If you run one listing from your laptop, you probably think about price when something feels off. If you run dozens or hundreds, you need a system. New data from AirDNA makes that split visible in the numbers. In the United States, among entire home and apartment listings, property managers and individual hosts do not price the same way. One group leans hard on dynamic pricing. The other still does most of the work by hand.
This post walks through what the data shows, why the gap might exist, and what it could mean for revenue, time, and how “professional” your operation looks to guests and platforms.
What the numbers say
The chart compares two groups in the U.S. market: individual hosts and property managers. It measures the share of listings that fall into three pricing styles: static, manual or rules based, and dynamic.
Individual hosts
- Static pricing: 10% of listings
- Manual or rules based pricing: 55%
- Dynamic pricing: 36%
Property managers
- Static pricing: 10% of listings
- Manual or rules based pricing: 35%
- Dynamic pricing: 55%
The headline is simple. More than half of manager led listings use dynamic pricing. For individual hosts, more than half still use manual or rules based approaches. Static pricing sits at the same modest level for both groups: 10%.
So the story is not “managers never touch price by hand” or “hosts never use automation.” It is a tilt. Managers tilt toward automation. Hosts tilt toward steering price themselves, with rules they set and adjust.
Why “manual or rules based” still dominates for many hosts
Manual and rules based pricing usually means you or your spreadsheet decide the base rate, weekend bumps, minimum stay, and maybe a few seasonal rules. It works. It is transparent. You feel in control.
For a small portfolio, that control matters. You know the house, the neighborhood, and the odd week when a local event moves demand. You might not trust a black box to price your only asset. Cost and complexity also matter. Dynamic pricing tools take setup, subscription fees, and a learning curve. If you have one listing and limited time, “good enough” manual pricing can feel like the rational choice.
The data suggests many hosts make that trade on purpose. Fifty five percent is not a fringe behavior. It is the majority path.
Why managers cluster around dynamic pricing
Property managers earn by scale. More listings mean more nights to price, more calendars to sync, and more risk if rates sit wrong for weeks. Dynamic pricing, in this context, usually means software that ingests market demand, competitor rates, seasonality, and sometimes events, then updates suggested prices often.
At volume, hand tuning every listing does not scale. A 55% share for dynamic pricing among managers fits a simple story: professional operators buy efficiency and consistency, and they accept algorithmic help to protect revenue across a portfolio.
That does not mean every manager trusts the number blindly. In practice, many teams use dynamic pricing as a starting point and still apply rules and overrides. The chart’s category split still captures something real: the default system is more likely to be automated for managers than for individual hosts.
The one number both groups share
Static pricing is 10% for both groups. That is worth a pause.
Static here means the price does not move much with the market. It is the “set it and forget it” end of the spectrum. That both cohorts land on the same low share suggests static pricing is not the norm for either group in this slice of the market. The real divide is between manual or rules based work and full dynamic systems, not between “frozen” and everything else.
What this might mean for revenue and risk (without claiming a guarantee)
This infographic does not show dollars earned. It shows how people price, not how much they make. So we should be careful.
Still, the gap invites a fair question. If demand shifts quickly and competitors move their rates, a manual stack that updates weekly can leave money on the table in peak windows, or keep prices too high when softness hits. Dynamic tools try to reduce that lag. They also introduce their own risks: bad inputs, overaggressive surges, or settings that do not fit a unique property.
For an individual host, the “right” answer may stay hybrid: dynamic suggestions plus human judgment. For a manager, the “right” answer may be dynamic first, with policies on top. The AirDNA split does not pick a winner. It shows which default each group tends to use.
Guests and platforms do not see your dashboard
Travelers see final prices and availability. They do not see whether you clicked a number in or a model suggested it. Over time, though, pricing discipline shows up in occupancy, review patterns, and how often you need to discount to save a week. Operators who treat pricing as a system, manual or dynamic, often behave more consistently than those who only react when something breaks.
If you are an individual host reading this and you sit in the 55% manual bucket, you are in good company. The data says that is typical. If you are curious about closing the gap to how managers operate, the next step is not necessarily a full handoff to software. It can be a trial on one listing, clear guardrails, and a honest look at how often you actually refresh rates today.
Bottom line
AirDNA’s U.S. data on entire home and apartment listings shows a clear pattern: property managers are more likely than individual hosts to use dynamic pricing (55% versus 36%), while individual hosts are more likely to rely on manual or rules based pricing (55% versus 35%). Static pricing is uncommon for both, at 10% each.
The picture is one of professionalization and scale. Managers standardize on tools that reprice with the market. Hosts, often running smaller operations, still do the steering themselves. Neither side is uniform. The spread matters because pricing is one of the levers that turns search views into booked nights. Where you sit on that spectrum is a choice about time, control, cost, and how aggressively you want market data in the room when you set tonight’s rate.
Source: AirDNA, as cited on the original graphic. For the latest methodology and markets, refer to AirDNA’s own reports and definitions.