AI Raw Land Due Diligence

The Problem

Land diligence is a manual data hunt—so deals stall and hidden risks slip into underwriting

Organizations face these key challenges:

1

Analysts spend hours jumping between county sites, GIS portals, PDFs, and spreadsheets to build a single parcel view

2

Red flags (access, zoning limits, wetlands/flood, utility proximity, liens/encumbrances) get discovered late—after time and money are already spent

3

Valuations are inconsistent because comps and assumptions vary by analyst, market, and available data

4

No clean audit trail: decisions rely on screenshots/notes, making it hard to reproduce conclusions or defend them to IC/lenders

Impact When Solved

Faster go/no-go screeningMore consistent underwriting and valuationsScale acquisitions without scaling headcount

The Shift

Before AI~85% Manual

Human Does

  • Search and download assessor/recorder, zoning, and permitting documents parcel-by-parcel
  • Manually interpret GIS layers (flood, wetlands, slopes) and summarize constraints
  • Find and adjust comps, build valuation ranges, and write the diligence memo
  • Coordinate vendors (title, survey, Phase I/soil) and reconcile findings back into the model

Automation

  • Basic mapping/GIS viewers and spreadsheet templates
  • Keyword search across PDFs and folders
  • Rule-of-thumb checklists maintained manually
With AI~75% Automated

Human Does

  • Define deal strategy and thresholds (acceptable risks, target use, required entitlements)
  • Review AI-flagged exceptions and make final go/no-go and pricing decisions
  • Approve sources/assumptions for investment committee and lender packages

AI Handles

  • Ingest and normalize parcel data from county records, GIS layers, FEMA/USFWS/state sources, listings, and historical sales
  • Extract key fields from PDFs/scan images (ordinances, plats, permits) and link evidence to each conclusion
  • Run geospatial checks (access/road frontage, proximity to utilities, slope, flood/wetlands overlap) and generate risk/constraint summaries
  • Automate comp selection and market analysis; produce valuation ranges with confidence scores and rationale

Real-World Use Cases

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