Residential Burden Shift
Alea Wilbur
2023-07-11
Getting Data & Data Prep
# 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. Would be needed if creating a loop for calculating all munis
# municipality names and their agency number
muni_agency_names <- DBI::dbGetQuery(
ptaxsim_db_conn,
"SELECT DISTINCT agency_num, agency_name, minor_type
FROM agency_info
WHERE minor_type = 'MUNI'
OR agency_num = '020060000'
"
)
muni_agency_names <- muni_agency_names %>%
select(-minor_type)
#Makes a list of ALL taxing agencies, including TIFs, SSAs, etc.
# all agency names, numbers, and types
# includes TIF and non-TIF agencies
all_taxing_agencies <- DBI::dbGetQuery(
ptaxsim_db_conn,
"SELECT agency_num, agency_name, major_type, minor_type
FROM agency_info
"
)
muni_agency_nums<- all_taxing_agencies %>%
filter(minor_type %in% c("MUNI")) %>%
select(agency_num)
muni_tax_codes <- DBI::dbGetQuery(
ptaxsim_db_conn,
glue_sql("
SELECT*
FROM tax_code
WHERE agency_num IN ({muni_agency_names$agency_num*})
AND year = 2021
",
.con = ptaxsim_db_conn
)
)# %>% select(-agency_rate)
# Agency number and agency name for all TIFs
TIF_agencies <- DBI::dbGetQuery(
ptaxsim_db_conn,
"SELECT DISTINCT agency_num, agency_name, major_type, minor_type
FROM agency_info
WHERE minor_type = 'TIF'
"
)
unique_tif_taxcodes <- DBI::dbGetQuery(
ptaxsim_db_conn,
glue_sql("
SELECT DISTINCT tax_code_num
FROM tax_code
WHERE agency_num IN ({TIF_agencies$agency_num*})
AND year = 2021
",
.con = ptaxsim_db_conn
)
)
## Read in summarized tax code level data for exemptions and taxbills.
#taxbills_by_Class_per_TC <- read_csv("taxbills_inMunis_perTC.csv") %>%
# mutate(tax_code = as.character(tax_code),
# tax_code_frozen_eav = replace_na(tax_code_frozen_eav, 0),
# tax_code_tif_increment = ifelse((tax_code_frozen_eav > tax_code_eav), 0, (tax_code_eav-tax_code_frozen_eav)),
# )
# tax_code_tif_increment = tax_code_eav-tax_code_frozen_eav) %>%
#select(-c(year:tax_code_rate, tax_code_eav, tax_code_revenue, tax_code_distribution_pct))
# get rid of rpm variables, tax_amt_exe,pre and post exemption variables,
#exemptions_by_class_per_TC <- read_csv("all_exemptions_by_TC.csv") %>%
# mutate(tax_code_num = as.character(tax_code_num))
tif_distrib <- DBI::dbGetQuery(
ptaxsim_db_conn,
glue_sql("
SELECT *
FROM tif_distribution
WHERE tax_code_num IN ({muni_tax_codes$tax_code_num*})
AND year = 2021
",
.con = ptaxsim_db_conn
)
) %>% mutate(tax_code_num = as.character(tax_code_num))
## Add agency names since those weren't included originally in the table:
# all_taxing_agencies <- all_taxing_agencies %>%
# left_join(muni_agency_names, by = c("first5", "first6")) %>%
# rename(muni_name = agency_name.y,
# muni_num = agency_num.y,
# agency_name = agency_name.x,
# agency_num = agency_num.x)
#
# # combine taxing agency names and agency type to data table that has eav and extension values
# agency_data <- right_join(agency_dt, all_taxing_agencies) %>%
# # get rid of unneeded columns to make table outputs smaller
# select(-c(cty_dupage_eav:cty_livingston_eav, lim_numerator, lim_denominator)) %>% # drop some of the unused variables
# arrange(agency_num)muni_pins <- DBI::dbGetQuery(
ptaxsim_db_conn,
glue_sql(
"SELECT DISTINCT pin, class, tax_code_num
FROM pin
WHERE tax_code_num IN ({muni_tax_codes$tax_code_num*})
AND year = 2021
",
.con = ptaxsim_db_conn
))
#all_muni_pins <-read_csv("all_muni_pins.csv")
# Normal output from lookup_pin() command. Includes all types of exemptions
exe_dt <- lookup_pin(2021, muni_pins$pin) %>%
setDT(key = c("year", "pin"))
taxbills_current <- tax_bill(2021,
muni_pins$pin,
pin_dt = exe_dt, # default option
simplify = FALSE)
head(taxbills_current)
TC_bills_current <- taxbills_current %>%
left_join(class_dict, by = c("class" = "class_code")) %>% # add major property types to pin data
group_by(major_class_code, major_class_type, tax_code) %>%
summarize(
final_tax_to_dist = sum(final_tax_to_dist),
final_tax_to_tif = sum(final_tax_to_tif),
tax_amt_exe = sum(tax_amt_exe), # revenue lost due to exemptions
tax_amt_pre_exe = sum(tax_amt_pre_exe), # total rev before all exemptions
tax_amt_post_exe = sum(tax_amt_post_exe), # total rev after all exemptions
rpm_tif_to_cps = sum(rpm_tif_to_cps), # not used
rpm_tif_to_rpm = sum(rpm_tif_to_rpm), # not used
rpm_tif_to_dist = sum(rpm_tif_to_dist), # not used
tif_share = mean(tif_share), # not used
) %>%
mutate(stage = "With exemptions")
TC_bills_current
write_csv(TC_bills_current, "TC_bills_current.csv")
# Changes all exemptions to 0 for all pins.
# This table will then go INSIDE of the taxbill() comand
# for no exemptions simulation
no_exe_dt <- lookup_pin(2021, muni_pins$pin)# %>%
# mutate(across(starts_with("exe_"), ~0)) %>%
# setDT(key = c("year", "pin"))
no_exe_dt[, tax_code := lookup_tax_code(year, pin)]
exe_cols <- names(no_exe_dt)[startsWith(names(no_exe_dt), "exe_")]
taxcode_sum_no_exe <- no_exe_dt[,
.(exe_total = sum(rowSums(.SD))),
.SDcols = exe_cols,
by = .(year, tax_code)
]
# recalculate the base
# change agency data table and their tax rate
t_agency_dt_no_exe <- lookup_agency(2021, no_exe_dt$tax_code)
t_agency_dt_no_exe[
taxcode_sum_no_exe,
on = .(year, tax_code),
agency_total_eav := agency_total_eav + exe_total
]
no_exe_dt[, (exe_cols) := 0][, c("tax_code") := NULL]
taxbills_no_exemps <- tax_bill(2021,
pin_vec = muni_pins$pin,
agency_dt = t_agency_dt_no_exe,
pin_dt = no_exe_dt,
simplify = FALSE
)[
, stage := "No exemptions"
]
head(taxbills_no_exemps)
class_dict$class_code <- as.character(class_dict$class_code)
TC_bills_noexemps <- taxbills_no_exemps %>%
left_join(class_dict, by = c("class" = "class_code")) %>% # add major property types to pin data
group_by(major_class_code, major_class_type, tax_code) %>%
summarize(
final_tax_to_dist = sum(final_tax_to_dist),
final_tax_to_tif = sum(final_tax_to_tif),
tax_amt_exe = sum(tax_amt_exe), # revenue lost due to exemptions
tax_amt_pre_exe = sum(tax_amt_pre_exe), # total rev before all exemptions
tax_amt_post_exe = sum(tax_amt_post_exe), # total rev after all exemptions
rpm_tif_to_cps = sum(rpm_tif_to_cps), # not used
rpm_tif_to_rpm = sum(rpm_tif_to_rpm), # not used
rpm_tif_to_dist = sum(rpm_tif_to_dist), # not used
tif_share = mean(tif_share), # not used
)
TC_bills_noexemps
write_csv(TC_bills_noexemps, "TC_bills_noexemps.csv")
rm(taxbills_no_exemps)Muni Land Use
EAV
using the pin data from ptaxsim, I calculate the eav of all properties, exempt EAV, the current taxbase, and taxbase if there were not exemptios
exempt EAV (summed from all exemption types)
current taxbase (using the
tif_distribtable and percent of taxcode rev that goes to the tif). If a taxcode is a TIF taxcode, then do (EAV-exempt EAV) * (1-%rev that goes to TIF). If the tax code is not a TIF tax code, then use the EAV-exempt EAV.taxbase without exemptions: If a taxcode is a TIF taxcode, then do EAV * (1-%rev that goes to TIF). If the tax code is not a TIF tax code, then use the eav
class_dict <- read_csv("class_dict.csv")
# use exemptions in tax codes to summarize EAV.
# More accurate that calculating it from revenue collected.tax rate in tax bill data
exemptions_by_class_per_TC <- read_csv("all_exemptions_by_TC.csv") %>%
mutate(tax_code_num = as.character(tax_code_num)) %>%
left_join(muni_tax_codes) %>%
full_join(muni_agency_names) %>%
left_join(nicknames) %>%
# merge with TIF distrib table to calculate the percent of the EAV that goes to the TIF vs the district
left_join(tif_distrib, by=c("tax_code_num", "year", "tax_code_rate")) %>%
mutate(exempt_EAV = (exe_homeowner + exe_senior + exe_freeze + exe_longtime_homeowner +
exe_disabled + exe_vet_returning + exe_vet_dis + exe_abate) ,
in_TIF = ifelse(tax_code_num %in% unique_tif_taxcodes$tax_code_num, 1, 0),
tax_base_current = ifelse(in_TIF==1, (eav-exempt_EAV)*(1-tax_code_distribution_pct/100), eav-exempt_EAV),
tax_base_noexemps = ifelse(in_TIF==1, (eav)*(1-tax_code_distribution_pct/100), eav),
ResidentialProps = ifelse(major_class_code %in% c("2", "3", "9"), "Residential", "Commercial"),
PropType = case_when(
major_class_code %in% c("3","9") ~ "Multi-Family",
major_class_code == "2" ~ "Single-Family",
TRUE~ "Commercial-Industrial"))
grouped_exemptions <- exemptions_by_class_per_TC %>%
# group_by(agency_name, major_class_code, major_class_type, ResidentialProps, PropType) %>%
group_by(clean_name,ResidentialProps, agency_name ) %>%
summarize(eav = sum(eav),
exempt_EAV = sum(exempt_EAV, exe_abate, na.rm=TRUE),
tax_base_current = sum(tax_base_current, na.rm=TRUE),
tax_base_noexemps = sum(tax_base_noexemps, na.rm=TRUE)) %>% ungroup()
grouped_exemptions# calculate totals with ONLY EAV outside of TIFs
muni_eav <- grouped_exemptions %>%
group_by(clean_name, agency_name) %>%
summarize(muni_EAV_includesTIF = sum(eav), # all EAV in the municipality that exists
muni_tax_base_current=sum(tax_base_current), # taxable EAV based on current exemptions
muni_tax_base_noexemps = sum(tax_base_noexemps)) %>% # taxable EAV pre-exemptions
ungroup()
perc_residential <- full_join(grouped_exemptions, muni_eav) %>%
filter(ResidentialProps == "Residential") %>%
mutate(percent_residential = eav / muni_EAV_includesTIF)# %>% select()
perc_residentialPercent Residential is the Residential EAV outside of TIFs / Municipality EAV outside of TIFs.
# #lowest % of eav from residential property types
# perc_residential %>% select(-ResidentialProps) %>%
# arrange(percent_residential) %>% head()
#
# #highest % of eav from residential property types
# perc_residential %>% select(-ResidentialProps) %>%
# arrange(percent_residential) %>% tail()
#
perc_residential %>% group_by(ResidentialProps)%>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = percent_residential)) +
geom_sf(aes(geometry = geometry), color = "black") + theme_void()+
labs(title = "Residential EAV / Total EAV") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank()) +#+
scale_fill_stepsn(colors = c("white","darkblue"),
# limits = c(0,.6),
n.breaks = 5, show.limits=TRUE,
name = "% Residential", label = scales::percent)
Homeowner’s Exemptions Exempt EAV / Residential EAV
exemptions_to_resEAV_ratios <- grouped_exemptions %>%
filter(ResidentialProps == "Residential") %>%
mutate(exemptEAV_pctof_resEAV = exempt_EAV/eav,
nontif_ratio = exempt_EAV / tax_base_noexemps) %>%
select(agency_name, exemptEAV_pctof_resEAV, nontif_ratio) %>%
arrange(nontif_ratio)
exemptions_to_resEAV_ratios %>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = exemptEAV_pctof_resEAV)) +
geom_sf(aes(geometry = geometry), color = "black") + theme_void()+
labs(title = "Exemptions / Residential EAV (in and out of TIFs)") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
scale_fill_stepsn(colors = c("white", "darkblue"),
# limits = c(0,.6),
n.breaks = 5, show.limits=TRUE,
name = "% Residential EAV \nthat is exempt", label = scales::percent)
exemptions_to_resEAV_ratios %>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = nontif_ratio)) +
geom_sf(aes(geometry = geometry), color = "black") + theme_void()+
labs(title = "Non-TIF EAV only: Homestead Exemptions / Residential EAV", caption = "Village of Phoenix skews graph. Dropped in map below") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
scale_fill_stepsn(colors = c("white", "darkblue"),
# limits = c(0,.6),
n.breaks = 5, show.limits=TRUE,
name = "% Residential EAV \nthat is exempt", label = scales::percent)
exemptions_to_resEAV_ratios %>%
filter(nontif_ratio<.5) %>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = nontif_ratio)) +
geom_sf(aes(geometry = geometry), color = "black") + theme_void()+
labs(title = "Percent of Residential EAV that is Tax Exempt",
subtitle = "Non-TIF EAV only: Homestead Exemptions / Residential EAV", caption = "Drops Village of Phoenix because it skews map colors
(78% of their residential EAV is tax exempt)") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
scale_fill_stepsn(colors = c("white", "darkblue"),
# limits = c(0,.6),
n.breaks = 5, show.limits=TRUE,
name = "% Residential EAV \nthat is exempt", label = scales::percent)
Tax Rates
TC_bills_noexemps <- read_csv("TC_bills_noexemps.csv") %>% mutate(tax_code = as.character(tax_code))
# noexemps_rev<- taxcodes_noexemps %>%
# left_join(muni_agency_names) %>%
# select(tax_code, major_class_code, major_class_type, final_tax_to_dist, agency_name) %>%
# rename(final_tax_to_dist_noexemps = final_tax_to_dist)
taxcodes_noexemps <- left_join(TC_bills_noexemps, muni_tax_codes,
by = c("tax_code" = "tax_code_num")) %>%
left_join(muni_agency_names) %>%
left_join(nicknames)
noexemps_munisummary <- taxcodes_noexemps %>%
group_by(clean_name, agency_name) %>%
summarize(MuniLevy = sum(final_tax_to_dist, na.rm = TRUE), #amount billed by muni within cook county
nonTIF_EAV = sum(final_tax_to_dist/(tax_code_rate/100), na.rm = TRUE), # within cook county
final_tax_to_dist = sum(final_tax_to_dist, na.rm=TRUE),
TIF_increment_EAV = sum(final_tax_to_tif/(tax_code_rate/100), na.rm=TRUE), # within cook county
Exempt_EAV = sum(tax_amt_exe/(tax_code_rate/100), na.rm=TRUE), # within cook county
Total_EAV = sum((tax_amt_exe+final_tax_to_dist+final_tax_to_tif)/(tax_code_rate/100), na.rm = TRUE)) %>%
mutate(taxrate_noexemps = MuniLevy/nonTIF_EAV,
# nonTIF_EAV= nonTIF_EAV + Exempt_EAV,
)
# select(agency_name, tax_rate_noexemps, everything()) %>%
# arrange(desc(tax_rate_noexemps))
noexemps_munisummary6 Highest and 6 lowest tax rates. View the extremes on both sides
Current_Taxrates %>% filter(tax_rate_current > 0.25 )Current_Taxrates %>% filter( tax_rate_current < 0.08)Map of Tax Rate Change
burden_shift %>%
filter(major_class_code == 2) %>% # all property types have same composite tax rate
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = tax_rate_current)) +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
# scale_fill_gradientn(colors = c("white", "maroon"),
scale_fill_stepsn(colors = c("white", "#76C3D6", "#1A5E7D"),
limits = c(0,.35),
show.limits=TRUE,
n.breaks = 5,
name = "Tax Rate", label = scales::percent)+
geom_sf(aes(geometry = geometry), color = "black") +
labs(title = "Current composite tax rates with current exemptions in place" ,
caption = "Highest composite tax rate is in Park Forest (41.4%.)
Lowest composite tax rate is in Oak Brook (6.3%) and Chicago (6.7%).")
burden_shift %>%
filter(major_class_code == 2) %>% # all property types have same composite tax rate
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = taxrate_new)) +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
scale_fill_stepsn(colors = c("white", "#76C3D6", "#1A5E7D"),
limits = c(0,.35),
n.breaks = 5, show.limits=TRUE,
name = "Tax Rate", label = scales::percent)+
geom_sf(aes(geometry = geometry), color = "black") +
labs(title = "New composite tax rates if exemptions were eliminated" ,
caption = "Data from CCAO & PTAXSIM. Unincorporated areas are filled with gray.")
Current_Taxrates %>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = taxrate_change*100)) +
geom_sf(aes(geometry = geometry), color = "black") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
scale_fill_stepsn(colors = c("#ffffcc","#a1dab4" ,"#41b6c4","#2c7fb8", "#253494"),
# low = "#ffffcc", mid = "#41b6c4", high = "#253494",
# space = "Lab",
n.breaks = 6, show.limits=TRUE,
name = "Percentage Point Difference")+
labs(title = "Change in Composite Tax Rate if Exemptions are Removed")
# scale_fill_stepsn(colors = c("white", "#76C3D6", "#1A5E7D"),
# limits = c(0,.35),
Current_Taxrates %>%
# filter(clean_name != "Park Forest") %>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = taxrate_change*100)) +
geom_sf(aes(geometry = geometry), color = "black") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
scale_fill_stepsn(colors = c("#ffffcc","#a1dab4" ,"#41b6c4","#2c7fb8", "#253494"),
# low = "#ffffcc", mid = "#41b6c4", high = "#253494",
# space = "Lab",
n.breaks = 5, show.limits=TRUE,
name = "Percentage Point Difference")+
labs(title = "Change in Composite Tax Rate if Exemptions are Removed", subtitle = "Drops Park Forest")
# as a dot graph ##
order <- burden_shift %>%
as_tibble() %>%
summarize(agency_name = unique(agency_name),
clean_name = unique(clean_name),
tax_rate_current = unique(tax_rate_current),
taxrate_new = unique(taxrate_new)) %>%
arrange(tax_rate_current) %>%
select(agency_name, clean_name, tax_rate_current)
median(order$tax_rate_current)## [1] 0.1212head(order)tail(order)# look at ones that changed the most
burden_shift %>%
filter(tax_rate_current < (median(tax_rate_current))+0.002 & tax_rate_current > (median(tax_rate_current))-0.002 |
(tax_rate_current > 0.25 | tax_rate_current < 0.08 )) %>%
# (tax_rate_current < (median(tax_rate_current))+0.005 & tax_rate_current > (median(tax_rate_current))-0.005 )) %>%
filter(PropType == "Single-Family") %>%
ungroup() %>%
select(clean_name, tax_rate_current, taxrate_new, agency_name) %>%
pivot_longer(c("tax_rate_current", "taxrate_new"),
names_to = "type", values_to = "tax_rate") %>%
left_join(order) %>%
ggplot(aes(x = tax_rate*100, y= reorder(clean_name, tax_rate_current)))+
geom_line(aes(group = clean_name))+
geom_point(aes(color=type), size=3 )+
theme_minimal() +
theme(
legend.title = element_blank(),
plot.title.position = "plot",
# panel.background = element_rect(fill='transparent'), #transparent panel bg
plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
)+
scale_color_brewer(palette="Paired", labels = c("Exemptions", "No Exemptions"), direction = 1)+
labs(title = "Difference in Composite Tax Rate if there were No Exemptions",
subtitle = "Ordered by Current Composite Tax Rate", x = "Composite Tax Rate (%)", y = "" , caption = "For the highest, median, and lowest municipality composite tax rates ")
# ordered by change in tax rate if exemptions were removed.
# as a dot graph ##
order <- burden_shift %>%
as_tibble() %>%
group_by(agency_name, clean_name) %>%
summarize(tax_rate_current = mean(tax_rate_current),
taxrate_new = mean(taxrate_new),
taxrate_change = mean(taxrate_change)) %>%
arrange(taxrate_change)
median(order$taxrate_change) # median rate change is 1.4 percentage points## [1] 0.01361head(order)# this one probably not the best Delete code soon.
# look at ones that changed the most
# burden_shift %>%
# filter(tax_rate_current < (median(tax_rate_current))+0.002 & tax_rate_current > (median(tax_rate_current))-0.002 |
# (tax_rate_current > 0.25 | tax_rate_current < 0.08 )) %>%
# filter(PropType == "Single-Family") %>%
# ungroup() %>%
# select(clean_name, tax_rate_current, taxrate_new, agency_name) %>%
# pivot_longer(c("tax_rate_current", "taxrate_new"),
# names_to = "type", values_to = "tax_rate") %>%
# left_join(order) %>%
# ggplot(aes(x = tax_rate*100, y= reorder(clean_name, taxrate_change)))+
# geom_line(aes(group = clean_name))+
# geom_point(aes(color=type), size=3 )+
# theme_minimal() +
# theme(
# legend.title = element_blank(),
# plot.title.position = "plot",
# # panel.background = element_rect(fill='transparent'), #transparent panel bg
# plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
# )+
# scale_color_brewer(palette="Paired", labels = c("Exemptions", "No Exemptions"), direction = 1)+
#
# labs(title = "Difference in Composite Tax Rate if there were No Exemptions", subtitle = "Ordered by Change in Tax Rate: Top and Bottom Observations",
# x = "Composite Tax Rate (%)", y = "" , caption = "For the highest and lowest composite tax rates")
burden_shift %>%
filter(taxrate_change < 0.002 |taxrate_change > 0.055 |
taxrate_change < (median(taxrate_change))+0.00059 & taxrate_change > (median(taxrate_change))-0.00059
) %>%
filter(PropType == "Single-Family") %>%
ungroup() %>%
select(clean_name, tax_rate_current, taxrate_new, agency_name) %>%
pivot_longer(c("tax_rate_current", "taxrate_new"),
names_to = "type", values_to = "tax_rate") %>%
left_join(order) %>%
ggplot(aes(x = tax_rate*100, y= reorder(clean_name, taxrate_change)))+
geom_line(aes(group = clean_name))+
geom_point(aes(color=type), size=3 )+
theme_minimal() +
theme(
legend.title = element_blank(),
plot.title.position = "plot",
# panel.background = element_rect(fill='transparent'), #transparent panel bg
plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
)+
scale_color_brewer(palette="Paired", labels = c("Exemptions", "No Exemptions"), direction = 1)+
labs(title = "Difference in Composite Tax Rate if there were No Exemptions", subtitle = "Ordered by Change in Tax Rate: Highest, Median, and Smallest Differences",
x = "Composite Tax Rate (%)", y = "" )
Single-Family (Class 2) Burden Shift
Change in Tax Burden for Class 2 Properties:
burden_shift %>%
mutate(burden_current = ifelse(burden_current>1, 1, burden_current)) %>%
filter(major_class_code == 2) %>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = burden_current)) +
geom_sf(aes(geometry = geometry), color = "black") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
# scale_fill_gradientn(
scale_fill_stepsn(colors = c("#ffffcc","#a1dab4" ,"#41b6c4","#2c7fb8", "#253494"),
show.limits=TRUE, n.breaks = 6,
name = "Burden with \nExemptions", labels = scales::percent
)+
labs(title = "Current share of property tax burden", subtitle = "for Class = 2 Property Types")
burden_shift %>%
filter(major_class_code == 2) %>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = burden_noexemps)) +
geom_sf(aes(geometry = geometry), color = "black") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
#scale_fill_gradientn(
# scale_fill_binned(
#colors = c("#f0f9e8", "#0868ac"),
# colors = c('#a6611a','#018571'),
#limits = c(0,.6),
# n = 6,
# name = "Burden w/o \nExemptions", label = scales::percent)+
scale_fill_stepsn(colors = c("#ffffcc","#a1dab4" ,"#41b6c4","#2c7fb8", "#253494"),
# low = "#ffffcc", mid = "#41b6c4", high = "#253494",
# space = "Lab",
n.breaks = 6, na.value = "grey50",
guide = "coloursteps",
aesthetics = "fill", show.limit = TRUE,
name = "Burden w/o \nExemptions",
label = scales::percent
)+
labs(title = "New share of property tax burden", subtitle = "for Class = 2 Property Types")
burden_shift %>% filter(clean_name != "Phoenix") %>%
filter(major_class_code == 2) %>%
mutate(burden_change = ifelse(burden_change<0,0, burden_change))%>%
# burden_change = ifelse(burden_change>1,1, burden_change))%>%
full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
ggplot(aes(fill = (0-burden_change*100)) )+
geom_sf(aes(geometry = geometry), color = "black") + theme_void()+
labs(title = "Change in Residential Share of Tax Burden") +
theme_classic() +
theme(axis.ticks = element_blank(), axis.text = element_blank())+
scale_fill_stepsn(colors = c("#253494", "#2c7fb8", "#41b6c4", "#a1dab4", "#ffffcc"),
# low = "#ffffcc", mid = "#41b6c4", high = "#253494",
# space = "Lab",
n.breaks = 5,
na.value = "grey50",
guide = #"legend",
"coloursteps",
show.limit = TRUE,
name = "Percentage Point \nDifference")
# scale_fill_steps( low= "white", high = "darkblue",
# #colors = c("darkblue","white"),
# show.limits = TRUE,
# n.breaks = 5,
# name = "Difference in Burden")
# geom_sf(data = countyIL, fill=NA, color="dark gray")# as a dot graph ##
order <- burden_shift %>%
ungroup %>% as_tibble() %>%
# filter(ResidentialProps == "Residential") %>%
filter(PropType == "Single-Family") %>%
select(agency_name, clean_name, burden_current, burden_change)
# burder_shift_ordered <- burden_shift %>%
# ungroup() %>%
# select(agency_name, current_burden, no_exemptions_burden) %>%
# pivot_longer(c("current_burden", "no_exemptions_burden"),
# names_to = "type", values_to = "pct_burden") %>%
# left_join(order)
# look at ones that changed the most
burden_shift %>% filter(PropType == "Single-Family")%>%
summarize(median_change = median(burden_change),
median_current = median(burden_current),
median_noexemps = median(burden_noexemps)) # 0.0442
burden_shift %>%
filter(PropType == "Single-Family") %>%
filter(burden_current > 0.9 |burden_current < .3 |
( (burden_current < median(burden_current) + 0.01 )& (burden_current > median(burden_current) - 0.01)) )%>%
#filter(ResidentialProps == "Residential") %>%
ungroup() %>%
select(agency_name, clean_name, burden_current, burden_noexemps, burden_change) %>%
arrange(burden_change) %>%
mutate(
burden_noexemps = ifelse(burden_noexemps > 1, 1, burden_noexemps)) %>%
pivot_longer(c("burden_current", "burden_noexemps"),
names_to = "type", values_to = "pct_burden") %>%
inner_join(order) %>%
ggplot(aes(x = pct_burden*100,
y= reorder(clean_name, burden_change)))+
# y= reorder(clean_name, burden_current)))+
geom_line(aes(group = clean_name))+
geom_point(aes(color=type), size=3 )+
theme_minimal() +
theme(#legend.position = "none",
legend.title = element_blank(),
plot.title.position = "plot",
# panel.background = element_rect(fill='transparent'), #transparent panel bg
plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
)+
scale_color_brewer(palette="Paired", labels = c("No Exemptions", "Exemptions"), direction = 1)+
labs(title = "Change in Single-family Residential Tax Burden",
subtitle = "Ordered by Current Burden Levels",
x = "Share of Levy (%)", y = "" ,
caption = "Residential Tax Burden is the Share of the property tax collected that was paid for by
single-family home owners in property classes 2.") #+
# scale_x_continuous(label = scales::percent)### Change in Burden
burden_shift %>%
ungroup() %>%
filter(PropType == "Single-Family") %>%
# filter(burden_change > 0.3 | burden_change < .01) %>%
select(agency_name, clean_name, burden_current, burden_noexemps,
burden_change) %>%
mutate(
burden_current = ifelse(burden_current > 1, 1, burden_current)) %>%
pivot_longer(c("burden_current", "burden_noexemps"),
names_to = "type", values_to = "pct_burden") %>%
inner_join(order) %>%
ggplot(aes(x = pct_burden, y= reorder(clean_name, burden_current)))+
geom_line(aes(group = clean_name))+
geom_point(aes(color=type), size=3 )+
theme_minimal() +
theme(#legend.position = "none",
legend.title = element_blank(),
plot.title.position = "plot",
# panel.background = element_rect(fill='transparent'), #transparent panel bg
plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
)+
scale_color_brewer(palette="Paired", labels = c("No Exemptions", "Exemptions"), direction = 1)+
labs(title = "Change in Single-family Residential Tax Burden",
x = "Share of Levy (%)", y = "" ,
caption = "Residential Tax Burden is the Share of the property tax collected that was paid for by
single-family home owners in property classes 2.")
burden_shift %>%
ungroup() %>%
filter(PropType == "Single-Family") %>%
filter(burden_noexemps > 0.2 | burden_noexemps < .001) %>%
select(agency_name, clean_name, burden_current, burden_noexemps,
burden_change) %>%
mutate(
burden_current = ifelse(burden_current > 1, 1, burden_current)) %>%
pivot_longer(c("burden_current", "burden_noexemps"),
names_to = "type", values_to = "pct_burden") %>%
inner_join(order) %>%
ggplot(aes(x = pct_burden, y= reorder(clean_name, burden_change)))+
geom_line(aes(group = clean_name))+
geom_point(aes(color=type), size=3 )+
theme_minimal() +
theme(#legend.position = "none",
legend.title = element_blank(),
plot.title.position = "plot",
# panel.background = element_rect(fill='transparent'), #transparent panel bg
plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
)+
scale_color_brewer(palette="Paired", labels = c("No Exemptions", "Exemptions"), direction = 1)+
labs(title = "Change in Single-family Residential Tax Burden",
x = "Share of Levy (%)", y = "" ,
caption = "Residential Tax Burden is the Share of the property tax collected that was paid for by
single-family home owners in property classes 2.")
Summary table of Burden Shift: 3 Property Categories
Same data that is used above but property types are combined into 3 categories:
Single-family (Class 2),
Multi-Family (Class 3 & 9),
and Commercial-Industrial (all other Classes: 1,4, 5, 6, 7, 8, 9)
# Current Burden:
munis_3property_types <- burden_shift %>%# mutate(
# burden_noexemps = ifelse(is.na(burden_noexemps), 0, burden_noexemps)) %>%
# mutate(burden_current = ifelse(burden_current>1, 1, burden_current)) %>%
ungroup() %>%
group_by(agency_name, clean_name, PropType) %>%
summarize(#final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE),
burden_current = sum(burden_current, na.rm = TRUE),
burden_noexemps = sum(burden_noexemps, na.rm = TRUE),
burden_change = sum(burden_change, na.rm = TRUE))
#write.csv(munis_3property_types, "1_usemetable.csv")
proptypes3_current <- munis_3property_types %>%
mutate(burden_current = ifelse(is.na(burden_current), 0, burden_current)) %>%
pivot_wider( id_cols = clean_name , names_from = "PropType", values_from = "burden_current",names_prefix="Current - ", values_fill = 0 ) %>%
select(clean_name, `Current - Single-Family`, everything()) %>%
arrange(-`Current - Single-Family`)
proptypes3_current[2:4] <- sapply(proptypes3_current[2:4], function(x) scales::percent(x, accuracy=.01) )
proptypes3_current# __Tax Burden if there were no exemptions:__
proptypes3_noexemps <- munis_3property_types %>%
mutate(burden_noexemps = ifelse(is.na(burden_noexemps), 0, burden_noexemps)) %>%
pivot_wider( id_cols = clean_name , names_from = "PropType", values_from = "burden_noexemps", names_prefix = "W/O Exemptions - ", values_fill = 0)
proptypes3_noexemps[2:4] <- sapply(proptypes3_noexemps[2:4], function(x) scales::percent(x, accuracy=.01))
proptypes3_noexempsChange in Share of Burden if there were no exemptions:
# burden_shift %>% ungroup() %>%
# group_by(agency_name, PropType) %>%
# summarize(district_rev_collected = sum(district_rev_collected),
# current_burden = sum(current_burden),
# no_exemptions_burden = sum(no_exemptions_burden))
props_wide <- munis_3property_types %>%
pivot_wider(id_cols = clean_name ,
names_from = "PropType",
values_from = "burden_change", values_fill = 0) %>% select(clean_name, `Single-Family`, everything()) %>%
arrange(desc(`Single-Family`) )
props_wide[2:4] <- sapply(props_wide[2:4], function(x) scales::percent(x, accuracy=.001))
props_wideproptypes3_comparisontable<- left_join(proptypes3_current, proptypes3_noexemps, by = "clean_name")
proptypes3_comparisontableproptypes3_burdenchange <- munis_3property_types %>%
mutate(burden_noexemps = ifelse(is.na(burden_change), 0, burden_change)) %>%
pivot_wider( id_cols = agency_name , names_from = "PropType", values_from = "burden_change", values_fill = 0) %>%
arrange(desc(`Single-Family`))
proptypes3_burdenchange[2:4] <- sapply(proptypes3_burdenchange[2:4], function(x) scales::percent(x, accuracy=.01))
proptypes3_burdenchangeExporting data to Excel file
library(openxlsx)
dataset_names <- list(
'Land Use' = perc_residential,
'Burden Table' = burden_table,
# 'EAVoutsideTIFs' = EAV_outside_TIFS_byClass,
'Composite Tax Rates' = Current_Taxrates,
'MuniEAV' = muni_eav, #included as columns in Burden Table too
# 'ResidentialEAV' = table_ResidentialEAV, # doesn't exist?
# 'Burden with Exemps' = burden_with_exemptions,
# 'Burden without Exemps' = burden_noexemps,
'Burden Shift-3PropTypes' = proptypes3_burdenchange,
'3 Property Types'= munis_3property_types,
'TaxcodeData-NoExemptions'=taxcodes_noexemps,
'TaxcodeData-Current' = taxcodes_current)
write.xlsx(dataset_names, file = 'data_for_slides.xlsx')