Predictive maintenance is one of many much-hyped tech promises in multifamily property operations. Vendors market sensor networks, AI dashboards, and smart analytics platforms that claim to anticipate equipment failures before they disrupt residents or drain budgets. In theory, predictive systems should reduce emergency repairs, extend asset life, and stabilize operating costs.
In practice, however, adoption is far more complicated. Many operators also question whether predictive maintenance is a transformative operational shift or simply another overhyped proptech narrative.
In this report, our conversations with multifamily investors, asset managers, technologists, and operators reveal a consistent theme: predictive maintenance is powerful when layered on top of strong operational fundamentals, but it is not a replacement for disciplined property management.
Preventive vs. predictive maintenance
Despite the surge in technology offerings, preventive maintenance — not predictive — remains the dominant model across most multifamily portfolios.
Lumi Ispas, a long-time investor, educator, and president of a 100-unit Chicago condominium building, says predictive systems are still largely absent in smaller-scale ownership environments.
“As an investor and when talking to small property management companies, we all do preventive maintenance, not predictive maintenance,” Ispas explains. “In the small investor world where each unit has its own HVAC system, water heater, and electrical panel, we don’t really have the tools to do predictive maintenance.”
Preventive maintenance relies on scheduled servicing, inspections, and lifecycle planning. It is proven, understandable, and easier to implement across decentralized equipment environments typical in garden-style or small multifamily buildings.
Many operators also note confusion between preventive and predictive approaches.
Preventive maintenance reduces risk through routine care; predictive maintenance attempts to forecast failure using data trends. The distinction matters because predictive strategies require infrastructure — sensors, integration platforms, and analytical capacity — that many portfolios lack.
Cost, complexity, and culture
If predictive maintenance offers such clear advantages, why hasn’t its adoption accelerated faster? Operators consistently cite integration challenges and organizational readiness as the primary barriers.
Jeff Goodman, a licensed real estate agent for Brown Harris Stevens in New York City, notes that legacy building infrastructure complicates retrofits. “Older buildings weren’t designed for unified sensor platforms,” he says. “Even retrofitting can be expensive and difficult.” Training and operational maturity are equally important. Predictive data without context can create false confidence.
“It doesn’t matter how many dashboards you have if the property management team doesn’t know how to interpret them,” Goodman adds.
Andrew Hanson, a multifamily owner and asset manager with more than two decades of experience, argues that maintenance fundamentals still determine performance.
“Predictive maintenance is often an overused buzzword,” Hanson says. “Technology should support disciplined preventive maintenance and capital planning, not replace them.”
Hanson organizes maintenance into six practical categories: reactive, corrective, preventive, condition-based, deferred, and capital replacement. Operators who understand these cycles often achieve better asset stability than those chasing technology alone.
What predictive maintenance actually promises
At its best, predictive maintenance shifts operations from reactive firefighting to proactive intervention. Goodman describes predictive maintenance as a system in which operational data serves as a decision-support engine.
“How often a boiler kicks on, or when an elevator starts behaving slightly off, that data allows repairs to happen before residents experience an emergency,” Goodman says. “The result is less downtime and meaningful cost savings.”
Simon Soloff, president and co-founder of EnTech, frames predictive maintenance as an operational intelligence layer. “Using real-time data like temperature, pressure, run hours, and fault codes allows owners to anticipate failures before they disrupt service,” Soloff explains. “For example, early detection of boiler heat exchanger issues or pump degradation helps avoid emergency repairs and tenant complaints.”
The benefits include reduced emergency service calls, lower insurance risk exposure, extended equipment life, improved resident satisfaction, and more predictable maintenance budgets.
Data quality: the hidden constraint
Technology vendors emphasize another barrier to establishing a predictive maintenance program: data quality. Barry Kunst, VP of Marketing at Solix Technologies, argues that predictive platforms fail when built on fragmented information.
“Owners buy shiny proptech tools expecting magic,” Kunst says. “But trying to run predictive analytics on siloed data is like building a skyscraper on sand.”
Without governed data — accurate asset histories, sensor calibration, documentation, and maintenance records — predictive systems generate unreliable outputs.
Kunst describes an emerging approach he calls evidence-backed analytics: systems that trace asset performance history and connect it to financial risk modeling. The goal is accountability: understanding not only when a failure occurs, but why predictive signals did or did not catch it.
While large portfolios experiment with comprehensive predictive systems, many operators find success in targeted applications. Ben Mizes, president of Clever Real Estate and a multifamily investor, advocates incremental adoption. “Starting small is key,” Mizes says. “Pick a high-risk area like plumbing leaks or HVAC.”
Leak detection sensors offer a clear example. Early alerts can prevent catastrophic water damage, insurance claims, and tenant displacement. Smart thermostats and remote HVAC monitoring also allow proactive servicing before resident complaints escalate. “The ROI becomes obvious once you avoid a major repair,” Mizes notes.
Targeted deployment allows operators to test workflows, staff readiness, and integration challenges before committing to broader predictive infrastructure.
Predictive maintenance as operational evolution
Across our interviews, a consensus emerged: predictive maintenance is not a plug-and-play solution. It is an operational evolution. It requires reliable asset and maintenance data, skilled staff capable of interpreting analytics, clear integration with preventive maintenance programs, and capital planning aligned with lifecycle management.
When these foundations are in place, predictive tools can meaningfully reduce disruptions and improve long-term asset performance.
Soloff summarizes the inflection point: “Once owners see measurable savings and improved reliability, adoption accelerates quickly.”
Practical adoption over hype
For multifamily operators, predictive maintenance is neither a silver bullet nor empty hype. Small portfolios may continue relying primarily on preventive maintenance for the foreseeable future. Larger operators, particularly those managing centralized systems or high-value assets, are better positioned to extract value from predictive models.
The industry is unlikely to abandon traditional maintenance frameworks. Instead, predictive systems will increasingly serve as decision-support tools, helping operators anticipate problems, allocate capital more intelligently, and protect resident experience.
The technology is real. The value is situational. And for multifamily owners, the smartest path forward is pragmatic experimentation grounded in operational fundamentals.
– Nick Pipitone
