Studying the Impacts of Environmental Amenities and Hazards with Nationwide Property Data: Best Data Practices for Interpretable and Reproducible Analyses

Nolte, Christoph; Boyle, Kevin J.; Chaudhry, Anita M.; Clapp, Christopher M.; Guignet, Dennis; Hennighausen, Hannah; Kushner, Ido; Liao, Yanjun; Mamun, Saleh; Pollack, Adam; Richardson, Jesse J.; Sundquist, Shelby; Swedberg, Kristen; Uhl, Johannes

doi:10.2139/ssrn.3900806

Cited by 16 publications

(15 citation statements)

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“…9 , our approach identifies the effect of rezoning properties into the SFHA as the result of FIRM updates over time. To build the panel model, we employ an extensively processed version of the Zillow ZTRAX database 26 , a comprehensive dataset of property transactions in the United potential social and economic consequences of overvaluation in US housing markets remain uncertain.…”

Section: Resultsmentioning

confidence: 99%

Unpriced climate risk and the potential consequences of overvaluation in US housing markets

et al. 2023

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Climate change impacts threaten the stability of the US housing market. In response to growing concerns that increasing costs of flooding are not fully captured in property values, we quantify the magnitude of unpriced flood risk in the housing market by comparing the empirical and economically efficient prices for properties at risk. We find that residential properties exposed to flood risk are overvalued by US$121–US$237 billion, depending on the discount rate. In general, highly overvalued properties are concentrated in counties along the coast with no flood risk disclosure laws and where there is less concern about climate change. Low-income households are at greater risk of losing home equity from price deflation, and municipalities that are heavily reliant on property taxes for revenue are vulnerable to budgetary shortfalls. The consequences of these financial risks will depend on policy choices that influence who bears the costs of climate change.

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Section: Resultsmentioning

confidence: 99%

Unpriced climate risk and the potential consequences of overvaluation in US housing markets

et al. 2023

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“…In our view, these findings point toward a substantial potential problem for the reproducibility and transparency of large‐scale flood loss estimation research. In the United States, as in many other countries, the aggregation of large‐scale property data is the domain of commercial endeavors, which has historically limited access to such data to well‐funded institutions and research groups, with only few notable exceptions (Nolte et al, 2021). Recent efforts to make property‐level flood risk estimates more widely available for the purpose of noncommercial research (e.g., by the First Street Foundation) are therefore laudable initiatives.…”

Section: Discussionmentioning

confidence: 99%

“…Following Nolte et al (2021), we identify residential buildings as those with corresponding ZTRAX building codes (RR101, RR102, RR000, and RR999) and at most two building footprints linked to a parcel. Remotely sensed building footprint data can omit buildings that are under tree cover or newly constructed.…”

Section: Methodsmentioning

confidence: 99%

“…By contrast, in large‐scale analyses (i.e., over a broader spatial domain), loss estimation relies on spatially aggregated representations of hazards, structures, or both (Aerts et al, 2013; Aznar‐Siguan & Bresch, 2019; Czajkowski et al, 2013; FEMA, 2021a; Johnson et al, 2020; Montgomery & Kunreuther, 2018; Narayan et al, 2017; Reguero et al, 2018; Remo et al, 2016; Wing et al, 2018; Zuzak et al, 2020). This is in part due to limited access to high‐resolution data (de Moel et al, 2015; Nolte, 2020; Nolte et al, 2021). Recent efforts to develop high‐resolution flood hazard estimates over large scales for noncommercial use (First Street Foundation, 2020) have the potential to change this practice and have resulted in notable exceptions (Armal et al, 2020; Wing et al, 2022; Wobus et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study

Pollack

Wing

Nolte

2022

J Flood Risk Management

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Large-scale estimations of flood losses are often based on spatially aggregated inputs. This makes risk assessments vulnerable to aggregation bias, a wellstudied, sometimes substantial outcome in analyses that model fine-grained spatial phenomena at coarse spatial units. To evaluate this potential in the context of large-scale flood risk assessments, we use data from a highresolution flood hazard model and structure inventory for over 1.3 million properties in Massachusetts and examine how prominent data aggregation approaches affect the magnitude and spatial distribution of flood loss estimates. All considered aggregation approaches rely on aggregate structure inventories but differ in whether flood hazard is also aggregated. We find that aggregating only structure inventories slightly underestimates overall losses (À10% bias), and when flood hazard data is spatially aggregated to even relatively small spatial units (census block), statewide aggregation bias can reach +366%. All aggregation-based procedures fail to capture the spatial covariation of inputs distributions in the upper tails that disproportionately generate total expected losses. Our findings are robust to several key assumptions, add important context to published risk assessments and highlight opportunities to improve flood loss estimation uncertainty quantification.

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“…Second is transferability: due to data limitations, damage estimates are often required for settings different from where the damage relationships are derived, calling for extrapolation [12][13][14]20 . A third is parsimony: input data demands should be light enough to facilitate application in a variety of settings and under common data constraints (Table S1) 12,20,22 . In addition to the criteria above, we propose interpretability as an additional priority for damage relationships.…”

Section: Introductionmentioning

confidence: 99%

New Empirical Models for Flood Loss Prediction and Implications for the Coterminous United States

Pollack

Wing

Pinter

et al. 2022

Preprint

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Flood-loss estimates are needed for floodplain development and mitigation projects, for setting fair insurance rates, and for guiding climate adaptation policy. Currently, flood-loss models, including depth-damage functions (DDFs) widely used in the U.S., lack empirical validation commensurate with the geographic extent and diversity of structures and flood exposure over which these predictions are needed. Using data from 845,776 U.S. National Flood Insurance Program claims, we validate DDFs and create alternative models grounded in empirical data and validation. These alternative models more accurately predict average observed damages for many types of structures and hazard compared to current DDFs which omit important variables and interactions that drive observed losses. We find that a major bottleneck in flood-loss estimation is the development and validation of flood-loss models for both damaged and undamaged homes, a gap FEMA could help close.

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Studying the Impacts of Environmental Amenities and Hazards with Nationwide Property Data: Best Data Practices for Interpretable and Reproducible Analyses

Cited by 16 publications

References 0 publications

Unpriced climate risk and the potential consequences of overvaluation in US housing markets

Unpriced climate risk and the potential consequences of overvaluation in US housing markets

Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study

New Empirical Models for Flood Loss Prediction and Implications for the Coterminous United States

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