Before You Spend a Dollar
I used Market Finder to identify Knox County as my target market. I pulled targeted lists in SiftMap using base criteria and distressors. Now I am going to walk you through what I did next. You need to run this same process in your market.
Sold Properties shows what actually sold in the last 6 months. Not what should sell. Not what you hope will sell. What did sell, to whom, and at what price. This is the difference between marketing based on assumptions and marketing based on evidence.
Sold Properties overview for Knox County, TN. This is my starting point. Yours will look like this for your county.
Access requirement: Sold Properties requires SiftMap Pro, included with Expert ($499/mo) and AI ($1,250/mo) plans. Also available as a standalone add-on at $297/mo.
The Validation Loop
A 4-step cycle you run quarterly. Each pass sharpens your filters and reduces wasted marketing spend.
1. Assume
Set base criteria in SiftMap using Market Finder data. Price range from the 60% window. Equity 30%+. Year built based on your market's housing stock age. Ownership 5+ years. These are educated guesses. Not facts.
2. Verify
Open Sold Properties for your county. Set the date range to the last 6 months. Switch to the Investor Transactions tab. Apply the AI score filter at 50 to 100. Now you are looking at what actually sold to investors in your target area.
3. Correlate
Which distressors show up on properties that sold? At what price points? In which zip codes? Click individual transactions to find buyers, see what they paid, and identify repeat purchasers. The patterns here are your marketing playbook.
4. Refine
Take your findings back to SiftMap. Tighten criteria that do not match sold data. Expand criteria where you are missing real transactions. Build refined presets. Run this loop again next quarter because markets shift.
Three Tabs, Three Perspectives
Each tab answers a different question. All three together give you the complete picture.
All Transactions
Every sale in your area. The baseline.
Investor Transactions
LLC/company purchases. Your competition.
In My Records
Properties in your CRM that sold.
All Transactions
This tab shows every completed sale in your selected county and time range. It is the baseline for understanding total market velocity. How many properties are moving? At what price points? This is not where you spend most of your time, but it establishes the denominator for your prediction rate calculations.
All Transactions view. Total market volume in your target area.
Investor Transactions
This is the primary analysis tab. It filters to transactions where an LLC or company purchased from an individual. This captures investor acquisitions from homeowners. It excludes LLC-to-LLC transfers, manufactured homes, and double closings. When you see 90+ investor transactions per month in Knox County, that tells you the market supports volume.
Investor Transactions tab. LLC/company purchases from individuals.
In My Records
The accountability check. This tab shows transactions that match properties already in your CRM. If you have 5,000 records in DataSift and 200 properties sold last quarter, how many of those 200 were in your database? A high match rate means your targeting works. A low match rate means your filters need adjustment. This is the tab that answers "am I marketing to properties that actually trade?"
In My Records tab. Your CRM properties that actually sold.
What the Data Tells You
Top Patterns breaks down every transaction by five dimensions. Click each one to learn what to look for.
Top Patterns view. Five dimensions of transaction analysis.
Price Range
Property Type
Year Built
Bed / Bath
Distressors
Price Range
Does your 60% price window from Market Finder match where investor deals actually cluster? If Market Finder said $200K to $600K but 70% of investor transactions are below $350K, your upper range is dead weight. Tighten the window and save marketing dollars on properties investors are not buying.
Property Type
Are you targeting the right asset types? Most investors default to single-family residential. But in some markets, townhomes or small multi-family represent a disproportionate share of investor transactions. If 15% of investor purchases are duplexes and you are only targeting SFR, you are leaving deals on the table.
Year Built
Your year-built filter in SiftMap is a starting point. Sold Properties reveals the real distribution. If you set "2000 or earlier" but the data shows a heavy cluster of investor purchases in 1960-1980 construction, you know where the renovation-ready inventory sits. Adjust accordingly.
Bed / Bath Count
Bedroom and bathroom distribution tells you about the exit strategy landscape. If 3-bed/2-bath properties dominate investor transactions, that is the bread-and-butter flip profile. If 2-bed/1-bath properties show up at a disproportionately low price point, that might signal a wholesale opportunity most investors overlook.
Distressors
The most important pattern. Which distressor flags were present on properties at the time of sale? Free and clear might represent 40% of transactions. Senior owners might be 25%. Tax delinquent might only be 8%. This distribution tells you exactly which lists to prioritize in SiftMap. Stack the distressors that correlate with actual sales, not the ones that sound motivated.
The Lists That Actually Convert
Stop guessing which distressors matter. The sold data shows you which ones appear on properties that actually sold.
Distressor flags on investor-purchased properties. The data picks your lists.
Free & Clear
No mortgage. High equity means more flexibility in deal structure. Often the single largest distressor category in sold data.
Senior Owner
Second highest reason people sell. Life transitions drive motivation. Target with multi-channel niche sequential marketing.
Absentee Owner
Combined with senior status and multi-property ownership, absentee owners are gold leads. The triple-stack produces the highest quality lists.
Tax Delinquent
7.5% of FL private sellers are tax delinquent. Smaller volume but high motivation. A reliable niche sequential marketing list.
Multi-Property (3-6)
Ty's 2021 niche: 14 contracts, roughly 3 houses each, $1.3M revenue. Owners managing multiple properties are more likely to sell one at a discount.
15+ Years Owned
"Tired landlord" equivalent. Small niche list, high conversion. Long-term owners often have significant equity and declining motivation to manage.
Assumptions vs. Reality
List Selection
"I picked tax delinquent because it sounds motivated."
Price Range
"Market Finder said $200K-$600K, so that is my SiftMap filter."
Geographic Focus
"I chose the top 3 zip codes by volume from Market Finder."
List Selection
"42% of investor transactions had free and clear status. Tax delinquent was only 8%. Free and clear is my primary list."
Price Range
"70% of investor purchases were below $350K. My upper range was dead weight. Tightened to $150K-$350K."
Geographic Focus
"Top volume zip had 112 DOM. Zip #4 by volume had 47 DOM and more investor transactions. Swapped my focus."
Does the Algorithm Work?
Sold Properties shows AI scores at time of sale. This lets you measure whether the prediction model matches reality in your market.
AI score distribution on investor-purchased properties. Prediction rates above 60% validate the model.
List Size
Full volume. All properties matching base criteria and distressors.
Signal Quality
Includes low-probability properties. Marketing spend is diluted across unlikely sellers.
Best For
Bulk sequential marketing where volume justifies broad targeting (20K+ records).
List Size
Roughly 50% reduction. The AI filter cuts the list to properties with higher sell probability.
Signal Quality
Higher concentration of investor-likely properties. Marketing spend hits harder per dollar.
Best For
Niche sequential marketing where quality matters more than volume (under 1,000 records).
Three AI Score Types
Investor Off-Market
Predicts likelihood a property sells to an investor off-market. The primary score for acquisitions-focused operators.
Included in AI plan ($1,250/mo)Realtor Score
Predicts likelihood a property lists on the MLS. Useful for agents and novation strategies.
$297/mo add-onInvestor On-Market
Scores MLS-listed properties nationwide for investor purchase likelihood. Built for novators and on-market buyers.
$297/mo add-onWhere Investors Actually Buy
My Market Finder analysis identified top zip codes by volume. Sold Properties confirms which ones have real investor activity. Do the same comparison in your market.
In my Knox County analysis, Market Finder surfaced zip codes like 37914 (East Knox) and 37918 (North Knox/Fountain City) as high-activity areas. But total sales volume and investor transaction volume are not the same thing. A zip code can have 50 total sales and 30 investor transactions. Another can have 80 total sales and only 5 investor transactions. Sold Properties separates signal from noise.
Switch to the Investor Transactions tab. Sort by zip code. Compare the investor transaction count against your Market Finder rankings. You will often find that your #1 volume zip is not your #1 investor zip. The zip codes with the highest ratio of investor transactions to total sales are your best targets for niche sequential marketing.
Do
Cross-reference Market Finder zip rankings with Investor Transaction counts. Focus on zips where investors are actively buying, not just where homes are selling.
Don't
Chase the highest-volume zip without checking days on market. Knox County's 37902 (downtown) shows 112 DOM despite high volume. That is slow absorption and a warning sign.
Sharpen the Signal
Sold Properties offers advanced filters to slice the data. Each filter narrows the view to answer a specific question.
Advanced filter panel. Layer these to isolate the exact transaction profile you want to study.
Filter by how long the seller owned the property before selling. Ranges: 0-3 years, 3-5 years, 5-10 years, 10-20 years, 20+ years. If your SiftMap base criteria uses "5+ years owned," check whether most investor transactions match that threshold. If 40% of investor purchases came from owners with 10+ years, your 5-year minimum is confirmed. If most come from 0-3 year owners, you may be targeting the wrong profile.
Custom equity range (0-100%). Your SiftMap base criteria likely uses 30%+ equity. Sold Properties validates whether that threshold matches reality. If the median equity on investor-purchased properties is 77% (like Knox County foreclosures), your 30% floor is correct but you might create a separate high-equity preset at 60%+ for premium targeting.
Select multiple distressor categories to see overlap. How many investor transactions involved properties that were both senior-owned AND free-and-clear? This stacking analysis tells you which distressor combinations produce the richest lists. The triple-stack of absentee + senior + multi-property is where conversion rates peak.
Filter transactions by construction year. This validates your SiftMap year-built setting. If you set "2000 or earlier" but the data clusters in 1960-1980, you know the renovation-ready inventory in your market has a specific age profile. Use this to tighten your SiftMap filter and avoid spending marketing dollars on newer homes with less motivation.
Save your validated filter combinations as presets for quick access. Create separate presets for different analysis angles: "Knox County Investor 6mo," "High Equity Senior," "Sub-$300K Distressed." Organize into folders by market or analysis type. Revisit quarterly to confirm the patterns still hold.
The 20-Minute Validation Routine
Run this routine before launching any new marketing campaign. Seven steps. Twenty minutes. Saves thousands in wasted spend.
Open Sold Properties
Navigate to Sold Properties in DataSift. Select your target county. I am using Knox County as my example. Use your county.
Set Time Range
Last 6 months. This captures seasonal patterns without drowning in stale data. For a first pass, 6 months gives you enough volume to spot trends.
Switch to Investor Transactions
Click the Investor Transactions tab. This filters to LLC/company purchases from individuals. Ignore total sales. Investor transactions are your competition and your market signal.
Review Top Patterns
Check price range distribution, property type breakdown, year-built clusters, and distressor flags. Write down anything that surprises you or contradicts your SiftMap filters.
Click Individual Transactions
Find buyers. See what they paid. Identify repeat purchasers (2+ purchases means active buyer). Note which neighborhoods they are concentrating in.
Document Your Findings
Record the key patterns below. This becomes your validation record for comparing against next quarter's analysis.
Adjust SiftMap Filters
Take your findings back to SiftMap. Tighten price range. Add or remove distressor layers. Build a refined preset. Your marketing now targets what is actually selling, not what you assumed.
Validation Notes
Close the Loop
Here is where the Market Analysis cluster comes together. I am comparing what my Market Finder analysis showed for Knox County with what Sold Properties confirmed. Do this same comparison in your market.
Median Sale Price
$314,000 (Knoxville city). Used to set the 60% price window for SiftMap base criteria.
Top Zip Codes
37914 (East Knox), 37918 (Fountain City), 37920 (South Knox) ranked by total volume and investor count.
Investor Volume
90+ investor transactions per month county-wide. Confirms the market supports acquisitions volume.
Investor Purchase Median
Check whether investor purchases cluster below the city median. If 70% of investor transactions are under $300K, your true price ceiling is lower than Market Finder's headline number.
Active Investor Zips
Confirm your Market Finder top zips have real investor activity. You may find a surprise zip with high investor ratio that was not in the original top 3.
Distressor Profile
Market Finder does not show distressors. Sold Properties fills this gap. The distressor distribution on sold properties becomes your SiftMap stacking strategy.
Terminology
Click any card to flip and see the definition.
Sold Properties
Click to flip
Sold Properties
DataSift tool showing completed real estate transactions with distressor flags, AI scores, and buyer data. The validation layer for every SiftMap filter assumption. Requires SiftMap Pro access.
Investor Transaction
Click to flip
Investor Transaction
A sale where an LLC or company purchases from an individual. Excludes LLC-to-LLC transfers, manufactured homes, and double closings. The primary analysis filter in Sold Properties.
Prediction Rate
Click to flip
Prediction Rate
Percentage of AI-scored properties that actually sold to investors. Rates above 60% indicate the model is working in your market. Measured by comparing AI scores at time of sale against actual investor transactions.
Top Patterns
Click to flip
Top Patterns
Aggregate analysis view in Sold Properties showing distributions across price range, property type, year built, bed/bath count, and distressor categories. The five dimensions that reveal your market's transaction fingerprint.
Distressor Correlation
Click to flip
Distressor Correlation
Mapping which property distress flags (free and clear, senior owner, tax delinquent, absentee) appear on properties at the time of sale. The insight that turns Sold Properties analysis into SiftMap list-building decisions.
The Validation Loop
Click to flip
The Validation Loop
Four-step cyclical framework: Assume (set SiftMap criteria), Verify (check Sold Properties), Correlate (match distressors to transactions), Refine (adjust filters). Run quarterly to keep targeting current.
In My Records
Click to flip
In My Records
Sold Properties tab filtering to properties already in your CRM. Shows whether your marketing targets actually trade. A high match rate validates your targeting. A low match rate signals filter adjustment needed.
AI Score
Click to flip
AI Score
Weighted 0-100 score calculated from 1,800 data points. Predicts likelihood of an investor purchase. Filter Sold Properties by AI score (50-100) to validate the model against real transactions in your market.
Base Criteria
Click to flip
Base Criteria
Starting SiftMap filters: asset type (SFR), estimated value (60% range), year built (market-dependent), years owned (5+), equity (30%+). These are hypotheses validated by Sold Properties analysis, not fixed rules.
Quarterly Review
Click to flip
Quarterly Review
Scheduled re-analysis of Sold Properties to catch market shifts. Compare current quarter's distressor distribution, price clusters, and investor activity against previous cycles. The third cycle is where assumptions stop and data-driven decisions begin.
Test Your Understanding
Seven questions on the Validation Loop framework. No trick questions.
1. What is the primary purpose of Sold Properties analysis in the Market Analysis workflow?
2. What are the four steps of the Validation Loop?
3. What does the "In My Records" tab show?
4. What does the Investor Transactions tab specifically filter for?
5. What AI score range is recommended when filtering Sold Properties for validation?
6. Why should distressor correlation from Sold Properties guide your SiftMap list building?
7. How often should you repeat the Validation Loop?
Resources & Further Reading
Continue building your Market Analysis workflow with these resources.
SiftMap Mastery
Build targeted property lists with validated filters. Your upstream list-building tool.
Market Finder Workflow
The 4-phase drill-down that identified your target market. Knox County deep breakdown included.
Foreclosure Analysis
Apply the same analytical rigor to any first-to-market niche. The 5-Lens Framework in action.
Sold Properties Help Article
Official DataSift documentation on the Sold Properties feature and all filter options.
Deal Flow Tech Stack SOP
Complete tool reference with pricing, setup guides, and blueprint recommendations.
Case Studies Library
Real investor results across all blueprints. See how validated targeting translates to closed deals.