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Airbnb and other short-term rental services are a topic of increasing interest and concern for urban researchers, policymakers and activists, because of the fear that short-term rentals are facilitating gentrification. This article presents a framework for analyzing the relationship between short-term rentals and gentrification, an exploratory case study of New York City, and an agenda for future research. We argue that Airbnb has introduced a new potential revenue flow into housing markets which is systematic but geographically uneven, creating a new form of rent gap in culturally desirable and internationally recognizable neighbourhoods. This rent gap can emerge quickly—in advance of any declining property income— and requires minimal new capital to be exploited by a range of different housing actors, from developers to landlords, tenants and homeowners. Performing spatial analysis on three years of Airbnb activity in New York City, we measure new capital flows into the short- term rental market, identify neighbourhoods whose housing markets have already been significantly impacted by short-term, identify neighbourhoods which are increasingly under threat of Airbnb-induced gentrification, and measure the amount of rental housing lost to Airbnb. Finally, we conclude by offering a research agenda on gentrification and the sharing economy.
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Airbnb and the Rent Gap: Gentrification Through the
Sharing Economy
Forthcoming in Environment and Planning A: Economy and Space
David Wachsmuth and Alexander Weisler
School of Urban Planning, McGill University
Abstract: Airbnb and other short-term rental services are a topic of increasing interest and
concern for urban researchers, policymakers and activists, because of the fear that short-term
rentals are facilitating gentrification. This article presents a framework for analyzing the
relationship between short-term rentals and gentrification, an exploratory case study of New
York City, and an agenda for future research. We argue that Airbnb has introduced a new
potential revenue flow into housing markets which is systematic but geographically uneven,
creating a new form of rent gap in culturally desirable and internationally recognizable
neighbourhoods. This rent gap can emerge quickly—in advance of any declining property
income— and requires minimal new capital to be exploited by a range of different housing
actors, from developers to landlords, tenants and homeowners. Performing spatial analysis on
three years of Airbnb activity in New York City, we measure new capital flows into the short-
term rental market, identify neighbourhoods whose housing markets have already been
significantly impacted by short-term, identify neighbourhoods which are increasingly under
threat of Airbnb-induced gentrification, and measure the amount of rental housing lost to
Airbnb. Finally, we conclude by offering a research agenda on gentrification and the sharing
Keywords: gentrification, Airbnb, short-term rentals, rent gap, urban political economy
Corresponding author:
David Wachsmuth, School of Urban Planning, McGill University, Montreal, Canada, H3A 0C2
Wachsmuth and Weisler forthcoming !2
New York’s short-term rental showdown
In October 2013, New York State’s Attorney General issued a subpoena to the
short-term rental service Airbnb, demanding that the firm hand over its records on
hosts operating in the state, so that a law passed a few years earlier regulating home
sharing in New York City could be properly enforced. The company refused and filed a
motion in court to have the subpoena thrown out. What followed was a seven-month
legal standoff culminating at the State Supreme Court. In May 2014 the court
overturned the Attorney General’s subpoena as being overly broad, but the next day
the Attorney General filed a new, narrower subpoena. A week later, the two parties
announced a settlement, which included Airbnb handing over the requested
Over the next several years, the public relations battle heated up. At the end of
2015, Airbnb undertook a data transparency exercise, voluntarily sharing a one-day
snapshot of data from New York City with lawmakers. But independent analysts
demonstrated that the company had carried out an unprecedented purge of listings
just days beforehand, raising persuasive doubts about the data’s representativeness
and accuracy (Cox and Slee 2016). In 2016 a white paper found that Airbnb hosts are
prone to reject African-American guests even if it means a loss in possible income
(Edelman et al. 2017), fueling a flurry of media scrutiny as well as a vague commitment
to change from the company’s new “director of diversity” (Benner 2016b). In April,
Airbnb (2016) released a report “Airbnb and Economic Opportunity in New York City’s
Predominantly Black Neighborhoods”, which used testimony from families saving for
college and African-American business owners to make the case that Airbnb helps
middle-class African-American families make ends meet in New York. Their report
boasted that usage had risen more than 50% faster in Black neighbourhoods than in
the city as a whole. Critics of the company were quick to point out that the most
obvious interpretation of this fact is that Airbnb is helping to gentrify these
neighbourhoods by taking affordable long-term rental units off the market. In
particular, an independent study of New York City’s predominantly Black
neighborhoods found that white hosts consistently earned a dramatically larger share
of revenue on Airbnb than their share of the population (Cox 2017).
And by the end of 2016 the company found itself in another legal standoff with
the State government. That October, New York Governor Cuomo signed a bill into law
which made it illegal simply to advertise a rental of less than thirty days in New York
Wachsmuth and Weisler forthcoming !3
City; the previous law had required the Mayor’s Office to investigate whether a
transaction had actually occurred. Airbnb promptly challenged the new law in court.
But, two months later, in what was seen as a shocking about-face, the company
dropped the lawsuit under the condition that hosts—rather than Airbnb itself—face
the up-to-$7,500 fines (Benner 2016a). This capitulation capped a month in which
Airbnb decided to call truces with some of the city governments which had been most
hostile to it, agreeing to cooperate with regulatory efforts in the US and Europe. The
company’s retreat started in its hometown of San Francisco, when a federal judge
dismissed their request for an injunction against new legislation that vowed to fine
Airbnb $1,000 per day per illegal listing in the city (Said 2016). For the first time
Airbnb agreed to directly police its hosts by limiting listings to one per host and
eventually blocking the rental of a unit for more than 90 days; the company also
promised to release user information to authorities.
As this brief timeline of Airbnb in New York suggests, cities and communities
around the world are increasingly grappling with the impact of short-term rentals on
their housing markets, and the question of whether and how to regulate the matter.
Cities across North America and Europe have seen legislative showdowns fuelled by
housing activism. Barcelona’s leftist mayor Ada Colau swept to office in 2015 with a
platform that explicitly linked Airbnb with housing stress. Berlin has cracked down on
short-term rentals in hopes of keeping housing affordable. Pricier capitals London
and Amsterdam have limited rentals to 90 nights and 60 nights per year, respectively.
And even while New York City and San Francisco dominate the US discourse, a range
of mid-size cities across the country have challenged the company’s business practices,
while others have reached amicable arrangements.
Yet, despite the enormous and growing policy and public interest in the impact
of short-term rentals on housing affordability, there has so far been little scholarly
investigation of this problem. In this article we address this deficit by presenting a
framework for analyzing Airbnb and gentrification, an exploratory case study of New
York City, and an agenda for future research. We argue that Airbnb has introduced a
new potential revenue flow into housing markets which is systematic but
geographically uneven, creating a new form of rent gap in culturally desirable and
internationally recognizable. This rent gap can emerge quickly—in advance of any
declining property income— and requires minimal new capital to be exploited by a
range of different housing actors, from developers to landlords, tenants and
homeowners. Performing spatial analysis on three years of Airbnb activity in New York
Wachsmuth and Weisler forthcoming !4
City, we measure new capital flows into the short-term rental market, identify
neighbourhoods whose housing markets have already been significantly impacted by
short-term, identify neighbourhoods which are increasingly under threat of Airbnb-
induced gentrification, and measure the amount of rental housing lost to Airbnb.
Finally, we conclude by offering a research agenda on gentrification and the sharing
Airbnb, the sharing economy, and housing affordability
Alongside the ride sharing company Uber, Airbnb is one of the two leading
lights of the so-called “sharing economy”, a contentious concept built on the peer-to-
peer exchange of goods and services enabled by recent advances in information
technology. The sharing economy has its free-market triumphalist advocates (e.g.
Hopkins 2016), as well as liberal-progressive defenders who view it as an opportunity
for destabilizing market-oriented consumerism and individual ownership (e.g.
Sundararajan 2016). An emerging line of radical critique, meanwhile, conceptualizes it
as a new kind of deregulatory right-wing populism (Morozov 2016; Slee 2016).
Airbnb is a short-term housing rental service whose platform connects travelers
with hosts. Its customers interact with the service much as they would interact with a
hotel—making bookings for accommodation—but it is the hosts who list and charge
for occupancy of their sofa, spare room, or entire unit, while Airbnb takes a
commission of 8% to 18% per booking. The company launched in 2008 and enjoyed
early successes during the Democratic National Convention in Denver, Colorado and
the annual South by Southwest music festival in Austin, Texas that year. It now counts
villas, castles, and luxury penthouses among its listings. At the close of 2017, the
company boasted over four million listings around the world and was valued at $31
billion—more than the Hilton and Marriot international hotel chains.
Airbnb has effectively created a new category of rental housing—short-term
rentals—which occupies a lacuna between traditional residential rental housing and
hotel accommodation. Airbnb is by no means the sole provider of short-term rentals
but it is by all accounts the dominant force; its closest competitor, Austin-based
HomeAway, lists about half as many units worldwide. Nonetheless, Airbnb’s impact on
cities and housing markets is not well understood, since the company takes great pains
to cloud its operations from scrutiny. Airbnb’s business model has been particularly
controversial because it so clearly flouts existing housing and land-use regulations in
Wachsmuth and Weisler forthcoming !5
many or even most of the cities in which it operates, and does so in a fashion which
appears to undermine policies aimed at protecting the supply of affordable housing.
Airbnb and its advocates insist that these regulations must be updated to
accommodate the new possibilities presented by the sharing economy. Opponents
argue that Airbnb aims to avoid the regulation and taxation imposed on other
businesses and threatens affordable housing in cities.
The company’s practices have inspired a curious oppositional coalition of
tenant associations, community groups, municipal governments, and hotels.
Municipalities and affordable housing advocates share concerns about the effect of
short-term rentals on the housing market, particularly in cities and neighbourhoods
where demand is putting upward pressure on rents. Airbnb and related platforms have
made it easier and more lucrative for landlords and property managers to offer units
as year-round short-term rentals than as long-term residential rentals. Accordingly,
legislators and activists in cities from Boston to Berlin have begun to target short-term
rentals as a housing affordability problem. “Cities are struggling to address urgent
shortages of affordable housing and there is evidence that commercial interests in the
[short-term rental] industry are removing residential units from housing markets and
thereby contributing to even higher rents,” read a letter to the US Federal Trade
Commission signed by urban lawmakers from across the United States (Partnership
for Working Families 2016). Several cities worldwide (most notably Berlin and
Barcelona) have pursued near-total bans on the service, while 2016 saw a flurry of
short-term rental legislation in cities across North America. In many municipalities,
short-term rentals were already illegal according to pre-existing law, and new
legislation has been used to increasing municipal monitoring and enforcement
capacities. Several cities, such as Philadelphia and San Jose, have legalized short-term
rentals but attempted to tax them, while others such as Phoenix have adopted an
entirely laissez-faire posture.
In general, municipalities recognize the huge amount of untaxed income
enabled by Airbnb and argue that the service or users should pay their share. The New
York Attorney General, for instance, estimates from subpoenaed data that the city
should have received over $33 million in hotel room occupancy taxes alone from
Airbnb between 2010 and 2014. Additionally, the anonymity provided by Airbnb
means it is unlikely that hosts paid the necessary taxes at any level. Finally, municipal
regulators have displayed reticence to confront small-time users—those who may
occasionally rent out a spare room to supplement their incomes—instead focusing on
Wachsmuth and Weisler forthcoming !6
so-called “commercial users”. Commercial users rent out multiple units on a full-time
basis, and their share of the overall short-term rental market has been rising steadily,
to approximately one third of overall Airbnb revenues in 2016 by one estimate
(Stulberg 2016).
Alongside a small but growing number of researchers, community groups and
housing advocates in cities across the world have begun to sound the alarm about the
impact Airbnb is having on affordable housing in their communities, highlighting
above all issues of racialized gentrification and displacement (see, e.g., BJH Advisors
2016; Lee 2016; Samaan 2015; New York Communities for Change 2015; Wachsmuth et
al. 2017; Wachsmuth et al. 2018; Wieditz 2017). In a 2015 white paper, the Los Angeles
Alliance for a New Economy (LAANE) estimated that homesharing platforms took 11
units off the local rental market each day, accounting for a significant portion of new
housing built since 2010 that was intended to slow rent increases. They found that
professional landlords accounted for most of the profit of Airbnb and their
competitors. From 2014 to 2015, the number of total listings skyrocketed while the
presence of leasing companies increased from 6% to 9% of users, accounting for up to
37% of all revenue. Meanwhile, the share of hosts renting out a spare room decreased
from 52% to just 36%, taking in only 16% of all income. LAANE argues that short-term
rentals have offset municipal efforts to increase housing stock; in popular
neighbourhoods, the number of full-time short-term rental units is up to four times
higher than the number of new units built since 2010. The study found that rents were
rising much faster than average in popular Airbnb neighbourhoods, for which the
platform has written travel guides on its website (Samaan 2015).
A study by New York Communities for Change and Real Affordability for All
found that Airbnb took approximately 20% of vacancies off the market in certain
Manhattan and Brooklyn zip codes, and up to 28% in the East Village neighbourhood,
even though it is technically illegal to rent an entire unit for less than 30 days. Overall,
they estimated that the 20 neighbourhoods most popular on Airbnb have lost 10% of
rental units (NYCC and RAFA 2015). These neighbourhoods are also featured in
Airbnb’s neighbourhood guides. The company dismissed the report as “lies, fuzzy
math and faulty stats” (Fermino 2015)—a curious inversion of the many critiques
lodged against Airbnb’s own dubious claims of providing for the local economy.
Quality of life is also a concern for residents who have seen their
neighbourhoods transformed into de facto hotel districts (Cócola Gant 2016). In the
fall of 2016, residents of New Orleans, still recovering from Hurricane Katrina, held a
Wachsmuth and Weisler forthcoming !7
jazz funeral at city hall (with coffins reading “RIP real neighbors” and “RIP affordable
housing”) to mourn neighbourhoods lost to Airbnb tourism in a protest (Litten 2016).
Meanwhile, hotel associations complain that short-term rentals effectively function as
hotels but have an unfair advantage because they don’t pay taxes and don’t comply
with safety and zoning regulations. Hotels also fear—plausibly, it turns out (Zervas et
al. 2016)—that this grey-market enterprise will take away from their business.
The short-term rent gap: Gentrification without redevelopment
These debates and controversies in cities around the world provide significant
circumstantial evidence that short-term rentals are implicated in gentrification.
Accordingly, we now proceed to demonstrate that there is fire to go with this smoke.
Our argument is that Airbnb and other facilitators of short-term rental housing are
indeed systematically driving gentrification and displacement. Airbnb 1)
simultaneously opens and provides a means for closing new technology-driven rent
gaps, but it does so 2) by raising potential rentier income without any need for
redevelopment, 3) in a geographically uneven fashion, concentrating in
neighbourhoods with extralocal tourist appeal which do not necessarily overlap with
areas gentrifying due to more traditional state or market factors.
Because Airbnb is, first of all, a mechanism for producing new revenue flows
through land ownership, our theoretical point of entry is the rent gap. Neil Smith
(1979) first proposed the rent gap model to offer a structural explanation for
gentrification in American inner-city contexts such as New York City and Philadelphia.
At its core, the rent gap model describes a situation where the actual economic returns
to properties tend to decline or stagnate while potential economic returns tend to
increase. In neighbourhoods where this “gap” between actual and potential returns
systematically increases, the result will be a correspondingly increasing incentive for
real estate capital to direct new housing investment flows. As these investment flows
drive up housing prices, attract more affluent newcomers, and displace existing poorer
residents, the result is gentrification.
Smith developed this model in an American urban setting featuring a host of
specific cultural, social, and political-economic features, but the core of the rent gap
model is relatively independent of these features. It simply states that where actual
rents and potential rents diverge, a structural incentive for capital reinvestment begins
to assert itself, and this incentive can be seen at work in cities around the world (Slater
Wachsmuth and Weisler forthcoming !8
2015; Lees et al. 2016). And as research on rural (Ghose 2004) and wilderness
gentrification (Darling 2005) demonstrates, these conditions can exist in even non-city
spaces, with much the same result. Smith mainly discusses the case where the
divergence between actual rent and potential rent occurs because of devalourization
and neighbourhood decline—the common empirical picture of pre-gentrified
neighbourhoods. But he also allows for the possibility that rent gaps could emerge in
previously stable neighbourhoods, thanks to sudden shocks which drive up potential
But it is also possible to conceive of a situation in which, rather than the
capitalized ground rent being pushed down through devalorization, the
potential ground rent is suddenly pushed higher, opening up a rent gap in a
different manner. This might be the case, for example, when there is rapid and
sustained inflation, or where strict regulation of a land market keeps potential
ground rent low, but is then repealed. (Smith 1996: 68)
Indeed, Hackworth (2002: 828) (following Hammel 1999) has argued that rent gaps are
increasingly likely to form through rising potential ground rent rather than decreases
in actual ground rent, “because the surrounding core of reinvestment has lifted the
economic potential of all centrally located parcels”.
The fact that short-term rentals have produced—effectively out of thin air—a
new potential revenue stream in housing markets suggests the possibility that Airbnb
is systematically creating rent gaps in cities around the world. This is our argument:
across certain neighbourhood types (primarily still-gentrifying areas and now-affluent,
formerly gentrifying areas), the new, technologically-enabled possibility of short-term
rentals systematically raises potential ground rents—and thus creates rent gaps even
where there has been little or no devalourization of existing housing. For dedicated
entrepreneurs, monthly income from short-term rental properties can substantially
exceed what could be realized through conventional long-term residential leases,
particularly in cities with strong rent control regimes. And for “amateur” homeowners
or tenants, the prospect of monetizing a spare room or staying with friends for an
occasional weekend while their residence is rented similarly increases the overall rent
achieved through the property. Airbnb is in effect shifting the “highest and best use”
of residential housing in neighbourhoods with sufficient extra-local tourist interest,
and the result is a rent gap.
Our thanks to Benjamin Theresa for drawing our attention to this point.
Wachsmuth and Weisler forthcoming !9
This argument builds in important respects on the concept of “transnational
gentrification” proposed by Sigler and Wachsmuth (2016). Relying on a case study of
the redevelopment of a historic neighbourhood in Panama City, they argue:
[In Panama City], localised disinvestment presents an opportunity for
reinvestment capital not because of the neighbourhood’s changing relationship
with metropolitan growth dynamics, but because of the neighbourhood’s
changing relationship with a transnational middle class, for whom globalisation
has rendered a physically distant locale increasingly accessible both logistically
and imaginatively as a lifestyle destination. (Sigler and Wachsmuth 2016: 708)
The standard model of the rent gap—and gentrification in general—is a metropolitan
scaled process where a neighbourhood declines but metropolitan growth sets the
stage for reinvestment (Hammel 1999). Transnational gentrification, by contrast, occurs
where rent gaps are globally scaled, and can create significant crisis for local residents
who are forced to pay housing prices being set by global rather than local demand.
Airbnb is an instance of this phenomenon; the service offers the opportunity for local
capital to take advantage of extra-local demand.
So what kind of rent gap does Airbnb produce? It is in part technological; the
potential economic returns to the very same apartment may be higher now than they
were a few years ago, for no other reason than the availability of a website which
allows short-term visitors to stay there. At the small scale, leaving for the weekend
didn’t formerly create a feasible opportunity for tenants to rent out their apartment.
And at the large scale, even if there had been sufficient flows of tourism to keep an
apartment continuously occupied with short-term visitors, what landlord could have
handled the necessary logistics to find these tourists, collect payment, and manage the
schedule? While a small number of cities have historically received gigantic inflows of
tourists at specific times of year (e.g. Edinburgh, Scotland, which hosts the Fringe
Festival each August), and hence saw the development of a dedicated short-term rental
sector even prior to the growth of web-based tourism, these cities are the minority.
Airbnb’s technology platform creates new potential housing revenue flows in a far
larger cohort of cities because it solves many of the logistical problems associated with
short-term rentals in a general fashion.
Airbnb’s rent gap is thus technological, but it is also culturally mediated.
Anyone can list their apartment on the service, but real economic activity only exists in
areas where there is strong extra-local tourism demand. Some of these locations will
be in pre-existing hotel districts and central business districts, but others will be in
Wachsmuth and Weisler forthcoming !10
areas which do not have large hotel presences but nevertheless have cultural cachet—
such as Williamsburg in New York, the Mission District in San Francisco, and inner
East London.
While Airbnb opens up new technology- and culture-driven rent gaps by
introducing the possibility of short-term rentals into formerly long-term housing
units, it also offers the means of closing those same gaps. Contrast this with, for
example, a major rezoning which raises potential ground rents in an area. The
municipality takes the action which helps produce a rent gap, but other actors are
necessary for realizing the higher rents—banks, developers, and the like. With Airbnb,
the very same factor which creates the possibility of higher returns to housing also
creates the means of achieving those returns. This decreases the turnaround time
necessary to close the rent gap. Clark (1995: 1496), for example, illustrates the lifecycle
of a property’s rent gap using a 30- or 60-year time horizon, over which time actual
rents fall and fall, before beginning to rise as speculation on the property’s
redevelopment prospects increases. He concludes that “The force of the rent gap is
already history during the years just prior to redevelopment.” By contrast, the rent gap
which short-term rentals produce is literally a short-term rent gap. For properties
which have a new highest and best use thanks to the emergence of Airbnb over the
last few years, there was no sustained period of speculation and gradually increasing
actual rents, but rather an exogenous shock to potential rent.
Moreover, little or no new investment is necessary to capitalize on an Airbnb
rent gap. Again, a comparison with received wisdom on gentrification is instructive
here, since nearly every analysis of gentrification concerns cases where the “gap” itself
needs to get large enough to justify the high cost of new construction or major
renovations. In Smith’s (1979: 545) original analysis of the rent gap, he explained that:
Gentrification occurs when the gap is wide enough that developers can
purchase shells cheaply, can pay the builders’ costs and profit for rehabilitation,
can pay interest on mortgage and construction loans, and can then sell the end
product for a sale price that leaves a satisfactory return to the developer.
Not a single one of these steps is necessary for converting an existing residential unit
to a short-term rental. While serious Airbnb entrepreneurs may well refurbish their
units to increase their success with the service, the only necessary step for converting a
long-term rental to a short-term rental is to remove the existing tenant. This means that
relatively small rent gaps can motivate conversion to short-term rentals; no new
mortgages need to be taken out, or contractors hired. In other words, Airbnb enables
Wachsmuth and Weisler forthcoming !11
gentrification without redevelopment. Figure 1 schematically illustrates the distinctive
rent gap dynamics which short-term rentals generate.
There are two other immediate implications of the short-term nature of
Airbnb’s rent gaps. The first is that owners of rental units in areas where there is
strong tourist demand for short-term rentals face strong economic incentives to evict
existing tenants, or to not find new tenants when previous ones depart, in order to
quickly and cheaply realize the higher possible rents. The second is that the growth in
short-term rentals is very likely to be coming at the expense of long-term rental
housing, as the latter gets converted to the former to take advantage of new rent gaps.
Either in the short-term with actual evictions, or over a slightly longer timescale as
long-term rental housing is “organically” converted to short-term rentals, the result
will be the displacement of an existing, lower-income population and the arrival of
higher-income newcomers. This outcome differs from a conventional gentrification
scenario because the newcomers are temporary visitors rather than permanent
residents. But, unlike a situation where the housing is demolished and replaced with
hotels—a situation of housing converted to tourism accommodation which would not
usually be described as gentrification—the housing being used as short-term rentals
Figure 1. Variations of the rent gap: A) In Smith’s (1979) original analysis, a gap can open
between gradually declining actual ground rent and the potential ground rent were the
property to be redeveloped or put to the “highest and best use”. When this rent gap becomes
big enough, redevelopment and gentrification may follow. B) The minimal capital needed to
take advantage of an Airbnb rent gap means that the gap can become large enough to
motivate landowner action much sooner than with a traditional disinvestment-driven rent
gap. This causes the point at which a rent gap becomes effective to shift to the left (i.e. sooner
in time) on the figure. C) Airbnb can cause potential income to rise sharply, creating a rent
gap well in advance of any declining property income.
Wachsmuth and Weisler forthcoming !12
remains potentially in circulation, if higher-income tenants or owner-occupiers are
able to outbid Airbnb tourists.
By creating higher potential returns to property through the possibility of
short-term rentals, Airbnb produces rent gaps, and thereby should be expected to
drive gentrification and displacement. But the “opportunity” Airbnb offers to
landlords and tenants is highly uneven, because it directly depends on the magnitude
of tourist demand for short-term accommodation. Accordingly, we should not expect
Airbnb’s rent gaps, and the resulting gentrification and displacement, to be equitably
distributed across urban space.
As a first approximation, Airbnb demand is likely to be particularly
concentrated in the following two neighbourhood types: 1) areas near the central
business district which have historically featured high rates of hotels, hostels, B&Bs
and other forms of short-term tourist accommodation—i.e. areas with strong pre-
existing tourist demand; and 2) residential areas with strong cultural cachet, good
public transit, and leisure amenities—i.e. gentrifying or recently gentrified areas,
which haven’t historically hosted tourists in large numbers. Conversely, Airbnb
demand is likely to be weak in poor and racialized neighbourhoods lacking (white,
middle-class) tourist-friendly cultural amenities, as well as more suburban areas with
poorer public transit connectivity to the central city.
From a gentrification-theoretical perspective, therefore, we should expect
Airbnb-induced gentrification pressures to overlap incompletely with other drivers of
gentrification. Short-term rentals may exacerbate housing pressures in already wealthy
areas experiencing so-called “super-gentrification” (Lees 2003) as well as in areas
undergoing more traditional 2nd- or 3rd-wave (Hackworth and Smith 2001)
gentrification processes, particularly in their more advanced stages. Meanwhile, in
poor neighbourhoods which are experiencing gentrification pressures but which are
not (yet) understood as desirable destinations for extra-local visitors, short-term
rentals may not be a significant exacerbator of these pressures.
Is Airbnb gentrifying New York?
To substantiate this theoretical argument, we now turn to a case study of
Airbnb’s activities in New York City over the last several years. We measure and
Our thanks to an anonymous reviewer for raising this issue.
Wachsmuth and Weisler forthcoming !13
describe Airbnb’s impact on ground rent flows throughout the city and document the
emergence of new Airbnb-driven rent gaps in specific neighbourhoods. Data on all
188,000 Airbnb listings which were active in the New York metropolitan region
between September 2014 and August 2017 was obtained from the consulting firm
Airdna. The data includes canonical information about listing type (private room or
whole house), asking prices, and other per-listing variables, along with daily per-listing
occupancy and revenue estimates. The primary study focus is New York City proper
during the year September 2016 to August 2017; 67,100 Airbnb listings received at
least one reservation in this geography and time period. To compensate for uncertainty
in the per-listing occupancy and revenue estimates, and to facilitate comparison with
five-year estimates from the 2015 American Community Survey data concerning
housing and demographic characteristics, listing data is aggregated at the census-tract
scale using a novel method for overcoming the random spatial obfuscation which
Airbnb applies to listing locations. Full methodological details are available in the
attached appendix.
An overview of Airbnb’s activity in New York City
Short-term rentals are not a new phenomenon in New York City; the State’s
Multiple Dwelling Law, which regulates and generally prohibits them, in fact dates
back to 1929. But contemporary policy and community attention to short-term rentals
emerged in the early 2000s, when legislators and community organizations in
Manhattan began to receive increasing numbers of complaints about apartment
buildings being converted to short-term rentals. Complaints were most common on
the West Side, which already hosted the city’s largest concentration of single-room-
occupancy housing. Residents had begun to notice tourists or frequent visitors to
neighbouring units, and registered complaints about safety and quality of life, as well
as fears of being evicted as their buildings transformed into de-facto hotels. In
response, a group of legislators and civil society actors formed an Illegal Hotel
Working Group in 2005. An investigation by the group identified hundreds of illegal
hotel conversions and documented the impacts of these conversions both on
individual tenants (harassment, security concerns and loss of quality of life) and on the
city as a whole (loss of housing supply and municipal revenue, and damage to
legitimate hotels) (Illegal Hotel Working Group 2008).
In retrospect, these illegal hotels were a precursor to the “sharing economy”
version of short-term rentals, of which Airbnb is now by far the dominant player.
Wachsmuth and Weisler forthcoming !14
Indeed, the scale of Airbnb’s activities renders earlier concerns in New York about
illegal hotels almost quaint by comparison. While in the mid-2000s the Illegal Hotel
Working Group (2008) identified 224 illegal hotel conversions in New York City, over
the one-year period from September 2016 to August 2017 there were 67,100 active
Airbnb listings in New York City. New York City is Airbnb’s third largest market
worldwide (after London and Paris), generating more than $650 million in host
revenue over the year. Figure 2 shows the total distribution of active listings across the
entire region in this period, revealing hotspots in"Midtown Manhattan (near the
Figure 2. Density map of active Airbnb listings in the New York region (September 2016 –
August 2017), with important New York City neighbourhoods indicated
Wachsmuth and Weisler forthcoming !15
existing Manhattan hotel district, an area with a long history of illegal hotels), the
Lower East Side, and Williamsburg and Bushwick in Brooklyn.
As Figure 3 demonstrates, Airbnb’s growth in New York City has slowed down
considerably over the September 2016 August 2017 period in comparison with the
previous two years. On an annual basis, the number of active listings (i.e. listings which
received at least one reservation during the month) increased 4.5% from 64,200 to
67,100, while host revenue increased 14% from $576 million to $657 million. Entire-
home listings make up just over half (51%) of all active New York City listings, but earn
a disproportionate 75% of all platform revenue. Slightly over one quarter (28%) of
revenue is earned by hosts with multiple entire-home listings or three or more private-
room listings, who cannot be solely renting their primary residence and are therefore
necessarily commercial operators rather than “home sharers” per se. However, both
the share of entire-home listings and the share of commercial operators have declined
somewhat in the last year, following a settlement with New York State’s attorney
Active monthly listings (seasonally adjusted)
7,5 0 0
Sep 14
Feb 15
Jul 15
Dec 15
May 16
Oct 16
Mar 17
Aug 17
New York City Manhattan Brooklyn Rest of MSA
Monthly host revenue (seasonally adjusted)
Sep 14
Feb 15
Jul 15
Dec 15
May 16
Oct 16
Mar 17
Aug 17
Figure 3. Seasonally adjusted revenue-earning listings (left) and monthly host revenue (right)
in the New York region (September 2014 – August 2017)
Wachsmuth and Weisler forthcoming !16
general in 2016 in which Airbnb agreed to start enforcing a one-host, one-home policy
in New York City. The company claims to have removed 4,800 entire-home listings
operated by hosts with multiple such listings since November 2015 (Airbnb 2018).
Airbnb’s two rent gaps: open and closed
The theoretical core of the rent gap model is simple—where potential ground
rent sufficiently exceeds actual (or capitalized) ground rent, redevelopment and hence
gentrification will tend to occur. But operationalizing this model is difficult. Indeed,
arguably only Clark’s (1988) painstaking examination of 120 years of land
redevelopment in Malmö, Sweden has fully succeeded in doing so. The core of the
problem is that “potential ground rent” and “capitalized ground rent” are abstract
rather than concrete concepts, and hence are not available for direct observation or
measurement (Clark 1995). By contrast, the two most readily observable concepts—
contract rent and land price—do not necessarily capture the key theoretical
proposition of land rent—that it is the economic surplus accruing to a landowner.
We now proceed to provide two major empirical indicators with respect to the
distribution and intensity of Airbnb-induced rent gaps in New York City: 1) the
proportion of total residential contract rent generated from Airbnb, and 2) the
proportion of neighborhood median long-term contract rent earned on average by
hosts of frequently rented entire-home listings on Airbnb. Neither of these indicators
is claimed to directly measure potential or capitalized ground rent, or the difference
between the two which is the rent gap. Instead, they are used as proxies for these
abstract concepts, and there are compelling theoretical reasons to believe they will
adequately describe the existence and relative size of rent gaps. Both of these
indicators are measured at the neighborhood (i.e. census tract) scale, in accordance
with Hammel’s (1999) arguments about land rent and scale. (Hammel argues that
capitalized ground rent is determined at the neighborhood scale, and that potential
ground rent is determined at the metropolitan scale, but with a parcel’s location
within that scale—i.e. its neighborhood—being decisive.)
While gentrification researchers generally expect rent gaps to be filled through
new capital investment—renovations and redevelopments—in the case of Airbnb this
often won’t be necessary. Property owners can simply supply furniture and switch
their units from residential leases to short-term rentals. If there has been an Airbnb-
induced rent gap, we should not expect to see large new capital investments; instead
we should expect to see existing rental housing revenue flows diverted into Airbnb,
Wachsmuth and Weisler forthcoming !17
and new revenue flows created. At an aggregate level, this has indeed occurred over
the September 2014 to August 2017 study period, as Figure 4 demonstrates. In the last
year, Airbnb accounted for almost 2% of all residential rent payments in New York.
More dramatically, between 2015 and 2016 (the last year of very rapid Airbnb growth in
New York), Airbnb accounted for more than 20% of all growth in residential rent flows
—a number which rises to nearly 50% in Manhattan.
These rent flows can be decomposed by neighbourhood, and the result—the
proportion of total residential contract rent generated from Airbnb—is a post-hoc
measurement of neighborhoods where Airbnb drove up potential ground rent, and
where short-term rentals proliferated as a result. Put differently, these are areas where
an Airbnb rent gap opened up and was filled through new short-term rental activity.
The reasoning here is as follows. On urban land zoned for residential uses, there are
effectively only two sources of rentier income: rent from long-term tenants and rent
from short-term tenants. The latter did not exist at any meaningful scale as recently as
five years ago. Neighborhoods with large proportions of total rent now being earned
through Airbnb are neighborhoods where, over the last several years, one or both of
two things occurred with frequency: residential landlords converted existing long-
term rental units to dedicated short-term rentals, or short-term rentals were
introduced to supplement the existing tenure arrangement in a unit. The second
possibility is self-evidently an increase in the total land rent (since new economic
surplus is being generated), while the first possibility will generally represent the same,
since landlords are presumably only converting existing long-term rentals to short-
term rentals in situations where they stand to realize an economic return to doing so.
Airbnb share
of residential
rents (2015)
Airbnb share
of residential
rents (2016)
Airbnb share
of residential
rents (2017)
Airbnb share
of residential
rent increase
Airbnb share
of residential
rent increase
New York City
Figure 4. Airbnb’s share of total annual residential rents in New York City, Manhattan and
Brooklyn, alongside its share of the annual growth in residential rents (September 2014 –
August 2017)
Wachsmuth and Weisler forthcoming !18
As a consequence, the share of a neighborhood’s total residential rental revenue which
now flows through Airbnb should be a reliable guide to the size of rent gap which had
emerged thanks to the advent of a new rentier economic opportunity, and which has
already been filled.
Figure 5 displays this indicator—the proportion of total residential contract
rent generated from Airbnb—spatially for the first and third years of the study period.
It demonstrates, first of all, that Airbnb as a new revenue stream from housing has
consistently been most consequential in"Times Square, the Lower East Side, and
Williamsburg. These are the areas where Airbnb created a rent gap, and where
!"#$ !"#%
Figure 5. The rent gap which has already been closed, shown by the percentage of residential
rent payments which now flow through Airbnb in 2015 and 2017
Wachsmuth and Weisler forthcoming !19
landlords have shifted housing supply into short-term rentals to capitalize on that rent
gap. Importantly, these three neighbourhoods are all “post-gentrified”, in the sense
that they saw massive increases in rents and massive displacement over the last several
decades, and now have been to a greater or lesser extent transformed into wealthy
neighbourhoods. Airbnb has had"its biggest impact to date, in other words, not at the
gentrification “frontier” (Smith 1996), but in areas that have already been pervasively
restructured by capital. It is further intensifying gentrification and displacement
dynamics where these dynamics have already been acute. Figure 5 further
demonstrates, however, that Airbnb’s impact has been growing rapidly in several more
peripheral areas of the city. Harlem in North Manhattan and Bedford-Stuyvesant in
Central Brooklyn have both seen Airbnb’s share of total residential rents increase
dramatically over the last two years.
A complementary picture of Airbnb’s impact emerges through examining how
much landlords can earn on the service relative to prevailing rents in their
neighbourhoods. We capture this by measuring the proportion of neighborhood
median long-term contract rent earned on average by frequently rented entire-home
listings on Airbnb. These are areas where individual landlords are making the most
money on Airbnb relative to what they could have been making with traditional long-
term rentals. This indicator is a prospective measurement of neighborhoods where
Airbnb has driven up potential ground rent in a manner which has not (yet) been
addressed through new short-term rental activities. The logic of this indicator is that,
on a neighborhood scale, if operators of high-intensity short-term rentals are earning
substantially more income than traditional long-term rental landlords, the latter will
face economic incentives to convert from long-term rentals to short-term rentals. In
any individual case there will be some friction to be overcome in this conversion
(existing tenants need to be removed, the landlord needs to arrange for key
management and cleaning, and so on), so we shouldn’t expect an inflection point
wherever short-term rents exceed long-term rents. But, in line with the rent gap
model, the larger the divergence between these two income sources, the larger the gap
between the actual ground rent earned by traditional landlords and the potential
ground rent were they to convert to the “highest and best use” of short-term rentals.
Wachsmuth and Weisler forthcoming !20
When this rent gap becomes large enough, we should expect to see short-term rental
conversions occur.
Figure 6 displays this indicator spatially and reveals a different geography from
Figure 5. While the Lower East Side remains a hotspot on this map, with average full-
time Airbnb revenues in the range of 200-300% of median rents, the other major areas
of Airbnb activity—Williamsburg and Midtown Manhattan—have significantly receded
in importance. The two previously second-tier neighbourhoods of Harlem in North
Manhattan and Bedford-Stuyvesant in Brooklyn have advanced in importance. These
are areas where there is not yet a lot of Airbnb activity in absolute terms, but where
the landlords who are using Airbnb are making a lot more money than they would
have in the long-term rental market. These are the neighbourhoods at greatest risk for
Airbnb-induced gentrification in the near future. And whereas current Airbnb impacts
were concentrated in already-gentrified areas, these at-risk neighbourhoods are all still
very clearly at the gentrification frontier.
Comparing these two patterns—the percentage of housing revenue that now
flows through Airbnb, and the percentage of the median rent which an average full-
time Airbnb property earns—allows us to see where Airbnb has already had a major
impact on neighbourhood change and where it is likely to have an impact in the
future. The first pattern indicates where Airbnb has opened and closed a rent gap. The
The major assumption of this indicator is that the extent to which contract rents map onto actual
ground rent is agnostic to long-term or short-term rentals. This will be true if the ownership costs for
long-term and short-term rentals are reasonably similar. Since ground rent in general is the economic
surplus accruing to land ownership, a contract rent of X monotonically implies a higher actual land rent
than a contract rent of Y if X > Y and the ownership costs are the same. If, by contrast, the higher
contract rent can only be achieved through correspondingly higher investments by the rentier, then this
monotonic relationship between contract rent and actual ground rent will not hold. In general, this
problem is why contract rent is an unreliable guide to actual ground rents, and hence to the existence of
rent gaps: if major reinvestments are needed to achieve higher contract rents, the actual economic
return to the rentier may not be any higher under a new, higher-earning land use. However, as
previously discussed, short-term rentals have a key characteristic which answers this problem, which is
that major investments are not required to convert long-term rental properties to short-term rentals; it
is effectively just a change in tenant, with some additional furniture needing to be purchased. The costs
of maintaining short-term tenants may well be slightly higher than long-term ones, particularly because
of the need to clean the apartment frequently, but Airbnb hosts charge dedicated cleaning fees which
should mitigate this cost. In sum, we feel confident assuming that variations in contract rent between
long-term and short-term rentals adequately reflect underlying variations between actual ground rent
for long-term rentals and these properties’ potential ground rent as short-term rentals, as indicated by
the contract rent generated by other nearby short-term rentals.
Wachsmuth and Weisler forthcoming !21
second pattern indicates where there is still money to be made for landlords by
converting long-term rental housing to short-term rentals—where Airbnb has opened
a rent gap which hasn’t been closed. A third pattern—where the first two intersect—
indicates where rent gaps are closing but not yet closed, where new Airbnb revenue
has been considerable but landlords continue to face incentives to introduce new
short-term rentals.
These three patterns are synthesized in the first panel of Figure 7, which
presents a vulnerability index for Airbnb-induced gentrification in New York. First,
shown in blue, are the areas which have had their housing supply heavily impacted by
Figure 6. The rent gap which is still open, shown by the profitability of an average frequently
rented entire-home Airbnb listing compared to the median 12-month rent in the
Wachsmuth and Weisler forthcoming !22
Airbnb, but which may be close to reaching an equilibrium (a closed rent gap). Large
areas of Midtown Manhattan, Lower Manhattan and Williamsburg fit this profile.
Second, shown in red, are the areas which haven’t yet been seriously impacted by
Airbnb, but are in real danger of it in the near future, because of how much more
money landlords in these areas are making by using Airbnb (an open rent gap).
Figure 7. An Airbnb gentrification vulnerability index (left) identifying neighbourhoods with
closed rent gaps (“high current impact”), open rent gaps (“high risk of future impact”), and
partially closed rent gaps (“both current impact and future risk”). The index’s juxtaposition
with race (right) indicates that the likely next frontiers of Airbnb-induced gentrification in
New York are racialized (and particularly African-American) neighbourhoods.
Wachsmuth and Weisler forthcoming !23
Harlem in Manhattan and Bedford-Stuyvesant in Brooklyn fit this profile. Last, shown
in purple, are the areas which have already been heavily impacted by Airbnb, but
where there appears to be more impact still to come (a not-yet closed rent gap). The
Lower East Side and parts of"Harlem and Brooklyn fit this profile.
The second panel of Figure 7 demonstrates the strong overlap between the
patterns of Airbnb-induced gentrification and racial segregation. Airbnb has had its
greatest impact so far in largely non-Hispanic white neighbourhoods, while the areas
it is increasingly threatening are largely African American and Hispanic
neighbourhoods. Households in areas suffering high current impact of Airbnb in New
York are only 34% non-white, while households in areas at high risk of future impact
are on average 71% non-white. (Across New York City 61% of households are non-
white—a figure which drops to 52% region-wide.) Given emerging research
demonstrating the prevalence of racial discrimination on Airbnb (Cox 2017; Edelman
et al. 2017), the pattern identified here implies the impending arrival of a new
intensification"of racialized gentrification in New York.
The consequences of Airbnb’s rent gaps for New York households
The preceding section demonstrated the opening and closing of Airbnb-
induced rent gaps in New York City. But the question of the consequences of these
rent gaps is still to be answered; as Slater (2015: 12) remarks, “a challenge for students
of rent gap theory is…to illustrate specifically how the opening and closing of rent
gaps leads to the agony of people losing their homes.” In the case of short-term
rentals, the mechanism is unfortunately straightforward. Beyond the neighbourhood
quality-of-life issues researchers have already documented (Cócola Gant 2016),
Airbnb’s impact on housing availability and affordability can be documented in two
interrelated ways: through a reduction of housing stock available for long-term
residents, and through increased rents and housing prices.
The growth of Airbnb in a housing market does not necessarily lead to a
reduction in housing units for long-term residents. If Airbnb hosts are exclusively
casual, part-time users of the platform, who rent their primary residence while they
are out of town or rent a spare room that would not have otherwise housed a tenant,
then even a large short-term rental sector would be compatible with no long-term
housing loss. It is hard to imagine how this situation could emerge organically, but
strong state regulation of the short-term rental industry could in theory achieve such a
result. If, on the other hand, Airbnb usage is concentrated in units which are
Wachsmuth and Weisler forthcoming !24
dedicated to short-term rentals throughout the year, then the opening and closing of
Airbnb-induced rent gaps is coming at the expense of local residents, for whom
housing options have been reduced.
In a pioneering discussion of gentrification and displacement, Marcuse (1985)
introduced a distinction between “direct displacement” and “exclusionary
displacement”. The former is the scenario most commonly associated with
gentrification-induced displacement: landlords evicting tenants in order to raise rents
or redevelop. But gentrification can also cause displacement through the indirect
mechanism of rendering unobtainable what would have otherwise been viable,
affordable housing for a family—as Marcuse (1985: 206) puts it, “a household excluded
from living where it would otherwise have lived”.
The data suggest that both forms of displacement are occurring in New York
City as a result of the growth of Airbnb. In 2017 there were 12,200 whole-unit listings
rented 60 days or more and available 120 days or more (hereafter “frequently rented”),
and 5,700 whole-unit listings rented 120 days or more and available 240 days or more
(hereafter “very frequently rented”). These figures can be taken, respectively, as high-
end and low-end estimates for housing units removed from the long-term rental
market, since apartments offered for rent on Airbnb at least a third of the year are
unlikely to have a full-time tenant, and apartments offered for rent two thirds of the
year almost certainly do not. If we compare this number with the amount of
normal"housing in the region, we can estimate what portion of each neighbourhood’s
housing stock has been lost to Airbnb. As the first panel of Figure 8 indicates, many
census tracts appear to have seen"three percent or more of their long-term rental
housing converted into Airbnb hotels. (A further 10,000 private rooms were rented 60
days or more and available 120 or more, and many of these will have displaced long-
term renters as well, but we have excluded these from the analysis to err on the
conservative side.) There is no way to estimate how many tenants were forcibly evicted
or harassed out of their apartments to free up units for Airbnb (direct displacement),
and how many units were simply converted to short-term rentals after they “naturally”
became vacant (exclusionary displacement). But in either case, the result has been a
large and concentrated loss of rental housing in the city. To put the numbers in
perspective, the city-wide rental vacancy rate was 2.7% in 2015 (US Department of
Housing and Urban Development 2016). A roughly equivalent percentage of rental
housing in Lower Manhattan and Williamsburg has been converted, or is at risk of
being converted, into full-time Airbnb use. The second panel of Figure 8 provides the
Wachsmuth and Weisler forthcoming !25
same estimates of Airbnb-induced housing loss in a different context, by taking these
estimates as a percentage of available-for-rent housing, as opposed to all housing. The
pattern is considerably noisier, since it relies on higher-error estimates of housing
which is vacant but for rent, but it is meant to demonstrate that the number of
housing units Airbnb has potentially removed from the long-term housing market
forms a consistently large proportion of the housing stock which would actually be
available to a household looking for an apartment.
Figure 8. Two estimates of the proportion of housing removed or threatened to be removed
from the long-term housing market by Airbnb for the year September 2016 – August 2017:
frequently rented entire-home listings as a proportion of total housing (left) or available-for-
rent housing (right)
Wachsmuth and Weisler forthcoming !26
Besides direct and exclusionary displacement in neighbourhoods with high
levels of short-term rental activity, there is also the prospect that Airbnb has increased
housing costs either in specific neighbourhoods or city-wide. There are two plausible
mechanisms through which this could occur. First, when long-term rentals are
converted to short-term rentals, this reduces the effective supply of available housing,
and should therefore cause prices for the remaining supply to be higher. Furthermore,
since New York’s housing market features extremely low price elasticity of supply
(McLaughlin 2016), these upward price impacts are likely to persist beyond the near
term, as new housing supply cannot easily be introduced in response to the increased
demand from short-term rentals. Second, by increasing the economic potential of
some residential properties, Airbnb should cause purchase prices for these properties
to increase, and hence the overall equilibrium market price to increase also.
It is outside the scope of this paper to directly estimate the impact of Airbnb’s
growth on housing affordability in New York City. To properly control for endogenous
effects, any such analysis would need to be comparative across many different cities in
different geographical and social contexts. Barron et al. (2018) conducted such a study
using a custom dataset of all Airbnb activity in the United States, and concluded that a
10% increase in exogenously-determined Airbnb listings leads to a 0.42% increase in
rents and a 0.76% increase in house prices. Nationwide, they estimate that Airbnb is
responsible for a 1% increase in residential rents and a 2% increase in housing prices
from 2012 to 2016, with the effects concentrated in cities such as New York where
Airbnb activity is highest. Wachsmuth et al. (2018) applied this model to New York City
to estimate that three years of Airbnb growth (from September 2014 to August 2017)
led to an increase of approximately $380 per year in the city-wide median new rent. In
neighbourhoods with very high Airbnb activity growth, this amount is considerably
In summary, there appear to be both concentrated and diffuse impacts of
Airbnb’s rent gaps on New York City. In the areas where short-term rentals have
proliferated, there has been substantial loss of long-term housing, driving both direct
and exclusionary displacement. City-wide, this reduction in effective housing supply
has plausibly translated into a general increase in rents and housing prices.
Wachsmuth and Weisler forthcoming !27
Conclusions: A research agenda for gentrification and short-term
The purpose of this paper has been to analyze the intersection of gentrification
and short-term rentals. Using a case study of New York City, we have argued that
Airbnb has introduced a new potential investment flow into housing markets which is
systematic but geographically uneven, creating a new form of rent gap in culturally
desirable and internationally recognizable neighbourhoods which have generally
already been subject to extensive gentrification. This rent gap can emerge quickly—in
advance of any declining property income—and requires minimal new capital to be
exploited by a range of different housing actors, from developers to landlords, tenants
and homeowners. We now conclude by offering several synthetic observations about
the New York case and a series of themes for future research on gentrification and
short-term rentals, in the hope of developing a more consistent body of knowledge to
inform scholars, policymakers and activists.
The first issue which the New York case study poses is the policy question of
Airbnb’s impact on housing supply in the city. There are two ways of looking at this.
On the one hand, New York City has 2.2 million renter-occupied housing units, and
only 12,200 frequently rented, entire-home Airbnb listings. Therefore, looking at the
total stock of housing at the urban scale, only half of a percent of New York’s rental
housing has been converted to short-term rentals. On the other hand, looking at the
change in housing supply—particularly at the neighbourhood scale—paints a direr
picture. Only 16,300 new housing units were permitted in New York in 2016, while
23,200 units were completed (New York City Rent Guidelines Board 2017). This means
that Airbnb activity has negated something like half to three quarters of a year’s worth
of new housing supply in the city. In the Manhattan submarket, only 4,000 new units
of housing were permitted in 2016, while there were 7,000 frequently rented whole-
unit Airbnb listings on the island. In other words, what appears superficially to be the
construction of new housing supply in the city is to a large extent the production of
new unlicensed hotels.
A second question posed by the New York case is whether the rentier economic
activity facilitated by Airbnb is positive sum or simply redistributive. In other words, is
Airbnb just shifting profit-making opportunities from land sectorally (away from the
hotel industry to Airbnb hosts) and spatially (away from the Midtown Manhattan hotel
district to other parts of the city), or is it driving an overall increase in land rents?
Wachsmuth and Weisler forthcoming !28
There can be little doubt that an increasingly major redistribution of rentier activity is
underway. The $657 million of annual revenue generated by Airbnb in New York City
is still dwarfed by the nearly $10 billion of annual hotel room revenue in the city.
However, hotel revenues have been flat or declining in New York for several years, and
most observers attribute this fact in part to the explosion of short-term rentals in this
time period. Moreover, the hotel industry in New York is highly concentrated around
Times Square in Manhattan (although Brooklyn and Queens hotel activity has been
growing much faster than Manhattan), while Airbnb activity is distributed across a
wider area of Midtown and Lower Manhattan and North Brooklyn. In other words, it is
likely that Airbnb has facilitated a sectoral and spatial redistribution of tourism
spending away from the (spatially concentrated) hotel industry towards the (spatially
dispersed) short-term rental market. Accompanying this redistribution is an increase
in overall land rents in New York, because a much larger (globalized) pool of demand is
bidding for the use of that land. But this effect is likely to be more modest in New York
than in smaller cities, where global demand from transnational gentrification (Sigler
and Wachsmuth 2016) will be proportionally larger (see below). On balance, therefore,
while the rise of short-term rentals in New York implies some expansion of overall
land rents due to expanded global demand, it is appears to be more significantly a
redistributive de facto rezoning of residential areas to commercial hotel use, carried
out by a private corporation.
Our research also raises a number of themes for future research on
gentrification and short-term rentals, as well as the broader landscape of the “sharing
economy”. The first theme is uneven development at both the urban and global scales. As
our examination of New York has demonstrated, short-term rental activity is
distributed in a highly uneven fashion across the urban landscape. In New York the
clusters were most pronounced in the city’s traditional tourism area and in several
neighbourhoods which have not historically been major tourism draws but do have
internationally recognizable cultural cachet. Does this pattern exist in other cities?
Furthermore, the neighbourhoods with the most Airbnb activity are not necessarily
the neighbourhoods where the impact on existing rental housing is strongest—a
situation we captured in New York with the vulnerability index (Figure 7, above).
Understanding geographically-specific vulnerability patterns in other cities is thus an
urgent research task. At a global scale, meanwhile, the question is the differential
exposure of cities to transnational gentrification (Sigler and Wachsmuth 2016):
transnational corporate power facilitating the arrival of transnational tourist demand
Wachsmuth and Weisler forthcoming !29
for local housing. With short-term rentals supplying a large and growing source of
housing demand which is almost completely disconnected from local economies and
labour markets, cities face the prospect of heterarchy in their land markets to the
extent that they are exposed to this demand. Urban researchers should try to
understand both the variation in this exposure and appropriate governance responses
to it.
A related theme is displacement; just as the impact of short-term rentals on
neighbourhoods is geographically uneven, it is also almost certainly socially uneven.
Short-term rentals are removing rental housing from the market, but are conversions
from standard rental apartments to de facto Airbnb hotels more or less likely to
displace existing residents than more traditional forms of gentrification-related urban
redevelopment? Our quantitative empirical analysis of New York was unable to
measure displacement directly, and without observation and qualitative research,
future research will be likewise limited to making neighbourhood-scale inferences
about likely displacement. Yet displacement is ultimately the key moral stakes of
gentrification (Slater 2009) and understanding the extent to which short-term rentals
are displacing people from their homes is a correspondingly vital topic for future
A third issue is everyday life; how are short-term rentals transforming the fabric
of everyday life in the neighbourhoods in which they are proliferating, and at other
spatial scales? The sharing economy is not just a new economic opportunity for its
“users”, but also a new and perhaps unprecedented commodification of everyday life;
as Slee (2016: 10), puts it, the sharing economy “is extending a harsh and deregulated
free market into previously protected areas of our lives”. Understanding the
parameters and implications of this development is a major opportunity for interview-
and ethnography-based qualitative research. Likewise, as we discussed above, short-
term rentals generate new economic incentives among rentiers that potentially
crosscut existing political interests. Interviews with both small-scale and large-scale
Airbnb landlords—along with tenants attempting to host short-term rentals
clandestinely—would help unpack the varied ways in which money is flowing through
housing markets and transforming the private sphere.
A fourth theme is regulation and regulatory conflict. Existing research suggests
that a commonality to the business models of firms in the corporate sharing economy
is disruption of existing governance arrangements more than existing market
structures (Geobey 2018). Accordingly, cities around the world are currently
Wachsmuth and Weisler forthcoming !30
scrambling to develop regulations on short-term rentals, but we still have very little
understanding about which attempts at regulation have proven effective so far, and
which have proven political feasible. Relatedly, as our discussion of Airbnb regulation
in New York has demonstrated, regulators do not always speak with one voice, or even
share basic interests with respect to the so-called sharing economy. Researchers thus
need to understand the political economy of short-term rentals better: what leads
different state and civil society actors to take different positions on how short-term
rentals should be regulated, and what leads them to invest significant resources into
securing their desired outcomes?
The final theme for future research is labour. Despite the label “sharing
economy”, Airbnb—along with Uber, the other leading firm in the corporate sharing
economy—does not actually involve sharing, in the sense of non-monetary exchange
(Eckhardt and Bardhi 2015). Instead Airbnb and Uber have both rolled out a kind of
flexibility-slash-precarity for their users, operators and intermediaries. Uber’s drivers
are “liberated” from the need to obtain expensive taxi medallions, but they are also
“liberated” from union benefits, job security, and regulatory protections (Slee, 2014).
Meanwhile, Airbnb operators frequently outsource cleaning and key management
labour which is generally unionized in the hotel sector, simultaneously rendering this
work more precarious and less visible to guests, who experience short-term rentals as
peer-to-peer exchanges. What are the conditions of labour in the sharing economy?
The explosive growth of Airbnb—from a few hundred thousand nights booked
in 2010 to 25 million in 2015, 50 million in 2016, and 100 million in 2017—makes clear
the urgent need for better understanding the impact of short-term rentals on urban
housing markets and the regulatory options available for controlling them. At their
core, short-term rentals are facilitating a massive and perhaps unprecedented
intensification of the commodification of housing. Airbnb and other “sharing
economy” corporations are transforming our cities, while communities (aided in many
cases by civil society and state actors) are resisting that transformation and articulating
other visions for “sharing” in the city. Critical urban researchers should seize the
opportunity to contribute to these visions.
Wachsmuth and Weisler forthcoming !31
The authors would like to thank David Chaney, Danielle Kerrigan, and Andrea Shilolo
for their research assistance, and Neil Brenner, Ahmed El-Geneidy, Emily Grise, Brian
McCabe, Adrian Phillips, and Benjamin Theresa for their help and feedback with
earlier drafts of this paper.
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Methodological appendix
This appendix describes in detail the data sources used in the paper and the
methodologies employed to analyze the data.
Data sources
The spatial analysis in this paper was conducted using a combination of
proprietary data on Airbnb activity obtained from the consulting firm Airdna and
public data on housing and demographics from the American Community Survey.
Airdna is a firm that specializes in scraping and aggregating data from the publicly
available Airbnb website and aggregating the data they find, and it is one of the two
widely relied upon third-party estimates of Airbnb’s activities. (The other is Murray
Cox’s open-data effort Inside Airbnb.) It would be preferable to do this analysis with
official, accurate data from Airbnb, but the company has historically been secretive
about its data, even when faced with legal requirements,"and when they have released
data, observers have concluded that they have done so in a misleading fashion (Cox
and Slee 2016).
The data provided by Airdna for this study is the complete property file for all
Airbnb listings in the New York–Newark–Jersey City, NY–NJ–PA Metropolitan
Statistical Area (henceforth the “New York MSA”) as of September 1, 2017, along with
the daily activity of each of these properties for the time period September 1, 2014
through August 30, 2017. For the sake of simplicity, the three years in this period are
often shortened in this paper as follows:
- “2015”: September 2014 – August 2015
- “2016”: September 2015 – August 2016
- “2017”: September 2016 – August 2017
The property file includes many listings which are now defunct as well as many
listings which were added to Airbnb only shortly prior to the end of the study period
and therefore haven’t yet generated much or any activity. After data cleaning, the
property file"contains 188,137 listings, 155,558 of which were located in New York City
proper. (Although data was available and analyzed for the entire New York MSA, the
analytical focus was on New York City. One reason for this choice is that the New York
MSA includes substantial summer vacation communities on Long Island and the New
Jersey Shore, which present a completely different type of short-term rental activity
Wachsmuth and Weisler forthcoming !37
that would confound the paper’s city-centric analysis.) Out of this total pool of listings,
85,300 across the MSA and 67,100 in New York City received at least one reservation
between September 2016 and August 2017—the main study period. (Hereafter, figures
which are the result of estimation are rounded off to avoid giving the impression of
perfect accuracy.) When the paper discusses “active” listings, it is these 85,300 or
67,100 to which it is referring. Because of a high rate of churn in listings activity, in any
given month the number of active listings is much smaller; in New York City, each
month there were between 16,100 and 25,700 listings receiving at least one
The entry for each listing in the property file provides a large assortment of
metadata, including:
- The listing type: private room, shared room, or whole-unit
- The location of the listing: latitude and longitude coordinates
- Unit details:"the number of bedrooms and bathrooms, and the maximum
number of guests
- Rental policies: the cancellation policy and security deposit, the cleaning fee,
check-in and check-out times, etc.
- Other details: the listing URL, the number of photos included in the
The daily activity file provides, for each property, the following information for
each day:
- Occupancy status: available, reserved, or blocked
- Price: listed nightly price
- Reservation ID: if the property is reserved, an ID number for the reservation
which can be used to calculate the length of individual reservations
With the exception of occupancy status (and hence reservation ID), all these
variables are directly observed from the Airbnb website, and thus completely accurate.
For 2014 and most of 2015, the occupancy status data was also taken directly from
Airbnb. But at the end of 2015, Airbnb stopped disclosing when a non-available
property was reserved or was simply blocked from new reservations, which made it
impossible to precisely measure occupancy. In response, Airdna developed a machine
learning model to estimate this information based on a combination of its existing
Wachsmuth and Weisler forthcoming !38
historical dataset of activity and other information which remained publicly available
(e.g. reviews and ratings). While the activity dataset for 2016 and 2017 therefore cannot
be fully accurate, it is the most accurate third-party estimate available. Moreover, our
use of this dataset in the paper is for the most part limited to relative comparison of
different neighborhoods (e.g. which neighborhood has relatively high amounts of
Airbnb revenue). Since the estimates were produced with a consistent methodology,
there should be relatively little risk of high levels of spatially autocorrelated error.
Airbnb is the largest home-sharing platform, with a particular dominance in
cities, but it is still only one of several large corporate players in this industry. Since we
do not have data for competitors (most significantly VRBO, HomeAway, and, the result is that all of our estimates of the size and impact of “home
sharing” will be systematic under-estimates. Since the thrust of the paper’s argument
is that short-term rentals are having an important impact on New York’s housing
market, this should strengthen the force of our conclusions to the extent that we are
able to demonstrate, with only a subset of the entire market data, that these impacts
are real.
Determining the spatial location of listings
Airbnb provides exact latitude and longitude coordinates for each listing, but it
is well known that these coordinates have been obfuscated to protect user privacy.
Using a dataset of known Airbnb locations, we experimentally determined that this
obfuscation is a random shift in the listing’s coordinates by 0 to 150 m. While this
amount may initially appear small, for census-tract level analysis it is potentially fatal,
because of the high possibility that a listing apparently located in census tract A may
actually have originated in census tract B prior to the spatial obfuscation. In fact,
145,300 listings in New York City—which amount to 93.4% of the total—fall within 150
m of a census tract boundary, and therefore would potentially be misidentified for
census-tract level analysis.
To address this problem of spatial imprecision, the first author developed a
method for producing more reliable estimates of the actual location of a listing, given
its reported latitude and longitude coordinates (which is also used in Wachsmuth et al.
2017, 2018). The method is based on the idea of dasymetric mapping, where
population density maps enumerated at a relatively coarse spatial scale can be
improved by mediating them through land use data. Given that a listing’s true location
must lie within a 150-m-radius buffer surrounding the reported coordinates, we
Wachsmuth and Weisler forthcoming !39
exploit the fact that an Airbnb listing must be located in an actual unit of housing to
transform the buffer into a probability surface weighted by the density of housing
units. The weighting is performed at the smallest feasible census geography, the block
group. Each listing is then randomly assigned an originating block group from its
probability surface, and the results are aggregated at the census scale for analysis.
Seasonal adjustment of Airbnb data
Figure 3 in the paper presents monthly trends for listing and revenue growth.
Any such attempt to measure growth trends of short-term rental activity must contend
with the fact that this activity is highly seasonal. In order to identify underlying trends,
we constructed seasonality indices for each variable analyzed in the paper. Using the
“ratio-to-moving-average” method, we calculated seasonal indices for the 35-month
period October 2014 to August 2017. The values for the active-listing and revenue
indices are indicated in Figure A1.
Monthly reserved listings Monthly revenue
Figure A1. Seasonality curves for Airbnb activity in New York
Wachsmuth and Weisler forthcoming !40
Calculating the proportion of total residential contract rent generated from
The indicator used in the paper to estimate rent gaps which have opened and
been (partially or completely) closed is the proportion of total residential contract rent
generated from Airbnb. This indicator is calculated on a per-census-tract basis, and
takes the form:
The numerator is the sum of all host revenue earned on Airbnb in a given
census tract over a given year. The denominator is this sum plus the amount of
contract rent generated in the long-term rental market, as measured by the American
Community Survey. The specific measure used was “aggregate gross rent” (2015 ACS
five-year estimates, table B25065), calculated at the census-tract scale. Gross rent is the
sum of the contract rent and any"utility payments not included in the contract rent,
and is provided in order to increase comparability between cases where utilities are
included in the rent and where they are not. Including utility payments in the measure
of long-term rents weakens the comparability of long-term rents with Airbnb host
revenue, since it overstates the actual revenue long-term landlords receive by bundling
their own revenue with money that will be passed along (or paid directly) to utility
providers. However, the benefits of having a consistent measurement of long-term
rents between neighbourhoods outweighs this downside. Moreover, the effect of
including utility payments will be to underestimate the share of rent payments which
are generated through Airbnb, so this choice is a conservative one. In Figure 5, the
indicator is calculated for 2015 (i.e. September 2014 August 2015) and 2017 (i.e.
September 2016 August 2017), and data for census tracts with fewer than three
revenue-earning Airbnb listings in the relevant time period is not displayed.
Calculating the proportion of neighborhood median long-term contract rent
earned on average by hosts of frequently rented entire-home listings on Airbnb
The indicator used to estimate rent gaps which have opened and are not (yet)
closed is the proportion of neighborhood median long-term contract rent earned on
average by hosts of frequently rented entire-home listings on Airbnb. This is
calculated on a per-census-tract basis, and takes the form:
Re n tAirbnb
Re n tAirbn b +Re ntLongter m
Wachsmuth and Weisler forthcoming !41
The numerator is the average annual revenue earned by a frequently rented
entire-home listing in a given census tract. The denominator is “median gross rent”
from the American Community Survey (2015 ACS 5-year estimates, table B25064). The
intuition guiding this variable construction is that, in the absence of strong policies to
prevent property owners from converting long-term rentals to short-term rentals, a
rough revenue equilibrium should emerge between the two. A landlord earning $2,000
per month in rent for an apartment in a neighbourhood where they could earn $4,000
per month on Airbnb will have a strong incentive to convert to a short-term rental.
This is a rent gap. If enough landlords take advantage of these opportunities, we would
expect 12-month rents to rise somewhat (in response to demand-side competition for
a shrinking stock"of rental units) and Airbnb rates to fall somewhat (in response to
supply-side competition for a relatively fixed tourist demand). Some time later, we
might find that median rents have risen to $2,400 and average Airbnb revenues have
fallen to $2,800. Now the rent gap is smaller, and there will be less pressure on
landlords to convert long-term rentals to short-term rentals.
The Airbnb gentrification vulnerability index
The Airbnb gentrification vulnerability index combines the two previous
indicators into a single synthetic picture of the areas of New York where rent gaps have
opened and the areas where rent gaps have closed. Three regions are indicated in
Figure 7: areas of high current impact, areas at high risk of future impact, and areas of
both high current impact and future risk. Areas of high current impact were defined as
those census tracts whose Airbnb revenue as a proportion of total rental revenue
(indicator 1) was more than two standard deviations higher than the regional mean.
Areas at high risk of future impact were defined as those census tracts belonging to
statistically significant clusters of high average revenue earned by frequently rented
entire-home listings on Airbnb in proportion to neighborhood median long-term
contract rent (indicator 2). Cluster analysis (using an Anselin local Moran’s i) was used
to mitigate the noisiness of the underlying pattern: the selected areas were high-high
clusters. Areas of both high current impact and future risk were defined as those
census tracts meeting both of the previous criteria.
AvgRentLongter m
Wachsmuth and Weisler forthcoming !42
“Non-white households” in the second panel of Figure 7 were calculated as
The numerator is the total of owner- and renter-occupied households from the
American Community Survey table “Tenure (white alone, not Hispanic or Latino
householder)” (2015 ACS 5-year estimates, table B25003H). The denominator is the
variable “occupied” from the American Community Survey table “Occupancy
status” (2015 ACS 5-year estimates, table B25002).
Housing lost to Airbnb
While in theory a “full-time” Airbnb rental is one for which there is no primary
occupant (tenant or owner) living in the unit year-round, in practice it is impossible to
verify this status unit by unit. Instead, attempts to estimate Airbnb’s impact on
housing markets generally choose an occupancy threshold beyond which a unit is
considered unlikely to be occupied by a long-term resident. Inside Airbnb (2017), for
instance, defines “frequently rented” units in New York City as those rented on Airbnb
for 60 or more days per year, arguing that “Entire homes or apartments highly
available and rented frequently year-round to tourists, probably don’t have the owner
present, are illegal, and more importantly, are displacing New Yorkers”.
We define two occupancy thresholds to estimate conversions from long-term
housing to short-term rentals. We use the term “frequently rented” to describe listings
rented at least 60 nights a year, and available for rent at least 120 nights a year. Sixty
days of occupancy rules out most scenarios of occasional short-term rental, such as a
landlord taking advantage of a one-month gap between long-term tenants, or a family
leaving on a one-month summer vacation. Setting an additional constraint of 120 days
of availability prevents the inclusion of listings which are rented relatively infrequently
but with extremely high efficiency; for example, a homeowner who was out of town
every weekend and listed their unit on Airbnb would only have 104 days of availability,
and so would not be counted as “full-time” by our criteria even if they managed to rent
the unit for 60 of those days. We use the term “very frequently rented” to describe
listings rented at least 120 nights a year, and available for rent at least 240 nights a year.
While it is likely that very few frequently rented listings can also house long-term
resident, it would be nearly impossible for a very frequently rented listing to have a
1Un it sWhite
Un it sOccupied
Wachsmuth and Weisler forthcoming !43
long-term resident, since these listings are on Airbnb for at least 8 months of the year
and have short-term tenants for at least 4 months. Figure A2 shows the count of
entire-home listings in New York City which meet each of these definitions of
“frequently rented” plus a set of several other definitions.
The first panel of Figure 8 shows the proportion of each census tract’s housing
stock composed of frequently rented entire-home listings. This is calculated as follows:
The denominator is the total number of housing units, occupied or not, in a
census tract, as given by the American Community Survey (2015 ACS 5-year estimates,
table B25001).
The second panel of Figure 8 shows the proportion of each census tract’s
“available for rent” housing stock composed of frequently rented entire-home listings.
This contextualizes Airbnb’s impact on housing availability more realistically from the
perspective of a household searching for an apartment. The proportion is calculated as
L i st i n gsRented 60, Availa ble120
Un it sTotal
60 days reserved
60 reserved, 120 available
90 reserved, 120 available
120 reserved
120 reserved, 240 available
7,5 00
Figure A2. Entire-home listings in New York City at different thresholds of “frequently
Wachsmuth and Weisler forthcoming !44
The denominator is meant to capture all the housing which is either available
for rent or would be available if it were not being rented short-term on Airbnb. The
second term is the variable “for rent” from the American Community Table “Vacancy
status” (2015 ACS 5-year estimates, table B25004).
Cox M and Slee T (2016) How Airbnb’s Data Hid the Facts in New York City. Policy
report. Available online at <
the-facts-in-new-york-city.pdf>. Last accessed July 4, 2017.
Wachsmuth D, Chaney D, Kerrigan D, Shillolo A, and Basalaev-Binder R (2018) The
High Cost of Short-term Rentals in New York City. Policy report. Urban Politics and
Governance research group, School of Urban Planning, McGill University.
Wachsmuth D, Kerrigan D, Chaney D, and Shillolo A (2017) Short-term Cities:
Airbnb’s Impact on Canadian Housing Markets. Policy report. Urban Politics and
Governance research group, School of Urban Planning, McGill University.
L i st i n gsRented 60, Availa ble120
L i st i n gsRented60, Avai la ble120 +Un i t sVacan tForRent
... A túlzott turizmussal súlytott városokban a legtöbb konfliktust az éjszakai zaj okozta (Dirksmeier & Helbrecht 2015, Pinkster & Boterman 2017, amely összefüggésben áll a parti-és alkoholturizmussal (Füller & Michel 2014, Smith et al. 2017, Sommer & Helbrecht 2017. Jellemző probléma továbbá a közterületek túlzsúfoltsága (García-Hernández et al. 2017, Vianello, 2016; a szemetelés (Sommer & Helbrecht 2017); dzsentrifikáció (Gravari-Barbas & Guinand 2017); valamint a lakásárak emelkedése és a lakók elköltözése (Mermet 2017, Wachsmuth & Weisler 2018. Az elmúlt években számos esettanulmány is megjelent, amelyek dokumentálják a helyi lakosok egyre növekvő tiltakozását és ellenállását a városi desztinációkban (Colomb & Novy 2016, Vianello 2016, Pinke-Sziva et al. 2019a. ...
... Az árnyaltabb kép megismerése lényeges és fontos megjegyezni, hogy nem minden kutató csatlakozik ahhoz az elképzeléshez, hogy kizárólag a turizmus okozza az overtourism jelenséget. Ennek egyik egyértelmű példája a dzsentrifikáció, ahol a turizmus szerepe vitatott (Gravari-Barbas & Guinand 2017), bár tény, hogy a turisztikai aktivitás súlyosbíthatja a meglévő dzsentrifikációt különösen az Airbnb eredményeként (Wachsmuth & Weisler 2018). Az éjszakai gazdaság (Night Time Economy -NTE) a turizmus szempontjából is vitatott terület, mivel az éjszakai élet a városokban a turizmustól függetlenül létezik, és ennek az éjszakai zaj elkerülhetetlen következménye. ...
A TANULMÁNY CÉLJA A globális méreteket öltött koronavírus-járvány alapjaiban változtatja meg a turisztikai desztinációk és azok közül is főként a nemzetközi vendégforgalomra építkező célterületek működését. Az utazási korlátozások következtében szignifikánsan csökkent a nemzetközi turistaérkezések száma Budapesten 2020-ban. Az elmúlt években a főváros nagy mértékben a külföldi vendégekre alapozta a költségvetését, ezért a külső hatásoknak rendkívül kitetté vált. A tanulmányunk célja, hogy képet adjon a városi turizmus pandémia által okozott kríziséről és abból való kiút egyik lehetőségének, a belföldi utazók megcélzásának vizsgálatát. ALKALMAZOTT MÓDSZERTAN A főváros külföldi turistáknak való kitettsége a belföldi turisták Budapestre csábításával ellensúlyozható, amelyhez meg kell ismerni a főváros turisztikai szolgáltatásait potenciálisan igénybe vevők körét: a belföldi látogatókat. Ezen célcsoport tagjai eltérő élményeket keresnek a fővárosba történő látogatásuk során, melyet egy több mint 100 válaszadót elérő kvalitatív kutatás keretében ismertünk meg. A kutatás fő célja az volt, hogy feltárjuk az egyes korcsoportok véleményét a fővárosról, a fővárosba történő utazásaikat támogató és hátráltató tényezőkről és a számukra vonzó élményígéretekről. LEGFONTOSABB EREDMÉNYEK Láthatóvá vált a kutatás során, hogy a belföldiek, különösen a pandémia előtt legalább évente, de akár többször is felkeresték a fővárost, de jellemzően nem éjszakáznak itt. A pandémia leginkább a gyakori látogatók szokásait változtatta meg, ők havi rendszerességet követően inkább félévente érkeztek a fővárosba. A fővárosról alkotott kép meglehetősen változatos, inkább az idősebb és a fiatal felnőttek rendelkeznek pozitív imázsképpel. A motivációk sorában a kulturális attrakciók, rendezvények és a barátok látogatása mindenképpen fontos, amíg a gátló tényezők között a város drágasága, a tömeg és nagyvárosokkal szembeni ellenérzés, valamint a korlátozott szabadidő jelenik meg. GYAKORLATI JAVASLATOK Eredményeink alapján feltárásra kerülhetnek azon turisztikai termékek és élményelemek, melyek fejlesztése hosszú távon növelheti Budapest belföldi turisztikai keresletét.
... The industries of individual mobility, housing, and hospitality services have been deeply involved in the process of innovation (more or less disruptive; Guttentag, 2015) related to the diffusion of digital platforms (Fields, Rogers, 2019). In this sense, Uber (in the market of vehicles for hire with a driver) and Airbnb (in the market of short-term rental) might be considered paradigmatic cases, attracting special attention in the empirical research (Barron et al., 2021;Cocola-Gant, Gago, 2019;Wachsmuth, Weisler, 2018). Both Uber and Airbnb entered the traditional markets of services offering a limited product differentiation with respect to the incumbents. ...
... There is a flourishing literature on the sharing economy as an urban phenomenon, pertaining both to the advantages that the city offers to the location of its activities and the (possibly negative) external effects of platforms. Focusing on Airbnb, a first strand of the literature concentrates on the Airbnb-led gentrification in several cities (see for instance : Wachsmuth, Weisler, 2018;Cocola-Gant, Gago, 2019) and the relationship between the increase in Airbnb and the increase in rental rates and house prices (see for instance : Horn, Merante, 2017;Garcia-López et al., 2019;Barron et al., 2020); a second strand of the literature highlights the role of population density, spatial proximity, and socio-economic specialization (Rauch, Schleicher, 2005;Davidson, Infranca, 2016) in attracting the location of Airbnb listings. With specific reference to the advantages of agglomeration, reinterpreted by Duranton and Puga (2004) as mechanisms of sharing, matching, and learning, Davidson and Infranca (2016) claim that the urban character of the sharing economy refers both to the localization of practices and to the role of platforms as agents of urban transformation. ...
Airbnb has been at the forefront of the reshaped hospitality industry for more than a decade. The Covid-19 pandemic, by limiting people’s mobility, radically affected tourism and hospitality sectors with a drastic reduction in the apparently incessant advance of Airbnb spread, in Italy and beyond. As data in 2020 are not yet fully available, this article depicts the spatial distribution of Airbnb in Italy in 2017-19 as a basis situation for future analysis inquiring the effects of the pandemic. The study is conducted by using a dynamic panel model, with GMM-SYS estimation. Results show that, despite sharing economy is proposed as fair and equipotential, Airbnb turns out to be highly selective. The evidence indicates that «access» alone, even if favored by platforms, does not guarantee market power, and performances are much more concentrated than listings. Moreover, the urban appeal of Airbnb is confirmed; traditional hospitality turns out to be a significant predictor of Airbnb presence and performances; the economic condition of unemployment is positively associated with Airbnb presence.
... The changes observed were, according to many, contradictory to the basic principles of the sharing economy [16]. The so-called "platform economy" [17] fuelled tourism, the financialization of housing and gentrification, including in previously non-touristic neighbourhoods (e.g., [18,19]). Prior to the pandemic, the rapid proliferation of STRs in cities began to feature more heavily in the discussion of overtourism, including in Polish cities (e.g., [20,21]). ...
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The article presents the impact of the COVID-19 pandemic on urban tourism activity on short-term rental (STR) platforms in Central and Eastern Europe (CEE). It offers empirical evidence of how Airbnb and HomeAway (Vrbo) changed in Polish cities during the COVID-19 pandemic. A case study of Polish cities was also used to investigate what pandemic-induced scenarios of that impact are presented in the literature. In particular, the study identifies “loser” cities, in which the pandemic consolidated and deepened the decline in active STR volume, and “winner” cities, in which the volume and dynamics of the active STRs increased during the pandemic.
... Local neighborhoods that typically do not receive much tourist expenditure may benefit from Airbnb users' dispersed spending [40]. Wachsmuth and Weisler [75] pointed out that Airbnb listings tend to be geographically uneven, creating new rent gaps in culturally desirable neighborhoods. ...
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This study explored key dimensions of customers’ experiences with peer-to-peer (P2P) accommodations through Airbnb in China and developed a corresponding measurement scale. Thirty-four in-depth interviews were conducted with Airbnb customers. A pilot study and main survey were implemented to collect data. Exploratory factor analysis was performed, and five factors related to the customer experience were extracted: tangible and sensory experiences, host, cultural experience, interaction with peer guests, and location. Confirmatory factor analysis was performed using data gathered from the main survey. This study contributes to the literature by capturing customers’ holistic experiences in the Chinese market via empirical testing. Theoretical and practical implications are also discussed.
... Il s'agit du phénomène de l'érosion de l'espace de logement par sa substitution avec des appartements de brève location (en particulier via Airbnb) ou des hôtels. Ces processus d'hôtelisation et d'airbnbisation intéressent les villes, en particulier les métropoles à haute vocation touristique, par exemple Los Angeles (Lee 2016), Barcelone (Garcia-López et autres 2019) ou New York (Wachsmuth et Weisler 2018), où l'étalement hors contrôle de ces habitations produit un embourgeoisement par le tourisme (Schild 2018) ayant un effet déterminant sur le prix des logements. ...
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Notre thèse principale est que les rituels peuvent favoriser l’inclusion si l’on met l’accent sur leur com- munalité, sur leur aspect transformateur et sur leur convivialité, qui est nécessaire pour que ces derniers se reproduisent. Pour démontrer cette thèse, nous avons analysé et comparé huit rituels urbains (à Genève, Montréal et Turin). Le livre s’achève sur des recommandations aux responsables des politiques urbaines.
... L'obbligo di registrazione degli alloggi in affitto breve (Parigi), la sospensione del rilascio di licenze per gli appartamenti turistici nel centro storico (Barcellona), il limite degli affitti brevi ad un massimo di due mesi annui e l'introduzione di un registro pubblico (Amsterdam), la sospensione dei permessi per casevacanza (Berlino) sono alcune delle misure introdotte da città europee (Wachsmuth & Weisler, 2018;Adamiak, 2018). ...
Il capitolo illustra, nello scenario nazionale e internazionale, il fenomeno della sharing economy per il turismo è strettamente legata ai modelli della cosiddetta "shadow hospitality", diffusi su piattaforme peer-to-peer quali Airbnb. Questa nuova tendenza del turismo ha attirato l'attenzione di studiosi, operatori, attivisti ed istituzioni per l'impatto che genera sulle comunità locali, sugli spazi e servizi pubblici, così come sul patrimonio culturale tangibile ed intangibile. Il cosiddetto "effetto Airbnb" sta influenzando il settore abitativo, la tipologia delle strutture commerciali e le tradizioni produttive locali, provocando sovente processi di gentrification ed erosione del genius loci. L'espulsione degli abitanti e delle attività economiche radicate nelle tradizioni produttive locali dalle aree centrali, più attrattive per la fruizione turistica, verso aree periferiche sempre più marginalizzate, genera profonde sperequazioni, deteriora la pax sociale e altera il carattere urbano frutto di stratificazioni e contaminazioni culturali. L'obiettivo del capitolo è riflettere sul ruolo delle politiche locali e nazionali nel "governare" il processo in atto, bilanciando gli effetti negativi illustrati con le opportunità di sviluppo locale e di valorizzazione del patrimonio culturale che l'industria del turismo può offrire. Si partirà, quindi, da una breve introduzione dello scenario della turistificazione e del suo impatto sugli equilibri territoriali ed urbani. In tale contesto si focalizzerà l'attenzione sul ruolo della cosiddetta shadow hospitality, per concludere introducendo una prospettiva di ricerca mirata a delineare un possibile modello di governance in grado di bilanciare le sperequazioni che si stanno creando all'interno delle città e tra territori sovraesposti e aree marginalizzate.
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Clustering of tourism-related activities leads to tourism gentrification because of urban tourism. This study proposes a methodology for identifying pattern of tourism intensification in cities. The main objectives were to conduct an in-depth measurement of tourism intensification and to consider the conflict of interest between stakeholders involved. The proposed indicator system is based on several variables measured by neighbourhoods as territorial units: citizen initiatives, population and tourist accommodations and facilities. All indicators were tested in two Spanish port cities: Malaga and Valencia. Geographic Information System mapping showed common patterns in both cities, such as tertiary use concentrated in the old town, new tourist offers around it, and emerging clusters far away. Statistical analysis showed a high spatial correlation between neighbourhoods with several citizen initiatives and a wide range of Airbnb listings or cultural facilities. The spatial analysis and correlations between tourism intensification indicators may serve as a basis for local administrations to propose an urban policy that deals with overtourism in the short and medium terms. This work serves as a basis for new research on urban tourism: redefinition and addition of variables, as well as further development of comparative case studies or field studies that check the datasets.
To explain the continuous hold of a single touristic real estate investor over the greater part of Crete's easternmost peninsula, Cape Sidero, for a period of over thirty years, this article examines the production of rent gaps on ‘exceptional’ rural land through increasing potential rent rather than a falling capitalized rent. I examine Neil Smith's ‘alternative’ rent gap hypothesis as it applies to two main factors: the production of and sustained control over land of monopolistic quality, which has no fixed value and is resistant to depreciation; and the dramatic neoliberal reworking of land markets through institutional and legislative changes, which produce legally ‘exceptional’ spaces. I employ the conceptual lens of the rent gap to examine how opening up a rent gap on ‘exceptional land’ based solely on the promise of (re)development can be a sufficient driver of land dispossession. Simply sustaining this promise can perpetuate land with monopolistic quality as a site of rent‐generating possibility, and while this process might never lead to (re)development, it can result in the submersion of the landscape into a captive, limbo state, stealing its future.
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In this introductory essay, we explore definitions of the ‘sharing economy’, a concept indicating both social (relational, communitarian) and economic (allocative, profit-seeking) aspects which appear to be in tension. We suggest combining the social and economic logics of the sharing economy to focus on the central features of network enabled, aggregated membership in a pool of offers and demands (for goods, services, creative expressions). This definition of the sharing economy distinguishes it from other related peer-to-peer and collaborative forms of production. Understanding the social and economic motivations for and implications of participating in the sharing economy is important to its regulation. Each of the papers in this special issue contributes to knowledge by linking the social and economic aspects of sharing economy practices to regulatory norms and mechanisms. We conclude this essay by suggesting future research to further clarify and render intelligible the sharing economy, not as a contradiction in terms but as an empirically observable realm of socio-economic activity.
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In this paper, I explore the impacts of holiday rentals in the historic centre of Barcelona. The intention is to contribute towards a conceptualisation of this unexplored phenomenon with the aim of better understanding why it represents the new gentrification battlefront in several tourist destinations. I suggest that the rhetoric of the sharing economy conceals the fact that holiday rentals are actually a new business opportunity for investors, tourist companies and individual landlords and, for this reason, long-term residents represent a barrier to capital accumulation. I show that there is an increasing conversion of housing into accommodation for visitors and that such conversion involves different forms of displacement. Importantly, when residents move out, the only buyers tend to be tourist investors. In such a context, I suggest that the growth of vacation flats produces conditions that solely enable the reproduction of further accommodation for visitors, rather than for long-term residential use. I call this process 'collective displacement', that is to say, a substitution of residential life by tourism. Ultimately, throughout this paper I suggest the importance of undertaking critical research relevant to those experiencing urban inequalities. Documenting and producing data about the way in which displacement takes place can be a crucial political tool for those who are fighting for staying put.
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At the beginning of the twenty-first century, proclamations rang out that gentrification had gone global. But what do we mean by 'gentrification' today? How can we compare 'gentrification' in New York and London with that in Shanghai, Johannesburg, Mumbai and Rio de Janeiro? This book argues that gentrification is one of the most significant and socially unjust processes affecting cities worldwide today, and one that demands renewed critical assessment. Drawing on the 'new' comparative urbanism and writings on planetary urbanization, the authors undertake a much-needed transurban analysis underpinned by a critical political economy approach. Looking beyond the usual gentrification suspects in Europe and North America to non-Western cases, from slum gentrification to mega-displacement, they show that gentrification has unfolded at a planetary scale, but it has not assumed a North to South or West to East trajectory the story is much more complex than that. Rich with empirical detail, yet wide-ranging, Planetary Gentrification unhinges, unsettles and provincializes Western notions of urban development. It will be invaluable to students and scholars interested in the future of cities and the production of a truly global urban studies, and equally importantly to all those committed to social justice in cities.
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Drawing upon the case of Panama's Casco Antiguo, this paper establishes the theoretical concept of 'transnational gentrification': a process of neighbourhood change both enabled by and formative of a spatially embedded transnational 'gentry' whose locational mobility creates new possibilities for profitable housing reinvestment in geographically disparate markets where such possibilities would not have otherwise existed. Globalisation does not just create a common political-economic structure driving urban change or a common ideology for a gentrifying cohort. In this case, it creates historically and geographically specific connections between places, which themselves can become pathways along which gentrification processes propagate, connecting local capital to international consumer demand. The case of the Casco Antiguo offers a provocative inversion of a standard critical narrative of globalisation, whereby capital is freed from national constraints and able to roam globally while people largely remain place-bound. In the Casco Antiguo, residents are transnational and proper ty developers are local. It has now been more than a decade since Neil Smith (2002) first described gentrifica-tion as a 'global strategy', and the transfor-ma tion in scholarly understandings of what is now widely also seen as a global phenomenon in that it is geographically ubiquitous (Atkinson and Bridge, 2005); found at nearly had previously been understood as a local process has been remarkable. At the mo st basic level, of course, gentrification is a local process rooted in neighbourhood-scale social class dynamics and transformations of me tropolitan-scale property markets, but it
In an experiment on Airbnb, we find that applications from guests with distinctively African American names are 16 percent less likely to be accepted relative to identical guests with distinctively white names. Discrimination occurs among landlords of all sizes, including small landlords sharing the property and larger landlords with multiple properties. It is most pronounced among hosts who have never had an African American guest, suggesting only a subset of hosts discriminate. While rental markets have achieved significant reductions in discrimination in recent decades, our results suggest that Airbnb's current design choices facilitate discrimination and raise the possibility of erasing some of these civil rights gains.
Peer-to-peer markets, collectively known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. The authors explore the economic impact of the sharing economy on incumbent firms by studying the case of Airbnb, a prominent platform for short-term accommodations. They analyze Airbnb's entry into the state of Texas and quantify its impact on the Texas hotel industry over the subsequent decade. In Austin, where Airbnb supply is highest, the causal impact on hotel revenue is in the 8%-10% range; moreover, the impact is nonuniform, with lower-priced hotels and hotels that do not cater to business travelers being the most affected. The impact manifests itself primarily through less aggressive hotel room pricing, benefiting all consumers, not just participants in the sharing economy. The price response is especially pronounced during periods of peak demand, such as during the South by Southwest festival, and is due to a differentiating feature of peer-to-peer platforms-enabling instantaneous supply to scale to meet demand.
In this paper I recapitulate the origins, structure, and purpose of Neil Smith's rent gap theory, and assess the frequently discussed but rarely dissected empirical studies of rent gaps, in order to trace the key analytical and political shifts Smith effected (from consumer preference to mortgage capital circulation, from “natural areas” to state structures, from house prices to capital depreciation, and from middle class demand to class struggle), as well as posit some possible extensions of the theory vis à vis territorial stigmatisation and displacement. This tracing and extending in place, I then consider the rent gap in the context of the emerging body of work on planetary urbanisation, and argue that the theory helps to expose and confront new geographies of structural violence—planetary rent gaps—where the constitutive power of speculative landed developer interests in processes of capitalist urbanisation can be analysed and challenged. If, as David Harvey has recently argued, rent “has to be brought forward into the forefront of analysis … [to] bring together an understanding of the ongoing production of space and geography and the circulation and accumulation of capital” (2010:183, The Enigma of Capital and the Crises of Capitalism, Profile), then it is important to consider what we can learn from the rent gap today, rather than relegate it, as so many seem to do, to something that has already been debated or exhausted in the large literature on gentrification.