In this study, we utilize Bayesian methods to construct a comprehensive spatio-temporal model for real estate launch prices in the city of São Paulo. Our approach involves the decomposition of the price series into trend and cycle components, as well as the inclusion of spatially continuous and time-varying random effects. Additionally, we incorporate a set of explanatory variables to account for hedonic aspects. Within the hedonic components, we not only consider intrinsic property characteristics but also take into consideration neighborhood features and the economic environment. With the application of this model, we have successfully estimated equilibrium prices for various locations, offering a more transparent interpretation of property price dynamics spanning from January 2000 to December 2013 within São Paulo city.
Spatial autoregressive models have been adapted to model data with both a geographic and a compositional nature. Interpretation of parameters in such a model is intricate. Indeed, when the model involves a spatial lag of the dependent variable, this interpretation must focus on the so-called impacts rather than on parameters and when moreover the dependent variable of this model is of a compositional nature, this interpretation should be based on elasticities or semi-elasticities. Combining the two difficulties, we provide exact formulas for the evaluation of these elasticity-based impact measures which have been only approximated so far in some applications. We also discuss their decomposition into direct and indirect impacts taking into account the compositional nature of the dependent variable. Finally, we also propose more local summary measures as exploratory tools that we illustrate on a toy data set and on real data.
We suggest the use of outdegrees from graph theory to rank locations in terms of their contagiousness. We show that outdegrees are equal to the column sums of spatial autoregressive matrices, which may be estimated using econometric methods for spatial panel data. In contrast to outdegree, R is invalid for 'traffic light' shading because it fails to distinguish between the export and import of contagion between sub-national locations. Simulation methods are used to illustrate the concept of outdegrees and its structural determinants in terms of centrality, indigenous contagion and spatial contagion. An empirical illustration is presented for Israel. A secondary criterion for traffic light shading involves the stochastic structure of morbidity shocks, which induce 'spiking' through their autoregressive persistence, conditional heteroscedasticity and diffusion jump parameters.
Many empirical studies in developing countries show that the expansion of the middle class is associated with better governance, economic growth and poverty reduction. Thus, assessing the weight of the middle class and exploring its determinants should be of primary interest to policy makers. We find several papers analyzing the determinants of middle class in developing countries, among others, we can cite the manuscripts of Martinez and Parent (Middle class determinants in Latin America (2000–2010): a gender perspective, 2012), Filali and Bouabid (Région et développement 44: 79–101, 2016). These studies were conducted in a non-spatial framework. However, the distribution of household income classes in one region can be expected to influence those of neighboring regions. This analysis uses a spatial ordered probit model which allows interregional spatial interactions and heteroscedasticity to define the determinants of Tunisian middle class. Based on the economic approach in defining this class, I use the total annual consumption expenditure as an approximation of the permanent household income. I identified three subcategories: a lower middle class, an intermediate class and an upper class.Model parameters are estimated in a Bayesian structure using MCMC simulation, by sampling sequentially from the complete set on conditional distributions for all parameters. The results indicate that the spatial model outperforms the standard model and that the age of the head of the household, a high level of education, the number of employees in the household, living in an urban area and home ownership have a positive effect on the probability of being in the upper income classes. Furthermore, the spatial coefficient is significantly positive, showing that spatial relationships should be reflected in model specification. This observation is confirmed by the mean values of regional specific errors (_i) estimates. Indeed, a clustering pattern of similar values is visible.
