Analysis of prices in the housing market using mixed models

Abstract

Hedonic models, commonly applied for analyzing prices in the property market, do not always fulfil their role, mainly due to the application of simplified assumptions concerning the distribution of variables, the nature of relations or spatial heterogeneity. Classical regression models assumed that the variation of the explained variable (price) is explained by the effect of market features (fixed effects) and the residual component. The hierarchical structure of market data, both as regards market segments and the spatial division, suggests that statistical models of prices should also include random effects for selected subgroups of properties and interactions between variables. The mixed model provides an alternative for constructing various regression models for individual groups or for using binary variables within one model. With its appropriate structure, it makes it possible to take into account both the spatial heterogeneity and to examine the effects of individual features on prices within various property groups. It can also identify synergy effects. The article presents the issue of mixed modelling in the property market and an example of its application in a market of dwellings in Olsztyn. The research used transaction data from the price and value register, supplemented with spatial data. The obtained model was compared with classical regression models and geographically weighted regression. The study also covered the usefulness of mixed models in the mass evaluation of properties, and the possibility of using them in spatial analyses and for the development of property value maps.