Table 3.1: Summary of Homestead Exemption Use in Cook County (Tax Year 2023
Metric
Total*
PINs
1,864,161
PINs (w/ exemps.)
1,000,590
EqAV
237.91 B
Taxed EAV
198.84 B
Exempt EAV
15.58 B
GHE
9.89 B
Senior
2.71 B
Senior Freeze
2.88 B
Other Exemps.
0.09 B
*EAV in billions.
EqAV is the 2023 equalization factor multiplied by assessed value. Thus, Taxed EAV and Exempt EAV equal EqAV. It can be thought of as the “maximum available tax base” in a world without exemptions.
Figure 3.2: Share of FMV that is tax exempt due to Homestead Exemptions Same as previous image but in different color scheme that indicates high and low instead of using a midpoint for the color gradient.
The County-wide municipal-median percent of FMV exempt from property taxes in tax year 2023 was Orland Hills with 12.06%.
The percent of municipal EAV exempt from property taxes are greatest across Cook County’s south suburbs and lowest in the northern most suburbs of the county.
Figure 4. Exempt EAV: City vs. Suburbs
Code
## makes table in margin for suburbs and citygeom_sums <- muni_cl_sums |>filter(year == params$year) |>select(clean_name, muni_c_all_exemptions) |>mutate(clean_name =ifelse(is.na(clean_name), "Unincorporated", clean_name),Geography =ifelse(clean_name =="Chicago", "City", "Suburbs"),Geography =ifelse(is.na(clean_name), "Unincorporated", Geography),Geography =factor(Geography, levels =c("Suburbs", "City", "Unincorporated"))) |>group_by(Geography) |>summarize("ExemptEAV"=sum(muni_c_all_exemptions, na.rm=TRUE))geom_sums |>flextable() |> flextable::set_caption(caption ="Includes all exemption types.")
Geography
ExemptEAV
Suburbs
9,633,250,071
City
5,942,859,453
Code
muni_cl_sums |>filter(year == params$year) |>select(clean_name, muni_c_exe_homeowner:muni_c_exe_vet_dis) |>mutate(clean_name =ifelse(is.na(clean_name), "Unincorporated", clean_name),Geography =ifelse(clean_name =="Chicago", "City", "Suburbs"),Geography =ifelse(is.na(clean_name), "Unincorporated", Geography),Geography =factor(Geography, levels =c("Suburbs", "City", "Unincorporated"))) |>group_by(Geography) |>summarize("GHE"=sum(muni_c_exe_homeowner, na.rm=TRUE),"Senior Homestead"=sum(muni_c_exe_senior, na.rm=TRUE),"Senior Freeze"=sum(muni_c_exe_freeze, na.rm=TRUE),"Other Exemptions"=sum(muni_c_exe_longtime_homeowner+muni_c_exe_disabled+muni_c_exe_vet_returning+ muni_c_exe_vet_dis_lt50 + muni_c_exe_vet_dis_50_69+muni_c_exe_vet_dis_ge70)) |>pivot_longer(cols =c(`GHE`:`Other Exemptions`), names_to ="Type" ) |>mutate(Type =factor(Type, levels =c("GHE", "Senior Homestead", "Senior Freeze", "Other Exemptions"#"Senior Freeze", "Senior Homestead", "GHE" ) )) |>ggplot(aes(x=Type, y = value/1e9, fill = Geography )) +geom_col( position ="dodge" ) +geom_text(aes(label =round(value/1e9, digits=1)), vjust=-0.5, position =position_dodge(.9)) +theme_minimal() +labs(title =paste0("Exempt EAV in Cook County, Tax Year ", params$year), subtitle ="by Type and Geography", y ="EAV (Billions)", x ="Exemption Type") +scale_y_continuous(limits =c(0, 8)) +scale_fill_manual(values =c("blue3", "deepskyblue3", "gray") )
Figure 3.3: Exempt EAV in Cook County Exemption use varies between suburban Cook County and the City of Chicago which may point to broader socioeconomic patterns as well as residents’ eligibility and update. Note: Our current calculations undervalue the disabled veterans exemption.
Exemption use varies between suburban Cook County (9633250071 EAV) and the City of Chicago (5942859453 EAV) , which may point to broader socioeconomic patterns as well as residents’ eligibility and uptake.
Figure 5. Value of residential exemptions by type in Cook County over time, Tax Years 2006 - 2023
Trends in the total EAV reductions due to homestead exemptions reflect both real estate market shifts and the legislative changes in their availability, eligibility, and value.
Effect on Composite Tax Rates
Figure 6 will not be recreated but is just a bar chart of the change in tax rate in the table below
Table 1. Change in composite property tax rates due to exemptions, tax year 2023.
Code
muni_ratechange <-read_csv(paste0("../Output/muni_ratechange_", params$year, "_test.csv"))muni_ratechange |>select(clean_name, current_rate_avg, rate_noExe, change_noExe, final_tax_to_dist ) |>mutate(change_noExe = change_noExe *100) |> DT::datatable(rownames =FALSE, colnames =c('Municipality'='clean_name','Current Comp. Rate'='current_rate_avg', 'Hypothetical Rate'='rate_noExe', 'Composite Tax Rate Change'='change_noExe', 'Composite Levy'='final_tax_to_dist'),caption ="Table 1 in Report for all Municipalities: Current and Hypothetical Composite Tax Rates if GHE $0") |>formatCurrency('Composite Levy', digits =0) |>formatPercentage(columns =c(2,3), digits =2)
Table 3.2: Searchable table containing all municipalities and the hypothetical tax rate change if exempt EAV became taxable.
Table 3.3: Table 1 in Exemption Report. Shows the top 5, bottom 5, and median 5 municipalities, ranked by change in composite tax rate. Includes all exemption types in calculation of rate change.
Municipality
With Exemptions
Without Exemptions
Percent Point Difference
Phoenix
27.48
19.77
7.68
Park Forest
23.61
18.50
5.07
Riverdale
25.38
20.22
5.01
Dolton
20.11
15.68
4.25
Calumet City
20.69
17.02
3.96
Roselle
9.61
8.67
0.94
Orland Hills
9.04
7.95
0.93
Bridgeview
11.37
10.44
0.92
Morton Grove
9.51
8.46
0.92
Merrionette Park
9.75
8.85
0.90
Glencoe
8.12
7.91
0.21
Kenilworth
8.34
8.16
0.18
Winnetka
7.69
7.51
0.18
Hinsdale
6.86
6.69
0.17
Hodgkins
9.14
8.76
0.06
Figure 7. Map of Spatial Patterns in Composite Tax Rate Change
Change in composite tax rate if exempt EAV was added back to the the taxable tax base (i.e. if there were no exemptions).
Code
muni_ratechange |>mutate(across(c(current_rate_avg, rate_noExe, change_noExe ), ~.*100)) |>left_join(nicknames, by ="clean_name") |>mutate(agency_name =ifelse(agency_name =="TOWN CICERO", "CITY OF CICERO", agency_name),shpfile_name =ifelse(agency_name =="TOWN CICERO", "CITY OF CICERO", agency_name) ) |>full_join(muni_shp, by =c("agency_name"="AGENCY_DESC")) |>ggplot(aes(fill = change_noExe)) +geom_sf(aes(geometry = geom), color ="black") +theme_void() +theme(axis.ticks =element_blank(), axis.text =element_blank())+scale_fill_stepsn(colors =c( "#F7FEF5",# "#e4f1e0","#d4f6cc","#47ba24","#1F6805","#133C04"),show.limits=TRUE, limits =c(0, 15),breaks =c(0, 2.5, 5, 7.5, 10, 15),na.value =NA,name ="Rate Change from \nExemptions" )
Effect on Tax Burdens
Figure 8. Dolton example of Share of levy paid by property type
CMAP used a donut chart, we will use a bar chart that represents 100% on the x a axis. Values are currently an ungrouped table below
Table 3.4: Ungrouped values for donut chart in report. Tax Burden is the Revenue Collected from a Major Class / the combined levy from local taxing agencies (non-TIF) in Dolton, IL.
Major Class
Current Tax Burden
Alt. Burden
2
72.23
78.14
5
23.26
18.30
1
1.51
1.19
8
1.16
0.91
3
0.81
0.64
6
0.74
0.59
9
0.25
0.20
4
0.06
0.05
0
0.00
0.00
Figure 9. Change in Share of Tax Burden
Was made in Excel. Not recoded for website yet.
Table 2. Change in share of property tax burden
Table 2: Change in the share of property tax burden due to exemptions for single-family, multi-family, and commercial and industrial properties, tax year 2023
Table 3.5: Table 3. Estimated Bill Change if exempt EAV from the GHE were added to back to the tax base.
Code
cholton_15000_summarytable <- pin_data |>select(pin, av, class, tax_code_num, tax_bill_total, av_certified, exe_homeowner:exe_abate, clean_name, eq_av, all_exemptions, zero_bill, has_HO_exemp, taxed_eav, final_tax_to_dist, final_tax_to_tif) |>filter(clean_name %in%c("Chicago", "Dolton", "Glencoe")) |>filter(between(av, 14800, 15200)) |>filter(class >199& class <300) |># merge in muni residential median AVleft_join(C2_munistats_filtered |>select(clean_name, median_av)) |>left_join(muni_ratechange |>select(clean_name, rate_noExe, rate_noGHE, rate_current)) |># +/- 500 from municipalities median residential AV# Removes properties that received other types of exemptionsfilter(exe_senior ==0& exe_freeze ==0& exe_longtime_homeowner ==0& exe_disabled ==0& exe_vet_returning ==0& exe_vet_dis_lt50 ==0& exe_vet_dis_50_69 ==0& exe_vet_dis_ge70 ==0& exe_abate ==0) |>arrange(av) |>mutate(bill_current = rate_current* taxed_eav,bill_noexemps = rate_noGHE*(eq_av-all_exemptions+exe_homeowner),bill_change = bill_noexemps - bill_current) |># group_by(clean_name, has_HO_exemp) |>summarize(AV =median(av),`Median AV in Muni`=first(median_av), # median_av was calculated earlier: C2 median AV for the muni `EqAV`=round(median(eq_av)),`Taxed EAV`=round(median(taxed_eav)),bill_cur =round(median(bill_current)),bill_new =round(median(bill_noexemps)),bill_change =round(median(bill_change)),pincount=n(),perceived_savings =round(median(all_exemptions*rate_current)) ) |># merge in clean_names variableleft_join(nicknames) |>select(clean_name, AV, has_HO_exemp, bill_cur, bill_new, bill_change, perceived_savings, `Median AV in Muni`, `EqAV`, `Taxed EAV`#everything() ) |> DT::datatable(rownames =FALSE, colnames =c('Claims GHE'='has_HO_exemp','Current Bill'='bill_cur','Hyp. Bill'='bill_new', 'Bill Change'='bill_change', 'Perceived Savings'='perceived_savings'),caption ="Change in Tax Bill for Properties with an Assessed Value of $15,000.") |>formatCurrency(columns =c(2, 4:9), digits =0)cholton_15000_summarytable
Table 4.
Municipalities with the largest and smallest reductions in tax base (as a share of residential EAV due to exemptions) and median property values
Figure 10. Tax Burden Shift from Current GHE
Share of municipal property tax levy paid by Class 2 properties with and without homestead exemptions, tax year 2023
Code
# as a dot graph ## order <- mc_burden |>filter(class_1dig ==2) |>select(clean_name, pct_taxburden_current, burden_shift)slice <- mc_burden |>filter(class_1dig ==2) |>select(clean_name, pct_taxburden_current, burden_shift) |>arrange(pct_taxburden_current) |>slice(1:5, 63:67, 127:131)median_burden <-median(order$pct_taxburden_current)median_shift <-median(order$burden_shift)# median burden change is 5.9 percentage points# current median burden is 70.3% of the levymc_burden |>filter(clean_name %in% slice$clean_name) |>#filter(!clean_name %in% cross_county_lines$clean_name)|>filter(class_1dig ==2) |># filter(burden_current > 0.938 |burden_current < .17 |# ( (burden_current < median(burden_current) + 0.01 )& (burden_current > median(burden_current) - 0.01)) )|> ungroup() |>select(clean_name, pct_taxburden_current, hyp_pct_taxburden, burden_shift) |>arrange(burden_shift) |>pivot_longer(c("pct_taxburden_current", "hyp_pct_taxburden"), names_to ="type", values_to ="pct_burden") |>inner_join(order) |>ggplot(aes(x = pct_burden, y=reorder(clean_name, - pct_taxburden_current)))+# y= reorder(clean_name, burden_current)))+geom_vline(xintercept = median_burden, linetype =3)+geom_line(aes(group = clean_name))+geom_hline(yintercept =5.5, linetype =2)+geom_hline(yintercept =10.5, linetype =2)+geom_point(aes(color=type), size=3 )+theme_minimal() +theme( legend.title =element_blank(),plot.title.position ="plot",plot.background =element_rect(fill='transparent', color=NA) )+scale_color_brewer(palette="Paired", labels =c("Current Burden", "Burden if \nNo Exemptions" ), direction =1) +scale_x_continuous(labels = scales::percent) +labs(title ="Change in Class 2 Residential Tax Burden", subtitle ="Ordered by Current Tax Burden",x ="Share of Levy (%)", y ="" , caption =paste0("Dotted line represents median Class 2 burden (", round(median_burden*100), "% of the levy). \nResidential Tax Burden is theshare of the property tax collected that was paid for by property owners with Class 2 properties.")) +geom_label(label ="Class 2 pays small share of \nlevy; very little residential", x=.32, y =13, label.size =1, size =3)+geom_label(label =paste0("Class 2 pays median share of \nlevy (", round(median_burden*100), "%), mix of land use"), x=.42, y =7.5, label.size =1, size =3) +geom_label(label ="Class 2 pays nearly all of levy, \nhighly residential", x=.70, y =3, label.size =1,size =3)
Figure 3.5
Figure 11. Zero Dollar Bills
Code
muni_mc_sums |>left_join(nicknames) |>filter(major_class_code ==2) |>group_by(year, Triad) |>summarize(zerodollar_count =sum(zero_bill)) |>ggplot(aes(x=year, y = zerodollar_count, fill = Triad)) +geom_bar(position ="stack", stat ="identity") +theme_minimal()+theme(legend.position ="top")+labs(title =element_text("Number of $0 Taxbills")) +scale_y_continuous(labels = scales::comma, limits =c(0, 35000), name ="")
Figure 3.6: Number of tax bills that were $0 shown here is different than the value shown in the exemption report. This is due to missing disabeled veteran exemption amounts that were not identified when writing the original report.
Table 6
Not recreated in code yet.
Additional notes
Multiple ways to measure “burden”:
Share of tax base = (taxed EAV / taxed EAV in Muni)
Tax Burden = tax paid / levy
Effective rate = tax paid / property value
Source Code
---title: "Tax Year 2023"params: year: 2023---{{< include exemption-report-template.qmd >}}
