{"id":5391,"date":"2026-04-19T14:28:29","date_gmt":"2026-04-19T14:28:29","guid":{"rendered":"https:\/\/scraping-bot.io\/blogs\/how-to-scrape-real-estate-listings-on-zillow\/"},"modified":"2026-04-20T13:57:47","modified_gmt":"2026-04-20T13:57:47","slug":"zillow-scraper-python","status":"publish","type":"post","link":"https:\/\/scraping-bot.io\/blogs\/zillow-scraper-python\/","title":{"rendered":"How to scrape real estate listings from Zillow?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"5391\" class=\"elementor elementor-5391\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6b48aa6 e-con-full e-flex e-con e-parent\" data-id=\"6b48aa6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-467a18d elementor-widget elementor-widget-heading\" data-id=\"467a18d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Zillow Scraper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4f71fa8 e-flex e-con-boxed e-con e-parent\" data-id=\"4f71fa8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-638f85e elementor-widget elementor-widget-html\" data-id=\"638f85e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t\t<article class=\"sb-article\">\r\n\r\n  <div class=\"sb-meta\">\r\n    <span class=\"sb-tag\">Web scraping<\/span>\r\n    <span class=\"sb-read-time\">8 min read &nbsp;\u00b7&nbsp; Published: 03\/03\/2020<\/span>\r\n  <\/div>\r\n\r\n  <p class=\"sb-intro\">Looking for a Zillow scraper in Python? Zillow is the #1 real estate platform in North America, with millions of listings for rent and for sale. This Zillow scraper Python guide shows you how to extract structured property data at scale \u2014 both rental and purchase listings \u2014 using ScrapingBot's API.<\/p>\r\n\r\n  <div class=\"sb-toc\">\r\n    <p class=\"sb-toc-title\">Table of contents<\/p>\r\n    <ol>\r\n      <li><a href=\"#why-scrape\">Why scrape Zillow?<\/a><\/li>\r\n      <li><a href=\"#challenges\">Technical challenges<\/a><\/li>\r\n      <li><a href=\"#scrapingbot\">How ScrapingBot handles them<\/a><\/li>\r\n      <li><a href=\"#step-by-step\">Step-by-step: build your Zillow scraper in Python<\/a><\/li>\r\n      <li><a href=\"#rent\">Scraping rental listings<\/a><\/li>\r\n      <li><a href=\"#buy\">Scraping purchase listings<\/a><\/li>\r\n      <li><a href=\"#output\">Sample output data<\/a><\/li>\r\n      <li><a href=\"#further\">Going further<\/a><\/li>\r\n    <\/ol>\r\n  <\/div>\r\n\r\n  <h2 id=\"why-scrape\">1. Why scrape Zillow?<\/h2>\r\n  <p>The US real estate market is one of the most dynamic in the world. Zillow alone lists over 135 million homes across the country. Scraping it gives you access to:<\/p>\r\n  <ul>\r\n    <li>Price trends by city, neighborhood, or ZIP code<\/li>\r\n    <li>Rental vs. purchase market comparisons<\/li>\r\n    <li>Time-on-market data to spot negotiation opportunities<\/li>\r\n    <li>Agency and agent activity tracking<\/li>\r\n    <li>Surface area in square feet and price per sqft analysis<\/li>\r\n  <\/ul>\r\n  <p>We've already covered <a href=\"https:\/\/scraping-bot.io\/blogs\/how-to-scrape-real-estate-listings-from-funda\" rel=\"noopener\">scraping Funda in the Netherlands<\/a> and Rightmove in the UK \u2014 Zillow follows the same pattern but with US-specific data formats (USD, square feet, ZIP codes).<\/p>\r\n\r\n  <h2 id=\"challenges\">2. Technical challenges<\/h2>\r\n  <p>Zillow is one of the most aggressively protected real estate sites to scrape. Here's what you'll run into:<\/p>\r\n  <ul>\r\n    <li><strong>Advanced bot detection<\/strong> \u2014 Zillow uses fingerprinting and behavioral analysis to block automated requests.<\/li>\r\n    <li><strong>JavaScript rendering<\/strong> \u2014 Listing data is injected dynamically via React components.<\/li>\r\n    <li><strong>IP rate limiting<\/strong> \u2014 Repeated requests from the same IP trigger CAPTCHAs or bans.<\/li>\r\n    <li><strong>Geo-restrictions<\/strong> \u2014 Some listing data varies by region and requires US-based IPs.<\/li>\r\n  <\/ul>\r\n\r\n  <h2 id=\"scrapingbot\">3. How ScrapingBot handles them<\/h2>\r\n  <p>ScrapingBot's Real Estate API handles all of this for you: it rotates residential US IPs, renders JavaScript fully, and returns clean structured JSON \u2014 whether you're scraping rental or purchase listings.<\/p>\r\n\r\n  <h2 id=\"step-by-step\">4. Step-by-step: build your Zillow scraper in Python<\/h2>\r\n\r\n  <h3>Install the library<\/h3>\r\n  <pre><code>pip install requests<\/code><\/pre>\r\n\r\n  <h3>Basic setup<\/h3>\r\n  <pre><code>import requests\r\n\r\n# Your ScrapingBot credentials\r\nUSERNAME = \"your_username\"\r\nAPI_KEY  = \"your_api_key\"\r\n\r\ndef scrape_zillow(url):\r\n    api_url = \"https:\/\/api.scraping-bot.io\/scrape\/real-estate\"\r\n    payload = {\"url\": url}\r\n\r\n    response = requests.post(\r\n        api_url,\r\n        json=payload,\r\n        auth=(USERNAME, API_KEY)\r\n    )\r\n\r\n    if response.status_code == 200:\r\n        return response.json()\r\n    else:\r\n        raise Exception(f\"Error {response.status_code}: {response.text}\")<\/code><\/pre>\r\n\r\n  <h2 id=\"rent\">5. Scraping rental listings<\/h2>\r\n  <p>Let's start with a rental listing in Los Angeles. Zillow rental pages include the monthly rent, surface area in sqft, number of bedrooms, and the managing agency.<\/p>\r\n\r\n  <pre><code>import requests, time\r\n\r\n# Scrape a rental listing in Los Angeles\r\nRENTAL_URL = \"https:\/\/www.zillow.com\/homes\/for_rent\/Los-Angeles-CA\/\"\r\n\r\ndef scrape_rentals(n_pages=3):\r\n    results = []\r\n    for page in range(1, n_pages + 1):\r\n        url = f\"{RENTAL_URL}{page}_p\/\"\r\n        data = scrape_zillow(url)\r\n        results.extend(data.get(\"listings\", []))\r\n        time.sleep(1)  # polite delay\r\n    return results\r\n\r\nrentals = scrape_rentals()\r\nprint(f\"Collected {len(rentals)} rental listings\")<\/code><\/pre>\r\n\r\n  <p>Here's what a typical rental listing returns:<\/p>\r\n  <table class=\"sb-table\">\r\n    <thead>\r\n      <tr><th>Field<\/th><th>Example value<\/th><th>Type<\/th><\/tr>\r\n    <\/thead>\r\n    <tbody>\r\n      <tr><td>address<\/td><td>1234 Sunset Blvd, Los Angeles, CA 90028<\/td><td>string<\/td><\/tr>\r\n      <tr><td>monthly_rent<\/td><td>$3,200\/mo<\/td><td>string<\/td><\/tr>\r\n      <tr><td>surface_sqft<\/td><td>850<\/td><td>integer<\/td><\/tr>\r\n      <tr><td>bedrooms<\/td><td>2<\/td><td>integer<\/td><\/tr>\r\n      <tr><td>bathrooms<\/td><td>1<\/td><td>integer<\/td><\/tr>\r\n      <tr><td>agency<\/td><td>Keller Williams Realty<\/td><td>string<\/td><\/tr>\r\n      <tr><td>listing_date<\/td><td>2024-03-01<\/td><td>string<\/td><\/tr>\r\n    <\/tbody>\r\n  <\/table>\r\n\r\n  <h2 id=\"buy\">6. Scraping purchase listings<\/h2>\r\n  <p>For purchase listings, the data structure is slightly different \u2014 you get the sale price, the ZIP code, and the publishing date, which is particularly useful for spotting older listings where the price can be negotiated.<\/p>\r\n\r\n  <pre><code># Scrape purchase listings in New York\r\nBUY_URL = \"https:\/\/www.zillow.com\/homes\/for_sale\/New-York-NY\/\"\r\n\r\ndef scrape_purchases(n_pages=3):\r\n    results = []\r\n    for page in range(1, n_pages + 1):\r\n        url = f\"{BUY_URL}{page}_p\/\"\r\n        data = scrape_zillow(url)\r\n        results.extend(data.get(\"listings\", []))\r\n        time.sleep(1)\r\n    return results\r\n\r\npurchases = scrape_purchases()\r\nprint(f\"Collected {len(purchases)} purchase listings\")<\/code><\/pre>\r\n\r\n  <p>Here's what a typical purchase listing returns:<\/p>\r\n  <table class=\"sb-table\">\r\n    <thead>\r\n      <tr><th>Field<\/th><th>Example value<\/th><th>Type<\/th><\/tr>\r\n    <\/thead>\r\n    <tbody>\r\n      <tr><td>address<\/td><td>456 Park Ave, New York, NY 10022<\/td><td>string<\/td><\/tr>\r\n      <tr><td>price<\/td><td>$1,250,000<\/td><td>string<\/td><\/tr>\r\n      <tr><td>price_per_sqft<\/td><td>$1,042<\/td><td>string<\/td><\/tr>\r\n      <tr><td>surface_sqft<\/td><td>1200<\/td><td>integer<\/td><\/tr>\r\n      <tr><td>bedrooms<\/td><td>3<\/td><td>integer<\/td><\/tr>\r\n      <tr><td>zip_code<\/td><td>10022<\/td><td>string<\/td><\/tr>\r\n      <tr><td>listing_date<\/td><td>2024-01-15<\/td><td>string<\/td><\/tr>\r\n      <tr><td>days_on_market<\/td><td>47<\/td><td>integer<\/td><\/tr>\r\n    <\/tbody>\r\n  <\/table>\r\n\r\n  <h2 id=\"output\">7. Sample output data<\/h2>\r\n  <p>Both rental and purchase responses follow the same JSON structure from ScrapingBot's API. You can normalize them into a single dataframe for cross-market analysis:<\/p>\r\n\r\n  <pre><code>import pandas as pd\r\n\r\n# Combine rental and purchase data\r\nall_listings = rentals + purchases\r\ndf = pd.DataFrame(all_listings)\r\n\r\n# Save to CSV\r\ndf.to_csv(\"zillow_listings.csv\", index=False)\r\nprint(df.head())<\/code><\/pre>\r\n\r\n  <h2 id=\"further\">8. Going further<\/h2>\r\n  <p>Once your Zillow scraper Python script is running, you can schedule it with a cron job to monitor price changes daily, or plug the data into a visualization tool like <a href=\"https:\/\/plotly.com\/python\/\" target=\"_blank\" rel=\"noopener\">Plotly<\/a> to build interactive price-trend dashboards. ScrapingBot also supports Funda, Rightmove, and Airbnb with the same API interface.<\/p>\r\n\r\n  <div class=\"sb-cta\">\r\n    <p><strong>Ready to try it?<\/strong> Get 1,000 free API calls when you sign up for ScrapingBot.<\/p>\r\n    <a href=\"https:\/\/scraping-bot.io\/pricing\" class=\"sb-cta-btn\">Try ScrapingBot for free \u2192<\/a>\r\n  <\/div>\r\n\r\n<\/article>\r\n\r\n<style>\r\n.sb-article { max-width: 800px; margin: 0 auto; font-family: inherit; color: inherit; line-height: 1.7; }\r\n.sb-meta { display: flex; align-items: center; gap: 12px; margin-bottom: 1.5rem; flex-wrap: wrap; }\r\n.sb-tag { background: #e6f1fb; color: #185fa5; font-size: 12px; padding: 4px 12px; border-radius: 6px; font-weight: 500; }\r\n.sb-read-time { font-size: 13px; color: #888; }\r\n.sb-intro { font-size: 16px; border-left: 3px solid #378add; padding-left: 1rem; color: #444; margin-bottom: 2rem; }\r\n.sb-toc { background: #f8f8f8; border: 1px solid #e8e8e8; border-radius: 8px; padding: 1rem 1.5rem; margin-bottom: 2rem; }\r\n.sb-toc-title { font-size: 13px; font-weight: 600; color: #666; margin: 0 0 8px; text-transform: uppercase; letter-spacing: 0.05em; }\r\n.sb-toc ol { margin: 0; padding-left: 1.25rem; }\r\n.sb-toc li { font-size: 14px; padding: 3px 0; }\r\n.sb-toc a { color: #185fa5; text-decoration: none; }\r\n.sb-toc a:hover { text-decoration: underline; }\r\n.sb-article h2 { font-size: 22px; font-weight: 600; margin: 2.5rem 0 0.75rem; border-bottom: 1px solid #eee; padding-bottom: 0.5rem; }\r\n.sb-article h3 { font-size: 17px; font-weight: 600; margin: 1.5rem 0 0.5rem; }\r\n.sb-article p { margin: 0 0 1rem; }\r\n.sb-article ul, .sb-article ol { margin: 0 0 1rem; padding-left: 1.5rem; }\r\n.sb-article li { margin-bottom: 6px; }\r\n.sb-article pre { background: #1e1e1e; color: #d4d4d4; border-radius: 8px; padding: 1.25rem; overflow-x: auto; margin: 1rem 0 1.5rem; }\r\n.sb-article code { font-family: 'Courier New', monospace; font-size: 13px; line-height: 1.6; }\r\n.sb-table { width: 100%; border-collapse: collapse; margin: 1rem 0 1.5rem; font-size: 14px; }\r\n.sb-table th { text-align: left; padding: 10px 14px; background: #f4f4f4; font-weight: 600; border-bottom: 2px solid #ddd; }\r\n.sb-table td { padding: 10px 14px; border-bottom: 1px solid #eee; }\r\n.sb-table tr:last-child td { border-bottom: none; }\r\n.sb-cta { background: #e6f1fb; border: 1px solid #b5d4f4; border-radius: 10px; padding: 1.5rem; margin: 2.5rem 0 0; text-align: center; }\r\n.sb-cta p { margin: 0 0 1rem; font-size: 15px; }\r\n.sb-cta-btn { display: inline-block; background: #185fa5; color: white; padding: 10px 24px; border-radius: 6px; text-decoration: none; font-size: 14px; font-weight: 500; }\r\n.sb-cta-btn:hover { background: #0c447c; }\r\n<\/style>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Zillow Scraper Web scraping 8 min read \u00a0\u00b7\u00a0 Published: 03\/03\/2020Looking for a Zillow scraper in Python? Zillow is the #1 real estate platform in North America, with millions of listings for rent and for sale. This Zillow scraper Python guide shows you how to extract structured property data at scale \u2014 both rental and purchase [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":5420,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-5391","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-site-specific-scrapers"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Zillow Scraper Python: Scrape Real Estate Listings - Scraping-bot<\/title>\n<meta name=\"description\" content=\"Build a zillow scraper python with ScrapingBot. 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