scholarly journals Correlates of Representation Errors in Internet Data Sources for Real Estate Market

2019 ◽  
Vol 35 (3) ◽  
pp. 509-529
Author(s):  
Maciej Beręsewicz
Keyword(s):  
Real Estate ◽  
Mixed Model ◽  
Real Estate Market ◽  
Data Sources ◽  
The Internet ◽  
Representation Error ◽  
Urban Rural ◽  
Administrative Units ◽  
Real Estate Prices ◽  
Made In

Abstract New data sources, namely big data and the Internet, have become an important issue in statistics and for official statistics in particular. However, before these sources can be used for statistics, it is necessary to conduct a thorough analysis of sources of nonrepresentativeness. In the article, we focus on detecting correlates of the selection mechanism that underlies Internet data sources for the secondary real estate market in Poland and results in representation errors (frame and selection errors). In order to identify characteristics of properties offered online we link data collected from the two largest advertisements services in Poland and the Register of Real Estate Prices and Values, which covers all transactions made in Poland. Quarterly data for 2016 were linked at a domain level defined by local administrative units (LAU1), the urban/rural distinction and usable floor area (UFA), categorized into four groups. To identify correlates of representation error we used a generalized additive mixed model based on almost 5,500 domains including quarters. Results indicate that properties not advertised online differ significantly from those shown in the Internet in terms of UFA and location. A non-linear relationship with the average price per m2 can be observed, which diminishes after accounting for LAU1 units.

scholarly journals Evaluation of the Applicability of Statistical Methods in Studies on Price Dynamics on the Real Estate Market

2013 ◽  
Vol 21 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Sebastian Kokot ◽  
Marcin Bas
Keyword(s):  
Real Estate ◽  
Traditional Approach ◽  
Real Estate Market ◽  
Price Dynamics ◽  
The Real ◽  
The Real Estate ◽  
Transaction Prices ◽  
Time Period ◽  
The Real Estate Market ◽  
Real Estate Prices

Abstract The specific character of the real estate market is the reason why observations of transaction prices seen as statistical variables are taken in a non-standard way. In the traditional approach each time period or specific moments of time are attributed with one observation of a studied variable per one object. In the case of the real estate market, this is not possible since transactions relate to different objects, i.e., properties, and occur at irregular, or even random, moments. This is why traditional methods used to examine the dynamics of economic phenomena must be adapted to specific conditions on the real estate market. Keeping that in mind, the aim of this paper is to adapt classical statistical examination methods of dynamics to specific conditions of the real estate market followed by the actual examination of the dynamics of real estate prices in three sub-segments of the housing market in Szczecin. On its basis, the authors evaluate various methods of examining real estate price dynamics in terms of their applicability in real estate appraisal procedures and, in a broader perspective, present characteristic phenomena that can be observed on the real estate market.

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

2015 ◽  
Vol 23 (2) ◽  
pp. 102-111 ◽  
Author(s):  
Radosław Cellmer ◽  
Katarzyna Szczepankowska
Keyword(s):  
Probability Distribution ◽  
Real Estate ◽  
Statistical Models ◽  
Real Estate Market ◽  
Model Parameters ◽  
The Real ◽  
The Real Estate ◽  
Transaction Prices ◽  
The Real Estate Market ◽  
Real Estate Prices

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.

scholarly journals The use of mean values for reporting real estate prices

2021 ◽  
Vol 64 (04) ◽  
pp. 513-532
Author(s):  
Melita Ulbl ◽  
Andraž Muhič
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Mean Values ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Special Challenge ◽  
The Republic ◽  
Real Estate Prices ◽  
The One

The proper and unambiguous reporting of the real estate market is one of the main requirements for ensuring its transparency. Reporting on the prices of real estate realised on the market is a special challenge here. For this purpose, averages are generally used, requiring both the reporter and the reader to be well acquainted with the rules of individual types of averages on the one hand and the specificities and heterogeneity of the real estate market on the other. In this paper, we present the specifics of individual mean values that can be used for this purpose. These characteristics are analysed in more detail and presented in the case of the Slovenian housing market. The purpose of this paper is to present the dilemmas faced in Slovenia when reporting on real estate prices on the market and present the solutions that the Surveying and Mapping Authority of the Republic of Slovenia will begin to introduce in its reports on the real estate market.

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 Evaluation of Residential Housing Prices on the Internet: Data Pitfalls

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Ming Li ◽  
Guojun Zhang ◽  
Yunliang Chen ◽  
Chunshan Zhou
Keyword(s):  
Real Estate ◽  
Central City ◽  
Housing Prices ◽  
Housing Price ◽  
Data Sources ◽  
City Centre ◽  
The Internet ◽  
Residential Housing ◽  
For Profit ◽  
Comparison Results

Many studies have used housing prices on the Internet real estate information platforms as data sources, but platforms differ in the nature and quality of the data they release. However, few studies have analysed these differences or their effect on research. In this study, second-hand neighbourhood housing prices and information on five online real estate information platforms in Guangzhou, China, were comparatively analysed and the performance of neighbourhoods’ raw information from four for-profit online real estate information platforms was evaluated by applying the same housing price model. The comparison results show that the official second-hand residential housing prices at city and district level are generally lower than those issued on four for-profit real estate websites. The same second-hand neighbourhood housing prices are similar across each of the four for-profit real estate websites due to cross-referencing among real estate websites. The differences of housing prices in the central city area are significantly fewer than those in the periphery. The variation of each neighbourhood’s housing prices on each website decreases gradually from the city centre to the periphery, but the relative variation stays stable. The results of the four hedonic models have some inconsistencies with other studies’ findings, demonstrating that errors exist in raw information on neighbourhoods taken from Internet platforms. These results remind researchers to choose housing price data sources cautiously and that raw information on neighbourhoods from Internet platforms should be appropriately cleaned.

scholarly journals Research and Analysis of a Real Estate Virtual E-Commerce Model Based on Big Data Under the Background of COVID-19

2021 ◽  
Vol 33 (6) ◽  
pp. 1-16
Author(s):  
Delong Zhu
Keyword(s):  
Big Data ◽  
Real Estate ◽  
Virtual Communities ◽  
Rapid Development ◽  
Real Estate Market ◽  
The Internet ◽  
Real Estate Industry ◽  
The Real ◽  
The Real Estate ◽  
Model Based

The sudden attack of the new crown virus in 2020 has brought an unprecedented impact on the real estate market economy and has completely disrupted people's work and life rhythm! With the rapid development of the Internet, the Internet has penetrated into all aspects of people's lives. As soon as e-commerce was introduced, it was loved by the majority of young people and brought tremendous changes to people's lives. Based on this, this paper studies a real estate virtual e-commerce model based on big data. In the study of this model, this paper combines the advantages of e-commerce and virtual communities to design a more effective virtual e-commerce model. The analysis of e-commerce and virtual communities shows that the virtual e-commerce model designed in this paper is a more effective model, and the real estate virtual e-commerce model based on big data technology can serve the real estate industry. Do a good job in the sales reform of the real estate industry.

scholarly journals Construction of social indicators using information measuring principles. Case study of real estate prices simulation model

2011 ◽  
Vol 52 ◽  
Author(s):  
Aleksandras Krylovas ◽  
Natalja Kosareva ◽  
Laura Gudelytė
Keyword(s):  
Real Estate ◽  
Simulation Model ◽  
Empirical Data ◽  
Social Indicators ◽  
Real Estate Market ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Testing Tool ◽  
Real Estate Prices

The article analyses the data concerning the real estate market of last years in Vilnius. The approach of dichotomous diagnostic operators as general testing tool creation from the empirical data is used and deterministic clusterization of catchments is provided. Based on this analysis, questionnaire concerning the information about catchment is constructed for the evaluation of aggregated appartment price.

scholarly journals ANALYSIS OF PRICING FACTORS IN REAL ESTATE MARKET

2019 ◽  
Vol 6 (2) ◽  
pp. 79-85
Author(s):  
Maria Chernyshova ◽  
Arina Malenkaya ◽  
Tatyana Mezhuyeva
Keyword(s):  
Real Estate ◽  
Supply And Demand ◽  
Market Price ◽  
Real Estate Market ◽  
Physical Factors ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Pricing Factors ◽  
Real Estate Prices

In the real estate market price depends on supply and demand is formed under the influence of social, economic and physical factors. The article presents the results of the analysis of pricing factors in the real estate market, the forecast of real estate prices in 2019.

scholarly journals COVID-19 Pandemic, Urban Resilience and Real Estate Prices: The Experience of Cities in the Yangtze River Delta in China

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 960
Author(s):  
Chuanhao Tian ◽  
Xintian Peng ◽  
Xiang Zhang
Keyword(s):  
Real Estate ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
Real Estate Market ◽  
River Delta ◽  
The Yangtze River ◽  
Urban Resilience ◽  
Residential Land ◽  
Real Estate Prices ◽  
The Yangtze River Delta

The COVID-19 pandemic has severely impacted the urban real estate market around the world. This study regards the impact of the pandemic as a quasi-natural experiment, using the Difference in Difference model (DID) to examine the short-term impact of this severe public health crisis on the residential land and housing markets in the Yangtze River Delta. The study found that the COVID-19 pandemic has had a significant inhibitory effect on the average price of urban residential land and houses in the Yangtze River Delta. Although the currency oversupply has caused real estate prices in all cities to rise, the price of urban residential land decreased by 13.7% for each additional unit of epidemic severity. The greater the city’s resilience to the pressure of the COVID-19 pandemic, the faster its residential land prices will recover. Empirical research on the new house samples confirmed this conclusion. Local governments should continue to improve their ability to manage abnormal conditions, not only to prevent the spread of the epidemic, but also to gradually promote the recovery of the urban economy, strengthen urban resilience to better respond to health crises, and achieve sustainable urban development.

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