive hosting periods. We discuss each concern in order. First, we instrument for price
with plausibly exogenous demand fluctuations which are typically caused by seasonality or
special events in a city.9 We use the Google searches for accommodations in a given city
as the instrument. We believe that these measures are plausibly exogenous to the supply
shocks in a given city because travelers typically need to search before they know the price.
Furthermore, because we control for market and seasonality fixed effects, obvious sources
of host supply shocks, such as hosts going on vacation at a particular time of the year, are
eliminated.
Next we discuss the supply of Airbnb over time. Figure 6 displays three measures of the
size of Airbnb plotted over time for our sample: active listings, available listings, and booked
listings. There are three observation to note. First, the share of active or available listings
that are booked varies greatly. The booking rate is especially high during periods of high
demand such as New Year’s Eve and the late summer. This suggests that Airbnb supply is
highly elastic. Second, the number of available listings actually decreases during New Year’s
Eve. One reason for this is that calendar updating behavior is endogenous. Many hosts do
not pro-actively take the effort to block a date on their calendar when they are unavailable.
However, when they receive a request to book a room, they typically reject the guest and
update their calendar accordingly. Since a larger share of listings are sent inquiries during
high demand periods, the calendar is also more accurate during those times. Therefore, the
naively calculated availability measure suffers from endogeneity and is even couter-cyclical
(high when demand is low, and low otherwise). Lastly, the gap between active listings and
9In fact, such a demand fluctuation played a key role in Airbnb’s founding. According to co-founder Joe
Gebbia, “We started the company by accident.... There was a design conference coming to the city, but
hotels were sold out. The size of our apartment could easily fit airbeds on the floor, so we decided to rent
themout.... Wenettedcloseto$1000.”. Fullinterview: https://allentrepreneur.wordpress.com/2009/
08/26/travel-like-a-human-with-joe-gebbia-co-founder-of-airbnb/
9
available listings is increasing over time, suggesting attrition in active listings. Therefore,
the meaning of an active listing does not stay constant over the entire period of study.
To address these concerns, we create a new adjusted measure of available listings. This
measure includes any listings which are listed as available for a given date or were sent an
inquiry for a given date and later became unavailable. Therefore, it does not suffer from
the issue of demand-induced calendar updating. Figure 7 displays our proposed measure
against the naive measure of available listings. The new measure does not suffer from drops
in availability during high demand periods. We use this measure throughout the rest of
the paper unless otherwise noted. Figure 8 displays the series of bookings and our new
measure of available listings for Austin. There are particularly large spikes in bookings and
availability during the South by Southwest Festival, a particularly stark example of a supply
response to a demand shock.
Table 4 contains our estimates of Equation 1 for all of Airbnb and for entire properties
and private rooms separately. Notably, we use the average price of hotels rather than the
averagepriceofAirbnbduetothefactthatsomedateandmarketcombinationsinoursample
contain no transactions.10 Turning first to column (1), a 1% increase in the average hotel
daily rate increases Airbnb bookings by 2.2%. This effect decreases to 1.9% when use data
only for entire properties and 2.1% when we use data only for private rooms. This suggests
that hosts with private rooms are typically closer to the margin between hosting and not
hosting, conditional on availability. Lastly, our estimate of χ, the elasticity of bookings with
respect to available listings is slightly above .3% in all cases. This implies that a significant
portion of seemingly available listings are not actually available for the variation in price
levels observed in the data.
5 Effect of Peer Entry on Hotels
So far we have shown how the diffusion of peer production differs across cities and how this
heterogeneity is related to the cost structure of professional and peer producers, as well as
the variability of demand. In this section we document the effects of peer entry on hotels.
Before describing our empirical strategy, we discuss the two most important challenges
to identifying the effect of Airbnb. First, consider the hypothetical scenario where Airbnb
supply grows randomly across markets. In this scenario, regressing the outcomes of hotels
across markets on the Airbnb supply would yield an unbiased estimate of the causal effect
of Airbnb. However, Airbnb does not grow randomly across markets. In fact, as previously
10When the number of bookings or available listings, we add 1 before taking the logarithm. Excluding
these observations does not affect the estimates in a meaningful manner.
10