Oliver Valentine Eboy
Currently work as a Lecturer in University Malaysia Sabah in the Geography Department. Have experiences in the research of GIS, property valuation modelling and spatial statistics for nearly 14 years. Provide technical consultation of property rating valuation modelling to Kota Kinabalu City Hall (DBKK). Obtain Diploma in Property Valuation studies, Bachelor and Master of Science degree in Geoinformatic from UniversitiTeknologi Malaysia (UTM). Currently on study leaves to pursue PhD education.

The Capabilities of Modelling Property rating Valuation using Spatial Regression Method
As one of the main source of revenue to the Malaysian Government, it is compulsory for property tax to be imposed to all properties. However, property tax rates required to be valued every five years to accommodate the present market value as stated in Local Authorities Act 1976 (Act 171). Usually, the revaluation exercise was conducted using comparable method and this has been carried out manually. This involves exhaustive, time consuming and costly processes as large area and many properties need to be covered. Recently, there has been a growing trend in developing a property valuation model using spatial statistics method which is capable of estimating property values of large quantities in a short time with little manpower needed and low in cost. Traditionally, the spatial statistics of Ordinary Least Squared (OLS) method was used to produce the model but apparently it has severe limitation especially pertaining modelling error. Therefore, another modelling method in a form of Spatial Regression Model (SRM) was adopted as an alternative to the OLS method. The SRM was capable to eliminate model error in which the OLS unable to achieved. This study demonstrates the development of property rating valuation model using the OLS and SRM method to estimate residential property value for the area under Kota Kinabalu City Hall (DBKK) jurisdiction. The capabilities of the OLS and SRM were examined based on the acceptability and accuracy of the model which conducted through a series of model tests. The outcome from these tests would show which model best represent the study area. The findings indicated that model error of exists in the dataset of the study area in which the SRM be able to rectify it. Subsequently, the SRM standout as the best property rating valuation model for DBKK area compared to OLS. Ultimately, this study had proved the capabilities of SRM in producing the property rating valuation model even with problematic dataset. In addition to that, it could also easily produce property value map thus improve the management of property rating valuation in local authority.
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