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.

The study aimed at examining the spatial pattern of residential densification in housing submarkets of Bida, an ancient traditional town in Nigeria. The study adopted the 2015 standard residential density of Niger State Urban Development Board to determine the level of residential density and occupancy rates of the various submarkets of the town. The study also adopted primary method of data collection through the use of satellite images, handheld GPS and georeferencing of demarcated areas and the buildings, using point features and vector approach in ArcGIS environment to achieve the area coverage, number of buildings and buildings per hectare (ha) in the housing submarkets. The finding of the study reveals that in 2008 Town housing submarket has the highest area coverage, followed by the Project Quarters and then GRA, but in terms of residential density, four housing submarkets of Town, Rahmatu Dangana, Gbazhi and Wadata have high densities above the other seven submarkets. The study further reveals that in the year 2013, additional eight housing submarkets have high residential densities, GRA medium density while Eyagi and Prject Quarters had low densities respectively. It was therefore recommended that there is the need for rational densification (planned densification) for urban development in order to check the increasing unplanned residential density that reduces the green and open spaces in urban environment.