AI is already having a major impact on multifamily operations, moving from experimentation to everyday use across leasing, resident communications, and customer service.
But AI is still often equated with automating repetitive and predictable customer interactions, with conversational leasing and customer support tools like EliseAI serving as a primary point of reference. These systems work, but they have also anchored expectations, reinforcing the idea that AI is primarily a front-of-house efficiency tool rather than a driver of deeper operational change.
That view is about to shift. This report explores six areas of AI application in multifamily operations from which the impact on NOI will become obvious in 2026 – but may not be clear today – ranging from vendor management to predictive maintenance to renewals. These reflect some of the most notable areas of growing AI adoption flagged by our Advisory Council over the past six months.
And we’ll also tackle one bonus category that’s too nascent for 2026 but will lead to meaningful NOI improvement in 2027 and beyond.
1. Resident Screening and Fraud Detection
Application fraud has become one of the most acute operational risks in multifamily, and the scale of the problem is now hard to ignore. According to a recent survey by the National Multifamily Housing Council, more than 93 percent of multifamily owners report having been a victim of application fraud in the past twelve months, and among those seeing an increase, the average reported growth in fraud was over 40 percent year over year. With eviction timelines stretching for months in many jurisdictions, a single bad approval can translate into significant lost rent, legal costs, and operational distraction.
Ironically, AI is both a driver of the problem and a critical part of the solution. Generative tools now make it trivial for applicants to create highly realistic pay stubs, bank statements, and employment letters, lowering the barrier to fraud across the market. Traditional screening tools were never designed to operate in this environment, where documents may look perfect but be entirely synthetic.
This is where AI based fraud detection is starting to matter. Models trained to recognize patterns in authentic financial documents are increasingly effective at identifying AI generated or manipulated files, often flagging inconsistencies that are invisible to human reviewers. At the same time, these systems improve as they process more applications, learning from confirmed fraud outcomes and legitimate approvals to refine risk signals over time.
Given the need, there has been a proliferation of new entrants in this category, both new proptech point solutions as well as PMS-provided tools. Insights by Blueprint profiled the top providers in a report last year, giving Snappt (independent) and Yardi’s RentGrow top marks. Survey respondents on our Advisory Council praised both products’ fraud detection capabilities, ease of use, and reporting capabilities, although some noted that Snappt’s Yardi integration leaves much to be desired.
For operators, regulatory and fair housing considerations remain central. Most operators are not looking to fully automate approval decisions; instead, confidence is growing around human in the loop models, where AI surfaces risk, prioritizes review, and standardizes analysis, while final decisions remain with trained staff.
In a landscape where fraud is rising and manual review does not scale, AI is becoming less a nice to have and more a necessary layer of defense.
2. Vendor Management
The story of 2026 may not be about rolling out new tools as much as it’s about consolidating existing tooling.
After a decade of rapid vendor proliferation, many multifamily and broader CRE operators report deep proptech fatigue, driven not by a lack of innovation but by tools that fail to integrate, overpromise on ROI, or ignore how buildings actually operate.
Survey data from the Insights by Blueprint Advisory Council highlights the paradox at the center of this moment. While 83 percent of respondents report proptech fatigue and feel overwhelmed by vendor outreach, 58 percent still believe their current tech stacks lack key capabilities. The core issue is not too much technology, but poorly managed technology ecosystems. Fragmentation across systems has made vendor sprawl itself an operational burden, with property managers juggling dozens of logins and manually reconciling data across platforms. As a result, vendor management in 2026 is more about integration discipline, consolidation, and ongoing performance evaluation than selecting yet another point solution.
AI is playing a subtle but important role in solving the new challenge of vendor management. While dozens of lengthy vendor contracts with complex terms can be too much for a human to manage, LLMs are excellent at turning long documents into structured, human-digestible data. Apps like Revyse can flag where operators are paying for duplicative functionality and make consolidation recommendations complete with flags on contract expiration dates, terms, and notice periods.
Technology is also tranforming how operators can work with vendors. With low-code and generative AI tools, firms can now prototype internal solutions before committing to third-party platforms, shifting leverage away from vendors and raising expectations around security, compliance, and long-term viability. This dynamic favors proptech companies that act as partners rather than product sellers, integrate cleanly into existing systems, and demonstrate measurable NOI impact.
Notably, two of our top ten proptech case studies of 2025 featured vendor management use cases.
3. Rent Collection
Multifamily operators are entering 2026 facing sustained pressure on rent collections, with on-time payment rates continuing a multi-year decline and economic stress showing few signs of easing. Industry data underscores the challenge: on-time rent payments fell to 83.6 percent in July 2025, down more than 200 basis points year over year, while broader full-payment forecasts have reached their weakest levels since the height of the pandemic. Despite these trends, most operators remain slow to adopt AI-driven tools for rent remediation, even as traditional approaches strain already lean on-site teams.
This disconnect highlights one of the most underappreciated applications of AI in multifamily today: collections, delinquency prevention, and arrears management.
While AI adoption has largely centered on leasing and resident communications, AI-powered rent collection systems are quietly demonstrating their ability to capture revenue earlier and reduce staff workload. We dug deep into this in an Insights by Blueprint report late last year, exploring the opportunity and evaluating the primary vendors serving the space.
In general, AI-powered collections platforms integrate with existing property management systems and payment processors, ingesting lease data, payment histories, communications, and behavioral signals to tailor outreach strategies at scale. Unlike manual workflows, AI systems act immediately after rent deadlines, adapt cadence and tone based on tenant behavior, and operate continuously across portfolios.
The benefits extend beyond automation. AI collection agents introduce consistency and neutrality into a process that is often emotionally charged and operationally fragmented. By centralizing communications, documenting every interaction, and timestamping partial payments or promises, these systems strengthen compliance, reduce conflict, and support more defensible remediation or eviction decisions when necessary. Case studies show material gains, including double-digit increases in collection revenue and hundreds of labor hours saved per month.
More advanced use cases push even further upstream. AI-driven delinquency forecasting leverages sentiment analysis, payment behavior, and external financial signals to identify risk before a tenant misses rent. This allows operators to intervene earlier with payment plans, assistance programs, or adjusted communication strategies. Similar models have long been standard in financial services, an industry that now invests tens of billions annually in AI-driven prediction and risk modeling, yet real estate has only begun to apply these techniques at scale.
4. Security
Artificial intelligence is rapidly reshaping security and surveillance in multifamily housing, turning what was once a reactive, low-tech function into a strategic operational lever tied to risk management, resident experience, and cost control. Vendors such as Deep Sentinel, Cloudastructure, and BluBØX are scaling quickly, offering AI-powered camera systems that analyze video feeds in real time, detect behavioral anomalies, and escalate credible threats within seconds. What once sounded futuristic is now commercially viable, and multifamily has emerged as one of the most active adopters.
At its core, AI-enhanced surveillance has evolved far beyond basic motion detection. Advances over the past five years in neural networks, object recognition, tracking, and scene understanding allow systems to interpret context, follow movement patterns, and even generate natural-language summaries of activity. These capabilities enable security to shift from passive recording to proactive intervention, a transition that operators increasingly view as essential rather than optional.
The economic arguments for AI-powered security are compelling. AI-powered camera systems, which may cost a few thousand dollars per device, are increasingly compared to far more expensive full-time security personnel. Beyond cost savings, AI systems provide continuous monitoring, structured reporting, and searchable archives that improve collaboration with law enforcement and reduce liability exposure. Some operators also report meaningful insurance discounts and six-figure annual labor savings, strengthening the ROI case even when crime reduction is difficult to quantify directly.
But adoption requires discipline. Operators are learning to phase deployments, prioritize higher-risk assets, and favor cloud-based, “bring your own camera” platforms that avoid costly rip-and-replace retrofits. Transparency with residents is also key, as poorly disclosed monitoring creates reputational risk.
AI-enhanced surveillance is part of a broader shift toward unified, data-driven building operations, where security is integrated with access control and portfolio-wide analytics.
5. Renewals
As lease churn has fallen from roughly 55 percent to 40 percent over the past decade, renewals now account for the majority of a property’s rent roll. Yet most operators still manage renewals with far less rigor than new leasing. As we covered in an Insights by Blueprint report late last year, AI is beginning to close that gap by turning renewals into a data-driven, continuously managed function rather than a reactive exercise.
The core advantage AI brings to renewals is visibility into resident intent. Instead of assuming residents stay because moving is inconvenient, AI-powered platforms ingest behavioral, financial, and engagement data from across the resident lifecycle to predict renewal likelihood well before lease expiration. Signals such as communication patterns, portal activity, service requests, pricing sensitivity, and response timing allow models to segment residents and trigger tailored outreach months in advance.
AI also changes how renewals are executed. Modern renewal systems automate personalized, multi-channel communications across email, text, chat, and voice, adjusting cadence, language, and incentives based on how residents engage. These systems learn over time, scoring residents and refining offers in ways manual workflows cannot replicate. Vendors such as Renew, EliseAI, and RealPage’s Lumina AI illustrate how agentic AI can act as a “retention engine,” coordinating pricing logic, outreach, and self-service options while freeing on-site teams from repetitive tasks.
Just as important, AI enables renewals to scale across portfolios. Tasks once handled exclusively by leasing offices, such as price requests, incentive selection, roommate changes, and notice-to-vacate workflows, can now be pushed directly to residents through AI-driven interfaces. This reduces labor intensity while creating consistency across assets and markets. Upsell opportunities, from parking to storage to pet-related fees, can also be surfaced dynamically during the renewal cycle rather than treated as one-off offers.
As with pricing algorithms, AI allows operators to integrate market data, competitive pricing, occupancy trends, and internal cost structures into renewal decisions in near real time, reducing the risk of underpricing or unnecessary vacancy. As agentic AI becomes embedded in major property management platforms, renewals will no longer be a side process but a core operating system.
6. Maintenance Ticket Routing and Dispatch
For multifamily operators, maintenance has always been one of the most operationally dense and labor intensive parts of the business, and it is precisely for that reason that it is becoming one of the most fertile areas for applied AI. The first wave of impact will not come from robots fixing toilets or replacing HVAC units, but from software that finally understands what a maintenance ticket is actually asking for and what to do with it once it arrives.
AI models trained on years of work order history can already parse resident language, infer urgency, identify likely root causes, and route tickets to the right team or vendor with far more consistency than manual triage ever allowed. In practice, this means fewer miscategorized tickets, faster response times for true emergencies, and less wasted labor on truck rolls that never should have happened. Over time, these systems will also learn asset specific behavior, recognizing that a recurring complaint in one building points to a failing component, while the same language in another property is usually a resident use issue.
The more interesting shift comes as triage blends into planning. As AI connects maintenance tickets to inventory, warranties, vendor performance, and historical resolution times, operators gain a dynamic view of maintenance demand that supports smarter staffing, better preventative maintenance scheduling, and more predictable costs. The result is not just faster fixes, but a maintenance operation that behaves less like a reactive cost center and more like a continuously improving system.
Bonus Category: Predictive Maintenance
Finally, one category of AI application may not be ready for prime time in 2026 but is worth watching closely given its high potential: predictive maintenance.
Typically, buildings perform inspections and maintenance of major building systems like HVAC and elevators on set calendar schedules. Beyond that, contractors and maintenance teams are only going to show up when a system breaks and resident complaints begin rolling in. And by that point, it’s too late to avoid disruption, frustrated residents, and costly repairs.
AI presents a potential solution in the form of predictive maintenance. Rather than simply conducting preventive maintenance on a set schedule, predictive maintenance algorithms use sensor data – vibrations, emissions, efficiency, and more – to predict when a building system is likely to fail. When those failure signals come in, maintenance teams can be dispatched to inspect the unit and repair any issues before outright failure happens.
Think about it this way: predictive maintenance algorithms have been trained on years of building system performance data, including thousands of failures. Through that training data, they can create “profiles” of how a system is likely to behave before failing. Thalo Labs, for instance, uses sensors on HVAC equipment to predict failure by measuring compressor outlet temperatures and chemical emissions, identifying units at high likelihood of failure. Elevators are another system likely to benefit from predictive maintenance technology, as we covered in a recent Insights by Blueprint report.
While predictive maintenance technology is still in the early innings, the declining cost of sensors is a key enabler of predictive algorithms. Wherever there’s sensor data, AI can likely generate insights earlier and with higher confidence than humans.
-Brad Hargreaves
