All notable changes to Realtor.com Agent Scraper — Agents, Listings & Reviews will be documented in this file.
- Full Agent Profiles — License number, supervising broker, office (name + address + phones), typed contacts, ratings, served areas, specializations, and languages on every agent — the depth thin "$1/1K lead" tools strip out
- City-Scale Discovery — Search any US market by
"City, ST" and paginate the entire agent directory with stable, deduped results
- Recruiting Signal Built In —
sold-last-year + active for-sale-count + ratings per agent so you can rank and target top producers
- Two Modes —
search (city directory) and lookup (direct by agent fulfillment id)
- Deep Enrichment Toggles —
includeForSale, includeSoldProperties, includeForRent, includeReviews attach each agent's full portfolio and review corpus with independent per-agent caps
- Full Sold History — Hundreds to thousands of past transactions per agent with last-sold price + date
- Reviews + Recommendations Corpus — Ratings, comments, reviewer type, sub-ratings (responsiveness, market expertise, negotiation, professionalism), and agent replies
- HTML Cohort Report — Top-broker breakdown, productivity medians, license/phone coverage, and per-query totals saved to the key-value store
- Resilient by Default — Automatic key failover so transient upstream blips never fail a run
- Brokerage recruiters get license + broker + productivity data on every agent in a market and can run outreach in minutes
- Lead-gen SaaS founders build agent contact-and-enrichment products without maintaining scrapers or proxies
- Data engineers join Realtor.com agents to internal CRM records via a stable fulfillment id + license number
- Analytics teams track top-producer concentration and broker market share over time
- Reputation teams pull the full reviews corpus with negotiation and responsiveness sub-scores
- Brokerage recruiters poaching top producers in a target metro using sold-last-year rankings
- Lead-gen SaaS founders productising agent contact + license enrichment for real-estate buyers
- Real-estate analytics teams monitoring agent productivity and broker share month over month
- CRM/data engineers enriching internal agent records with served areas, reviews, and recommendations
- Reputation-intelligence teams benchmarking agent reviews and sub-ratings across a market