By:
Dave Kingsley
I. Introduction
The widespread belief that nursing home companies operate on a “low margin” is one of the most destructive myths blocking meaningful reform of the U.S. nursing home system. For nearly four decades, Clifton, Larson, Allen (CLA)—one of the nation’s most influential accounting firms—has reinforced this myth through its Annual SNF Cost Comparison and Industry Trends Report* – a publication based entirely on CMS Cost Report data. Despite its authoritative tone, the CLA report is methodologically unsound and substantively misleading. Yet it remains a powerful political instrument, routinely deployed to persuade legislators that Medicaid underfunds nursing homes and that regulatory oversight should be relaxed.
This Bulletin is a rebuttal to CLA’s latest edition—the 40th Annual SNF Cost Comparison and Industry Trends Report—which, like its predecessors, has changed little in structure or substance. We argue that the report is deceptive, grounded in a statistical sleight of hand that obscures the true financial performance of the privatized nursing home sector. Despite its serious analytic flaws and its distorted portrayal of industry finances, the report continues to exert outsized influence on public policy, shaping legislative decisions in ways that ultimately harm residents, families, and the public interest.
*See the report here: cla_s_40th_snf_cost_comparison_and_industry_trends_report.pdf.
The core problem is that CLA presents a fundamentally distorted picture of how money actually moves through modern nursing home chains. The report treats the licensed facility as if it were the economic unit of the enterprise, ignoring the upstream extraction of revenue through related‑party real estate companies, management firms, and home‑office allocations. To understand why CLA’s conclusions are so misleading—and why its “low margin” narrative is analytically indefensible—it is necessary to clarify how capital flows through the multi‑entity structures that dominate the for‑profit sector.
The following points outline the structural flaws in CLA’s approach and the realities of financial extraction in today’s nursing home industry
II. Structural Flaws in CLA’s Approach
• The licensed facility is a financial pass‑through, not the economic unit of the enterprise.
Facility‑level operating margin is meaningless as a measure of profitability for modern nursing home chains. “Operating costs” at the facility include payments to related‑party vendors, home‑office entities, and management companies—entities that are often owned by the same investors who own the facility. By design, the facility’s books show little or no profit because profit has already been extracted upstream.
• The CMS Cost Report captures only what remains at the licensed facility after upstream extraction.
Cost Reports do not record profits siphoned off through related‑party transactions, inflated management fees, or home‑office allocations. Most for‑profit chains operate as multi‑entity corporate structures in which revenue is systematically shifted away from the facility before the operating margin is calculated.
• Related‑party real estate companies (PROPCOs) extract profit by charging above‑market rent.
Facilities commonly lease their buildings from property companies owned by the same investors. These leases—often triple‑net—require the facility to pay rent plus insurance, taxes, and maintenance. Inflated rent payments convert real estate profit into a facility “expense,” artificially depressing the operating margin reported to CMS.
• Non‑cash expenses further depress reported margins.
Depreciation, amortization, and interest—none of which reflect cash outflows from the facility—are included as expenses deducted from net revenue
.• Operating margin is distorted by skewed data and inappropriate statistical presentation.
CLA’s charts obscure the extreme skew in facility‑level margins. A small number of facilities with large negative margins pull down the mean, while the median and
III. How CLA’s Figures Misrepresent Nursing Home Finance
Figure 1 and Figure 2 are a misuse of statistics and a misrepresentation of nursing home finance. It would appear to the lay public (including legislators and advocates) that nursing home operators are losing money or at the very least struggling to make an adequate return on their investment.
Figure 1: Median Operating Margin
CLA’s bar chart misuses operating margin by:
- presenting a single median that conceals distribution
- including Public Health Emergency (PHE) funding as if it were patient‑care revenue
- implying volatility where none exists
- reinforcing the narrative of “low margins” while ignoring upstream extraction
Figure 2: Provider Distribution by Operating
This chart:
- uses arbitrary bins that hide the right tail of high‑margin facilities
- exaggerates the appearance of financial distress
- treats facilities with +4% and +20% margins as identical
- omits the ownership‑level extraction that explains negative margins
Skew in the data is deceptively displayed in Figure 2. The bars on the left side indicate that 32.% and 29.6% were -4% or lower in 2019 and 2024 respectively. But what cant’ be known from the bars is how skewed the data were to the left versus to the right as represented by the bars representing margins of 4% higher or lower.
IV. Disaggregated Data: What the Numbers Actually Show
Valid analysis requires disaggregation. The national data (Table 1) show:
- NOI across 9,264 for‑profit facilities is only 0.78%
- Related‑party and home‑office payments consume 13.29% of revenue
- Corporations and LLCs extract the most through related‑party channels
- Negative NOI does not indicate distress—it indicates extraction
Table 1
Figure 4
U.S. nursing homes are nested within states. The data in Table 2 pertain to the State of Massachusetts, which has a somewhat lower NOI and smaller spread between NOI and expenditures for related parties and home office allocations. While NOI percent overall is -2.62, expenditures for home office allocations and related parties are ~11% of net patient revenue. With tax write downs at the parent/holding co level, it is apparent that robust amounts of capital are flowing upstream. The Massachusetts data are illustrated in Figure 5.
The Massachusetts data confirm the pattern that we expect to see in the data:
- Corporations: –3.83% NOI with 11.47% related‑party payments
- LLCs: –2.29% NOI with 11.33% related‑party paymen
Table 2
Figure 5
These facilities appear “unprofitable” only because profit has been shifted to related entities.
V. Why Facility‑Level Margins Cannot Inform Public Policy
Facility‑level operating margins are analytically incapable of informing public policy because:
- The facility is not the economic unit of the enterprise.
- Facility‑level margins systematically understate profitability.
- They obscure the financial mechanisms that matter for policy.
When legislators rely on facility‑level margins, they are not seeing financial reality. They are seeing the residue left after profit extraction.
VI. Policy Implications
- Reimbursement must be based on consolidated financial statements, not facility‑level margins.
- States must require full transparency of related‑party transactions.
- Medicaid rate‑setting must incorporate liquidity, working capital, and cash conversion.
- Regulators must account for financial engineering, not just facility‑level books.
- Workforce policy must be insulated from claims of “low margins.”
- Public reporting systems must be modernized to reflect enterprise‑level finance.
VII. Conclusion
The CLA report is not a neutral financial analysis. It is a political document that reinforces the industry’s preferred narrative of financial fragility by relying on a metric—facility‑level operating margin—that is structurally incapable of capturing the true financial performance of nursing home chains.
The evidence is clear:
The problem is not underfunding. The problem is extraction.
Reform is impossible until policymakers stop looking at the facility and start looking at the enterprise.
APPENDIX A. Statistical Diagnostics for Facility‑Level Operating Margins (NOI)
Distributional Evidence Demonstrating the Distortion in CLA’s Facility‑Level Margin Analysis
This appendix presents the distributional characteristics of facility‑level net operating income (NOI) using SPSS’s Explore procedure. These diagnostics provide empirical support for the argument made in the main text: facility‑level operating margins are dominated by extreme anomalies and cannot be used to characterize the financial performance of nursing home chains.
The appendix is organized into four components:
- Descriptive statistics for raw and trimmed data
- Boxplots illustrating distributional shape
- Stem‑and‑leaf displays showing density and extremes
- Interpretive notes linking the diagnostics to the methodological critique in the Bulletin
A1. Descriptive Statistics: Raw vs. Trimmed % Net Operating Income
A1.1 Raw Data (Untrimmed)
The untrimmed distribution of % NOI is characterized by extreme negative values that collapse the mean and create the illusion of a chronically unprofitable industry.
Table A.1
Key statistics (FigureA1):
- Mean: –3.1%
- Median: 1.0%
- 5% Trimmed Mean: .1%
- Minimum: -4072%
- Maximum: +52%
- Skewness: –45.2
- Kurtosis: 2422.7
These values indicate a distribution that is not remotely normal and not economically plausible. The extreme negative tail reflects accounting artifacts, related‑party flows, and data anomalies—not true operating performance.
A1.2 Trimmed Data (Economically Screened)
After removing facilities with implausible revenue, expense, or net operating values, the distribution becomes stable and interpretable.
Table A.2
Key statistics (Table A2):
- Mean: 3.40%
- Median: 2.87%
- Minimum: –10.00%
- Maximum: 51.96%
- Skewness: 0.54
- Kurtosis: 0.47
A2. Boxplots: Visualizing Distributional Shape
Figure A1. Boxplot of Raw % Net Operating Income (Untrimmed)
The raw boxplot shows a compressed central box near zero and a long tail of extreme negative values extending downward. These outliers dominate the distribution and render the mean meaningless. Case 3,050, still visible in the raw plot, is a prime example of an anomaly that should be excluded from regression modeling.
Figure A2. Boxplot of Trimmed % Net Operating Income
The trimmed boxplot displays a conventional distribution: a centered box, moderate whiskers, and a handful of high‑positive outliers consistent with strong chain‑level performance. The shape is economically plausible and statistically stable.
A3. Stem‑and‑Leaf Displays: Density and Extremes
Stem‑and‑leaf plots provide a compact, intuitive visualization of the distribution. They preserve individual values while revealing density, symmetry, and the presence of extreme cases.
Figure A3. Stem‑and‑Leaf Plot of Raw % Net Operating Income (Untrimmed)
The raw stem‑and‑leaf display reveals a profoundly asymmetric distribution:
- 245 extreme negative cases dominate the left tail.
- Only 14 extreme positive cases appear on the right.
- Stems between –2 and –1 contain dense clusters of negative values.
Interpretation:
The imbalance between negative and positive extremes visually demonstrates the structural distortion caused by outliers. This is the statistical foundation of the “low‑margin” illusion.
Figure A4. Stem‑and‑Leaf Plot of Trimmed % Net Operating Income
After trimming:
- Negative extremes disappear entirely.
- A small number of high‑positive cases remain, reflecting strong chain‑level performance.
- The stems cluster around the center, producing a balanced, interpretable distribution.
Interpretation:
The trimmed stem‑and‑leaf plot confirms that the industry’s true operating margins are positive and moderately dispersed. The disappearance of negative extremes validates the trimming criteria and exposes the methodological flaw in CLA’s unfiltered presentation.
A4. Interpretation and Link to Main Text
The diagnostics in Appendix A support three central claims made in the Bulletin:
1. Facility‑level operating margins are not meaningful indicators of profitability.
The raw distribution is dominated by extreme negative anomalies that reflect accounting practices, not economic performance.
2. CLA’s use of untrimmed facility‑level margins produces a systematically distorted picture.
The extreme skew and kurtosis in the raw data invalidate the use of simple means and bar charts.
3. Once anomalies are removed, the industry’s financial position is clearly positive.
The trimmed distribution shows margins centered around 3–4%, with moderate dispersion and no negative extremes.
These findings reinforce the Bulletin’s conclusion:
The “low‑margin” narrative is a statistical illusion created by outliers and methodological shortcuts.