Descriptive Statistics

Flood Factor scores, recent residential sales, and FEMA individual assistance claims

Purpose

This page documents several descriptive statistics used in the dissertation text. The calculations summarize:

the number and share of residential properties with elevated First Street Foundation Flood Factor scores;
the number of high-risk residential properties that sold after Flood Factor information became publicly available;
the number of FEMA individual assistance claims associated with selected Cook County flood disasters, by flood insurance status.

This page is designed as a transparent calculation record. The code is shown so readers can see how each statistic was produced.

Data sources

The calculations below use the following files.

File	Description	Source note
`data/raw/parcel_universe_currentyear.csv`	Parcel universe file used to define the broader parcel population.	This file was downloaded from *FILL IN SOURCE* on *FILL IN DATE*.
`data/processed/floodfactor_scores.csv`	First Street Foundation Flood Factor scores joined to parcel identifiers.	This file was created from *FILL IN SOURCE / API / EXPORT* on *FILL IN DATE*.
`data/raw/residential_pins_ever.csv`	Residential parcel identifiers and property class information.	This file was downloaded from *FILL IN SOURCE* on *FILL IN DATE*.
`data/processed/res_sales.rds`	Processed residential sales file.	This file was created from *FILL IN SOURCE / PROCESSING SCRIPT*.
`data/processed/City_Name_Mapping.csv`	Crosswalk from raw municipality names to cleaned municipality names.	This file was manually created / downloaded from *FILL IN SOURCE*.
`data/raw/indiv_assistance_V2_CookCounty.csv`	FEMA Individual Assistance records for Cook County.	This file was downloaded from *FILL IN FEMA DATA SOURCE* on *FILL IN DATE*.

Residential parcels and Flood Factor scores

The first step loads the parcel universe, First Street Foundation Flood Factor scores, and residential parcel identifiers. Residential parcels are restricted to the 2023 residential PIN file and then joined to the Flood Factor score data.

Code

puniverse <- read_csv("data/raw/parcel_universe_currentyear.csv")

ff_scores <- read_csv("data/processed/floodfactor_scores.csv")

res_pins <- read_csv("data/raw/residential_pins_ever.csv") |>
  filter(year == 2023) |>
  distinct(pin, class)


res_sales <- read_rds("data/processed/delete_probably/res_sales.rds") |>
  group_by(pin) |>
  summarize(year = max(year), .groups = "drop")

recent_res_sales <- res_sales |>
  filter(year > 2019)

pins <- left_join(res_pins, ff_scores)

Residential parcels with high Flood Factor scores

This calculation counts residential class 200 properties with a Flood Factor score greater than 4. In this file, env_flood_fs_factor > 4 corresponds to Flood Factor scores of 5 or higher.

Code

high_ff_residential_count <- pins |>
  filter(env_flood_fs_factor > 4, class > 199, class < 300) |>
  summarize(n = n()) |>
  pull(n)

high_ff_residential_count

[1] 198309

The share of residential parcels with high Flood Factor scores is calculated as follows.

Code

share_high_ff_residential <- high_ff_residential_count / nrow(res_pins)

share_high_ff_residential

[1] 0.1236108

Code

percent(share_high_ff_residential, accuracy = 0.1)

[1] "12.4%"

Recent sales among high Flood Factor properties

This section identifies high Flood Factor properties that sold after 2019. This helps describe the number of properties for which flood risk information may have been capitalized into transaction prices after the public release of Flood Factor information.

Code

high_ff_recent_sales <- pins |>
  filter(env_flood_fs_factor > 4, pin %in% recent_res_sales$pin) |>
  distinct(pin)

n_high_ff_recent_sales <- nrow(high_ff_recent_sales)

n_high_ff_recent_sales

[1] 48232

The share of high Flood Factor residential properties with a recent sale is calculated below.

Code

share_high_ff_with_recent_sale <- n_high_ff_recent_sales / high_ff_residential_count

share_high_ff_with_recent_sale

[1] 0.2432164

Code

percent(share_high_ff_with_recent_sale, accuracy = 0.1)

[1] "24.3%"

Flood Factor thresholds

The next table summarizes the number of parcels above several Flood Factor thresholds. The table separates residential class 200 parcels from other parcels.

Code

puni_pins_coded <- pins |>
  mutate(
    FFS_4plus = env_flood_fs_factor >= 4,
    FFS_5plus = env_flood_fs_factor >= 5,
    FFS_6plus = env_flood_fs_factor >= 6,
    Res_C2 = class > 199 & class < 300
  )

ff_threshold_summary <- puni_pins_coded |>
  group_by(Res_C2) |>
  summarize(
    FFS_4plus = sum(FFS_4plus, na.rm = TRUE),
    FFS_5plus = sum(FFS_5plus, na.rm = TRUE),
    FFS_6plus = sum(FFS_6plus, na.rm = TRUE),
    n = n(),
    .groups = "drop"
  ) |>
  mutate(
    share_FFS_4plus = FFS_4plus / n,
    share_FFS_5plus = FFS_5plus / n,
    share_FFS_6plus = FFS_6plus / n
  )

ff_threshold_summary |>
  mutate(
    across(starts_with("share_"), ~ percent(.x, accuracy = 0.1))
  )

Table 1: Number of parcels above selected First Street Foundation Flood Factor thresholds, by residential class 200 status.

# A tibble: 2 × 8
  Res_C2 FFS_4plus FFS_5plus FFS_6plus       n share_FFS_4plus share_FFS_5plus
  <lgl>      <int>     <int>     <int>   <int> <chr>           <chr>          
1 FALSE       3639      2604      2248   17408 20.9%           15.0%          
2 TRUE      274782    198309    166660 1586893 17.3%           12.5%          
# ℹ 1 more variable: share_FFS_6plus <chr>

FEMA Individual Assistance claims

This section summarizes FEMA Individual Assistance claims for selected Cook County flood disaster declarations.

The FEMA Individual Assistance file was downloaded from FILL IN FEMA DATA SOURCE. The file is filtered to Cook County, Illinois, and to selected flood-related disaster numbers.

Code

city_names <- read_csv("./data/processed/City_Name_Mapping.csv")

ind_assist_raw <- read_csv("./data/raw/indiv_assistance_V2_CookCounty.csv")

ind_assist <- ind_assist_raw |>
  filter(incidentTypeCode != "B", disasterNumber != "1763") |>
  mutate(
    year = str_sub(declarationDate, 1, 4),
    year = as.numeric(year),
    joinyear = case_when(
      year <= 2010 ~ 2000,
      year < 2020 & year > 2010 ~ 2010,
      year > 2020 ~ 2020,
      TRUE ~ year
    ),
    tract = str_sub(censusGeoid, 6, 9),
    tract = str_pad(tract, width = 6, pad = "0", side = "right")
  ) |>
  left_join(city_names)

Cook County disaster records

The object below keeps all Cook County records, excluding disaster number 4819.

Code

cook_assist <- ind_assist |>
  filter(county == "Cook (County)", disasterNumber != "4819")

cook_assist |>
  count(disasterNumber, declarationDate, incidentTypeCode, sort = TRUE) |> nice_dt()

Selected 2023 flood disaster declarations

The dissertation text focuses on selected 2023 Cook County flood events. This section filters to disaster numbers 4728 and 4749.

Code

selected_flood_assist <- ind_assist |>
  filter(
    county == "Cook (County)",
    disasterNumber %in% c("4728", "4749", 4728, 4749)
  )

selected_flood_assist |>
  count(disasterNumber, declarationDate, sort = TRUE) |> 
nice_dt()

The table below counts Individual Assistance claims with positive ihpAmount, grouped by disaster number and flood insurance status.

Code

claims_by_insurance <- selected_flood_assist |>
  filter(ihpAmount > 0) |>
  group_by(disasterNumber, floodInsurance) |>
  summarize(
    claimcount = n(),
    .groups = "drop"
  ) |>
  arrange(desc(claimcount)) |>
  pivot_wider(
    names_from = floodInsurance,
    values_from = claimcount,
    values_fill = 0
  )

nice_dt(claims_by_insurance)

Table 2: FEMA Individual Assistance claims with positive IHP amounts for selected Cook County flood disasters, by flood insurance status.

Notes for revision

Before publishing this page, update the source notes above by filling in:

the exact source for each raw data file;
the download date or API access date;
whether the file is publicly redistributable (only due to file size constraints on GitHub);
whether the website hosts the raw file, a processed file, or only summary statistics;

--- title: "Descriptive Statistics" subtitle: "Flood Factor scores, recent residential sales, and FEMA individual assistance claims" format: html: toc: true toc-depth: 2 code-fold: show code-tools: true execute: echo: true warning: false message: false --- ```{r} #| label: setup #| include: false library(tidyverse) library(readr) library(stringr) library(DT) library(scales) nice_dt <- function(df, page_length = 10, paging = NULL, caption = NULL, ...) { # automatically determine whether paging is needed if (is.null(paging)) { paging <- nrow(df) > page_length } DT::datatable( df, rownames = FALSE, caption = caption, options = list( paging = paging, pageLength = page_length, searching = FALSE, info = paging, lengthChange = FALSE, autoWidth = TRUE ), ) } ``` # Purpose This page documents several descriptive statistics used in the dissertation text. The calculations summarize: 1. the number and share of residential properties with elevated First Street Foundation Flood Factor scores; 2. the number of high-risk residential properties that sold after Flood Factor information became publicly available; 3. the number of FEMA individual assistance claims associated with selected Cook County flood disasters, by flood insurance status. This page is designed as a transparent calculation record. The code is shown so readers can see how each statistic was produced. # Data sources The calculations below use the following files. | File | Description | Source note | |---|---|---| | `data/raw/parcel_universe_currentyear.csv` | Parcel universe file used to define the broader parcel population. | This file was downloaded from **___FILL IN SOURCE___** on **___FILL IN DATE___**. | | `data/processed/floodfactor_scores.csv` | First Street Foundation Flood Factor scores joined to parcel identifiers. | This file was created from **___FILL IN SOURCE / API / EXPORT___** on **___FILL IN DATE___**. | | `data/raw/residential_pins_ever.csv` | Residential parcel identifiers and property class information. | This file was downloaded from **___FILL IN SOURCE___** on **___FILL IN DATE___**. | | `data/processed/res_sales.rds` | Processed residential sales file. | This file was created from **___FILL IN SOURCE / PROCESSING SCRIPT___**. | | `data/processed/City_Name_Mapping.csv` | Crosswalk from raw municipality names to cleaned municipality names. | This file was manually created / downloaded from **___FILL IN SOURCE___**. | | `data/raw/indiv_assistance_V2_CookCounty.csv` | FEMA Individual Assistance records for Cook County. | This file was downloaded from **___FILL IN FEMA DATA SOURCE___** on **___FILL IN DATE___**. | # Residential parcels and Flood Factor scores The first step loads the parcel universe, First Street Foundation Flood Factor scores, and residential parcel identifiers. Residential parcels are restricted to the 2023 residential PIN file and then joined to the Flood Factor score data. ```{r} #| label: load-residential-flood-factor-data puniverse <- read_csv("data/raw/parcel_universe_currentyear.csv") ff_scores <- read_csv("data/processed/floodfactor_scores.csv") res_pins <- read_csv("data/raw/residential_pins_ever.csv") |> filter(year == 2023) |> distinct(pin, class) res_sales <- read_rds("data/processed/delete_probably/res_sales.rds") |> group_by(pin) |> summarize(year = max(year), .groups = "drop") recent_res_sales <- res_sales |> filter(year > 2019) pins <- left_join(res_pins, ff_scores) ``` ## Residential parcels with high Flood Factor scores This calculation counts residential class 200 properties with a Flood Factor score greater than 4. In this file, `env_flood_fs_factor > 4` corresponds to Flood Factor scores of 5 or higher. ```{r} #| label: count-residential-high-flood-factor high_ff_residential_count <- pins |> filter(env_flood_fs_factor > 4, class > 199, class < 300) |> summarize(n = n()) |> pull(n) high_ff_residential_count ``` The share of residential parcels with high Flood Factor scores is calculated as follows. ```{r} #| label: share-residential-high-flood-factor share_high_ff_residential <- high_ff_residential_count / nrow(res_pins) share_high_ff_residential percent(share_high_ff_residential, accuracy = 0.1) ``` # Recent sales among high Flood Factor properties This section identifies high Flood Factor properties that sold after 2019. This helps describe the number of properties for which flood risk information may have been capitalized into transaction prices after the public release of Flood Factor information. ```{r} #| label: recent-sales-high-ff-properties high_ff_recent_sales <- pins |> filter(env_flood_fs_factor > 4, pin %in% recent_res_sales$pin) |> distinct(pin) n_high_ff_recent_sales <- nrow(high_ff_recent_sales) n_high_ff_recent_sales ``` The share of high Flood Factor residential properties with a recent sale is calculated below. ```{r} #| label: share-high-ff-with-recent-sale share_high_ff_with_recent_sale <- n_high_ff_recent_sales / high_ff_residential_count share_high_ff_with_recent_sale percent(share_high_ff_with_recent_sale, accuracy = 0.1) ``` # Flood Factor thresholds The next table summarizes the number of parcels above several Flood Factor thresholds. The table separates residential class 200 parcels from other parcels. ```{r} #| label: tbl-flood-factor-thresholds #| tbl-cap: "Number of parcels above selected First Street Foundation Flood Factor thresholds, by residential class 200 status." puni_pins_coded <- pins |> mutate( FFS_4plus = env_flood_fs_factor >= 4, FFS_5plus = env_flood_fs_factor >= 5, FFS_6plus = env_flood_fs_factor >= 6, Res_C2 = class > 199 & class < 300 ) ff_threshold_summary <- puni_pins_coded |> group_by(Res_C2) |> summarize( FFS_4plus = sum(FFS_4plus, na.rm = TRUE), FFS_5plus = sum(FFS_5plus, na.rm = TRUE), FFS_6plus = sum(FFS_6plus, na.rm = TRUE), n = n(), .groups = "drop" ) |> mutate( share_FFS_4plus = FFS_4plus / n, share_FFS_5plus = FFS_5plus / n, share_FFS_6plus = FFS_6plus / n ) ff_threshold_summary |> mutate( across(starts_with("share_"), ~ percent(.x, accuracy = 0.1)) ) ``` # FEMA Individual Assistance claims This section summarizes FEMA Individual Assistance claims for selected Cook County flood disaster declarations. The FEMA Individual Assistance file was downloaded from **___FILL IN FEMA DATA SOURCE___**. The file is filtered to Cook County, Illinois, and to selected flood-related disaster numbers. ```{r} #| label: load-fema-individual-assistance city_names <- read_csv("./data/processed/City_Name_Mapping.csv") ind_assist_raw <- read_csv("./data/raw/indiv_assistance_V2_CookCounty.csv") ind_assist <- ind_assist_raw |> filter(incidentTypeCode != "B", disasterNumber != "1763") |> mutate( year = str_sub(declarationDate, 1, 4), year = as.numeric(year), joinyear = case_when( year <= 2010 ~ 2000, year < 2020 & year > 2010 ~ 2010, year > 2020 ~ 2020, TRUE ~ year ), tract = str_sub(censusGeoid, 6, 9), tract = str_pad(tract, width = 6, pad = "0", side = "right") ) |> left_join(city_names) ``` ## Cook County disaster records The object below keeps all Cook County records, excluding disaster number 4819. ```{r} #| label: cook-county-assistance-records cook_assist <- ind_assist |> filter(county == "Cook (County)", disasterNumber != "4819") cook_assist |> count(disasterNumber, declarationDate, incidentTypeCode, sort = TRUE) |> nice_dt() ``` ## Selected 2023 flood disaster declarations The dissertation text focuses on selected 2023 Cook County flood events. This section filters to disaster numbers 4728 and 4749. ```{r} #| label: selected-cook-county-flood-disasters selected_flood_assist <- ind_assist |> filter( county == "Cook (County)", disasterNumber %in% c("4728", "4749", 4728, 4749) ) selected_flood_assist |> count(disasterNumber, declarationDate, sort = TRUE) |> nice_dt() ``` The table below counts Individual Assistance claims with positive `ihpAmount`, grouped by disaster number and flood insurance status. ```{r} #| label: tbl-fema-claims-by-flood-insurance #| tbl-cap: "FEMA Individual Assistance claims with positive IHP amounts for selected Cook County flood disasters, by flood insurance status." claims_by_insurance <- selected_flood_assist |> filter(ihpAmount > 0) |> group_by(disasterNumber, floodInsurance) |> summarize( claimcount = n(), .groups = "drop" ) |> arrange(desc(claimcount)) |> pivot_wider( names_from = floodInsurance, values_from = claimcount, values_fill = 0 ) nice_dt(claims_by_insurance) ``` # Notes for revision Before publishing this page, update the source notes above by filling in: - the exact source for each raw data file; - the download date or API access date; - whether the file is publicly redistributable (only due to file size constraints on GitHub); - whether the website hosts the raw file, a processed file, or only summary statistics;