OP: Please note that you can fit a line that predicts price based on the mileage (linear regression). For any car that the actual price is smaller than the one predicted is a good deal - that's what you want to have most visible. Obviously, you can use more variables for predicting the price, but the point is that cars that deviate from the fitted curve are either a bad deal or a good deal and that's where machine learning gives you monetary value on your web service. You could make even one step further give API for other websites to use your "bad deal"/"good deal" prediction engine and set your revenue this way.

[EDIT] The more variables you add the more accurate your model can be. You could even use NLP to extract the narrative about the car. You would disrupt KBB, by providing more user friendly interface. It does not have to be the visuals but just the fact that you can boil down the number of all cars that sell down to a hand full number of cars that deviate from the fitted curve. Even if you are not able to tell whether the deviation makes it a good or a bad deal, you are selecting a smaller number of cars worth looking at than any other website. At least you build a time sever which is worth money, too.

[EDIT2] I think I had an epiphany. The distance from the fitted curve is a buyer's risk. Someone comes to your website and you ask what car and what risk. Some people don't want a good deal, they want a fair deal; they want a car that is priced just right. Others might venture to get cars that are outliers, maybe those car have an engine modification or someone is in a rush to sell. In any case you would have a criteria for searching cars that no one has. KBB gives you the sound and fair deal prices but yours index could be calculated in near-real-time and thus more accurate. All depends how you implement regression model.

Note to self: excellent data mining application with interesting visualization