Hand-Reg Domain Research Report: Zone File Pattern Mining

Research Result First

This report documents a live hand-registration research run based on the workflow in the hand-reg domain research guide. Instead of starting from auction lists or expired domains, the process starts with approved zone files from ICANN CZDS, extracts active domains across multiple TLDs, studies naming patterns, generates .com candidates, and validates availability through direct WHOIS and registrar bulk search.

The strongest result from this run is:

Recommended Action

Buy at most 1-2 names from this first batch:

• assistpayroll.com - strongest buy candidate
• assistfiling.com - acceptable second buy if keeping a small hand-reg basket

1. Data Source and Approved Zone Files

The research started by requesting zone file access through ICANN CZDS. The following TLD zone files were approved and downloaded:

Summary

• Total extracted domain rows across the approved TLDs: 16,832,269
• Non-org cross-TLD signal file created from .app, .dev, .studio, .agency, .media, and .finance
• .org was kept as a separate signal source because it is very large and can introduce nonprofit or legacy-domain noise

2. Cross-TLD Signal Mining

The first serious filter was to identify SLDs that appear across multiple TLDs. This is useful because repeated usage across TLDs often indicates that a word or phrase is not random. It may already be used by real products, agencies, tools, or service businesses.

The non-org cross-TLD scan found:

This step also revealed an important filtering lesson: raw substring matching creates noise. For example, syntax contains tax, and claw contains law, but neither is useful for tax or legal domain research. The filter was adjusted to focus on edge patterns where the keyword appears at the beginning or end of the SLD.

3. Pattern Findings

The best research value did not come from copying exact SLDs. Most strong exact-match .com names are already taken. The value came from learning modifiers and naming directions that appear repeatedly across active TLDs.

Key Insight

The zone file does not directly tell us which .com hand-reg is available. It tells us which naming patterns real websites are already using. The hand-reg opportunity appears after translating those patterns into new .com combinations and then checking availability.

4. Candidate Generation

Based on the pattern scan, a small candidate generator was created using commercial roots and modifiers.

Example roots:

• tax
• refund
• filing
• ledger
• audit
• payroll
• books
• clinic
• dental
• therapy
• legal
• contract
• privacy

Example modifiers:

• assist
• agent
• bridge
• compass
• flow
• forms
• hub
• kit
• lens
• pilot
• stack
• sync
• vault
• wise

This generated 600 .com candidates. The first WHOIS batch focused on 120 names, then the interesting subset was retried and verified more carefully.

5. Direct .com WHOIS Results

The following candidates were checked directly against .com registry WHOIS. This is stronger than only relying on a registrar interface because it confirms whether the registry sees a match.

Taken during the same check:

6. Registrar Confirmation

The strongest four names were then checked manually through a registrar bulk search:

This confirmation matters because direct WHOIS can show registry availability, but the registrar check confirms that the names are not being treated as premium inventory and can be registered at normal pricing.

7. Buyer Signal Notes

The external buyer signal is directional rather than conclusive. The goal is not to prove that a buyer already exists for the exact name. The goal is to confirm that the category has real commercial behavior.

8. Final Verdict