PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | vol. 22, iss. 3 | 54--62
Tytuł artykułu

The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the traditional approach, geostatistical modeling involves analyses of the spatial structure of regionalized data, as well as estimations and simulations that rely on kriging methods. Geostatistical methods can complement traditional statistical models of property transaction prices, and when combined with those models, they offer a comprehensive tool for spatial analysis that is used in the process of developing land value maps. Transaction prices are characterized by mutual spatial correlations and can be considered as regionalized variables. They can also be regarded as random variables that have a local character and a specific probability distribution. This study explores the possibilities of applying geostatistical methods in spatial modeling of the prices of undeveloped land, as well as the limitations associated with those methods and the imperfect nature of the real estate market. The results are discussed based on examples, and they cover both the modeling process and the generated land value maps.(original abstract)
Rocznik
Strony
54--62
Opis fizyczny
Twórcy
  • University of Warmia and Mazury in Olsztyn, Poland
Bibliografia
  • ANDO A., UCHIDA R., 2004, The space-time structure of land prices in Japanese metropolitan areas, Annals of Regional Science, vol. 38, pp. 655-674.
  • BASU S., THIBODEAU T., 1998, Analysis of spatial autocorrelation in house prices, Journal of Real Estate Finance and Economics, Kluwer Academic Publishers, vol. 17 (1), pp. 61-85.
  • BOURASSA S. C., CANTONI E., HOESLI M., 2007, Spatial Dependence, Housing Submarkets, and House Price Prediction, Journal of Real Estate Finance and Economics, vol. 35, pp. 143-160.
  • CHICA-OLMO J., 2007, Prediction of Housing Location Price by a Multivariate Spatial Method: Cokriging, Journal of Real Estate Research, vol. 29 (1), pp. 95-114.
  • CHICA-OLMO J., CANO-GUERVOS R., CHICA-OLMO M., 2013, A Coregionalized Model to Predict Housing Prices, Urban Geography, vol. 34 (3), pp. 395-412.
  • CHILES J., DELFINER B., 1999, Geostatistics. Modelling Spatial Uncertainty, John Wiley & Sons Inc.
  • CICHOCIŃSKI P., 2009, Próba zastosowania metod geostatystycznych do taksacji nieruchomości, Roczniki Geomatyki, tom VII, z. 4(34), s. 17-30. (An attempt to use geostatistical methods for property taxation).
  • COLAKOVIC M., VUCETIC D., 2012, Possibility of Using GIS and Geostatistic for Modeling the Influence of Location on the Value of Residential Properties, FIG Working Week 2012, Rome, Italy, 6-10 May 2012, www.fig.net/pub/fig2012.
  • CRESSIE N., 1993, Statistics for Spatial Data, revised edition, John Willey & Sons, New York.
  • DUBIN R. A., 2003, Robustness of Spatial Autocorrelation Specifications: Some Monte Carlo Evidence. Journal of Regional Science, vol. 43, no 2, pp. 221-248.
  • DUBIN R., KELLEY PACE R., THIBODEAU T. G., 1999, Spatial Autoregression Techniques for Real Estate Data, Journal of Real Estate Literature, vol. 7, American Real Estate Society, pp. 79-95.
  • GELFAND A. E., ECKER M. D., KNIGHT J. R., SIRMANS C. F., 2004, The Dynamics of Location in Home Price, Journal of Real Estate Finance and Economics, vol. 29, no 2, pp. 149-166.
  • GILLEN, K., THIBODEAU, T.G., WACHTER, S., 2001, Anisotropic Autocorrelation in House prices. Journal of Real Estate Finance and Economics, vol. 23, no 1, pp. 5-30.
  • HENGL T., 2007, A Practical Guide to Geostatistical Mapping of Environmental Variables, European Commission, Joint Research Centre, Institute for Environment and Sustainability.
  • ISAAKS E., SRIVASTAVA R., 1989, Applied Geostatistics, Oxford University Press.
  • JOURNEL, A. G., HUIJBREGTS, C.J., 1978. Mining Geostatistics, Academic Press Inc, London, UK.
  • KOKESZ Z., 2010, Uwarunkowania stosowania krigingu zwyczajnego do sporządzania map izoliniowych, Biuletyn Państwowego Instytutu Geologicznego, nr 439, s. 403-408. (Conditions of the use of ordinary kriging for isolinear mapping).
  • KRIGE D. G., 1951, A statistical approach to some mine valuations. Problems on the Witwatersrand, Journal of the Chemical Metallurgical and Mining Society of South Africa, December 1951, pp. 119-139.
  • KRIGE D. G., 1962, Statistical applications in mine valuation, J. Inst. Mine Survey South Africa, vol. 12 (2), pp. 95-136.
  • KUCHARSKA-STASIAK E., 1999, Nieruchomość a rynek, Wydawnictwo Naukowe PWN, Warszawa. (Real estate and the market).
  • KULCZYCKI P, 2005, Estymatory jądrowe w analizie systemowej, Wydawnictwa Naukowo-Techniczne, Warszawa. (Kernel estimators in the analysis of the system).
  • KULCZYCKI M., LIGAS M., 2007, Zastosowanie analizy przestrzennej do modelowania danych pochodzących z rynku nieruchomości, Studia i Materiały Towarzystwa Naukowego Nieruchomości, vol. 15 nr 3-4, s.145-153. (Spatial analysis and real estate markets' modelling).
  • LIGAS M., 2009, Zastosowanie modelu regresja-kriging do predykcji wartości nieruchomości, Studia i Materiały Towarzystwa Naukowego Nieruchomości, vol. 17, nr 1, s. 7-16. (Application of regression-kriging model to real estate value prediction).
  • LONGLEY P., GOODCHILD M., MAGUIRE D., RHIND D., 2005, Geographic Information Systems and Science, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester.
  • MARTINEZ M. G., LORENZO J., RUBIO N., 2000, Kriging methodology for regional economic analysis: Estimating the housing price in Albacete, International Advances in Economic Research, vol. 6 (3), pp 438-450.
  • MATHERON G., 1963, Principles of Geostatistics, Economic Geology, no 58 pp. 1246-1266.
  • MATHERON G., 1971, The Theory of Regionalized Variables and its Applications, Les Cahiers du Centre de Morphologie Mathematique de Fontainbleau nr 5, Ecole Nationale Superieure des Mines de Paris.
  • MCBRATNEY A., ODEH I., BISHOP T., DUNBAR M., SHATAR T., 2000, An overview of pedometric techniques for use in soil survey, Geoderma 97 (3-4), pp. 293-327.
  • MONTERO, J.M., LARRAZ B., PAEZ A., 2009, Estimating Commercial Property Prices: An Application of Cokriging with Housing Prices as Ancillary Information, Journal of Geographical Systems 11 (4), pp. 407-25.
  • NAMYSŁOWSKA-WILCZYŃSKA B., 2006, Geostatystyka. Teoria i zastosowania, Oficyna Wydawnicza Politechniki Wrocławskiej. (Geostatistics. Theory and practice).
  • SARMA D. D., 2009, Geostatistics with Applications in Earth Sciences, Springer.
  • SCHABENBERGER O., GOTWAY A. C., 2005, Statistical methods for spatial data analysis, Chapman & Hall/CRC.
  • SILVERMAN B.W., 1986, Density Estimation for Statistics and Data Analysis, New York, Chapman and Hall.
  • STACH A., 2009, Analiza i modelowanie struktury przestrzennej, [w] Zwoliński Z. (red.) GIS - platforma integracyjna geografii, Bogucki Wydawnictwo Naukowe, Poznań. (Analysis and modelling of spatial structure).
  • TU Y., H. SUN, S. YU, 2007, Spatial autocorrelations and Urban housing market segmentation, Journal of Real Estate Finance and Economics, vol. 34, pp. 385-406.
  • WACKERNAGEL H., 1998, Multivariate geostatistics: an introduction with applications, 2nd Edition, Springer- Verlag.
  • WEBSTER R., OLIVER M., 2001, Geostatistics for Environmental Scientists Statistics in Practice, Wiley, Chichester.
  • ZAWADZKI J., 2005, Wykorzystanie metod geostatystycznych w badaniach środowiska, Oficyna Wydawnicza Politechniki Warszawskiej. (Using geostatistical methods for environmental studies).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171300337

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.