Abstract
The spatial distribution of prices is closely linked with the urban real estate market. Property prices are one of the key indicators of economic activity because they influence economic decisions. Decision-makers and consumers often need information about the spatial distribution of prices, but spatial-temporal analyses of the real estate market are based on the prices quoted in different locations across years (epochs). Due to this idiosyncrasy, the resulting datasets are dispersed (different across years) and difficult to compare. For this reason, the existing interpolation methods are not always effective in analyses of the real estate market. A different approach to interpolating real estate prices that supports the generation of continuous interpolated surfaces while maintaining the values of measurement points is thus needed. This paper proposes a method for replacing dispersed spatial data with a regular GRID structure. The GRID structure covers the measured object with a regular network of nodes, which supports uniform interpolation at every point of the analyzed space and a comparison of interpolation models in successive epochs (years). The proposed method was tested on a selected object. The results indicate that the GRID structure can be used in analyses of highly complex real estate markets where input data are incomplete, irregular and dispersed.
Highlights
The market of residential property is defined as “geographic areas where the price per unit of housing quantity is constant” [1]
According to Chou [18], spatial interpolation relies on two fundamental assumptions: The surface of the price variable is continuous, and the price variable is spatially dependent
During the generation of GRID structures, the values in interpolation nodes are computed with the use of measurement points located near the mapped node within a given search radius R (Figure 2b)
Summary
The market of residential property is defined as “geographic areas where the price per unit of housing quantity (defined using some index of housing characteristics) is constant” [1]. The spatial distribution of prices is closely linked with the real estate market. Decision-makers and consumers often need information about the spatial distribution of prices. The hedonic regression method and the repeat-sales method are most frequently used in real estate market analyses [3,4,5,6,7,8,9,10,11]. The emergence of geographic information system (GIS) tools contributed to the development of surface models with the use of spatial interpolation methods (geostatistical methods) [12,13,14,15,16,17]. The presence of spatial correlations between prices resulting from neighborhood effects has been observed by many authors [3,19,20,21,22], and it supports price analyses with the use of various interpolation methods
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