This past year, we’ve been digging into how operations metrics are changing in the age of AI — through our own data, conversations with our customers, renter surveys, and more. In our latest report, Measuring Success in Multifamily: Setting New Performance Benchmarks in the Age of AI, we share what we’ve learned.

We found that, with the increased adoption of AI-enabled property management solutions like ApartmentAdvisor ASSIST, that operators of every size are reevaluating their key performance indicators and resetting targets to keep up with technology advancements. The bottom line is: Your residents have new expectations, your competitors are implementing AI and automation, and your success metrics have been changed forever.

In this article, we break down three leasing metrics that AI has changed forever. To learn more, download our full report.

How to Measure Your Leasing Team’s Success

When you measure your leasing operation’s success, you are likely looking at one benchmark above all others: occupancy rate. It’s true — your occupancy rate is the ultimate indicator of leasing success, but it’s shaped by a set of core metrics that reveal how your portfolio is actually performing. This includes your lead-to-tour-to-lease ratios, lead response time, and cost-per-lease, among others. Tracking these KPIs as you implement AI and automation into your leasing process can help you understand what’s working, what’s slowing you down, and where the technology can help to remove friction and improve conversion.

The right CRM can help you make sense of this information. ApartmentAdvisor’s Lease ASSIST, for example, gives you an easy view into how all of your leasing agents are performing — while also enabling them to do more with their time, thanks to a 24/7 AI-powered assistant, built-in screening and tour scheduling, and a ranked lead pipeline.

Overview: Three Leasing Benchmarks that AI Changed Forever

* Industry Benchmarks reflect typical performance ranges aggregated from multifamily operators and published industry research (see Sources). Actual results vary by asset class, building age, staffing model, and market conditions.

** AI Targets suggest projected gains achievable with effective AI-enabled workflows and should be viewed as optimization goals rather than guaranteed results.

Lead-to-Tour-to-Lease Ratios

What It Is: The percentage of inbound leads that turn into tours, and the percentage of tours that turn into signed leases.

Why It Matters: A well-managed leasing team will spend the most amount of time with leads that are likely to turn into signed leases. The tighter your lead:tour:lease ratio, the stronger the impact on your occupancy rates, revenue, and admin and leasing spend.

How It’s Changing: Before automated pre-qualification processes, it was up to your leasing team to engage every lead and have a conversation with them to figure out if their preferences, incomes, and credit scores matched a unit’s requirements. Now, your AI assistant can handle pre-qualification, tour scheduling, and follow-up, ensuring your leasing team is focused on the meaningful interactions with the right leads to close more deals.

Key Takeaway: Tours alone aren’t a strong indicator of leasing team success; the number of completed pre-qualified tours is the metric you should be looking at. If leads aren’t pre- qualified correctly, then your leasing team may be wasting time on tours that have very little chance of converting into revenue while leaving more qualified prospects on the backburner.

Lead Response Time

What It Is: The amount of time that passes from when a prospect submits an inquiry to when they receive their first response from your team.

Why It Matters: On average, at least half of multifamily leads come in after business hours, resulting in delayed responses that give renters time to look for a unit elsewhere. Responding quickly increases engagement and conversion likelihood.

How It’s Changing: With AI, it’s never been easier to respond to leads instantly, 24/7. In the past, it may have taken a busy leasing team an hour or more
to simply respond to the first inquiry a prospective tenant sends. Now, leasing teams have the power to engage every lead instantly, answer questions, and start the screening process.

Key Takeaway: Instant response keeps prospects engaged at peak interest, ultimately driving up conversion.

Average Days on Market

What It Is: The number of days between when a vacant unit is listed for rent and when a lease is signed.

Why It Matters: Units that sit on the market for longer than average often point to inefficiencies in your leasing process or a disconnect between your rent pricing and market demand.
Longer vacancy periods translate directly to lost rent and can weaken your property’s market reputation over time.

How It’s Changing: AI-enabled systems can automate listing syndication, lead screening, and tour scheduling, reducing manual steps that slow leasing velocity. AI-driven market analytics can also identify pricing gaps and demand patterns to help you adjust strategy to shrink days to lease.

Key Takeaway: Speed to lease reflects both operational efficiency and market alignment. By automating routine leasing tasks and using data to fine tune your pricing strategy, you can improve this metric.

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