Scraping Large-scale Real Estate Listings Data
Being a leading real estate services and investment firm, the client wanted to build and maintain a database of the real estate listings which would be used to power their business intelligence capability and improve service portfolio with better data-backed research.
Extracting real estate data from some of the most popular real estate listing portals from the US. The data feed needed a daily push of the new listings in an easy-to-consume manner for the database ingestion. It was also entailed that the crawling infrastructure must support high volume data extraction going up to millions of listings on a weekly basis without compromising on the quality.
The required data points were:
- Street Name
- Zip Code
- Facts and Features
- Real Estate Provider
Solution: Site-specific crawling and extraction
The client delivered a list of source websites to be crawled. The data had to be extracted on a daily basis to collect fresh information every day. Our team set up crawlers to fetch the data points from the provided source sites. Since every website in the source list had a different structure and design, site specific crawl and extraction was the offering that suited this case.
Once we were done with the initial setup, the crawlers started delivering data. According to the client's preference, the data was to be uploaded to the client’s Azure servers in XML format. After stabilizing the crawlers, PromptCloud was able to deliver millions of real estate listings on a weekly basis.
Benefits to the client:
- The client didn’t have to worry about any of the technical aspects in the process
- The setup was completed in just 3 days and the data flow was consistent since then
- Our team set up monitoring for the sources to make sure no data was missed
- The complicated cases of dynamic websites were effortlessly handled by our tech stack
- The client was able to start their market research using the delivered data within a short time span
- The cost of data was significantly lower than what the client would incur with an in-house web scraping setup