Land Acquisition Intel

Land Acquisition Intelligence Skill

Fetches real parcel data + infrastructure, scores every parcel against your use case criteria, outputs ranked CSVs of top sites. Zero paid APIs. Runs fully local.

Critical: Always Follow This Order

1. Understand intent — what land use, what region, any custom requirements
2. Resolve region — get center lat/lon + bbox
3. Run fetch script — parcels + infrastructure
4. Run score script — vectorized scoring with use-case weights
5. Report top candidates — table + Google Maps links + file paths

Never skip steps. Never invent scores without running the scripts.

Step 1: Understand Intent

Ask (or infer from context) before running:

Required:

• What is the land for? (datacenter / warehouse / solar / industrial / agricultural / custom)
• What region? (city, county, state, or coordinates)

Optional (infer defaults if not given):

• Minimum acreage? (default: none)
• Maximum distance from highway? (default: none)
• Any specific infrastructure required? (power/rail/fiber/water)
• Budget tier? (high-value = prefer smaller parcels near infra; land-banking = larger, more remote OK)

If use case is custom or unclear: ask "What's the primary value driver for this site? Power access, logistics, labor, cost, or something else?"

Step 2: Resolve Region

Extract city/county/state from user input. Map to coordinates:

Built-in coords (Western US defaults):

• Carson City NV: 39.1638, -119.7674
• Reno NV: 39.5296, -119.8138
• Las Vegas NV: 36.1699, -115.1398
• Sparks NV: 39.5349, -119.7527
• Fernley NV: 39.6077, -119.2125
• Phoenix AZ: 33.4484, -112.0740
• Salt Lake City UT: 40.7608, -111.8910
• Boise ID: 43.6150, -116.2023
• Denver CO: 39.7392, -104.9903
• Portland OR: 45.5051, -122.6750

For any other city: use Nominatim geocoder (free, no key):

https://nominatim.openstreetmap.org/search?q=CITY+STATE&format=json&limit=1

Default bbox: ~60-mile radius from center point.

Step 3: Fetch Data

Run: python3 scripts/fetch_data.py --lat LAT --lon LON --state STATE --output /tmp/land-intel/raw/

Fetches (all free, no API keys):

Expected raw files:

/tmp/land-intel/raw/
├── parcels_raw.csv         # APN, acres, lat/lon, county
├── osm_buildings.csv       # OSM land use proxy
├── highways.csv
├── interchanges.csv
├── rail.csv
├── substations.csv
├── transmission_lines.csv
├── water_infra.csv

If a fetch fails: log it, skip that layer, continue. Missing infra = 999km default = lower score for that factor only.

Step 4: Score Parcels

Run: python3 scripts/score_parcels.py --raw /tmp/land-intel/raw/ --use-case USE_CASE --output /tmp/land-intel/output/

USE_CASE options: datacenter, warehouse, solar, agricultural, industrial, custom

Scoring Factors (all vectorized, no loops)

Universal factors (all use cases):

• Acreage fit: 0-20pts — calibrated to use-case ideal range
• Highway access: 0-20pts — distance to nearest major road
• Zoning compatibility: 0-15pts — industrial > commercial > agricultural > unknown > residential
• Water access: 0-5pts — nearest water body or tower

Use-case specific weights:

Custom use case: prompt user for 3-5 scoring priorities, assign 100pts across them.

Scoring outputs:

• [use_case]_candidates.csv — top 5,000 sites, all score components
• all_candidates_ranked.csv — full dataset with composite score

Motivated seller signals (added to every parcel where data available):

• Out-of-state owner: +15pts signal
• Estate/trust in owner name: +20pts signal
• Tax delinquent: +30pts signal
• Long hold (pre-2010 acquisition): +15pts signal

Step 5: Report Results

After scripts complete, present findings in this format:

## Land Scouting Results: [USE CASE] in [REGION]

### Data Summary
- Parcels scored: X across N counties
- Infrastructure layers: highways ✓, rail ✓, power ✓, water ✓

### Top 10 Sites

| # | Score | Location | County | Acres | Key Factor | APN | Map |
|---|-------|----------|--------|-------|------------|-----|-----|
| 1 | 94 | 39.1638,-119.7674 | Carson City | 42ac | sub=0.3km | 1004169 | [link] |
...

### Score Distribution
Min: X | Median: X | Max: X | Sites >80: X

### Files Saved
- /tmp/land-intel/output/[use_case]_candidates.csv
- /tmp/land-intel/output/all_candidates_ranked.csv

Google Maps link format: https://maps.google.com/?q=LAT,LON

Always include top 10 in the response. Offer to show more or filter by criteria.

Error Handling

No parcel data:

• Use OSM buildings + 500m synthetic grid as fallback
• Note: "County parcel data unavailable, using OSM proxy — APN/owner data will be missing"

OSM rate limited (HTTP 429):

• Sleep 3s, retry once
• Split into smaller bbox quadrants
• Skip layer if still failing

Dependencies missing:

pip install pandas numpy requests tqdm shapely pyproj geopandas --break-system-packages

Region not found:

• Use Nominatim geocoder on city name
• If still fails: ask user for coordinates directly

Optional Enrichment

After base scoring, offer these if user wants to go deeper:

• Zoning details: actual zoning codes from county assessor (Municode or county GIS)
• Ownership lookup: cross-reference APN with county assessor for owner name/address
• Active listings overlay: Redfin CSV endpoint (recursive bbox subdivision, no API key)
• BLM/federal ownership: PADUS or BLM NILS layer — critical for Western US
• Power capacity: substation IDs from OSM → call utility for available MW
• Environmental flags: FEMA flood zones, EPA brownfields, wetlands

Prompt: "Base scoring complete. Want me to enrich the top 20 with zoning codes, ownership data, or active listing prices?"

Scope

Works well for:

• Any US county with public ArcGIS parcel data (most do)
• Western US: Nevada, Utah, Arizona, Idaho, Oregon, Colorado, California
• Eastern US: NC OneMap, county GIS portals (see references/state-endpoints.md)
• Any use case expressible as infrastructure proximity + acreage

Doesn't cover:

• International parcels
• Permitting or entitlement research
• Environmental impact analysis
• Residential property or single-family land
• Title/lien searches

Data freshness:

• OSM: real-time
• County parcel layers: typically updated annually (check SourceDate field)
• Infrastructure: OSM community-maintained, generally reliable