Get a price estimate for your camper van or van build — powered by machine-learning models trained on real marketplace listings.
Likely listing range
This estimate reflects what similar vans are typically listed for, not necessarily the final negotiated sale price.
Flat-fee remote review for one specific used camper van listing
$25 per listing
We review one van listing and provide a fixed-scope written report covering build quality, systems, layout, visible red flags, and asking price fairness.
This report is designed to help buyers evaluate a specific used camper van before moving forward. Our review is performed remotely and is limited to the photos, description, specifications, and other information available in the listing at the time of review.
We begin the review once we have both your submitted listing link and confirmed payment.
The typical report may address the following, depending on availability of thorough information within the seller's listing.
High-level takeaways, major strengths, key concerns, and our suggested next step.
How complete, credible, and informative the listing appears, including any important gaps or unanswered questions.
General observations on visible craftsmanship, finish quality, and signs of thoughtful or questionable execution.
A remote assessment of the visible or disclosed electrical, water, HVAC, and other major systems, including notable strengths, limitations, or red flags.
Observations on the apparent quality and appropriateness of major appliances, materials, and system components.
Our perspective on how logical, usable, and practical the layout appears for real-world travel and day-to-day use.
High-level observations on the chassis, age, mileage, exterior condition, modifications, and other visible vehicle-related considerations.
Our view on whether the asking price appears strong, fair, or aggressive based on the visible quality, components, and overall listing.
Key items we recommend clarifying with the seller, including useful questions, documents, or photos to request.
A practical bottom-line recommendation, including major green flags, yellow flags, red flags, and suggested next steps.
This is a remote, fixed-scope review of one specific van listing. The report is limited to the information, photos, and representations available at the time of review. It is not a mechanical inspection, safety certification, title review, legal opinion, or guarantee of condition. We do not dismantle, test, or physically inspect the vehicle. Buyers should still perform their own due diligence, including an in-person inspection and an appropriate mechanical inspection before purchase.
We begin the review once we have both your submitted listing link and confirmed payment.
Please submit the form and complete payment to start your request.
The Van Valuator calculator estimates what the market is currently willing to pay for a van based on your specific features and configuration. Behind the interface is a multi-stage statistical and machine-learning pipeline trained on a large historical database of secondary-market listings — DIY conversions and professional builds — collected across multiple marketplaces. Each prediction represents a best estimate of the current market listing price for a van matching your specifications, adjusted for recency, geography, and evolving market dynamics. The calculator also quantifies uncertainty, and accounts for price adjustment post-listing while providing model-derived explanations of the top features influencing your price.
Your estimate is produced by a gradient-boosted ensemble model that learns a function based on bespoke van or vehicle input features. These models construct many shallow decision trees sequentially, each correcting the residual error of the ensemble so far. This results in a flexible, nonlinear function capable of representing:
The displayed estimate is the model’s point prediction of current listing price. The range you see reflects model uncertainty, variance in historical listings of similar vans, and natural differences in how sellers represent and maintain their builds. It also provides a conservative representation to account for expected negotiation between buyers and sellers, which may cause actual transaction prices to deviate from listing prices (which our dataset is trained on).
GBDTs model the price as:
F(x) = Σm=1..M γm hm(x)
where each hₘ(x) is a small decision tree trained to minimize the remaining prediction error. This approach is well-suited for diverse tabular data and handles missing fields gracefully while capturing nonlinearities without manual feature engineering - perfect for custom van build data.
To decompose your specific estimate, we compute SHAP (Shapley Additive) values—a game-theoretic method that decomposes a prediction into additive contributions from each input. SHAP shows:
To understand broad market patterns, we also fit a regularized linear model estimating average marginal effects of individual features:
minimizeβ (‖y − Xβ‖² + λ₁‖β‖₁ + λ₂‖β‖₂²)
Elastic Net Regression blends feature selection and stability, helping estimate directional guidance values such as “a shower typically adds $X–$Y.” This complements the nonlinear predictions of the main model.
The “typical van” snapshot is computed from the overall distribution of vans in our current database, independent of any individual user’s inputs. For key numeric fields (such as price, mileage, year, sleeping capacity, and seating), we report the median value across all listings. For other features like showers, toilets, 4x4, solar, inverters, and other core amenities, we show the share of listings that include each feature. Taken together, these medians and prevalence rates provide a high-level picture of what is most common in the market right now.
The top features displayed in the “Top features that drive prices” section are chosen based on their global SHAP importance — the average absolute contribution of each feature across the entire training dataset — rather than being tailored to any single van. They highlight which attributes explain the most variation in list prices in the market as a whole.
The length of each bar reflects the feature’s relative importance, using its global SHAP importance compared with the other features shown. The arrow and accompanying text summarize the direction and approximate dollar magnitude of the effect estimated by the Elastic Net model — for example, whether a feature typically increases or reduces prices, and by roughly how much per unit or when present versus absent.
Dollar ranges such as “a shower adds $X–$Y” come from the Elastic Net regression, which estimates global average marginal effects while controlling for correlated build variables.
Regional price differences are calculated directly from recent listing data, rather than from model residuals. Over a recent time window, we compute the median listing price in each state and compare it with the overall median price across all states. This produces a simple descriptive view of which regions currently have higher or lower asking prices.
regional premium = median_pricestate − median_priceoverall
The card shows these premiums as both dollar and percentage differences relative to the overall median. Because this view is based directly on recent median listing prices (and is not model-adjusted), it is best interpreted as a snapshot of how asking prices differ across regions in the current market.
We estimate recent market movement using the Elastic Net model’s time coefficient on listing age. Intuitively, this captures how prices tend to change per day after controlling for van features. Over a chosen window (for example, the last 90 days), we scale that per-day effect by the window length and express it as an approximate percentage change relative to the current median listing price.
The resulting percentage change is converted into a dollar estimate by applying it to the observed median listing price in the dataset, yielding an approximate “typical” dollar increase or decrease over the selected window, after controlling for van features.
The sparkline is built directly from recent listing data. For each day in the selected window, we compute the median list price of all vans created on that day and plot those daily medians as a time series. This provides a real, data-level view of how asking prices have moved over time, with smoothing coming from using daily medians rather than individual listings.
All estimates come with uncertainty stemming from listing variability, feature ambiguity, DIY build variation, seller presentation, and regional supply/demand patterns. The range shown reflects these uncertainties as well as the fact that we model listing prices, which may differ from final sale prices. We expect that our listing-range confidence bands account for this fact.
This tool provides informational estimates only and does not constitute an appraisal, valuation, or financial advice. Predictions are modeled from public listing data and may not represent actual transaction prices. Conversions Consulting makes no guarantees of accuracy or completeness. Use at your own discretion.
