Estimating the value of real estate has applications in fields as diverse as taxation, buying and renting properties, expropriation and urban regeneration. Determining the most objective, accurate and acceptable value for real estate by considering spatial criteria is therefore important. One stochastic method used to determine real estate values is 'nominal valuation'. In this approach, criteria that may affect land value are subjected to various spatial analyses, and pixel-based value maps can be produced using GIS. Land value maps are in raster data format and need to be compared with the actual market values. Pixelresolution analyses are required that depend on the selected grid dimensions. First of all, nominal value maps were produced using a nominal valuation model, using criteria for proximity, visibility and terrain. These were weighted in order to produce a nominal asset value-based map according to the 'Best Worst Method'. Changes in the unit land values were examined for maps at various resolutions; a resolution of 10 metres emerged as the ideal pixel size for valuation maps.
Today, thanks to the internet connection, the borders are disappearing and accessing information is more comfortable. Besides desktop applications, number of web-based applications which instant changes can be seen by all users are increasing day by day. The diversity of web-based applications that are currently used in presenting spatial information to users is also progressing. Using open source libraries, developers can develop web applications for their own purposes. Three dimensional (3D) visualization on the web is a commonly used approach in geographic information systems (GIS) applications. In this article, a 3D web application is developed using Cesium open source javascript library. Vector data layers containing attribute data on global, country and city scales are visualized on the web application. Moreover, raster data layers which produced in other projects like GIS-based land valuation application is also visualized in three dimension. It is pointed out that the output products obtained from different studies can be accessed and visualized through the web browser without installing an additional program or add-ons on the users' computers.
Geographic Information Systems (GIS) and Machine Learning methods are now widely used in mass property valuation using the physical attributes of properties. However, locational criteria, such as as proximity to important places, sea or forest views, flat topography are just some of the spatial factors that affect property values and, to date, these have been insufficiently used as part of the valuation process. In this study, a hybrid approach is developed by integrating GIS and Machine Learning for mass valuation of residential properties. GIS‐based Nominal Valuation Method was applied to carry out proximity, terrain, and visibility analyses using Ordnance Survey and OpenStreetMap data, than land value map of Great Britain was produced. Spatial criteria scores obtained from the GIS analyses were included in the price prediction process in which global and spatially clustered local regression models are built for England and Wales using Price Paid Data and Energy Performance Certificates data. Results showed that adding locational factors to the property price data and applying a novel nominally weighted spatial clustering algorithm for creating a local regression increased the prediction accuracy by about 45%. It also demonstrated that Random Forest was the most accurate ensemble model.
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