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

## TL;DR

This paper presents a deep recurrent neural network model that predicts vehicle license plate auction prices in Hong Kong, leveraging NLP techniques to account for the meaning of characters, achieving high accuracy and enabling price estimation and search functionalities.

## Contribution

It introduces a novel deep RNN approach for license plate price prediction, treating the problem as an NLP task, and demonstrates significant improvements over previous models.

## Key findings

- The deep RNN explains over 80% of price variations.
- Retraining the model improves prediction accuracy.
- The model can be extended to estimate price distributions and serve as a search engine.

## Abstract

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.