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Utilization of Artificial Neural Network and GIS for property market valuation

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5 Author(s)

The increasingly rapid advances in technology development, approaches to integrate the property market valuation with Geographical Information System (GIS). Application of GIS in Property Valuation field very helpful for property market valuation information to presented in map and table using ArcGIS 9.3 software. It purpose is to facilities the access and searching the information. Before this, information about property using old files and Microsoft Excel but using GIS spatial and attribute data will be shown. To display property market valuations in Google Earth of each category like residential that have divided into two; double storey house and bungalow, commercial and industrial areas, data that are needed such as property data from JPPH, land parcel from JUPEM, and land use from MBSA. From the property market valuation, can determine high or low market value based on attribute data. In visualization, can analysis the factors of highest and lowest market value based on geographic that have been produced using ArcGIS ang Google Earth. After that, the detail analysis also can determine using ArcGIS 9.3 software such as using overlay and proximity analysis tools. From that data, changes of the market value can be determined the increase market value for each categories of property. Analysis of the property market valuation is performed using mathematical software like Microsoft Excel, SPSS and Artificial Neural Networks (ANN) software. Microsoft Excel produces analysis in graph and bar chart of highest and lowest market valuation for each category of property. After that, the changes of the market valuation also can determine. SPSS shows the relationship of property market value with an area. Lastly, using ANN software can predict property market valuation based on algorithms. For prediction property market valuation just not in qualitative factors but can variable in quantitative to make a good analysis for each property categories; residential, commercial and i ndustry.

Published in:

Signal Processing and its Applications (CSPA), 2011 IEEE 7th International Colloquium on

Date of Conference:

4-6 March 2011