FEDS Paper: Premiums or Peril — QuantCalc Tools Rate & Fee Analysis

FEDS Paper: Premiums or Peril — QuantCalc Tools Rate & Fee Analysis (QuantCalc Tools Desk Note, 2026-10-29) — This briefing summarizes the supplied source material at https://www.federalreserve.gov/econres/feds/premiums-or-peril.htm. Imported financial records below are historical snapshot data and have not been checked against current official registers.

Answer-First Summary: Erik Heitfield , Mallick Hossain, and Katie Merritt

Using fine-grained data on 465,000 Florida home sales over twelve years and semiparametric machine learning methods, we examine how home prices respond to weather-related risk factors. After controlling for geography, home characteristics, and transaction features, we find that home prices are negatively related to property-level expected weather losses derived from catastrophe models, insurance premiums, and exposure to recent hurricanes, but with notably different magnitudes. Expected weather losses show the strongest association with prices and are consistent with markets rationally capitalizing anticipated future losses into current property valuations. Insurance premiums have a statistically significant but economically more modest negative effect on home prices, suggesting that housing market participants may view premium differences across properties as more transitory. Recent hurricane exposure has only a marginal effect on house prices, suggesting that hurricane risk is well-understood in Florida so that realized weather events convey little new information about future losses.

Imported Financial Source Snapshot (Not Independently Verified)#

The following fields are copied from an imported historical snapshot. They have not been checked against current official regulator registers. Do not treat them as proof of authorization, current license status, or an official warning.

• INTO (INTO) — Imported score: 2.7/10 | Imported risk field: unknown | Source license records: [No license record in this snapshot]

Core Analysis (QuantCalc Tools Rate & Fee Analysis)#

Erik Heitfield , Mallick Hossain, and Katie Merritt

Using fine-grained data on 465,000 Florida home sales over twelve years and semiparametric machine learning methods, we examine how home prices respond to weather-related risk factors. After controlling for geography, home characteristics, and transaction features, we find that home prices are negatively related to property-level expected weather losses derived from catastrophe models, insurance premiums, and exposure to recent hurricanes, but with notably different magnitudes. Expected weather losses show the strongest association with prices and are consistent with markets rationally capitalizing anticipated future losses into current property valuations. Insurance premiums have a statistically significant but economically more modest negative effect on home prices, suggesting that housing market participants may view premium differences across properties as more transitory. Recent hurricane exposure has only a marginal effect on house prices, suggesting that hurricane risk is well-understood in Florida so that realized weather events convey little new information about future losses.

Imported Financial Source Snapshot (Not Independently Verified)#

The following fields are copied from an imported historical snapshot. They have not been checked against current official regulator registers. Do not treat them as proof of authorization, current license status, or an official warning.

• INTO (INTO) — Imported score: 2.7/10 | Imported risk field: unknown | Source license records: [No license record in this snapshot]

Key Quantitative & Regulatory Takeaways#

• Primary Subject: FEDS Paper: Premiums or Peril
• Figures & Entities in Supplied Text: no source facts provided
• Provided Source: Source link

Structured Fact & Metric Comparison Table#

Frequently Asked Questions (FAQ)#

What are the exact figures and regulatory details reported in FEDS Paper: Premiums or Peril?#

The supplied text states: Erik Heitfield , Mallick Hossain, and Katie Merritt

Using fine-grained data on 465,000 Florida home sales over twelve years and semiparametric machine learning methods, we examine how home prices respond to weather-related risk factors. After controlling for geography, home characteristics, and transaction features, we find that home prices are negatively related to property-level expected weather losses derived from catastrophe models, insurance premiums, and exposure to recent hurricanes, but with notably different magnitudes. Expected weather losses show the strongest association with prices and are consistent with markets rationally capitalizing anticipated future losses into current property valuations. Insurance premiums have a statistically significant but economically more modest negative effect on home prices, suggesting that housing market participants may view premium differences across properties as more transitory. Recent hurricane exposure has only a marginal effect on house prices, suggesting that hurricane risk is well-understood in Florida so that realized weather events convey little new information about future losses.

Imported Financial Source Snapshot (Not Independently Verified)#

The following fields are copied from an imported historical snapshot. They have not been checked against current official regulator registers. Do not treat them as proof of authorization, current license status, or an official warning.

• INTO (INTO) — Imported score: 2.7/10 | Imported risk field: unknown | Source license records: [No license record in this snapshot]

Has QuantCalc Tools independently verified this tools data?#

No independent verification was performed by this rewrite step. The supplied source link is https://www.federalreserve.gov/econres/feds/premiums-or-peril.htm.

Imported Financial Source Snapshot (Not Independently Verified)#

The following fields are copied from an imported historical snapshot. They have not been checked against current official regulator registers. Do not treat them as proof of authorization, current license status, or an official warning.

• INTO (INTO) — Imported score: 2.7/10 | Imported risk field: unknown | Source license records: [No license record in this snapshot]

Quantitative Verification & Analytical Model#