Prediction of housing deficit in Mérida, Venezuela, by artificial neural networks
AbstractThis work combines the tools of Radial Basis Function (RBF) and Multivariate Analysis to predict insufficient housing supply in the state of Merida, Venezuela. An alternative indicator to the commonly one used was built in order to evaluate this phenomenon. Data covering the number of families at the same house, house property, overcrowding level, housing physical condition, and public utilities condition were extracted from The Household Sampling Survey (HSS), 1994-2005. It is outstanding that RBF showed an acceptable level of effectiveness and capacity of adapting itself to this kind of problem. In general, results obtained during training and generalization stages reached very low average quadratic errors, a good level of success in the prognosis and robustness of the trained models.
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Bibliographic InfoArticle provided by Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela in its journal Economía.
Volume (Year): 35 (2010)
Issue (Month): 29 (January-june)
Contact details of provider:
Postal: Facultad de Ciencias Económicas y Sociales. Instituto de Investigaciones Económicas y Sociales. Campus Universitario Liria, Edificio G, Tercer Nivel. Mérida 5101, Estado Mérida, Venezuela
Phone: +58 74 401111 ext. 1081
Fax: +58 74 401120
Web page: http://iies.faces.ula.ve/
More information through EDIRC
Qualitative deficit; quantitative deficit; multiple correspondence analysis; artificial neural networks; scores.;
Find related papers by JEL classification:
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