A shared rent algorithm drew an antitrust challenge

The rent calculation behind an empty apartment

A vacant apartment looks like lost income: no tenant means no rent for that month. But a landlord setting prices faces a tradeoff. Accept a tenant at today’s rate, or wait and try to lease the unit later at a higher one. If a pricing system predicts that higher rents will more than make up for the vacancy, it can recommend holding out.

RealPage’s software was designed to help landlords make those decisions using rental-market data and pricing recommendations. Rather than relying only on one property manager’s view of nearby listings, the system could draw on information from many participating landlords. That gave it a broader picture of the market—and made its recommendations consequential beyond any single building.

When competitors follow the same signal

Normally, landlords compete for tenants: if one asks too much, renters can choose another apartment, putting pressure on the first landlord to lower the price. But when many landlords use the same system, their pricing decisions can become more aligned. A recommendation to wait for higher rents may be easier to follow when the algorithm’s view of the market reflects conditions across many properties.

That does not mean every landlord followed every recommendation or that a vacant unit automatically raised anyone else’s rent. The concern is about the combined effect: if enough apartments are held off the market, renters have fewer available choices, and landlords may face less pressure to offer a lower price.

The cost of treating homes as inventory

This approach applies a familiar business calculation—whether to sell now or wait for a better price—to housing, where the “inventory” is someone’s potential home. For an individual owner, one empty unit may seem like a calculated bet. Across a market, repeated bets can mean fewer apartments offered to renters at once, even while buildings have units sitting vacant.

That is the tension behind the algorithm’s influence: a recommendation can look like ordinary price optimization for each property, yet shape the choices renters encounter across many properties at the same time.