THE USE OF NEURAL NETWORKS IN THE SPATIAL ANALYSIS OF PROPERTY VALUES

The real-estate market is "where" a multiplicity of economic, cultural, social and demographic factors are synthesised with respect to choices regarding the qualitative and locational aspects of a property. The spatial analysis of the real-estate market and, in particular, of the factors which contribute to determining prices, is a very useful instrument in outlining the geography of the economic development of vast areas. The aim of the paper is the construction of a simulation model, on a spatial level, of real-estate values with reference to the housing market in the urban area of the city of Treviso (I). The model was built using a neural network which gives the possibility of analysing the marginal contribution of single real-estate characteristics independently of the a priori choice of the interpolation function; at the same time it works well even in the presence of statistical correlation among the explicative variables, a serious drawback in multiple regression models. The work is divided into several parts. First, a synthetic picture of the real-estate market of the area studied has been drawn up with reference to the main conditioning factors. Then the problem of the selection of a neural network model for the appraisal of property values is presented. Finally, there is the description of the procedure for the spatialization of obtained results from the neural model for the definition of a values map. The results shows the notable interpretative and predictive capacity of the neural model and it seems very useful in appraisals. Furthermore, the mapping of value fluctuations enables first-hand verification of the "goodness" of the assessed model and its capacity to portray the real situation. The general approach presented seems, therefore, useful both as an instrument of support for urban and territorial planning, as well as a permanent monitoring system of the real-estate market with the aim of creating an informative system of support for the analysis of real-estate investment.Land Economics/Use,

Insight versus Effort. Communicating the Creative Process Leading to New Products

Studies of the creative process identify two relevant sources of new ideas and products: Insight, a sudden, dreamlike, illuminating experience; and effort, deliberate, structured, hard work. With the aim of investigating the communication of the creative process,this research proposes that consumers hold associations between insight and arts, and between effort and sciences. These lay theories induce differential evaluations of new products: consumersevaluate more favorably artistic and scientific products presented as the outcome of insight or effort, respectively. The strength of the proposed effects, however,depends on the level of consumer expertise in the relevant product domain. We maintain that,as audience expertise increases, lay theories become less relevant and the effects of creative process narratives are attenuated. Five studies support the proposed conceptual framework and showthatnarratives of thecreative process influence the evaluations of new products, depending on the product domain and on consumer expertise

THE USE OF NEURAL NETWORKS IN THE SPATIAL ANALYSIS OF PROPERTY VALUES

The real-estate market is "where" a multiplicity of economic, cultural, social and demographic factors are synthesised with respect to choices regarding the qualitative and locational aspects of a property. The spatial analysis of the real-estate market and, in particular, of the factors which contribute to determining prices, is a very useful instrument in outlining the geography of the economic development of vast areas. The aim of the paper is the construction of a simulation model, on a spatial level, of real-estate values with reference to the housing market in the urban area of the city of Treviso (I). The model was built using a neural network which gives the possibility of analysing the marginal contribution of single real-estate characteristics independently of the a priori choice of the interpolation function; at the same time it works well even in the presence of statistical correlation among the explicative variables, a serious drawback in multiple regression models. The work is divided into several parts. First, a synthetic picture of the real-estate market of the area studied has been drawn up with reference to the main conditioning factors. Then the problem of the selection of a neural network model for the appraisal of property values is presented. Finally, there is the description of the procedure for the spatialization of obtained results from the neural model for the definition of a values map. The results shows the notable interpretative and predictive capacity of the neural model and it seems very useful in appraisals. Furthermore, the mapping of value fluctuations enables first-hand verification of the "goodness" of the assessed model and its capacity to portray the real situation. The general approach presented seems, therefore, useful both as an instrument of support for urban and territorial planning, as well as a permanent monitoring system of the real-estate market with the aim of creating an informative system of support for the analysis of real-estate investment