---
title: "Airbnb Data: What's Actually Inside a Scraped Listings Snapshot"
description: "What Airbnb data really contains once you scrape it — prose prices, fused city fields, compressed ratings — and how to clean it. Explore 465K+ listings live."
url: "https://ouro.foundation/posts/aliraza/airbnb-data-whats-actually-inside-a-scraped-listings-snapshot"
id: "01a07bc0-a0dc-7e3c-aa95-16f96df70135"
type: "post"
---

# Airbnb Data: What's Actually Inside a Scraped Listings Snapshot

Post by @aliraza in #scraping (all) · 2026-09-07

- Asset id: `01a07bc0-a0dc-7e3c-aa95-16f96df70135`
- Page: https://ouro.foundation/posts/aliraza/airbnb-data-whats-actually-inside-a-scraped-listings-snapshot

Most people who go looking for **Airbnb data** expect a clean table: a price column, a bedroom count, a city, a rating. What a scrape actually returns is closer to a page of marketing copy that happens to be comma-separated. The price is a sentence. The capacity is prose. The city is glued to the stay type inside a single field. None of that makes the data less useful — but it does mean the first hour with any Airbnb dataset is spent parsing, not analysing.

This is a walkthrough of what one snapshot really holds, which fields lie about their type, and what the data simply cannot tell you. You can follow along against the live [Airbnb data dashboard](https://datasets.datafetchpro.com/airbnb/pulse), which runs the same 190K+ listings described here.

## One row, honestly described

Here's a single listing from a real crawl, field by field:

| Field | Value |
| ----- | ----- |
| `listing_id` | 782102547563500637 |
| `name` | The Explorer House *Dogs Welcome* |
| `property_type` | Home in Alabaster |
| `avg_rating` | 4.9 |
| `review_count` | 83 |
| `price_display` | $1,058 for 5 nights, originally $1,149 |
| `price_per_night` | 211.60 |
| `bedrooms` / `beds` / `bathrooms` | 3 bedrooms / 5 beds / 2 baths |
| `latitude` / `longitude` | 33.24894 / -86.80906 |
| `badge` | Guest favorite |
| `scraped_at` | 2026-08-26 01:49:49+00 |

Nothing here is wrong. But only three of those fields — the ID, the coordinates and the timestamp — arrive as the type they represent.

## The five fields that aren't what they claim to be

### 1. The price is a sentence

`price_display` reads *"$1,058 for 5 nights, originally $1,149"*. Two numbers and a stay length live in one string, and the stay length isn't constant across rows. Divide the wrong figure and your nightly rate is off by a factor of five. Worse, the visible total is the **discounted** one — the `originally` figure is the anchor. Any Airbnb data analysis that ignores this reports a market as cheaper than it is and loses the discount signal entirely, which is one of the more interesting variables in the set. The [pricing view](https://datasets.datafetchpro.com/airbnb/pricing) treats discount depth as a first-class dimension for exactly that reason.

### 2. The city hides inside the stay type

`property_type` is *"Home in Alabaster"* — stay type and city fused. There is no state column, no clean city column. Splitting on `" in "` gets you most of the way there and then breaks on names that contain the word. The reliable geography in Airbnb data is the coordinate pair, which is why the [map view](https://datasets.datafetchpro.com/airbnb/map) derives location from latitude and longitude rather than from text.

### 3. Capacity is prose with half-units

`3 bedrooms`, `5 beds`, `2.5 baths`. Strip the noun, keep the decimal, and remember that studios say something other than a number. Beds and bedrooms diverge often enough that per-bed pricing is a genuinely different metric from per-bedroom pricing — and per-bed is usually the more honest comparison across a mixed supply.

### 4. Ratings barely vary

The sample above shows 4.9, 5.0, 5.0, 4.9, 4.9. That's not a lucky draw; it's the whole distribution. Airbnb ratings compress into the top tenth of the scale, so `avg_rating` on its own separates almost nothing. Review **count** does the work instead — it proxies tenure and booking volume, and it's the variable worth weighting by. The [trust view](https://datasets.datafetchpro.com/airbnb/trust) pairs the two rather than ranking on stars alone.

### 5. Badges are inconsistently present

`Guest favorite` and `Superhost` are real signals, but their fill rate shifts between crawls. Compare two snapshots naively and you'll conclude that half the market lost a badge overnight. It didn't. Badge deltas are the single easiest way to generate a confident, wrong finding from Airbnb data.

## What a single snapshot can and can't answer

One scrape is a still photograph. It answers structural questions well:

- What does supply look like here — whole homes or rooms inside someone's house?
- What's the median nightly rate, and per bed?
- How deep are the discounts, and who is discounting?
- How concentrated is the review volume? The stay-type and room-share splits on the [demand view](https://datasets.datafetchpro.com/airbnb/demand) are built on precisely these questions, and they're answerable from a single file.

What one snapshot cannot answer is anything with a direction: is this market getting more expensive, is inventory growing, did that host cut their rate. For that you need two crawls and an ID match — which is what the [movers view](https://datasets.datafetchpro.com/airbnb/movers) and its comparison export exist for. That matched file is worth calling out: rebuilding it from two separate downloads means redoing the join and re-deriving every delta yourself, including the clamps for review counts that occasionally tick downward.

## What Airbnb data will never contain

Scraped listing data is supply-side only. It does not include:

- **Occupancy or bookings.** Calendar availability isn't in a search result, and inferring occupancy from blocked dates is an estimate, not an observation.
- **Revenue.** Rate multiplied by an assumed occupancy is a model, and it should be labelled as one.
- **Host identity.** A badge is not a portfolio, and there's no operator key to group listings by.
- **Stay dates.** In this dataset `checkin` and `checkout` are empty on every row, so the dates behind a quoted rate are unknown. They're excluded from the export rather than shipped empty, because a present-but-blank column implies it carries something. Being clear about these gaps is more useful than papering over them. A dataset that quietly imputes occupancy is harder to trust than one that says it doesn't have it.

## Working with it

The practical shape of a clean Airbnb data pipeline is short:

1. Parse `price_display` into total, original total, nights and a true nightly rate.
2. Split `property_type` into stay type and city — then verify the city against coordinates.
3. Cast capacity strings to numbers, keeping half-baths.
4. Derive per-bed rate and discount percentage.
5. Weight ratings by review count before comparing anything.
6. Key on `listing_id` and `scraped_at` so snapshots can be joined later. Every one of those steps is already computed in the [Airbnb Data — 465K+ Scraped Listings with Nightly Rates, Ratings, Badges & Coordinates](https://ouro.foundation/datasets/aliraza/airbnb-data-465k-scraped-listings-with-nightly-rates-ratings-badges-coordinates) , which ships 30 columns — raw and derived — in CSV, JSON or NDJSON, with the dashboard's filters carried into the export. The derived columns come from the same SQL as the charts, so a figure in your file and the same figure on screen can't drift apart.

If you'd rather see the shape of it before downloading anything, start with the [Airbnb data dashboard](https://datasets.datafetchpro.com/airbnb/pulse) and filter your way to the slice you care about.

---

## FAQ

**What is Airbnb data?** In this context, structured records of live Airbnb listings — nightly rate, original rate, rating, review count, stay type, capacity, badge and coordinates — captured at a specific timestamp from public search results.

**Is scraped Airbnb data accurate?** It's accurate to the moment of capture, which is why every row carries `scraped_at`. Rates shift with dates, demand and discounts, so a snapshot should be read as an observation, not a permanent price.

**What format does the Airbnb dataset come in?** CSV, JSON or NDJSON, up to 25,000 rows per download, with a column picker and a separate cross-snapshot comparison export.

**Can I get occupancy or revenue figures?** Not from listing-side data. Those require booking-side signals or modelling, and anything presented as occupancy in a scraped Airbnb dataset is an estimate.

## Assets in this post

- [Airbnb Data — 465K+ Scraped Listings with Nightly Rates, Ratings, Badges & Coordinates](https://ouro.foundation/datasets/aliraza/airbnb-data-465k-scraped-listings-with-nightly-rates-ratings-badges-coordinates) (dataset, id `01a07bb8-2e87-7953-bccc-f83a4c0ae4b2`)