Nowadays, the housing prices are one of the burning issues in some of the current main economies in the world (such is the case of the United Estates of America, the United Kingdom, Spain, among others, as well as in Poland). This is the reason of the increasing literature about methodologies for estimating them, because the valuation of the real estate properties is crucial for fiscal issues, it influences the consumption and the rate of economic growth, etc. Model-driven orientation has been traditionally used for estimating housing prices (hedonic models, spatial autorregresive models, mixed regressive spatial autorregresive models, models with spatial dependence in the error term, spatial Durbin models, etc.). But there is another alternative approach, the data-driven approach, which includes, among others, shape functions, methods based on inverse distance weighting, kriging and cokriging, the last two ones having shown to provide excellent results due to the fact that they take into account the structure of the spatial dependence existing in the data. Having said that, the paper shows how kriging and cokriging work, and provides their equations in several situations (constant and unknown mean, existence of trend, homotopic, partial and complete heterotopic cases, etc.). It includes three different applications in the real estate market of Toledo (Spain). Out of the real estate valuation context, the univariate and multivariate kriging methods are used to estimate electoral results in the region of Valencia (Spain) and to analyze the neighbourhood crime in Columbus (Ohio,USA).

JEL classification: C21, C31, R31, P25.

Keywords: kriging, cokriging, variogram, spatial dependence, real estate valuation.

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