How a $278M Real Estate Team Finds Better Content with AI
Most real estate agents do not have a content problem.
They have a boring-content problem.
They post the same market stats, the same listing graphics, the same "rates changed again" updates, and then wonder why nobody shares it.
The content that actually grows an audience usually comes from a different place. It comes from stories people already care about before they ever think of them as real estate content.
That is how I use AI in our own real estate business.
Not to replace agents. Not to replace local knowledge. Not to automate trust.
I use it to find better raw material.
Our team did roughly $278M in production, and over the past year I grew from about 1,500 followers to more than 30,000. A big reason was learning how to find local stories before they became obvious.
Here is the system.
Watch the walkthrough
1. I make AI search for local stories before everyone else sees them
Most agents wait until a story is already everywhere.
By then, the moment is usually gone.
I use Manus to look for stories earlier in the cycle:
- county votes
- planning agendas
- new restaurants
- road projects
- business openings
- school board issues
- development fights
- closings
- public meeting notes
- economic development updates
- local government pages
Those are the places where better content starts.
A national real estate headline might be interesting to agents, but local stories are what people in your market actually talk about.
If a restaurant is opening, a road project is going to change traffic, a school board vote is getting attention, or a development fight is heating up, people care because it affects their daily life.
That gives you a content angle that is bigger than real estate but still connected to your local expertise.
2. I make it search places most agents skip
Most agents look in the same obvious places:
- Google News
- Facebook groups
- other agents' posts
Those can help, but they are usually late signals.
The better signals are often buried in sources agents do not want to read:
- county websites
- planning commission packets
- zoning agendas
- economic development pages
- meeting videos
- school board documents
- transportation updates
- local news outlets
- city newsletters
- public hearing notices
That is exactly the kind of work AI is good at.
It can scan boring sources, summarize what matters, and surface stories that deserve a second look.
The key is not asking AI, "What should I post today?"
That gives you generic ideas.
The better prompt is: "Search these local sources and find stories that people in my market would actually talk about. Then explain why each story matters."
That turns AI into a local research assistant instead of a caption generator.
3. I make it score the stories before I waste time on them
Not every story deserves a video.
Not every story deserves a newsletter.
Not every story deserves your time.
So I make AI score ideas before I create around them.
The system is simple:
- Red means high viral potential
- Orange means strong local interest
- Yellow means useful
- Green means lower priority
The score is based on practical questions:
- Would a local person send this to someone else?
- Is there a clear headline or hook?
- Does this affect daily life?
- Is there a visual angle?
- Is there controversy, nostalgia, convenience, money, traffic, food, schools, or development involved?
- Can I explain it quickly?
- Is it tied to a place people recognize?
That keeps me from spending hours on content nobody was ever going to care about.
It also helps separate "useful" from "share-worthy."
A useful post might build trust.
A share-worthy post can grow your audience.
You need both, but you should know which one you are making before you start.
The mistake most agents make with AI content
Most agents use AI at the very end of the process.
They already picked the topic, then they ask AI to write the caption.
That is fine, but it is not where the leverage is.
The real leverage is using AI at the beginning:
- Find better stories
- Pull from sources nobody else is checking
- Score the ideas before creating
- Turn the winners into scripts, newsletters, posts, and follow-up content
If your topic is weak, even a great caption will not save it.
If your topic is strong, the content gets easier.
A simple Manus prompt you can adapt
Here is a simple version of the workflow:
Search local sources for story ideas in [MARKET]. Focus on county websites, city pages, planning agendas, economic development updates, transportation news, school board sources, local news outlets, and public meeting notes.
Find stories that could matter to homeowners, buyers, sellers, or local residents.
For each story, return:
- Headline
- Source URL
- Short summary
- Why locals would care
- Real estate angle, if any
- Suggested content hook
- Viral score: Red, Orange, Yellow, or Green
- Reason for the score
Do not give generic real estate tips. Find real local stories.
You can run that weekly, daily, or before planning your next batch of videos.
For a heavier daily research workflow, make the prompt more specific. The more specific the job, the better the output.
Here is a sanitized version of the Tier 3 local-story research prompt:
Conduct Tier 3 Deep Research for [MARKET] viral-potential stories for the current week. Find the top 20 [MARKET] stories with the highest viral potential for local social media, email, and real estate/community audience engagement.
Prioritize [PRIMARY COUNTY/AREA 1], [PRIMARY COUNTY/AREA 2], and [PRIMARY COUNTY/AREA 3], followed by [SECONDARY AREAS].
Required story mix:
- 8 Government & Development stories
- 10 Restaurant & Business stories
- 2 School Board stories
Time window: Prioritize stories published, posted, discussed, approved, opened, closed, announced, or debated within the last 7 to 14 days.
Research official government pages, economic development pages, planning pages, meeting agendas, board packets, public notices, local outlets, YouTube uploads, and relevant social signals.
Score each story as:
- Red: highest viral potential
- Orange: strong local interest
- Yellow: moderate local interest
- Green: lower priority
Create a formatted Excel spreadsheet named "[MARKET] Viral Stories Research - Week of [CURRENT DATE].xlsx" with a dark blue header, frozen top row, category-colored rows, clickable URLs, and viral potential color badges.
Email the spreadsheet to [PRIMARY RECIPIENT] and [SECONDARY RECIPIENT] with the subject "[MARKET] Viral Stories Research - Week of [CURRENT DATE]" and include a bulleted summary of all 20 stories in the email body with Category, County/Area, Headline, one-sentence viral hook, and Source name for each.
That prompt works because it defines the market, source types, category mix, scoring system, output file, formatting requirements, and delivery format.
What Manus costs for this workflow
As of the pricing page I checked in June 2026, Manus showed two relevant paid plans:
- Customizable monthly usage: $100 per month, 20,000 credits per month, 300 refresh credits every day, 20 concurrent tasks, and 20 scheduled tasks.
- Extended usage for productivity: $300 per month, 63,000 credits per month, 300 refresh credits every day, a free Cloud Computer, 20 concurrent tasks, and 20 scheduled tasks.
For this exact local-story research workflow, I would think about the cost this way:
- Daily research: use the Extended plan. Budget $300 per month. If it runs roughly 30 times per month, the subscription cost is about $10 per daily report before any extra credit purchases.
- Weekly research: the Customizable plan may be enough if this is the main scheduled workflow and the research scope is controlled. Budget $100 per month, or about $25 per weekly report. Use Extended if the runs are very heavy or if you are running several scheduled workflows.
- Monthly research: the Customizable plan should usually be the starting point. Budget $100 per month if you want the workflow available on demand, but the effective cost is high if you only run one report.
The clean recommendation: use Extended for a serious daily local-news research engine, start with Customizable for weekly or monthly research, and watch actual credit usage after the first few runs.
The output is not the finished content.
It is the raw material.
That is the point.
How to turn one story into multiple assets
Once a story scores well, I can turn it into several pieces of content:
- short-form video script
- Instagram caption
- newsletter blurb
- blog outline
- YouTube short description
- talking points for a market update
- follow-up post if the story develops
That is how one good local story becomes a week of content.
But again, the win starts with finding the right story.
AI did not replace the agent
This matters.
AI did not replace our agents.
It gave us more leverage.
The agent still needs judgment. The agent still needs local knowledge. The agent still needs taste.
But AI can help with the work that slows most people down:
- searching
- scanning
- summarizing
- comparing
- scoring
- drafting
- repurposing
That gives you more time to do the part humans are better at: deciding what matters and explaining it in a way people trust.
The bottom line
If you want better real estate content, do not start by asking AI for captions.
Start by asking it to find better stories.
The agents who win with AI will not be the ones posting the most generic AI-written content.
They will be the ones using AI to see opportunities earlier, research faster, and create around topics people already care about.
That is the difference.
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