Per-Bed Reporting KPIs: What Top Coliving Operators Actually Track

Per-bed inventory unlocks per-bed reporting, and per-bed reporting unlocks decisions you can't make from property-level dashboards. This guide covers the 8 bed-level KPIs top coliving operators track, why each matters, and how to set up tracking that drives real operational changes.

RevPAB (Revenue Per Available Bed)

The most important bed-level metric. RevPAB = (Total Revenue ÷ Total Bed-Nights Available) × 30. This normalizes both occupancy and pricing into one number. A property running at 100% occupancy with low rates can have lower RevPAB than a property at 90% with optimized rates. Top coliving operators run RevPAB of £25-45 depending on market. RevPAB should trend up over time; flat or declining RevPAB signals operational decay. Track per-bed, per-floor, per-property, and portfolio-wide.

Vacancy Duration Per Bed

Average days between move-out and next move-in, calculated at bed level. Property-level averages mask problems, one always-vacant bed pulls the property average up while 28 other beds turn over fast. Bed-level tracking exposes this. Healthy vacancy duration: 7-14 days for in-demand beds, 14-30 days for tougher beds. Beds vacant 30+ days repeatedly need investigation, pricing too high? Bad housemate dynamic in the room? Physical issue (broken AC, leak, view of wall)? Inventory analytics surface these patterns.

Bed-Level Renewal Rate

% of residents in a specific bed who renew vs move-out. Some beds have 80% renewal rates (residents love them); others have 20% (residents bail at first opportunity). Pattern recognition: poor renewal beds often have specific issues, bad neighbor, noise from common area, smaller window, specific layout problem. Address the bed-specific issue, and renewal rates climb. This metric is invisible at property level.

Bed-Level NPS

When residents are surveyed, attribute responses to their specific bed. Aggregate NPS by bed, room, floor, and property. Surprising patterns emerge, sometimes one room consistently produces detractors regardless of who's in it. That's an operational signal: investigate the room (housemate dynamic, physical issue, location problem). Sometimes one floor produces consistently low NPS, that's a building-level operational issue (cleaning, noise, common areas).

Application-to-Move-In Conversion Per Bed

Of applications received specifically for a bed, % that convert to move-in. Beds with low conversion (under 30%) typically have: pricing issues (rate too high relative to attributes), poor presentation (photos don't sell the bed), or applicant friction (response time, paperwork delays). Beds with high conversion (over 70%) are sales gems, replicate their pricing, photos, and copy across similar beds.

Per-Bed Maintenance Frequency

Tickets per bed per year. Average healthy: 2-4 tickets/bed/year. Beds running 6+ tickets/year have underlying physical issues (old furniture, plumbing problems, hot/cold spots), proactive replacement is cheaper than recurring tickets. Beds running 0 tickets/year for 18+ months might indicate residents who don't report issues (possibly because they're disengaged, foreshadowing churn). Both extremes warrant investigation.

Time-on-Market Distribution

Histogram of how long beds stayed listed before being filled. Healthy distribution: 70% under 14 days, 20% 14-30 days, 10% 30+ days. Skewed distribution where 30%+ beds take 30+ days indicates either pricing issues or marketing channel problems. Compare distributions across properties to find your problem properties.

Per-Bed Lifetime Revenue

Total revenue generated by a specific bed since first occupancy. Some beds generate £15,000+ per year (long stays, premium rates), others generate £8,000 (high turnover, average rates). Pattern recognition: beds with high lifetime revenue typically have stable housemate dynamics; beds with low lifetime revenue often have rotation problems. Use this to identify which beds need housemate-matching attention.

Setting Up Bed-Level Reporting

Most PMS platforms can't produce these reports natively. JumboTiger's reporting module ships with all 8 bed-level KPIs as standard dashboards. If using another PMS, you'll need to: extract bed-level data via API, pipe into Tableau/Power BI/Looker Studio, build the dashboards manually. Plan for 2-3 weeks of setup. Once running, review weekly in ops meetings.

Final Thoughts

Per-bed reporting is the natural follow-on from per-bed inventory. Without bed-level metrics, you're blind to operational patterns that drive RevPAB, NPS, and renewal rates. Invest in proper tracking infrastructure once, build it into weekly ops cadence, and you'll spot issues months before they become crises. The operators tracking these metrics consistently outperform the ones running on property-level averages.