You've priced a listing carefully. You used recent comps. You consulted the latest MLS statistics showing the market is "stable." And now your listing is sitting.
What happened?
The problem isn't your pricing instincts. It's that those broad market statistics you're relying on probably don't reflect what's actually happening in your specific neighborhood.
Let's look at a real example. The Pikes Peak MLS report for October 2025 shows an average sales price of $565,356 and a median sales price of $479,450. Compared to October 2024, that's a -2.5% change in average price and -0.3% change in median price.
Seems reasonable, right? The market is basically flat.
Except it's highly likely these numbers don't represent your specific listing. Are there areas of town that have increased? Absolutely. Are there areas that have decreased more than 0.3%? Absolutely.
The statistics are so generic that they often don't apply to individual properties. You're making pricing decisions based on data that averages together everything from Old Colorado City bungalows to Black Forest estates.
So maybe you get more specific. You look at niche market reports like the CAR Local Market Update.
For October 2025, the Black Forest/Elbert market shows a median sales price decrease of 19.5% over the last year. That's dramatic - surely that's more actionable than the county-wide data?
But here's the issue: In October 2024, there were 6 sales with a median of $934,500. In October 2025, there were 6 sales with a median of $752,500.
Only 6 sales each month. In a market area with a wide range of home types and prices.
That 19.5% decrease probably has more to do with different types of homes selling than homes actually losing that much value. The median is being skewed by which specific properties happened to close that month.
Look at Calhan/Ramah in the same report: 9 sales in 2024, 8 sales in 2025. The median shows an increase from $403,810 to $537,500. But with such small sample sizes, a single luxury sale can swing the entire statistic.
When you compare medians at two points in time, you're ignoring everything that happened between those points - which is exactly where all your comparable sales are likely coming from.
Plus, medians can be significantly skewed by an outlier or two when sample sizes are small. You're not getting a true picture of market movement; you're getting a snapshot of which specific properties happened to sell during those two months.
Instead of comparing two median points in time, you need to look at the actual trend across all sales.
Here's how it works:
Why four timeframes? Because markets don't move in straight lines. Looking at multiple timeframes helps you spot when the market direction changes. Maybe the 12-month trend shows slight appreciation, but the 3-month trend shows prices leveling off or declining. That's critical information for pricing a listing today.
I built PropertyBrain specifically to solve this problem. It runs this exact analysis and gives you the trends for your specific market area. Check it out here.
You can do this same analysis yourself:
The trendline equation will look something like: y = 150x + 450000
That "150" means prices are increasing by approximately $150 per day in that market. Multiply by 30 for monthly change, or 365 for annual change.
Even if you don't run your own analysis, you can use this knowledge to critically evaluate published statistics:
Here's how you know if your trend analysis is accurate: Apply it to your sold comps.
If you've correctly identified the market trend, your time-adjusted sold comps should fall pretty closely in line with your current under contract and pending listings. Sometimes with active listings too, if they're priced well and selling close to list price.
If your adjusted sold comps are significantly higher or lower than what's currently going under contract, your trend analysis needs adjustment.
This verification step is crucial - it's your reality check that you're seeing the market accurately.
A quick note on defining your market area (we'll dive much deeper into this when we talk about comp selection in a few weeks):
Think like the buyer.
If a buyer had a home search set up and your listing appeared in their results, what else would show up? What neighborhoods? Just your subdivision or several nearby ones? What price range, size range, design style, year built, and garage count would that typical buyer consider?
Your market area should reflect realistic buyer behavior, not arbitrary boundaries like zip codes.
Those county-wide statistics aren't wrong - they're just not specific enough to be useful for individual pricing decisions. And niche market reports can be too easily skewed by small sample sizes.
You need hyperlocal trend data that actually reflects what's happening in your specific market area. Whether you use a tool like PropertyBrain, create your own Excel analysis, or simply approach published statistics more critically, the goal is the same: understand what's really happening in your neighborhood, not what's happening across the entire county.
Your listings - and your clients - deserve better than generic market data.
