How to analyze your Airbnb reviews.

Why star ratings alone tell you almost nothing

The overall star rating is a courtesy score. Most guests give five stars for any acceptable stay, which compresses the signal: a 4.9 and a 4.7 listing can have wildly different underlying problems. The real information lives one layer down, in data Airbnb collects but barely surfaces:

• Six category scores per review — cleanliness, accuracy, check-in, communication, location, value. Guests rate these separately from the overall stars, and they're far more honest here.
• Issue and praise tags — structured checkboxes like "dusty," "great communication," "hard to find" that guests click during the review flow. These are literally your guests filing categorized bug reports.
• Dates and listing IDs — which make trends and per-property comparison possible.

All of it is in your free Airbnb data export. One host's export we analyzed contained 4,271 reviews spanning a decade — years of operational feedback that had never been looked at as a dataset.

The four questions worth answering

You can compute dozens of metrics from review data. Four cover most of the value:

1. Where am I weak? Category averages per listing, ranked worst-first. The worst category is your to-do list; the best is your listing copy.
2. Am I getting better or worse? Each category's average by year. A 4.4 cleanliness that was 4.7 two years ago is an emergency; a 4.4 that was 4.1 is a recovery in progress.
3. What specifically do guests flag? Tag frequency. Ten "dusty" tags beat any amount of guessing about what "clean better" means — see our cleanliness score guide for turning tags into fixes.
4. Which property drags the account? The same three measures compared across listings. Multi-listing hosts almost always find one property doing disproportionate damage to the blended averages.

The spreadsheet method (free, some elbow grease)

If you're comfortable in Excel or Google Sheets, the pipeline is:

1. Get the export — request it from Airbnb and locate the reviews file (the JSON containing reviewsReceived).
2. Flatten JSON to rows — one row per review; columns for date, listing ID, overall rating, the six category scores, and tags. This is the painful step: the file is machine-formatted, can run 10–20 MB, and doesn't open cleanly in a spreadsheet. A JSON-to-CSV converter or a short script is the usual route.
3. Pivot — averages by listing and by year for each category; a tag count table (tags come as lists, so split them to one-per-row first).
4. Repeat when new reviews arrive — request a fresh export and re-run the pipeline.

This genuinely works, and if you only want a one-time snapshot of a single listing, it may be all you need.

The dashboard method (free for one listing, instant)

The MyGuestMap host dashboard exists because step 2 above filters out most normal hosts. You upload the reviews file exactly as Airbnb sent it — no editing, no conversion — and get the four questions answered on the spot:

• Category scores per listing, ranked worst-first, each with its sample size
• Cleanliness trend by year
• Recurring tags aggregated per listing — what guests consistently flag and consistently praise
• Re-upload anytime; the new export replaces the old data

It's free for one listing of your choice, with no card — multi-listing analysis is the paid tier. It never asks for your Airbnb login; it only reads the file Airbnb gave you, and your data stays private to your account.