I would ask your friend: "Would you buy a car if the only thing you knew about it was its age?" I assume he would say no.
Then I would ask him another question: "Would you be interested in the age of a used car before deciding to purchase it?" I assume he'd say yes.
So, while the age of a used car is relevant to determining its value, it is by no means the only factor driving its market price, pardon the pun. Other things are relevant to its price: mileage, make, model, wear and tear, maintenance history, etc. etc. These other factors are probably more important to the value of the car than its simple age.
Importantly, these other relevant factors are likely to be correlated with a car's age AND correlated with its sale price (i.e. relevant). This is important because it means you are likely to have bias on the coefficient estimate on the effect of age on a car's value/sales price, due to these "omitted variables". Google "omitted variable bias" for more information.
In a nutshell, your friend's model is misattributing all changes in a car's price to its age.
So, I would tell your friend that his linear "car price model" is incomplete and misleading. Selling his car based solely on the results of his silly regression would result in him losing money (and selling quickly!) or not selling the car at all because it's overpriced.