What 2026 Multifamily Fraud Data Reveals About the Pet and ESA Documentation Problem

Application fraud in multifamily is no longer an occasional nuisance. It has become a recurring operational reality that the industry is now measuring at a portfolio level. MRI Software’s 2026 Multifamily Real Estate Pulse Check, based on responses from more than 700 multifamily professionals across North America, found that 91% of respondents encounter fraud at least every few months and 26% encounter it weekly. Only 4% reported no fraud at their properties.

Those numbers reflect a broader fraud environment in multifamily, but they also have a direct, underappreciated connection to one of the most common documentation problems housing operators face: pet and ESA records. Most fraud conversations in multifamily focus on income verification and credit history. The documentation that accompanies pets and assistance animals is in the same vulnerable position, and most properties have no structured process for evaluating it.

What the Fraud Data Actually Shows

The MRI report identifies the two most common fraud types operators are dealing with in 2026. Credit history fraud and fake AI-generated documents were reported as the two most common types of fraud. “As AI has advanced, the ability of bad actors has gone with it,” said Carla Hinson, VP of Solution and Innovation at MRI Software. “Over half of the respondents had encountered fraud. That is significant.”

AI-generated fake documents are not staying in the income verification lane. The same tools that produce convincing fake pay stubs and bank statements are now producing ESA letters. Online ESA letter services already had a documentation quality problem before generative AI made it easier to produce credible-looking clinical letterhead. The MRI data confirms that the overall fraud environment has accelerated, and ESA documentation sits squarely in that environment.

Confidence in fraud detection strategies is mixed, with 51% of property managers expressing confidence compared to 28% of executives. That gap between where the problem is being felt and where confidence in detecting it sits is significant. The same pattern applies specifically to ESA documentation: leasing staff see the letters and process them, but they rarely have a structured evaluation framework or the portfolio visibility to recognize when the same provider is generating letters for multiple applicants across properties.

The Fraud Confidence Gap by Role

Role	Confidence in Fraud Detection	Implications for ESA Review
Property managers	51% confident	Close to the documentation, but lack portfolio context
Executives	28% confident	Portfolio visibility reveals patterns site-level staff cannot see
Gap	23 percentage points	ESA fraud that looks manageable at one property may be systematic across a portfolio

Where Pet and ESA Documentation Fits in the Broader Fraud Picture

Housing operators understand income fraud because there are established tools for catching it: background checks, employment verification, and credit pulls. The tools being used to identify fraud include background checks at 91%, followed by employment verification at 88%, and resident or AI screening platforms at 85%.

None of those tools screen ESA documentation. An applicant can clear every standard screening check and still submit a letter that was generated by an online service after a two-minute questionnaire. There is no equivalent of a background check for ESA documentation quality, which means the evaluation gap is structural rather than accidental.

The financial exposure compounds this. A fraudulent income document costs the property money when a tenant defaults on rent. A fraudulent ESA letter costs the property money through waived pet fees, waived deposits, waived monthly pet rent, and, in some cases, the cost of damage caused by an animal whose actual risk profile was never assessed.

The confidence gap between property managers and executives on fraud prevention suggests application fraud may look manageable at the property level but more troubling across full portfolios. For ESA documentation, that dynamic is even sharper because the evaluation has never been systematized the way income verification has.

Why AI-Generated Fake Documents Raise the Stakes for ESA Specifically

ESA letters are particularly vulnerable to AI document generation because the format is predictable. A letter that confirms a disability, names the animal, includes a license number, and appears on professional letterhead passes a visual inspection. AI tools can now produce that output with enough variation to avoid simple pattern detection.

Multifamily operators are embracing AI quickly, but integration problems are still doing more to slow adoption than ROI concerns or staff training gaps. Leasing has become the clearest first target for AI because it sits directly at the intersection of revenue, repetitive work, and resident experience.

That same intersection is where fraudulent ESA documentation is doing the most damage. A leasing team using AI tools to accelerate application processing is also a team with less time to scrutinize individual documents. Faster throughput without a structured ESA documentation standard means more fraudulent letters get approved.

The countermeasure is not slower leasing. It is a verification standard that runs independently of the general application process. What the letter says matters less than who wrote it, whether that person has a documented patient relationship, and whether the same provider appears across multiple applications in the portfolio.

What a Systematic Response Looks Like

The MRI data points to a shift that is already underway in how multifamily operators think about fraud: from a site-level problem to a portfolio-level data problem. That framing applies directly to ESA documentation.

Properties that review ESA letters one at a time, at the leasing desk, with no cross-property visibility, are operating in the same information gap that makes income fraud difficult to catch. The properties best positioned to detect fraudulent ESA documentation are those with a centralized review process, a consistent written standard for what counts as reliable documentation, and the ability to flag patterns across applications.

The practical steps:

• Define a written documentation standard and train every leasing team member on it before any request is processed
• Route every ESA accommodation request through a centralized review rather than resolving it at the site level
• Track provider names and credentials across applications to identify high-volume, low-relationship sources
• Log every decision with the documentation on file, so patterns become visible at the portfolio level
• Review logs quarterly to identify approval rates, documentation quality trends, and any concentration of requests from the same providers

The goal is not to make ESA accommodation harder for residents with legitimate needs. It is to apply the same verification rigor to ESA documentation that the industry is already applying to income and credit documents, because the fraud environment affecting both has become the same environment.

Conclusion

The MRI Software data confirms what housing operators have been experiencing at the property level: fraud is frequent, AI-generated documents are a growing share of that fraud, and confidence in detection is inconsistent across roles. ESA documentation sits in that same fraud environment and has been largely excluded from the systematic response the industry is building. Treating ESA letters as a distinct documentation category with its own verification standard, tracked at the portfolio level rather than reviewed one at a time at a leasing desk, closes a gap that the broader fraud data makes clear is no longer small.

Frequently Asked Questions

How often are multifamily operators encountering fraud in 2026?

According to MRI Software's 2026 Pulse Check, 91% of operators encounter fraud at least every few months, and 26% encounter it weekly.

What are the most common types of fraud in multifamily right now?

Credit history fraud and AI-generated fake documents are the two most common types reported in the MRI survey.

Why is ESA documentation particularly vulnerable to AI-generated fraud?

ESA letters follow a predictable format that AI tools can replicate convincingly. Without a standard for verifying the provider's patient relationship, a visually credible letter can pass a leasing team review.

What verification tools do most operators use for general fraud detection?

Background checks at 91%, employment verification at 88%, and resident or AI screening platforms at 85%. None of these tools assesses ESA documentation quality.

Why do executives have lower confidence in fraud detection than property managers?

Executives have portfolio visibility that reveals patterns site-level staff cannot see. What looks manageable at a single property becomes a different picture across a full portfolio.

What is the most practical first step for addressing ESA documentation fraud?

Define a written documentation standard, centralize the review process, and start tracking provider information across applications at the portfolio level.