Is Algorithmic Pricing Raising Your Rent?

A Data-Driven Investigation

RealPage's software lets landlords share secret pricing data, coordinating rent hikes instead of competing for tenants. The result? Higher rents, fewer concessions, and no real choice for renters.

Landlords aren't setting prices—an algorithm is. RealPage collects and analyzes private rent data from competitors, then recommends price hikes landlords follow almost automatically. This isn't competition—it's algorithmic price-fixing.

This isn't just bad for renters—it's bad for the market. RealPage controls 80% of the apartment pricing software industry, creating a monopoly that locks landlords in and locks renters out of fair pricing.

When landlords collude, renters lose. Lawsuits are underway, but millions of renters have already paid the price. Housing should be a competitive market—not a rigged game.

$1,803

DC's Fair Market Rent (1 Bedroom Apartment, $2,045 for 2 bedrooms )

$2,053

DC's Average Rent (All Apartments)

$2,150

RealPages's Average Rent (DC, All Apartments)

Jump to Key Findings

Or scroll down to learn how Algorithmic Collusion works.

Scroll down to learn about the major factors that determine rent prices.

What factors into rent?

and how does RealPage compare?

Two major factors affect rent prices: the market and the condition of the property. We use the year built and metropolitation area to compare RealPage-managed properties to the rest of the market.

Scroll down to understand who RealPage serves and in what markets

Contextualizing RealPage

Who does RealPage serve and how did the LRO merger affect prices?

Realpage isn't serving mom and pop landlords: they court large property management companies. In Spring 2017, RealPage acquired their main rival, LRO. This merger allowed RealPage to collect more data and increase their market share.

Scroll down to understand who RealPage serves and in what markets

Key Findings & Policy Implications

Our analysis reveals a significant association between RealPage's market presence and rent dynamics. The guided journey through housing supply, rent adjustments, occupancy rates, and robust counterfactual tests suggests that algorithmic pricing may contribute to rent inflation.

Policy Implications:

  • Regulators and policymakers should consider the impact of pricing technologies on housing affordability.
  • Enhanced transparency and oversight might be needed to ensure fair competition in rental markets.
  • Stakeholders—from tenants to property managers—could benefit from clearer insights into how technology drives market outcomes.

These findings underscore the need for further investigation and proactive policy responses in an era marked by rising housing instability and technological disruption.

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