Real Estate Scraping: Automate Property Data Collection with ScrapingBot<\/title>\r\n<\/head>\r\n<body>\r\n\r\n<article class=\"sb-article\">\r\n\r\n <div class=\"sb-meta\">\r\n <span class=\"sb-tag\">Real estate<\/span>\r\n <span class=\"sb-read-time\">5 min read \u00b7 Published: 30\/06\/2023<\/span>\r\n <\/div>\r\n\r\n <p class=\"sb-intro\">Looking to automate real estate data collection? This guide explains how <strong>real estate scraping<\/strong> with ScrapingBot's API lets you extract property listings, sale prices, and market data automatically \u2014 in JSON format, updated in real time, from any real estate website.<\/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=\"#what-is\">What is real estate scraping?<\/a><\/li>\r\n <li><a href=\"#why\">Why automate real estate data collection?<\/a><\/li>\r\n <li><a href=\"#what-data\">What data can you extract?<\/a><\/li>\r\n <li><a href=\"#scrapingbot\">ScrapingBot: specialist in real estate data extraction<\/a><\/li>\r\n <li><a href=\"#step-by-step\">Step-by-step: build your real estate scraper in Python<\/a><\/li>\r\n <li><a href=\"#output\">Sample JSON output<\/a><\/li>\r\n <li><a href=\"#use-cases\">Key use cases<\/a><\/li>\r\n <li><a href=\"#further\">Going further: automated property monitoring<\/a><\/li>\r\n <\/ol>\r\n <\/div>\r\n\r\n <h2 id=\"what-is\">1. What is real estate scraping?<\/h2>\r\n <img decoding=\"async\" src=\"https:\/\/scraping-bot.io\/blogs\/wp-content\/uploads\/2026\/04\/real-estate-illustration.webp\" alt=\"Real estate scraping - extract property listings and prices automatically\" class=\"sb-float-right\" \/>\r\n <p><strong>Real estate scraping<\/strong> is the automated extraction of property data from real estate websites \u2014 listings, sale prices, surface areas, locations, and availability \u2014 using a scraper API. It replaces manual browsing and copy-pasting with a fully automated, reliable, and scalable data pipeline.<\/p>\r\n <p>Instead of visiting dozens of property websites one by one, a <strong>real estate scraper<\/strong> visits them automatically, extracts structured data, and returns it in <strong>JSON format<\/strong> ready for analysis, database storage, or integration into your application.<\/p>\r\n <p>ScrapingBot is a specialist in automatic data extraction from websites \u2014 including the most complex real estate platforms with JavaScript rendering and anti-bot protections.<\/p>\r\n\r\n <h2 id=\"why\">2. Why automate real estate data collection?<\/h2>\r\n <p>The real estate market moves fast. New properties appear daily, prices change, and listings go offline within hours of being posted. Manual data collection simply cannot keep pace. Here's what automated <strong>real estate data extraction<\/strong> gives you:<\/p>\r\n <ul>\r\n <li><strong>Speed<\/strong> \u2014 collect hundreds of listings in minutes instead of hours<\/li>\r\n <li><strong>Reliability<\/strong> \u2014 automatic and reliable data collection, updated as frequently as you need<\/li>\r\n <li><strong>Coverage<\/strong> \u2014 monitor an entire city or region simultaneously, across multiple platforms<\/li>\r\n <li><strong>Time savings<\/strong> \u2014 eliminate repetitive browsing and focus on analysis and decision-making<\/li>\r\n <li><strong>Up-to-date database<\/strong> \u2014 automatically refresh your property database with new listings as soon as they appear on the market<\/li>\r\n <\/ul>\r\n <p>Whether you're a real estate agent, a property investor, a proptech startup, or a data analyst, <strong>real estate scraping<\/strong> gives you a decisive competitive advantage.<\/p>\r\n\r\n <h2 id=\"what-data\">3. What data can you extract?<\/h2>\r\n <p>A well-configured <strong>real estate scraper<\/strong> can extract all the key data points from property listings \u2014 houses, apartments, commercial properties, and land:<\/p>\r\n <ul>\r\n <li><strong>Sale or rental price<\/strong> \u2014 current listing price, price per m\u00b2, price history<\/li>\r\n <li><strong>Property details<\/strong> \u2014 surface area, number of rooms, bedrooms, bathrooms, floor<\/li>\r\n <li><strong>Location data<\/strong> \u2014 address, city, neighborhood, ZIP code, GPS coordinates<\/li>\r\n <li><strong>Listing date<\/strong> \u2014 when the property first appeared on the market<\/li>\r\n <li><strong>Agency or seller<\/strong> \u2014 contact details of the listing agent<\/li>\r\n <li><strong>Property description<\/strong> \u2014 full text description for NLP analysis<\/li>\r\n <li><strong>Photos<\/strong> \u2014 image URLs for visual processing pipelines<\/li>\r\n <\/ul>\r\n <p>All of this data is returned in structured <strong>JSON format<\/strong>, ready to be imported into your real estate database, CRM, or analytics platform.<\/p>\r\n\r\n <h2 id=\"scrapingbot\">4. ScrapingBot: specialist in real estate data extraction<\/h2>\r\n <p>ScrapingBot is a specialist in <strong>automatic data extraction from websites<\/strong>, with a dedicated Real Estate API that handles the technical complexity for you: JavaScript rendering, IP rotation, CAPTCHA bypass, and structured JSON output \u2014 regardless of the source platform.<\/p>\r\n <p>The same API works across all major real estate platforms \u2014 property portals, agency websites, auction sites, and classifieds. Authentication is via a single <strong>API key<\/strong>, and integration into your existing Python workflow takes just a few lines of code.<\/p>\r\n <p>We've already covered scraping <a href=\"https:\/\/scraping-bot.io\/blogs\/how-to-scrape-real-estate-listings-from-funda\" target=\"_blank\" rel=\"noopener\">Funda in the Netherlands<\/a> and Rightmove in the UK \u2014 the same API interface applies to any real estate website globally.<\/p>\r\n\r\n <h2 id=\"step-by-step\">5. Real estate scraping: step-by-step Python tutorial<\/h2>\r\n\r\n <h3>Install the library<\/h3>\r\n <pre><code>pip install requests pandas<\/code><\/pre>\r\n <p>The <a href=\"https:\/\/pypi.org\/project\/requests\/\" target=\"_blank\" rel=\"noopener\">requests library<\/a> handles API calls. <a href=\"https:\/\/pandas.pydata.org\/docs\/\" target=\"_blank\" rel=\"noopener\">pandas<\/a> is used to structure and export your property database.<\/p>\r\n\r\n <h3>Basic setup<\/h3>\r\n <pre><code>import requests\r\n\r\nUSERNAME = \"your_username\"\r\nAPI_KEY = \"your_api_key\"\r\n\r\ndef scrape_property(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 <h3>Scraping new listings from a city<\/h3>\r\n <p>To monitor new properties for sale in a specific city and automatically update your database, loop through listing pages with a polite delay:<\/p>\r\n\r\n <pre><code>import requests, time, pandas as pd\r\n\r\n# Search for properties for sale in Paris\r\nSEARCH_URL = \"https:\/\/www.example-realestate.com\/for-sale\/paris\/\"\r\n\r\ndef scrape_listings(n_pages=5):\r\n results = []\r\n for page in range(1, n_pages + 1):\r\n url = f\"{SEARCH_URL}?page={page}\"\r\n data = scrape_property(url)\r\n listings = data.get(\"listings\", [])\r\n results.extend(listings)\r\n print(f\"Page {page}: {len(listings)} properties found\")\r\n time.sleep(1) # polite delay\r\n return results\r\n\r\nlistings = scrape_listings()\r\nprint(f\"Total: {len(listings)} properties collected\")<\/code><\/pre>\r\n\r\n <h3>Automating and refreshing your property database<\/h3>\r\n <p>To keep your <strong>real estate database<\/strong> up to date automatically, schedule your scraper to run daily and detect new listings:<\/p>\r\n\r\n <pre><code>import sqlite3, schedule, time\r\nfrom datetime import datetime\r\n\r\ndef save_listing(listing):\r\n conn = sqlite3.connect(\"real_estate.db\")\r\n cursor = conn.cursor()\r\n\r\n cursor.execute(\"\"\"\r\n CREATE TABLE IF NOT EXISTS properties (\r\n id INTEGER PRIMARY KEY AUTOINCREMENT,\r\n title TEXT,\r\n price REAL,\r\n surface_m2 REAL,\r\n price_per_m2 REAL,\r\n rooms INTEGER,\r\n city TEXT,\r\n zip_code TEXT,\r\n listing_date TEXT,\r\n url TEXT UNIQUE,\r\n scraped_at TEXT\r\n )\r\n \"\"\")\r\n\r\n try:\r\n cursor.execute(\"\"\"\r\n INSERT INTO properties\r\n (title, price, surface_m2, price_per_m2, rooms, city,\r\n zip_code, listing_date, url, scraped_at)\r\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\r\n \"\"\", (\r\n listing.get(\"title\"),\r\n listing.get(\"price\"),\r\n listing.get(\"surface_m2\"),\r\n listing.get(\"price_per_m2\"),\r\n listing.get(\"rooms\"),\r\n listing.get(\"city\"),\r\n listing.get(\"zip_code\"),\r\n listing.get(\"listing_date\"),\r\n listing.get(\"url\"),\r\n datetime.utcnow().isoformat()\r\n ))\r\n conn.commit()\r\n print(f\"New listing saved: {listing.get('title')}\")\r\n except sqlite3.IntegrityError:\r\n pass # listing already exists\r\n finally:\r\n conn.close()\r\n\r\ndef daily_scrape():\r\n listings = scrape_listings(n_pages=10)\r\n for listing in listings:\r\n save_listing(listing)\r\n\r\n# Run every day at 8am\r\nschedule.every().day.at(\"08:00\").do(daily_scrape)\r\n\r\nwhile True:\r\n schedule.run_pending()\r\n time.sleep(60)<\/code><\/pre>\r\n\r\n <h2 id=\"output\">6. Sample JSON output<\/h2>\r\n <p>ScrapingBot's <strong>real estate scraping<\/strong> API returns a structured JSON object for each property listing. Here's a typical response:<\/p>\r\n\r\n <pre><code>{\r\n \"title\": \"3-bedroom apartment, city center\",\r\n \"price\": 385000,\r\n \"currency\": \"EUR\",\r\n \"surface_m2\": 78,\r\n \"price_per_m2\": 4936,\r\n \"rooms\": 4,\r\n \"bedrooms\": 3,\r\n \"bathrooms\": 1,\r\n \"floor\": 3,\r\n \"city\": \"Paris\",\r\n \"zip_code\": \"75011\",\r\n \"listing_date\": \"2026-04-15\",\r\n \"agency\": \"Agence Centrale Immobilier\",\r\n \"url\": \"https:\/\/example-realestate.com\/listing\/12345\",\r\n \"scraped_at\": \"2026-04-21T08:00:00Z\"\r\n}<\/code><\/pre>\r\n\r\n <p>Full field reference for the JSON property data output:<\/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>title<\/td><td>3-bedroom apartment, city center<\/td><td>string<\/td><\/tr>\r\n <tr><td>price<\/td><td>385000<\/td><td>float<\/td><\/tr>\r\n <tr><td>currency<\/td><td>EUR<\/td><td>string<\/td><\/tr>\r\n <tr><td>surface_m2<\/td><td>78<\/td><td>float<\/td><\/tr>\r\n <tr><td>price_per_m2<\/td><td>4936<\/td><td>float<\/td><\/tr>\r\n <tr><td>rooms<\/td><td>4<\/td><td>integer<\/td><\/tr>\r\n <tr><td>bedrooms<\/td><td>3<\/td><td>integer<\/td><\/tr>\r\n <tr><td>city<\/td><td>Paris<\/td><td>string<\/td><\/tr>\r\n <tr><td>zip_code<\/td><td>75011<\/td><td>string<\/td><\/tr>\r\n <tr><td>listing_date<\/td><td>2026-04-15<\/td><td>string<\/td><\/tr>\r\n <tr><td>agency<\/td><td>Agence Centrale Immobilier<\/td><td>string<\/td><\/tr>\r\n <tr><td>scraped_at<\/td><td>2026-04-21T08:00:00Z<\/td><td>string (ISO 8601)<\/td><\/tr>\r\n <\/tbody>\r\n <\/table>\r\n\r\n <h2 id=\"use-cases\">7. Key use cases for real estate scraping<\/h2>\r\n <p>Once your <strong>real estate data extraction<\/strong> pipeline is running, the data unlocks a wide range of high-value applications:<\/p>\r\n <ul>\r\n <li><strong>Property price comparison<\/strong> \u2014 compare sale prices across neighborhoods, cities, or property types to identify undervalued listings<\/li>\r\n <li><strong>New listing alerts<\/strong> \u2014 automatically detect new properties as soon as they appear on the market, before the competition<\/li>\r\n <li><strong>Automated property database<\/strong> \u2014 build and maintain a comprehensive, always up-to-date database of properties for sale<\/li>\r\n <li><strong>Price trend analysis<\/strong> \u2014 track price evolution over time by city, ZIP code, or property type<\/li>\r\n <li><strong>Market intelligence<\/strong> \u2014 monitor supply and demand dynamics, average days on market, and price-per-m\u00b2 benchmarks<\/li>\r\n <li><strong>Investment opportunity detection<\/strong> \u2014 identify properties priced below market value for investment purposes<\/li>\r\n <\/ul>\r\n\r\n <h2 id=\"further\">8. Going further: automated property monitoring<\/h2>\r\n <p>Once your <strong>real estate scraping<\/strong> pipeline is running, you can plug the JSON output into a <a href=\"https:\/\/plotly.com\/python\/\" target=\"_blank\" rel=\"noopener\">Plotly<\/a> dashboard to visualize price trends by city or neighborhood, or connect it to a notification system to alert you instantly when a new property matching your criteria appears on the market. For large-scale deployments across multiple cities or countries, ScrapingBot's API scales effortlessly \u2014 the same interface handles hundreds of thousands of listings per day. ScrapingBot also supports e-commerce price scraping, email extraction, and social media data collection with the same unified API.<\/p>\r\n\r\n <div class=\"sb-cta\">\r\n <p><strong>Ready to automate your real estate data collection?<\/strong> Get 500 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-float-right { float: right; margin: 0 0 1rem 1.5rem; max-width: 280px; width: 40%; height: auto; border-radius: 8px; }\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>\r\n\r\n<\/body>\r\n<\/html>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4a8348f e-flex e-con-boxed e-con e-parent\" data-id=\"4a8348f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\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> Real estate 5 min read \u00a0\u00b7\u00a0 Published: 30\/06\/2023Looking to automate real estate data collection? This guide explains how real estate scraping with ScrapingBot’s API lets you extract property listings, sale prices, and market data automatically \u2014 in JSON format, updated in real time, from any real estate website. Table of contents What is real […]<\/p>\n","protected":false},"author":4,"featured_media":5438,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[6],"tags":[],"class_list":["post-5409","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-web-scraping-in-general"],"acf":[],"yoast_head":"\n<title>Real Estate Scraping : Extract Property Data in JSON with Python<\/title>\n<meta name=\"description\" content=\"Extract property listings and sale prices automatically with ScrapingBot's real estate scraping API. 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\n\t\t\t\t\t\r\n\r\n\r\n \r\n \r\n Real Estate Scraping: Automate Property Data Collection with ScrapingBot<\/title>\r\n<\/head>\r\n<body>\r\n\r\n<article class=\"sb-article\">\r\n\r\n <div class=\"sb-meta\">\r\n <span class=\"sb-tag\">Real estate<\/span>\r\n <span class=\"sb-read-time\">5 min read \u00b7 Published: 30\/06\/2023<\/span>\r\n <\/div>\r\n\r\n <p class=\"sb-intro\">Looking to automate real estate data collection? This guide explains how <strong>real estate scraping<\/strong> with ScrapingBot's API lets you extract property listings, sale prices, and market data automatically \u2014 in JSON format, updated in real time, from any real estate website.<\/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=\"#what-is\">What is real estate scraping?<\/a><\/li>\r\n <li><a href=\"#why\">Why automate real estate data collection?<\/a><\/li>\r\n <li><a href=\"#what-data\">What data can you extract?<\/a><\/li>\r\n <li><a href=\"#scrapingbot\">ScrapingBot: specialist in real estate data extraction<\/a><\/li>\r\n <li><a href=\"#step-by-step\">Step-by-step: build your real estate scraper in Python<\/a><\/li>\r\n <li><a href=\"#output\">Sample JSON output<\/a><\/li>\r\n <li><a href=\"#use-cases\">Key use cases<\/a><\/li>\r\n <li><a href=\"#further\">Going further: automated property monitoring<\/a><\/li>\r\n <\/ol>\r\n <\/div>\r\n\r\n <h2 id=\"what-is\">1. What is real estate scraping?<\/h2>\r\n <img decoding=\"async\" src=\"https:\/\/scraping-bot.io\/blogs\/wp-content\/uploads\/2026\/04\/real-estate-illustration.webp\" alt=\"Real estate scraping - extract property listings and prices automatically\" class=\"sb-float-right\" \/>\r\n <p><strong>Real estate scraping<\/strong> is the automated extraction of property data from real estate websites \u2014 listings, sale prices, surface areas, locations, and availability \u2014 using a scraper API. It replaces manual browsing and copy-pasting with a fully automated, reliable, and scalable data pipeline.<\/p>\r\n <p>Instead of visiting dozens of property websites one by one, a <strong>real estate scraper<\/strong> visits them automatically, extracts structured data, and returns it in <strong>JSON format<\/strong> ready for analysis, database storage, or integration into your application.<\/p>\r\n <p>ScrapingBot is a specialist in automatic data extraction from websites \u2014 including the most complex real estate platforms with JavaScript rendering and anti-bot protections.<\/p>\r\n\r\n <h2 id=\"why\">2. Why automate real estate data collection?<\/h2>\r\n <p>The real estate market moves fast. New properties appear daily, prices change, and listings go offline within hours of being posted. Manual data collection simply cannot keep pace. Here's what automated <strong>real estate data extraction<\/strong> gives you:<\/p>\r\n <ul>\r\n <li><strong>Speed<\/strong> \u2014 collect hundreds of listings in minutes instead of hours<\/li>\r\n <li><strong>Reliability<\/strong> \u2014 automatic and reliable data collection, updated as frequently as you need<\/li>\r\n <li><strong>Coverage<\/strong> \u2014 monitor an entire city or region simultaneously, across multiple platforms<\/li>\r\n <li><strong>Time savings<\/strong> \u2014 eliminate repetitive browsing and focus on analysis and decision-making<\/li>\r\n <li><strong>Up-to-date database<\/strong> \u2014 automatically refresh your property database with new listings as soon as they appear on the market<\/li>\r\n <\/ul>\r\n <p>Whether you're a real estate agent, a property investor, a proptech startup, or a data analyst, <strong>real estate scraping<\/strong> gives you a decisive competitive advantage.<\/p>\r\n\r\n <h2 id=\"what-data\">3. What data can you extract?<\/h2>\r\n <p>A well-configured <strong>real estate scraper<\/strong> can extract all the key data points from property listings \u2014 houses, apartments, commercial properties, and land:<\/p>\r\n <ul>\r\n <li><strong>Sale or rental price<\/strong> \u2014 current listing price, price per m\u00b2, price history<\/li>\r\n <li><strong>Property details<\/strong> \u2014 surface area, number of rooms, bedrooms, bathrooms, floor<\/li>\r\n <li><strong>Location data<\/strong> \u2014 address, city, neighborhood, ZIP code, GPS coordinates<\/li>\r\n <li><strong>Listing date<\/strong> \u2014 when the property first appeared on the market<\/li>\r\n <li><strong>Agency or seller<\/strong> \u2014 contact details of the listing agent<\/li>\r\n <li><strong>Property description<\/strong> \u2014 full text description for NLP analysis<\/li>\r\n <li><strong>Photos<\/strong> \u2014 image URLs for visual processing pipelines<\/li>\r\n <\/ul>\r\n <p>All of this data is returned in structured <strong>JSON format<\/strong>, ready to be imported into your real estate database, CRM, or analytics platform.<\/p>\r\n\r\n <h2 id=\"scrapingbot\">4. ScrapingBot: specialist in real estate data extraction<\/h2>\r\n <p>ScrapingBot is a specialist in <strong>automatic data extraction from websites<\/strong>, with a dedicated Real Estate API that handles the technical complexity for you: JavaScript rendering, IP rotation, CAPTCHA bypass, and structured JSON output \u2014 regardless of the source platform.<\/p>\r\n <p>The same API works across all major real estate platforms \u2014 property portals, agency websites, auction sites, and classifieds. Authentication is via a single <strong>API key<\/strong>, and integration into your existing Python workflow takes just a few lines of code.<\/p>\r\n <p>We've already covered scraping <a href=\"https:\/\/scraping-bot.io\/blogs\/how-to-scrape-real-estate-listings-from-funda\" target=\"_blank\" rel=\"noopener\">Funda in the Netherlands<\/a> and Rightmove in the UK \u2014 the same API interface applies to any real estate website globally.<\/p>\r\n\r\n <h2 id=\"step-by-step\">5. Real estate scraping: step-by-step Python tutorial<\/h2>\r\n\r\n <h3>Install the library<\/h3>\r\n <pre><code>pip install requests pandas<\/code><\/pre>\r\n <p>The <a href=\"https:\/\/pypi.org\/project\/requests\/\" target=\"_blank\" rel=\"noopener\">requests library<\/a> handles API calls. <a href=\"https:\/\/pandas.pydata.org\/docs\/\" target=\"_blank\" rel=\"noopener\">pandas<\/a> is used to structure and export your property database.<\/p>\r\n\r\n <h3>Basic setup<\/h3>\r\n <pre><code>import requests\r\n\r\nUSERNAME = \"your_username\"\r\nAPI_KEY = \"your_api_key\"\r\n\r\ndef scrape_property(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 <h3>Scraping new listings from a city<\/h3>\r\n <p>To monitor new properties for sale in a specific city and automatically update your database, loop through listing pages with a polite delay:<\/p>\r\n\r\n <pre><code>import requests, time, pandas as pd\r\n\r\n# Search for properties for sale in Paris\r\nSEARCH_URL = \"https:\/\/www.example-realestate.com\/for-sale\/paris\/\"\r\n\r\ndef scrape_listings(n_pages=5):\r\n results = []\r\n for page in range(1, n_pages + 1):\r\n url = f\"{SEARCH_URL}?page={page}\"\r\n data = scrape_property(url)\r\n listings = data.get(\"listings\", [])\r\n results.extend(listings)\r\n print(f\"Page {page}: {len(listings)} properties found\")\r\n time.sleep(1) # polite delay\r\n return results\r\n\r\nlistings = scrape_listings()\r\nprint(f\"Total: {len(listings)} properties collected\")<\/code><\/pre>\r\n\r\n <h3>Automating and refreshing your property database<\/h3>\r\n <p>To keep your <strong>real estate database<\/strong> up to date automatically, schedule your scraper to run daily and detect new listings:<\/p>\r\n\r\n <pre><code>import sqlite3, schedule, time\r\nfrom datetime import datetime\r\n\r\ndef save_listing(listing):\r\n conn = sqlite3.connect(\"real_estate.db\")\r\n cursor = conn.cursor()\r\n\r\n cursor.execute(\"\"\"\r\n CREATE TABLE IF NOT EXISTS properties (\r\n id INTEGER PRIMARY KEY AUTOINCREMENT,\r\n title TEXT,\r\n price REAL,\r\n surface_m2 REAL,\r\n price_per_m2 REAL,\r\n rooms INTEGER,\r\n city TEXT,\r\n zip_code TEXT,\r\n listing_date TEXT,\r\n url TEXT UNIQUE,\r\n scraped_at TEXT\r\n )\r\n \"\"\")\r\n\r\n try:\r\n cursor.execute(\"\"\"\r\n INSERT INTO properties\r\n (title, price, surface_m2, price_per_m2, rooms, city,\r\n zip_code, listing_date, url, scraped_at)\r\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\r\n \"\"\", (\r\n listing.get(\"title\"),\r\n listing.get(\"price\"),\r\n listing.get(\"surface_m2\"),\r\n listing.get(\"price_per_m2\"),\r\n listing.get(\"rooms\"),\r\n listing.get(\"city\"),\r\n listing.get(\"zip_code\"),\r\n listing.get(\"listing_date\"),\r\n listing.get(\"url\"),\r\n datetime.utcnow().isoformat()\r\n ))\r\n conn.commit()\r\n print(f\"New listing saved: {listing.get('title')}\")\r\n except sqlite3.IntegrityError:\r\n pass # listing already exists\r\n finally:\r\n conn.close()\r\n\r\ndef daily_scrape():\r\n listings = scrape_listings(n_pages=10)\r\n for listing in listings:\r\n save_listing(listing)\r\n\r\n# Run every day at 8am\r\nschedule.every().day.at(\"08:00\").do(daily_scrape)\r\n\r\nwhile True:\r\n schedule.run_pending()\r\n time.sleep(60)<\/code><\/pre>\r\n\r\n <h2 id=\"output\">6. Sample JSON output<\/h2>\r\n <p>ScrapingBot's <strong>real estate scraping<\/strong> API returns a structured JSON object for each property listing. Here's a typical response:<\/p>\r\n\r\n <pre><code>{\r\n \"title\": \"3-bedroom apartment, city center\",\r\n \"price\": 385000,\r\n \"currency\": \"EUR\",\r\n \"surface_m2\": 78,\r\n \"price_per_m2\": 4936,\r\n \"rooms\": 4,\r\n \"bedrooms\": 3,\r\n \"bathrooms\": 1,\r\n \"floor\": 3,\r\n \"city\": \"Paris\",\r\n \"zip_code\": \"75011\",\r\n \"listing_date\": \"2026-04-15\",\r\n \"agency\": \"Agence Centrale Immobilier\",\r\n \"url\": \"https:\/\/example-realestate.com\/listing\/12345\",\r\n \"scraped_at\": \"2026-04-21T08:00:00Z\"\r\n}<\/code><\/pre>\r\n\r\n <p>Full field reference for the JSON property data output:<\/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>title<\/td><td>3-bedroom apartment, city center<\/td><td>string<\/td><\/tr>\r\n <tr><td>price<\/td><td>385000<\/td><td>float<\/td><\/tr>\r\n <tr><td>currency<\/td><td>EUR<\/td><td>string<\/td><\/tr>\r\n <tr><td>surface_m2<\/td><td>78<\/td><td>float<\/td><\/tr>\r\n <tr><td>price_per_m2<\/td><td>4936<\/td><td>float<\/td><\/tr>\r\n <tr><td>rooms<\/td><td>4<\/td><td>integer<\/td><\/tr>\r\n <tr><td>bedrooms<\/td><td>3<\/td><td>integer<\/td><\/tr>\r\n <tr><td>city<\/td><td>Paris<\/td><td>string<\/td><\/tr>\r\n <tr><td>zip_code<\/td><td>75011<\/td><td>string<\/td><\/tr>\r\n <tr><td>listing_date<\/td><td>2026-04-15<\/td><td>string<\/td><\/tr>\r\n <tr><td>agency<\/td><td>Agence Centrale Immobilier<\/td><td>string<\/td><\/tr>\r\n <tr><td>scraped_at<\/td><td>2026-04-21T08:00:00Z<\/td><td>string (ISO 8601)<\/td><\/tr>\r\n <\/tbody>\r\n <\/table>\r\n\r\n <h2 id=\"use-cases\">7. Key use cases for real estate scraping<\/h2>\r\n <p>Once your <strong>real estate data extraction<\/strong> pipeline is running, the data unlocks a wide range of high-value applications:<\/p>\r\n <ul>\r\n <li><strong>Property price comparison<\/strong> \u2014 compare sale prices across neighborhoods, cities, or property types to identify undervalued listings<\/li>\r\n <li><strong>New listing alerts<\/strong> \u2014 automatically detect new properties as soon as they appear on the market, before the competition<\/li>\r\n <li><strong>Automated property database<\/strong> \u2014 build and maintain a comprehensive, always up-to-date database of properties for sale<\/li>\r\n <li><strong>Price trend analysis<\/strong> \u2014 track price evolution over time by city, ZIP code, or property type<\/li>\r\n <li><strong>Market intelligence<\/strong> \u2014 monitor supply and demand dynamics, average days on market, and price-per-m\u00b2 benchmarks<\/li>\r\n <li><strong>Investment opportunity detection<\/strong> \u2014 identify properties priced below market value for investment purposes<\/li>\r\n <\/ul>\r\n\r\n <h2 id=\"further\">8. Going further: automated property monitoring<\/h2>\r\n <p>Once your <strong>real estate scraping<\/strong> pipeline is running, you can plug the JSON output into a <a href=\"https:\/\/plotly.com\/python\/\" target=\"_blank\" rel=\"noopener\">Plotly<\/a> dashboard to visualize price trends by city or neighborhood, or connect it to a notification system to alert you instantly when a new property matching your criteria appears on the market. For large-scale deployments across multiple cities or countries, ScrapingBot's API scales effortlessly \u2014 the same interface handles hundreds of thousands of listings per day. ScrapingBot also supports e-commerce price scraping, email extraction, and social media data collection with the same unified API.<\/p>\r\n\r\n <div class=\"sb-cta\">\r\n <p><strong>Ready to automate your real estate data collection?<\/strong> Get 500 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-float-right { float: right; margin: 0 0 1rem 1.5rem; max-width: 280px; width: 40%; height: auto; border-radius: 8px; }\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>\r\n\r\n<\/body>\r\n<\/html>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4a8348f e-flex e-con-boxed e-con e-parent\" data-id=\"4a8348f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\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> Real estate 5 min read \u00a0\u00b7\u00a0 Published: 30\/06\/2023Looking to automate real estate data collection? This guide explains how real estate scraping with ScrapingBot’s API lets you extract property listings, sale prices, and market data automatically \u2014 in JSON format, updated in real time, from any real estate website. Table of contents What is real […]<\/p>\n","protected":false},"author":4,"featured_media":5438,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[6],"tags":[],"class_list":["post-5409","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-web-scraping-in-general"],"acf":[],"yoast_head":"\n<title>Real Estate Scraping : Extract Property Data in JSON with Python<\/title>\n<meta name=\"description\" content=\"Extract property listings and sale prices automatically with ScrapingBot's real estate scraping API. 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