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Financial analysis · adoption-ready estimate
Real Estate License Trigger Feed | Reach Agents at the Moment It Matters
If an entrepreneur "adopted" this product today, here's the realistic math.
Fermi summary
Land 30 companies at $500/mo and you're at $180k ARR - but there's only a 14% chance you get there in year 1, and you'll burn $47k to find out.
Market size (TAM)
$60.0M
~4,000 US companies (regional mortgage lenders, large brokerage recruiters, real estate tech SaaS vendors, E&O insurers) that actively sell to agents × ~$15,000 avg annual spend on trigger/prospecting data feeds
Year-1 ARR range
$36k - $540k
midpoint $180k
Investment to production
$47k
Data pipeline: $23k to reliably scrape, parse, and normalize all 50 state licensing portals with change-detection and deduplication. API/del
Probability of success
14%
P(reaching mid case in 12 months)
Expected take-home Y1
$-28100
probability-weighted, after investment
Go-to-market motion
Cold email + LinkedIn to VP Sales or Head of Recruiting at regional mortgage companies and large independent brokerages, offering a 2-week live trial of the license trigger feed, targeting 3-5 closes/month at $400-600 MRR.
Key risks
- State licensing portals vary wildly in structure, CAPTCHA usage, and scraping legality - achieving reliable, fresh coverage across all 50 states may take 6-9 months of engineering, leaving embarrassing data gaps during early sales cycles
- Established data aggregators (CoreLogic, ATTOM, Reonomy) or real estate CRMs (Follow Up Boss, kvCORE, LionDesk) could bundle license-trigger alerting as a feature, turning your standalone product into a line item they cut
- License trigger frequency per market is inherently limited - new licensee waves are seasonal and finite, so buyers may churn after 3-6 months once they've saturated their initial target pool of fresh agents
Generated by the Wishdeal Factory financial-analysis agent. Numbers are honest Fermi estimates, not guarantees. Real outcomes depend on the operator. The studio is bullish on the engineering quality, agnostic on the business outcome.