Base-price models are the industry standard in STR dynamic pricing, popular because they’re simple and scalable. The math needs no probabilities or advanced statistics, and the input — widely available market data — is easy to come by. We use “base-price model” to describe any model that requires a base price set or managed by a user.
Where the data comes from
These models are fed primarily by data scraped off the OTAs — pricing and availability for the other short-term rentals in your area. A scraper is just a robot doing what you could do by hand: visit Atlanta listings on Airbnb, record price and availability for each, repeat thousands of times into a database. You can buy this data or build the scraper yourself.
From market average to a percentage curve
The scraped prices for your city are aggregated: for each future date, hundreds or thousands of observations condense into one market-average price. That curve has peaks and troughs — weekends, summer, holidays, events — created by your competitors’ variable pricing. The model then converts each day’s market average into a percentage above or below the average of the whole dataset (say, $213), producing a curve of percentages instead of dollars.
From percentage to your price
The base price is the starting point — the average rate you expect the property to command. Say I have a 3-bedroom in Atlanta I think will average $300; I set that as my base price, and the model multiplies it by each day’s market factor from the percentage curve. The result tracks the market trend exactly, just from a different starting point.
What makes it “dynamic”?
The model becomes dynamic through the frequency it’s run — but it only changes pricing when its inputs change. When the scraper runs again and picks up the price moves your neighbors have made, your pricing updates. Day-over-day changes are usually small: hundreds of scraped properties are hard to move, and managers typically make small adjustments — big swings usually signal an error.
The base-price model by itself is not dynamic; it becomes dynamic by the frequency of running it. But the model won’t change pricing unless the inputs change.
Benefits
- Simplicity — no historical data required, so a brand-new property with no booking history can run on day one.
- Accessible data — you can buy scraped market data or build a scraper for almost any market in the world.
- Easy to understand — more advanced models need dedicated revenue professionals with statistics and economics backgrounds.
The cost
The biggest criticism is that it doesn’t truly “optimize” revenue. Optimization is a mathematical process that uses probabilities to find the revenue-maximizing price; a base-price model just takes a static price and decides how much to nudge it up or down from market trends. Think of it as a “follow your neighbor” model — if your neighbors price well, it performs well; if you don’t trust their pricing, invest in an optimization model.