$210K with a two-person team on county data
$210K from 7 deals with only $5,300 in expenses. Two people and county data.
About
Kyle Blake is new to the real estate investment arena. He began his career working for an acquisitions company in St. Louis, where he gained insight into real estate investments and discovered DataSift. Obtaining his license, he ventured out on his own, honing in on his market niche and enhancing his expertise through DataSift.
Operating market
Kyle confidently maneuvers the vibrant real estate scene in St. Louis, Missouri, employing savvy strategies to secure lucrative opportunities.
Favorite features
The Challenge
Kyle started probate wholesaling in St. Louis in September 2023 with no probate experience. No pipeline, no aged database, no team beyond himself. His early calling was random: dial whatever records he could find and hope motivation showed up. Without a niche, every conversation started cold and nothing compounded from one week to the next.
The doubt that comes with a small operation never fully went away either. Kyle went almost all of the first quarter of his $210K year without closing a deal. The next year opened the same way: 15 houses walked, 13 offers made, nothing locked up yet. In his own words, there is a little bit of a fear for when the next deal is going to come. What changed his trajectory was not more volume. It was dropping random calling for first to market probates and not-interested follow-up, a system he picked up in his first couple months on DataSift.
What They Built
Pulling probate data straight from the county, every day
Kyle hired one VA with a single job: pull fresh probate filings from the county every single day. About 200 probates a month fit his criteria. No list vendors and no stale data, so he is often the first investor a family hears from. His entire expense line for the year landed at $5,300.
We do first to market probates and we made right around 210,000. I just looked at my expenses the other day for my accountant and my taxes. And I spent like 5,300 bucks or something like that.
One channel, one caller, zero texts
Kyle runs a single channel. He click-to-calls the daily list himself and skips SMS entirely. One person calling 200 tightly filtered records a month beats a team spraying thousands of stale ones. The channel discipline is what holds overhead to an average of $441 a month.
Turning not-interested probates into his first closings
His first traction came from the pile most investors write off. Within his first couple months on DataSift, structured follow-up on not-interested probate leads produced three deals. That aged-lead discipline stayed in the system as the operation matured.
Keeping spreads big on only seven deals
Low volume forces deal quality. Seven closings carried the entire year because the average spread held right around $30K.
I think the biggest deal we closed was 56k. We had a couple of $50,000 deals, a couple 30,000. I think the lowest one was 17.5 that we did last year. I think we did that over seven deals.
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
Kyle faced data disarray and operational inefficiencies in his real estate pursuits until an illuminating 'A-ha!' moment unfolded with the integration of DataSift. This platform harmonized his scattered data into a centralized system, instantly transforming chaos into a well-organized database. This shift eradicated manual tasks, empowering Kyle with efficient lead management capabilities.
With DataSift's structured automation, Kyle witnessed a profound change in his workflow, drastically cutting down time spent on lead acquisition. Armed with an organized database, he swiftly extracted crucial insights, elevating his decision-making and confidence in navigating real estate's competitive landscape. This transformative revelation marked a pivotal turning point, propelling Kyle towards leveraging data effectively and amplifying his success in the industry.
"Anybody that's hesitant about jumping in, join the team, join the community, it'll change your life for sure."
The Results
The year's ledger: $210K in assignment fees across 7 wholesale deals, an average spread right around $30K per deal. The biggest was $56K, a couple came in near $50K, a couple around $30K, and the smallest was $17.5K. Against that revenue sat $5,300 in total expenses, roughly $441 a month. That works out to about 2.5 percent of revenue spent to earn it.
Before DataSift, Kyle was calling random lists with no probate experience. Within his first couple months on the system he pulled three deals from not-interested follow-up. A year later the same two-person operation, Kyle plus one data VA, worked about 200 probates a month into $210K. All seven closings were wholesales, no listings.
We pull the list every single day. Just myself, cold calling. I don't even send text messages.
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