Hypothetical Taxbills: Remove General Homestead Exemptions

Bring in data

Bills were pulled from PTAXSIM amd summed to the pin level in files 1_...rmd and 2...rmd. Exemption data per PIN was pulled from PTAXSIM in file 3_.....rmd.

ptaxsim_db_conn <- DBI::dbConnect(RSQLite::SQLite(), "./ptaxsim.db/ptaxsim-2021.0.4.db")


muni_agency_names <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  "SELECT DISTINCT agency_num, agency_name, minor_type
  FROM agency_info
  WHERE minor_type = 'MUNI'
  OR agency_num = '020060000'  

  "
)

muni_tax_codes <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  glue_sql("
  SELECT DISTINCT agency_num, tax_code_num
  FROM tax_code
  WHERE agency_num IN ({muni_agency_names$agency_num*})
  AND year = 2021
  ",
  .con = ptaxsim_db_conn
  )
) %>% 
  mutate(tax_code_num = as.numeric(tax_code_num))

tax_codes <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  glue_sql("
  SELECT DISTINCT tax_code_num, tax_code_rate
  FROM tax_code
  WHERE year = 2021
  ",
  .con = ptaxsim_db_conn
  )
) %>% mutate(tax_code_num = as.numeric(tax_code_num))

taxbills_current <- read_csv("./Output/Dont_Upload/1_Get_All_Pins-CookPinTaxbills_2021_Actual.csv")
# 22,453,875 tax bills in 2021 in municipalities. 
# taxing agency-PIN is the unit of observation here (only partial property tax bills)
# Each PIN has multiple rows (because it is taxed by multiple taxing agencies)

sapply(taxbills_current, function(x) sum(is.na(x)))


# 1,825,816 billed properties with 14-digit PINs in incororated areas
# 1,864,594 in incorported and unincorporated areas.
pin14_bills_current <- taxbills_current %>%
  group_by(tax_code, class, pin) %>%
  
  mutate(total_bill = final_tax_to_dist + final_tax_to_tif) %>% # from each taxing agency
  
  summarize(
    total_billed = sum(total_bill, na.rm = TRUE), # total on someone's property tax bill
    av = first(av),
    eav = first(eav),
    # pin_count_in_parcel = n(),
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE),
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), # revenue lost due to exemptions
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), # total rev before all exemptions
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE), # total rev after all exemptions
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  )  %>% 
  mutate(propclass_1dig = str_sub(class, 1, 1))

# head(pin14_bills_current)

sapply(pin14_bills_current, function(x) sum(is.na(x)))


# Now group again and sum values for tax code and each property class!

# There are 28,381 property class - tax code group combinations in incorporated areas.
# There are 28,534 property class - tax code group combos in incorporated + unincorporated areas

rm(taxbills_current)

Add exemption types to pin level tax bills

Need to merge PIN level data of tax bills and exemptions per PIN if the goal is to calculate new tax rates for different scenarios AND view individual level tax bill changes. (i.e. if we want to look at the “median taxpayer” then need to use this method).

Note: The EAV from the exemption table is the original EAV (AV * equalizer) without consider TIFs or exemptions.

31,000 properties have $0 tax bills based on exemption data table made from PTAXSIM’s pin table. (Using the tax bill data allows for more in depth look at tax bills and can create variables such as small_bill if bills were less than $100 after exemptions were applied).

pin_eav <- read_csv("./Output/Dont_Upload/3_Exemption_Details_output-all_cook_pin_exemptions_2021_actual.csv") %>%
  #filter(class_code !=0) %>%
  
  mutate(all_exemptions = exe_homeowner + exe_senior + exe_freeze + exe_longtime_homeowner + 
           exe_disabled + exe_vet_returning + exe_vet_dis_lt50 + exe_vet_dis_50_69 + exe_vet_dis_ge70 + exe_abate) %>%
  mutate(zero_bill = ifelse(eav <= all_exemptions, 1, 0),
         has_HO_exemp = ifelse(exe_homeowner > 0, 1, 0)) %>%
  rename(equalized_AV = eav,
         pin_AV = av)

# table(pin_eav$zero_bill)

table(pin_eav$major_class_code, pin_eav$zero_bill)

# table(pin_eav$major_class_code, pin_eav$has_HO_exemp)



## Add exemption types and values to the tax bill data at PIN level
pin_data <- left_join(pin14_bills_current, pin_eav, by = c("pin", "class" = "class_code" ))

## Add tax code tax rate to PIN level data
pin_data <- left_join(pin_data, tax_codes, by = c("tax_code" = "tax_code_num"))

Calculate new tax rates

# taxcodes_by_class_current <- pin_data %>% 
#   group_by(tax_code, class)  %>%
#   
#   summarize(
#     av = sum(av),
#     eav = sum(eav),
#     equalized_AV = sum(equalized_AV),
#     pins_in_class = n(),
#     current_exemptions = sum(all_exemptions),
#     HO_exemps = sum(exe_homeowner),
#     tax_code_rate = first(tax_code_rate),
#     final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
#     final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
#     tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
#     tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
#     tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
#     rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
#     rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
#     rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
#     tif_share = mean(tif_share, na.rm=TRUE), # not used
#   ) %>%
#   
#   mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) %>%
#   rename(cur_comp_TC_rate = tax_code_rate) %>%
#   mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100),
#          new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100) + HO_exemps) %>%
#   mutate(new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav)*100) %>%
#   mutate(new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate)) %>%
#   select(tax_code, class, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, everything())
# 
# 
# head(taxcodes_by_class_current) 


taxcodes_current <- pin_data %>% 
  group_by(tax_code)  %>%
  summarize(
    av = sum(av),
    eav = sum(eav),
    equalized_AV = sum(equalized_AV),
    pins_in_class = n(),
    current_exemptions = sum(all_exemptions),
    HO_exemps = sum(exe_homeowner),
    tax_code_rate = first(tax_code_rate), 
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) %>%
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) %>%
  rename(cur_comp_TC_rate = tax_code_rate) %>%
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100),
         new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100) + HO_exemps) %>%
  mutate(new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav)*100) %>%
  mutate(new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate)) %>%
  select(tax_code, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, everything())


#head(taxcodes_current) 


taxcode_taxrates <- taxcodes_current %>% 
  select(tax_code, cur_comp_TC_rate, new_comp_TC_rate, current_exemptions, HO_exemps)


### Summarizing it from tax code to municipality weights the rates incorrectly (since some tax codes have waaaay more pins than others) ### 


# taxcode_taxrates %>% 
#   right_join(muni_tax_codes, by = c("tax_code" = "tax_code_num")) %>% 
#   group_by(agency_num) %>% 
#   summarize(cur_comp_TC_rate = mean(cur_comp_TC_rate, na.rm=TRUE),
#             new_comp_TC_rate = mean(new_comp_TC_rate, na.rm = TRUE)) %>% right_join(muni_agency_names) %>%
#   arrange(agency_name) 
# %>% write_csv("4c_tc_taxrates.csv")

Finding the “Median Property” or “Average Taxpayer”

The median assessed value is used to determine the median property for various groups (e.g. the municipality, the 1-digit major property class, and 3-digit property class).

The assessed value and original equalized assessed values come from the pin data table within PTAXSIM. This table also has every type of exemption that the property received and the amount of EAV that was exempt due to the exemption.

pin_data2 <- read_csv("./Output/4C_joined_PINs_bills_and_exemptions.csv")

head(pin_data2)

dim(pin_data2)

## [1] 1864594      53

pin_data2 <- pin_data2 %>% 
  mutate(
    bill_current = (final_tax_to_dist + final_tax_to_tif),
    bill_noexemps = new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
    bill_change = bill_noexemps - bill_current)


muni_taxrates <- pin_data2 %>% 
  group_by(agency_name)  %>%
  
  summarize(
    av = sum(av, na.rm = TRUE),
    eav = sum(eav, na.rm = TRUE),
    equalized_AV = sum(equalized_AV, na.rm = TRUE),
    pins_in_class = n(),
    current_exemptions = sum(all_exemptions, na.rm = TRUE),
    HO_exemps = sum(exe_homeowner, na.rm = TRUE),
    tax_code_rate = mean(tax_code_rate, na.rm = TRUE), # Changed from first() to mean() on Nov 1
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) %>%
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) %>%
  rename(cur_comp_TC_rate = tax_code_rate) %>%
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100),
         new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100) + HO_exemps) %>%
  mutate(new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav)*100) %>%
  mutate(new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate)) %>%
  select(agency_name, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, everything())

muni_taxrates 

muni_taxrates %>% write_csv("./Output/4C_muni_taxrates.csv")

Bill Distributions

pin_data2 %>%
  filter(major_class_code == 2 & bill_current < 30000 & agency_name == "VILLAGE OF DOLTON") %>% ggplot() +
  geom_histogram(aes(x= bill_current, bins = 100))+
    geom_vline(aes(xintercept=median(bill_current, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,20000)) +
  scale_y_continuous(limits = c(0, 1500))+
  labs(x="Dolton's Actual Tax Bills With Current Exemptions", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & bill_noexemps < 30000 & agency_name == "VILLAGE OF DOLTON") %>% ggplot() +
  geom_histogram(aes(x= bill_noexemps, bins = 100))+
    geom_vline(aes(xintercept=median(bill_noexemps, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,20000)) + 
    scale_y_continuous(limits = c(0, 1500))+

  labs(x="Dolton's Tax Bills Without General Homestead Exemptions", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF GLENCOE") %>% ggplot() +
  geom_histogram(aes(x= bill_current, bins = 100))+
    geom_vline(aes(xintercept=median(bill_current, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,60000)) + 
#    scale_y_continuous(limits = c(0, 1500))+

  labs(title="Glencoe's Current Tax Bill Distribution", x = "Dollars", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF GLENCOE") %>% ggplot() +
  geom_histogram(aes(x= bill_noexemps, bins = 100))+
    geom_vline(aes(xintercept=median(bill_noexemps, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,60000)) + 
#    scale_y_continuous(limits = c(0, 1500))+

  labs(title="Glencoe's Tax Bills Without General Homestead Exemptions", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & bill_current < 30000 & agency_name == "CITY OF CHICAGO") %>% ggplot() +
  geom_histogram(aes(x= bill_current, bins = 100))+
    geom_vline(aes(xintercept=median(bill_current, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,20000)) +
  scale_y_continuous(limits = c(0, 80000)) +
  labs(title="Chicago's Actual Tax Bills With Current Exemptions", x= "Dollars", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2  & agency_name == "CITY OF CHICAGO") %>% ggplot() +
  geom_histogram(aes(x= bill_noexemps, bins = 100))+
    geom_vline(aes(xintercept=median(bill_noexemps, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-5,20000)) + 
  scale_y_continuous(limits = c(0, 80000)) +

  labs(title="Chicago's Tax Bills Without General Homestead Exemptions", x="Dollars", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF DOLTON") %>% 
  ggplot() +
  geom_histogram(aes(x= bill_change, bins = 100))+
    geom_vline(aes(xintercept=median(bill_change, na.rm=TRUE), color = "red")) +
    theme_classic()+
  theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-2800,2800)) +
  scale_y_continuous(limits = c(0, 2000))+
  labs(title="Dolton's Change in Tax Bills", subtitle =  "Hypothetical with no General Homestead Exemptions", x = "Change in Tax Bill ($)", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "CITY OF CHICAGO") %>% ggplot() +
  geom_histogram(aes(x= bill_change, bins = 100))+
    geom_vline(aes(xintercept=median(bill_change, na.rm=TRUE), color = "red")) +
    theme_classic()+ theme(legend.position = "none") + 

  scale_x_continuous(limits = c(-2800,2800)) + 
  scale_y_continuous(limits = c(0, 200000))+

  labs(title ="Chicago's Change in Tax Bills", subtitle = "Hypothetical with no General Homestead Exemptions", x = "Change in Tax Bill ($)", y = "Number of Tax Bills")

pin_data2 %>%
  filter(major_class_code == 2 & agency_name == "VILLAGE OF GLENCOE") %>% ggplot() +
  geom_histogram(aes(x= bill_change, bins = 100))+
    geom_vline(aes(xintercept=median(bill_change, na.rm=TRUE), color = "red")) +
    theme_classic()+theme(legend.position = "none") + 

#  scale_x_continuous(limits = c(-5,20000)) + 
#    scale_y_continuous(limits = c(0, 1500))+

  labs(title="Glencoe's Change in Tax Bills Without General Homestead Exemptions", y = "Number of Tax Bills", x = "Dollars")

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class >199 & class < 300) %>% 
  arrange(av) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, zero_bill, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            mean_bill_cur = mean(bill_current, na.rm=TRUE),
            median_bill_cur = median(bill_current),
            mean_bill_new = mean(bill_noexemps, na.rm=TRUE),
            median_bill_new = median(bill_noexemps),
            mean_change = mean(bill_change, na.rm=TRUE),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class >199 & class < 300) %>% 
  arrange(av) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            mean_bill_cur = mean(bill_current, na.rm=TRUE),
            median_bill_cur = median(bill_current),
            mean_bill_new = mean(bill_noexemps, na.rm=TRUE),
            median_bill_new = median(bill_noexemps),
            mean_change = mean(bill_change, na.rm=TRUE),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class >199 & class < 300) %>% 
  arrange(av) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            mean_bill_cur = mean(bill_current, na.rm=TRUE),
            median_bill_cur = median(bill_current),
            mean_bill_new = mean(bill_noexemps, na.rm=TRUE),
            median_bill_new = median(bill_noexemps),
            mean_change = mean(bill_change, na.rm=TRUE),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

For all Class 2 Properties:

In Chicago: If they didn’t claim an exemption (n = 331,492), median bill decreases by $168. If they do currently claim an exemption (n = 401,450), the median bill increases $330 (BUT they think they are saving $671 due to exempt EAV * higher tax rate amount that shows up on their tax bills!)

In Dolton: If they didn’t claim an exemption (n = 2,633), median bill decreases by $1241. If they do currently claim an exemption (n = 5,639), the median bill increases $1090 (BUT they think they are saving $2791 due to exempt EAV * higher tax rate amount that shows up on their tax bills!)

	has_HO exemp	median bill_cur	median bill_new	median change	pin count	perceived savings
Chicago	0	3389.3	3043.7	-168.2	331492	0.0
Chicago	1	3757.1	4029.7	330.5	401450	671.6
Dolton	0	6051.9	4792.4	-1241.1	2633	0.0
Dolton	1	3802.3	4868.6	1090.4	5639	2791.5

If we do not care whether they do or do not currently receive the general homestead exemption, then the median change is a decrease of $3 in Chicago (732,942 pins) and an increase of $769 in Dolton (8,272 pins).

	median bill_current	median bill_new	median change	pincount	perceived savings
Chicago	3619	3642.87	-2.90	732942	669.7
Dolton	4305	4846.94	769.12	8272	2293.7

The median Class 2 tax payer thinks they are saving $670 when they really are saving no money due to current exemptions within Chicago and the median taxpayer in Dolton thinks they are saving \$2,294 but are only saving $770 due to the higher tax rates that result from the decrease in taxable EAV that occurs from those that claim the general homested exemption.

tax_bill_change_HO <- pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class == 203) %>% 
  arrange(av) %>%
    mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            median_bill_cur = round(median(bill_current)),
            median_bill_new = round(median(bill_noexemps)),
            median_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe))

tax_bill_change_HO

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class == 203) %>% 
  arrange(av) %>%
    mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            median_bill_cur = round(median(bill_current)),
            median_bill_new = round(median(bill_noexemps)),
            median_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe))

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class == 203) %>% 
  arrange(av) %>%
    mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, zero_bill, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            median_bill_cur = round(median(bill_current)),
            median_bill_new = round(median(bill_noexemps)),
            median_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe))

	has_HO_exemp <dbl>	median_bill_cur <dbl>	median_bill_new <dbl>	median_change <dbl>	pincount <int>	perceived_savings <dbl>
CITY OF CHICAGO	0	3418	2970	-369	32128	0.0
CITY OF CHICAGO	1	3151	3268	269	102677	6712
VILLAGE OF DOLTON	0	6298	5006	-1295	1103	0.0
VILLAGE OF DOLTON	1	3441	4629	1186	2506	2792

If only “removing”/“refunding” the general homestead exemption:

For Chicago class 213 properties, the median tax bill would go down $370 for those who don’t currently have a general homestead exemption and would increase $270 for people who do currently claim the general homestead exemption. 32K people don’t claim the exemption and 102K people do.

Dolton class 213 median properties have a decrease of $1295 if they didn’t claim homestead exemptions currently and increased $1186 f they did already claim the general homestead exemption. 1100 people do not claim the exemption and 2500 people do claim the general homestead exemption.

Class 213 Properties	median bill_cur	median bill_new	median change	pincount	perceived savings
Chicago	3151	3258	175	134805	670
Dolton	4152	4770	1009	3609	2294

Compare AV=10,000

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class > 199 & class < 300) %>% 
  arrange(av) %>%
    filter(av > 9500 & av < 10500) %>%

 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, zero_bill, has_HO_exemp) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            median_bill_cur = median(bill_current),
            median_bill_new = median(bill_noexemps),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class >199 & class < 300) %>% 
  arrange(av) %>%
    filter(av > 9500 & av < 10500) %>%

 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, has_HO_exemp) %>% 
    summarize(median_AV = median(av),
            median_equalizedAV =  median(equalized_AV),
            median_EAV = median(eav),
            median_bill_cur = median(bill_current),
            median_bill_new = median(bill_noexemps),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class >199 & class < 300) %>% 
  arrange(av) %>%
  filter(av > 9500 & av < 10500) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name) %>% 
    summarize(median_AV = median(av),
            median_equalizedAV =  median(equalized_AV),
            median_EAV = median(eav),
            median_bill_cur = median(bill_current),
            median_bill_new = median(bill_noexemps),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class ==203) %>% 
  arrange(av) %>%
  filter(av > 9500 & av < 10500) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name)

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class == 203) %>% 
  arrange(av) %>%
  filter(av > 9500 & av < 10500) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name) %>%
    summarize(median_AV = median(av),
            median_equalizedAV =  median(equalized_AV),
            median_EAV = median(eav),
            median_bill_cur = median(bill_current),
            median_bill_new = median(bill_noexemps),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class == 203) %>% 
  arrange(av) %>%
  filter(av > 9500 & av < 10500) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, has_HO_exemp) %>%
    summarize(median_AV = median(av),
            median_equalizedAV =  median(equalized_AV),
            median_EAV = median(eav),
            median_bill_cur = median(bill_current),
            median_bill_new = median(bill_noexemps),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

compare AV of 15K

Glencoe doesn’t have PINs that are in the 10,000 AV range… So same format as tables above but uses AV value = $15,000 +/- 500

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
#  filter(class == 203) %>% 
    filter(class >199 & class < 300) %>% 

  arrange(av) %>%
  filter(av > 14500 & av < 15500) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name, has_HO_exemp) %>%
    summarize(median_AV = median(av),
            median_equalizedAV =  median(equalized_AV),
            taxable_eav = mean(equalized_AV-all_exemptions+exe_homeowner),
            median_bill_cur = median(bill_current),
            median_bill_new = median(bill_noexemps),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

pin_data2 %>% 
filter(pin_AV > 0) %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
#  filter(class == 203) %>% 
    filter(class >199 & class < 300) %>% 

  arrange(av) %>%
  filter(av > 14500 & av < 15500) %>%
 # group_by(agency_name, has_HO_exemp) %>% 
  mutate(#taxable_eav = final_tax_to_dist / tax_code_rate,
    # current bill = current tax rate * portion of levy billed
         bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name) %>%
    summarize(median_AV = median(av),
            median_equalizedAV =  median(equalized_AV),
            taxable_eav = mean(equalized_AV-all_exemptions+exe_homeowner),
            median_bill_cur = median(bill_current),
            median_bill_new = median(bill_noexemps),
            median_change = median(bill_change),
            perceived_savings = median(tax_amt_exe),
            cur_comp_TC_rate = mean(cur_comp_TC_rate),
            new_comp_TC_rate = mean(new_comp_TC_rate),
            pincount=n()
)

Class 300 & All Other Classes

pin_data2 %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class >299 & class < 400) %>% 
  arrange(av) %>%
    mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            median_bill_cur = round(median(bill_current)),
            median_bill_new = round(median(bill_noexemps)),
            median_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe))

pin_data2 %>% 
  filter(agency_name %in% c("CITY OF CHICAGO", "VILLAGE OF DOLTON", "VILLAGE OF GLENCOE")) %>%
  filter(class>399) %>% 
  arrange(av) %>%
    mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_AV-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  group_by(agency_name) %>% 
  summarize(median_AV = median(av),
            median_EAV = median(eav),
            median_bill_cur = round(median(bill_current)),
            median_bill_new = round(median(bill_noexemps)),
            median_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = median(tax_amt_exe))