GIS based property valuation: Objectifying the value of view
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Automated valuation models evolved substantially since the ‘80s and are now the main practice for property valuation in The Netherlands, where they are used both for taxation purposes and the property trade market. Important factors for the value of one’s property are physical conditions of the house and the influence of the property’s location. The latter, however, is often insufficiently represented in an automated valuation model. Incorporation of the spatial character of properties in property valuation can be pursued in two ways. On one hand the model can be improved in the data collection phase by inserting additional locational variables, on for example the quality of the surroundings, in the valuation model. On the other hand, the modelling process itself can be improved by exploiting spatial statistical methods to specify the regression model. Much literature has been written on the two fields, though little is touching both. The developments in the field of Geographical Information Systems eased the spatial approaches in automated valuation. GIS technologies offer the possibility to objectify information that was traditionally collected in a subjective manner, such as the view from a property. Furthermore, GIS technologies facilitate spatial regression models, that account for spatial errors. A main spatial error in regression analysis occurs when the property values are not functioning independent, since properties close to each other often show similar values. This paper demonstrates the use of GIS applied to automated regression to estimate the value of a view on two clusters in a residential urban housing market. The outcomes of the study show that including the spatial variables on view, automatically computed with GIS, improve the property price predictions. Also, the spatial approach in regression modelling significantly improves the model fit. In the two clusters the best prediction model is the one that combines both spatial approaches.