US nationwide, short-term rental software: 2026-08-05
What is the best tool for an Airbnb host to set nightly pricing and analyze a market before buying?
4 of 5
sources answered OK
run day 2026-08-05
12
name entries logged
all engines, with repeats across engines
| Engine | Status | Names as extracted | Raw answer SHA-256 |
|---|---|---|---|
| OpenAI | FAILED | 3c29eeb396cc113e96b990ae64adaf82b7424ba792ccd7454fe55ab304d2876a | |
| OpenAI (rerun 1) | QUOTA_BLOCKED | 76ce496a03db293d05242ac57a898b05d6ffc56a98c18cb3da081c1633f3957d | |
| Anthropic | OK | AirDNA; Airbtics; Mashvisor; Wheelhouse | 0da1826054974e0056dff5c95ee8c6809c864d3bf0f9253c96a98b2f0adab5ca |
| Anthropic (rerun 1) | OK | AirDNA; Wheelhouse | d579241985f922b2da6f99e049edcc0aa593eca6698963eac0adbf4384def48b |
| Perplexity | OK | Wheelhouse | bf70d055bbf4a3c2a0f2164c71de73d487cf34cf215aecd22812cc37767eeef7 |
| Perplexity (rerun 1) | OK | AirDNA; Wheelhouse | 9fa05a094bd09b13f196f1cfb02eaf6e4929d939d6c5bb81272321e72cb887ff |
| Perplexity (rerun 2) | OK | Wheelhouse | dc6e8ee0d4fbf4b5c83ed60741d311c6fa40c8f820b7a0fe403a10e535596142 |
| Perplexity (rerun 3) | OK | AirDNA; Wheelhouse | 6eb25767fdea888de4b4bb846eebefede9a5ddf211ee8cb4ac7f88af128eae2b |
| Perplexity (rerun 4) | OK | Wheelhouse | e78b935657ece7843ad08a7c592e10c6846c77d99188ac82f4089b48388f395a |
| Gemini | QUOTA_BLOCKED | bbb60975737d2d4c0a2be2fcce2672ea67eab66a9f4d9668409b359534a3e62d | |
| Gemini (rerun 1) | QUOTA_BLOCKED | 85301021431954f30218b819593f1fae942dafac2375126c102d3229b80875a0 | |
| Gemini (rerun 2) | OK | AirDNA; Beyond (formerly Beyond Pricing; Market Analysis; Market Dashboards; Pricing Model; Wheelhouse | cf3cfde66a941a4f97230b33bc6c94e7b6930ad52c750311fbb854b576aa40d8 |
| Gemini (rerun 3) | QUOTA_BLOCKED | 93cce9cc78150eceec7afb39cb6c9387743b1c8bf0f93fa5fd7d228767ff48b6 | |
| Gemini (rerun 4) | QUOTA_BLOCKED | a261e678abcb55ae3d9187661e556fe290cead4df7f2d1973b29c53fcdf56c37 | |
| Google search | OK | 0 names extracted | 56b4cdee66a4cbf5377064fe92d2110a7aa54f41dbf4d2a6fa0eea814eaada3d |
| Google search (rerun 1) | OK | 0 names extracted | 3b42458a28616942cd037ddbc59fd4d0ac43ab44f1368fa015c416f5dcc0ebef |
| Google search (rerun 2) | OK | 0 names extracted | 701acec68950772ee3d556d547e759fa21acd4545c4e3c6cfd04ff587814caac |
| Google search (rerun 3) | OK | 0 names extracted | 798fe79c6a2ca184c0ed22e96c32c87a04848a83a93ff8d016b75928f1c55ec3 |
| Google search (rerun 4) | OK | 0 names extracted | e50b980041d4d1d89d03e67dc8fdea7225ba86b21d59f31700379dae3eead5fe |
Changes
Earliest run day in the window. Nothing to compare.
Names are shown as extracted from each answer. Some entries are headings or terms, not businesses. That is a known issue in the Edition 1 files and it is logged on the corrections page. A count is not a ranking.
This day as a file: 2026-08-05.csv (SHA-256 8194f5a0995cf54e4a7dd3c5ea455ecfca23063ca7401a6d92f0ff4779e9a32f). Whole window: all.csv. Raw answers sit in the public corpus under the file names in the CSV.
Compiled by Terry J Carter, Carter Enterprise LLC. Data window 2026-08-05 to 2026-10-07. Source files generated 2026-10-08. Page built 2026-10-09. Method · Corrections