Data-Driven CRE Prospecting: Using Property Data to Find Your Next Deal

The best CRE prospecting isn't random. It's targeted. And the brokers who consistently find deals before their competition do it by using data to identify who's most likely to transact — and why — before anyone else does.

This is data-driven CRE prospecting: using property ownership records, transaction history, financing data, and market trends to build a list of high-probability prospects and reach them at exactly the right moment.

The Data Sources That Matter

Not all property data is created equal. Here's what's actually useful for prospecting and where to find it:

County Assessor and Recorder Records

Every county maintains public records of property ownership, assessed values, and recorded documents including deeds, mortgages, and sometimes leases. This data is free, comprehensive, and often overlooked by brokers who rely solely on paid platforms. The challenge is that it requires more work to extract actionable intelligence — but that friction is also your competitive advantage.

CoStar and CompStak

For lease data specifically, these platforms are the most comprehensive available. Lease expirations, tenant names, square footage, and rental rates — all searchable by submarket and property type. If you're doing tenant rep, this is non-negotiable.

CMBS Loan Data

Commercial mortgage-backed securities loans are publicly reported through EDGAR and specialty providers. Loan maturity dates are gold for prospecting — owners with maturing loans often need to sell, refinance, or recapitalize. Finding them 12-18 months before maturity puts you in the right conversation at the right time.

Transaction Comps

Recent sale transactions in a submarket tell you who's been buying — and those buyers often want to keep buying. Building a prospecting list from recent acquirers in your market is one of the highest-conversion strategies available.

The Signals That Predict Transactions

Data is only valuable if you know what to look for. These are the signals that most reliably predict an owner or tenant is about to make a real estate decision:

For Building Owners (Sellers)

• Long ownership tenure (10+ years) — significant embedded gain creates motivation to harvest
• Loan maturity in 12-24 months — forces a decision: sell, refi, or find equity
• Recent major tenant departure — vacancy changes the economics and often the owner's plans
• Ownership by an estate or trust — beneficiaries often have different objectives than the original owner
• Out-of-market ownership — remote owners are often less connected to local brokers and more open to conversations

For Tenants

• Lease expiring in 12-18 months — the primary trigger for tenant rep assignments
• Significant headcount growth or reduction — space needs change with the business
• Recent funding round — often signals expansion; growth companies need more space
• Merger or acquisition activity — always creates real estate decisions
• Industry-specific pressures — regulatory changes, supply chain shifts, or market forces that affect a specific industry's footprint needs

Building a Data-Driven Prospect List

Here's a repeatable process for building a high-quality CRE prospect list from property data:

1. Define your target criteria — property type, size range, submarket, and the primary trigger signal you're targeting (lease expiration, loan maturity, long tenure, etc.)
2. Pull the raw data — from county records, CoStar, CMBS databases, or your preferred data source
3. Score by probability — how many trigger signals does this prospect have? Multiple signals = higher priority
4. Find decision-maker contact information — LinkedIn, company websites, county records, professional directories
5. Segment by timing and deal size — different urgency and effort levels for different prospect types
6. Build your outreach around the signal — reference the specific reason you're reaching out in your message

Turning Data Into Conversations

The power of data-driven prospecting isn't just in finding the right prospects — it's in the quality of outreach it enables. When you know a building owner has a loan maturing in 14 months, your email isn't a generic cold pitch. It's a timely, relevant message that demonstrates you understand their situation.

"Your loan on the industrial flex at 4200 Commerce Drive comes due in Q1 2027. Given where cap rates have moved in the submarket, I wanted to share a quick picture of what the disposition market looks like — in case it's relevant to your planning."

That email gets a different response than "I'm a broker who works in your area." The data is what makes the difference.

Scaling Data-Driven Outreach

The bottleneck for most brokers isn't finding the data — it's writing personalized outreach at scale for hundreds of prospects. A list of 200 building owners with loan maturities represents 200 different situations that all deserve a tailored message.

That's where MogulAim changes the equation. Import your data-driven prospect list, and the AI writes personalized outreach for each contact — referencing their specific property, market, and the signal that put them on your list. Sequences run automatically. You get notified when someone engages.

Data finds the opportunity. Personalized outreach opens the door. The deal happens when you show up with both.