Policy and market forces delay real estate price declines on the US coast

Coastal communities, often situated precariously close to sea level, continue to attract residents and investment despite the looming threat of sea-level rise (SLR) and increased storm intensity. Sea-level rise will inevitably submerge large portions of low-lying coastal areas, rendering some uninhabitable within this century. Millions of US households face inundation risks by 2100. Paradoxically, coastal real estate commands a premium, appreciating faster than inland properties, and attracting higher-income residents. This apparent contradiction—high prices persisting despite impending inundation—suggests that property prices don't fully reflect SLR and flooding risks. While some evidence shows that vulnerable properties are discounted, inflated prices relative to market fundamentals remain common. The strong desire for coastal living leads to rebuilding after storms with even larger homes, and adaptation measures like beach nourishment are easily justified by avoided property value losses. To understand these phenomena, a model is needed that integrates real estate markets, coastal amenities, hazards, and policies responding to coastal change. This study addresses this need by developing a coupled human-natural system model to examine long-term interactions between real estate markets and the physical coastal system. Existing empirical studies struggle to isolate the SLR signal in property prices due to other market fluctuations and the long timeframe of SLR impacts (often extending beyond a typical 30-year mortgage). Coupled systems modeling offers a powerful tool to explore these complex interactions and the implications of long-term SLR.

The study builds on a growing body of literature on coupled human and natural systems, acknowledging the limitations of simply superimposing economic models onto physical or biological systems. Early bioeconomic models studied fisheries, but the field expanded with spatially explicit data on land use, urbanization, and conservation. Progress has focused on modeling nonlinear feedbacks between physical processes and human responses across space and time. In coastal systems, understanding the coastal-economic zone requires integrating physical coastal systems and economic behavior. Previous models have incorporated human behavior with geophysical models of coastal evolution, and simplified dynamics of coastal change with detailed economic decision-making. However, this research adds to this literature by endogenizing real estate values and demographic changes as functions of SLR risk within a model including beach erosion, storm risk, beach width effects on property value, and local public finance decisions related to beach rebuilding. The authors note the potential for misinterpreting empirical results and drawing incorrect policy implications without modeling the coupled system. Even simple coupled models can exhibit non-intuitive behaviors, highlighting the need for modeling to evaluate empirical evidence and understand surprising findings. Coupled systems modeling can also yield substantially different estimates compared to simpler models, such as estimates of beach width value that are more than double those ignoring the coupling.

The researchers developed the Coastal Home Ownership Model (C-HOM) to study the evolution of coastal real estate markets, resident incomes, and shoreline management decisions over a 150-year horizon. Income is defined relative to US marginal income tax rates (10-37%). The model length allows for consideration of longer horizons than a typical 30-year mortgage, an initial 50-year period without SLR to understand internal mechanisms, and the evaluation of long-term SLR and storm climate effects. C-HOM is a coupled system model with feedbacks between agent actions (buying/selling property, voting, beach management) and the physical system (erosion, beach width). Exogenous forces influencing the system include SLR, storm risk, and competing property markets. The model assumes sufficient SLR to cause widespread inundation and examines mechanisms delaying market collapse and how coastal defense interventions might influence demographic change. The model framework incorporates agent actions and outcomes (risk assessment, property valuations, owner agent flux, beach nourishment decisions), and external forcings (SLR, storm risk, influence of competing property markets). Four scenarios are simulated: a baseline with 90% beach nourishment subsidy and constant outside markets; a 50% subsidy reduction; a 90% subsidy with appreciating outside markets; and a 90% subsidy with depreciating outside markets. Each scenario involves an initial barrier height of 1m for the first 50 years, with 1m of SLR over the subsequent 100 years, reaching total inundation at year 150. The model uses an asset-price approach linking property sales price to capitalized housing market rent, modifying the user cost of housing model to parameterize variations in incentives as a function of demographics, environment, and economic conditions. The model includes feedbacks in the coupled human-natural system, where physical environmental changes (erosion, SLR, storms) affect real estate values, and human responses (beach nourishment) create feedbacks. Agent-based modeling captures nonlinearities and heterogeneity, with agents having heterogeneous risk perceptions and tolerances, and using finite-time forecasts for decision-making. Empirical findings on amenity values and climate risks are incorporated, reflecting how coastal markets respond to environmental changes. The model also reflects how public adaptation investments capitalize into real estate prices, incorporating federally subsidized beach nourishment and its influence on local housing market price volatility. The model comprises a fixed number of properties, agents generating housing bid prices, and an investor agent potentially purchasing multiple units. Agents decide to buy or rent based on the user cost of housing, and the model determines equilibrium house price and investor market share. The model considers two market segments (oceanfront and non-oceanfront), beach management decisions (subsidy and self-financing), and willingness to pay for coastal living influenced by beach width. The model includes risk premium incorporating background risk and climate risks (oceanfront exposure, storms, SLR), and a risk multiplier reflecting heterogeneous beliefs and tolerance. Agent distribution adjustments reflect demographic changes due to interactions with outside markets, through arbitrage and herding effects. Expected capital gains are formed based on past market returns with varying time scales for different agents. Shoreline management decisions are made through community voting on nourishment plans based on cost-benefit analysis comparing tax burdens and property value increases.

The baseline scenario demonstrates that property value doesn't immediately reflect SLR risks. The interaction of risk, investment, and income obscures the SLR signal. Even with constant outside markets, when SLR impacts prices, arbitrage opportunities maintain high prices as high-income buyers enter. A 50% reduction in beach nourishment subsidies leads to more volatile property values, a precipitous decline after decades, an immediate decrease in mean beach width, and increased median owner income tax rates (reflecting high-income agent influx). Investors own substantially less housing. When outside markets appreciate, coastal real estate appreciates even after SLR onset, beach width increases due to rising housing values justifying nourishment, and investors are quickly driven out by high-income owners. If outside markets decline, however, the influx of high-income agents reverses, expected returns and property values plummet despite continued nourishment, high-income owners leave, investors buy all real estate, and lower-income renters increase. The results highlight three mechanisms delaying the full capitalization of SLR risk in property values: tax advantages for high-income owners (increasing benefits of mortgage deductions), interactions of tax policy with outside markets (high-income agents exploiting arbitrage opportunities), and policies artificially increasing property value (beach nourishment subsidies). Removing subsidies leads to faster price adjustments to climate risk, but also faster coastal gentrification. The study finds that subsidies for beach nourishment are ultimately maladaptive, delaying downward price adjustments and associated demographic shifts. The research also suggests that public funds might be better spent on managed retreat, buyout with rentbacks programs (allowing owners to remain temporarily while facilitating retreat), investments in adaptive housing technology, rather than shore stabilization.

C-HOM's findings explain the persistence of high coastal property values despite increasing climate risks, highlighting the coupled nature of coastal real estate markets and the physical environment. The model shows how policy interventions, income inequality, and outside market dynamics interact to delay market adjustments to SLR risks. The demographic shift toward wealthier residents is identified as a form of coastal gentrification, sustained by policies such as beach nourishment subsidies. The study demonstrates how tax policy, outside markets, and nourishment subsidies interact to dampen environmental signals and delay the full reflection of SLR risks in property values. The study's findings raise questions about the causal impact of climate risk on property values, challenging the notion that markets fully reflect such risks in the short term, and proposing mechanisms (tax advantages for high-income owners, interaction with outside markets, and nourishment subsidies) that explain the persistent high values. The study also examines the potential tradeoff between coastal management policies that maintain the socioeconomic composition of coastal communities and those that allow property markets to accurately reflect long-term climate risks. The exit of high-income residents under declining outside markets represents a form of climate gentrification, with the study showing the possibility of both coastal and climate gentrification occurring in distinct time periods within the same community.

This study demonstrates that policy interventions, coupled with market dynamics, significantly delay the capitalization of sea-level rise risks into coastal property values. The model highlights the complexities of coastal management and the trade-offs between maintaining the socioeconomic status quo and allowing markets to accurately reflect risk. Future research could explore other policies (flood insurance, dune building, seawalls), incorporate racial dynamics into the model, and account for changes in housing supply. Investigating alternative uses of public funds (managed retreat, buyouts with rentbacks, adaptive housing technologies) should be prioritized.

The model simplifies certain aspects of the real world, such as the fixed housing supply, and the assumption of perfect information on SLR for investors. Incorporating housing supply dynamics and heterogeneous climate beliefs within a spatial model would enhance the model's realism. The model uses income as an indicator of coastal gentrification, while considering other factors such as race would improve its scope.