Overview
Filtering parcels at the subdivision level can help you identify highly targeted opportunities within a market. However, it’s important to understand that this strategy is more time-intensive and typically produces smaller datasets compared to broader approaches like zip code or county-level filtering.
In this guide, we’ll walk through how to identify and filter subdivision-level opportunities using Land Insights, along with key considerations to help you decide when (and if) this strategy makes sense.
When Should You Use Subdivision Filtering?
Subdivision filtering is best used when:
You already know a market well
You’ve identified a specific subdivision with strong activity
You plan to repeatedly market to the same area
For most users, broader strategies (zip code or county) will be faster, more scalable, and easier to manage.
Step 1: Identify a Target Market
Start by selecting a county or zip code you’re interested in.
Example:
Search for a county (e.g., Coconino County)
Enable MLS Comp Data
Step 2: Set Your Market Criteria
To properly evaluate demand, apply the following filters:
Acreage Range: Example: 1–15 acres
Timeframe: Set to 1 year
This ensures you’re looking at recent activity (not outdated demand)
MLS Comp Toggle: Turn ON
Step 3: Analyze Market Activity
Once filters are applied, zoom into the map to view activity.
You’ll notice:
Green markers = Sold properties
Blue markers = Active listings
What to Look For:
High number of sold (green) properties
Low number of active (blue) listings
This indicates strong demand and low competition.
Step 4: Identify a Subdivision
As you zoom in:
Look for clearly defined parcel grids (these often indicate subdivisions)
Confirm consistent lot sizing and layout
Once identified, evaluate:
How many properties sold in the past year
How many are currently listed
Step 5: Evaluate Pricing Trends
Before moving forward, analyze pricing:
Are current listings higher than past sold prices?
✅ Good sign — potential margin
Are prices declining or inconsistent?
⚠️ Risky — may be harder to sell
Ideal Scenario:
Past comps: ~$4,000/acre
Current listings: ~$5,000+/acre
This creates room for competitive pricing and profitable deals.
Step 6: Draw a Polygon Around the Subdivision
Once you’ve chosen a subdivision:
Use the Polygon Tool
Outline the subdivision area
Apply your filters:
Acreage
Ownership filters
Deduplication
Step 7: Review Your Dataset
After filtering, review your results.
Important to note:
Subdivision lists are typically small
Adding filters (like out-of-state owners) reduces them even further
Example:
Initial dataset: ~200+ records
After filtering: ~70 records
Key Limitations of Subdivision Filtering
Before committing to this strategy, keep these in mind:
Time-Consuming: Requires extensive map analysis
Low Volume: Smaller mailing lists
Disorganized Data: Multiple small datasets can become difficult to manage
Scaling Challenges: Hard to hit large mailing goals (e.g., 5k–10k/month)
Recommended Approach
While subdivision filtering can work, most users will benefit from:
Targeting zip codes or counties
Focusing on speed to marketing
Maintaining consistent mailing volume
Why This Matters:
Consistency beats precision in most cases.
If your goal is:
10,000 mailers/month → aim for 2,500 per week
Staying consistent with outreach will have a bigger impact than spending excessive time searching for the “perfect” subdivision.
What Makes a Strong Subdivision Market?
If you do pursue this strategy, look for:
20+ sold properties in the last year
Only 0–3 active listings
Stable or increasing prices
These are rare but highly targeted opportunities.
Final Thoughts
Subdivision filtering can be effective in specific scenarios, but it requires significant effort and doesn’t scale easily. For most users, focusing on broader markets and executing consistent marketing campaigns will produce better and faster results.
If you choose to use this strategy, treat it as a supplemental approach—not your primary one.
💬 Questions or need help?
If you have any questions, concerns, or run into issues with ownership data, our support team is here to help.
📧 Email us at: [email protected]
We’re always happy to assist.🤠