The aim of the paper is to open the issue of multiple shrinkage trajectories in a context of extended urbanisation (Keil 2018) by delineating the different trajectories of Romanian cities. We employed principal component analysis to allow for a multi-criterial classification of Romanian cities based on k-means cluster analysis. Beyond the dominant representation of shrinkage as a process that is mainly correlated with population loss and economic decline, this paper calls for bridging together distinct dimensions which have been either under-studied, such as the aspect of human development, or studied separately across the existing literature, such as governance of shrinkage and economic growth. Therefore, the typology developed here accounts for understanding the process of shrinkage as a complex process, having multiple causes, which determine peculiar trajectories. The outcome confirms the existence of distinct and highly localised shrinkage identities (Martinez-Fernandez, Audirac, et al. 2012). We show that regrowth is not strictly related to the urban core, but it has more to do with a process of complexification of the landscape and social relations existing at the periphery of the city. Shrinking core cities coexists with growing peri-urban areas.

This paper examines the rapid increase of prices in the residential sector of Cluj-Napoca in the context of the housing affordability crisis (Wetzstein, 2017). By using insight from the Growth Regimes literature, we look at the internal demand as a main driver of rapid price rise. As Kohl and Spielau (2018) argue, the monetary conditions needed for export-led growth regimes are restricting the outputs of the construction sector, creating under-supplied, demand-driven housing markets. We propose three alternative hypotheses regarding the major agent driving the prices within the city as major source of demand: the employees in knowledge-intensive services, the diffuse regional savings of employees in search for some yields, the specialized real estate investors. We use OLS and spatial regression (lag and error) to model the price per square meter using the social composition of the neighbourhoods, the within and out-of-town origin of investors, and the source of money (bank loans vs. cash payment) to demonstrate that the existing crisis is driven by the middle class’s savings that also benefits from gentrification, while speculative investments in the housing markets are rather limited.

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