This article aims at testing the possibilities of applying hierarchical spatial autoregressive models to create land value maps in urbanized areas. The use of HSAR (Hierarchical Spatial Autoregressive) models for spatial differentiation of prices in the property market supports the multilevel diagnosis of the structure of this phenomenon, taking into account the effect of spatial interactions. The article applies a two-level hierarchical spatial autoregressive model, which will permit the evaluation of interactions and control spatial heterogeneity at two levels of spatial aggregation (general and detailed). The results of the research include both the evaluation of the impact of location on prices (taking into account non-spatial factors) and the creation of the average land price map, taking into consideration the spatial structure of the city. In empirical studies, the HSAR model was compared with classic LM (Linear Model), HLM (Hierarchical Linear Model), and SAR (Spatial Autoregressive) models to perform comparative analyses of the results.
This paper presents a novel approach to the modelling and simulation of real estate transactions. The main purpose of the study was to develop the theoretical foundations for building simulation models of transaction locations and real estate prices. Pursuing this objective involved a spatial market analysis based on geostatistics to develop maps of the dynamics and spatial activity of the real estate market. The research was conducted by presenting the issue against the background of the literature of the subject and by conducting an experiment, which involved developing an original procedure of providing simulated market data. The study deals with the market for non-built-up land real estate with a residential function in the city of Olsztyn (Poland). The time range concerned the years 2004–2015. Information on 932 real estate transactions was adopted for the study. A set of additional information on virtual transactions was generated during the study; this information can supplement market data for markets of low activity or if there are information gaps. Geoinformation analyses were performed in order to determine new trends in price levels and spatial activity of a real estate market. Overall, this resulted in generating maps of simulated transaction densities, a map of simulated prices and a map of the probability of a specific price occurring.
Simulation modelling performs a prognostic function through model research and the shaping of the future. Thorough insight into the analysed system and exploring its characteristics for the selection of optimal tools of analysis is an extremely significant process that precedes the stage of the simulation itself. For modelling and transaction simulation, the problem concerning the optimal range of the kernel function used for exploring the spatial activity of a property market should be addressed first. A probability function is the basis for the subsequent phase of research, which allows one to answer the question of whether the transaction density in a given year can be reflected in the transactions of the following year and subsequent years, and whether transaction distribution is correlated, in any way, with the transaction density in the previous year. The final results of the work are maps of the dynamics of transactions on the market and of the simulated transaction density.
Wartość i cena są pojęciami zbliżonymi pochodzącymi z teorii ekonomii. Nie można jednak traktować ich równoznacznie. Wartość nieruchomości, w kontekście jej ceny transakcyjnej, przedstawia wyrażony w jednostkach pieniężnych miernik przedmiotowych cech rynkowych nieruchomości. Cena transakcyjna stanowi natomiast wypadkową określonej wartości rynkowej nieruchomości z obiektywnymi cechami rynkowymi nieruchomości oraz subiektywnymi cechami negocjacyjnymi uczestników transakcji. W pracy podjęto kolejną próbę analizy poziomu cen i wartości nieruchomości w wybranych gminach województwa warmińsko-mazurskiego. Segment badawczy stanowiły nieruchomości gruntowe niezabudowane i zabudowane będące własnością trzech wybranych gmin (horyzont 2010–2013) oraz nieruchomości rolne należące zarówno do osób prywatnych, jak i stanowiące własność Skarbu Państwa (horyzont 2010–2014). Głównym celem prowadzonych badań było zidentyfikowanie różnic między szacowanymi wartościami nieruchomości oraz cenami osiąganymi w procesie transakcyjnym. W pracy przedstawiono, zarówno w formie analitycznej, jak i graficznej, efekty badań z podaniem możliwych przyczyn występujących rozbieżności.
In both the global and the domestic approach, the real estate market is a multifaceted domain of study, constituting a specific and imperfect system. Researchers have to rely on increasingly advanced analytical tools to capture the structural complexity of real estate markets. Real estate prices are influenced by contradictory behaviors of market participants. This observation prompted the authors to analyze the income and price elasticity of demand for housing by calculating elasticity coefficients in view of changes in housing prices and the Veblen effect. This problem was analyzed based on a review of the literature and the results of an experiment. The results of the current study can be used to confirm the presence of the Veblen effect on the housing market based on the adopted criteria. The coefficients of price and income elasticity of demand for housing were calculated in view of the price dynamics on the real estate market to paint a more complete picture of reality and explain market processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.