Predicting Used Vehicle Prices

Sarp Ata Kanca, Yagmur Beren Sengezken

ECON 465 Stage 3

Predicting Used Vehicle Prices

Authors

Sarp Ata Kanca

Yagmur Beren Sengezken

Economic Question

Research Question

To what extent do vehicle age, mileage, condition, and engine size predict the market price of a used vehicle?

Why Does It Matter?

  • Used vehicle markets are economically important.
  • Consumers need accurate price expectations.
  • Sellers need efficient pricing strategies.
  • Vehicle depreciation affects household wealth.

Dataset

Source

Kaggle: Craigslist Cars & Trucks Dataset

Sample

  • Original observations: 426,880
  • Cleaned sample: 170,439 observations

Variables

Price: Vehicle Listing Price , Odometer:Miles Driven , Cylinders:Engine Size

Year: Manufacturing Year , Condition:Vehicle Condition

Price Distribution

Observation

  • Strong right-skewed distribution.
  • Extreme high-value listings present.
  • Log transformation improves model performance.

Model A

Baseline Regression

[ log(price)=_0+_1(year)+_2(odometer)]

Economic Intuition

  • Newer vehicles should be worth more.
  • Higher mileage should reduce value.

Model B

Extended Regression

[ log(price)=_0+_1(year)+_2(odometer)+_3(condition)+_4(cylinders)]

Why Add These Variables?

  • Vehicle quality matters.
  • Engine size affects consumer demand.
  • More realistic representation of market valuation.

Model Comparison

Metric Model A Model B
RMSE 0.8344 0.7392
R?? 0.1383 0.3237

Result

Model B clearly performs better.

  • Lower prediction error
  • Higher explanatory power

Cross-Validation Results

5-Fold Cross Validation

Metric Mean
RMSE 0.729
R’2 0.339

Interpretation

  • Model performance is stable across samples.
  • Results generalize well to unseen observations.

Key Findings

Vehicle Age

  • Positive coefficient
  • Newer vehicles command higher prices

Odometer

  • Negative coefficient
  • More mileage lowers market value

Condition

  • “Like New” vehicles receive significant premiums
  • Salvage vehicles receive substantial discounts

Cylinders

  • Engine size contributes to valuation differences

Economic Interpretation

Depreciation

Vehicle prices decrease as usage increases.

Consumer Valuation

Buyers pay premiums for:

  • Newer vehicles
  • Better condition
  • Larger engines

Market Implications

Observable quality characteristics strongly influence market outcomes.

Limitations

Limitation 1

Important variables were omitted:

  • Manufacturer
  • Model
  • Fuel type
  • Location

Limitation 2

Data is self-reported by sellers.

Listing prices may differ from final transaction prices.

Future Research

Potential Improvement

Include:

  • Brand effects
  • Fuel efficiency
  • Geographic market differences

New Economic Question

Do vehicle brands create a measurable price premium after controlling for age, mileage, condition, and engine size?

Conclusion

Main Takeaways

Vehicle age and mileage are significant predictors.

Condition substantially improves prediction accuracy.

Model B outperforms Model A.

Results are consistent with economic theory of depreciation.

Thank You

Questions?