How to Scrape Redfin Real Estate Listings in 2026

If you want to scrape Redfin at scale — pulling thousands of clean, structured US home listings per region instead of copy-pasting one property card at a time — this guide walks through exactly how to do it. Redfin is one of the richest public sources of US residential real estate data: every listing carries price, beds, baths, square footage, lot size, MLS number, year built, days on market, HOA dues, exact coordinates and broker detail. The problem is that the Redfin website is built for humans browsing one map at a time, not for bulk export. Below is how to turn any Redfin city, zip, neighborhood or county into an analysis-ready dataset.

Why Redfin listing data is worth scraping

Redfin is a brokerage as well as a portal, which means its listing feed is unusually clean and standardized compared to scraping raw MLS portals or aggregator sites that stitch together inconsistent feeds. Each home is normalized into the same shape, and the data is public — you don’t need a login, a Redfin account, cookies or a developer API key to read it.

What makes the dataset valuable is the combination of fields on every record:

• Pricing detail — list or sold price and price per square foot, so you can normalize across home sizes instantly.
• Physical attributes — beds, baths (split into full and partial), square footage, lot size, stories and year built.
• Market timing — status and days on market, plus sold dates for closed transactions.
• Identity — the MLS number alongside Redfin’s own listingId and propertyId, which makes joining against other datasets straightforward.
• Geospatial — exact latitude and longitude on every home, so the dataset is map-ready and ready for spatial joins (school zones, flood maps, transit, census tracts).

That last point matters more than people expect. A lot of “real estate data” you can buy comes without coordinates, which forces you to geocode addresses yourself — slow, error-prone and expensive at volume. Redfin embeds lat/lng directly, so the data drops straight into a map or a spatial database.

▶ Run the Redfin Scraper — turn any Redfin city, zip or county into a structured dataset of homes with price, beds, baths, sqft, MLS# and exact coordinates. No login, no API key. $3.50 per 1,000 listings.

Input modes: how you target a location

The scraper accepts locations in two flexible ways, and you can mix them freely in a single run.

1. Redfin search URLs

The most precise mode. Go to redfin.com, search a city, zip, neighborhood or county, press Enter, and copy the address-bar URL. Paste it into the searchUrls array. Because you’re handing the actor the exact same URL Redfin’s own UI uses, any region Redfin can render — including narrow neighborhood or county pages — is fair game.

https://www.redfin.com/city/30818/TX/Austin

2. Raw US zip codes

If you don’t want to fish for URLs, just drop a 5-digit US zip code straight into the input. The actor resolves it to the corresponding Redfin region. This is ideal when you already have a list of target zips — say, the zips inside an investor’s buy-box — and want to iterate over them programmatically.

{
  "searchUrls": [
    "https://www.redfin.com/city/30818/TX/Austin",
    "90210"
  ]
}

There’s also a locations array that behaves the same way and is merged with searchUrls, so you can keep URLs and zips in separate lists if that’s cleaner for your pipeline.

For-sale, sold and rental coverage

A single dropdown, listingType, switches what kind of inventory you pull:

• forSale (default) — active homes currently on the market. This is your live inventory feed.
• sold — recently closed transactions. This is the backbone of comps and price-trend analysis, because sold prices (not asking prices) are what actually moves a valuation model. Sold records carry the sold date so you can time-bucket them.
• rent — rental listings, for anyone modeling cap rates, rent-vs-buy or building a rental comps set.

The ability to flip the same location between active, sold and rental inventory with one parameter is what lets you build a complete picture of a market rather than just a snapshot of what’s listed today.

Filters, sorting and pagination

You don’t have to pull everything and filter later — the scraper applies filters during the run so you only pay for and store what you want:

• minPrice / maxPrice — keep only homes in a USD price band.
• minBeds — keep only homes with at least N bedrooms.
• sortBy — recommended, newest, priceLow or priceHigh. Sorting by newest is handy for lead-gen, where you want fresh listings first.
• maxResults — a global cap on homes saved. Defaults to 1000; set it to 0 for unlimited.

Under the hood, the actor opens each region over a US residential connection and reads the listings Redfin embeds in the page, then paginates the region’s listing feed up to 350 homes per page, walking deep into the thousands for large metros. Records stream to the dataset as they’re collected and are de-duplicated by listing ID, so a busy market with overlapping pages won’t produce duplicate rows. The residential proxy is required because Redfin only serves listing data to normal US browsers — it comes pre-configured, so you leave the proxy setting alone.

Practical note: because pagination caps at 350 homes per page and walks the whole region, a single large-metro run can return several thousand homes. Use maxResults to keep runs bounded when you’re testing, then raise or zero it out for full pulls.

The output schema

Every home is saved as one flat, structured record — no nested digging required. Here’s a realistic example of a single for-sale listing exactly as the actor emits it:

{
  "listingId": 216325500,
  "propertyId": 31265191,
  "mlsId": "4199242",
  "mlsStatus": "Active",
  "address": "6105 Highlandale Dr",
  "city": "Austin",
  "state": "TX",
  "zip": "78731",
  "price": 1750000,
  "beds": 4,
  "baths": 3.5,
  "fullBaths": 3,
  "partialBaths": 1,
  "sqFt": 3145,
  "lotSize": 15816,
  "pricePerSqFt": 556,
  "stories": 3,
  "yearBuilt": 1976,
  "propertyType": "House",
  "hoaFee": null,
  "status": "Active",
  "daysOnMarket": 1,
  "latitude": 30.3467215,
  "longitude": -97.7626719,
  "numPhotos": 39,
  "url": "https://www.redfin.com/TX/Austin/6105-Highlandale-Dr-78731/home/31265191",
  "sourceUrl": "https://www.redfin.com/city/30818/TX/Austin",
  "scrapedAt": "2026-06-04T07:00:00.000Z"
}

Field reference

• address / city / state / zip — full property location.
• price / pricePerSqFt — list or sold price and price per square foot.
• beds / baths / fullBaths / partialBaths — room counts, with baths split into full and partial.
• sqFt / lotSize / stories / yearBuilt — size and construction detail.
• propertyType — House, Condo, Townhouse, Multi-Family, Land, etc.
• hoaFee — monthly HOA dues when known (null when not disclosed).
• status / daysOnMarket / soldDate — listing status and timing.
• mlsId / listingId / propertyId — the MLS number plus Redfin’s own identifiers.
• latitude / longitude — exact map coordinates on every home.
• numPhotos / photos — photo count and Redfin photo descriptor.
• url — direct link to the Redfin listing; sourceUrl records which region URL produced the row; scrapedAt timestamps the capture.

A few schema choices to make early when you load this into a warehouse. Keep listingId (or mlsId) as your stable join key — addresses get reformatted and prices change, but the IDs don’t. Always retain sourceUrl so you can attribute every row back to the region query that produced it. And log scrapedAt, because real estate inventory turns over fast: a home that was active on Monday may be pending by Friday, and you’ll want to know when each snapshot was taken.

Use cases

The breadth and structure of the data make it useful across several roles:

• Real estate investors — build comps from sold data, track active inventory inside a buy-box (price band + min beds + target zips), and spot price-per-sqft outliers that signal a deal or an overpriced flip.
• Proptech and data teams — feed normalized listing datasets into AVMs, dashboards and ML models. The included coordinates mean you skip a geocoding step entirely, and consistent fields across the whole country mean less cleanup before modeling.
• Market analysts — measure inventory levels, days-on-market trends and price movement by metro or zip over time. Run the same regions on a schedule and the de-dup-by-ID behavior gives you a clean time series of what’s new.
• Agents and brokerages — monitor active, sold and rental inventory in the markets you serve, and benchmark your listings against comparable homes.
• Lead generation — surface brand-new listings (sortBy: newest) and broker names by area to feed an outreach pipeline. Fresh-on-market homes are time-sensitive leads, and a daily scheduled run captures them the moment they appear.

The common thread is that a one-off pull of a single neighborhood is a curiosity, but a scheduled feed across your target markets, refreshed regularly, becomes a real intelligence asset — comps that stay current, inventory you can trend, and leads you catch while they’re fresh.

▶ Run the Redfin Scraper — pull for-sale, sold and rental homes across any US market, filtered by price and beds, with coordinates and MLS numbers on every row. $3.50 per 1,000 listings.

FAQ

Do I need a Redfin login or API key to scrape it?

No. The actor reads only public Redfin listing data — no account, no cookies and no developer API key. You point it at a location and it returns structured records.

How do I target a specific location?

Two ways, and you can mix them. Either paste a Redfin URL (search on redfin.com and copy the address bar) or just type a 5-digit US zip code. City, zip, neighborhood and county pages are all supported.

How many homes can I get per location?

Redfin’s listing feed is walked up to 350 homes per page and paginated across the whole region — typically thousands of homes in a large metro. Use maxResults to cap the volume (default 1000, or 0 for unlimited).

Can I get sold homes and rentals, not just for-sale?

Yes. Set listingType to sold for recently closed transactions (the basis for comps) or rent for rental listings. The default is forSale.

Why is a residential proxy required?

Redfin serves listing data only to normal US browsers, so the actor connects through Apify’s US residential proxies by default. It’s pre-configured — just leave the proxy setting as it is.

Are exact coordinates included?

Yes. Every home carries latitude and longitude, so the dataset is immediately ready for mapping, spatial joins and geospatial analysis without a separate geocoding step.

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This guide — how-to-scrape-redfin-real-estate-listings.mdx — covered everything you need to scrape Redfin real estate listings at scale: input modes, for-sale/sold/rental coverage, pagination, the full output schema and the use cases that turn raw listing data into a durable real estate intelligence feed.