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Artificial  
Intelligence-Driven  
Rent Pricing Tools  
& the Housing Crisis
MCKYNZIE CLARK, AUSTIN NELSON,  
SANJANA UTIRAMERUR



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Kynzie (she/her) is a researcher and student based in New York City. 
Eager to understand the growing intersection between technology, law, 
and networks of care, Kynzie is pursuing a Master of Social Work degree 
at Columbia University as a Fisher Cummings Fellow. In her studies, she 
hopes to leverage her experience in advocacy, research, and policy work 
to advance equity and systems change work. Previously, Kynzie studied 
political science and Russian language at Yale University. Outside of 
classes, Kynzie interns at The Bronx Defenders and works with the 
Center for Intimacy Justice.

MCKYNZIE 
CLARK



COLUMBIA SOCIAL WORK REVIEW, VOL. XXIII  |   51   

Austin Nelson (she/her) is a Master of Social Work student at Columbia 
University with a background in research, community engagement, 
and data ethics. Her professional experience spans direct service 
with unhoused populations, trauma-informed youth programming, 
and nonprofit development. Austin’s research interests center on the 
intersection of technology, equity, and housing justice, drawing from 
previous work on data ethics in the ICT4D space and gender-based 
violence interventions. She brings a critical, justice-oriented lens to 
examining the role of AI in rental markets, with attention to accessibility, 
bias, and systemic inequality.

50  |  COLUMBIA SOCIAL WORK REVIEW, VOL. XXII 

AUSTIN  
NELSON



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Sanjana is an MSW student on the advanced clinical practice track at 
the School of Social Work. She is passionate about working with diverse 
populations navigating systemic inequity, particularly in healthcare. In 
the future, she hopes to attain her LCSW license and provide accessible 
trauma-informed therapy to marginalized communities.

INSPIRATION
Our interest in the intersection of AI and the housing crisis emerged 
from my broader focus on social justice and technology. As social work 
students passionate about policy and ethics, we were struck by how 
AI tools—often promoted as neutral or innovative—are increasingly 
shaping access to basic human rights like housing. We were inspired to 
write this piece after reading about biased tenant screening algorithms 
and models that reinforce housing discrimination. Researching this topic 
was both eye-opening and frustrating. There was a wealth of information 
on AI development, but far less on how these tools impact low-income 
communities or exacerbate inequality.

SANJANA  
UTIRAMERUR



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AI AND THE HOUSING CRISIS

ABSTRACT
This policy brief explores the use of artificial intelligence (AI) in 
rent pricing tools that corporate landlords and property management 
companies use for rental housing, and its consequences for the housing 
market. Across the United States, landlords have become increasingly 
reliant on AI-driven rent pricing tools to raise rents and boost their 
profits. This technology, which uses both sensitive proprietary data and 
publicly available information, is reducing housing accessibility, often 
driving tenants from their homes. As AI becomes increasingly pervasive 
in our everyday lives, it is essential that we interrogate its uses, especially 
in those that have as many collateral consequences as housing. We offer 
an overview of rent regulation history, the underlying AI technology, and 
existing policy, and make our own policy recommendations.

Keywords: artificial intelligence, housing policy, tenants’ rights, data 
rights, privacy

MCKYNZIE CLARK, AUSTIN NELSON, & SANJANA UTIRAMERUR

ARTIFICIAL INTELLIGENCE–DRIVEN RENT 
PRICING TOOLS AND THE HOUSING CRISIS
Rental housing, like other societal systems, has been increasingly shaped 
by artificial intelligence (AI) technology in recent years. This shift, 
however, is not just a technological evolution—it represents a radical 
departure from traditional rent-setting methods and carries significant 
implications for privacy and equity. This article argues that artificial 
intelligence, particularly through rent pricing tools, has adversely 
affected the already competitive rental housing markets in urban 
settings by exacerbating discrimination and enabling collusion between 
landlords. We start by exploring the history of how rental prices have 
traditionally been set in the United States as well as the evolution of 
machine learning and AI. Then, we explain how AI-driven rent pricing 
tools, such as RealPage, affect urban rental housing markets. Finally, we 
look at policies regulating rental housing, data protection, and AI and 
offer our own policy recommendations.
 
I. BACKGROUND
CONTEXT REGARDING RENT IN THE U.S.

Historically, rent-setting in the United States has been shaped by a 
complex interplay of economic forces, housing regulations, and anti-
Black racism. Before the 1930s, homeownership was less common 
than it is today, with most Americans renting their homes due to 
high down payments and short loan terms that made buying property 
difficult (Gordon, 2005). Traditionally, rent-setting was a localized 
process where individual landlords determined rental prices based on 
property characteristics and neighborhood demand. The first rent control 
laws were adopted in the 1920s in response to urbanization, housing 
shortages, rent increases, and growing tenant advocacy following World 
War I (Rajasekaran et al., 2019).

During the Great Migration, which spanned most of the twentieth 
century, millions of African Americans migrated to northern cities 



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in search of economic opportunities and to obtain freedom from 
oppressive Jim Crow laws in the South. However, these migrants 
were met with discriminatory housing policies that acted as barriers 
to wealth accumulation and homeownership. Starting in the 1930s, 
the Home Owners Loan Corporation created color-coded maps that 
graded neighborhoods based on their perceived lending risk, which was 
often directly related to racial demographics (Kaplan & Valls, 2007). 
Predominantly Black neighborhoods were labeled “hazardous” and 
outlined in red, and the people living there were systematically denied 
access to credit, home loans, and mortgage financing. The practice led 
to the use of the term “redlining,” which was institutionalized by the 
Federal Housing Administration mortgage insurance program. The 
program made homeownership far more affordable for white families 
by offering low-down-payment, long-term loans backed by government 
insurance (Gordon, 2005). As a result of redlining practices, these loans 
were unavailable to Black families, reinforcing racial segregation and 
discriminatory housing practices. 

Additionally, racially restrictive covenants legally prevented Black 
families from purchasing or renting homes in white neighborhoods 
(Coates, 2014). When written into property deeds, these covenants 
explicitly prohibited sales to nonwhite buyers. Therefore, Black families 
were forced into overcrowded, deteriorating areas where landlords 
exploited high demand by charging inflated rent prices. 

Redlining and other discriminatory housing policies created the 
conditions for predatory practices to thrive, further preventing Black 
homeownership. Contract selling was a deceptive home-buying scheme 
in which Black families, denied access to traditional mortgages, were 
forced to purchase homes through high-risk installment contracts. Unlike 
conventional home loans, these contracts did not grant the buyer equity, 
and missing even a single payment could result in immediate eviction, 
allowing the seller to retain the property and all previous payments. 

Real estate agents used the tactic of blockbusting—spreading fear that 
Black families moving into the neighborhood would cause property 

values to plummet. The tactic drove white homeowners to sell their 
properties at reduced prices. The agents would then resell these homes 
to Black buyers at inflated prices, profiting from racial segregation and 
housing instability (Coates, 2014; Ross, 2008). 

Enabled by Federal Housing Administration loans and reinforced by 
these racist predatory practices, white families conducted “white flight,” 
moving to the suburbs to avoid integration after desegregation mandates. 
This practice further exacerbated economic and housing disparities. 
This migration deprived urban centers of crucial tax revenue, leading 
to deteriorating public services, housing conditions, and schools, all of 
which primarily impacted Black residents (Dilworth & Gardner, 2019).

The Fair Housing Act (FHA) of 1968, a direct outcome of the Civil 
Rights Movement, aimed to eliminate discrimination in housing based on 
race, religion, or national origin. Although this act made redlining illegal, 
the legacy of redlining continues to shape housing patterns, as Black 
communities still often face disinvestment, limited access to credit, lower 
homeownership rates, and high rental costs (Dilworth & Gardner, 2019). 
Many families of color remain in formerly redlined areas that suffer 
from underinvestment and gentrification pressures. The current renting 
population is increasingly diverse, with people of color, young adults, 
and low-income families making up significant portions (Dilworth & 
Gardner, 2019). 

CONTEXT REGARDING ARTIFICIAL 
INTELLIGENCE AND DATA

Artificial intelligence refers broadly to inanimate machine operations 
designed to replicate human cognition. The field of AI is relatively new: 
The term was only coined in 1956 by Dartmouth College professor John 
McCarthy, who explored “thinking machines” such as Alan Turing’s 
Enigma (Lawrence Livermore National Laboratory, n.d.). Today, when 
people refer to AI, they are most often referring to a process known as 
machine learning or a specific type of machine learning called deep 
learning. According to MIT Sloan professor Thomas W. Malone, 

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machine learning has become a critical method that has shaped most AI 
development for the last ten to fifteen years (Brown, 2021). 

The logic behind machine and deep learning is relatively intuitive. 
Machine learning (ML), simply put, is the process of training a computer 
program or system to perform tasks without explicit instructions. It 
uses simplistic structures, such as (but not limited to) decision trees 
and linear regressions. Deep learning (DL) is more sophisticated and 
teaches computers to process data in a way that attempts to mimic human 
neural networks. DL tools require much larger datasets than their ML 
counterparts and can be used to recognize complex patterns in data 
across a number of dimensions to make new predictions or insights.

While the complexity DL offers has proved tremendously helpful in 
a number of applications, it presents problems for others, especially 
for data containing social factors (such as socioeconomic status, race, 
gender, or sexuality). All AI algorithms, both ML and DL, are only 
as good as the data they are trained on, and biased inputs result in 
biased outputs. The problem of bias has dominated most critiques of 
AI technology, and fairly so. Examples of “algorithmic bias” that either 
inadequately represent1 or even adversely affect people of color2 are in no 
short supply. Bias presents an even bigger challenge to DL algorithms. 
Bias can be deeply embedded within the training data required to make 
DL algorithms function, and the complexity of DL neural networks 
makes it extremely difficult to identify, let alone address, instances of 
bias.

While there are many strategies to try to calibrate algorithms fairly with 
respect to factors such as race and gender, completely removing bias is 
not possible (Kleinberg et al., 2017). In his book The Alignment Problem, 
programmer and researcher Brian Christian (2020) has referred to the 

1 For example, Google Photos facial recognition has failed to identify Black people as human. 

2 For example, the Correctional Offender Management Profiling for Alternative Sanctions 
(COMPAS) algorithm has produced results that disproportionately and negatively affected Black 
men.

impossibility of achieving perfect fairness as a “brute mathematical fact” 
for any means of classification, human or machine (p. 70).
 
HOW DOES AI AFFECT RENTAL HOUSING 
MARKETS?

So far, we have established two key elements of DL algorithms that will 
help us explain how AI affects rental markets: 1) They analyze patterns 
across datasets to make predictions, and 2) they can and will be biased, 
and that bias is practically impossible to remove. 

DL algorithms are mainly used in the rental housing market through AI-
driven rent pricing tools. These typically operate by analyzing data from 
various sources, such as recent rental listings in an area and sales data, 
to offer market predictions and suggest optimal rent levels to maximize 
landlord profits. Prima facie, the process mirrors the way that most 
landlords and property managers determine rental rates without AI: They 
survey the area, consider competitor rates, and calculate other factors that 
affect what they think is the best rate for them to charge tenants (Vicks, 
2024; Policy Memo: Rent-Setting Software Algorithms, 2024). 

However, upon closer inspection, we find that DL rent pricing tools differ 
from traditional rent pricing in that:
• They are able to process far higher volumes of data.
• They are accessible to multiple landlords and property managers, 

leading to pricing collusion.
• They bring an inflated perceived trustworthiness that AI tends to 

confer.
• They are more prone to hard-to-find bias that disproportionately 

affects renters of color. 

We analyze these issues further using the RealPage/YieldStar rent pricing 
tool as a case study due to its popularity and impact (Vogell et al., 2022).
 
REALPAGE AND YIELDSTAR

RealPage is a Texas-based property management software company 

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that provides a technology platform that “enables real estate owners 
and managers to change how people experience and use rental space” 
across over 24 million units in North America, Europe, and Asia 
(RealPage, n.d.). Among the many products RealPage offers is a tool 
called YieldStar, an “asset optimization system that enables owners 
and managers to optimize rents to achieve the overall highest yield, or 
combination of rent and occupancy, at each property” (RealPage, n.d.). 
YieldStar aggregates both public and proprietary data to set rent prices 
across entire regions (Policy Memo: Rent-Setting Software Algorithms, 
2024). This data includes tenants’ rent data, credit checks, criminal 
background information, survey data from landlords and competitors, 
historical data, and sales transaction data (RealPage, n.d.). 

The result has been a sharp and sustained increase in rental costs 
nationwide, especially in cities like New York, where renters are made 
to spend upwards of 30% of their total income on rent (Siegel & Bram, 
2024). The technology has also emboldened landlords to raise rates 
higher than they otherwise would. In the words of RealPage executive 
Andrew Bowen, “I think [AI is] driving [rate increases], quite honestly 
… As a property manager, very few of us would be willing to actually 
raise rents double digits within a single month by doing it manually” 
(Vogell et al., 2022). Without meaningful intervention, these technologies 
will only deepen existing inequalities, further entrenching a system 
designed to prioritize profit over people’s right to a stable home.

RealPage allows landlords to circumvent price-fixing regulations by 
enabling them to access data from other landlords and companies without 
direct cooperation, effectively reducing competition and inflating the 
housing market. In 2024, the U.S. Department of Justice, in collaboration 
with eight state attorneys general, filed a civil suit against RealPage for 
alleged unlawful monopolistic practices that reduce competition among 
landlords (U.S. Department of Justice, 2024). Moreover, a federal suit 
in North Carolina accuses the software of violating sections 1 and 2 of 
the Sherman Anti-Trust Act by monopolizing interstate commerce and 
restricting competition in the marketplace. This suit is ongoing and has 

been amended as of January 7, 2025, to include six apartment landlords 
as defendants (U.S. Department of Justice, 2025).  

II. POLICY LANDSCAPE
RENTAL POLICY

Prior to the introduction of AI pricing tools such as RealPage, landlords 
determined rent pricing through market analysis and cost considerations, 
factoring in the economic climate. Traditionally, property managers rely 
on comparable market analysis, a process that reviews rental prices for 
similar properties within a given region, to determine competitive rent 
pricing (Pagourtzi et al., 2003). To ensure profitability, landlords and 
property managers must consider operational costs such as insurance, 
mortgage payments, and utilities. These factors, combined with a 
consideration of current economic conditions such as inflation and 
employment rates, would be used to set a rental price for each property 
(Dias & Duarte, 2019).

In the United States, several federal regulations exist to protect against 
discrimination and monopolistic practices in the housing market. The 
aforementioned FHA of 1986 prohibits housing discrimination on 
the basis of sex, race, religion, national origin, disability, or familial 
status (Fair Housing Act [FHA] 1968/2023). The U.S. Department of 
Housing and Urban Development (HUD) enforces the FHA and oversees 
affordability initiatives such as Section 8 vouchers, which assist low-
income families seeking affordable housing. 

While HUD specifies that AI tools for tenant screening, advertising, and 
mortgage decisions must comply with the Fair Housing Act, there are no 
specific federal regulations regarding rent pricing tools. Furthermore, the 
free housing market is largely protected by the Sherman Anti-Trust Act 
of 1890, which prohibits price-fixing agreements between competitors, 
exclusive contracts, and monopolizing a market for products or services 
(Sherman Anti-Trust Act, 1890). In accordance with this act, landlords 
and property managers are prohibited from sharing data about their rental 
units and colluding to inflate rental prices. But as previously mentioned, 

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RealPage has been accused of violating the Sherman Anti-Trust Act 
by allowing landlords and property managers to access rental data, 
effectively restricting competition and increasing inflation in rent pricing.
 
TECH POLICY

Artificial intelligence is relatively new and thus loosely regulated 
by federal law, leaving the majority of regulations to the state level. 
Currently, the Federal Trade Commission (FTC) and the National 
Institute of Standards and Technology (NIST) have issued broad 
guidelines regarding transparency and consumer protection in AI 
algorithms (Federal Trade Commission, 2024). Although the NIST 
provides a suggested framework for transparency and data protection, no 
comprehensive legislation specifically addresses these concerns (NIST, 
2024). 

Regarding AI tools in housing, rent pricing tools are loosely regulated 
through sector-specific policies by HUD and the Federal Housing 
Finance Agency. However, concerns have risen that AI pricing tools may 
contribute to discrimination in the housing market by deriving algorithms 
that rely on historical data and patterns of discrimination against certain 
ethnic and socioeconomic subgroups. Moreover, the use of artificial 
intelligence reduces human oversight and creates a lack of transparency 
on how pricing decisions are made. As consumers across the United 
States express concern over artificial intelligence, states have begun to 
introduce legislation around the use of AI tools in housing. 

As every state faces unique housing challenges, each state has taken 
a slightly different approach to regulating AI pricing tools. California 
is currently facing a housing crisis, as the state has one of the highest 
median rent and home prices in the nation. As of September 2024, the 
qualifications for a mortgage on a mid-tier home were more than double 
the median household income for the previous year (Bentz, 2024). 
Despite growing concerns, the state has not enacted comprehensive 
legislation to address AI pricing tools. Currently, the data privacy of 
California residents is protected by the California Consumer Privacy Act 

and the California Privacy Rights Act, both of which restrict data sharing 
by businesses and grant consumers greater control over personal data 
collected by AI systems (California Privacy Protection Agency, n.d.). 
Connecticut and Virginia have enacted similar legislation in an effort to 
increase transparency and protect consumers’ sensitive data. 

Although no specific state legislation addresses AI tools in housing, 
broader civil rights regulations in California prohibit discriminatory 
practices in housing. For example, both the California Fair Employment 
and Housing Act and California Government Code Section 12955 
prohibit discrimination in housing and employment on the basis of race, 
color, religion, ancestry, national origin, disability, medical condition, 
marital status, sexual orientation, sex, or age (Housing discrimination, 
1980). While existing legislation is broad, these regulations make it 
unlawful for AI pricing tools to result in algorithmic discrimination. 
However, more specific legislation is required to combat price increases 
that affect the affordability of rent in California. 

As of now, Colorado is the only state to enact comprehensive legislation 
on the development and distribution of artificial intelligence systems: 
The Colorado Artificial Intelligence Act (CAIA) will become effective 
February 1, 2026 (Consumer Protections for Artificial Intelligence, 
2024). The CAIA targets high-risk artificial intelligence systems in 
sectors such as education, employment, housing, and healthcare to 
reinforce standards set by the Fair Housing Act and protect against 
algorithmic discrimination, defined by unlawful differential treatment 
that disfavors groups based on protected classifications. The legislation 
imposes regulations on AI by requiring developers and deployers of high-
risk AI systems to use reasonable care in protecting consumers against 
algorithmic discrimination. For example, developers would be required 
to provide documentation to deployers on data used to train the system, 
how it was evaluated, and its intended outputs and use. Furthermore, 
developers and distributors are required to clearly display the potential 
risks of algorithmic discrimination on websites for public use. If the AI 
system presents a risk for algorithmic discrimination, the developer is 
required to notify the Colorado attorney general within a 90-day period. 

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These specific limitations on AI systems are designed to combat 
algorithmic discrimination but do not specifically address antitrust laws 
or monopolization of the market. AI pricing tools such as RealPage 
remain in a gray area, where they must adhere to antidiscrimination 
laws but may still provide enough rental data that landlords and property 
managers may take advantage of it to manipulate rental prices. This gap 
in regulation raises concerns over what AI pricing tools may accomplish 
without stricter oversight. 

Furthermore, the current administration has been very vocal about its 
intentions to continue using and developing AI technologies without 
“barriers” such as bias prevention or data protections (White House, 
2025). The United States also refused to sign the international AI Action 
Statement at the Paris AI Action Summit earlier this year (Kleinman & 
McMahon, 2025).
 
III. POLICY OPTIONS
There are a number of options that U.S. policymakers can and should 
consider to address issues stemming from AI-driven rent pricing. These 
options address various regulatory fields involved in the problem, 
including transparent use of AI, access to personal data, and housing. The 
three most prominent policy options—strengthening federal oversight 
of the FHA, introducing AI and data regulation, and adopting rent 
controls—are outlined in this section. 

STRENGTHENING FEDERAL OVERSIGHT  
OF THE FHA

Instituting a federal mandate stating that landlords and property owners 
must disclose the use of AI pricing tools and other factors contributing 
to rent pricing to tenants could ameliorate some of the harmful results of 
AI-priced housing. With ensured disclosure, renters are presented with 
enough information to make calculated decisions on whether to utilize 
AI tools. Additionally, a mandate could create a federal registry of AI 

systems used in the housing market, requiring developers to submit 
documentation to demonstrate compliance with antidiscrimination 
standards set by the FHA (FHA, 1968/2023). Developers and distributors 
of AI housing tools would be subject to annual audits under the review 
of HUD. The HUD would then assess potential algorithmic bias, 
discriminatory outcomes, and data integrity.

One advantage of a federal mandate is that it ensures a consistent 
national standard for AI tools and alignment with antidiscrimination 
laws in the housing market. A mandate in addition to a national registry 
would address gaps in sector-specific federal oversight. However, laws 
prohibiting algorithmic discrimination and requiring transparency may 
face lobbying or other pushback from AI developers and stakeholders, 
and significant federal resources may be needed to implement and 
enforce the mandate.
 
AI AND DATA REGULATION

Introducing financial incentives for states to adopt comprehensive 
legislation similar to CAIA could help address algorithmic discrimination 
and monopolization of AI pricing tools. In this scheme, federal grants 
would be provided to states that adopt comprehensive AI legislation. 
State incentives could encourage tailored solutions to state-specific issues 
in the housing market. However, without federal oversight, there may be 
inconsistent regulations and protections across states. 

RENT CONTROL

Lastly, the issue of AI rent pricing tools would not exist if not for the 
state of the U.S. housing and rental market. One possible method for 
addressing the hypercompetitive nature of rental housing is through rent 
control and/or antigouging legislation (collectively, “rent regulations”). 
American cities are more susceptible to AI rent pricing tools because 
of the lack of policy limitations constraining the range of rent prices 
landlords can ask for. The U.S. housing market is relatively unregulated 

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as a whole, especially in comparison to many Western European nations. 
Because AI-driven rent pricing tools encourage landlords to raise rents 
by higher amounts within shorter time frames, reducing the rate by which 
a landlord could increase rent would limit the potential impact of the AI 
recommendations on the rental market.

State interventions in rent controls were quite common in socialist states 
to maintain the competitiveness of socialist economies on a global scale 
(Lux et al., 2013). In postsocialist and primarily capitalist economies, 
state interventions in the rental housing market generally take the form 
of social housing or private rental-sector regulation. Since World War II, 
most of these interventions have been to the private rental sector. Some 
of the strictest systems of state intervention in rental regulations can be 
found in Sweden and Denmark, where the state regulates rents for all 
running and newly signed leases (Sardo, 2024, p. 228). 

Of course, traditional rent control measures have faced criticism for 
their inflexibility and for their tendency to disincentivize upkeep and 
maintenance of units. New York City itself has a long history of rent 
controls that have been found to be less than successful. Researchers 
at the Wharton School found that a review of twentieth-century rent 
control policies revealed they had negative impacts on rental structure 
quality, especially in smaller prewar-constructed buildings (Gyourko & 
Linneman, 1990, p. 399). Furthermore, socialized policies often fail to 
achieve popular support in the U.S., a country whose historic and cultural 
commitment to the free market is well established. 

While rent regulation policies may be difficult to enact at a federal level 
in the United States, it would be advisable for states or municipalities 
to adopt a larger role in reviewing lease agreements. Moreover, state 
governments may consider a liberalization of private rent contracts in 
combination with rental regulation and tenant protections. This would 
address the volatility of the rental market, collusion concerns, and 
ostensibly concerns over abuse of private data. Even requiring landlords 
to justify changes in rent would increase transparency in the rental 

market and could discourage opaque decision-making methods such as 
those fostered by rent pricing algorithms.
 
IV. CONCLUSION AND RECOMMENDATIONS
Addressing the impacts of AI-driven rent-setting tools, such as RealPage, 
requires a strategy that balances ambition with practicality, combining 
strengthened federal oversight, state-level incentives, and well-designed 
rent control measures. 

Federal oversight would provide a critical foundation, setting consistent 
nationwide standards in data protection and aligning AI systems with 
the existing Fair Housing Act. Requiring transparency, establishing a 
registry of AI tools, and conducting regular HUD audits could create 
accountability and curb the negative effects of rent pricing tools. For 
this reason, and a host of other AI-based challenges we continue to 
face, it is more important than ever to develop a comprehensive policy 
framework addressing the use of AI and data privacy rights borrowing 
from the strong precedents set by the EU in the General Data Protection 
Regulation (GDPR), Digital Services Act (DSA), and AI Act. However, 
achieving comprehensive federal legislation may face steep political 
challenges, so it is necessary to supplement these efforts with policies 
that can be implemented more readily at the state and local levels.

State incentives offer a pragmatic way to drive meaningful change while 
respecting the unique challenges of local housing markets. By providing 
grants to states that adopt comprehensive AI legislation, like Colorado’s 
Artificial Intelligence Act, the federal government could empower states 
to innovate while remaining aligned with broader national goals. This 
approach allows for regionally tailored solutions that can be implemented 
without waiting for federal consensus, creating a framework where 
states lead the way in testing and refining policies to address algorithmic 
discrimination and monopolistic practices in the housing sector.

Rent control measures round out this strategy by directly addressing the 

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immediate harms of AI-driven rent increases. These policies, which cap 
excessive rent hikes and require landlords to justify significant increases, 
protect tenants while remaining adaptable to local conditions. Paired with 
complementary tools like housing vouchers and incentives for affordable 
housing development, they provide stability in the rental market and 
safeguard vulnerable populations. 

Together, federal oversight, state-level innovation, and rent regulations 
form a cohesive and actionable plan to address both the systemic and 
immediate challenges posed by AI-driven housing systems. These 
approaches, both individually and collectively, would increase housing 
equity and decrease exploitative data practices. 

Overall, localized rent control measures provide the best first step toward 
addressing long-standing systemic inequities in the rental housing 
market and preventing AI-driven rent pricing tools from exacerbating 
these inequities in the market. The recently severely reduced size of the 
federal government as well as polarized views about the extent to which 
AI should be regulated (if at all) play heavily into this recommendation. 
Since rent controls have historically correlated with decreased quality 
of rental units, they should be introduced alongside stronger landlord 
accountability measures or increased enforcement of housing court 
decisions where rental habitability can be disputed.

 

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Christian, B. (2020). The alignment problem: Machine learning and human values. W. W. Norton.

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