Not every vendor has a digital footprint. Some of the most valuable listings an agent will ever handle come from people who have never posted on social media, never left a Google review, and are selling a property for the first time in decades.

In Episode 11 of The AI Edit, Samantha McLean addresses the gap: how do you use AI to prepare for a vendor who doesn’t exist online?

Sam introduces “Betty” – a made-up vendor who is 80 years old, bought her house in 1982, raised her family there, and is selling for the first time in 44 years. “Betty” has no digital footprint. But, as Sam points out, her house does – and her demographic does.

The episode opens with a practical distinction. When it comes to property management and investor listings, the approach to copy is fundamentally different. An investor doesn’t need the emotional language of a residential listing – they need the facts, the tenant appeal, and a maintenance budget. Sam flags this as a common blind spot in AI-generated content: the same prompts don’t work for every vendor type.

For example:

Instead of trying to “research” the seller (Betty) online, you can use what the property shows you.

Visual cue = a visual hint you can see in an image (like Google Earth/Street View) that suggests what life at the property might be like.

You pull up images of the driveway, garden, and front of the house, then ask AI to look for signs the owner might be struggling with the upkeep (called “physical stress” here).

Examples of stress might be

  • Overgrown garden (hard to maintain)
  • Accessibility issues (steps, steep drive, narrow paths — could be difficult to move around)
  • Deferred maintenance (things that look like they’ve been put off: peeling paint, worn paths, messy exterior)
  • Then you use the AI’s list to create “help-first” value-adds: practical help you offer before you talk about selling, marketing, or your fee.
  • Example: “Before photos, I can organise a gardener to tidy up.”

Another prompt can help you the generational gap. If the agent is a 25-year-old used to texting and email, and the vendor is a 75-year-old couple who value tradition and face-to-face respect, the prompt asks AI to critique the agent’s standard pre-listing kit. What should be removed? QR codes, DocuSign links, anything that signals a process designed for a different generation. What should replace it? Trust-building elements that match the vendor’s expectations.

Key takeaways:

  • Not every vendor has a digital footprint – but their house and their demographic do. AI can build a profile from location and life stage rather than online data.
  • The “empathy analysis” uses three prompts: psychological profiling, visual cue analysis (via Google Earth), and a generational bridge audit of the pre-listing kit.
  • Property management and investor listings need a fundamentally different approach to copy – features, tenant appeal, and maintenance budgets rather than emotional language.
  • Demographic research at the suburb level should come before any marketing is written. Profile the audience first.
  • The generational bridge audit is practical and confronting – QR codes and DocuSign links may be alienating the very vendors agents are trying to win.

The AI Edit is a weekly series of short clips from the AI First Agent Accelerator. New episodes weekly.

Want Samantha to present to your team or at your next event? Get in touch. Want the full system? Visit aiagentcourse.com.