Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network

In Chinese societies where superstition is of paramount importance, vehicle license plates with desirable numbers can fetch for very high prices in auctions. Unlike auctions of other valuable items, however, license plates do not get an estimated price before auction. In this paper, I propose that the task of predicting plate prices can be viewed as a natural language processing task, because the value of a plate depends on the meaning of each individual character on the plate as well as the semantics. I construct a deep recurrent neural network to predict the prices of vehicle license plates in Hong Kong based on the characters on a plate. Trained with 13-years of historical auction prices, the deep RNN outperforms previous models by significant margin.