Investigation finds many tenant screening services, relying on algorithms to find infractions and widely used by landlords, often produce flawed reports
and the people they hurt. Superb reporting. https://themarkup.org/... Matthew Goldstein / @mattgoldstein26 : Denied an apartment because of a mistake in a background check report? It's more common than you think and the big screening companies that sell reports to landlords often act as if it's the cost of doing business. Story by @lkirchner of @themarkup and me https://www.nytimes.com/... Evan Simko-Bednarski / @simko_bednarski : Important work by @lkirchner with @MattGoldstein26 on the industry behind cheap, fast, and sometimes wrong tenant background checks used by 90% of landlords nationwide. https://www.nytimes.com/... Bassam Khawaja / @bassam_khawaja : A tenant screening industry valued at $1 billion is producing cheap and fast background reports for an estimated 9 out of 10 landlords in the US—but false reports with no human review are keeping people out of housing: https://www.nytimes.com/... @nytimesbusiness : Tenant screening companies produce cheap and fast reports for an estimated 9/10 U.S. landlords. A review of hundreds of lawsuits filed against the companies shows how automated reports can wrongly label people criminals. With @TheMarkup https://www.nytimes.com/... @nahronational : “The screening process happens so quickly and the competition for apartments can be so fierce that prospective renters don't always know why they were turned down, much less whether an incorrect background report was the cause.” https://ow.ly/... @brutapologist : This sucks for this person but the problem isn't automation, it's the fact a criminal history can disqualify you from housing, which everyone needs regardless of what they've done https://twitter.com/... Sandra Park / @sandrapark : Tenant screening companies, now valued as a $1 billion industry, don't even bother to make sure their records are accurate. The impact is devastating, particularly on people of color, who tend to share common names. https://www.nytimes.com/... @nytimesbusiness : With @TheMarkup, we reviewed hundreds of federal lawsuits filed against tenant screening companies over the past 10 years that show how automated reports can wrongly label people https://www.nytimes.com/... Marcelo Rochabrn / @mrochabrun : One of the most befuddling things about NY was this “tenant blacklist” database, which would flag you if you had taken a bad landlord to court. This information wasn't easy to buy, so tenant screening companies would send minions to manually copy names from the court's computer https://twitter.com/... Jon Keegan / @jonkeegan : My colleague @lkirchner doing what @themarkup is all about right here. Telling the story of innocent people denied housing after sloppy tools used by landlords and public housing authorities incorrectly identified them based on their name - in some cases just PART of their name https://twitter.com/... Josh Sternberg / @joshsternberg : Computer algorithms that scan everything from terror watch lists to eviction records spit out flawed tenant screening reports. And almost nobody is watching. https://themarkup.org/... Samuel Ashworth / @samuelashworth : It's hard to think of anything more important than the work that @lkirchner and @themarkup are doing in explaining the sinister power of the automated, algorithm-driven systems that offer convenience - but no accountability. https://twitter.com/... Dan Immergluck / @danimmergluck : Good, important story/thread on automated tenant screening. Some ML folks may be really doing some damage here (and the advantages of having a name like Immergluck vs one like Jansen). https://twitter.com/... Jeffrey Vagle / @jvagle : With very little fanfare, we find ourselves living in the film Brazil. https://themarkup.org/...
Context & Ripple Effects
This investigation lands mid-arc in a documented fight over automated housing gatekeepers. Housing advocates had already flagged in automated tools from companies like CoreLogic that criminal-record data fails to capture nuance, and the reporting here quantifies the stakes: a roughly $1 billion industry whose reports reach an estimated nine out of ten U.S. landlords, built on hundreds of lawsuits showing renters wrongly labeled as criminals.
The piece also predates the next wave it helped set up — three years later, advocates were warning about landlords adopting AI screening tools with even less transparency, making this investigation the baseline record of error rates the later debate cites.
First-order effects
- Renters with clean records are being denied apartments over mistakes in reports sold by screening companies, who treat erroneous denials as the cost of doing business rather than a defect to fix.
Second-order effects
- Civil lawyers are building litigation playbooks against these systems — the same strategy documented months later as civil-rights suits targeting automated denial of basic services — forcing screening vendors into court costs and accuracy defenses.
Third-order effects
- If the lawsuit pattern holds, algorithmic gatekeepers across services face the accountability pressure already visible in adjacent domains like sentencing and policing algorithms, pushing regulators toward accuracy and appeal requirements for automated decisions.
The trend: Automated decision systems in housing are moving from opaque convenience tools toward contested infrastructure, with error documentation and litigation setting the terms of adoption.