Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network
This addresses the need for price estimation in license plate auctions, which is important for buyers and sellers in Chinese societies, but it is incremental as it applies existing deep RNN methods to a new domain-specific problem.
The paper tackles the problem of predicting auction prices for vehicle license plates in Hong Kong by treating it as an NLP task, achieving a model that explains over 80% of price variations and outperforms previous models significantly.
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.