A spatio-temporal, Gaussian process regression, real-estate price predictor

Henry Crosby; Paul Davis; Theo Damoulas; Stephen A. Jarvis

doi:10.1145/2996913.2996960

A spatio-temporal, Gaussian process regression, real-estate price predictor

Henry Crosby^*, Paul Davis, Theo Damoulas, Stephen A. Jarvis

^*Corresponding author for this work

Engineering and Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

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
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	https://doi.org/10.1145/2996913.2996960
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

Conference

Conference	24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
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

Access to Document

10.1145/2996913.2996960

Cite this

Crosby, H., Davis, P., Damoulas, T., & Jarvis, S. A. (2016). A spatio-temporal, Gaussian process regression, real-estate price predictor. In M. Renz, M. Ali, S. Newsam, M. Renz, S. Ravada, & G. Trajcevski (Eds.), 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 Article 68 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). Association for Computing Machinery . https://doi.org/10.1145/2996913.2996960

Crosby, Henry ; Davis, Paul ; Damoulas, Theo et al. / A spatio-temporal, Gaussian process regression, real-estate price predictor. 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. editor / Matthias Renz ; Mohamed Ali ; Shawn Newsam ; Matthias Renz ; Siva Ravada ; Goce Trajcevski. Association for Computing Machinery , 2016. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

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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.",

keywords = "Gaussian Process Regression, Machine Learning, Real Estate Valuation, Space Time Cube, Universal Kriging",

author = "Henry Crosby and Paul Davis and Theo Damoulas and Jarvis, {Stephen A.}",

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year = "2016",

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language = "English",

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editor = "Matthias Renz and Mohamed Ali and Shawn Newsam and Matthias Renz and Siva Ravada and Goce Trajcevski",

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Crosby, H, Davis, P, Damoulas, T & Jarvis, SA 2016, A spatio-temporal, Gaussian process regression, real-estate price predictor. in M Renz, M Ali, S Newsam, M Renz, S Ravada & G Trajcevski (eds), 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016., 68, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Association for Computing Machinery , 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016, Burlingame, United States, 31/10/16. https://doi.org/10.1145/2996913.2996960

A spatio-temporal, Gaussian process regression, real-estate price predictor. / Crosby, Henry; Davis, Paul; Damoulas, Theo et al.
24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. ed. / Matthias Renz; Mohamed Ali; Shawn Newsam; Matthias Renz; Siva Ravada; Goce Trajcevski. Association for Computing Machinery , 2016. 68 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Davis, Paul

AU - Damoulas, Theo

AU - Jarvis, Stephen A.

PY - 2016/10/31

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N2 - 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.

AB - 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.

KW - Gaussian Process Regression

KW - Machine Learning

KW - Real Estate Valuation

KW - Space Time Cube

KW - Universal Kriging

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M3 - Conference contribution

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BT - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016

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A2 - Ali, Mohamed

A2 - Newsam, Shawn

A2 - Renz, Matthias

A2 - Ravada, Siva

A2 - Trajcevski, Goce

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Y2 - 31 October 2016 through 3 November 2016

ER -

Crosby H, Davis P, Damoulas T, Jarvis SA. A spatio-temporal, Gaussian process regression, real-estate price predictor. In Renz M, Ali M, Newsam S, Renz M, Ravada S, Trajcevski G, editors, 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. Association for Computing Machinery . 2016. 68. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). doi: 10.1145/2996913.2996960

A spatio-temporal, Gaussian process regression, real-estate price predictor

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this