Weekly deals from filtered pre-foreclosure lists
14 years in real estate. 1 lead per day from 1 caller using preset filters = 4-6 deals/month.
About
Since stepping into the real estate arena in 2014, Jordan Rabb has navigated the New England market with a keen eye for multi-family properties, working closely with realtors and leveraging financing strategies to grow his portfolio.
Operating market
Jordan's real estate journey is rooted in the diverse and competitive New England area, where he has honed his expertise in identifying lucrative opportunities.
Favorite features
The Challenge
Jordan Rabb spent 14 years in real estate, and 11 or 12 of them were part time. He ran a bar business, worked with a realtor, and chased multifamily properties with FHA loans and money raised from friends and family. Some years he bought nothing. Some years he bought one or two. When COVID closed the bars in 2020, he went full time and found wholesaling through YouTube and the Steve Trang podcast.
His first data setup showed the problem. A hired caller pulled lists from PropStream, dialed them four times on a three line dialer, and said the answer was more spend. One month cost $5,000 for the caller, the lists, and the skip tracing, and Jordan could not name a single data source behind his leads. Even after joining DataSift he stayed a skeptic. He left for four months to test a CRM with heavier automations, calls it "an enormous waste of time," and came back.
What They Built
Grading every data source by answer rate
Jordan runs his whole marketing operation on preset filters. Every record shows its source: pre-foreclosure, deceased, driving for dollars. When answer rates drop, he changes the list source or the skip trace, not the budget. He tested county court records against his old PropStream pulls and found the courthouse list reached sellers first.
When I talk to my cold callers, they're telling me, this data set is answering the phone, this data isn't. And really all that comes down to is the preset filter.
One caller, one short sale lead per day
His caller cannot dial the whole database in a day, so Jordan filters it to the records that hit hardest. Owner occupied pre-foreclosures answer worst, so vacant, out of state, deceased, and banking records get top priority, targeted for short sales. That daily set produces one short sale lead per day.
If we put one cold caller every single day, we know we will get one short sale lead out of them.
A three phase pipeline instead of bulk dialing
Phase one holds raw data nobody calls. Phase two is the go or no go queue his caller works daily, one attempt each. Phase three is where a prospector researches the no answers: age, deceased status, relatives, caller ID name checks. Only hand picked records get direct mail. He stopped bulk cold calling entirely.
Data traps that catch process errors
Jordan built filters he calls data traps: saved searches that should always return zero. A marketing record with an open task, or an acquisitions record without one, means someone broke process. His cleanup filters also reset voicemail counters when records leave phase two, so a returning record starts clean.
Before and After
Before DataSift
- Achieved Data Clarity
- Consistent Deal Flow
- Inconsistent Deal Flow
- Poor Marketing Flow
- Little Data Clarity
- Relied On Google Sheets
- Consistent Deal Flow
- Data Clarity / Organization
- Wholesales, Flips And Does Retail
- Strong Marketing Flow
- Didn't Understand Data Management
- Strong Real Estate Agent
- Wanted To Break Into Wholesaling But Didn't Know How
- Healthy Scale
- Data Driven Decisions
- Consistent Deals
- Strong Marketing Flow
- Inconsistent
- Little Data Insight
- Unorganized Bulk Marketing
- Strategic Focus on Niches
- Enhanced Lead Flow and Certainty
- Streamlined Marketing Process
- Efficient Use of Data
- Direct Mail & Deep Prospecting
- Broad, Unfocused Strategy
- Inconsistency in Results
- Dependency on Luck
- Marketing Clarity
- 6 Figure Months
- High Deal Volume
- Strong Marketing Flow
- Proper Data Management
- Weak Marketing Flow
- Poor Data Management
- Lack of Focus
- Manual Efforts
- Successful Scale
- Total Data Clarity
- Clear Marketing Approach
- Large Deal Spreads
- Improper Data Management
- Poor Marketing Method
- Inconsistent Deal Flow
- Strong Marketing Flow
- Clear Data Management
- Clear Data Management
- Successful Scale
- Many ways to close deals
- Large deal spreads
- Poor data management
- Unable to scale properly
- Poor marketing flow
- Smaller Deal Spreads
- Increased Deal Volume
- Platform Utilization
- Realtor Collaboration
- Funding Efforts
- Lots Of Deals
- Data Driven Decisions
- Strong Marketing Flow
- Sales And Marketing Clarity
- Poor Data Management
- Poor Marketing Flow
- Relied On Bulk Marketing
- Lack of Clarity
- Automated Acquisition
- Strong Marketing Flow
- Data Driven Decisions
- Structured Follow-ups
- Increased Lead Generation
- Achieved Sales And Marketing Clarity
- Manual Acquisition Processes
- Ineffective Marketing Flow
- Minimal Lead Insights
- Poor Follow Up Flow
- Inconsistent Lead Generation
- Lacked Sales And Marketing Clarity
After DataSift
- Achieved Data Clarity
- Consistent Deal Flow
- Inconsistent Deal Flow
- Poor Marketing Flow
- Little Data Clarity
- Relied On Google Sheets
- Consistent Deal Flow
- Data Clarity / Organization
- Wholesales, Flips And Does Retail
- Strong Marketing Flow
- Didn't Understand Data Management
- Strong Real Estate Agent
- Wanted To Break Into Wholesaling But Didn't Know How
- Healthy Scale
- Data Driven Decisions
- Consistent Deals
- Strong Marketing Flow
- Inconsistent
- Little Data Insight
- Unorganized Bulk Marketing
- Strategic Focus on Niches
- Enhanced Lead Flow and Certainty
- Streamlined Marketing Process
- Efficient Use of Data
- Direct Mail & Deep Prospecting
- Broad, Unfocused Strategy
- Inconsistency in Results
- Dependency on Luck
- Marketing Clarity
- 6 Figure Months
- High Deal Volume
- Strong Marketing Flow
- Proper Data Management
- Weak Marketing Flow
- Poor Data Management
- Lack of Focus
- Manual Efforts
- Successful Scale
- Total Data Clarity
- Clear Marketing Approach
- Large Deal Spreads
- Improper Data Management
- Poor Marketing Method
- Inconsistent Deal Flow
- Strong Marketing Flow
- Clear Data Management
- Clear Data Management
- Successful Scale
- Many ways to close deals
- Large deal spreads
- Poor data management
- Unable to scale properly
- Poor marketing flow
- Smaller Deal Spreads
- Increased Deal Volume
- Platform Utilization
- Realtor Collaboration
- Funding Efforts
- Lots Of Deals
- Data Driven Decisions
- Strong Marketing Flow
- Sales And Marketing Clarity
- Poor Data Management
- Poor Marketing Flow
- Relied On Bulk Marketing
- Lack of Clarity
- Automated Acquisition
- Strong Marketing Flow
- Data Driven Decisions
- Structured Follow-ups
- Increased Lead Generation
- Achieved Sales And Marketing Clarity
- Manual Acquisition Processes
- Ineffective Marketing Flow
- Minimal Lead Insights
- Poor Follow Up Flow
- Inconsistent Lead Generation
- Lacked Sales And Marketing Clarity
What changed
Jordan's discovery of DataSift through YouTube and subsequent participation in the auto lead gen challenge was transformative. Despite briefly exploring other strategies, he quickly realized the unparalleled value DataSift offered, crediting it as the cornerstone for scaling his business. The comprehensive support and marketing capabilities provided by DataSift, coupled with Tyler's insightful courses, were instrumental in his success.
Jordan Rabb's journey with DataSift illustrates the platform's impact on empowering real estate professionals with the tools and knowledge to significantly scale their operations and achieve new heights of success.
"Sift is the only reason I was able to scale my business".
The Results
Since 2023 the team has averaged four deals a month, and five or six a month in the second half of that year. Call it 4 to 6 deals a month, which is 48 to 72 deals a year, from one cold caller producing one short sale lead per day. When Jordan wants two leads a day, he pulls more data, filters it down, and adds a second caller.
Before the system he got one lead a month if he was lucky, on $5,000 a month of spend. Now he manages around eight leads a day and can trace every one back to its source, list, and skip trace.
Nothing beats just picking up the phone and following up with your leads.
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