Automatisierter Immobilien-Extraktor

• •Google Sheets API-Anmeldedaten

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  "name": "Automated Real Estate Listing Extractor",
  "tags": [],
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        "content": "## 🔹 **SECTION 1: 🔁 Schedule Trigger — Automate Workflow**\n\n### 🧩 1. 📅 Schedule Trigger\n\n**Node Name:** `Schedule Trigger`  \n**What it does:**  \nAutomatically triggers the workflow every 6 hours, no manual intervention needed. Keeps your data fresh and updated regularly.\n\n🧠 **Beginner Benefit:**  \n\n> Set it once and forget it — your workflow runs automatically on schedule without any extra effort.\n\n---\n\n## 🔹 **SECTION 2: 🌐 Scrapeless Crawler — Fetch Webpage Data**\n\n### 🧩 2. 🕷️ Scrapeless Crawler\n\n**Node Name:** `Scrapeless Crawler`  \n**What it does:**  \nSends a request to Scrapeless API to crawl the target real estate webpage. Returns the page content in Markdown format for easy parsing later.\n\n**Example URL:**  \nhttps://www.loopnet.com/search/commercial-real-estate/los-angeles-ca/for-lease/\n\n🧠 **Beginner Benefit:**  \n\n> Leverage powerful scraping as a service — no need to write complicated crawler code yourself.\n\n---\n"
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        "content": "\n\n## 🔹 **SECTION 4: 🕵️ Parse Listings — Extract Property Data**\n\n### 🧩 3. 🔍 Parse Listings (Code Node)\n\n\n**Node Name:** `Parse Listings`\n**What it does:**\nHandles the entire extraction and cleaning process in a single code node to simplify the workflow and improve performance.\n\n\n### ✅ **Step 1: Extract Markdown Text**\n\n* Extracts the core Markdown-formatted text from the complex HTML response returned by Scrapeless.\n* Automatically removes unwanted HTML tags, scripts, and ads, keeping only the meaningful page content.\n\n---\n\n### ✅ **Step 2: Parse Key Information**\n\n* Uses regex and string manipulation to extract critical fields from the Markdown text, including:\n\n  * 🏢 **Property Title**\n  * 🔗 **Link**\n  * 📐 **Size**\n  * 🏗️ **Year Built**\n\n* Outputs clean, structured **JSON objects** that are easy to pass to downstream nodes.\n\n---\n\n### ✅ **Step 3: Clean & Format Data**\n\n* Filters out unnecessary fields, keeping only the relevant ones:\n\n  * `title`\n  * `link`\n  * `size`\n  * `yearBuilt`\n\n* Formats the output to be clean and ready for export to Google Sheets, Notion, Slack, databases, or other platforms.\n\n---\n\n### 🧠 **Beginner Benefit:**\n\n> Extracts text, parses listings, and cleans data in one step, saving time and reducing node complexity. Produces structured, ready-to-use data for your business needs.\n\n"
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        "url": "https://www.loopnet.com/search/commercial-real-estate/los-angeles-ca/for-lease/",
        "resource": "crawler",
        "operation": "crawl",
        "limitCrawlPages": 2
      },
      "credentials": {
        "scrapelessApi": {
          "id": "B73pdQXNjpqNbIhs",
          "name": "Scrapeless account"
        }
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      "typeVersion": 1
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      "name": "Zeile in Tabelle anhängen oder aktualisieren",
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          "mode": "list",
          "value": "gid=0",
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        "content": "\n\n## 🔹 **SECTION 6: 📊 Append to Google Sheets — Save Data**\n\n### 🧩 6. 📈 Append to Google Sheets\n\n**Node Name:** `Google Sheets Append`  \n**What it does:**  \nAppends the parsed and cleaned property data into a Google Sheets spreadsheet for easy review and analysis.\n\n🧠 **Beginner Benefit:**  \n\n> Automatically keeps your spreadsheet up-to-date with fresh listings — no copy/paste required.\n\n"
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      "name": "Parse Listings",
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        "jsCode": "const markdownData = [];\n$input.all().forEach((item) => {\n\titem.json.forEach((c) => {\n\t\tmarkdownData.push(c.markdown);\n\t});\n});\n\nconst results = [];\n\nfunction dataExtact(md) {\n\tconst re = /\\[More details for ([^\\]]+)\\]\\((https:\\/\\/www\\.loopnet\\.com\\/Listing\\/[^\\)]+)\\)/g;\n\n\tlet match;\n\n\twhile ((match = re.exec(md))) {\n\t\tconst title = match[1].trim();\n\t\tconst link = match[2].trim()?.split(' ')[0];\n\n\t\t// Extract a snippet of context around the match\n\t\tconst context = md.slice(match.index, match.index + 500);\n\n\t\t// Extract size range, e.g. \"10,000 - 20,000 SF\"\n\t\tconst sizeMatch = context.match(/([\\d,]+)\\s*-\\s*([\\d,]+)\\s*SF/);\n\t\tconst sizeRange = sizeMatch ? `${sizeMatch[1]} - ${sizeMatch[2]} SF` : null;\n\n\t\t// Extract year built, e.g. \"Built in 1988\"\n\t\tconst yearMatch = context.match(/Built in\\s*(\\d{4})/i);\n\t\tconst yearBuilt = yearMatch ? yearMatch[1] : null;\n\n\t\t// Extract image URL\n\t\tconst imageMatch = context.match(/!\\[[^\\]]*\\]\\((https:\\/\\/images1\\.loopnet\\.com[^\\)]+)\\)/);\n\t\tconst image = imageMatch ? imageMatch[1] : null;\n\n\t\tresults.push({\n\t\t\tjson: {\n\t\t\t\ttitle,\n\t\t\t\tlink,\n\t\t\t\tsize: sizeRange,\n\t\t\t\tyearBuilt,\n\t\t\t\timage,\n\t\t\t},\n\t\t});\n\t}\n\n\t// Return original markdown if no matches found (for debugging)\n\tif (results.length === 0) {\n\t\treturn [\n\t\t\t{\n\t\t\t\tjson: {\n\t\t\t\t\terror: 'No listings matched',\n\t\t\t\t\traw: md,\n\t\t\t\t},\n\t\t\t},\n\t\t];\n\t}\n}\n\nmarkdownData.forEach((item) => {\n\tdataExtact(item);\n});\n\nreturn results;\n"
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