Predicting Auction Price of Vehicle License Plate with Deep Residual Learning

TL;DR

This paper introduces a deep neural network model that predicts auction prices of Chinese vehicle license plates, providing price estimates, distributions, and feature representations, outperforming simpler models.

Contribution

It presents an end-to-end deep learning approach for license plate price prediction, including distribution estimation and feature extraction, which is novel in this context.

Findings

Convolutional neural networks outperform recurrent networks in accuracy.

The model effectively estimates license plate auction prices and distributions.

The system is deployed as an online estimator and search engine.

Abstract

Due to superstition, license plates with desirable combinations of characters are highly sought after in China, fetching prices that can reach into the millions in government-held auctions. Despite the high stakes involved, there has been essentially no attempt to provide price estimates for license plates. We present an end-to-end neural network model that simultaneously predict the auction price, gives the distribution of prices and produces latent feature vectors. While both types of neural network architectures we consider outperform simpler machine learning methods, convolutional networks outperform recurrent networks for comparable training time or model complexity. The resulting model powers our online price estimator and search engine.