Diploma in Residual Value Models and Asset Remarketing
Online postgraduate diploma of 800 hours in residual value models and asset remarketing. 8 modules covering asset residual value, market data, statistical models, electric vehicles, residual risk management, remarketing and its digitalisation, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Understand the factors that drive asset residual value.
Collect and clean used-vehicle market data.
Build statistical residual value models.
Assess the residual risk of electric vehicles.
Manage the residual risk of a portfolio.
Optimise remarketing channels.
Digitalise the sales process for used assets.
Who it's for
- Residual value analysts.
- Remarketing managers.
- Risk modellers.
- Renting product managers.
- Vehicle appraisers.
- Used-vehicle pricing analysts.
- Engineering or science graduates seeking to move into renting and leasing.
What you'll work with
- IFRS 16 (leases)
- IFRS 9 (financial instruments)
- IAS 16 (property, plant and equipment)
- IAS 36 (impairment of assets)
- Regulation (EU) 2023/1542 (batteries and waste batteries)
- Spanish Law 23/2003 (guarantees in the sale of consumer goods)
- EBA Guidelines (loan origination and monitoring)
- Python with XGBoost
- R
- Tableau
- SAS
- Microsoft Excel
8 modules
1.1The concept of residual value1.2Economic and accounting depreciation1.3Vehicle depreciation factors1.4Mobility assets: cars, vans and trucks1.5Industrial machinery and equipment1.6Asset life cycle1.7Residual value risk1.8Impact on leasing and renting1.9Second-hand markets1.10Case study: depreciation curve of a model
2.1Transaction data sources2.2Price guides and their methodology2.3Auction data2.4Mileage and condition2.5Equipment and options2.6Seasonality effects2.7In-house remarketing data2.8Cleaning and normalisation2.9Used-vehicle price indices2.10Practical exercise: used-vehicle price database
3.1Linear and logarithmic regression3.2Survival models3.3Segment-based models3.4Gradient boosting and decision trees3.5Key explanatory variables3.6Out-of-sample validation3.7Prediction intervals3.8Model explainability3.9Drift monitoring3.10Lab: residual value model with XGBoost
4.1Uncertainty of electric vehicle residual values4.2Battery state of health4.3Battery certificates4.4Risk of technological obsolescence4.5Software updates and value4.6Incentives and their market effect4.7Used-vehicle export markets4.8Second-life batteries4.9Regulatory risk from low-emission zones4.10Case study: residual value scenarios for an electric vehicle
5.1Setting the contractual residual value5.2Risk policies5.3Provisions and impairment5.4Residual value hedges and insurance5.5Manufacturer buy-back5.6Portfolio analysis5.7Stress testing5.8Model governance5.9Reporting to management5.10Workshop: exposure analysis of a portfolio
6.1Remarketing channels6.2Physical and online auctions6.3Sales to trade and private buyers6.4Reconditioning and its cost6.5Return inspection and appraisal6.6Damage and customer charges6.7Reverse logistics of returns6.8Export of used vehicles6.9Remarketing indicators6.10Practical exercise: choosing the optimal channel for a batch
7.1Digital sales platforms7.2Dynamic pricing7.3Image-based inspection with artificial intelligence7.4Used-vehicle certification7.5Used-vehicle warranties7.6Used-vehicle financing7.7Customer experience7.8Integration with fleet systems7.9Results analytics7.10Case study: designing an auction platform
8.1Selecting the portfolio8.2Data analysis8.3Residual value model8.4Scenarios and risks8.5Remarketing strategy8.6Digitalisation plan8.7Assessing the economic impact8.8Conclusions and lines of improvement8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
Training is 100% online on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so it fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with XGBoost, R, Tableau, SAS and Microsoft Excel, in free, academic or evaluation versions. Exercises are based on real procedures: a residual value model built on market data, exposure analysis of a renting portfolio and a comparison of remarketing channels. Assessment is continuous by module, plus the final integrated project.
Standards covered: IFRS 16, IFRS 9, IAS 16, IAS 36, Regulation (EU) 2023/1542, Spanish Law 23/2003 on guarantees, EBA Guidelines on loan origination and monitoring.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer able to run the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Python with XGBoost, R, Tableau, SAS and Microsoft Excel, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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