The Use of a Grid Structure for Reconstructing and Forecasting the Value of Real Estate in Selected Measurement Epochs

Abstract

The absence of sufficiently long time series of data relating to real estate prices in a selected location prevents accurate analyses and the development of precise forecasts that play an important role in a market economy. New methods and solutions are being sought to address this problem. This paper proposes an original method for reconstructing, forecasting and archiving data relating to real estate value. The proposed method involves a GRID (regular square nets) structure and it relies on the prices quoted in successive years (epochs) of measurement in a selected object. Irregularly distributed measurement data (real estate prices) acquired in successive years are transformed into a regular GRID structure to develop digital surface models that describe the distribution of data. The nodes of the GRID structure are described with the coefficients of an approximating polynomial to reconstruct and forecast real estate value in a specific location at any point in time. A GRID structure supports a comparison of changes in real estate value over time in a given node or group of nodes selected from successive measurement epochs. Individual coefficients of an approximating polynomial are generated, allocated to selected nodes, and automatically adapted to local changes in value. As a result, the observed changes can be described in a given period of time. Source data covering multiple epochs are replaced with a single file containing coefficients of approximating polynomials to reduce the size of the stored datasets and facilitate data management.

Application of Polynomial Interpolation for Iterative Complementation of the Missing Nodes in a Regular Network of Squares Used for the Construction of a Digital Terrain Model

“…A complete grid structure for each measurement epoch is also required during the analyses of changes in the surface model over time. An adequate density of such a structure allows analyses of changes in DTM in successive measurement epochs to be carried out at any point of the space under analysis [53,54].…”

Adjusting the Regular Network of Squares Resolution to the Digital Terrain Model Surface Shape

“…Automatically obtained information often requires thorough processing prior to being entered into a spatial information system; this particularly applies to the dynamic processing of large volumes of data collected in various measurement epochs, and to carry out analyses of large surface structures in real-time [29][30][31][32][33][34]. One of the structures that enable a reduction in data volume, data organisation and redundancy reduction is a regular network of squares [35][36][37][38][39].…”