What data tools do and do not tell you
Short-term rental analytics platforms such as AirDNA collect listing and booking signals and turn them into estimates for revenue, occupancy, and rates. They are estimates, not audited financial statements. Treat them as a way to compare markets and properties, and verify against other sources before committing capital.
The core metrics
- ADR (average daily rate): average price per booked night.
- Occupancy: booked nights divided by available nights.
- RevPAR (revenue per available rental night): ADR x occupancy. It blends price and demand into one number.
- Revenue: total booked revenue over a period, usually annual.
- Active listings: the supply you will compete with.
- Lead time and length of stay: how far ahead guests book and how long they stay.
Example (illustrative only): Two listings each earn about $54,000 per year. One achieves it at ADR $450 and 33 percent occupancy; the other at ADR $200 and 74 percent. The first has fewer bookings and more variance per booking. The second depends on high turnover and cleaning cost. Same revenue, different business.
Step 1: Define your property precisely
Before you filter, write down the bedroom count, bathroom count, maximum guests, and the amenities you plan to offer (pool, hot tub, game room, pet friendly). Filtering to the wrong profile is the most common data error.
Step 2: Build a comp set
- Filter by the same bedroom count and similar capacity.
- Restrict to a tight geography, such as a neighborhood or a set number of miles from the anchor attraction.
- Require similar amenities, especially the ones that command premium rates.
- Keep only listings with a meaningful operating history, so partial years do not distort averages.
- Aim for at least five to ten comps, then read the actual listings.
Step 3: Look at the distribution, not the average
Averages hide the spread. Sort your comps by revenue and note the top quartile and bottom quartile. If your underwriting requires top quartile results to break even, the deal is fragile.
Example (illustrative only): Suppose ten comps earn between $30,000 and $90,000, with a median of $52,000. Underwriting to the median, or below, is more defensible than underwriting to the $90,000 outlier.
Step 4: Understand why top performers are top performers
Open the best listings and study them. Common reasons include a unique feature, exceptional photography, an unusual location, strong review scores, or very active pricing. Ask whether you can realistically replicate those advantages. If not, discount the numbers.
Step 5: Read seasonality
Monthly views reveal peaks and troughs. A market that earns most of its income in three months carries more risk than one with steadier demand. Pair this with our seasonality planning guide, and check whether your cash reserves can cover slow months.
Step 6: Check supply trends
Rising active listings with flat demand can compress occupancy and rates. Look at the trend in listing counts over the past few years, and consider whether new construction or new regulation could change supply.
Step 7: Cross-check with other sources
- Browse live listings and check calendars for blocked or booked dates. Blocked dates are not always bookings.
- Ask local property managers about typical performance.
- Compare with local lodging tax reports, when available.
- Review recent guest reviews to gauge what guests value and complain about.
Common data-reading mistakes
- Comparing gross revenue to your profit. Revenue is not income. Subtract expenses using the framework in STR underwriting basics.
- Using a market-wide average. The average of all listings includes studios, mansions, and part-time listings.
- Ignoring listing age. A listing open for four months tells you little about a full year.
- Treating a market score as a verdict. Composite scores summarize assumptions you may not share.
- Assuming past equals future. Regulation, supply, and travel demand change.
- Confusing hosts who over-report with hosts who under-report. Some data comes from estimates, so cross-checks matter.
A simple worksheet
- Property profile (beds, baths, capacity, amenities).
- Comp list with ADR, occupancy, revenue, listing age, review score.
- Median and quartiles for revenue.
- Monthly seasonality chart.
- Supply trend note.
- Your conservative revenue assumption and the reasoning for it.
If you can explain your revenue assumption in two sentences, referencing specific comps, you are using the data well. If you cannot, keep researching.
Turning research into a written conclusion
After pulling data, write a short paragraph that a skeptical friend could check. It should name the comp set, the median revenue, the range, the seasonal pattern, and the revenue figure you will use in underwriting. Writing forces you to notice gaps. If you cannot say why your property should earn what you assume, the number is a hope rather than an estimate.
Red flags in a data pull
- Very few comps, so one outlier drives the average
- Comps that differ sharply in bedroom count or amenities
- Revenue that depends on a single month or a single event
- Rapid supply growth in the last twelve months
- Listings with unusually high rates but almost no reviews
Refresh your research before you make an offer, and again before closing, because data changes and a stale pull can mislead.
Educational only: this guide is general education, not financial or investment advice, and figures shown are illustrative.