How are you prioritizing distressed leads before skip tracing?
Investor · Hendersonville, NC · Member since 2016 · 498 posts · 286 votes
A big lead list can be a trap.
If I pull 5,000 “distressed” properties, I don’t really have 5,000 leads. I have 5,000 records that still need to be sorted.
The useful question is not just whether the owner is absentee, high equity, tax delinquent, or in pre-foreclosure.
It’s why this property is worth researching before the next one.
That could be repeat code issues, long-open violations, tax pressure, vacancy, failed listings, bad ownership history, or some other sign that the property has become a headache.
I’m interested in how other people are filtering before they spend money on skip tracing, mail, or calls. What actually moves a property to the top of your list?
great point on the pre-market side. For off-market leads I look for a combination of signals before spending any outreach budget — vacancy indicators, tax delinquency, code violations, and absentee owners tend to cluster together. A landlord showing up in two or three of those categories at once is almost always worth the call. The more distress signals stacking on one property the higher I prioritize it. Still refining my system but that layered approach has been the most efficient filter so far.
well we use Deal machine to pull data as bulk and make some cold callers go through them
and we get the answer of your question by making the CC ask them why they wanna sell,
the good thing here is once the cold caller sounds legit and truthful the owners drop their guard down and spell out the issue
Great question! My approach is to filter by days on market, price reductions, and vacancy signals first. Properties with 60+ days on market, multiple price cuts, and listed as as-is are my top priority before I spend any time skip tracing. I also look at the listing description for keywords like "motivated", "must sell", "estate sale" or "relocating" — those signal a seller who needs out fast.
Makes sense. DealMachine plus callers can work if the callers are good.
I’m more interested in what happens before the call list gets handed over. If I have 5,000 records, I don’t really want the caller figuring out live which ones are worth time.
Justice, I like the DOM/price cut/as-is angle for listed properties. I’m also trying to think through the pre-market version of that: code issues, tax problems, vacancy, repeat complaints, landlords who are just tired of dealing with the property.
That’s the part I’m working on too, how to narrow the list before spending money on the outreach.
great point on the pre-market side. For off-market leads I look for a combination of signals before spending any outreach budget — vacancy indicators, tax delinquency, code violations, and absentee owners tend to cluster together. A landlord showing up in two or three of those categories at once is almost always worth the call. The more distress signals stacking on one property the higher I prioritize it. Still refining my system but that layered approach has been the most efficient filter so far.
great point on the pre-market side. For off-market leads I look for a combination of signals before spending any outreach budget — vacancy indicators, tax delinquency, code violations, and absentee owners tend to cluster together. A landlord showing up in two or three of those categories at once is almost always worth the call. The more distress signals stacking on one property the higher I prioritize it. Still refining my system but that layered approach has been the most efficient filter so far.
That’s pretty much how I see it too. One signal by itself can be noise, but when vacancy, tax issues, code violations, absentee ownership, or other problems stack on the same property, it starts to look a lot more worth researching.
The hard part is getting that stacked view before you spend money on skip tracing or hand the list to callers.
The filter that actually compresses the list is motivation signal, not just distress signal. High equity plus tax delinquent is still noise if there's no filing attached. A lis pendens or probate filing means someone is already in a process with a deadline. That's what separates 'might sell someday' from 'has a reason to move this month.'
A big lead list can be a trap.
If I pull 5,000 “distressed” properties, I don’t really have 5,000 leads. I have 5,000 records that still need to be sorted.
The useful question is not just whether the owner is absentee, high equity, tax delinquent, or in pre-foreclosure.
It’s why this property is worth researching before the next one.
That could be repeat code issues, long-open violations, tax pressure, vacancy, failed listings, bad ownership history, or some other sign that the property has become a headache.
I’m interested in how other people are filtering before they spend money on skip tracing, mail, or calls. What actually moves a property to the top of your list?
Completely agree with this.
A “distressed list” without prioritization is usually just expensive noise.
The biggest shift for us came when we stopped treating lead generation as a volume problem and started treating it as a probability problem.
Properties with layered distress signals consistently outperform broad absentee/high-equity pulls.
Things like:
…usually create far more meaningful conversations than generic lists.
Most people spend too much time increasing lead count instead of increasing lead quality before outreach even starts.
That’s a good distinction. A distress signal tells you something may be wrong with the property or ownership situation. A motivation signal tells you there may be a timeline or event forcing a decision.
I still think the best lists usually have both. Code issues, tax problems, vacancy, etc. can point to property pressure, but something like probate, lis pendens, failed sale, or another deadline can make it more urgent.
The trick is figuring out which combinations actually produce conversations instead of just making the spreadsheet look more “distressed.”
Yeah. Both stacked is the goal. The filing just tells you which ones to sort first when you don't have time to work all of them.
Good question — this is actually where a lot of time gets wasted if there’s no filtering layer upfront.
From what I’ve seen, most operators don’t have a consistent “pre-skip trace” stage — they either:
skip trace everything (expensive + noisy), or
rely on gut feeling from lists (hit or miss)
The more structured approach I’ve seen is adding a quick pre-screening step before spending money on data enrichment or skip tracing. Things like:
basic equity position
rough condition signals (if available)
absentee vs owner-occupied
days on list / urgency indicators
simple cashflow or exit potential check
That first filter usually determines whether a lead is even worth paying to contact.
I’ve been experimenting with a lightweight tool that helps with this “first-pass prioritization layer” — basically helping rank and structure distressed leads before any skip tracing or outreach happens — but still refining it based on how different operators actually source lists.
Curious how others are doing it right now — are you mostly prioritizing manually, or using any scoring system before you run skip trace?
This is great topic I have been focusing on targeting more “distressed signals” within the zip codes we market in.
I have cold callers calling through the data and sorting them through mobiles and landlines cold call and use sms but I need to do a better stacking my lists with better distress signals like absentee owner a water shut off list thinking about getting deal machine which I’ve seen is pretty good in stacking data with certain list.
This is great topic I have been focusing on targeting more “distressed signals” within the zip codes we market in.
I have cold callers calling through the data and sorting them through mobiles and landlines cold call and use sms but I need to do a better stacking my lists with better distress signals like absentee owner a water shut off list thinking about getting deal machine which I’ve seen is pretty good in stacking data with certain list.
That makes sense
you’re already operating at the data + outbound level, so the next bottleneck is usually not “more lists,” it’s how cleanly you can prioritize and convert the signals once they hit your system.
Absentee + utilities + equity stacking is strong, but most teams still lose efficiency on the intake side (too many signals, not enough structured follow-up logic).
Curious
when your cold callers are flagging leads, how are you currently organizing and tracking which ones get immediate follow-up vs long-term nurture?
the stacking approach is right but what actually made a difference for me was automating what happens after the list gets prioritized. you can have the best filtered list in the world but if follow up isn't running consistently on those leads you're still leaving deals on the table.
built a system where once a lead hits a certain threshold of distress signals it automatically goes into a sequence. no manual sorting, no handing off to a caller to figure it out live. The prioritization and the outreach are connected so nothing just sits there
A big lead list can be a trap.
If I pull 5,000 “distressed” properties, I don’t really have 5,000 leads. I have 5,000 records that still need to be sorted.
The useful question is not just whether the owner is absentee, high equity, tax delinquent, or in pre-foreclosure.
It’s why this property is worth researching before the next one.
That could be repeat code issues, long-open violations, tax pressure, vacancy, failed listings, bad ownership history, or some other sign that the property has become a headache.
I’m interested in how other people are filtering before they spend money on skip tracing, mail, or calls. What actually moves a property to the top of your list?
Justice nailed it with the stacking concept — vacancy, tax delinquency, code violations, absentee owners clustering together is exactly the right filter. And Dominic, your follow-up is the real question: "The hard part is getting that stacked view before you spend money on skip tracing."
Here's what changed how I approach this: I stopped trying to build that stacked view from paid tools and started pulling it directly from county public records. Three independent government departments in every county already publish the exact data you're trying to cross-reference:
1. Tax Collector — tax delinquent lists (often free, sometimes published quarterly)
2. Code Enforcement — open violations, repeat complaints, boarded properties
3. Clerk of Court — probate filings, lis pendens, liens
When the same property shows up in all three — tax delinquent AND code violations AND a probate filing — that's not a guess. That's three independent government sources documenting the same distress from three different angles. Carson's distinction between distress signal and motivation signal is exactly this: the tax collector tells you there's financial pressure, code enforcement tells you the property is deteriorating, and the clerk of court tells you there's a legal process with a deadline. When all three converge on one address, you've got both distress AND motivation before you make a single call.
The best part: this costs $0. No DealMachine subscription, no paid lead list, no PropStream. Every county in America publishes this data on their websites. For Henderson County specifically — Henderson County Tax Office for delinquencies, Henderson County Code Enforcement for violations, Henderson County Register of Deeds and Clerk of Court for probate and liens.
Okechukwu asked what "headache" signals to look for first — the answer is whichever three your county publishes most consistently. Start with tax delinquent (almost every county publishes this), add code enforcement violations (most cities have this searchable), then layer in probate/lis pendens from the clerk. Where all three overlap on the same property, that's your priority list. No skip tracing needed to know those owners are under pressure — the government already documented it for you.
Andre mentioned water shut-offs as a distress signal — that's a fourth data source some cities publish. The more independent sources converge on the same address, the higher the probability. Sara Joe's framing of this as a probability problem not a volume problem is exactly right, and county records let you build that probability stack without spending a dime on data enrichment.