AI for CRE Sale Comps from Public Records: 4 Options

By Jude Lee · · Comparison

Two CRE brokers reviewing a county deed document and a comparable sales map on dual monitors

The comp is never just the deed

Every investment sales shop keeps a comp set, and every comp set is maintained by someone’s patience. A recorded deed in most U.S. counties gives you grantor, grantee, legal description, recording date, and — in many jurisdictions — a documentary transfer tax stamp you can back into a price. What it almost never gives you is the thing the comp needs to be useful: in-place NOI, going-in cap rate, the occupancy at closing, whether the price was allocated across a five-property portfolio, whether it was an entity transfer that never touched the recorder, or whether the buyer was a 1031 exchanger paying a premium for timing.

That gap is why comps stay manual. It’s also why “AI for comps” gets oversold. A language model reading a deed PDF will happily produce a confident cap rate it has no basis for. The practical question isn’t whether AI can read the documents — it can, reliably, with a human checking — it’s which parts of the workflow you hand to software and which parts stay on the phone.

What an “AI agent” actually means for this job

An AI agent, as distinct from a chatbot, carries out multi-step tasks and takes actions: it checks a source on a schedule, opens documents, extracts fields, compares them to what’s already in your database, writes new records, and drafts the email that asks a broker to confirm the price. The agent doesn’t decide what a comp is worth. It removes the clicking, retyping, and dedupe work between the recorder’s website and your comp sheet.

The connective tissue is usually MCP — the Model Context Protocol, an open standard published by Anthropic for giving an AI assistant governed access to specific tools and data. It is the standard the major assistant vendors have adopted, and it has begun appearing in real estate software: HousingWire covered Lofty’s launch of an MCP server for brokerages — one vendor shipping one endpoint, which is a data point rather than an industry trend. My expectation, clearly labeled as expectation, is that more CRE platforms will follow and that the first versions will be read-only and narrow in scope. (Reviewed as of early 2026; this area moves quickly.)

The model can read the deed. It cannot verify the price. Design the workflow around that one sentence and most of the vendor hype sorts itself out.

Four ways to run the comp pipeline

1. Manual research and a shared sheet

An analyst watches recordings, pulls deeds, checks assessor records, calls the listing broker, and types the result into Excel or your CRM. It is slow, and it is also the only option that produces genuine verification. Many small shops should stay here longer than they think — if you add four comps a month, no system pays for itself.

2. Commercial data platforms and their built-in AI

CoStar, Crexi, Reonomy and similar providers already do the aggregation at scale, and most have shipped AI search or summarization features. If your markets are well covered, buying the data is almost always cheaper than rebuilding it; see our breakdown of how CoStar, Crexi and Reonomy differ. The limits are coverage gaps in secondary markets, licensing terms that restrict what you can export into your own systems, and the fact that their comp is not your comp — your in-house knowledge of a deal’s structure never gets back into the record.

3. A general AI assistant with uploaded documents

Drop a deed, a closing statement, and a rent roll into Claude or ChatGPT and ask for a structured comp record. This works well for one-off extraction and costs nearly nothing to try. Before you do it with live deal documents, check the account’s data-retention and model-training settings, which differ between consumer, team and enterprise tiers and change over time — read the provider’s current documentation rather than trusting what was true last year. Deeds, closing statements and rent rolls frequently sit under NDAs, client confidentiality obligations, or platform data-licensing terms that restrict third-party processing, so use an enterprise or team account with training disabled for anything client-identifiable, and have counsel check your NDA language if you’re unsure. Even then, a general assistant doesn’t monitor sources, write to your database, or remember your schema. It’s a document reader, not a pipeline.

4. A custom agent connected to your own systems via MCP

Here you build a small MCP server over your comp database, your deal history, and whatever public sources you’re permitted to pull, then let an assistant query and write through it. The agent proposes a new comp record with fields extracted and a confidence note; a human approves it. The same pattern we describe for connecting Claude to CRE systems through a custom MCP server applies directly, and it’s the only option where your brokers’ verified knowledge accumulates into an asset you own.

Platform AI features
Coverage and licensed data you can’t replicate. Fast to adopt, no engineering. You adapt to their schema, their export limits, and their roadmap. Best when your markets are well covered and your team is small.
Custom MCP agent
Works across your sources, writes into your schema, and captures deal color that no vendor has. Requires build and maintenance, plus someone who owns data quality. Best when you have proprietary comps, odd markets, or heavy volume.

Sizing the cost before you commit

Don’t start from a vendor’s savings claim. Start from your own measurements, using this as a template rather than a result:

Then ask what the recovered hours get reallocated to. In brokerage, retrieval hours converted into verification calls are worth more than the hours themselves, because a verified cap rate is what wins the pitch. If the hours just evaporate into the day, the saving is real but invisible — say so to your partners before you budget for it. Our brokerage automation payback walkthrough uses the same transparent-assumption approach.

AI takes slices of a job, not whole jobs

You’ll see informal “rules” circulating about how much of a task AI can take over — budget a third for change management, expect roughly a third of the work to be automatable, don’t trust more than a third of unverified output. There is no recognized industry standard behind any of these, and the versions conflict with each other. Treat whichever one you encounter as somebody’s rule of thumb and ask for the evidence underneath it. The durable idea is simply that AI rarely absorbs a whole role; it absorbs slices of specific workflows. For comps, the slice is retrieval and structuring — and sizing that slice honestly is exactly what the worked example above is for.

What stays human

Agents break on exactly the cases that make a comp interesting: entity transfers with no recorded price, allocated portfolio pricing, sale-leasebacks where the rent is the real deal, assemblages, and anything non-arm’s-length. They also break quietly — an extraction error looks identical to a correct one until someone recognizes the building. Keep a human approval step on every record that enters the database, and have the agent flag low-confidence fields rather than fill them.

Which brings up the question brokers actually ask: will AI replace commercial real estate brokers? Not for this work. The comp’s value lives in the relationship that produces the verified number, and in the judgment about whether a trade is comparable at all. What AI replaces is the transcription around that judgment.

A two-week pilot

  1. Pick one submarket and one asset class

    Narrow scope makes failures visible. Twenty recent trades is plenty.

  2. Write the schema first

    Define every field you want on a comp record, including a verification-status field and a source field. The schema is the actual product.

  3. Test extraction with a general assistant

    Upload ten deeds and closing documents against your schema, using an account whose retention settings you’ve checked. Score field-level accuracy by hand. This costs almost nothing and tells you whether a build is worth scoping.

  4. Check your data rights

    Confirm what your platform licenses permit you to export, and confirm the county’s disclosure rules with the recorder or assessor directly. Involve counsel if you plan to automate retrieval at volume.

  5. Only then decide build vs buy

    If the platforms cover your markets and your volume is modest, buy. If you’re accumulating proprietary comps your vendors will never have, scope a small MCP server over your own database and keep humans on approval.

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