Screenshot via Airbnb

A "renters rights" exchange could spread costs and benefits more evenly across city neighborhoods.

Airbnb is a convenient, cheap, and fun alternative for travelers in search of lodging. It’s also a wicked problem for city regulators.

Airbnb drains housing supplies in cities where rents are already too high. It can disrupt neighborhood life, strain relationships between landlords and renters, and put tourists at risk. And contrary to the company’s own claims about spreading economic benefits to areas outside city centers, the neighborhoods reaping the most money from the service are often the ones already bustling.

That last complexity is demonstrably true in London, which had the third-highest number of Airbnb listings in the world in 2015. A paper prepared in advance of the 2016 International World Wide Web Conference investigates London’s Airbnb market to determine who, and what neighborhoods, benefit most from Airbnb’s economic down-trickle. The findings aren’t entirely striking, though the policy implications suggested by the researchers would mark a major change.

Supply has changed, but demand is constant

Matching public-facing data (such as listings and reviews) scraped from the Airbnb website to U.K. census data, Ordnance Survey data, and Foursquare usage data (to measure neighborhood popularity, more or less), the study authors found that between 2012 and 2015 the supply of Airbnb listings in London has changed in terms of geography and host demographic. Study co-author Daniele Quercia of Bell Laboratories in Cambridge, U.K., writes on his website:

Early adopting areas were centrally located and were characterized by young, tech-savvy and ethnically-mixed residents, some of whom were students. After less than two years, Airbnb had started to penetrate areas of two types: it penetrated areas either of [home] owners or of medium/low-income renters.

In other words, over time, Airbnb supply spread out beyond the desirable, high-income center-city. When it came to actual demand, however, Quercia and his co-authors did not find any such distribution. Airbnb guests continued to rent and review units located in neighborhoods with housing markets characteried by “young, tech-savvy, employed, well-educated, and multi-ethnic residents.” Though there is rental supply in London’s suburban, lower-income housing markets, demand has not matched it.

Sharing rights

Based on the finding that large numbers of Airbnb units in certain neighborhoods aren’t being rented at nearly the same rate as those in others, the paper makes some brow-raising policy recommendations.

Rather than imposing blanket limitations on Airbnb citywide, or on the flipside allowing Airbnb to continue business as usual, the authors suggest that cities like London consider a scheme of “transferable sharing rights.” The idea is that the city could distribute a scarce number of “rental rights” to city residents in such a way that allows a more even spread of both the economic benefits (income for renters and revenues for local businesses) and negative externalities (impacts on housing markets and disruption of community life).

Residents could buy and sell these rights from one another on a digital marketplace, depending on their desire to participate. The price of these rights would be based on “both real-time market demand and municipal policies.” And city councils, or even neighborhood councils, could generate some revenue through fees charged for rights exchanges.  

“Algorithmic regulation”

A rental rights exchange is a pretty cool idea. But as the authors write, basing such a regulatory scheme on “real-time market demand” would mean regulating largely by collecting and analyzing huge amounts of data. Beloved by certain tech leaders, this is a concept known as “algorithmic regulation.” In the context of regulating Airbnb, it’s slightly questionable.

First, it assumes that Airbnb would be open and transparent in sharing rental data when there’s ample reason to believe otherwise. Second, even with good and complete data, it’s untrue that simply running an algorithm reveals important insights about, say, where residents should pay more for rental rights. That still requires human labor and judgment, which can be flawed, to say the least, especially in a context that demands real-time answers.

Third, and maybe most worryingly, regulating with an algorithm means that regulations are no longer legible to all citizens. When not everyone can understand the code or the data that’s guiding decisions at City Hall, especially for something as essential and complicated as housing, that’s a sad day for democracy. Current housing policy may be arcane and convoluted, but at least it’s printed in a language that most people can read, and that advocates can cite.

Its certainly useful for leaders to know which neighborhoods are most affected by Airbnb, for better or for worse. And spreading the platform’s economic benefits to less-touristic neighborhoods, while reducing stress on areas in high demand, is a noble idea. But a system that accomplishes these goals, with plenty of checks and balances, won’t be as simple as slapping an algorithm on the problem. If regulating Airbnb in the city’s best interest is a tricky task, it’s one that stands to gets even trickier the longer it’s avoided.

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