Many studies have unveiled the importance of variation in residential property values overtime, but failed to cover different types of residential property value and location. The aim of this research is therefore to model residential property rental value in Bida from 2015 to 2020 with the aid of Geographic Information System (GIS). The study focused on the rental values of the residential property and rental value variation across space. Data collected for this paper includes residential rental values and geographic coordinates from 196 residential properties in the study area, comprising 101 one-bedroom, 80 two-bedroom and 15 three-bedroom apartments. Inverse Distance Weighted (IDW) interpolation tool of ArcGIS was employed in analysing the data. It was found out that the core areas of the town commands lower rental values while the southern part of the town commands higher rental values. It was also found out that one bedroom apartment is the most dominant residential rental property followed by the two bedroom apartments and three bedroom apartments respectively. Geospatial database was produced for each model in a bid to ascertain the level of changes with time. The benefits associated with the application of GIS technology were established by this research and recommends its application to other property values modelling.

The subject of the paper is the change trends in the trade in undeveloped land and real properties designated for agricultural and forestry purposes in Poland with respect to the value and surface area of real properties sold. Another aim of the study is to present the differences between average transaction prices of undeveloped real properties designated, among others, for residential, industrial, agricultural and forestry purposes. The research covers the period starting from the accession of Poland into the European Union structures. The Polish real property market has been very active in the analysed period. The analysis of tendencies in real property sales transaction values in Poland in the time period from 2004 to 2015 demonstrates that the increasing trend prevailed from 2004 to 2008. Then, in 2009, the value of transactions decreased in comparison to the preceding year.

The construction sector is linked to the general development of a country. There is a lot of data scattered and not properly explored in relation to the buildings constructed. However, if these scattered data on the behaviour of the real estate market are organized, combined with knowledge of civil engineering, this merger of information can mitigate some evaluation problems, especially those that are overvalued for unknown or dubious reasons. Thus, there is a need for models capable of working with limited data to analyse the causal relationships between explanatory variables and sales prices and, from there, predict property values. The purpose of this article is the innovative use of simple building inspection strategies to predict the market price for residential apartments. For this, 19 samples of residential apartments are used in the city of Niterói, Rio de Janeiro, Brazil, in February 2021. The methodology uses the results of the survey of civil engineering and converts them into heuristic terms predicting the price of the property. With this, the imprecision, uncertainty, and subjectivity of human expression combined with the knowledge of civil engineering result in a plausible solution and easy application in the market. Finally, the use of fuzzy logic in the evaluation of properties is an adequate unconventional method, in addition to avoiding repetition in regression coefficients in binary logic. To check the reliability of the method, the comparison between the market values of the samples and the values predicted by the fuzzy logic is used. The result according to the mean absolute percentage error (MAPE) can be interpreted as a good result (7%).

The article describes peculiarities of formation of the estimated constituent in the structure of the state land cadastre in Ukraine. The author gives analysis of the current Ukrainian practice of consideration of the intended use of a land plot while making normative monetary evaluation of non-agricultural land plots within and outside settlements. The conducted analysis of the methodology of normative monetary evaluation of the land of settlements and non-agricultural land outside the settlements confirms that in the process of evaluation, each of the methodologies considers the intended use of land plots in a different way, causing different value correlation between the lands of different categories and kinds of intended use. Differentiation of the estimated indices of the methodology of normative monetary evaluation of non-agricultural lands within and outside settlements absolutely disagree with one another. The article supplies conclusions on the necessity to transfer to a consistent approach of such differentiation without reference to the land plot location within or outside the settlements. Basing on the analysis of statistical information on the sale of state- and communally-owned lands, the author defines the coefficients of correlation between the sale price of state- and communally-owned lands in 2017−2018 in Ukraine in terms of the kinds of intended use and the figures of the indices of differentiation of normative monetary evaluation of non-agricultural lands within and outside settlements depending on the intended use of the land plot. The author proposes changes to the approaches to differentiation of the indices of evaluation of the lands of different intended use on the base of market prices. The work also outlines the main problems and possible directions of transition to a large-scale evaluation of lands in Ukraine.

One of the most challenging tasks in modelling of house pricing is to take into account the location factors. Geographically Weighted Regression (GWR) as a local regression model is an extremely effective instrument for spatial data analysis. The aim of the study is to model the relationships between a residential real estate price (per sq.m) and both building and location characteristics for Gomel using GWR. The data of the Belarus’ National Cadastral Agency on real estate transactions (apartments) in Gomel in 2019 are used as initial. The global Moran I index has been used to estimate a spatial autocorrelation of the dependent variable (price per square meter of residential real estate). Several factors having the impact on the apartment sale prices have been determined. Independent variables having been used in analysis can be divided into building characteristics and spatial characteristics. The building characteristics section includes the number of rooms within the property, property area (square meters), building age, number of floors in the building, floor of the property. The spatial characteristics group contains proximity to city centre, recreation areas, supermarkets, bus stops, healthcare and educational facilities. A regression model of housing price in Gomel has been developed. Mapping variable regression coefficients allows exploring spatial features of the impact of the different explanatory variables on the property price. Geographically weighted regression modelling has revealed the pricing peculiarities inherent for certain areas of the city.