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.

Airbnb and gentrification in New York

6 vulnerability index

[Update: a draft of the full paper is now available for download.]

For the last several months I’ve been working with Alexander Weisler (a recent MUP graduate from the School of Urban Planning at McGill) on a paper which explores the connection between short-term rentals and gentrification. We use a case study of Airbnb in New York City, based on a lot of number crunching, GIS, and interviews with community organizations and policymakers. The paper is nearly finished, and I’ll upload it here once it’s ready. But in the meantime (and recognizing that it will be a year or more before the paper makes it through peer review and the publishing process), I wanted to provide a quick tour of the arguments and evidence, using the near-final maps I’ve spent the last several weeks making.

The thesis of the paper is that Airbnb is systematically creating a new kind of rent gap. Following Neil Smith’s original argument, we normally think of rent gaps as emerging where localized disinvestment drives down the money landowners earn from their properties, even as overall city-wide growth increases the potential money they could earn if they were to renovate or redevelop. Once this gap between actual and potential profit gets big enough, developers take an interest, and reinvestment—and hence gentrification—is likely to occur.

What we see with Airbnb is also a rent gap, but it’s not one that relies on disinvestment or developers. Instead, the opportunity which the service provides to rent out an apartment to short-term tourists instead of long-term tenants is driving up potential landlord earnings without any disinvestment having occurred—and without any need for big expensive renovations to capitalize on the opportunity. All a landlord needs to do is evict current tenants or decide not to find new ones when a lease ends. So this is an enormous new opportunity for profit-making in urban housing markets where there is external tourist demand, and an enormous new risk of displacement and gentrification.

Building off these ideas, the paper makes three specific arguments: First, 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. Second, Airbnb offers a means of exploiting its own rent gaps to a range of different housing actors, from developers to landlords, tenants and homeowners, and the actions these different actors take to exploit new rent gaps have very different impacts on urban housing markets. Third, even though Airbnb-induced gentrification frequently runs counter to entrenched urban governance interests, municipal regulators are severely hampered in their ability to effectively rein in short-term rentals, in large part due to scalar governance constraints and the demands of inter-urban competition.

How much rental housing has New York lost to Airbnb?

In order to estimate the impact Airbnb has had on New York housing, I’ve relied on data scraped from the public Airbnb website by the consulting firm Airdna, covering all Airbnb activity across the New York region from 2015. What follows is a series of maps using that data in combination with data from the US Census and the American Community Survey.

Figure 1 shows all Airbnb listings for 2015, aggregated by census tract. (There are approximately 110,000.) It reveals hotspots in Hell’s Kitchen and Chelsea (near the existing Manhattan hotel district, and an area with a long history of illegal hotels), the Lower East Side, and Williamsburg and Bushwick in Brooklyn.

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Figure 1: Total estimated number of Airbnb listings per census tract in the New York region.

(Incidentally, I looked at data from the entire tristate New York metropolitan region, but almost 90% of Airbnb listings are in the immediate vicinity of Manhattan shown on the map. The exceptions were the Hamptons and the Jersey Shore, where arguably Airbnb has simply made existing vacation rentals easier to accomplish rather than facilitate large-scale conversions of permanent rental housing to short-term rentals, so I felt justified mostly ignoring these areas.)

This map is a decent approximation of what you’ll see if you go to and search for listings in New York, but it doesn’t do a very good job of estimating the actual impact of the service on housing in the city. Many of these 110,000 listings were just rented once or twice, or even not at all.

So I narrowed in on whole-unit listings that were occupied more than two months a year. “Whole-unit listings” means I excluded cases where people were renting a spare bedroom; every time a whole-unit listing is rented, we know there’s no one else living in the unit. And two months a year of occupancy is a reasonable threshold for a “full-time” listing (used by other researchers as well), because it’s a period of time that isn’t compatible with the standard 12-month residential lease, even allowing for some transition time between tenants. There will surely be some false positives with apartments that are in the long-term rental market most of the year but just on Airbnb in the summer, but also many false negatives with all the private-room listings that would have formerly been ads for roommates on Craigslist—an important source of affordable housing that is hard to measure in a study like this. (In the paper I discuss how the results differ if we change the full-time assumptions.)

All told, around 21,000 housing units in New York were rented full-time on Airbnb in 2015. [Edit: following some pushback from Airbnb’s CEO, here are some additional details about this estimate. If I change the two-month threshold to three months, the estimate drops to 16,000, and if I change it to four months, the estimate drops to 12,000. There’s a lot of uncertainty in this parameter, but in both cases, the underlying patterns—depicted in the rest of the maps—remain the same, so I am reasonably confident about these estimates, based on the third-party data I have access to. Those who are interested can read a thorough discussion of my methodology.] If we compare this number with the amount of normal rental units in the region, we can estimate what portion of each neighbourhood’s rental housing stock has been lost to Airbnb. This is shown in Figure 2:

Figure 2: The percentage of long-term rental housing converted to full-time Airbnb activity.

The pattern is similar to Figure 1, although the clustering in Lower Manhattan and North Brooklyn is more pronounced. And the numbers themselves are shocking—many census tracts have seen five percent or more of their long-term rental housing converted into Airbnb hotels. It’s impossible to estimate how many tenants were forcibly evicted or harassed out of their apartments to free up units for Airbnb, and how many units were simply converted to short-term rentals after they “naturally” became vacant. But in either case, the result has been a huge and concentrated loss of rental housing in the city.

I consolidate these estimates of how much long-term rental housing has been directly lost to Airbnb, by neighbourhood, in Figure 3:

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Figure 3: A neighbourhood-level summary of the rental apartments Airbnb has removed from New York’s housing system.

Two points to note here. First, Airbnb’s impact on housing in New York is highly uneven—it’s so far limited mostly to Lower Manhattan and to Northern Brooklyn. This means that any city- or region-wide aggregate statistics about Airbnb’s presence in New York are going to be highly misleading. No surprise that it is precisely these sorts of statistics that Airbnb leans on….

Second, this map arguably understates Airbnb’s impact on Williamsburg—which already looks quite high at 2.8% of the neighbourhood’s rental housing stock currently being rented full-time on the service. This is because the southern edge of Williamsburg has almost no Airbnb activity, thanks to the large and insular Hasidic Jewish population in the area. If you remove the 4,500 or so housing units from this area, the rest of Williamsburg ticks up over a 3% Airbnb-to-rental-housing ratio.

Airbnb’s rent gap

Beyond estimating how much long-term rental housing New York has lost to Airbnb, the paper also attempts to demonstrate the existence of an Airbnb rent gap in New York. Here the relevant metrics are less about housing units and more about money—revenue flows through the urban housing market. As Neil Smith explained nearly forty years ago, gentrification is “a back to the city movement by capital, not people”.

The existence of a rent gap means that, systematically across a neighbourhood, landowners can earn more money from some different use of their property than from the existing use, which creates an incentive to reinvestment and hence gentrification. As I discussed above, we normally think of rent gaps leading to new capital investment—renovations and redevelopments—but in the case of Airbnb this generally won’t be necessary. Property owners will just switch their units from residential leases to short-term rentals. So if there has been an Airbnb-induced rent gap, we shouldn’t expect to see big new capital expenditures; instead we should expect to see routine housing revenue flows (which are mostly composed of rent and mortgage payments) diverted into Airbnb. Figure 4 provides a rough estimate of this activity—an estimate of where Airbnb has created and already plugged a rent gap:

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Figure 4: The percentage of ongoing housing revenue flows (e.g. rents and mortgages) which Airbnb now accounts for—a measure of the rent gap which Airbnb has created and filled.

Unsurprisingly, the basic pattern is similar to the previous maps, although the clustering is even more acute. Airbnb as a new revenue stream from housing has been most consequential in Times Square, the Lower East Side, and Williamsburg. These are the areas where Airbnb created a rent gap, and where landlords have shifted housing 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”, 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.

However, we get a very different picture of Airbnb’s impact if we look at how much landlords can earn on the service when compared to prevailing rents in their neighbourhoods. In other words, leaving aside for the moment the question of where total Airbnb revenue flows have been highest, where are individual landlords making the most money on Airbnb relative to what they could have been making with traditional rentals? Figure 5 answers this question:

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Figure 5: How much monthly revenue the average full-time Airbnb listing generates, compared to the median rent in each census tract—a measure of the potential profit landlords can make by converting housing from long-term to short-term rental, i.e. the rent gap Airbnb has created but not yet filled.

This is a completely different geography from the previous maps. While the Lower East Side remains a hotspot on this map, with average full-time Airbnb revenues in the range of 200% of median rents, the other major areas of Airbnb activity—Williamsburg and Hell’s Kitchen—have significantly receded in importance. Meanwhile, three new neighbourhoods have appeared: Harlem in North Manhattan, Bedford-Stuyvesant in Brooklyn, and Union City and its surrounding areas in New Jersey. These are areas where there isn’t yet a lot of Airbnb activity in absolute or even relative 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.

Put differently, Figure 5 shows the neighbourhoods which appear to have large and unfilled rent gaps—where there is money to be made but where landlords haven’t yet seized on the opportunity. 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 local housing and where it is likely to have an impact in the future. The first pattern indicates where Airbnb has already had a major impact on local housing—where it has created and filled a rent gap. The 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 created a rent gap which hasn’t yet been filled.

I combined these two patterns by performing a cluster analysis on the two variables to identify the areas of New York which stand out either in terms of high current impact or high risk of future impact. The result is Figure 6, a “clustered vulnerability index” for Airbnb-induced gentrification in New York:

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Figure 6: A clustered vulnerability index of Airbnb-induced gentrification in the New York region, indicating that neighbourhoods now at risk are predominantly African-American ones.

There are three important types of neighbourhood which emerged here. First, shown in blue, are the areas which have had their housing supply heavily impacted by Airbnb, but which may be close to reaching an equilibrium (a closed rent gap). Most of 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). Harlem and Bedford-Stuyvesant in New York fit this profile, as do parts of Hudson County in New Jersey. 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 Williamsburg fit this profile.

An important point which the map doesn’t communicate is that the blue already-gentrified areas are predominantly white neighbourhoods, while the red “high risk” areas are all heavily African-American neighbourhoods. When you combine this fact with research showing how prevalent racial discrimination is on Airbnb, this implies that a major new intensification of racialized gentrification is coming to these areas.

I am playing around with versions of the vulnerability index map to reflect this situation. But I’m also planning to use this index as a sort of template for looking at other cities, particularly where there are community organizations and policymakers interested in fighting back against gentrification and displacement—and against Airbnb’s increasingly obvious role in facilitating these destructive processes.

Competitive Multi-City Regionalism: Growth Politics Beyond the Growth Machine


Competitive multi-city regionalism in Florida (source: author)

I’m happy to announce that a new paper of mine has just been released by Regional Studies in early access. The paper is titled “Competitive Multi-City Regionalism: Growth Politics Beyond the Growth Machine”, and it is one of several of my recent or forthcoming papers in which I’m grappling with the emergence of new, very large-scale local economic development partnerships in the United States.

I call the phenomenon “competitive multi-city regionalism”, and here’s the abstract of the paper:

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.

In a recent paper in Economic Geography, I argued that one functional basis for competitive multi-city regionalism is corridor-scaled infrastructure development. In this new paper in Regional Studies, I instead look at the way that different incentives to ground-up regionalism are produced throughout the multi-scalar state, corresponding to a variety of different functional bases, spatial scales, and institutional configurations.

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, 26 September 2016.

When It Comes to Sustainability, We’re Ranking Our Cities Wrong

It’s been two weeks since the publishing of my commentary with Daniel Aldana Cohen and Hillary Angelo in Nature on the limits of current urban environmental thinking, and the response so far has been terrific. We argued that policymakers and researchers need to expand the way we define and measure urban sustainability in both social and spatial terms, and while this argument won’t be surprising to critical urban scholars, our goal was to boil it down to its most accessible and intuitive formula.

Building on this goal, I recently sat down with Vanessa Quirk from Metropolis Magazine to unpack some of the arguments we made in Nature, including the idea that the current “leaders” in urban sustainability policy such as New York and San Francisco only look like leaders because we do a very poor job of properly capturing their broader environmental impacts.

Part of the reason why wealthy cities are so wealthy is not that they removed themselves from global manufacturing, but that they occupy a very privileged position there. The banks are located in New York, the same banks that finance all the factories. It seems pretty unjust to say, “Look at how successful New York’s been at reducing carbon emissions” when New York is the center of all the global activity that pollutes other parts of the world. New York has exported its pollution. That’s partly why we say that a lot of sustainability gains actually turn out to be “regressive redistributions.”

We also discuss the difficulties with ranking or comparing cities’ environmental performance. Even if good consumption-based measures of carbon footprints were common, I argue, we’d still have intractable problems of how we define cities and regions:

If we’re comparing cities, we have to compare whole regions. You don’t just look at the city, because the city boundary is a historical accident. We look at the whole urban region that’s all functionally interconnected.

But the problem is that the way that these are defined really varies across place. Even just within the United States, the census bureau defines urban regions based on counties, but if you look at the southwest the counties are huge. But in the northeast they’re tiny. Even just within the U.S., where statistics are really good, it’s very hard to compare cities. If you look at it internationally, forget it. At the end of the day the problem is we don’t have the data to do a good job of comparing cities. Period.

The entire interview is available at Metropolis Magazine, and the original commentary is available at Nature.