The Effectiveness of Real Estate Market Versus Efficiency of Its Participants
Real estate markets (REMs) may be classified as strong-form efficient, semi-strong-form efficient or weak-form efficient. Efficiency measures the level of development or goal attainment in a complex social and economic system, such as the real estate market. The efficiency of the real estate market is the individual participant's ability to achieve the set goals. The number of goals is equivalent to the number of participants. Every market participant has a set of specific efficiency benchmarks which can be identified and described. In line with the theory of rational expectations, every participant should make decisions in a rational manner by relying on all available information to make the optimal forecast. The effectiveness of the real estate market is a function of the efficiency of individual market participants.
This paper attempts to prove the following hypothesis: the effectiveness of a real estate market may be identified by analysing the effectiveness of its participants. The authors also discuss methods based on the rough set theory which can influence the efficiency and efficacy of market participants, and consequently, the effectiveness of the real estate market and its participants.
Recently, it has become popular to streamline the way of managing territorial units by adapting the marketing approach to a territorial dimension. The majority of cities and communes in Poland have realized that, in order to achieve their set goals under conditions of fierce competition for limited resources, it is necessary to introduce territorial marketing as one of the key and significant own tasks to be implemented. The objective of the article is to develop principles of the effective use and management of the area of a commune by carrying out suitable marketing projects, based on an analysis of the social, economic and geopolitical situation of the commune, with particular emphasis placed on location factors.
This study proposes a decision support subsystem in real estate management. Owing to the complex and multi-layered character of the discussed problem, only selected aspects of real estate management are discussed in this paper. The described system will play the role of a relatively simple and effective “assistant” which is expected to maximize the effectiveness of a decision and shorten decision-making time. The author has made an attempt to develop a subsystem as an adviser to subjects operating in the real estate management. This system was developed accounting for and combining the classical economic and real estate market theories with the implementation of non-classical methods in the data mining category in an effort to increase its effectiveness. The rough set theory has been proposed as a tool that supports analytical processes. Fuzzy logic best reproduces expert knowledge, and it is one of the most effective tools for solving “vaguely defined” problems.
The given work is an attempt to prove the hypothesis that: the reduction of uncertainty in the real estate management decision-making process is possible by the development of the advisory system based on the rough set theory. The main aim of this work is to increase the efficiency and efficacy of entities operating in the real estate management, thus influencing the effectivness of the entity and management.
Space as a public good should be used in a way that is consistent with recognized social, cultural, aesthetic, economical and ecological values. The optimization of space is associated with its limitations, thus it should be subjected to rational management. Optimizing the function of city space involves identifying the most mismatched features of the area and a proposal to convert them into functions best suited with respect to the existing natural and anthropogenic, social, economic and ecological conditions. The selection criteria of the optimal use of land will be presented, as well as the parameters characterizing them and the possibility of using chosen multi-criteria methods of analysis. Social, economic and ecological criteria adopted for the analysis are the basis for the sustainable development of an area and coincide with factors which ought to be taken into account during the development of land-planning documents.
Preliminary data analyses in decision-making systems and procedures are very important for numerous reasons, in particular because the accumulation and analysis of large data sets is costly and time-consuming. The effective use of decision support systems, including on the real estate market, requires the elimination of noise. The authors have proposed to eliminate redundant data with the use of the modified method for evaluating the capacity of the data set, which is applied in the process of classifying the condition of real estate markets. The proposed procedure (subsystem) is an attempt to improve the effectiveness of analyses relating to the development of methods for rating real estate markets. The proposed solutions will be simulated on the example of leading real estate markets in Poland and Italy.
Rating systems developed in Poland and other countries are generally used to evaluate the performance of businesses, organizations, institutions and even entire economies. Comprehensive solutions for assessing real estate markets and individual properties have never been proposed (several systems for evaluating mostly commercial real estate have been developed). This deficiency could be attributed to an absence of databases describing the real estate market and market changes as well as a shortage of coherent methods for analyzing real estate markets. In most cases, however, market phenomena may be difficult to classify because they involve behavioral, social and stochastic elements.
This article analyzes the existing systems for rating and ranking markets in different Polish regions and cities. They were compared with information about the classification of real estate markets on the example of selected property markets in Poland. Selected categories were evaluated to determine whether rating methods for real estate markets, including housing markets, should be developed for different Polish cities and regions. The growth potential of local real estate markets was also analyzed.
In the valuation of a property subject to development, the valuer may consider the potential aspect of the value of both land to be improved and a building to be refurbished. These kinds of valuations are complex, especially when a prudent assessment of value is required. In general terms, all properties may have potential development which, in some cases, can be termed “hope”. In particular, uncertainty regarding the change in the legal framework may create expectations as to the uncertain variation of property value in the future. In these cases, it may be necessary to deal with hope value or future value, trying to reach the value of a property subjected to uncertain changes. Hope value is the difference between the existing use value and the price that the market might pay for future transformation. The main aim of the paper is the elaboration of a methodology to determine the hope value. In this work, a real option model for the valuation of hope value in the real estate market will be applied to a small sample of residential properties located in Olsztyn that are subject to possible transformation. The possibility of a transformation may create expectations and may influence the value of the property. Although the applications of these methods to real estate valuation are fairly recent, the International Valuation Standards have included real option theory in the income approach as a valuation method since 2011.
This paper aims to determine the influence of selected variables on residential property price indices for the European countries, with particular attention paid to Italy and Poland, using a rough set theory and an approach that uses a committee of artificial neural networks. Additionally, the overall analysis for each European country is presented.
Quarterly time series data constituted the material for testing and empirical results. The developed models show that the economic and financial situation of European countries affects residential property markets. Residential property markets are connected, despite the fact that they are situated in different parts of Europe.
The economic and financial crisis of countries has variable influence on prices of real estate. The results also suggest that methodology based on the rough set theory and a committee of artificial neural networks has the ability to learn, generalize, and converge the residential property prices index.
This paper presents a streamlined sub-system of decision-making in a real estate market with incomplete data. As we currently observe, various entities collect data and use databases, which entails a problem with their quality and completeness. This results from the specifics of the real estate market, particularly from the nature of the available information, access to it and integral uncertainty.
In the first part of this paper, we will present substantive guidelines for the development of a procedure for supplementing missing information. Afterwards, in order to verify the feasibility and effectiveness of the procedure, an implementation simulation will be conducted on the selected example. We would like to emphasize that all decisions are made under the conditions of an information gap.