There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 820 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 809 | - |
dc.citation.title | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING | - |
dc.citation.volume | 79 | - |
dc.contributor.author | Lu, Zhenyu | - |
dc.contributor.author | Im, Jungho | - |
dc.contributor.author | Quackenbush, Lindi J. | - |
dc.contributor.author | Yoo, Sanglim | - |
dc.date.accessioned | 2023-12-22T03:38:11Z | - |
dc.date.available | 2023-12-22T03:38:11Z | - |
dc.date.created | 2013-09-23 | - |
dc.date.issued | 2013-09 | - |
dc.description.abstract | This study proposed a new method to predict residential property value using remote sensing data as a major data source substitute to traditional inputs in house price estimation models. An optimized regional regression (ORR) approach was proposed in this study. This approach integrated a differential evolution optimization algorithm along with the ordinary least square regression to improve house value prediction accuracy. In addition to ORR, four other regression methods, random forest, Cubist regression trees, geographically weighted regression, and global ordinary least square, were also employed to provide a comparison. Results showed that models using remote sensing data are capable of acquiring accurate house price information. In addition, the volume of residential buildings proved to be an efficient substitute for total living area, the most important variable of the house price estimation model (i.e., a hedonic model). The ORR approach yielded the most accurate predictions followed by the geographically weighted regression. Further investigation indicated that the ORR approach has three major advantages: it is effective, stable, and the results are readily interpretable. | - |
dc.identifier.bibliographicCitation | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, v.79, no.9, pp.809 - 820 | - |
dc.identifier.doi | 10.14358/PERS.79.9.809 | - |
dc.identifier.issn | 0099-1112 | - |
dc.identifier.scopusid | 2-s2.0-84883778115 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/3917 | - |
dc.identifier.url | http://www.ingentaconnect.com/content/asprs/pers/2013/00000079/00000009/art00003 | - |
dc.identifier.wosid | 000330093400005 | - |
dc.language | 영어 | - |
dc.publisher | AMER SOC PHOTOGRAMMETRY | - |
dc.title | Remote sensing-based house value estimation using an optimized regional regression model | - |
dc.type | Article | - |
dc.relation.journalWebOfScienceCategory | Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology | - |
dc.relation.journalResearchArea | Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.