Build a comprehensive portfolio teardown of any institutional SFR investor using Parcl Labs Portfolio Hunter
Build a comprehensive portfolio teardown of any institutional SFR investor using Parcl Labs Portfolio Hunter
npx skills add https://github.com/ParclLabs/parcl-ai-connect --skill competitor-analysis이 명령을 Claude Code에 복사하여 붙여넣어 스킬을 설치하세요
| name | competitor-analysis |
| description | Build a comprehensive portfolio teardown of any institutional SFR investor using Parcl Labs Portfolio Hunter |
| user-invocable | true |
| argument-hint | [investor name] |
| allowed-tools | Read, Grep, Glob, Bash, Write, Edit, WebFetch |
Generates a comprehensive Excel workbook analyzing any institutional SFR investor across 8 analytical dimensions using Parcl Labs Portfolio Hunter data.
Target investor: $ARGUMENTS (e.g., any institutional SFR operator name)
Use search_investors to query the investor name and retrieve their investor_id.
Use investor_msa_activity to get market-level portfolio breakdown. Rank markets by property count. Take the top 10 for detailed analysis.
rental_price_index for top 10 markets by property countinvestor_properties for full property-level datainvestment_activity for full transaction historyinvestor_rental_properties (filter rental_status = "Active Rental") and investor_for_sale_propertiesinvestor_properties on parcl_property_id to get sq_ftNever 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 | Resolve investor name → investor_id |
investor_msa_activity | Market-level portfolio breakdown |
investor_properties | Full property-level data (sqft, beds, baths, year_built, acquisition_price) |
investor_rental_properties | Active and off-market rental listings with pricing and DOM |
investor_for_sale_properties | Current for-sale listings with pricing and unrealized P&L |
investment_activity | Purchase and sale transaction history |
rental_price_index | Market-level rental price index for benchmarking |
After downloading all CSVs, use the bundled script to build the formatted workbook:
python3 ${CLAUDE_SKILL_DIR}/scripts/build_workbook.py \
--msa-activity <msa_activity.csv> \
--properties <properties.csv> \
--rentals <rentals.csv> \
--for-sale <for_sale.csv> \
--transactions <transactions.csv> \
--rpi <rpi.csv> \
--output <output.xlsx> \
--investor "Investor Name"
The script handles MSA ID normalization (some endpoints return float-formatted IDs like 2900174.0), property sqft joins for $/sqft rent comparisons, and all conditional formatting.
If the script is not available, follow the 8-tab structure above using openpyxl directly.
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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
Direct mail target list with route-optimized delivery zones for HVAC outreach
Generate an institutional-grade market study PDF with price performance, supply-demand, and investor metrics