Categorical Variable Problem In Real Estate Submarket Determination With Gwr Model

Abstract

Abstract Real estate market analysis can involve many aspects. One of them is the study of the influence of various factors on prices and property values. For this type of issues, different kinds of measures and statistical models are often used. Many of them do not give unambiguous results. One of the reasons for this is the fact that the real estate market is characterized by the concept of local markets, which may be affected in different ways by economic, social, technical, environmental and other factors. Incorporating the influence of local markets, otherwise known as submarkets, into models often helps improve the precision of mass real estate valuation results. The delineation of submarket boundaries can be done in several different ways. One tool that is helpful in these types of situations are geographically weighted regression (GWR) models. The problem that may arise when using such models is related to the nature of some market factors, which may be of a qualitative nature. Because neighborhoods of individual properties may lack variability in terms of some variables, estimating GWR models is significantly difficult or impossible. The study will present an approach in which the categorical variables are transformed into a single synthetic variable, and only this variable will constitute the explanatory variable in the model. Areas where the slope parameters of the GWR model are similar were considered a submarket. The purpose of this paper is to determine the boundaries of submarkets in the study area and to compare the results of modeling the value of real estate using models that do not take local markets into account, as well as those that take into account local markets determined by experts and using the GWR model.