How to build a Property Diligence Agent

TL;DR

This agent automates property due diligence by gathering, verifying, and summarizing public records, comparable sales, and inspection reports for any property address—delivering a comprehensive, AI-generated report in minutes.

What It Does:

• Accepts a property address from the user.

• Verifies the location and retrieves latitude/longitude using a geocoding API.

• Queries county/city databases for:

  • Detailed property records (ownership, parcel, lot size, etc.).

  • Recent comparable sales (“comps”) near the property.

• Allows upload of inspection reports or other relevant documents.

• Uses AI to summarize:

  • Public records,

  • Comparable sales,

  • Inspection findings.

• Generates a final, comprehensive due diligence report.

Who It’s For:

• Real estate lenders and underwriters

• Property investors and analysts

Time to Value:

• Immediate: Enter an address and upload any inspection docs—get a full due diligence report in minutes, not hours or days.

Output:

• A clear, AI-generated due diligence report summarizing:

  • Key property details (owner, parcel, lot size, etc.)

  • Recent comparable sales

  • Inspection findings (if provided)

• All data sources and summaries are included for transparency.

Common Pain Points of Property Diligence

• Manual, time-consuming research across multiple databases and websites

• Inconsistent or missing property records

• Difficulty finding recent, relevant comparable sales

• Tedious extraction of key details from lengthy inspection reports

• Risk of missing critical information due to human error

What This Agent Delivers

• Automated, multi-source data gathering (public records, comps, inspections)

• Reliable geocoding and property verification

• AI-powered summarization of complex or unstructured data

• Consistent, comprehensive due diligence reports

• Drastically reduced research time and effort

Step-by-Step Build (StackAI Nodes)

1) Text Input (in-0)

What it does:

• Accepts the property address from the user.

Goal:

• Provide a starting point for all downstream data gathering.

2) Location Verifier (action-2)

What it does:

• Sends the address to a geocoding API (OpenStreetMap Nominatim) to get latitude and longitude.

Goal:

• Ensure the address is valid and obtain coordinates for spatial queries.

3) Python (python-0)

What it does:

• Processes the geocoding API response to create a small bounding box (envelope) around the property’s coordinates.

Goal:

• Prepare a geometry parameter for querying spatial databases.

4) API for Property Details (action-0)

What it does:

• Uses the bounding box to query a county GIS/parcel database for detailed property records.

Goal:

• Retrieve authoritative public records for the property.

5) API for Comparables (action-1)

What it does:

• Queries a county or city database for recent comparable sales (comps) near the property address.

Goal:

• Gather market data for valuation and risk assessment.

6) Inspection Reports (doc-1)

What it does:

• Allows the user to upload inspection reports or other relevant documents.

Goal:

• Incorporate on-the-ground property condition data into the analysis.

7) Comparables (llm-0)

What it does:

• Uses AI to summarize the comparable sales data, highlighting key sales, prices, and trends.

Goal:

• Extract actionable insights from raw comps data.

8) Public Records (llm-2)

What it does:

• Uses AI to summarize the public property records, extracting owner, parcel, lot size, and other key details.

Goal:

• Present a concise summary of the property’s official records.

9) Comparables 1 (llm-1)

What it does:

• Uses AI to generate a comprehensive due diligence report, combining public records, comparables, and inspection findings.

Goal:

• Deliver a final, decision-ready report for the user.

10) Output (out-0)

What it does:

• Displays the final due diligence report to the user.

Goal:

• Present the results in a clear, accessible format.

11) Output 1 (out-1)

What it does:

• Optionally displays the raw property details data for transparency or debugging.

Goal:

• Provide access to underlying data if needed.