The Underestimated Cost of Standing Still

1. AirDNA’s own research characterises 2026 as the strongest year for STR investment since 2021. What is driving that optimism, and which markets or segments within that picture are you most cautious about?

The strongest-year framing needs an update, because 2026 hasn’t gone to script. When we made the call at the end of 2025, the thesis was straightforward: home prices cooling, rates briefly dipping below 6%, supply decelerating, the STR premium at its best since 2022. The buying window looked wide open.

Then the year threw us a curveball: the U.S. war in Iran and the closure of the Strait of Hormuz set off an energy shock that pushed inflation back to 4.2% by May and sent mortgage rates back above 6%, with the Fed holding steady and signaling it might hike rather than cut. That pushed many would-be buyers back to the sidelines.

What kept the optimism intact was supply. We’ve cut our 2026 U.S. listing-growth forecast to 2.7%, down from the 4.6% we projected in December, and that deceleration has changed the math for existing owners considerably. Occupancy is holding just above 57%, pricing power has returned, and RevPAR is tracking up roughly 2.9% for the year, almost entirely on rate. Our Repeat Rent Index shows established hosts recovering from inflation faster than the broader market. The same pattern is visible in Europe: listings grew just 2.6% year-over-year in May, with ADR up 6.8%, which suggests the supply story isn’t uniquely American.

The honest read for 2026 is that it’s a stronger year to own than to buy. The investment entry-point thesis hasn’t disappeared: it’s been pushed into 2027, when the energy shock fades, and real rate gains become more meaningful.

On the cautious side, supply remains the key dividing line. Florida Gulf markets (Cape Coral, Sarasota, St. Pete) are seeing listings outpace demand, erasing gains that would otherwise be seen in a tighter market. Supply-constrained markets like San Francisco are posting double-digit RevPAR growth by contrast. Beyond supply, I’m watching the mountain markets that took a hit from snow drought this ski season, the border and gateway markets exposed to the drop in Canadian and Western European inbound travel, and hotels, which have opened 2026 with stronger occupancy growth than most people anticipated. The macro tail risk is real: a breakdown in the Iran ceasefire, or a stock-market correction that dents the wealth effect sustaining high-end travel, would tighten the picture faster than the supply data alone would suggest.

2. You spent a decade at CBRE analyzing traditional hospitality before moving into STR data. Now that short-term rentals have matured significantly as an asset class, how has the way institutional investors underwrite STR deals evolved, and what do they still get wrong?

Institutional capital is moving into STR, but not in the way most people assume. The asset itself is still almost entirely individually owned. Funds aren’t buying up portfolios of vacation homes the way they buy multifamily or hotels, and I don’t expect that to change significantly. Where the money is actually going is a layer up: into the management of these assets and into the infrastructure driving the industry. Data platforms, pricing engines, distribution software, and property management companies consolidating thousands of individual listings under a single operating umbrella are where investment activity and innovation are concentrated.

The underwriting mistake I see most often comes straight out of my CBRE years. In traditional hospitality, you’re underwriting a stabilized operating asset with decades of performance history behind it. Short-term rentals don’t behave that way. Too many investors still approach it as passive real estate when it’s fundamentally an operating business. The return comes from execution, not from the building itself. Which is exactly why the smarter capital is going into management and technology rather than trying to own the dirt.

3. Adapt, AirDNA’s new AI-native dynamic pricing product, is launching this summer, a significant move from pure market intelligence into revenue management. What was the argument internally for making that leap?

We’ve been informing pricing decisions for years. Hosts come to AirDNA to understand their market and benchmark their rates, and our data has always been the foundation on which good pricing decisions are built. Launching Adapt is about taking the next step, from giving hosts the full picture to actually acting on it. 

And that’s not a small step. Giving hosts the data to make better decisions is one thing, but setting the price for them every night, and being right enough that they trust you to keep doing it, is another. So we built Adapt on three tracks: the models that read the market and price your listing against it (not the other way around), the integrations that let Adapt run inside the tools people already use, and the settings that turn a price into a strategy.

Pricing models are only as good as the data and context you give them, and we had the context. Many tools in this category don’t have the depth of data we do at AirDNA, and many run on a single channel, so the model sees only a thin slice of the market. The ones that pull from multiple channels usually analyze them in isolation, which means you’re either double-counting demand or missing it entirely. We’re building the engine on top of billions of AirDNA data points across millions of listings, ten-plus years in the making, stitched together across every major platform, with the AI embedded deep enough to understand the whole market before it prices a single night. We had everything we needed to build this; the argument for building Adapt basically made itself.

But getting the price right was never going to be enough on its own. Hosts need to understand why their rates look the way they do and be in control of the strategy behind them. Most tools in this category have spent years layering rule on top of rule to get there, and the result is something most hosts can’t actually navigate. We built Adapt to work the other way. Tell it what you care about, getting booked, protecting rate, maximizing revenue, and it builds the min-stays, gap, and lead-time discounts, and seasonal adjustments that get a host to their occupancy target, their rate floor, their revenue goal. It builds your comp set across every channel and explains every number in plain English. If you want to reprice Christmas, you can. If you want to know why a specific day is priced the way it is, you just ask.

That’s the whole point. Market intelligence is something you check now and then, but a system running your rates every night is something you can’t operate without.

4. If you had to name the single most underestimated risk facing STR operators in the next 24 months, what would it be?

Falling behind in how guests actually book. The real revenue risk for most operators is running a listing on settings that made sense two years ago while behavior shifts underneath them.

Booking windows have compressed to the shortest since the pandemic, around 20 days in urban markets. Stays are getting shorter almost everywhere. Travelers are choosing value and drive-to trips. A five-night minimum in a market now booking three-night stays simply doesn’t show up for the demand that exists.

That misalignment is more costly this year because the entire return is coming from rate, with occupancy broadly flat. When growth is price-led, operators who keep adjusting with looser minimums, sharper pricing, and a close eye on local competitors will capture it. The ones holding static get left behind.

I won’t minimize the macro risks, and regulation, especially, is not priced in the way people assume. A single city ordinance can reset a submarket overnight. But that’s structural, managed at the portfolio level. The risk most operators underestimate day to day, and the one fully in their control, is the cost of standing still while the market moves.