Advertisement

Decision Trees Analysis in a Low Tension Real Estate Market: The Case of Troina (Italy)

  • Alberto ValentiEmail author
  • Salvatore Giuffrida
  • Fabio Linguanti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9157)

Abstract

Troina is a town in the central mountainous area of Sicily, in the Province of Enna, and well represents the general social and economic profile of this territory. Its real estate market is assumed in this study as one of the most significant ones for the description of this profile, because of its characteristics that, especially during the current economic-financial crisis, are particularly evident. The study of this market has been carried out as a basis for a possible redevelopment capital-centered policy, so that both urban/architectural and real estate characteristics have been considered within the proposed pattern. This pattern is based on the decision trees technique, a data mining procedure that allows defining the different submarkets under some specified hypotheses. The different aggregations we have figured out express different ways of assuming the real estate market profile and the directions of any policy that could boost the preservation of the historical urban context instead of promoting the outward urban spreading with further land consumption.

Keywords

Minor inner old towns Real estate market segmentation Decision trees Decision trees Mass appraisal 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Acciani, C., Gramazio, G.: L’Albero di Decisione quale nuovo possibile percorso valutativo. Aestimum 48, 19–38 (2006)Google Scholar
  2. 2.
    Anderberg, M.R.: Cluster analysis for application. Academic Press, New York (1973)zbMATHGoogle Scholar
  3. 3.
    Bertrand, P.: Structural properties of pyramidal clustering. Partitioning data sets. In: Cox, I., Hansen, P., Julesz, B. (eds.) DIMACS Series in discrete mathematics and theoretical computer science, vol. 19, pp. 35–53. American Mathematical Society, Providence (1995)Google Scholar
  4. 4.
    Bonner, R.: On some clustering techinques. International Business Machine Journal of Research and Development. 8, 22–32 (1964)zbMATHGoogle Scholar
  5. 5.
    Breiman, L., Friedman, J.H., Olsen, R.A., Stone, C.J.: Classification and regression trees, pp. 203–215. Wadsworth International Group, Belmont (1984)Google Scholar
  6. 6.
    Cunningham, K.M., Ogilvie, L.C.: Evaluation of hierarchical grouping techniques: a preliminary study. Computer Journal 15, 209–213 (1972)CrossRefGoogle Scholar
  7. 7.
    Drucker, H., Cortes, C.: Boosting decision trees. Adv Neural Inf Process Syst 8, 479–485 (1996)Google Scholar
  8. 8.
    Forte, C.: Elementi di estimo urbano. Etas Kompass, Milano (1968)Google Scholar
  9. 9.
    Everitt, B., Landau, S., Morvene, L., Stahl, D.: Cluster Analysis, 5th edn. John Wiley and sons Ltd. (2011)Google Scholar
  10. 10.
    Fraley, C., Raftery, A.E.: Model-Based Clustering, Discriminant Analysis, and Density Estimation. Journal of the American Statistical Association 97(458) (2002)Google Scholar
  11. 11.
    Freund, Y., Schapire, R.: A decision-theoretical generalization of on-line learning and an application to boosting. J Computer Syst Sci 55, 119–139 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Gastaldi, T.: Data Mining: Alberi Decisionali e Regole Induttive per la profilazione del cliente. Science & Business 4(5–6), 16–25 (2002)Google Scholar
  13. 13.
    Giuffrida, S., Ferluga, G., Valenti, A.: Capitalization rates and “real estate semantic chains”. An application of clustering analysis. International Lournal of Business Intelligence and Data Mining (2015)Google Scholar
  14. 14.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. Springer-Verlag, New York (2001)CrossRefzbMATHGoogle Scholar
  15. 15.
    Hepşen, A., Vatansever, M.: Using Hierarchical Clustering Algorithms for Turkish Residential Market. International Journal of Economics and Finance 4, 138–150 (2012). doi: 10.5539/ijef.v4n1p138 Google Scholar
  16. 16.
    Maitra, R., Volodymyr, M., Soumendra, L.N.: Bootstrapping for Significance of Compact Clusters in Multi-dimensional Datasets, Jasa (2012)Google Scholar
  17. 17.
    Rizzo, F.: Nuova economia. Aracne, Roma (2013)Google Scholar
  18. 18.
    Simonotti, M.: La segmentazione del mercato immobiliare urbano per la stima degli immobili urbani. Atti del XXVIII Incontro di studio Ce.S.E.T, Roma (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alberto Valenti
    • 1
    Email author
  • Salvatore Giuffrida
    • 1
  • Fabio Linguanti
    • 2
  1. 1.Department of Civil Engineering and ArchitectureUniversity of CataniaCataniaItaly
  2. 2.Special Educational Department of ArchitectureUniversity of CataniaCataniaItaly

Personalised recommendations