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Airbnb does have an API, but it is not a public, city-wide search API for any developer. Access is tied to Airbnb programs and approved partner purposes. For a Python analysis, the practical alternatives are a published snapshot from Inside Airbnb for a city it covers, a commercial data service such as AirDNA, or—if you represent an eligible government or tourism organization—Airbnb’s City Portal. Which route fits depends on the city, the data fields and dates you need, and the terms under which you can use the data.
Does Airbnb have a public API?
Airbnb has an API, but it is program-based rather than a general public feed of listings and prices by city. Airbnb’s API Terms, last updated October 15, 2025, describe access for host services and partner functionality. Airbnb decides which scopes an organization receives; program requirements include accepting API terms, a mutual NDA, applicable partner terms, and a data-security review. The terms limit API data to authorized program purposes and prohibit building databases or performing pricing analysis with API content. They also prohibit use of undocumented API interfaces. See Airbnb’s API Terms of Service.
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That distinction matters: an authorized API integration is not a back door to city-wide market research. If your purpose is to compare listings or prices across a city, use a dataset or service whose stated terms cover that use rather than assuming API access permits it.
How can you get Airbnb listing data for a city?
First decide whether you need a dated snapshot of listing-level records, commercial market analytics, or official data access for an institution. These options are not interchangeable.
#1 Best Overall
| Route | What it can provide | Coverage and freshness | Access and use considerations |
|---|---|---|---|
| Inside Airbnb | Downloadable files for selected cities and regions; depending on the place and snapshot, these may include detailed listings, calendars, reviews, summary listings, and neighborhood files. | City availability, snapshot dates, and file types vary. Inside Airbnb says quarterly data for the last year is available for each region; check the selected region’s actual snapshot date and files. | The download page labels the data CC BY 4.0. Its data policies also say to take only what is needed, not scrape the project site, and not republish the data. Follow both the license and the project’s policies. |
| AirDNA | Commercial short-term-rental data and analytics covering Airbnb, Vrbo, and Booking.com, with selected CSV exports from market and property charts. | Coverage and available metrics depend on market and product. It is a provider service, not Airbnb’s public API. | AirDNA’s documentation says some download capability is unavailable on the free subscription. Check current market coverage, plan limits, export terms, licensing, and suitability before paying. |
| Airbnb City Portal | Local data and insights for city partners. | For eligible government officials or tourism organizations partnering with Airbnb, not a self-serve feed for every developer. | Request access through the institutional route described by Airbnb; eligibility and access are not stated as general public availability. |
| Airbnb personal data export | Your own account data in HTML, Excel, or JSON. | Account-specific, not city-wide listing or price data. | Useful for an account holder seeking their own information; not a substitute for market data. |
AirDNA describes its methodology as combining daily pricing and calendar availability collected from public pages with reservation data shared by property managers and hosts. Those are AirDNA’s own descriptions of its product and methods, not a guarantee of complete coverage or a statement that it is an Airbnb-authorized feed. Its help documentation dated June 19, 2026 discusses the collection methodology; documentation dated June 9, 2026 describes CSV exports and subscription qualifications.
How can you get Airbnb prices by city in Python?
1. Check whether the city has a usable snapshot
On Inside Airbnb’s Get the Data page, select the city or region and inspect the listed snapshot date and available files. Do this before designing your analysis: some places or snapshots may not include the calendar or other file you need. Record the city, snapshot date, file name, and download source in your project notes. The data is a dated snapshot, not live inventory or a complete feed of every Airbnb listing.
Download the relevant CSV or compressed CSV manually. Inside Airbnb advises downloading the data once rather than retrieving it on every analysis run. Its data page identifies the license as CC BY 4.0, while its policies say not to republish the data; check those terms before sharing data or outputs.
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2. Load the file and inspect its actual schema
File names and columns can differ between cities and snapshots, so do not assume a particular price, calendar, or listing-ID column exists. Install pandas if needed, then point the script at the file you downloaded. Pandas can read ordinary CSV files and commonly compressed CSV formats when compression is inferable from the file name.
python -m pip install pandasfrom pathlib import Path
import pandas as pd
file_path = Path("data/replace-with-your-downloaded-file.csv.gz")
df = pd.read_csv(file_path, low_memory=False)
print("Rows:", len(df))
print("Columns:")
print(df.columns.tolist())
print("First rows:")
print(df.head())
print("Missing values by column:")
print(df.isna().sum().sort_values(ascending=False).head(20))Use the data dictionary linked from the selected city’s download page to understand field definitions. A column name alone is not enough to establish whether a value represents a nightly asking price, a calendar date’s price, a minimum stay, or another measure.
3. Parse only fields you have verified
After confirming the field meanings in the matching data dictionary, replace the example names below with the exact columns in your downloaded file. This pattern makes conversion failures visible instead of silently treating malformed values as valid prices.
# Replace these with columns confirmed in this snapshot's data dictionary.
price_column = "REPLACE_WITH_VERIFIED_PRICE_COLUMN"
if price_column not in df.columns:
raise KeyError(f"{price_column!r} is not in this file; inspect df.columns first")
# Handles common currency symbols and thousands separators, but verify
# the file's currency and formatting before relying on the result.
price_text = df[price_column].astype("string").str.replace(",", "", regex=False)
price_text = price_text.str.replace(r"[^0-9.\-]", "", regex=True)
df["price_numeric"] = pd.to_numeric(price_text, errors="coerce")
print("Parsed values:", df["price_numeric"].notna().sum())
print("Unparsed or missing:", df["price_numeric"].isna().sum())
print(df["price_numeric"].describe())The conversion is only a parsing example, not a claim about the file’s currency or what its price field measures. Confirm currency, units, snapshot date, and whether a field is an advertised price or a calendar-specific amount before comparing values. Exclude or separately investigate missing, zero, and implausible values according to the analysis question; do not assume missing means unavailable or free.
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A listing-level snapshot and a calendar file answer different questions. A listing file may help describe records captured at the snapshot date; a calendar file, when available, may contain date-level availability and prices. Neither should be treated as a guaranteed record of completed bookings or future realized revenue. Define whether you are comparing advertised prices, available dates, or some other measure, and document the date range and inclusion rules.
Only after identifying the correct verified price field should you calculate a summary. For example, if the chosen file contains one verified numeric value per relevant observation, a basic group summary is:
# Replace with the verified grouping column for your question.
group_column = "REPLACE_WITH_VERIFIED_GROUP_COLUMN"
if group_column not in df.columns:
raise KeyError(f"{group_column!r} is not in this file")
summary = (
df.dropna(subset=["price_numeric"])
.groupby(group_column)["price_numeric"]
.agg(observations="count", median="median", mean="mean")
.reset_index()
)
print(summary.head())Choose the grouping column and unit of observation deliberately. A mean or median across all rows can be misleading if rows represent different dates, if the same listing appears repeatedly, or if missing availability changes which observations enter the calculation. Record your method so another reader can reproduce the result from the same snapshot.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you download Airbnb data without scraping?
Yes. For cities present on Inside Airbnb, download a published city file and work with it locally in Python. A commercial data provider or an eligible institutional route may fit other requirements.
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How to choose a route for your project
- Need a downloadable snapshot for a covered city? Check Inside Airbnb’s city page for its date, files, and definitions; use the dataset within its license and project policies.
- Need broader market analytics or multiple platforms? Assess AirDNA’s current coverage, export features, subscription limits, and license against your specific use before purchasing.
- Represent a city government or tourism organization? Ask about Airbnb City Portal access and eligibility; it is an institutional partnership route.
- Need only your own account information? Use Airbnb’s personal data export, which is not city-wide market data.
- Need an authorized integration for a partner service? Apply through Airbnb’s API program and confirm the exact granted scopes and allowed purposes. Do not assume API authorization extends to pricing analysis or database building.
Before choosing, compare the requested city’s coverage, snapshot age and cadence, listing-level versus aggregate fields, date and price granularity, download or export access, license and redistribution terms, subscription limits, and any institutional eligibility requirement. A city’s presence in one provider does not establish that another provider has the same data or update schedule.
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