Urban landscape in relation to building cycles and bid-rent curves

• Main Authors: Kristjánsdóttir, Sigríður, Elíasson, Lúðvík
• Format: Book Part
• Language: English
• Published: University of Strathclyde Publishing 2022
• Subjects: Architecture; Reykjavík; Iceland
• Online Access: https://strathprints.strath.ac.uk/80495/; https://strathprints.strath.ac.uk/80495/1/Kristjansdottir_Eliasson_ISFU_2021_Urban_landscape_in_relation_to_building_cycles_and_bid_rent_curves.pdf; https://doi.org/10.17868/strath.00080495

Conzen (1960) mentions that fringe belts may form when city growth is halted. Whitehand (1972) uses bid-rent curve analysis to show that different types of development take place beyond the current city edge during building cycle upswings than during downturns. He divided Glasgow into belts of fixed width and derived the proportion of the area at each distance where new development for houses and institutions took place. The study period was split into several sub-periods, each conforming to a particular boom or slump in the housing market. Whitehand found that the development of Glasgow did largely follow the predictions of the bid-rent theory. This theory is tested in Reykjavík, Iceland. As the city of Reykjavík expands from its core on a peninsula towards the mainland, the centre of growth of new building and development moves ever further away from the city centre. Although economic fluctuations appear to be echoed in the building cycle, periods of economic downturn at most slow down the rate of the expansion of Reykjavík (Kristjánsdóttir 2007). This paper expands on previous studies by adding twenty years of data covering turbulent times in the global and local economies, showing a stronger coherence with predictions of the bid-rent theory than previously recorded. In the wake of the global financial crisis the economy experienced its first housing slump since immediately following the Great Depression. This gives an opportunity to revisit the application of bid-rent theory and housing slumps in explaining the relative share of residential housing in new developments. Here this is done by looking at time series for the volume of housing construction.