Bridging the Gap Between Revealed and Stated Preferences in Flood-prone Housing Markets

Koning, Koen de; Filatova, Tatiana; Bin, Okmyung

doi:10.1016/j.ecolecon.2017.01.022

Cited by 34 publications

(50 citation statements)

References 49 publications

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“…This discount distorts our estimates of the flood dummy coefficient in the simulations, and is likely governed by spatial correlation between location in the flood zone and other housing attributes. This is further explained in detail in de Koning, Filatova, and Bin ().…”

Section: Resultsmentioning

confidence: 93%

“…The initial model is set up by uploading the entire GIS map of actual properties and their attributes. At the start of the simulation, the expected sales prices for properties are assigned using HA of the actual sales data combined with spatial interpolation (kriging) of the residuals (de Koning, Filatova, and Bin ). The model dynamics are realized through a sequence of steps: (i) households who own a property may decide to put it on a market, (ii) real estate agents advise sellers on their asking price given the current market trends, (iii) buyers search for a property that gives them the highest utility under the budget constraint and place a bid, (iv) sellers engage in price negotiations with the buyers who place the highest bid, and (v) real estate agents update their price prediction based on the recent realized bilateral transactions.…”

Section: Methodsmentioning

confidence: 99%

“…The weights ( A ij ), which indirectly measure preferences of an agent j for a particular attribute i , are based on de Koning, Filatova, and Bin (), which examines HA of actual sales using only the key housing characteristics that drive the variation in sales prices . Table presents the ANOVA results, illustrating the fraction of variance explained by each input variable (including the residual variance), which captures neighborhood quality.…”

Section: Methodsmentioning

confidence: 99%

“…The actual probability of a certain number of floods (N) experienced by a household in 100‐year and 500‐year flood zones depends on the length of residence (YR). Following de Koning, Filatova, and Bin (), the probability of N floods (P N ) is estimated as:

P_{N} = p N * {(1 - p)}^{italicYR - N} * ((), \frac{italicYR}{N}),

where p is the annual probability of a flood occurring, reflected by the 100‐year and 500‐year flood zone. Consequently, the expected utility ( EU ) a household receives from owning a property equals:

italicEU = \sum_{N = 0} U_{N} italiclosses * P_{N}

given that U_{italicN losses} = U_{0 L} - U_{L} * N

where U N losses is the utility gain for a property considering a specific number of floods.…”

Section: Methodsmentioning

confidence: 99%

“…Given the spatial correlation/multicollinearity among these spatial attributes, we assume that neighborhood quality is represented by the residuals of the hedonic regression. We apply spatial interpolation (kriging) to quantify the impact of neighborhood quality on property prices (de Koning, Filatova, and Bin ). To endow agents with heterogeneous incomes and housing budgets based on the empirical data (Statista ; United States Census Bureau ; Quigley and Raphael ), the results of an ANOVA of the main housing attributes are used as proxies for household's preferences for the corresponding attributes as described in the next section.…”

Section: Study Area and Datamentioning

confidence: 99%

See 4 more Smart Citations

Capitalization of Flood Insurance and Risk Perceptions in Housing Prices: An Empirical Agent‐Based Model Approach

Koning¹,

Filatova²,

Bin

2019

Southern Economic Journal

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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.

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Section: Resultsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

P_{N} = p N * {(1 - p)}^{italicYR - N} * ((), \frac{italicYR}{N}),

italicEU = \sum_{N = 0} U_{N} italiclosses * P_{N}

given that U_{italicN losses} = U_{0 L} - U_{L} * N

where U N losses is the utility gain for a property considering a specific number of floods.…”

Section: Methodsmentioning

confidence: 99%

Section: Study Area and Datamentioning

confidence: 99%

See 3 more Smart Citations

Capitalization of Flood Insurance and Risk Perceptions in Housing Prices: An Empirical Agent‐Based Model Approach

Koning¹,

Filatova²,

Bin

2019

Southern Economic Journal

Self Cite

View full text Add to dashboard Cite

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A Typology for Characterizing Human Action in MultiSector Dynamics Models

Yoon¹,

Lankao

Yang

et al. 2022

Preprint

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In modern society, sectors delivering services critical to economic productivity, environmental protection, and human wellbeing are inextricably linked through a network of interdependencies. The societal importance of cross-sectoral interactions is made especially apparent during periods of failure, manifested either abruptly or gradually, which can result in major economic loss, disrupted communities, environmental impact, and human casualties (Helbing, 2013). During Hurricanes Katrina and Sandy, for example, sudden failures in flood protection, energy and food provision, and communications cascaded into an impairment of critical services including healthcare provision, ultimately leading to the loss of human life (

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Uncertainty analysis in multi-sector systems: Considerations for risk analysis, projection, and planning for complex systems

Srikrishnan

Wong

Lamontagne

et al. 2022

Preprint

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Simulation models of multi-sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi-sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both singleand multi-sector systems. Here we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi-sector model and their interactions with policy design as they emerge at different stages of analysis: (1) inference and model calibration; (2) projecting future outcomes; and (3) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi-sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

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Bridging the Gap Between Revealed and Stated Preferences in Flood-prone Housing Markets

Cited by 34 publications

References 49 publications

Capitalization of Flood Insurance and Risk Perceptions in Housing Prices: An Empirical Agent‐Based Model Approach

Capitalization of Flood Insurance and Risk Perceptions in Housing Prices: An Empirical Agent‐Based Model Approach

A Typology for Characterizing Human Action in MultiSector Dynamics Models

Uncertainty analysis in multi-sector systems: Considerations for risk analysis, projection, and planning for complex systems

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