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Folia Oeconomica Stetinensia

The Journal of University of Szczecin

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Estimating The Size Of The Secondary Real Estate Market Based On Internet Data Sources

Maciej Beręsewicz
Published Online: 2015-06-03 | DOI: https://doi.org/10.1515/foli-2015-0012


As a result of the growing digitization of society and the development of electronic economy, current statistical data sources, including administrative registers, do not satisfy the information needs of society. Therefore, there are growing gaps in the statistical coverage of a number of sectors of the economy. One example of such a gap is the secondary real estate market, which is only partially accounted for by official statistical data sources. On the other hand new data sources such as the Internet or Big Data tend to decrease information gap in official statistics. The Web portals that specialise in brokerage on real estate market should be not neglected as a data source for statistics. Therefore, the aim of the paper is to use two Web portals devoted to the housing market to estimate supply measured in the number of flats offered to sale in Poznań, Poland. In addition, classification and quality of Web portals will be discussed.

Keywords: secondary real estate market; internet data sources; statistical data sources; Petersen estimator; capture–recapture; supply on real estate market

JEL classification: C13; C42; C81; L85


  • Beręsewicz, M. (2014). Próba zastosowania różnych miar odległości w uogólnionym estymatorze Petersena. Taksonomia: klasyfikacja i analiza danych – teoria i zastosowania. Taksonomia 22: klasyfikacja i analiza danych – teoria i zastosowania. Wrocław: Uniwersytet Ekonomiczny we Wrocławiu.Google Scholar

  • Beręsewicz, M. & Klimanek, T. (2013). Wykorzystanie estymacji pośredniej uwzględniającej korelację przestrzenną w badaniu cen mieszkań. Taksonomia 21: klasyfikacja i analiza danych – teoria i zastosowania. Wrocław: Uniwersytet Ekonomiczny we Wrocławiu.Google Scholar

  • Daas, P., Roos, M., de Blois, C., Hoekstra, R., ten Bosch, O., & Ma, Y. (2011). New data sources for statistics: experiences at Statistics Netherlands. The Hague/Herleen: Statistics Netherlands.Google Scholar

  • Fellegi, I. & Sunter, A. (1969). A Theory for Record Linkage. Journal of the American Statistical Association, 64, 328, 1183–1210.Google Scholar

  • Gołata, E. & Dehnel, G. (2013). Rozbieżności szacunków NSP 2011 i BAEL. Taksonomia 20: klasyfikacja i analiza danych – teoria i zastosowania (pp. 120–130). Wrocław: Uniwersytet Ekonomiczny we Wrocławiu.Google Scholar

  • Groves, R., Fowler, M.F.J. Jr., Couper, M., Lepkowski, J.M., Singer, E. & Tourrangeau, R. (2010). Survey methodology. New York: Wiley.Google Scholar

  • Hoekstra, R., ten Bosch, O. & Harteveld, F. (2010). Automated Data Collection from Web Sources for Official Statistics: First Experiences. Heerlen. The Netherlands: Statistics Netherlands.Google Scholar

  • IWGDMF (1995), International Working Group for Disease Monitoring and Forecasting. Capture-recapture and multiple-record systems estimation II: Applications. American Journal of Epidemiology, 142, 1059–1068.Google Scholar

  • Lavallee, P. & Rivest, L.-P. (2012). Capture-Recapture Sampling and Indirect Sampling. Journal of Offcial Statistics, 28, 1.1–27.Google Scholar

  • Lazer., D., Kennedy, R., King, G. & Vespignani, A. (2014). The parable of Google Flu: traps in Big Data analysis. Science, 14 March 2014.Web of ScienceCrossrefGoogle Scholar

  • Miller, G. (2011). Social Scientists Wade Into the Tweet Stream. Science 333 (6051), 1814–1815.Web of ScienceGoogle Scholar

  • Paradysz, J. (2007). Rejestry administracyjne jako źródło zasilania w statystyce regionalnej. In: Statystyka regionalna w jednoczącej się Europie, ed. J. Paradysz. Poznań: Uniwersytet Ekonomiczny w Poznaniu.Google Scholar

  • R Core Team (2014). R: A language and environment for statistical computing [computer software]. R Foundation for Statistical Computing. Vienna. Austria, www.R-project.org.

  • Roszka, W. (2012). System statystyki publicznej oparty na zintegrowanych źródłach danych. Przegląd Statystyczny, 59, 2.Google Scholar

  • Rozporządzenie z dnia 29 marca 2001 r. Ministra Rozwoju Regionalnego i Budownictwa w sprawie ewidencji gruntów i budynków (DzU 2001.38.454).Google Scholar

  • Statistics Finland (2004). Use of registers and administrative data sources for statistical purposes – best practices in Statistics Finland. Handbook 45. Helsinki.Google Scholar

  • UNECE (2007). Register-based statistics in the Nordic countries: review of best practices with focus on population and social statistics. United Nations Publication.Google Scholar

  • Wallgren, A. & Wallgren, B. (2014). Register-Based Statistics: Statistical Methods for Administrative Data. Chichester: John Wiley & Sons.Google Scholar

  • Wolter, K.M. (1986). Models for Census Data Some Coverage Error. Journal of the American Statistical Association, 81 (394), 338–346.Google Scholar

  • Zhang, L-C. (2015). On modelling register coverage errors. Journal of Official Statistics (forthcoming).Google Scholar

About the article

Received: 2014-07-01

Accepted: 2014-11-06

Published Online: 2015-06-03

Published in Print: 2014-12-01

Citation Information: Folia Oeconomica Stetinensia, Volume 14, Issue 2, Pages 259–269, ISSN (Online) 1898-0198, DOI: https://doi.org/10.1515/foli-2015-0012.

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© University of Szczecin. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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