Experimental spatial modelling of commercial property stock using GIS

This novel research project draws upon the experience of a small number of experimental research projects in seeking to extend some of the frontiers to the spatial modelling of commercial real estate markets. In so doing, it explores new ways of capturing, integrating, representing, illustrating and modelling commercial real estate data with other spatial variables.There is tacit understanding of the relationship between the distribution of commercial properties, and spatial factors such as proximity to footfall, transport and other infrastructure. However, there has been surprisingly little research that has been able to illustrate these tangled market relationships using spatial analyses, underpinned by empirical quantitative data. This research project has developed a methodology to visualise the distribution of rateable value, used as a proxy for the attractiveness of commercial property, across a pilot study area, in this case, the City of York, in North Yorkshire, England.The project has experimented with the use of grid squares to analyse geo-spatial relations of commercial real estate variables (such as, rental value, stock, vacancy, availability) with other spatial variables (such as, infrastructural facilities, transportation nodes). The project has confirmed that grid squares are more effective at representing data that are unevenly distributed across urban space at city level, than other artificial delineations, such as area postcodes, political boundaries or streets. The grid square approach can be further enhanced using 3D extrusions which facilitate simultaneous representation of an additional data characteristic, for example total stock combined with average rental value by location. Finally, modelling was conducted using hexagonal rather than grid squares, which revealed that hexagonal tiles are potentially more accurate, due to the proximity of data to the centroid of the tile (effectively losing the corners) and more efficacious at representing linear spatial patterns of of commercial property market data due to hexagons having 50% more directions of alignment than square tiles.