The Best Property Data Software to Find Roofs That Need Replacing (Honest 2026 Comparison)

Every roofing owner who has ever paid for property data eventually asks the same question out loud, usually after a bad mailing campaign: which of these houses actually has an old roof? You bought the list. You paid for the skip trace. You mailed 5,000 postcards. And the three jobs you closed came from streets you could have walked yourself. The data told you who owns the house and what it sold for in 2014. It told you almost nothing about the one thing that decides whether you have a job there: how worn out the roof is.

That gap is the whole problem with most "property data software" when a roofer tries to use it for prospecting. The category was built for real estate investors, mortgage lenders, and insurance underwriters. Roofers borrowed it because it was the only data around. So you end up paying for a tool that's genuinely excellent at answering a question you don't have (who is the owner of record, what's the assessed value, is there a lien) and silent on the question you do have (is this roof 8 years old or 23 years old).

This breakdown is written from the contractor's chair. It walks through what each major category of tool actually does, who it's genuinely good for, what it costs in rough terms, and — most importantly — where it leaves you guessing. Then it gets specific about the one signal that actually predicts a replacement: roof age, expressed as a range, paired with the storm history that roof has personally absorbed. By the end you'll be able to look at any vendor's demo and know within five minutes whether it can find old roofs or whether it's just an expensive list of mailing addresses.

No tool on this list pays to be here. Where a product is good, it's called good. Where the data is borrowed from somewhere else and re-skinned, that's called out too. A tight trade compares notes honestly, so that's what this is.

The one signal that actually predicts a replacement

Before comparing any software, you have to be clear about what you're hunting. A roof gets replaced for one of three reasons:

1. It aged out. Asphalt shingles in most of the country give you somewhere between 15 and 25 years of real service life depending on the product, the slope, the ventilation, and the sun exposure. A 3-tab economy shingle on a hot south-facing slope in Phoenix is cooked years before an architectural shingle on a shaded northern roof in Minnesota. But age is the dominant driver. A roof that went on in 2003 is a candidate today no matter what.
2. A storm broke it. Hail bruises the mat and knocks granules loose; wind lifts and creases tabs. A 9-year-old roof that took a 1.75-inch hail core can need replacing years ahead of its calendar age.
3. It was installed badly or has a defect. Less common, harder to see from data, usually surfaces during the free inspection.

For prospecting, the two you can target at scale are age and storm exposure. Everything else is found on the ladder. So the real question for any property data tool becomes brutally simple:

Can this software tell me, house by house, roughly how old the roof is, and what storms that specific roof has taken?

Hold every vendor to that question. Most of them can't answer either half of it, and the good ones are honest about which half they cover.

Why "year built" is a trap

The single most common mistake roofers make with property data is using year built as a stand-in for roof age. It feels reasonable. A house built in 1998 is 28 years old, so the roof must be due, right?

Not even close. That roof has almost certainly been replaced once already — and the re-roof is invisible in nearly every property dataset. Year built tells you when the structure went up. It says nothing about the roof currently on it. In an established neighborhood, year-built data will send you to:

• Homes that were re-roofed three years ago (no job there, you just annoyed them)
• Homes that are genuinely due (good)
• Homes that were re-roofed eight years ago and are mixed in with the other two, indistinguishable

You cannot separate those three groups with assessor data. The re-roof rarely pulls a permit that's cleanly attributable, and even when it does, permit coverage is wildly inconsistent county to county. So a list built on "homes built before 2005" is mostly noise. You'll knock a lot of new roofs.

The same trap hides inside Zillow, Redfin, and county GIS portals: the field you see is year built or last-sale-year, not roof age. Treat any tool that leans on year built as a demographic tool, not a condition tool.

Expected service life, by material and exposure

To judge whether a roof-age range means a real job, you need a working sense of how long each roof type lasts. These are general field expectations, not guarantees — ventilation, slope, attic heat, color, and install quality all move them — but they anchor your targeting:

The practical lesson: an architectural-shingle roof reading 18–22 years old is a strong candidate; the same range on a standing-seam metal roof is noise. A targeting layer that can flag roof type alongside age saves you from chasing roofs that have another 20 years in them. When you read an age range, always read it against the likely material and the local climate. A 20-year-old roof in the relentless sun of central Texas or Arizona is further along than a 20-year-old roof in coastal Oregon, and good targeting bakes that exposure in rather than treating every region the same.

The categories of software roofers actually use

There is no single "roof replacement finder" category, which is exactly why this is confusing. Roofers cobble together tools from four different worlds, each built for someone else first:

The takeaway: a measurement tool measures the house you already chose. A property data platform tells you who owns it and whether they can afford you. A hail map tells you where weather happened. None of those, on their own, tells you which house is due. That's a fourth, narrower category — and it's the one most roofers don't realize exists, so they overpay for the other three and fill the gap with guesswork.

Let's go through the real tools.

Property and owner-data platforms

These are the heavyweights of "property data software." They are genuinely powerful. They are also almost entirely about the owner and the transaction, not the roof.

PropStream, BatchLeads, and the investor-data tools

What they're genuinely good at. PropStream and BatchLeads (and cousins like REISkip and ListSource) are investor-grade data engines. You can filter a whole county by equity, ownership length, absentee status, tax delinquency, estimated value, and pull skip-traced phone numbers and mailing addresses in bulk. For building a mailing list of homeowners who can afford a roof and have owned long enough to need one, they're efficient and cheap on a per-record basis. Pricing posture is subscription plus per-skip-trace fees — affordable for the volume you get.

Where they fail you. None of them know roof age or roof condition. The closest signal is year built, which is the trap described above. You can filter to "single-family, owned 12+ years, built 1995–2008, 60%+ equity" and you'll get a clean, mailable list — of homes whose roofs might be 2 years old or 22 years old. You're buying affordability and reachability, not roof condition. That's a real input (a broke renter is a bad prospect no matter how old the roof is), but it's the back half of targeting, not the front half.

Best for: Roofers who already know how to identify due roofs another way and need owner/mailing/equity data to enrich and reach them. Excellent enrichment layer, poor targeting layer.

Regrid, ATTOM, CoreLogic, and the parcel/data-licensing world

What they're genuinely good at. Regrid offers nationwide parcel boundaries and standardized assessor fields. ATTOM and CoreLogic are the wholesale data houses — much of the property data you see in other products is licensed from them underneath. If you want clean parcel geometry, ownership, and assessor attributes at scale via API to build something yourself, this is the raw material. CoreLogic in particular also sits underneath a lot of insurance and weather risk products.

Where they fail you. Same ceiling. The standardized fields are owner, parcel, assessed value, year built, building characteristics from the assessor. Roof material is sometimes present but is notoriously stale and often blank or wrong (it reflects original construction, not the current roof). No roof age. No per-house storm impact. These are foundations, not finished targeting.

Best for: Larger operations or franchises with a developer who wants to build internal tooling on licensed parcel data, then layer condition data on top.

Zillow, Redfin, county GIS portals (the free tier)

What they're good at. Free, fast spot-checks. Confirming an address, eyeballing the last sale, checking the lot, seeing a Street View image. Genuinely useful for a one-off look before a knock.

Where they fail you. Not built for bulk prospecting, terms of service generally prohibit scraping, and again — year built and last sale, never roof age. The aerial image in Zillow is often years old. County GIS is free and authoritative for parcels and permits, but coverage and freshness vary enormously by county, and digging permits by hand doesn't scale past a few addresses.

Best for: Free per-address sanity checks. Not a prospecting engine.

The data-freshness and coverage problem nobody mentions in the demo

There's a quieter issue that bites every property-data buyer eventually, and no sales rep brings it up: the data underneath is only as current and as complete as its source, and roofs change faster than assessor records do.

• Assessor data lags reality. County records update on their own schedule — sometimes annually, sometimes slower. A home that sold last month, got re-roofed, or changed hands may still show the old owner and old attributes for a year or more. If your targeting depends on owner length or recent sale, expect a slice of every list to be stale.
• Roof-material fields are often blank or wrong. When a property dataset has a "roof material" field at all, it usually reflects original construction and gets carried forward unchanged through every re-roof. Don't trust it as a condition signal.
• Permit coverage is wildly uneven. Re-roof permits are the one record that could date a roof, but coverage varies enormously county to county, many re-roofs never pull a clean attributable permit, and digitization is inconsistent. You can mine permits for a small footprint by hand; it does not scale, and the gaps are silent.
• Aerial imagery has a date. Any imagery-based read — including roof-age estimation — is only as current as the underlying capture. Ask any imagery vendor how fresh their imagery is and how often it refreshes. A read from imagery shot three years ago is judging a roof three years younger than it is today.

The point isn't that the data is useless — it's that you should know exactly which fields are fresh and which are stale before you build a campaign on them. Treat sale and owner data as "probably right, verify the close ones," treat year-built and roof-material fields as structural trivia, and treat any age or condition signal as a dated estimate that the ladder confirms.

Aerial measurement tools (a different category entirely)

This is where a lot of roofers get confused, because these tools look like roof software and they're excellent. But they answer "how big is this roof," not "which roof should I go after."

EagleView, Hover, Roofr, GAF QuickMeasure

What they're genuinely good at. EagleView is the long-standing standard for aerial roof reports — measurements, pitch, facets, waste factors, derived from aerial and now drone imagery, accurate enough that insurers and large contractors rely on them. Hover turns a few smartphone photos into a 3D model with measurements and is great for both roof and full-exterior. Roofr bundles instant measurement reports with a CRM and proposal flow at a contractor-friendly price. GAF QuickMeasure is a low-cost measurement report. All four are legitimately good products that save estimators real time and reduce measurement errors and material over-ordering.

Where they fail you for prospecting. You have to already know the address. These tools measure a roof you've chosen. They don't scan a neighborhood and tell you which roofs are old. They have no roof-age signal and no targeting layer — that's not what they're for and they don't claim to be. Asking EagleView "which houses on this street need a roof" is asking a tape measure to pick your customers.

Best for: Estimating and proposals once you have a prospect. Pair them with a targeting layer; don't expect them to be one. This is the "measure the house" category, not the "which house" category, and the distinction is the whole game.

Storm and hail mapping tools

For restoration-focused roofers, storm data is the lifeblood. These tools are real and useful — and frequently oversold.

HailTrace, Interactive Hail Maps, HailRecon, and weather-verification reports

What they're genuinely good at. HailTrace, Interactive Hail Maps, and similar services map where hail and damaging wind occurred, often with estimated hail sizes and swaths, sometimes with property-level notifications when a storm crosses your area. For knowing where a storm hit so you can deploy crews fast, and for pulling a date-and-location weather-verification report to attach to a documentation file, they're valuable. The underlying radar and storm-report data traces back to NOAA's Storm Prediction Center and the National Weather Service, which publish hail and wind reports publicly.

Where they fail you. Two real limits. First, a hail map shows where it hailed — a swath over a ZIP code — not which individual roofs that hail actually wore out. Two roofs under the same swath can be in completely different shape: a 4-year-old roof shrugs off 1-inch hail that finishes a 17-year-old roof next door. The map can't see the difference because it doesn't know the age or the per-roof impact. Second, storm tools go quiet in calm years and quiet regions. If you're outside a hail alley, or it's a slow season, the map gives you nothing to work — even though plenty of roofs around you are simply aging out. Storm data answers "where was the weather," not "which roof did it break," and not "who's due when the sky is clear."

Best for: Restoration crews timing deployments after a confirmed event, and pulling factual date/location weather verification for documentation. Not a year-round targeting engine on its own.

The missing category: roof-age and due-roof targeting

Notice what nothing above does: take a neighborhood and rank the roofs by how worn out they are. Property tools give you owners. Measurement tools size a roof you already picked. Storm tools show weather. The actual prospecting question — which of these specific houses has a roof old enough or beaten-up enough to replace — falls into the gap.

That gap is a distinct category. The job is to estimate, per address, a roof-age range (not an exact install date — nobody can promise that from imagery, and any vendor who claims an exact date is overselling), and to pair it with the storm history that particular roof has absorbed, so you can sort a street from most-due to least-due before anyone climbs a ladder.

This is where RoofPredict fits, and it's worth being precise about what it is and isn't, because the honest version is more useful than a pitch.

What RoofPredict actually does

RoofPredict reads aerial imagery and per-roof weather history and returns, for each address in an area you scan:

• A roof-age range (for example, 18–22 years), not an exact date. Imagery-based age estimation is a range by nature; treating it as a range is the honest framing and it's still plenty to target on.
• A per-roof storm history — the hail and wind that specific roof has taken, modeled house by house rather than as a swath over the ZIP. The difference from a hail map is the point: a hail map shows where it hailed; the model estimates which roofs that weather actually wore out.
• A risk-style score combining age and storm exposure, so you can rank a street from most likely due to least likely.

What that gives a roofer is the front half of targeting that every tool above is missing: a ranked list of the houses worth your time, so your crew knocks and your mail hits the worn-out roofs and skips the new ones. From there you still do the real work — the free inspection confirms condition, you measure with whatever measurement tool you already use, you close it like you always have.

Honest limits (because these build trust, not erode it)

• Age is a range, not a date. RoofPredict will tell you a roof reads as roughly 16–20 years; it will not hand you a 2007 install certificate. If a vendor promises exact install dates from imagery, be skeptical.
• A forecast is odds, not proof. A high score means a roof is likely due. The ladder is still the ground truth. The tool sharpens where you spend gas, mail, and payroll; it doesn't replace the inspection.
• It's a targeting layer, not a measurement or CRM tool. It doesn't replace EagleView or Hover for measurements, and it isn't an investor data platform. It's the layer those tools don't cover: which house. You can feed its output into the CRM and mailing you already run, and it can run targeted mail for you, but the core job is telling you which roofs are due.
• It's not a lead service. You're not buying a homeowner who's been resold to five competitors. You're getting your own streets and your own customer book sorted by which roofs are worn out, so you own the work instead of renting it.

That last point is the real positioning. A lead site rents you a customer five other roofers also bought. A hail map sells you weather and goes quiet in calm years. A measurement tool sizes a roof after you've already found it. A targeting layer turns the streets and the old estimate list you already have into work you own and can repeat, storm or not.

A practical workflow: turning data into knocked doors

Software only matters if it changes what your crew does Monday morning. Here's a concrete workflow that uses the categories together, in the order that actually works.

Step 1 — Define the area, not the whole metro

Pick a real, workable area: a set of neighborhoods within reasonable drive time of your shop, ideally with housing stock old enough to have aging roofs. Established subdivisions built 18–28 years ago are prime, because their original roofs are now hitting end of life — but remember you're targeting roofs, so confirm age, don't assume from year built.

Step 2 — Rank the roofs by how due they are

This is the step most roofers skip because the tools they own can't do it. Run the area through a roof-age targeting layer and get back a ranked list: roofs reading 18+ years at the top, recent storm-hit roofs flagged, new roofs filtered out. If you don't have a targeting tool, the manual fallback is brutal but possible — Street View spot-checks plus permit digging plus driving the streets — and it does not scale past a tiny footprint. This is exactly the work software should be doing for you.

Step 3 — Enrich with owner and reachability data

Now layer in the property-data platform's strength. Take your ranked due-roof list and enrich each address with owner name, owner-occupied vs. absentee, length of ownership, and a rough equity/affordability read. Drop the renters and the obviously-can't-afford-it parcels. You've now got due roofs owned by people who can say yes. This is the right order: condition first, affordability second. Doing it the other way (the common mistake) means you enrich 5,000 random homes and most of their roofs are fine.

Step 4 — Choose the channel per house

• Knock the densest clusters of top-ranked roofs — your reps spend the day on streets where most doors are real candidates instead of burning hours on new roofs.
• Mail the rest of the ranked list with a postcard that references something concrete (an aging roof in the neighborhood), not generic "free inspection" filler.
• Mine your own old list — past estimates and past customers whose roofs have now crossed into the due range. This is money already in your book; a targeting layer will surface which old estimates are now ripe.

Knocking the ranked list, specifically. Hand a canvasser a street where most doors are real candidates and two things change. First, conversations get easier because the rep can open with something concrete and true about the neighborhood's roofs rather than a generic pitch. Second — and this is the one owners underrate — a green rep who knocks good doors closes something in their first weeks, makes money, and stays. Rep churn is one of the most expensive problems in residential roofing, and a big driver of it is new hires burning out on dead streets. Ranked targeting doesn't just raise conversion; it keeps your crew intact. A workable per-rep cadence on a dense due-roof block is roughly 40–60 doors a day with a real conversation at maybe a quarter to a third of them; on a year-built-noise street those same hours produce far fewer real conversations because most roofs visibly don't need anything.

Mailing the ranked list, specifically. Generic "free roof inspection" postcards get thrown out because every roofer in the market sends them. A piece that references something specific and honest — that there are roofs in the neighborhood reaching the age where they tend to be replaced, and you're already working the area — reads as relevant rather than spam. Keep it factual: never imply you know their exact install date, never promise a free roof, never mention deductibles. USPS Every Door Direct Mail can be efficient for blanketing a tight cluster of due roofs by carrier route, while a targeted addressed mailing to your enriched due-roof list is better when the due roofs are scattered. Either way, the targeting decides the ROI long before the design does. Two or three touches to the same ranked list over a season beats one touch to a list three times the size — repetition to the right roofs compounds; a single hit to the wrong ones evaporates.

Step 5 — Inspect, measure, document, close

At the door and on the roof, your existing process takes over. Free inspection confirms actual condition. Pull your measurement report (EagleView/Hover/Roofr). If it's a storm job, document the contractor's own observations carefully — photos, measurements, dated weather verification — and keep everything factual. Close it.

Worked example: 2,000 homes, two approaches

Say you're working a 2,000-home footprint with a $4,000 mail budget at roughly $0.80 all-in per piece (so ~5,000 pieces of capacity across rounds), two canvassers, and a slow storm season.

The year-built approach (the common one): You pull "built 1998–2008, owned 10+ years" from an investor data tool — say 1,100 homes match. You mail all of them. But maybe a third of those roofs have already been re-roofed in the last decade and another chunk aren't quite due. You're paying to reach roofs that are 4 and 7 years old alongside the genuinely due ones, with no way to tell them apart. Your canvassers knock the same 1,100 blind. Response is diluted by every new roof in the mix, and your reps burn days on doors that were never going to convert.

The roof-age approach: You rank the same 2,000 roofs by age and storm exposure first. Maybe 380 read as genuinely due (18+ years or storm-hit). You enrich just those for owner/equity, drop the renters, and you're left with ~300 due roofs owned by reachable homeowners. You mail those 300 with a sharper message and put your canvassers on the three streets where due roofs cluster densely. Same budget, same crew — but every dollar and every knock is aimed at a worn-out roof instead of diluted across a pile of new ones. You also stop annoying the ~700 homeowners whose roofs are fine, which protects your brand in a neighborhood you'll work for years.

The math isn't a promised conversion lift — it's simpler than that. You stopped spending on roofs that were never jobs. That's the entire value of condition-first targeting: not magic, just no waste.

What this software actually costs (rough, honest ranges)

Nobody publishes clean numbers, and exact pricing moves, so treat these as directional posture rather than quotes — but you should walk into a buying conversation with a sense of the shape:

• Investor data platforms (PropStream, BatchLeads) run as a monthly subscription in the low hundreds, plus small per-record fees for skip tracing and phone append. Cheap per record at volume; the cost is your time turning a raw list into a targeted one.
• Parcel/data-licensing (Regrid, ATTOM, CoreLogic) ranges from modest self-serve parcel access up to enterprise data-licensing contracts. Only worth it if you have someone to build on the raw data.
• Measurement reports (EagleView, Hover, Roofr, QuickMeasure) are mostly priced per report, from a few dollars for a basic measurement up to more for detailed multi-structure reports, sometimes bundled into a CRM subscription. You pay per roof you measure, which is the right model since you only measure prospects.
• Storm/hail mapping is typically a monthly subscription, with notification tiers costing more. Worth it in a hail market, dead weight in a calm one.
• Roof-age targeting is priced around the scan — a small per-report cost for the underlying read, with monthly tiers for ongoing area coverage. The thing to compare isn't sticker price; it's cost per useful door. A cheap list of year-built noise that mostly hits new roofs is more expensive per real job than a pricier list that's already condition-sorted.

The trap is comparing tools on monthly price instead of on cost-per-job. A $200/month tool that sends you to 1,100 mostly-fine roofs costs you far more in wasted mail, gas, and payroll than a tool that costs a bit more and hands you 300 genuinely due ones. Always divide by the doors that were actually worth knocking, not the doors you got.

Your own CRM is the cheapest property data you'll ever own

Before you spend a dollar buying outside data, look at the data you already paid for and forgot about. Every roofing company that's been operating for a few years is sitting on two goldmines:

1. Old estimates that didn't close. The homeowner who got three bids in 2017 and went with someone else, or stalled, has a roof that's now four-plus years older and may finally be ready. You already have their address, their roof, and a prior relationship. A targeting layer can re-score that whole list and surface which old estimates have now crossed into the due range — turning a dead spreadsheet into a warm call list.
2. Past customers whose roofs are aging. If you did a repair or a partial five or eight years ago, or roofed the house before that under a previous owner, those roofs are quietly marching toward replacement. A maintenance touch at the right moment is one of the highest-trust, lowest-cost jobs you can land.

The reason this gets skipped is that re-scoring an old list by current roof age is exactly the work no investor-data tool does — they'll tell you the owner and the equity again, but not whether the roof is now due. Run your own book through an age-and-storm targeting layer the same way you'd run a new neighborhood, and you'll often find a season of jobs sitting in your existing CRM before you ever buy a new list. It's money already in your book; the only thing missing was knowing which records are ripe right now.

Five mistakes that waste the most money

Patterns that show up over and over when roofers buy property data:

1. Treating year built as roof age. Covered throughout — it's the single most expensive error because it quietly poisons the whole list with re-roofed homes you can't separate out.
2. Enriching before you've sorted by condition. Skip-tracing and appending phone numbers to thousands of homes whose roofs are fine is paying to reach the wrong people faster. Sort by roof condition first; enrich only the due ones.
3. Buying the cheapest list and counting the savings, not the jobs. A cheap unranked list feels frugal until you total the wasted mail, gas, and rep hours. Measure cost per useful door.
4. Confusing a measurement tool with a targeting tool. EagleView measuring a roof you picked is not the same as a tool picking the roof. Buying measurement and expecting prospecting leaves the front half of the funnel empty.
5. Leaning on a hail map year-round. Storm tools are powerful right after an event and nearly silent the rest of the time. If your only data source is weather, your pipeline lives and dies with the sky. Pair storm data with age data so calm seasons still produce work.

How to evaluate any property data tool for finding old roofs

When a vendor demos, run them through this checklist. It cuts through marketing fast.

1. Does it give roof age, or year built? If the age field is really "year built," it's a demographic tool, not a condition tool. Ask directly: "Is this the age of the structure or the age of the current roof?"
2. Is roof age a range or a claimed exact date? A range is honest. An exact install date "from imagery" is a red flag.
3. Does it model storms per house or per ZIP/swath? Per-house impact is far more useful than a swath map for separating the 17-year-old hit roof from the 4-year-old one beside it.
4. Does it work in calm seasons and calm regions? If the tool only lights up after a storm, it can't keep your pipeline full year-round.
5. Does it rank/sort the area, or just dump a list? You want streets sorted most-due to least-due, not 5,000 unranked addresses.
6. Can you get a sample on a street you know? Hand the vendor a street where you know the roofs. See if their output matches reality. This is the single best test — your own knowledge is the answer key.
7. Does it enrich or integrate with what you already run? Owner/mailing data, CRM, mailing — does it plug into your stack or fight it?
8. What does it honestly NOT do? A vendor who can clearly state their limits is more trustworthy than one who claims to do everything.

The seventh point matters more than people expect: the best stack is usually a roof-age targeting layer for the front half (which house), an owner-data platform for enrichment (who/affordability), and a measurement tool for the estimate (how big). Buying one tool and expecting it to do all three is how roofers end up disappointed with whatever they bought.

A note on storm and claims work — stay on the right side of the line

Many roofers reading this do storm restoration, where data and documentation collide with insurance. A hard boundary worth stating plainly, because it protects your license and your business:

You document your own inspection, scope, photos, measurements, and the factual weather verification for the address. That's the contractor's job and it's entirely proper. What you don't do is represent the homeowner to the carrier, interpret their policy, tell them what they're entitled to recover, or negotiate the settlement. The homeowner files; the insurer decides; the contractor documents their own scope. Coverage disputes, denials, and appraisal questions route to a licensed public adjuster or attorney — not to your sales rep at the kitchen table.

Where documentation software helps is on the contractor side: turning your inspection photos, your estimate, your field notes, and dated factual weather verification into an organized, page-cited file, and flagging where your own documentation has gaps (a missing photo, a slope you didn't measure, a supplement item your scope supports but your paperwork doesn't yet back up). That's how restoration contractors stop leaking legitimately earned revenue to missed scope and dead supplements — by documenting their own work thoroughly, with human review guarding anything that goes to the insurer. RoofClaimRCM is built for exactly that contractor-side documentation job, and it keeps the do-not-say line firmly in view: factual support, internal review, document requests — never homeowner representation or settlement negotiation.

Keep your storm targeting and your claims documentation honest and factual, and the data works for you instead of becoming a liability.

So which is the "best" property data software?

There's no single winner, because these tools answer different questions. The honest answer depends on what you're missing:

• Best for owner/equity/mailing data: PropStream or BatchLeads. Cheap per record, deep filters, great enrichment — just don't expect them to know roof age.
• Best raw parcel data to build on: Regrid, ATTOM, or CoreLogic, if you have a developer and a plan.
• Best for measuring a roof you've already chosen: EagleView, Hover, or Roofr — excellent at measurement, silent on targeting.
• Best for where a storm hit: HailTrace or Interactive Hail Maps, with the caveat that a swath isn't a per-roof verdict and they go quiet in calm years.
• Best for the actual question — which roofs are due, ranked, house by house, storm or not: a roof-age targeting layer like RoofPredict, which is a different category from all of the above. It's the front half of targeting (which house, by age and per-roof storm exposure) that owner-data, measurement, and hail-map tools each leave out.

The roofers who win the data game aren't the ones who buy the most software. They're the ones who get the order right: figure out which roofs are worn out first, then enrich for affordability, then measure, then close — and stop paying to reach roofs that were never going to be jobs. Get that order right and even a modest budget outperforms a fat one aimed at year-built noise.

If you want to see whether age-and-storm targeting matches the roofs you already know, the most honest test is the one from the checklist: hand a tool a street where you know the roofs and see if it reads them right. RoofPredict will run that on an area you choose so you can check it against your own eyes — book a demo, point it at streets you know, and decide for yourself whether it found the due roofs. That's the test that matters, and it's the one every vendor on this list should be willing to take.

FAQ

What property data software can tell me the actual age of a roof?

Standard property data platforms (PropStream, BatchLeads, Regrid, ATTOM, CoreLogic) give you year built, not roof age — and year built is misleading because most older homes were re-roofed at least once and that re-roof is invisible in assessor data. To estimate the age of the current roof, you need a roof-age targeting layer that reads aerial imagery, like RoofPredict, which returns an age range per address (for example 18–22 years) rather than an exact date. Treat any tool claiming an exact install date from imagery with skepticism.

Why can't I just use year-built data to find old roofs?

Because year built is the age of the structure, not the roof currently on it. A house built in 1998 has almost certainly been re-roofed at least once, and that re-roof rarely shows up cleanly in property data. A year-built list mixes homes re-roofed three years ago, homes re-roofed eight years ago, and genuinely due homes into one pile you can't separate. You end up mailing and knocking new roofs alongside old ones. Year built is a demographic signal, not a condition signal.

Is EagleView or Hover good for finding which roofs to replace?

No — and they don't claim to be. EagleView, Hover, Roofr, and GAF QuickMeasure are measurement tools: you give them an address you've already chosen and they return precise measurements, pitch, and square footage for estimating. They have no roof-age or targeting signal. They measure the house; they don't pick the house. Use them after a roof-age targeting layer has told you which doors to knock.

Do hail maps tell me which roofs need replacing?

Hail maps (HailTrace, Interactive Hail Maps, and similar, ultimately built on NOAA Storm Prediction Center data) tell you where hail and wind occurred — a swath over an area — not which individual roofs that weather actually wore out. A 4-year-old roof and a 17-year-old roof under the same swath are in very different shape. Hail maps also go quiet in calm seasons and outside hail regions, so they can't keep a pipeline full year-round on their own. They're best for timing deployments after a confirmed storm and for pulling factual weather verification for documentation.

What's the difference between a roof-age targeting tool and a lead service?

A lead service sells you a homeowner who has usually been resold to several competitors, so you're competing on speed and price for a contact you don't own. A roof-age targeting layer like RoofPredict instead ranks the roofs in an area you choose by age and per-roof storm exposure, so you can knock, mail, and mine your own old estimate list against the worn-out roofs. You own the work and can repeat it, storm or not, instead of renting a shared contact.

How accurate is roof age estimated from aerial imagery?

It's an estimate expressed as a range, not an exact date — and that's the honest framing. Imagery-based estimation can reliably separate a clearly new roof from a clearly aged one and place a roof in a band like 16–20 years, which is more than enough to target on. It cannot hand you a precise install certificate. A high score means a roof is likely due, not proven due; the free inspection on the ladder remains the ground truth. The tool sharpens where you spend gas, mail, and payroll.

What's the right order to use these tools together?

Condition first, affordability second, measurement third. Rank the roofs in your area by age and storm exposure with a targeting layer; enrich just the due roofs with owner, occupancy, and equity data from a property-data platform to drop renters and unreachable parcels; then pull a measurement report for the homes you inspect. Doing it backwards — enriching thousands of random homes first — wastes budget on roofs that are perfectly fine.

Can property data software help with storm and insurance claims?

It can help on the contractor-documentation side only. Software can organize your own inspection photos, estimate, field notes, and dated factual weather verification into a page-cited file and flag gaps in your own documentation. It must not cross into representing the homeowner, interpreting their policy, telling them what they're owed, or negotiating the settlement — the homeowner files, the insurer decides, and coverage disputes route to a licensed public adjuster or attorney. RoofClaimRCM is built for that contractor-side documentation work with human review on anything insurer-facing.

How do I test whether a property data tool actually finds old roofs?

Hand the vendor a street where you already know the condition of the roofs and have them run it. Your own knowledge is the answer key. If the tool's age ranges and due-roof rankings match what you see with your own eyes, it works; if it's just returning year built or unranked addresses, it doesn't. A vendor confident in their data will run that sample on a street you choose.

Does any one tool do targeting, owner data, and measurement all at once?

Not well. Each category was built for a different buyer — investors, lenders, estimators, restoration crews — so the best results come from a small stack: a roof-age targeting layer for which house, an owner-data platform for who and affordability, and a measurement tool for the estimate. Expecting a single product to do all three is the most common reason roofers end up disappointed with whatever they bought.