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Multi-source urban data fusion for property value assessment: A case study in Philadelphia
- Source :
- Neurocomputing. 404:70-83
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- The property value assessment in the real estate market still remains as a challenges due to incomplete and insufficient information, as well as the lack of efficient algorithms. House attributes, such as size and number of bedrooms, are currently being employed to perform the estimation by professional appraisers and researchers. Numerous algorithms have been proposed; however, a better assessment performance is still expected by the market. Nowadays, there are more available relevant data from various sources in urban areas, which have a potential impact on the house value. In this paper, we propose to fuse urban data, i.e., metadata and imagery data, with house attributes to unveil the market value of the property in Philadelphia. Specifically, two deep neural networks, i.e., metadata fusion network and image appraiser, are proposed to extract the representations, i.e., expected levels, from metadata and street-view images, respectively. A boosted regression tree (BRT) is adapted to estimate the market values of houses with the fused metadata and expected levels. The experimental results with the data collected from the city of Philadelphia demonstrate the effectiveness of the proposed model. The research presented in this paper also provides the real estate industry a new reference to the property value assessment with the data fusion methodology.
- Subjects :
- 0209 industrial biotechnology
Computer science
Property (programming)
Cognitive Neuroscience
Decision tree
02 engineering and technology
Sensor fusion
computer.software_genre
Computer Science Applications
Metadata
020901 industrial engineering & automation
Artificial Intelligence
Value (economics)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Data mining
computer
Multi-source
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 404
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........527308a8ac5963ff843e111d2e5ed839