There’s a saying from the early days of baseball – often misattributed to Yogi Berra or Ted Williams, but most likely born from the bat of Wee Willie Keeler in the 1890s: “Hit ‘em where they ain’t.”
It’s not just a lesson in slap hitting. It’s strategy in its purest form: win by aiming where the competition isn’t.
Right now, in real estate content marketing, agents are still swinging at the same pitches. And AI is only making it worse.
Everyone’s churning out the same blog posts:
- “Top 10 Restaurants in [Your Town]”
- “Best Parks for Dogs in [City]”
- “Pros and Cons of Moving to [Place]”
Helpful? Maybe. But also shallow, saturated, and dead on arrival in search results. That’s not strategy. That’s sameness. And AI didn’t come here to make you average faster.
Look
If you’re using AI to write what humans were already doing well, you’re automating the noise, not rising above it.
But if you use AI to uncover what humans can’t, trends hidden in sales data, buyer movement patterns, and pricing shifts across neighborhood lines, that’s when you start hitting where they ain’t.
That’s how you stop competing and start leading.
Most Use AI Like an Intern
They’re using ChatGPT to bang out low-value listicles and surface-level guides.
Fast? Sure. Cheap? Definitely.
But so is every other agent with a login.
If your blog content could be replaced by a few Google searches and a Fiverr rewrite, you’re not building a brand. You’re building filler.
The real opportunity isn’t in creating more content.
It’s in creating unduplicatable content.
Here’s What AI Shouldn’t Be Writing
These types of blogs can be useful, but they’re also dangerously easy to clone, commoditize, and skip:
- “Weekend Things to Do in [Town]”
- “Best Neighborhoods for Families in [City]”
- “First-Time Homebuyer Checklist”
These posts are human-range. Anyone can write them. So AI writing them doesn’t make you special, it just makes you faster at being forgettable. Ouch.
Here’s What AI Should Be Writing
1. Micro-Trend Market Analysis
“What $1.2M homes in [ZIP] are doing that $950K homes aren’t”
Export all listings from the past 60 days in your MLS. Filter into two price tiers (e.g. $900K–$999K and $1.1M–$1.3M). Use ChatGPT to compare each group’s average DOM, price/sqft, number of photos, and listing language. Ask AI to summarize 3–5 key differences and what they imply about buyer expectations at each tier. See our SOP on how to pull this off
2. Out-of-State Buyer Mapping
“Where California buyers are landing in [Town], and why it matters”
Pull leads from your CRM with out-of-state phone numbers or “move from” info. Sort by state (e.g. California, Oregon), then cross-tab their target neighborhoods. Drop the dataset into AI and ask it to group incoming states by most common destination and budget. Highlight patterns: walkability, school zones, or value-driven moves. See our SOP on how to pull this off.
3. Price-Per-Square-Foot Heatmap
“Where $500/sqft still gets you value – and where it’s overpriced”
Export sold listings from the past 90 days with price and square footage. In Google Sheets, calculate price per sqft. Group by ZIP or neighborhood. Ask AI to identify which areas are below, near, or above the market average. Create a color-coded table or map showing where buyers still get value. See our SOP on how to pull this off.
4. Historical Forecasting
“How interest rates have shaped [City]’s home prices over the last 30 years – and what’s next”
Grab historical average interest rates (FRED or Bankrate) and pair them with local home price trends from MLS archives or public records. Input both into AI and ask it to chart correlations – e.g. “What happened to home prices when rates rose above 7%?” Then ask AI to forecast what a future rate shift could mean for pricing. Take it with a grain of salt and publish away!
5. Real Cost of Ownership Report
“It’s not just your mortgage. What it really costs to live in [Neighborhood]”
Choose 3–5 homes from the same price range across different neighborhoods. Gather estimated taxes, HOA dues, insurance, utilities, and any Mello-Roos or hidden fees. Ask AI to create a side-by-side monthly cost breakdown to show buyers that a $700K home in one area can cost more monthly than a $750K home in another.
6. Sentiment Index by Neighborhood
“The neighborhoods locals actually love – based on 3,200 reviews and posts”
Scrape Tripadvisor, Nextdoor, Reddit, and public forums. Use natural language processing to score neighborhoods by tone, frequency, complaints, and praise. Show where it feels good to live.
Bonus. Or The Opposite: What Locals Complain About
“The 3 things neighbors actually hate – based on 2,000 online posts”
AI summarizes sentiment across Reddit, Nextdoor, Google, Tripadvisor, and Facebook Groups. You frame the “complaint clusters.”
— This is how you create content that can’t be substituted —
AI doesn’t just write.
It processes. It connects dots. It finds what humans miss.
And when you combine that with your human insight, local nuance, and real estate strategy, you create content that no lazy blog farm can replicate.
You’re not faster. You’re better.
So Stop Hitting Where Everyone’s Standing
“Top 10 restaurants” will get outranked by TripAdvisor.
“Best parks for dogs” will get beat by Yelp.
But a deep-dive into buyer behavior by school zone, renovation ROI by block, or neighborhood sentiment analysis?
That gets shared. That builds trust. That gets leads.
Hit where they ain’t.
Write what they can’t.
If this is all too much – we’re here to help turn this kind of complexity into your unfair advantage. You focus on clients. We’ll make the blog magic happen.
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