  In Chinese societies, superstition is of paramount importance, and vehicle
license plates with desirable numbers can fetch very high prices in auctions.
Unlike other valuable items, license plates are not allocated an estimated
price before auction. I propose that the task of predicting plate prices can be
viewed as a natural language processing (NLP) task, as the value depends on the
meaning of each individual character on the plate and its semantics. I
construct a deep recurrent neural network (RNN) to predict the prices of
vehicle license plates in Hong Kong, based on the characters on a plate. I
demonstrate the importance of having a deep network and of retraining.
Evaluated on 13 years of historical auction prices, the deep RNN's predictions
can explain over 80 percent of price variations, outperforming previous models
by a significant margin. I also demonstrate how the model can be extended to
become a search engine for plates and to provide estimates of the expected
price distribution.
