Compact development and preferences for social mixing in location choices: results from revealed preferences in Santiago, Chile
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Date
2021
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Abstract
Even though densification and social mixing are declared objectives of many nowadays urban planning paradigms, their simultaneous implementation is usually questioned by different actors and is not frequent in practice. In a market economy, understanding potential demand for this class of development, from different types of households, is essential to define public policies oriented to achieve both compact development (CD) and social mixture. To understand the preferences of households and potential demand, we implement a location choice model based on a bid–rent framework and spatial latent classes (LC), using census data and location attributes. By using spatial LC, we do not impose exogenous definitions of which zones are perceived as CD or suburban, rendering a robust method to identify variation in preferences. We apply the model to Santiago de Chile, where social mixing in dense and well-located areas is being intensely discussed. We find strong differences in households' valuation of attributes between spatial classes. Results show that social mixing is more difficult in dense, well-connected areas than in suburban areas because higher-income households are more sensitive to the socioeconomic context of the location in compact areas. Besides showing evidence on household preferences and their implications for social-mixing policies, this paper also provides a proof of concept for the use of spatial LC (proposed in previous work by the authors), showing this is a robust methodology allowing to generate behavior-based classifications for urban areas. The paper also contributes methodologically, by deriving the elasticity formulation for bid-auction location choice models, which allows quantifying the importance of location attributes in location probability.
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Bid elasticities, Compact development, Discrete choice, Latentclasses, Location choice models, Social mixing, Spatial heterogeneity