Free real estate data download: rent benchmarks and short-term-rental listings

Search for a free real estate data download and you mostly find gated dashboards and PDFs with the numbers stripped out. Two sources skip all of that: HUD's FY2026 Fair Market Rent tables and Inside Airbnb's listing snapshots. Both are files you download and keep. HUD's data is US government work, public domain, no conditions attached. Inside Airbnb publishes under CC BY 4.0, which allows commercial work as long as the credit line stays with the data. This page walks through both downloads, links the free samples inline, and shows how the two datasets work together in a rental screening workflow.

The two sources, and what each license allows

One caution about "free real estate data" you find elsewhere: aggregator sites republish Zillow or MLS numbers under terms that rarely permit redistribution, and several market them as public. The two sources above state their terms on the record. For license details pack by pack, the public domain datasets guide lists the exact credit strings.

Download 1: HUD Fair Market Rents FY2026

FMR is HUD's rent benchmark: what a modestly priced unit should rent for in each area, and the ceiling housing agencies use for voucher payments. The FY2026 release parses into three tables. fmr-by-zip-2026.csv carries 51,895 rows, one per ZIP, with fmr_0br through fmr_4br, metro status, area name, and state. fmr-by-county-2026.csv carries 3,229 county rows with medians and maxes per bedroom size. state-summary.csv carries 52 rows naming the highest- and lowest-rent county per state.

Two of the files are free here with no signup:

• sample.csv22 rows, ZIP-level schema · 1.7 KB
• sample.json22 records · 5.6 KB
• state-summary.csvall 52 rows, complete · 3.9 KB

The column-by-column breakdown lives in HUD fair market rent data, explained, and SAFMR vs FMR covers when ZIP-level rents differ from metro-wide ones.

Download 2: Airbnb listings across 6 US cities

Inside Airbnb publishes periodic snapshots of each city's listings. Six of them are normalized here into one 12-column schema: 90,169 listings, snapshot dates June through August 2026. Each row holds the nightly price in USD, room type, ZIP-level neighbourhood, blurred coordinates (about 11 meters), minimum_nights, availability_365, and review counts. Host names and exact coordinates are stripped upstream.

Free files with no signup:

• sample.csv20 rows, Austin · 2.6 KB
• sample.json20 records · 7.7 KB
• cities-index.csvall 6 rows, complete per-city index · 1.1 KB

The Airbnb dataset sample page explains all 12 columns, and Inside Airbnb alternative covers what a normalized six-city copy adds over the raw per-city downloads.

How investors and analysts use the two together

Both files join on ZIP. The Airbnb neighbourhood column holds a ZIP code area, and the HUD table has one FMR row per ZIP, so a spreadsheet VLOOKUP or one SQL join puts a monthly benchmark next to every listing. Three questions that combination answers:

• Screen a market before touring it. Compare a city's median nightly rate (Austin's sits at USD 217 in the June 2026 snapshot) against the local FMR for the same bedroom count, using the free cities-index.csv and state-summary.csv.
• Flag pricing that depends on premium demand. FMR is what HUD judges a modest unit to rent for, so a long-term listing priced far above the FMR ceiling is a bet on a stronger tenant than the market median.
• Test short-term-rental feasibility by ZIP. High FMR plus high availability_365 points to a ZIP where short-term leases face little long-term competition and the underlying rent level still supports a fallback strategy.

Every number needed for these checks is on this page or in the free files above.

Query the full files free, no key

Twenty or 22 rows prove a schema but answer no real question. The dataset-mcp server covers that step: read-only queries against the complete files, 100 rows per call, filters on any column, no API key.

npx --allow-git=all -y github:jayjex/dataset-mcp

Five listings in downtown Austin, by ZIP:

query_dataset("airbnb-six-cities", {where: [{column: "neighbourhood", op: "=", value: "78701"}], limit: 5})

Setup steps and more worked examples live on the for agents page.

Free sample vs full pack

Full packs: the complete HUD FY2026 tables (51,895 + 3,229 + 52 rows) and the 90,169-row Airbnb six-city pack, each with data dictionary and source checksums. Checkout and download run through Getly.

Licenses in one paragraph

HUD FY2026 FMR data is US government data in the public domain: use it in client work, models, or republished tables with no credit required. The Inside Airbnb rows are CC BY 4.0: commercial use allowed, and the line "Data by Inside Airbnb, InsideAirbnb.com, CC BY 4.0" stays with any redistribution. The public domain datasets guide maps both cases with the exact credit strings.

FAQ

Where can I download free real estate data as CSV?

Two sources cover most rental-analysis needs. HUD publishes its FY2026 Fair Market Rent tables as US government data: 51,895 ZIP-level rows with rents for studios through 4-bedroom units. Inside Airbnb publishes listing snapshots under CC BY 4.0, and the six-city pack here holds 90,169 listings in one schema. On this page, a 22-row HUD sample, the complete 52-row state summary, a 20-row Airbnb sample, and the complete 6-row city index all download free with no signup.

Is HUD fair market rent data free for commercial use?

Yes. HUD's FMR data is US government work in the public domain, so you can ship it in client deliverables, models, or republished tables with no credit line required. Inside Airbnb data is the different case: CC BY 4.0 allows commercial use, but the credit line "Data by Inside Airbnb, InsideAirbnb.com, CC BY 4.0" must stay with any redistribution.

Can I check the full files before downloading them?

Yes. The dataset-mcp server answers read-only queries against the complete files, free, with no API key: 100 rows per call, filters on any column. The query example above returns five listings in downtown Austin, and get_stats returns min, max, mean, and top values for any column.

How do investors use rent benchmarks together with listings data?

The two files join on ZIP code: the Airbnb neighbourhood column holds a ZIP code area, and the HUD table has one FMR row per ZIP. Analysts compare a market's median nightly rate against the local FMR for the same bedroom count, flag long-term listings priced far above the FMR ceiling, and screen ZIPs where high FMR and high availability point to both short-term and long-term demand.