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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallUse a licensed, dated dataset rather than an undocumented Airbnb endpoint. For repeatable public-data analysis, download the regional listings.csv.gz and calendar.csv.gz files from Inside Airbnb, filter the calendar by date and listing ID in pandas, and retain availability, nightly price, currency, stay constraints, and the snapshot date. The result is a defensible date-keyed table—but it is a snapshot, not a guaranteed live quote or fee-inclusive checkout total.
What you can—and cannot—scrape
An Airbnb calendar row can answer questions such as “Was this listing marked available on 2025-08-14?” and “What nightly display price did the dataset record?” It does not, by itself, provide a current booking quote. Cleaning fees, service fees, taxes, discounts, and other checkout adjustments can be separate from the nightly value.
Your source determines both freshness and permission. Airbnb’s API Terms of Service limit API use to permitted host-service or documented program purposes. They prohibit retaining static copies or building databases from API content, analyzing or optimizing pricing data outside permitted use, exceeding volume limits, and using undocumented APIs. The terms state: “For clarity, any Airbnb application program interface that is not listed on developer.airbnb.com is undocumented and may not be used; any use of such undocumented application program interface is a breach of these API Terms.” (Airbnb API Terms, §2.2(G), last updated 15 October 2025.)
Public visibility is not the same as permission to automate collection. Before collecting or redistributing anything, check Airbnb’s current terms, robots rules, applicable privacy and computer-access law, and the license attached to the dataset you use.
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Choose an input that matches your use case
| Input | Freshness | What it contains | Permission and operational trade-off |
|---|---|---|---|
| Inside Airbnb regional files | Quarterly data for the last year, published as dated regional snapshots | listings.csv.gz for listing metadata and calendar.csv.gz for date-level availability and nightly price |
Free downloads under CC BY 4.0; easy to archive and reproduce locally. The download page lists regions, country archives, and data requests. |
| Authorized Airbnb integration | Depends on the documented program | Only the fields and access scopes that Airbnb’s partner documentation permits | Requires eligibility, authentication, and compliance with the API terms. Do not substitute an undocumented endpoint. |
| Academic daily collection | Daily updates in a separate research pipeline | The University of Glasgow UBDC record describes property characteristics, booking-calendar updates, policies, host information, and reviews | The 2025 UBDC record describes coverage from June 2021 of 30 Scottish travel-to-work areas and 10 other UK areas, with monthly estimates for 30 months through December 2023. Aggregated data are restricted to UBDC staff for non-commercial academic research, although collection code is openly available. |
| Third-party hosted collector | Typically a live or scheduled run | For example, the airbnb-listings-collector example stores one row per listing/date and exposes nightly display price, fee components, total price, metadata, and availability | Operationally convenient but still subject to Airbnb’s rules. The repository’s internal StaysPdpSections request is not evidence of authorization. Verify terms and program status before commercial use. |
Inside Airbnb lists dated examples such as the Albany snapshot from 05 January 2025. Record that source date in every export so a reader can distinguish a historical observation from a current quote.
Define the observation before writing code
Set the date and party assumptions
Write down the destination or listing IDs, check-in and check-out dates, number of guests, desired currency, and the metric you will report. A calendar dataset is normally one row per listing and stay date; a trip total requires a separate calculation and may still omit checkout fees.
Use the calendar schema correctly
The documented calendar fields are date, available, price, minimum_nights, maximum_nights, and, optionally, reservation_id. Treat price as the nightly amount in the listing’s currency. Keep unavailable dates as rows with an explicit availability value instead of deleting them.
Keep price types separate
Name your output column nightly_price, not total_price. A fee-inclusive total is a different observation. The open collector example separates nightly display price, cleaning fee, service fee, taxes, and total price and warns that its Price field is not the total. Never silently convert currencies; retain the original currency code and document any later conversion rate and date.
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Download and inspect the public files
- Open Inside Airbnb’s Get the Data page and choose the region and dated release closest to your study period.
- Download both
listings.csv.gzandcalendar.csv.gz. Keep the compressed originals unchanged, along with the page’s region and snapshot date. - Inspect the headers before filtering. Column availability can vary by release; fail loudly when a required field is absent rather than silently producing an incomplete report.
Complete pandas workflow for a date-keyed table
The script below reads local compressed files, normalizes IDs and prices, filters an inclusive date range, optionally restricts to listing IDs, joins selected listing metadata, preserves unavailable dates, checks duplicates, and writes a CSV. Set SOURCE_SNAPSHOT_DATE to the date shown on the download page.
from pathlib import Path
import re
import pandas as pd
LISTINGS_FILE = Path("listings.csv.gz")
CALENDAR_FILE = Path("calendar.csv.gz")
OUTPUT_FILE = Path("airbnb_prices_by_date.csv")
SOURCE_SNAPSHOT_DATE = "2025-01-05" # replace with the release date you downloaded
START_DATE = "2025-08-01"
END_DATE = "2025-08-07" # inclusive
LISTING_IDS = set() # e.g. {"123456", "987654"}; empty means all
def parse_money(value):
"""Return a numeric amount while retaining missing values."""
if pd.isna(value):
return pd.NA
text = str(value).strip()
if not text:
return pd.NA
# Handles values such as "$1,234.50" without assuming a currency.
cleaned = re.sub(r"[^0-9.\-]", "", text)
try:
return float(cleaned) if cleaned else pd.NA
except ValueError:
return pd.NA
def require_columns(frame, required, name):
missing = sorted(set(required) - set(frame.columns))
if missing:
raise ValueError(f"{name} is missing required columns: {missing}")
listings = pd.read_csv(LISTINGS_FILE, compression="gzip", low_memory=False)
calendar = pd.read_csv(CALENDAR_FILE, compression="gzip", low_memory=False)
require_columns(calendar, {"listing_id", "date", "available", "price"}, "calendar.csv.gz")
require_columns(listings, {"id"}, "listings.csv.gz")
# IDs are strings so large numeric IDs do not lose precision or gain .0 suffixes.
calendar["listing_id"] = calendar["listing_id"].astype("string").str.strip()
listings["listing_id"] = listings["id"].astype("string").str.strip()
calendar["date"] = pd.to_datetime(calendar["date"], errors="coerce").dt.date
if calendar["date"].isna().any():
raise ValueError("calendar contains an unparseable date")
start = pd.Timestamp(START_DATE).date()
end = pd.Timestamp(END_DATE).date()
if end < start:
raise ValueError("END_DATE must be on or after START_DATE")
subset = calendar.loc[calendar["date"].between(start, end)].copy()
if LISTING_IDS:
subset = subset.loc[subset["listing_id"].isin({str(x) for x in LISTING_IDS})]
# Normalize availability without turning unknown values into false.
subset["available"] = subset["available"].map({
True: True, False: False, "t": True, "f": False,
"true": True, "false": False, "TRUE": True, "FALSE": False
}).astype("boolean")
subset["nightly_price"] = subset["price"].map(parse_money)
# Keep a source currency if the release provides one; otherwise leave it missing.
if "currency" in subset.columns:
subset["currency"] = subset["currency"].astype("string")
elif "currency" in listings.columns:
subset = subset.merge(
listings[["listing_id", "currency"]].drop_duplicates("listing_id"),
on="listing_id", how="left"
)
else:
subset["currency"] = pd.NA
# Preserve stay constraints when present.
for column in ("minimum_nights", "maximum_nights"):
if column not in subset.columns:
subset[column] = pd.NA
metadata_names = [
"listing_id", "room_type", "accommodates", "bedrooms",
"latitude", "longitude", "neighbourhood_cleansed"
]
metadata_names = [c for c in metadata_names if c in listings.columns]
metadata = listings[metadata_names].drop_duplicates("listing_id")
result = subset.merge(metadata, on="listing_id", how="left", validate="many_to_one")
# A listing/date should occur once after the join.
duplicates = result.duplicated(["listing_id", "date"], keep=False)
if duplicates.any():
raise ValueError("duplicate listing/date rows found after joining metadata")
# Non-negative prices are valid when present; unavailable rows may have no price.
negative = result["nightly_price"].notna() & (result["nightly_price"] < 0)
if negative.any():
raise ValueError("negative nightly price encountered")
result["source_snapshot_date"] = SOURCE_SNAPSHOT_DATE
result["price_type"] = "nightly display price"
result = result[[
"listing_id", "date", "available", "nightly_price", "currency",
"minimum_nights", "maximum_nights", "source_snapshot_date", "price_type",
*[c for c in metadata_names if c != "listing_id"]
]].sort_values(["listing_id", "date"])
result.to_csv(OUTPUT_FILE, index=False)
print(f"Wrote {len(result):,} rows to {OUTPUT_FILE}")
print(result.head().to_string(index=False))The parser removes display symbols and commas but does not infer a currency. If a release stores a currency only in listing metadata, the join carries it across; if no currency field exists, the result stays missing rather than guessing.
Validate the result before analysis
Check date coverage
For each listing, compare the number of rows with the number of calendar dates requested. Missing dates can mean the file has no record for that listing, the listing was not in the selected snapshot, or your filter was wrong. Do not fill gaps with an invented price.
Check availability and constraints
Review the counts of available, unavailable, and unknown values. For available stays, compare the requested length with minimum_nights and maximum_nights when those fields are present. A date can show a price yet still be unusable for a particular stay length.
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Check joins and identifiers
Deduplicate listing metadata before joining and enforce one row per listing_id/date. Keep IDs as strings throughout; converting them to floating-point numbers can change their text representation and break joins.
Check missing prices
An unavailable or blocked date may have no nightly price. Report that missingness explicitly. A zero value and a missing value are not interchangeable.
Nightly price versus a trip total
To estimate room nights, sum nightly values only for the requested dates that are available and compatible with the stay constraints. Label that calculation as a nightly subtotal. It is not a checkout total unless you also have the applicable cleaning fee, service fee, taxes, discounts, and currency treatment. A live booking flow can change after the snapshot was published.
If you need fee-inclusive data, use a source that explicitly supplies those components and document whether it is an authorized API response, a licensed dataset, or a third-party run. The Airbnb Calendar API schema describes the date-level fields, but a schema page is not permission to call an undocumented Airbnb service.
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Freshness, reproducibility, and scaling
Label every observation
Store the regional release date (or API retrieval timestamp), source URL, region, code version, and any filters. A quarterly Inside Airbnb file is suitable for periodic analysis and historical comparisons, not a promise of today’s inventory.
Use conservative collection behavior
If an authorized integration or permitted hosted service is doing live collection, define date ranges explicitly, batch requests, respect published limits, retry transient failures with backoff, and log responses. The open collector’s README recommends a one-second default delay, two to three seconds for large runs, batching, and proxies when scaling. Those operational suggestions do not authorize its internal endpoint.
Keep raw and derived data separate
Archive the original compressed files read-only. Write normalized tables and analyses to new files, and retain a manifest containing the snapshot date and checksums. This makes a later result auditable even after the publisher replaces an older download.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common errors and fixes
- “FileNotFoundError”: Put both compressed files in the paths configured at the top of the script, or replace them with absolute paths. Do not unzip and then leave
compression="gzip"pointed at a nonexistent archive. - Required-column error: Inspect
calendar.columnsandlistings.columns. Releases can differ; map a documented equivalent column deliberately instead of silently dropping it. - All prices are missing: Display prices may include currency symbols, non-breaking spaces, or blanks. Inspect raw values, extend the parser for the observed format, and retain the currency separately.
- No rows after date filtering: Confirm that your dates fall inside the snapshot’s coverage and that the calendar uses the same date convention. Print
calendar["date"].min()andcalendar["date"].max()after parsing. - Duplicate listing/date rows: Check for duplicate calendar records and a non-unique metadata join. Deduplicate only when you can explain why duplicates exist; otherwise stop and investigate.
- Unexpectedly high totals: You probably summed nightly prices and called the result a final price. Rename it as a subtotal and obtain fee components from a permitted source.
- Blocked or challenged live requests: Stop increasing concurrency. Verify that the integration is authorized, reduce request volume, obey rate limits, and use the dated public files when a historical snapshot meets your need.
Or skip the browser setup
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cURL
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://www.airbnb.com/rooms/123456 -o shot.webpPython
import requests
r = requests.get(
"https://api.screenshotneo.com/v1/shot",
params={"access_key": "YOUR_API_KEY", "url": "https://www.airbnb.com/rooms/123456"},
timeout=90,
)
r.raise_for_status()
open("shot.webp", "wb").write(r.content)Node.js
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://www.airbnb.com/rooms/123456' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);
if (!res.ok) throw new Error(`ScreenshotNeo returned ${res.status}`);
const fs = await import('node:fs/promises');
await fs.writeFile('shot.webp', Buffer.from(await res.arrayBuffer()));ScreenshotNeo has 63 options, including full-page capture with lazy images loaded, CSS-selector element capture, dark mode, 12 device presets plus custom viewports, retina scale, PDF paper and page controls, custom CSS and JavaScript, clicks, selector or network-idle waits, ad/tracker/request blocking, headers, cookies, user agent, authorization, timezone, geolocation, transparent backgrounds, resizing, chosen cache TTLs, signed image links, asynchronous jobs with signed webhooks, bulk capture for 100 URLs per call, usage API, OpenAPI specification, and compatibility with parameter names used by other screenshot APIs. The Free plan includes 1,000 screenshots per month with no card; paid plans start at $5 for 3,000 shots, and yearly billing gives two months free. Create a free ScreenshotNeo account to start.
FAQ
Is there a public Airbnb calendar dataset?
Yes. Inside Airbnb publishes regional quarterly downloads and country archives, including detailed listings and calendar files, under CC BY 4.0. Availability is regional and dated, so select the release that matches your geography and study period.
Why keep unavailable dates instead of dropping them?
An unavailable row is an observation about inventory, not a malformed price. Retaining it lets you distinguish “blocked or booked” from “the dataset had no record,” which is essential when measuring availability rates.
Can I redistribute a table generated from the files?
Check the license for the exact release and your intended use. Inside Airbnb states CC BY 4.0 for its downloads, while the UBDC collection is restricted to internal non-commercial academic research. Attribution and any additional legal obligations still apply.
Frequently Asked Questions
Is there a public Airbnb calendar dataset?
Yes. Inside Airbnb publishes regional quarterly downloads and country archives, including detailed listings and calendar files, under CC BY 4.0. Availability is regional and dated, so select the release that matches your geography and study period.
Why keep unavailable dates instead of dropping them?
An unavailable row is an observation about inventory, not a malformed price. Retaining it lets you distinguish “blocked or booked” from “the dataset had no record,” which is essential when measuring availability rates.
Can I redistribute a table generated from the files?
Check the license for the exact release and your intended use. Inside Airbnb states CC BY 4.0 for its downloads, while the UBDC collection is restricted to internal non-commercial academic research. Attribution and any additional legal obligations still apply.
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Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
