Real Estate And Hospitality

When: User wants to find and compare property listings in a specific area.

Pipeline

1. Search properties -> tri_angle/redfin-search
   • Key input: location, propertyType, minPrice, maxPrice
2. Get details -> tri_angle/redfin-detail
   • Pipe: results[].url -> startUrls
   • Key input: startUrls

Output fields

Step 1: address, price, beds, baths, sqft, url, status Step 2: description, yearBuilt, lotSize, priceHistory[], taxHistory[], schools[]

Airbnb market analysis

When: User wants to analyze Airbnb listings, pricing, and reviews in a destination.

Pipeline

1. Search listings -> tri_angle/new-fast-airbnb-scraper
   • Key input: location, checkIn, checkOut, maxItems
2. Get reviews -> tri_angle/airbnb-reviews-scraper
   • Pipe: results[].url -> startUrls
   • Key input: startUrls, maxReviews

Output fields

Step 1: name, price, rating, reviews, type, amenities[], url, images[] Step 2: text, rating, date, reviewerName

Gotcha

Airbnb pricing varies by date. Always set checkIn and checkOut for accurate pricing. For market analysis, run multiple date ranges to capture seasonal variation.

Real estate lead scoring and agent routing

When: User wants to qualify inbound leads from listing portals by budget signals and urgency, then route them to the right agent.

Pipeline

1. Search matching properties -> tri_angle/redfin-search
   • Key input: location, minPrice, maxPrice (from lead payload)
2. Enrich lead with LinkedIn signals -> harvestapi/linkedin-profile-scraper
   • Key input: profileUrls (optional - use only when lead email resolves to a LinkedIn profile)

Output fields

Step 1: address, price, beds, baths, status, url Step 2: headline, currentCompany, experience[] (income/seniority signals)

Gotcha

The LinkedIn enrichment step is optional - only run it when the lead’s identity is known and a LinkedIn profile URL is available. The core routing logic (hot/warm/cold tier + agent assignment) runs on the MLS webhook payload itself, with scraping used as enrichment. Lead scoring and routing output fields are AI-generated: leadTier, assignedAgent, routingReason.

Construction and pre-market property discovery

When: User wants to find new-construction projects or pre-market inventory before they appear on major listing portals.

Pipeline

1. Scrape construction portals -> apify/playwright-scraper
   • Key input: startUrls (local MLS or construction project portal URLs), proxyConfiguration
2. Extract clean text -> lukaskrivka/article-extractor-smart
   • Pipe: results[].url -> urls

Output fields

Step 1: raw HTML / structured page data Step 2: projectName, price, location, possessionDate, constructionStatus

Gotcha

No market-specific Actor exists for most construction portals (e.g., 99acres). Run apify actors search "real estate" --user-agent apify-agent-skills/apify-ultimate-scraper to check for community-built options before using apify/playwright-scraper. For JS-heavy portals, playwright-scraper is required. Step 2 cleans raw output into structured fields - pipe all Step 1 URLs through it.

Multi-source property comparison

When: User wants to compare listings across Zillow, Realtor, Zumper, and other US/UK sources.

Pipeline

1. Aggregate listings -> tri_angle/real-estate-aggregator
   • Key input: location, propertyType, sources (Zillow, Realtor, Zumper, Apartments.com, Rightmove)

Output fields

Step 1: address, price, beds, baths, sqft, source, url, listingDate