Access county-level data on NFIP policies

Usage

get_nfip_policies(
  state_abbreviation,
  county_geoids = NULL,
  file_name = "fima_nfip_policies_2025_10_14.parquet",
  api = FALSE
)

Arguments

state_abbreviation: A 2 letter state abbreviation (e.g. TX).
county_geoids: A character vector of five-digit county codes.
file_name: The name (not the full path) of the Box file containing the raw data.
api: If TRUE, query the API. If FALSE (default), read from file_name.

Value

A dataframe of project-level funding requests and awards, along with variables that can be aggregated to the county level.

state_fips: A two-digit state identifier.
state_abbreviation: The two-character abbreviation of the state.
county_code: The five-digit county identifier.
county_name: The name of the county.
census_tract: The 11-digit census tract code.
policy_cost: The cost of the policy summing the calculated premium, reserve fund assessment, federal policy fee, and HFIAA surcharge.
policy_count: The number of insured units in active status associated with the policy.
policy_rated_flood_zone: The NFIP flood zone.
policy_premium_total_cost: The policy premium; negative values indicate a refund.
policy_date_termination: The date when the policy is no longer in effect, either because it was cancelled or lapsed.
policy_date_effective: The effective date of the flood policy.
building_occupancy_type: The occupancy type of the building.
building_replacement_cost: The insurer's estimated cost to replace the building.

Details

The following dataset houses information on NFIP policies (both historic and current). In order to filter to current policies, the analyst will need to filter on the policy_date_termination and policy_date_effective columns.

The dataset also contains both residential and commercial policies. In order to filter to residential policies, the analyst can filter out the "non-residential" occupancy type.

Examples

if (FALSE) { # \dontrun{
 test <- get_nfip_policies(
      state_abbreviation = "TX",
      county_geoids = c("48201"),
      file_name = "fima_nfip_policies_2025_10_14.parquet",
      api = FALSE) |>
    dplyr::filter(
      !occupancy_type %in% c("non-residential"), ### only residential claims,
      policy_date_termination >= as.Date("2025-10-15"),
      policy_date_effective <= as.Date("2025-10-15")) |>
    dplyr::group_by(county_geoid)|>
    dplyr::summarise(avg_policy_cost = mean(policy_cost))
} # }