Real Estate Listing Scraper
Every week, it scrapes LoopNet for new commercial listings and saves the structured data to your Google Sheet.
What it does
You receive new commercial real estate listings from LoopNet every week, automatically organized in your Google Sheet so you can analyze titles, links, and sizes without manual entry.
Step by step
- Configure an n8n cron trigger to execute the workflow once per week.
- Set up a Scrapeless HTTP request node targeting LoopNet's commercial listings URL with appropriate headers.
- Implement a Google Sheets append rows action using the extracted data fields.
Where the LLM does the work
- Generate robust regular expressions to parse specific HTML structures or Markdown fragments from Scrapeless output into clean JSON fields.
- Draft error-handling logic for edge cases where listing metadata is missing or formatted inconsistently across different property types.
- Refine the parsing code to handle dynamic content loading patterns that static regex might miss.
Watch out for
LoopNet frequently updates its DOM structure so you must add manual monitoring alerts when parsing fails unexpectedly.
Scrapeless proxies can sometimes return cached or incomplete pages, so implement a retry mechanism with distinct user agents.
Google Sheets has row limits per API call, so batch your inserts if the weekly scrape returns more than 50 listings at once.
Regex parsers are brittle against minor HTML changes, so wrap the parsing step in a try-catch block to prevent workflow crashes.
Tools that fit
Code API Parsing Markdown with regex
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Real Estate Listing Scraper: Every week, it scrapes LoopNet for new commercial listings and saves the structured data to your Google Sheet. ## Specification - What it does: You receive new commercial real estate listings from LoopNet every week, automatically organized in your Google Sheet so you can analyze titles, links, and sizes without manual entry. - Trigger: On a schedule (Scheduled · weekly) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - Scrapeless: Web scraping LoopNet listings - n8n: Workflow orchestration - Google Sheets: Storing structured property data - Code: Parsing Markdown with regex ## Known pitfalls, handle each one explicitly in your implementation 1. LoopNet frequently updates its DOM structure so you must add manual monitoring alerts when parsing fails unexpectedly. 2. Scrapeless proxies can sometimes return cached or incomplete pages, so implement a retry mechanism with distinct user agents. 3. Google Sheets has row limits per API call, so batch your inserts if the weekly scrape returns more than 50 listings at once. 4. Regex parsers are brittle against minor HTML changes, so wrap the parsing step in a try-catch block to prevent workflow crashes. ## Reference implementation https://n8n.io/workflows/5878 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automated-real-estate-listing-scraper/ via usecasesforagents.com
Frequently asked questions
Can I use a different Web Scraping than Scrapeless?
Yes. Scrapeless is only the example web scraping in this recipe. The same flow works with Airtop, Apify, Bright Data and Decodo. Swap the web scraping connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Also fits Real Estate.
Analysts get a ready-made pattern for turning raw numbers into a report someone will actually read.