How to vet expired domains in bulk before an auction

Keep an untouched copy of the auction export. In a working sheet, retain the domain, auction URL, closing time, asking price and any other information you will need when bidding. Add a separate normalized hostname column for lookups.

Remove protocols, paths and tracking parameters from that lookup column. Use one bare hostname per row, lower-case consistently and deduplicate the hostnames. Keep a mapping to the original auction rows; a repeated listing should not trigger repeated paid checks.

For a 5,000-row export, count unique hostnames before estimating the cost. Start with a small sample to check that the imports and output columns work. The Windows desktop app is useful for sustained list work; its bulk checking, filtering and export features use the same account credits.

A successful domain metrics request costs one credit for Moz, Majestic and Pretty fields together. If all 5,000 rows are unique and succeed, the first pass consumes 5,000 credits. Backlink requests are separate: reviewing a 100-domain shortlist with the backlinks endpoint consumes another 100 credits when all requests succeed.

5,000 unique successful requests consume 5,000 credits. That is about $3.97–$6.80 in consumed credit value at the current pack rates. Credits are bought in packs: the entry pack is $34 for 25,000 credits. This is not a $3.97 checkout option. Compare the current DomDetailer credit packs.

Do not assume a client timeout prevented server-side work. Keep the completed results, check the account and investigate uncertain requests before submitting them again.

Run the bulk DA, PA and Trust Flow checker. Keep DA, TF, CF, raw Spam Score, referring domains and topical categories visible. Read the documented Spam Score scale before choosing a numerical filter.

A screening rule manages your workload; it does not certify a domain. Retain uncertain and failed rows for review. Check a sample of rejected rows too, so an overly broad threshold does not discard names that meet the project's purpose.

Illustrative review: a candidate has DA 38, TF 16 and CF 27. Its ratio is about 0.59. That combination earns further investigation, not an automatic bid. The name, topic, current references and purchase price still need to fit.

Review available archive captures at different dates, rather than one conveniently chosen homepage. Look for changes of purpose, unexplained redirects, unrelated content and target paths that no longer make sense. Archive gaps are gaps in evidence, not proof of a clean history.

Compare the visible history with Topical Trust Flow categories. A plausible history for a travel resource does not automatically support an unrelated finance project. Write your intended use next to the candidate and explain the fit.

Confirm the auction's actual status and current registration details before bidding. Registry snapshot observations and old link metrics cannot establish that a name is currently available to buy.

Use the bulk backlink checker on shortlisted domains. Read source URLs, destination URLs, anchors and nofollow flags together. Visit representative source pages and confirm that useful links remain present and lead to a sensible destination.

Look for concentration: many rows from one domain, repeated sitewide placements or identical anchors. Those patterns need explanation, but do not prove abuse on their own. A one-per-referring-domain sample is useful for breadth; it hides repeated placements, so use the full returned sample when that distinction matters.

The endpoint returns up to 5,000 rows and an observed profile summary. Neither is a complete live inventory of every backlink. Keep the lookup date and any uncertainty with the decision.

Add a final decision column: retain, reject or investigate. Keep short notes on name suitability, former topic, live link evidence, costs and unanswered questions. An export with reasons is more useful than a sheet sorted by the highest DA.

Set a bid limit based on the project and its evidence. Do not infer traffic, guaranteed rankings or a resale price from metric scores. If the name is valuable to you independently of the old links, record that as the reason for buying.

For repeatable processing in your own workflow, use the Python metrics-to-CSV guide or controlled Google Sheets batch menu. Use exports within the DomDetailer terms of service; API access does not grant redistribution rights.