Tax Year 2024

Supporting Content for Cook County C&I Report

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

options(scipen = 999)

library(tidyverse)
library(glue)
library(sf)
library(DT)
library(flextable)
#install.packages("ggtext")
library(ggtext)

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

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

muni_sums <- read_csv("../Output/ptaxsim_muni_level_2006to2024.csv")


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



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





set_flextable_defaults(theme_fun = theme_vanilla, 
                       padding = 2,
                       line_spacing = 1,
                       big.mark = ",",
                       )

options(DT.options = list())

FitFlextableToPage <- function(ft, pgwidth = 6){

  ft_out <- ft |> autofit()

  ft_out <- width(ft_out, width = dim(ft_out)$widths*pgwidth /(flextable_dim(ft_out)$widths))
  return(ft_out)
}



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


eq_factor <- read_csv("../Necessary_Files/eq_factor.csv") |>
  filter(year == params$year) |>
  select(eq_factor = eq_factor_final) |>
  as.numeric()

incentive_majorclasses <- c("6", "7A", "7B", "8A", "8B")

commercial_classes <- c(401:435, 490, 491, 492, 496:499,
                        500:535,590, 591, 592, 597:599, 
                        700:799,
                        800:835, 891, 892, 897, 899)  

industrial_classes <- c(480:489,493, 
                        550:589, 593,
                        600:699,
                        850:890, 893
                        )
cmap_colors = c("#1e478e", "#6dae4f", "#d3b42b", "#008fd5", "#ca3428", 
                "#ade0ee", "#3e6730", "#d0e4a4")

# All have less than 50 percent of their taxed EAV within Cook County
cross_county_lines <- c(
  # very small amounts in Cook County, like less than 15%:
  # Deer Park, "Frankfort", "Homer Glen",  "Oak Brook", "East Dundee", "University Park",  
 "030270000", "030440000", "030585000", "030890000", "030320000", "031280000",
  
  # 
  # "Bensenville", "Hinsdale", "Roselle", "Deerfield", Elgin, 
  "030080000", "030560000", "031120000",  "030280000",  "030340000", "030270000",
 
  # Closer to ~50% in Cook County
  # Buffalo Grove,  Bartlett,  Burr Ridge, Hanover Park, Steger 
  "030150000","030050000", "030180000","030500000", "031210000"
  )

cross_county_line <- c(
  "Deer Park", "Frankfort", "Homer Glen",  "Oak Brook", "East Dundee", "University Park",  
  "Bensenville", "Hinsdale", "Roselle", "Deerfield", "Elgin",
  "Buffalo Grove",  "Bartlett",  "Burr Ridge", "Hanover Park", "Steger"
                    )

Municipalities that are dropped due to having EAV outside of Cook County: Frankfort, Homer Glen, Oak Brook, East Dundee, University Park, Bensenville, Hinsdale, Roselle, Deer Park, Deerfield, Elgin, Buffalo Grove, Bartlett, Burr Ridge, Hanover Park, Steger.

# Workaround for identifying more project IDs. 
# Used Appeal ID to create unique identifier to group PINs.
bor <- read_csv("../Output/borappeals.csv") |> 
  mutate(project_appellant = paste(project_id, sep = "-", appellant))
# modelsummary::datasummary_skim(bor)


# Cleaned PIN-Project list after cleaning the commercial valuation dataset found online. 
# Another temporary work-around until we (maybe) have full keypin list:
# proj_xwalk <- read_csv("../Output/all_keypins.csv")  
proj_xwalk <-  readxl::read_xlsx("../Output/projects_checked_MAINFILE.xlsx") 
# all commercial valuation properties but made with not-quite-clean data from commercial valuation dataset on Cook County Data Portal (which was made from combining the Methodology worksheets) 
# Values are also only the FIRST PASS assessments and do not include appeals or changes in values

# Join project IDs to PINs:
pin_data <- pin_data |>   
# select(-incent_prop) |> 
  left_join(proj_xwalk |> select(-incent_prop)
)

# original class_dict variables already in 0_joined data
# but I do want the new-ish variables (comparable properties, and land use type) I created in the file to be brought in:
class_dict <- read_csv("../Necessary_Files/class_dict_expanded.csv") |>
  select(class_code, comparable_props, land_use=Alea_cat)


pin_data <- pin_data |> 
  left_join(class_dict, by =  c("class" = "class_code")) |>
  mutate(clean_name = ifelse(is.na(clean_name), "Unincorporated", clean_name)) 

# BOR data source shortfall: We only have the data if they appeal!
# sales data

bor_pins <- bor |> 
  group_by(pin) |> 
  arrange(desc(tax_year)) |>
  summarize(pin = first(pin),              # grabs first occurrence of unique PIN
            class_bor = list(unique(class)),
            appellant = first(appellant),
            project_id = first(project_id), 
            timesappealed = n() ) |>
  mutate(proj_appellant = paste(project_id, "-", appellant))

pin_data <- pin_data |> left_join(bor_pins, by = "pin")

# now do it the other way and compare

pin_data <- pin_data |> 
  mutate( both_ids = project_id,
          both_ids = ifelse(is.na(both_ids), main_keypin, both_ids),
          both_ids = ifelse(is.na(both_ids) & between(class, 300, 899), pin, both_ids))

Total Value should equal Current Taxable Value + non-Taxable Value where non-Taxable Value = Value in TIF Increment + Reduced Value from Policy Choices where Reduced Value = Tax Exempt Value from Homeowners exemptions or abatements + Reduced Taxable Value from lower levels of assessments due to incentive classifications:

\[\mbox{Total Value = Taxed Value + Untaxable Value}\]

where

\[\mbox{Untaxable Value = TIF Increment + Exemptions + Abatements + Reduced Taxable Value from Lower Incentive Class Assessment Ratios}\]

where

\[\mbox{Reduced Taxable Value from Incentive Classification Levels of Assessments}\] \[\mbox{which then equals } {0.25 \ast EAV - \approx0.10 \ast EAV}\]

Cook County Total Value

\[ \mbox{AV = Fair Market Value * Level of Assessment} \]

\[ \mbox{Tax Rate} = \frac{\mbox{Amount Levied by Taxing Districts}}{\mbox{Taxable Value}} \]

Taxed Value refers to what taxing agencies did tax to pay for their levies. We use the portion of the tax bill that does NOT go to TIFs to calculate the portion of the composite levy paid by each PIN and then sum up from there.

\[ \mbox{Final Tax to District} = \mbox{Portion of Levy Paid by PIN} = {\mbox{Tax Code Rate}}*{\mbox{Taxable Value of PIN}} \]

\[\mbox{Equalized Assessed Value} = {\frac{\mbox{final tax to dist + final tax to TIF}}{\mbox{tax code rate}} + \mbox{Exemptions + Abatements}}\]

\[\mbox{Taxed EAV} = {\frac{\sum{final\_tax\_to\_dist}}{\mbox{tax code rate}} = \mbox{EAV - Exemptions - Abatements}}\]

cook_sums |> 
  filter(year == params$year) |>
  select(cty_PC, cty_fmv, cty_fmv_taxed) |>
    mutate(cty_pct_fmv_taxed = scales::percent(cty_fmv_taxed/cty_fmv, accuracy = 0.01)) |>
  flextable() |> 
  set_header_labels(cty_PC = 'PINs', 
                   # cty_projects = "Project IDs", 
                    cty_fmv_taxed = 'Taxed FMV', 
                    cty_fmv_untaxable_value = 'FMV not Taxed\nfor Levy',
                    cty_fmv = 'Total FMV', 

                    cty_pct_fmv_taxed = 'FMV Taxed (%)'
  ) |>   
  FitFlextableToPage()

PINs	Total FMV	Taxed FMV	FMV Taxed (%)
1,859,067	679,122,487,676	560,845,108,013	82.58%

Table 13.1: FMV of PINs in Cook County Taxed FMV represents the property value that was actually taxed by local taxing jurisdictions(equal to the amount levied) but converted to FMV. We use the portion of an individual’s tax bill that does NOT go to a TIF to calculate the composite levy for taxing jurisdictions. This means that the “levy” actually represents the amount collected by the taxing agencies, not the amount that was levied. Therefore the composite levy does not include unpaid taxbills.

tbl <-  cook_sums |>
  filter(year ==params$year) |>
  mutate(cty_pct_fmv_both_incent = cty_fmv_incentive / cty_fmv_comandind ,
         cty_pct_PC_both_incent = cty_PC_withincents / cty_PC_comandind         ) |>
  select(cty_fmv_comandind, #cty_fmv, 
          cty_pct_fmv_both_incent, 
         cty_pct_fmv_incentinTIF,
         cty_pct_PC_both_incent, cty_PC_comandind, cty_PC_incents_inTIFs, #cty_pct_PC_both_incent_inTIF, 
         cty_pct_fmv_incents_tif_increment) |>
 mutate(across(contains("pct_"), scales::percent, accuracy = .01))

tbl |>
  flextable() |> 
  align(align = "right") |>
  set_header_labels(
    cty_fmv_comandind = 'Com. & Ind. FMV',
    #  cty_fmv = 'Total FMV in Cook',
    cty_pct_fmv_both_incent = '% of Com. & Ind. FMV  w/ Incent.',
    cty_pct_fmv_incentinTIF = '% of Com. & Ind. FMV  w/ Incent. in TIF',
    cty_pct_fmv_incents_tif_increment = '% of Com. & Ind. FMV in TIF Increment',
    
    cty_PC_comandind = 'PIN Count',
    cty_PC_incents_inTIFs = "Incent. PINs in TIF",
    cty_pct_PC_both_incent = '% of Com. & Ind. PINs w/ Incent.',
    cty_pct_PC_both_incent_inTIF = '% of Incent. PINs in TIF'
  ) |>
  FitFlextableToPage()

Com. & Ind. FMV	% of Com. & Ind. FMV w/ Incent.	% of Com. & Ind. FMV w/ Incent. in TIF	% of Com. & Ind. PINs w/ Incent.	PIN Count	Incent. PINs in TIF	% of Com. & Ind. FMV in TIF Increment
118,711,589,076	12.60%	39.39%	4.86%	96,431	2,084	28.78%

Table 13.2: Commercial and Industrial PINs in Cook County

12.60% of industrial and commercial PINs (aka "revenue producing PINs") FMV has an incentive classification (4.86% when using PIN counts). Of the PINs that have incentive classification, 39.39% of the FMV is located within a TIF (4.86% when using PIN counts).

28.78% is in TIF Increment.

#table_cook |> 
  
tbl <- cook_sums |>
  filter(year == params$year) |>
  mutate(cty_pct_fmv_com_incent = cty_fmv_comwithincent / cty_fmv_commercial,#) |>
         cty_pct_fmv_com_incent_inTIF = cty_fmv_com_incent_inTIF / cty_fmv_comwithincent,
         
         cty_pct_PC_com_incent_inTIF = cty_PC_com_incent_inTIF / cty_PC_com_incent) |>
  
  select(cty_PC_commercial, cty_PC_com_incent, cty_pct_incent_ofcomPC, 
         cty_pct_PC_com_incent_inTIF, cty_fmv_commercial, cty_pct_incent_ofcomPC, 
         cty_pct_fmv_com_incent, cty_pct_fmv_com_incent_inTIF) |>
  
  mutate(across(contains("pct_"), scales::percent, accuracy = .01))

tbl |>
  flextable() |> 
    align(align = "right") |>
  set_header_labels(cty_fmv_commercial = 'Commercial FMV',
                    cty_PC_commercial = 'Commercial Pin Count', 
                    cty_PC_com_incent = 'Com. PIN Count w/ Incent.',
                    cty_pct_incent_ofcomPC = 'Com. PINs w/ Incent.',
                    cty_pct_fmv_com_incent = '% of Com. FMV  w/ Incent.', 
                    
                    cty_pct_fmv_com_incent_inTIF = '% of Com. FMV  w/ Incent. in TIF', 
                    cty_PC_com_incent_inTIF = "Com. Incent. PINs in TIF", 
                    cty_pct_PC_com_incent = '% of Com. PINs w/ Incent.',
                    cty_pct_PC_com_incent_inTIF = '% of Incent. PINs in TIF'
  ) |>   FitFlextableToPage()

Commercial Pin Count	Com. PIN Count w/ Incent.	Com. PINs w/ Incent.	% of Incent. PINs in TIF	Commercial FMV	% of Com. FMV w/ Incent.	% of Com. FMV w/ Incent. in TIF
70,273	1,033	1.47%	38.82%	89,887,762,331	4.61%	53.19%

Table 13.3: Commercial PINs in Cook County

4.61% of commercial PIN FMV has an incentive classification (1.47% when using PIN counts). Of the PINs that have incentive classification, 53.19% of the FMV is located within a TIF (38.82% when using PIN counts).

tbl <- cook_sums |>
  filter(year == params$year) |>
  mutate(cty_pct_fmv_ind_incent = cty_fmv_indwithincent / cty_fmv_industrial,
         cty_pct_fmv_ind_incent_inTIF = cty_fmv_ind_incent_inTIF / cty_fmv_indwithincent,
         
         cty_pct_PC_ind_incent_inTIF = cty_PC_ind_incent_inTIF / cty_PC_ind_incent) |>
  
  select(cty_PC_industrial, cty_PC_ind_incent, cty_PC_ind_incent_inTIF, 
         cty_pct_incent_ofindPC, cty_pct_fmv_ind_incent_inTIF, cty_pct_PC_ind_incent_inTIF,
         
         cty_fmv_industrial, cty_pct_fmv_ind_incent,  
  ) |>
  mutate(across(contains("pct_"), scales::percent, accuracy = .01)) 

tbl |>
  flextable() |> 
  align(align = "right") |> 
  set_header_labels(cty_fmv_industrial = 'Industrial FMV',
                    cty_PC_industrial = 'Industrial Pin Count', 
                    cty_PC_ind_incent = 'Ind. PIN Count w/ Incent.',
                    cty_pct_incent_ofindPC = 'Ind. PINs w/ Incent.',
                    cty_pct_fmv_ind_incent = '% of Ind. FMV  w/ Incent.', 
                    
                    cty_pct_fmv_ind_incent_inTIF = '% of Ind. FMV  w/ Incent. in TIF', 
                    cty_PC_ind_incent_inTIF = "Ind. Incent. PINs in TIF", 
                    cty_pct_PC_ind_incent = '% of Com. PINs w/ Incent.',
                    cty_pct_PC_ind_incent_inTIF = '% of Incent. PINs in TIF'
  ) |> 
  FitFlextableToPage()

Industrial Pin Count	Ind. PIN Count w/ Incent.	Ind. Incent. PINs in TIF	Ind. PINs w/ Incent.	% of Ind. FMV w/ Incent. in TIF	% of Incent. PINs in TIF	Industrial FMV	% of Ind. FMV w/ Incent.
26,158	3,658	1,683	13.98%	34.10%	46.01%	28,823,826,745	37.51%

Table 13.4: Industrial PINs in Cook County

37.51% of industrial FMV has an incentive classification (13.98% when using PIN counts). Of the PINs that have incentive classification, 34.10% of the FMV is located within a TIF ( 46.01% when using PIN counts).

cook_sums |>
  filter(year == params$year) |>
  select(cty_fmv, cty_fmv_tif_increment, cty_fmv_exemptions, cty_fmv_abatements, 
         ) |>
mutate(cty_pct_fmv_untaxable = (cty_fmv_tif_increment + cty_fmv_exemptions + cty_fmv_abatements)/cty_fmv) |>
  mutate(across(contains("pct_"), scales::percent, accuracy = .01)) |>
  flextable() |> 
    align(align = "right") |>  
  set_header_labels(
                   cty_fmv = 'Total FMV', 
                    cty_fmv_tif_increment = 'TIF Increment FMV' ,
                    cty_fmv_exemptions = 'Exempt Value: Exemptions',
                    cty_fmv_abatements = 'Exempt Value: Abatements',
                 #   cty_fmv_untaxable_value = 'Value not Taxable \nfor Levy',
                    cty_pct_fmv_untaxable = 'County FMV not Taxed (%)'
                 ) |>
  FitFlextableToPage()

Total FMV	TIF Increment FMV	Exempt Value: Exemptions	Exempt Value: Abatements	County FMV not Taxed (%)
679,122,487,676	61,784,381,147	56,141,291,078	0	17.36%

Table 13.5: Untaxable FMV in Cook County.

cook_sums |>
  filter(year == params$year) |>  
  select(cty_av, cty_av_taxed, cty_untaxable_value_av, cty_pct_av_taxed, cty_pct_av_untaxable ) |>
  mutate(across(contains("pct_"), scales::percent, accuracy = .01)) |>
  flextable() |> 
    align(align = "right") |>  set_header_labels(
                   cty_av = 'Total AV', 
                    cty_av_taxed = 'Taxed AV' ,
                    cty_untaxable_value_av = 'AV Not Taxed',
                    cty_pct_av_taxed = '% Taxed',
                    cty_pct_av_untaxable = '% Not Taxed') |>
   FitFlextableToPage()

Total AV	Taxed AV	AV Not Taxed	% Taxed	% Not Taxed
83,458,163,843	68,596,533,659	16,409,549,964	82.19%	19.66%

Table 13.6: Untaxable AV in Cook County. Taxed AV represents the property value that was actually taxed by local taxing jurisdictions.

cook_sums |>
  filter(year == params$year) |>
  select(cty_fmv, cty_fmv_inTIF, cty_fmv_tif_increment, 
         cty_fmv_incentive, cty_fmv_incents_inTIFs, cty_fmv_incents_tif_increment) |>
  flextable() |> 
  set_header_labels(
                    cty_fmv = 'Total FMV', 
                    cty_fmv_inTIF = 'FMV in TIFs',
                    cty_fmv_tif_increment = 'TIF Increment FMV' ,
                    cty_fmv_incents_inTIFs = 'FMV from Incent. Class in TIF',
                    cty_fmv_incentive = "FMV with Incent.Class.", 
                    cty_fmv_incents_tif_increment = 'FMV with Incent. Class. in TIF Increment') |>   
  FitFlextableToPage()

Total FMV	FMV in TIFs	TIF Increment FMV	FMV with Incent.Class.	FMV from Incent. Class in TIF	FMV with Incent. Class. in TIF Increment
679,122,487,676	120,932,595,191	61,784,381,147	14,956,985,713	5,891,556,760	4,305,354,674

Table 13.7: FMV of properties with incentive classifications and TIF increment. Value in TIFs, value within the TIF that can be taxed by local taxing jurisdictions, value of properties that have reduced levels of assessments from incentive classifications, and the value that is both in a TIF and has a reduced LOA.

Taxed value is the amount of value that was actually taxed in order to pay for taxing agencies levies. It includes frozen EAV within an area + taxable EAV for residential properties net exemptions and abatements. It also includes the equalized assessed value of incentive properties at their current, lower assessment ratios. final_tax_to_dist is used to calculate the amount that was collected by local government agencies and then divided by the tax rate to calculate the amount of value that was taxed, or the taxable equalized assessed value (TEAV).

The Taxed Value, when converted to the Fair Market Value (FMV) represents the amount of value that was taxed out of the full FMV available in Cook County.

Untaxable EAV includes homeowner exemptions for 200 level properties, abatements for other property class types, EAV in the TIF increment, and EAV that has been reduced due to incentive classifications.

cook_MC_sums <- read_csv("../Output/ptaxsim_cook_MC_level_2006to2024.csv") 

cook_MC_sums |>  
  filter(year == params$year) |>
      mutate(across(contains(c("pct_", "rate")), scales::percent, accuracy = .01)) |>
      mutate(across(is.numeric, round, digits = 0)) |>

  select(
    "Property Type" = class_group, 
    "Homeowner Exempt FMV"= cty_mc_fmv_exemptions,
    "Abated FMV" = cty_mc_fmv_abatements,
    "$0 Bill Count" = cty_mc_zero_bill,
    "$ Levy Paid" = cty_mc_levy,
  #  "% of Levy Paid" =  pct_mc_levy_paid, 
    "FMV in Major Class" =  cty_mc_fmv,
    "Taxed AV" = cty_mc_av_taxed,
   # "Project Count" = cty_mc_projects,
    "Avg Composite Tax Rate" = cty_mc_current_rate_avg,
 'FMV in TIFs' = cty_mc_fmv_inTIF, 
   'TIF Increment FMV' = cty_mc_fmv_tif_increment)  |> 
  flextable() |> 
  align(align = "right")

Property Type	Homeowner Exempt FMV	Abated FMV	$0 Bill Count	$ Levy Paid	FMV in Major Class	Taxed AV	Avg Composite Tax Rate	FMV in TIFs	TIF Increment FMV
0	0	0	93,279	14,361	0	52,480	9.86%	0	0
1	0	0	5,029	110,431,839	5,446,604,190	419,892,652	10.24%	2,389,141,250	1,113,782,921
2	56,127,802,945	0	24,663	10,557,379,804	499,767,211,980	41,780,428,821	9.11%	57,158,146,720	25,801,076,108
3	188,839	0	29	998,308,846	54,074,286,640	4,385,238,578	8.19%	16,767,153,840	10,138,872,450
4	0	0	6	23,307,608	601,863,935	99,164,739	9.31%	286,875,585	106,040,209
5A	13,210,489	0	509	4,130,663,966	85,156,134,688	17,427,701,620	9.12%	30,720,749,976	15,394,260,529
5B	88,805	0	40	946,587,981	17,996,604,740	3,325,439,744	9.22%	7,200,083,220	4,692,359,430
6	0	0	1	236,377,492	9,747,786,025	761,017,479	10.59%	3,121,976,440	2,138,159,522
7A	0	0	0	9,129,465	468,038,430	35,319,406	9.68%	250,674,677	157,452,280
7B	0	0	21	37,422,505	3,172,185,693	174,481,796	8.14%	1,712,364,163	1,433,528,815
8A	0	0	0	22,336,030	506,111,295	44,221,512	17.75%	242,557,120	99,325,050
8B	0	0	0	28,649,246	1,062,864,270	58,052,385	19.68%	563,984,360	476,889,007
9	0	0	6	19,749,267	1,122,795,790	85,522,448	8.20%	518,887,840	232,634,824

The Fair Market Value (FMV) is also called the Market Value for Assessment Purposes and can be calculated from the av / loa, or the Assessed Value divided by the Level of Assessment. However, the values used for the level of assessment are an approximation for incentive properties since we do not have the PIN level assessment ratios.

Municipality Level Stats

Ignore stats for these Municipalities. Simple rounding errors may cause bizarre results for rate changes & other calculations. These municipalities are dropped from summary tables in this website but are included in exported files.

• Frankfort has 1 PIN in Cook County
• East Dundee has 2
• Homer Glen has 3
• University Park has 4
• Oak Brook, Deer Park, Deerfield, & Bensenville each have less than 75 PINs in Cook County, IL

Frequency of Incentives

Fair Market Value of Incentive PINs

landuse_incentshares <- pin_data |>  
  filter(!agency_num %in% cross_county_lines) |>

  filter(land_use != "Land") |>
  group_by(clean_name, land_use) |> 
  summarize(pin_count = n(),
            incent_PC = sum(ifelse(class >=600 & class <=900, 1, 0), na.rm=TRUE),
            fmv_incentive = sum(ifelse(class >=600 & class <=900, fmv, 0), na.rm = TRUE),
            fmv_group = sum(fmv, na.rm=TRUE),
  ) |>
  mutate(
    fmv_incentive = ifelse(fmv_incentive == 0, NA, fmv_incentive),
    pct_incent = fmv_incentive / fmv_group,
    pct_incent = ifelse(is.na(pct_incent), 0, pct_incent),
pct_incent = scales::percent(pct_incent)
)  

indust_top10 <- landuse_incentshares |>
  select(clean_name, land_use, fmv_incentive, incent_PC) |> 
  filter(land_use == "Industrial") |>
  arrange(desc(fmv_incentive)) |>
  head(10) |> 
  select(-land_use)

indust_top10 |>
  flextable() |> 
  set_header_labels(clean_name = "Municipality", incent_PC = '# of Incent. PINs',
fmv_incentive = "FMV from Incentive\nClass Properties"
) |>
  set_table_properties( layout = "autofit", width = .75)

Municipality	FMV from Incentive Class Properties	# of Incent. PINs
Chicago	1,979,059,970	493
Elk Grove Village	935,174,200	210
Franklin Park	833,824,150	84
Bedford Park	671,990,980	154
North Lake	636,227,120	21
Melrose Park	606,945,600	117
Des Plaines	355,953,705	59
Matteson	337,527,380	16
Mc Cook	315,423,540	48
Mt Prospect	291,506,020	48

Table 13.8: Top 10 Municipalities using Industrial Incentives

commerc_top10 <- landuse_incentshares |>
  select(clean_name, land_use, fmv_incentive, incent_PC) |> 
  filter(land_use == "Commercial") |>
  arrange(desc(fmv_incentive)) |>
  head(10) |> 
  select(-land_use)
  
commerc_top10 |>
  flextable() |> 
#align(align = "right", j = 2:4) |>
  set_header_labels(clean_name = "Municipality", 
                    incent_PC = '# of Incent. PINs', 
                    fmv_incentive = "FMV from Incentive\nClass Properties"
                    ) |>
  set_table_properties( layout = "autofit", width = .75)

Municipality	FMV from Incentive Class Properties	# of Incent. PINs
Chicago	3,217,094,693	223
Glenview	71,563,900	3
Matteson	63,645,520	24
Evergreen Park	51,106,697	6
Calumet City	41,480,025	73
South Holland	41,391,110	51
Chicago Heights	32,634,725	69
Orland Park	29,190,280	10
Oak Forest	28,584,525	34
Melrose Park	27,004,127	6

Table 13.9: Top 10 Municipalities using Commercial Incentives

Share of Total FMV with Incentive Classification

Combined Industrial and Commercial FMV with incentive classification divided by total FMV in municipality. Not included in a table in the report.

One way of showing areas that have reduced their tax base significantly through the use of incentive classifications.

table_muni_percentages <- muni_sums |>   
  filter(year == params$year)|>
  filter(!clean_name  %in% cross_county_line)


muni_count <- n_distinct(table_muni_percentages$clean_name)
no_incents <- n_distinct(table_muni_percentages$clean_name[table_muni_percentages$pct_fmv_w_incentclass == 0])
median = round(muni_count/2)
median_5 = c( (median-2) : (median+2) )
bottom_5 = c( (muni_count-4) : muni_count )

table_muni_percentages |>
  select(clean_name, pct_fmv_w_incentclass) |> 
  arrange(desc(pct_fmv_w_incentclass)) |>
  slice(c(1:5, median_5)) |>
  
  mutate(pct_fmv_w_incentclass = scales::percent(pct_fmv_w_incentclass, digits = 2)) |>
  flextable() |> 
  border_remove() |>
  hline_top() |>
  hline(i = c(5,10)) |>
  
  set_header_labels(clean_name = "Municipality", pct_fmv_w_incentclass = "% FMV") |>
  add_footer_lines(top = FALSE, values = paste0("There are ", no_incents, "  municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.") ) |>
  
  align(j = 2, align = "right") |>
  align(j=2, align = "right", part = "header") |>
  bold(i = 8) |>

  set_table_properties( layout = "autofit")

Municipality	% FMV
Mc Cook	59.50%
Bedford Park	48.60%
North Lake	37.50%
Markham	31.40%
Franklin Park	28.00%
Chicago Ridge	1.50%
Hazelcrest	1.50%
Chicago	1.40%
Palatine	1.40%
Tinley Park	1.40%
There are 29 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 13.10: Municipalities with the largest share of FMV property with incentive classification.

library(sf)
library(ggpattern)

no_incentives <-  pin_data |> 
  group_by(clean_name) |> 
  filter(sum(incent_prop) == 0) |> 
  distinct(clean_name)


no_incentives <- no_incentives |>
  left_join(muni_shp, by = c("clean_name" = "MUNICIPALITY")) 

# unincorp_areas <- muni_shp |> filter(!MUNICIPALITY %in% munilevel$clean_name)
#anti_join(munilevel, muni_shp, by = c("clean_name" = "MUNICIPALITY"))

# unincorp_areas2 <- muni_shp |> filter(!MUNICIPALITY %in% munilevel$shpfile_name)

table_muni_percentages |>
    left_join(nicknames) |>

  select(clean_name, pct_fmv_w_incentclass, shpfile_name) |>
 filter(!is.na(clean_name) & clean_name != "Unincorporated" ) |>
  full_join(muni_shp, by = c("shpfile_name" = "MUNICIPALITY")) |>
  
  ggplot(aes(fill = pct_fmv_w_incentclass)) +
  geom_sf(aes(geometry = geom), color = "black") +
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
  scale_fill_steps2(
    high = "brown", low = "black",
    show.limits=TRUE,
    nice.breaks=FALSE,
    na.value="gray",
    n =4,
    name = "Municipality FMV\nwith Incentive\nClassification",
    labels = scales::percent
) +
   geom_sf_pattern(data = no_incentives, aes(geometry = geom), pattern = 'crosshatch', pattern_spacing = 0.015, pattern_density = 0.1, fill = "white", alpha = .5, color = 'gray40')

Figure 13.1: Percent of Municipality FMV with Incentive Classification % = FMV from Incentive Class properties / Muni FMV

table2 <- pin_data |> 
    filter(land_use != "Land") |>
  group_by(clean_name, incent_prop) |>   # projects can be counted twice if the project has incentive and normal commercial/industrial prop classes.
  summarize(pin_count = n(),
         project_count = n_distinct(main_keypin), 
         av_adjusted=sum(ifelse(between(class, 600, 899), av*2.5, av)),
         av=sum(av, na.rm=TRUE),
fmv=sum(fmv)) 

datatable(table2,
          rownames= FALSE,
          colnames = c('Municipality' = 'clean_name',   
                       'Incentivized?' = 'incent_prop', 
                       'PIN Count' = 'pin_count', 
                       'Project Count' = 'project_count', 
                       'Taxable AV' = 'av',
                       'AV w/o Incentive Classification' = 'av_adjusted',
                       'FMV' = 'fmv')) |>
  formatCurrency(c('Taxable AV',  'AV w/o Incentive Classification', 'FMV'), digits = 0)

Table 13.11: PINs and value summarized by if the property has an incentive class or not in a municipality. AV Adjusted is the amount of assessed value that could be taxed if the property were assessed at 25% instead of the lower level of assessment of approximately 10%.

Share of Commercial & Industrial FMV with Incentive Classification

muni_incent_share <- pin_data |> 
  # drop munis mostly outside of Cook and unincorporated PINs
filter(!agency_num %in% cross_county_line & 
         !is.na(clean_name) ) |>

  filter(land_use == "Industrial" | land_use == "Commercial") |>
  group_by(clean_name, agency_num) |> 
  summarize(pin_count = n(),
            incent_PC = sum(ifelse(class >=600 & class <900, 1, 0), na.rm=TRUE),
            fmv_w_incent = sum(ifelse(class >=600 & class <900, fmv, 0), na.rm = TRUE),
           # project_count = n_distinct(main_keypin),
            fmv_group = sum(fmv, na.rm=TRUE),
            comind_fmv =sum(fmv, na.rm=TRUE),
            pins_inTIF = sum(in_tif, na.rm=TRUE)) |>
  mutate(
    fmv_w_incent = ifelse(fmv_w_incent == 0, NA, fmv_w_incent),
    pct_fmv_w_incent = fmv_w_incent/comind_fmv,
    pct_fmv_w_incent = ifelse(is.na(pct_fmv_w_incent), 0, pct_fmv_w_incent)) |>  
  select(clean_name, pct_fmv_w_incent, agency_num, fmv_w_incent, comind_fmv) 


muni_count <- n_distinct(muni_incent_share$clean_name)
no_incents <- n_distinct(muni_incent_share$clean_name[muni_incent_share$pct_fmv_w_incent == 0])
median = round(muni_count/2)
median_5 = c( (median-2) : (median+2) )
bottom_5 = c( (muni_count-4) : muni_count )


muni_incentshare_slice <- muni_incent_share  |>
  ungroup() |>
  
  filter(!agency_num %in% cross_county_lines) |>
  mutate(pct_fmv_w_incentclass = fmv_w_incent/comind_fmv,
         pct_fmv_w_incentclass = ifelse(is.na(pct_fmv_w_incentclass), 0, pct_fmv_w_incentclass)) |>
    select(clean_name, pct_fmv_w_incentclass) |>

  arrange(desc(pct_fmv_w_incentclass)) |>
  mutate(pct_fmv_w_incentclass = scales::percent(pct_fmv_w_incentclass, accuracy = 0.01 ) ) |>
  slice(c(1:5, median_5))

muni_incentshare_slice |>
  flextable() |> 
  border_remove() |>
  align(j = 2, align = "right", part = "all") |>
  hline_top() |>
  hline(i = c(5,10)) |>
  set_header_labels(clean_name = "Municipality", pct_fmv_w_incentclass = "% FMV with\nIncent. Class.") |>
  add_footer_lines(top = FALSE, values = c(paste0("There are ", no_incents, " municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.") )) |>
  bold(i = 8) |>
  set_table_properties( layout = "autofit")

Municipality	% FMV with Incent. Class.
Markham	77.50%
Sauk Village	73.33%
North Lake	66.09%
Country Club Hills	65.77%
Mc Cook	62.93%
Merrionette Park	9.85%
Palos Heights	9.76%
Countryside	9.57%
Arlington Heights	9.27%
Forest Park	8.42%
There are 31 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 13.12: Municipalities with the largest share of Commercial and Industrial property with incentive classification. Uses values obtained from PTAXSIM, and levels of assessment from CCAO’s Github.

figure7 <- muni_incent_share |>
  select(clean_name, pct_fmv_w_incent) |>
  left_join(nicknames)|>
filter(clean_name != "Unincorporated") |>
  full_join(muni_shp, by = c("shpfile_name" = "MUNICIPALITY")) 

figure7$pct_fmv_w_incent <- cut(
  figure7$pct_fmv_w_incent,
  breaks = c(0, 0.075, 0.15, 0.22, 0.30, as.numeric(max(muni_incent_share$pct_fmv_w_incent))),
  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")
    

figure7 |>
  ggplot(aes(fill = pct_fmv_w_incent)) +
  geom_sf(aes(geometry = geom), color = "black") +
  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
        ) +
#   scale_fill_steps2(
#     high = "#1e478e", low = "black",
#     show.limits=TRUE,
#     nice.breaks=FALSE,
#     na.value="gray",
#     n =4,
#     name = "C&I FMV with\nIncentive Class",
#     labels = scales::percent
# ) +
  
     # scale_fill_stepsn(colors = c("#ffffcc","#a1dab4" ,"#41b6c4","#2c7fb8", "#253494"),
     #                    show.limits=TRUE, 
     #                 limits = c(0,1),
     #                 na.value = "white", # "gray70",
     #                 n.breaks = 6,
     #                    name = "Burden with \nExemptions", 
     #                 labels = scales::percent
     #                 
     #                )+
   geom_sf_pattern(data = no_incentives, aes(geometry = geom), pattern = 'crosshatch', pattern_spacing = 0.015, pattern_density = 0.1, fill = "white", alpha = .5, color = 'gray40')+
  ggtitle(label = "Commercial & Industrial FMV with Incentive Classification")

Figure 13.2: Share of Commercial and Industrial FMV that has an incentive property class.

figure7alt <- muni_incent_share |>
  select(clean_name, pct_fmv_w_incent) |>
  left_join(nicknames)|>
filter(clean_name != "Unincorporated") |>
  full_join(muni_shp, by = c("shpfile_name" = "MUNICIPALITY")) 

figure7alt$pct_fmv_w_incent_label <- cut(
  figure7alt$pct_fmv_w_incent,
  breaks = c(0, 0.15, 0.30, 0.45, 0.60, as.numeric(max(muni_incent_share$pct_fmv_w_incent))),
  labels = c(
    "<15%",
    "15–29.99%",
    "30–44.99%",
    "45–59.99%",
    "≥60%"
  ),
  include.lowest = TRUE
)

# Define color palette
exemption_colors <- c(
  "<15%" = "#ffffcc",
  "15–29.99%" = "lightblue",
  "30–44.99%" = "#4292C6",
  "45–59.99%" = "#253494",
  "≥60%" = "#00004d")


figure7alt |>
  ggplot(aes(fill = pct_fmv_w_incent_label)) +
  geom_sf(aes(geometry = geom), color = "black") +
  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
        ) +
   geom_sf_pattern(data = no_incentives, aes(geometry = geom), pattern = 'crosshatch', pattern_spacing = 0.015, pattern_density = 0.1, fill = "white", alpha = .5, color = 'gray40')+
  ggtitle(label = "Commercial & Industrial FMV with Incentive Classification")

Figure 13.3: Share of Commercial and Industrial FMV that has an incentive property class.

Share of Industrial FMV with Incentive Classification within each Municipality

indust_share_full <- muni_sums  |>
  
  filter(year == params$year & !clean_name %in% cross_county_line) |>
  ungroup() |>
  mutate(pct_fmv_indust_incent =  muni_fmv_indust_incent/muni_fmv_industrial,
         pct_fmv_indust_incent = ifelse(is.na(pct_fmv_indust_incent), 0, pct_fmv_indust_incent)) |>
  select(clean_name, pct_fmv_indust_incent) |> 
  arrange(desc(pct_fmv_indust_incent)) 

muni_count <- n_distinct(indust_share_full$clean_name)
no_incents <- n_distinct(indust_share_full$clean_name[indust_share_full$pct_fmv_indust_incent == 0])
median = round(muni_count/2)
median_5 = c( (median-2) : (median+2) )
bottom_5 = c( (muni_count-4) : muni_count )

indust_share <- indust_share_full |>
  slice(c(1:5, median_5)) |>
  mutate(pct_fmv_indust_incent = scales::percent(pct_fmv_indust_incent, accuracy = 0.01 ) ) 

indust_share |>
  flextable() |> 
  border_remove() |>
  align(j = 2, align = "right", part = "all") |>
  hline_top() |>
  hline(i = c(5,10)) |>
  set_header_labels(clean_name = "Municipality", pct_fmv_indust_incent = "% FMV with\nIncent. Class.") |>
  add_footer_lines(top = FALSE, values = paste0("There are ", no_incents, "  municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.") ) |>
  bold(i = 8) |>
  set_table_properties( layout = "autofit")

Municipality	% FMV with Incent. Class.
Country Club Hills	94.65%
Markham	91.31%
Homewood	90.55%
North Lake	88.17%
Matteson	84.64%
Niles	24.03%
Skokie	23.11%
Palos Hills	23.02%
Hazelcrest	21.51%
Chicago	21.27%
There are 37 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 13.13: Percent of Industrial FMV within each Municipality that has incentive classification. 37 municipalities do not use industrial incentives.

indust_max = max(indust_share_full$pct_fmv_indust_incent)
figure7B <- indust_share_full |>
  select(clean_name, pct_fmv_indust_incent) |>
  left_join(nicknames)|>
filter(clean_name != "Unincorporated") |>
  full_join(muni_shp, by = c("shpfile_name" = "MUNICIPALITY")) 

figure7B |>
  ggplot(aes(fill = pct_fmv_indust_incent)) +
  geom_sf(aes(geometry = geom), color = "black") +
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
  scale_fill_steps2(
    high = "brown", low = "black",
    show.limits=TRUE,
    nice.breaks=FALSE,
    na.value="gray",
    n =4,
    name = "FMV with\nIncentive Class",
    labels = scales::percent) +
   geom_sf_pattern(data = no_incentives, aes(geometry = geom), pattern = 'crosshatch', pattern_spacing = 0.015, pattern_density = 0.1, fill = "white", alpha = .5, color = 'gray40')+
  ggtitle(label = "Industrial FMV with Incentive Classification")

Figure 13.4: Share of Industrial FMV that has an incentive property class.

Share of Commercial FMV with Incentive Classification within each Municipality

commerc_share_full <- muni_sums  |>
  
  filter(year == params$year & !clean_name %in% cross_county_line) |>
  ungroup() |>
  mutate(pct_fmv_commerc_incent =  muni_fmv_commerc_incent/muni_fmv_commercial,
    pct_fmv_commerc_incent = ifelse(is.na(pct_fmv_commerc_incent), 0, pct_fmv_commerc_incent)) |>
  select(clean_name, pct_fmv_commerc_incent) |> 
  arrange(desc(pct_fmv_commerc_incent))

muni_count <- n_distinct(commerc_share_full$clean_name)
no_incents <- n_distinct(commerc_share_full$clean_name[commerc_share_full$pct_fmv_commerc_incent == 0])
median = round(muni_count/2)
median_5 = c( (median-2) : (median+2) )
bottom_5 = c( (muni_count-4) : muni_count )

commerc_share <- commerc_share_full |>
  slice(c(1:5, median_5))  |>
  mutate(pct_fmv_commerc_incent = scales::percent(pct_fmv_commerc_incent, accuracy = 0.01 ) ) 

commerc_share |>
  flextable() |> 
  border_remove() |>
  align(j = 2, align = "right", part = "all") |>
  hline_top() |>
  hline(i = c(5,10)) |>
  set_header_labels(clean_name = "Municipality", pct_fmv_commerc_incent = "% FMV with\nIncent. Class.") |>
  add_footer_lines(top = FALSE, values = paste0("There are ", no_incents, "  municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.") ) |>
  
  bold(i = 8) |>
  set_table_properties( layout = "autofit")

Municipality	% FMV with Incent. Class.
Sauk Village	47.22%
Posen	30.91%
Calumet Park	30.46%
Ford Heights	30.00%
South Holland	28.67%
Arlington Heights	1.97%
Oak Lawn	1.81%
Countryside	1.74%
Hoffman Estates	1.66%
Chicago Ridge	1.45%
There are 46 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 13.14: Percent of Commercial FMV within each Municipality that has incentive classification.

figure7C <- commerc_share_full |>
  select(clean_name, pct_fmv_commerc_incent) |>
  left_join(nicknames)|>
filter(clean_name != "Unincorporated") |>
  full_join(muni_shp, by = c("shpfile_name" = "MUNICIPALITY")) 

figure7C |>
  ggplot(aes(fill = pct_fmv_commerc_incent)) +
  geom_sf(aes(geometry = geom), color = "black") +
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
  scale_fill_steps2(
    high = "brown", low = "black",
    limits = c(0, indust_max),
    show.limits=TRUE,
    nice.breaks=FALSE,
    na.value="gray",
    n =4,
    name = "FMV with\nIncentive Class",
    labels = scales::percent
) +
   geom_sf_pattern(data = no_incentives, aes(geometry = geom), pattern = 'crosshatch', pattern_spacing = 0.015, pattern_density = 0.1, fill = "white", alpha = .5, color = 'gray40')+
  ggtitle(label = "Commercial FMV with Incentive Classification")

Figure 13.5: Share of Commercial FMV that has an incentive property class.

The Costs of Incentives

Shifted Tax Revenue and Tax Burden

Read in CCAO level of assessment rates for each property class. Read in Summary file for Municipalities from 2006 to 2024.

muni_rate <- read_csv("../Output/ptaxsim_muni_level_2006to2024.csv") |>
  filter(year == params$year) |>
  left_join(nicknames) |> 
  select(clean_name, muni_current_rate_avg, muni_pct_eav_taxed) |>
  rename(cur_munilevel_rate = muni_current_rate_avg) |> 
  filter(!clean_name %in% cross_county_line)

muni_ratechange <- read_csv(paste0("../Output/website_files/muni_ratechange_", params$year, ".csv")) |> 
  filter(!clean_name %in% cross_county_line)

Not Taxed Value - PIN aggregated to County Level

cook_ratechange <- pin_data |>
  filter(!clean_name  %in% cross_county_line) |> 
  summarize(
    
    ## New Tax Base Calculations
    new_TEAV_noIncents = sum(ifelse(class >= 600 & class < 900,
                                    (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor), na.rm=TRUE),
    
    ## NewTax Base Calculations
    new_TEAV_noCommercIncents = sum(ifelse(class >= 600 & class < 900 & class %in% commercial_classes,
                                           (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor), na.rm=TRUE),

   new_TEAV_noIndustIncents = sum(ifelse(class >= 600 & class < 900 & class %in% industrial_classes,
      (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor), na.rm=TRUE),

    new_TEAV_noC6 = sum(ifelse( class >=600 & class < 700, 
(taxed_av*eq_factor/loa)*0.25 , taxed_av*eq_factor), na.rm=TRUE),

    new_TEAV_noC7 = sum(ifelse(class >=700 & class < 800,
(taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor), na.rm=TRUE),

    new_TEAV_noC8 = sum(ifelse(class >=800 & class < 900, (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor), na.rm=TRUE),
    
    new_TEAV_vacant_props = sum(ifelse(class >=600 & class <900,  # if incentive classes were assessed at 0% (aka not taxedat all) or if the building lost its value completely without the incentive classification.
                                       0, taxed_av*eq_factor), na.rm=TRUE),
    
    new_TEAV_noExemps = sum(taxed_av*eq_factor + exe_total_adj, na.rm=TRUE), # does not include abatements
    new_TEAV_noAbates = sum(taxed_av*eq_factor + exe_abate, na.rm=TRUE), # include only abatements, not other exemption types
  
    # TIF increment above the frozen EAV
    forgone_TIF_EAV = sum(fmv_tif_increment * loa * eq_factor, na.rm=TRUE),
 
    cty_PC = n(),
    # projects = n_distinct(both_ids), # mostly for industrial and commercial properties
    pins_withincents = sum(incent_prop == 1),
    fmv_incentive = sum(ifelse(incent_prop == 1, fmv, 0), na.rm = TRUE),
    #fmv_taxed =  sum(taxed_fmv, na.rm=TRUE),
    fmv_incents_inTIFs = sum(ifelse(incent_prop == 1 & final_tax_to_tif > 0, fmv, 0), na.rm = TRUE),
    fmv_inTIF = sum(fmv_inTIF, na.rm=TRUE),
    fmv_tif_increment = sum(fmv_tif_increment, na.rm=TRUE),
 #   fmv_untaxable_value = sum(untaxable_value_fmv , na.rm=TRUE),
    fmv_exemptions = sum(exe_total_adj/eq_factor/loa, na.rm=TRUE),
    fmv_abatements = sum(exe_abate/eq_factor/loa, na.rm=TRUE),
    zero_bill = sum(zero_bill, na.rm=TRUE),
    fmv_residential = sum(ifelse(res_prop==1, fmv, 0), na.rm = TRUE),
    fmv_C2 = sum(ifelse(c2_prop == 1, fmv, 0), na.rm = TRUE),
    
    fmv_industrial = sum(ifelse(class %in% industrial_classes, fmv, 0), na.rm = TRUE),
    fmv_commercial = sum(ifelse(class %in% commercial_classes, fmv, 0), na.rm = TRUE),
    
    current_rate_avg = mean(tax_code_rate),
    avg_C2_bill_noexe = mean(ifelse(between(class,200,299) & exe_total_adj == 0, (final_tax_to_dist + final_tax_to_tif), NA), na.rm=TRUE),
    avg_C2_bill_withexe = mean(ifelse(between(class,200,299) & exe_total_adj > 0, (final_tax_to_dist + final_tax_to_tif), NA), na.rm=TRUE),
    av_taxed = sum(taxed_av, na.rm = TRUE),
    untaxable_value_av = sum(untaxable_value_av, na.rm=TRUE),
    av = sum(av),
    eav_taxed = sum(taxed_av*eq_factor), 
    eav_untaxable = sum(untaxable_value_eav, na.rm=TRUE),
    fmv = sum(fmv, na.rm=TRUE),
    pins_in_class = n(),
    exe_total_adj = sum(exe_total_adj),   # in EAV
    abatements = sum(exe_abate),            # in EAV
    eav_incents_inTIFs = sum(ifelse(incent_prop == 1 & in_tif == 1, taxed_eav, 0), na.rm = TRUE),

    final_tax_to_dist = sum(final_tax_to_dist), ## Amount of Total Levy
    final_tax_to_tif = sum(final_tax_to_tif),
    eq_av = sum(eq_av)) |>
  mutate(
   reducedEAV_comind_incents =  new_TEAV_noIncents - eav_taxed,
   reducedEAV_commerc_incents  = new_TEAV_noCommercIncents - eav_taxed,

   reducedEAV_indust_incents  = new_TEAV_noIndustIncents - eav_taxed,
    # Absolute maximum TEAV: No Exemptions, no abatements, no TIFS, no Incentive properties
    # Commercial and industrial assessed at 25%
    TEAV_max = eav_taxed + exe_total_adj + abatements + forgone_TIF_EAV +    reducedEAV_comind_incents,

    # no exemptions or incentive classifications:
    TEAV_neither = eav_taxed + exe_total_adj + reducedEAV_comind_incents,

    rate_noExe = final_tax_to_dist / new_TEAV_noExemps,
    rate_noCommercInc = final_tax_to_dist / new_TEAV_noCommercIncents,
    rate_noIndustInc = final_tax_to_dist / new_TEAV_noIndustIncents,

    rate_noAbate = final_tax_to_dist / new_TEAV_noAbates,
    rate_noInc = final_tax_to_dist / new_TEAV_noIncents ,
    rate_neither = final_tax_to_dist / TEAV_neither, 
    rate_noTIFs = final_tax_to_dist / (eav_taxed + forgone_TIF_EAV),
    rate_vacant = final_tax_to_dist / new_TEAV_vacant_props,
    rate_lowest = final_tax_to_dist / TEAV_max ,
    rate_current = final_tax_to_dist / eav_taxed,
    change_noInc = rate_current - rate_noInc,

    forgonerev_noExe = rate_noExe * exe_total_adj,
    forgonerev_noAbate = rate_noAbate * (new_TEAV_noAbates - eav_taxed),
    forgonerev_noIncents = rate_noInc * (new_TEAV_noIncents - eav_taxed),
    forgonerev_noCommercIncents = rate_noInc * (new_TEAV_noCommercIncents - eav_taxed),

    forgonerev_noIndustIncents = rate_noInc* (new_TEAV_noIndustIncents - eav_taxed),

    forgone_noExe_or_Inc = rate_neither * (TEAV_neither - eav_taxed),
    forgonerev_noTIFs = rate_noTIFs * forgone_TIF_EAV,
    forgonerev_vacant = rate_vacant * (new_TEAV_vacant_props - eav_taxed),
    forgonerev_maxTEAV = rate_lowest * (TEAV_max - eav_taxed)
)

cook_ratechange |>
 mutate(across(contains("rate"), scales::percent, accuracy = .01),
        change_noInc = round(change_noInc*100, digits = 2)) |>

select("Current Composite Rate" = rate_current, 
       "Rate if Incentive Props LoA is 25%" = rate_noInc, 
       "Tax Rate Change" =  change_noInc) |> 

  flextable()

Current Composite Rate	Rate if Incentive Props LoA is 25%	Tax Rate Change
8.20%	8.02%	0.19

cook_ratechange |> 
select("Commerc. EAV w/Incent" = reducedEAV_commerc_incents, 
       "Indust EAV w/ Incent" = reducedEAV_indust_incents,
       "CommInd EAV w/ Incent" = reducedEAV_comind_incents,
       "Homeowner Exemption Exempt EAV" = exe_total_adj) |> 
  flextable()

Commerc. EAV w/Incent	Indust EAV w/ Incent	CommInd EAV w/ Incent	Homeowner Exemption Exempt EAV
1,081,216,392	3,658,169,347	4,739,385,739	16,722,709,858

Table 13.15: Change in taxable EAV due to Incentive Classification

$15 Billion EAV is tax exempt due to homeowners exemptions. All incentive properties combined only have $4 billion EAV that is tax exempt (due to the decreased level of assessment which results in less AV, and therefore, EAV)

Estimates for Revenue Shifted to Non-Incentive Class Properties

Take new tax rate and apply it to new taxbase.

table7 <- cook_ratechange |> 
  select(contains("forgonerev") ) 

table7 |> 
  flextable()

forgonerev_noExe	forgonerev_noAbate	forgonerev_noIncents	forgonerev_noCommercIncents	forgonerev_noIndustIncents	forgonerev_noTIFs	forgonerev_vacant	forgonerev_maxTEAV
1,268,500,835	0	380,034,456	86,698,890	293,335,566	2,012,962,665	-266,863,549	3,262,542,725

Naive Revenue Shift

Uses old current tax rate and multiplies it by the new taxbase.

burden_shift <- pin_data |>
  filter(!agency_num %in% cross_county_lines & 
           !is.na(clean_name) & clean_name!="Unincorporated" )  |>
  summarize(
    # for homestead exemptions
    mostnaive_forgone_tax_amt_exe = sum(tax_amt_exe),    
  
    # more accurate but still uses current tax rate instead of recalculated tax rate:
    forgonerev_from_exemptions  = sum(ifelse(class >= 200 & class < 300,
          (((av*eq_factor) - (taxed_av*eq_factor))) * tax_code_rate, 0), na.rm=TRUE),

   # amount of EAV from taxing an additional 15% of the AV if incentive properties didn't exist
   # using current tax rate for each property at the tax code level
    forgonerev_from_comind_incents  = sum(ifelse(class >= 600 & class < 900,
          (((taxed_av*eq_factor)*0.25 - (taxed_av*eq_factor))) * tax_code_rate, 0), na.rm=TRUE),

    forgonerev_commerc_incents  = sum(ifelse(class >= 600 & class < 900 & class %in% commercial_classes,
          (((taxed_av*eq_factor)*0.25 - (taxed_av*eq_factor))) * tax_code_rate, 0), na.rm=TRUE),

    forgonerev_indust_incents  = sum(ifelse(class >= 600 & class < 900 & class %in% industrial_classes,
         (((taxed_av*eq_factor)*0.25 - (taxed_av*eq_factor))) * tax_code_rate, 0), na.rm=TRUE),
   
    #  forgonerev_noTIFs = rate_current/100 * ,
       # TIF increment above the frozen EAV
    forgonerev_TIFs = sum(fmv_tif_increment * loa * eq_factor*tax_code_rate, na.rm=TRUE),
   
   # if incentive properties had no tax value (i.e. owners left, or fully tax exempt)
   # also equal to the current amount collected from incentive properties
     forgonerev_vacant = sum(ifelse(class >= 600 & class < 900,  taxed_av*eq_factor * tax_code_rate, 0), na.rm = TRUE)
   )|>
  rename(
    `Naive Est. Homeowner Exemptions` = mostnaive_forgone_tax_amt_exe,

    `Homeowner Exemptions` = forgonerev_from_exemptions,
    `C&I Incentives` = forgonerev_from_comind_incents,
    `Commercial Incentive`= forgonerev_commerc_incents,
    `Industrial Incentive` = forgonerev_indust_incents,
    `TIFs` = forgonerev_TIFs,
    `Incent Props become Vacant` = forgonerev_vacant
  ) 

burden_shift |> flextable()

Naive Est. Homeowner Exemptions	Homeowner Exemptions	C&I Incentives	Commercial Incentive	Industrial Incentive	TIFs	Incent Props become Vacant
1,520,237,369	2,068,748,072	-245,085,882	-51,047,047	-194,038,835	2,065,107,660	326,781,176

Change in Composite Property Tax Rate Due to Incentives and other Policy Scenarios

Tables - Difference in Composite Tax Rates

muni_ratechange_full <- muni_ratechange |> 
  select(clean_name, rate_current, rate_noExe, change_noExe) |> 
  arrange(desc(change_noExe) )


muni_count <- n_distinct(muni_ratechange_full$clean_name)
no_incents <- n_distinct(muni_ratechange_full$clean_name[muni_ratechange_full$change_noExe < 0.0001])
median = round(muni_count/2)
median_5 = c( (median-2) : (median+2) )
bottom_5 = c( (muni_count-4) : muni_count )

table1JD_full <- muni_ratechange_full

table1JD <- muni_ratechange_full |>
  slice(c(1:5, median_5, bottom_5)) 

table1JD  |>
  mutate(across(is.numeric, scales::percent, accuracy = 0.01) ) |>
  flextable() |> 
  border_remove() |>
  hline_top() |>
  hline(i = c(5,10,15)) |>
  set_header_labels(clean_name = "Municipality", rate_current = "Current Comp.\nTax Rate", rate_noExe = "Tax Rate if No\ Exemptions",
                    change_noExe = "Rate Change") |>
  bold(i = 8) |>
  set_table_properties( layout = "autofit") |>
    align(j = 2:4, align = "right", part = c("all"))

Municipality	Current Comp. Tax Rate	Tax Rate if No Exemptions	Rate Change
Phoenix	34.08%	13.85%	20.23%
Park Forest	25.36%	18.29%	7.08%
Riverdale	27.34%	21.29%	6.05%
Calumet City	23.32%	18.17%	5.15%
Dolton	21.11%	16.02%	5.08%
Crestwood	9.31%	8.10%	1.20%
Prospect Heights	10.78%	9.58%	1.20%
Chicago Ridge	10.92%	9.84%	1.08%
Orland Hills	9.00%	7.95%	1.05%
Riverside	10.97%	9.92%	1.05%
Winnetka	7.94%	7.75%	0.19%
Rosemont	9.20%	9.10%	0.10%
Hodgkins	9.10%	9.03%	0.07%
Mc Cook	10.76%	10.69%	0.07%
Bedford Park	11.96%	11.90%	0.06%

Table 13.16: Composite Tax Rate Change from hypothetical scenario of taxing property that is current exempt due to exemptions.

muni_ratechange_full <- muni_ratechange |> 
  select(clean_name, rate_current, rate_noInc, change_noInc) |> 
  arrange(desc(change_noInc) )


muni_count <- n_distinct(muni_ratechange_full$clean_name)
no_incents <- n_distinct(muni_ratechange_full$clean_name[muni_ratechange_full$change_noInc < 0.0001])
median = round(muni_count/2)
median_5 = c( (median-2) : (median+2) )
bottom_5 = c( (muni_count-4) : muni_count )

table3JD_full <- muni_ratechange_full
table3JD <- muni_ratechange_full|>
  slice(c(1:5, median_5)) 

table8 <- muni_ratechange_full|>
  slice(c(1:5, median_5)) 

table8  |>
  mutate(across(is.numeric, scales::percent, accuracy = 0.01) ) |>
  flextable() |> 
  border_remove() |>
  hline_top() |>
  hline(i = c(5,10)) |>
  set_header_labels(clean_name = "Municipality", rate_current = "Current Comp.\nTax Rate", rate_noInc = "Tax Rate if No\nIncent. Class.",
                    change_noInc = "Rate Change") |>
  bold(i = 8) |>
  add_footer_lines(paste0("There are ", no_incents,  " municipalities that do not use incentives and have a majority of their taxable EAV within Cook County."), top = FALSE) |>
  set_table_properties( layout = "autofit") |>
    align(j = 2:4, align = "right", part = c("all"))

Municipality	Current Comp. Tax Rate	Tax Rate if No Incent. Class.	Rate Change
Phoenix	34.08%	24.15%	9.93%
Ford Heights	25.70%	21.46%	4.24%
North Lake	11.23%	7.64%	3.60%
Bedford Park	11.96%	8.42%	3.54%
Matteson	14.37%	11.34%	3.03%
Robbins	12.86%	12.67%	0.20%
Tinley Park	10.29%	10.09%	0.20%
Schaumburg	9.59%	9.40%	0.19%
Chicago Ridge	10.92%	10.74%	0.18%
Glenview	7.67%	7.49%	0.18%
There are 29 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 13.17: Composite Tax Rate Change from hypothetical scenario of taxing incentive property at 25% of their FMV instead of 10% of their FMV.

muni_ratechange |> 
  select(clean_name, change_noInc) |> 
 mutate(change_noInc = round(change_noInc*100, digits = 1)) |>
  filter(clean_name != "Unincorporated") |>
  left_join(nicknames) |>
  full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>
  ggplot(aes(fill = change_noInc)) +
  geom_sf(aes(geometry = geom), color = "black") +
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
  scale_fill_steps2(
    low = "lightblue", high = "#1e478e",
    show.limits=TRUE,
    nice.breaks=FALSE,
    na.value = "gray50",
    n =4,
    name = "Tax Rate Change\nin Percentage Pts") +
  
   geom_sf_pattern(data = no_incentives, aes(geometry = geom), pattern = 'crosshatch', pattern_spacing = 0.015, pattern_density = 0.1, fill = "white", alpha = .5, color = 'gray40')

Figure 13.6: Hypothetical change in composite tax rate if all value that currently receives incentive classification became assessed at 25%.

muni_ratechange_full <- muni_ratechange |> 
  mutate(change_vacant=rate_vacant-rate_current)|>
  select(clean_name, rate_current, rate_vacant, change_vacant) |> 
  arrange(desc(change_vacant))


muni_count <- n_distinct(muni_ratechange_full$clean_name)
no_incents <- n_distinct(muni_ratechange_full$clean_name[abs(muni_ratechange_full$change_vacant) < 0.0001])
median = round(muni_count/2)
median_5 = c( (median-2) : (median+2) )
bottom_5 = c( (muni_count-4) : muni_count )

table9 <- muni_ratechange_full|>
  slice(c(1:5, median_5)) 

table9  |>
  mutate(across(is.numeric, scales::percent, accuracy = 0.01) ) |>
  flextable() |> 
  border_remove() |>
  hline_top() |>
  hline(i = c(5,10)) |>
  set_header_labels(clean_name = "Municipality", rate_current = "Current Comp.\nTax Rate", rate_vacant = "Tax Rate if No\nIncent. Class.",
                    change_vacant = "Rate Change") |>
  bold(i = 8) |>
  add_footer_lines(paste0("There are ", no_incents,  " municipalities that do not use incentives and have a majority of their taxable EAV within Cook County."), top = FALSE) |>
  set_table_properties( layout = "autofit") |>
    align(j = 2:4, align = "right", part = c("all"))

Municipality	Current Comp. Tax Rate	Tax Rate if No Incent. Class.	Rate Change
Phoenix	34.08%	46.96%	12.88%
North Lake	11.23%	16.38%	5.15%
Ford Heights	25.70%	30.63%	4.93%
Bedford Park	11.96%	16.61%	4.65%
Matteson	14.37%	17.55%	3.18%
Midlothian	13.06%	13.20%	0.14%
Robbins	12.86%	13.00%	0.14%
Palatine	10.29%	10.43%	0.14%
Tinley Park	10.29%	10.43%	0.14%
Chicago Ridge	10.92%	11.05%	0.13%
There are 32 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 13.18: Composite Tax Rate Change Maximum increase in composite property tax rate due to incentives. (five largest and five median). Scenario from if properties that currently receive incentives lost all their value (i.e. most extreme scenario for if we didn’t have incentives and nobody used the land so it had no value.)

# as a dot graph ## 
# create order of dots
order <- muni_ratechange |>  
  as_tibble() |> 
  filter(change_noInc > 0) |> 
  arrange(change_noInc) |>
  select(clean_name, change_noInc) |>
  distinct()


# make dot graph
muni_ratechange |> 
  filter(change_noInc > .005) |>
  filter(change_noInc > 0) |>

  select(clean_name, rate_current, rate_noInc, change_noInc) |> 
  distinct() |>
  pivot_longer(c("rate_current", "rate_noInc"), 
               names_to = "type", values_to = "tax_rate") |> 
  left_join(order) |>
    filter(change_noInc > 0 ) |>
  mutate(clean_name = if_else(clean_name == "Mc Cook", "McCook", clean_name)) |>
  ggplot(aes(x = tax_rate, y= reorder(clean_name, change_noInc)))+
  geom_line(aes(group = clean_name))+ 
  geom_point(aes(fill=type), size=3, pch = 21, color = "black" )+
  theme_minimal() + 
  theme( 
    legend.title = element_blank(),
    plot.title.position = "plot",
    plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
  )+
  scale_fill_brewer(palette="Paired", labels = c("Incentives", "No Incentives"), direction = 1) +
  labs(title = "Difference in Composite Tax Rate if Assessed at 25%",
       subtitle = "Ordered by Comp. Rate Change", x = "Composite Tax Rate (%)", y = "")

Figure 13.7: Ordered by amount of change in the composite tax rate.

# as a dot graph ## 
# create order of dots
order <- muni_ratechange |>  
  as_tibble() |> 
 filter(change_noInc >0) |>
  arrange(rate_current) |>
  select(clean_name, rate_current)

# make dot graph
muni_ratechange |> 
 filter(change_noInc > .005) |>
  select(clean_name, rate_current, rate_noInc) |> 
  distinct() |>
  arrange(rate_current) |>
  pivot_longer(c("rate_current", "rate_noInc"), 
               names_to = "type", values_to = "tax_rate") |> 
  inner_join(order) |>
  ggplot(aes(x = tax_rate, y= reorder(clean_name, rate_current)))+
  geom_line(aes(group = clean_name))+ 
  geom_point(aes(fill=type), size=3, pch = 21, color = "black" 
             )+
  theme_minimal() + 
  theme( 
    legend.title = element_blank(),
    plot.title.position = "plot",
    plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
  )+
  scale_x_continuous(labels = scales::percent)+
  scale_fill_brewer(palette="Paired", labels = c("Incentives", "No Incentives"), direction = 1)+

  
  labs(title = "Difference in Composite Tax Rate if Assessed at 25%",
       subtitle = "Ordered by Current Composite Tax Rate", x = "Composite Tax Rate (%)", y = "")

Figure 13.8: Change in tax rate if incentive properties were assessed at 25% of their FMV instead of their reduced level of assessment. Only shows municipalities that had more than 1/2 percentage point change in tax rate.

# as a dot graph ## 
# create order of dots
order <- muni_ratechange |>  
  as_tibble() |> 
  filter(change_noInc > 0) |>
  arrange(rate_current) |>
  select(clean_name, rate_current) |> distinct()

# make dot graph
muni_ratechange |> 
  filter(change_noInc > 0) |>
  select(clean_name, rate_current, rate_noInc, #rate_neither, 
         rate_vacant, rate_noExe) |> 
  distinct() |>
  arrange(rate_current) |>
  pivot_longer(c("rate_current", 
                 "rate_noInc", 
                 "rate_vacant", 
                 "rate_noExe"# ,
                #  "rate_neither"
), 
               names_to = "type", values_to = "tax_rate") |> 
  inner_join(order) |>
  ggplot(aes(x = tax_rate, y= reorder(clean_name, rate_current)))+
  geom_line(aes(group = clean_name))+ 
  geom_point(aes(fill=type), size=3, pch = 21, color = "black" )+
  theme_minimal() + 
  theme( 
    legend.title = element_blank(),
legend.position = "bottom",
plot.title.position = "plot",
    plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
  )+
  scale_fill_brewer(palette = "RdGy",
                    labels = c("Current Rate", 
                             #  "No Exemps & LoA is 25%",
                               "No Exemptions", 
                               "No Incentives:\nLoA 25%",
                               "Incententive Classification\nLoA = 0%"
                    ), 
                    direction = -1) +
    scale_x_continuous(labels = scales::percent)+

  labs(title = "Composite Tax Rate Scenarios",
       subtitle = "Ordered by Current Composite Tax Rate", 
       x = "Composite Tax Rate (%)", y = "")

Figure 13.9: Multiple taxrate scenarios. All municipalities that had a taxrate change from altering the level of assessment for incentive class properties.

# as a dot graph ## 
# create order of dots
order <- muni_ratechange |>  
  as_tibble() |> 
  filter(change_noInc > .005 | clean_name == "Chicago") |>
  arrange(rate_current) |>
  select(clean_name, rate_current) |> distinct()

# make dot graph
figure9_data <- muni_ratechange |> 
  filter(change_noInc > .005) |>
  select(clean_name, rate_current, rate_noInc, rate_vacant) |> 
  distinct() |>
  arrange(rate_current) |>
  pivot_longer(c("rate_current", 
                 "rate_noInc", 
                 "rate_vacant"
  ), 
  names_to = "type", values_to = "tax_rate") |> 
  inner_join(order) 

figure9_data|>
  ggplot(aes(x = tax_rate, y= reorder(clean_name, rate_current)))+
  geom_line(aes(group = clean_name))+ 
  geom_point(aes(fill=type), size=3, pch = 21, color = "black" )+
  theme_minimal() + 
  theme( 
    legend.title = element_blank(),
#    legend.position = "bottom",
    legend.position = "none",

    plot.title.position = "plot",
    plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg

      axis.text.y = element_markdown(size = 9) # enable HTML styling in axis labels

)+
  scale_fill_manual(values = c("#ade0ee", "#1e478e", "#6dae4f"),
                    labels = c("Current Rate", 
                               "No Incentive Classes: \nLoA = 25%",
                               "Incentive Classification\nLoA = 0%") ) +  

    scale_x_continuous(labels = scales::percent) +
  labs(#title = "Composite Tax Rate Scenarios",
       #subtitle = "Ordered by Current Composite Tax Rate", 
       x = "Composite Tax Rate (%)", y = "")

Figure 13.10: Figure 9 in Incentive Report. Only shows municipalities that had more than 1/2 percentage point change in tax rate.

# as a dot graph ## 
# create order of dots
order <- muni_ratechange |>  
  as_tibble() |> 
  filter(change_noInc > .005) |>
  arrange(rate_current) |>
  select(clean_name, rate_current) |> distinct()

# make dot graph
figure9_data <- muni_ratechange |> 
  mutate(lowchange = ifelse(change_noInc < 0.02, TRUE, FALSE)
         ) |>
  filter(change_noInc > .005) |>
  select(clean_name, rate_current, rate_noInc, rate_vacant, lowchange) |> 
  distinct() |>
  arrange(rate_current) |>
  pivot_longer(c("rate_current", 
                 "rate_noInc", 
                 "rate_vacant"
  ), 
  names_to = "type", values_to = "tax_rate") |> 
  inner_join(order)  |>
  mutate(dotcolor = case_when(
          type == "rate_current" ~ "#ade0ee",
          type == "rate_noInc" ~ "#1e478e",
          type == "rate_vacant" ~ "#6dae4f"
          
         ),
         dotalpha = case_when(
          lowchange == TRUE ~ .8,
          T ~ 1 ),
      dotlabel = ifelse(lowchange, 
                              paste0("<span style='color:#B3B3B3;'>", clean_name, "</span>"), 
                              clean_name)
           
           )


 label_vec <- setNames(figure9_data$dotlabel, figure9_data$clean_name)


figure9_data  |>
  ggplot(aes(x = tax_rate, y= reorder(clean_name, rate_current)))+
  geom_line(aes(group = clean_name, alpha = dotalpha))+ 
  geom_point(aes(fill=dotcolor, alpha = dotalpha ), size=3, pch = 21, color = "black")+
  theme_minimal() + 

  scale_fill_identity(  labels = c("Current Rate", 
                               "No Incentive Classes: \nLoA = 25%",
                               "Incentive Classification\nLoA = 0%")) + 
  scale_x_continuous(labels = scales::percent) +
  scale_y_discrete(labels = label_vec) + 

  labs(#title = "Composite Tax Rate Scenarios",
       #subtitle = "Ordered by Current Composite Tax Rate", 
       x = "Composite Tax Rate (%)", y = "")+
    theme( 
    legend.title = element_blank(),
    legend.position = "none",
   # legend.position = "bottom",
    plot.title.position = "plot",
    plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg
    axis.text.y = ggtext::element_markdown(size = 9), # enable HTML styling in axis labels
    axis.text.y.left = ggtext::element_markdown(size = 9)

  )

Figure 13.11: Figure 9 in Incentive Report. Only shows municipalities that had more than 1/2 percentage point change in tax rate. Municipalities with less than X amount are in grayscale.

addcook <- cook_ratechange |>   
  select(rate_current, rate_noInc, rate_vacant, rate_noExe) |>
  mutate(clean_name = "Cook",
         lowchange = TRUE) |>
  select(clean_name, rate_current,rate_noInc, rate_vacant, lowchange, rate_noExe)
# as a dot graph ## 
# create order of dots
order <- muni_ratechange |>  
  as_tibble() |> 
    filter(change_noInc > .005 | clean_name == "Chicago" |  change_noExe > 0.01) |>

 # filter((change_noInc > .005 | clean_name == "Chicago" | change_noExe > 0.005) & !is.na(clean_name)) |>
  arrange(rate_current) |>
  select(clean_name, rate_current) |> distinct() |>
  rbind(addcook |> select(clean_name, rate_current))

# make dot graph
figure9_data <- muni_ratechange |> 
  mutate(lowchange = ifelse(change_noInc < 0.015 & change_noExe < 0.015, TRUE, FALSE)
         ) |>
    filter(change_noInc > .015 | clean_name == "Chicago" | clean_name == "Cook" | change_noExe > 0.015) |>

 # filter(change_noInc > .005 | clean_name == "Chicago" | clean_name == "Cook" | change_noExe > 0.01) |>

  select(clean_name, rate_current, rate_noInc, rate_vacant, lowchange, rate_noExe) |> 
  rbind(addcook) |>

  distinct() |>
  arrange(rate_current) |>
  pivot_longer(c(
                 "rate_noInc", 
                # "rate_vacant", 
                 "rate_noExe",
                "rate_current"), 
               names_to = "type", values_to = "tax_rate") |> 
  inner_join(order)  |>
  mutate(
    dotcolor = case_when(
     
      type == "rate_noInc" ~ "#ffffcc", #"#4292C6",
      #type == "rate_vacant" ~ "#6dae4f",
      type == "rate_noExe" ~ "#4292C6",
       type == "rate_current" ~ "#253494",
    ),
    dotalpha = case_when(
      lowchange == TRUE ~ .8,
      T ~ 1 ),
    dotlabel = ifelse(lowchange, 
                      paste0("<span style='color:#B3B3B3;'>", clean_name, "</span>"), 
                      clean_name)
           )

label_vec <- setNames(figure9_data$dotlabel, figure9_data$clean_name)


figure9_data  |>
  ggplot(aes(x = tax_rate, y= reorder(clean_name, rate_current)))+
  geom_line(aes(group = clean_name, alpha = dotalpha), show.legend = c(alpha = FALSE)
            )+ 
  geom_point(aes(fill=dotcolor, alpha = dotalpha ), color = "black", size=3, pch = 21, 
             show.legend = c(fill = TRUE, alpha = FALSE)
             )+
  theme_minimal() + 
  theme( 
    legend.title = element_blank(),
    #legend.position = "none",
    legend.position = "bottom",
    plot.title.position = "plot",
    plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg
    axis.text.y = ggtext::element_markdown(size = 9), # enable HTML styling in axis labels
    axis.text.y.left = ggtext::element_markdown(size = 9)    
  ) +
  scale_fill_identity(labels = rev(
    c(
      "No Incentive Classes",
      "No Exemptions",
      "Current Rate")
  ),
  guide = "legend") +
  scale_x_continuous(labels = scales::percent) +
  scale_y_discrete(labels = label_vec) + 
  labs(#title = "Composite Tax Rate Scenarios",
    #subtitle = "Ordered by Current Composite Tax Rate", 
    x = "Composite Tax Rate (%)", y = "")

Figure 13.12: Image for Academic Paper. Only shows municipalities that had more than 1/2 percentage point change in tax rate. Municipalities with less than X amount are in grayscale.

Export Tables

library(readxl)

today_date <- Sys.Date()

cook_sums <- cook_sums |>  filter(year == params$year)

cook_MC_sums <- cook_MC_sums |> filter(year == params$year)

muni_sums <- muni_sums |>  filter(year == params$year)

tablelist <- list(
  "Table1" = table1JD, # increase in comp rate due to exemptions
  "Table1_full" = table1JD_full, # increase in comp rate due to exemptions

  # "Table2" =         # FMV of incentive PINs. Separate word doc formatted for paper
  "Table3" = table3JD,   # Increase in comp rate due to incentives. Table 8 in incentive report, table 3 in JD report
  "Table3_full" = table3JD_full,   # Increase in comp rate due to incentives. Table 8 in incentive report, table 3 in JD report
  "Figure1JD_data" = Figure1JD_data,
  "Figure2JD_data" = median_exempt,
  "Figure3JD_data" = mc_burden,
  "Figure4JD_data" = figure7alt,
  "Figure5JD_data" = figure9_data,
  "Muni Stats" = muni_sums,
  "Muni Rate Change" = muni_ratechange,

  "Cook Sums" = cook_sums,
  "Cook Class Sums" = cook_MC_sums,
  "Cook Rate Change" = cook_ratechange
)

file_name = paste0("../Output/Report Content/calculations_report_content_", params$year, "_", today_date, ".xlsx")

writexl::write_xlsx(tablelist, file_name)
 
# readme <- c(
# 
#   "Muni names and share of FMV with incentive property",
#   "Muni names and share of FMV with incentive property",
#   "Muni names and share of FMV with incentive property",
#   
#   "Muni Share_all includes all municipalities and their share of FMV with incentives out of all FMV in the municipality.",
#   "IndustShare_all is same as Indust_share but includes all munis.",
#   "CommercShare_all is same as Commerc_share but includes all munis.") %>% 
#   as.data.frame()

#if(params$year == 2023){
paper_tables <- list(
  # "README"= readme,
  # Table 2 is from yearly trends.  Incentive FMV by year
  # Figure 1 is from yearly trends.  Bar Chart - Aggregate Incentive FMV by year
  # Figure 2 is from yearly trends.  Line Chart - Indexed Incentive FMV
  # Figure 3 is from yearly trends.  Line Chart - FMV for single and multi family vs C&I FMV
  # Figure 5. Line Chart - Increase in FMV indexed to 2011 for Select property types


  "Table 3" = commerc_top10,
  "Table 4" = indust_top10,
  # Table 5 is from yearly trends file

  "Figure 4" = figure7,
  "Figure 5A" = figure7B,
  "figure 5B" = figure7C,
  # Table 6 is from yearly trends file
  
  "Table 8" = table8, # if assessment ratio was 25% instead of 10%
  "Table 9" = table9, # if properties with incentives were vacant (i.e. 0 FMV)

 "Figure 9 data" = figure9_data,   # dot plot 
   # Figure X is from yearly trends. FMV Growth by Incent Class and Land Use
   # Table X is from yearly trends. Table for figure X
   # Table X+1 is from yearly trends. Change from vacant land to incentive PIN
   # Table X+2. Regression models
  "Muni Share_all" = muni_incent_share,
  "IndustShare_all" = indust_share_full,
  "Commerc_Share_all" = commerc_share_full
# "All Cook Rate Scenarios" = cook_ratechange
)


writexl::write_xlsx(paper_tables, paste0( "../Output/Incentive Report/incentive_report_tables_taxyear_", params$year, "_", today_date, ".xlsx"))

#}