scholarly journals Modeling the Real Estate Prices in Olsztyn under Instability Conditions

2012 ◽  
Vol 11 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Mirosław Bełej ◽  
Sławomir Kulesza

Abstract The paper deals with the description of the issues related to the dynamics of the real estate market in terms of sharp, unexpected changes in the housing prices which have been observed in the last decade in many European countries due to some macroeconomic circumstances. When such perturbations appear, the real estate market is said to be structurally unstable, since even a small variation in the control parameters might result in a large, structural change in the state of the whole system. The essential problem addressed in the paper is the need to define and discriminate between the intervals of stable and unstable real estate market development with special attention paid to the latter. The research aims at modeling hardly explored field of discontinuous changes in the real estate market in order to reveal the bifurcation edge. Assuming that the periods of sudden price changes reflect an intrinsic property of the real estate market, it is shown that the evolution path draws for most of the time a smooth curve onto the stability area of the equilibrium surface, and only briefly penetrates into the instability area to hop to another equilibrium state.

2020 ◽  
Vol 12 (1) ◽  
pp. 346 ◽  
Author(s):  
Alice Barreca ◽  
Rocco Curto ◽  
Diana Rolando

Urban vibrancy is defined and measured differently in the literature. Originally, it was described as the number of people in and around streets or neighborhoods. Now, it is commonly associated with activity intensity, the diversity of land-use configurations, and the accessibility of a place. The aim of this paper is to study urban vibrancy, its relationship with neighborhood services, and the real estate market. Firstly, it is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city. Secondly, the influence of urban vibrancy on the listing prices of existing housing is analyzed by performing spatial analyses. To achieve this, the presence of spatial autocorrelation is investigated and spatial clusters are identified. Therefore, spatial autoregressive models are applied to manage spatial effects and to identify the variables that significantly influence the process of housing price determination. The results confirm that housing prices are spatially autocorrelated and highlight that housing prices and NeSI are statistically associated with each other. The identification of the urban areas characterized by different levels of vibrancy and housing prices can effectively support the revision of the urban development plan and its regulatory act, as well as strategic urban policies and actions. Such data analyses support a deep knowledge of the current status quo, which is necessary to drive important changes to develop more efficient, sustainable, and competitive cities.


2018 ◽  
Vol 8 (11) ◽  
pp. 2321 ◽  
Author(s):  
Alejandro Baldominos ◽  
Iván Blanco ◽  
Antonio Moreno ◽  
Rubén Iturrarte ◽  
Óscar Bernárdez ◽  
...  

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.


2020 ◽  
Vol 6 (1) ◽  
pp. 1-26
Author(s):  
C. Aguilera Alvial

This article studies the fundamentals of housing prices based on the Real Index of Housing Prices (IRPV), given that in recent times in Chile there has been a sustained increase in price levels and seeks to find evidence on the existence of a possible speculative bubble in the real estate market. Following the methodology of various Chilean and international authors, the Engle & Granger Co-integration methodology was applied. Furthermore, the results of the previous methodology were compared using the Johansen Co-integration test. Then a method to find structural breaks is applied. As a result, evidence is found to not reject the existence of a bubble in the real estate market. It is found that only interest rates co-integrate in the long term with the evolution of house prices, while the other fundamentals present a spurious relationship.


2021 ◽  
pp. 1-4
Author(s):  
Diederik Boertien ◽  
Antonio López-Gay

Real estate has traditionally been an important economic resource for Spanish households. The development of the real estate market in Spain during the 21st century brings forth two very different stories. The first story is one of obstacles to access housing. It has become increasingly hard to buy or rent a home. Housing prices have risen considerably in urban areas while people’s income changed very little. The second story is one of accumulation of properties. Housing has been, and continues to be, a form of saving, investment and speculation for small and large property-owners. Falling housing prices permitted resourceful households to accumulate more properties during the financial crisis. These two stories lead to the following question: How did changes in the ownership of properties impact inequality in Spain? In this Perspectives Demogràfiques, we analyse how developments in the real estate market are connected to wealth inequality in Spain. The results point at a polarization of access to property; both the number of households without property and the number of households with multiple properties increased over time. Because real estate is the most important form of household’s wealth, the accumulation of properties has become a non-negligible part of wealth inequality between households in Spain.


Author(s):  
V. Zapototska ◽  
O. Levytska ◽  
I. Horyn

In this article we consider the theoretical and applied principles of formation of the cost of residential areas of Lviv. Some factors of supply were evaluated such as: availability of housing, the exploitaition of housing, foreign direct investments, the amount of construction works. The assessment of activity indicators of the real estate market in the regions was done. Maximum of residential real estate of the secondary market of Lviv, which were on sale in 2015, was observed in FrankIvskiy region (20.0% of all objects), because it has a high degree of intensity of functioning of the real estate market in this segment. However, in Sykhivskiy region the development of secondary real estate is retarded, despite of the significant amounts of housing. An analysis of the price indices of housing in the city allowed to the authors to identify five areas of pricing, to analyze property values of the areas of the city and to outline the reasons of differentiation. The first – Central area – includes Galitskiy array. The second – middle zone – consists of Zaliznichniy, Frankivskiy, Shevchenkivskiy and Lychakivskyi arrays and Lychakiv, Pogulyanka and the New Lviv. The third – peripheral urban area – covers Levandivka, Sriblyastiy, Veliki Kravchitsi, Znesinnia, Mayorivka, Kozelnyky, Sihiv and Sykhivskiy array Bodnarivka, Kulparkiv, Zamarstyniv and Zboyischa. The fourth – peripheral area – includes Syhnivka and Ryasne. The fifth – neighborhood peripheral zone– applies to the Lysynachi and Ryasne-2. The authors managed to create a map of the potential fields in a cost of residential development in the city. The amount of new buildings in the city’s area also was analyzed in the work. According to the forecast which was made by using analytical methods of smoothing and leveling till May 2017, prices in secondary market of all areas of Lviv will gradually decrease in average house. Naturally, the highest values in prices will occur in the central and middle areas. The reason is that the investigated territory is the historical center of the city, which has a high level of service industry. This part of city has the highest level of industrial production and sales of industrial products. It also constantly focuses on development of trade and providing the local population with qualitative goods and services. Housing prices will be the lowest in peripheral approximate, peripheral and remote peripheral areas of Lviv, which are the youngest and the most isolated among other areas of the city. There are also green areas in Lviv which are characterized by insufficient availability of social facilities. The main problem here is transport infrastructure. It needs development and improvement because the locals daily faced with serious problems both during arrival at work or school and when they return home. In the work also were conducted the calculations of tightness connection (correlation) parameters of the commissioning of housing, retail turnover volume of enterprises of direct investment, the quantity of people and the average nominal wage by an average of one full-time employee. The result of the reseach is a tight connection between the commissioning of housing and retail turnover of enterprises and average nominal wages on average one staff member. It has a linear character and it’s positive. However, direct investments and quantity of population in general are not related to the investigation process.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alina Stundziene ◽  
Vaida Pilinkienė ◽  
Andrius Grybauskas

Purpose This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania. Design/methodology/approach The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations. Findings The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle. Practical implications Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary. Originality/value This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.


Author(s):  
Alejandro Baldominos ◽  
Antonio José Moreno ◽  
Rubén Iturrarte ◽  
Óscar Bernárdez ◽  
Carlos Afonso

The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, $k$-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.


2021 ◽  
Vol 32 (5) ◽  
pp. 459-468
Author(s):  
Vaida Pilinkienė ◽  
Alina Stundziene ◽  
Evaldas Stankevičius ◽  
Andrius Grybauskas

The COVID-19 pandemic caused a number of challenges worldwide regarding not only the human health perspective, but also the economic situation. Quarantine, imposed in many countries, forced a substantial part of businesses to close or narrow down their activities, thus leaving corporations and employees without any or with lower income. If national governments had not undertaken any actions to save national economies, the consequences could have been even more devastating. The real estate market is an important part of economy. Instability in the real estate market can cause financial problems, vulnerability of population’s welfare and other negative effects. This research aims to assess the impact of the economic stimulus measures on the real estate market under the conditions of the COVID-19 pandemic in Lithuania. The research methods include comparative analysis, correlation analysis, stationarity test, regression analysis and the ARDL models. The results indicate that the economic stimulus measures only partially contribute to stabilization of the real estate market in Lithuania. The drop in housing prices was 2.9 percent lower because of the economic stimulus in the second quarter of 2020. Maintenance of household cash and deposits as well as lending to business enterprises are the measures that allow to stabilize the real estate market in the shortest time under the conditions of the economic shock. The other governmental support measures are also important, especially if they are aimed at preserving jobs.


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