Statistical Modelling of the Market Value of Dwellings, on the Example of the City of Kraków

Abstract

The analysis of a city’s spatial development, in terms of a location that meets the needs of its inhabitants, requires many approaches. The preliminary assessment of the collected material showed that there was real estate in the database whose price did not have market characteristics. For the correct formulation of the valuation model, it is necessary to detect and eliminate or reduce the impact of these properties on the valuation results. In this study, multivariate analysis was used and three methods of detecting outliers were verified. The database of 8812 residential premises traded on the primary market in Kraków was analyzed. In order to detect outliers, the following indices were determined: projection matrix, Mahalanobis distances, standardized chi test and Cook distances. Critical values were calculated based on the formulas proposed in the publication. The probability level was P = 0.95. The article shows that the selected methods of eliminating outliers—the methods of standardized residuals and the Cook’s distance method give similar regression models. Further analysis (with the use of classification tree methods) made it possible to distinguish zones that are homogeneous in terms of price dispersion. In these zones, a set of features influencing real estate prices were determined.

Renewable Energy Sources in the Residential Property Market, Exemplified by the City of Krakow (Poland)

“…In the literature on the subject, it is emphasized that it is a function of fundamental economic variables. If ecological factors are disregarded, such investments are considered outliers [5], and one environmental sector is becoming an important part of the construction industry [6], which can be seen both in the European market [7] and the Polish market [8]. Furthermore, due to the specificity of local markets, individual economic factors may influence their development with varying strengths [9].…”

A Survey of Methods and Input Data Types for House Price Prediction

“…While both data sets include structural, temporal and standard spatial features, socioeconomic and POI data are also covered in [23]. Two data sets available on request include basic features [24,25]. Two other data sets that can be requested also contain advanced spatial data, that is, network distances [26,27].…”

“…The building blocks of more advanced tree-based methods are decision trees. In [25], classification and regression trees (CART) and chi-square automatic interaction detector (CHAID) trees are built to determine the effect of the independent variables on house prices in Kraków. The popular CART method includes both regression and classification, while the CHAID method implements classification via a chi-square test to determine the best split.…”

“…In contrast, the three articles published in 2017 and 2021 employ NN and DT, which contribute to lower model novelty. Regarding input data types used, only one article [25] does not use basic spatial information, which causes its data novelty score to fall back to three, although it is among the most recently published articles.…”