AirDNA advertises its short-term rental data as up to about 94.9% accurate. A host on r/airbnb_hosts nearly wired money on the strength of that number: Rentalizer projected $85,000 in annual STR revenue “with high confidence” for a $700,000 property. The trailing 12 months of actual booking data for that same address showed $30,000.
This is not investment advice. It’s a comparison of three revenue-estimation tools used at the scouting and underwriting stage, before an offer goes in. But a bad projection has real consequences: it’s the gap between a property that cash-flows and one that doesn’t cover its mortgage. None of these tools guarantee a specific address will hit their number, and none of them should be treated as a substitute for pulling real comps and underwriting conservatively.
The short version: AirDNA has the deepest dataset and is the closest thing the industry has to a standard reference, but its headline accuracy figure describes market-level averages, not a promise about one address. Mashvisor’s real value isn’t STR data depth — it’s the side-by-side long-term-rental-versus-STR comparison. Rabbu is the honest free option: a solid first-pass number with no paywall, backed by a thinner dataset than AirDNA’s for serious multi-market work. Treat any of the three as a starting filter, not a final answer, and verify every number against real comps before making an offer.
What Each Tool Actually Is (and What the “AI” Really Does)
AirDNA runs two core products: Rentalizer, which projects revenue for a single address, and MarketMinder, which tracks market-level occupancy, ADR, and supply trends. AirDNA has also added a “Rentalizer Agent” AI research assistant on top of the same underlying dataset — reportedly the largest STR comp database of the three.
Mashvisor markets an “AI Property Finder” and neighborhood-level analysis, but the feature that actually differentiates it is showing long-term rental and short-term rental projections for the same property, side by side.
Rabbu offers a free revenue estimator and comp lookup by address. It’s free because Rabbu’s business model runs through its marketplace and agent-referral side, not subscription fees for data.
None of these tools are “AI” in the sense of a model reasoning about what makes one specific property unique. They’re statistical comp-averaging engines trained on historical booking data, wrapped in AI branding for marketing purposes. The branding does a lot of lifting. The value sits in the underlying comp dataset, not the label on the front end.
The Accuracy Question: What “94.9%” Actually Means
AirDNA’s own published figures — roughly 94.9% for occupancy-related metrics and 96.2% for revenue accuracy — are AirDNA’s claims about its own data. Third-party reviews and community reports consistently describe these figures as market-level aggregate accuracy: how closely AirDNA’s occupancy and ADR trends track a whole market’s actual performance, not a promise about a single address.
A tool can be highly accurate on a market’s average and still be materially wrong on one property. Unique floor plans, amenity gaps, micro-location differences, new-supply saturation, and seasonality all move the needle on a specific listing in ways a market average smooths over.
The $700,000 near-miss above is not an isolated data point. Another host on r/airbnb_hosts put it more bluntly: “Airdna is garbage. They report twice the projected income for my property as I actually make.” A frequent AirDNA user on r/ShortTermRentals offered a more measured diagnosis: “Most likely, it’s a combo of you under performing and AirDNA being overly optimistic. Find actual comps in your area… I see AirDNA projections that are way off quite frequently. I use it everyday.”
Property-level estimates tend to skew optimistic because Rentalizer models what a well-managed, well-reviewed, well-priced listing could earn — not what a new, unoptimized listing with zero reviews will actually earn in its first year. The same accuracy question shows up in property valuation tools that use AI to estimate what a house is worth, and the answer is the same one: the headline number is a starting filter, not a closing document.
AirDNA — Best for Serious Multi-Market Scouting
AirDNA fits investors actively comparing multiple markets or zip codes, not just underwriting one property. The dataset is the deepest of the three, with the most granular filters by bedroom count, property type, and comp radius.
Pricing is reported (confirm current rates on airdna.co) at roughly $15–40+ per month for a single-market Starter tier with Rentalizer and comp access, with Professional and multi-market tiers priced higher. A limited free Rentalizer teaser is also available, though it withholds most of the detail.
AirDNA’s strength is depth. It’s reportedly the tool DSCR lenders reference most often as supporting documentation. The watch-outs: it’s pricier than Mashvisor at comparable tiers, property-level estimates skew optimistic per the community reports above, and some hosts report AirDNA revising historical data retroactively, which complicates tracking a market’s trend over time.
The subscription earns its cost for investors seriously underwriting several markets at once. It’s overkill for someone asking a single “should I buy this one house” question — Rabbu answers that for free.
Mashvisor — Best for the LTR-vs-STR Strategy Decision
Mashvisor fits investors who haven’t decided whether a specific property should run as a long-term rental or an Airbnb. The side-by-side cash-on-cash return and cap rate comparison for both strategies on the same address is the actual product here, not raw STR data depth.
Pricing is reported (confirm current rates on mashvisor.com) at roughly $18–100+ per month across tiers, with a short free trial. The platform combines MLS, STR, and LTR data in one interface and includes a property search and filter function for finding deals, not just analyzing ones already found.
Community feedback flags the STR data specifically as more aggregated and less granular than AirDNA’s, and some users report mixed experiences with support responsiveness and billing — worth noting as reported sentiment rather than a verified pattern.
Mashvisor isn’t the right purchase for an investor who has already committed to STR. It earns its subscription only when the LTR-versus-STR question is genuinely open.
Rabbu — Best Free First-Pass
Rabbu fits a fast, no-cost gut check on a specific address before committing to a paid subscription. The core estimator is genuinely free: the basic revenue number isn’t gated behind a sales call or a “contact an agent” wall.
The strength is transparency and simplicity. The watch-out is dataset size: Rabbu’s comp pool is smaller than AirDNA’s for deep multi-market underwriting, and community reports describe the two tools’ numbers diverging meaningfully on the same address. One host on r/ShortTermRentals summed up the trade cleanly: “I really like Rabbu for its user friendliness and that they don’t hide everything behind a paywall or contact an agent. BUT I do find when I take a property over to Air DNA, the revenue is crazy different. I don’t like relying on just one set of data anyways.”
A separate risk applies specifically to Rabbu’s marketplace listings, not the standalone calculator. One r/ShortTermRentals user warned: “Use lots of caution, there are many fraudulent properties. Just today I found a 2mo old listing that literally burned to the ground 4 months ago. Lots of financials are fake… AirDNA and other tools will likely give you a better feel for actual CF than the often faked Rabbu #s.” That’s a marketplace-listing problem, distinct from the free calculator’s reliability.
For an investor who isn’t even sure they’ll buy an STR yet, Rabbu is the right starting point — free, fast, no login wall. Upgrading to AirDNA makes sense once the market shortlist narrows.
Where All Three Fall Apart: Rural and Low-Inventory Markets
Accuracy scales directly with comp density. Estimating revenue is straightforward when 200 nearly identical listings sit in the same area with rich booking history. It’s a different problem entirely for a unique or standalone property in a market with thin STR inventory.
A host on r/airbnb_hosts described the pattern precisely: “I think the overall accuracy would be very location/market specific. It’s not hard to estimate revenue when there are 200 other listings that are essentially the same in an area with no shortage of STR data. As opposed to a unique or standalone house in a market less populated with STRs.”
New and emerging markets, along with rural areas generally, have thin comp data across all three tools. That’s a data-availability problem, not a brand failure specific to one vendor. In these markets, manual comps carry more weight than any tool’s projection: active competing listings, their calendars, their review counts, and direct outreach to nearby hosts.
Investors also weighing a flip instead of a rental run into a related version of this problem — flippers face similar tradeoffs comparing deal-analysis tools that lean on comp density they don’t always have. Neither situation means the tool is wrong. It means the data underneath it is too thin to trust at face value.
Regulation: A Projection Is Worthless If STRs Are Banned
None of these three tools verify whether short-term rentals are legally permitted at a given address. A $100,000 projected annual revenue number means nothing if the city bans STRs outright or caps permits below what’s available.
Local STR ordinances, permit caps, and zoning rules need to be checked before any projection factors into an offer — confirming short-term rental compliance rules is a separate step that none of these revenue tools perform. Regulatory risk runs highest in markets that have recently tightened rules: new permit caps, primary-residence requirements, or added occupancy taxes. None of that shows up in a revenue estimate, because none of these tools are built to track it.
This is the step every revenue projection tool quietly skips — and the one that can zero out an entire deal regardless of how good the underlying comp data was.
How Lenders Actually Treat These Projections
DSCR loans qualify a property based on its income potential rather than the borrower’s personal income, and many DSCR lenders accept an AirDNA-style report as supporting documentation. One investor on r/ShortTermRentals framed it directly: “My personal favorite is AirDNA… I’ve found that dscr lenders rely on AirDNA as gold standard.”
But the lender’s own appraisal — specifically the rental income analysis on Form 1007 — governs the actual underwriting decision, not the number a revenue tool produces. Lenders typically look for a debt service coverage ratio around 1.1 to 1.25 or higher, and a favorable AirDNA or Mashvisor projection alone doesn’t secure approval. Down payment requirements commonly reported in the 20–30% range for DSCR STR loans, along with credit minimums, still apply regardless of what any tool projects.
A strong number from any of these platforms is a useful talking point in a lender conversation. It is not a guarantee. The appraiser’s number is what closes the loan.
Do You Even Need a Paid Tool? A Free-First-Pass Workflow
A practical sequence for most first-time STR buyers, before spending anything on a subscription:
- Step 1: Run the target address through Rabbu’s free calculator for a no-cost baseline number.
- Step 2: Haircut that number. Community heuristics vary — one host on r/airbnb_hosts reported: “Not at all, multiply airdna by like .65 … I have one and so far the .65x is pretty accurate.” A YouTube commenter offered a rougher rule: “Basicely you can trust it but you have to check the listing manually and half the revenue, because their datas are only based on the prices and available dates displayed by airbnb.” Treat any gross projection as a ceiling, not an expectation.
- Step 3: Subtract fees before getting attached to the number. As one host on r/airbnb_hosts put it: “They’re good, but I think their numbers are before deducting fees… For most owners, this will be 30% of revenue or more. After you include maintenance, insurance, property tax, HOA and other expenses, you could be looking at 50% of revenue going towards expenses that don’t even include the mortgage payment or management fees.”
- Step 4: Pay for AirDNA or Mashvisor only once seriously comparing multiple markets, or once the LTR-versus-STR question genuinely needs answering — not for a single one-off property check.
- Step 5: Cross-reference four or five real active comps manually — calendars, review counts, current pricing — before relying on any tool’s number in an offer.
Most first-time STR buyers don’t need a subscription running several hundred dollars a year to reach a go/no-go decision. They need a free number, a 35–50% haircut, and their own comps.
Our Verdict: Which Investor Should Use Which
A first-time buyer checking one or two specific properties should start with Rabbu’s free estimator, apply the haircut above, and verify with manual comps before doing anything else.
An investor genuinely undecided between long-term and short-term rental strategy on a specific property gets the most direct answer from Mashvisor’s side-by-side comparison — that’s the tool built for exactly this fork.
An investor actively scouting multiple markets, or needing documentation a DSCR lender will recognize, gets more value from AirDNA’s depth than either alternative, and the subscription cost is justified by the scale of the decision.
Every path converges on the same final step: verify the number independently before wiring money. Once a property closes, dynamic pricing tools take over the revenue side of the equation, and day-to-day guest and operations management is a separate toolset entirely from anything covered here.
The Bottom Line
AirDNA, Mashvisor, and Rabbu are first-pass filters, not underwriting engines. Each is genuinely useful for the specific job it’s built for — but none of them knows a specific address better than a walk-through, a spreadsheet of real comps, and a conservative haircut applied by the person actually buying the property.
The practical next step: run the free Rabbu number first, haircut it by a third to a half, subtract realistic fees and operating expenses, confirm STRs are legally permitted at that exact address, then decide whether the deal still pencils before spending a dollar on a subscription.
The tool that gets it “right” is the one whose number was verified independently — not the one with the best-sounding accuracy stat on its homepage.
Frequently Asked Questions
Is AirDNA’s 94.9% accuracy claim real?
The figure is AirDNA’s own published claim, and it describes market-level aggregate accuracy — how closely AirDNA’s occupancy and revenue trends track a whole market’s actual performance. It is not a verified promise about how close the projection will land for one specific address, and community reports show property-level projections diverging significantly from actual results in some cases.
Which is more accurate: AirDNA or Mashvisor?
Neither has independently audited, property-level accuracy figures available for direct comparison. AirDNA’s larger dataset generally makes it more reliable for STR-specific projections, while Mashvisor’s advantage lies in comparing STR against long-term rental returns on the same property rather than in raw STR data precision.
Is Rabbu actually free?
The core revenue estimator and comp lookup are free to use, with no paywall on the basic number. Rabbu’s business model runs through its marketplace and agent-referral services rather than subscription fees, though separate caution applies to financial claims on individual marketplace listings.
Can I use an AirDNA report to get a DSCR loan?
Many DSCR lenders reportedly accept an AirDNA-style report as supporting documentation, and some community members describe it as close to an industry standard for that purpose. The lender’s own appraisal, specifically the Form 1007 rental income analysis, is what actually governs loan approval — not the report itself.
How much should I discount a Rentalizer or Mashvisor estimate?
There’s no single verified discount rate, but community heuristics commonly range from roughly a 35% haircut (multiplying the projection by about 0.65) to cutting the number in half, depending on how optimized the surrounding comps are. Treat any gross projection as a ceiling and verify against manually pulled comps before relying on it.
Do these tools work in rural or low-inventory markets?
Accuracy drops in rural and low-inventory markets across all three tools, because the underlying comp data is thinner wherever fewer similar STR listings exist nearby. This is a data-availability limitation shared by all of them, not a flaw specific to one vendor — manual comps should carry more weight than any tool’s number in these markets.
