Quantifying terrain factor using GIS applications for real estate property valuation
This thesis studies the use of GIS applications to derive adjustment figures for the terrain factor in property valuation tasks. It aims at suggesting a quantitative approach alternative to evaluate the terrain factor as opposed to traditional methods and current industry practices where terrain is qualitatively judged based on visual observation at site and subjected to individual opinion. In this study, the terrain factor is considered by analysing the slope and surface roughness elements of terrain. To achieve this, slope and surface roughness values are generated from available open source digital elevation models (DEMs) within the Esri ArcGIS software environment. For the purposes of this study, the Shuttle Radar Topography Mission (SRTM) DEM developed by National Geospatial Intelligence Agency (NGA) and United States National Aeronautics and Space Administration (NASA) as well as the Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM jointly developed by Ministry of Economy, Trade and Industry, Japan (METI) and NASA, were used to derive terrain values. The output adjustments were tested on several hypothetical valuation cases, consisting of small and large properties, to see the effects of DEM resolution upon the results. In order to test the accuracy of the proposed adjustment outputs and applicability of the study methods, feedbacks from industry experts were collected via an online survey for analysis. Upon analysing the feedbacks, this study finds that industry experts are of the opinion that the terrain adjustments proposed by this method are reasonable for use in the industry practice, although some apprehensions were also noted, as property valuers tend to exercise caution when using automated valuation methods. The proposed method is simple to apply and does not require advanced knowledge of GIS functions to operate. Therefore, considering the positive feedback from the valuation community, it could pave way towards future incorporation of geostatistical methods/ components in value analysis.