  Predicting the price of a house remains a challenging issue that needs to be
addressed. Research has attempted to establish a model with different methods
and algorithms to predict the housing price, from the traditional hedonic model
to a neural network algorithm. However, many existing algorithms in the
literature are proposed without any finetuning and customization in the model.
In this paper, the author attempted to propose a novel neural network-based
model to improve the performance of housing price prediction. Inspired by the
modular neural network, the proposed model consists of a three-level neural
network that is capable to process information in parallel. The author compared
several state-of-the-art algorithms available in the literature on the Boston
housing dataset to evaluate the effectiveness of the proposed model. The
results show that the proposed model provides better accuracy and outperforms
existing algorithms in different evaluation metrics. The code for the
implementation is available
https://github.com/wijayarobert/MultiLevelDenseLayerNN
