Tax Year 2021

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 (%)
1,864,594	564,078,586,339	464,644,694,576	82.37%

Table 10.1: 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
106,504,761,139	10.20%	39.53%	3.80%	96,200	1,214	24.44%

Table 10.2: Commercial and Industrial PINs in Cook County

10.20% of industrial and commercial PINs (aka "revenue producing PINs") FMV has an incentive classification (3.80% when using PIN counts). Of the PINs that have incentive classification, 39.53% of the FMV is located within a TIF (3.80% when using PIN counts).

24.44% 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
71,118	835	1.17%	37.84%	84,128,815,755	4.07%	52.20%

Table 10.3: Commercial PINs in Cook County

4.07% of commercial PIN FMV has an incentive classification (1.17% when using PIN counts). Of the PINs that have incentive classification, 52.20% of the FMV is located within a TIF (37.84% 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.
25,082	2,817	898	11.23%	33.71%	31.88%	22,375,945,384	33.25%

Table 10.4: Industrial PINs in Cook County

33.25% of industrial FMV has an incentive classification (11.23% when using PIN counts). Of the PINs that have incentive classification, 33.71% of the FMV is located within a TIF ( 31.88% 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 (%)
564,078,586,339	44,138,431,759	54,949,509,783	13,382,918	17.57%

Table 10.5: 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
70,733,758,749	58,268,604,987	13,658,042,180	82.38%	19.31%

Table 10.6: 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
564,078,586,339	91,482,762,913	44,138,431,759	10,861,564,025	4,294,012,280	2,654,664,070

Table 10.7: 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	96,779	0	0	0	10.75%	0	0
1	2,748	0	4,632	105,599,039	4,358,002,460	354,420,382	11.23%	1,843,011,040	811,597,121
2	54,934,117,722	86,882	40,940	8,855,545,702	410,547,209,260	33,720,353,390	10.00%	42,781,852,900	18,247,024,676
3	7,486,595	1,009,352	33	853,432,585	41,422,095,310	3,597,924,029	9.07%	10,108,420,990	5,367,465,968
4	0	0	6	21,863,896	532,586,910	89,281,220	10.23%	195,351,425	86,180,740
5A	7,790,020	7,985,670	502	4,146,504,887	80,205,629,680	16,778,821,180	9.89%	25,394,220,936	13,059,850,873
5B	112,699	4,010,709	19	876,094,029	14,904,980,524	2,812,083,799	10.16%	6,365,768,352	3,653,885,240
6	0	0	0	194,534,160	7,052,047,695	570,394,284	11.67%	2,305,071,670	1,349,017,317
7A	0	0	0	4,972,971	330,871,270	20,987,270	10.49%	254,849,110	126,368,573
7B	0	290,305	0	35,596,175	2,589,747,270	163,203,417	8.76%	1,326,940,140	973,133,431
8A	0	0	0	25,327,201	500,214,000	42,614,581	20.62%	203,965,750	70,639,234
8B	0	0	0	15,304,493	388,683,790	24,077,040	21.40%	203,185,610	135,505,514
9	0	0	6	23,676,326	1,246,518,170	94,444,396	8.75%	500,124,990	257,763,071

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	1,404,401,120	460
Elk Grove Village	726,896,050	247
Bedford Park	526,447,670	153
North Lake	490,863,310	19
Franklin Park	489,621,430	100
Melrose Park	285,148,740	87
Mc Cook	280,518,540	45
Des Plaines	260,638,670	53
Mt Prospect	250,025,790	42
Schaumburg	225,985,410	80

Table 10.8: 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	2,626,124,593	180
Glenview	74,639,040	3
Matteson	60,182,120	21
Calumet City	48,137,470	61
Evergreen Park	45,506,210	5
South Holland	36,105,920	46
Lansing	33,281,550	26
Chicago Heights	32,485,680	56
Orland Park	28,475,590	11
Homewood	24,525,680	20

Table 10.9: 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	68.40%
Bedford Park	48.80%
North Lake	40.30%
Hodgkins	37.90%
Sauk Village	30.10%
Maywood	1.50%
Park Forest	1.50%
Chicago Ridge	1.40%
Tinley Park	1.40%
Chicago	1.30%
There are 29 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 10.10: 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 10.1: 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 10.11: 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.
Sauk Village	78.47%
Mc Cook	74.73%
North Lake	71.46%
Ford Heights	56.32%
Phoenix	52.47%
Orland Hills	8.05%
Arlington Heights	7.88%
Palatine	7.78%
Broadview	7.43%
Schaumburg	7.36%
There are 32 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 10.12: 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 10.2: 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 10.3: 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.
Homewood	91.22%
Phoenix	90.58%
Richton Park	89.07%
North Lake	84.90%
Sauk Village	82.97%
Dolton	20.53%
Hoffman Estates	19.43%
Forest Park	19.38%
Skokie	19.33%
Chicago	19.29%
There are 38 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 10.13: 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 10.4: 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	47.81%
Ford Heights	35.89%
Posen	31.13%
Phoenix	28.72%
Matteson	27.26%
Evanston	1.31%
North Lake	1.27%
Des Plaines	1.26%
Oak Park	1.13%
Niles	0.78%
There are 51 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 10.14: 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 10.5: 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 2021.

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
8.64%	8.46%	0.18

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
1,004,405,702	2,653,769,934	3,658,175,636	32,414,579,152

Table 10.15: 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
2,356,860,606	817,500.1	309,405,487	84,951,808	224,453,679	1,601,209,207	-214,622,125	3,693,294,199

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
1,584,759,724	1,975,234,158	-204,793,025	-49,321,200	-155,471,825	1,569,027,965	273,057,367

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
Park Forest	41.33%	19.48%	21.85%
Phoenix	29.81%	11.09%	18.72%
Riverdale	30.66%	19.55%	11.11%
Hazelcrest	24.25%	13.82%	10.43%
Dolton	24.85%	14.50%	10.35%
Prospect Heights	11.38%	8.78%	2.60%
Chicago Ridge	12.47%	9.92%	2.55%
Orland Hills	10.61%	8.08%	2.54%
Riverside	12.26%	9.74%	2.52%
Westchester	9.52%	7.04%	2.48%
Winnetka	8.64%	8.12%	0.52%
Rosemont	10.43%	10.19%	0.24%
Mc Cook	12.91%	12.70%	0.21%
Bedford Park	13.27%	13.07%	0.19%
Hodgkins	10.01%	9.83%	0.18%

Table 10.16: 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
North Lake	13.54%	8.82%	4.72%
Ford Heights	24.56%	19.96%	4.60%
Bedford Park	13.27%	8.95%	4.31%
Mc Cook	12.91%	8.81%	4.10%
Phoenix	29.81%	25.89%	3.92%
Prospect Heights	11.38%	11.10%	0.28%
Glenview	8.37%	8.14%	0.23%
Robbins	14.96%	14.74%	0.22%
Schaumburg	10.11%	9.89%	0.22%
Tinley Park	12.66%	12.44%	0.22%
There are 29 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 10.17: 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 10.6: 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
North Lake	13.54%	21.05%	7.51%
Bedford Park	13.27%	19.55%	6.28%
Mc Cook	12.91%	18.71%	5.80%
Ford Heights	24.56%	29.03%	4.47%
Phoenix	29.81%	33.16%	3.35%
Prospect Heights	11.38%	11.57%	0.19%
Glenview	8.37%	8.53%	0.16%
Schaumburg	10.11%	10.27%	0.16%
Chicago Ridge	12.47%	12.63%	0.16%
Robbins	14.96%	15.11%	0.15%
There are 31 municipalities that do not use incentives and have a majority of their taxable EAV within Cook County.

Table 10.18: 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 10.7: 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 10.8: 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 10.9: 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 10.10: 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 10.11: 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 10.12: 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"))

#}