{"id":5753,"date":"2026-04-22T10:24:45","date_gmt":"2026-04-22T10:24:45","guid":{"rendered":"https:\/\/scraping-bot.io\/blogs\/?p=5753"},"modified":"2026-04-22T19:37:01","modified_gmt":"2026-04-22T19:37:01","slug":"bien-ici-scraper-python","status":"publish","type":"post","link":"https:\/\/scraping-bot.io\/blogs\/bien-ici-scraper-python\/","title":{"rendered":"How to Scrape Bien’ici Real Estate Listings with Python?"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\r\n\r\n\r\n \r\n \r\n Bien'ici Scraper in Python \u2014 Complete Guide 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\">Web scraping<\/span>\r\n <span class=\"sb-read-time\">9 min read  \u00b7  Published: 22\/04\/2026<\/span>\r\n <\/div>\r\n\r\n <p class=\"sb-intro\">Looking for a Bien'ici scraper Python guide? The French real estate market moves fast. Whether you're tracking price trends, monitoring competitor listings, or building a data pipeline, this guide shows you how to extract structured property data at scale from Bien'ici 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 Bien'ici?<\/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 Bien'ici scraper Python<\/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 Bien'ici?<\/h2>\r\n <p>Bien'ici is one of France's leading real estate portals, with tens of thousands of active listings at any given time \u2014 apartments, houses, new developments, rentals. It is used by buyers, sellers, agents, and investors alike. Scraping it gives you access to:<\/p>\r\n <ul>\r\n <li>Price history and market trends by neighbourhood or postal code<\/li>\r\n <li>Listing density and time-on-market data<\/li>\r\n <li>Rental vs. sale inventory ratios by geographic area<\/li>\r\n <li>Competitor agency activity and their mandates<\/li>\r\n <li>DPE data (energy class, GHG emissions) for environmental performance analysis<\/li>\r\n <\/ul>\r\n\r\n <div class=\"sb-img-block\">\r\n <img decoding=\"async\" src=\"https:\/\/scraping-bot.io\/blogs\/wp-content\/uploads\/2026\/04\/bien_ici_appartement.webp\" alt=\"Bien'ici scraper python \u2014 listing example Paris 15th\" class=\"sb-screenshot\" loading=\"lazy\" \/>\r\n <p class=\"sb-img-caption\">A Bien'ici listing: interactive 3D map and property detail (Paris 15th, \u20ac542,900, 70 m\u00b2)<\/p>\r\n <\/div>\r\n\r\n <h2 id=\"challenges\">2. Technical challenges<\/h2>\r\n <p>Before diving into the code, it's important to understand why scraping Bien'ici isn't as simple as a basic HTTP request:<\/p>\r\n <ul>\r\n <li><strong>Anti-bot protection<\/strong> \u2014 Bien'ici detects headless browsers and blocks requests with suspicious headers.<\/li>\r\n <li><strong>JavaScript rendering<\/strong> \u2014 Listing data is loaded dynamically, so basic HTTP requests won't capture it.<\/li>\r\n <li><strong>IP rate limiting<\/strong> \u2014 Too many requests from one IP triggers temporary bans.<\/li>\r\n <li><strong>Pagination<\/strong> \u2014 Search results span many pages that need systematic traversal.<\/li>\r\n <li><strong>Listing identifiers<\/strong> \u2014 Bien'ici URLs use internal IDs (e.g. <code>ag756375-491979461<\/code>) that require specific handling.<\/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 abstracts all of this complexity: it rotates IPs automatically, renders JavaScript, and returns clean structured JSON \u2014 no browser automation needed on your end. The result of your Bien'ici scraper Python calls is a ready-to-use <code>data<\/code> object containing title, price, surface area, number of rooms, energy class, and much more.<\/p>\r\n\r\n <h2 id=\"step-by-step\">4. Step-by-step: build your Bien'ici scraper 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 request \u2014 scraping a single listing<\/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\n# The Bien'ici listing you want to scrape\r\nTARGET_URL = \"https:\/\/www.bienici.com\/annonce\/achat\/paris-15e-arrondissement_75015\/appartement\/ag756375-491979461\"\r\n\r\ndef scrape_bienici(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}\")\r\n\r\ndata = scrape_bienici(TARGET_URL)\r\nprint(data)<\/code><\/pre>\r\n\r\n <h3>Scraping multiple listings (with pagination)<\/h3>\r\n <p>Bien'ici structures its search URLs with a page parameter. Here's how to iterate through multiple pages and aggregate results:<\/p>\r\n <pre><code>import requests, time\r\n\r\n# Search: apartments for sale in Paris, page by page\r\nBASE_SEARCH = \"https:\/\/www.bienici.com\/recherche\/achat\/paris-75\/appartement?page={page}\"\r\n\r\ndef scrape_pages(n_pages=5):\r\n results = []\r\n for page in range(1, n_pages + 1):\r\n url = BASE_SEARCH.format(page=page)\r\n data = scrape_bienici(url)\r\n listings = data.get(\"listings\", [])\r\n results.extend(listings)\r\n print(f\"Page {page} \u2014 {len(listings)} listings retrieved\")\r\n time.sleep(1) # polite delay between requests\r\n return results\r\n\r\nlistings = scrape_pages(n_pages=3)\r\nprint(f\"Total collected: {len(listings)} listings\")<\/code><\/pre>\r\n\r\n <h3>Extracting and cleaning the JSON data<\/h3>\r\n <p>Once the response is retrieved, here's how to access the key fields and store them cleanly:<\/p>\r\n <pre><code>def parse_listing(raw):\r\n d = raw.get(\"data\", {})\r\n return {\r\n \"title\": d.get(\"title\"),\r\n \"price\": d.get(\"price\"),\r\n \"currency\": d.get(\"currency\"),\r\n \"surface_m2\": d.get(\"surfaceArea\"),\r\n \"rooms\": d.get(\"numberOfRooms\"),\r\n \"bedrooms\": d.get(\"numberOfBedrooms\"),\r\n \"energy_class\": d.get(\"energyClass\"),\r\n \"ghg_class\": d.get(\"greenhouseGazClass\"),\r\n \"monthly_rent\": d.get(\"monthlyRent\"),\r\n \"insee_code\": d.get(\"codeInsee\"),\r\n \"listing_url\": d.get(\"siteURL\"),\r\n }\r\n\r\n# Example usage\r\ndata = scrape_bienici(TARGET_URL)\r\nlisting = parse_listing(data)\r\nprint(listing)<\/code><\/pre>\r\n\r\n <h2 id=\"output\">5. Sample output data<\/h2>\r\n <p>Here's what a typical response looks like for a Bien'ici listing \u2014 in this case a 4-room, 70 m\u00b2 apartment in the 15th arrondissement of Paris:<\/p>\r\n\r\n <div class=\"sb-img-block\">\r\n <img decoding=\"async\" src=\"https:\/\/scraping-bot.io\/blogs\/wp-content\/uploads\/2026\/04\/bien_ici_retour_JSON.webp\" alt=\"Bien'ici scraper python \u2014 JSON response from ScrapingBot\" class=\"sb-screenshot\" loading=\"lazy\" \/>\r\n <p class=\"sb-img-caption\">JSON response returned by ScrapingBot: structured data ready to use (status 200, no captcha detected)<\/p>\r\n <\/div>\r\n\r\n <table class=\"sb-table\">\r\n <thead>\r\n <tr>\r\n <th>Field<\/th>\r\n <th>Example value<\/th>\r\n <th>Type<\/th>\r\n <\/tr>\r\n <\/thead>\r\n <tbody>\r\n <tr><td>title<\/td><td>4-Room Apartment \u2014 Tenanted Sale<\/td><td>string<\/td><\/tr>\r\n <tr><td>price<\/td><td>542900<\/td><td>integer<\/td><\/tr>\r\n <tr><td>currency<\/td><td>EUR<\/td><td>string<\/td><\/tr>\r\n <tr><td>surfaceArea<\/td><td>70<\/td><td>integer<\/td><\/tr>\r\n <tr><td>surfaceAreaUnit<\/td><td>sqm<\/td><td>string<\/td><\/tr>\r\n <tr><td>numberOfRooms<\/td><td>4<\/td><td>integer<\/td><\/tr>\r\n <tr><td>numberOfBedrooms<\/td><td>2<\/td><td>integer<\/td><\/tr>\r\n <tr><td>energyClass<\/td><td>D:208<\/td><td>string<\/td><\/tr>\r\n <tr><td>greenhouseGazClass<\/td><td>D:28<\/td><td>string<\/td><\/tr>\r\n <tr><td>monthlyRent<\/td><td>null<\/td><td>null<\/td><\/tr>\r\n <tr><td>codeInsee<\/td><td>75115<\/td><td>string<\/td><\/tr>\r\n <tr><td>host<\/td><td>www.bienici.com<\/td><td>string<\/td><\/tr>\r\n <tr><td>statusCode<\/td><td>200<\/td><td>integer<\/td><\/tr>\r\n <tr><td>captchaFound<\/td><td>false<\/td><td>boolean<\/td><\/tr>\r\n <tr><td>siteURL<\/td><td>https:\/\/www.bienici.com\/realEstateAds-one.json?\u2026<\/td><td>string<\/td><\/tr>\r\n <\/tbody>\r\n <\/table>\r\n\r\n <div class=\"sb-note\">\r\n <strong>\ud83d\udca1 Good to know:<\/strong> The <code>energyClass<\/code> field returns the raw DPE value (e.g. <code>D:208<\/code>), letting you extract both the letter rating and the kWh\/m\u00b2\/year figure for comparative analysis.\r\n <\/div>\r\n\r\n <h2 id=\"further\">6. Going further<\/h2>\r\n <p>Once you have the raw data from your Bien'ici scraper Python script, you can pipe it into a CSV with <a href=\"https:\/\/pandas.pydata.org\/docs\/\" target=\"_blank\" rel=\"noopener\">pandas<\/a>, store it in a PostgreSQL database, or feed it into a price-trend dashboard by arrondissement. You can also cross-reference it with sources such as DVF (Demandes de Valeurs Fonci\u00e8res) data for in-depth market analysis.<\/p>\r\n <p>ScrapingBot also supports SeLoger, Leboncoin Immobilier, PAP, and other French portals via the same API interface \u2014 ideal for building a multi-source aggregator.<\/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-img-block { margin: 1.5rem 0 2rem; }\r\n.sb-screenshot { width: 100%; border-radius: 8px; border: 1px solid #ddd; box-shadow: 0 2px 12px rgba(0,0,0,0.08); display: block; }\r\n.sb-img-caption { font-size: 13px; color: #888; margin-top: 0.5rem; text-align: center; font-style: italic; }\r\n.sb-note { background: #fffbea; border: 1px solid #f0e28a; border-radius: 8px; padding: 1rem 1.25rem; margin: 1rem 0 1.5rem; font-size: 14px; color: #5a4a00; }\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\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>  Web scraping 9 min read \u00a0\u00b7\u00a0 Published: 22\/04\/2026Looking for a Bien’ici scraper Python guide? The French real estate market moves fast. Whether you’re tracking price trends, monitoring competitor listings, or building a data pipeline, this guide shows you how to extract structured property data at scale from Bien’ici using ScrapingBot’s API.Table of contents Why […]<\/p>\n","protected":false},"author":3,"featured_media":5794,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-5753","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>Bien'ici Scraper Python: Scrape Real Estate Listings - Scraping-bot<\/title>\n<meta name=\"description\" content=\"Build a Bien'ici scraper in Python with ScrapingBot's API. 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