Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

Choice, Deferral and Consistency

In this paper we study decision making in situations where the individual’s preferences are not assumed to be complete. First, we identify conditions that are necessary and sufficient for choice behavior in general domains to be consistent with maximization of a possibly incomplete preference relation. In this model of maximally dominant choice, the agent defers/avoids choosing at those and only those menus where a most preferred option does not exist. This allows for simple explanations of conflict-induced deferral and choice overload. It also suggests a criterion for distinguishing between indifference and incomparability based on observable data. A simple extension of this model also incorporates decision costs and provides a theoretical framework that is compatible with the experimental design that we propose to elicit possibly incomplete preferences in the lab. The design builds on the introduction of monetary costs that induce choice of a most preferred feasible option if one exists and deferral otherwise. Based on this design we found evidence suggesting that a quarter of the subjects in our study had incomplete preferences, and that these made significantly more consistent choices than a group of subjects who were forced to choose. The latter effect, however, is mitigated once data on indifferences are accounted for.

Nondictatorial Arrovian Social Welfare Functions: An Integer Programming Approach

In the line opened by Kalai and Muller (1997), we explore new conditions on prefernce domains which make it possible to avoid Arrow's impossibility result. In our main theorem, we provide a complete characterization of the domains admitting nondictorial Arrovian social welfare functions with ties (i.e. including indifference in the range) by introducing a notion of strict decomposability. In the proof, we use integer programming tools, following an approach first applied to social choice theory by Sethuraman, Teo and Vohra ((2003), (2006)). In order to obtain a representation of Arrovian social welfare functions whose range can include indifference, we generalize Sethuraman et al.'s work and specify integer programs in which variables are allowed to assume values in the set {0, 1/2, 1}: indeed, we show that, there exists a one-to-one correspondence between solutions of an integer program defined on this set and the set of all Arrovian social welfare functions - without restrictions on the range.