Rent Algorithms Artificially Inflate Housing Costs: FBI Investigation & the Colorado Bill that Sought to Address Antitrust Concerns
Landlords and property managers’ use of rent setting algorithms to determine housing costs across the US is under scrutiny, as the FBI investigates algorithm services such as RealPage over antitrust and collusion allegations. A bill from Colorado’s 2024 legislative session sought to address the use of these rent setting algorithms, but died in the last days of session due to disagreements over amendments.
HB24-1057 Prohibit Algorithmic Devices Used for Rent Setting attempted to clarify that under the Colorado Consumer Protection Act, the use of algorithms containing non-public competitor data is illegal collusion for the purpose of price-fixing, and the bill as introduced would have prohibited the use of these anticompetition algorithms as a deceptive trading practice.
One such rent algorithm service called RealPage admits that its algorithm is helping drive rents higher with marketing like “Pricing Optimization That Outperforms the Market 2%-5%,” on its website.
In the inception of RealPage, which is one of the first and biggest rent-setting algorithm services in the United States, one property management company found that turnover rates increased about 15 percentage points after it implemented the algorithm service. But that wasn’t a problem for the business: despite having to replace more renters, the company’s revenue grew by 7.4%.
The CEO of the property management company was quoted saying, “The net effect of driving revenue and pushing people out was $10 million in income,” Campo said. “I think that shows keeping the heads in the beds above all else is not always the best strategy.”
So we see that from the outset of rent-setting algorithms, the express goal has always been to use private data to artificially drive up prices through anti competitive collusion between landlords and management companies, and this practice has driven the rise in rent costs across Colorado.
In Denver alone, the cost of thousands of units is determined with rent-setting algorithms. CFI analysis comparing California and Oregon’s rent rates to that of the Denver metro area from 2011-2024 finds that rent rates in the Denver metro area have risen faster than most of the major cities in California and Oregon that were analyzed.
Too many Coloradans are struggling to keep up with rising rent costs, and the use of these rent-setting algorithms further contributes to and exacerbates our housing affordability crisis. On average across the state, a person must work 85 hours a week at minimum wage to afford a modest one bedroom apartment at the statewide average Fair Market Rent, which is about $1,600/month. For comparison, spending around $900 on rent is considered affordable for a Coloradan who earns 30% AMI––that’s almost half of the Fair Market Rent rate.
Across Colorado, people on fixed incomes, working people, and their families struggle to afford to remain housed. Essential service workers, teachers, and nurses often cannot afford to live close to where they work. A single mother earning an average teacher’s salary in Colorado cannot afford to house and support her family without a second job.
Renters are considered cost burdened if they spend 30% or more of their household income on housing costs, and in Colorado, 51% of renters are cost burdened, according to 2022 ACS data. Housing is simply unaffordable for most Coloradans, and rent setting algorithms that artificially inflate housing prices cannot be allowed to continue flagrantly disregarding antitrust laws that are in place to prevent these anti-competitive practices.
Housing is necessary for Coloradans to be able to thrive. Access to affordable and stable housing is essential for economic mobility, community-building, and public health and safety. More technological innovation demands more guardrails, especially as it relates to basic human needs like housing. As we continue to follow federal action on this issue, we hope to see a bill aiming to robustly protect renters from the use of rent setting algorithms return and succeed next session.
