Predicting Property Rating Values Using Geographically Weighted Regression
Currently, the market value for rating valuation applied in Malaysia is the single property valuation technique. This technique is not efficient enough, involving high costs and large labor force because rating involves valuation of large number of properties. Multiple Regression Analysis (MRA) w...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/12448/1/ITMA_2010_1A.pdf |
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| Summary: | Currently, the market value for rating valuation applied in Malaysia is the single
property valuation technique. This technique is not efficient enough, involving high
costs and large labor force because rating involves valuation of large number of
properties. Multiple Regression Analysis (MRA) was applied due to these
weaknesses. However, the MRA fails to account for the spatial effects (spatial
heterogeneity and spatial dependence) inherent in property data. In this study, the
Geographically Weighted Regression (GWR) model is introduced as a new method
to value rating properties. The GWR model is able to capture spatial heterogeneity
by allowing different relationships to occur between variables at different points in
space.
This study has two objectives. The first objective is to determine the attributes to be
used for MRA and GWR model in this study. Data for this study were collected from
two local authorities to represent rent and transaction data-based rating. Data for rent was obtained from Majlis Perbandaran Kajang (MPKj) and data for transaction was
obtained from Majlis Perbandaran Kulai (MPKu). Final attributes for rent–based
rating area are land area, main floor area, ancillary floor area, type of ceiling,
property position, property type, age of building, distance to centre business of
district and neighborhood quality and the attributes for transacted-based rating area
are land area, main floor area, additional floor area and floor finishing.
The second objective is to compare the performances of the GWR model with the
MRA model in predicting rating values in the study areas. The result of R2, Adjusted
R2, F-test and standard error of estimates proved that the GWR model provides better
fitness compared to the MRA model. Residual analyses also reveal the same
conclusion where residual for the GWR model is smaller in absolute values and
probability distribution close to normal. The GWR model has also successfully
captured spatial heterogeneity in almost all attributes. The prediction assessment of
out-sample observations also revealed that the GWR model is able to produce better
prediction. The ability of the GWR model to capture spatial effects is the main
reason for this model to perform better; the GWR model is able to solve spatial
heterogeneity problem explicitly and spatial dependence problem implicitly. Thus,
the GWR which has been proven to be able to produce accurate prediction with small
number of attributes should be used for rating valuation in Malaysia. |
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