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What “AI in rentals” actually includes
“AI” is an umbrella term in housing technology. The U.S. Government Accountability Office (GAO) describes property technology as software, digital platforms and other digital tools used for advertising, touring, leasing and financial management. In its 2025 review, GAO examined four commonly used categories that incorporate algorithms or machine learning:
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| Use case | What the system does | Potential convenience | Key risk |
|---|---|---|---|
| Advertising and search | Targets ads and personalizes listing results using user or market data. | Faster discovery of homes that appear to match a renter’s preferences. | People may receive different housing information or be excluded from opportunities because of protected characteristics or inferred traits. |
| Tenant screening | Compiles credit, criminal, employment, income, rental-payment and eviction information; some tools generate a score or recommendation. | Owners can process applications more quickly and consistently. | Inaccurate, incomplete, duplicated, outdated or mismatched records can produce an unjustified denial that is difficult to explain. |
| Rent-setting software | Produces data-driven pricing recommendations for owners or managers. | May help analyze large amounts of market information. | Recommendations could contribute to higher rents or raise competition concerns, depending on how the software and data are used. |
| Facial recognition and access | Uses biometric matching to control entry to an apartment building or amenity. | Industry groups and public housing agencies interviewed by GAO described possible security benefits. | Misidentification can affect some demographic groups, and surveillance data may be collected or used without meaningful renter consent. |
These categories are examples, not a complete census of every automated system used in housing. A chatbot answering leasing questions, software that flags maintenance requests or a fraud-detection model may also involve automation, but the reviewed evidence does not establish how widespread those uses are or whether they improve outcomes overall.
Where renters may notice benefits
Searching and touring
Digital listing platforms can let a renter filter neighborhoods, price ranges and amenities, receive personalized results and arrange a viewing without several phone calls. Virtual tours and online applications can reduce travel and paperwork. Those are process benefits: they may save time for some users, but the reviewed sources do not provide a representative comparison showing that AI-assisted searches are faster, cheaper or more successful across the rental market.
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Application processing
Screening software can assemble information from multiple databases and return a recommendation to a property manager. Automation may reduce manual data entry and help a manager handle a high volume of applications. A recommendation is not proof that an applicant is likely to pay or that the underlying records are correct. The value of speed depends on a human checking the report and giving the applicant a meaningful way to correct errors.
Building operations and access
Facial-recognition systems are sometimes proposed as a way to manage entry. GAO found that the public housing agencies and industry associations it interviewed described potential security advantages. “Potential” matters: those interviews do not demonstrate that biometric access is safer than alternatives in every building, nor do they settle questions about consent, retention or how a misidentification is resolved.
How automation can harm applicants
Bad data can become a bad decision
The Federal Trade Commission (FTC) lists common background-report problems: information belonging to another person, incomplete case outcomes, duplicate records, outdated records, and records that should be sealed or expunged. A system can process those errors in seconds and still produce a wrong recommendation. If a landlord relies on it without review, an applicant may be denied for a debt, case or eviction that is not theirs or no longer reportable.
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Opaque scoring makes the problem harder. A renter may see only “does not meet criteria” rather than the specific record, rule or threshold that drove the result. Owners can also struggle to explain a vendor’s model. A fast answer is not a fair answer if no one can identify the evidence behind it.
Discriminatory targeting and unequal access
HUD announced Fair Housing Act guidance on May 2, 2024, stating that the law applies to tenant screening and housing advertising when artificial intelligence or algorithms perform those functions. Targeting can limit who sees an advertisement or what information is shown. Even if a landlord does not specify a protected characteristic, an algorithm may use correlated data that has a similar effect. HUD Acting Secretary Adrianne Todman said: “Housing providers, tenant screening companies, advertisers, and online platforms should be aware that the Fair Housing Act applies to tenant screening and the advertising of housing, including when artificial intelligence and algorithms are used to perform these functions.”
GAO reported that, in February 2025, it observed HUD had removed the screening guidance from the agency’s public website. GAO said HUD explained in March 2025 that the guidance was taken down during a policy review and that the website was being updated while the guidance remained in effect. That account does not support a simple claim that the guidance was either permanently withdrawn or unchanged; renters and housing providers should check current agency information and applicable law.
Biometric privacy and misidentification
Facial recognition creates a different risk from a credit or rental-history error. A false match can block a legitimate resident or guest, while a database can retain sensitive information about people who never agreed to ongoing surveillance. GAO’s review notes possible demographic differences in misidentification and the possibility that surveillance information is used without renter consent. Before accepting a biometric system, ask what is collected, where it is stored, how long it is retained, who can access it and what non-biometric alternative exists when the system fails.
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Rent-setting software and the price question
Rent-setting tools analyze market and property data to recommend prices. A recommendation can influence what an owner posts, how quickly a price changes or how units are offered. That is distinct from tenant screening: the issue is not whether one applicant is qualified, but how software may affect prices and competition across many units.
GAO discusses the potential for higher rents and identifies a civil antitrust lawsuit brought by the U.S. Department of Justice and state attorneys general against RealPage as an allegation in a complaint. An allegation is not a proven finding. The reviewed material does not establish that rent-setting software has raised rents across the market, nor does it provide a representative comparison of software-assisted and conventional pricing. Treat claims about a particular lawsuit, vendor or market effect according to their procedural status.
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What the evidence can—and cannot—tell you
GAO interviewed representatives of 12 property-technology companies, 10 public housing agencies and nine advocacy or industry groups. GAO expressly characterizes these as nongeneralizable samples selected for expertise or use. They can show which concerns and applications knowledgeable participants described; they cannot provide a market-wide adoption rate, error rate, satisfaction score or estimate of how AI changes housing access.
The reviewed sources contain no representative cross-market test of AI versus non-AI rental tools on cost, speed, accuracy, fairness or renter outcomes. Therefore, claims that AI has already made renting cheaper, faster or fairer overall go beyond the available evidence. A sensible conclusion is narrower: automation is present in important parts of the process, its effects vary by deployment, and oversight determines whether convenience outweighs risk.
A renter’s practical playbook
Before paying an application or screening fee
The FTC advises asking what information a landlord uses to decide whether to rent to you. Ask whether the property uses a screening company, what categories are considered, and whether you can review the criteria before paying. Keep the answers, fee receipt and application confirmation.
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After an adverse decision
If a landlord takes negative action because of a screening report, federal consumer-reporting rules give you specific steps. Use this sequence:
- Read the adverse-action notice. It should identify the reporting company that supplied the report.
- Request a free copy from that reporting company within 60 days. The FTC says the request deadline runs from the date of the adverse-action notice.
- Check every identifying detail. Look for another person’s information, duplicate entries, missing case outcomes, outdated records, or sealed and expunged matters.
- Dispute errors with the reporting company. The company generally must investigate a dispute within 30 days.
- Send supporting documents and keep delivery records. Include identification and court, creditor or housing records that show why an entry is wrong.
- Ask the landlord to reconsider. Provide the corrected report or dispute result and ask which criterion caused the decision.
- Check local protections. State and local law may provide additional rights or deadlines beyond the federal process.
Correction is not guaranteed to produce approval: a landlord may apply lawful criteria using accurate information. The purpose of the process is to prevent an applicant from being judged on someone else’s record or information that should not appear.
If access technology misidentifies you
Report the problem immediately to building management and request a manual entry method. Ask what biometric data was used, whether an incident was logged, and how long records are retained. Document dates, names and any resulting lockout or fee. If management cannot explain the process, consult local tenant or privacy authorities about remedies available where you live.
How owners and managers should evaluate a tool
A low price or impressive accuracy claim is not enough. Before deployment, require written answers to these questions:
- Task: Does the system advertise, screen, price, control access or perform another function? What decision is it allowed to influence?
- Data: Which sources are used, how current are they, and how are identity matches verified?
- Human review: Can a trained employee inspect the underlying record, override the recommendation and document the reason?
- Explanation and challenge: Will applicants receive a clear adverse-action notice and a practical method to dispute information?
- Privacy: What consent, security, retention and deletion controls apply, especially to biometric data?
- Fair-housing testing: How is the deployment monitored for discriminatory advertising, screening or access outcomes?
- Vendor accountability: Who investigates errors, how quickly are corrections distributed, and what happens if the vendor changes its model?
These questions are safeguards derived from concerns identified by GAO, HUD and the FTC; they are not an endorsement of any particular vendor or certification.
So, game-changer or hype?
AI is a meaningful change in the mechanics of renting, but not a demonstrated cure for housing affordability, access or fairness. It can make a search, tour, application workflow or building entry more convenient. It can also scale a flawed record, hide a decision behind an unexplained score, expose sensitive information or narrow who sees an opportunity. The most reliable way to judge a deployment is to identify the exact task, inspect the data and demand human review, explanation, correction and recourse. Until representative evidence shows consistent gains without unacceptable harms, “useful in some jobs, risky in others” is more accurate than either a revolution or a dismissal.
Quick Recap
Questions to ask when comparing two rental AI deployments
| Question | Why it matters |
|---|---|
| What decision does it perform? | Advertising, screening, pricing and access create different legal and personal risks. |
| What data does it use? | Errors, stale records and proxy variables can change outcomes. |
| Can a person override it? | Human review provides a path to catch mismatches and exceptional circumstances. |
| How is a renter informed? | Clear explanations let applicants understand and challenge an adverse result. |
| What privacy controls exist? | Consent, retention and deletion determine the lasting impact of collected data. |
| Could it restrict access or raise prices? | Targeting and pricing systems can affect who sees homes and what they cost. |
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