No abstract

No abstract

This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.

This work stems from the widely accepted hypothesis that housing price is determined by two types of characteristic: structural and locational. Under this hypothesis, a methodology is presented for the spatial estimation of housing prices and locational rents: the Iterative Residual Kriging (IRK) method. The methodological aspects are complemented by the presentation of an application of this methodology to the case of housing in Granada, Spain.

User satisfaction sets a basic example for public service quality, especially those considered as basic necessities. The quality in transport service related to conceptualization and measurement, as a decisive demand key, presents challenges for both economic and mobility policies.Several operator companies are involved in the transport sector. Therefore this report aims to design a model of overall satisfaction based on the level of satisfaction with a specific set of factors, considering individual characteristics of users and the differential effect of different bus lines.The current presentation has applied a combined method, using Nonlinear Principal Component Analysis (NLPCA) and a Logit Multilevel Model (LMLM) in two-steps.