The Crackdown on Rent-Setting Software: How New Rules Could Reshape U.S. Rents

What rent-setting software does and why it spread

Over the past decade, algorithmic rent-setting tools have become standard for many large apartment operators. These services, offered by companies such as RealPage, pull in vast amounts of lease data from subscribing landlords—current asking rents, signed lease terms, vacancy levels, renewal behavior, and concessions. They then generate daily recommendations for each unit or floor plan, telling managers whether to push rents higher, hold steady, or discount to fill vacancies.[wboc.com]

The appeal is obvious: instead of relying solely on local staff judgment and manual comps, a centralized system promises to optimize revenue across thousands of units. For owners, this can mean higher net operating income, more consistent underwriting assumptions, and a clear paper trail for pricing decisions.

Critics, however, argue that when many competing landlords feed confidential data into the same system and lean on its recommendations, the result can look less like independent pricing and more like coordinated behavior. Because the software can see real-time supply, demand, and competitor pricing across a market, it may allow landlords to collectively maintain higher rents than a truly competitive market would support.[wboc.com]

Greystar’s multistate settlement over algorithmic pricing

Those concerns are no longer just academic. In November 2025, Greystar, which manages more than 946,000 rental units nationwide, agreed to pay $7 million to settle allegations from nine states that its use of rent-setting algorithms contributed to inflated rents.[wboc.com]

According to reporting on the settlement, the states involved include California, Colorado, Connecticut, Illinois, Massachusetts, Minnesota, North Carolina, Oregon, and Tennessee.[wboc.com] While Greystar has not admitted wrongdoing, the agreement underscores how seriously state enforcers are treating the intersection of shared data, pricing algorithms, and housing costs.

Separately, Oregon reached its own settlement with Greystar earlier in 2025 over claims related to RealPage’s software. Under that agreement, Greystar agreed that it will no longer use third-party pricing services such as RealPage within Oregon.[opb.org] That restrictions-on-future-conduct piece is just as important for the broader market as the dollars involved, because it shows states are willing not only to seek money but also to block certain forms of rent-setting technology.

For institutional landlords and property managers, the message is clear: any system that pools sensitive pricing and occupancy data across supposed competitors is likely to face intense scrutiny.

Portland’s push to ban algorithmic rent-setting tools

Portland is now emerging as a test case for how local governments may try to limit these tools directly. A proposal before the Portland City Council would ban landlords from using “algorithmic pricing” services that scan regional rent and vacancy data submitted by landlords and then recommend monthly rents.[opb.org]

Under the proposed ordinance, landlords that use covered software could face city fines of up to $1,000 per violation. Tenants would also gain a private right of action, allowing them to sue landlords that use prohibited tools and seek up to $1,000 per violation, with each month of rent set using price-fixing software treated as a separate violation.[portlandmercury.com][opb.org]

After the idea was first floated in spring 2025, landlord groups raised concerns that the language was overly broad and could unintentionally sweep in small owners or even simple spreadsheet-based analysis. In response, the latest draft clarifies what is not covered: it exempts affordable housing providers, allows rent coordination within a single company’s portfolio, and excludes landlords who own fewer than six units.[opb.org][portlandmercury.com]

Supporters of the ban argue that these changes keep the focus on large-scale use of shared-data algorithms while avoiding collateral damage to small operators. Opponents maintain that the measure is unnecessary, could slow new development, and may expose the city to costly litigation. Regardless of the final vote, the proposal illustrates how local housing policy is starting to factor in not just how much rent is charged, but how those numbers are generated.

Beyond Portland: growing local and state actions

Portland is not alone. Several other cities and states have begun to target algorithmic rent-setting more explicitly. Reporting on the Greystar settlement notes that cities including Philadelphia and Seattle have passed ordinances restricting rent-setting software, and that new state laws in large coastal markets now limit or ban its use.[wboc.com]

This trend is part of a broader reevaluation of housing data and transparency. On the sales side, some states are “non-disclosure” jurisdictions, meaning that home sale prices are not required to be reported in public records. In those states, buyers and sellers must rely on multiple listing services and private data providers to understand market values.[redfin.com][redfin.com]

In rentals, by contrast, many of the new rules are moving in the opposite direction—away from private, pooled datasets that only large landlords and vendors can see, and toward more explicit boundaries on data sharing and price coordination. The underlying question is who gets to see what information, and whether sharing it through a common algorithm crosses the line from sophisticated market analysis into collective pricing.

What landlords, investors, and renters should watch next

For landlords and property managers, the immediate takeaway is risk management. If you use—or are considering—third-party software that:

• ingests competitors’ nonpublic rent and occupancy data, and
• produces explicit rent recommendations across a market,

you should assume that regulators or plaintiffs’ attorneys may examine how closely those recommendations are followed and whether your competitors are plugged into the same system.

Operators can reduce risk by:

• Reviewing contracts and product descriptions for any rent-setting or revenue-management software
• Documenting independent reasons for pricing decisions (local comps, property condition, unit-level demand)
• Limiting the sharing of nonpublic, granular leasing data with third parties unless clearly necessary
• Training staff on what the software does—and does not—decide, so they can explain pricing choices if challenged

For renters, new rules can create leverage. Policies like Portland’s proposal would give tenants the right to sue if banned software is used and to seek statutory damages. Even in markets without explicit bans, public settlements and investigations may encourage landlords to be more transparent about how rents are set.

Investors should factor these legal and operational risks into underwriting. Revenue-management upside that once looked like a free efficiency gain may now carry real compliance costs and litigation exposure. On the flip side, markets that adopt clear, stable rules about data sharing and pricing practices may ultimately provide more predictable operating environments.

The bottom line: algorithmic tools in rental housing are moving from a quiet back-office function to a regulated, closely watched part of the business model. Whether you own a handful of units or a national portfolio, now is the time to understand exactly how your rents are being set—and how that process will hold up under a brighter spotlight.