Sebastian Heslin-Rees – Project I
New insights into workplace and retail dynamics for English and Welsh cities
These two pieces of work are both using Whythawk data on commercial properties in England and Wales, at Lower Super Output Area (LSOA). Both are making use of existing methodologies applied to a different scenario to produce new insight on commercial property rent and spatial location.
A commercial geodemographic classification of workplace zones
This research endeavour will utilise the newly available Whythawk dataset to construct a model for presenting and thus, understanding the spatial distributions of workers and workplaces across England and Wales. Largely, this will involve clustering workplaces of similar characteristics to distil a set of key workplace types, which can subsequently be mapped and analysed. In addition, the dataset has made available details of workplaces that have not been present in previous workplace datasets, such as distinguishing different workplace functions within multi-level building complexes. Consequently, this could provide additional insights and novel avenues for academic research and policy initiatives.
Predicting commercial rents using novel Machine Learning approaches
Using novel big data, this study will assess mass market appraisal within the English and Welsh commercial rental market. Mass market appraisal is the valuation of properties at a given time, and is required to ensure each property makes the appropriate tax contribution. This study will use a large volume of data on commercial business type, rental and rateable values and numerous external environmental variables. A range of machine learning algorithms will be used to predict and appraise the commercial rental market in England and Wales.
The outputs will include academic papers focused on methodologies employed, CDRC datasets and detailed maps. These projects are expected to help property professionals better understand commercial rental pricing and businesses who use and occupy these spaces and also researchers who are interested in how property values interact with other aspects of the environment.