How commercial real estate teams automate LOI drafting

Why does LOI drafting take so long?

A letter of intent is a short document, usually two to four pages, and most of it is non-binding. The time does not go into the writing. It goes into everything around the writing.

A typical draft starts with pulling property details from a listing platform like CoStar, LoopNet, or Crexi, or from an offering memorandum sitting in someone's inbox. Then the deal terms come out of an underwriting model in Excel: purchase price, earnest money, due diligence period, closing timeline. Then someone finds the latest version of the firm's Word template, fills in the blanks, fixes the formatting the template broke, and routes the draft to a partner for review. Then it goes out by email or DocuSign.

Each step is small. Together they cost an afternoon. And when a mostly non-binding letter costs an afternoon, teams only send LOIs on deals they already feel sure about. That defeats the point of the instrument. An LOI exists to start conversations cheaply.

How much LOI volume is actually enough?

Here is a pattern worth walking through. Picture a commercial real estate investment firm sending 1 to 3 LOIs per week. The target is 5 per day. Not 5 per week. Per day.

That sounds aggressive until you consider what an LOI is for. In acquisitions, LOIs are at-bats. Most get ignored or rejected, and that is fine. The ones that land open negotiations you would never have had otherwise. A team sending 25 letters a week simply sees more of the market than a team sending 2.

The interesting part is what was holding the firm back. It was not conviction, capital, or deal flow. It was drafting throughput. The acquisitions strategy was being set by administrative capacity, which is backwards. Strategy should set the volume, and the paperwork should keep up.

What does an automated LOI workflow look like?

The goal is a pipeline where a person makes the decisions and the system does the typing. In practice it has four stages:

• Deal intake. Target properties and terms live somewhere structured: the underwriting model, a deal pipeline in a CRM like HubSpot or Salesforce, or even a well-kept spreadsheet. The system reads terms from there instead of asking anyone to retype them.
• Drafting. A document generation step merges those terms into the firm's own template language. With current AI models this goes further than mail merge: the system can adapt the cover email to the specific broker and flag any term that deviates from your usual ranges.
• Review. Every draft lands in a queue. A partner reads it, edits if needed, and approves. Nothing leaves the building unreviewed.
• Sending and tracking. The approved LOI goes out through email or DocuSign, gets logged against the deal, and schedules its own follow-up reminder.

The template language stays locked. Attorneys approve it once, and the system only fills in the variables. That is what makes the speed safe.

What stays human in the process?

Everything that involves judgment. Which deals to pursue. What price to offer. How hard to push on the due diligence timeline. How to handle the phone call that comes back. The automation removes typing, formatting, and chasing, none of which was ever the valuable part of an acquisitions job.

A useful test: if a task requires knowing the market or the counterparty, a person should do it. If a task could be done by a careful assistant working from a checklist, it is a candidate for the system.

Is this worth setting up for your team?

The math is straightforward. Estimate the hours one LOI currently costs, multiply by the volume you actually want, and compare that against what you send today. For the firm in this example, hand-drafting 25 LOIs a week would consume more hours than a full-time hire.

LOI drafting is also one instance of a broader pattern: document generation from structured terms. The same pipeline handles proposals, quotes, purchase agreements, and engagement letters. If your team produces repetitive documents on a template, the workflow transfers directly.

For the wider picture on real estate operations, from speed-to-lead to scheduling, see our page on AI automation for real estate.

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