Delivering keynotes is fun, mainly because they usually require the person delivering them to pull together multiple strands of thought into a single coherent narrative.
I've given a couple lately on the topic of automation and how it seems likely to change multifamily property management. One took place a few weeks ago at the MX Summit in Rapid City, South Dakota, where—as good fortune would have it—the topic tied into an excellent piece of research just released by the event's host, Property Meld.
First, a bit of background. The MX Summit is a phenomenally well-attended maintenance event, primarily targeted toward the long tail of property management companies. Most operators in that segment have sub-1,000-unit portfolios of scattered sites, single-family, and smaller multifamily properties.
As I've written extensively in these pages, one of the most interesting things about this part of the industry is how familiar the owners are with maintenance operations. There are good reasons for that: maintenance can make or break the profitability of smaller operators. It is also logistically harder than in mainstream multifamily, where each property has a permanent maintenance team. But there's also another crucial difference, and it has to do with renewals.
A high-stakes game
Renewals are, of course, a big deal throughout rental housing, typically accounting for more than 50% of the revenue of a given property or portfolio. They become an even bigger issue further down the scale of rental housing properties.
When a tenant moves out of a single-family home, for example, it's often the case that the owner will take that property off the rental market altogether. Churn, therefore, doesn't just mean losing revenue and vacancy loss—it can mean losing that unit of inventory forever. The incentives to figure out renewals are consequently even higher in this market than they are in the mainstream of multifamily.
That makes any opportunity to reduce the likelihood of churn worth pursuing. And while most renewals are driven by life circumstances outside the operator's control, there is still a subset of renewal decisions that are influencible. To the extent residents' decisions are influenceable, maintenance experience tends to be the biggest driver.
Knowing what we don't know
The trouble is, maintenance delivery is a big, multifactoral problem, and resident sentiment toward maintenance can be unpredictable. Most companies track satisfaction with maintenance outcomes, but that only gives us a narrow indicator of maintenance performance. It doesn't tell us how the accumulation of maintenance experience affects long-term sentiment toward, and ultimately the likelihood of renewal.
The Property Meld study that was presented at this year's MX Summit sheds light on this very problem. It analyzed millions of work orders, categorized them by work order type and the time at which they occurred during the lifetime of a lease, and looked for correlation with renewal decisions.
The objective was to identify specific issues and resolutions that had the greatest impact on renewal decisions. One example (see slide), included in the white paper, shows that within the first 90 days, certain repair types—unsurprisingly, HVAC, water heaters, and toilets—are far more influential in renewal decisions than, say, a dishwasher, a tub, or a door-related issue.
These findings may seem intuitive on the surface, but they point to something that is likely to become increasingly important as we move further into the world of agentic AI. As I have presented in multiple keynotes, renewal decisions can often be highly revealing of things that happened much earlier in the lease lifecycle.
A new framing for renewals
We tend in multifamily to think about renewals in terms of the transaction—how much we should charge and whether the conversation may or may not lead to a negotiation. Those negotiations frequently turn on service issues that have often been long-standing for the resident.
If we can identify problems earlier in the tenancy that drive renewal decisions, we should stop waiting until renewal time to address them. A better approach is to identify and prioritize high-salience issues as they arise. Historically, this has been difficult because it's hard to maintain meaningful conversations with hundreds of residents across a 12-month lease.
But in the world of agentic AI, we not only have the capacity to converse with residents continuously, we also have the intelligence to understand what really matters. That creates the potential to rethink how we organize and prioritize maintenance.
That is a very different way to operate. Many multifamily companies bonus maintenance teams on retention, but it would be unusual to prioritize maintenance tasks based on their impact more than nine months from now. Maintenance teams are typically busy with turns and the work orders already in the queue. As AI accumulates data and increasingly identifies smarter ways to operate properties, it seems likely that it will redefine how we prioritize activities.
That may lead us to some more fundamental change: all of today's maintenance technology is designed to optimize the property manager's resources, managing work orders, optimizing the team's time, and so on. If we look to the best providers of logistics, think Amazon, Uber, etc., their vast and sophisticated networks are ultimately designed to remove friction from purchase decisions. Amazon's same-day service, for example, exists to make it easier for consumers to buy more.
It is normal to deploy the immense power of AI to orientate organizations to do the things that make consumers buy more. That, for me, is an interesting takeaway from Property Meld's excellent study, and from my own research into automation and sentiment analysis, both of which I will be writing much more about in the coming weeks.