Design of methodology for estimation of parcel investment attractiveness using spatially adjusted predictive modelling

Abstract

Determining investment attractiveness is an important task, in the meaning of economic and administration operation. The investment attractiveness of a land parcel can be assessed quantitatively as a value of dynamics in its sale price. In addition, spatial parameters can be accounted when estimating the investment attractiveness. To ensure prediction of land parcel sale prices and their dynamics we implemented ensemble regression analysis. Methodology was tested on publically accessible data of Russian cadastral agency. To elaborate an investment attractiveness clustering of cadastral units depending on price and spatial parameters, we conducted k-means clustering with Silhouette metric control.