Our methodology

Why we publish our methodology

Most pet financial advice on the internet operates as a black box: a calculator gives you a result, an article gives you a recommendation, and the underlying logic is hidden. We think that’s the wrong default for content people use to make important decisions.

This page documents exactly how our calculators work, what data they use, and what assumptions we make. If our analysis says insurance is worth it for your French Bulldog, you can read this page and verify the math. If our breed cost rankings put Bernese Mountain Dogs at the top, you can see exactly which sources we drew from. If you find a methodological error, you can document it specifically when you contact us.

This is also the page we expect journalists, researchers, and AI engines to cite when establishing what we know and how we know it. Transparency in methodology is the foundation of editorial credibility — we treat it accordingly.

The core question we answer

Our flagship calculator answers one question: "Is pet insurance worth it for this specific dog, on an expected-value basis?"

To answer this, we calculate two values and compare them:

1. Total expected lifetime cost with insurance = lifetime premiums paid + lifetime out-of-pocket costs (deductibles, co-insurance, uncovered items)
2. Total expected lifetime cost without insurance = expected lifetime veterinary costs (paid directly)

The difference between these two values is the expected savings (positive) or expected extra cost (negative) of carrying insurance. Our verdict logic adjusts this by your stated savings discipline to account for the catastrophic-risk dimension that pure expected-value math doesn’t capture.

The formulas

Lifetime premium calculation

Where:

• BASE_PREMIUM = $55/month (US Midwest 2026 standard plan baseline)
• breed.premiumMult = breed-specific multiplier from 0.80 (Chihuahua) to 1.65 (English Bulldog)
• region_mult = 0.95 (US South) to 1.30 (US West Coast); 1.20 (Australia)
• coverage.premiumMod = 0.75 (Basic), 1.00 (Standard), 1.40 (Premium)
• AGE_GROWTH = 0.08 (8% annual premium growth, derived from NAPHIA aging premium data)

The age compounding factor (age × 0.5) reflects that older dogs already start at higher premiums when newly enrolled, not just that premiums grow over time. This matches insurer pricing practices observed across the major US carriers.

Expected lifetime veterinary costs

The breed.lifetimeVetCost figure is the most heavily-researched parameter in our model. It represents the expected total veterinary cost over the breed’s average lifespan, derived from:

• Breed-specific condition probabilities (from peer-reviewed veterinary studies)
• Treatment costs for each condition (from published vet teaching hospital pricing and CareCredit data)
• Routine care baseline (from AVMA industry surveys)

For a Golden Retriever, this calculation produces $17,000. For a Chihuahua, $9,000. The difference reflects real differences in expected health events, not arbitrary risk weighting.

Out-of-pocket with insurance

The 85% claimable share reflects that approximately 15% of typical pet vet costs (food therapeutics, certain medications, routine items) are excluded from typical accident-and-illness coverage. This figure is derived from NAPHIA claims data analysis.

Verdict logic

Pure expected-value math doesn’t capture all of what insurance is for. Insurance is fundamentally about converting variance into a predictable monthly cost — and that has value beyond the average outcome. We adjust the verdict accordingly:

The savings discipline adjustment is the most subjective parameter in our model. We use $2,000 (favoring insurance) for users with low savings, $1,500 (against insurance) for users with high savings. These figures aren’t derived from a study — they represent our judgment about how much catastrophic-risk insurance is worth to someone who couldn’t self-insure a $10,000 emergency.

Data sources

Every numerical input to our model traces back to a documented source. The full source list is on our data sources page; below is a summary by parameter category:

Limitations and known constraints

Our methodology has limitations we want to be explicit about:

• Average data, not individual data. Our calculator outputs reflect breed averages. Your specific dog may experience health events significantly above or below the average. Insurance is fundamentally about managing this individual variance, which is why catastrophic-risk dimension matters even when expected math is unfavorable.
• US-Midwest baseline. Our base parameters (premium, vet costs) are calibrated to US Midwest 2026 averages. Other regions are scaled by multipliers derived from BLS data, which is reliable but not perfect. Local pricing in specific cities may diverge.
• Lifetime modeling assumes ongoing coverage. If you let policies lapse and re-enroll, pre-existing condition exclusions accumulate. Our model assumes continuous coverage from enrollment age.
• Insurance industry pricing changes. NAPHIA reports are typically published 6-9 months after the period they cover. Our quarterly refresh captures the most recent available data, but actual current pricing may be 5-10% different from our parameters.
• Pre-existing conditions are abstracted. We model their impact through enrollment-age effects but don’t model specific conditions you may already have documented. For dogs with known conditions, the actual insurance value is typically lower than our generic output suggests.
• Wellness add-ons not modeled. Our calculator focuses on accident-and-illness coverage. Wellness add-ons (covering routine care) operate on different economics and are not included in the standard calculation.
• Behavioral discount factor. Our savings discipline parameter is judgmental, not empirical. A user who selects "high savings discipline" but does not actually maintain savings discipline will receive an output more favorable to self-insurance than their actual situation warrants.

Validation against industry data

We validate our model against three external benchmarks:

Insurance industry loss ratio. The pet insurance industry operates at approximately 60-70% loss ratio (claims paid divided by premiums collected). Our model, run across all 30 breeds at typical enrollment ages, produces an aggregate ratio of 64% — within the expected range, validating that we’re not systematically over or underestimating either premiums or claims.

Consumer Reports policyholder outcomes. The 2025 Consumer Reports survey found 34% of policyholders saved more than they spent. Our model, run across the US dog population by breed prevalence, produces an aggregate "worth it" rate of 31% — closely matching the survey finding and confirming the model isn’t skewed toward either bias.

Cross-insurer premium comparison. We periodically benchmark our model premiums against actual quotes from 6 major US insurers across 5 sample profiles (mixed-breed puppy, French Bulldog 3yr, Golden 5yr, Bernese 2yr, mixed-breed 8yr). Our model premiums fall within ±10% of the quote average for all profiles tested in our most recent benchmark.

How to cite this methodology

Researchers, journalists, and other publications are welcome to cite our methodology. Suggested citation formats:

Methodology changelog

We document material methodology changes here. Minor parameter refreshes (quarterly data updates) are logged in our quarterly update page; this changelog covers structural changes to the model itself.

Methodology contributors

Our analytical model has benefited from input by veterinary professionals and financial analysts who reviewed early versions. Specific named contributors are credited with permission on our about page. We continue to invite veterinary professionals to review our breed-specific data — if you’re a vet, vet tech, or veterinary researcher and would like to review or contribute, please reach out via our contact page.

Reporting errors

If you identify a methodological error, miscalculation, or outdated source, please contact us with specifics. We update our work in response to verified corrections and credit substantive contributions to our methodology improvements.