Radosław Cellmer

The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses

Abstract In the traditional approach, geostatistical modeling involves analyses of the spatial structure of regionalized data, as well as estimations and simulations that rely on kriging methods. Geostatistical methods can complement traditional statistical models of property transaction prices, and when combined with those models, they offer a comprehensive tool for spatial analysis that is used in the process of developing land value maps. Transaction prices are characterized by mutual spatial correlations and can be considered as regionalized variables. They can also be regarded as random variables that have a local character and a specific probability distribution. This study explores the possibilities of applying geostatistical methods in spatial modeling of the prices of undeveloped land, as well as the limitations associated with those methods and the imperfect nature of the real estate market. The results are discussed based on examples, and they cover both the modeling process and the generated land value maps.

The Use of the Geographically Weighted Regression for the Real Estate Market Analysis

Abstract The article presents a method for developing geographically weighted regression models for analyzing real estate market transaction prices and evaluating the effect of selected property attributes on the prices and value of real estate. The property attributes were evaluated on a grading scale to determine the relative (percentage) indicators characterizing the relationships on the real estate market. The market data were analyzed to evaluate the influence of infrastructure availability on the prices of land in Olsztyn. The results were used to assess the effect of every utility service on the property transaction prices.

USE OF SPATIAL AUTOCORRELATION TO BUILD REGRESSION MODELS OF TRANSACTION PRICES

Abstract This paper presents the principles of studying global spatial autocorrelation in the land property market, as well as the possibilities of using these regularities for the construction of spatial regression models. Research work consisted primarily of testing the structure of the spatial weights matrix using different criteria and conducting diagnostic tests of two types of models: the spatial error model and the spatial lag model. The paper formulates the hypothesis that the application of spatial regression models greatly increases the accuracy of transaction price prediction while forming the basis for the creation of cartographic documents including, among others, maps of land value.

Use of Statistical Models for Simulating Transactions on the Real Estate Market

Abstract The regularities and relations between real estate prices and the factors that shape them may be presented in the form of statistical models, thanks to which the diagnosis and prediction of prices is possible. A formal description of empirical observation presented in the form of regressive models also offers a possibility for creating certain phenomena in a virtual dimension. Market phenomena cannot be fully described with the use of determinist models, which clarify only a part of price variation. The predicted price is, in this situation, a special case of implementing a random function. Assuming that other implementations are also possible, regressive models may constitute a basis for simulation, which results in the procurement of a future image of the market. Simulation may refer both to real estate prices and transaction prices. The basis for price simulation may be familiarity with the structure of the analyzed market data. Assuming that this structure has a static character, simulation of real estate prices is performed on the basis of familiarity with the probability distribution and a generator of random numbers. The basis for price simulation is familiarity with model parameters and probability distribution of the random factor. The study presents the core and theoretical description of a transaction simulation on the real estate market, as well as the results of an experiment regarding transaction prices of office real estate located within the area of the city of Olsztyn. The result of the study is a collection of virtual real properties with known features and simulated prices, constituting a reflection of market processes which may take place in the near future. Comparison between the simulated characteristic and actual transactions in turn allows the correctness of the description of reality by the model to be verified.

Forms and Effectiveness of the Client's Influence on the Market Value of Property - Case Study

Abstract Although real estate valuation is supposed to make the market transparent, it has been noted to be partial in many countries. Analysis of literature and results of the statistical analysis of survey responses of Polish property appraisers indicated that: Property valuers operate in an environment which exerts influence on the final result of valuation. The attempts of the client to influence the valuation process and their effectiveness do not depend on the gender and age of the property appraiser. The problem of valuation bias should be seen in the weakness of the system enforcing compliance with ethical standards, with this being an area which requires reinforcement in many countries.

Using Kernel density estimation for modelling and simulating transaction location

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.

Analysis of Housing Development Activity in Poland from 2005-2014

Abstract The growth of both the construction market and the property market depends on various macroeconomic and legal factors, as well as on demographic, institutional, stock and local conditions. The aim of this research was to determine the spatial differentiation, dynamics and determinants of housing development activity in Poland in the context of historical and current legal conditions. This activity was measured, first of all, by the number of construction contracts and the number of completed buildings and dwelling units. During the research, an attempt was made to establish determinants of construction activity, by analyzing social, demographic and economic factors concerning individual districts. With this aim in view, the study used statistical panel data models, constructed on the basis of data created as a result of combining time series of observations for cross-sectional units. The results of the research are presented not only in the form of statistical models, but also as a series of cartographic studies, prepared with the application of GIS tools, presenting the current status of housing development activity in Poland.

Simulation Modeling Concept Of Property Market Transactions

Abstract The real estate market is a specific and imperfect field of research, and its complex structure and the presence of information gaps necessitate the use of advanced analytical tools. One such tool is simulation modeling, which has a variety of practical applications and can be used to model real-life systems characterized by a high degree of complexity and a high share of random components. In this paper, virtual data was used to simulate transactions on the local real estate market. Simulation tools were applied to generate additional information about transactions, their spatial distribution and transaction prices. The applicability of the iterative Monte Carlo approach with a standard regression model and a spatial regression model was evaluated.