Browser Skills

Field-tested against www.zillow.com on 2026-04-18 using http_get (no browser).

Quick summary

Search listing pages (/homes/, /sold/, /rentals/) — http_get works with full Chrome headers. Returns ~973 KB HTML with all listing data embedded in __NEXT_DATA__ JSON.
Individual property detail pages (/homedetails/) — http_get returns HTTP 403 unconditionally. No header combination bypasses this.
Internal API endpoints (/async-create-search-page-state, /graphql/) — 403 for all server-side requests regardless of headers.
Redfin — http_get works; HTML contains both JSON-LD per listing and a stingray JSON API.

What works: search listing pages via `__NEXT_DATA__`

Zillow search pages embed all listing data in <script id="__NEXT_DATA__">. This is standard Next.js SSR output — it is the same data Zillow's React app hydrates from.

Required headers — The single-word User-Agent ("Mozilla/5.0") used by http_get internally gets 403. You must pass a full Chrome UA plus Accept/Accept-Language headers:

python

import re, json
from helpers import http_get

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
    "Accept-Language": "en-US,en;q=0.9",
}

def extract_listings(html):
    """Parse Zillow __NEXT_DATA__ and return list of listing dicts."""
    m = re.search(r'<script id="__NEXT_DATA__"[^>]*>(.*?)</script>', html, re.DOTALL)
    if not m:
        return []
    d = json.loads(m.group(1))
    sps = d['props']['pageProps']['searchPageState']
    return sps['cat1']['searchResults']['listResults']

html = http_get("https://www.zillow.com/homes/San-Francisco,-CA_rb/", headers=HEADERS)
listings = extract_listings(html)
print(len(listings))  # 41 — always 41 per page

Fields available in each listing card

The listResults array is the canonical source. Each entry includes:

Field	Source	Example
`zpid`	listing	`15081707`
`address`	listing	`"212 Spruce St, San Francisco, CA 94118"`
`addressStreet`, `addressCity`, `addressState`, `addressZipcode`	listing	split address components
`price`	listing	`"$4,395,000"` (formatted string)
`unformattedPrice`	listing	`4395000` (int, use for math)
`beds`	listing	`4`
`baths`	listing	`4`
`area`	listing	`4133` (sqft)
`latLong`	listing	`{'latitude': 37.78867, 'longitude': -122.45361}`
`statusType`	listing	`"FOR_SALE"` / `"FOR_RENT"` / `"RECENTLY_SOLD"`
`detailUrl`	listing	full `https://www.zillow.com/homedetails/...` URL
`zestimate`	listing	`4857200` (Zillow AI estimate, int)
`imgSrc`	listing	thumbnail URL
`has3DModel`	listing	`True`/`False`
`hasOpenHouse`	listing	`True`/`False`
`openHouseStartDate`, `openHouseEndDate`	listing	ISO strings
`isFeaturedListing`	listing	sponsored/featured flag
`brokerName`	listing	`"Sotheby's International Realty"`
`statusText`	listing	`"FOR SALE"` display string
`hdpData.homeInfo.price`	nested	raw price int (matches `unformattedPrice`)
`hdpData.homeInfo.zestimate`	nested	raw zestimate int
`hdpData.homeInfo.rentZestimate`	nested	monthly rent estimate
`hdpData.homeInfo.homeType`	nested	`"SINGLE_FAMILY"`, `"CONDO"`, `"TOWNHOUSE"` etc.
`hdpData.homeInfo.daysOnZillow`	nested	int
`hdpData.homeInfo.taxAssessedValue`	nested	int
`hdpData.homeInfo.lotAreaValue` + `lotAreaUnit`	nested	e.g. `2957.724`, `"sqft"`
`hdpData.homeInfo.priceForHDP`	nested	reliable sold price for recently-sold listings

python

# Full extraction snippet
listing = listings[0]
hi = listing.get('hdpData', {}).get('homeInfo', {})

record = {
    "zpid":         listing['zpid'],
    "address":      listing['address'],
    "price_raw":    listing.get('unformattedPrice') or hi.get('price'),
    "beds":         listing.get('beds'),
    "baths":        listing.get('baths'),
    "sqft":         listing.get('area'),
    "lat":          listing['latLong']['latitude'],
    "lon":          listing['latLong']['longitude'],
    "status":       listing['statusType'],
    "zestimate":    listing.get('zestimate'),
    "rent_zest":    hi.get('rentZestimate'),
    "home_type":    hi.get('homeType'),
    "days_listed":  hi.get('daysOnZillow'),
    "tax_assessed": hi.get('taxAssessedValue'),
    "url":          listing['detailUrl'],
}

Total result count and pagination

python

d = json.loads(re.search(r'<script id="__NEXT_DATA__"[^>]*>(.*?)</script>', html, re.DOTALL).group(1))
sps = d['props']['pageProps']['searchPageState']

# Total listings in this search
total = sps['categoryTotals']['cat1']['totalResultCount']
print(total)  # 1037

# Each page returns exactly 41 listings. Add /<N>_p/ for subsequent pages:
# Page 2: https://www.zillow.com/homes/San-Francisco,-CA_rb/2_p/
# Page 3: https://www.zillow.com/homes/San-Francisco,-CA_rb/3_p/

max_pages = (total + 40) // 41

Scrape all pages

python

import re, json, time
from helpers import http_get

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
    "Accept-Language": "en-US,en;q=0.9",
}

def get_listings(city_slug, page=1):
    """city_slug: e.g. 'San-Francisco,-CA', 'Seattle,-WA', 'Austin,-TX'"""
    if page == 1:
        url = f"https://www.zillow.com/homes/{city_slug}_rb/"
    else:
        url = f"https://www.zillow.com/homes/{city_slug}_rb/{page}_p/"
    html = http_get(url, headers=HEADERS)
    m = re.search(r'<script id="__NEXT_DATA__"[^>]*>(.*?)</script>', html, re.DOTALL)
    d = json.loads(m.group(1))
    sps = d['props']['pageProps']['searchPageState']
    total = sps['categoryTotals']['cat1']['totalResultCount']
    listings = sps['cat1']['searchResults']['listResults']
    return listings, total

all_listings = []
listings, total = get_listings("San-Francisco,-CA")
all_listings.extend(listings)

max_pages = (total + 40) // 41
for page in range(2, min(max_pages + 1, 6)):   # cap at 5 pages for demo
    time.sleep(1.0)   # polite delay
    page_listings, _ = get_listings("San-Francisco,-CA", page)
    all_listings.extend(page_listings)

print(f"Fetched {len(all_listings)} of {total} listings")

URL patterns that work (all confirmed)

URL pattern	Status	Notes
`/homes/{city}_rb/`	Works	For-sale listings
`/homes/{city}_rb/{N}_p/`	Works	Pagination
`/homes/for_sale/{city}/0-1800000_price/`	Works	Price filter (max)
`/homes/3-_beds/{city}/`	Works	Bed count filter
`/homes/{zip}_rb/`	Works	ZIP code search
`/san-francisco-ca/rentals/`	Works	Rental listings
`/san-francisco-ca/sold/`	Works	Recently sold
`/homedetails/{address}/{zpid}_zpid/`	403	Single property detail
`/async-create-search-page-state`	403	Internal search API
`/graphql/`	400/403	GraphQL endpoint

Rental listings

Rental search pages use the same __NEXT_DATA__ structure. However, rental listing cards have a different schema — individual units are nested, not a flat price:

python

html = http_get("https://www.zillow.com/san-francisco-ca/rentals/", headers=HEADERS)
listings = extract_listings(html)

r = listings[0]
# Multi-unit buildings:
# r['units'] = [{'price': '$3,485+', 'beds': '0', 'roomForRent': False}, ...]
# r['minBaseRent'] = 3485
# r['maxBaseRent'] = 7130
# r['availabilityCount'] = 23

# Single-unit rentals:
# r['price'] = '$2,500/mo'
# r['unformattedPrice'] = 2500

# Check which type:
if r.get('isBuilding'):
    price_range = f"${r['minBaseRent']}–${r['maxBaseRent']}/mo"
    units = r.get('units', [])
else:
    price = r.get('unformattedPrice') or r.get('hdpData', {}).get('homeInfo', {}).get('price')

Sold listings

Sold pages (/sold/) work identically. Key difference: statusType is "RECENTLY_SOLD" and price comes from hdpData.homeInfo.priceForHDP (not the price field which is None in sold cards):

python

html = http_get("https://www.zillow.com/san-francisco-ca/sold/", headers=HEADERS)
listings = extract_listings(html)

for l in listings:
    hi = l.get('hdpData', {}).get('homeInfo', {})
    sold_price  = hi.get('priceForHDP')      # actual sold price
    zestimate   = hi.get('zestimate')
    tax_value   = hi.get('taxAssessedValue')
    print(l['address'], f"${sold_price:,}", f"zest=${zestimate}")
# 999 Green St APT 1702, San Francisco, CA 94133 $3,200,000 zest=$3,403,400
# 1041 Vallejo St, San Francisco, CA 94133 $6,250,000 zest=None

Total sold inventory in San Francisco: 18,109 (all time in Zillow's database, paginated 41/page).

Bot detection behavior

Zillow detects bot status server-side and embeds window.__USER_SESSION_INITIAL_STATE__ and props.isBot in the page.
In field testing, the page returned isBot: False with the Chrome User-Agent — Zillow does not block the search pages.
The page does embed captcha strings in the HTML (for the CAPTCHA challenge widget code), but the challenge is NOT triggered for search pages.
/homedetails/ pages do trigger blocking — every property detail URL tested returned HTTP 403. This is enforced before serving HTML, not via JavaScript CAPTCHA.
Rate limiting: 3 rapid sequential requests to /homes/ all succeeded. Observed no 429s. Add time.sleep(0.5–1.0) between pages as a courtesy.

What you do NOT get from `http_get`

Because property detail pages are blocked (403), you lose:

Full property description text
All listing photos (you only get imgSrc thumbnail from search)
Detailed home facts (year built, parking, HVAC, school scores)
Price history
Nearby comparable sales (comps)
Agent contact info

To get these, you must navigate to the /homedetails/ URL in a browser session. The browser is not blocked (Zillow relies on JS challenges and fingerprinting that only trigger in browser context).

Alternative: Redfin (field-tested, more accessible)

Redfin allows http_get with no blocking for both HTML pages and its internal API.

Redfin JSON-LD per listing (easiest)

Each Redfin search results page embeds one <script type="application/ld+json"> per listing with structured property data:

python

import re, json
from helpers import http_get

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
    "Accept-Language": "en-US,en;q=0.9",
}

html = http_get(
    "https://www.redfin.com/city/17151/CA/San-Francisco/filter/property-type=house",
    headers=HEADERS
)
print(len(html))  # ~1.6 MB

# Extract all SingleFamilyResidence JSON-LD entries
properties = []
for s in re.findall(r'<script type="application/ld\+json">(.*?)</script>', html, re.DOTALL):
    try:
        d = json.loads(s)
        if isinstance(d, list):
            for item in d:
                if item.get('@type') in ('SingleFamilyResidence', 'House', 'Residence', 'Apartment'):
                    properties.append(item)
    except Exception:
        pass

prop = properties[0]
print("Name:", prop['name'])               # "662 Hampshire St, San Francisco, CA 94110"
print("Address:", prop['address'])
# {'@type': 'PostalAddress', 'streetAddress': '662 Hampshire St',
#  'addressLocality': 'San Francisco', 'addressRegion': 'CA',
#  'postalCode': '94110', 'addressCountry': 'US'}
print("Rooms:", prop['numberOfRooms'])     # 3
print("Floor size:", prop['floorSize'])    # {'@type': 'QuantitativeValue', 'value': 3350, 'unitCode': 'FTK'}
print("URL:", prop['url'])
# https://www.redfin.com/CA/San-Francisco/662-Hampshire-St-94110/home/1533754

Note: The JSON-LD schema does NOT include price (Redfin omits offers from the LD+JSON). Use the stingray API below for price.

Redfin stingray API (structured JSON with price)

Redfin's internal GIS/search API returns rich structured data including price, MLS ID, beds, baths, sqft, agent info, and remarks. Responses are prefixed with {}&& — strip it before parsing:

python

import json
from helpers import http_get

def redfin_search(region_id, region_type=6, num_homes=20, page=1, uipt="1,2,3,4,5,6"):
    """
    region_type: 6=city, 2=zipcode, 5=county
    uipt: property types (1=house, 2=condo, 3=townhouse, 4=multi-family, 5=land, 6=other)
    """
    url = (
        f"https://www.redfin.com/stingray/api/gis"
        f"?al=1&num_homes={num_homes}&ord=redfin-recommended-asc"
        f"&page_number={page}&region_id={region_id}&region_type={region_type}"
        f"&sf=1,2,3,5,6,7&status=9&uipt={uipt}&v=8"
    )
    raw = http_get(url, headers={
        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
        "Referer": "https://www.redfin.com/",
        "Accept": "*/*",
    })
    # Strip the {}&& CSRF prefix Redfin prepends to all API responses
    assert raw.startswith('{}&&'), f"Unexpected prefix: {raw[:10]}"
    return json.loads(raw[4:])

data = redfin_search(region_id=17151)  # 17151 = San Francisco, CA
homes = data['payload']['homes']

home = homes[0]
print("Address:", home['streetLine']['value'])  # "875 California St #703"
print("City/State/Zip:", home['city'], home['state'], home['zip'])
print("Price:", home['price']['value'])          # 3300000
print("Beds:", home['beds'])                     # 3
print("Baths:", home['baths'])                   # 2.5
print("Sqft:", home['sqFt']['value'])            # 1828
print("$/sqft:", home['pricePerSqFt']['value'])  # 1805
print("Lot size:", home['lotSize']['value'])      # 9448
print("Year built:", home['yearBuilt']['value'])  # 2021
print("Days on market:", home['dom']['value'])    # 1
print("MLS ID:", home['mlsId']['value'])          # "426115342"
print("MLS Status:", home['mlsStatus'])           # "Active"
print("Lat/Long:", home['latLong']['value'])
print("URL:", home['url'])                        # "/CA/San-Francisco/..."
print("Remarks:", home['listingRemarks'][:100])

Redfin region IDs

City	region_id	region_type
San Francisco, CA	`17151`	`6` (city)
Los Angeles, CA	`17152`	`6`
New York, NY	`17834`	`6`
Seattle, WA	`16163`	`6`

To find other region IDs: search on Redfin, look at the URL (e.g. /city/17151/CA/San-Francisco) — the number is the region_id.

Redfin stingray response structure

code

data['payload']['homes'][i]
  .streetLine.value      → street address string
  .city / .state / .zip  → strings
  .price.value           → int (asking price in dollars)
  .sqFt.value            → int (square feet)
  .pricePerSqFt.value    → int
  .beds                  → int
  .baths                 → float (2.5 = 2 full + 1 half)
  .fullBaths / .partialBaths → ints
  .lotSize.value         → int (sq ft)
  .yearBuilt.value       → int
  .dom.value             → days on market (int)
  .mlsId.value           → MLS listing number (string)
  .mlsStatus             → "Active", "Pending", etc.
  .listingId             → Redfin internal int
  .propertyId            → Redfin internal int
  .latLong.value         → {'latitude': float, 'longitude': float}
  .url                   → relative URL "/CA/San-Francisco/..."
  .listingRemarks        → description text (may be truncated)
  .keyFacts              → [{'description': str, 'rank': int}]
  .listingTags           → ['SWEEPING CITY VIEWS', ...]
  .hoa.value             → HOA monthly (int)
  .location.value        → neighborhood name string
  .sashes                → [{'sashTypeName': 'New'/'Price Drop'/...}]
  .photos.value          → photo token string
  .numPictures           → int

Alternative APIs (no scraping required)

If you need property data without scraping Zillow or Redfin at scale:

API	Free tier	Key data
ATTOM Data (attomdata.com)	Trial available	Ownership, AVM, tax, sale history, building characteristics
Rentcast (rentcastapi.com)	50 req/mo free	Rental estimates, comps, market data
RapidAPI: Zillow56	~100 req/mo free	Wraps Zillow data (unofficial, use at own risk)
HouseCanary	Paid	AVM, market risk, rental value
Redfin API (unofficial, above)	Unlimited	MLS listing data
US Census / HUD	Free, no key	Median home values by geography, affordability

Gotchas

Single User-Agent word triggers 403. http_get passes "Mozilla/5.0" as default User-Agent — this gets blocked. Always pass the full Chrome UA via the headers= argument.
price field is None for sold and rental multi-unit listings. Use unformattedPrice for for-sale, hdpData.homeInfo.priceForHDP for sold, and minBaseRent/maxBaseRent for rentals.
/homedetails/ is unconditionally blocked. Tested with full browser headers, Referer, Sec-Fetch-* headers — all return HTTP 403. Only the browser bypasses this.
41 listings per page, hardcoded. Zillow always returns exactly 41 results per page from listResults. mapResults was empty in all tests (server-side response only).
isBot: False doesn't mean you're safe. Zillow correctly identifies server-side requests and blocks /homedetails/. The isBot flag in __NEXT_DATA__ is False for search pages but the restriction is enforced at route level for detail pages.
Captcha strings in HTML do not mean CAPTCHA is active. The search page includes the captcha widget JavaScript (for lazy loading if needed) but does not serve a challenge — confirmed by successfully parsing listing data from the same HTML.
Redfin {}&& prefix on all API responses. Strip with raw[4:] before json.loads(). If the prefix changes, the assertion fails explicitly.
Redfin JSON-LD omits price. The SingleFamilyResidence schema objects do not include an offers field — use the stingray API for pricing.
Redfin stingray API returns all listing fields wrapped in {'value': X, 'level': N} dicts. Always read .value for numeric fields (e.g. home['price']['value'], not home['price']). Level 1 means data is public; 2 means potentially restricted.
Zillow total count can exceed 800 but pagination caps at page ~20. Zillow caps search results at around 800 listings even if totalResultCount shows 1037. Narrow by ZIP code, neighborhood, or price range to stay within bounds.
URL filter syntax for Zillow: Beds: 3-_beds prefix; price: 0-1800000_price suffix; ZIP: use {zip}_rb instead of city slug. Test by building the URL in a browser and copying the pattern.

zillow

Use this skill

What the agent will read

Scraping & Data Extraction

Quick summary

What works: search listing pages via `__NEXT_DATA__`

Fields available in each listing card

Total result count and pagination

Scrape all pages

URL patterns that work (all confirmed)

Rental listings

Sold listings

Bot detection behavior

What you do NOT get from `http_get`

Alternative: Redfin (field-tested, more accessible)

Redfin JSON-LD per listing (easiest)

Redfin stingray API (structured JSON with price)

Redfin region IDs

Redfin stingray response structure

Alternative APIs (no scraping required)

Gotchas

Use this skill

What the agent will read

Scraping & Data Extraction

Quick summary

What works: search listing pages via __NEXT_DATA__

Fields available in each listing card

Total result count and pagination

Scrape all pages

URL patterns that work (all confirmed)

Rental listings

Sold listings

Bot detection behavior

What you do NOT get from http_get

Alternative: Redfin (field-tested, more accessible)

Redfin JSON-LD per listing (easiest)

Redfin stingray API (structured JSON with price)

Redfin region IDs

Redfin stingray response structure

Alternative APIs (no scraping required)

Gotchas

What works: search listing pages via `__NEXT_DATA__`

What you do NOT get from `http_get`