Using GIS and large-scale digital data to implement hedonic pricing studies

Abstract

This paper describes how a standard GIS package can be used to convert large-scale vector digital data (point, line and annotation features) into polygons using standardised and replicable methods. Building area, garden and land use polygons are all derived from such data (Ordnance Survey LandLine.Plus). These entities are then combined with further sources of digital data to derive more re ned information such as property types. Finally, complex DEMs are developed for use in visibility studies. The variables calculated are subsequently employed in a property valuation study where many are found to be signi cant determinants of property price. The main exception is variables relating to viewsheds, although it is argued that this does not invalidate the techniques used in their deviation but highlights the di culties involved in modelling a large number of variables in a property price analysis.

Hedonic Pricing of Climate Change Impacts to Households in Great Britain

“…Lake et al (2000). They derive more refined information of property characteristics (like walking distances or car travel times) for Scotland.…”

Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

“…Recent years have brought a great interest in applying spatial statistics to hedonic price modelling, which was partially caused by increasing Geographic Information Systems (GIS) development and the applications of big data. The use of GIS tools has been particularly significant for the evaluation of the impact of environmental/spatial attributes in property values [1][2][3]. This has resulted in the introduction of advanced geostatistical methods and Geographically Weighted Regression (GWR) as efficient methodologies for capturing spatial heterogeneity and spatial autocorrelation in housing markets versus multiple regression as a global model [4,5].…”

Estimating the residence price by linear regression model and Geographical Information Systems (GIS

“…Lake et.al. (1998;2000) have used GIS for the first time in real estate evaluation and residence pricing. This study has put forth that highways, rail road's and industrial buildings have a negative impact on the view of the residence thereby decreasing its price Schernthanner and Asche (2010) have carried out the 2009 residence market analysis for the city of Potsdam in Germany by using GIS.…”