Enhancing the performance of a neural network with entity embeddings: an application to real estate valuation

In contrast to the brilliant success of deep learning approach in dealing with unstructured data such as image and natural language, it has not shown noticeable achievements in handling structured data, that is, tabular format data. Categorical data types form a considerable portion of structured data, and a neural network, the most universal implementation algorithm for deep learning, is inefficient in processing these data types. This is a reason for the poor performance of the neural network applied to the structured data. In this study, a neural network is used to estimate land prices in the Gyunggi province, South Korea. To enhance the performance of the network when most input variables are categorical, the architecture of the neural network is specified using the entity embedding layers, a technique to reveal the continuity inherent in categorical data. This study demonstrates that information in the categorical data can be efficiently extracted by the entity embedding technique. The network architecture proposed in this study can be applied in valuation practices where categorical data are abundant. In addition, the interpretation of the resultant embedding layers can enhance the explainability of the deep learning approach, promoting its rapid adoption in the real estate industry.