Average synergy breakdown across sampled score documents
Key comparison table
| Signal | Sample result | Why it matters |
|---|---|---|
| Looking-only profiles | 164 of 200 | Current sample is seeker-heavy, which changes how top-of-funnel content should speak. |
| Offering profiles | 12 of 200 | Supply-side content remains underbuilt compared with seeker demand. |
| Top target city | New York, New York | The current sampled profile demand is heavily concentrated in New York. |
| Strongest score dimension | Personality | Core personal fit is currently outperforming housing alignment in the sampled model. |
| Weakest score dimension | Housing | Space and household setup remain a frequent source of mismatch. |
What the sampled data suggests
The current CoHabby sample points to a useful tension: personality and lifestyle alignment look relatively strong, while housing alignment remains weaker. That means the hard problems in shared housing are often not just who the person is, but whether the actual setup can support the match.
In practical terms, better matching content should keep pushing on commute fit, guest expectations, room setup, and how shared space is used.
Why seeker-heavy demand matters
The sampled profile set is heavily weighted toward people looking for rooms or housemates rather than offering space. That reinforces the need for deeper lister-funnel SEO in Phase 2 and stronger content for listing creators.
How to read this report correctly
This is an aggregate sample snapshot, not a universal claim about every renter or every room listing. It is most useful as directional insight: what appears strong, what appears weak, and where CoHabby’s current match data says attention belongs next.
Download the data
Sources and supporting references
CoHabby anonymized aggregate sample
Derived from a 2026-03-13 snapshot of 100 `synergy_scores` docs and 200 `users` docs using aggregate-only fields.
Use the research in your next search or listing decision
These reports are built to support better room listings, stronger screening, and fewer bad-fit placements.