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Algorithmic pricing means crunch time for ethics and compliance officers

report from ProPublica in October crushed the fiction that revenue management software in the residential housing market doesn’t hurt renters. The troubling ProPublica report and its aftermath raise questions for all compliance officers, not just those in the real estate space: Is algorithmic pricing legal? And even if it’s legal, is it ethical?

ProPublica found that when a critical mass of landlords in an area use a software called YieldStar — which deploys an algorithm that analyzes information gathered from thousands of landlords — rents “suggested” to landlords rise faster than if humans are involved, often a lot faster.

There are two reasons. First, landlords don’t like apartments to sit empty. They think higher occupancy rates always mean more revenue. They’re wrong, as the YieldStar software proved. Instead, landlords can raise rents to more than make up for the empty apartments.

The second reason YieldStar works is that it removes humans from the rent-setting process, freeing corporate landlords to hike prices for tenants who remain.

As reporter Heather Vogell and data specialist Haru Coryne explained, before YieldStar, leasing agents were sometimes too “empathetic” toward renters, especially those who belonged to the agents’ peer group. YieldStar solves that by moving rent-setting decisions off-site and out of human hands.


The company that owns YieldStar is Houston-based RealPage. It feeds confidential information from property management clients into YieldStar. The algorithm produces “suggested” rents every day, which allows landlords to “continuously maximize asset value with precision pricing capabilities,” according to YieldStar’s website.

It takes vast amounts of data from landlords for RealPage’s price-setting algorithm to work properly. The company acquired more data by acquiring more property owners and leasing managers — ten companies since 2016, including its closest competitor, Lease Rent Options, in 2017.

RealPage successfully tested the algorithm in Houston and moved on from there. It now has more than 30,000 customers, including many of the most prominent property managers in the United States.

ProPublica analyzed five of the country’s top ten property managers as of 2020. They all used RealPage’s pricing software “in at least some buildings, and together they control thousands of apartments in metro areas such as Denver, Nashville, Atlanta and Seattle.” Rents in those markets for typical two-bedroom apartments rose 30 percent or more between 2014 and 2019, ProPublica said.

In one neighborhood in Seattle, ProPublica found that ten property managers oversaw 70 percent of all apartments, and all ten used RealPage’s pricing software.

“Apartment managers can reject the software’s suggestions, but as many as 90 percent are adopted, according to former RealPage employees,” ProPublica said.

Because of the ProPublica report, Senator Amy Klobuchar and other lawmakers sent a letter to the head of the DOJ’s antitrust division, asking for action. The letter said,

We are concerned that the use of this rate setting software essentially amounts to a cartel to artificially inflate rental rates in multifamily residential buildings. In addition, we are concerned about potential anticompetitive coordination taking place through the RealPage User Group, a forum of over 1,000 RealPage clients that work together on a variety of subjects, including revenue management and screening. The conduct in these markets raises significant anticompetitive concerns.

ProPublica reported in late November that the DOJ launched an antitrust investigation into whether RealPage’s rent-setting software is “facilitating collusion among landlords.”


The problems with algorithmic pricing are well-known to the feds. What’s surprising is that the first case involving price-setting software dates from the late 1980’s.

That’s when most major U.S. airlines began using routing and booking software that allowed them to share non-public pricing information. Between 1988 and 1992, the arrangement may have cost airline customers more than $1 billion, the DOJ said. By 1994, eight airlines reached settlements or consent decrees for price fixing. The airlines agreed to change what information they share through the software.

Jump forward 23 years. In June 2017, the DOJ and Federal Trade Commission submitted a scholarly white paper to the OECD titled “Algorithms and Collusion.” The white paper cited the airlines’ case but wasn’t industry specific. It recognized that while algorithmic pricing might sometimes help consumers by “facilitating rapid competitive response,” it can result in competitors unlawfully communicating through an intermediary to set prices or restrict output.

“This is sometimes called a ‘hub-and-spoke’ conspiracy,” the white paper said. “In this scenario, a purchaser or supplier (the ‘hub’) reaches separate agreements with each competitor (the ‘spokes’), with additional evidence demonstrating a common understanding among the competitors (the ‘rim’), for instance by assurances from the hub that each spoke would adhere to the agreement only if all the others did.”

A month before the United States submitted the white paper to the OECD, the acting chair of the FTC, Maureen Ohlhausen, gave a speech on the intersection of antitrust law and algorithmic pricing.

In a free market, she said, individual actors are free to set their prices based on all the information legally available to them. But she also described the heart of the problem with algorithmic pricing:

Let’s just change the terms of the hypothetical slightly to understand why. Everywhere the word “algorithm” appears, please just insert the words “a guy named Bob.” Is it ok for a guy named Bob to collect confidential price strategy information from all the participants in a market, and then tell everybody how they should price? If it isn’t ok for a guy named Bob to do it, then it probably isn’t ok for an algorithm to do it either.

Ohlhausen asked important questions: Are there opportunities for mischief in the black box nature of all this? Will pricing algorithms allow firms to collude or increase prices in ways that ultimately go undetected by enforcement agencies? Does antitrust doctrine need to change to reflect the greater use of automated decision-making across markets?


Questions Ohlhausen didn’t ask may be the most important of all: Is it ethical to replace humans with machines when pricing goods and services? Should we remove empathy from transactions with consumers? Is it right for landlords or merchants to always optimize revenue, regardless of the effect on individuals, families, communities, and beyond?

The public debate about algorithmic pricing has mainly involved airlines and apartments. What about food, medicines, electricity, refrigerators, cars, insurance, clothes, school tuition — you name it? Do we want a world where machines set the price of everything we need? Machines that won’t negotiate with us, listen to our stories, or give us more time?

It’s not too soon for compliance officers everywhere to think about these questions. Algorithmic pricing has arrived, and so have tough choices, whether we see them now or not.

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