AirDNA vs STRProfitMap: Which One Actually Helps You Make Smarter STR Investments?

If you're evaluating short-term rental investments, you've probably come across AirDNA. It's the biggest name in STR data, and for good reason. They've been aggregating Airbnb and Vrbo listing data for years. But bigger doesn't always mean better when real money is on the line.

We built STRProfitMap because we kept running into the same problem: the data looked great on a dashboard but fell apart during underwriting. This comparison breaks down where each tool fits in an investor's workflow, and where the gaps can cost you.

The Core Workflow of STR Investment

Every serious STR investment follows the same steps: identify a market, pull comparable listings, underwrite the deal, and stress-test your assumptions. The quality of your comps determines the quality of your underwriting. Occupancy and average daily rates swing hard with seasonality, local regulations, and property type. A tool that averages across mismatched listings (active and inactive, studios and five-bedrooms) can make a bad deal look investable.

What AirDNA Does Well

AirDNA built its reputation on scale. The platform aggregates millions of listings across global markets and offers five years of historical data, six months of forward-looking projections, and market-wide investability scores. For broad questions like "Should I look at Denver or Dallas?", that macro view is genuinely useful. You can spot trending markets, compare metro-level demand, and get a general sense of where STR revenue is heading.

Where it falls short for underwriting:

• AirDNA relies on scraped data that includes inactive and unreliable listings. We ran a side-by-side test in Oakhurst, California: AirDNA returned over 1,000 listings, but only about 600 held up as active, entire-home rentals with real booking history.
• Revenue averages get inflated when your dataset mixes luxury cabins with underperforming studios. That noise makes deal-level analysis unreliable.
• No full dataset export to Excel means you're stuck underwriting inside their interface instead of your own models.
• No STR regulation data, which is a growing risk factor as cities like New York, San Francisco, and dozens of smaller markets impose permit caps and occupancy restrictions.

How STRProfitMap Approaches It Differently

We designed STRProfitMap for the decision stage: the point where you're evaluating a specific property in a specific ZIP code and need numbers you can trust.

Curated comps, not raw scrapes. We filter out the noise upfront. Every listing in our dataset is an entire-home property with at least nine months of rental history and verified guest stays. That means the averages you see actually reflect what a performing STR looks like in that market.

Interactive profit mapping. Our map color-codes ROI from dark green (high return) to red (low return) and lets you filter by bedroom count, property type, price range, and performance metrics. Overlay layers show saturation indexes and local STR regulations so you can spot overbuilt ZIP codes or markets with permit caps before you make an offer.

AI Buy Box. We process guest reviews to surface the amenities that actually drive revenue (hot tubs, game rooms, EV chargers, whatever matters in that specific market) and generate a revenue potential score for your target property configuration.

Excel export. Download full comp sets and run your own sensitivity analysis. Your underwriting model, your assumptions, your spreadsheet.

Case Study: Walking Away from a Cincinnati Studio

Here's an example of what cleaner data actually changes. An investor was evaluating a studio apartment in Cincinnati. AirDNA's market dashboard showed strong average revenue across the metro, and the deal looked promising on paper.

When they pulled our ZIP-level export and filtered to studio-only comps, the picture changed. Most of the high-earning listings driving that metro average were three- and four-bedroom houses with hot tubs and game rooms. Completely different properties competing for a different guest segment. The actual studio comps showed a lower revenue ceiling and inconsistent occupancy that didn't pencil against the lease commitment.

The investor passed on the deal. Sometimes the best investment decision is the one you don't make. But you can only get there if your comps actually match your property.

Head-to-Head Comparison

Market coverage AirDNA: Global, millions of listings STRProfitMap: U.S.-focused, curated dataset

Data quality AirDNA: Raw scraped data (includes inactive listings) STRProfitMap: Filtered to verified, active entire-home rentals

Best for AirDNA: Macro market research and trend spotting STRProfitMap: Deal-level underwriting and comp analysis

Historical data AirDNA: 5 years STRProfitMap: Growing (focused on actionable recency)

Forward projections AirDNA: 6 months STRProfitMap: AI-driven revenue scoring by property config

Regulation data AirDNA: Not included STRProfitMap: STR regulation overlay by market

Saturation analysis AirDNA: Limited STRProfitMap: ZIP-level saturation index

Excel export AirDNA: Not available STRProfitMap: Full comp set export

Amenity insights AirDNA: Not available STRProfitMap: AI-powered review analysis

Which One Should You Use?

If you're in the exploration phase, scanning metros, comparing broad market trends, deciding which regions deserve a closer look, AirDNA's scale and historical depth give you a solid starting point.

But exploration isn't underwriting. When you're ready to evaluate a specific deal, you need comps that match your property type, a dataset that isn't inflated by inactive listings, and the ability to export everything into your own financial model. That's where STRProfitMap picks up.

Many investors use both: AirDNA to build a shortlist of markets, STRProfitMap to vet the actual deals.

See the Difference Yourself

Look at a free ZIP export and compare the comps side by side. When you strip out the noise, the numbers tell a different story, and that story is what keeps your capital safe.