Recent floods in America, Europe, Asia and Africa reminded societies across the world of the need to revisit their climate adaptation strategies. Rapid urbanization coinciding with a growing frequency and intensity of floods requires transformative actions in cities worldwide. While abandoning flood prone areas is sometimes discussed as a public climate adaptation option, little attention is paid to studying cumulative impacts of outmigration as an individual choice. To explore the aggregated consequences of households' outmigration decisions in response to increasing flood hazards, we employ a computational agent-based model grounded in empirical heuristics of buyers' and sellers' behaviour in a flood-prone housing market. Our results suggest that pure market-driven processes can cause shifts in demographics in climate-sensitive hotspots placing low-income households further at risk. They get trapped in hazard zones, even when individual risk perceptions and behavioural location preferences are independent of income, suggesting increasing climate gentrification as an outcome of market sorting.@en

Validation is a vital step in any model that aspires to have an impact. In agent-based computational economics in particular, it is essential because, unlike most neoclassical economic models, the models are vastly complex and often unique in their kind [1, 2]. Importantly, they often require disaggregated data to specify attributes, behavioural rules and interactions among agents. Therefore, agent-based models require more time and effort to validate. There are numerous ways of validation in agent-based modelling discussed in the literature, e.g [1, 3–7]. Reviewing them is beyond the scope of this paper. Here we will focus on two dimensions of validation: empirical versus theoretical validation and micro- versus macroscale validation. In our agent-based model of the housing market, we have applied both ends of the two spectra.@en

Federally regulated or insured lenders in the United States are mandated to require flood insurance on properties that are located in areas at high risk of flooding. Despite the existence of this mandatory flood insurance requirement, take-up rates for flood insurance have been low, and the federal government's exposure to uninsured property losses from flooding remains substantial. Meanwhile, the value of capital at risk varies significantly with flood events and changing risk perceptions, which necessitates mechanisms that stabilize these dynamics. In this article we discuss how a scenario of complete insurance uptake, under various risk attitudes, affects the value of properties in the 100-year and 500-year flood zones. Our results indicate that an increase in flood insurance uptake may provide such a mechanism by lowering the value of capital at risk in the flood zone consistently, independent of homeowners' risk attitudes. We apply an empirical adaptive agent-based model to examine the capitalization of insurance costs, risk premiums, and their interaction in housing prices. Our approach combines widely-used empirical hedonic analysis with the computational economic framework. We highlight the usefulness of our method in capturing the marginal implicit price of homeowners' preferences that may change over time and separately assess the effect of various factors and policies on property values, illustrating the agent-based modeling as a valuable complement to traditional hedonic analysis.@en

Coastal areas around the world are urbanizing rapidly, despite the threat of sea level rise and intensifying floods. Such development places an increasing number of people and capital at risk, which calls for public flood management as well as household level adaptation measures that reduce social vulnerability to flooding and climate change. This study explores several private adaptation responses to flood risk, that are driven by various behavioral triggers. We conduct a survey among households in hazard-prone areas in eight coastal states in the USA, of which, some have recently experienced major flooding. While numerous empirical studies have investigated household-level flood damage mitigation, little attention has been given to examining the decision to retreat from flood zones. We examine what behavioral motives drive the choices for flood damage mitigation and relocation separately among property buyers and sellers. Hence, we focus on the drivers that shape demand for future development in flood-prone cities. We find that households’ choices to retreat from or to avoid flood zones (1) are highly sensitive to information that provokes people's feelings of fear, and (2) rely on hazardous events to trigger a protective action, which ideally would take place well before these events occur. We highlight that major flooding may cause a potential risk of large-scale outmigration and demographic changes in flood-prone areas, putting more low-income households at risk. Therefore, coordinated policies that integrate bottom-up drivers of individual climate adaptation are needed to increase urban resilience to floods.@en

Property prices are affected by changing market conditions, incomes and preferences of people. Price trends in natural hazard zones may shift significantly and abruptly after a disaster signalling structural systemic changes in property markets. It challenges accurate market assessments of property prices and capital at risk after major disasters. A rigorous prediction of property prices in this case should ideally be done based only on the most recent sales, which are likely to form a rather small dataset. Hedonic analysis has been long used to understand how various factors contribute to the housing price formation. Yet, the robustness of its assessment is undermined when the analysis needs to be performed on relatively small samples. The purpose of this study is to suggest a model that can be widely applicable and quickly calibrated in a changing environment. We systematically study four statistical models: starting from a typical standard hedonic function and gradually changing its functional specification by reducing the hedonic analysis to some basic property characteristics and applying kriging to control for neighbourhood effects. Across different sample sizes we find that the latter performs consistently better in the out-of-sample predictions than other traditional price prediction methods. We present the specific improvements to the traditional spatial hedonic model that enhance the model’s prediction accuracy. The improved model can be used to monitor price changes in risk-prone areas, accounting for changes in flood risk and at the same time controlling for autonomous market responses to flood risk.@en