Vehicle Depreciation

Utilizing the time series of vehicle valuations to determine how vehicles retain value over time

We can calculate the rate of depreciation for vehicles at any valuation. We assume that the current_date is some time after the vehicle purchase and that the model year of the vehicle was roughly when the vehicle was purchased (though a more exact purchase date would of course be more accurate. We also assume that when the model year is the same as the current year, that the vehicle was purchased 1 month ago.

For more information on the calculation of annualized rate of deprecation, see, for example, https://www.investopedia.com/terms/a/appreciation.asp.

df['years_since_model_year'] = \
            (df['current_date'].map(lambda x: x.replace(tzinfo=None)) \
                    - pd.to_datetime(df['year'], format = '%Y'))/np.timedelta64(1,'Y')
                                          df['annualized_rate_of_appreciation'] = 100*((df['retail_price']/df['msrp']).pow(1/df['years_since_model_year'].replace(0,1.0/12.0)) - 1)

For aggregating rate of depreciation for DIMO-connected vehicles by make we only use vehicles assumed to be owned for at least one year (i.e. the model year ended at least one year ago), obtaining the following

Average Rate of Depreciation by make