Development Of Spatial Model In Property Rating Valuation
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Date
2015-02
Authors
EBOY, OLIVER VALENTINE
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Abstract
According to Local Authorities Act 1976 (Act 171), property tax rates are required to
be valued every five years to accommodate the present market value. Usually, the
revaluation activity involves exhaustive, time consuming and costly processes
because it involves large areas and many properties that are needed to be covered.
There are various property valuation models being developed using spatial statistics
method to estimate property values of large quantities in a short time involving small
manpower and at low cost. However, it has been difficult to produce either one
property valuation model that is suitable for the study area or subdivides it to many
models in order to ensure accurate of the model produced. Furthermore, the type of
variables used in model development and which variables have the most influence in
determining the property rating also are difficult to be examined. Therefore, the aim
of this study is to develop property rating valuation model in Kota Kinabalu City
Hall (DBKK) using Ordinary Least Squares (OLS) and Spatial Regression Model
(SRM). The model developed could accurately estimate the property rating and
eliminate the error. These spatial models are developed based on the 1997 residential
property valuation data obtained from DBKK and subsequently tested to measure its
accuracy and reliability. The study found that using segmentation approach of the
data based on different building types are suitable to be represented with separate
models. However, the overall study area is best represented by SRM as OLS model
contain spatial autocorrelation error. Similarly, the model based on building type in
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this study is also suitable to be represented by SRM. The findings shows that data
segmentation based on building type model performed better and each building type
model produced different number of significant variables that influence the property
rating value. This was proven based on the big marginal accuracy differences of R2
achieved by the highest building type model of intermediate terrace with 84.3%
compared to the overall model with 59.2%. The study also found out that each
building type has it own significant variables that influence the most of its value.
Spatial statistics can be used to produce residential property rating valuation model.
This approach is also suitable for large valuation dataset with fast processing and low
in cost. The model developed is suitable to be used by DBKK or local authorities in
management of the property rating valuation data.
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Keywords
Development Of Spatial Model , In Property Rating Valuation