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

Coastal communities along the US East and Gulf coasts face increasing risks from sea-level rise (SLR) and storms, yet coastal property values continue to sell at a premium and have appreciated faster than non-coastal real estate, with coastal residents having higher incomes than non-coastal residents. This apparent paradox—persistent high prices and a demographic shift toward high-income owners despite inevitable long-run inundation—aligns with evidence that property prices do not fully reflect SLR and flood risks, and that storm-related price discounts dissipate within a few years. Empirically isolating the SLR signal is challenging due to other market fluctuations, discounting of long-horizon risks, and potential nonlinearities as SLR progresses beyond historical experience. The study’s purpose is to model how real estate markets interact with the evolving physical coastal system over long horizons to understand mechanisms delaying capitalization of climate risks and to evaluate policy responses. The authors develop the Coastal Home Ownership Model (C-HOM), a coupled human-natural, agent-based model that endogenizes property values, demographic change, storm and SLR risk, beach width, and local public finance for nourishment, to examine long-term trajectories and policy tradeoffs as communities approach inundation.

The paper situates its contribution within the literature on coupled human–natural systems, tracing roots to bioeconomic models of fisheries from the 1960s and a subsequent expansion using spatially explicit data to study land use, urbanization, and conservation. Advances have emphasized nonlinear feedbacks between physical processes and human responses, highlighting that economic or physical models alone cannot capture coastal co-evolution. In coastal contexts, prior work coupled beach nourishment to property markets, incorporated local public finance decisions, parameterized storm risk, and examined how environmental amenities (beach width, views, proximity) and hazards (storms, floods) capitalize into housing prices. Empirical studies show partial and transitory capitalization of flood and SLR risks, post-storm rebuilding with larger homes, and that adaptation investments (e.g., nourishment, dunes, hard structures) capitalize into prices. The authors note that without coupled modeling, empirical findings can be misinterpreted, and that coupling can significantly change estimated values (e.g., the value of beach width). The study adds by endogenizing real estate values and demographic changes as functions of SLR risks while incorporating beach erosion, storm risk, beach width effects, and local finance decisions for nourishment, and by examining the role of outside markets and tax policy in masking climate risk signals.

The authors develop C-HOM, an agent-based, coupled human–natural model of a coastal housing market with fixed housing supply divided into two segments: oceanfront and non-oceanfront. The model operates over 150 years: years 1–50 without SLR, years 51–150 with SLR rising linearly by 1 m (starting at t=50), reaching total inundation at year 150. Physical dynamics: beach width evolves with linear erosion and discrete nourishment events; mean sea level increases linearly during the SLR phase. Beach nourishment can add width and is implemented through community voting; costs include fixed and variable components, with local financing via temporary property tax increments and exogenous subsidy rates. Agent system: a fixed number n of potential owner-household agents and one outside institutional investor compete. Each agent either owns or rents one housing unit per period; the investor can own multiple units and rents to non-owning residents, paying a management cost. Prices and rents are determined via a user-cost of housing framework. Asset pricing: sale price P equals capitalized rents R discounted by a capitalization rate r reflecting mortgage interest (discounted by the owner’s marginal income tax rate due to mortgage interest and property tax deductibility), depreciation, a risk premium, and expected capital gains. Owner bid prices and rent bids follow modified user-cost equations; rents are decomposed into a constant housing services value and a willingness-to-pay (WTP) component for coastal amenities. WTP includes a base component and a beach-width component parameterized by a hedonic value of beach width, with stronger effects for oceanfront properties; expected beach width is formed from recent history. Risk premium includes background market risk and climate-related risks (oceanfront exposure, storms, SLR), scaled by agent-specific belief/tolerance multipliers; climate risk increases nonlinearly as barrier elevation approaches mean sea level. Agents have heterogeneous income tax rates (reflecting US brackets), WTP, and risk multipliers drawn from evolving distributions. Expected capital gains are heterogeneous, based on past price returns measured over agent-specific horizons (1–30 years). Market clearing and investor share: owner bid prices are sorted; the investor selects a market share for which it can outbid owners on prices while offering rents low enough to ensure zero vacancy relative to owner rent bids. External markets and demographics: exogenous outside market prices for oceanfront-equivalent and non-oceanfront-equivalent properties serve as benchmarks. Deviations between local and outside prices drive adjustments in agent parameter distributions through a combination of short-run herding (positive feedback) and long-run arbitrage (negative feedback), shifting mean income, WTP, and risk tolerance to induce influx or exit of higher- or lower-income agents. Nourishment decision-making: residents periodically evaluate nourishment plans over a 10-year horizon (intervals from every 2 to 5 years). Costs (fixed + sand volume-based) are discounted; financing uses 5-year amortized local bonds with oceanfront properties bearing a higher cost share. Benefits are the increase in property values under each plan relative to no nourishment, accounting for expected beach width and tax increments. A plan is implemented if a majority of resident-owners vote in favor. Scenarios: four main scenarios are simulated with constant erosion and storm probability and 1 m SLR over 100 years: (1) Baseline—90% nourishment subsidy; outside markets constant in real terms; agent flux enabled. (2) Subsidy cut—subsidy reduced to 50%; outside markets constant. (3) Outside appreciation—subsidy 90%; outside markets double over 50 years starting at year 50 and remain elevated. (4) Outside decline—subsidy 90%; outside markets constant for 50 years after SLR onset then decline to 10% of initial value over the next 50 years. Additional experiments include disabling agent flux and combining subsidy cuts with rising outside markets. Empirical context: Fig. 1 presents motivating evidence from a first-stage hedonic analysis of 23,184,659 US property sales (1989–2016) that estimates county-year fixed effects for coastal vs inland counties, controlling for property characteristics.

• In the baseline (90% subsidy; outside markets constant), property values do not immediately reflect SLR risks at SLR onset (t=50). Modest declines occur initially; an influx of relatively high-income owner-occupants (drawn by arbitrage with outside markets and higher tax advantages) sustains prices for decades despite rising risk. Around ~60 years after SLR onset, values begin to decline rapidly as risks continue to rise and the high-income influx saturates. Investors’ market share falls toward zero as owner-occupants dominate. Beach width remains wide due to frequent nourishment supported by subsidies and owner votes.
• Cutting the nourishment subsidy from 90% to 50% at SLR onset increases price volatility and leads to a sharper, earlier precipitous decline in values, especially for oceanfront properties. Mean beach width initially decreases due to reduced subsidization, later stabilizing as nourishment resumes at lower frequency. Median owner income tax rates increase as relatively higher-income agents enter sooner (accelerating gentrification). Investors’ market share declines more quickly as high-income owners crowd out investors and lower-income renters.
• When outside markets appreciate (values double over 50 years), coastal property prices rise well after SLR onset and remain above baseline even as barrier elevation approaches zero. Initially, nourishment frequency is unchanged; as prices rise, nourishment becomes more frequent and beach width increases relative to baseline. High-income owners enter sooner, and investors are rapidly crowded out.
• Combining subsidy reduction with rising outside markets shows that appreciation in outside markets can offset subsidy removal over the long run: prices continue to increase with SLR even as mean beach width declines initially; later, self-financed nourishment increases and beach width dynamics resemble the baseline, with a faster influx of high-income owners and quicker investor exit.
• If outside markets later decline (to 10% of initial levels), expected returns and property values plummet sharply despite continued nourishment maintaining beach width. Over ~30 years, high-income owners exit; investors purchase most or all properties and rent them to lower-income occupants, reversing earlier gentrification dynamics.
• Overall mechanisms: subsidies for nourishment, tax advantages for high-income owners (mortgage interest and property tax deductibility), and stable or appreciating outside markets all dampen SLR’s impact on coastal property values, but only delay precipitous declines as total inundation approaches. Removing subsidies improves risk capitalization (prices reflect flood risk) but accelerates coastal gentrification. Appreciating outside markets can sustain high values and trigger more frequent nourishment; declining outside markets can reverse gentrification and precipitate sharp value declines before inundation.

The coupled modeling reveals why high coastal property values persist despite growing climate risks. Three mechanisms dampen and delay capitalization of SLR risk: (1) Tax policy advantages for higher-income owner-occupants increase their effective willingness to pay by lowering user costs via mortgage interest and property tax deductions, encouraging bids on desirable coastal properties. (2) Outside market dynamics create arbitrage: when coastal prices dip relative to broader desirable markets, higher-income agents enter, propping up prices; when outside markets appreciate, rising tax bases justify greater amenity investment (e.g., more frequent nourishment). (3) Nourishment subsidies directly inflate coastal amenity values, delaying downward price adjustment. Together, these mechanisms sustain high values and induce coastal gentrification in the near-to-medium term, yet cannot prevent eventual price collapses as inundation nears. Policy tradeoffs emerge: reducing subsidies improves market efficiency by allowing faster risk capitalization but accelerates gentrification and pushes out lower-income households; maintaining subsidies slows demographic turnover but masks climate risk signals and delays necessary adjustments. Declining outside markets can reverse gentrification, leading to investor dominance and lower-income rental occupancy. The findings suggest that smoothing transitions—both price trajectories and demographic shifts—may be preferable to abrupt declines, with implications for adaptation strategy design.

The study develops C-HOM, a coupled human–natural, agent-based housing market model linking coastal physical dynamics, climate risks, amenities, and policy. It shows that nourishment subsidies, tax advantages for high-income owners, and appreciating outside markets delay—but do not prevent—steep declines in coastal property values as inundation approaches. Policy implications include a tradeoff between equity and efficiency: removing subsidies allows prices to reflect long-run risks sooner but accelerates gentrification; maintaining subsidies tempers demographic change but perpetuates mispricing. The authors suggest reallocating public funds from nourishment toward adaptation pathways that smooth transitions, such as managed retreat and buyouts with rentbacks, and toward incentives for adaptive, less durable coastal housing technologies. Future research should enrich the coupled modeling with spatial dynamics, heterogeneity in climate beliefs and demographics (e.g., race), rising nourishment costs, alternative adaptation instruments (insurance, dunes, seawalls, zoning), regulation of investor short-term rentals, interactions across neighboring communities, and endogenous housing supply adjustments.

• The model uses income as the primary indicator of gentrification; other dimensions (e.g., race) are not modeled.
• Housing supply is fixed, consistent with built-out barrier islands, so endogenous supply adjustments (new construction or retreat) are not considered.
• Physical processes are simplified: linear erosion, no explicit dune dynamics, and storm risk is parameterized rather than simulated as stochastic events with damage.
• Nourishment costs, sediment dynamics, and project externalities are simplified; potential cost escalation with cumulative sand use is not modeled.
• Investor behavior is represented by a single outside investor; heterogeneity among investors and short-term rental markets (e.g., restrictions on STRs) are abstracted.
• Agent entry/exit rates and belief heterogeneity are stylized; the speed and mechanisms of demographic flux are uncertain empirically.
• External market prices are exogenous; feedbacks from broader macroeconomic or climate policy changes are not endogenized.
• Model calibration relies on literature-based parameters; while sensitivity analyses are referenced, full empirical identification of all mechanisms is beyond scope.