Propensity to Sell Data Benchmarks 2026: What Good Looks Like

Introduction

Most real estate investors are still buying lists the same way they did in 2018. Spray and pray. And then they wonder why their contact-to-deal ratio feels broken.

The shift that’s actually happening: predictive analytics and propensity-to-sell scoring are moving from “nice to have” to table stakes — and 2026 is the year the gap between investors using this data and those ignoring it gets harder to close.

HousingWire covered the tools driving this in early 2025, and by January 2026, economists from NAR — including Lawrence Yun, Nadia Evangelou, and Jessica Lautz — were already pointing to data-driven decision-making as one of the defining factors shaping where deals get done this year.

Sale propensity analysis isn’t new. But most investors are using it wrong (or not at all). Research via Diva Portal frames proactive engagement — not reactive list-pulling — as what actually separates operators who win from those burning through skip traces.

Pro tip: If you’re building your outreach strategy after a lead gets cold, you’ve already lost the timing game. Propensity scoring is about catching sellers before they’re ready to list publicly — that window is everything.

This article breaks down what “good” actually looks like in 2026, with real benchmarks to measure against.

Key Takeaways

• Propensity to sell scoring is moving from “nice to have” to essential in 2026.
• Predictive analytics are shaping real estate deals more than ever.
• Proactive engagement, not reactive list-pulling, sets successful investors apart.

What is Propensity to Sell Data Benchmarks for Real Estate Investors in 2026: What Good Looks Like?

At its core, propensity to sell is a score — a prediction of how likely a given property owner is to sell within a defined time window. Not “they own a distressed property,” but a ranked probability built from dozens of overlapping signals: equity position, time-owned, life events, tax delinquency, absentee ownership, and more.

The “benchmark” part is where most investors get fuzzy.

A benchmark isn’t just having the score. It’s knowing what a good score threshold looks like for your market, your outreach channel, and your offer type. Say you’re running cold calls into a mid-size Midwest market — a propensity score that works as a cutoff in Phoenix might leave you calling the wrong half of your list entirely. Context is everything.

NAR’s Research department — alongside economists like Nadia Evangelou, Lawrence Yun, and Jessica Lautz — published their 2026 outlook in January of this year, and the underlying theme is market uncertainty requiring smarter targeting, not more volume. Proactive engagement and strategic foresight aren’t buzzwords; they’re the actual mechanism that separates a 3% conversion list from a 10% one, per Diva Portal’s research on sale propensity modeling.

The shift here is real. HousingWire’s guide on predictive analytics for real estate — published March 2025 — maps out how machine learning models are now doing what manual list-pulling never could: weighting signals dynamically based on what’s actually predicting deals right now, not what worked two years ago.

Pro tip: Don’t benchmark your propensity scores against national averages. Pull a sample of your closed deals from the last 12 months and work backwards — what score range did those sellers actually carry? That’s your real floor.

Good benchmarks in 2026 look like calibrated thresholds, not raw scores. The investors winning with this data aren’t chasing every high-score lead — they’re narrowing to a defined band and dialing with intent.

Why This Matters for Your Business

Most investors don’t feel the cost of bad targeting directly. It bleeds out slowly — in wasted dials, burned-out callers, and deals that never materialize from lists that looked fine on paper.

That’s the real business case for propensity scoring. Not “better data” in the abstract, but fewer wasted touches on owners who won’t sell in any foreseeable timeline.

NAR’s research team — Nadia Evangelou, Lawrence Yun, Jessica Lautz, and Danielle Hale — flagged ongoing inventory constraints and affordability pressure in the 2026 Real Estate Outlook, published in January 2026. Tight markets don’t reward broad outreach. They reward precision.

Key Stat: A Diva Portal research paper on sale propensity analysis identifies proactive engagement and strategic foresight as the factors separating successful practitioners from everyone else — not bigger lists, not more dials.

Practically speaking, that means working a scored list in BatchLeads or PropStream looks fundamentally different than just filtering by equity or ownership length. You’re sequencing outreach around probability — highest-score owners first, with skip tracing and call cadence built around when they’re statistically most likely to be receptive.

The HousingWire guide on predictive analytics (published March 2025) makes a similar case: the tools exist, the data pipelines exist, and the main gap now is adoption discipline.

Here’s where I’d push back on the “just get better data” narrative, though — the data’s only as useful as the follow-up system behind it. A scored list sitting in a spreadsheet does nothing. Pair it with a dialer workflow and consistent outreach, and the math changes.

The business impact is operational, not just tactical. Fewer wasted contacts means your callers spend more time in real conversations and less time on dead ends.

Key Strategies and Best Practices

Most people treat propensity scoring like it’s a one-time list pull. Run the query, export the CSV, start dialing. That’s not a strategy — that’s just buying a slightly fancier list.

The real edge comes from building a feedback loop. Your scoring data should get sharper every week based on what’s actually converting, not just what the algorithm thinks will convert on paper.

Start with signal stacking, not single-variable filtering. A property with tax delinquency alone isn’t a great lead. Pair that with absentee ownership, 10+ years of equity, and a life event trigger — now you’ve got something. Tools like BatchLeads and PropStream both let you layer these filters, but most investors use maybe two or three variables when they could be running six or seven simultaneously.

Pro tip: Build two separate lists — a “hot” tier (4+ signals stacked) and a “warm” tier (2-3 signals). Work them with different cadences. Don’t burn your best leads with the same generic script you’d use on a cold equity pull.

The Diva Portal research on sale propensity analysis makes a point worth taking seriously: proactive engagement and strategic foresight are what separate operators who use this data well from those who don’t. That’s a fancy way of saying — don’t wait for a lead to “ripen.” If someone scores in the top tier of your propensity model, they’re likely being targeted by three other investors already.

Speed matters here. A lot.

On the tech side, Itransition’s predictive analytics overview outlines how platforms connecting tools like Power BI, Azure, and CRM systems (Salesforce, Dynamics 365) can tie property data directly into your outreach pipeline. You probably don’t need enterprise infrastructure for this. But you do need your data, your dialer, and your CRM talking to each other — even if it’s REsimpli and Mojo Dialer duct-taped together with a spreadsheet.

Three things I’d prioritize:

• Refresh your lists monthly. Propensity scores decay. A 90-day-old pull is stale.
• Track disposition outcomes back to the original score tier. This is how you validate whether your model is actually working.
• Don’t skip the manual review on top-tier leads. Run a quick BatchLeads property check before your caller dials — equity position, ownership length, any recent activity.

HousingWire’s 2025 guide on predictive analytics covers tool comparisons worth bookmarking, especially if you’re evaluating whether to build scoring in-house or pull it from a data provider directly.

One thing I’d actually skip? Over-engineering your scoring model before you’ve validated your follow-up process. A great list with a broken follow-up cadence still dies on the vine.

Tools and Technology Comparison

Not all propensity data is created equal — and the tool you use to source it shapes everything downstream: how fresh the signals are, how many variables feed the score, and whether you can actually act on it without a data science degree.

Here’s a quick breakdown of where the main platforms sit:

PropStream’s a solid starting point, honestly. But if you’re running serious volume — say 400+ dials a day across a focused market — you’ll hit the ceiling on its scoring depth fast. BatchLeads closes that gap a bit with its skip tracing layer baked in alongside propensity filters.

REsimpli’s underrated for teams that want everything in one place. The scoring isn’t the most sophisticated, but you can build disposition workflows, track your contact rates, and manage your follow-up sequences without stitching together five different tools. That matters more than people admit.

Pro tip: Don’t confuse a “motivated seller” tag with an actual propensity score. A lot of platforms slap that label on owners who haven’t paid taxes in 90 days. That’s one signal. A real score is pulling 20–40 variables and ranking output probabilistically — that’s the distinction worth asking vendors about directly.

HousingWire’s March 2025 guide on predictive analytics tools is worth a read if you’re evaluating platforms — it covers the main options without being a sponsored roundup.

For enterprise-level infrastructure, Itransition covers how Salesforce, Azure, and Power BI can be wired into custom propensity models — more relevant if you’ve got a dev team or you’re running an institutional operation, less so if you’re a team of three.

The honest take: most investors don’t need the fanciest stack. They need one tool with reliable scoring, connected to a dialer like Mojo Dialer or CallTools, and a CRM that tracks which score tiers are actually converting. Start tight. Expand once you know what’s working.

Step-by-Step Implementation

Pull a propensity-to-sell list. Export it to a spreadsheet. Start dialing. That’s what most investors do — and it’s also why most investors plateau.

A real implementation looks different. It’s a system, not a one-time action.

Step 1: Pull and segment your scored list. In BatchLeads or PropStream, filter by your highest-propensity tier first — don’t mix scores into one undifferentiated blob. You want three buckets: high, medium, and “watch list.” Work high first, always.

Step 2: Sync to your CRM before you dial a single number. Load the segmented list into REsimpli or whatever CRM you’re running. Tag by score tier. This sounds obvious but most people skip it — then lose track of who they’ve touched and at what stage.

Step 3: Build your call cadence around the score tier.

• High-propensity owners: 6-8 touch attempts, multi-channel (call + SMS + direct mail)
• Mid-tier: 3-4 attempts, calls only, then move on
• Watch list: Drop into a slow drip — monthly touch, no heavy dial volume

Pro tip: Don’t exhaust your best leads with 12 aggressive dials in a week. High-propensity doesn’t mean desperate to sell right now — it means the conditions are right. Patience + consistency wins here.

Step 4: Track disposition data obsessively. Every “not interested,” every callback, every “call me in 60 days” — that’s feedback your scoring model needs. REsimpli handles this reasonably well natively. If you’re running a bigger operation, something with Power BI integration (like the stack Itransition outlines) lets you actually visualize where conversions cluster by score band.

Step 5: Re-score monthly, not quarterly. Life events move fast. The NAR’s research team — Evangelou, Yun, Lautz, Hale — have been watching inventory signals shift faster than normal seasonal patterns heading into 2026. Stale scores cost deals.

Proactive engagement and strategic foresight — per Diva Portal’s analysis of sale propensity frameworks — are what separate investors who use this data well from those who just bought a fancier list.

Common Mistakes to Avoid

Most of the damage happens before anyone picks up the phone.

Mistake 1: Treating score thresholds as fixed. A “high propensity” cutoff that worked six months ago might be pulling in noise now. Markets shift — NAR economists Nadia Evangelou, Lawrence Yun, Jessica Lautz, and Danielle Hale flagged supply-side volatility as a watchpoint for 2026 — and your score floors need to flex with conditions, not stay locked where you set them at launch.

Mistake 2: Over-relying on a single data vendor. I’ve seen investors pull everything from one source and assume the model’s doing heavy lifting. It’s not always. Cross-referencing BatchLeads signals against PropStream — or even spot-checking with county records — catches gaps a single feed will miss entirely.

Mistake 3: Skipping the feedback loop. Predictive models need outcomes to improve. If you’re not tagging dispositions in REsimpli or your CRM and sending that back to your scoring logic somehow, you’re just running the same flawed model forever. Research published via Diva Portal frames proactive engagement and strategic foresight as the actual differentiators in sale propensity analysis — not just having the data.

Pro tip: Don’t call your high-propensity tier last. Sounds obvious, but dial priority often gets reshuffled when a VA or caller builds their own queue. Lock the call order at the list level before it exports.

Mistake 4: Confusing freshness with accuracy. A list pulled today isn’t automatically better than one from three weeks ago if it hasn’t been scrubbed. Stale phone numbers drag contact rates down fast — and HousingWire’s predictive analytics guide covers skip-tracing as a non-negotiable step, not an optional cleanup.

One more thing I’d add: don’t ignore the signals that aren’t in your platform’s default score. Divorce filings, probate leads, code violations — those aren’t always baked in. Check what your tool is actually modeling before you trust the output blindly.

What This Means Going Forward

The gap between investors running propensity-scored outreach and those still working raw county lists isn’t closing — it’s widening. NAR’s research team flagged supply-side volatility as a defining watchpoint for 2026. That means markets are going to keep rewarding whoever gets to the right seller first.

Proactive targeting wins. Reactive dialing doesn’t.

Research on sale propensity analysis frames it plainly: the shift here is toward data-driven decision-making, and the investors who build that muscle now won’t have to scramble later. I’d honestly argue most people are still one or two steps behind on this — and don’t realize it until their pipeline runs dry.

Pro tip: Don’t wait until your current list “stops working” to reassess your scoring model. By then you’ve already burned weeks of dials and goodwill with your callers.

HousingWire’s predictive analytics guide is a solid next read if you want to go deeper on tooling and methodology.

Here’s the one thing to do this week: pull your last 90 days of conversion data, map it back to the propensity scores those deals came from, and recalibrate your threshold. Don’t guess at what “high propensity” means — let your own results tell you.

If the outreach side is the bottleneck, book a strategy call with our team at Televista and we’ll walk through what a scored-list calling campaign actually looks like in practice.

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