The Crow’s Eye View
Reverse-Engineering 6,600 Property Sales to Build a Lead Scoring System
We're not going to pretend we have proven conversion numbers or a huge client list. We don't. What we do have is 6,600+ closed residential sales from two Georgia counties, broken down and analyzed to figure out what actually matters when someone sells.
This is how we built our lead scoring model from the raw, messy data.
Why Most Lead Generation Fails
We've covered why most lead generation fails in a previous post. The short version: companies optimize for volume because it's easier to sell big numbers than quality filtering.
They come up with a basic schema, hit go, and never look back. Minimal effort for change so long as they scale it enough for profitability. They drown you in quantity and never worry about quality.
We wanted to build something different. Not more leads, better leads. To do that, we needed to understand what actually predicts a sale.
What We Analyzed
Every residential sale in Richmond and Columbia counties over the past few years. Every closed transaction.
We broke down:
Property characteristics: equity, ownership duration, improvements
Owner profiles: local vs. out-of-area, single property vs. multiple
Market context: sale price vs. assessment, time on market
Transaction patterns: seasonal trends, cash vs. financed
There are a lot of impressive sounding correlations, and I could probably woo someone with fancy graphs and charts, but those won’t close deals. We were looking for patterns that repeat in success.
We didn't look at web scrapes, social media signals, or predictive AI models. Just public records and MLS data for closed transactions.
Vacant Properties Are the Strongest Signal. 5% of all sales in our dataset involved vacant properties.
That doesn't sound like much until you compare it to how rare vacant properties actually are in the housing stock.
But here's what matters: certain agents specialize in vacant properties.
We found an agent who specialized in vacant properties, with some closing 5x the market average. She had other characteristics that shone in her sales that were intriguing, it showed her niche and how she excelled there.
She's not taking whatever walks through the door. She's built a model around vacant property acquisition. She knows how to find motivated sellers, structure offers, and close deals other agents skip.
That’s the kind of work we aim to support. Driven agents who know their market. We want to help them go a step further.
The Scoring Model
We built a 100-point system based on what showed up in recently closed sales. We’re analyzing long term data as well, but markets change, so this study was only 2024-2025 for relevancy.
A 75-point lead isn't guaranteed to sell. It means properties with those combinations showed up repeatedly in our closed sales. That's the difference between data and guessing. This may sound simple, but it was quite an undertaking. We’ll have to manually check the data for changing market conditions as we continue to ensure the validity of this model.
Local Market Context
National lead platforms use the same scoring everywhere. But our Georgia counties have a median sale price of $220k against a median assessed value of $95k. This suggests either rapid appreciation, lagging assessments, or both. Either way, it’s not a distressed market where sellers are desperate.
We see:
Properties selling at well above their assessed value.
High cash transaction rates
Significant out-of-area ownership
Sales concentrated in specific property types
Generic national scoring doesn't account for this. Our model was built specifically for markets like this one.
What We Don't Do
We don't predict the future. Anyone claiming they can is lying.
We don't guarantee conversion rates. Market conditions shift, agent skill varies, and seller motivation isn't always visible in public data.
We don't automate everything. Human review matters when data conflicts or edge cases appear.
What we do: identify properties that share characteristics with deals that already closed, score them by signal strength, and match them to agents who specialize in those transaction types.
We don’t believe in a one-size-fits-all methodology for our country’s real estate market. We’re focusing our efforts on the CSRA first. Here's what we have:
A scoring model built from actual closed sales
A few hundred high-scoring leads (60+ points) ready to go
Target agents identified who specialize in the property types we score highest
A hypothesis that these leads convert better than generic lists
What we're doing now
Calling agents and testing whether our leads interest them.
If the agent I mentioned looks at our vacant property list and says "send them," we've validated the approach. If she says "already have those" or "not interested," the model needs work. We’ll continue to refine it.
Analysis is worthless if the end user doesn't want the output.
Why This Is Different
Most lead companies optimize for volume because volume sells. Big numbers look good.
We optimize for signal. We'd rather deliver 100 leads with 7-8 conversions than 1,000 leads with 5 conversions.
The difference:
We started with what sold, not what sounds good
We built market-specific scoring, not generic formulas
We match lead types to specialist agents, not broadcast lists
We validate before we scale
This is the same discipline we used in intelligence work. Understand the environment. Test assumptions. Verify before committing. Don't mistake activity for results.
What's Next
We're in controlled testing. Calling agents who match our lead profiles, seeing if our analysis translates to interest.
If you specialize in:
Vacant property acquisitions
Out-of-area or inherited properties
Distressed or motivated sellers
Markets with cash transactions
We might have what you need.
If you're frustrated with generic lead lists and low conversion, we can show you a different approach.
Reach out to us at contact@crowseyellc.com or connect on LinkedIn. We'll look at your market and specialization, see if our data matches what you actually close.
—
Chris, Founder
Crow's Eye LLC
Data Shouldn’t Be a Wall
Data needs to be useful, or it’s just another obstacle
One big problem I keep seeing with data is it gets treated like a prize for gatekeepers. It’s not always intentional, sometimes it’s just too complicated and the team is missing a good translator. Other times it ends up locked up in expensive systems, buried under jargon, or hidden behind CRMs bloated with feature creep that either wasn’t asked for, or could’ve been prevented by better delivery to begin with.
You pay more, you click more, but you don’t actually see or understand more. You drown in detail and miss the point. It’s no wonder so many people throw up their hands and go back to gut instinct.
This is backwards and preventable.
Data should push you forward with confidence, not stand before you like cliff you can’t climb.
At Crow’s Eye, we don’t see data as some magic black box. We’ve been on both sides of this problem. We’ve seen useless datasets dressed up in beautiful delivery get funded and locked into contracts long after their actual value was gone. We’ve also worked with excellent datasets that got lost in translation.
Data is raw material. On its own, it’s messy and confusing. But cleaned up, shaped right, and put in context, it becomes something you can actually build with.
That’s why we want to talk about democratizing data. It isn’t about making everyone into a statistician or piling on more dashboards nobody checks. It’s about lowering the barrier so people can actually use the information that’s already there.
Data needs to be:
Readable. Strip out the clutter. Show the core facts in a way that doesn’t require three coffees and a decoder ring.
Accessible. Don’t just hand over raw exports. Give people a way to explore, filter, and compare without needing to learn a whole new system.
Enabling. Help more people bring their strengths to the table, whether it’s the owner who knows the market, the rookie with a sharp eye, or the veteran analyst who just needs a clean foundation to work from.
The truth is that most businesses don’t fail because they lack data. They fail because the data they have is too messy, too complicated, or too expensive to put to work. That’s how you end up with wasted time, bloated contracts, and smart people stuck making decisions in the dark.
It doesn’t have to be that way.
At Crow’s Eye, our work is about stripping out that friction. We build pipelines that keep the raw stuff clean, views that highlight what matters and skip the rest., and delivery methods that match what people actually need, not just what looks good on a slide deck. When data is handled right, it stops being a wall and starts being a tool.
Let’s stop hiding it behind contracts, complicated CRMs, and jargon. Let’s make it usable, shareable, and real. That’s how we all get further, faster, better.
That’s the work we’re doing at Crow’s Eye.
Let’s build this together.
Reach out to us at contact@crowseyellc.com or connect on LinkedIn
Why Most Real Estate Leads Fail (and How to Fix It)
Most agents don’t have a shortage of leads. They have a shortage of qualified ones. Lists, dialers, and online forms produce plenty of names, but only a few ever turn into clients.
Crow’s Eye is a veteran-run small business, built on disciplined analysis rather than volume.
The Conversion Gap
The National Association of Realtors reports that the average real estate lead conversion rate falls between 0.5% and 1.2%. For every 200 leads, that’s only one or two deals.
Top teams are converting 7% to 9%. That gap isn’t explained by more calls or longer hours. It comes down to better qualification.
What Qualification Really Means
Most lead companies call a prospect “qualified” if they filled out a form or clicked an ad. That’s not qualification. That’s just contact information with a timestamp.
True qualification looks deeper:
Readiness: Are there signs they’re preparing to act soon, based on behavior or circumstance?
Authority: Can they make the buying or selling decision, or are they just gathering info?
Motivation: Are they driven to move for downsizing, relocating, investing, or are they under financial pressure?
Fit: Does their situation line up with what the agent does best?
Context: How does this prospect’s potential value compare to what’s happening in the broader market and their specific neighborhood?
You can’t get this from a software filter. It takes judgment, market knowledge, and the discipline to pass on leads that look fine on paper but won’t close in reality.
Why Local Markets Matter
What works in Atlanta can fail completely in Savannah. Price points, buyer behavior, and local culture all shift how people act. Buyers in Atlanta’s northern suburbs often plan around school districts, while Savannah’s market is more influenced by lifestyle factors like proximity to the historic core or the coast. Those motivators change how prospects engage.
Most providers use the same playbook everywhere. They optimize for volume, not results
At Crow’s Eye, we look at each market on its own terms: recent sales, active inventory, seasonal patterns, and the local economy. We don’t just find people who might buy or sell. We find people who are likely to work with you, in your area, right now.
Using Tech the Right Way
Automation handles the heavy lifting of pulling data and spotting patterns. The best value comes when people review that data, add context, and decide which leads actually have intent.
We focus on questions that matter to an agent:
Which prospects show the clearest signs they’re ready?
What context about their situation can help you in the first call?
How should current market conditions shape your approach?
What This Means for Your Business
When you work with properly qualified leads:
Conversion rates improve because you’re speaking to people who are actually ready.
Your time goes further because you’re not chasing dead ends.
Confidence grows because you know the data behind your pipeline.
No shortcuts or inflated promises. Just disciplined analysis and leads filtered by people who understand what it takes to close in real estate.
Want to See the Difference?
We’re running test pilots in new areas. We’re also active in a few markets where we’ve already refined our process.
Crow’s Eye is a small business built by veteran analysts. We apply the same mission-tested discipline to market data that we once applied in the field.
Reach out to us at contact@crowseyellc.com or connect on LinkedIn. We’ll look at your market, your challenges, and see if our approach makes sense for you.
Real estate is a relationship business. Good relationships start with the right data.
—
Chris, Founder
Crow’s Eye LLC
Why We Built Crow’s Eye
Why we built Crow’s Eye
There is a lot of techno-babble corporate jargon heavy noise in the data analysis world. Everyone is pushing under-analyzed lists and dashboards to their customers. Most of it is fully automated using either machine learning or “AI insights” that are unsupervised and completely disconnected from the users that are supposed to benefit from it.
We decided Crow’s Eye would not be another data mill.
We come from an industry where a strong practical foundation in analysis is key. There’s a place for automation, machine learning, and even AI in a workflow that can greatly benefit the analyst and the customer, but they’re just tools to be added to your arsenal. As the saying goes, you can’t build a castle on sand. In our previous industry there was little to no room for guessing and very real world consequences with tight timelines. This experience has shaped how we view our work and why we’re focused, thorough, and precise from the point of discovery on through to delivery.
We built Crow’s Eye because small business don’t need more data, they need the right data, delivered by people who know how to deliver it to any audience.
We’re Not Playing The Same Game
We’re not trying to scale fast.
We’re not buying scraped lists and flipping them with new branding.
We don’t outsource the core work or replace our people with AI.
Every lead we send it out is reviewed by a human analyst. Every area is uniquely analyzed to help us reach maximum potential on the leads to our customers.
We use automation where it speeds up our workflow, but never to the point of reducing the quality of our analysis.
We’re small on purpose, it’s how we stay sharp.
The reason we put this team together isn’t just to make money. We are building a space for veterans seeking work and fulfillment after service, as well as displaced analysts whose value is being dismissed for the AI gold rush. We come from humble beginnings, and we remember what it’s like to do work that mattered for real people. We want to give that back to as many people as we can.
What This Blog’s For
This isn’t a place for us to posture and puff our chests at the competition.
This is where we talk about what we’re doing, what we’re learning, and how we think about and approach problems with analysis.
Sometimes it’ll get a bit into the weeds for the more technical crowd, sometimes it will be more personal, but we’ll be honest throughout.
We’re just getting started.
Welcome to The Crow’s Eye View.
Reach out to us at contact@crowseyellc.com or connect on LinkedIn.