The New York Times recently ran a story that describes the inner workings of the tenant online screening business, where companies create background reports for landlords on prospective apartment renters. These are companies that access multiple public databases to aggregate data on a specific individual that the landlord can use to determine whether to rent to that person. The article is a scary take-down of a segment of the data business that decided to compete largely on price, and in the process threw quality out the window.
This is not a small segment of the data industry. Indeed, it is estimated that there are over 2,000 companies involved in generating both employment background and tenant screening reports, generating over $3.2B annually. Companies in this segment range from a handful of giants to tiny mom-and-pop operators.
As the Times article notes, the tenant screening segment of the business is largely unregulated. In the tight market for rental apartments, landlords can afford to be picky and apartments rent quickly, so prospective renters typically will lose an apartment before they can get an erroneous report corrected. And with no central data source and lots of small data vendors, it’s impossible to get erroneous data corrected permanently.
The Times article pins the problem in large part on the widespread use of wildcard and Soundex name searches designed to search public databases exhaustively. And with lots of players and severe price pressure, most of the reports that are generated are fully automated. In most cases, landlords simply get a pile of whatever material results from these broad searches. In some cases, the data company provides a score or simply a yes/no recommendation to the landlord. Not surprisingly, landlords prefer these summaries to wading through and trying to assess lengthy source documents.
The core problem is that in this corner of the industry, we have the rare occurrence of unsophisticated data producers selling to unsophisticated data users. Initially, these data producers differentiated themselves by trying to tap the greatest number of data sources (terrorist databases, criminal databases, sex offender databases). This strategy tapped out pretty quickly, which is why these companies shifted to selling on price. To do this, they had to automate, meaning they began to sell reports based on broad searches with no human review. There are also a lot of data wholesalers in this business, meaning it is fast and relatively inexpensive to set yourself up as a background screening company.
There is also a more subtle aspect to this business that should interest all data producers. The use of broad wild card searches is ostensibly done because “it’s better to produce a false positive than a false negative.” This sounds like the right approach on the surface, but hiding underneath is an understanding that the key dynamic of this business is a need to deliver “hits,” otherwise known as negative information. This is where the unsophisticated data user comes into play. Landlords evaluate and value their background screening providers based on how frequently they find negative information on an applicant. If landlords don’t see negative information regularly, they begin to question the value of the screening company, and become receptive to overtures from competitors who claim they do more rigorous screening. In other words, the more rigorous your data product, the more you are exposed competitively.
There’s a lesson here: if you create a data product whose purpose is to help users identify problems, you need to deliver problems frequently in order to succeed. This sets up a warped incentive where precision is the enemy of profit. Place this warped incentive in a market with strong downward price pressure, and the result is messy indeed.