scholarly journals Information Capacity Database in the Rating Model on the Basis of Polish and Italian Real Estate Markets

2016 ◽  
Vol 24 (3) ◽  
pp. 40-51
Author(s):  
Małgorzata Renigier-Biłozor ◽  
Andrzej Biłozor
Keyword(s):  
Real Estate ◽  
Large Data ◽  
Real Estate Market ◽  
Data Sets ◽  
Data Set ◽  
Real Estate Markets ◽  
The Real Estate ◽  
Redundant Data ◽  
Modified Method ◽  
Effective Use

AbstractPreliminary 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.

scholarly journals Predicting real estate market trends and value using pre-processing and sentiment text mining analysis

2021 ◽  
pp. 35-43
Author(s):  
Nikolay Sinyak ◽  
Singh Tajinder ◽  
Jaglan Madhu Kumari ◽  
Vitaliy Kozlovskiy
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Real Estate ◽  
Real Estate Market ◽  
Media Analysis ◽  
Market Information ◽  
Real Estate Markets ◽  
The Real Estate ◽  
Crowd Wisdom ◽  
Market Trends

Ubiquitous growth in the text mining field is unprecedented, where social media mining is playing a significant role. Gigantic growth of text mining is becoming a potential source of crowd wisdom extraction and analysis especially in terms of text pre-processing and sentiment analysis. The analysis of a potential influence of sentiment on real estate markets controversially discussed by scholars of finance, valuation and market efficiency supporters. Therefore, it’s a significant task of current research purview which not only provide an appropriate platform for the contributors but also for active real estate market information seekers. Text mining has gained the widespread attention of real estate market information users which is almost on explosion level. Accessibility of data on such behemoth scale mandates regular and critical analysis of this information for various perspectives’ plausibility. Rich patterns of online social text can be exploited to extract the relevant real estate information effectively. As text mining plays a significant and crucial role in discovery of these insights therefore its challenges and contribution in social media analysis must be explored extensively. In this paper, we provide a brief about the current summary of the modern state of text mining in pre-processing and sentiment for the real estate market analysis. Empha-sis is placed on the resources and learning mechanism available to real estate researchers and practitioners, as well as the major text mining tasks of interest to the community. Thus, the main aim of this chapter is to expound and intellectualize the domains of social media which are accessible on an extraordinary range in the field of text mining real estate for predicting real estate market trends and value.

Data Analysis on the Influencing Factors of the Real Estate Price

2021 ◽  
pp. 52-66
Author(s):  
Huang-Mei He ◽  
Yi Chen ◽  
Jia-Ying Xiao ◽  
Xue-Qing Chen ◽  
Zne-Jung Lee
Keyword(s):  
Data Analysis ◽  
Real Estate ◽  
Influencing Factors ◽  
Real Estate Market ◽  
Training Data ◽  
Market Demand ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

scholarly journals COVID-19 Infects Real Estate Markets: Short and Mid-Run Effects on Housing Prices in Campania Region (Italy)

2020 ◽  
Vol 9 (7) ◽  
pp. 114 ◽  
Author(s):  
Vincenzo Del Giudice ◽  
Pierfrancesco De Paola ◽  
Francesco Paolo Del Giudice
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Estate Market ◽  
Campania Region ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Short Run ◽  
Predator Model ◽  
Real Estate Prices

The COVID-19 (also called “SARS-CoV-2”) pandemic is causing a dramatic reduction in consumption, with a further drop in prices and a decrease in workers’ per capita income. To this will be added an increase in unemployment, which will further depress consumption. The real estate market, as for other productive and commercial sectors, in the short and mid-run, will not tend to move independently from the context of the aforementioned economic variables. The effect of pandemics or health emergencies on housing markets is an unexplored topic in international literature. For this reason, firstly, the few specific studies found are reported and, by analogy, studies on the effects of terrorism attacks and natural disasters on real estate prices are examined too. Subsequently, beginning from the real estate dynamics and economic indicators of the Campania region before the COVID-19 emergency, the current COVID-19 scenario is defined (focusing on unemployment, personal and household income, real estate judicial execution, real estate dynamics). Finally, a real estate pricing model is developed, evaluating the short and mid-run COVID-19 effects on housing prices. To predict possible changes in the mid-run of real estate judicial execution and real estate dynamics, the economic model of Lotka–Volterra (also known as the “prey–predator” model) was applied. Results of the model indicate a housing prices drop of 4.16% in the short-run and 6.49% in the mid-run (late 2020–early 2021).

scholarly journals Assessment of Predictor Importance with the Example of the Real Estate Market

2016 ◽  
Vol 16 (2) ◽  
pp. 29-39
Author(s):  
Mariusz Kubus
Keyword(s):  
Feature Selection ◽  
Real Estate ◽  
Linear Models ◽  
Predictive Modelling ◽  
Real Estate Market ◽  
Comparative Approach ◽  
Single Family ◽  
Data Set ◽  
The Real Estate ◽  
Leave One Out

Abstract Regression methods can be used for the valuation of real estate in the comparative approach. However, one of the problems of predictive modelling is the presence of redundant or irrelevant variables in data. Such variables can decrease the stability of models, and they can even reduce prediction accuracy. The choice of real estate’s features is largely determined by an appraiser, who is guided by his/her experience. Still, the use of statistical methods of a feature selection can lead to a more accurate valuation model. In the paper we apply regularized linear regression which belongs to embedded methods of a feature selection. For the considered data set of real estate land designated for single-family housing we obtained a model, which led to a more accurate valuation than some other popular linear models applied with or without a feature selection. To assess the model’s quality we used the leave-one-out cross-validation.

scholarly journals Optimization of the variables selection in the process of real estate markets rating

2015 ◽  
Vol 6 (4) ◽  
pp. 139 ◽  
Author(s):  
Małgorzata Renigier-Biłozor ◽  
Andrzej Biłozor
Keyword(s):  
Decision Making ◽  
Real Estate ◽  
Real Estate Market ◽  
Market Potential ◽  
Coherent System ◽  
Essential Information ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

The growing significance of the real estate market prompts investors to search for factors and variables which support cohesive analyses of real estate markets, market comparisons based on diverse criteria and determination of market potential. The specificity of the real estate market is determined by the unique attributes of property. The Authors assume that developing real estate market ratings identifies the types of information and factors which affect decision-making on real estate markets. The main objective of  real estate market ratings is to create a universal and standardized classification system for evaluating the real estate market. One of the most important problems in this area is collecting appropriate features of real estate market and development dataset. The main problem involves the selection and application of appropriate features, which would be relevant to the specificity of information related to the real estate market and create a kind of coherent system aiding the decision-making process. The main aim of this study is the optimization of  set of variables that were used to develop the real estate market ratings.  For this purpose, Hellwig’s method of integral capacity of information was applied. In this particular case, the method shows what set of variables provides information most sufficiently. The results lead to obtaining the necessary set of features that constitute essential information which describes the situation on the local real estate market.

scholarly journals The Role of Macroprudential Policy in Bank Housing Loans Portfolio Quality Assurance

2021 ◽  
Vol 59 (2) ◽  
pp. 281-296
Author(s):  
Mirela Mitrašević
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Residential Real Estate ◽  
Macroprudential Policy ◽  
Mortgage Loans ◽  
Real Estate Markets ◽  
The Real Estate ◽  
Business Conditions ◽  
Policy Measures ◽  
The Impact

Abstract The subject of this paper is the contemporary trend in residential real estate markets in European countries and their impact on the quality of banks’ housing loan portfolios. Due to the fact that these are the markets that still have not fully recovered from the previous financial crisis, and at the time of writing were exposed to significant uncertainty related to the effects of specific business conditions caused by COVID-19, the research on the risks related to these markets and tools which can mitigate their consequences are of paramount importance. Given the fact that the importance of monitoring the emergence of systemic risks in the financial system and the design of macroprudential tools for Bosnia and Herzegovina is yet to come, one of the aims of the paper is to present the results of the research on the effectiveness of certain macroprudential policy measures for mitigating the impact of price fluctuations in residential real estate markets. A special attention is paid to the challenges that the real estate market and mortgage loans have been facing during the crisis caused by the COVID-19 pandemic. The paper provides a basis for future researches examining to which extent the applied macroprudential policy measures in some countries have been effective in hitherto unprecedented business conditions

Analysis of Land Markets Intended for Single-Family Housing for Different Suburban Areas

2017 ◽  
Author(s):  
Anna Przewiezlikowska
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Single Family ◽  
Real Property ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Suburban Areas ◽  
Family Housing ◽  
Comparative Description

The aim of this article is the comparative description of two real estate markets based on the procedures for real property valuation. The study concerned only the land, which was undeveloped, intended for single-family housing in two communes located in the district of Krakow and three communes from the district of Kielce. The analyses were performed at four-year intervals and the comparison of the real estate markets was conducted. The first part contains the description of the areas covered by the research studies and the analyses of the real estate market and market trends. The next stage includes the descriptions of the two test real properties which are the subject of valuation and the fundamental comparative criterion. Then, the algorithms and methods of the calculations are presented. The practical part contains the description of individual markets, the implementation of the analyses and calculations, the comparison of the study areas and conclusions. The comparative analysis of the performed simulations of valuations was carried out first and then followed by a collective summary of descriptive statistics of all the real estate bases and the comparative description of the structures of the databases showing meaningful differences between Krakow and Kielce region.

Normalisation of the Speculative Frame Method and Its Application to the Housing Market in Poland

2021 ◽  
Vol 8 (1) ◽  
pp. 36-46
Author(s):  
Justyna Brzezicka ◽  
◽  
Radosław Wisniewski ◽  
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Time Series Data ◽  
Real Estate Market ◽  
Series Data ◽  
Data Sets ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Frame Method ◽  
Real Estate Prices

This article proposes the normalisation of the speculative frame method for identifying real estate bubbles, price shocks, and other disturbances in the real estate market. This index-based method relies on time series data and real estate prices. In this article, the speculative frame method was elaborated and normalised with the use of equations for normalising data sets and research methodologies. The method is discussed on the example of the Polish housing market.

scholarly journals RATING METHODOLOGY FOR REAL ESTATE MARKETS – POLAND CASE STUDY

2014 ◽  
Vol 18 (2) ◽  
pp. 198-212 ◽  
Author(s):  
Malgorzata Renigier-Biłozor ◽  
Radoslaw Wisniewski ◽  
Arturas Kaklauskas ◽  
Andrzej Biłozor
Keyword(s):  
Decision Making ◽  
Real Estate ◽  
National Economy ◽  
Real Estate Market ◽  
Market Potential ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Local Character ◽  
The Real Estate Market

The development of the real estate market is conditioned by a variety of endogenous and exogenous factors. Selected factors determine the local character of the real estate market, whereas others contribute to its classification as one of the main branches of the national economy. Rapid economic growth and the search for new investment opportunities have turned the real estate market into a highly competitive arena where various players carry out diverse investment strategies. Investors search for similarities that would enable them to develop risk minimizing strategies. Ratings are a modern tool that can be deployed in analyses and predictions of real estate market potential. This paper proposes a methodology for developing real estate market ratings, and it identifies the types of information and factors which affect decision-making on real estate markets. The following research hypotheses are formulated and tested in the article: 1) a real estate market can be rated in view of its significance for the local and national economy, 2) real estate market ratings support market participants in the decision-making process.

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