Airbnb and the Rent Gap: Paper forthcoming in Environment and Planning A

Figure 7

Figure 7 from the paper: an Airbnb gentrification vulnerability index of New York City

I’m happy to announce that my paper with Alex Weisler, “Airbnb and the Rent Gap: Gentrification Through the Sharing Economy” has now been accepted in Environment and Planning A: Economy and Space, and will be published soon. The paper argues that short-term rentals have facilitated a form of gentrification that does not require redevelopment, by introducing a major new revenue flow into urban land markets. We use spatial analysis to precisely measure the impacts of Airbnb on NYC’s land and housing markets, and discuss the implications.

Here is the 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 economy.

The paper also includes an extensive methodological appendix, which should allow any other researcher to replicate or extend our methodology in other cities, and provides transparency about how we reached the conclusions we did.

Finally, I want to note that my experience with peer review on this paper was the best I’ve ever had. We received 3 detailed reviews that thoughtfully engaged with the paper’s arguments, and the final paper is way better than it would’ve been without the reviews.

The final author draft of the paper is freely available to download.

Green and Gray: New Ideologies of Nature in Urban Sustainability Policy

Wachsmuth and Angelo 2018, Figure 4

“Green surface, gray substrate” in Masdar City (source: LAVA)

I’m pleased to announce that a new paper by Hillary Angelo and me has just been published in the Annals of the American Association of Geographers. It’s called “Green and Gray: New Ideologies of Nature in Urban Sustainability Policy”, and it reflects nearly a decade of thinking and research on our part.

In a nutshell, the paper argues that when policymakers, planners and other policy actors talk about “urban sustainability”, they are actually drawing on two quite different underlying ideas about cities and the environment. We call these “green urban nature” and “grey urban nature”. Green urban nature is the return of verdant, living nature to the city—trees, gardens, and postindustrial greening. Gray urban nature is the idea of the city as inherently sustainable thanks to its concentration of people and technology, embodied in urban density schemes, public transit, and energy-efficient buildings.

We use brief case studies of sustainability planning in three global urban contexts (the Ruhr Valley, Germany; Vancouver, Canada; and Masdar City, Abu Dhabi) to demonstrate several different concrete configurations of green and gray urban nature. One of them is what we call “green surface, gray substrate”—substantively high-tech (“gray”) urban sustainability policy which is dressed up with green visual signifiers to help intuitively communicate its environmental content.

Here’s the abstract of the paper:

In the past two decades, urban sustainability has become a new policy common sense. This article argues that contemporary urban sustainability thought and practice is coconstituted by two distinct representational forms, which we call green urban nature and gray urban nature. Green urban nature is the return of nature to the city in its most verdant form, signified by street trees, urban gardens, and the greening of postindustrial landscapes. Gray urban nature is the concept of social, technological, urban space as already inherently sustainable, signified by dense urban cores, high-speed public transit, and energy-efficient buildings. We develop Lefebvre’s ideas of the realistic and transparent illusions as the constitutive ideologies of the social production of space to offer a framework for interpreting contemporary urban sustainability thinking in these terms and concretize this argument through case studies of postindustrial greening in the Ruhr Valley, Germany; municipal sustainability planning in Vancouver, Canada; and the Masdar smart city project in Abu Dhabi. We conclude by examining the implications of green and gray urban natures for the politics of urban sustainability.

The final author draft of the paper is freely available to download. The Version of Record of this manuscript has been published and is available in the Annals of the American Association of Geographers, 22 February 2018.

The High Cost of Short-term Rentals in New York City

Screen Shot 2018-01-30 at 13.09.53 copy.png

I’m thrilled to announce the release of my research team’s new report, “The High Cost of Short-term Rentals in New York City”. The report was conducted by myself and the graduate researchers I supervise in UPGo, the Urban Politics and Governance research group at McGill.

The report provides a comprehensive analysis of Airbnb activity in New York City and the surrounding region in the last three years (September 2014 – August 2017). Relying on new methodologies to analyze big data, we set out to answer four questions:

  1. Where is Airbnb activity located in New York, and how is it changing?
  2. Who makes money from Airbnb in New York?
  3. How much housing has Airbnb removed from the market in New York?
  4. Is Airbnb driving gentrification in New York?

Our key findings are as following:

  • Two Thirds of Revenue from Likely Illegal Listings: Entire-home/apartment listings account for 75% ($490 million) of total Airbnb revenue and represent 51% of total listings. 87% of entire-home reservations are illegal under New York State law, which means that 66% of revenue ($435 million) and 45% of all New York Airbnb reservations last year were illegal.
  • 13,500 Units of Lost Housing: Airbnb has removed between 7,000 and 13,500 units of housing from New York City’s long-term rental market, including 12,200 frequently rented entire-home listings that were available for rent 120 days or more and 5,600 entire-home listings available for rent 240 days or more.
  • $380 More in Rent: By reducing housing supply, Airbnb has increased the median long-term rent in New York City by 1.4% over the last three years, resulting in a $380 annual rent increase for the median New York tenant looking for an apartment this year. In some Manhattan neighborhoods the increase is more than $700.
  • 4,700 Ghost Hotels: There are 4,700 private- room listings that are in fact “ghost hotels” comprising many rooms in a single apartment. These ghost hotels have removed 1,400 units of housing from the long-term rental market, and are a new tactic for commercial Airbnb operators to avoid regulatory scrutiny.
  • 28% of Revenue: Commercial operators that control multiple entire-home/apartment listings or large portfolios of private rooms are only 12% of hosts but they earn more than 28% of revenue in New York City.
  • Top 10% of Hosts: The top 10% of hosts earned a staggering 48% of all revenue last year, while the bottom 80% of hosts earned just 32%.
  • 200% and $100K More: The median host of a frequently rented entire-home/apartment listing earned 55% more than the median long-term rent in its neighborhood last year. This disparity between short-term and long-term rents is driving Airbnb-induced housing loss and gentrification. Nearly 300 unique listings earned $100,000 or more last year.
  • Racialized Revenue: White neighborhoods make systematically more money on Airbnb than non-white neighborhoods. Neighborhoods with high existing Airbnb revenue (generally in Midtown and Lower Manhattan) are disproportionately white. But the fastest-growing neighborhoods for Airbnb (particularly Harlem and Bedford-Stuyvesant) are disproportionately African American.
  • 72% of the Population: Nearly three quarters of the population in neighborhoods at highest risk of Airbnb-induced gentrification across New York is non-white, as Airbnb continues to have a strongly racialized impact across the city.

The report was commissioned by the ShareBetter coalition, and can be downloaded from their website:High-Cost-Short-Term-Rental-Airbnb-Report1-793x1024.png

I posted a thread on Twitter running through the main findings and excerpting a bunch of the graphics, which is available here:

This New York report follows on our August release of “Short-term Cities: Airbnb’s Impact on Canadian Housing Markets”, and it’s likely to be our last such release for the near future, since we are now sitting on a giant pile of research findings which need to be written up for academic journals. But I’m very happy to have this report out in the public domain, and I’m really proud of what we’ve accomplished with it. In a separate post I’ll compile some of the press coverage the report has received so far.

Airbnb and gentrification in New York: full paper now available

Figure 6

A few months ago, I wrote several posts previewing upcoming research about Airbnb’s impact on housing in New York City. I’m happy to announce that the full paper featuring this research is now available in draft form. It is titled Airbnb and the Rent Gap: Gentrification Through the Sharing Economy, and is co-authored with Alexander Weisler, a recent Master’s in Urban Planning graduate whom I supervised at McGill. Here is the paper’s 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 in 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. Performing spatial analysis on twelve months of Airbnb activity in the New York region, we measure the amount of rental housing lost to Airbnb, measure new capital flows into the short- term rental market, identify neighbourhoods whose housing markets have already been significantly impacted by short-term rentals at the cost of long-term rental housing, and identify neighbourhoods which are increasingly under threat of Airbnb-induced gentrification.

We are going to be submitting the paper for peer review shortly, but wanted to provide an advanced copy online, since it will be many months until the paper is in print.

The paper is available at ResearchGate, and for direct download for those without ResearchGate accounts. Feedback is welcome!

Me at the 2017 AAG

The 2017 annual meeting of the American Association of Geographers kicks off tomorrow in Boston, and, despite some serious concerns about the inclusivity of the event in Trump’s America, I’m looking forward to attending. This year, perhaps unwisely, I am giving four separate presentations. In chronological order….

On Thursday morning at 8:00, in Constitution A on the Sheraton second floor, I’ll be participating in the “Whither the Growth Machine I: Financialization and the Rescaling of the Growth Machine” session organized by John Stehlin and Alexander Tarr. My paper is titled “Post-City Politics”, and applies the logic of the local growth machine to some of the new, decidedly non-local configurations of growth-oriented urban governance. The abstract is as follows:

In 1968, Melvin Webber declared the impending arrival of the “post-city era”, in which the growing importance of knowledge production activities would dramatically reduce place-boundedness, and the city would disappear through the generalization of its social order. This prediction turned out to be wrong—city and regional agglomerations are more important to national and international social orders than ever before—but in this paper I argue that we are now witnessing the arrival of “post-city politics” in the United States.

Scholarship on growth machines and the new urban politics relies on a fundamental correspondence between the structure of local economies as common labor and property markets, and the institutional arenas of urban politics. My analysis shows that such a correspondence can no longer be assumed. The characteristic questions of growth machine analysis—urban development and elite growth coalitions—are less and less strongly tied to the city as a specific bounded, modular spatial configuration in the US. Suburbanization, economic globalization, state rescaling, and urban financialization have destabilized the structural basis for local growth coalition formation. Consequently, local elites are driven to look for new governance configurations to address the regulatory problems they encounter at the local scale. Localities are no longer stable containers for the politics of growth.

I substantiate this argument through a comparative analysis of new large-scale growth coalitions across the US. The era of strong and enduring local growth machines—of city-bound urban politics—is fading. This paper is an exploration of what is rising to take its place.

Immediately after this session finishes, I’ll be heading over to Room 111 in the Plaza Level of Hynes for the “Planetary Urbanization 2: Ecology, Politics and the Anthropocene” session organized by Christian Schmid. Although I’m listed as the session introducer, this is actually just an accounting fiction to get around AAG regulations, and I’ll be presenting a full paper. It is “Green and grey: New ideologies of nature in urban sustainability politics”, a paper I have co-written with Hillary Angelo. The abstract is as follows, although in the presentation we will focus more than the abstract suggests on the city-centrism of contemporary urban sustainability discourse:

In the past two decades, cities have come to be understood as environmental solutions instead of environmental problems. The rise of “urban sustainability” as a new policy common sense is the most notable product of this shift. This paper offers a framework for interpreting contemporary ideas of urban sustainability, based on a distinction between green urban nature and grey urban nature as two representations of city-environment relations. Green urban nature is the return of nature to the city in its most verdant form, signified by street trees, urban gardens, and the greening of post-industrial landscapes. Grey urban nature is the concept of social, technological urban space as already inherently sustainable, signified by dense urban cores, high-speed public transit, and energy-efficient buildings. First, we sketch out an intellectual history of the city-nature binary, and introduce the concept of “post-binary” urban nature to characterize contemporary discourse which sees that binary as having been superseded. Next, we develop Lefebvre’s ideas of the realistic and transparent illusions as the constitutive ideologies of the social production of space to argue that the post-binary imaginary is in fact still constituted by green and grey representational dimensions. Finally, we concretize the implications of this argument through a case study of the Masdar smart city project in Abu Dhabi, and then discuss urban sustainability as a distinctively post-binary concept.

Later on Thursday I’m honoured to be the discussant for the Territory, Politics, Governance annual lecture, organized by the Regional Studies Association. The lecture, taking place at 3:20 PM in Constitution A on the Sheraton second floor, is being given by Maarten Hajer, and is titled “Imagining the post-fossil city: Why is it so difficult to think of new possible worlds?”. The abstract of Dr. Hajer’s talk is as follows:

Why is it so difficult to think of new possible urban futures? Countless papers and reports start with the reiteration that the trend towards urbanization will continue. ‘In 2050 up to 70% of the world’s population is expected to live in cities.’ While recognizing this macro-trend it is clear that building cities according to the principles that emerged over the 20th Century, with a dominant role for auto-mobility, and widely dispersed ‘enclavism’, will lead to an environmental disaster. Yet the transition to ‘post-fossil urbanization’ is slow in coming. Prof. Hajer argues that this has to do with the fact that we lack new imaginaries, new appealing conceptions of future city life. In his talk he will reflect on the question why we have lost the capacity to imagine alternative urban futures. Learning from the literature on Science Fiction and practices like ‘research by design’ Hajer aims to recoup our capacity to think of alternative possible worlds.

I’m really looking forward to engaging with Dr. Hajer on the future of cities and the environment.

Finally, on Sunday afternoon at 2:00 PM in the Berkeley room on the third floor of the Marriott, I’m excited to be participating in an author-meets-critics session for Theresa Enright’s incredible book “The Making of Grand Paris: Metropolitan Urbanism in the Twenty-First Century”. It’s a terrific book and a terrific set of scholars discussing it, so it should be a lot of fun, despite the end-of-conference time slot!

Needless to say, I’m also looking forward to attending some sessions where I don’t have to give a presentation. I haven’t had a chance to look at the full program yet, but the “Contradictions of the Climate Friendly City” sessions look excellent, and that’s where I’m planning to spend my Friday afternoon.

“Competitive Multi-City Regionalism”, now available in print

I’m happy to announce that my Regional Studies article, “Competitive Multi-City Regionalism: Growth Politics Beyond the Growth Machine”, is now available in print. It’s in Vol 51, Issue 4 of Regional Studies, and here’s the abstract:

Local growth politics are increasingly conducted at scales that confound the assumptions of growth machine theory. This paper analyzes ‘competitive multi-city regionalism’ in the United States – local growth coalitions collaborating on economic development across multiple city-regions. It introduces the concept of ‘scalar logics of regionalism’ to characterize the multiple regionalism projects at work throughout the state–economy nexus, and develops a comparative case study of regionalism initiatives in Arizona, Florida and Ohio to demonstrate the importance of interactions and conflict between different scalar logics in determining the multi-scalar outcomes of local growth politics.

I’ll be presenting related research at the American Association of Geographers annual meeting in Boston next month, as part of a paper session titled “Whither the Growth Machine?”. Next week I’ll post a preview of this paper along with my other conference activities.

The final author draft of the paper is freely available to download. The Version of Record of this manuscript has been published and is available in Regional Studies 51 (4).

How do we measure Airbnb’s impact on housing and gentrification?

My post on March 13 summarizing an upcoming paper I’m writing on Airbnb and gentrification in New York with Alexander Weisler (a former graduate student of mine) caused a bit of a stir, including some pushback from Airbnb’s CEO. So, in the interests of transparency, I wanted to discuss the methodology I used to generate my conclusions in more detail. This post will allow anyone to replicate, confirm or challenge the findings, by clarifying both the analytical procedure and the various assumptions I made along the way.

To begin with, I carried out my analysis using proprietary data provided by the consultancy Airdna. This is a firm that specializes in scraping 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 excellent open-data effort Inside Airbnb; something that is on my near-term agenda is to systematically compare the estimates generated by Airdna and Inside Airbnb, as a means of “triangulating” the reliability of third-party data.)

It would be much better to do this analysis with official, accurate data from Airbnb, but they are extremely secretive about their data, even when faced with legal requirements, and when they have released data, observers have concluded that they’ve done so in a misleading fashion. So myself and other researchers have to settle for third-party data.

The data I used is the complete property file for all listings in the entire New York metropolitan statistical area as of September, 2016. This includes many listings which are presumably now defunct (e.g. where the listing’s specific web address was last successfully scraped in 2014), as well as many listings which have only recently been added to Airbnb and haven’t yet generated much or any activity. The file contains 141,657 listings, 110,478 of which have had verified activity since September 2015—one year before the end date of the dataset.

Where I set the threshold for an “active” listing could potentially change the results quite a bit (and probably explains the difference between my estimates and those from Inside Airbnb, as I discuss below), but what I found was that most of the old listings had very little activity, so the numbers of revenue-generating listings didn’t change much. For instance, by setting the threshold at September 2015, I excluded 31,179 listings, but only about 501 (1.7%) of these excluded listings had any revenue listed at all, and only 321 (1%) had more than $1000 in estimated annual revenue.

The entry for each listing provides a large assortment of metadata. The metadata I focused on was:

  • The listing type: private room, shared room, or whole-unit
  • Occupancy details: how many days it was booked, listed and blocked
  • Price and revenue details: the average daily rate and the total estimated annual revenue
  • The location of the listing: city, zipcode, neighbourhood, and latitude and longitude coordinates

But I also had access to other information about each listing, including:

  • 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, and the like
  • Other details: the listing URL, the number of photos included in the listing, etc.

I imported this data into ArcGIS, and turned each listing into a point on the map, using its latitude and longitude coordinates. Here’s the distribution of all points across the New York metropolitan region (New York City is shown in dark grey):

Update 1.png

Because of how many points there are, it’s not easy to get a good sense of their distribution, but a large majority of listings across the region are concentrated in a very small space. Out of the 110,478 “active” listings:

  • 91,811 (83.1%) are in New York City
  • 4,126 (3.7%) of the listings are on the eastern part of Long Island, in and around the Hamptons, where there are large numbers of vacation rentals in the summertime
  • 94,786 (85.8%) of the listings are in the area shown in my maps in my previous blogpost (Manhattan, Northern Brooklyn, parts of Queens and the New Jersey side of the Hudson River)

There’s a certain amount of uncertainty about these numbers, because they were captured at different points of time, and the totals here are probably closer to a maximal than a minimal estimate of Airbnb activity, since it’s hard to know when to disqualify an old listing than it is to know when to start counting a new listing.

Estimating Airbnb activity across the region

In order to get a better sense of the distribution of listings across the region, and in order to perform the subsequent analysis with data from the census and the American Community Survey, I aggregated the listings at the census-tract scale (census tracts are roughly equivalent to small neighbourhoods, with approximately 4,000 residents). I did this using a polygon-to-point spatial join operation in ArcGIS, identifying for each of the 140,000 listings the census tract it lies within:

Update 2.png

This worked well, but there were some problems that needed to be sorted out, because the latitude and longitude coordinates taken from listings on Airbnb’s website are randomized by up to 150 m, in order to protect users’ anonymity. Generally this randomization isn’t a problem, because most points are simply shifted a little bit within the same census tract, and for every point that gets randomly shifted across the line from census tract A to B, there’s probably another point that gets randomly shifted from B to A. For aggregate-level analysis of the kind I’m doing, this is acceptable random error. But occasionally points got shifted into census tracts that don’t have any population in them. For example, here is a detail view of the area around Central Park in Manhattan:

Update 3.png

There are a handful of points in the middle of the park, which doesn’t make sense. Similarly, there are some points which ended up in census tracts with fewer than 50 total units of housing according to the American Community Survey (e.g. prisons and university dormitories which have a lot of people living there but close to no owner- or renter-occupied housing). So I designed a method for using information about the distribution of housing units on a block-by-block scale to probabilistically estimate which census tract a given listing is likely to have been drawn from. (This is the subject of a forthcoming GIS methods paper.)

Once I sorted these details out, I was able to aggregate all the points across census tracts. For the subsequent analysis, I also isolated various subsets of the listings. In total, I recorded the following subsets: (The main number is with an “active” cut-off of Sep. 2015. In brackets afterwards are pseudo-confidence-intervals with a Jan. 2016 cut-off on the low end and no cut-off on the high end.)

  • 110,478 (95,870 – 141,657) active listings
  • 59,668 (51,224 – 76,531) active whole-unit listings
  • 21,345 (19,700 – 21,482) “full-time” whole-unit listings, comprising 14,813 (14,048 – 14,829) listings that were booked more than 60 days in the last year plus 6,532 (5,652 – 6,653) listings where less than a year’s worth of data was available that had the same proportion of bookings

Only 70% of the listings in the NYC region generated any revenue at all last year—a proportion which is more or less the same for whole-unit listings and private rooms. Also, slightly more than half of the total listings were for whole units. The distribution of all listings was shown in Figure 1 of my blogpost yesterday:

1 all listings (updated)

The subsequent maps all used the whole-unit listings booked for more than 60 days (which I called “full-time”).

What counts as a “full-time” listing?

One of Airbnb CEO Brian Chesky’s specific complaints about my analysis was that two months of occupancy doesn’t equal “full time”. Is he right? Why did I choose a 60-day cut off? What is “full time”, anyway? This is actually a tough question to answer, and I’m not yet sure that I’ve got the right answer, but here’s why I set the threshold where I did.

To begin with, there are different reasons you might be interested in defining “full-time” Airbnb occupancy. For instance, if you’re thinking about becoming an Airbnb host, you’d probably want to feel confident that your unit would be rented enough of the time to justify not doing something else with it (such as renting it with a standard 12-month lease, or selling it to someone else). But my reason is because I’m trying to assess Airbnb’s impact on the long-term residential rental housing market. A frequent accusation levelled against the service is that it is effectively encouraging the conversion of apartments into hotels, and I want to see if there’s evidence for this accusation. So I’m looking for a threshold which does a good job of separating units which are rented on Airbnb but probably still have a long-term tenant living in them from units which are rented on Airbnb enough that they probably don’t have a long-term tenant living in them.

The metric I am using is the number of days per year that a unit is occupied. And, to begin with, there’s no single threshold that will accurately classify every case. There are probably people who travel extremely frequently, and are able to keep a unit as their primary residence while still renting it on Airbnb for 200 days a year. And there are probably people who listed their unit year-round but set too high a price or are in an area with insufficient demand, and it only rented 25 days in total. Still, if the threshold is too low, we will get lots of false positives—for example by counting as “full-time” an apartment which was on Airbnb for a few weeks after one long-term tenant moved out and before another moved in, or an apartment which the long-term inhabitant puts on Airbnb during periods of occasional travel. On the other hand, if we set the threshold too high, we will get lots of false negatives, and end up underestimating the impact Airbnb is having on the rental market.

There’s no hard and fast rule here, but the threshold should probably be more than one month, since a one-month gap between long-term tenants isn’t uncommon. (Although it’s less common in New York, where turnaround times of two or even one week are standard, than in other cities.) A plausible high end for the threshold might be 100 days, because that is the number of days a unit would be rented if its permanent occupant managed to successfully rent it on Airbnb every single weekend of the year.

We also have to consider that almost no apartment has a 100% occupancy rate. Even if you have a competitively priced listing in a high-demand neighbourhood, sometimes you’ll get a Friday-Monday booking followed by a Wednesday-Sunday booking, and you won’t find anyone to rent your apartment for Monday and Tuesday night. In fact, according to my data and to Inside Airbnb—the gold standard third-party watchdog of Airbnb’s activity—the occupancy rate for frequently rented, whole-unit listings is a little more than 50%. This means, for instance that the average listing occupied 45 days a year was actually available on the website (and thus likely not occupied by a primary resident) for 90 days.

Inside Airbnb sets 60 days a year as its “frequently booked” threshold. As the site puts it, “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.” So I opted to use the same standard, although I’m still not certain that’s the best trade off between false negatives and false positives.

Here’s what happens to the estimated number of full-time listings as the threshold increases:

Update 4.png

It’s a negative logarithmic curve, which means that the values drop off quite quickly at first, and then the rate of decrease slows down. In other words, the overall estimate of full-time listings is relatively volatile in the lower portion of the threshold’s range—roughly for thresholds under 120 days. At the same time, I experimented with different thresholds and didn’t find that the basic findings of the study changed very much. The vulnerability index I created looks more or less the same with an estimated 20,000 full-time housing units lost to Airbnb (at a 60-day threshold) or with an estimated 12,000 units lost (at a 120-day threshold).

As a sidenote, Inside Airbnb’s overall estimates of Airbnb usage in New York are quite a bit smaller than mine. It’s not quite an apples-to-apples comparison, because their data was compiled in December 2016, and mine was in September 2016, but they estimate 40,227 active listings in New York City, 11,232 of which are full-time, whole-unit listings. My data is for the entire New York metropolitan region, but my equivalent figures for New York City are 91,811 active listings and 17,985 full-time, whole-unit listings. This is a big difference, and I believe it is mostly because Inside Airbnb has a much more aggressive threshold for determining which listings are in active use: there needs to have been a user review left for the listing in the last six months. If I move my “last-active date” threshold from September 2015 to January 2016, my numbers start to look closer to theirs; I estimate 78,798 active listings and 16,338 full-time, whole-unit listings. If I move my threshold to March 2016 (so roughly the same 6-month window that Inside Airbnb uses), I estimate 64,791 active listings and 14,903 full-time, whole-unit listings.

I think Inside Airbnb’s more aggressive filtering almost certainly gives a better “snapshot” of the current state of Airbnb usage in the city, but my tentative opinion is that aggregating data over a year the way I have done gives a better account of Airbnb’s impact on housing stock in the medium-term, particularly taking into account seasonal variation. This is still an open question in my mind, though.

The result of all this is that there’s a lot of uncertainty in interpreting Airbnb rental data, even when the underlying data is relatively clear. And it’s certainly possible that I’m setting my thresholds too aggressively. At the same time, for the purposes of estimating Airbnb’s impact on rental markets, I believe my assumptions remain quite conservative, for two reasons. First, I’m using the actual number of days a listing is occupied, instead of the number of days it is available to be occupied. The larger the number of days a listing is available on Airbnb, even if it isn’t rented each of those days, the more likely it becomes that the apartment isn’t being lived in by traditional long-term tenants. As an example, nearly 30,000 whole-unit listings in New York are available 240 days—or approximately 8 months—per year. This is how the estimate changes with the threshold:

Update 5.png

Second, I totally exclude private-room rentals. In New York it is very common for renters to split apartments with roommates. And so it is likely that many of the frequently-rented private room rentals in the city are coming at the expense of “roommate wanted” ads on Craigslist, and thus directly reducing available long-term rental housing in the city. For reference, I counted 16,239 private-room listings rented for 60 days or more.

One the other hand, I don’t think any of this uncertainty actually matters very much except for the very specific task of estimating how much permanent rental housing has been lost to Airbnb. This is an important issue, so it’s good to have rigorous and defensible estimates. But for the issues relating to land economics and gentrification—above all the rent gap—my analysis turns out very similarly even with a wide range of different underlying estimates.

How to measure Airbnb’s impact on the housing market?

Assuming we have good estimates for the number of full-time, whole-unit rentals on Airbnb (which is a big assumption, given the discussion in the previous section!), measuring Airbnb’s impact on the housing market is very straightforward.

I simply took the counts of renter-occupied housing by census tract from the American Community Survey (table B25003, 2015 five-year estimates) and added my estimates of full-time Airbnb-occupied units to get the total number of existing rental housing plus “could be rental if it weren’t on Airbnb” housing. I divided the Airbnb rental estimates by this amount to get the percentage of rental units estimated to be full-time on Airbnb. This informed Figures 2 and 3 from my blogpost:

2 full-time rental percentage

3 community boards

How to measure Airbnb’s rent gap?

The idea of Neil Smith’s concept of the rent gap is that over time, as a neighbourhood’s properties deteriorate, the actual revenue landowners are able to earn from their properties also tends to decline, but the possible revenue (were the properties to be redeveloped or renovated) tends to increase. If this “gap” between actual and potential revenue gets large enough, eventually it becomes likely that redevelopment capital arrives to take advantage of the profit-making opportunity. The result is renovations, new construction, displacement of existing tenants, and the arrival of more affluent tenants and homeowners—gentrification.

My idea about Airbnb and gentrification is that short-term rentals have created a new form of rent gap: one driven by sharply rising potential revenue, rather than gradually falling actual revenue. I have tried to measure this gap in two ways, one aimed at estimating how much new housing revenue has been generated thanks to Airbnb (i.e. where the rent gap was created and then filled), and one aimed at identifying areas where new potential profit-making opportunities are still quite prevalent (i.e. where the rent gap is growing and not yet filled).

To estimate the “filled” rent gap, I compared the amount of revenue generated in each census tract by Airbnb rentals with the total rental and homeowner costs otherwise incurred in these areas. Quantitative measurements of gentrification often want to look at capital expenditures (for renovations or new developments) as an index of new investments into the housing market. But, as I described in the previous blogpost, addressing the Airbnb rent gap typically requires little or no capital expenditures. So I came to the conclusion that the best way to measure the already-filled rent gap was simply to look at operating revenues from housing (also known as annual land rents).

The specific measures of ongoing housing revenue I used were “aggregate gross rent” (2015 ACS five-year estimates, table B25065) and “aggregate selected monthly owner costs” (2015 ACS five-year estimates, table B25089). Gross rent is the sum of the contract rent and any utility payments not included in the contract rent (in order to increase comparability between cases where utilities are included in the rent and where they are not). “Selected monthly owner costs” is meant to be the same bundle of expenses for homeowners (i.e. substituting mortgage payments for rent). In both cases, I used the aggregate amount of these payments made in a census tract, to approximate the total volume of routine money (i.e. not including capital expenditures) that flows through the housing market.

In the same way that we want to normalize our estimates of Airbnb’s impacts on rental housing stock by considering percentages of total housing stock instead of just the raw counts of full-time Airbnb rentals, we want to normalize our estimates of Airbnb’s impacts on housing revenue by considering percentages of total revenue streams instead of just the raw amounts of money Airbnb hosts earn. Figure 4 from the blogpost showed the result of this analysis—the areas where the Airbnb-generated rent gap was largest, but has now been filled:

4 income ratio

To measure the “unfilled” rent gap, I compared Airbnb host revenues with what those hosts likely could have earned on the traditional rental market. The intuition here 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. If you are a landlord earning $2000/month in rent for an apartment, but you could be earning $4000/month if you put that same apartment on Airbnb, you will have a strong incentive to get rid of your current tenant and do just that. This, of course, is the rent gap. If enough landlords take advantage of these opportunities, we should 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 $2400 and average Airbnb revenues have fallen to $2800. Now the rent gap is much smaller, and there will be less pressure on landlords to convert long-term rentals to short-term rentals.

In order to measure the size of this outstanding rent gap, I compared the average revenue earned by full-time, whole-unit Airbnb listings in a given census tract with the median contract rent in that tract (2015 ACS 5-year estimates, table B25058). “Contract rent” is the direct revenue a landlord receives every month from a tenant, and is the most comparable to Airbnb revenues of the several different measures of rent payments that the ACS provides. The results of this comparison were shown in Figure 5 of my blogpost:

5 median rent

What is the vulnerability index?

As described above, I calculated two measurements of the Airbnb-induced rent gap in New York—one for the rent gap which has already been plugged, and one for the rent gap which is still open—and I wanted to combine them in a single vulnerability index.

I started by using cluster analysis on each of these maps to filter out some of the noise and identify consistent patterns across space. The particular kind of cluster analysis I used was the Anselin Local Moran’s I statistic, which analyzes a spatial distribution of features to identify statistically significant hot-spots (clusters of high values near other high values), cold-spots (clusters of low values near other low values), and outliers (high spots near mostly low spots, and vice versa). For instance, here’s what the cluster analysis found for my measure of the open rent gap, the ratio of average whole-unit, full-time Airbnb property revenue to median rent:

Update 6.png

The pink areas are “high-high clusters” of census tracts that all have high ratios of Airbnb-revenue-to-median-rent, which means these are the areas most at risk of future Airbnb-induced gentrification. The red areas are also census tracts with high ratios, but they are isolated from other such areas, so they don’t represent big vulnerabilities. (Although investigating what is going on in these outliers could be interesting for future research.)

I performed the same cluster analysis on the measure of the already-filled rent gap (Airbnb revenue as a percentage of overall housing revenue), and similarly extracted the high-high clusters. I then combined these two distributions into a single map, noting the areas of overlap, to produce the vulnerability index:

6 vulnerability index

For the time being I’ve left this map in its relatively “raw” form, with individual census tracts highlighted or not, but I’ve also considered consolidating this information at the neighbourhood scale. (For instance, showing the entire Lower East Side as purple, and all of Bed-Stuy as red.) This would sacrifice some of the map’s precision, but increase its readability. Such experiments are still to come….

Final thoughts

The purpose of this post is to make explicit all the assumptions, approximations, and decisions that went into generating my analysis of Airbnb and gentrification in New York. It should be clear, at a minimum, that there is a large amount of uncertainty in this sort of analysis, for two reasons. First, since Airbnb doesn’t release any public data about its activities, researchers and policymakers alike are forced to rely on third-party estimates, which are inherently less accurate than official dat would be. Second, even if completely accurate data were available, there are some basic epistemological uncertainties—for instance, how many days does an apartment need to be rented on Airbnb before it is no longer in the regular long-term rental stock?

For both of these reasons, I would be very grateful for constructive criticism about any of the above, in order to improve the estimates and get us collectively closer to an evidence-based discussion about the impact of short-term rentals on our cities. The paper that this material is taken from will be submitted for peer review shortly, which will provide another opportunity for criticism. Once the methodology is rock-solid, I plan to apply it to other cities, and to develop further tools for comparing cities in a easy-to-understand and action-oriented way.