Updated 20 September 2026: first published on Elite Agent in May 2025. The article has been refreshed as an AI basics guide to creating a clear suburb report with checked data and Gamma.
My Year 12 home economics teacher, Mrs Henderson, once made mashed potatoes that looked delicious. Butter, chives, perfect creamy peaks. We were ready for a taste.
Then she added electric blue food colouring.
“Who’d like the first taste?” she asked. Funnily enough, nobody put up a hand.
We eat with our eyes. The same principle applies to the suburb reports we send to clients: presentation affects whether someone wants to read them.
But a beautiful report still needs sound information. For an agent learning AI basics, a useful first project is to turn a small set of checked local figures into a readable suburb snapshot.
The original Sora example from 2025 accompanied the blue-potato story. It is a historical example, not part of the reporting workflow below.
How do I make a suburb report with AI?
Start with the question your reader wants answered. A homeowner considering a sale may need different context from an investor comparing rental markets.
Gather the reports you are permitted to use. Record the suburb, property type, reporting period and definition beside each figure. Keep houses and units separate, and distinguish advertised prices from sale prices.
Give AI that material to organise. If your tool can search, open the sources it cites yourself. A confident answer with several links is still a draft.
Organise a short suburb report for [AUDIENCE] about [SUBURB, STATE], as at [DATE].
Use this checked source material: [REPORT EXTRACTS, LINKS, DATES AND DEFINITIONS].
Ask about missing context before drafting. Use only supplied evidence. For each figure retain its source, reporting period, property type and definition. Mark unavailable information as unavailable; do not estimate it.
Suggest a clear heading, three supported market indicators, a short explanation of what each measures and three questions a local owner might ask. Answer only where the evidence supports an answer.
Keep house and unit data separate. Do not combine incompatible periods, invent buyer demographics or predict capital growth. Separate facts from interpretation. End with source notes and a checklist of gaps or conflicting figures for me to review.Turn the checked draft into a Gamma document
Open every cited source and correct the draft before designing it. Then paste or import the checked text into Gamma, keeping its source notes and dates.
The screenshots below show the interface used for the original 2025 tutorial. Menu names and available features may differ today.
Choose the audience and language, then organise the content into short sections. A heading, a few useful figures and an explanation usually give the reader a clearer starting point than a crowded page.
Choose a theme and layout that suit your agency. Use Gamma to style the approved text. If it condenses or rewrites anything, compare the result with your checked version. Avoid asking it to add market facts to fill space.
You can also refer to the original Gamma suburb snapshot tutorial. The written steps here let you complete the exercise without relying on the recording’s interface.
What should I check before sharing the PDF?
Compare the designed report with your approved draft. Check that figures, dates, definitions and qualifications survived formatting. Make sure charts and images do not suggest information you have not established.
Use Gamma’s export guide to export a PDF, then open the actual file. Check small text, page breaks, links and clipped content.
Ask a colleague to find the reporting period and the source of one figure. If those are hard to find, improve the layout before sending it to clients.
The aim is a report people can read and understand. The design should help the information earn its place on the page.
The original version appeared in Samantha McLean’s AI Powered Agents newsletter. Read the refreshed source article on AI Powered Real Estate by Samantha McLean on Substack.