Course

AI for Real Estate Agents and Loan Officers

Listing package to accepted offer to funded loan, without inventing a price or a rate.

IntermediateReal estate

0/88 stops · about 2 hours · final quiz · certificate

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Start with

• AI at Work: FoundationsStart from zero and finish with three things you can actually use.

Who this is for

Real estate agents, brokers, loan officers and transaction coordinators, especially anyone who carries the listing side and the lending side of the same deal. Every example follows Daniel, a lists and sells homes in coastal Southern California and writes the loans for many of his own buyers, twenty years in, with a two person team.

You will be able to

• Build a listing package with description, feature list, showing notes and the seller update, fair housing checked on every draft
• Run a buyer consultation from a prep sheet and hand the lender side a needs summary with no financial detail in it
• Draft offers, counters and the explanation email without a number Claude invented
• Keep a loan file moving with a conditions list, a plain English borrower update and a timeline
• Follow up after open houses and with past clients in a voice that gets replies
• Hold both rulebooks, the team templates and the market facts in one Project the whole team works inside

Stops

1. 1 Two rulebooksFair housing on the marketing side, lending rules on the loan side, and one Project that carries both into every chat the team starts. Read once
2. 2 The listing packageProperty facts and photos in, description at three lengths, feature list, showing notes and the seller update out, every draft fair housing checked. Weekly
3. 3 The buyer consultationA prep sheet built from the CRM record, the questions only you can ask, and a needs summary the lending side can use with no financial detail in it. Daily
4. 4 Offers and countersThree offers turned into one comparison the seller can read, the counter explained in plain English, and not a single number Claude produced. Daily
5. 5 The loan fileUnderwriting conditions turned into a list a borrower can act on, an update in plain English, and a timeline with no date the loan system has not given. Daily
6. 6 Open house and past-client follow-upForty names off a sign-in sheet into forty short notes that sound like you, plus the once a year message to past clients that actually gets a reply. Weekly
7. 7 Market updates and contentAn MLS export turned into a neighbourhood update, a newsletter and a video script, with every claim traceable to a column and no forecast anywhere. Weekly
8. 8 Daniel's weekThree routines on a timer, one ask from a house he is standing in, and the capstone that runs a listing from package to accepted offer with its loan file attached. Weekly

Final quiz