Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values

Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values

The limitation and highly complex process of discrete measurement of location have encouraged searching for alternative approach to derive locational compensation factor in the prediction of property values. A new approach by use of Geographical Information System-Multiple Regression Analysis-genera...

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Main Author: Chin, Chui Vui
Format: Thesis
Language:English
Published: 2006
Subjects:
GA Mathematical geography
Cartography
Online Access:http://eprints.utm.my/id/eprint/5144/1/ChinChuiVuiMFKSG2006.pdf
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spelling my-utm-ep.51442018-02-28T06:55:19Z Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values 2006-01 Chin, Chui Vui GA Mathematical geography. Cartography The limitation and highly complex process of discrete measurement of location have encouraged searching for alternative approach to derive locational compensation factor in the prediction of property values. A new approach by use of Geographical Information System-Multiple Regression Analysis-generated location value response surface (GIS-MRA-generated LVRS) approach was proposed in this study. The method used LVRS in a GIS to model locational influence from MRAgenerated residuals, with no locational variables on residential property values in the local context by integrating spatial & aspatial data in term of developing a hybrid predictive model. This study has three main objectives. First, to discuss the pertinent factors influencing residential property values, including the location factors. Second, to develop a predictive model of residential property values, whereby the locational factor is modelled by GIS-MRA-generated LVRS. Third, to examine the usefulness of GIS-MRA-generated LVRS to improve the quality of the regression model. To achieve these objectives, this study was divided into two main parts. The first part comprised the theories of value, residential property value factors, location modelling, MRA modelling, and the spatial interpolation techniques. The second part, comprised the development of hybrid models, whereby the locational factors was modelled by GIS-MRA-generated LVRS approach and examining its ability to improve regression modelling. As many as125 individual terraced units in three adjoining housing schemes (Taman Pelangi, Taman Sentosa and Taman Sri Tebrau) in Johor Bahru were used for model estimation, while 14 transacted units were set aside for predictive purposes. Results have shown models applying LVRS have managed to improve overall model’s statistical quality and predictive performance by achieving higher proportion of “reasonably accurate� prediction as compared to the traditional MRA models. The LVRS has allowed a clearer spatial visual picture of the location influence to be captured at all level in the study area. The location factor influence to property value can be modelled in a more effective way. 2006-01 Thesis http://eprints.utm.my/id/eprint/5144/ http://eprints.utm.my/id/eprint/5144/1/ChinChuiVuiMFKSG2006.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Geoinformation Science and Engineering Faculty of Geoinformation Science and Engineering
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic GA Mathematical geography
Cartography
spellingShingle GA Mathematical geography
Cartography
Chin, Chui Vui
Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values
description The limitation and highly complex process of discrete measurement of location have encouraged searching for alternative approach to derive locational compensation factor in the prediction of property values. A new approach by use of Geographical Information System-Multiple Regression Analysis-generated location value response surface (GIS-MRA-generated LVRS) approach was proposed in this study. The method used LVRS in a GIS to model locational influence from MRAgenerated residuals, with no locational variables on residential property values in the local context by integrating spatial & aspatial data in term of developing a hybrid predictive model. This study has three main objectives. First, to discuss the pertinent factors influencing residential property values, including the location factors. Second, to develop a predictive model of residential property values, whereby the locational factor is modelled by GIS-MRA-generated LVRS. Third, to examine the usefulness of GIS-MRA-generated LVRS to improve the quality of the regression model. To achieve these objectives, this study was divided into two main parts. The first part comprised the theories of value, residential property value factors, location modelling, MRA modelling, and the spatial interpolation techniques. The second part, comprised the development of hybrid models, whereby the locational factors was modelled by GIS-MRA-generated LVRS approach and examining its ability to improve regression modelling. As many as125 individual terraced units in three adjoining housing schemes (Taman Pelangi, Taman Sentosa and Taman Sri Tebrau) in Johor Bahru were used for model estimation, while 14 transacted units were set aside for predictive purposes. Results have shown models applying LVRS have managed to improve overall model’s statistical quality and predictive performance by achieving higher proportion of “reasonably accurate� prediction as compared to the traditional MRA models. The LVRS has allowed a clearer spatial visual picture of the location influence to be captured at all level in the study area. The location factor influence to property value can be modelled in a more effective way.
format Thesis
qualification_level Master's degree
author Chin, Chui Vui
author_facet Chin, Chui Vui
author_sort Chin, Chui Vui
title Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values
title_short Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values
title_full Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values
title_fullStr Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values
title_full_unstemmed Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values
title_sort using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values
granting_institution Universiti Teknologi Malaysia, Faculty of Geoinformation Science and Engineering
granting_department Faculty of Geoinformation Science and Engineering
publishDate 2006
url http://eprints.utm.my/id/eprint/5144/1/ChinChuiVuiMFKSG2006.pdf
_version_ 1747814567101071360

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