Abstract
This paper introduces a novel four-stage methodology for real-estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression-kriging, random forests and an M5P-decision-tree. The output is integrated into a commercial real estate decision engine.
Original language | English |
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Title of host publication | 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 |
Editors | Matthias Renz, Mohamed Ali, Shawn Newsam, Matthias Renz, Siva Ravada, Goce Trajcevski |
Publisher | Association for Computing Machinery |
ISBN (Electronic) | 9781450345897 |
DOIs | |
Publication status | Published - 31 Oct 2016 |
Event | 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 - Burlingame, United States Duration: 31 Oct 2016 → 3 Nov 2016 |
Publication series
Name | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems |
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Conference
Conference | 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 |
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Country/Territory | United States |
City | Burlingame |
Period | 31/10/16 → 3/11/16 |
Bibliographical note
Publisher Copyright:© 2016 ACM.
Keywords
- Gaussian Process Regression
- Machine Learning
- Real Estate Valuation
- Space Time Cube
- Universal Kriging
ASJC Scopus subject areas
- Earth-Surface Processes
- Computer Science Applications
- Modelling and Simulation
- Computer Graphics and Computer-Aided Design
- Information Systems