Finding Similar Properties and their Taxbills

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

# has EAV values, extensions by agency_num
agency_dt <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  "SELECT *
  FROM agency
  WHERE year = 2021
  "
)

# grabs all unique muni names & numbs
# don't forget Cicero
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'  

  "
)


# list of all taxcodes in municipalities. 
# This does NOT include unincorporated tax codes!!
doltchi_tax_codes <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  glue_sql("
  SELECT*
  FROM tax_code
  WHERE (agency_num = '030210000' OR agency_num = '030310000')
  AND year = 2021
  ",
  .con = ptaxsim_db_conn
  )
) %>% mutate(tax_code = as.character(tax_code_num))

taxbills_current <- read_csv("./Output/1_Get_All_Pins-CookPinTaxbills_2021_Actual.csv")
# 22,453,875 tax bills in 2021 in municipalities (incorporated areas). 
# 22,972,902 tax bills in all of Cook County in 2021 (incorporated and unincorporated)
# DOES INCLUDE unincorporated tax bills based on how we pulled the data in Step 1.



# 1,825,816 billed properties with 14-digit PINs  for incorporated areras
# 1,864,594 billed properties in Cook county (incorporated and unincorporated)
# 
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)



DoltonChicago <- pin14_bills_current %>% 
  filter(tax_code %in% doltchi_tax_codes$tax_code_num) %>%
  filter(class != "0")


DoltonChicago <- DoltonChicago %>% 
  mutate(tax_code = as.character(tax_code))%>%
  left_join(doltchi_tax_codes) %>% 
  group_by(agency_num, class) %>% arrange(av)

write_csv(DoltonChicago, "./Output/Cholton_taxbills.csv")

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

class_dict <- read_csv("./Necessary_Files/class_dict.csv")

taxcode_taxrates <- read_csv("./Output/2_taxcode_taxrates.csv")


DoltonChicago <- read_csv("./Output/Cholton_taxbills.csv") %>% 
  arrange(av) %>%     
  left_join(taxcode_taxrates) %>%
    mutate(propclass_1dig = str_sub(class, 1, 1))

Max composite tax rate in a tax code in Chicago is 9.1% and minimum composite tax rate is 6.7%. Chicago has 665 tax codes.

Max composite tax rate in a tax code in Dolton is 27.9% and minimum composite tax rate is 22.9%. Dolton has 13 tax codes.

# DoltonChicago %>% 
#   group_by(agency_num, tax_code) %>% 
#   summarize(max_comprate = max(tax_rate_current),
#             min_comprate = min(tax_rate_current)) %>% arrange(-max_comprate)

DoltonChicago %>% group_by(agency_num) %>% 
  summarize(max_comprate = max(tax_rate_current, na.rm=TRUE),
            mean_comprate = mean(tax_rate_current, na.rm=TRUE),
            min_comprate = min(tax_rate_current, na.rm=TRUE)) %>% arrange(-mean_comprate)

## # A tibble: 2 × 4
##   agency_num max_comprate mean_comprate min_comprate
##   <chr>             <dbl>         <dbl>        <dbl>
## 1 030310000        0.279         0.252        0.229 
## 2 030210000        0.0908        0.0671       0.0670

DoltonChicago %>% filter(agency_num == "030310000") %>%
  group_by(agency_num, class) %>% 
  summarize(max_comprate = max(tax_rate_current, na.rm=TRUE),
            mean_comprate = mean(tax_rate_current, na.rm=TRUE),
            min_comprate = min(tax_rate_current, na.rm=TRUE),
            pin_count = n(),
            av = mean(av),
            eav = mean(eav),
            tax_amt_post_exe = mean(tax_amt_post_exe),
            tax_amt_exe = mean(tax_amt_exe),
            tax_amt_pre_exe = mean(tax_amt_pre_exe)) %>% 
  arrange(-pin_count) %>% head() %>% kbl()

agency_num	class	max_comprate	mean_comprate	min_comprate	pin_count	av	eav	tax_amt_post_exe	tax_amt_exe	tax_amt_pre_exe
030310000	203	0.27915	0.2517154	0.22937	3609	8462.172	25409.362	3971.0875	2351.644	6322.732
030310000	234	0.27915	0.2437240	0.22937	2374	11825.945	35509.757	5704.2086	2924.786	8628.995
030310000	202	0.27915	0.2637175	0.22937	990	4833.689	14514.148	2225.9500	1615.417	3839.347
030310000	211	0.27915	0.2555963	0.22937	286	14033.490	42138.364	9064.7549	1370.885	10435.640
030310000	100	0.27915	0.2578337	0.22937	221	6529.937	19607.348	5015.0933	0.000	5015.093
030310000	299	0.27915	0.2618058	0.22937	221	2607.186	7828.552	665.1598	1384.339	2049.500

DoltonChicago %>% filter(agency_num == "030210000") %>%
  group_by(agency_num, class) %>% 
  summarize(max_comprate = max(tax_rate_current, na.rm=TRUE),
            mean_comprate = mean(tax_rate_current, na.rm=TRUE),
            min_comprate = min(tax_rate_current, na.rm=TRUE),
            pin_count = n(),
            av = mean(av),
            eav = mean(eav),
            tax_amt_post_exe = mean(tax_amt_post_exe),
            tax_amt_exe = mean(tax_amt_exe),
            tax_amt_pre_exe = mean(tax_amt_pre_exe)) %>% 
  arrange(-pin_count) %>% head() %>% kbl()

agency_num	class	max_comprate	mean_comprate	min_comprate	pin_count	av	eav	tax_amt_post_exe	tax_amt_exe	tax_amt_pre_exe
030210000	299	0.08435	0.0671080	0.06697	286419	25845.293	77605.65	4879.667	333.8783	5213.531
030210000	203	0.08244	0.0670568	0.06697	134808	21963.844	65950.83	3504.307	917.8658	4422.150
030210000	211	0.09078	0.0670501	0.06697	120872	36681.360	110143.14	6528.777	858.2396	7387.016
030210000	202	0.08244	0.0670643	0.06697	55917	16138.675	48459.62	2488.226	761.6435	3249.829
030210000	205	0.09078	0.0670673	0.06697	36918	30049.316	90229.10	5194.381	856.2097	6050.591
030210000	100	0.09078	0.0671145	0.06697	32109	6197.712	18609.82	1254.988	0.0000	1254.988

DoltonChicago %>% 
  filter(class == "203") %>% 
  arrange(av)%>%
  group_by(agency_num, class) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Class 203, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)

Chicago and Dolton, Class 203, Measures of the Middle
agency_num	class	medianbill	meanbill	medianAV	meanAV
030210000	203	3151	3504	20000	21964
030310000	203	4152	3971	8874	8462

DoltonChicago %>% 
  filter(class == "205") %>% 
  group_by(agency_num, class) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Class 205, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)

Chicago and Dolton, Class 205, Measures of the Middle
agency_num	class	medianbill	meanbill	medianAV	meanAV
030210000	205	4168	5194	25000	30049
030310000	205	3515	4006	6980	7834

DoltonChicago %>% 
  filter(class == "211") %>% 
  group_by(agency_num, class) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Class 211, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)

Chicago and Dolton, Class 211, Measures of the Middle
agency_num	class	medianbill	meanbill	medianAV	meanAV
030210000	211	4640	6529	28000	36681
030310000	211	8878	9065	15192	14033

DoltonChicago %>% 
  filter(class == "234") %>% 
  group_by(agency_num, class) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Class 234, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)

Chicago and Dolton, Class 234, Measures of the Middle
agency_num	class	medianbill	meanbill	medianAV	meanAV
030210000	234	2420	2953	15999	18781
030310000	234	6043	5704	11990	11826

Using similarily Assessed Value properties

Chicago has 655 tax codes in its borders and Dolton has 13 tax codes in its borders.

Class 203

One story residence, any age, 1,000 to 1,800 sq. ft.

DoltonChicago %>% 
  filter(agency_num == "030210000" ) %>% #& class == "203" & between(av,9950,10050)) %>% 
  group_by(tax_code) %>% 
  summarize(count = n(),            
            avg_current_comprate = mean(tax_rate_current, na.rm=TRUE)
  ) %>% arrange(-avg_current_comprate) %>% head()

## # A tibble: 6 × 3
##   tax_code count avg_current_comprate
##      <dbl> <int>                <dbl>
## 1    70039   212               0.0908
## 2    70048    89               0.0844
## 3    72185   214               0.0824
## 4    70071    78               0.0790
## 5    77098   125               0.0782
## 6    72157    35               0.0758

DoltonChicago %>% 
  filter(agency_num == "030310000") %>% # & class == "203" & between(av, 9950, 10050)) %>%
  group_by(tax_code) %>%
  summarize(count = n(),
            avg_current_comprate = mean(tax_rate_current, na.rm=TRUE)
  ) %>% arrange(-avg_current_comprate) %>% head()

## # A tibble: 6 × 3
##   tax_code count avg_current_comprate
##      <dbl> <int>                <dbl>
## 1    37035  4038                0.279
## 2    37039   259                0.230
## 3    37034  4486                0.229
## 4    37036    83              NaN    
## 5    37037    36              NaN    
## 6    37038     5              NaN

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions….

For property class 203 PINs with assessed values between $9,000 and $11,000, the average change in tax bill would be $75 more in Chicago and $205 more in Dolton compared to their current tax bills ($1475 and $4312 respectively).

The “average” property tax payer would think they are saving $624 in Chicago and $2708 in Dolton due to exemptions. This number appears on their taxbill and calculated by the full EAV * current tax rate and does NOT consider the change in tax rate that would occur if levies are held constant and all EAV became taxable.

Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(av,9000,11000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(av, 9000, 11000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Class 203 Comparison, AV ~ $10,000 (9000-11000 range)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))

Property Class 203 Comparison, AV ~ $10,000 (9000-11000 range)
Chicago		Dolton
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	24
bill_current	1475	bill_current	4312
bill_hyp	1550	bill_hyp	4517
bill_change	74	bill_change	205
tax_amt_post_exe	1475	tax_amt_post_exe	4312
tax_amt_exe	624	tax_amt_exe	2708
tax_amt_pre_exe	2099	tax_amt_pre_exe	7020
pin_count	4801	pin_count	1466
av	10424	av	9769
eav	31301	eav	29333

Changing the range of PINs included in the calculation alters the “Median Property Statistic”

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions….

For property class 203 PINs with assessed values between $8,000 and $12,000, the average change in tax bill would be $98 more in Chicago and $55 more in Dolton compared to their current tax bills ($1513 and $4342 respectively).

Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(av,8000,12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(av, 8000, 12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Major Class 2 Comparison, AV ~ $10,000 (AV range $8000-$12000)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago Class 203, 8K-12K Property Stats" = 2, "Dolton Class 203, 8L-12K AV Property Stats" = 2))

Property Major Class 2 Comparison, AV ~ $10,000 (AV range $8000-$12000)
Chicago Class 203, 8K-12K Property Stats		Dolton Class 203, 8L-12K AV Property Stats
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	24
bill_current	1513	bill_current	4342
bill_hyp	1611	bill_hyp	4396
bill_change	98	bill_change	55
tax_amt_post_exe	1513	tax_amt_post_exe	4342
tax_amt_exe	676	tax_amt_exe	2519
tax_amt_pre_exe	2190	tax_amt_pre_exe	6860
pin_count	10480	pin_count	2541
av	10871	av	9380
eav	32643	eav	28166

Major Class 2

Average and median tax bills and assessed values are calculated below for ALL property class types within the the broader “Residential” property class type (property classes that have the first digit “2”, or Major Class Type 2)

The median AV is used to select a range of pins (based on their AV) to calculate the average current bill, hypothetical bill, and hypothetical change in tax bill for a “median property”.

This is done because some properties receive multiple exemptions while others receive none. Using the literal median pin can skew the summary statistics of that specific pin receives no exemptions or multiple exemptions. The average for the range of “median PINs” is created to smooth out the variation within the observations.

DoltonChicago %>% 
  filter(propclass_1dig == "2") %>% 
  arrange(av) %>%
  group_by(agency_num) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            pin_count = n()
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Major Class 2, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)

Chicago and Dolton, Major Class 2, Measures of the Middle
agency_num	medianbill	meanbill	medianAV	meanAV	pin_count
030210000	3619	5244	21119	29123	732952
030310000	4304	4364	9132	8929	8275

# 
# DoltonChicago %>% 
#   filter(propclass_1dig == "3") %>% 
#   arrange(av) %>%
#   group_by(agency_num) %>%
#   summarize(medianbill = median(total_billed),
#             meanbill = mean(total_billed),
#             medianAV = median(av),
#             meanAV = mean(av),
#             pin_count = n()
#             )%>% 
#  # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
#   kbl(caption= "Chicago and Dolton, Major Class 3, Measures of the Middle", digits=0, booktabs = T) %>%   
#   kable_styling(full_width = T)

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago’s hypothetical tax bill would be $1570 (an $14 increase from $1556) and Dolton’s would be $4610 (a $30 increase from $4580).

On average, residents would think they “saved” $501 in Chicago and $2588 in Dolton (based on tax_amt_exe which also shows up on their tax bill based on the “naive” pre-tax exemption tax bill amount on the tax bill). The amount saved per person will depend on how many exemptions they qualified for in the first place. This is a rough average for all types of exemptions and includes those that received no exemptions and multiiple exemptions.

Values were calculated by selecting pins with AVs between $9000 and $11,000 and then calculating the average current bill, change in bill, and other statistics seen in the table. Pin count tells you the number of pins that were included in the AV range used for the “median property.”

Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & propclass_1dig == "2" & between(av,9000,11000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & propclass_1dig == "2" & between(av, 9000, 11000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Major Class 2 Comparison, AV ~ $10,000 (AV range $9000-$11000)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))

Property Major Class 2 Comparison, AV ~ $10,000 (AV range $9000-$11000)
Chicago		Dolton
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	24
bill_current	1556	bill_current	4580
bill_hyp	1570	bill_hyp	4610
bill_change	14	bill_change	30
tax_amt_post_exe	1556	tax_amt_post_exe	4580
tax_amt_exe	501	tax_amt_exe	2588
tax_amt_pre_exe	2057	tax_amt_pre_exe	7168
pin_count	34215	pin_count	1823
av	10199	av	9869
eav	30623	eav	29635

Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & propclass_1dig == "2" & between(av,8000,12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & propclass_1dig == "2" & between(av, 8000, 12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Major Class 2 Comparison, AV ~ $10,000 (AV range $8000-$12000)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))

Property Major Class 2 Comparison, AV ~ $10,000 (AV range $8000-$12000)
Chicago		Dolton
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	25
bill_current	1579	bill_current	4796
bill_hyp	1592	bill_hyp	4698
bill_change	13	bill_change	-99
tax_amt_post_exe	1579	tax_amt_post_exe	4796
tax_amt_exe	504	tax_amt_exe	2539
tax_amt_pre_exe	2083	tax_amt_pre_exe	7335
pin_count	62480	pin_count	3885
av	10329	av	9943
eav	31015	eav	29855

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago’s hypothetical tax bill would be $1592 (an $13 increase from $1579) and Dolton’s would be $4598 (a $99 DECREASE from $4596).

Values were calculated by selecting pins with AVs between $8000 and $12,000 and then calculating the average current bill, change in bill, and other statistics seen in the table. Pin count tells you the number of pins that were included in the AV range used for the “median property.”

Increasing the number of pins included in the measurement of “average bill” and “average bill change” completely changed the results for Dolton. Using the “median” value must be done super carefully.

If we “removed” only homeowners exemptions or only senior exemptions, then the median statistic would be more reliable… potentially.

Class 205

Two or more story residence, over 62 years, up to 2,200 sq. ft

Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "205" & between(av,8000,12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")


Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "205" & between(av, 8000, 12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Class 205 Comparison, AV ~ $10,000 (8000 to 12000 AV range)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))

Property Class 205 Comparison, AV ~ $10,000 (8000 to 12000 AV range)
Chicago		Dolton
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	27
bill_current	1503	bill_current	5147
bill_hyp	1530	bill_hyp	4755
bill_change	27	bill_change	-392
tax_amt_post_exe	1503	tax_amt_post_exe	5147
tax_amt_exe	622	tax_amt_exe	2353
tax_amt_pre_exe	2124	tax_amt_pre_exe	7500
pin_count	2832	pin_count	55
av	10544	av	9427
eav	31660	eav	28305

Class 211

Two to six residential apartments, any age.

Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "211" & between(av,17000,19000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")


Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "211" & between(av, 17000, 19000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Class 211 Comparison, AV ~ $18,000") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))

Property Class 211 Comparison, AV ~ $18,000
Chicago		Dolton
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	24
bill_current	3040	bill_current	11459
bill_hyp	2782	bill_hyp	8211
bill_change	-258	bill_change	-3249
tax_amt_post_exe	3040	tax_amt_post_exe	11459
tax_amt_exe	628	tax_amt_exe	1231
tax_amt_pre_exe	3668	tax_amt_pre_exe	12690
pin_count	6747	pin_count	54
av	18217	av	17707
eav	54701	eav	53170

Class 234

Split level residence, with a lower level below grade, all ages, all sizes

Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "234" & between(av,12000,13000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")


Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "234" & between(av, 12450, 12750)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Class 234 Comparison, AV ~ $12,500") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))

Property Class 234 Comparison, AV ~ $12,500
Chicago		Dolton
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	24
bill_current	1763	bill_current	5944
bill_hyp	1824	bill_hyp	5861
bill_change	61	bill_change	-82
tax_amt_post_exe	1763	tax_amt_post_exe	5944
tax_amt_exe	779	tax_amt_exe	3176
tax_amt_pre_exe	2542	tax_amt_pre_exe	9120
pin_count	591	pin_count	215
av	12613	av	12606
eav	37872	eav	37851

Other ways to compare locations (that I don’t like as much)

Median Chicago AV vs Median Dolton AV

## Chicago #

Chi1 <- DoltonChicago %>% 
  arrange(av)%>%
  filter(agency_num == "030210000" & class == "203" & between(av,18000,22000)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    hypothetical_taxrate = mean(taxrate_new, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")
  # kbl(caption= "Chicago, Class 203, Tax Bill ~$3150", digits=0, booktabs = T)


## Dolton ## 

Dol1<- DoltonChicago %>% 
  arrange(av) %>%
  filter(agency_num == "030310000" & class == "203" & between(av,8000,12000)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    hypothetical_taxrate = mean(taxrate_new, na.rm=TRUE),

    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")


both_dt <- cbind(Chi1, Dol1)


kbl(both_dt, booktabs = T, digits = 0, caption = "Property Class 203 Comparison, Chicago Median AV vs Dolton Median AV") %>%
  kable_styling(full_width = T) %>%
add_header_above(c("Chicago Median AV 203 Property" = 2, "Dolton Median AV 203 Property" = 2))

Property Class 203 Comparison, Chicago Median AV vs Dolton Median AV
Chicago Median AV 203 Property		Dolton Median AV 203 Property
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	24
hypothetical_taxrate	0	hypothetical_taxrate	0
bill_current	3132	bill_current	4342
bill_hyp	3280	bill_hyp	4396
bill_change	148	bill_change	55
tax_amt_post_exe	3132	tax_amt_post_exe	4342
tax_amt_exe	897	tax_amt_exe	2519
tax_amt_pre_exe	4029	tax_amt_pre_exe	6860
pin_count	28012	pin_count	2541
av	20009	av	9380
eav	60082	eav	28166

Median Chicago Taxbill vs Median Dolton Taxbill

## Chicago #

Chi1 <- DoltonChicago %>% 
  arrange(av)%>%
  filter(agency_num == "030210000" & class == "203" & between(total_billed,3100,3300)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")
  # kbl(caption= "Chicago, Class 203, Tax Bill ~$3150", digits=0, booktabs = T)


## Dolton ## 

Dol1<- DoltonChicago %>% 
  arrange(av) %>%
  filter(agency_num == "030310000" & class == "203" & between(total_billed,3100,3300)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values") #%>% 
  # kbl(caption= "Dolton, Class 203, Tax Bill ~ $3150", digits=0, booktabs = T)


# av = 
#exempt eav = 


both_dt <- cbind(Chi1, Dol1)


kbl(both_dt, booktabs = T, digits = 0, caption = "Property Class 203 Comparison, Tax Bill ~ $3,150 (Chicago's median current bill)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))

Property Class 203 Comparison, Tax Bill ~ $3,150 (Chicago’s median current bill)
Chicago		Dolton
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	25
bill_current	3181	bill_current	3194
bill_hyp	3268	bill_hyp	3408
bill_change	87	bill_change	214
tax_amt_post_exe	3181	tax_amt_post_exe	3194
tax_amt_exe	822	tax_amt_exe	2164
tax_amt_pre_exe	4003	tax_amt_pre_exe	5358
pin_count	6292	pin_count	84
av	19884	av	7156
eav	59706	eav	21487

Chi1a <- DoltonChicago %>% filter(agency_num == "030210000" & class == "203" & between(total_billed,4100,4200)) %>%
  arrange(av)%>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
                ) %>% 
  pivot_longer(comp_taxrate:eav, names_to = "Stats", values_to = "Values") 
# kbl(caption= "Chicago, Class 203, Tax Bill ~$3150", digits=0, booktabs = T)


## Chicago ## 

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
#DoltonChicago %>% filter(agency_num == "030310000" & class == "203" & between(total_billed,3140,3160))

Dol1a <- DoltonChicago %>% filter(agency_num == "030310000" & class == "203" & between(total_billed,4100,4200)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)) %>% 
  pivot_longer(comp_taxrate:eav, names_to = "Stats", values_to = "Values") #%>% 
# kbl(caption= "Dolton, Class 203, Tax Bill ~ $3150", digits=0, booktabs = T)


# av = 
#exempt eav = 


both_dt <- cbind(Chi1a, Dol1a)


kbl(both_dt, booktabs = T, digits = 0, caption = "Property Class 203 Comparison, Tax Bill ~ $4150 (Dolton's median current bill)") %>%
  kable_styling(full_width = T) %>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))

Property Class 203 Comparison, Tax Bill ~ $4150 (Dolton’s median current bill)
Chicago		Dolton
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	24
bill_current	4158	bill_current	4151
bill_hyp	4193	bill_hyp	4202
bill_change	35	bill_change	52
tax_amt_post_exe	4158	tax_amt_post_exe	4151
tax_amt_exe	901	tax_amt_exe	2431
tax_amt_pre_exe	5059	tax_amt_pre_exe	6581
pin_count	1173	pin_count	79
av	25133	av	9068
eav	75468	eav	27228

both_dt <- cbind(Chi1, Dol1a)


kbl(both_dt, booktabs = T, digits = 0, caption = "Property Class 203 Comparison, Median Dolton vs Median Chicago") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago Median 203 Property" = 2, "Dolton Median 203 Property" = 2))

Property Class 203 Comparison, Median Dolton vs Median Chicago
Chicago Median 203 Property		Dolton Median 203 Property
Stats	Values	Stats	Values
comp_taxrate	7	comp_taxrate	24
bill_current	3181	bill_current	4151
bill_hyp	3268	bill_hyp	4202
bill_change	87	bill_change	52
tax_amt_post_exe	3181	tax_amt_post_exe	4151
tax_amt_exe	822	tax_amt_exe	2431
tax_amt_pre_exe	4003	tax_amt_pre_exe	6581
pin_count	6292	pin_count	79
av	19884	av	9068
eav	59706	eav	27228

Using a current tax bill ~$3500:

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago’s hypothetical tax bill would be $3644 (an $87 increase from $3557) and Dolton’s would be $3692 (a $136 increase from $3555).

DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(tax_amt_post_exe, 3500, 3600)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav))  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Chicago, Class 203, Current Bill ~ $3,550", digits=0)

Chicago, Class 203, Current Bill ~ $3,550
Stats	Values
comp_taxrate	7
bill_current	3557
bill_hyp	3644
bill_change	87
tax_amt_post_exe	3557
tax_amt_exe	846
tax_amt_pre_exe	4403
pin_count	3141
av	21866
eav	65656

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(tax_amt_post_exe,3500,3600)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Dolton, Class 203, Current Bill ~ $3,550", digits=0)

Dolton, Class 203, Current Bill ~ $3,550
Stats	Values
comp_taxrate	24
bill_current	3555
bill_hyp	3692
bill_change	137
tax_amt_post_exe	3555
tax_amt_exe	2190
tax_amt_pre_exe	5745
pin_count	55
av	8129
eav	24408

Using a current tax bill ~$3700:

Value was chosen randomly as roughly between the median property AV in Dolton and Chicago.

For post-exemption bills around $3700: Chicago has $26,448 AV and $79,420 in EAV. Chicago’s Composite tax rate for the properties examined is 6.7%. Dolton has an AV of $7,631 and $22,914 in EAV. Dolton’s average composite tax rate for the properties examined was 24.2%.

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago’s hypothetical tax bill would be $4503 (an $802 increase from $3700) and Dolton’s would be $3479 (a $220 DECREASE from $3700).

## Chicago ## 
DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(tax_amt_post_exe, 3695, 3705)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,

    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Chicago NOT ENOGUH PINS, Class 203, Current Bill ~$3,700", digits=0)

Chicago NOT ENOGUH PINS, Class 203, Current Bill ~$3,700
Stats	Values
comp_taxrate	7
bill_current	3700
bill_hyp	4503
bill_change	803
tax_amt_exe	1626
tax_amt_pre_exe	5325
pin_count	47
av	26449
eav	79418

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)

## Dolton ## 
DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(tax_amt_post_exe,3695,3705)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),  
    bill_change = bill_hyp - bill_current,
    tax_amt_pre_exe = mean(tax_amt_pre_exe), # the naive amount that appears on peoples taxbills
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Dolton NOT ENOUGH PINS, Class 203, Current Bill ~ $3,700" , digits=0)

Dolton NOT ENOUGH PINS, Class 203, Current Bill ~ $3,700
Stats	Values
comp_taxrate	24
bill_current	3700
bill_hyp	3479
bill_change	-221
tax_amt_pre_exe	5421
pin_count	4
av	7631
eav	22914

Expanded taxbill range:

## Chicago ## 
DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(tax_amt_post_exe, 3600, 3800)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,

    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Chicago, Class 203, Current Bill ~$3,700", digits=0)

Chicago, Class 203, Current Bill ~$3,700
Stats	Values
comp_taxrate	7
bill_current	3713
bill_hyp	3679
bill_change	-34
tax_amt_exe	719
tax_amt_pre_exe	4431
pin_count	5195
av	22013
eav	66098

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)

## Dolton ## 
DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(tax_amt_post_exe,3600,3800)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),  
    bill_change = bill_hyp - bill_current,
    tax_amt_pre_exe = mean(tax_amt_pre_exe), # the naive amount that appears on peoples taxbills
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Dolton, Class 203, Current Bill ~ $3,700", digits=0)

Dolton, Class 203, Current Bill ~ $3,700
Stats	Values
comp_taxrate	24
bill_current	3703
bill_hyp	3870
bill_change	167
tax_amt_pre_exe	5987
pin_count	124
av	8462
eav	25408

Using pre-exemption taxbill amount of $3700:

For pre exemption tax bills around $3700 in Chicago, average AV is $18,378, EAV is $55,184. Average current taxbill is $2901.

Chicago’s Composite tax rate for the properties examined is 6.7%. Dolton’s average composite tax rate for the properties examiend was 24.2%.

For pre exemption tax bills around $3700 in Dolton, average AV is $5,235, EAV is $15,719. Average current taxbill is $3044.

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago’s hypothetical tax bill would be $2990 (an $89 increase from $2901) and Dolton’s would be $2377 (a $667 DECREASE from $3044).

Uses a tax bill range of 3600 to 3800 to increase “median pins” used for summary stats.

## Chicago ## 
DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(tax_amt_pre_exe, 3600, 3800)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Chicago, Class 203, Naive Pre-Exemp Bill ~ $3,700", digits=0)

Chicago, Class 203, Naive Pre-Exemp Bill ~ $3,700
Stats	Values
comp_taxrate	7
bill_current	2737
bill_hyp	2898
bill_change	161
tax_amt_post_exe	2737
tax_amt_exe	895
tax_amt_pre_exe	3632
pin_count	6162
av	18023
eav	54119

## Dolton ##
DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(tax_amt_pre_exe,3600,3800)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Dolton, Class 203, Naive Pre-Exemp Bill ~ $3,700", digits=0)

Dolton, Class 203, Naive Pre-Exemp Bill ~ $3,700
Stats	Values
comp_taxrate	26
bill_current	1850
bill_hyp	2361
bill_change	512
tax_amt_exe	1833
tax_amt_pre_exe	3683
pin_count	29
av	4796
eav	14401

---
title: "4b_Similar Properties"
author: "Alea Wilbur"
date: "`r Sys.Date()`"
output:
  html_document:
    code_folding: hide
    code_download: yes
---

```{r setup, warning=FALSE, message=FALSE, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)


library(tidyverse)
library(data.table)
library(gstat)
library(ptaxsim)
library(kableExtra)

```

# Finding Similar Properties and their Taxbills

```{r}
library(glue)
ptaxsim_db_conn <- DBI::dbConnect(RSQLite::SQLite(), "./ptaxsim.db/ptaxsim-2021.0.4.db")

# has EAV values, extensions by agency_num
agency_dt <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  "SELECT *
  FROM agency
  WHERE year = 2021
  "
)

# grabs all unique muni names & numbs
# don't forget Cicero
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'  

  "
)


# list of all taxcodes in municipalities. 
# This does NOT include unincorporated tax codes!!
doltchi_tax_codes <- DBI::dbGetQuery(
  ptaxsim_db_conn,
  glue_sql("
  SELECT*
  FROM tax_code
  WHERE (agency_num = '030210000' OR agency_num = '030310000')
  AND year = 2021
  ",
  .con = ptaxsim_db_conn
  )
) %>% mutate(tax_code = as.character(tax_code_num))

```

```{r eval = FALSE}
taxbills_current <- read_csv("./Output/1_Get_All_Pins-CookPinTaxbills_2021_Actual.csv")
# 22,453,875 tax bills in 2021 in municipalities (incorporated areas). 
# 22,972,902 tax bills in all of Cook County in 2021 (incorporated and unincorporated)
# DOES INCLUDE unincorporated tax bills based on how we pulled the data in Step 1.



# 1,825,816 billed properties with 14-digit PINs  for incorporated areras
# 1,864,594 billed properties in Cook county (incorporated and unincorporated)
# 
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)



DoltonChicago <- pin14_bills_current %>% 
  filter(tax_code %in% doltchi_tax_codes$tax_code_num) %>%
  filter(class != "0")


DoltonChicago <- DoltonChicago %>% 
  mutate(tax_code = as.character(tax_code))%>%
  left_join(doltchi_tax_codes) %>% 
  group_by(agency_num, class) %>% arrange(av)

write_csv(DoltonChicago, "./Output/Cholton_taxbills.csv")
```

```{r}
nicknames <- readxl::read_excel("./Necessary_Files/muni_shortnames.xlsx")

class_dict <- read_csv("./Necessary_Files/class_dict.csv")

taxcode_taxrates <- read_csv("./Output/2_taxcode_taxrates.csv")


DoltonChicago <- read_csv("./Output/Cholton_taxbills.csv") %>% 
  arrange(av) %>%     
  left_join(taxcode_taxrates) %>%
    mutate(propclass_1dig = str_sub(class, 1, 1))



```


> Max composite tax rate in a tax code in Chicago is 9.1% and minimum composite tax rate is 6.7%. Chicago has 665 tax codes. 

> Max composite tax rate in a tax code in Dolton is 27.9% and minimum composite tax rate is 22.9%. Dolton has 13 tax codes. 



```{r}
# DoltonChicago %>% 
#   group_by(agency_num, tax_code) %>% 
#   summarize(max_comprate = max(tax_rate_current),
#             min_comprate = min(tax_rate_current)) %>% arrange(-max_comprate)

DoltonChicago %>% group_by(agency_num) %>% 
  summarize(max_comprate = max(tax_rate_current, na.rm=TRUE),
            mean_comprate = mean(tax_rate_current, na.rm=TRUE),
            min_comprate = min(tax_rate_current, na.rm=TRUE)) %>% arrange(-mean_comprate)

DoltonChicago %>% filter(agency_num == "030310000") %>%
  group_by(agency_num, class) %>% 
  summarize(max_comprate = max(tax_rate_current, na.rm=TRUE),
            mean_comprate = mean(tax_rate_current, na.rm=TRUE),
            min_comprate = min(tax_rate_current, na.rm=TRUE),
            pin_count = n(),
            av = mean(av),
            eav = mean(eav),
            tax_amt_post_exe = mean(tax_amt_post_exe),
            tax_amt_exe = mean(tax_amt_exe),
            tax_amt_pre_exe = mean(tax_amt_pre_exe)) %>% 
  arrange(-pin_count) %>% head() %>% kbl()

DoltonChicago %>% filter(agency_num == "030210000") %>%
  group_by(agency_num, class) %>% 
  summarize(max_comprate = max(tax_rate_current, na.rm=TRUE),
            mean_comprate = mean(tax_rate_current, na.rm=TRUE),
            min_comprate = min(tax_rate_current, na.rm=TRUE),
            pin_count = n(),
            av = mean(av),
            eav = mean(eav),
            tax_amt_post_exe = mean(tax_amt_post_exe),
            tax_amt_exe = mean(tax_amt_exe),
            tax_amt_pre_exe = mean(tax_amt_pre_exe)) %>% 
  arrange(-pin_count) %>% head() %>% kbl()
``` 



```{r}

DoltonChicago %>% 
  filter(class == "203") %>% 
  arrange(av)%>%
  group_by(agency_num, class) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Class 203, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)



DoltonChicago %>% 
  filter(class == "205") %>% 
  group_by(agency_num, class) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Class 205, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)

DoltonChicago %>% 
  filter(class == "211") %>% 
  group_by(agency_num, class) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Class 211, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)

DoltonChicago %>% 
  filter(class == "234") %>% 
  group_by(agency_num, class) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Class 234, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)


```

## Using similarily Assessed Value properties 

Chicago has 655 tax codes in its borders and Dolton has 13 tax codes in its borders. 



### Class 203

One story residence, any age, 1,000 to 1,800 sq. ft.


```{r}
DoltonChicago %>% 
  filter(agency_num == "030210000" ) %>% #& class == "203" & between(av,9950,10050)) %>% 
  group_by(tax_code) %>% 
  summarize(count = n(),            
            avg_current_comprate = mean(tax_rate_current, na.rm=TRUE)
  ) %>% arrange(-avg_current_comprate) %>% head()

DoltonChicago %>% 
  filter(agency_num == "030310000") %>% # & class == "203" & between(av, 9950, 10050)) %>%
  group_by(tax_code) %>%
  summarize(count = n(),
            avg_current_comprate = mean(tax_rate_current, na.rm=TRUE)
  ) %>% arrange(-avg_current_comprate) %>% head()
```


If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions....

For property class 203 PINs with assessed values between \$9,000 and \$11,000, the average change in tax bill would be \$75 more in Chicago and \$205 more in Dolton compared to their current tax bills (\$1475 and $4312 respectively). 

The "average" property tax payer would think they are saving \$624 in Chicago and $2708 in Dolton due to exemptions. This number appears on their taxbill and calculated by the full EAV * current tax rate and does NOT consider the change in tax rate that would occur if levies are held constant and all EAV became taxable. 


```{r}
Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(av,9000,11000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(av, 9000, 11000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Class 203 Comparison, AV ~ $10,000 (9000-11000 range)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))
```


> Changing the range of PINs included in the calculation alters the "Median Property Statistic" 

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions....

For property class 203 PINs with assessed values between \$8,000 and \$12,000, the average change in tax bill would be \$98 more in Chicago and \$55 more in Dolton compared to their current tax bills (\$1513 and $4342 respectively). 

```{r}
Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(av,8000,12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(av, 8000, 12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Major Class 2 Comparison, AV ~ $10,000 (AV range $8000-$12000)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago Class 203, 8K-12K Property Stats" = 2, "Dolton Class 203, 8L-12K AV Property Stats" = 2))
```



### Major Class 2

Average and median tax bills and assessed values are calculated below for ALL property class types within the the broader "Residential" property class type (property classes that have the first digit "2", or Major Class Type 2)

The median AV  is used to select a range of pins (based on their AV) to calculate the average current bill, hypothetical bill, and hypothetical change in tax bill for a "median property". 

This is done because some properties receive multiple exemptions while others receive none. Using the literal median pin can skew the summary statistics of that specific pin receives no exemptions or multiple exemptions. The average for the range of "median PINs" is created to smooth out the variation within the observations. 

```{r}
DoltonChicago %>% 
  filter(propclass_1dig == "2") %>% 
  arrange(av) %>%
  group_by(agency_num) %>%
  summarize(medianbill = median(total_billed),
            meanbill = mean(total_billed),
            medianAV = median(av),
            meanAV = mean(av),
            pin_count = n()
            )%>% 
 # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
  kbl(caption= "Chicago and Dolton, Major Class 2, Measures of the Middle", digits=0, booktabs = T) %>%   
  kable_styling(full_width = T)

# 
# DoltonChicago %>% 
#   filter(propclass_1dig == "3") %>% 
#   arrange(av) %>%
#   group_by(agency_num) %>%
#   summarize(medianbill = median(total_billed),
#             meanbill = mean(total_billed),
#             medianAV = median(av),
#             meanAV = mean(av),
#             pin_count = n()
#             )%>% 
#  # pivot_longer(medianbill:meanAV, names_to = "Stats", values_to = "Values")  %>%
#   kbl(caption= "Chicago and Dolton, Major Class 3, Measures of the Middle", digits=0, booktabs = T) %>%   
#   kable_styling(full_width = T)
```

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago's hypothetical tax bill would be \$1570 (an \$14 increase from \$1556) and Dolton's would be \$4610 (a \$30 increase from $4580).   

On average, residents would think they "saved" \$501 in Chicago and \$2588 in Dolton (based on tax_amt_exe which also shows up on their tax bill based on the "naive" pre-tax exemption tax bill amount on the tax bill). The amount saved per person will depend on how many exemptions they qualified for in the first place. This is a rough average for all types of exemptions and includes those that received no exemptions and multiiple exemptions. 

Values were calculated by selecting pins with AVs between \$9000 and $11,000 and then calculating the average current bill, change in bill, and other statistics seen in the table. Pin count tells you the number of pins that were included in the AV range used for the "median property."

```{r}
Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & propclass_1dig == "2" & between(av,9000,11000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & propclass_1dig == "2" & between(av, 9000, 11000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Major Class 2 Comparison, AV ~ $10,000 (AV range $9000-$11000)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))
```


```{r}
Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & propclass_1dig == "2" & between(av,8000,12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & propclass_1dig == "2" & between(av, 8000, 12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Major Class 2 Comparison, AV ~ $10,000 (AV range $8000-$12000)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))
```
If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago's hypothetical tax bill would be \$1592 (an \$13 increase from \$1579) and Dolton's would be \$4598 (a \$99 DECREASE from $4596).   

Values were calculated by selecting pins with AVs between \$8000 and $12,000 and then calculating the average current bill, change in bill, and other statistics seen in the table. Pin count tells you the number of pins that were included in the AV range used for the "median property."


> Increasing the number of pins included in the measurement of "average bill" and "average bill change" completely changed the results for Dolton. Using the "median" value must be done super carefully. 

> If we "removed" only homeowners exemptions or only senior exemptions, then the median statistic would be more reliable... potentially. 



### Class 205

Two or more story residence, over 62 years, up to 2,200 sq. ft


```{r}
Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "205" & between(av,8000,12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")


Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "205" & between(av, 8000, 12000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Class 205 Comparison, AV ~ $10,000 (8000 to 12000 AV range)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))
```


### Class 211

Two to six residential apartments, any age.


```{r}
Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "211" & between(av,17000,19000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")


Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "211" & between(av, 17000, 19000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Class 211 Comparison, AV ~ $18,000") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))
```



### Class 234

Split level residence, with a lower level below grade, all ages, all sizes


```{r}
Chi3 <- DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "234" & between(av,12000,13000)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")


Dol3 <- DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "234" & between(av, 12450, 12750)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe, na.rm=TRUE),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe, na.rm=TRUE),
    tax_amt_pre_exe = mean(tax_amt_pre_exe, na.rm=TRUE),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")

both_dt <- cbind(Chi3, Dol3)


kbl(both_dt, booktabs = T, digits = 0, 
    caption = "Property Class 234 Comparison, AV ~ $12,500") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))
```


# Other ways to compare locations (that I don't like as much)

## Median Chicago AV vs Median Dolton AV

```{r}


## Chicago #

Chi1 <- DoltonChicago %>% 
  arrange(av)%>%
  filter(agency_num == "030210000" & class == "203" & between(av,18000,22000)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    hypothetical_taxrate = mean(taxrate_new, na.rm=TRUE),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")
  # kbl(caption= "Chicago, Class 203, Tax Bill ~$3150", digits=0, booktabs = T)


## Dolton ## 

Dol1<- DoltonChicago %>% 
  arrange(av) %>%
  filter(agency_num == "030310000" & class == "203" & between(av,8000,12000)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    hypothetical_taxrate = mean(taxrate_new, na.rm=TRUE),

    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")


both_dt <- cbind(Chi1, Dol1)


kbl(both_dt, booktabs = T, digits = 0, caption = "Property Class 203 Comparison, Chicago Median AV vs Dolton Median AV") %>%
  kable_styling(full_width = T) %>%
add_header_above(c("Chicago Median AV 203 Property" = 2, "Dolton Median AV 203 Property" = 2))


```


## Median Chicago Taxbill vs Median Dolton Taxbill


```{r}


## Chicago #

Chi1 <- DoltonChicago %>% 
  arrange(av)%>%
  filter(agency_num == "030210000" & class == "203" & between(total_billed,3100,3300)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")
  # kbl(caption= "Chicago, Class 203, Tax Bill ~$3150", digits=0, booktabs = T)


## Dolton ## 

Dol1<- DoltonChicago %>% 
  arrange(av) %>%
  filter(agency_num == "030310000" & class == "203" & between(total_billed,3100,3300)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
  ) %>% 
  pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values") #%>% 
  # kbl(caption= "Dolton, Class 203, Tax Bill ~ $3150", digits=0, booktabs = T)


# av = 
#exempt eav = 


both_dt <- cbind(Chi1, Dol1)


kbl(both_dt, booktabs = T, digits = 0, caption = "Property Class 203 Comparison, Tax Bill ~ $3,150 (Chicago's median current bill)") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))




Chi1a <- DoltonChicago %>% filter(agency_num == "030210000" & class == "203" & between(total_billed,4100,4200)) %>%
  arrange(av)%>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)
                ) %>% 
  pivot_longer(comp_taxrate:eav, names_to = "Stats", values_to = "Values") 
# kbl(caption= "Chicago, Class 203, Tax Bill ~$3150", digits=0, booktabs = T)


## Chicago ## 

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
#DoltonChicago %>% filter(agency_num == "030310000" & class == "203" & between(total_billed,3140,3160))

Dol1a <- DoltonChicago %>% filter(agency_num == "030310000" & class == "203" & between(total_billed,4100,4200)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)) %>% 
  pivot_longer(comp_taxrate:eav, names_to = "Stats", values_to = "Values") #%>% 
# kbl(caption= "Dolton, Class 203, Tax Bill ~ $3150", digits=0, booktabs = T)


# av = 
#exempt eav = 


both_dt <- cbind(Chi1a, Dol1a)


kbl(both_dt, booktabs = T, digits = 0, caption = "Property Class 203 Comparison, Tax Bill ~ $4150 (Dolton's median current bill)") %>%
  kable_styling(full_width = T) %>%
add_header_above(c("Chicago" = 2, "Dolton" = 2))


both_dt <- cbind(Chi1, Dol1a)


kbl(both_dt, booktabs = T, digits = 0, caption = "Property Class 203 Comparison, Median Dolton vs Median Chicago") %>%
  kable_styling(full_width = T)%>%
add_header_above(c("Chicago Median 203 Property" = 2, "Dolton Median 203 Property" = 2))
```


## Using a current tax bill ~$3500:

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago's hypothetical tax bill would be \$3644 (an \$87 increase from \$3557) and Dolton's would be \$3692 (a \$136 increase from $3555).

```{r}
DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(tax_amt_post_exe, 3500, 3600)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav))  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Chicago, Class 203, Current Bill ~ $3,550", digits=0)

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)
DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(tax_amt_post_exe,3500,3600)) %>%
    summarize(              
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av), 
    eav = mean(eav)) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Dolton, Class 203, Current Bill ~ $3,550", digits=0)
```





## Using a current tax bill ~$3700:

Value was chosen randomly as roughly between the median property AV in Dolton and Chicago. 

For post-exemption bills around \$3700: Chicago has $26,448 AV and \$79,420 in EAV. Chicago's Composite tax rate for the properties examined is 6.7%. Dolton has an AV of \$7,631 and \$22,914 in EAV. Dolton's average composite tax rate for the properties examined was 24.2%.

If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago's hypothetical tax bill would be \$4503 (an \$802 increase from \$3700) and Dolton's would be \$3479 (a \$220 DECREASE from $3700). 

```{r}
## Chicago ## 
DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(tax_amt_post_exe, 3695, 3705)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,

    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Chicago NOT ENOGUH PINS, Class 203, Current Bill ~$3,700", digits=0)

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)

## Dolton ## 
DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(tax_amt_post_exe,3695,3705)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),  
    bill_change = bill_hyp - bill_current,
    tax_amt_pre_exe = mean(tax_amt_pre_exe), # the naive amount that appears on peoples taxbills
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Dolton NOT ENOUGH PINS, Class 203, Current Bill ~ $3,700" , digits=0)
```



> Expanded taxbill range:


```{r}
## Chicago ## 
DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(tax_amt_post_exe, 3600, 3800)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,

    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Chicago, Class 203, Current Bill ~$3,700", digits=0)

# Chicago only has 2 pins that are similar to Dolton's median pin (which had a lot of matches)

## Dolton ## 
DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(tax_amt_post_exe,3600,3800)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),  
    bill_change = bill_hyp - bill_current,
    tax_amt_pre_exe = mean(tax_amt_pre_exe), # the naive amount that appears on peoples taxbills
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  ) %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Dolton, Class 203, Current Bill ~ $3,700", digits=0)
```



## Using pre-exemption taxbill amount of $3700:

For pre exemption tax bills around \$3700 in Chicago, average AV is \$18,378, EAV is \$55,184. Average current taxbill is $2901.


Chicago's Composite tax rate for the properties examined is 6.7%.
Dolton's average composite tax rate for the properties examiend was 24.2%.

For pre exemption tax bills around \$3700 in Dolton, average AV is \$5,235, EAV is \$15,719. Average current taxbill is $3044.


If holding the levy constant and acknowledging the change in tax rates that would occur from having additional taxable EAV within the taxing jurisdictions, Chicago's hypothetical tax bill would be \$2990 (an \$89 increase from \$2901) and Dolton's would be \$2377 (a \$667 DECREASE from $3044). 

Uses a tax bill range of 3600 to 3800 to increase "median pins" used for summary stats. 

```{r}
## Chicago ## 
DoltonChicago %>% 
  filter(agency_num == "030210000" & class == "203" & between(tax_amt_pre_exe, 3600, 3800)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_post_exe = mean(tax_amt_post_exe),
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Chicago, Class 203, Naive Pre-Exemp Bill ~ $3,700", digits=0)


## Dolton ##
DoltonChicago %>% 
  filter(agency_num == "030310000" & class == "203" & between(tax_amt_pre_exe,3600,3800)) %>%
  summarize(
    comp_taxrate = mean(tax_code_rate),
    bill_current = mean(tax_amt_post_exe),
    bill_hyp = mean(eav*taxrate_new, na.rm=TRUE),
    bill_change = bill_hyp - bill_current,
    tax_amt_exe = mean(tax_amt_exe),
    tax_amt_pre_exe = mean(tax_amt_pre_exe),
    pin_count = n(),
    av = mean(av),
    eav = mean(eav)
  )  %>% 
    pivot_longer(cols = comp_taxrate:eav, names_to = "Stats", values_to = "Values")%>%
  kbl(caption= "Dolton, Class 203, Naive Pre-Exemp Bill ~ $3,700", digits=0)
```



4b_Similar Properties

Alea Wilbur

2023-09-24