Beware the automated landlord

Tenants of the automated landlord are effectively paying two rents: one in money, the other in information for data harvesting, writes Desiree Fields

September 9, 2017
6 min read


Desiree FieldsDesiree Fields is an urban geographer at the University of Sheffield


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‘Rent Easy. Earn Waypoints™ for doing things most renters do anyway: Sign a 2 or 3 year lease. Pay rent on time. Pass tune-ups. Refer friends… Live Well. Use Waypoints™ to get these items and more for free: Free rent… Home upgrades… Appliances.’ (Waypoint Homes, 2015)

Waypoints are a ‘customised loyalty and rewards solution’ designed by INCENTCO, a company drawing on technology, marketing, and industry experts to develop ‘incentive platforms’ for real estate, furniture rental, and health care, among other businesses. For Waypoint Homes, a rental company controlling more than 30,000 formerly foreclosed homes in the US and backed by Starwood Capital Group and Colony Capital, the points ‘gamify’ the subjectivities and behaviours of ‘good tenants’, with renters earning points for behaviours aligned with the interests of landlords.

That is, ‘doing things most renters do anyway’ will also increase revenue per home, while many of the rewards tenants can get, such as appliances, smart home accessories and other home upgrades, also add value to the rental properties.

The US housing bust, which resulted in the repossession of millions of homes and a surge in demand for rental housing, has converged with the ascendance of the new tech boom. Advances in (big) data generation and processing, cloud-based platforms, mobile computing, and algorithmic decision making are being used as technologies of abstraction and aggregation that allow for new forms of large-scale investment in the rental market.

Rent extraction at scale

Private equity firms like Blackstone, Colony Capital, and others are capitalising on the vacant pink stucco homes, overgrown yards and abandoned, mosquito-infested swimming pools left behind after 2008. First, they have rapidly assembled large, geographically dispersed portfolios of rental homes. Achieving this scale has then allowed them to securitise the stream of monthly payments from tenants, and these rent-backed financial instruments have met with strong demand from capital markets.

Rent payments have thus shifted: no longer the mere fulfillment of a contractual obligation between tenant and landlord, rent represents an asset base for the construction of financial products. As the example of Waypoints shows, information-communication technologies and data practices serve as techniques by which landlords realise rents.

In this instance, we see an incentive platform being used to encourage tenants to lock into long-term leases, which often come with rent increases, thereby securing the revenue stream to keep payments flowing to bondholders. In turn, landlords can use up-front payments from holders of rent-backed securities to finance expansion, with the new asset class also generating fees for a range of intermediaries, such as the credit rating agencies that rate the instruments and the traders that market them. Meanwhile, rewards like home upgrades flow back to the landlord in the form of enhanced property value, realised as rent at the point of resale.

Eviction can even be outsourced through software-as-a-service – already compatible with leading property management platforms

At every stage of the rental process, information infrastructures and practices of data use, reuse, and analysis help automate landlording. Max-bid apps allow geographically-removed investors to make local markets knowable and target the most desirable properties. Automating rent payments and maintenance requests allows for the management of portfolios of thousands of homes clustered in the US Sun Belt. Incentive platforms (like those Waypoints) gamify rent extraction. Eviction can even be outsourced through software-as-a-service – already compatible with leading property management platforms.

The entrance of institutional investors into the rental market is driving the development of new applications and techniques for data generation, processing, and use to inform their investment strategies and manage their portfolios.

For example, rental market intelligence (RentRange, CoreLogic), portfolio surveillance systems (Green River Capital’s Rental Asset Management and Performance system), and online marketplaces for rental investment (Roofstock, Investability) are now sites of technological innovation and expansion of the digital economy.

The role that such information infrastructures and data practices are playing in constituting the single-family rental market as a financial asset class can be seen as what information theorists like Mark Lycett, Viktor Mayer-Schonberger, and Kenneth Cukier term ‘datafication’, or the use of data to create value. Information can be abstracted from and about specific, socio-spatially fragmented properties and tenants, and rebundled easily, efficiently, and affordably.

Datafication allows for the aggregation on which the creation of new financial assets depends, and, crucially, is a self-perpetuating process, leaving new forms of data capital in its wake. For example, tenant-facing systems that automate rent-collection and maintenance requests provide a constant flow of property-level data, information that becomes data capital in the sense that firms can feed it back into their max bid algorithms, analyse rent levels, and search out efficiencies on maintenance costs.

Datafication and the landlord-system

But even as datafication makes legible previously unknown spaces and populations and informs the production of new financial asset classes, this process is often opaque to those directly affected by it, in this case renters themselves: a situation that forecloses critical reflection. The automation of so many aspects of the rental and property management process complicates the figure of the landlord: while one firm may have monopoly control over the property, a whole series of technological intermediaries is tasked with sustaining that monopoly control via systems that collect, process, and circulate information about tenants and the spaces in which they carry out their daily lives.

This raises questions about who owns that information, how it is governed, how it circulates, and to what ends it is used – and to whose benefit. Tenants of the automated landlord are effectively (and unwittingly) paying two rents: one consisting of money, the other of information, extracted as they do things like renew their lease or request a leaky tap be fixed.

Harvesting this data, in turn, creates new opportunities for capital accumulation. For example, lists of tenants who occasionally pay rent just a few days late might be sold to a data broker, and ultimately used to target ads for credit cards, payday loans, or ‘sharing economy’ services that allow a middle class stretched thin to use their homes and cars to draw in new income streams.

Ultimately, datafication in the sphere of rental housing is working to advance the interests of financial actors by reconnecting homes into flows of global capital. At the same time, it further entangles tenants with largely unaccountable systems of information extraction and commodification. The struggle for a right to the city is unavoidably about encountering and questioning these entanglements collectively. If, as legal scholar Frank Pasquale argues, opacity and obfuscation are core to the operation of the financial and tech industries, then demystifying and making their operations visible is a political act that can open up opportunities for critique and struggle about how datafication shapes urban life.

This article is from a collection titled Our Digital Rights to the City. You can access the full booklet at meatspacepress.org. The collection was edited by Joe Shaw and Mark Graham, with design by Irene Beltrame.


Desiree FieldsDesiree Fields is an urban geographer at the University of Sheffield