| Title |
Smart Real Estate Assessments Using Structured Deep Neural Networks |
| ID_Doc |
40365 |
| Authors |
Xu, HP; Gade, A |
| Title |
Smart Real Estate Assessments Using Structured Deep Neural Networks |
| Year |
2017 |
| Published |
|
| DOI |
|
| Abstract |
In a smart city, effective and accurate real estate assessments governed by a local government is crucial for determining the property taxes. Such assessments have never been trivial, and inappropriate assessments may result in disputes between property owners and the local government. In this paper, we introduce a deep learning approach to smartly and effectively assessing real estate values. We propose a systematic method to derive a layered knowledge graph and design a structured Deep Neural Network (DNN) based on it. Neurons in a structured DNN are structurally connected, which makes the network time and space efficient; and thus, it requires fewer data points for training. The structured DNN model has been designed to learn from the most recently captured data points; therefore, it allows the model to adapt to the latest market trends. To demonstrate the effectiveness of the proposed approach, we use a case study of assessing real properties in small towns. A structured DNN was designed to match with a layered knowledge graph for property assessments in the real estate domain, which results in a significant reduction of neurons and connections between them. The experimental results show that a structured DNN outperforms conventional multivariate linear regression models, fully-connected neural networks, and prediction methods used by the leading real estate companies. |
| Author Keywords |
Smart city; deep learning; real estate assessments; layered knowledge graph; structured deep neural networks |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000464418300168 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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