How Bankrate and NerdWallet Calculate Insurance Rates (And What They Don't Tell You)

The Assumption Most Consumers Make

When a consumer visits Bankrate to check auto insurance rates, then visits NerdWallet to get a second opinion, the implicit assumption is that these are independent analyses. Different companies, different research teams, different data. The expectation is that comparing multiple sources provides a more complete and reliable picture of the market.

That assumption is wrong. Both sites — along with The Zebra, Forbes Advisor, LendingTree, ValuePenguin, Insurify, and US News — rely on the same underlying data provider for their insurance rate comparisons: Quadrant Information Services.

This is not a secret. Both Bankrate and NerdWallet disclose it in their methodology sections. But the disclosure is typically buried in fine print that most readers never reach, and the implications are significant for anyone trying to make an informed insurance decision.

Both Use Quadrant — The Same Data Pipeline

Bankrate's insurance methodology page states that rates are provided by Quadrant Information Services. NerdWallet's methodology disclosures reference Quadrant as well. The Zebra explicitly cites its partnership with Quadrant for rate filing data.

What this means: when you see "average annual premium for a 35-year-old driver with good credit" on any of these sites, the number originates from the same data processing pipeline. Quadrant collects rate filings from state regulators, builds modeled rate sets for standardized driver profiles, and licenses those rate sets to publishers. Each publisher then selects which carriers, states, and profiles to highlight — but the underlying premium calculations come from Quadrant.

The editorial presentation differs. Bankrate might emphasize annual costs while NerdWallet focuses on monthly payments. One site might feature a 30-year-old driver; another might use a 40-year-old profile. But these are presentation choices applied to the same base data. The rates themselves are not independently calculated.

How the Modeling Process Works

To understand what published rate comparisons actually represent, it helps to trace the data through the full processing chain:

Step 1: A carrier — say GEICO — files a rate change with the California Department of Insurance through SERFF. The filing contains the complete pricing structure: base rates for each coverage line, territorial factors for every ZIP code, vehicle rating groups, driver classification tables, discount and surcharge schedules, and the algorithm that combines everything into a final premium.

Step 2: Quadrant's team extracts data from the filing and builds a rating model that approximates the carrier's pricing algorithm. This model is designed to produce premium estimates for standardized driver profiles.

Step 3: Quadrant runs its model for a set of sample profiles — typically defined by age, marital status, driving record, credit tier, vehicle type, and coverage level — and produces modeled premiums for each carrier, state, and profile combination.

Step 4: Publishers license these modeled premiums and present them as "average rates" or "typical costs" in their comparison content. The publisher's editorial team may select specific profiles, states, or carriers to highlight, and may add context, rankings, or recommendations around the data.

By the time a consumer sees a number like "State Farm: $1,842/year in California," that figure has passed through multiple layers of processing, modeling, and editorial selection — each of which introduces assumptions and potential divergence from what any individual consumer would actually be quoted.

What Gets Lost in Translation

The modeling and averaging process strips away precisely the information that matters most for individual rate decisions:

Territorial rating factors. Auto insurance premiums vary dramatically by ZIP code. In California, the difference between the most and least expensive ZIP codes can exceed $1,000 per six-month term for the same driver. Filed rate structures contain precise territorial factors for every ZIP code. Published comparisons typically use state averages or averages for a handful of metro areas — obscuring the geographic variation that drives most of the premium difference consumers experience.

Discount stacking. Carriers offer dozens of discounts — multi-policy, good student, defensive driving, anti-theft device, paperless billing, pay-in-full, loyalty, and more. The interaction between these discounts (which stack multiplicatively, not additively) significantly affects the final premium. Published comparisons typically model a "clean" driver with few or no discounts applied, missing the real-world scenario where discount stacking can reduce premiums by 20-40%.

Tier placement. Many carriers use tiered rating structures where drivers are placed into pricing tiers based on credit, claims history, and other factors. Tier placement can cause a 50% or greater swing in premium for the same base rate structure. Published comparisons typically assume a single tier (often "preferred" or "standard"), which may not reflect where any given consumer would actually be placed.

Surcharge schedules. The cost impact of an at-fault accident, a speeding ticket, or a DUI varies significantly by carrier. Some carriers are relatively forgiving; others apply severe surcharges. Published comparisons rarely capture this variation because their sample profiles typically assume a clean record.

Coverage interaction effects. How premiums change when you adjust deductibles, add umbrella coverage, or modify liability limits is carrier-specific and often nonlinear. A $250 vs $500 comprehensive deductible might cost $40 more per term with one carrier and $120 more with another. Published comparisons fix coverage at a single level, missing these dynamics entirely.

Why Averages Mislead

The fundamental problem with published rate comparisons is not that the data is wrong — it is that averages are the wrong tool for a decision that depends entirely on individual circumstances.

Consider an analogy: if the average temperature in San Francisco is 62 degrees, that tells you almost nothing about whether to bring a jacket on a specific day in July (when it might be 55 and foggy) versus September (when it might be 85). The average collapses meaningful variation into a single number that describes no actual day.

Insurance rates work the same way. Telling a consumer that "the average annual premium for State Farm in California is $1,842" is technically derived from data but practically meaningless for any individual decision. Your actual premium depends on your ZIP code, your vehicle, your driving record, your credit tier, your coverage selections, and which discounts you qualify for. Published averages capture none of this.

This is why two consumers can read the same Bankrate comparison, choose the carrier ranked "cheapest," and get wildly different results when they actually request quotes. The average said one thing; their individual rating factors said something different.

The Alternative: Filed Rates and Rate Change History

The information that published comparisons strip away — territorial factors, tier structures, discount tables, surcharge schedules — is all contained in the original SERFF regulatory filings. These filings are public records, and they contain far more useful information than any modeled average.

With filed rate data, you can answer questions that published comparisons cannot:

• Which carrier has the lowest territorial factor for my specific ZIP code?
• How much did GEICO raise rates in my state in the last filing, and when did it take effect?
• Which carrier applies the smallest surcharge for an at-fault accident?
• How do premiums compare at my actual coverage level, not a standardized one?

TrueFactor is built on this primary-source filing data. Rather than modeling premiums for sample profiles, the platform preserves the actual filed rate structures and makes them queryable and comparable. Explore carrier rate comparisons built on regulatory data, or read about our methodology to understand how we process SERFF filings.

Published comparisons serve a purpose — they provide a general sense of relative market positioning. But for consumers making a decision about an expense that can exceed $2,000 per year, and for insurance professionals who need precise competitive intelligence, the filed rates tell a more complete and more accurate story.

See also: Where Insurance Rate Comparisons Get Their Data | Most Expensive ZIP Codes for Auto Insurance in California