September 6, 2026 · Equipment Capital Index
How We Actually Compute the Equipment Capital Index Rate Index
Most “equipment financing rate” content online is written once and never touched again — a single blended national average, sourced from nowhere in particular, that stays the same whether it’s 2024 or 2026. We built the Rate Index to be the opposite of that: a live, per-category, per-machine figure computed from a fixed formula against real inputs, refreshed as the underlying data changes. This post is the full explanation of exactly how, for anyone who wants to check our work rather than take “trust us” for an answer.
The amortization math
Every monthly payment figure is computed with the standard amortizing-loan formula:
payment = P * r * (1 + r)^n / ((1 + r)^n - 1)where P is the financed amount (estimated equipment price plus a $750.00 doc/origination fee, less an estimated residual value for operating leases), r is the monthly interest rate (base APR ÷ 12), and n is the term in months. There’s nothing proprietary or hidden in this — it’s the same formula behind any standard loan calculator. The part that actually matters is what feeds into P and the base APR, which is where most “instant rate” sites get vague.
All server-side math runs on Python’s decimal.Decimal type with ROUND_HALF_UP rounding to the cent, specifically not floating-point arithmetic. Floating-point rounding drift is a real, measurable source of error when you’re computing amortization schedules across hundreds of periods — small enough to miss in a spot check, large enough to matter if you’re reconciling a real invoice against it.
Where the base APR comes from
The base APR isn’t a single blended number. It’s built per equipment category from a mix of real public sources:
- State DOT equipment rental-rate schedules — several state transportation departments publish public hourly/daily equipment rental benchmarks used for public-works contract bidding, which we use as a market reference point for what renting (as opposed to owning) that equipment class actually costs.
- Federal GSA surplus auction data — realized sale prices from federal equipment auctions, used to sanity-check our estimated market price against what buyers have actually paid.
- The Federal Reserve’s published Bank Prime Loan Rate — the base reference rate that commercial equipment financing is priced relative to.
Each of these is a real, checkable public source, not a synthesized number — the Press page lists the exact count of independent public sources and live data points feeding the current index, updated automatically as new rows are added.
What we deliberately don’t do
We don’t backfill a missing spec with a plausible-sounding guess. If a machine’s operating weight, lift capacity, or a category-specific spec isn’t independently verifiable from a real source, that field stays empty rather than getting filled with an estimate dressed up as data. This shows up directly in how we handle indexing: pages below a quality threshold are marked noindex until the missing data is actually filled in, even though that means a meaningful share of the site’s page count isn’t in Google’s index at any given time. We’d rather have fewer complete pages than a larger number of thin ones — see the full methodology for the exact threshold and mechanics.
This also means we sometimes leave a spec gap unfilled indefinitely. A recent example: several telehandler models in our dataset share a spec (max lift height) that’s genuinely a single fixed number for that exact model code, so we backfilled it after verifying it against manufacturer spec sheets — but one entry’s recorded engine and weight didn’t match any real telehandler with that model name closely enough to trust, so it stayed without that field rather than guessing. That’s a small example of a rule we apply consistently across roughly 500 individual machine pages: a real number from a checkable source, or no number at all.
Reproducing it yourself
The underlying dataset — every machine, its specs, and the sourced comparables — is published as an open dataset on GitHub under CC BY 4.0, with a citation file for anyone using it in research. If you want to check a specific figure rather than take this post’s word for it, that’s the place to do it.