In the real estate industry, Days on Market (DOM) is one of the most important attribute that is normally used to appraise real estate properties. In the academic sector, DOM is seemingly attracting a lot of researchers. DOM can be define as the length of time (i.e. in days) a real estate listing takes in passive market. In our paper, a novel hybrid neural network model is proposed to solve DOM prediction problem. Our proposed model extracts features using both CNN-based Attention (CNNA), and Bidirectional LSTM (BLSTM) modules. Furthermore, we concatenate their outputs and pass the results through a prediction (MLP) block, for predictions to be made. In implementing our model, overfitting was experienced as a challenge. In order to combat overfitting in our network we introduce Dropout layers in almost all the modules. Moreover, we present confidence intervals for four attributes in our dataset by using either percentile bootstrap confidence interval (CI) or percentile bias corrected accelerated ($B{C}_a$) bootstrap CI, depending on the estimated distribution of an attribute. Finally, we appraise our model by experimenting with dataset of a famous real estate agency in Shanghai. The experimental outcomes clearly prove the superiority of the projected approach for solving DOM prediction problem.

在房地产行业中，“上市天数”（DOM）是通常用于评估房地产的最重要属性之一。在学术领域，DOM似乎吸引了很多研究人员。DOM可以定义为房地产列表在被动市场中占据的时间长度（即天数）。在本文中，提出了一种新颖的混合神经网络模型来解决DOM预测问题。我们提出的模型使用基于CNN的注意力（CNNA）和双向LSTM（BLSTM）模块提取特征。此外，我们将它们的输出连接起来，并将结果通过预测（MLP）块传递，以进行预测。在实施我们的模型时，过度拟合是一个挑战。为了解决我们网络中的过度拟合问题，我们在几乎所有模块中引入了Dropout层。此外，$乙C_{\mathrm{一种}}$）引导CI，具体取决于属性的估计分布。最后，我们通过试验上海一家著名房地产代理商的数据集来评估我们的模型。实验结果清楚地证明了该方法在解决DOM预测问题方面的优越性。