AI-related tools can shape what homes people see, how a home is valued, whether a rental application raises concerns, and how housing is managed. They can make searches and estimates faster, but their usefulness depends on the data and the decision at stake. When a system is hard to explain or its information is wrong, the consequences can reach beyond inconvenience—to housing access, financing, privacy, and home equity.
How AI is being used in real estate
In its 2025 reports, the U.S. Government Accountability Office (GAO) examined selected digital tools used in rental housing and homebuying. The reports describe a process-wide spread of technology, not a claim that every product uses AI or that housing decisions are generally automated. A digital platform may use algorithms for one task while people make other decisions.
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| Stage | Examples of tools | What they may do |
|---|---|---|
| Finding a home | Online real estate platforms and advertising systems | Surface listings, personalize search results, deliver housing ads, or answer questions through virtual assistants and chatbots. |
| Estimating value and financing | Automated valuation models (AVMs) and automated underwriting systems | Estimate a property’s value or help a lender evaluate creditworthiness. |
| Closing a purchase | Electronic closing tools | Support digital steps in the homebuying process. |
| Applying to rent | Tenant-screening software | Analyze applicant information for a housing provider. |
| Setting or managing a rental | Rent-setting software and digital management tools | Recommend rents or help owners manage units. |
| Entering a building | Facial-recognition technology | Help control building access. |
GAO’s July 2025 rental review identified 34 property-technology tools, then selected four categories for closer examination. That figure is not a census of the market. The report also drew on interviews with 12 selected technology providers and 10 deliberately selected public housing agencies (PHAs); neither interview sample can be generalized to the entire industry or all PHAs. GAO’s September 2025 homebuying review likewise examined selected products, not every platform or transaction.
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Digital rental tools can make it easier to search, view, and lease housing, while helping owners manage units, according to GAO’s 2025 rental report. In homebuying, GAO says digital tools can streamline parts of the process and reduce costs. These are potential benefits, not guarantees for every user or property.
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Search and administration
Listings, digital communications, and automated administrative steps can reduce friction in finding or managing housing. Personalization can also help people navigate a large set of listings, though the same systems may influence which options they see.
Automated home estimates
GAO’s September 2025 homebuying report contrasts AVM estimates produced in seconds with traditional appraisals that can take more than a week. It says a traditional appraisal generally can cost a homebuyer $350–$600, while an automated estimate may cost the buyer little. Those are GAO’s report comparisons—not a current nationwide quote, a provider guarantee, or proof that an AVM is suitable for a particular property.
Where errors and opaque decisions matter
The stakes depend on what a tool influences. A poor listing recommendation may waste time; a mistaken screening result can affect access to a rental, and an unreliable valuation can affect a mortgage or a homeowner’s ability to draw on equity. Some tools provide recommendations or information rather than making the final decision, but an opaque recommendation can still shape what a human decision-maker does.
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Rental screening: a wrong record can be hard to untangle
GAO’s July 2025 review reports agency enforcement actions against tenant-screening companies involving inaccurate or outdated data. If an applicant receives an adverse result without a clear explanation, it may be difficult to identify which record or factor drove it and how to correct the underlying information. GAO’s discussion concerns selected tools and cases; it does not establish that every screening product is inaccurate.
For a screening concern, ask the housing provider what information influenced the decision, which screening company supplied it, and how to request review or correct a record. Keep copies of notices and relevant documents. The available steps depend on the decision and the laws that apply; an opaque score by itself does not establish that discrimination occurred.
Valuations: speed does not establish accuracy
An AVM estimates value using data and a model. GAO notes that limited comparable sales can make estimates less reliable, especially for older or less-homogeneous properties, and that consumers may not be able to see the model’s inputs or judge its accuracy. The Consumer Financial Protection Bureau (CFPB) explains that model design and data can carry human bias into an estimate.
A high estimate could contribute to an unaffordable mortgage; a low one could restrict access to home equity. Before relying on an estimate, consider whether there are enough relevant comparable properties, whether the model accounts for the home’s distinctive features and condition, and whether a human review or another valuation is available. Treat the result as an estimate, not an independent inspection or a guaranteed sale price.
Advertising and steering
Algorithms can personalize listings or housing advertisements. GAO describes concerns that advertising systems and chatbots could steer people toward selected listings or neighborhoods. Personalization can be convenient, but a person may not know what options were filtered out or why. That is a fair-housing concern, not proof that every targeted ad or personalized result is discriminatory.
Rent recommendations and market effects
GAO reports advocacy groups’ concern that rent-setting software could weaken renters’ bargaining power when multiple property owners use recommendations as a market benchmark. It also summarizes research linking software use with more responsive rent adjustments. Those findings do not prove that one software product alone caused a market-wide rent increase; effects and relationships vary with conditions.
Privacy and surveillance
Online platforms may collect sensitive consumer information. In rental housing, facial recognition may offer a security benefit, according to industry associations and the PHAs GAO interviewed, while creating risks of misidentification and privacy loss. Advocacy groups raised concerns that surveillance information could be used without renters’ consent. That is a reported concern, not evidence of a universal practice.
What U.S. rules and oversight do—and do not—cover
Fair housing obligations apply to algorithmic uses
In a May 2, 2024 announcement, the U.S. Department of Housing and Urban Development (HUD) said the Fair Housing Act applies to tenant screening and housing advertising when AI or algorithms are used. HUD described the Act as prohibiting intentional discrimination and practices with an unjustified discriminatory effect, and urged screening that is fair, transparent, and accurate. The announcement concerned guidance; it was not a new statute.
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A federal AVM rule has a defined scope
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 does not cover every home-value estimate, listing algorithm, or rental tool.
Oversight details continue to matter
GAO’s 2025 rental report recommended that HUD provide public housing agencies with more specific written direction about facial recognition. All 10 PHAs in GAO’s selected interview sample wanted additional direction, but the sample was nongeneralizable and does not establish what all PHAs think. 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. These recommendations point to questions about implementation and oversight; they are not findings that every covered tool violates the law.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions to ask before relying on a housing algorithm
The following checks are a practical way to assess a tool, not an official government checklist. Focus first on what the tool actually does and how much the outcome matters.
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- What decision does it influence? Find out whether the system recommends, ranks, screens, estimates, or makes a decision, and who is responsible for the final outcome.
- Where does its data come from? Ask what information is used, how current it is, and whether it adequately covers the property type or applicant involved.
- How well does it work across cases? Ask how accuracy is evaluated across different property types and groups, especially where the result could affect housing access or financing.
- Can someone explain and correct the result? Look for a way to understand the main factors, challenge incorrect information, and request human review.
- What information is collected and shared? Ask what data the platform or property operator collects, how it is used, and what notice or consent is provided.
- Which obligations apply? The relevant protections may depend on whether the tool is used for advertising, tenant screening, lending, or another purpose. Fair-housing, fair-lending, and consumer-protection duties remain relevant when firms use automated tools.
What the evidence does not establish
The 2025 GAO reports are examinations of selected tools, stakeholders, and cases—not a measurement of every product or the entire U.S. housing market. The sources discussed here do not establish a market-wide AI adoption rate, total industry revenue, or a count of people harmed by these systems. Claims about prevalence or effects should be kept within those limits.
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