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AI-enabled tools can make it faster to find a home, estimate a property’s value, screen rental applicants, or manage a building. They can also steer people, rely on inaccurate data, or make consequential decisions that are hard to understand or challenge. The effects depend on the specific tool, the decision it influences, and the quality of its data—not on AI as a single, universal system.
Where AI appears in homebuying and renting
Property technology, or proptech, includes software, digital platforms, and other digital tools used in housing. The U.S. Government Accountability Office (GAO) examined selected tools in two 2025 reviews. Its rental review focused on advertising platforms, tenant-screening tools, rent-setting software, and facial-recognition technology. Its homebuying review focused on online real-estate platforms, automated valuation models (AVMs), automated underwriting systems, and electronic closing tools. These categories show how technology can touch many stages of housing; they do not mean every product uses AI or that every housing decision is automated.
Searching, advertising, and communicating
Online platforms can help people search listings, view estimated values, explore financing, and communicate with chatbots or virtual assistants. Algorithms may personalize which listings a person sees or how housing advertisements reach different audiences. That can reduce search friction, but it also means the selection or delivery of housing options may be shaped by a system the user cannot see.
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AVMs estimate property values using data and statistical models. Lenders may use automated underwriting systems to evaluate creditworthiness, while digital tools can support electronic closing. These are distinct applications: a quick online estimate is not the same thing as a lender’s underwriting decision, and neither should be treated as an all-purpose measure of a home or borrower.
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Rental applications and building operations
Landlords or housing providers may use software to screen applicants or recommend rents. Some use facial recognition to control building access. These tools can affect whether an applicant advances, what rent is proposed, or how residents enter a property, but the degree of automation and human involvement can vary.
What these tools can make faster
GAO describes potential advantages on both sides of the market: digital rental tools can make it easier to search, view, and lease housing and help owners manage units, while homebuying tools can streamline parts of the process and reduce costs. These are potential benefits, not guarantees that a particular tool will be accurate, inexpensive, or suitable for every person or property.
Automated estimates versus traditional appraisals
GAO’s 2025 homebuying review contrasts AVM estimates that can be produced in seconds with traditional appraisals that can take more than a week. It also reports that a traditional appraisal generally can cost a homebuyer $350–$600, while an automated estimate may cost the buyer little. Those figures describe GAO’s comparison, not a current nationwide quote or a promise about any provider. A fast estimate may be useful for orientation; it does not automatically replace a professional appraisal or establish a property’s value for every purpose.
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How AI can create housing risks
The important question is not simply whether a tool uses AI. It is what the tool influences, what information it uses, how errors are handled, and whether a person can understand or contest the result.
Discrimination and steering
Housing advertising and screening can affect who learns about a home or who is considered for it. GAO describes concerns that advertising algorithms or chatbots could steer people toward selected listings or neighborhoods, and that screening algorithms may be difficult for applicants and housing providers to understand. These are fair-housing risks, not proof that every algorithm discriminates.
In a May 2, 2024 announcement, the U.S. Department of Housing and Urban Development (HUD) said the Fair Housing Act applies to housing advertising and tenant screening when AI or algorithms are used. HUD’s announcement describes existing legal duties: the Act prohibits intentional discrimination and practices with an unjustified discriminatory effect. The announcement is guidance, not a new statute.
Bad or outdated screening information
A screening result can affect access to a rental, yet inaccurate or stale information may be difficult to spot when the process is opaque. GAO’s 2025 rental review reports agency enforcement actions against tenant-screening companies over inaccurate or outdated data. That finding concerns selected tools and cases; it does not establish how often errors occur across the entire rental market.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteIf a screening result contributes to a rejection, a practical first step is to ask the housing provider what information and decision process were involved, how to obtain or correct the underlying record, and whether a person can review the result. These questions help identify whether the problem is a wrong record, a mismatch, or a decision rule that needs explanation.
Rent recommendations and market effects
Rent-setting software can help owners adjust prices in response to information and market conditions. GAO summarizes advocacy-group concerns that when multiple owners use algorithmic recommendations as a benchmark, renters may have less bargaining power. GAO also describes research connecting software use with more responsive rent adjustments. These relationships vary with conditions; they do not prove that any single tool caused a market-wide rent increase.
Valuation errors and inherited bias
AVMs can make estimates more consistent and may reduce some forms of subjective judgment. But models depend on their data and design. Historical housing-price data can carry forward past inequities, while limited comparable sales can make estimates less reliable—especially for older or less-homogeneous properties. Opaque models also make it harder for consumers to assess which inputs shaped a result.
The Consumer Financial Protection Bureau has explained that human bias can enter through data and model design. A valuation that is too high can contribute to an unaffordable mortgage; one that is too low can limit access to home equity. An AVM can be a useful reference, but it should not be treated as a verified appraisal simply because it returns a precise-looking number.
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Online platforms may collect sensitive consumer information. Facial recognition at rental properties may offer a security benefit, according to industry associations and public housing agencies GAO interviewed, but it also raises misidentification and privacy concerns. Advocacy groups cited by GAO raised concerns about surveillance information being used without renter consent; that is a reported concern, not a claim that all providers use information this way.
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What the evidence does—and does not—show
GAO identified 34 rental proptech tools in its 2025 review, then selected four categories for closer examination. It interviewed 12 proptech companies and 10 public housing agencies; the selected interviews are nongeneralizable samples, not a census of providers or agencies. GAO’s homebuying review likewise examines selected tools and stakeholders. The reports illuminate mechanisms and risks, but they do not establish a market-wide adoption rate, total industry revenue, or a count of people harmed by AI in housing.
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Fair housing duties still apply
HUD’s May 2024 guidance emphasizes that housing providers, tenant-screening companies, advertisers, and online platforms remain subject to Fair Housing Act requirements when they use algorithms or AI. The presence of software does not remove responsibility for discriminatory outcomes. HUD urged fair, transparent, and accurate screening.
A limited federal rule for certain AVMs
In July 2024, six federal agencies announced a final rule establishing quality-control standards for AVMs used by mortgage originators and secondary-market issuers in covered transactions secured by a consumer’s principal dwelling. The controls address confidence in estimates, protection against data manipulation, conflicts of interest, random sample testing and review, and compliance with nondiscrimination laws. This rule covers specified AVM uses; it is not a general rule for every home-value estimate, listing algorithm, or rental tool.
Further guidance remains part of oversight
GAO’s 2025 rental report recommended that HUD provide public housing agencies with more specific written direction on facial recognition. All 10 agencies in GAO’s selected interview sample wanted additional direction, but that deliberately selected, nongeneralizable sample cannot establish the view of all public housing agencies. GAO’s homebuying report recommended written direction from the Federal Housing Finance Agency to Fannie Mae and Freddie Mac concerning fair-lending compliance and supervision. The recommendations underscore that implementation and oversight details matter even where legal duties already exist.
How to evaluate a real-estate AI tool
Whether you are a renter, homebuyer, or housing provider, evaluate the tool by the consequence of the decision—not by a vendor’s claim that it is advanced or automated. These questions are a practical synthesis of the risks and controls described by GAO and federal agencies, not an official checklist.
- What does it influence? Clarify whether the tool ranks listings, estimates value, screens an applicant, recommends rent, or supports a final decision. The higher the stakes, the more important it is to know who reviews the output.
- Where does its data come from, and how current is it? Ask about coverage and freshness. For screening, find out how mismatched or outdated records can be corrected; for valuation, ask whether the property type and local sales data are well represented.
- How well does it work across properties and people? Ask how accuracy is assessed across different property types and groups, and whether the provider tests for disparities or unreliable results.
- Can someone explain or correct the result? Look for a clear explanation of relevant inputs, a way to challenge inaccurate information, and a route to human review or appeal.
- What information is collected and shared? Ask what data the platform or property operator collects, how it is used, what notice is provided, and whether meaningful consent is available for surveillance-related tools.
- Which obligations apply? Depending on the tool and use, fair-housing, fair-lending, and consumer-reporting requirements may be relevant. Do not assume that a vendor’s software removes the housing provider’s responsibilities.
For an automated home estimate, compare the result with relevant local sales and ask what happens when there are few comparable properties. For a rental screening result, focus on the underlying record, correction route, and human review. In both cases, a precise output is not a substitute for a transparent process.
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