The City of Portland ordains.
Section 1. The Council finds:
- The City of Portland recognizes that the sale or use of certain revenue management software programs, known as algorithmic devices, in the rental housing market poses a significant threat to fair competition, housing affordability, and tenant protections.
- Algorithmic devices enable landlords to indirectly coordinate rental prices and occupancy levels by analyzing and sharing non-public competitor data, leading to artificially inflated rents and reduced housing access.
- Such practices disproportionately harm low-income residents, increase eviction rates, and destabilize Portland's housing market.
- Multiple lawsuits, including those filed by the Attorneys General of Arizona[1], United States Department of Justice[2], Multidistrict Litigation (JPML) has transferred a number of actions to the Middle District of Tennessee[3], and Office of the Attorney General for the District of Columbia[4] have highlighted the anti-competitive and harmful effects of these practices.
- To safeguard tenants, promote fair competition, and ensure market stability, this ordinance prohibits the use and sale of algorithmic devices for setting rents or managing occupancy levels in the City of Portland.
NOW, THEREFORE, the Council directs:
- Add City Code Section 30.01.088 as shown in Exhibit A.
- This ordinance takes effect 90 days after passage by Council.
Exhibits and Attachments
Impact Statement
Purpose of Proposed Legislation and Background Information
This ordinance affirms what has been true for over 100 years: price fixing and coordination are illegal practices that undermine free market competition. This is true whether an individual or entity is colluding with competitors in person in a “smoky back room” or through algorithm-driven price fixing services.
Price fixing and coordination facilitated by AI and algorithms are particularly pernicious for both renters and landlords seeking access to a fair, competitive marketplace. They leverage access to mass quantities of sensitive competitor data to “driv[e] every possible opportunity to increase price,” and ensure competitors transform into collaborators, “mov[ing] in unison versus against each other” such that the algorithm “as a middleman, and not the free market, determines the price that a renter will pay.”[1] This means that the landlords willing to collude and undercut small landlords relying on legitimate competitive strategies can profit through market manipulation rather than having to compete to offer a superior rental product. The result is inflated rental prices, market distortions, reduced housing supply, and housing instability.
This ordinance will prohibit anticompetitive practices driven by AI and algorithms, restoring the market competition critical to addressing the ongoing housing crisis.
Background & Justification:
AI or algorithmic price-fixing software, including but not limited to certain software products or modules from RealPage (controlling over 80 percent of the commercial revenue management software market) and Yardi, determine rental prices through data aggregation, machine learning models, and predictive analytics.
This is problematic for several reasons:
- First, the data aggregation process is designed to do just that – aggregate data and align or coordinate prices based on machine learning models and predictive analytics. This is the very definition of price collusion - essentially the virtual iteration of it, fixing and coordinating prices with granular competitor data, in near real-time. This is the algorithmic equivalent of gathering the city’s landlords in a conference room to share sensitive competitor data and agree to set and move prices in unison. This hinders the process of landlords competing independently on their pricing decisions and results in a high probability for coordinated rental price increases.
- Second, the AI software utilized to determine rental price recommendations is an opaque labyrinth. The machine learning and predictive analytics, such as regression models, neural networks, forecasting, algorithmic clustering, deployed by such companies make it very difficult to understand how they exactly work and how price recommendations are being made. More importantly, the software is deemed proprietary and kept hidden from public understanding and regulatory scrutiny.
- Lastly, beyond the blatantly anticompetitive practices facilitated by these tools, the ambiguity and lack of transparency with respect to how AI software is functioning to fix rental prices has the potential to quickly devolve into AI models favoring certain neighborhoods, demographics, or tenant profiles, even with the use of aggregated, anonymized data.
- Lawsuits have been filed at the federal and state levels against these companies, including by the U.S. Department of Justice, Oregon Attorney General, and other jurisdictions, citing violations of antitrust laws and harm to tenants.
- The ordinance is aligned with a current legislative proposal, Senate Bill 722 (2025), which would prohibit residential landlords from using software modules designed to set rents or occupancy rates and allow affected tenants to collect damages up to a specified amount.
- The ordinance amends Portland City Code Chapter 30.01.200, explicitly banning the use and sale of such software in the City of Portland. This ordinance does not prohibit landlords from using software that provides landlord support services (e.g., accounting or property management), so long as those tools do not facilitate anticompetitive practices such as price fixing or coordination.
- This legislation supports Portland’s tenant protection policies and reinforces commitments to housing affordability by prohibiting algorithmic price-fixing in rental housing.
[1] United States Department of Justice v. RealPage Inc., Complaint, 24-cv-00710
Financial and Budgetary Impacts
- No anticipated financial impact on the City’s budget, unless the Council allocates additional resources (i) to the City Attorney’s Office for enforcement, as compliance will primarily be upheld through civil penalties and tenant-initiated legal actions, or (ii) to the Portland Housing Bureau for policy education, outreach, and evaluation to enhance compliance and determine if the policy’s goals are being met.
- Potential fiscal benefits include:
- Reducing homelessness and eviction-related costs, which place a burden on social services, emergency housing, and public health resources.
- Preventing artificially inflated rental prices that contribute to the overall cost of living crisis in Portland.
Economic and Real Estate Development Impacts
Effect on Real Estate Development: Without data about the number of companies utilizing and housing units that are currently managed by the aforementioned software, specific impacts cannot be forecasted. However, the following facts can be considered:
- The Federal Department of Justice Complaint against RealPage cites RealPage executives stating that their software is aimed at “driving every possible opportunity to increase price” and that, among landlords, “there is greater good in everybody succeeding versus essentially trying to compete against one another in a way that actually keeps the entire industry down.” Explicitly acknowledging the benefits of price coordination, RealPage stated that when enough landlords used RealPage’s software, they would “move in unison versus against each other.” As one potential client put it succinctly to RealPage: “I always liked this product because your algorithm uses proprietary data from other subscribers to suggest rents and term. That’s classic price fixing . . . .” (emphasis added).
- In a market where units are priced without software advertised as a means of “driving every possible opportunity to increase price,” it is conceivable that prices paid by Portland renters will decline or at the very least, not grow as quickly as they would when price fixing software is aiming to maximize prices and landlord revenue. Roughly half (49.2%) of Portlanders rent, with over half of those households paying more than 30% of their household income to rent. This suggests that the most obvious and plausible economic impact is that some large portion of Portland renters will see an increase in discretionary income while some smaller number of landlords (not necessarily Portland residents) will receive less income derived from above-market pricing. Further, it is probable that additional discretionary income retained by renters will be directed into the local economy (e.g., local businesses and services).
- The ordinance does not prohibit software platforms offering accounting, vendor management, and related property management services. Landlords who find themselves using prohibited services on a software platform will still be able to use non-prohibited services on that platform. Thus, the operations of landlords and their management companies should not be affected so long as they do not engage in price fixing.
Tenant & Community Engagement:
- Housing advocates, tenant unions, and fair housing organizations provided feedback emphasizing the urgent need for regulation against algorithm-driven rent increases.
- Testimonies from low-income tenants and families facing rent hikes highlighted how algorithmic pricing has exacerbated displacement, homelessness, and financial strain.
Impact on Businesses & Housing Development:
- No anticipated impact on small landlords who set rents manually or based on fair market analysis.
- Large corporate landlords using these algorithmic tools may experience reduced profit margins, but the expectation is that market transparency and competition will improve.
- No effect on affordable housing development, as the ordinance exempts income-based rent calculation tools used for publicly funded housing projects.
Comparative Analysis with Peer Cities:
- San Francisco and Philadelphia have passed bills while Minneapolis, Washington, D.C., and other cities are exploring similar regulations against algorithmic rent-setting.
- This ordinance ensures Portland remains an equitable and competitive housing market, rather than one dominated by corporate price-fixing schemes.
Community Impacts and Community Involvement
- Impacted Communities:
- Algorithmic rental price-fixing software can drive rent increases, and low-income renters, Black and Indigenous communities, seniors, and disabled individuals are disproportionately affected by rent increases because they tend to be more rent-burdened.
- This ordinance helps stabilize housing costs and reduce displacement, benefiting rent-burdened families and historically marginalized communities.
- Neighborhood & Livability Impacts:
- Rising rents and corporate price-fixing have contributed to increased gentrification and displacement, challenges with which Portland has struggled, particularly in East Portland and historically Black neighborhoods.
- By banning algorithmic rental price-fixing, this ordinance ensures that housing affordability remains a priority for all Portlanders.
- Public Engagement & Testimony:
- Tenant advocacy groups, nonprofit housing organizations, and legal experts have been involved in shaping this ordinance.
- Small landlords and independent property owners have expressed support, noting that algorithmic pricing primarily benefits large corporate landlords at the expense of renters and smaller property owners.
- Expected testimony will include representatives from tenant rights organizations, housing justice organizations, and impacted renters who have experienced drastic rent hikes due to algorithm-driven rent increases.
100% Renewable Goal
Not applicable.
Financial and Budget Analysis
As the number of potential violations and their durations are unknown at the moment, the amount of resources needed by the City to enforce the language in the code is also unknown, as well as the amount of revenues that may be generated via civil penalties. Enforcement includes the involvement of the City Attorney and the Code Hearings Officer. Civil penalties charged to violators are up to $1,000 per violation, with each day of violation and each affected housing unit constituting a separate violation.
Document History
Document number: 2025-045