Can ChatGPT or Claude find development sites and property owners?
What a general assistant gets right, where it breaks, and what it needs behind it
Andrew Buchanan, CEO, MAIA Analytics
October 8, 2026 · 6 min read
Partly. A general assistant like ChatGPT or Claude can plan the search, explain the criteria and write the code, but on its own it has no parcel data, no spatial tools and no way to check its answer against the county record, so the list it returns is directionally right and rarely accurate enough to call from. To get a site list you can act on, the assistant needs parcel-level data and spatial tools behind it: either a stack you build and maintain, or an engine built for the job, such as MAIA, which your assistant can also call directly.
If you have already tried this, you probably know the feeling. The first answer looks great. Then you check five of the parcels and two don't exist, one is a church, and the owner on the fourth sold in 2019.
What happens when you ask ChatGPT to find sites?
Ask a general assistant for "industrial parcels over five acres near an interstate exit in Fulton County, owned by individuals" and it will usually do one of three things:
- Describe the method. Go to the county GIS portal, filter by land use, buffer the interchanges, export, join to the assessor roll. Useful, but it's homework, not an answer.
- Search the web. It finds listings and news articles, which covers the parcels that are already on the market and misses the ones that aren't.
- Make up rows. Plausible addresses, plausible acreages, plausible owners. This is the dangerous one, because it looks finished.
None of this is the model being bad. It's being asked to answer a question about places without being able to see the places.
Why does it go wrong?
A where-question is a join across datasets that a chat window doesn't hold:
- The parcels. Boundaries, acreage, land use and the owner of record live in county assessor and recorder systems, and every county publishes them differently. Some offer a download; some only a lookup page.
- The context. Zoning, flood zones, roads, substations and buildings are separate layers from separate agencies, and they have to be intersected with each parcel geometrically, not matched by name.
- The owner behind the owner. The owner of record is often an LLC or a trust. The person you want to call is one or two filings further back.
- Freshness. A model's training data is months to years old. Parcels split, merge and sell every week.
A language model can reason about all of this. It can't do the geometry on data it doesn't have, and it can't tell you which rows it guessed.
What does it take to build it yourself?
Plenty of teams try, and it's a reasonable instinct. The usual build looks like this:
| Piece | What it involves |
|---|---|
| Parcel data | A nationwide parcel subscription or county-by-county downloads, normalized to one schema |
| Context layers | Zoning, flood, infrastructure and land cover, each from a different source and format |
| Spatial engine | A database that can buffer, intersect and measure geometry (PostGIS or similar) |
| Agent tooling | Functions the model can call to query all of the above, with the schema explained to it |
| Owner research | Entity lookups, contact enrichment, and a check that the person still owns the parcel |
| Verification | A way to show where each value came from, so a person can trust a row before calling |
The first three are a data engineering project. The last three are where most internal builds stall: the demo works on one county, the API bill grows, and the answers stay "mostly right."
When building makes sense: you have an engineer who will own it for years, you work in a handful of counties, and your criteria rarely change. If that's you, build.
How does MAIA answer the same question?
MAIA is a geospatial search and research engine: an agent platform purpose-built for the physical world. You describe the sites you want in plain English, the way you just typed it into ChatGPT. MAIA finds every parcel that clears your bar, shows them on a map and in a table, researches the owners, and shows the source behind every answer.
The difference is what sits behind the agent:
- Parcels and owners already loaded, with zoning, buildings, roads and other datasets tied to each parcel. Coverage varies by county and source.
- Spatial tools built in. Distance, overlap and acreage are computed on the geometry, not estimated.
- More data when the question needs it. If you ask about flood zones or substations, MAIA's agent finds and adds the relevant public map layer when a suitable one is available.
- The method shown. Every answer shows its sources. If MAIA can't support a number, it says so instead of guessing.
Your criteria stay yours. MAIA doesn't have a thesis about your market; it finds every parcel that fits the one you bring.
Can my own ChatGPT or Claude use MAIA?
Yes. MAIA has an MCP server, so an assistant or agent that supports MCP (Claude, for example) can send MAIA a where-question and get back the answer, with a link to the full map and table. Your assistant keeps doing what it's good at, planning and writing, and MAIA does the part that needs the physical world.
FAQ
Can ChatGPT find the owner of a property?
It can tell you how to look it up, and with web browsing it can sometimes open a county lookup page for one address. It can't pull owner records for a whole county or check them in bulk, so it can't reliably give you the current owners for a list of parcels.
Why does ChatGPT give me parcels that don't exist?
Without parcel data to query, a language model fills a table with plausible-looking rows. Ask for sources on every row; anything without one should be treated as a guess.
Is it worth building my own parcel search tool with AI?
If you have an engineer to own it, a few counties, and stable criteria, it can be. Most teams underestimate the parcel normalization, the spatial joins and the owner research, which are where accuracy is won or lost.
What is MAIA?
MAIA is a geospatial search and research engine from MAIA Analytics. You ask a where-question in plain English and get every place that fits, the owner behind it, and the source for every value.
Does MAIA replace ChatGPT or Claude?
No. It gives them the physical world. People use MAIA in its own app, and assistants that support MCP can query it directly.
