WRIE · Methodology
WRIE Methodology: Model Inputs, Governance, Interventions & Measurement
How the SeniorCRE Workforce Retention Intelligence Engine™ (WRIE) works end-to-end — the data it uses, the data it does not, the humans in the loop, and how outcomes are measured. Written for operations, finance, HR, and audit reviewers evaluating predictive workforce analytics for senior housing & care.
Answer first, in plain language
The staffing report works until payroll disagrees with the schedule. This page explains, step by step, how staffing and pay data are brought into one record per employee, how the people most likely to leave in the next 30, 60, or 90 days are flagged, who decides what to do about it, and how the result is measured 90 days later. Nothing reaches an employee record without a manager confirming it, and every flag keeps the version and timestamp that produced it.
Status of everything described below
Every step, input, and formula on this page is built and exercised in SeniorCRE validation environments on synthetic or de-identified data. There is no completed operator-production deployment published, and no accuracy, savings, or payback figure is claimed. Buyers get a back-test against their own history instead. Operators keep the systems they run today and SeniorCRE governs what those systems produce, or they deploy SeniorCRE for the scheduling, time, and payroll functions they choose — that decision stays with the operator. Claim states are recorded in the WRIE Evidence Record.
The same answer, in the reviewer’s terms
WRIE scores caregiver and clinical-staff exit risk on a rolling 30/60/90-day horizon from operator-owned scheduling, HRIS, acuity, and payroll data on the governed operating record. Every prediction is versioned, human-confirmed, and routed to a defined intervention playbook. Outcomes are attributed at the 90-day tenure milestone and written back so the next model cycle learns from the result.
Model inputs
| Input | Definition | Source system |
|---|---|---|
| Scheduling history | Shift patterns, swaps, no-shows, OT density, consecutive-day count | Operator SoR / SeniorCRE scheduling |
| Tenure & role band | Hire date, role, care setting (AL / MC / SNF / IL) | HRIS / operator SoR |
| Acuity-weighted workload | Resident acuity mapped to caregiver assignments | SeniorCRE clinical (operator-controlled operating record) |
| PRN vs. FTE mix | Share of hours filled by PRN vs. FTE by unit / shift | Scheduling |
| Pay-period earnings variance | Rolling variance in gross earnings vs. baseline | Payroll / HRIS |
| Internal transfer history | Prior transfers, department moves, promotions | HRIS |
| Commute distance | Optional, where captured via HRIS geo field | HRIS (optional) |
Data WRIE does not use
- • Protected-class attributes (race, age, religion, disability, national origin, gender identity)
- • Sentiment scraped from social media or private messaging
- • Personal-device location or biometric data
- • Credit scores or non-employment financial data
Prediction governance
- Versioning: Every prediction is tagged with a model release ID and a scoring timestamp. Prior scores are never overwritten.
- Change control: Model changes require a named reviewer and a back-test against the prior four quarters before production release.
- Adverse-impact monitoring: Quarterly review across role, tenure band, and shift band. Findings are logged and remediation is tracked to closure.
- Human-in-the-loop: No prediction becomes an action without an operator-approved intervention. Overrides are logged in the immutable audit trail.
- Auditability: Every read of a prediction, every intervention decision, and every override writes to the append-only audit log per project data-governance policy.
Intervention workflow (five steps)
- Ingest. Scheduling, HRIS, and acuity data flow into the operator-owned operator-controlled operating record nightly.
- Predict. WRIE scores each employee for exit risk on a rolling 30/60/90-day horizon; every score is versioned.
- Confirm. A human manager reviews the prediction and confirms, defers, or overrides. Every decision is logged.
- Intervene. The confirmed prediction routes to a defined intervention playbook — schedule adjustment, PRN conversion, mentor pairing, retention conversation, retention bonus, or transfer offer.
- Measure. Outcomes are attributed at the 90-day tenure milestone and written back to the Operator-Controlled Operating Record so the next model cycle learns from the result.
Measurement periods
| Window | What it measures |
|---|---|
| Rolling 30 days | Near-term exit risk; triggers immediate schedule / conversation plays |
| Rolling 60 days | PRN conversion window; mentor pairing eligibility |
| Rolling 90 days | Retention-bonus attribution milestone; retained employees are credited here |
| 12-month portfolio | Cost-per-turnover ROI vs. pre-WRIE baseline the operator brings to the back-test |
Reviewer credentials
| Role | Scope of review | Affiliation |
|---|---|---|
| Clinical Operations Lead | Reviews model use against safe-staffing and acuity guidelines. | SeniorCRE clinical operations |
| Workforce Economist | Reviews cost-per-turnover benchmarks and ROI attribution. | SeniorCRE / external advisor |
| Data Governance Owner | Reviews inputs, adverse-impact monitoring, and audit-trail integrity. | SeniorCRE |
Methodology last reviewed July 23, 2026. Named reviewers and benchmark citations are refreshed each quarter.
Definitions: workforce intelligence vs. everything adjacent
- Workforce intelligence
- The practice of reconciling scheduling, payroll, HRIS, and acuity-weighted clinical demand into one governed record per employee, scoring exit risk against that record, routing confirmed risk to a human-owned intervention, and forecasting labor cost forward on the corrected data.
- HRIS reporting (what this is not)
- Descriptive reporting on transactions that already happened. Useful for compliance and headcount, but it produces no forward risk signal and no forecast.
- Workforce management (what this is not)
- Scheduling, time capture, and labor budgeting. It optimizes a shift or a pay period; it does not reconcile the record across systems or predict exits.
- People analytics (adjacent)
- Horizontal, cross-industry workforce modeling. Strong on method, but the senior-care demand signal — acuity-weighted care minutes, PBJ-reported hours, agency invoices — has to be built by the operator.
- Premium workforce analytics (high-turnover environments)
- Four things separate it from reporting: prediction on reconciled data rather than survey sentiment, a human-confirmed intervention workflow with an immutable audit trail, defensible attribution that survives an owner or lender review, and documented governance including input exclusions and adverse-impact monitoring.
Best practices for workforce intelligence and payroll forecasting
Eight practices, in dependency order. Each one is a precondition for the ones below it.
1.Reconcile before you model
One row per employee across scheduling, payroll, and HRIS, with agency workers held outside the employee table. Reconcile to the last filed payroll and, for skilled nursing, to submitted PBJ hours before any score is trusted.
2.Forecast at shift and role level, thirteen weeks out
Cost-center-level forecasting hides the overtime density and agency conversion that actually move labor cost. Model overtime, differentials and premium pay, PTO accrual, vacancy ramp, orientation hours, and merit cycles as explicit inputs.
3.Drive the forecast from census, not budget
Hours should scale with an occupancy and acuity forecast. A payroll forecast anchored to budget will be wrong in exactly the months that matter.
4.Price agency from invoices, not the rate card
Guarantees, cancellations, travel, and overtime on contract hours are where the variance lives. Reconcile invoices to worked shifts every cycle.
5.Publish forecast-to-actual variance weekly with a named owner
Track mean absolute percentage error by community, role, and shift. When error drifts, the upstream census, acuity, or reconciliation feed is usually the cause — not the forecast model.
6.Keep the human in the loop and keep the audit trail
No individual-level action fires from a score alone. Confirmation, suppression, action, and outcome are all dated and attributed to a person.
7.Freeze the baseline before the first intervention
Definitions, period, and data sources are fixed in writing and never restated. Without a frozen baseline there is no defensible ROI claim.
8.Emit compliance artifacts from the same run
PBJ-ready hours and staffing-measure inputs should be a byproduct of the forecast run, not a separate reporting project.
Evidence block
Model inputs: what a predictive turnover model actually reads
A predictive turnover model in senior housing & care reads operational signals from scheduling, payroll, and acuity-weighted clinical demand — not survey sentiment. These are the inputs SeniorCRE’s Workforce Retention Intelligence Engine™ (WRIE) uses, the reason each one carries signal, and the refresh cadence each requires.
| Signal | Source system | Why it is predictive | Cadence |
|---|---|---|---|
| Overtime density (OT hours / scheduled hours, rolling 4 weeks) | Payroll and time-and-attendance | Sustained OT concentration on the same employees is the most consistently observable precursor to voluntary exit in shift-based care work. | Daily |
| Consecutive-day and clopen patterns | Scheduling system | Recovery-time compression predicts call-offs first and exits second. | Daily |
| Shift-swap, no-show, and late-clock behavior | Scheduling + time clock | Disengagement usually appears as schedule friction weeks before a resignation. | Daily |
| Earnings variance vs. the employee’s own trailing 8-week average | Payroll | Falling or erratic take-home pay is a stronger driver than absolute wage level for caregivers and aides. | Per pay period |
| Acuity-weighted workload per worked hour | Clinical / care-plan data reconciled to census | Headcount can be flat while the work itself intensifies; unweighted ratios hide that entirely. | Daily |
| Early-tenure isolation (days 1–90: orientation completion, preceptor continuity, unit consistency) | HRIS + scheduling | A large share of caregiver separations occur inside the first ninety days, where the causes are onboarding-operational rather than compensation. | Weekly |
| Primary-caregiver continuity on assigned residents | Scheduling + assignment data | Continuity breakdown correlates with both resident-experience decline and caregiver exit. | Weekly |
| Open-shift exposure on the employee’s unit and shift | Scheduling | Chronic vacancy on a unit transfers load to the people who stay, which is how one exit becomes three. | Daily |
| Agency mix on the employee’s shift | Scheduling + agency invoices | High contract-labor mix changes both workload distribution and perceived pay equity on the floor. | Weekly |
| Manager-span and supervisor-change events | HRIS | Supervisor change is a discrete, dateable risk event that is trivially available and frequently unused. | Event-driven |
| Commute-band and internal transfer history | HRIS (coarse, non-address band) | Coarse distance bands carry signal without storing home address in the model. | Monthly |
| Role-and-market wage position vs. BLS OEWS for the same role and MSA | BLS OEWS (public) reconciled to payroll | Public wage benchmarks make a pay-position finding defensible to an owner or lender. | Annual (BLS release) with quarterly reconciliation |
Inputs excluded on purpose
Exclusions are part of the model, not an afterthought. Each exclusion below is enforced at the feature layer and is reviewable in the model documentation.
- Race, ethnicity, national origin, sex, religion
- Protected characteristics are excluded from features and from any derived interaction term. Adverse-impact monitoring is run on outputs instead.
- Age and date of birth
- Excluded as a feature. Tenure is used instead, which is the operationally relevant variable.
- Pregnancy, disability, leave type, and any health information
- Excluded. Leave presence may be used only as an availability fact for scheduling, never as a risk feature.
- Union status and protected concerted activity
- Excluded categorically. Prediction must not touch protected activity.
- Home address, precise geolocation, and personal device data
- Excluded. Only coarse commute bands are used.
- Survey sentiment as a primary driver
- Not excluded, but never a standalone trigger: response bias and cadence make it unusable as the basis for an individual-level action.
- Immigration status and visa detail
- Excluded from the model entirely; handled only in compliance workflows.
Evidence block
The 30/60/90-day intervention workflow
Analytics improve retention only when a named human confirms the risk, acts on the driver behind it, verifies the driver moved, and attributes the outcome at the ninety-day milestone. This is the workflow, with owners and exit criteria for each window.
- Step 1
Days 0–7 (confirm before you act)
Establish that the score reflects reality on the floor before anyone is contacted.
Owner: Community executive director with the regional workforce lead
- Review the ranked flight-risk list with the driver contributions shown per employee — never a bare score.
- Mark each name confirmed, not-confirmed, or insufficient-information. Not-confirmed is a first-class outcome and is retained for model review.
- Suppress any name where the driver is a data artifact (missed punch batch, mis-mapped unit, agency worker in the employee table).
- Record the confirmation decision, the decider, and the timestamp in the audit trail.
Exit criteria: Every high-risk name has a dated human decision. Nothing is auto-actioned.
- Step 2
Days 1–30 (remove the operational cause)
Act on the driver, not the score.
Owner: Scheduler and unit manager, tracked by the ED
- Overtime-density driver: rebalance the next two schedule cycles and cap consecutive days for the named employees.
- Earnings-variance driver: correct the schedule shortfall or the differential error before any conversation about pay.
- Early-tenure driver: assign a named preceptor, hold unit and shift constant for the remainder of the ninety days, and complete the day-14 and day-30 check-ins.
- Continuity driver: restore primary-caregiver assignment consistency on the affected residents.
- Log each action against the employee record with the driver it addresses.
Exit criteria: Each confirmed name has at least one logged action mapped to a specific driver, with a due date.
- Step 3
Days 31–60 (verify the driver actually moved)
Check that the operational condition changed, independent of whether the employee stayed.
Owner: Regional workforce lead
- Re-measure the driver metric for each named employee against its value on the confirmation date.
- Escalate any name where the driver is unchanged after two schedule cycles — that is a scheduling-capacity problem, not a retention problem.
- Review the unit-level pattern: three or more confirmed names on one unit and shift is a staffing-structure finding, not twelve individual conversations.
- Feed suppression reasons back into the data-quality backlog.
Exit criteria: Driver movement is recorded per name; unit-level findings are opened as structural items.
- Step 4
Days 61–90 (attribute honestly)
Close the loop at the ninety-day milestone with an attribution a CFO can defend.
Owner: Finance owner with the regional workforce lead
- Report retained / separated / transferred for every confirmed name, alongside the not-confirmed cohort as a comparison group.
- Attribute only where a driver moved and an action was logged; everything else is reported as unattributed.
- Publish the model’s precision at the operating threshold, plus the count of suppressed and not-confirmed names.
- Re-freeze the baseline for the next quarter and version the model.
Exit criteria: A dated ninety-day cohort report exists with attributed, unattributed, and suppressed counts separated.
Evidence block
Retention metric definitions
Most disagreements about retention numbers are definitional. These are the formulas, the numerator and denominator rules, and the measurement error each metric most often contains.
| Metric | Formula | Numerator / denominator rules | Most common error |
|---|---|---|---|
| Annualized voluntary turnover rate | (voluntary separations in period / average active headcount in period) × (12 / months in period) | Numerator excludes involuntary terminations, RIFs, and internal transfers. Denominator is average active headcount, not period-end headcount. | Mixing voluntary and involuntary separations, or using period-end headcount, which inflates the rate in a shrinking portfolio. |
| 90-day new-hire survival | hires still active on day 90 / hires in the cohort | Cohort is defined by hire date, not by month of report. Rehires within twelve months are flagged separately. | Reporting a rolling snapshot instead of a closed cohort, which makes the number move for reasons unrelated to retention. |
| Regrettable turnover | voluntary separations flagged regrettable / total voluntary separations | Regrettable requires a documented manager designation at separation, not a retrospective judgment. | Backfilling the flag after the fact, which makes the metric unauditable. |
| Agency share of worked hours | agency worked hours / (agency worked hours + employee worked hours) | Agency hours come from invoices reconciled to the schedule, not from the schedule alone. | Using scheduled rather than invoiced agency hours, which understates share whenever cancellations are billed. |
| Overtime density | OT hours / total worked hours, by role and shift | Computed at role-and-shift level; portfolio averages hide the concentration that drives exits. | Reporting a single portfolio OT percentage. |
| Vacancy duration (time-to-fill) | median days from requisition open to first worked shift | Ends at first worked shift, not at offer acceptance. | Measuring to offer acceptance, which omits the no-show and orientation-attrition window. |
| Primary-caregiver continuity | resident-days covered by the top-two assigned caregivers / total resident-days | Measured per resident, then rolled up. Agency coverage counts against continuity. | Measuring at the unit level, which averages away the residents with the worst continuity. |
| Cost-to-fill an open shift | incremental premium (agency rate differential + OT premium + guarantee + travel/orientation) for one covered shift | Incremental cost only — base hours the operator would have paid regardless are excluded. | Charging the full agency invoice as incremental, which overstates avoidable cost. |
| Fully loaded cost per separation | separation + vacancy coverage premium + recruiting + onboarding + ramp productivity loss | Built from the operator’s own payroll, invoice, and recruiting cost lines; industry ranges are used only to sanity-check. | Adopting a published per-separation figure as if it were the operator’s own cost. |
Evidence block
Retention ROI and agency-labor savings: the formulas
A retention ROI model is defensible when every term is defined, every rate comes from the operator’s own invoices and payroll, and internal effort is charged against the result. These are the formulas used, with the guardrail that keeps each one honest.
Avoided-separation value
AS = (B_sep − M_sep) × C_sep_loaded
- B_sep = separations in the frozen baseline period, same roles, same communities, seasonally aligned
- M_sep = separations in the measured period
- C_sep_loaded = fully loaded cost per separation, built from the operator’s own cost lines
Guardrail: Only counted where a driver moved and an action was logged. Report the rest as unattributed.
Avoided agency premium
AA = Σ (H_agency_baseline − H_agency_measured) × (R_agency − R_internal_loaded)
- H = invoiced agency hours by role and shift
- R_agency = effective invoiced agency rate including guarantees and cancellations
- R_internal_loaded = internal wage plus burden for the same role and shift
Guardrail: Rate differential only. Hours the operator would have paid internally are not savings.
Avoided overtime premium
AO = (H_ot_baseline − H_ot_measured) × 0.5 × R_base_loaded
- H_ot = overtime hours by role and shift
- R_base_loaded = base wage plus burden
Guardrail: Only the 0.5 premium portion counts. Counting the full OT hour double-counts base labor.
Census-protection value (reported separately, never blended)
CP = Δ occupied units × net revenue per unit per month × months
- Δ occupied units attributable to staffing capacity, evidenced by a documented admission hold released
Guardrail: Reported as a separate, clearly-labelled scenario line. Never added into the retention ROI total without a documented hold.
Net program value
NPV_program = (AS + AA + AO) − (platform cost + internal effort cost)
- internal effort cost = loaded hours spent by EDs, schedulers, and finance on the workflow
Guardrail: Internal effort is never excluded. A model that omits operator labor is not a model an owner will accept.
Sensitivity treatment
| Variable | How it is treated |
|---|---|
| Fully loaded cost per separation | Run low / base / high from the operator’s own cost lines. Publish all three; lead with low. |
| Attribution share | Default to attributing only the confirmed-and-actioned cohort. Show a 0% attribution floor case so the downside is visible. |
| Agency rate differential | Use the trailing three months of invoices, not the contracted rate card. |
| Seasonality | Align the baseline and measured periods to the same calendar quarters, or state the adjustment. |
| Portfolio change | Exclude acquisitions, dispositions, and units in lease-up from both periods. |
Evidence block
Limitations of this methodology
Stating the limits is part of the method. Each limitation below changes how a result should be read, and each is disclosed to buyers before a model is run against their data.
- No operator-production workflow is published as of October 16, 2026.
- Every capability statement is at the design or pre-pilot validation level. Ask for a back-test against your own history rather than a reference outcome.
- Prediction cannot establish causation.
- A flight-risk score identifies conditions that precede exits. It does not prove an intervention caused a retention. Attribution is reported, not asserted.
- Model quality is bounded by the worst input feed.
- Unreconciled agency invoices, missing punches, or mis-mapped units degrade scores faster than any modeling choice improves them.
- Small communities produce unstable rates.
- Below roughly 40 FTEs in a role, quarterly turnover rates swing on single events. Use counts and rolling twelve-month windows instead.
- Cold-start requires history.
- Roughly twelve to eighteen months of separation history per role family is needed before scores are worth acting on; before that the workflow still runs on driver metrics alone.
- Labor markets shift underneath the model.
- Wage compression, a new competitor opening, or a rate-card change invalidates a stale baseline. Re-freeze quarterly.
- Published industry cost ranges are not operator results.
- They size exposure. They are never presented as savings achieved, and no payback period is published.
- Adverse-impact risk is real and must be monitored.
- Protected characteristics are excluded as inputs, but outputs are still monitored for disparate distribution, and monitoring results are part of the audit record.
Evidence block
Cited case-study format
SeniorCRE publishes no operator-production outcome data today. When it does, every case study will follow this ten-section format, with each figure tagged by evidence class. Operators are welcome to demand the same format from any vendor on the workforce analytics shortlist.
| Section | What it must contain |
|---|---|
| 1. Operator profile | Portfolio size, care types, states, bed/unit count, and payer mix. No named operator without written consent. |
| 2. Frozen baseline | Named period, exact metric definitions used, and the data sources reconciled. Baseline is frozen before the first intervention and never restated. |
| 3. Data readiness at start | Which historical exports, representative records, sandbox feeds, or manual inputs were used, and which reconciliation exceptions were open at the start. |
| 4. Intervention log | Confirmed names, drivers addressed, actions logged, and dates. Includes suppressed and not-confirmed counts. |
| 5. Measured period result | The same metrics, same definitions, seasonally aligned, with attributed / unattributed / suppressed reported separately. |
| 6. Model performance | Precision and recall at the operating threshold, model version, and drift notes for the period. |
| 7. Economics | ROI formulas applied with the operator’s own cost lines, low/base/high sensitivity, and internal effort cost included. |
| 8. Confounders and limitations | Wage actions, market events, acquisitions, leadership changes, and anything else that could explain the result. |
| 9. Sources and verification | Public sources cited with access dates. Operator-supplied figures marked as operator-reported and reviewed by a named operator signatory. |
| 10. Evidence class | Every figure tagged: A public primary source, B vendor public materials, C operator-reported, D design target not yet operator-proven. |
Verification
Public sources used
Every benchmark reference below is a public primary source a reader can check without a vendor relationship.
- BLS Occupational Employment and Wage Statistics (OEWS)Wage position by role, state, and MSA.
- BLS Job Openings and Labor Turnover Survey (JOLTS)Sector-level quits and openings context for the baseline period.
- CMS Payroll-Based Journal (PBJ) staffing dataReported nurse-staffing hours and turnover measures for skilled nursing.
- CMS Care Compare / Five-Star staffing measuresPublicly reported staffing and turnover measures used for peer comparison.
- BLS Current Employment Statistics — healthcare and residential careEmployment trend context for the market in the measured period.
Cluster pages
Frequently asked questions
What data does WRIE use to predict turnover?
Scheduling history (shift patterns, swaps, no-shows, overtime density), tenure and role band, acuity-weighted workload, PRN vs. FTE mix, pay-period earnings variance, commute distance where captured, and internal transfer history. WRIE does not use protected-class attributes, sentiment scraped from social media, or personal-device data. All inputs are documented on the operator-owned operator-controlled operating record.
How is prediction governance handled?
Every prediction is versioned, timestamped, and attributed to a model release. Model changes go through a change-control record with a named reviewer. Adverse-impact monitoring runs quarterly across role, tenure band, and shift band. Operators can suppress or override any prediction; overrides are logged in the immutable audit trail. Nothing goes to an employee record without an operator-approved intervention.
What is the intervention workflow?
A prediction becomes an intervention only when a human manager confirms it. Interventions are drawn from a defined library — schedule adjustment, PRN conversion, mentor pairing, retention conversation, retention bonus, transfer offer — each with a documented playbook. Every intervention writes back to the Operator-Controlled Operating Record so the next model cycle learns from the outcome.
What are the measurement periods?
WRIE reports on rolling 30 / 60 / 90-day and 12-month windows. Cost-per-turnover attribution is calculated at the 90-day tenure milestone (the point at which a retention play is credited). Portfolio ROI is measured quarter-over-quarter against the pre-WRIE baseline the operator brings to the back-test.
Who reviews and signs off on the model?
The WRIE model is reviewed by SeniorCRE clinical operations leadership, a workforce economist, and a data-governance owner. Every published benchmark and every case study is dated and cited. Reviewer credentials and last-review date appear on the methodology page and are refreshed each quarter.
The owner’s language
What governed labor does to NOI, and to coverage, at one building
Labor is the largest controllable cost in the building, and it is the one an owner tests against a covenant they have already signed. So the workforce case has to land on the NOI line. Nothing below is a result we have measured — each amount is the operator’s own, taken from payroll, the schedule, agency invoices and separation history.
Line 01
Agency and premium hours above the governed plan
Ungoverned today: Scheduling, timekeeping and payroll are reconciled after the pay period, by hand, once the premium has already been spent.
Line 02
Care hours staffed against governed census
Ungoverned today: A signed deposit is counted as occupied, so hours are scheduled and paid against a bed nobody has moved into.
Line 03
Replacement cost of separations nobody saw coming
Ungoverned today: The exit signal sits in scheduling, payroll and credential data that no one reconciles into one record per employee, so the cost is recognized at the exit interview.
The three lines are the operator’s figures, not ours. What SeniorCRE supplies is the definition each line is measured on, which system holds authority for it, the reconciliation rule, and the lineage back to source — so the amount can be defended when it reaches the owner.
What that does to debt service coverage
Debt service coverage ratio = property NOI ÷ annual debt service.
Coverage is usually tested at the property, on trailing NOI, against a covenant the owner has already signed. That makes an NOI line item and a covenant test the same conversation — an amount that never reaches NOI never reaches coverage, and a definition nobody governs is where the amount goes missing.
Illustrative arithmetic on stated assumptions
One 100-unit assisted living community. Annual debt service of $1,200,000. Trailing NOI of $1,560,000, which tests at 1.30×.
If the owner’s own worksheet puts $120,000 of annual NOI on the lines above — care levels billed in period, hours staffed against governed census, occupancy read on one definition — NOI of $1,680,000 tests at 1.40×.
That is division, not evidence. The assumptions are stated so the owner can replace them; substitute the property’s own debt service and NOI and the arithmetic answers for that property instead of this one.
No email is required and nothing is gated. If the arithmetic does not clear the covenant on your own numbers, that is the answer, and we would rather you find it on your own sheet than in a briefing.
No operator runs SeniorCRE in operator production today, no third-party integration is live in operator production, and no operator outcome is claimed or independently validated. Figures above are arithmetic on the assumptions shown. Commercial terms are not published; they are provided under NDA in a written quote scoped to the portfolio.
Govern the truth before you automate the decision — and before you ask an owner to fund it.
Next: what is proven, and what is not
Now that the method is written down, read the evidence state of every claim on it — including the claims that remain unproven.