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

Authors:

Henry Crosby,

Paul Davis,

Theo Damoulas, and

Stephen A. JarvisAuthors Info & Claims

SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 2016

Article No.: 68, Pages 1 - 4

https://doi.org/10.1145/2996913.2996960

Published: 31 October 2016 Publication History

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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.

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Droj GKwartnik-Pruc ADroj L(2024)A Comprehensive Overview Regarding the Impact of GIS on Property ValuationISPRS International Journal of Geo-Information10.3390/ijgi1306017513:6(175)Online publication date: 25-May-2024
https://doi.org/10.3390/ijgi13060175
Xu XZhang Y(2023)Composite property price index forecasting with neural networksProperty Management10.1108/PM-11-2022-008642:3(388-411)Online publication date: 3-Nov-2023
https://doi.org/10.1108/PM-11-2022-0086
Xu XZhang Y(2023)A Gaussian process regression machine learning model for forecasting retail property prices with Bayesian optimizations and cross-validationDecision Analytics Journal10.1016/j.dajour.2023.1002678(100267)Online publication date: Sep-2023
https://doi.org/10.1016/j.dajour.2023.100267
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Published In

SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 2016

649 pages

ISBN:9781450345897

DOI:10.1145/2996913

General Chairs:
Mohamed Ali
University of Washington, Tacoma
,
Shawn Newsam
University of California, Merced
,
Program Chairs:
Matthias Renz
George Mason University, USA
,
Goce Trajcevski
Northwestern University, USA
,
Siva Ravada
Oracle Corporation, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 31 October 2016

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SIGSPATIAL'16

SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31 - November 3, 2016

California, Burlingame

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SIGSPACIAL '16 Paper Acceptance Rate 40 of 216 submissions, 19%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Droj GKwartnik-Pruc ADroj L(2024)A Comprehensive Overview Regarding the Impact of GIS on Property ValuationISPRS International Journal of Geo-Information10.3390/ijgi1306017513:6(175)Online publication date: 25-May-2024
https://doi.org/10.3390/ijgi13060175
Xu XZhang Y(2023)Composite property price index forecasting with neural networksProperty Management10.1108/PM-11-2022-008642:3(388-411)Online publication date: 3-Nov-2023
https://doi.org/10.1108/PM-11-2022-0086
Xu XZhang Y(2023)A Gaussian process regression machine learning model for forecasting retail property prices with Bayesian optimizations and cross-validationDecision Analytics Journal10.1016/j.dajour.2023.1002678(100267)Online publication date: Sep-2023
https://doi.org/10.1016/j.dajour.2023.100267
Xu XZhang Y(2022)Retail Property Price Index Forecasting through Neural NetworksJournal of Real Estate Portfolio Management10.1080/10835547.2022.211066829:1(1-28)Online publication date: 20-Oct-2022
https://doi.org/10.1080/10835547.2022.2110668
Bencure JTripathi NMiyazaki HNinsawat SKim S(2021)Factors affecting decision-making in land valuation process using AHP: a case in the PhilippinesInternational Journal of Housing Markets and Analysis10.1108/IJHMA-11-2020-0136ahead-of-print:ahead-of-printOnline publication date: 22-Mar-2021
https://doi.org/10.1108/IJHMA-11-2020-0136
Naumzik CZoechbauer PFeuerriegel S(2020)Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference ApproachProceedings of The Web Conference 202010.1145/3366423.3380298(2342-2353)Online publication date: 19-Apr-2020
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Crosby HDamoulas TJarvis S(2019)Road and travel time cross-validation for urban modellingInternational Journal of Geographical Information Science10.1080/13658816.2019.165887634:1(98-118)Online publication date: 29-Aug-2019
https://doi.org/10.1080/13658816.2019.1658876

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Automated real estate valuation with machine learning models using property descriptions

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