Wert-Berater, Inc. is an independent hospital feasibility study consultant preparing lender- and agency-ready analyses for new hospitals, expansions, acquisitions, specialty hospitals, and micro-hospital projects. Our studies evaluate service-area demand, patient-origin and discharge patterns, competitive market share, physician support, payer mix, reimbursement, service-line volume, development costs, operating economics, debt-service coverage, and downside sensitivity for SBA, USDA, conventional, institutional, and other financing structures.
Prepared for lenders, CDCs, and federal agencies to SBA SOP 50 10 8, USDA 7 CFR Part 5001, and conventional underwriting standards. Fiduciary duty runs to the lender and the agency, never the borrower. 4,000+ engagements since 1998 covering $41.2 billion in evaluated project value. So far in 2026: 41 engagements and $1.54 billion evaluated — 17 SBA, 11 USDA.
A hospital feasibility study consultant is engaged by the lender, the agency, or the capital provider to determine whether a proposed hospital can capture enough clinically appropriate volume, at a realistic net reimbursement, to cover its operating cost structure and service the proposed debt. Hospital credits fail differently from other commercial assets: the revenue is not a rate multiplied by an occupancy figure but a mix of encounters, each reimbursed at a different rate by a different payer, delivered by physicians who must be recruited and retained.
The work begins with the service area, defined from patient-origin evidence rather than a marketing radius. Drive-time bands are drawn to the proposed site and tested against where residents of each ZIP code currently receive care, because a hospital's realistic catchment is bounded by referral patterns and existing loyalties, not by geography alone. Population is then resolved by age cohort, since utilization is strongly age-dependent, and projected forward across the model horizon. Age-adjusted use rates are applied to that population to convert demographics into expected inpatient discharges, emergency department visits, and surgical cases — the three volume streams that drive almost all hospital revenue.
Expected demand is then contested against incumbent supply. State discharge databases establish, by DRG or service line, where the volume in the service area actually goes today and what share each existing hospital holds. Licensed bed counts, occupancy, and the outpatient competition — ambulatory surgery centers, freestanding emergency departments, imaging providers, and urgent care — are counted, because outpatient migration removes volume from a hospital pro forma that assumes historical inpatient patterns. Any realistic market-share assumption has to be defended against the specific incumbents that would have to lose the volume for the project to gain it.
Physician supply is analyzed as a hard constraint rather than a supporting detail. Licensing and CMS enrollment data establish the specialists practicing in the service area, their admitting relationships, and their age profile, and the study tests whether the admitters required by the volume forecast actually exist or whether the projection depends on recruitment. Where it depends on recruitment, the recruitment assumption is stated explicitly with its cost, its timeline, and the effect on the forecast if it does not occur on schedule — the most common single point of failure in a specialty hospital projection.
Volume is then converted to revenue through payer mix. Commercial, Medicare, Medicaid, and self-pay each reimburse at materially different levels, so net revenue per discharge and per encounter is built from the mix the service area demographics actually support, not from a blended average. Against that revenue the study builds the cost structure: clinical and administrative staffing at achievable wage rates, supplies and pharmacy, purchased services, malpractice and insurance, and the capital program — construction, equipment, and information systems — tested against the loan request. Ramp-up and stabilization are modeled explicitly over multiple years, because a hospital does not open at stabilized volume. The output is a ten-year model with sensitivity testing on volume, payer mix, and reimbursement, reporting debt-service coverage and the point at which coverage fails, alongside the regulatory pathway: certificate of need where the state requires it, state licensure, and CMS certification, each with its sequencing and its risk to the schedule.
The analysis is framed to the facility actually proposed. The three types below carry different volume structures, different physician dependencies, and different failure modes, and a study that treats them interchangeably will misprice the risk.
A general acute-care hospital carries a broad service-line portfolio and a correspondingly broad demand base: medical and surgical admissions, obstetrics where offered, an emergency department that functions as the principal admission source, and diagnostic and ancillary services. Feasibility turns on realistic market share against established incumbents, average daily census and bed utilization sufficient to spread a heavy fixed-cost base, and a payer mix that can carry the cost structure. Physician coverage must be broad enough to staff call across specialties, and the emergency department's conversion rate to inpatient admission is a critical modeled input rather than an assumption.
A specialty hospital — surgical, orthopedic, or cardiac — concentrates demand in a narrow set of service lines, which raises both margin potential and concentration risk. Volume typically depends on a small identifiable group of admitting and operating physicians, so the analysis examines each one's current case volume, where those cases are performed today, and what would have to change for them to move. Where a handful of physicians account for most of the forecast, that dependency is stated as a specific credit risk with its own sensitivity case. Payer mix in elective specialty work is usually more favorable than general acute care, but it is also more exposed to reimbursement policy changes affecting the specific procedures involved.
A micro-hospital operates a small licensed inpatient footprint attached to a substantial emergency department, and its economics are dominated by emergency volume rather than by inpatient census. Feasibility depends on whether the site captures enough emergency visits at an acceptable acuity and payer mix to support the fixed cost of standing an emergency department up around the clock. Because the inpatient component is small, there is little capacity to absorb a shortfall, so stabilization risk is concentrated and the ramp period is modeled tightly. Market positioning against nearby freestanding emergency departments and full-service hospitals is decisive, and self-pay exposure is tested directly because emergency demand is not selective.
Hospital feasibility — general acute care, specialty surgical and cardiac hospitals, freestanding emergency facilities, and the micro-hospital format — is underwritten on service-line economics inside a defined service area: discharges, surgical cases, and emergency visits the population generates, the shares incumbent systems already hold, and the payer mix that prices every projected encounter. The study sizes demand from state discharge data and use-rate benchmarks, tests the medical-staff plan against the physicians who actually admit, and treats regulatory posture — licensure, certificate-of-need where applicable, CMS certification path — as the gating architecture it is.
The methodology builds volume by service line from population use rates and documented market shares, prices it through a payer-mix model with commercial, Medicare, Medicaid, and self-pay yields stated separately, and stresses the census against physician-recruitment and ramp scenarios. Capital costs are benchmarked against healthcare construction data, and coverage is tested at the program minimum across census and payer-mix stress cases.
Every Wert-Berater financial model is fully linked with no hardcoded values, so any reviewer can stress any input. Deliverables comprise a complete narrative report and the linked Excel model, with ten-year pro forma, sensitivity analysis at ±5, 10, and 15 percent, interest-rate stress from +0.5 to +3.0 percent, and ratio analysis benchmarked against RMA and IBISWorld data.
SBA engagements are prepared to SOP 50 10 8, including its debt-service-coverage minimums of 1.15x operating and 1.00x global. USDA engagements follow RD Staff Instruction 5001 across the Business & Industry, Community Facilities, REAP, and Value-Added Producer Grant programs. Conventional engagements are built to the lender's stated coverage standard, typically 1.20x. Smaller and rural facilities reach USDA Community Facilities and B&I structures; specialty and physician-sponsored hospitals fit conventional and bond frameworks; the regulatory and reimbursement dimensions carry decisive weight in every program.
The firm's healthcare practice spans the medical-village, senior-care, and wellness-facility record, with the same payer-aware revenue discipline applied at hospital scale. Independence is non-negotiable: determinations follow the evidence and are not revised under pressure, and studies are built to pass lender, agency, and third-party review without exception items.
Completed engagements in the healthcare sector include an ambulatory surgery center study in the Maitland–Winter Park corridor of Florida and a medical office building study in Foley, Alabama. Rural and critical-access facilities carry a different reimbursement and regulatory analysis and are addressed separately in the firm’s rural and critical access hospital feasibility studies. Where a sponsor needs market data rather than a lender-grade determination, the firm also publishes custom hospital and ASC market reports as a lower-scope research alternative.
Hospital and healthcare feasibility determinations are prepared under the direction of Donald Safranek, MSc, President of Wert-Berater, Inc. The firm provides financial and market analysis for lenders and agencies; it does not provide medical, clinical, or legal advice, and it holds no clinical accreditation. Last reviewed 2 September 2026.
A hospital market feasibility study reconciles inpatient demand and outpatient demand rather than treating them as separate market totals. The service-area population establishes the potential patient base, while competitive hospitals and licensed bed supply define the capacity already available. Emergency department demand and outpatient services are modeled as distinct volume streams so the healthcare feasibility study does not count an encounter in more than one service line.
The model sets operating expenses against expected facility economics to calculate EBITDA. Break-even analysis then identifies the volume and net-revenue threshold at which the hospital covers its fixed and variable costs.
A hospital feasibility study — whether the project is a general acute-care facility, a specialty surgical or cardiac hospital, or a micro-hospital — must resolve questions that a generic commercial real-estate study never reaches. The engagement begins with a defined service area drawn from drive-time isochrones and county-level discharge geography, not from an arbitrary radius, and every subsequent projection is anchored to that boundary. The financial model prices each service line separately because a cardiac program, an orthopedic line, and an emergency department carry materially different payer mixes, staffing ratios, and supply costs.
The bound narrative and fully linked Excel workbook are delivered together. No value in the model is hardcoded, so a credit officer or agency reviewer can stress any input — census, rate, or cost — and watch every downstream ratio recalculate in real time.
Demand for hospital services is not estimated from population alone. The methodology layers multiple data sources to produce a defensible volume count at the service-line level. State hospital discharge databases — filed under each state health department's mandatory reporting program — provide the foundational case counts by diagnosis-related group, zip code of patient origin, and facility of treatment. Those records establish both the total discharges the service area generates and the shares each incumbent system currently captures.
Utilization rates are drawn from federal and state benchmarks and age-adjusted to the local population pyramid, because an older rural catchment and a younger suburban market produce materially different surgical and medical admission rates even at identical population sizes. Physician-supply data from state licensing registries and CMS enrollment records identifies which specialties are present, which are underserved, and which admitting physicians are realistically available to support a new facility's ramp.
Competitive supply is mapped from CMS cost reports, state licensure filings, and facility bed-count registries — not from secondary market reports that may lag by years. Freestanding emergency department locations are verified against state emergency-service licensing records. Payer-mix inputs are cross-referenced against CMS Medicare cost report public-use files and state Medicaid managed-care enrollment data. Where a certificate-of-need application has been filed by a competitor, the public docket is reviewed as part of the regulatory-supply analysis.
Hospital conclusions are only as defensible as the data behind them, and a lender reviewing the study needs to know where every material figure originated. The firm builds hospital analyses from primary public records rather than purchased summaries, so a reviewer can trace and re-verify the inputs independently.
State hospital discharge databases provide the inpatient volume record by facility, service line, and patient-origin ZIP code, which is the basis for both current market share and the demand forecast. State health department records and licensing files establish licensed bed counts, facility status, and service authorizations for every competitor in the service area. Certificate-of-need dockets, where the state operates a CON program, show what capacity has been applied for, approved, or denied — pipeline supply that must be netted against the demand base before any share assumption is credible.
Federal sources carry the financial and provider picture. Centers for Medicare & Medicaid Services cost reports supply audited facility-level financial and utilization data for existing hospitals, which anchors both competitor performance and the operating benchmarks applied to the subject. CMS provider enrollment and state licensing records establish the physician supply, specialty distribution, and practice locations underlying the admitter analysis. U.S. Census Bureau demographic series provide the population base and age-cohort projections to which utilization rates are applied. State Medicaid enrollment information informs the payer-mix assumption, and recognized industry operating benchmarks are used to test staffing ratios and expense loads for reasonableness.
Where a figure cannot be sourced to a record of this kind, the study states the assumption as an assumption and tests it in sensitivity rather than presenting it as an established fact.
Hospital feasibility cannot be determined from population alone. Two service areas with identical population counts can support very different hospitals, because what a hospital earns depends on which encounters it captures and who pays for them — not on how many people live nearby. A projection built from population growth without a service-line and payer structure beneath it is not a feasibility analysis.
Volume is therefore modeled by service line. Medical admissions, surgical cases, emergency visits, obstetrics where offered, and diagnostic and ancillary encounters each carry their own demand driver, their own competitive position, and their own contribution margin. Surgical volume can carry a facility that would be unprofitable on medical admissions alone; emergency volume generates admissions but arrives with the least favorable payer profile. Because these lines behave differently, each is forecast separately and each is tested separately in the downside case.
Payer mix then determines what that volume is worth. Commercial reimbursement, Medicare, Medicaid, and self-pay produce materially different net revenue for the identical clinical encounter, and the differences are large enough that payer mix, rather than volume, is frequently the variable that decides the outcome. Net revenue is therefore built per discharge and per encounter from the mix the service area actually supports, with contractual allowances and bad-debt exposure modeled explicitly rather than netted into a single rate.
Against that revenue sits a cost structure that is largely fixed in the short run. Clinical staffing must be in place before volume arrives, and is modeled at wage rates achievable in the local labor market rather than at national averages. Supplies, pharmacy, purchased services, and malpractice scale with activity and case mix. Physician recruitment carries its own cost and its own timeline, and where the volume forecast depends on physicians not yet in the market, the recruitment schedule becomes a determinant of the ramp. Capital cost — construction, equipment, and information systems — sets the debt burden the operating result must cover. The study carries all of it into a ten-year model with an explicit ramp to stabilization, and reports the combination of volume, payer mix, and reimbursement at which debt-service coverage fails.
Hospital feasibility studies are sensitive to a small number of inputs that carry disproportionate weight on the coverage ratio. Identifying those inputs early, and stress-testing each one explicitly, is what separates a study that survives lender scrutiny from one that does not.
Every stress case is run inside the linked model so the lender can see the coverage ratio at each combination of assumptions, not just at the base case.
SBA, USDA, and conventional lenders each bring a distinct review lens to a hospital credit, and the feasibility study must satisfy all three frameworks when the capital stack involves multiple programs.
Under SBA SOP 50 10 8, the study must demonstrate debt-service coverage of at least 1.15x on an operating basis and 1.00x on a global basis. For a hospital, the SBA reviewer will focus on whether projected revenues are supported by documented physician-admitter relationships, whether the payer mix is realistic given the service area’s Medicaid penetration, and whether the ramp assumption is conservative relative to the competitive environment. A micro-hospital seeking SBA financing faces particular scrutiny on the emergency-department revenue model, because ED volume is harder to project than elective surgical volume.
USDA Community Facilities financing — the program most commonly available to rural critical-access and small acute-care hospitals — requires that the study address community need, financial sustainability, and management capacity. RD Staff Instruction 5001 governs the analysis, and the agency’s field offices expect explicit discussion of the facility’s role in the local healthcare safety net.
Conventional lenders and bond underwriters focus on stabilized coverage, typically 1.20x or better, and on the sensitivity of that coverage to payer-mix deterioration and physician turnover. Specialty hospitals sponsored by physician groups receive additional scrutiny on Stark Law and anti-kickback compliance posture, because regulatory risk can impair CMS certification and eliminate Medicare revenue entirely. The feasibility study addresses each of these dimensions explicitly rather than leaving them to the borrower’s counsel to resolve outside the credit file.
Every hospital engagement delivers a written narrative report and a fully linked financial model. The report is prepared so a credit officer can follow the reasoning from evidence to determination, and the model is delivered live so any assumption can be substituted and the coverage impact observed immediately. The engagement covers:
Wert-Berater quotes a fixed fee for every engagement within one business day of receiving a project description. The fee does not change if the analysis takes longer than expected, and no portion of it is contingent on the study’s finding. That structure matters in hospital credits because the analysis sometimes produces a negative or conditional determination, and the firm’s fiduciary duty runs to the lender and reviewing agency, not to the borrower.
The engagement begins when the client delivers a complete data room. For a hospital project, that room typically includes the site and licensure documents, the proposed medical-staff plan with physician credentials and historical admit data, the architectural program with square footage and acuity assumptions, a construction budget with contingency line, any existing market studies or certificate-of-need filings, and the proposed financing term sheet. Incomplete data rooms extend the timeline; the firm identifies gaps at intake rather than discovering them mid-engagement.
Standard delivery is 10 to 15 business days from a complete data room. Rush delivery is available. The bound narrative report and fully linked Excel workbook are published to a secure client portal at delivery. The financial model remains live in the portal and recalculates when any input changes — a feature that matters when a lender asks a what-if question after the initial submission. The engagement closes with an explicit statement of conditions that identifies every assumption the determination depends on, so the credit file is complete and reviewable without supplemental correspondence.
Wert-Berater quotes a fixed fee within one business day of receiving a project description. The fee is stated upfront, does not change based on the outcome, and is never contingent on a favorable finding. Because hospital engagements vary significantly in scope — a micro-hospital differs from a multi-service-line acute-care facility — the quote is project-specific rather than published as a schedule.
Standard delivery is 10 to 15 business days from receipt of a complete data room. Rush delivery is available for time-sensitive SBA, USDA, or bond-financing deadlines. The most common cause of delay is an incomplete data room — missing physician credentialing records, an unsigned term sheet, or an incomplete construction budget. The firm identifies those gaps at intake so the clock starts only when the file is complete.
Three factors distinguish hospital credits: the revenue model depends on physician-admitter behavior that is difficult to contractually guarantee before opening; payer mix is more volatile because a single managed-care contract renegotiation can shift net revenue per case materially; and the regulatory pathway — licensure, certificate-of-need where applicable, and CMS certification — creates binary risks that can delay or eliminate revenue entirely. Each of those factors requires explicit scenario modeling, not a footnote.
Yes, where CON statutes apply. The study maps the regulatory posture of the project — whether a CON is required, whether one has been filed or granted, and what competing applications are pending in the public docket. CON status is treated as a gating condition because an unfavorable determination can eliminate the project before construction begins. In non-CON states the analysis addresses licensure sequencing and CMS certification timing instead.
Yes. Physician-sponsored specialty hospitals — surgical, cardiac, and orthopedic facilities are the most common formats — are analyzed under the same service-line methodology, with additional attention to Stark Law and anti-kickback compliance posture, because CMS certification and Medicare participation depend on it. The study identifies regulatory risk explicitly rather than deferring it to legal counsel outside the credit file, which is what SBA and conventional reviewers expect to see addressed.
The core data room includes site and licensure documents, the proposed medical-staff plan with physician credentials and historical admit patterns, an architectural program with acuity and square-footage assumptions, a construction budget with contingency, any existing market studies or CON filings, and the proposed financing term sheet. State discharge data and CMS cost reports are sourced by the firm. Gaps in the sponsor-provided file are identified at intake so they can be resolved before the engagement clock starts.
The consultant is retained by the lender, agency, or capital provider rather than the borrower, and determines whether a proposed hospital can capture enough clinically appropriate volume, at a realistic net reimbursement, to cover its cost structure and service the proposed debt. The work covers service-area definition from patient-origin evidence, age-adjusted utilization and demand forecasting, competitive share against incumbents, physician supply and admitter relationships, payer mix and net revenue, staffing and operating costs, capital cost, ramp-up and stabilization, sensitivity testing, debt-service coverage, and the regulatory pathway. The consultant states a determination and the conditions it rests on. The fee is fixed and never contingent on the finding.
Demand is derived, not assumed. The service area is defined from drive-time bands tested against where residents of each ZIP code currently receive care. Population within that area is resolved by age cohort and projected across the model horizon, because utilization is strongly age-dependent. Age-adjusted use rates are then applied to convert population into expected inpatient discharges, emergency department visits, and surgical cases. That gross demand is contested against incumbent supply using state discharge data, so the forecast reflects volume the project could realistically capture rather than all volume generated in the area.
The analysis is built from primary public records a reviewer can independently re-verify: state hospital discharge databases for inpatient volume by facility, service line, and patient-origin ZIP code; state health department and licensing records for bed counts and service authorizations; certificate-of-need dockets where the state operates a CON program; CMS cost reports for audited facility-level financial and utilization data; CMS provider enrollment and state licensing records for physician supply; Census demographic series for population and age-cohort projections; state Medicaid enrollment information for payer mix; and recognized industry operating benchmarks to test staffing and expense loads. Figures that cannot be sourced to a record of this kind are stated as assumptions and tested in sensitivity.
Market share is projected against named incumbents rather than assumed as a percentage. State discharge data establishes, by service line, where the volume in the service area goes today and what share each existing facility holds. The projection then identifies which specific competitors would have to lose volume for the subject to gain it, and what would cause that shift — a service not currently offered locally, materially shorter drive time, physician relocation, or capacity constraints at the incumbent. Outpatient competition from surgery centers, freestanding emergency departments, and imaging providers is netted separately, because outpatient migration removes volume from any forecast built on historical inpatient patterns.
Payer mix is built from the demographics and coverage profile the service area actually supports, then applied per service line rather than as a blended average. Commercial, Medicare, Medicaid, and self-pay reimburse materially differently for the identical clinical encounter, and the spread is wide enough that payer mix frequently decides the outcome even when volume is achievable. Net revenue is modeled per discharge and per encounter with contractual allowances and bad-debt exposure stated explicitly rather than netted into a single rate, and the mix is stress-tested, because a shift of a few points toward Medicaid or self-pay can move coverage below the required threshold.
The engagement delivers a written narrative report and a fully linked financial model. It covers service-area and market analysis, demand by service line, competitive-supply assessment including outpatient competition, physician supply and admitter analysis, payer-mix modeling, capital cost review tested against the loan request, operating projections built from local wage rates, a ten-year model with an explicit ramp to stabilization, sensitivity testing on volume and reimbursement, debt-service coverage at the operating and global level, and the regulatory pathway covering certificate of need where applicable, licensure, and CMS certification. The determination is stated with every condition it rests on.
Yes, and it is treated as a hard constraint rather than a supporting detail. Licensing and CMS enrollment data establish which specialists practice in the service area, their admitting relationships, and their age profile. The study then tests whether the admitters required by the volume forecast already exist or whether the projection depends on recruitment. Where it depends on recruitment, the assumption is stated explicitly with its cost, its timeline, and the effect on the forecast if it does not occur on schedule. In specialty hospital projections, where a small number of physicians often account for most of the forecast volume, that concentration is reported as a specific credit risk with its own sensitivity case.
Yes. Both are analyzed on their own economics rather than as scaled-down general hospitals. A specialty hospital concentrates demand in a narrow set of service lines, so the analysis examines the specific admitting and operating physicians the forecast depends on, their current case volume, and where those cases are performed today. A micro-hospital is dominated by emergency volume rather than inpatient census, so feasibility turns on whether the site captures enough emergency visits at an acceptable acuity and payer mix to carry the fixed cost of a round-the-clock emergency department, with self-pay exposure tested directly and stabilization risk modeled tightly because there is little inpatient capacity to absorb a shortfall.
Where a project qualifies under the programme, the study is prepared to the applicable USDA standard and addresses the matters the agency reviewer examines, including essential-community-facility purpose, service-area need, and the applicant’s ability to repay from operations. The firm prepares studies to USDA 7 CFR Part 5001 as well as SBA SOP 50 10 8 and conventional underwriting standards. Programme eligibility itself is determined by the agency and the lender, not by the consultant, and the study does not represent that any application will be approved.
Reviewers look for a determination they can rely on and a model they can re-run. In practice that means a service area defined from patient-origin evidence rather than a marketing radius, demand derived from age-adjusted utilization rather than population growth alone, a share assumption defended against named incumbents, physician supply tested as a constraint with recruitment risk disclosed, payer mix modeled per service line with contractual allowances stated, a capital budget tested against the loan request, an explicit ramp to stabilization, and sensitivity testing that identifies the combination of volume, payer mix, and reimbursement at which debt-service coverage fails. Independence matters as much as method: the fiduciary duty runs to the lender and the agency.
Sensitivity is run on the variables that actually move the outcome rather than on a uniform percentage applied to everything. Volume is flexed by service line, payer mix is shifted toward the less favourable payers, reimbursement is reduced, wage rates and staffing loads are raised to reflect local labour-market pressure, and the ramp to stabilization is extended to model slower-than-planned recruitment or licensure. Interest-rate stress is applied to the debt structure. The reported output is the combination at which debt-service coverage falls below the required threshold, together with how much headroom the base case holds against it, so the credit file records the distance to failure rather than a single optimistic figure.
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Wert-Berater, Inc. is an independent provider of feasibility studies and other related services. The firm does not provide financing or equity investment advice, and does not arrange, broker, or place debt or equity capital of any kind.
All appraisal assignments are performed by Bruce E. Jones, MAI, ASA-GC, BCA, CMEA, a member of the Appraisal Institute since 2006, a staff member of Wert-Berater, Inc. and owner of Special Purpose Realty Valuation.