The Data-Smart Way To Find Your Ideal Real Estate Clients

Most real estate teams chase static personas — “downsizers,” “move-up buyers,” “first-time buyers.” In the real world, your ideal customer profile (ICP) shifts constantly with the macro and micro real estate inventory and, yes, how connected your community is.

The tighter the social fabric, the clearer the movement patterns — and the easier it is to meet people where they’re headed. Here’s how to map and market to the ideal customer profile that’s represented in your communities and ZIP codes.

Start with turnover, not stereotypes

Before setting a budget or building marketing campaigns, analyze turnover in your target area over the past three months. Calculate it by dividing the total homes sold in the past three months by the total number of homes in the ZIP code you want to target.

How to calculate turnover yourself

Get total owner-occupied units → Your MLS, ACS or Census QuickFacts.

Get No. of sales in the period → Your MLS, Redfin, Zillow, County Recorder.

Formula: Turnover rate (%) = (homes sold ÷ total owner-occupied homes) × 100

Example:

• 92648 has ~11,000 households (ACS).
  
• If 110 homes sold in the past three months → turnover ≈ 1 percent quarterly or ~4 percent annualized.

The turnover percentage is a laggard and irrelevant; the important data is the list of 110 addresses. You’ll use this list to uncover who moved in and out.

How to obtain the consumer data on ‘who moved in and out’

• Run the addresses through Claritas, Melissa, or Mosaic to identify the “before” (sellers) and the “after” (buyers).
• Some providers (Melissa, CoreLogic, ATTOM) also offer change-of-address and deed transfer datasets, which is critical for tracking where the sellers moved to.

This profile data becomes your working ICP and the baseline for every decision you make next.

To gain actionable insights from this data, upload it into Perplexity / ChatGPT and begin asking questions. 

Here’s a prompt you can use in your farm to compare the mix of recent sellers versus recent buyers. This contrast tells you which segments are exiting and which are arriving:

I’m analyzing property turnover in [insert ZIP code]. I have address-level property transfer data showing recent sellers (those who moved out) and recent buyers (those who moved in). I’ve profiled each group using [Claritas / Melissa / Mosaic / CoreLogic / ATTOM] segmentation data.

Please compare the profile of recent sellers versus recent buyers. Identify:

• • Which demographic and lifestyle segments are leaving.
  • Which demographic and lifestyle segments are entering.
  • The key differences between sellers and buyers in terms of age, household composition, income, lifestyle clusters, etc.
  • What this contrast suggests about neighborhood change, turnover trends, and the evolving Ideal Customer Profile (ICP).

Finally, summarize what actionable insights I should draw from this comparison for targeting, marketing and positioning strategies in this farm.”

See who’s coming (and going)

Once you know the segment mix, get granular with your prompting. 

Prompts you can use:

• Which segments are most represented among the last 50 to 100 listings and sales? For example, are they simply retired, X in the city, blue collar boomers, etc.
• Which price ranges do they transact in? What amenities do they consistently trade toward (garage space, single-level living, yard size, walkability)? 
• Create an ideal customer profile “seller/buyer segment snapshot” for the top three lifestyle segments.

Don’t overlook the community

Neighborhoods with strong social ties often show clustered moves; friends follow friends across a few predictable corridors.

When your outreach strengthens those ties (through block events, school partnerships and hyperlocal groups), you’re not just “farming”; you’re shaping the pathways people use to make their next move.

Follow your segments to their next ZIP

Your sellers aren’t scattering; they’re clustering. Use change-of-address, deed transfer datasets and migration data (e.g., Redfin migration trends, NeighborhoodScout) to prove it.

Layer this data into Perplexity / ChatGPT and start prompting it with these questions:

• What are the top three ZIP codes (or cities) where recent sellers from our area have moved in the past few years? Does this pattern appear as clusters rather than random dispersion?
• Which demographic or lifestyle segments are most common among these outbound movers — and do these segment patterns align with any specific destinations?

You’ll often find clean handoffs — for example, local “suburban families” trading into nearby exurbs with larger lots, or “urban professionals” consolidating into transit-rich cores.

From there, build two lists:

• Outbound list: Homeowners in your farm who fit segments that fit the recent sellers profile.
• Inbound list: Homeowners (and renters on the cusp of buying) in your destination areas who match the segments most likely to move into your market.

Now your pipeline mirrors how people move.

Layer in the why: themes and triggers

Segments don’t move just because a spreadsheet says so. They move for themes you can plan around:

• affordability (rate and payment sensitivity)
• space (rooms, yards, garages)
• proximity (schools, caregiving, commute)
• life events (e.g., divorce), and lifestyle (walkability, recreation)

Create quarterly content around those themes for each segment snapshot — pricing explainers, side‑by‑side “payment for space” comparisons, commute-time maps, or “what you gain/lose” guides between origin and destination neighborhoods.

Protip: Keep it compliant. Build audiences from broad, aggregated data — not from protected characteristics — and use education-forward content that helps people compare options rather than pressure a decision. When discussing sensitive triggers like divorce, keep it high-level: Don’t target individuals or infer personal circumstances; make all outreach opt‑in and scenario‑based.

Size the play before you spend

Make sure the opportunity is worth pursuing before investing in marketing. Even if a target segment is a perfect fit, it won’t pay off if there aren’t enough prospects. 

If you find the total addressable market (TAM) is too small, expand your geographic area.

Make ICP your filter for every tactic

With segments, destinations and TAM in hand, run every tactic through the same filter:

• Creative: Show the trade people are making (“From tight townhome to half‑acre and a workshop in 18 minutes”).
• Offers: Lead with segment‑specific value (“Equity-to-payment plan for rate‑sensitive sellers,” “One‑level living tour for aging‑in‑place planners”).
• Channels: Match the media to the move (destination‑area paid social for inbound segments; direct mail and neighborhood events for outbound sellers; search for cross‑market queries).
• Cadence: Update segment snapshots monthly, rerun migration flows quarterly and revisit TAM before each new campaign.

A 15‑minute move‑mapping audit to grow market share

1. Pull three months of listings and sales for your farm. Compute turnover and note the top three price bands.
2. Overlay PRIZM or Mosaic for your farm. List the top three segments among recent sellers and buyers.
3. Use Census migration flows to find your top three outbound destinations. Pull PRIZM/Mosaic for those, and match inbound clusters to your outbound segments.
4. Draft one theme and one offer per top segment. Sanity-check TAM so you’re not over-investing in a micro-niche.

Do that, and your marketing stops guessing and starts mapping.

The payoff

When you market to motion instead of myths, your farm stops being a postcard route and starts running like a playbook. Communities with strong ties will show you their lanes, segmentation will name the travelers, and migration data will draw the map.

Meet people along that path, speak to the next chapter they’re already walking toward, and watch your market share move first.

Steven McCloskey is the owner of Motif46.