Best Zip Codes for Roofing Leads (And How to Find Yours)

A roofer asked me last month: "Just tell me — which zip codes in DFW are the best for roofing leads?" Fair question. Wrong answer.

Because the best zip codes for your shop are not necessarily the best for the shop down the street. The right answer depends on at least four variables: roof age distribution, household income, insurance claim density, and the neighborhood-replacement cascade effect — all weighted against your specific shop's close rates and job preferences.

This post is the framework I use to score zips. It's repeatable, data-driven, and gives you a personal answer in about 90 minutes of analysis work per metro.

Why "best zip" isn't a universal answer

The three biggest reasons one zip wins for some shops and not others:

1. Job preference / specialty. A shop that specializes in luxury architectural shingles wants different zips than a shop doing standard 3-tab insurance work. Same metro, different ideal zips.

2. Service area + drive time. A great zip 35 minutes from your average job site is worse than a mediocre zip 10 minutes away. Drive time eats margin.

3. Sales-team strength on different door types. If your reps are stronger with older retired homeowners than with younger families with kids, zip demographics matter. If they're equally strong, demographics matter less.

The framework below produces a score per zip. The same framework, applied to two different shops, will produce different rankings — because each shop's weights are different.

Variable 1: Roof age distribution

The highest-leverage variable. A zip where 30% of homes are in the 18-25 year roof age window will outperform a zip where only 8% are, by 3-4x.

How to measure:

Pull property records for the zip (free at most county appraisal districts). Filter by year-built. The roof age proxy is today's year − year_built for any home without a recent re-roof permit.

For a typical DFW zip, 4 distributions are possible:

Distribution	What it looks like	Roofer outcome
Young cohort	70%+ homes under 15 yrs old	Skip — low volume
Even spread	25% in each age bucket	Moderate volume, evergreen
Mature cohort	50%+ homes 18-25 yrs old	High volume, prime years
Aged cohort	50%+ homes 25+ yrs old	Mixed — many already replaced

The mature cohort zips are the sweet spot. Replacement decisions cluster within a 5-year band, and these zips have the highest density of homeowners in active decision-making.

In DFW, examples of mature-cohort zips (homes built 2000-2008):

• 75024 (West Plano)
• 75093 (Plano east)
• 75070 (McKinney south)
• 76016 (Arlington north)
• 75013 (Allen)

This is publicly-knowable data. Your local equivalents take ~10 minutes per zip to compute from year-built records.

Variable 2: Household income + replacement spend correlation

A roof replacement is a $10-25k decision. Homeowners in lower-income zips don't avoid replacement — they defer it. Higher-income zips replace on schedule; lower-income zips replace when it leaks.

Three ways this affects your prospecting:

1. Higher-income zips = higher close rates on full replacement (vs. repair)
2. Higher-income zips = bigger ticket sizes (premium shingle upgrades, full property work)
3. Lower-income zips = higher volume of repair work but lower per-job revenue

The metric to look at: median household income + median home value. Both are in census + zillow data, free.

For most residential roofing shops, the sweet spot is median income $100-180k. Higher than that and luxury construction firms dominate; lower and the deferred-maintenance problem reduces close rates.

DFW median income approximation for the zips above:

• 75024: ~$135k (Plano, high replacement-on-schedule)
• 75093: ~$165k (Plano, higher tier)
• 75070: ~$120k (McKinney, solid middle)
• 76016: ~$95k (Arlington, mixed — defers more)
• 75013: ~$130k (Allen, solid)

Variable 3: Insurance claim density

In storm-belt regions, insurance-driven replacement is a meaningful share of volume. The closer-to-real-time signal: how many roof-related insurance claims have been filed in this zip in the last 24 months?

Some state insurance departments publish this. Texas Department of Insurance publishes aggregate hail claim data by zip on a 6-12 month lag. Florida and Colorado have varying availability.

Where you can get this data:

• Texas: TDI bulletins (somewhat clunky to query)
• NOAA Storm Events: confirmed hail in the zip in the last 24 months — proxy for claim potential
• Local public adjuster networks: they track this for their own work; sometimes share informally

The signal: zips with confirmed hail events + above-average insurance claim density = likely high storm-driven replacement volume. Even if you're not pursuing insurance work directly, these zips have high replacement activity.

For DFW, the zips most affected by 2024-2026 hail events (rough order):

• 75150, 75180, 75181 (Mesquite, hit repeatedly)
• 76016, 76017 (Arlington, hail-active)
• 75044 (Garland)
• 75070, 75071 (McKinney south)

Variable 4: Neighborhood replacement cascade effect

The most overlooked variable. When 3+ homes on a block replace their roofs in a quarter, the next 5-10 on that block become 3-4x more likely to replace within the next 18 months.

The mechanism: social proof. Homeowners see their neighbors investing, they hear talk over the fence, they get the comparative-aging conversation ("my roof is the same age as the Johnsons'..."). Within 12-18 months, half a block can replace.

How to detect cascades at the zip level:

1. County permit data — pull all roof permits in the zip from the last 12 months
2. Geo-cluster the permits — anywhere 5+ permits sit within a 400-foot radius is a cascade zone
3. Multiple cascades = active zip — a zip with 4-6 active cascade zones is in an active-replacement cycle

Cascade-active zips outperform comparable zips by 2-3x on close rates because every door you knock benefits from "your neighbors just did this" social proof.

Putting it together: the 4-variable scorecard

For each zip in your candidate set, score 1-5 on each variable. Total score out of 20.

Variable	Score 1 (low)	Score 5 (high)
Roof age (% homes 18-25 yrs)	Under 10%	Over 30%
Income (median household)	Under $70k	$100-180k
Insurance (recent claims)	None	Active hail event past 24 mo
Cascade (active clusters)	0-1 in zip	4+ in zip

A zip scoring 15+ is a top-tier prospect zip for your shop. A zip scoring 8-12 is moderate. Below 8 = skip.

Important: weight the four variables based on YOUR shop's profile.

• Insurance-restoration specialists: triple-weight Variable 3 (insurance density)
• Retail-cash-job shops: triple-weight Variable 2 (income)
• High-volume door-knocking shops: triple-weight Variable 1 (roof age density)
• Storm-chase shops: triple-weight Variable 3 (insurance) + Variable 4 (cascade)

The same scoring framework, different weights, gives you a personalized answer instead of a generic best-zip list.

A worked example: scoring 5 DFW zips

Using equal weights, here's how the framework scores five DFW zips for a residential generalist shop:

Zip	Roof age	Income	Insurance	Cascade	Total	Rank
75024 (West Plano)	4	5	2	4	15	Top
75070 (McKinney south)	5	4	4	4	17	Top
75150 (Mesquite)	3	3	5	3	14	Moderate-top
76016 (Arlington north)	4	3	4	3	14	Moderate-top
75093 (Plano east)	4	5	2	3	14	Moderate-top

For a generalist DFW residential shop, 75070 ranks #1 — highest combined signal across all four variables. For an insurance-restoration specialist, 75150 (Mesquite) would top the list because of the Variable 3 weighting.

Automating the scoring

The four variables can all be pulled from public data, but doing it manually for 30+ zips per metro is a half-day of work per quarter.

This is where AI prospecting tools save time:

Roofbird automatically scores every zip in your service area on these four variables (plus the cascade signals at street level) and ranks them for you. Open the DFW sample dashboard to see the output. The trial loads 25 free pre-scored leads in your top zips.

Alternatively, build your own scorecard using:

• Year-built data: County appraisal district records (free)
• Income data: U.S. Census API or Zillow (free)
• Insurance data: state insurance commissioner bulletins + NOAA Storm Events
• Permit data: county building department records (free)

Plan for 30-60 min per zip to build the data manually, less if you script the queries.

What to do this week

1. List 8-12 zips you've worked in over the last 12 months
2. Score each one using the 4-variable framework (use equal weights for v1)
3. Sort by total score
4. Focus next month's prospecting on the top 3 zips
5. After 30 days, compare actual close rates by zip against the predicted ranking
6. Adjust the variable weights based on what you see in the data

The shops with the most efficient prospecting motions aren't the ones knocking everywhere. They're the ones knocking precisely in the right 3-5 zips, repeatedly, until those zips are saturated.

— Jake