Analyze property management fragmentation across SFR portfolios to identify consolidation opportunities
Analyze property management fragmentation across SFR portfolios to identify consolidation opportunities
npx skills add https://github.com/ParclLabs/parcl-ai-connect --skill pm-fragmentation이 명령을 Claude Code에 복사하여 붙여넣어 스킬을 설치하세요
Build a ranked rental property manager list with scale, geography, and contact info — separating third-party PMs, brokerages, institutional landlords, and listing platforms. Use for PM targeting or "who manages rentals in [market]".
Build an accidental landlord lead gen pipeline that identifies failed sellers who pivoted to renting as PM prospects
Rank brokers and agents by motivated seller distress signals, with configurable price segment and market
Identify SFR portfolios showing disposition signals as bulk acquisition targets in secondary markets
Build a comprehensive portfolio teardown of any institutional SFR investor using Parcl Labs Portfolio Hunter
Direct mail target list with route-optimized delivery zones for HVAC outreach
| name | pm-fragmentation |
| description | Analyze property management fragmentation across SFR portfolios to identify consolidation opportunities |
| user-invocable | true |
| argument-hint | [market name or 'top-10'] |
| allowed-tools | Read, Grep, Glob, Bash, Write, Edit, WebFetch |
Identifies fragmented property management relationships across SFR portfolios, surfacing portfolio owners who may benefit from consolidated PM services.
Target: $ARGUMENTS. Pass a single market name for single-market analysis, or "top-10" to anchor on the top 10 markets by institutional SFR presence.
Multi-market mode ("top-10"):
search_investors to find the largest institutional SFR operator's investor_id.investor_msa_activity and rank markets by property count. Take the top 10.Single-market mode:
search_locations to resolve the market name to a parcl_id.Run portfolio_search across each target MSA filtered to a minimum of 50 units. This captures institutional and mid-size operators large enough to need professional management but small enough that they may not have it figured out.
Exclude portfolios with more than 500 units nationally. These are institutional operators who self-manage or have locked-in PM relationships.
Use investor_rental_properties for every portfolio identified. Key fields:
enhanced_manager_agent_business: the PM company managing the listingenhanced_manager_property_manager: the specific property managerRaw data contains dozens of variants for the same PM company. Normalize aggressively:
Common normalizations:
For each portfolio in each market, calculate the share of known-PM-managed units per property manager. Classify:
| Classification | Criteria |
|---|---|
| Single PM | One PM manages everything |
| Concentrated | Top PM holds 85%+ share |
| Moderately Concentrated | Top PM holds 60-84% |
| Fragmented | Top PM holds 40-59% |
| Highly Fragmented | Top PM holds less than 40% |
| Self-Managed/Unknown | No identifiable PM |
Opportunity scoring:
Produce a single interactive HTML file (dark theme, all text bright white) with:
Multi-market mode:
Single-market mode:
No Unknown % column. It adds noise without changing the signal.
See examples/tampa-single-market.md for a complete single-market specification applied to Tampa.
Never display raw parcl_id values in user-facing output. Always resolve to human-readable names:
5452730 to "East Hampton (11937)")2900417 to "Tampa-St. Petersburg-Clearwater, FL")search_investorsNote: some MCP endpoints return IDs as floats with .0 suffix (e.g., 5452730.0). Strip the .0 before any lookup or display.
| Endpoint | Purpose |
|---|---|
search_investors | Find anchor investor for market selection |
investor_msa_activity | Market-level property counts for ranking |
search_locations | Resolve market names to parcl_ids |
portfolio_search | Find portfolios by market and size criteria |
investor_rental_properties | Property-level rental data with PM company details |