SPATIAL MODELING OF RESIDENTIAL REAL ESTATE VALUE BASED ON COMPREHENSIVE CONSIDERATION OF SOCIO-ECONOMIC FACTORS

Abstract

The relevance of the study is driven by the need to improve approaches to modeling residential real estate value in wartime conditions, when, along with traditional socio-economic factors, spatial characteristics of the territory and the consequences of military actions become significantly important. The subject of the study is the spatial patterns of value formation for one-room apartments in the secondary market within the vernacular districts of the Saltivka residential area in Kharkiv. The purpose of the article is to substantiate a spatial approach to modeling residential real estate value, according to which pricing is considered a result of the combined impact of several socio-economic factors, rather than a single dominant factor. To achieve this goal, a list of factors characterizing the studied territory was determined, the exclusion of parameters whose impact within the residential area is practically homogeneous from quantitative analysis was justified, a correlation analysis of the impact of individual factors on housing value was performed, and a complex multiple regression model was built. The methodological basis of the study includes methods of spatial, correlation, and regression analysis, statistical comparison of groups, and data generalization within vernacular districts. Vernacular districts were chosen as the spatial unit of the study, since they most fully reflect the boundaries of local residential areas formed by residents. The analysis was performed for one-room apartments in the secondary market as the most representative segment of residential real estate. Within 32 vernacular districts, the impact of transport accessibility, provision of social infrastructure, and the share of the territory damaged as a result of hostilities was analyzed. It was established that each of the studied factors separately explains no more than 6% of the variation in housing value, while the complex multiple regression model explains 10.8% of the variation, which is approximately twice as much as the strongest individual factor. The obtained results are consistent with the hypothesis about the multifactorial nature of pricing and indicate that the simultaneous consideration of several spatially differentiated factors provides a better description of housing value than analyzing each of them separately. The scientific novelty of the study lies in the use of vernacular districts as a spatial unit of analysis and the substantiation of an approach to modeling housing value based on the cumulative consideration of spatial factors within a typical residential area. The practical significance of the obtained results lies in the possibility of using the proposed approach to improve models of mass appraisal of residential real estate, support management decisions in the field of post-war urban reconstruction, and spatial planning.