1 research outputs found
Exploring new data sources to improve UK land parcel valuation
The paper describes a novel approach for building a UKwide
Automated Land Valuation Model and its implementation
into commercial online software. We examine existing
approaches to land valuation used in the UK, notably
Trade Area Analysis, Spatial Interaction and Comparable
Sales. We make the case that land use analysis, demographics
and societal preferences affect the potential income and
optimal use of parcels of land and hence the value of those
parcels. This hypothesis leads to the introduction of a number
of additional factors required to facilitate estimated land
value, including traffic flow, population and site suitability.
A number of artificial intelligence (AI) and machine learning
spatial-temporal techniques are introduced to predict the
value of all land parcels sold since 1995. We introduce a new
technique, which includes (i) the application of Support Vector
Machines to land use analysis; (ii) the use of predictive
techniques for macro-environmental factors; (iii) the use of
large, open-source data sets to improve valuation; (iv) industry
alignment in predefined industrial tool. A number of
different mathematical techniques are used to validate the
proposed model and we show that our model demonstrates
92% accuracy for residential pricing predictions