How to Qualify a Roof Prospect Before Knocking

A rep can effectively door-knock 25-30 homes in a 3-hour session before fatigue + conversation friction drop quality. Which means the 30 homes you pick MATTER more than the 30 you didn't pick. The roofers I know with the best canvassing CACs aren't the ones who knock the most doors. They're the ones who pre-qualify ruthlessly so every door is worth knocking.

Step zero of qualifying: was the address even in a hail path? The free US hail map answers that in ten seconds.

This post is the 4-question pre-knock screen that filters a 4,000-home zip down to the 30 worth your Saturday.

The 80/20 of canvassing conversion

Across residential roofing canvassing data, the pattern holds: roughly 80% of closed jobs come from 20% of doors knocked. The 80% of knocks on the wrong doors produce maybe 20% of closes — bad ROI, especially when rep labor is your biggest canvassing cost.

The 20% high-converting doors share four signals:

1. Roof age in the 18-25 year window
2. Visible condition signals (granule loss, curl, algae)
3. Neighborhood replacement cascade nearby
4. Owner tenure 5+ years (longer-term homeowners are more likely to invest)

Filter for these four, and the 30 doors you knock convert at 25-40% to inspection-scheduled, vs. 8-15% on random doors.

Pre-knock question 1: Roof age band

The single highest-leverage filter.

How to check:

• AI satellite age estimation (5cm-15cm resolution can estimate within ±4 years)
• County permit records (most accurate when available)
• Year-built + typical replacement cycle math

The filter:

• Keep: homes estimated 18-25 years old
• Skip: homes under 12 years old (low replacement likelihood)
• Marginal: homes 12-18 (consider with other signals)
• Marginal: homes 25+ (often already replaced or deferred)

Filtering by age band alone reduces a 4,000-home zip to ~800-1,000 candidates. Already a 4x improvement in knock efficiency.

Pre-knock question 2: Visible condition signals

Within the right age cohort, look for visible signs of replacement-readiness:

Tier 1 (high-priority signals):

• Visible granule loss exposing fiberglass mat
• Tarps installed (homeowner already responding to damage)
• Missing tabs / shingle sections
• Curl pronounced on multiple slopes

Tier 2 (moderate signals):

• Algae streaking
• Color fading
• Minor curl onset
• Mottled texture variation

Tier 3 (weak signals — by themselves, not enough):

• Material aging consistent with age band
• Tree overhang causing moss/debris
• Adjacent older roofs

The filter: keep homes with 2+ Tier 1 or Tier 2 signals. Skip if only Tier 3.

Combined with age filter, this reduces 800-1,000 candidates to ~300-500. Another 2-3x improvement.

Pre-knock question 3: Neighborhood replacement cascade

The single strongest social-proof signal. As I covered in the cascade post, homes within 400 feet of 3+ recent roof replacements are 3-4x more likely to replace within 18 months.

How to detect:

• County permit data — geo-cluster recent permits
• AI tools that score "neighbors replaced" as a signal
• Manual: Google Earth comparison of recent vs older imagery

The filter:

• Highest priority: homes in cascade zones (3+ recent neighbor replacements within 400ft)
• High priority: homes with 1-2 nearby replacements
• Standard priority: homes with no nearby cascade signals

A cascade-zone home in the right age + condition cohort converts at 35-50% to inspection. Outside a cascade zone with the same age + condition, it's 20-30%.

Pre-knock question 4: Owner tenure proxy

Longer-tenure homeowners are more likely to invest in replacement than recent buyers.

Why:

• They've watched the roof age (psychological attachment to the "decision is mine" framing)
• They have established financial position vs. new buyers stretched on the mortgage
• They've seen neighbors replace
• They have referral networks that reward the decision

How to check:

• Last sale date in property records (free at county appraisal districts)
• Filter: owner since 5+ years ago = high priority

The filter:

• High priority: owner since 5+ years
• Marginal: owner 2-5 years (depends on other signals)
• Skip: owner under 2 years (rarely replaces in first 2 years unless leak)

After all four filters, you typically have 30-100 high-confidence candidates per zip — vs. the original 4,000 homes. Worth knocking. Doors you'd skip naturally because they don't meet the criteria.

The 4-question screen in practice

For each home on your candidate list, run the 4-question scoring:

HOME: 4218 Oak Ridge Dr, Plano TX 75024

1. Roof age band:        22 years (AI estimate)     ✓
2. Visible signals:      Granule loss + algae       ✓
3. Cascade zone:         3 neighbors replaced       ✓
4. Owner tenure:         Owner since 2008           ✓

Score: 4/4
Action: Top-priority knock

vs.

HOME: 7104 Pine Ridge Dr, Plano TX 75024

1. Roof age band:        9 years (AI estimate)      ✗
2. Visible signals:      None significant            ✗
3. Cascade zone:         No                          ✗
4. Owner tenure:         Owner since 2023            ✗

Score: 0/4
Action: Skip

The math: if you knock 30 doors with average score 3/4+, your inspection conversion rate is 30-40%. If you knock 30 random doors averaging 1/4, your conversion is 8-12%. Same effort, 3x more inspections scheduled.

AI-assisted pre-screening (the modern way)

Doing this manually for 30-100 candidates per zip is doable in 1-2 hours. Doing it across a 4-zip service area weekly is a full day.

This is where AI tools save time — they score every home in your service area on all four signals automatically:

• AI age estimation (Question 1)
• Visible condition signals (Question 2)
• Cascade detection (Question 3)
• Owner tenure from public records (Question 4)

Output: ranked list with each home's score on all 4 signals. You pick the top 30 and knock.

Roofbird does end-to-end pre-screening — every home in your service area scored on age + condition + cascade + tenure, ranked, with the high-score candidates surfaced. The free trial includes 25 pre-screened leads in your area. See the DFW sample for what the output looks like.

A combined weekly pre-knock routine

For roofers running this manually + AI assist:

Monday (1 hour):

• Pull AI-scored list for top 2 zips
• Cross-reference top 100 with county permit data
• Drop anything with recent permit (false-positive flag)

Tuesday (30 min):

• Spot-check top 30 in Google Street View
• Drop anything visibly newer than AI estimated
• Final knock list: ~25-30 names

Wednesday or Thursday (30 min):

• Plan route: cluster by geographic proximity
• Print door hangers for backup at no-answer doors

Saturday (4 hours):

• Knock the 25-30 home list
• Track responses

Total prep: 2 hours. Total canvas: 4 hours. Expected output: 8-12 inspections scheduled, 2-4 jobs closed.

CAC: ~$120-200 vs. random-knock CAC of ~$400-600. The pre-screen is the highest-leverage hour in the week.

What to do this week

If you've been knocking without pre-screening:

1. Pick one zip in your service area
2. Build a 30-home candidate list using the 4-question screen
3. Knock those 30 this Saturday with the proper script
4. Compare your inspection-conversion rate vs. your historical random-knock rate

The conversion improvement is almost always meaningful — 1.5x to 3x. Once you see the numbers, the time spent pre-screening becomes obvious investment.

For AI-assisted pre-screening of your service area: Roofbird's free trial gives you 25 pre-screened leads, no card required.

— Jake