finding-real-estate-professionals-on-twitter

Finds real estate agents, brokers, property investors, and real estate professionals on Twitter/X using apidojo's Twitter User Scraper on Apify. Triggers when the user asks to: find real estate agents on Twitter, discover property professionals on X for outreach, build a list of realtors active on Twitter, find real estate investors or brokers on X, prospect real estate professionals via their Twitter bios, identify mortgage brokers or property managers on Twitter, or compile a real estate professional contact list from Twitter. Returns username, bio, follower count, verification status, location, and website per user. Ideal for PropTech SaaS vendors, mortgage product teams, and B2B service providers targeting real estate professionals.

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Inputs

Parameter	Type	Required	Default	Notes
startUrls	array	Optional	[]	Twitter profile or tweet URLs
twitterHandles	array	Optional	[]	Twitter usernames (without @)
twitterUserIds	array	Optional	[]	Twitter user IDs
getFollowers	boolean	Optional	false	Extract follower lists
getFollowing	boolean	Optional	false	Extract following lists
getRetweeters	boolean	Optional	false	Extract retweeters of a tweet URL
includeUnavailableUsers	boolean	Optional	false	Include unavailable/suspended users
maxItems	number	Optional	Unlimited	Maximum users to return
customMapFunction	string	Optional	—	JavaScript function to transform each output object

How to Run

Using run_actor.js (recommended)

bash

# Quick answer (table)
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}'

# Save as CSV
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}' --output results.csv --format csv

# Save as JSON
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}' --output results.json --format json

REST API fallback

bash

curl -X POST "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}'

If Apify MCP is available: Use the Apify MCP call_actor tool with actor apidojo~twitter-user-scraper and the input above.

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Scoring & Ranking

Score each user by:

• followers → normalized 0-1 (cap at 50K), weight 0.30
• bio_match_score (contains: realtor, broker, real estate, property, agent, MLS) → 0 or 1, weight 0.40
• has_website → 0 or 1, weight 0.30

python

score = 0.30 * min(followers / 50000, 1.0) + 0.40 * int(bio_match) + 0.30 * int(has_website)

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Classification

Score	Tier	Label
≥ 0.70	A	PRIME_OUTREACH
0.40–0.69	B	HOT_CANDIDATE
< 0.40	C	LOW_PRIORITY

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Edge Cases

• Generic bio keywords: "house" or "home" match too broadly. Use "realtor", "real estate agent", "MLS".
• Personal accounts mixed in: Filter by followers > 200 and has website link.
• Bot accounts: Unusually high following-to-follower ratio — filter out.
• Keyword not in bio: Twitter user search matches bio text — results may vary if bio is non-standard.
• International agents: Use country-specific terms (e.g., "estate agent" for UK).