Development Of Spatial Model In Property Rating Valuation
| dc.contributor.author | EBOY, OLIVER VALENTINE | |
| dc.date.accessioned | 2016-04-15T07:32:33Z | |
| dc.date.available | 2016-04-15T07:32:33Z | |
| dc.date.issued | 2015-02 | |
| dc.description.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 xvii 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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/1907 | |
| dc.subject | Development Of Spatial Model | en_US |
| dc.subject | In Property Rating Valuation | en_US |
| dc.title | Development Of Spatial Model In Property Rating Valuation | en_US |
| dc.type | Thesis | en_US |
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