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In complex systems changes in individual expectations, especially driven by new
information and emotions (Lux 2009; Anand et al. 2011) could lead to major abrupt
shifts in the aggregated market dynamics. Specifically, certain currently very attrac-
tive coastal and delta areas might experience sudden out migration and housing
market collapse. Pryce and Chen (2011) argue that conventional models of housing
market dynamics in hazard-prone areas based on the historic data alone might not be
able to shed light on how and under what conditions property prices and spatial pat-
terns may change in a world with climate change. This also poses challenges for de-
signing policies as decision support tools, which omit behavioral adaptation triggered
by changing climate and emergence of potential regime shifts in economic systems,
could be misleading. Thus, the strengths of conventional tools that are based on the
decades of successful applications and validation could be reinforced by simulations,
such as agent-based computational economics (ACE) (Tesfatsion and Judd 2006) to
account for adaptive economic behaviour.
Current paper addresses this gap by integrating adaptive expectations about land
market dynamics and hedonic analysis of housing market dynamics in flood-prone
areas within a spatial agent-based land market model. As an ultimate goal we aim to
incorporate evolution of individual risk perception into spatial ACE model and explore
how the critical transitions in land markets emerge in spatial socio-economic systems
from the bottom up as new information about growing coastal hazards diffuses.
2
Methods: Empirical Agent-Based Land Market Model with
Adaptive Price Expectations
2.1
Model Assumptions
Our ACE combines the microeconomic demand, supply, and bidding foundations of
spatial economics models with the spatial heterogeneity of spatial econometric mod-
els in a single methodological platform. We model a coastal city where both coastal
amenities and flooding disamenities drive land market outcomes, facilitating separate
analysis of the effects of each driver on land rents and land development patterns. We
start with conventional urban economic model and gradually relax the assumptions of
perfect rationality and homogeneity among households as well as the assumption of
an instantly equilibrating land market. In particular, our ACE model is grounded in a
monocentric urban model (Alonso 1964) enriched by coastal amenities following (Wu
and Plantinga 2003; Wu 2006) and flood hazard probabilities following Frame
(1998). Thus, spatial goods in this ACE market are quite heterogeneous differentiated
by distance to CBD (D), coastal amenities (A), probability of hazard (P) and structural
housing characteristics.
Heterogeneous household agents (buyers and sellers) exchange heterogeneous spa-
tial goods (houses) via simulated bilateral market interactions with decentralized price
determination. It is challenging to model price expectations in urban property markets
characterized by high heterogeneity of goods, which are infrequently traded.
While ACE has made a major progress on modeling markets of homogeneous goods
