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Pricing Analytics · Econometrics · Market IntelligenceIndividual academic project

Hedonic Pricing Analysis of European Electric Vehicles

Hedonic pricing model to analyse how range, power, and segment explain electric-vehicle pricing in the European market.

Type

Individual academic project

Area

Pricing Analytics · Econometrics · Market Intelligence

Tools

R · ggplot2 · lmtest · sandwich · car · stargazer

Techniques

Hedonic pricing · OLS regression · Log-log model · Interaction effects · Robust errors HC1 · Diagnostics

Output

Pricing model + strategic interpretation

Value

Individual project where I analysed how range, power, and segment influence European EV prices using R, OLS regression, interaction effects, and robust errors to extract pricing and product implications.

1.000

EVs analysed

0,879

Adjusted R² of final model

0,334

Range elasticity — Economy/Mid-Range

0,572

Range elasticity — High-End

Executive summary

Quantitative case centred on a pricing question: how to value technical attributes and how that value changes by segment. The sector is automotive, but the logic is useful for product, consulting, and market intelligence beyond that context.

Business context

In markets with technical and comparable products, such as automotive, electronics, or SaaS, pricing does not depend only on cost. It depends on the perceived value of attributes, segment, positioning, and competition. This case uses EVs as a context to demonstrate transferable pricing analysis.

My role

Individual academic project in Econometric Techniques. I handled data preparation, variable selection, model specification, log-log OLS, segment dummy creation, interaction effects, HC1 robust errors, diagnostics, and business interpretation of elasticities.

Data & methods

  • European Cars Dataset filtered to 1,000 electric vehicles.
  • R and OLS regression.
  • Log-log hedonic model with range, power, segment, and range-by-segment interaction.
  • HC1 robust standard errors.
  • Breusch-Pagan, robust RESET, Jarque-Bera, VIF, and condition number.
  • Variable centering before the interaction to reduce structural multicollinearity.

Process

  1. 01Filter the dataset for EVs.
  2. 02Set price as the dependent variable.
  3. 03Transform variables using logarithms.
  4. 04Include range, power, and segment.
  5. 05Create a High-End dummy and range-by-segment interaction.
  6. 06Evaluate heteroscedasticity, specification, residual normality, and multicollinearity.
  7. 07Interpret elasticities and translate implications.

Key findings

  • Range should be treated as a central attribute in EV pricing studies.
  • The marginal valuation of range is not homogeneous: it depends on segment and positioning.
  • Estimated price-range elasticity was ~0.334 in Economy/Mid-Range and ~0.572 in High-End.
  • Range sensitivity in High-End was approximately 1.71 times that of Economy/Mid-Range.
  • Horsepower was no longer significant after controlling for range and segment.

Business implications

  • For product teams: define ranges according to valued attributes.
  • For pricing: justify range-related premiums by segment.
  • For marketing: adapt messages to value-driving attributes.
  • For market intelligence: compare competitors by attributes, not only final price.

Limitations

  • Academic/secondary dataset.
  • Does not include incentives, real discounts, or financing.
  • Does not prove pure causality.
  • Market price may differ from real transaction price.

What I would do next

  • Add promotion and incentive data.
  • Compare configurator price vs actual price.
  • Incorporate brand, equipment, and total cost of ownership.
  • Use non-linear models or machine learning.
  • Create a competitive dashboard by segment.

Assets

Download reportComing soonView notebookComing soonNotebook pending publication for a future reproducible version.

Suggested visuals

Scatter plot: price vs range.

Segment comparison.

Regression coefficient table.

Pricing implication cards.

R code snippet.

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