How to Produce Broker Opinions of Value (BOVs) at Scale with AI

To produce broker opinions of value at scale with AI, you stop treating each BOV as a bespoke research project and start treating it as a pipeline: a standard set of inputs, comparable sales and leases pulled and sourced, a valuation run across a few honest scenarios, a sub-market narrative drafted, and the whole thing assembled into your template. AI does the comp gathering, the first-pass valuation math, and the drafting. A broker reviews every value and signs it. That division is the point. A BOV is your professional opinion, and no model gets to have that opinion for you. What a model can do is take the ten hours of assembly that stand between a listing conversation and a finished BOV and turn them into an afternoon, so you can say yes to more of them.

This is written for the commercial broker or advisory team that wins business by showing up with a credible value fast, and loses it by taking two weeks to turn one around. The bottleneck is almost never the judgment. It is the grind: finding the comps, keying them into a model, writing the market section, formatting the document. Below is the pipeline we build with brokerage teams, decomposed into steps you can reason about, and honest about the one step that stays yours.

Step 1: Standardize the BOV Inputs Before You Automate Anything

The reason BOVs are slow is that every one starts from a blank page. The fix is to decide, once, what a BOV at your shop always contains: the subject property facts (address, asset type, size, year built, current occupancy, in-place income if you have it), the comp set you always pull (recent sales and active or recent leases within a defined radius and vintage band), the valuation approaches you stand behind, and the sections of the finished document. Write that down as a template.

This step is not glamorous and it is the one that makes the rest possible. Once a BOV has a fixed shape, a machine can fill that shape. The subject property gets entered once, the comp criteria are the same every time, and the output slots into a document your clients already recognize as yours. Standardizing first is also what keeps the AI honest later: when the structure is fixed, a missing comp or an unsupported number is obvious, because there is a labeled place it was supposed to go.

Step 2: Pull and Vet the Comparables, With a Source on Every One

The comp set is where a BOV lives or dies, and it is the most time-consuming part to build by hand. This is where AI earns its place first. Given the subject property and your criteria, it pulls candidate sale and lease comparables from the data sources you already license, and lays them out in the same fields every time: address, date, price or rent, size, cap rate where known, and the distance and vintage relative to the subject.

Two rules keep this trustworthy. First, every comparable carries a source, so a number you cannot trace does not make it into the value. Second, the broker vets the set before it feeds the valuation, because proximity in a database is not the same as true comparability, and only a person who knows the sub-market can throw out the comp that looks close on paper and wrong in reality. The AI assembles the candidate set fast and consistently. You decide which comps are actually comparable. That is the difference between a defensible BOV and a confident one.

Step 3: Run the Valuation Across Honest Scenarios

With a vetted comp set, the valuation math is mechanical, which means it is a good fit for automation and a bad place for a human to spend an evening. From the comps and the subject's income, the model produces a value range using the approaches you standardized: a sales-comparison view off the price-per-unit or price-per-foot, and an income view off the in-place or market NOI and a supportable cap rate.

The scenarios are what make it useful rather than a single false-precision number. For an occupied asset, run the value as-is. For one with vacancy, run it at current occupancy and at a stabilized assumption, and show both, so the owner sees the gap and what closing it is worth. Every assumption behind a scenario is labeled, so when a client asks why the number is what it is, the answer is on the page, not in your head. The model does the arithmetic across scenarios instantly. You choose which scenarios are honest for this asset and this owner.

Step 4: Draft the Sub-Market Narrative from the Same Inputs

A number without a story does not win the listing. The sub-market section, the part that explains what is happening in this asset's corner of the market and why your value makes sense, is also the part brokers most often leave thin because writing it from scratch is slow. AI drafts it from the same comp and market inputs you already assembled: recent transaction activity, the direction of rents and cap rates in the band, and how the subject sits against them.

Treat that draft as a first draft, because it is. The AI can summarize what the comps say, but it does not know the deal that fell out of contract last month or the tenant everyone in the sub-market knows is leaving, and that local color is often what convinces an owner you are the right broker. So the machine writes the structured, factual base of the narrative in seconds, and you add the judgment and the on-the-ground knowledge that a database will never have. That is a far better use of your time than formatting bullet points.

Step 5: Assemble the Document, Then Sign It Yourself

Now the pieces come together into the BOV your clients recognize: subject summary, comp set, valuation scenarios, sub-market narrative, all poured into your branded template automatically. What used to be an hour of copying between a spreadsheet and a slide deck is a generated draft. But assembly is not approval. Before a BOV goes to an owner, a broker reads the whole thing, checks that every value is supported, and puts their name on it.

This is the step that makes the whole pipeline safe to run at volume. The AI produced a complete, consistent draft, which means your review is a review and not a rebuild. You are checking judgment, not transcribing numbers. And because the structure is fixed and every figure is sourced, that review is fast: you are looking for the comp that should not be there and the assumption that is too aggressive, not proofreading formatting. A BOV is a professional opinion with your name on it, and the person who signs it has read every number. The machine got you to a finished draft. You are still the one giving the opinion.

What This Changes for a Brokerage

The point of automating BOV production is not to remove the broker. It is to remove the reason brokers ration BOVs. When a credible opinion of value takes ten hours to assemble, you only produce them for the listings you are confident you will win, which means the owner who was on the fence never gets the one document that would have earned your call. When a BOV takes an afternoon, you can lead with value across a whole prospecting list, and the unsolicited, well-supported BOV becomes an outreach tool instead of a favor you save for warm leads.

If you are thinking about where this fits, it sits next to the rest of the brokerage stack. The comp and market data still comes from the platforms you already use, and it is worth being deliberate about which, which we cover in our guide to the best CRE market data and comps platforms. The BOV is often the front of a longer document workflow, and the same discipline extends into turning a deal into a committee memo, which we decompose in our guide to producing an IC memo from an OM with AI. And a BOV built for outreach pairs naturally with a system for reaching the owners it is meant for, which we cover in automated outreach to property owners.

Frequently Asked Questions

How do you produce a broker opinion of value with AI?

You run it as a pipeline, not a bespoke project. Standardize what every BOV contains, then have AI pull comparable sales and leases from your licensed data with a source on each, run the valuation across honest scenarios such as as-is and stabilized, and draft the sub-market narrative from those same inputs, all poured into your template. A broker vets the comp set, chooses the scenarios, adds the local market color, and signs the value. The AI does the assembly and the first-pass math; the broker owns the opinion. That turns a ten-hour BOV into an afternoon without giving up the judgment that makes it credible.

Does AI decide the value of the property?

No, and it should not. The AI produces a value range from the vetted comps and the income approach, labels every assumption, and shows the scenarios, but a broker reviews it and signs the final opinion. A BOV is a professional opinion with your name on it, and the judgment about which comps are truly comparable, which scenarios are honest, and what the sub-market is really doing stays with the person who knows the market. The value of the automation is that your review is a review of a complete, sourced draft rather than an evening of building the model from scratch.

Can I trust AI-pulled comps in a valuation?

Only with two guardrails, both of which are built into the pipeline. First, every comparable carries a source, so an untraceable number never reaches the value. Second, the broker vets the comp set before it feeds the valuation, because proximity in a database is not the same as true comparability. AI is very good at assembling a consistent candidate set fast and very bad at knowing that a nearby sale was a distressed insider deal. The workflow uses it for the first and relies on you for the second, which is what keeps the resulting BOV defensible rather than merely fast.

Will automating BOVs replace what brokers do?

It replaces the assembly, not the broker. The slow part of a BOV is finding comps, keying a model, and formatting a document, none of which is where your value as a broker lives. Your value is in the judgment on comparability, the read on the sub-market, the relationship with the owner, and the opinion you are willing to sign. Automating the assembly lets you produce more credible BOVs and use them earlier in the relationship, which tends to mean more listings, not fewer brokers. The tool makes each BOV cheap enough to lead with instead of ration.

How is an AI BOV different from an appraisal?

A BOV is a broker's opinion of value used for listing and advisory decisions, produced quickly and without the regulatory formality of a certified appraisal, and this pipeline speeds up exactly that. It does not turn a BOV into an appraisal, and it should not be represented as one. What it changes is the cost of producing the broker opinion: the comp assembly, valuation scenarios, and market narrative that go into a BOV are drafted by AI and reviewed and signed by the broker, so you can offer a well-supported opinion of value at the speed the listing conversation actually needs.