Manufacturing & Industrial
Equipment Resale Valuation API
You are lending against a machine, insuring it, or deciding whether to sell it. All three decisions need a number that nobody can look up.
- Asset finance lenders
- Equipment dealers and auctioneers
- Insurers
- Fleet and asset managers
- Marketplace platforms
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
Used industrial equipment has no price screen. A five-year-old CNC machining centre with eleven thousand spindle hours is worth something quite specific, and establishing what involves an appraiser, a few phone calls and a view. That works for one asset and badly for a portfolio of four hundred, which is the position asset finance lenders and fleet managers are actually in.
Transaction evidence does exist, it is just scattered. Auction results are published, dealer listings are visible, and finance companies hold their own realisation history from repossessions and end-of-lease sales. Each source is biased in a known direction — auction prices sit below retail, listings are asks rather than sales, and a lender's own realisations skew toward distressed disposal — and a valuation model that ignores those biases will be consistently wrong in a predictable way.
What drives value beyond age is specific to equipment type. Hours or cycles on the primary wear component, configuration and tooling, maintenance evidence, emissions compliance for mobile plant, and regional demand. A machine that is worth keeping in one market is uncompetitive in another because of an emissions standard. Capturing that properly requires per-category modelling rather than one depreciation curve with a category dummy.
Who buys this
Asset finance lenders, Equipment dealers and auctioneers, Insurers, Fleet and asset managers, Marketplace platforms
What the API returns
What comes back
- Point valuation with a range, by condition grade and disposal channel
- Depreciation curve for the asset over a forward horizon
- Comparable transactions with the adjustments applied to each
- Channel differentials: auction, dealer retail, trade and private sale
- Residual value forecasts at specified future dates for lease and finance structuring
- Condition and hours sensitivity, so the effect of usage is explicit
- Regional adjustment with the demand basis stated
- Confidence banding driven by comparable availability, with sparse categories flagged
Endpoints
| Method | Path | Returns |
|---|---|---|
| POST | /v1/valuations | Value one asset by specification, hours and condition |
| POST | /v1/valuations/batch | Value a portfolio or fleet in one call |
| GET | /v1/valuations/{id}/comparables | Comparable transactions with adjustments shown |
| POST | /v1/residuals/forecast | Residual value at future dates for finance structuring |
| GET | /v1/categories/{category}/depreciation | Category depreciation curves and drivers |
| POST | /v1/feedback/realisations | Submit achieved sale prices to calibrate the model |
Request and response
illustrativeRequest
{
"asset": {
"category": "cnc_machining_centre",
"manufacturer": "Haas",
"model": "VF-4SS",
"year": 2021,
"hours": { "spindle": 11400 },
"configuration": ["4th_axis", "through_spindle_coolant", "20hp"],
"condition_grade": "good",
"maintenance_evidence": "service_records_complete"
},
"region": "GB",
"channels": ["auction", "dealer_retail", "trade"],
"residual_dates": ["2027-10-01", "2029-10-01"]
}Response
{
"valuation_id": "val_771204",
"valued_at": "2026-10-21T16:18:00Z",
"currency": "GBP",
"valuations": [
{ "channel": "dealer_retail", "point": 58400, "range": [52100, 64900] },
{ "channel": "trade", "point": 46200, "range": [41000, 51800] },
{ "channel": "auction", "point": 38900, "range": [33200, 44600] }
],
"confidence": { "band": "medium", "basis": "31 comparables in 24 months, 9 in region" },
"drivers": [
{ "driver": "spindle_hours", "effect": -0.14,
"detail": "11,400h against category median 7,200h at this age" },
{ "driver": "configuration", "effect": 0.07, "detail": "4th axis adds demand" },
{ "driver": "maintenance_evidence", "effect": 0.04 },
{ "driver": "region", "effect": -0.02, "detail": "GB slightly below EU average" }
],
"residuals": [
{ "date": "2027-10-01", "point": 49100, "range": [42800, 55900] },
{ "date": "2029-10-01", "point": 34600, "range": [27200, 42100] }
],
"comparables_summary": {
"count": 31, "median_adjustment_applied": 0.09,
"channels_represented": ["auction", "dealer_retail"]
},
"note": "Auction evidence dominates this category; retail figures carry wider uncertainty."
}Where the data comes from
| Published auction results | Realised prices with specification and hours — the most reliable transaction evidence available, though biased below retail |
| Dealer and marketplace listings | Asking prices and time-on-market, collected only where the platform's terms permit it |
| Your own realisation history | Achieved prices from repossessions, end-of-lease disposals and trade-ins; the single best calibration input and one lenders already hold |
| Equipment specification reference data | Model configurations, nameplate ratings and option codes, which make comparables genuinely comparable |
| New equipment list prices | Current and historical, which anchor depreciation as a proportion of replacement cost |
| Emissions and regulatory reference data | Engine emission stages and regional restrictions, which can move mobile plant values sharply |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- Per-category models rather than one curve with category dummies, because the wear driver, the configuration effects and the demand structure genuinely differ between a machining centre and an excavator
- Channel modelling as an explicit structure, since auction, trade and retail are different markets and a single “market value” conflates them
- Hedonic adjustment on comparables for hours, configuration, condition and region, with each adjustment reported so a valuer can see what was done
- Selection-bias correction for the fact that assets which come to auction are not a random sample of the population
- Time adjustment of comparables to the valuation date, using category-level price indices rather than general inflation
- Calibration against your own realisations, which is what moves the model from a market average to something that predicts your outcomes
- Confidence driven by comparable density, with sparse categories returning a wide range or declining to value rather than producing a falsely precise figure
- Backtesting against subsequent achieved sales, reported as realised error by category and channel
What it changes for the business
Portfolio revaluation at scale
Batch valuation replaces a sampling exercise with coverage of the whole book.
Better-structured finance
Residual forecasts with ranges let lease terms be priced against uncertainty rather than a flat assumption.
Faster lending decisions
An indicative valuation at application speeds up cases that currently wait on an appraiser.
Disposal timing
Depreciation curves and channel differentials inform when and where to sell rather than defaulting to auction.
How it fits into your product
How it embeds
- Single valuation calls from your origination or underwriting flow
- Batch portfolio revaluation on a schedule for impairment and reporting cycles
- Comparable evidence returned with adjustments, which is what an appraiser or credit committee asks to see
- Realisation feedback endpoint so achieved prices continuously recalibrate the model
- Deployment inside your infrastructure where book and collateral data cannot leave
- White-label valuation reports for dealer and customer-facing use
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 12 weeksDiscovery and data review
Weeks 1–2
We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.
Architecture and prototype
Weeks 3–4
You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.
Production build
Weeks 5–10
Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.
Integration and handover
Weeks 11–12
We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
How accurate can a valuation without inspection be?
It is an estimate conditional on the specification and condition you declare, and it cannot see a cracked casting or a worn spindle. Condition grade is an input, not something the model observes, so the valuation is only as good as the grading. For lending decisions above a materiality threshold we would expect this to inform and prioritise physical inspection rather than replace it, and we would say so in the output.
What about equipment categories with very few transactions?
Some categories genuinely cannot be valued statistically — specialised process plant, bespoke machines, anything with a handful of sales a decade. In those the API returns a wide range with low confidence, or declines to value and says why. Producing a confident-looking number from four comparables would be the worse answer, and the confidence band exists so that choice is visible rather than hidden.
Can you ingest dealer listing data?
Only where the platform's terms permit collection and the intended use. Several marketplaces prohibit systematic collection outright, and listings are asking prices rather than transactions in any case. Auction results plus your own realisations are the stronger foundation; listings add time-on-market and demand signal where they are available lawfully.
How much does our own realisation history help?
A great deal, and it is usually the most valuable dataset in the project. A lender's achieved disposal prices reflect the exact channel, timing and condition profile of their own book, which no market aggregate captures. It also corrects the auction bias that otherwise pulls valuations down systematically.
Are residual forecasts reliable enough for lease pricing?
Over two to three years, reasonably, on categories with dense evidence. Over five to seven years they carry real uncertainty from technology change, emissions regulation and demand cycles, which the ranges reflect. We report measured forecast error from backtesting by category and horizon, and we would resist a structure priced off the point estimate alone.
Related blueprints
Industrial Parts Matching API
Cross-references equivalent components across manufacturers using part numbers, dimensions and technical specifications.
Industrial Supplier Intelligence API
Capacity, financial distress and disruption signals for the suppliers a manufacturer depends on, delivered as monitoring rather than a one-off report.
Predictive Maintenance API
Failure probabilities, anomaly scores and maintenance recommendations for industrial equipment, served to your CMMS or plant software.
Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.