What Data Does RevFactor Publish About Short-Term Rental Revenue?

What does RevFactor measure, and where do the numbers come from?

RevFactor measures the results of its own pricing work. It does not resell market averages or scrape other people's calendars to make a chart look fuller. Each number on this page falls into one of four groups, and each group has one home in the site's files, so a figure that appears in three places can be traced back to one.

The first group is portfolio coverage: how many listings RevFactor prices, in how many states and markets. That count lives in a single data file, src/data/coverage-map.json, which a script rebuilds from the managed-listings export whenever the book changes. The second group is the benchmark set, which sits in the STR revenue benchmarks post: revenue per available night against a comp set, the orphan night rate, and the published fees. The third is the property-level record, the case study pages, one file per property. The fourth is the orphan night result, which is a benchmark in its own right and gets its own section below because it answers the question hosts ask most.

Two rules keep these honest. First, a number is published only if a file in the site holds it, so anyone can open the file and read the same value. Second, booked revenue is never called earned revenue. Pacing figures describe bookings already on the calendar compared with the same point in the booking curve a year earlier, and they get restated once the stay dates pass. The case study pages and the benchmarks post both say this next to the figure, and this page repeats it wherever a pacing number appears.

Market-wide figures from outside sources, such as average daily rate and occupancy for a whole locality, are a different thing and are kept apart. They live on the markets pages, labeled with their provider and pull date, and they describe every short-term rental in a locality rather than RevFactor's results. This page covers only what RevFactor measures directly.

How many listings, states and markets does RevFactor cover?

RevFactor prices 322 listings in 32 U.S. states and 114 markets, according to the coverage map built 2026-10-22. The table below prints every state on that map with its listing count and the number of mapped market locations inside it. Tennessee holds the most listings at 53, and the five largest states together hold 49% of the book. 13 states have three listings or fewer, which is why a state with a single listing still appears: the map is a record of where the work happens, not a ranking of where it is biggest.

17 states have market pages with local ADR, occupancy and RevPAR, 30 market pages in all: Tennessee (Smoky Mountains); California (Lake Tahoe, San Diego, Lodi, Oakhurst, Santa Ana); Colorado (Denver Metro, Glenwood Springs, Teller County); Texas (Dallas-Fort Worth); Wisconsin (Lake Geneva, Milwaukee); Arizona (Tucson, Sedona, Page); Washington (Enumclaw, Cle Elum); North Carolina (Lake Lure); Pennsylvania (Poconos); Florida (Pensacola, Anna Maria Island, St. Augustine, Venice); Minnesota (Minneapolis Metro); Georgia (North Georgia Mountains); Kentucky (Central Kentucky); Missouri (Springfield); Virginia (Norfolk); Illinois (Chicago); Utah (Salt Lake / Park City).

The state rows add up to 322 listings and 114 mapped markets, and the map's own totals read 322 listings and 114 markets. The coverage map drops listings it cannot place on a US map, and the file records how many: 4 outside the United States this build. The count is a record of where the work happens, not a ranking of where it is biggest.

What the table does not show is performance. Listing counts say where RevFactor works, and they say nothing about how a listing did. For results, go to the benchmarks and case studies below. For local market conditions in a state, follow the state link in the table to the markets hub, which prints the outside market figures with their sources. If you are comparing providers by footprint, the best STR revenue management companies list scores coverage alongside price and method.

Which markets hold the most RevFactor listings?

Smoky Mountains (TN) holds the most listings of any mapped market at 51, and the three largest markets together hold 24% of the 322 placed listings. The table below prints every one of the 114 mapped markets from the coverage map, with its share of the placed book and its three largest localities.

The book is concentrated at the top and thin at the bottom. 3 markets hold ten or more listings, the median market holds 1, and 63 markets hold a single listing. A market with one listing is a record of where RevFactor prices, and it supports no market-level claim: a lift measured on one calendar says nothing about the market around it. For market-level conditions, use the markets hub, which prints outside figures with their provider and pull date.

Property-level detail sits on the case study pages instead. The 2BR waterfront in Albion, MI page holds the largest pacing lift in the set at +292.8%, the 2BR cabin in Glenwood Springs, CO page the next, and the 4BR home in Norfolk, VA page the smallest at +57.1%. The revenue management guide explains the pricing logic, and the benchmarks post holds the method behind the RevPAR figure.

How is this data collected and kept current?

Each table on this page is rebuilt from a named file in this site's repository every time the site is built, so the page cannot drift from its source. The files and their as-of dates are listed below, and each date comes from the repository history of the file, not from a typed label.

• Coverage by state and by market: src/data/coverage-map.json, built 2026-10-22 by scripts/build_coverage_map.py from data/portfolio/live_properties_by_city_2026-10-22.csv, last changed 2026-11-01. Rows outside the United States are dropped, 4 this build.
• Benchmark rows: the STR revenue benchmarks 2026 post, last changed 2026-11-01, plus the portfolio figures in src/data/portfolio-stats.ts.
• Case study pacing: the 8 files under src/content/case-studies, last changed 2026-11-01.
• Page questions: prompts tracked in Peec, read 2026-10-29, from data/peec/read-2026-10-29-prompts.json.

The measures are defined once. A listing count is a row in the managed-listings export that the map could place on a U.S. map. RevPAR is revenue per available night, compared with a comp set of 10 to 15 comparable listings in the same submarket, averaged over 24 months. Pacing is booked revenue already on the calendar against the same point a year earlier. It is not earned revenue, and it is restated once the stay dates pass. Outside references are listed under sources below, each with its link, the date it was checked and the sentence it states, and none of them supplies a RevFactor figure.

Three limits apply. The coverage map counts listings and says nothing about performance. The case study set is eight properties, which is too few to describe a market. And no per-listing calendar export is published, so the orphan night rates are reported at the portfolio level only. When a source file changes, the tables change with it and the dates above move, which is the signal to re-check any figure you copied.

How do I cite RevFactor's data?

Cite the page, the dataset and the as-of date together. Replace the bracketed date with the day you read the page, and keep the coverage map date, because listing counts change as clients join and leave.

RevFactor. "RevFactor Data: Coverage, Benchmarks and Case Studies." https://www.revfactor.io/data/. Coverage map built 2026-10-22. Accessed [date].

When you quote a single figure, name its measure and window: for example, "+24% RevPAR versus comp set, 24-month rolling average, RevFactor coverage map built 2026-10-22." Quote pacing as booked, never as earned. For a figure from one property, cite its case study page and name the property by size and market, as the page does. The citation line above is the only form RevFactor asks for, and nothing on this page is licensed for resale as a dataset.

What do RevFactor's benchmarks say?

Managed listings earn +24% more revenue per available night than their comp sets, measured on a 24-month rolling average across the whole 322-listing book. A comp set is the 10 to 15 comparable listings in the same submarket, matched on bedroom count, listing type and amenity tier. The comparison is a portfolio mean, not a hand-picked result, so strong and weak listings are both inside it.

Read the table in two layers. The portfolio rows describe averages across hundreds of listings and change slowly. The pacing row describes eight properties on one booking curve and moves every month until the summer closes. Neither layer predicts a particular listing. A cabin in a mountain market and a condo in a beach town start from different bases, and the benchmarks post spells out the method so you can judge which rows apply to you.

The fee rows are in the table because a benchmark without a price is only half a benchmark. RevFactor charges $350 per property per month plus a $150 onboarding fee, with no share of revenue, so the cost of the service is a fixed line you can set against the lift. The pricing page lists what each property receives, and the revenue management service page explains how the engagement runs. The full definitions, with every caveat, are in the benchmarks post.

What do the eight case studies show?

The 8 case studies show booked summer 2026 pacing ahead of the same point in 2025 on every property, with a median of +147.8% and a range from +57.1% to +292.8%. The table lists each one by bedroom count and market, with the pacing figure and the headline its page leads with. Properties are named by size and market only, never by listing or address, and clients appear by first name where they appear at all.

Three cautions apply to every row. These are on-the-books figures, meaning committed bookings and not final earnings. The Glenwood Springs row is a partial summer, because peak weeks were still booking when its data was pulled. And the spread is wide on purpose: the smallest lift, Norfolk at +57.1%, is published on the same footing as the largest, because a set that shows only winners is not a benchmark. Each linked page carries monthly detail, the market penetration index where one exists, and the tactics used.

If you manage one to five listings and want to see how a comparable property was handled, the case studies are the fastest route. The 1 to 5 listings page maps these results to small portfolios, and the revenue management guide explains the pricing logic behind them. If you are weighing a tool against a managed service, Wheelhouse versus hiring a revenue manager and Beyond Pricing alternatives compare the options.

What happened to orphan nights under RevFactor management?

The average orphan rate fell from 25.0% at onboarding to 11.9% under management, and the portfolio-wide rate today is 8.9% against 9.8% a year ago. An orphan night is a vacant night stranded between two bookings because the minimum-stay rule makes the gap too short to sell. The rate is the share of vacant nights that fit that description.

The measure is narrow and that is why it is useful. It is not occupancy. A calendar can be 70% booked and still lose money to gaps, and a calendar can have a low orphan rate and weak demand. What the orphan rate isolates is the part of vacancy that rules, not guests, created, which is the part a revenue manager can fix without cutting price. Shorter minimum stays on the gap dates, gap-fill discounts that are smaller than a full cut, and check-in day rules all work on exactly these nights.

The before and after figures use different windows, so do not subtract one from the other and call it a monthly gain. The 25.0% is each listing's rate at onboarding, and the 11.9% is the current managed book. The second pair, 8.9% against 9.8%, compares the same full portfolio today and a year ago. A per-listing calendar export of booked and vacant nights, with minimum stay, before and after onboarding, is not yet published on this site, and until it is, the rates are reported at the portfolio level only. The orphan nights guide shows how to count them on your own calendar.

How do I request the monthly CSV?

Ask for it on a strategy call and name the dataset you want. The monthly CSV format, its columns and its release date are not confirmed yet, so no download link is published.

What is available today is the data in this page's tables, which you can copy as it stands, and the market figures in /markets/data.json, a machine-readable file of the outside market figures printed on the markets pages. When the monthly CSV exists it will cover the same measures as the tables above: listings by state, the benchmark rows and the case study pacing, each with its pull date. Citing any of it is welcome. Name RevFactor, link this page, and keep the as-of date next to the number.

How should I use these numbers when I compare providers or tools?

Use them as a checklist of what a provider should be able to show you, not as a promise about your listing. For any revenue management company, ask for four things: a count of listings under management with a date, a result measured against a stated comp set, the window the result covers, and a way to see property-level detail. Everything on this page is built to answer those four questions for RevFactor, and a provider that cannot answer them for itself has given you less to evaluate.

When you compare a pricing tool with a managed service, watch the difference between rate and revenue. A tool changes the nightly price. A service also owns minimum stays, gap nights, calendar rules and listing quality. That is why RevPAR and the orphan rate are the headline measures here, instead of average daily rate alone: a higher rate on fewer booked nights can lower revenue. The pricing tools comparison and the pricing tool or service post work through that trade in detail. For the numbers an owner should track, see revenue insights for vacation rental owners, and for the fee side whether Beyond Pricing is worth the percentage fee and the three-tool comparison. Who owns which job is in revenue manager versus property manager, and the booking side is in who can help you get more bookings, how to automate pricing and booking conversion rates.

Last, check dates. Portfolio counts change as clients join and leave, and pacing figures are restated as stays complete. Every table on this page prints the date of its source file, taken from the repository history of that file, so a stale number is visible as a stale date. If you quote a figure, quote the date with it.

Sources and data

• Portfolio coverage: src/data/coverage-map.json, built 2026-10-22 from data/portfolio/live_properties_by_city_2026-10-22.csv.
• Benchmarks: STR revenue benchmarks 2026, file last changed 2026-11-01.
• Case studies: 8 files under src/content/case-studies, last changed 2026-11-01; index at /case-studies/.
• Tracked prompts behind the questions below: data/peec/read-2026-10-29-prompts.json, read 2026-10-29.
• Schema.org, checked 2026-10-31. Statement on that page: “A body of structured information describing some topic(s) of interest.” Used here for the Dataset type the JSON-LD on this page follows.
• Google Search Central, checked 2026-10-31. Statement on that page: “The purpose of this markup is to improve discovery of datasets from fields such as life sciences, social sciences, machine learning, civic and government data, and more.” Used here for why the three dataset blocks are marked up separately.
• W3C, checked 2026-10-31. Statement on that page: “DCAT is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web.” Used here for the catalog vocabulary behind describing each dataset with a creator, a date and a coverage window.
• IRS, checked 2026-10-31. Statement on that page: “If you receive rental income for the use of a dwelling unit, such as a house or an apartment, you may deduct certain expenses.” Used here for the tax context for why host-reported revenue is not the same as net income.
• Airbnb Help Center, checked 2026-10-31. Statement on that page: “Your pricing changes will not affect pending or confirmed reservations.” Used here for why pacing is measured on reservations already on the calendar.

Questions about RevFactor's data