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.

What drives urban consumption in mainland china? The role of property price dynamics

This paper adds to the literature on wealth effects on consumption by disentangling house price effects on consumption for mainland China. In a stochastic modelling framework, the riskiness, rate of increase and persistence of house price movements have different implications for the consumption/housing ratio. We exploit the geographical variation in property prices by using a quarterly city-level panel dataset for the period 1998Q1 – 2009Q4 and rely on a panel error correction model. Overall, the results suggest a significant long run impact of property prices on consumption. They also broadly confirm the predictions from the theoretical model.

Saving and Re-building Lives: an Analysis of the Determinants of Disaster Relief

We analyse both theoretically and empirically, the factors that influence the amount of humanitarian aid which countries receive when they are struck by natural disasters. Our investigation particularly distinguishes between immediate disaster relief which helps the survival of victims and long term humanitarian aid given towards reconstruction and rehabilitation. The theoretical model is able to make predictions as well as explain some of the peculiarities in the empirical results. The empirical analysis, making use of some useful data sources, show that both short and long term humanitarian aid increase with number of people killed, financial loss and level of corruption, while GDP per capita has no effect. Number of people affected had no effect on short term aid, but significantly increased long term aid. Both types of aid increased if the natural disaster was an earthquake, tsunami or drought. In addition, short term aid increases in response to a flood while long term aid increases in response to storms.