Tax Year 2021

Current and Future Uses of Homestead Exemptions in Cook County Replication Materials

knitr::opts_chunk$set(warning = FALSE, message = FALSE)

options(scipen = 999)

library(tidyverse)
library(glue)
library(sf)
library(DT)
library(flextable)

nicknames <- readxl::read_excel("../Necessary_Files/muni_shortnames.xlsx")


county_sums <- read_csv("../Output/ptaxsim_cook_level_2006to2024.csv")

muni_sums <- read_csv("../Output/ptaxsim_muni_level_2006to2024.csv") |> 
 left_join(nicknames)


muni_mc_sums <- read_csv("../Output/ptaxsim_muni_MC_2006to2024.csv") |>
  rename_all(~str_replace(., "muni_mc_", ""))


# muni_cl_sums <- read_csv("../Output/ptaxsim_muni_class_summaries_2006to2023.csv") |>
#   left_join(nicknames)

muni_shp <- read_sf("../Necessary_Files/muni_shp.gpkg")

# # Create an empty data frame with a column named "year"
#  params <- data.frame(year = numeric(0))
# 
# # Add the value 2021 to the "year" column
#  params <- rbind(params, data.frame(year = 2024))

County-Wide Exemption Summary Statistics (Tax Year 2021)

tbl <- county_sums |>
    filter(year == params$year) |>
    group_by(year) |>
    summarize(
      'PINs' = sum(cty_PC),
      'PINs (w/ exemps.)' = sum(cty_PC_claimed_exe),
      'Taxed EAV' = sum(cty_taxed_eav),
      'Exempt EAV' = sum(exempt_allexemptions_eav),
      'GHE' = sum(exempt_GHE_eav + exempt_LTHO_eav),
      'Senior' = sum(exempt_SR_eav),
      'Senior Freeze' = sum(exempt_FR_eav),
      'Other Exemps.' = sum(exempt_allexemptions_eav -exempt_GHE_eav - exempt_SR_eav - exempt_FR_eav )
      ) |>
    pivot_longer(cols = c('PINs':'Other Exemps.'),
                 names_to = "Metric", values_to = "Total") |>
    select(-year) |> 
    mutate(
      Total = case_when(
        Metric %in% c("Other Exemps.", "EAV", #"EqAV", 
                      "Taxed EAV", "Exempt EAV","GHE", "Senior", "Senior Freeze") ~ 
          paste0(format(round(`Total` / 1e9, 2), big.mark = ","), " B"),
        TRUE ~ format(`Total`, big.mark = ",")
      )
    )


ft <- tbl |>
    flextable(cwidth = c(1, 1)) |>
    align(j = 2, align = "right", part = "header") |>  # Align header
    align(j = 2, align = "right", part = "body") |>  # Align body
    align(i = 5:8, j = 1, align = "center", part = "body") |>  # Align body

    footnote(i = 1, j = 2, value = as_paragraph("EAV in billions."), ref_symbols = "*", part='header') #Footnote in new header
  

ft

Table 1.1: Summary of Homestead Exemption Use in Cook County (Tax Year 2021

Metric	Total*
PINs	1,864,594
PINs (w/ exemps.)	1,029,968
Taxed EAV	174.96 B
Exempt EAV	16.51 B
GHE	10.20 B
Senior	2.63 B
Senior Freeze	3.23 B
Other Exemps.	0.49 B
*EAV in billions.

EqAV is the 2021 equalization factor multiplied by assessed value. Thus, Taxed EAV and Exempt EAV together equal EqAV. It can be thought of as the “maximum available tax base” in a world without exemptions.

Figure 3. using EAV out of Municipality’s EAV

median_exempt <- muni_sums |>
  filter(year== params$year)|>
  mutate(muni_pct_eav_exempt =(exempt_allexemptions_eav) / (muni_eav_taxed + exempt_allexemptions_eav)) |> 
  select(muni_pct_eav_exempt, clean_name, agency_name)

median_exempt$exemption_category <- cut(
  median_exempt$muni_pct_eav_exempt,
  breaks = c(0, 0.075, 0.15, 0.22, 0.30, as.numeric(max(median_exempt$muni_pct_eav_exempt))),
  labels = c(
    "<7.5%",
    "7.5–14.99%",
    "15.0–21.99%",
    "22.0–29.99%",
    "≥30%"
  ),
  include.lowest = TRUE
)


exemption_colors <- c(
  "<7.5%" = "#ffffcc",
  "7.5–14.99%" = "lightblue",
  "15.0–21.99%" = "#4292C6",
  "22.0–29.99%" = "#253494",
  "≥30%" = "#00004d")

midpoint = scales::percent(median(median_exempt$muni_pct_eav_exempt), accuracy = 0.01)

median_muni = median_exempt |>
  arrange(muni_pct_eav_exempt) |>
  filter(muni_pct_eav_exempt == median(muni_pct_eav_exempt)) |>
  select(clean_name) |> 
  as.character()

print(median_exempt |>
    mutate(agency_name = as.character(ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", as.character(agency_name) ))) |>
 full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>
  ggplot(aes(fill = exemption_category)) + 
  geom_sf(aes(geometry = geom), color = "black") + 
  theme_void()+ 
  labs(title = "Exempt EAV as a Percent of Municipality EAV") +
        theme(axis.ticks = element_blank(), 
              axis.text = element_blank()) +
        scale_fill_manual(
          na.value = "gray50",
          name = "% Exempt",
          values = exemption_colors,
          na.translate = FALSE
        ) )

Figure 1.1: Share of EAV that is tax exempt due to homestead exemptions

The County-wide municipal-median percent of EAV exempt from property taxes in tax year 2021 was Morton Grove with 12.61%.

Alt Figure 3. Exempt EAV out of Muni’s Residential EAV

median_exempt <- muni_mc_sums |>
  filter(year== params$year & major_class_code == "2")|>
  group_by(clean_name) |>
  mutate(muni_pct_eav_exempt =( exempt_allexemptions_eav )/  (eav_taxed + exempt_allexemptions_eav))    |> 
  select(year, major_class_code, muni_pct_eav_exempt, eav_c2_taxed, exempt_allexemptions_eav, clean_name, eav_taxed)


midpoint = scales::percent(median(median_exempt$muni_pct_eav_exempt), accuracy = 0.01)

median_muni = median_exempt |>
  arrange(muni_pct_eav_exempt) |>
  filter(muni_pct_eav_exempt == median(muni_pct_eav_exempt)) |>
  select(clean_name) |> 
  as.character()


median_exempt$exemption_category <- cut(
  median_exempt$muni_pct_eav_exempt,
  breaks = c(0, 0.075, 0.15, 0.22, 0.30, as.numeric(max(median_exempt$muni_pct_eav_exempt))),
  labels = c(
    "<7.5%",
    "7.5–14.99%",
    "15.0–21.99%",
    "22.0–29.99%",
    "≥30%"
  ),
  include.lowest = TRUE
)

# writexl::write_xlsx(median_exempt, "../Output/Figure2_JD.xlsx")
  
# Define color palette
exemption_colors <- c(
  "<7.5%" = "#ffffcc",
  "7.5–14.99%" = "lightblue",
  "15.0–21.99%" = "#4292C6",
  "22.0–29.99%" = "#253494",
  "≥30%" = "#00004d")

print(median_exempt |>
        left_join(nicknames) |> # to get name that joines with the muni_shp shapefiles
        mutate(agency_name = as.character(ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", as.character(agency_name) ) )) |>
        full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>
        ggplot(aes(fill = exemption_category)) + 
        geom_sf(aes(geometry = geom), color = "black") + 
        theme_void()+ 
        labs(title = "Exempt EAV as a Percent of Residential EAV") +
        theme(axis.ticks = element_blank(), 
              axis.text = element_blank()) +
        scale_fill_manual(
          na.value = "gray50",
          name = "% Exempt",
          values = exemption_colors,
          na.translate = FALSE
        ) 
      
)

Figure 1.2: Share of residential EAV that is tax exempt due to homestead exemptions. Denominator is a municipality’s residential EAV.

Alt Figure 3. Using FMV

median_exempt <- muni_sums |>
  filter(year == params$year)|>
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv
) |> 
  select(pct_fmv_exempt, clean_name)

midpoint = scales::percent(median(median_exempt$pct_fmv_exempt), accuracy = 0.01)


median_exempt$exemption_category <- cut(
  median_exempt$pct_fmv_exempt,
  breaks = c(0, 0.075, 0.15, 0.22, 0.30, as.numeric(max(median_exempt$pct_fmv_exempt))),
  labels = c(
    "<7.5%",
    "7.5–14.99%",
    "15.0–21.99%",
    "22.0–29.99%",
    "≥30%"
  ),
  include.lowest = TRUE
)

# Define color palette
exemption_colors <- c(
  "<7.5%" = "#ffffcc",
  "7.5–14.99%" = "lightblue",
  "15.0–21.99%" = "#4292C6",
  "22.0–29.99%" = "#253494",
  "≥30%" = "#00004d")


median_muni = median_exempt |>
  arrange(pct_fmv_exempt) |>
  filter(pct_fmv_exempt == median(pct_fmv_exempt)) |>
  select(clean_name) |> as.character()

print(median_exempt |>
        left_join(nicknames) |>
        mutate(agency_name = as.character(ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", as.character(agency_name)) ) ) |>
        full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>
        ggplot(aes(fill = exemption_category)) + 
        geom_sf(aes(geometry = geom), color = "black") + 
        theme_void()+ 
        labs(title = "Exempt FMV as a Percent of Municipality FMV",
             caption = sprintf("The County-wide municipal-median is %s", midpoint)) +
        theme_void() + 
        theme(axis.ticks = element_blank(), 
              axis.text = element_blank()) +
        scale_fill_manual(
          na.value = "gray50",
          name = "% Exempt",
          values = exemption_colors,
          na.translate = FALSE
        ) )

Figure 1.3: Share of FMV that is tax exempt due to Homestead Exemptions Same as previous image but in different color scheme that indicates high and low instead of using a midpoint for the color gradient.

The County-wide municipal-median percent of FMV exempt from property taxes in tax year 2021 was Harwood Heights with 14.60%.

The percent of municipal EAV exempt from property taxes are greatest across Cook County’s south suburbs and lowest in the northern most suburbs of the county.

Figure 4. Exempt EAV: City vs. Suburbs

## makes table in margin for suburbs and city
geom_sums <- muni_sums |> 
  filter(year == params$year) |> 
  select(clean_name, exempt_allexemptions_eav) |>
  mutate(clean_name = ifelse(is.na(clean_name), "Unincorporated", clean_name),
         Geography = ifelse(clean_name == "Chicago", "City", "Suburbs"),
         Geography = ifelse(is.na(clean_name), "Unincorporated", Geography),
         Geography = factor(Geography, levels = c("Suburbs", "City", "Unincorporated"))) |>
  group_by(Geography) |>
  summarize("ExemptEAV" = sum(exempt_allexemptions_eav, na.rm=TRUE))

geom_sums |> 
  flextable() |>
  flextable::set_caption(caption ="Includes all exemption types.")

Geography	ExemptEAV
Suburbs	9,536,518,203
City	6,976,414,646

muni_mc_sums |> 
  filter(year == params$year) |> 
 # select(clean_name, muni_c_exe_homeowner:muni_c_exe_vet_dis) |>
  mutate(clean_name = ifelse(is.na(clean_name), "Unincorporated", clean_name),
    Geography = ifelse(clean_name == "Chicago", "City", "Suburbs"),
         Geography = ifelse(is.na(clean_name), "Unincorporated", Geography),
         Geography = factor(Geography, levels = c("Suburbs", "City", "Unincorporated"))) |>
  group_by(Geography) |>
  summarize("GHE" = sum(exempt_GHE_eav, na.rm=TRUE),
            "Senior Homestead" = sum(exempt_SR_eav, na.rm=TRUE),
            "Senior Freeze" = sum(exempt_FR_eav, na.rm=TRUE),
            "Other Exemptions" = sum(exempt_LTHO_eav + exempt_DIS_eav + exempt_VET_eav)) |>
  
  pivot_longer(cols = c(`GHE`:`Other Exemptions`), names_to = "Type" ) |>
   mutate(Type = factor(Type, levels = c("GHE", "Senior Homestead", "Senior Freeze", "Other Exemptions" 
                                         #"Senior Freeze", "Senior Homestead", "GHE"
                                        )
                                        )) |>
  ggplot(aes(x=Type, y = value/1e9, fill = Geography
             )) +
  
  geom_col( position = "dodge" ) +
  geom_text(aes(label = round(value/1e9, digits=1)), vjust=-0.5, position = position_dodge(.9)) +
  theme_minimal() +
  labs(title = paste0("Exempt EAV in Cook County, Tax Year ", params$year), 
       subtitle = "by Type and Geography", 
       y = "EAV (Billions)", x = "Exemption Type") + 
  scale_y_continuous(limits = c(0, 8)) +
  scale_fill_manual(values = c("blue3", "deepskyblue3", "gray") )

Figure 1.4: Exempt EAV in Cook County Exemption use varies between suburban Cook County and the City of Chicago which may point to broader socioeconomic patterns as well as residents’ eligibility and update. Note: Our current calculations undervalue the disabled veterans exemption.

Exemption use varies between suburban Cook County (9,536,518,203 EAV) and the City of Chicago (6,976,414,646 EAV) , which may point to broader socioeconomic patterns as well as residents’ eligibility and uptake.

Figure 5. Value of residential exemptions by type in Cook County over time, Tax Years 2006 - 2021

#
county_sums |>
   # filter(year == params$year) |>
    group_by(year) |>
    summarize(
      
      'GHE' = sum(exempt_GHE_eav + exempt_LTHO_eav),
      'Senior homestead' = sum(exempt_SR_eav),
      'Senior freeze' = sum(exempt_FR_eav),
     # 'Other Exemps.' = sum(exempt_allexemptions_eav -exempt_GHE_eav - exempt_SR_eav - exempt_FR_eav), 
      "Veterans (combined)" = sum(exempt_VET_eav, na.rm = T),
      "Persons with disabilities" = sum(exempt_DIS_eav, na.rm=T),
        #    "Other Exemptions" = sum(muni_c_exe_disabled + muni_c_exe_vet_returning+ muni_c_exe_vet_dis_lt50 + muni_c_exe_vet_dis_50_69+muni_c_exe_vet_dis_ge70, na.rm = T)
) |>
  pivot_longer(cols = c(`GHE`:`Persons with disabilities`), names_to = "Type" ) |>
  mutate(Type = factor(Type, levels = c(
                                        "Persons with disabilities", "Veterans (combined)", "Senior freeze", "Senior homestead", "GHE"))) |>
  ggplot(aes(x=year, y = value/1000000000, fill = Type)) +
  
  geom_bar( stat = "identity" ) + 
  theme_minimal() +
  labs(title = "Residential Exempt EAV in Cook County", 
       subtitle = paste0("2006-", params$year), 
       y = "EAV (Billions)", x ="") +
  scale_fill_manual(values = c("deepskyblue3", "chartreuse2",  "darksalmon", "brown2", "blue4" )) +
                    scale_x_continuous(breaks = c(2006, 2010, 2015, 2020, params$year + 2))

Figure 1.5: Residential Exempt EAV in Cook County

Trends in the total EAV reductions due to homestead exemptions reflect both real estate market shifts and the legislative changes in their availability, eligibility, and value.

Effect on Composite Tax Rates

Figure 6 will not be recreated but is just a bar chart of the change in tax rate in the table below

Table 1. Change in composite property tax rates due to exemptions, tax year 2021.

muni_ratechange <- read_csv(paste0("../Output/website_files/muni_ratechange_", params$year, ".csv"))


muni_ratechange |> 
  select(clean_name,  current_rate_avg, rate_noExe, change_noExe, final_tax_to_dist) |> 
  mutate(change_noExe = round(change_noExe * 100, digits = 2)) |>
  DT::datatable(rownames = FALSE, colnames = c('Municipality' = 'clean_name','Current Comp. Rate' = 'current_rate_avg', 'Hypothetical Rate' = 'rate_noExe',  'Composite Tax Rate Change'='change_noExe', 

                       'Composite Levy' = 'final_tax_to_dist'),
          caption = "Table 1 in Report for all Municipalities: Current and Hypothetical Composite Tax Rates if GHE $0") |>
  formatCurrency('Composite Levy', digits = 0) |>
  formatPercentage(columns = c(2,3), digits = 2)

Table 1.2: Searchable table containing all municipalities and the hypothetical tax rate change if exempt EAV became taxable.

muni_ratechange |>
  filter(!is.na(clean_name)) |> 
  select(clean_name,  current_rate_avg, rate_noExe, change_noExe 
) |>
  mutate(across(c(current_rate_avg, rate_noExe, change_noExe ), ~.*100)) |>
  arrange(desc(change_noExe)) |>
  slice(c(1:5, 69:73, 121:125)) |>
    #slice(c(1:5, 58:62, 115:119)) |>

  flextable() |> 
  border_remove() |>
  hline_top() |>
  hline(i = c(5,10)) |>
  
  set_header_labels(
    clean_name = "Municipality", 
    current_rate_avg = "With Exemptions",
    rate_noExe = "Without Exemptions",
    change_noExe = "Percent Point Difference"
    
  ) |>
  align(j = 2, align = "right") |>
  align(j=2, align = "right", part = "header") |>
  set_table_properties( layout = "autofit") |> bold(i = 8)

Table 1.3: Table 1 in Exemption Report. Shows the top 5, bottom 5, and median 5 municipalities, ranked by change in composite tax rate. Includes all exemption types in calculation of rate change.

Municipality	With Exemptions	Without Exemptions	Percent Point Difference
Park Forest	41.78	19.48	21.85
Phoenix	29.95	11.09	18.72
Riverdale	31.05	19.55	11.11
Hazelcrest	24.99	13.82	10.43
Dolton	25.20	14.50	10.35
Deer Park	10.63	8.40	2.39
Bridgeview	13.02	10.70	2.38
Broadview	12.21	9.80	2.38
Alsip	12.92	10.55	2.36
Morton Grove	10.67	8.13	2.34
Glencoe	9.53	8.88	0.64
Kenilworth	9.84	9.30	0.54
Winnetka	8.66	8.12	0.52
Hinsdale	7.49	7.03	0.49
Rosemont	10.57	10.19	0.24

Figure 7. Map of Spatial Patterns in Composite Tax Rate Change

Change in composite tax rate if exempt EAV was added back to the taxable tax base (i.e. if there were no exemptions).

muni_ratechange  |>
    mutate(across(c(current_rate_avg, rate_noExe, change_noExe ), ~.*100)) |>

  left_join(nicknames, by = "clean_name") |>
  mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name),
         shpfile_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) |>
  full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>

  ggplot(aes(fill = change_noExe)) + 
  geom_sf(aes(geometry = geom), color = "black") + 
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
   scale_fill_stepsn(colors = c( "#F7FEF5",
                                # "#e4f1e0",
                                "#d4f6cc",
                                "#47ba24",
                                "#1F6805",
                                "#133C04"),
                        show.limits=TRUE, 
                     limits = c(0, 15),
                     breaks = c(0, 2.5, 5, 7.5, 10, 15),
                     na.value = NA,
                        name = "Rate Change from \nExemptions"  
                    )

order <- burden_shift %>% 
  as_tibble() %>%
  group_by(agency_name, clean_name) %>%
    summarize(tax_rate_current = median(tax_rate_current), 
            taxrate_new = median(taxrate_new),
            taxrate_change = median(taxrate_change)) %>%
  arrange(taxrate_change)

median(order$taxrate_change)

burden_shift %>%  
  filter(taxrate_change < 0.0027 |taxrate_change > 0.06  |
           taxrate_change < (median(taxrate_change))+0.0007 & taxrate_change > (median(taxrate_change))-0.00075
         ) %>% 
    filter(Alea_cat == "Owner Occupied") %>%

 # filter(PropType == "Single-Family") %>%
  ungroup() %>% 
  select(clean_name, tax_rate_current, taxrate_new, agency_name) %>% 
  pivot_longer(c("tax_rate_current", "taxrate_new"), 
               names_to = "type", values_to = "tax_rate") %>% 
  left_join(order) %>%
  ggplot(aes(x = tax_rate*100, y= reorder(clean_name, taxrate_change)))+
  geom_line(aes(group = clean_name))+ 
   geom_point(aes(color=type), size=3 )+
  geom_hline(yintercept = 10.5, linetype =2)+
    geom_hline(yintercept = 5.5, linetype =2)+
  theme_minimal() + 
  theme( 
    legend.title = element_blank(),
               plot.title.position = "plot",
    legend.position = "none",
     #   panel.background = element_rect(fill='transparent'), #transparent panel bg
    plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
   )+
        scale_color_brewer(palette="Paired", labels = c("Exemptions", "No Exemptions"), direction = 1)+

  labs(title = "Difference in Composite Tax Rate if there were No Exemptions", 
       subtitle = "Ordered by Change in Tax Rate: Highest, Median, and Smallest Differences",
       caption = "Median change is 1.38 percentage points",
       x = "Composite Tax Rate (%)", y = "" )

Effect on Tax Burdens

Figure 8. Dolton example of Share of levy paid by property type

#read_csv(paste0("../../Output/Dont_Upload/0_Joined_PIN_data_", params$year, ".csv") )

pin_data <- read_csv(paste0("../Output/Dont_Upload/0_joined_PIN_data_", params$year, ".csv"))


mc_burden <- read_csv(paste0("../Output/website_files/muni_mc_burden_", params$year, ".csv"))


burden_shift <- read_csv(paste0("../Output/website_files/muni_burden_shift_", params$year, ".csv"))

mc_burden |> 
  mutate(group = case_when(class_1dig == 2 ~ "Class 2",
                         class_1dig == 3 ~ "Class 3",
                         T ~ "All Other Classes"))|>
  
  select(clean_name, group,  
         pct_taxburden_current, hyp_pct_taxburden) |>
  group_by(clean_name, group) |>
  summarize(`Current Tax Burden Share` = sum(pct_taxburden_current),
           `Tax Burden Share without Exemptions` =sum(hyp_pct_taxburden)) |>
  filter(clean_name=="Dolton") |>
  pivot_longer(cols = c(`Current Tax Burden Share`, `Tax Burden Share without Exemptions`), 
                names_to = "type", values_to = "value") |>
  ggplot(aes(x = group, y= value, group = type, fill = type) )+ 
  geom_col(position = "dodge") +  
  geom_text(aes(label = scales::percent(round(value, digits=3))), 
              vjust=-0.5, position = position_dodge(.9)) + 
  theme_classic()+
  theme(legend.title = element_blank(), legend.position = "bottom",
        axis.title.x  = element_blank()) + 
  scale_y_continuous(labels = scales::percent, name = "Share of Levy")

burden_c2 <- mc_burden |> 
  filter(class_1dig == 2) |> 
  select(clean_name, #pct_taxbase, 
         burden_shift,  pct_taxburden_current, hyp_pct_taxburden)

burden_shift |>  
  select(-c(muni_levy, muni_taxbase,))|>
  select(clean_name, Group, pct_taxburden_current, hyp_pct_taxburden, everything()) |>
  DT::datatable(rownames = FALSE, 
                colnames = c(
                  'Municipality' = 'clean_name',
                  'Current Taxbase' = 'group_taxbase', 
                  'Hyp. Taxbase' = 'hyp_group_taxbase',  
                  'Taxes Paid'='group_taxes_current', 
                  'Hyp Taxes Paid' = 'hyp_group_taxes',
                  'Share of Levy'='pct_taxburden_current',
                  'Share of Taxbase' = 'pct_taxbase_current',
              "Hyp. Share of Levy"  =  'hyp_pct_taxburden',
              "Burden Shift" = 'burden_shift'
                  ),
                caption = "Table 2 in Report for all Municipalities: Current and Hypothetical Composite Tax Rates if GHE $0"
      ) |>
  formatCurrency(columns = c(5:8), digits = 0) |>
  formatPercentage(columns = c(3,4,9,10), digits = 2)

mc_burden |> 
    mutate(pct_taxburden_current = round(pct_taxburden_current * 100, digits = 2),
           hyp_pct_taxburden = round(hyp_pct_taxburden * 100, digits = 2) ) |>

  filter(clean_name == "Dolton") |> 
  select(`Major Class`= class_1dig, `Current Tax Burden` = pct_taxburden_current, `Alt. Burden` = hyp_pct_taxburden) |> 
  arrange(desc(`Current Tax Burden`)) |>
  flextable::flextable()

Table 1.4: Ungrouped values for donut chart in report. Tax Burden is the Revenue Collected from a Major Class / the combined levy from local taxing agencies (non-TIF) in Dolton, IL.

Major Class	Current Tax Burden	Alt. Burden
2	65.40	79.31
5	29.78	17.86
1	1.66	0.97
8	1.20	0.70
3	0.94	0.55
6	0.56	0.33
9	0.37	0.21
4	0.07	0.04
0	0.00	0.00

Figure 9. Change in Share of Tax Burden

Was made in Excel. Not recoded for website yet.

Table 2. Change in share of property tax burden

Table 2: Change in the share of property tax burden due to exemptions for single-family, multi-family, and commercial and industrial properties, tax year 2021

burden_shift |>  
    select(clean_name, Group, burden_shift) |>
  mutate(burden_shift = round(burden_shift, digits =4)) |>
pivot_wider(id_cols=clean_name, names_from = Group, values_from = burden_shift) |>
  arrange((`Single-family`)) |> 
  DT::datatable(rownames = FALSE) 

Table 1.5: Supporting Table for Table 2 Change in tax burden if EAV that is current tax exempt due to homestead exemptions became taxable. Measured in percentage point change.

burden_c2 |> 
  mutate(burden_shift = burden_shift*100) |> # multiply by 100 to make it percentage point change without formatting to percent below.
  datatable(rownames = FALSE,
          colnames = c('Municipality' = 'clean_name', 
                       'Burden Shift, Pct Pt Change' = 'burden_shift', 
                       'Current Tax Burden\nC2 Tax Collected / Muni Levy' = 'pct_taxburden_current', 
                       "Hypothetical Tax Burden\nHyp. C2 Tax Collected / Muni Levy" = 'hyp_pct_taxburden'),
          caption = "Table 2 in Exemption Report: Current Share of Taxable EAV and Share of Levy Paid by Class 2 Properties"
           ) |>
  formatPercentage(c(2,3,4), digits = 2) |>
  formatRound(c(2), digits = 2)

Figure 9 Follow up. Current Tax Burden Map

burden_c2 |>
  left_join(nicknames) |>
  mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) |>
  full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>

  ggplot(aes(fill = pct_taxburden_current)) + 
  geom_sf(aes(geometry = geom), color = "black") + 
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
   scale_fill_stepsn(colors = c("#ffffcc","#a1dab4" ,"#41b6c4","#2c7fb8", "#253494"),
                        show.limits=TRUE, 
                     limits = c(0,1),
                     na.value = "gray70",
                     n.breaks = 6,
                        name = "Burden with \nExemptions", 
                     labels = scales::percent
                     
                    )+
  labs(title = "Current share of property tax burden", 
       subtitle = "for Class = 2 Property Types")

Table 3.

Difference in taxbills for those that do and do not claim homeowner exemption

## removes properties that have more than one exemption type
C2_munistats_filtered <- pin_data |> 
  filter(class > 199 & class < 300) |>
    filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0) |>
  group_by(clean_name)  |>
  arrange(av) |>
summarize(
    median_eq_av = round(median(eq_av)),
    median_taxed_eav = round(median(eq_av-exe_total_adj)),
    median_av = round(median(av)), 
    avg_av = round(mean(av)),
    avg_eq_av = round(mean(eq_av)),
    avg_taxed_eav = round(mean(eq_av-exe_total_adj)),

    C2_pins_in_muni = n(),
    C2_current_exemptions = sum(exe_total_adj, na.rm = TRUE),
    C2_HO_exemps = sum(exe_homeowner, na.rm = TRUE),
  )

C2_munistats_filtered |>  
  DT::datatable(rownames = FALSE, 
                colnames = c(
                  'Median AV*eq_factor' = 'median_eq_av',
                  'Average Eq.AV' = 'avg_eq_av',
                  'Median Taxed EAV' = 'median_taxed_eav', 
                  'Median AV' = 'median_av',  
                  'Average AV'='avg_av', 
                  'Class 2 Hyp Taxes Paid' = 'avg_taxed_eav',
                  'Class 2 Pin Count'='C2_pins_in_muni',
                  'Class 2 Exempt EAV' = 'C2_current_exemptions'
                  )      
                ) |>
  formatCurrency(columns = c(2:7, 9,10), digits = 0)

## Grouped by if they have a $0 tax bill and had the GHE per muni


muni_median_summarytable <- pin_data |> 
  select(pin, av, class, tax_code_num, tax_bill_total, av_certified, exe_homeowner:exe_abate, clean_name, eq_av, exe_total_adj, zero_bill, has_HO_exemp, taxed_eav, final_tax_to_dist, final_tax_to_tif) |>

  filter(class > 199 & class < 300) |> 
  
  # merge in muni residential median AV
  left_join(C2_munistats_filtered |> select(clean_name, median_av, median_eq_av, median_taxed_eav)) |>
  
  left_join(muni_ratechange |> select(clean_name, rate_noExe, rate_noGHE, rate_current)) |>
  # +/- 500 from municipalities median residential AV
  filter(av < median_av+200 & av > median_av-200) |>
  
  # Removes properties that received other types of exemptions
  filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0) |>
  
  arrange(av) |>
  
  mutate(#bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_current = rate_current* taxed_eav,
         bill_noexemps =  rate_noGHE*(eq_av-exe_total_adj+exe_homeowner),
         bill_change = bill_noexemps - bill_current) |>
  # 
  group_by(clean_name, 
           has_HO_exemp) |> 
  
  summarize(AV = median(median_av), # median_av was calculated earlier: C2 median AV for the muni 
            `Taxable EAV` = round(median(eq_av)),
            `Taxed EAV` = round(median(taxed_eav)),
            bill_cur = round(median(bill_current)),
            bill_new = round(median(bill_noexemps)),
            bill_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = round(median(exe_total_adj*rate_current))
            ) |> 
  
  # merge in clean_names variable
  left_join(nicknames) |>
  
  select(clean_name, has_HO_exemp, bill_cur, bill_new, bill_change, perceived_savings,  AV, `Taxable EAV`, `Taxed EAV`, 
         ) 

muni_median_summarytable |>
  DT::datatable(rownames = FALSE, 
                colnames = c(
                  'Claims GHE' = 'has_HO_exemp',
                  'Current Bill' = 'bill_cur',
                  'Hyp. Bill' = 'bill_new', 
                  'Bill Change' = 'bill_change',  
                  'Perceived Savings'='perceived_savings'))  |>
  formatCurrency(columns = c(3:9), digits = 0)

Table 1.6: Change in Tax Bill if Exempt EAV from GHE were added back to tax base.

muni_median_summarytable |>
  filter(clean_name %in% c("Chicago", "Dolton", "Glencoe")) |>
  mutate(across(bill_cur:`Taxed EAV`, scales::dollar )) |>
 rename(Municipality = clean_name,
                  'Claims GHE' = 'has_HO_exemp',
                  'Current Bill' = 'bill_cur',
                  'Hyp. Bill' = 'bill_new', 
                  'Bill Change' = 'bill_change',  
                  'Perceived Savings'='perceived_savings' )  |>
  
  flextable() 

Table 1.7: Report Table 3. Estimated Bill Change for a municipality’s median valued property if exempt EAV from the GHE were added back to the tax base.

Municipality	Claims GHE	Current Bill	Hyp. Bill	Bill Change	Perceived Savings	AV	Taxable EAV	Taxed EAV
Chicago	0	$4,244	$4,073	-$170	$0	$21,000	$63,054	$63,054
Chicago	1	$3,571	$3,427	-$143	$1,346	$21,000	$63,057	$53,057
Dolton	0	$6,653	$5,341	-$1,312	$0	$8,940	$26,772	$26,772
Dolton	1	$4,187	$3,362	-$826	$4,970	$8,940	$26,850	$16,850
Glencoe	0	$20,889	$20,341	-$548	$0	$73,170	$219,197	$219,197
Glencoe	1	$19,985	$19,461	-$524	$1,906	$73,170	$219,663	$209,708

cholton_15000_summarytable <- pin_data |> 
  
  select(pin, av, class, tax_code_num, tax_bill_total, av_certified, exe_homeowner:exe_abate, clean_name, eq_av, exe_total_adj, zero_bill, has_HO_exemp, taxed_eav, final_tax_to_dist, final_tax_to_tif) |>
  filter(clean_name %in% c("Chicago", "Dolton", "Glencoe")) |>
  filter(between(av, 14800, 15200)) |>

  filter(class > 199 & class < 300) |> 
  
  # merge in muni residential median AV
  left_join(C2_munistats_filtered |> select(clean_name, median_av)) |>
  
  left_join(muni_ratechange |> select(clean_name, rate_noExe, rate_noGHE, rate_current)) |>
  # +/- 500 from municipalities median residential AV
  # Removes properties that received other types of exemptions
  filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0 ) |>
  
  arrange(av) |>
  
  mutate(
         bill_current = rate_current* taxed_eav,
         bill_noexemps =  rate_noGHE*(eq_av-exe_total_adj+exe_homeowner),
         bill_change = bill_noexemps - bill_current) |>
  # 
  group_by(clean_name, 
           has_HO_exemp) |> 
  
  summarize(AV = median(av),
    `Median AV in Muni` = first(median_av), # median_av was calculated earlier: C2 median AV for the muni 
            `EqAV` = round(median(eq_av)),
            `Taxed EAV` = round(median(taxed_eav)),
            bill_cur = round(median(bill_current)),
            bill_new = round(median(bill_noexemps)),
            bill_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = round(median(exe_total_adj*rate_current))
            ) |> 
  
  # merge in clean_names variable
  left_join(nicknames) |>
  
  select(clean_name, AV, has_HO_exemp, bill_cur, bill_new, bill_change, perceived_savings,  `Median AV in Muni`, `EqAV`, `Taxed EAV`
         #everything() 
         ) |>
  mutate(across(bill_cur:`Taxed EAV`, scales::dollar )) |>
 rename(Municipality = clean_name,
                  'Claims GHE' = 'has_HO_exemp',
                  'Current Bill' = 'bill_cur',
                  'Hyp. Bill' = 'bill_new', 
                  'Bill Change' = 'bill_change',  
                  'Perceived Savings'='perceived_savings' )  |>
  
  flextable()
  # DT::datatable(rownames = FALSE, 
  #               colnames = c(
  #                 'Claims GHE' = 'has_HO_exemp',
  #                 'Current Bill' = 'bill_cur',
  #                 'Hyp. Bill' = 'bill_new', 
  #                 'Bill Change' = 'bill_change',  
  #                 'Perceived Savings'='perceived_savings'),
  #               caption = "Change in Tax Bill for Properties with an Assessed Value of $15,000.")  |>
  # formatCurrency(columns = c(2, 4:9), digits = 0)

cholton_15000_summarytable

Table that uses pins valued at ~$15,000. Creates unrealistic bill change for some municipalities if they do not have properties valued at 15K.

Municipality	AV	Claims GHE	Current Bill	Hyp. Bill	Bill Change	Perceived Savings	Median AV in Muni	EqAV	Taxed EAV
Chicago	14,999	0	$3,031	$2,909	-$122	$0	$21,000	$45,037	$45,037
Chicago	14,999	1	$2,358	$2,263	-$95	$1,346	$21,000	$45,037	$35,037
Dolton	14,975	0	$11,174	$8,971	-$2,203	$0	$8,940	$44,965	$44,965
Dolton	14,945	1	$8,638	$6,935	-$1,709	$4,970	$8,940	$44,875	$34,762
Glencoe	15,067	0	$4,312	$4,198	-$113	$0	$73,170	$45,242	$45,242
Glencoe	15,067	1	$3,359	$3,270	-$88	$1,906	$73,170	$45,242	$35,242

Table 4.

Municipalities with the largest and smallest reductions in tax base (as a share of residential EAV due to exemptions) and median property values

muni_sums |> 
  filter(year == params$year) |>
  group_by(clean_name) |> 
  summarize(pct_res_exempt = muni_fmv_exempt / muni_fmv_residential) |>  
  arrange(desc(pct_res_exempt) ) |>
    left_join(C2_munistats_filtered) |> 
  mutate(prop_value = median_av*10,
         pct_res_exempt = scales::percent(pct_res_exempt, accuracy = 0.01) ) |>
  mutate(across(c(median_eq_av:avg_taxed_eav, prop_value), scales::dollar)) |>
  select(Municipality = clean_name, `Share Res. Exempt` = pct_res_exempt,
         `Median Prop. Value` = prop_value, everything())

Figure 10. Tax Burden Shift from Current GHE

Share of municipal property tax levy paid by Class 2 properties with and without homestead exemptions, tax year 2021

# as a dot graph ## 

order <- mc_burden |>
  filter(class_1dig == 2) |>
    select(clean_name, pct_taxburden_current, burden_shift)

slice <-  mc_burden |>
  filter(class_1dig == 2) |>
    select(clean_name, pct_taxburden_current, burden_shift) |>
  arrange(pct_taxburden_current) |>
  slice(1:5, 63:67, 127:131)



median_burden <- median(order$pct_taxburden_current)
median_shift <- median(order$burden_shift)

# median burden change is 5.9 percentage points
# current median burden is 70.3% of the levy

mc_burden |> 
  filter(clean_name %in% slice$clean_name) |>
#filter(!clean_name %in% cross_county_lines$clean_name)|>
  filter(class_1dig == 2) |>
#  filter(burden_current > 0.938 |burden_current < .17 |
 #          ( (burden_current < median(burden_current) + 0.01 )& (burden_current > median(burden_current) - 0.01)) )|> 
  ungroup() |> 
      select(clean_name, pct_taxburden_current, hyp_pct_taxburden, burden_shift) |>
  arrange(burden_shift) |>
  pivot_longer(c("pct_taxburden_current", "hyp_pct_taxburden"), 
               names_to = "type", values_to = "pct_burden") |> 
  inner_join(order) |>
  ggplot(aes(x = pct_burden, 
             y= reorder(clean_name, - pct_taxburden_current)))+
  # y= reorder(clean_name, burden_current)))+
  geom_vline(xintercept = median_burden, linetype = 3)+
  geom_line(aes(group = clean_name))+ 
  geom_hline(yintercept = 5.5, linetype = 2)+
  geom_hline(yintercept = 10.5, linetype = 2)+
  geom_point(aes(color=type), size=3 )+

  theme_minimal() + 
  theme( 
    legend.title = element_blank(),
    plot.title.position = "plot",
 plot.background = element_rect(fill='transparent', color=NA) 
  )+
  scale_color_brewer(palette="Paired", labels = c("Burden if \nNo Exemptions","Current Burden"), direction = 1) +
  scale_x_continuous(labels = scales::percent) +

  
  labs(title = "Change in Class 2 Residential Tax Burden", 
       subtitle = "Ordered by Current Tax Burden",
  x = "Share of Levy (%)", y = "" , 
  caption = paste0("Dotted line represents median Class 2 burden (", round(median_burden*100), "% of the levy).  \nResidential Tax Burden is theshare of the property tax collected that was paid for by property owners with Class 2 properties.")) +
  
    geom_label(label = "Class 2 pays small share of \nlevy; very little residential", x=.32, y = 13, label.size = 1, size = 3)+
  
    geom_label(label = paste0("Class 2 pays median share of \nlevy (", round(median_burden*100), "%), mix of land use"), x=.42, y = 7.5, label.size = 1, size = 3) +
  
    geom_label(label = "Class 2 pays nearly all of levy, \nhighly residential", x=.70, y = 3, label.size = 1,size = 3)

Figure 1.6

order <- mc_burden |>
  filter(class_1dig == 2) |>
    select(clean_name, pct_taxburden_current, burden_shift)

slice <-  mc_burden |>
  filter(class_1dig == 2) |>
    select(clean_name, pct_taxburden_current, burden_shift) |>
  arrange(pct_taxburden_current) |>
  slice(1:5, 63:67, 127:131)



median_burden <- median(order$pct_taxburden_current)
median_shift <- median(order$burden_shift)

# median burden change is 5.9 percentage points
# current median burden is 70.3% of the levy

mc_burden |> 
  filter(clean_name %in% slice$clean_name) |>
#filter(!clean_name %in% cross_county_lines$clean_name)|>
  filter(class_1dig == 2) |>
#  filter(burden_current > 0.938 |burden_current < .17 |
 #          ( (burden_current < median(burden_current) + 0.01 )& (burden_current > median(burden_current) - 0.01)) )|> 
  ungroup() |> 
      select(clean_name, pct_taxburden_current, hyp_pct_taxburden, burden_shift) |>
  arrange(burden_shift) |>
  pivot_longer(c("pct_taxburden_current", "hyp_pct_taxburden"), 
               names_to = "type", values_to = "pct_burden") |> 
  inner_join(order) |>
  ggplot(aes(x = pct_burden, 
             y= reorder(clean_name, - pct_taxburden_current)))+
  # y= reorder(clean_name, burden_current)))+
  geom_vline(xintercept = median_burden, linetype = 3)+
  geom_line(aes(group = clean_name))+ 
  geom_hline(yintercept = 5.5, linetype = 2)+
  geom_hline(yintercept = 10.5, linetype = 2)+
  geom_point(aes(color=type), size=3 )+

  theme_minimal() + 
  theme( 
    legend.title = element_blank(),
    plot.title.position = "plot",
 plot.background = element_rect(fill='transparent', color=NA) 
  )+
  scale_color_brewer(palette="Paired", labels = c("Burden if \nNo Exemptions","Current Burden" ), direction = 1) +
  scale_x_continuous(labels = scales::percent) +

  
  labs(title = "Change in Class 2 Residential Tax Burden", 
       subtitle = "Ordered by Current Tax Burden",
  x = "Share of Levy (%)", y = "" , 
#  caption = paste0("Dotted line represents median Class 2 burden (", round(median_burden*100), "% of the levy).  \nResidential Tax Burden is theshare of the property tax collected that was paid for by property owners with Class 2 properties."
#)
) #+
  
#    geom_label(label = "Class 2 pays small share of \nlevy; very little residential", x=.32, y = 13, label.size = 1, size = 3)+
  
 #   geom_label(label = paste0("Class 2 pays median share of \nlevy (", round(median_burden*100), "%), mix of land use"), x=.42, y = 7.5, label.size = 1, size = 3) +
  
 #   geom_label(label = "Class 2 pays nearly all of levy, \nhighly residential", x=.70, y = 3, label.size = 1,size = 3)

Figure 1.7

Figure 11. Zero Dollar Bills

muni_mc_sums |> 
  left_join(nicknames) |>
  filter(major_class_code == 2) |> 
  group_by(year, Triad) |>
  summarize(zerodollar_count = sum(zero_bills)) |> 
  ggplot(aes(x=year, y = zerodollar_count, fill = Triad)) + 
  geom_bar(position = "stack", stat = "identity") + theme_minimal()+
  theme(legend.position = "top")+
  labs(title = element_text("Number of $0 Taxbills"), xlab = NULL,) +
  scale_y_continuous(labels = scales::comma, limits = c(0, 41000), name = "")

Figure 1.8: Number of tax bills that were $0 shown here is different than the value shown in the exemption report. This is due to missing disabled veteran exemption amounts that were not identified when writing the original report.

Table 6 - Cost of Covering Cook County’s current Homestead Exemptions

class8munis <- read_csv("../Output/old_files/datarequests_Class8Munis.csv")


cross_county_lines <- c("030440000", "030585000", "030890000", "030320000", "031280000",
                        "030080000", "030560000", "031120000", "030280000", "030340000",
                        "030150000","030050000", "030180000","030500000", "031210000")

taxcodes_current <- pin_data %>% 
  group_by(tax_code_num)  %>%
  summarize(
    av = sum(av),
    taxed_eav = sum(taxed_eav),
    equalized_AV = sum(eq_av),
    pins_in_class = n(),
    current_exemptions = sum(exe_total_adj),
    HO_exemps = sum(exe_homeowner),
    tax_code_rate = first(tax_code_rate), 
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!!
   # final_tax_to_dist_AWM = sum(final_tax_to_dist_AWM, na.rm=TRUE),
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
   # tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) %>%
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) %>%
  rename(cur_comp_TC_rate = tax_code_rate) %>%
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate),
         new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate) + HO_exemps,
         new_taxable_eav2 = final_tax_to_dist/(cur_comp_TC_rate) + current_exemptions,

         ) %>%
  mutate(
    new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav),
        new_comp_TC_rate2 = (final_tax_to_dist / new_taxable_eav2),

         ) %>%
  mutate(
    new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate),
        new_comp_TC_rate2 = ifelse(is.nan(new_comp_TC_rate2), cur_comp_TC_rate, new_comp_TC_rate2),

         ) %>%
  select(tax_code_num, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, new_taxable_eav2, new_comp_TC_rate2, everything())

better_cost_est_ghe = taxcodes_current |> 
  summarize(better_cost_est = sum(new_comp_TC_rate*(HO_exemps)))
                                            
better_cost_est_allexe = taxcodes_current |> 
  summarize(better_cost_est = sum(new_comp_TC_rate2*(current_exemptions)))



tbl_southtriad_exemps <- muni_sums |>
  left_join(nicknames) |>
  filter(Triad == "South") |>
  filter(!clean_name %in% cross_county_lines) |>
  filter(year == params$year) |>
  group_by(year) |>
  summarize(
    'PINs' = sum(muni_PC_total),
    'PINs (w/ exemps.)' = sum(muni_PC_claimed_exe),
    "EqAV" = sum(total_value_eav),
    'Taxed EAV' = sum(muni_eav_taxed),
    'Exempt EAV' = sum(total_value_eav),
    "GHE" = sum(exempt_SR_eav, na.rm=TRUE),
    "Senior Homestead" = sum(exempt_FR_eav, na.rm=TRUE),
    "Senior Freeze" = sum(exempt_FR_eav, na.rm=TRUE),
    "Other Exemptions" = sum(exempt_LTHO_eav + exempt_DIS_eav + exempt_VET_eav)) |>
  pivot_longer(cols = c('PINs':'Other Exemptions'),
               names_to = "Metric", values_to = "Total") |>
  select(-year)
tbl_southtriad_exemps

South Triad PIN Counts, Taxbase & Exemptions

tbl_class8muni_exemps <- muni_sums |>
  filter(clean_name %in% class8munis$clean_name) |>
  filter(!clean_name %in% cross_county_lines) |>
  filter(year == params$year) |>
  group_by(year) |>
  
  summarize(
    'PINs' = sum(muni_PC_total),
    'PINs (w/ exemps.)' = sum(muni_PC_claimed_exe),
    "EqAV" = sum(total_value_eav),
    'Taxed EAV' = sum(muni_eav_taxed),
    'Exempt EAV' = sum(total_value_eav),
    "GHE" = sum(exempt_SR_eav, na.rm=TRUE),
    "Senior Homestead" = sum(exempt_FR_eav, na.rm=TRUE),
    "Senior Freeze" = sum(exempt_FR_eav, na.rm=TRUE),
    "Other Exemptions" = sum(exempt_LTHO_eav + exempt_DIS_eav + exempt_VET_eav)) |>
  pivot_longer(cols = c('PINs':'Other Exemptions'),
               names_to = "Metric", values_to = "Total") |>
  select(-year)
tbl_class8muni_exemps

Municipalities Eligible for Class 8 Incentive Properties - PIN Counts, Taxbase & Exemptions

tbl <- muni_sums |>
  filter(!clean_name %in% cross_county_lines) |>
  filter(year == params$year) |>
  group_by(year) |>
  summarize(
    'PINs' = sum(muni_PC_total),
    'PINs (w/ exemps.)' = sum(muni_PC_claimed_exe),
    "EqAV" = sum(total_value_eav),
    'Taxed EAV' = sum(muni_eav_taxed),
    'Exempt EAV' = sum(total_value_eav),
    "GHE" = sum(exempt_SR_eav, na.rm=TRUE),
    "Senior Homestead" = sum(exempt_FR_eav, na.rm=TRUE),
    "Senior Freeze" = sum(exempt_FR_eav, na.rm=TRUE),
    "Other Exemptions" = sum(exempt_LTHO_eav + exempt_DIS_eav + exempt_VET_eav)) |>
  pivot_longer(cols = c('PINs':'Other Exemptions'),
             names_to = "Metric", values_to = "Total") |>
  select(-year) 
tbl

PIN Counts, Taxbase & Exemptions for Municipalities with a majority of their Taxbase within Cook County

It would take $923,281,641 to cover the cost of the GHE without the tax rate increasing from the hypothetical rate to the current rate.

It would take $2,416,746,380 to cover the cost of all exemptions without the tax rate increasing from the hypothetical rate to the current rate.

Not finished for class 8 munis or south triad totals. But done the same way as GHE and all exemption scenarios.

Additional notes

Multiple ways to measure “burden”:

• Share of tax base = (taxed EAV / taxed EAV in Muni)
  
• Tax Burden = tax paid / levy
  
• Effective rate = tax paid / property value