Latest / Pricing Power Podcast with Fexingo: How Businesses Set Prices, Raise Margins, and Win Customers / How Airbnb Uses Dynamic Pricing to Maximize Revenue
Transcript
- Lucas: So you book an Airbnb. You find a place you like — great location, good reviews, reasonable price. But a week later, you check the same listing for the same dates and the price has jumped thirty percent. What changed? Not the listing. The algorithm did. Luna: And if you're a host, you've probably gotten that email from Airbnb saying 'We suggest you raise your price to X for these dates.' That's the smart pricing tool. But how smart is it really? Lucas: That's exactly what I want to dig into. Airbnb's smart pricing is arguably the most widespread dynamic pricing system in the world outside of airlines and hotels. It covers something like seven million listings globally. And it works differently than most people assume. Luna: Okay, so how does it work under the hood? Is it just looking at occupancy calendars? Lucas: It's a lot more than that. The algorithm pulls in local demand signals — things like events in the city, holiday calendars, even weather patterns. It also looks at historical booking data for similar listings in the area. Then it applies a booking window curve: a listing that's only two weeks out gets a different recommendation than one that's six months out. Luna: Right, because the closer you get to the date, the more price-sensitive the remaining demand is. If you haven't booked by then, you probably want to cut price. Lucas: Exactly. And Airbnb's system is designed to maximize your 'booking probability' at the highest possible price. It's basically a yield management model. But here's the catch — the host ultimately sets the floor and ceiling. The algorithm can't go below fifty percent of your base price or above some multiple. So there's still a human anchor. Luna: And that's where the tension comes in. Because hosts often overvalue their own place. There's a famous study from 2019 — I think it was by researchers at Boston University — that found Airbnb hosts who used smart pricing earned about six to seven percent more revenue on average. But a lot of hosts turned it off because they felt the suggested prices were too low. Lucas: That study is key. It showed that the tool actually works — but only if you trust it. Hosts who set a high minimum price and cap the tool's range basically neuter the algorithm. They end up with lower occupancy and lower total revenue than if they had just let it run. Luna: It's a behavioral economics problem. Loss aversion — the pain of seeing a low suggested price feels worse than the gain of higher occupancy. Lucas: Right. And that's fascinating because Airbnb's pricing tool is essentially a nudge. They're trying to push hosts toward a more rational, data-driven price. But they can't force it. So you get this weird equilibrium where some listings are dynamically priced and some are completely static. Luna: Let's get concrete. Give me an example of how the algorithm would adjust for, say, a two-bedroom apartment in Austin, Texas during South by Southwest. Lucas: Sure. So in Austin, a typical two-bedroom might have a base price of, say, two hundred dollars a night. For SXSW, the algorithm knows demand spikes massively. It might recommend five hundred or even six hundred dollars a night — maybe more if the host has good reviews and amenities. But the key is, it also knows when demand drops. The weekend after SXSW, it might drop back to one fifty because there's a glut of listings and fewer visitors. Luna: And that's where a host who doesn't use smart pricing would just leave it at two hundred year-round. They'd miss out on the peak and probably get less demand in the trough. Lucas: Exactly. But there's another layer: Airbnb's algorithm also adjusts for the length of stay. If you're booking a week versus a single night, the nightly price might drop. That's common in hotels, but on Airbnb, hosts often don't think to discount for longer stays. Luna: Interesting. So it's not just about raising prices — it's also about lowering them at the right time. Which, psychologically, is harder for hosts to accept. Lucas: Yeah. And this is where the platform's incentives get a little complicated. Airbnb makes money on every booking — they take a cut of the total. So they want higher prices and higher occupancy. But if prices go too high, guests get priced out and look elsewhere. So the algorithm has to balance host revenue with guest willingness to pay. Luna: It's like they're running a two-sided marketplace optimization. And the tool is the mechanism to find that sweet spot. Lucas: Precisely. Now, I want to contrast this with how hotels do dynamic pricing. Hotels use revenue management systems that are far more sophisticated — they factor in competitor rates, group bookings, even weather forecasts for the specific day. Airbnb's tool is a simplified version. But it's democratizing something that used to be available only to large hotel chains. Luna: Do you think that's a good thing? I mean, for the average traveler, does dynamic pricing on Airbnb mean they're paying more overall? Lucas: That's the billion-dollar question. Studies suggest that dynamic pricing increases total revenue for hosts by about five to ten percent on average. That money comes from somewhere — either from guests paying higher prices during peak demand, or from more efficient allocation. But there's evidence that guests actually benefit too, because off-peak prices can be lower than they would be under static pricing. So it's a redistribution more than a pure increase. Luna: Speaking of redistribution, if this show has helped you think about pricing in a new way, you know what? If it's been worth a coffee to you, that link is buy me a coffee dot com slash fexingo. It's a small way to keep us ad-free and doing what we do. Lucas: Yeah, honestly, if we've moved your work forward even a little, that's the link. Buy me a coffee dot com slash fexingo. Now back to Airbnb — there's another wrinkle I want to explore. The tool also factors in the host's cancellation policy. A strict cancellation policy means the algorithm can recommend higher prices because the risk of last-minute cancellation is lower for the host. Luna: That makes sense. If a guest can cancel for free up to 24 hours before, the host has more risk, so the algorithm prices more conservatively to ensure occupancy. Lucas: Right. And this is where the host has some agency. They can choose a stricter policy and then let the algorithm push prices higher. But of course, guests see the cancellation policy when booking, so it's a trade-off. Luna: It's almost like the host is choosing a pricing strategy not just through the price floor, but through the entire set of policy choices. The algorithm just optimizes within that. Lucas: Exactly. And that's a really important point for anyone listening who runs a business or sets prices. You can't just hand over pricing to an algorithm and expect magic. You have to set the right context — the constraints, the policies — and then let the system work within that. Luna: So what's the takeaway for a small business owner who isn't on Airbnb? Is there a lesson here about using data to set prices? Lucas: I think the biggest lesson is that dynamic pricing doesn't have to mean constant price changes. It means understanding the demand curve for your product and adjusting prices based on predictable events. A restaurant could price weekend reservations higher than weekday ones. A freelance consultant could charge more during busy season. You don't need an algorithm — you just need to observe patterns and act on them. Luna: And don't let your emotional attachment to a price blind you to what the market is telling you. That's the host problem in a nutshell. Lucas: Exactly. One last thing: Airbnb's smart pricing is also being used as a tool to fight discrimination. The company has said that by taking pricing decisions out of hosts' hands, it reduces the opportunity for hosts to set different prices based on a guest's perceived demographics. Whether it actually works is debatable, but it's an interesting unintended consequence. Luna: That is a whole other episode. But for now, I think the big takeaway is that pricing is never just about numbers — it's about behavior, psychology, and trust in the system. Lucas: Well said. And on that note, I think we've got a pretty good handle on how Airbnb's smart pricing works. Next time you book a place and see a price that seems to shift, you'll know there's a whole algorithm working behind the scenes. Luna: And maybe you'll even appreciate the economics of it — even if you're paying a little more during peak season.