Disaster on the Horizon: The Price Effect of Sea Level Rise

Bernstein, Asaf; Gustafson, Matthew; Lewis, Rory

doi:10.2139/ssrn.3073842

Cited by 93 publications

(133 citation statements)

References 38 publications

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“…Third, by using daily hurricane forecasts from NOAA, this paper adds to recent climate finance research that analyzes how NOAA forecasts are reflected in asset prices. Drawing mixed conclusions, several papers (see Bernstein, Gustafson, and Lewis (2018); Giglio, Maggiori, Rao, Stroebel, and Weber (2018); Murfin and Spiegel (2019)) use NOAA sea level rise predictions to examine whether residential real estate prices reflect sea level rise risks. Our use of NOAA forecasts is substantially different, because for hurricanes we can observe multiple isolated events from inception to resolution, whereas NOAA's forecasts for sea level rise are long-term and cannot yet be compared to realizations.…”

Section: Related Literaturementioning

confidence: 99%

Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics

Kruttli

Tran

Watugala

2019

FEDS

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We investigate the uncertainty dynamics surrounding extreme weather events through the lens of option and stock markets by identifying market responses to the uncertainty regarding both potential hurricane landfall and subsequent economic impact. Stock options on firms with establishments exposed to the landfall region exhibit increases in implied volatility of 5-10 percent, reflecting impact uncertainty. Using hurricane forecasts, we show that landfall uncertainty and potential impact uncertainty are reflected in prices before landfall. We find no evidence that markets incorporate better hurricane forecasts than those from NOAA. Improvements to hurricane forecasts could have economically significant effects in financial markets.

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Section: Related Literaturementioning

confidence: 99%

Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics

Kruttli

Tran

Watugala

2019

FEDS

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“…2 Rosen (1974) provides a theoretical framework enabling the sale price of a house to be modeled with a hedonic function of its physical and location attributes. Following this seminal paper, a strand of literature has been developed using the numerical portion of real estate transactions data to assess the price impacts for certain housing characteristics and neighborhood disamenities (Palmquist, 1984, Lindenthal, 2017, Muehlenbachs et al, 2015, Bernstein et al, 2018. 3 Although Liberti and Petersen (2018) and Garmaise and Moskowitz (2004) point out that houses are heterogeneous goods for which some characteristics cannot be easily captured by numerical data ("hard" information), the widely used hedonic framework does not provide a solution to control for unobserved heterogeneities that cannot be easily transmitted in impersonal ways ("soft" information).…”

Section: Introductionmentioning

confidence: 99%

Information Value of Property Description: A Machine Learning Approach

Shen

Ross

2018

SSRN Journal

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This paper employs a ML-Hedonic approach to quantify the value of uniqueness, a type of "soft" information embedded in real estate advertisements. We first propose an unsupervised learning algorithm to quantify levels of semantic deviation ("uniqueness") in descriptions, the textual portions of real estate advertisements. We then estimated the impact of description uniqueness on real estate transaction outcomes using linear hedonic pricing models. The results indicate textual data disseminate information that numerical data cannot capture, and property descriptions effectively narrow the information gap between structured real estate data and the houses by conveying "soft" information about unique house features. A one standard deviation (0.08) increase in description uniqueness compared to neighboring properties leads to a 5.6% increase in property sale prices and a 2.3-day delay in the closing time, controlling for house characteristics, transaction circumstances, and agent unobservables. This paper provides theoretical and empirical insights on how to utilize the emerging Machine Learning tools in economic research.

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“…The finance literature has also remained resolutely anchored on the impacts of environmental changes on firm performance or asset valuation (Griffin et al, 2015;Brown et al, 2017;Tulloch et al, 2017). Fewer studies have incorporated scientific findings to quantitatively measure or reflect environmental risks in asset pricing or risk management (Burke et al, 2015;Dietz et al, 2016;Balvers et al, 2017;Bernstein et al, 2019). There is little research that investigates how firms and industries are affected by the planetary boundary processes and conditions.…”

Section: Linking Planetary Boundaries To Financementioning

confidence: 99%

The future trajectory for environmental finance: planetary boundaries and environmental, social and governance analysis

Ding

Daugaard

Linnenluecke

2020

Accounting & Finance

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Urgent issues such as climate change have drawn increasing attention from finance scholars. Most research has situated a corporate response within the context of the environmental, social and governance performance. However, other disciplines express concerns around environmental degradation within broader frameworks, such as the Planetary Boundaries framework. We highlight the different conceptualisations of ESG and planetary boundaries and call for further research that links finance research to the Planetary Boundaries framework. We describe how contributions in the Accounting & Finance Special Issue on Environmental Finance advance research in this area and explore implications for future research that responds to the imperatives of remaining within Planetary Boundaries.

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Disaster on the Horizon: The Price Effect of Sea Level Rise

Cited by 93 publications

References 38 publications

Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics

Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics

Information Value of Property Description: A Machine Learning Approach

The future trajectory for environmental finance: planetary boundaries and environmental, social and governance analysis

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