Real estate market can be thought of as an open, dynamic system. It means that it is able to exchange stimuli with other open systems, and that its state evolves in a way that might be described mathematically. It turns out that two main processes contribute to the overall evolution of the real estate market: long-term, predictable evolution, interrupted by sharp changes of catastrophic origin. In this picture, national housing funds play an important role in supporting the housing finance: on one hand they could either stimulate or suppress the real estate market influencing the availability of the mortgage credit, but on the other hand, they could also help to stabilize prices. In this study, an attempt was made to determine the degree of relationship between the volume of mortgage financing from national housing funds and the dynamics of real estate prices.
The real estate market, as an open, complex and dynamic system, responds to changes in the environment of economic, legal or social conditions, although the pace and direction of these changes depends on the level of inertia of this system. At the same time, this market stimulates the market environment through prices. This study attempts to identify cause-and-effect relationships in the scope of the impact of selected economic and social indicators on prices of residential premises, as well as to identify the effects of price changes on these indicators. The time horizon of the study covered the years from 2008 to 2018. In the studies, to assess the stationarity of time series, an extended Dickey-Fuller test was used for the model with a free expression and linear trend, a vector autoregression model (VAR) was then constructed and Granger tests and impulse response analysis were performed using the Impulse Response Function (IRF). As a result, it was demonstrated that the response of real estate prices to the impulse from explanatory variables appears between the first and the fourth quarters, and expires after about three years.
This paper deals with selected theoretical issues pertaining to the setting of asking prices by housing developers. Determinants of the buyer’s and seller’s reservation prices have been identified. The advantages and disadvantages, in terms of behavioral economics, of the pricing strategies practiced by housing developers have been indicated. The strategy based on fixing an asking price roughly equal to the estimated market value of the property was compared with the strategy based on offering an inflated asking price (with the assumption of price negotiations). A second comparison concerned the strategy of price disclosure compared with the strategy of price non-disclosure.
The reflections contained within the article were based on behavioral economics and marketing theory. The discussion was based largely on foreign articles, observed examples of pricing policy carried out by housing developers in Poland, and information obtained from housing developers and real estate brokers who are active on the primary market.
The real estate market is regarded as a part of the capital market. Just as they invest in securities, investors allocate their funds in real estate, hoping to make a sound profit. There are many tools that support the process of investing on the stock exchange, such as a technical analysis. There are also proven methods that help predict future prices of assets on the basis of their historic quotations.
The article is an attempt to transfer the Japanese method of candlestick charting used in the technical analysis of securities onto the real estate market. The method has been implemented on the residential real estate market due to the relatively large number of transactions being concluded there.
The presented research aims to contribute to the concerns regarding the evolutionary dynamics of the real estate market, seen as an open system flowing from one equilibrium state into another. In such quasi-stable states, real estate markets are thought to change only slightly with elapsed time, but occasionally, a sudden jump, during which the markets undergo changes of structural origin, might occur as well. Hence, the paper contains an analysis of the dynamics of time series of housing prices in order to distinguish between processes of different time scales. Research was performed assuming a priori distributions of the variables, and using the autocorrelation function together with the partial autocorrelation function for detailed data analysis.
This article aims to present the diversity in the rates of fees for advertisements on selected bank buildings located in the right-of-way, as well as the manner of calculating them. The research covered fees, incurred by a selected bank brand, for advertisements placed on all outlets of the bank in the area of the entire country in terms of the location of the buildings, means of establishing fees, rate of the basic fee, as well as costs incurred by the bank for placing advertisements in the right-of-way according to the state at the end of 2014.
Currently, one can distinguish a few means of right-of-way occupation for which fees are collected, including: for the purpose of carrying out construction works, locating technical infrastructure equipment or buildings unrelated to the needs of road or traffic management, and for placing advertisements (outdoor advertising). The basic rate of payments for occupying the right-of-way by advertising results directly from the ordinances regarding roads passed by local government units or thematic regulation pertaining to national roads. The maximum basic rate is specified by law.
This paper makes it possible to determine the scale of financial burdens incurred by a bank resulting from marking the outlets, as well as the differences in determining and calculating fees for signboards occupying the areas of right-of-ways among the many local road authorities.
The patterns and relations between real estate prices and the factors which shape them can be presented, among others, in the form of traditional statistical models, as well as by means of geostatistical methods. In the case of research involving the diagnosis and prediction of transaction prices, the key role is played by the spatial aspect, hence the particular significance of geostatistical methods using spatial information.
The main goal of the conducted research is to determine the probability of the occurrence of a price in a given location in space by means of geostatistical simulation - indicator kriging. Indicator kriging does not use the entirety of information included in a dataset, and can, therefore, be useful in situations when the assumptions involving the spatial stationarity of the examined phenomenon are not fulfilled by an entire dataset, but are fulfilled by a certain part of the set. The maps of the probability with which a regionalized variable (price) takes on particular values, limited by arbitrarily selected cutoff values, were prepared by means of indicator kriging. An alternative approach to the preparation of price probability maps is the determination of the spatial distribution of areas in which, with the assumed probability, the value of the price falls within the predetermined ranges.
The paper presents both the essence as well as a theoretical description of the geostatistical simulation of a transaction on the real estate market, as well as the results of an experiment involving the transaction prices of real properties located in the north-western part of the city of Olsztyn.
The result of the research is a set of virtual information about the places in which the transactions have occurred and about the prices of real estate, constituting a reflection of the market processes which may take place in the near future.
Due to the specific characteristics of cities, such as the intensity of the use of space, the supply of green areas is limited. These areas are subjected to constant pressure (demand for new transportation or construction functions), which usually results in their reduction. As a consequence of this, green areas have become a limited asset in urban environments. City dwellers, seeking a high quality of life in the urban environment, pay attention to the proximity of greenery to their place of residence. This is, therefore, a factor that greatly influences market prices of residential real estate. In the study, the dependency of transaction prices of residential premises on the vicinity of urban greenery was subjected to analysis. Additionally, questionnaire studies were conducted with respect to the evaluation of the significance of environmental factors when choosing a place to live, which translates over to the decisions made by consumers on the real estate market.
The aim of the article was to assess the similarities of average price changes in the residential market in 34 European countries in 2010-2016. The first part of the study concerned tendencies of changes in average prices in residential markets in the studied countries, while the second part analyzed co-occurrence of changes in these countries in time. The study covered the period after the first wave of the financial crisis in Europe and took into account the second wave of crisis in several euro area countries. Price indices, trend functions, price ranges, linear correlation coefficients and shape similarity measure were utilized for conducting this study.
European countries, in general, differed with respect to changes in prices in the residential market. 12 countries were characterized by a trend of increasing price indices. 18 countries were classified as correcting countries, as during the studied period they were distinguished by a clear change in trend. Four countries with a downward trend during the study period were also identified. Furthermore, a differentiation between the countries was found due to the values of price ranges during the studied period. Studies of co-occurrence in time were conducted with the use of linear correlation coefficients mainly for groups of rising countries and falling countries. The study was conducted using measures of shape similarity, which allowed for an identification of converging, leading and following markets for some countries.
Real estate valuation uses 3 main approaches: income, cost and comparative. When applying the comparative method, correction coefficients based on similar real estate transactions are determined. In practice, the coefficients and similar real estate objects are usually determined by using qualitative approach based on the valuators’ experience. The paper provides an analytical method for the determination of correction coefficient, which limits subjectivity when using the comparative method for valuation. The provided analytical approach also integrates macroeconomic indicators in the calculation process. It also addresses issues when available historical real estate transaction data is limited. A machine learning approach was applied to determine the average price of real estate in the region, with the possibility of using this information to obtain correction coefficients where historical data was unavailable. Alternative research usually focuses on final price estimation of the selected real estate object; however, the valuation standard of Tegova released in 2018 does not allow for applying analytically based approaches for individual real estate object evaluation; these approaches can be used only as a supportive tool for valuators.