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
- A VA Works Every No-Contact Lead in DataSift
- Daily Follow-Ups Run From the Sift Line Tasks
- Nine Deals Under Contract After the Stacked Niche Switch
- Cold Callers Sent KPIs by Hand Outside the System
- Expensive PPC Spend With No Return
- Follow-Up Leads Vanished Into an Abyss in the Old CRM
- 60,000 Records a Quarter Feeding His Bulk Callers
- Four Contracts Signed in 10 Days
- Three Deals a Week, Two Weeks Running
- First Direct to Seller Push in 30 Years of Business
- A $10,000 Mail Campaign That Produced Nothing
- 15 Years Buying Only From the MLS and Wholesalers
- New lender agreed 12 days after the first collapsed
- $105,696 net profit after closing costs
- Nine properties contracted at $360K, sold at $450K
- Hard money lenders would not lend on rural portfolios
- Local title companies refused assignments and passthroughs
- Seller would only sell one house, priced far over appraisal
- Still Runs on a $500 Monthly Budget
- An $85K Deal Off a 50-Name Stacked List
- 13 Contracts in Six Weeks From 250 Prospects
- About $40K Spread Across Six Months, Still Behind
- Five Contracts in a Row Fell Through
- Shotgun Lists Across Too Many Zip Codes
- One Contract Per 25-35 Leads, Half the Typical Ratio
- Each Caller Brings 16-18 Leads a Week
- 50,000 Free County Records Called on a Four-Month Cycle
- Two Partners Dialing Everything Themselves From 2017 to 2019
- VA Cleaned Every County Download by Hand in Excel
- Another Blanket ListSource Pull Whenever the Pipeline Ran Dry
- Same county-data system expanding into Texas and Miami
- $800K in revenue from six or seven deep-research deals
- About 20 people across data, calling, research, and sales
- PropStream tax delinquent lists arrived after owners had already paid
- Two years hunting a golden list that did not exist
- Bandit signs at intersections, cold calling all day from a coffee shop
- Four deals closed faster than his first three
- 8,200 dials and 25 leads in one November
- Four VAs running one calling and texting protocol
- Generic YouTube advice with no sequence to follow
- Back at a sales job four months after going full time
- Spent $35,000 on marketing with zero return
- About 35 Leads Per Deal Across 347 Total Leads
- $253K In Cash And Equity From One Package Deal
- 40 Properties Wholesaled In Under Four Months
- 12-Hour Days And A 45-Minute Commute
- No Deals Found Through Realtors, MLS, Or Craigslist
- Cold Calling From A Printed Spreadsheet In The Parking Lot
- Pulls Nearly All His Data Straight From the County
- Runs Jacksonville Virtually on $1,000 a Month
- Locked a Contract Four Days After His January Restart
- Scattered Across SMS, Facebook Ads, and a Website
- Partner's $2 an Hour Caller Hit a 1% Contact Rate
- Called Lists Three Times Then Trashed Them
- 50,000 sqft warehouses in negotiation, one to two leads daily
- $59,000 spread on his first big wholesale deal
- Every eviction filed in New Jersey, worked daily
- Old CRM buried callers in highlight and right-click drama
- Phenomenal months, then crickets for months
- Overnight pharmacy shifts, 84 hours in a single week
- Knows the playbook works and is replicating it
- First off-market contract four months after starting
- $250,000 assignment fee on deal one
- Weak cash position after funding ground-up projects
- Questioned whether real estate money was even real
- Two years spending on development deals, not making much back
- $40,000 month tracking with fixed costs
- Five first to market deals since April
- Probate filings pulled daily in multiple counties
- Two mail campaigns, response rates he did not like
- Costs fluctuated month to month with list spend
- Custom Podio build, broad absentee and vacant lists
- Owner buried beside her son after two years unclaimed
- Runs every heir search solo now
- Five heirs found, contacted, and signed across Texas
- Needed a specialist for her first deep heir search
- Paper records only, fathers listed by initials
- Probate leads dead-ended with no findable heirs
- Every No Recycled On A Two-Year Rehash Cycle
- Three VAs Clean Data Before A Single Dial
- Three First To Market Niche Filters Worked Daily
- Loose Onboarding And Under-Corrected Bad Hires
- Team Swung From Large To Small To Solo
- Broad Marketing Across Every Niche At Once
- Mornings and evenings back with his family
- 8 person crew handles whole projects in sets of two
- 40+ properties across three portfolios in about six months
- Afraid to take on payroll and a crew
- The bottleneck on his own projects
- Software job eight to five, real estate five to midnight
- 55% Answer Rate On One-By-One Click To Call
- Free County Data Replaced Every Paid List
- $45K Fee Closed One Week After The Callback
- Podio, Slack, And Multiple VAs, No Traction
- 30,000-Record Lists Dialed Two Or Three Times Through
- At Least $40K Wasted On Lists And VAs
- Four Marketing Attempts on a Couple of Lists
- A Three-Person Team at About $1,800 Cost Per Contract
- Almost Two Years Nomadic Across Asia, Europe, and the Middle East
- Behind a Jacksonville Desk 95 Percent of the Time
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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