Yearly Trends - Incentive Classifications

knitr::opts_chunk$set(warning = FALSE, message = FALSE)

options(scipen = 999)

library(tidyverse)
library(glue)
library(sf)
library(DT)
library(flextable)

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

cook_sums <- read_csv("../Output/ptaxsim_cook_level_2006to2023_new.csv") 

muni_shp <- read_sf("../Necessary_Files/muni_shp.gpkg")


knitr::opts_chunk$set(warning = FALSE, message = FALSE)

set_flextable_defaults(theme_fun = theme_vanilla, 
                       padding = 2,
                       line_spacing = 1,
                       big.mark = ",",
                       )

options(DT.options = list())

FitFlextableToPage <- function(ft, pgwidth = 6){

  ft_out <- ft %>% autofit()

  ft_out <- width(ft_out, width = dim(ft_out)$widths*pgwidth /(flextable_dim(ft_out)$widths))
  return(ft_out)
}

# Create an empty data frame with a column named "year"
params <- data.frame(year = numeric(0))

# Add the value 2021 to the "year" column
params <- rbind(params, data.frame(year = 2023))

Time Trends - FMV Growth 2011-2023

Pin Counts

year_count <- 13

comm_ind_pins <- read_csv("../Output/comm_ind_PINs_2011to2023_timeseries.csv")

commind_together <- comm_ind_pins  %>%
#  left_join(class_dict |> select(class, land_use)) |>
 # filter(land_use %in% c("Industrial", "Commercial")) |>
  group_by(year) %>%
  summarize(
    proptype_pincount = n(),
            n_incent = sum(ifelse(incent_prop == "Incentive", 1, 0)),
            n_nonincent = sum(ifelse(incent_prop == "Non-Incentive", 1, 0)))  %>% mutate(pct_incent = n_incent / proptype_pincount)

  

table <- comm_ind_pins %>% 
    filter(land_use %in% c("Industrial", "Commercial")) |>

  group_by(year, land_use) %>%
  summarize(
    proptype_pincount = n(),
            n_incent = sum(ifelse(incent_prop == "Incentive", 1, 0)),
            n_nonincent = sum(ifelse(incent_prop == "Non-Incentive", 1, 0))) %>%
  mutate(pct_incent = n_incent / proptype_pincount) %>%
  arrange(year) %>%
  mutate(year = as.character(year)) %>%
filter(!land_use %in% c("Exempt", "Other Residential", "Owner Occupied", "Rental") ) %>% 
  select(year, land_use, n_incent, pct_incent) |>
  pivot_wider(id_cols = year, names_from = land_use, 
              values_from = c(n_incent,pct_incent)) %>% ungroup() |>
  select(-year)

cbind(commind_together, table) %>% 
  select(Year = year, 
         "C&I Incentive Parcels" = n_incent, 
         "Share of C&I" = pct_incent,
         "Commercial Pin Count" = n_incent_Commercial, 
         "Share of Commercial" = pct_incent_Commercial,
         "Industrial Pin Count" = n_incent_Industrial, 
         "Share of Industrial" = pct_incent_Commercial,
         "C&I PIN Count" = proptype_pincount) %>% 
  mutate(Year = as.character(Year)) |>
  mutate(across(contains("Share"), scales::percent, digits = 2)) |>
  flextable() %>%
  add_header_row(values = c(" ", "Incent C&I", "Commercial", "Industrial","All C&I"), colwidths = c(1,2,2,2, 1))

	Incent C&I		Commercial		Industrial		All C&I
Year	C&I Incentive Parcels	Share of C&I	Commercial Pin Count	Share of Commercial	Industrial Pin Count	Share of Industrial	C&I PIN Count
2011	2,174	2.049%	377	0.531%	1,797	0.531%	106,085
2012	2,267	2.135%	393	0.551%	1,874	0.551%	106,170
2013	2,380	2.242%	426	0.598%	1,954	0.598%	106,151
2014	2,468	2.330%	449	0.635%	2,019	0.635%	105,902
2015	2,623	2.477%	533	0.753%	2,090	0.753%	105,892
2016	2,851	2.692%	579	0.817%	2,272	0.817%	105,915
2017	3,016	2.851%	652	0.924%	2,364	0.924%	105,769
2018	3,226	3.055%	714	1.012%	2,512	1.012%	105,593
2019	3,361	3.185%	751	1.062%	2,610	1.062%	105,523
2020	3,419	3.248%	788	1.119%	2,631	1.119%	105,278
2021	3,554	3.379%	819	1.171%	2,735	1.171%	105,182
2022	4,278	4.071%	862	1.239%	3,416	1.239%	105,081
2023	4,427	4.218%	882	1.265%	3,545	1.265%	104,949

comm_ind_pins %>% 
  filter(land_use %in% c("Commercial", "Industrial")) %>%
 group_by(year, land_use) %>%
  summarize(
  PC = sum(incent_prop=="Incentive") )%>%
  ggplot() + 
  geom_col(aes(x=year, y = PC, fill = land_use)) + 
  scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year),                                      
    breaks = c(min(comm_ind_pins$year), 2010, 2015, 2020, params$year)) + 
  theme_classic() + 
  theme(legend.position =  "bottom", legend.title = element_blank(), 
                          axis.title.x =   element_blank())

commind_together_fmv <- comm_ind_pins %>%
  filter(land_use %in% c("Industrial", "Commercial")) |>
  group_by(year) %>%
  summarize(
    fmv_CI = sum(fmv, na.rm=TRUE),
    fmv_incent = sum(ifelse(incent_prop == "Incentive", fmv, 0))
          )  %>% 
  mutate(pct_incent = (fmv_incent / fmv_CI))

  

table_fmv <- comm_ind_pins %>% 
    filter(land_use %in% c("Industrial", "Commercial")) |>

  group_by(year, land_use) %>%
  summarize(
    fmv = sum(fmv, na.rm=TRUE),
    fmv_incent = sum(ifelse(incent_prop == "Incentive", fmv, 0), na.rm=TRUE)
          )  %>% 
  mutate(pct_incent = (fmv_incent / fmv) / 10000) %>%  ## should standardize percentage formatting....
  arrange(year) %>%
  select(year, land_use, fmv_incent, pct_incent) |>
  pivot_wider(id_cols = year, names_from = land_use, 
             values_from = c(fmv_incent, pct_incent)) %>% ungroup() |>
  select(-year)

cbind(commind_together_fmv, table_fmv) %>% 
  select(Year = year, "C&I Incentive FMV" = fmv_incent, "Share of C&I" = pct_incent,
                  "Commercial Incent. FMV" = fmv_incent_Commercial, "Share of Commercial" = pct_incent_Commercial,

         "Industrial Incent. FMV" = fmv_incent_Industrial, "Share of Industrial" = pct_incent_Industrial, fmv_CI) %>% 
    mutate(Year = as.character(Year)) %>%
  mutate(across(contains("Share"), scales::percent, digits = 2)) |>

  flextable() %>%
  add_header_row(values = c(" ", "Commercial & Industrial", "Commercial", "Industrial","All C&I"), colwidths = c(1,2,2,2, 1))

	Commercial & Industrial		Commercial		Industrial		All C&I
Year	C&I Incentive FMV	Share of C&I	Commercial Incent. FMV	Share of Commercial	Industrial Incent. FMV	Share of Industrial	fmv_CI
2011	4,517,119,648	5.80%	23,466,989,713,379	3.77%	28,119,835,568,929	17.97%	77,894,868,288
2012	4,731,517,657	6.19%	24,035,816,950,486	3.93%	28,713,619,088,032	18.74%	76,481,941,537
2013	4,982,856,965	6.62%	25,737,770,106,294	4.26%	29,022,129,056,412	19.54%	75,269,976,797
2014	5,252,674,092	7.01%	27,120,411,200,218	4.49%	29,374,428,685,553	20.19%	74,950,805,536
2015	5,971,685,137	7.47%	34,673,161,888,967	5.33%	31,151,056,185,480	20.90%	79,957,648,137
2016	6,195,459,765	7.61%	38,336,853,582,387	5.79%	34,493,612,547,072	22.72%	81,394,230,129
2017	6,659,059,793	8.05%	43,731,085,414,019	6.52%	36,971,068,186,936	23.64%	82,711,416,633
2018	7,880,502,820	8.60%	53,590,962,149,774	7.14%	41,566,445,681,653	25.12%	91,604,521,901
2019	9,213,592,010	9.23%	60,648,428,933,550	7.51%	49,715,948,188,710	26.10%	99,805,152,061
2020	9,455,060,085	9.83%	60,219,559,400,868	7.88%	52,014,613,469,607	26.31%	96,190,663,158
2021	10,765,549,788	10.24%	68,101,588,258,158	8.19%	60,134,520,912,660	27.35%	105,139,150,171
2022	12,608,755,833	11.26%	75,632,100,948,127	8.62%	82,890,793,074,152	34.16%	112,005,710,967
2023	13,006,606,963	11.39%	78,107,284,362,912	8.82%	90,982,096,580,565	35.45%	114,221,919,106

comm_ind_pins %>% 
  filter(land_use %in% c("Commercial", "Industrial")) %>%
 group_by(year, land_use) %>%
  summarize(
  fmv = sum(ifelse(incent_prop=="Incentive", fmv/1000000000, 0) ))%>%
  ggplot() + 
  geom_col(aes(x=year, y = fmv, fill = land_use)) + 
  scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year), 
    breaks = c(min(comm_ind_pins$year), 2009, 2012, 2015, 2018, params$year)) + 
  theme_classic() +
  labs(y = "FMV ($ Billions)")+
  theme(legend.position =  "bottom", legend.title = element_blank(), 
                          axis.title.x =   element_blank())

The file comm_ind_inmunis_timeseries_2006to2023.csv comm_ind_PINs_2011to2023_timeseries.csv contains all PINs that had an incentive property class for at least 1 year. It includes all observations for a property during the years that it existed, even if it is not an incentive class property in that year.

Only includes PINs in incorporated areas.

comm_ind_pins %>%
  filter(incent_prop == "Incentive") %>%
  # group_by(year, land_use) %>%
  reframe(PC=n(), .by= c(year, land_use)) %>%
  distinct() %>%
   reframe(land_use, PC,
     both_PC = sum(PC), .by=year) %>% distinct() %>% 
  arrange(year) %>%
  group_by(land_use) %>%
  mutate(ratio_2011 = PC/ PC[year==2011],
         ratio_both_2011 = both_PC/both_PC[year==2011]) %>% 
  ggplot() +
  geom_line(aes(x=year, y = ratio_2011, group = land_use, color = land_use)) + 
  geom_line(aes(x=year, y =ratio_both_2011), lty = 1) + 
  theme_bw() + 
    scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year), 
    breaks = c(min(comm_ind_pins$year), 2009,  2015, 2018, 2021, params$year))

comm_ind_pins %>%
  filter(incent_prop == "Incentive") %>%
   reframe(fmv,
           type_fmv = sum(fmv, na.rm=TRUE), .by = c(year,land_use)
          ) %>%
   reframe(land_use, type_fmv,
          fmv_incent = sum(fmv, na.rm=TRUE), .by=year) %>% distinct() %>% 
  arrange(year) %>%
  group_by(land_use) %>%
  mutate(ratio_2011 = type_fmv/ type_fmv[year==2011],
         ratio_both_2011 = fmv_incent/fmv_incent[year==2011]) %>% 
  ggplot() +
  geom_line(aes(x=year, y = ratio_2011, group = land_use, color = land_use)) + 
  geom_line(aes(x=year, y =ratio_both_2011), lty = 1) + 
    scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year), 
    breaks = c( 2009, 2012, 2015, 2018, 2021, params$year)) + 
  theme_classic()

pins_per_year <- comm_ind_pins %>% 
  group_by(year) %>%
  summarize("Gains Incent" = sum(gain_incent, na.rm=TRUE),
            "Loses Incent" = sum(lose_incent, na.rm=TRUE),
            "PINs with Incent. Class." = sum(incent_prop=="Incentive"),
            "PINs in Sample" = n(),
            "Became Exempt" = sum(became_exempt, na.rm=TRUE))

pins_per_year

Table 8.1: Number of PINs that gain or lose incentive classification each year.

df_2011_class8change <- comm_ind_pins|>
  group_by(pin) |>
      mutate(class8years = sum(class_1dig == 8)) |>

  mutate(class8_change = case_when(
      class8years == year_count ~ "Always Class 8",
      class8years == 0 ~ "Never Class 8",
      TRUE ~ "Changes to C8")
  ) |> 
  ungroup() |>
group_by(year, class8_change) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE),
  ) |> ungroup() |>
  group_by( class8_change) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1)  



# df_2011_class8change %>% 
#   filter(year == 2022) %>%
#   select(class8_change, fmv_group_growth, base_year_fmv, group_year_fmv, everything(), -year)

df_2011_class8change %>% 
  filter(year == 2023) %>%
  select(class8_change, fmv_group_growth, base_year_fmv, group_year_fmv, everything(), -year)

comm_ind_pins <- comm_ind_pins %>% 
  group_by(pin) |>
  mutate(class8years = sum(class_1dig == 8),
         class8_change = case_when(
           class8years == year_count ~ "Always Class 8",
           class8years == 0 ~ "Never Class 8",
           sum(lag(class_1dig) != 8 & class_1dig == 8) > 0 ~ "Becomes Class 8",
           sum(class_1dig != 8 & lag(class_1dig) == 8 ) > 0 ~ "Leaves Class 8",
           TRUE ~ "Other"),
         becomes8 = ifelse(lag(class_1dig) != 8 & class_1dig == 8, 1, 0),
         becomes7 = ifelse(lag(class_1dig) != 7 & class_1dig == 7, 1, 0),
         becomes6 = ifelse(lag(class_1dig) != 6 & class_1dig == 6, 1, 0),
         switches_to8 = ifelse(sum(incent_prop == "Incentive")> 1 & becomes8 ==1, 1, 0),
         # is_exempt = ifelse(class %in% c("0", "EX"), 1, 0),
         #  was_exempt = ifelse(lag(class) %in% c("0","EX"), 1, 0)
  ) %>%
        ungroup()

 classpins_per_year <- comm_ind_pins |>
   group_by(year) %>%
  summarize("Becomes 8" = sum(becomes8==1, na.rm=TRUE),
            "Total 8" = sum(class_1dig == 8),

            "6 or 7 to 8" = sum(switches_to8 == 1, na.rm=TRUE),

            "Becomes 7" = sum(becomes7 == 1, na.rm=TRUE),
            "Total 7" = sum(class_1dig == 7),

            "Becomes 6" = sum(becomes6 == 1, na.rm=TRUE),
            "Total 6" = sum(class_1dig == 6),
            "PINs with Incent. Class." = sum(incent_prop=="Incentive"),
          #  "Tax Exempt" = sum(is_exempt),
            "PINs in Sample" = n(),
            #"Became Exempt" = sum(became_exempt, na.rm=TRUE)
          )
classpins_per_year %>%  flextable::flextable()

year	Becomes 8	Total 8	6 or 7 to 8	Becomes 7	Total 7	Becomes 6	Total 6	PINs with Incent. Class.	PINs in Sample
2,011	0	608	0	0	64	0	1,502	2,174	106,085
2,012	45	641	43	1	64	93	1,562	2,267	106,170
2,013	55	668	52	9	72	131	1,640	2,380	106,151
2,014	43	702	39	11	80	120	1,686	2,468	105,902
2,015	88	762	88	66	147	157	1,714	2,623	105,892
2,016	105	864	105	27	165	187	1,822	2,851	105,915
2,017	87	924	76	23	189	152	1,903	3,016	105,769
2,018	56	951	54	52	243	209	2,032	3,226	105,593
2,019	66	979	66	24	265	153	2,117	3,361	105,523
2,020	38	983	38	24	285	119	2,151	3,419	105,278
2,021	67	1,035	67	25	301	124	2,218	3,554	105,182
2,022	673	1,703	672	22	318	116	2,257	4,278	105,081
2,023	116	1,813	2	25	296	141	2,318	4,427	104,949

Table 8.2: Number of PINs that became each major class type each year.

Explore PINs that became Class 8 or Other Incentive in South Triad

# comm_ind_pins %>% 
#   filter(class8_change == "Becomes Class 8") %>% 
#   left_join(bor, by = c("pin", "year" = "tax_year")) %>%
#   filter(!is.na(appellant)) %>%
#   
#   arrange(pin, desc(year)) %>%
#   select(year, appellant, project_id, class.x, everything())

# example of PIN that was class 8 and then became un-incentivized in 2017
# comm_ind_pins %>% filter(pin == "16271000330000")


comm_ind_pins %>% 
  filter(class8_change == "Becomes Class 8" & Triad == "South") %>% 
  arrange(pin) %>%
  select(year, class, everything())

Table 8.3: Number of PINs that became each major class 8 in the South triad.

comm_ind_pins %>% 
  filter(becomes8 == 1 & Triad == "South") %>% 
  arrange(pin) %>%
  select(year, class, everything())
comm_ind_pins %>% 
  filter(becomes7== 1& Triad == "South") %>% 
  arrange(pin) %>%
  select(year, class, everything())
comm_ind_pins %>% 
  filter(becomes6 == 1 & Triad == "South") %>% 
  arrange(pin) %>%
  select(year, class, everything())

Table 8.4: South Triad Number of PINs that became each major class type each year.

note to self: status is coded incorrectly. There are ‘Gains Incentive’ PINs that are also Controls if they existed less than 12 years. Became_taxed is wrong too

df_2011_incentchange <- comm_ind_pins|>
group_by(year, incent_change) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE),
  ) |> ungroup() |>
  group_by( incent_change) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1)  



df_2011_incentchange %>% 
  #filter(year == params$year) %>%
  filter(year == 2023) |>
  select(incent_change, fmv_group_growth, base_year_fmv, group_year_fmv, everything(), -year)

Table 8.5: Aggregate FMV Growth by Incentive Classification. Changes Sometime includes properties that gained or lost an incentive classification. incent_status in tables below breaks up Changes Sometime into more detailed categories.

comm_ind_pins %>%
  ungroup() %>% 
  group_by(year, incent_status) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE)) |> 
  ungroup() |>
group_by(incent_status) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1) |> 
  filter(year == params$year)  %>%
  select(incent_status, fmv_group_growth, group_pin_count, everything(), -year)

Table 8.6: Aggregate FMV Growth by incent_status. Calculations for PINs counts in header of table in report.

comm_ind_pins %>%
  group_by(year, landuse_change) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE)) |> 
  ungroup() |>
  group_by(landuse_change) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    base_year_n = group_pin_count[year == min(year)],
    n_group_growth = group_pin_count/base_year_n -1,
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1) %>%
    filter(year == params$year) %>%   
  select(landuse_change, fmv_group_growth, group_pin_count)

Table 8.7: Aggregate FMV Growth from 2011 to 2023 by landuse_change.

df_2011_bal <- comm_ind_pins %>%
  group_by(year, landuse_change, incent_status) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE)) |> 
  ungroup() |>
  group_by(landuse_change, incent_status) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    base_year_n = group_pin_count[year == min(year)],
    n_group_growth = group_pin_count/base_year_n -1,
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1) 

df_2011_bal %>% 
  select(year, landuse_change, incent_status, fmv_group_growth, group_pin_count) %>% 
  filter(year == params$year) %>%   
  select(-year)

Table 8.8: Growth from 2011 to 2023 - Change in Land Use by Incentive Class Status. Non-winsorized version of table used in Table X of report.

df_2011_bal %>% 
  ggplot() + 
  geom_line(aes(x=year, fmv_group_growth, group = incent_status, color = incent_status)) + 
  labs( title = "FMV Growth Since 2011 by Land Use",) + 
  theme_bw()  +  
  facet_wrap(~landuse_change)

Figure 8.1: Includes the ‘Excluded’ Category

df_2011_bal %>% 
  filter(incent_status != "Excluded" & landuse_change != "Excluded" & landuse_change != "Exempt Sometime") %>%
  ggplot() + 
  geom_line(aes(x=year, fmv_group_growth, group = incent_status, color = incent_status)) + 
  labs( title = "FMV Growth Since 2011 by Land Use", 
        caption = "PINs that did not exist during all years of the sample frame were excluded from the image (n=10,809)."
        ) + 
  scale_color_brewer(direction = -1) + 
  theme_bw()  +  
    scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year), 
    breaks = c( 2009, 2012, 2015, 2018, 2021, params$year)) + 
  facet_wrap(~landuse_change)

Figure 8.2: Excludes the ‘Excluded’ Category and ‘Exempt Sometime’ properties.

df_2011_bal %>%  
  mutate(year = as.factor(year)) %>%
  ggplot() + 
  geom_line(aes(x=year, y=fmv_group_growth, group = landuse_change, color = landuse_change)) +
  theme_bw()  +  
  facet_wrap(~incent_status, nrow = 1) +
  scale_x_discrete(breaks = c(2012, params$year)) + 
  scale_y_continuous(breaks = c(-1, 0, 1, 2, 3, 4, 10), labels = scales::percent,) +
  scale_color_brewer(direction = -1) + 

  labs(title= paste0("Growth from 2011 to ", params$year),
       subtitle = "Incentive Classification Status by Land Use Change",
       y = "FMV Growth since 2011", x = NULL,
       caption =  "Values are indexed to 2011 FMV") +
  theme(legend.title = element_blank(), legend.position = "bottom")

Figure 8.3: Growth from 2011 to 2023. Faceted by if a PIN changed landuse during the sample period. Indexed to fair market value during 2011

df_2011_bal %>%  
  filter(landuse_change != "Excluded" & incent_status != "Excluded" & landuse_change != "Exempt Sometime") %>%
  mutate(year = as.factor(year)) %>%
  ggplot() + 
  geom_line(aes(x=year, y=fmv_group_growth, group = landuse_change, color = landuse_change)) +
  theme_bw()  +  
  facet_wrap(~incent_status, nrow = 1) +
  scale_x_discrete(breaks = c(2012, params$year), ) + 
  scale_y_continuous(breaks = c(-1, 0, 1, 2, 3, 4, 10), labels = scales::percent,) +
  labs(title= "Growth from 2011", 
       subtitle = "Incentive Classification Status by Land Use Change",
       y = "FMV Growth since 2011", x = NULL,
       caption =  paste0("Values are indexed to 2011 FMV. Excludes PINs that were tax exempt some years or did not exist for all years between 2011 and ", params$year)) +
  theme(legend.title = element_blank(), legend.position = "bottom")

Figure 8.4: Aggregate FMV Growth from 2011 to 2023. Faceted by PIN incentive status during the sample period. Indexed to fair market value during 2011.

Trends for Incentive Report - FMV Growth 2011-2022

Pin Counts

# Create an empty data frame with a column named "year"
params <- data.frame(year = numeric(0))

# Add the value 2021 to the "year" column
params <- rbind(params, data.frame(year = 2022))


year_count <- 12

comm_ind_pins <- read_csv("../Output/comm_ind_PINs_2011to2022_timeseries.csv")

commind_together <- comm_ind_pins  %>%
#  left_join(class_dict |> select(class, land_use)) |>
 # filter(land_use %in% c("Industrial", "Commercial")) |>
  group_by(year) %>%
  summarize(
    proptype_pincount = n(),
            n_incent = sum(ifelse(incent_prop == "Incentive", 1, 0)),
            n_nonincent = sum(ifelse(incent_prop == "Non-Incentive", 1, 0)))  %>% mutate(pct_incent = n_incent / proptype_pincount)

  

table <- comm_ind_pins %>% 
    filter(land_use %in% c("Industrial", "Commercial")) |>

  group_by(year, land_use) %>%
  summarize(
    proptype_pincount = n(),
            n_incent = sum(ifelse(incent_prop == "Incentive", 1, 0)),
            n_nonincent = sum(ifelse(incent_prop == "Non-Incentive", 1, 0))) %>%
  mutate(pct_incent = n_incent / proptype_pincount) %>%
  arrange(year) %>%
  mutate(year = as.character(year)) %>%
filter(!land_use %in% c("Exempt", "Other Residential", "Owner Occupied", "Rental") ) %>% 
  select(year, land_use, n_incent, pct_incent) |>
  pivot_wider(id_cols = year, names_from = land_use, 
              values_from = c(n_incent,pct_incent)) %>% ungroup() |>
  select(-year)

cbind(commind_together, table) %>% 
  select(Year = year, 
         "C&I Incentive Parcels" = n_incent, 
         "Share of C&I" = pct_incent,
         "Commercial Pin Count" = n_incent_Commercial, 
         "Share of Commercial" = pct_incent_Commercial,
         "Industrial Pin Count" = n_incent_Industrial, 
         "Share of Industrial" = pct_incent_Commercial,
         "C&I PIN Count" = proptype_pincount) %>% 
  mutate(Year = as.character(Year)) |>
  mutate(across(contains("Share"), scales::percent, digits = 2)) |>
  flextable() %>%
  add_header_row(values = c(" ", "Incent C&I", "Commercial", "Industrial","All C&I"), colwidths = c(1,2,2,2, 1))

	Incent C&I		Commercial		Industrial		All C&I
Year	C&I Incentive Parcels	Share of C&I	Commercial Pin Count	Share of Commercial	Industrial Pin Count	Share of Industrial	C&I PIN Count
2011	2,174	2.064%	377	0.531%	1,797	0.531%	105,339
2012	2,267	2.150%	393	0.551%	1,874	0.551%	105,424
2013	2,380	2.258%	426	0.599%	1,954	0.599%	105,399
2014	2,468	2.347%	449	0.635%	2,019	0.635%	105,149
2015	2,623	2.495%	533	0.753%	2,090	0.753%	105,139
2016	2,851	2.711%	579	0.817%	2,272	0.817%	105,158
2017	3,016	2.872%	652	0.924%	2,364	0.924%	105,012
2018	3,226	3.078%	714	1.012%	2,512	1.012%	104,825
2019	3,361	3.208%	751	1.062%	2,610	1.062%	104,754
2020	3,419	3.272%	788	1.119%	2,631	1.119%	104,506
2021	3,554	3.405%	819	1.172%	2,735	1.172%	104,377
2022	4,278	4.103%	862	1.239%	3,416	1.239%	104,262

comm_ind_pins %>% 
  filter(land_use %in% c("Commercial", "Industrial")) %>%
 group_by(year, land_use) %>%
  summarize(
  PC = sum(incent_prop=="Incentive") )%>%
  ggplot() + 
  geom_col(aes(x=year, y = PC, fill = land_use)) + 
  scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year),                                      
    breaks = c(min(comm_ind_pins$year), 2010, 2015, 2020, params$year)) + 
  theme_classic() + 
  theme(legend.position =  "bottom", legend.title = element_blank(), 
                          axis.title.x =   element_blank())

commind_together_fmv <- comm_ind_pins %>%
  filter(land_use %in% c("Industrial", "Commercial")) |>
  group_by(year) %>%
  summarize(
    fmv_CI = sum(fmv, na.rm=TRUE),
    fmv_incent = sum(ifelse(incent_prop == "Incentive", fmv, 0))
          )  %>% 
  mutate(pct_incent = (fmv_incent / fmv_CI))

  

table_fmv <- comm_ind_pins %>% 
    filter(land_use %in% c("Industrial", "Commercial")) |>

  group_by(year, land_use) %>%
  summarize(
    fmv = sum(fmv, na.rm=TRUE),
    fmv_incent = sum(ifelse(incent_prop == "Incentive", fmv, 0), na.rm=TRUE)
          )  %>% 
  mutate(pct_incent = (fmv_incent / fmv) / 10000) %>%  ## should standardize percentage formatting....
  arrange(year) %>%
  select(year, land_use, fmv_incent, pct_incent) |>
  pivot_wider(id_cols = year, names_from = land_use, 
             values_from = c(fmv_incent, pct_incent)) %>% ungroup() |>
  select(-year)

cbind(commind_together_fmv, table_fmv) %>% 
  select(Year = year, "C&I Incentive FMV" = fmv_incent, "Share of C&I" = pct_incent,
                  "Commercial Incent. FMV" = fmv_incent_Commercial, "Share of Commercial" = pct_incent_Commercial,

         "Industrial Incent. FMV" = fmv_incent_Industrial, "Share of Industrial" = pct_incent_Industrial, fmv_CI) %>% 
    mutate(Year = as.character(Year)) %>%
  mutate(across(contains("Share"), scales::percent, digits = 2)) |>

  flextable() %>%
  add_header_row(values = c(" ", "Commercial & Industrial", "Commercial", "Industrial","All C&I"), colwidths = c(1,2,2,2, 1))

	Commercial & Industrial		Commercial		Industrial		All C&I
Year	C&I Incentive FMV	Share of C&I	Commercial Incent. FMV	Share of Commercial	Industrial Incent. FMV	Share of Industrial	fmv_CI
2011	4,517,119,648	5.80%	23,463,939,288,755	3.77%	28,116,292,510,285	17.97%	77,884,805,324
2012	4,731,517,657	6.19%	24,032,673,113,974	3.93%	28,709,924,212,184	18.74%	76,471,970,301
2013	4,982,856,965	6.62%	25,734,545,891,214	4.26%	29,018,119,667,260	19.54%	75,260,356,329
2014	5,252,674,092	7.01%	27,117,499,539,386	4.49%	29,370,285,923,681	20.19%	74,942,268,880
2015	5,971,685,137	7.47%	34,669,713,596,431	5.33%	31,146,767,739,560	20.90%	79,949,126,657
2016	6,195,459,765	7.61%	38,332,981,959,927	5.79%	34,487,750,832,512	22.72%	81,384,963,409
2017	6,659,059,793	8.05%	43,726,864,762,435	6.52%	36,964,969,114,216	23.64%	82,702,363,261
2018	7,880,502,820	8.60%	53,586,419,102,006	7.14%	41,559,964,771,893	25.12%	91,595,579,109
2019	9,213,592,010	9.23%	60,643,227,333,318	7.51%	49,704,379,040,070	26.10%	99,793,793,205
2020	9,455,060,085	9.83%	60,212,906,092,288	7.88%	52,003,434,592,659	26.31%	96,177,970,965
2021	10,765,549,788	10.24%	68,095,574,757,210	8.19%	60,122,900,149,280	27.35%	105,127,558,771
2022	12,608,755,833	11.26%	75,625,420,472,263	8.62%	82,874,583,156,680	34.16%	111,993,215,703

comm_ind_pins %>% 
  filter(land_use %in% c("Commercial", "Industrial")) %>%
 group_by(year, land_use) %>%
  summarize(
  fmv = sum(ifelse(incent_prop=="Incentive", fmv/1000000000, 0) ))%>%
  ggplot() + 
  geom_col(aes(x=year, y = fmv, fill = land_use)) + 
  scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year), 
    breaks = c(min(comm_ind_pins$year), 2009, 2012, 2015, 2018, params$year)) + 
  theme_classic() +
  labs(y = "FMV ($ Billions)")+
  theme(legend.position =  "bottom", legend.title = element_blank(), 
                          axis.title.x =   element_blank())

The file comm_ind_inmunis_timeseries_2006to2022.csv comm_ind_PINs_2011to2022_timeseries.csv contains all PINs that had an incentive property class for at least 1 year. It includes all observations for a property during the years that it existed, even if it is not an incentive class property in that year.

Only includes PINs in incorporated areas.

comm_ind_pins %>%
  filter(incent_prop == "Incentive") %>%
  # group_by(year, land_use) %>%
  reframe(PC=n(), .by= c(year, land_use)) %>%
  distinct() %>%
   reframe(land_use, PC,
     both_PC = sum(PC), .by=year) %>% distinct() %>% 
  arrange(year) %>%
  group_by(land_use) %>%
  mutate(ratio_2011 = PC/ PC[year==2011],
         ratio_both_2011 = both_PC/both_PC[year==2011]) %>% 
  ggplot() +
  geom_line(aes(x=year, y = ratio_2011, group = land_use, color = land_use)) + 
  geom_line(aes(x=year, y =ratio_both_2011), lty = 1) + 
  theme_bw() + 
    scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year), 
    breaks = c(min(comm_ind_pins$year), 2009,  2015, 2018, 2021, params$year))

comm_ind_pins %>%
  filter(incent_prop == "Incentive") %>%
   reframe(fmv,
           type_fmv = sum(fmv, na.rm=TRUE), .by = c(year,land_use)
          ) %>%
   reframe(land_use, type_fmv,
          fmv_incent = sum(fmv, na.rm=TRUE), .by=year) %>% distinct() %>% 
  arrange(year) %>%
  group_by(land_use) %>%
  mutate(ratio_2011 = type_fmv/ type_fmv[year==2011],
         ratio_both_2011 = fmv_incent/fmv_incent[year==2011]) %>% 
  ggplot() +
  geom_line(aes(x=year, y = ratio_2011, group = land_use, color = land_use)) + 
  geom_line(aes(x=year, y =ratio_both_2011), lty = 1) + 
    scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year), 
    breaks = c( 2009, 2012, 2015, 2018, 2021, params$year)) + 
  theme_classic()

pins_per_year <- comm_ind_pins %>% 
  group_by(year) %>%
  summarize("Gains Incent" = sum(gain_incent, na.rm=TRUE),
            "Loses Incent" = sum(lose_incent, na.rm=TRUE),
            "PINs with Incent. Class." = sum(incent_prop=="Incentive"),
            "PINs in Sample" = n(),
            "Became Exempt" = sum(became_exempt, na.rm=TRUE))

pins_per_year

Table 8.9: Number of PINs that gain or lose incentive classification each year.

df_2011_class8change <- comm_ind_pins|>
  group_by(pin) |>
      mutate(class8years = sum(class_1dig == 8)) |>

  mutate(class8_change = case_when(
      class8years == year_count ~ "Always Class 8",
      class8years == 0 ~ "Never Class 8",
      TRUE ~ "Changes to C8")
  ) |> 
  ungroup() |>
group_by(year, class8_change) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE),
  ) |> ungroup() |>
  group_by( class8_change) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1)  



# df_2011_class8change %>% 
#   filter(year == 2022) %>%
#   select(class8_change, fmv_group_growth, base_year_fmv, group_year_fmv, everything(), -year)

df_2011_class8change %>% 
  filter(year == 2023) %>%
  select(class8_change, fmv_group_growth, base_year_fmv, group_year_fmv, everything(), -year)

comm_ind_pins <- comm_ind_pins %>% 
  group_by(pin) |>
  mutate(class8years = sum(class_1dig == 8),
         class8_change = case_when(
           class8years == year_count ~ "Always Class 8",
           class8years == 0 ~ "Never Class 8",
           sum(lag(class_1dig) != 8 & class_1dig == 8) > 0 ~ "Becomes Class 8",
           sum(class_1dig != 8 & lag(class_1dig) == 8 ) > 0 ~ "Leaves Class 8",
           TRUE ~ "Other"),
         becomes8 = ifelse(lag(class_1dig) != 8 & class_1dig == 8, 1, 0),
         becomes7 = ifelse(lag(class_1dig) != 7 & class_1dig == 7, 1, 0),
         becomes6 = ifelse(lag(class_1dig) != 6 & class_1dig == 6, 1, 0),
         switches_to8 = ifelse(sum(incent_prop == "Incentive")> 1 & becomes8 ==1, 1, 0),
         # is_exempt = ifelse(class %in% c("0", "EX"), 1, 0),
         #  was_exempt = ifelse(lag(class) %in% c("0","EX"), 1, 0)
  ) %>%
        ungroup()

 classpins_per_year <- comm_ind_pins |>
   group_by(year) %>%
  summarize("Becomes 8" = sum(becomes8==1, na.rm=TRUE),
            "Total 8" = sum(class_1dig == 8),

            "6 or 7 to 8" = sum(switches_to8 == 1, na.rm=TRUE),

            "Becomes 7" = sum(becomes7 == 1, na.rm=TRUE),
            "Total 7" = sum(class_1dig == 7),

            "Becomes 6" = sum(becomes6 == 1, na.rm=TRUE),
            "Total 6" = sum(class_1dig == 6),
            "PINs with Incent. Class." = sum(incent_prop=="Incentive"),
          #  "Tax Exempt" = sum(is_exempt),
            "PINs in Sample" = n(),
            #"Became Exempt" = sum(became_exempt, na.rm=TRUE)
          )
classpins_per_year %>%  flextable::flextable()

year	Becomes 8	Total 8	6 or 7 to 8	Becomes 7	Total 7	Becomes 6	Total 6	PINs with Incent. Class.	PINs in Sample
2,011	0	608	0	0	64	0	1,502	2,174	105,339
2,012	45	641	43	1	64	93	1,562	2,267	105,424
2,013	55	668	52	9	72	131	1,640	2,380	105,399
2,014	43	702	39	11	80	120	1,686	2,468	105,149
2,015	88	762	88	66	147	157	1,714	2,623	105,139
2,016	105	864	105	27	165	187	1,822	2,851	105,158
2,017	87	924	76	23	189	152	1,903	3,016	105,012
2,018	56	951	54	52	243	209	2,032	3,226	104,825
2,019	66	979	66	24	265	153	2,117	3,361	104,754
2,020	38	983	38	24	285	119	2,151	3,419	104,506
2,021	67	1,035	67	25	301	124	2,218	3,554	104,377
2,022	673	1,703	6	22	318	116	2,257	4,278	104,262

Table 8.10: Number of PINs that became each major class type each year.

Explore PINs that became Class 8 or Other Incentive in South Triad

# comm_ind_pins %>% 
#   filter(class8_change == "Becomes Class 8") %>% 
#   left_join(bor, by = c("pin", "year" = "tax_year")) %>%
#   filter(!is.na(appellant)) %>%
#   
#   arrange(pin, desc(year)) %>%
#   select(year, appellant, project_id, class.x, everything())

# example of PIN that was class 8 and then became un-incentivized in 2017
# comm_ind_pins %>% filter(pin == "16271000330000")


comm_ind_pins %>% 
  filter(class8_change == "Becomes Class 8" & Triad == "South") %>% 
  arrange(pin) %>%
  select(year, class, everything())

Table 8.11: Number of PINs that became each major class 8 in the South triad.

comm_ind_pins %>% 
  filter(becomes8 == 1 & Triad == "South") %>% 
  arrange(pin) %>%
  select(year, class, everything())
comm_ind_pins %>% 
  filter(becomes7== 1& Triad == "South") %>% 
  arrange(pin) %>%
  select(year, class, everything())
comm_ind_pins %>% 
  filter(becomes6 == 1 & Triad == "South") %>% 
  arrange(pin) %>%
  select(year, class, everything())

Table 8.12: South Triad Number of PINs that became each major class type each year.

note to self: status is coded incorrectly. There are ‘Gains Incentive’ PINs that are also Controls if they existed less than 12 years. Became_taxed is wrong too

df_2011_incentchange <- comm_ind_pins|>
group_by(year, incent_change) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE),
  ) |> ungroup() |>
  group_by( incent_change) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1)  



df_2011_incentchange %>% 
  #filter(year == params$year) %>%
  filter(year == 2023) |>
  select(incent_change, fmv_group_growth, base_year_fmv, group_year_fmv, everything(), -year)

Table 8.13: Aggregate FMV Growth by Incentive Classification. Changes Sometime includes properties that gained or lost an incentive classification. incent_status in tables below breaks up Changes Sometime into more detailed categories.

comm_ind_pins %>%
  ungroup() %>% 
  group_by(year, incent_status) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE)) |> 
  ungroup() |>
group_by(incent_status) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1) |> 
  filter(year == params$year)  %>%
  select(incent_status, fmv_group_growth, group_pin_count, everything(), -year)

Table 8.14: Aggregate FMV Growth by incent_status. Calculations for PINs counts in header of table in report.

comm_ind_pins %>%
  group_by(year, landuse_change) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE)) |> 
  ungroup() |>
  group_by(landuse_change) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    base_year_n = group_pin_count[year == min(year)],
    n_group_growth = group_pin_count/base_year_n -1,
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1) %>%
    filter(year == params$year) %>%   
  select(landuse_change, fmv_group_growth, group_pin_count)

Table 8.15: Aggregate FMV Growth from 2011 to 2022 by landuse_change.

df_2011_bal <- comm_ind_pins %>%
  group_by(year, landuse_change, incent_status) |>
  summarize(
    group_pin_count = n(),
    group_year_fmv = sum(fmv, na.rm=TRUE)) |> 
  ungroup() |>
  group_by(landuse_change, incent_status) %>%
  mutate(
    base_year_fmv = group_year_fmv[year == min(year)],
    base_year_n = group_pin_count[year == min(year)],
    n_group_growth = group_pin_count/base_year_n -1,
    fmv_group_growth = (group_year_fmv / base_year_fmv) -1) 

df_2011_bal %>% 
  select(year, landuse_change, incent_status, fmv_group_growth, group_pin_count) %>% 
  filter(year == params$year) %>%   
  select(-year)

Table 8.16: Growth from 2011 to 2022 - Change in Land Use by Incentive Class Status. Non-winsorized version of table used in Table X of report.

df_2011_bal %>% 
  ggplot() + 
  geom_line(aes(x=year, fmv_group_growth, group = incent_status, color = incent_status)) + 
  labs( title = "FMV Growth Since 2011 by Land Use",) + 
  theme_bw()  +  
  facet_wrap(~landuse_change)

Figure 8.5: Includes the ‘Excluded’ Category

df_2011_bal %>% 
  filter(incent_status != "Excluded" & landuse_change != "Excluded" & landuse_change != "Exempt Sometime") %>%
  ggplot() + 
  geom_line(aes(x=year, fmv_group_growth, group = incent_status, color = incent_status)) + 
  labs( title = "FMV Growth Since 2011 by Land Use", 
        caption = "PINs that did not exist during all years of the sample frame were excluded from the image (n=10,809)."
        ) + 
  scale_color_brewer(direction = -1) + 
  theme_bw()  +  
    scale_x_continuous(
    limits = c(min(comm_ind_pins$year), params$year), 
    breaks = c( 2009, 2012, 2015, 2018, 2021, params$year)) + 
  facet_wrap(~landuse_change)

Figure 8.6: Excludes the ‘Excluded’ Category and ‘Exempt Sometime’ properties.

df_2011_bal %>%  
  mutate(year = as.factor(year)) %>%
  ggplot() + 
  geom_line(aes(x=year, y=fmv_group_growth, group = landuse_change, color = landuse_change)) +
  theme_bw()  +  
  facet_wrap(~incent_status, nrow = 1) +
  scale_x_discrete(breaks = c(2012, params$year)) + 
  scale_y_continuous(breaks = c(-1, 0, 1, 2, 3, 4, 10), labels = scales::percent,) +
  scale_color_brewer(direction = -1) + 

  labs(title= paste0("Growth from 2011 to ", params$year),
       subtitle = "Incentive Classification Status by Land Use Change",
       y = "FMV Growth since 2011", x = NULL,
       caption =  "Values are indexed to 2011 FMV") +
  theme(legend.title = element_blank(), legend.position = "bottom")

Figure 8.7: Growth from 2011 to 2022. Faceted by if a PIN changed landuse during the sample period. Indexed to fair market value during 2011

df_2011_bal %>%  
  filter(landuse_change != "Excluded" & incent_status != "Excluded" & landuse_change != "Exempt Sometime") %>%
  mutate(year = as.factor(year)) %>%
  ggplot() + 
  geom_line(aes(x=year, y=fmv_group_growth, group = landuse_change, color = landuse_change)) +
  theme_bw()  +  
  facet_wrap(~incent_status, nrow = 1) +
  scale_x_discrete(breaks = c(2012, params$year), ) + 
  scale_y_continuous(breaks = c(-1, 0, 1, 2, 3, 4, 10), labels = scales::percent,) +
  labs(title= "Growth from 2011", 
       subtitle = "Incentive Classification Status by Land Use Change",
       y = "FMV Growth since 2011", x = NULL,
       caption =  paste0("Values are indexed to 2011 FMV. Excludes PINs that were tax exempt some years or did not exist for all years between 2011 and ", params$year)) +
  theme(legend.title = element_blank(), legend.position = "bottom")

Figure 8.8: Aggregate FMV Growth from 2011 to 2022. Faceted by PIN incentive status during the sample period. Indexed to fair market value during 2011.

Export Tables

library(readxl)

tablelist <- list(
  "Muni Stats" = munilevel,
  "Muni Rate Change" = muni_ratechange,
  
  "Cook Sums" = table_cook,
  "Cook Class Sums" = cty_MC_table,
  "Cook Rate Change" = cook_ratechange
)




#writexl::write_xlsx(tablelist, "Output/calculations_report_content_20240927.xlsx")

readme <- c(
  "Pin Change: Count of PINs gaining or losing an incentive class each year.",
  "Major Class Change: Counts of PINs becoming major class types each year.",
  "Top 10 Commercial Incents is based on summed FMV from properties that have commercial incentive classes.",
  "Top 10 Industrial Incents is based on summed FMV from properties that have industrial incentive classes.",
  
  "Muni Shares shows the top 5, middle 5, and bottom 5 munis based on the share of FMV that has an incentive property out of all FMV in the municipality. Note: Municipalities without commercial or industrial property are excluded from this list.",
  "Commerc_Share shows the top 5, middle 5, and bottom 5 municipalities based on share of commercial incentive FMV out of the commercial FMV within the municipality.Note: There are at least 50 municipalities that do not have commercial incentive properties. The bottom 5 displayed are alphabetically last, but there are 50 tied at 0%.",
            "Indust_Share shows the top 5, middle 5, and bottom 5 municipalities based on the share of industrial incentive FMV out of the industrial FMV within the municipality. Note: There are at least 38 municipalities that do not have industrial incentive properties. The bottom 5 displayed are alphabetically last, but there are 38 tied at 0%.",
  
  "Muni Share_all includes all municipalities and their share of FMV with incentives out of all FMV in the municipality.",
  "IndustShare_all is same as Indust_share but includes all munis.",
  "CommercShare_all is same as Commerc_share but includes all munis.") %>% as.data.frame()

paper_tables <- list(
  "README"= readme,
  
  "Pin Change per Year" = pins_per_year,
  "Top 10 Commercial Incents" = commerc_top10,
  "Top 10 Indust Incents" = indust_top10,
  
  "Muni Shares" = muni_incentshare_slice,
  "Indust_Share" = indust_share,
  "Commerc_Share" = commerc_share,

  "Muni Share_all" = muni_incent_share,
  "IndustShare_all" = indust_share_full,
  "Commerc_Share_all" = commerc_share_full,
  
  "Rate Change" = muni_ratechange_sliced
  
)

# writexl::write_xlsx(paper_tables, "../Output/incentive_report_tables.xlsx")