Introduction

Diff-in-Diff models

For our analysis of the effectiveness of the New Markets Tax Credit (NMTC) and Low Income Housing Tax Credit (LIHTC) as a tool for community revitalization and the reduction of social vulnerability in neighborhoods across the Middle Atlantic Division, we will employ a Diff-In-Diff model of linear regression.

Diff-In-Diff models are a statistical technique that allows us to analyze the differences in differences of changes across time periods to determine whether our program intervention resulted in an additional increase or decrease beyond what we would expect following general trends in similar controls.

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Dependent Variables: SVI Variables, House Price Index, Median Home Values, and Median Income

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Independent Variables: NMTC and LIHTC Data

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Library

# Load packages
library(here)         # relative filepaths for reproducibility
library(rio)          # read excel file from URL
library(tidyverse)    # data wrangling
library(stringi)      # string data wrangling
library(tidycensus)   # US census data
library(ggplot2)      # data visualization
library(kableExtra)   # table formatting
library(scales)       # palette and number formatting
library(unhcrthemes)  # data visualization themes
library(ggrepel)      # data visualization formatting to avoid overlapping
library(rcompanion)   # data visualization of variable distribution
library(ggpubr)       # data visualization of variable distribution
library(moments)      # measures of skewness and kurtosis
library(tinytable)    # format regression tables
library(modelsummary) # format regression tables

Load Functions

import::here( "fips_census_regions",
              "load_svi_data",
              "merge_svi_data",
              "census_division",
              "slopegraph_plot",
              "census_pull",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_courtney.R"),
             .character_only = TRUE)

# Load API key, assign to TidyCensus Package
source(here::here("analysis/password.R"))
census_api_key(.census_api_key)

## To install your API key for use in future sessions, run this function with `install = TRUE`.

Data

# Load NMTC AND LIHTC data sets

svi_divisional_nmtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_nmtc.rds")))

svi_national_nmtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_nmtc.rds")))

svi_divisional_lihtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_lihtc.rds")))

svi_national_lihtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_lihtc.rds")))

House Price Index Data

hpi_df <- read.csv("https://www.fhfa.gov/DataTools/Downloads/Documents/HPI/HPI_AT_BDL_tract.csv")

hpi_df_10_20 <- hpi_df %>% 
  mutate(GEOID10 = str_pad(tract, 11, "left", pad=0)) %>% 
  filter(year %in% c(2010, 2020))  %>%
 select(GEOID10, state_abbr, year, hpi) %>%
  pivot_wider(names_from = year, values_from = hpi) %>%
  mutate(housing_price_index10 = `2010`,
         housing_price_index20 = `2020`) %>%
  select(GEOID10, state_abbr, housing_price_index10, housing_price_index20)

# View data
hpi_df_10_20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	state_abbr	housing_price_index10	housing_price_index20
01001020100	AL	132.35	152.78
01001020200	AL	123.78	123.37
01001020300	AL	158.57	167.01
01001020400	AL	165.11	179.60
01001020501	AL	172.55	180.96
01001020502	AL	158.75	164.25

# Drop state_abbr column for joining
hpi_df_10_20 <- hpi_df_10_20 %>% select(-state_abbr)

CBSA Crosswalk Data

msa_csa_crosswalk <- rio::import("https://r-class.github.io/paf-515-course-materials/data/raw/CSA_MSA_Crosswalk/qcew-county-msa-csa-crosswalk.xlsx", which=4)

msa_csa_crosswalk <- msa_csa_crosswalk %>% 
  mutate(county_fips = str_pad(`County Code`, 5, "left", pad=0),
         cbsa = coalesce(`CSA Title`, `MSA Title`),
         cbsa_code = coalesce(`CSA Code`, `MSA Code`),
         county_title = `County Title`)  %>% 
  select(county_fips, county_title, cbsa, cbsa_code)

msa_csa_crosswalk %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_fips	county_title	cbsa	cbsa_code
01001	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01003	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01005	Barbour County, Alabama	Eufaula, AL-GA MicroSA	C2164
01007	Bibb County, Alabama	Birmingham-Hoover-Cullman, AL CSA	CS142
01009	Blount County, Alabama	Birmingham-Hoover-Cullman, AL CSA	CS142
01015	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150

Census Data

states <- list(svi_national_nmtc$state %>% unique())
states

## [[1]]
##  [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "DC" "FL" "GA" "HI" "ID" "IL" "IN"
## [16] "IA" "KS" "KY" "LA" "ME" "MD" "MA" "MI" "MN" "MS" "MO" "MT" "NE" "NV" "NH"
## [31] "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT"
## [46] "VT" "VA" "WA" "WV" "WI" "WY"

census_pull10 <- lapply(states, census_pull, yr = 2010)

census_pull10_df <- census_pull10[[1]] %>%  
  # Drop margin of error column
  select(-moe) %>%
  # Add suffix to variable names
  mutate(variable = paste0(variable, "_10")) %>%
  # Pivot data frame
  pivot_wider(
    names_from = variable,
    values_from = c(estimate)
  )

census_pull10_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_10	Median_Home_Value_10
01001020100	Census Tract 201, Autauga County, Alabama	31769	120700
01001020200	Census Tract 202, Autauga County, Alabama	19437	138500
01001020300	Census Tract 203, Autauga County, Alabama	24146	111300
01001020400	Census Tract 204, Autauga County, Alabama	27735	126300
01001020500	Census Tract 205, Autauga County, Alabama	35517	173000
01001020600	Census Tract 206, Autauga County, Alabama	24597	110700
01001020700	Census Tract 207, Autauga County, Alabama	22114	93800
01001020801	Census Tract 208.01, Autauga County, Alabama	30841	258000
01001020802	Census Tract 208.02, Autauga County, Alabama	29006	145100
01001020900	Census Tract 209, Autauga County, Alabama	24841	108000

census_pull19 <- lapply(states, census_pull, yr = 2019)

census_pull19_df <- census_pull19[[1]] %>% 
  # Select columns
  select(GEOID, NAME, variable, estimate, moe) %>% 
  # Create individual FIPS columns for state, county, and tract
  mutate(FIPS_st = substr(GEOID, 1, 2),
         FIPS_county = substr(GEOID, 3, 5),
         FIPS_tract = substr(GEOID, 6, 11)) %>%
# Los Angeles, CA Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "037" & FIPS_st == "06" & FIPS_tract == "137000"), "930401", FIPS_tract )) %>%
# Pima County, AZ Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "002704"), "002701", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "002906"), "002903", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004118"), "410501", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004121"), "410502", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004125"), "410503", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "005200"), "470400", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "005300"), "470500", FIPS_tract2 )) %>%
# Madison County, NY Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030101"), "940101", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030102"), "940102", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030103"), "940103", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030200"), "940200", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030300"), "940300", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030401"), "940401", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030403"), "940403", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030600"), "940600", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030402"), "940700", FIPS_tract2 )) %>%
# Oneida County, NY Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024800"), "940000", FIPS_tract2 )) %>% 
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024700"), "940100", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024900"), "940200", FIPS_tract2 )) %>%  
                      # Move columns in data set
                      relocate(c(FIPS_st, FIPS_county, FIPS_tract, FIPS_tract2),.after = GEOID) %>%
                      # Create new GEOID column
                      mutate(GEOID = paste0(FIPS_st, FIPS_county, FIPS_tract2)) %>% 
                      # Drop newly created FIPS columns and margin of error
                      select(-FIPS_st, -FIPS_county, -FIPS_tract, -FIPS_tract2, -moe) %>% 
                      # Add suffix
                      mutate(variable = paste0(variable, "_19")) %>%
                      # Pivot data set
                      pivot_wider(
                        names_from = variable,
                        values_from = c(estimate)
                      ) 

census_pull19_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_19	Median_Home_Value_19
01001020100	Census Tract 201, Autauga County, Alabama	25970	136100
01001020200	Census Tract 202, Autauga County, Alabama	20154	90500
01001020300	Census Tract 203, Autauga County, Alabama	27383	122600
01001020400	Census Tract 204, Autauga County, Alabama	34620	152700
01001020500	Census Tract 205, Autauga County, Alabama	41178	186900
01001020600	Census Tract 206, Autauga County, Alabama	21146	103600
01001020700	Census Tract 207, Autauga County, Alabama	20934	82400
01001020801	Census Tract 208.01, Autauga County, Alabama	31667	322900
01001020802	Census Tract 208.02, Autauga County, Alabama	33086	171500
01001020900	Census Tract 209, Autauga County, Alabama	32677	156900

inflation_adj = 1.16

# Join 2010 and 2019 Median Income and Home Value Data
census_pull_df <- left_join(census_pull10_df, census_pull19_df[c("GEOID", "Median_Income_19", "Median_Home_Value_19")], join_by("GEOID" == "GEOID"))

# Create new inflation adjusted columns for 2010 median income and median home value, find changes over time
census_pull_df <- census_pull_df %>% 
                   mutate(Median_Income_10adj = Median_Income_10*inflation_adj,
                          Median_Home_Value_10adj = Median_Home_Value_10*inflation_adj,
                          Median_Income_Change = Median_Income_19 - Median_Income_10adj,
                          Median_Income_Change_pct = (Median_Income_19 - Median_Income_10adj)/Median_Income_10adj,
                          Median_Home_Value_Change = Median_Home_Value_19 - Median_Home_Value_10adj,
                          Median_Home_Value_Change_pct = (Median_Home_Value_19 - Median_Home_Value_10adj)/Median_Home_Value_10adj)

# View data
census_pull_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct
01001020100	Census Tract 201, Autauga County, Alabama	31769	120700	25970	136100	36852.04	140012	-10882.04	-0.2952900	-3912	-0.0279405
01001020200	Census Tract 202, Autauga County, Alabama	19437	138500	20154	90500	22546.92	160660	-2392.92	-0.1061307	-70160	-0.4366986
01001020300	Census Tract 203, Autauga County, Alabama	24146	111300	27383	122600	28009.36	129108	-626.36	-0.0223625	-6508	-0.0504074
01001020400	Census Tract 204, Autauga County, Alabama	27735	126300	34620	152700	32172.60	146508	2447.40	0.0760709	6192	0.0422639
01001020500	Census Tract 205, Autauga County, Alabama	35517	173000	41178	186900	41199.72	200680	-21.72	-0.0005272	-13780	-0.0686665
01001020600	Census Tract 206, Autauga County, Alabama	24597	110700	21146	103600	28532.52	128412	-7386.52	-0.2588807	-24812	-0.1932218
01001020700	Census Tract 207, Autauga County, Alabama	22114	93800	20934	82400	25652.24	108808	-4718.24	-0.1839309	-26408	-0.2427027
01001020801	Census Tract 208.01, Autauga County, Alabama	30841	258000	31667	322900	35775.56	299280	-4108.56	-0.1148426	23620	0.0789227
01001020802	Census Tract 208.02, Autauga County, Alabama	29006	145100	33086	171500	33646.96	168316	-560.96	-0.0166719	3184	0.0189168
01001020900	Census Tract 209, Autauga County, Alabama	24841	108000	32677	156900	28815.56	125280	3861.44	0.1340054	31620	0.2523946

NMTC Data

svi_divisional_nmtc_df0 <- left_join(svi_divisional_nmtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_divisional_nmtc_df1 <- left_join(svi_divisional_nmtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_divisional_nmtc_df <- left_join(svi_divisional_nmtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_divisional_nmtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	ET_HHTYPE_10	EPL_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_dollars_formatted	post10_nmtc_dollars_formatted	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
34001000100	34001	000100	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2907	1088	983	1127	2907	38.76849	0.8482	1	144	1433	10.048849	0.7544	1	280	435	64.36782	0.9529	1	204	548	37.22628	0.2998	0	484	983	49.23703	0.7813	1	468	1759	26.60603	0.8634	1	532	2543	20.92017	0.8978	1	250	8.599931	0.17770	0	944	32.47334	0.94170	1	186	1851	10.04862	0.2706	0	266	678	39.233038	0.8981	1	177	2611	6.779012	0.7778	1	1928	2907	66.32267	0.7743	1	1088	113	10.386029	0.6229	0	9	0.8272059	0.7223	80	983	8.138352	0.8657	1	265	983	26.95829	0.7354	0	2907	0.3512	4.1451	0.8935	5	3.06590	0.7944	3	0.7743	0.7667	1	3.2975	0.8414	1	11.28280	0.8862	10	2157	941	784	1182	2157	54.79833	0.9571	1	242	1058	22.873346	0.9922	1	215	342	62.86550	0.9780	1	316	442	71.49321	0.9481	1	531	784	67.72959	0.9893	1	396	1274	31.083202	0.9497	1	266	2157	12.331943	0.9041	1	185	8.576727	0.09430	0	552	25.59110	0.8128	1	297	1605	18.504673	0.74880	0	83	510	16.27451	0.6090	0	251	2020	12.425743	0.87100	1	1852	2157	85.85999	0.8476	1	941	118	12.5398512	0.6385	0	0	0.00000	0.3216	0	67	784	8.545918	0.8657	1	212	784	27.04082	0.7502	1	0	2157	0.0000000	0.1517	4.7924	0.9850	5	3.13590	0.8217	2	0.8476	0.8400	1	2.7277	0.6085	2	11.50360	0.9104	10	Yes	$0	$0	Census Tract 1, Atlantic County, New Jersey	23841	227800	19425	155600	27655.56	264248	-8230.56	-0.2976096	-108648	-0.4111592	139.73	111.11	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001000200	34001	000200	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3189	2217	1473	519	3189	16.27469	0.4806	0	109	1558	6.996149	0.5179	0	573	955	60.00000	0.9323	1	199	518	38.41699	0.3261	0	772	1473	52.41005	0.8418	1	405	2579	15.70376	0.6491	0	484	3547	13.64533	0.7154	0	847	26.560050	0.96290	1	436	13.67200	0.08181	0	608	3005	20.23295	0.8466	1	42	857	4.900817	0.1204	0	422	3072	13.736979	0.8799	1	1792	3189	56.19316	0.7390	0	2217	901	40.640505	0.8693	1	0	0.0000000	0.3251	48	1473	3.258656	0.7064	0	250	1473	16.97217	0.6444	0	3189	0.3512	3.2048	0.6963	1	2.89161	0.7231	3	0.7390	0.7317	0	2.8964	0.6887	1	9.73181	0.7340	5	3510	2046	1353	1021	3510	29.08832	0.7682	1	121	1852	6.533477	0.6717	0	343	696	49.28161	0.9273	1	416	657	63.31811	0.8696	1	759	1353	56.09756	0.9321	1	553	2338	23.652695	0.8871	1	354	3510	10.085470	0.8530	1	643	18.319088	0.60310	0	1002	28.54701	0.9055	1	450	2508	17.942584	0.72330	0	237	786	30.15267	0.8539	1	534	3375	15.822222	0.90620	1	2534	3510	72.19373	0.7818	1	2046	906	44.2815249	0.8690	1	0	0.00000	0.3216	0	119	1353	8.795270	0.8711	1	324	1353	23.94678	0.7255	0	0	3510	0.0000000	0.1517	4.1121	0.9003	4	3.99200	0.9781	3	0.7818	0.7747	1	2.9389	0.7011	2	11.82480	0.9310	10	Yes	$0	$0	Census Tract 2, Atlantic County, New Jersey	26736	320400	25729	187900	31013.76	371664	-5284.76	-0.1704005	-183764	-0.4944358	184.06	165.71	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001000300	34001	000300	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3997	1823	1357	1401	3968	35.30746	0.8164	1	382	2238	17.068811	0.9376	1	176	329	53.49544	0.8855	1	604	1028	58.75486	0.7947	1	780	1357	57.47973	0.9165	1	920	2677	34.36683	0.9346	1	1351	4149	32.56206	0.9811	1	314	7.855892	0.14370	0	937	23.44258	0.55900	0	319	3054	10.44532	0.3000	0	187	782	23.913044	0.7498	0	1080	3671	29.419777	0.9742	1	3357	3997	83.98799	0.8419	1	1823	363	19.912233	0.7535	1	0	0.0000000	0.3251	150	1357	11.053795	0.9136	1	651	1357	47.97347	0.8585	1	3997	0.3512	4.5862	0.9691	5	2.72670	0.6360	1	0.8419	0.8336	1	3.2019	0.8054	3	11.35670	0.8920	10	3801	1640	1226	1857	3801	48.85556	0.9333	1	226	1800	12.555556	0.9267	1	111	280	39.64286	0.8339	1	608	946	64.27061	0.8842	1	719	1226	58.64600	0.9528	1	650	2275	28.571429	0.9337	1	1027	3801	27.019206	0.9914	1	380	9.997369	0.14040	0	1223	32.17574	0.9607	1	219	2578	8.494957	0.15680	0	268	909	29.48295	0.8456	1	940	3400	27.647059	0.97280	1	3318	3801	87.29282	0.8579	1	1640	262	15.9756098	0.6917	0	0	0.00000	0.3216	0	124	1226	10.114192	0.8955	1	477	1226	38.90701	0.8258	1	0	3801	0.0000000	0.1517	4.7379	0.9829	5	3.07630	0.8013	3	0.8579	0.8501	1	2.8863	0.6781	2	11.55840	0.9150	11	Yes	$0	$0	Census Tract 3, Atlantic County, New Jersey	21045	250500	19420	147300	24412.20	290580	-4992.20	-0.2044961	-143280	-0.4930828	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001000500	34001	000500	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3483	1241	1027	1938	3483	55.64169	0.9533	1	124	1630	7.607362	0.5830	0	227	446	50.89686	0.8549	1	478	581	82.27194	0.9799	1	705	1027	68.64654	0.9863	1	733	2077	35.29129	0.9396	1	727	3258	22.31430	0.9149	1	377	10.824002	0.30810	0	1055	30.28998	0.90140	1	268	2401	11.16202	0.3549	0	209	763	27.391874	0.7940	1	911	3077	29.606760	0.9746	1	3036	3483	87.16624	0.8550	1	1241	52	4.190169	0.4505	0	4	0.3223207	0.6567	113	1027	11.002921	0.9128	1	422	1027	41.09056	0.8250	1	3483	0.3512	4.3771	0.9379	4	3.33300	0.8766	3	0.8550	0.8467	1	3.1962	0.8026	2	11.76130	0.9229	10	3385	1185	945	1682	3364	50.00000	0.9391	1	72	1577	4.565631	0.4586	0	185	468	39.52991	0.8332	1	362	477	75.89099	0.9703	1	547	945	57.88360	0.9477	1	592	1983	29.853757	0.9422	1	738	3385	21.802068	0.9817	1	240	7.090103	0.05988	0	1129	33.35303	0.9689	1	135	2256	5.984043	0.04817	0	110	717	15.34170	0.5822	0	721	3076	23.439532	0.95690	1	3029	3385	89.48301	0.8727	1	1185	9	0.7594937	0.2382	0	0	0.00000	0.3216	0	103	945	10.899471	0.9072	1	263	945	27.83069	0.7560	1	0	3385	0.0000000	0.1517	4.2693	0.9283	4	2.61605	0.5709	2	0.8727	0.8648	1	2.3747	0.4357	2	10.13275	0.7921	9	Yes	$0	$0	Census Tract 5, Atlantic County, New Jersey	17287	197000	21700	131800	20052.92	228520	1647.08	0.0821367	-96720	-0.4232452	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001001100	34001	001100	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2204	1204	1204	1185	2204	53.76588	0.9457	1	219	927	23.624596	0.9830	1	97	172	56.39535	0.9094	1	462	1032	44.76744	0.4746	0	559	1204	46.42857	0.7197	0	346	1440	24.02778	0.8306	1	469	1942	24.15036	0.9360	1	363	16.470054	0.70200	0	578	26.22505	0.74410	0	442	1558	28.36970	0.9675	1	247	396	62.373737	0.9898	1	104	2051	5.070697	0.7260	0	2118	2204	96.09800	0.9204	1	1204	570	47.342193	0.8858	1	0	0.0000000	0.3251	14	1204	1.162791	0.4877	0	817	1204	67.85714	0.9413	1	2204	0.3512	4.4150	0.9451	4	4.12940	0.9805	2	0.9204	0.9114	1	2.9911	0.7243	2	12.45590	0.9597	9	1950	1267	1096	1131	1950	58.00000	0.9678	1	66	706	9.348442	0.8395	1	42	101	41.58416	0.8612	1	309	995	31.05528	0.1959	0	351	1096	32.02555	0.4782	0	510	1379	36.983321	0.9763	1	155	1950	7.948718	0.7660	1	392	20.102564	0.69880	0	447	22.92308	0.6712	0	570	1503	37.924152	0.99200	1	143	374	38.23529	0.9167	1	109	1841	5.920695	0.74640	0	1909	1950	97.89744	0.9529	1	1267	479	37.8058406	0.8464	1	0	0.00000	0.3216	0	33	1096	3.010949	0.6446	0	743	1096	67.79197	0.9414	1	0	1950	0.0000000	0.1517	4.0278	0.8848	4	4.02510	0.9798	2	0.9529	0.9442	1	2.9057	0.6869	2	11.91150	0.9365	9	Yes	$0	$0	Census Tract 11, Atlantic County, New Jersey	16008	238500	15402	162500	18569.28	276660	-3167.28	-0.1705656	-114160	-0.4126364	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001001300	34001	001300	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2153	1026	857	695	2153	32.28054	0.7840	1	266	1112	23.920863	0.9840	1	168	432	38.88889	0.6425	0	271	425	63.76471	0.8714	1	439	857	51.22520	0.8184	1	210	1471	14.27600	0.5989	0	463	2215	20.90293	0.8974	1	334	15.513237	0.64360	0	539	25.03484	0.66990	0	222	1687	13.15945	0.4984	0	265	608	43.585526	0.9249	1	92	1905	4.829396	0.7187	0	1993	2153	92.56851	0.8890	1	1026	147	14.327485	0.6885	0	0	0.0000000	0.3251	20	857	2.333722	0.6326	0	142	857	16.56943	0.6382	0	2153	0.3512	4.0827	0.8798	4	3.45550	0.9039	1	0.8890	0.8804	1	2.6356	0.5687	0	11.06280	0.8673	6	1632	917	770	591	1632	36.21324	0.8455	1	203	854	23.770492	0.9928	1	164	291	56.35739	0.9604	1	364	479	75.99165	0.9710	1	528	770	68.57143	0.9914	1	150	1056	14.204546	0.7232	0	97	1632	5.943627	0.6352	0	301	18.443628	0.60940	0	271	16.60539	0.2292	0	314	1361	23.071271	0.88960	1	148	410	36.09756	0.9025	1	0	1585	0.000000	0.06953	0	1512	1632	92.64706	0.8931	1	917	246	26.8266085	0.7912	1	16	1.74482	0.7855	1	0	770	0.000000	0.1194	0	219	770	28.44156	0.7606	1	14	1632	0.8578431	0.6722	4.1881	0.9131	3	2.70023	0.6211	2	0.8931	0.8850	1	3.1289	0.7740	3	10.91033	0.8638	9	Yes	$0	$0	Census Tract 13, Atlantic County, New Jersey	32444	233700	23773	162300	37635.04	271092	-13862.04	-0.3683280	-108792	-0.4013103	66.52	44.91	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001001400	34001	001400	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3736	1893	1503	2135	3736	57.14668	0.9592	1	411	1635	25.137615	0.9866	1	267	401	66.58354	0.9617	1	715	1102	64.88203	0.8844	1	982	1503	65.33599	0.9764	1	511	1696	30.12972	0.9005	1	527	4199	12.55061	0.6728	0	269	7.200214	0.11510	0	1598	42.77302	0.99350	1	345	2252	15.31972	0.6309	0	787	941	83.634432	0.9990	1	63	3192	1.973684	0.5448	0	3463	3736	92.69272	0.8905	1	1893	427	22.556788	0.7770	1	0	0.0000000	0.3251	22	1503	1.463739	0.5339	0	705	1503	46.90619	0.8533	1	3736	0.3512	4.4955	0.9585	4	3.28330	0.8640	2	0.8905	0.8818	1	2.8405	0.6671	2	11.50980	0.9048	9	3812	1724	1549	2291	3754	61.02824	0.9760	1	380	1547	24.563672	0.9934	1	117	240	48.75000	0.9237	1	753	1309	57.52483	0.7816	1	870	1549	56.16527	0.9326	1	472	1913	24.673288	0.8987	1	294	3802	7.732772	0.7558	1	363	9.522560	0.12310	0	1463	38.37880	0.9885	1	508	2339	21.718683	0.85640	1	564	948	59.49367	0.9910	1	201	3159	6.362773	0.76130	1	3389	3812	88.90346	0.8683	1	1724	571	33.1206497	0.8294	1	0	0.00000	0.3216	0	83	1549	5.358296	0.7754	1	661	1549	42.67269	0.8448	1	10	3812	0.2623295	0.4739	4.5565	0.9673	5	3.72030	0.9522	4	0.8683	0.8605	1	3.2451	0.8159	3	12.39020	0.9595	13	Yes	$0	$0	Census Tract 14, Atlantic County, New Jersey	16780	255800	17487	139100	19464.80	296728	-1977.80	-0.1016091	-157628	-0.5312205	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001001500	34001	001500	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	1074	901	752	656	1074	61.08007	0.9700	1	43	270	15.925926	0.9242	1	30	70	42.85714	0.7276	0	366	682	53.66569	0.6910	0	396	752	52.65957	0.8458	1	266	921	28.88165	0.8886	1	121	1064	11.37218	0.6131	0	385	35.847300	0.99020	1	129	12.01117	0.06170	0	321	993	32.32628	0.9846	1	62	195	31.794872	0.8408	1	125	1050	11.904762	0.8562	1	965	1074	89.85102	0.8717	1	901	636	70.588235	0.9304	1	0	0.0000000	0.3251	10	752	1.329787	0.5133	0	626	752	83.24468	0.9888	1	1074	0.3512	4.2417	0.9134	4	3.73350	0.9515	4	0.8717	0.8632	1	3.1088	0.7709	2	11.95570	0.9362	11	1601	976	810	1001	1601	62.52342	0.9797	1	204	563	36.234458	0.9989	1	74	110	67.27273	0.9848	1	224	700	32.00000	0.2097	0	298	810	36.79012	0.6089	0	379	1145	33.100437	0.9610	1	272	1601	16.989382	0.9572	1	451	28.169894	0.93630	1	251	15.67770	0.1835	0	411	1350	30.444444	0.97330	1	196	446	43.94619	0.9511	1	220	1532	14.360313	0.89290	1	1435	1601	89.63148	0.8738	1	976	451	46.2090164	0.8742	1	0	0.00000	0.3216	0	24	810	2.962963	0.6412	0	546	810	67.40741	0.9401	1	15	1601	0.9369144	0.6832	4.5057	0.9617	4	3.93710	0.9752	4	0.8738	0.8659	1	3.4603	0.8774	2	12.77690	0.9705	11	Yes	$0	$0	Census Tract 15, Atlantic County, New Jersey	11549	172400	12339	NA	13396.84	199984	-1057.84	-0.0789619	NA	NA	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001001900	34001	001900	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	1656	1159	903	524	1656	31.64251	0.7755	1	72	879	8.191126	0.6337	0	41	50	82.00000	0.9900	1	407	853	47.71395	0.5494	0	448	903	49.61240	0.7888	1	210	1318	15.93323	0.6573	0	409	1748	23.39817	0.9270	1	134	8.091787	0.15270	0	262	15.82126	0.12460	0	268	1303	20.56792	0.8570	1	87	338	25.739645	0.7731	1	93	1617	5.751392	0.7497	0	1416	1656	85.50725	0.8482	1	1159	897	77.394305	0.9416	1	0	0.0000000	0.3251	91	903	10.077519	0.9007	1	516	903	57.14286	0.9002	1	1656	0.3512	3.7823	0.8187	3	2.65710	0.5936	2	0.8482	0.8400	1	3.4188	0.8829	3	10.70640	0.8361	9	1343	1022	751	694	1343	51.67535	0.9465	1	136	665	20.451128	0.9868	1	54	65	83.07692	0.9939	1	455	686	66.32653	0.9072	1	509	751	67.77630	0.9894	1	68	926	7.343413	0.4249	0	270	1343	20.104244	0.9746	1	161	11.988086	0.22700	0	281	20.92331	0.5297	0	212	1062	19.962335	0.80630	1	69	206	33.49515	0.8825	1	104	1225	8.489796	0.81120	1	1038	1343	77.28965	0.8047	1	1022	802	78.4735812	0.9398	1	0	0.00000	0.3216	0	34	751	4.527297	0.7388	0	376	751	50.06658	0.8789	1	0	1343	0.0000000	0.1517	4.3222	0.9357	4	3.25670	0.8567	3	0.8047	0.7974	1	3.0308	0.7381	2	11.41440	0.9049	10	Yes	$0	$0	Census Tract 19, Atlantic County, New Jersey	20965	223700	18803	254400	24319.40	259492	-5516.40	-0.2268313	-5092	-0.0196230	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001002300	34001	002300	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2251	1108	703	1183	2251	52.55442	0.9403	1	137	842	16.270784	0.9292	1	119	230	51.73913	0.8660	1	213	473	45.03171	0.4822	0	332	703	47.22617	0.7383	0	370	1322	27.98790	0.8776	1	811	2471	32.82072	0.9821	1	153	6.796979	0.09922	0	738	32.78543	0.94640	1	191	1646	11.60389	0.3890	0	189	519	36.416185	0.8771	1	480	1860	25.806452	0.9639	1	1961	2251	87.11684	0.8548	1	1108	72	6.498195	0.5264	0	0	0.0000000	0.3251	43	703	6.116643	0.8192	1	338	703	48.07966	0.8595	1	2251	0.3512	4.4675	0.9532	4	3.27562	0.8621	3	0.8548	0.8465	1	2.8814	0.6831	2	11.47932	0.9018	10	2410	1143	789	1222	2409	50.72644	0.9419	1	118	1396	8.452722	0.7982	1	103	219	47.03196	0.9109	1	428	570	75.08772	0.9667	1	531	789	67.30038	0.9884	1	729	1582	46.080910	0.9943	1	754	2410	31.286307	0.9952	1	84	3.485477	0.01502	0	592	24.56432	0.7651	1	258	1818	14.191419	0.52410	0	273	537	50.83799	0.9753	1	826	2255	36.629712	0.99160	1	2193	2410	90.99585	0.8826	1	1143	71	6.2117235	0.4953	0	0	0.00000	0.3216	0	202	789	25.602028	0.9894	1	362	789	45.88086	0.8609	1	0	2410	0.0000000	0.1517	4.7180	0.9818	5	3.27112	0.8607	3	0.8826	0.8746	1	2.8189	0.6488	2	11.69062	0.9229	11	Yes	$0	$0	Census Tract 23, Atlantic County, New Jersey	18704	223300	18470	116700	21696.64	259028	-3226.64	-0.1487161	-142328	-0.5494696	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210

svi_national_nmtc_df0 <- left_join(svi_national_nmtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_national_nmtc_df1 <- left_join(svi_national_nmtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_national_nmtc_df <- left_join(svi_national_nmtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_national_nmtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_dollars_formatted	post10_nmtc_project_cnt	post10_nmtc_dollars	post10_nmtc_dollars_formatted	nmtc_flag	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
01001020200	01001	020200	AL	Alabama	Autauga County	1	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.57540	0	49	439	11.16173	0.02067	105	291	36.08247	0.30190	0	154	730	21.09589	0.09312	339	1265	26.798419	0.8392	1	313	2012	15.55666	0.6000	0	204	10.09901	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.8351	1	15	1890	0.7936508	0.40130	0	1243	2020	61.534653	0.77810	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.780822	0.5406	0	115	730	15.7534247	0.83820	1	0	2020	0.0000	0.3640	0	2.70312	0.5665	1	3.27660	0.8614	3	0.77810	0.7709	1	2.53160	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.413633	0.6427	0	29	717	4.044630	0.41320	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.40410	0	139	1313	10.586443	0.5601	0	91	1533	5.936073	0.4343	0	284	16.163916	0.5169	0	325	18.49744	0.28510	0	164	1208.000	13.576159	0.4127	0	42	359.0000	11.6991643	0.39980	0	0	1651	0.0000000	0.09479	1116	1757.000	63.5173591	0.759100	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.46880	0	57	573.000	9.947644	0.7317	212	1757	12.0660216	0.9549	1	2.45440	0.4888	0	1.70929	0.10250	0	0.759100	0.752700	1	2.91300	0.6862	1	7.835790	0.4802	2	Yes	$0	0	0	$0	0	Census Tract 202, Autauga County, Alabama	19437	138500	20154	90500	22546.92	160660	-2392.92	-0.1061307	-70160	-0.4366986	123.78	123.37	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01001020700	01001	020700	AL	Alabama	Autauga County	1	South Region	6	East South Central Division	2664	1254	1139	710	2664	26.65165	0.6328	0	29	1310	2.213741	0.05255	0	134	710	18.87324	0.13890	187	429	43.58974	0.47090	0	321	1139	28.18262	0.28130	396	1852	21.382289	0.7478	0	345	2878	11.98749	0.4459	0	389	14.60210	0.6417	0	599	22.48499	0.4007	0	510	2168	23.52399	0.8752	1	228	712	32.022472	0.8712	1	0	2480	0.0000000	0.09298	0	694	2664	26.051051	0.51380	0	1254	8	0.6379585	0.2931	0	460	36.6826156	0.9714	1	0	1139	0.000000	0.1238	0	125	1139	10.9745391	0.74770	0	0	2664	0.0000	0.3640	0	2.16035	0.4069	0	2.88178	0.6997	2	0.51380	0.5090	0	2.50000	0.4882	1	8.05593	0.5185	3	3562	1313	1248	1370	3528	38.832200	0.8512	1	128	1562	8.194622	0.79350	1	168	844	19.905213	0.44510	0	237	404	58.66337	0.8359	1	405	1248	32.45192	0.60420	0	396	2211	17.910448	0.7857	1	444	3547	12.517620	0.7758	1	355	9.966311	0.1800	0	954	26.78271	0.79230	1	629	2593.000	24.257617	0.8730	1	171	797.0000	21.4554580	0.71860	0	0	3211	0.0000000	0.09479	1009	3562.000	28.3267827	0.466800	0	1313	14	1.0662605	0.3165	0	443	33.7395278	0.9663	1	73	1248	5.8493590	0.82110	1	17	1248.000	1.362180	0.1554	112	3562	3.1443010	0.8514	1	3.81040	0.8569	4	2.65869	0.58470	2	0.466800	0.462900	0	3.11070	0.7714	3	10.046590	0.7851	9	Yes	$0	0	0	$0	0	Census Tract 207, Autauga County, Alabama	22114	93800	20934	82400	25652.24	108808	-4718.24	-0.1839309	-26408	-0.2427027	95.94	108.47	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01001021100	01001	021100	AL	Alabama	Autauga County	1	South Region	6	East South Central Division	3298	1502	1323	860	3298	26.07641	0.6211	0	297	1605	18.504673	0.94340	1	250	1016	24.60630	0.32070	74	307	24.10423	0.11920	0	324	1323	24.48980	0.17380	710	2231	31.824294	0.8976	1	654	3565	18.34502	0.7018	0	411	12.46210	0.5001	0	738	22.37720	0.3934	0	936	2861	32.71583	0.9807	1	138	825	16.727273	0.5715	0	9	3155	0.2852615	0.25010	0	1979	3298	60.006064	0.77030	1	1502	14	0.9320905	0.3234	0	659	43.8748336	0.9849	1	44	1323	3.325775	0.7062	0	137	1323	10.3552532	0.73130	0	0	3298	0.0000	0.3640	0	3.33770	0.7351	2	2.69580	0.6028	1	0.77030	0.7631	1	3.10980	0.7827	1	9.91360	0.7557	5	3499	1825	1462	1760	3499	50.300086	0.9396	1	42	966	4.347826	0.45390	0	426	1274	33.437991	0.85200	1	52	188	27.65957	0.1824	0	478	1462	32.69494	0.61110	0	422	2488	16.961415	0.7638	1	497	3499	14.204058	0.8246	1	853	24.378394	0.8688	1	808	23.09231	0.58290	0	908	2691.100	33.740844	0.9808	1	179	811.6985	22.0525243	0.73230	0	8	3248	0.2463054	0.26220	1986	3498.713	56.7637257	0.717500	0	1825	29	1.5890411	0.3551	0	576	31.5616438	0.9594	1	88	1462	6.0191518	0.82690	1	148	1461.993	10.123166	0.7364	38	3499	1.0860246	0.7013	0	3.59300	0.8073	3	3.42700	0.91560	2	0.717500	0.711400	0	3.57910	0.9216	2	11.316600	0.9150	7	Yes	$0	0	0	$0	0	Census Tract 211, Autauga County, Alabama	17997	74000	20620	88600	20876.52	85840	-256.52	-0.0122875	2760	0.0321528	134.13	145.41	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01003010200	01003	010200	AL	Alabama	Baldwin County	1	South Region	6	East South Central Division	2612	1220	1074	338	2605	12.97505	0.2907	0	44	1193	3.688181	0.14720	0	172	928	18.53448	0.13090	31	146	21.23288	0.09299	0	203	1074	18.90130	0.05657	455	1872	24.305556	0.8016	1	456	2730	16.70330	0.6445	0	401	15.35222	0.6847	0	563	21.55436	0.3406	0	410	2038	20.11776	0.7755	1	64	779	8.215661	0.2181	0	0	2510	0.0000000	0.09298	0	329	2612	12.595712	0.31130	0	1220	38	3.1147541	0.4648	0	385	31.5573770	0.9545	1	20	1074	1.862197	0.5509	0	43	1074	4.0037244	0.40880	0	0	2612	0.0000	0.3640	0	1.94057	0.3398	1	2.11188	0.2802	1	0.31130	0.3084	0	2.74300	0.6129	1	7.10675	0.3771	3	2928	1312	1176	884	2928	30.191257	0.7334	0	29	1459	1.987663	0.13560	0	71	830	8.554217	0.03726	0	134	346	38.72832	0.3964	0	205	1176	17.43197	0.12010	0	294	2052	14.327485	0.6940	0	219	2925	7.487179	0.5423	0	556	18.989071	0.6705	0	699	23.87295	0.63390	0	489	2226.455	21.963167	0.8122	1	191	783.8820	24.3659136	0.77990	1	0	2710	0.0000000	0.09479	398	2927.519	13.5951280	0.251100	0	1312	13	0.9908537	0.3111	0	400	30.4878049	0.9557	1	6	1176	0.5102041	0.25900	0	81	1176.202	6.886570	0.6115	7	2928	0.2390710	0.4961	0	2.22540	0.4183	0	2.99129	0.76340	2	0.251100	0.249000	0	2.63340	0.5496	1	8.101190	0.5207	3	Yes	$0	1	408000	$408,000	1	Census Tract 102, Baldwin County, Alabama	23862	103200	26085	136900	27679.92	119712	-1594.92	-0.0576201	17188	0.1435779	128.38	166.27	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003010500	01003	010500	AL	Alabama	Baldwin County	1	South Region	6	East South Central Division	4230	1779	1425	498	3443	14.46413	0.3337	0	166	1625	10.215385	0.71790	0	151	1069	14.12535	0.04638	196	356	55.05618	0.73830	0	347	1425	24.35088	0.17010	707	2945	24.006791	0.7967	1	528	4001	13.19670	0.5005	0	619	14.63357	0.6436	0	790	18.67612	0.1937	0	536	3096	17.31266	0.6572	0	165	920	17.934783	0.6102	0	20	4021	0.4973887	0.32320	0	754	4230	17.825059	0.40230	0	1779	97	5.4525014	0.5525	0	8	0.4496908	0.4600	0	63	1425	4.421053	0.7762	1	90	1425	6.3157895	0.56910	0	787	4230	18.6052	0.9649	1	2.51890	0.5121	1	2.42790	0.4539	0	0.40230	0.3986	0	3.32270	0.8628	2	8.67180	0.6054	3	5877	1975	1836	820	5244	15.636918	0.3902	0	90	2583	3.484321	0.33610	0	159	1345	11.821561	0.10530	0	139	491	28.30957	0.1924	0	298	1836	16.23094	0.09053	0	570	4248	13.418079	0.6669	0	353	5247	6.727654	0.4924	0	1109	18.870172	0.6645	0	1144	19.46571	0.34110	0	717	4102.545	17.476956	0.6332	0	103	1286.1180	8.0085961	0.23410	0	0	5639	0.0000000	0.09479	868	5877.481	14.7682323	0.270900	0	1975	26	1.3164557	0.3359	0	45	2.2784810	0.6271	0	9	1836	0.4901961	0.25400	0	116	1835.798	6.318779	0.5811	633	5877	10.7708014	0.9507	1	1.97613	0.3410	0	1.96769	0.19610	0	0.270900	0.268600	0	2.74880	0.6077	1	6.963520	0.3406	1	Yes	$0	0	0	$0	0	Census Tract 105, Baldwin County, Alabama	21585	121100	28301	148500	25038.60	140476	3262.40	0.1302948	8024	0.0571201	191.57	213.49	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003010600	01003	010600	AL	Alabama	Baldwin County	1	South Region	6	East South Central Division	3724	1440	1147	1973	3724	52.98067	0.9342	1	142	1439	9.867964	0.69680	0	235	688	34.15698	0.62950	187	459	40.74074	0.40290	0	422	1147	36.79163	0.55150	497	1876	26.492537	0.8354	1	511	3661	13.95794	0.5334	0	246	6.60580	0.1481	0	1256	33.72718	0.9305	1	496	2522	19.66693	0.7587	1	274	838	32.696897	0.8779	1	32	3479	0.9198045	0.42810	0	2606	3724	69.978518	0.81840	1	1440	21	1.4583333	0.3683	0	321	22.2916667	0.9036	1	97	1147	8.456844	0.8956	1	167	1147	14.5597210	0.82090	1	0	3724	0.0000	0.3640	0	3.55130	0.7859	2	3.14330	0.8145	3	0.81840	0.8108	1	3.35240	0.8725	3	10.86540	0.8550	9	4115	1534	1268	1676	3997	41.931449	0.8814	1	294	1809	16.252073	0.96740	1	341	814	41.891892	0.94320	1	204	454	44.93392	0.5438	0	545	1268	42.98107	0.83620	1	624	2425	25.731959	0.9002	1	994	4115	24.155529	0.9602	1	642	15.601458	0.4841	0	1126	27.36331	0.81750	1	568	2989.000	19.003011	0.7045	0	212	715.0000	29.6503497	0.85920	1	56	3825	1.4640523	0.53120	2715	4115.000	65.9781288	0.773200	1	1534	0	0.0000000	0.1079	0	529	34.4850065	0.9685	1	101	1268	7.9652997	0.87950	1	89	1268.000	7.018927	0.6184	17	4115	0.4131227	0.5707	0	4.54540	0.9754	5	3.39650	0.90810	2	0.773200	0.766700	1	3.14500	0.7858	2	11.860100	0.9520	10	Yes	$0	1	8000000	$8,000,000	1	Census Tract 106, Baldwin County, Alabama	17788	81600	16453	104700	20634.08	94656	-4181.08	-0.2026298	10044	0.1061105	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011000	01003	011000	AL	Alabama	Baldwin County	1	South Region	6	East South Central Division	3758	2012	1576	1053	3758	28.02022	0.6597	0	66	1707	3.866432	0.16250	0	293	1297	22.59059	0.25080	83	279	29.74910	0.19030	0	376	1576	23.85787	0.15710	744	2723	27.322806	0.8465	1	996	4137	24.07542	0.8462	1	713	18.97286	0.8429	1	804	21.39436	0.3306	0	763	3295	23.15630	0.8670	1	155	1145	13.537118	0.4538	0	50	3475	1.4388489	0.51460	0	516	3758	13.730708	0.33300	0	2012	0	0.0000000	0.1224	0	606	30.1192843	0.9484	1	42	1576	2.664975	0.6476	0	96	1576	6.0913706	0.55620	0	0	3758	0.0000	0.3640	0	2.67200	0.5579	2	3.00890	0.7581	2	0.33300	0.3299	0	2.63860	0.5614	1	8.65250	0.6030	5	4921	1979	1732	1539	4908	31.356968	0.7523	1	150	2105	7.125891	0.72850	0	214	1471	14.547927	0.20260	0	59	261	22.60536	0.1167	0	273	1732	15.76212	0.07981	0	936	3332	28.091237	0.9206	1	861	4921	17.496444	0.8930	1	1039	21.113595	0.7653	1	1183	24.03983	0.64410	0	585	3738.000	15.650080	0.5371	0	81	1151.0000	7.0373588	0.19000	0	101	4546	2.2217334	0.61440	1244	4921.000	25.2794148	0.427800	0	1979	0	0.0000000	0.1079	0	527	26.6296109	0.9393	1	83	1732	4.7921478	0.77460	1	151	1732.000	8.718245	0.6904	20	4921	0.4064215	0.5688	0	3.37421	0.7528	3	2.75090	0.63780	1	0.427800	0.424200	0	3.08100	0.7597	2	9.633910	0.7366	6	Yes	$0	0	0	$0	0	Census Tract 110, Baldwin County, Alabama	19340	126400	23679	158700	22434.40	146624	1244.60	0.0554773	12076	0.0823603	129.69	188.85	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011406	01003	011406	AL	Alabama	Baldwin County	1	South Region	6	East South Central Division	3317	6418	1307	583	3317	17.57612	0.4181	0	70	1789	3.912800	0.16690	0	221	685	32.26277	0.57540	284	622	45.65916	0.52130	0	505	1307	38.63810	0.60430	168	2255	7.450111	0.2800	0	919	3677	24.99320	0.8623	1	452	13.62677	0.5791	0	673	20.28942	0.2668	0	366	2769	13.21777	0.4276	0	96	887	10.822999	0.3359	0	180	3066	5.8708415	0.77920	1	473	3317	14.259873	0.34330	0	6418	3976	61.9507635	0.9655	1	384	5.9831723	0.7063	0	17	1307	1.300689	0.4632	0	10	1307	0.7651109	0.08684	0	0	3317	0.0000	0.3640	0	2.33160	0.4577	1	2.38860	0.4323	1	0.34330	0.3401	0	2.58584	0.5335	1	7.64934	0.4576	3	3226	7850	1797	228	3215	7.091757	0.1241	0	72	2055	3.503650	0.33910	0	302	1139	26.514486	0.69300	0	230	658	34.95441	0.3131	0	532	1797	29.60490	0.52020	0	128	2726	4.695525	0.2384	0	530	3226	16.429014	0.8749	1	790	24.488531	0.8715	1	342	10.60136	0.05624	0	280	2884.000	9.708738	0.1832	0	58	792.0000	7.3232323	0.20270	0	15	3107	0.4827808	0.34070	15	3226.000	0.4649721	0.002512	0	7850	5394	68.7133758	0.9706	1	274	3.4904459	0.6697	0	23	1797	1.2799110	0.41980	0	26	1797.000	1.446856	0.1647	0	3226	0.0000000	0.1831	0	2.09670	0.3785	1	1.65434	0.08785	1	0.002512	0.002491	0	2.40790	0.4381	1	6.161452	0.2215	3	Yes	$0	0	0	$0	0	Census Tract 114.06, Baldwin County, Alabama	29838	252000	32201	224200	34612.08	292320	-2411.08	-0.0696601	-68120	-0.2330323	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011407	01003	011407	AL	Alabama	Baldwin County	1	South Region	6	East South Central Division	5187	6687	2066	1404	5172	27.14617	0.6423	0	172	1935	8.888889	0.63280	0	482	1433	33.63573	0.61530	367	633	57.97788	0.79510	1	849	2066	41.09390	0.67110	278	3618	7.683803	0.2906	0	1027	4945	20.76845	0.7735	1	1398	26.95200	0.9629	1	1263	24.34933	0.5302	0	596	3792	15.71730	0.5759	0	158	1633	9.675444	0.2833	0	29	4867	0.5958496	0.35240	0	170	5187	3.277424	0.07984	0	6687	2772	41.4535666	0.9251	1	197	2.9460147	0.6326	0	90	2066	4.356244	0.7729	1	0	2066	0.0000000	0.02586	0	0	5187	0.0000	0.3640	0	3.01030	0.6516	1	2.70470	0.6077	1	0.07984	0.0791	0	2.72046	0.6014	2	8.51530	0.5852	4	5608	7576	2543	1058	5602	18.886112	0.4835	0	32	2631	1.216268	0.05882	0	581	1979	29.358262	0.77080	1	309	564	54.78723	0.7671	1	890	2543	34.99803	0.67250	0	230	4433	5.188360	0.2698	0	776	5602	13.852196	0.8156	1	1527	27.228959	0.9205	1	567	10.11056	0.05099	0	615	5035.000	12.214498	0.3295	0	16	1746.0000	0.9163803	0.01566	0	0	5573	0.0000000	0.09479	441	5608.000	7.8637660	0.140300	0	7576	3055	40.3247096	0.9148	1	72	0.9503696	0.5383	0	0	2543	0.0000000	0.09796	0	125	2543.000	4.915454	0.4934	6	5608	0.1069900	0.4054	0	2.30022	0.4418	1	1.41144	0.04295	1	0.140300	0.139100	0	2.44986	0.4589	1	6.301820	0.2416	3	Yes	$0	0	0	$0	0	Census Tract 114.07, Baldwin County, Alabama	22317	292600	28418	241100	25887.72	339416	2530.28	0.0977406	-98316	-0.2896622	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011502	01003	011502	AL	Alabama	Baldwin County	1	South Region	6	East South Central Division	9234	4606	3702	3160	9213	34.29936	0.7632	1	282	4002	7.046477	0.47570	0	526	2158	24.37442	0.31260	582	1544	37.69430	0.33410	0	1108	3702	29.92977	0.33740	997	6176	16.143135	0.6201	0	2074	10111	20.51231	0.7670	1	1450	15.70284	0.7043	0	2491	26.97639	0.6984	0	1542	7577	20.35106	0.7842	1	684	2718	25.165563	0.7767	1	532	8697	6.1170519	0.78590	1	3275	9234	35.466753	0.60970	0	4606	214	4.6461138	0.5268	0	828	17.9765523	0.8689	1	89	3702	2.404106	0.6192	0	293	3702	7.9146407	0.64700	0	0	9234	0.0000	0.3640	0	2.96340	0.6387	2	3.74950	0.9623	3	0.60970	0.6040	0	3.02590	0.7475	1	10.34850	0.8024	6	14165	6867	6002	2853	14165	20.141193	0.5175	0	313	7047	4.441606	0.46620	0	1181	4164	28.362152	0.74500	0	887	1838	48.25898	0.6211	0	2068	6002	34.45518	0.65900	0	1667	10750	15.506977	0.7286	0	2527	14165	17.839746	0.8980	1	3082	21.757854	0.7907	1	2506	17.69149	0.24240	0	3004	11659.000	25.765503	0.9038	1	407	3482.0000	11.6886847	0.39940	0	364	13519	2.6925068	0.65290	2755	14165.000	19.4493470	0.346300	0	6867	441	6.4220183	0.5555	0	526	7.6598223	0.7585	1	93	6002	1.5494835	0.46540	0	184	6002.000	3.065645	0.3373	0	14165	0.0000000	0.1831	0	3.26930	0.7261	1	2.98920	0.76250	2	0.346300	0.343400	0	2.29980	0.3856	1	8.904600	0.6398	4	Yes	$0	2	8860000	$8,860,000	1	Census Tract 115.02, Baldwin County, Alabama	20411	162700	22820	180400	23676.76	188732	-856.76	-0.0361857	-8332	-0.0441473	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380

LIHTC Data

svi_divisional_lihtc_df0 <- left_join(svi_divisional_lihtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_divisional_lihtc_df1 <- left_join(svi_divisional_lihtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_divisional_lihtc_df <- left_join(svi_divisional_lihtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_divisional_lihtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	post10_lihtc_project_cnt	post10_lihtc_project_dollars	lihtc_flag	lihtc_eligibility	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
34001001400	34001	001400	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3736	1893	1503	2135	3736	57.14668	0.9592	1	411	1635	25.137615	0.9866	1	267	401	66.58354	0.9617	1	715	1102	64.88203	0.8844	1	982	1503	65.33599	0.9764	1	511	1696	30.129717	0.90050	1	527	4199	12.5506073	0.672800	0	269	7.200214	0.115100	0	1598	42.77302	0.9935	1	345	2252	15.319716	0.63090	0	787	941	83.63443	0.9990	1	63	3192	1.973684	0.54480	0	3463	3736	92.69272	0.8905	1	1893	427	22.556788	0.7770	1	0	0.000000	0.3251	0	22	1503	1.463739	0.5339	0	705	1503	46.90619	0.85330	1	0	3736	0.000000	0.3512	0	4.495500	0.9585	4	3.283300	0.8640	2	0.8905	0.8818	1	2.84050	0.66710	2	11.509800	0.9048	9	3812	1724	1549	2291	3754	61.028236	0.9760	1	380	1547	24.563672	0.9934	1	117	240	48.75000	0.9237	1	753	1309	57.52483	0.7816	1	870	1549	56.16527	0.9326	1	472	1913	24.673288	0.8987	1	294	3802	7.7327722	0.7558	1	363	9.522560	0.123100	0	1463	38.37880	0.9885	1	508	2339.000	21.718683	0.85640	1	564	948.0000	59.49367	0.9910	1	201	3159	6.3627730	0.7613	1	3389	3812.000	88.90346	0.8683	1	1724	571	33.1206497	0.8294	1	0	0.000000	0.3216	0	83	1549	5.358296	0.7754	1	661	1549.0000	42.672692	0.8448	1	10	3812	0.2623295	0.4739	0	4.5565	0.9673	5	3.720300	0.9522	4	0.8683	0.8605	1	3.2451	0.81590	3	12.390200	0.9595	13	0	0	0	Yes	Census Tract 14, Atlantic County, New Jersey	16780	255800	17487	139100	19464.80	296728	-1977.80	-0.1016091	-157628	-0.5312205	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001001500	34001	001500	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	1074	901	752	656	1074	61.08007	0.9700	1	43	270	15.925926	0.9242	1	30	70	42.85714	0.7276	0	366	682	53.66569	0.6910	0	396	752	52.65957	0.8458	1	266	921	28.881650	0.88860	1	121	1064	11.3721805	0.613100	0	385	35.847300	0.990200	1	129	12.01117	0.0617	0	321	993	32.326284	0.98460	1	62	195	31.79487	0.8408	1	125	1050	11.904762	0.85620	1	965	1074	89.85102	0.8717	1	901	636	70.588235	0.9304	1	0	0.000000	0.3251	0	10	752	1.329787	0.5133	0	626	752	83.24468	0.98880	1	0	1074	0.000000	0.3512	0	4.241700	0.9134	4	3.733500	0.9515	4	0.8717	0.8632	1	3.10880	0.77090	2	11.955700	0.9362	11	1601	976	810	1001	1601	62.523423	0.9797	1	204	563	36.234458	0.9989	1	74	110	67.27273	0.9848	1	224	700	32.00000	0.2097	0	298	810	36.79012	0.6089	0	379	1145	33.100437	0.9610	1	272	1601	16.9893816	0.9572	1	451	28.169894	0.936300	1	251	15.67770	0.1835	0	411	1350.000	30.444444	0.97330	1	196	446.0000	43.94619	0.9511	1	220	1532	14.3603133	0.8929	1	1435	1601.000	89.63148	0.8738	1	976	451	46.2090164	0.8742	1	0	0.000000	0.3216	0	24	810	2.962963	0.6412	0	546	810.0000	67.407407	0.9401	1	15	1601	0.9369144	0.6832	0	4.5057	0.9617	4	3.937100	0.9752	4	0.8738	0.8659	1	3.4603	0.87740	2	12.776900	0.9705	11	1	1497998	1	Yes	Census Tract 15, Atlantic County, New Jersey	11549	172400	12339	NA	13396.84	199984	-1057.84	-0.0789619	NA	NA	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34001002400	34001	002400	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3129	1759	1375	1916	3129	61.23362	0.9705	1	205	1075	19.069767	0.9574	1	28	60	46.66667	0.7987	1	670	1315	50.95057	0.6297	0	698	1375	50.76364	0.8102	1	632	2059	30.694512	0.90660	1	461	2365	19.4926004	0.873700	1	539	17.225951	0.744100	0	850	27.16523	0.7907	1	575	1736	33.122120	0.98650	1	237	594	39.89899	0.9035	1	312	2663	11.716110	0.85490	1	2357	3129	75.32758	0.8038	1	1759	1091	62.023877	0.9176	1	29	1.648664	0.7742	1	57	1375	4.145454	0.7529	1	696	1375	50.61818	0.87140	1	209	3129	6.679450	0.9003	1	4.518400	0.9608	5	4.279700	0.9831	4	0.8038	0.7960	1	4.21640	0.98320	5	13.818300	0.9835	15	2614	1726	1217	1579	2612	60.451761	0.9744	1	290	1171	24.765158	0.9939	1	69	127	54.33071	0.9521	1	538	1090	49.35780	0.5970	0	607	1217	49.87675	0.8624	1	697	1998	34.884885	0.9695	1	551	2614	21.0788064	0.9797	1	516	19.739862	0.679400	0	503	19.24254	0.3999	0	576	2111.000	27.285647	0.95060	1	257	567.0000	45.32628	0.9571	1	556	2368	23.4797297	0.9570	1	2029	2614.000	77.62050	0.8058	1	1726	1166	67.5550406	0.9204	1	0	0.000000	0.3216	0	115	1217	9.449466	0.8840	1	673	1217.0000	55.299918	0.8978	1	223	2614	8.5309870	0.9307	1	4.7799	0.9845	5	3.944000	0.9756	3	0.8058	0.7985	1	3.9545	0.96510	4	13.484200	0.9845	13	0	0	0	Yes	Census Tract 24, Atlantic County, New Jersey	14657	243300	17646	302900	17002.12	282228	643.88	0.0378706	20672	0.0732457	NA	NA	Atlantic County, New Jersey	Atlantic City, NJ MSA	C1210
34003015400	34003	015400	NJ	New Jersey	Bergen County	1	Northeast Region	2	Middle Atlantic Division	5086	2258	2100	1485	5063	29.33044	0.7447	0	195	2873	6.787330	0.4938	0	223	478	46.65272	0.7984	1	876	1622	54.00740	0.6974	0	1099	2100	52.33333	0.8405	1	640	3682	17.381858	0.70160	0	1579	6178	25.5584331	0.949900	1	603	11.856075	0.377400	0	961	18.89501	0.2410	0	534	5000	10.680000	0.31600	0	254	1232	20.61688	0.6975	0	681	4763	14.297712	0.88510	1	3916	5086	76.99567	0.8096	1	2258	1028	45.527015	0.8820	1	0	0.000000	0.3251	0	28	2100	1.333333	0.5139	0	643	2100	30.61905	0.76370	1	57	5086	1.120724	0.7485	0	3.730500	0.8072	2	2.517000	0.5136	1	0.8096	0.8017	1	3.23320	0.81730	2	10.290300	0.7914	6	7543	3570	3054	1638	7543	21.715498	0.6364	0	320	4251	7.527641	0.7462	0	238	752	31.64894	0.6832	0	1211	2302	52.60643	0.6776	0	1449	3054	47.44597	0.8252	1	877	5631	15.574498	0.7611	1	1093	7543	14.4902559	0.9339	1	981	13.005436	0.282700	0	1174	15.56410	0.1785	0	756	6369.000	11.869995	0.37380	0	303	2013.0000	15.05216	0.5737	0	970	7103	13.6562016	0.8846	1	5610	7543.000	74.37359	0.7916	1	3570	1871	52.4089636	0.8898	1	0	0.000000	0.3216	0	258	3054	8.447937	0.8637	1	301	3054.0000	9.855927	0.5207	0	15	7543	0.1988599	0.4315	0	3.9028	0.8603	3	2.293300	0.3701	1	0.7916	0.7845	1	3.0273	0.73680	2	10.015000	0.7805	7	0	0	0	Yes	Census Tract 154, Bergen County, New Jersey	24590	389800	35719	326900	28524.40	452168	7194.60	0.2522262	-125268	-0.2770386	NA	NA	Bergen County, New Jersey	New York-Newark-Bridgeport, NY-NJ-CT-PA CSA	CS408
34003018100	34003	018100	NJ	New Jersey	Bergen County	1	Northeast Region	2	Middle Atlantic Division	6907	2665	2569	1865	6863	27.17470	0.7140	0	242	3781	6.400423	0.4509	0	434	834	52.03837	0.8694	1	1123	1735	64.72622	0.8830	1	1557	2569	60.60724	0.9450	1	1521	4649	32.716713	0.92270	1	2703	7124	37.9421673	0.992200	1	1024	14.825539	0.598800	0	1336	19.34270	0.2674	0	452	5848	7.729138	0.11920	0	363	1614	22.49071	0.7280	0	1324	6571	20.149140	0.93510	1	4209	6907	60.93818	0.7551	1	2665	517	19.399625	0.7487	0	0	0.000000	0.3251	0	136	2569	5.293889	0.7960	1	1043	2569	40.59945	0.82350	1	0	6907	0.000000	0.3512	0	4.024800	0.8697	3	2.648500	0.5885	1	0.7551	0.7477	1	3.04450	0.74620	2	10.472900	0.8112	7	7668	2912	2816	1803	7664	23.525574	0.6750	0	370	4727	7.827375	0.7646	1	441	819	53.84615	0.9501	1	1122	1997	56.18428	0.7544	1	1563	2816	55.50426	0.9274	1	1879	5775	32.536797	0.9576	1	1695	7668	22.1048513	0.9829	1	1041	13.575900	0.316600	0	1193	15.55816	0.1784	0	711	6474.819	10.981001	0.31250	0	197	1914.1749	10.29164	0.3928	0	2045	7161	28.5574640	0.9756	1	5637	7667.630	73.51685	0.7875	1	2912	806	27.6785714	0.7973	1	0	0.000000	0.3216	0	150	2816	5.326704	0.7742	1	833	2816.0423	29.580522	0.7679	1	10	7668	0.1304121	0.3642	0	4.3075	0.9336	4	2.175900	0.2971	1	0.7875	0.7803	1	3.0252	0.73590	3	10.296100	0.8061	9	0	0	0	Yes	Census Tract 181, Bergen County, New Jersey	20495	390100	29008	354100	23774.20	452516	5233.80	0.2201462	-98416	-0.2174862	NA	NA	Bergen County, New Jersey	New York-Newark-Bridgeport, NY-NJ-CT-PA CSA	CS408
34005702101	34005	702101	NJ	New Jersey	Burlington County	1	Northeast Region	2	Middle Atlantic Division	1637	702	483	445	1637	27.18387	0.7142	0	63	456	13.815789	0.8857	1	0	0	NaN	NA	NA	222	483	45.96273	0.5037	0	222	483	45.96273	0.7085	0	31	903	3.433001	0.08742	0	14	1965	0.7124682	0.008765	0	0	0.000000	0.002836	0	696	42.51680	0.9928	1	62	898	6.904232	0.08018	0	103	452	22.78761	0.7331	0	0	1379	0.000000	0.07335	0	248	1637	15.14966	0.4224	0	702	25	3.561254	0.4247	0	0	0.000000	0.3251	0	0	483	0.000000	0.1459	0	0	483	0.00000	0.01044	0	0	1637	0.000000	0.3512	0	2.404585	0.4890	1	1.882266	0.1557	1	0.4224	0.4183	0	1.25734	0.03853	0	5.966591	0.2021	2	3997	1271	1235	304	3996	7.607608	0.1919	0	46	901	5.105438	0.5252	0	0	0	NaN	NA	NA	731	1235	59.19028	0.8107	1	731	1235	59.19028	0.9566	1	49	1973	2.483528	0.0993	0	27	3057	0.8832188	0.0568	0	0	0.000000	0.001592	0	1651	41.30598	0.9924	1	58	1412.011	4.107616	0.01556	0	91	1092.7925	8.32729	0.3046	0	32	3347	0.9560801	0.3989	0	1411	3996.883	35.30251	0.5750	0	1271	10	0.7867821	0.2414	0	0	0.000000	0.3216	0	27	1235	2.186235	0.5699	0	11	1234.9741	0.890707	0.0533	0	0	3997	0.0000000	0.1517	0	1.8298	0.3066	1	1.713052	0.1034	1	0.5750	0.5698	0	1.3379	0.06021	0	5.455752	0.1329	2	0	0	0	Yes	Census Tract 7021.01, Burlington County, New Jersey	33625	NA	44370	NA	39005.00	NA	5365.00	0.1375465	NA	NA	NA	NA	Burlington County, New Jersey	Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA	CS428
34005702204	34005	702204	NJ	New Jersey	Burlington County	1	Northeast Region	2	Middle Atlantic Division	3000	737	678	1208	2479	48.72933	0.9201	1	195	973	20.041110	0.9644	1	141	334	42.21557	0.7153	0	272	344	79.06977	0.9729	1	413	678	60.91445	0.9473	1	479	1828	26.203501	0.85830	1	243	2062	11.7846751	0.635000	0	375	12.500000	0.423100	0	875	29.16667	0.8714	1	247	1502	16.444740	0.69460	0	245	562	43.59431	0.9252	1	188	2656	7.078313	0.78350	1	1581	3000	52.70000	0.7268	0	737	0	0.000000	0.1009	0	0	0.000000	0.3251	0	118	678	17.404130	0.9662	1	79	678	11.65192	0.55970	0	521	3000	17.366667	0.9582	1	4.325100	0.9272	4	3.697800	0.9448	3	0.7268	0.7197	0	2.91010	0.69490	2	11.659800	0.9155	9	2496	766	711	436	2236	19.499105	0.5828	0	168	1119	15.013405	0.9569	1	109	476	22.89916	0.4367	0	172	234	73.50427	0.9602	1	281	710	39.57746	0.6774	0	205	1672	12.260766	0.6634	0	106	2181	4.8601559	0.5382	0	483	19.350962	0.662200	0	561	22.47596	0.6407	0	438	1641.664	26.680248	0.94370	1	95	606.3759	15.66685	0.5916	0	20	2331	0.8580009	0.3785	0	1043	2495.717	41.79160	0.6233	0	766	4	0.5221932	0.2121	0	0	0.000000	0.3216	0	51	711	7.172996	0.8340	1	39	710.5812	5.488465	0.3485	0	313	2496	12.5400641	0.9504	1	3.4187	0.7554	1	3.216700	0.8464	1	0.6233	0.6176	0	2.6666	0.57970	2	9.925300	0.7714	4	0	0	0	Yes	Census Tract 7022.04, Burlington County, New Jersey	17110	231600	24236	195800	19847.60	268656	4388.40	0.2211048	-72856	-0.2711869	114.52	126.87	Burlington County, New Jersey	Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA	CS428
34007600200	34007	600200	NJ	New Jersey	Camden County	1	Northeast Region	2	Middle Atlantic Division	2152	988	790	823	2152	38.24349	0.8438	1	219	1183	18.512257	0.9528	1	115	414	27.77778	0.3413	0	299	376	79.52128	0.9744	1	414	790	52.40506	0.8417	1	485	1266	38.309637	0.95530	1	317	2208	14.3568841	0.740200	0	171	7.946097	0.147200	0	627	29.13569	0.8700	1	273	1434	19.037657	0.80620	1	176	471	37.36730	0.8842	1	326	1961	16.624171	0.90730	1	2074	2152	96.37546	0.9239	1	988	54	5.465587	0.4952	0	0	0.000000	0.3251	0	20	790	2.531646	0.6528	0	354	790	44.81013	0.84340	1	0	2152	0.000000	0.3512	0	4.333800	0.9289	4	3.614900	0.9333	4	0.9239	0.9149	1	2.66770	0.58390	1	11.540300	0.9067	10	2111	878	725	851	2111	40.312648	0.8788	1	52	913	5.695509	0.5929	0	69	375	18.40000	0.2795	0	273	350	78.00000	0.9779	1	342	725	47.17241	0.8205	1	407	1376	29.578488	0.9404	1	209	2111	9.9005211	0.8481	1	177	8.384652	0.089330	0	516	24.44339	0.7586	1	453	1595.000	28.401254	0.96070	1	200	448.0000	44.64286	0.9538	1	68	2083	3.2645223	0.6378	0	2111	2111.000	100.00000	0.9976	1	878	14	1.5945330	0.3022	0	10	1.138952	0.7512	1	0	725	0.000000	0.1194	0	239	725.0000	32.965517	0.7908	1	0	2111	0.0000000	0.1517	0	4.0807	0.8946	4	3.400230	0.8937	3	0.9976	0.9885	1	2.1153	0.31270	2	10.593830	0.8340	10	0	0	0	Yes	Census Tract 6002, Camden County, New Jersey	19841	61400	18057	64600	23015.56	71224	-4958.56	-0.2154438	-6624	-0.0930024	NA	NA	Camden County, New Jersey	Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA	CS428
34007600400	34007	600400	NJ	New Jersey	Camden County	1	Northeast Region	2	Middle Atlantic Division	3245	1556	1038	2230	3118	71.52021	0.9895	1	355	1169	30.367836	0.9951	1	130	372	34.94624	0.5405	0	498	666	74.77477	0.9578	1	628	1038	60.50096	0.9442	1	649	1641	39.549056	0.96100	1	905	3153	28.7028227	0.968500	1	188	5.793528	0.066780	0	1123	34.60709	0.9638	1	447	2167	20.627596	0.85890	1	415	736	56.38587	0.9800	1	289	2933	9.853392	0.83120	1	3058	3245	94.23729	0.9033	1	1556	65	4.177378	0.4500	0	0	0.000000	0.3251	0	58	1038	5.587669	0.8052	1	509	1038	49.03661	0.86430	1	203	3245	6.255778	0.8951	1	4.858300	0.9853	5	3.700680	0.9459	4	0.9033	0.8945	1	3.33970	0.85670	3	12.801980	0.9715	13	3373	1557	1335	2125	3373	63.000296	0.9805	1	344	1020	33.725490	0.9987	1	100	409	24.44988	0.4886	0	678	926	73.21814	0.9591	1	778	1335	58.27715	0.9498	1	841	2163	38.881183	0.9823	1	332	3373	9.8428698	0.8461	1	629	18.648088	0.622700	0	1069	31.69286	0.9560	1	747	2304.000	32.421875	0.98120	1	440	788.0000	55.83756	0.9864	1	332	3128	10.6138107	0.8489	1	3282	3373.000	97.30210	0.9417	1	1557	100	6.4226076	0.5014	0	24	1.541426	0.7760	1	60	1335	4.494382	0.7365	0	793	1335.0000	59.400749	0.9136	1	100	3373	2.9647198	0.8250	1	4.7574	0.9834	5	4.395200	0.9861	4	0.9417	0.9332	1	3.7525	0.93580	3	13.846800	0.9859	13	0	0	0	Yes	Census Tract 6004, Camden County, New Jersey	9391	70000	12266	71500	10893.56	81200	1372.44	0.1259864	-9700	-0.1194581	NA	NA	Camden County, New Jersey	Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA	CS428
34007600700	34007	600700	NJ	New Jersey	Camden County	1	Northeast Region	2	Middle Atlantic Division	1723	572	409	457	1342	34.05365	0.8048	1	211	657	32.115677	0.9965	1	125	237	52.74262	0.8787	1	71	172	41.27907	0.3897	0	196	409	47.92176	0.7525	1	495	1142	43.345009	0.97370	1	249	1008	24.7023810	0.942400	1	124	7.196750	0.115000	0	437	25.36274	0.6912	0	144	734	19.618529	0.82660	1	93	271	34.31734	0.8621	1	320	1565	20.447284	0.93700	1	1626	1723	94.37028	0.9044	1	572	28	4.895105	0.4761	0	0	0.000000	0.3251	0	30	409	7.334963	0.8483	1	98	409	23.96088	0.71310	0	381	1723	22.112594	0.9664	1	4.469900	0.9540	5	3.431900	0.8994	3	0.9044	0.8955	1	3.32900	0.85130	2	12.135200	0.9465	11	1618	613	517	988	1618	61.063041	0.9762	1	70	655	10.687023	0.8840	1	85	233	36.48069	0.7849	1	128	284	45.07042	0.4921	0	213	517	41.19923	0.7111	0	477	957	49.843260	0.9969	1	172	1618	10.6304079	0.8688	1	207	12.793572	0.271700	0	604	37.33004	0.9856	1	270	1013.206	26.648079	0.94310	1	149	374.2530	39.81264	0.9275	1	256	1374	18.6317322	0.9302	1	1515	1617.787	93.64644	0.9007	1	613	69	11.2561175	0.6156	0	0	0.000000	0.3216	0	54	517	10.444874	0.9008	1	83	516.9681	16.055148	0.6382	0	18	1618	1.1124845	0.7048	0	4.4370	0.9503	4	4.058100	0.9811	4	0.9007	0.8926	1	3.1810	0.79120	1	12.576800	0.9657	10	1	0	1	Yes	Census Tract 6007, Camden County, New Jersey	16866	92700	15982	75500	19564.56	107532	-3582.56	-0.1831148	-32032	-0.2978834	NA	NA	Camden County, New Jersey	Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA	CS428

svi_national_lihtc_df0 <- left_join(svi_national_lihtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_national_lihtc_df1 <- left_join(svi_national_lihtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_national_lihtc_df <- left_join(svi_national_lihtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_national_lihtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	lihtc_eligibility	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
01005950700	01005	950700	AL	Alabama	Barbour County	1	South Region	6	East South Central Division	1753	687	563	615	1628	37.77641	0.8088	1	17	667	2.548726	0.06941	0	41	376	10.90426	0.01945	0	62	187	33.15508	0.24640	0	103	563	18.29485	0.04875	0	264	1208	21.85430	0.7570	1	201	1527	13.163065	0.4991	0	368	20.992584	0.89510	1	462	26.354820	0.66130	211	1085	19.44700	0.7505	1	107	399	26.81704	0.8048	1	0	1628	0.0000000	0.09298	861	1753	49.11580	0.7101	0	687	17	2.4745269	0.4324	0	38	5.5312955	0.6970	0	3	563	0.5328597	0.3037	0	19	563	3.374778	0.3529	0	233	1753	13.29150	0.9517	1	2.18306	0.4137	2	3.20468	0.8377	3	0.7101	0.7035	0	2.7377	0.6100	1	8.83554	0.6264	6	1527	691	595	565	1365	41.39194	0.8765	1	37	572	6.468532	0.6776	0	70	376	18.617021	0.38590	0	92	219	42.009132	0.47360	0	162	595	27.22689	0.44540	0	280	1114	25.13465	0.8942	1	105	1378	7.619739	0.5505	0	383	25.081860	0.88450	1	337	22.069417	0.51380	237	1041.0000	22.76657	0.8360	1	144	413.0000	34.86683	0.9114	1	11	1466	0.7503411	0.40700	711	1527.0000	46.56189	0.6441	0	691	13	1.8813314	0.3740	0	37	5.3545586	0.7152	0	0	595	0.0000000	0.09796	115	595.0000	19.327731	0.8859	1	149	1527	9.7576948	0.9470	1	3.44420	0.7707	2	3.55270	0.9403	3	0.6441	0.6387	0	3.02006	0.7337	2	10.66106	0.8537	7	Yes	Census Tract 9507, Barbour County, Alabama	15257	133700	17244	137500	17698.12	155092	-454.12	-0.0256592	-17592	-0.1134294	131.05	135.61	Barbour County, Alabama	Eufaula, AL-GA MicroSA	C2164
01011952100	01011	952100	AL	Alabama	Bullock County	1	South Region	6	East South Central Division	1652	796	554	564	1652	34.14044	0.7613	1	46	816	5.637255	0.33630	0	96	458	20.96070	0.19930	0	62	96	64.58333	0.89170	1	158	554	28.51986	0.29220	0	271	1076	25.18587	0.8163	1	155	1663	9.320505	0.3183	0	199	12.046005	0.47180	0	420	25.423729	0.60240	327	1279	25.56685	0.9151	1	137	375	36.53333	0.9108	1	0	1590	0.0000000	0.09298	1428	1652	86.44068	0.8939	1	796	0	0.0000000	0.1224	0	384	48.2412060	0.9897	1	19	554	3.4296029	0.7145	0	45	554	8.122744	0.6556	0	0	1652	0.00000	0.3640	0	2.52440	0.5138	2	2.99308	0.7515	2	0.8939	0.8856	1	2.8462	0.6637	1	9.25758	0.6790	6	1382	748	549	742	1382	53.69030	0.9560	1	40	511	7.827789	0.7730	1	110	402	27.363184	0.71780	0	45	147	30.612245	0.23070	0	155	549	28.23315	0.47730	0	181	905	20.00000	0.8253	1	232	1382	16.787265	0.8813	1	164	11.866860	0.27170	0	250	18.089725	0.26290	258	1132.0000	22.79152	0.8368	1	99	279.0000	35.48387	0.9162	1	33	1275	2.5882353	0.64520	1347	1382.0000	97.46744	0.9681	1	748	0	0.0000000	0.1079	0	375	50.1336898	0.9922	1	0	549	0.0000000	0.09796	37	549.0000	6.739526	0.6039	0	0	1382	0.0000000	0.1831	0	3.91290	0.8785	4	2.93280	0.7342	2	0.9681	0.9599	1	1.98506	0.2471	1	9.79886	0.7570	8	Yes	Census Tract 9521, Bullock County, Alabama	19754	58200	18598	66900	22914.64	67512	-4316.64	-0.1883791	-612	-0.0090651	NA	NA	NA	NA	NA
01015000300	01015	000300	AL	Alabama	Calhoun County	1	South Region	6	East South Central Division	3074	1635	1330	1904	3067	62.08021	0.9710	1	293	1362	21.512482	0.96630	1	180	513	35.08772	0.65450	0	383	817	46.87882	0.55040	0	563	1330	42.33083	0.70280	0	720	2127	33.85049	0.9148	1	628	2835	22.151675	0.8076	1	380	12.361744	0.49340	0	713	23.194535	0.45030	647	2111	30.64898	0.9708	1	298	773	38.55110	0.9247	1	0	2878	0.0000000	0.09298	2623	3074	85.32856	0.8883	1	1635	148	9.0519878	0.6465	0	6	0.3669725	0.4502	0	68	1330	5.1127820	0.8082	1	303	1330	22.781955	0.9029	1	0	3074	0.00000	0.3640	0	4.36250	0.9430	4	2.93218	0.7233	2	0.8883	0.8800	1	3.1718	0.8070	2	11.35478	0.9009	9	2390	1702	1282	1287	2390	53.84937	0.9566	1	102	1066	9.568480	0.8541	1	158	609	25.944171	0.67520	0	286	673	42.496285	0.48560	0	444	1282	34.63339	0.66340	0	467	1685	27.71513	0.9180	1	369	2379	15.510719	0.8562	1	342	14.309623	0.40850	0	548	22.928870	0.57100	647	1831.0000	35.33588	0.9862	1	202	576.0000	35.06944	0.9130	1	16	2134	0.7497657	0.40690	1896	2390.0000	79.33054	0.8451	1	1702	96	5.6404230	0.5329	0	0	0.0000000	0.2186	0	0	1282	0.0000000	0.09796	186	1282.0000	14.508580	0.8308	1	43	2390	1.7991632	0.7727	1	4.24830	0.9395	4	3.28560	0.8773	2	0.8451	0.8379	1	2.45296	0.4602	2	10.83196	0.8718	9	Yes	Census Tract 3, Calhoun County, Alabama	12211	41700	18299	51300	14164.76	48372	4134.24	0.2918680	2928	0.0605309	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015000500	01015	000500	AL	Alabama	Calhoun County	1	South Region	6	East South Central Division	1731	1175	743	1042	1619	64.36072	0.9767	1	124	472	26.271186	0.98460	1	136	461	29.50108	0.48970	0	163	282	57.80142	0.79190	1	299	743	40.24226	0.64910	0	340	1270	26.77165	0.8389	1	460	1794	25.641026	0.8722	1	271	15.655690	0.70190	0	368	21.259388	0.32190	507	1449	34.98965	0.9885	1	150	386	38.86010	0.9269	1	0	1677	0.0000000	0.09298	1559	1731	90.06355	0.9123	1	1175	50	4.2553191	0.5128	0	4	0.3404255	0.4480	0	0	743	0.0000000	0.1238	0	122	743	16.419919	0.8473	1	0	1731	0.00000	0.3640	0	4.32150	0.9362	4	3.03218	0.7679	2	0.9123	0.9038	1	2.2959	0.3818	1	10.56188	0.8244	8	940	907	488	586	940	62.34043	0.9815	1	59	297	19.865320	0.9833	1	100	330	30.303030	0.79220	1	58	158	36.708861	0.34970	0	158	488	32.37705	0.60200	0	199	795	25.03145	0.8930	1	118	940	12.553192	0.7770	1	246	26.170213	0.90530	1	118	12.553192	0.08233	383	822.5089	46.56484	0.9984	1	30	197.8892	15.16000	0.5363	0	0	889	0.0000000	0.09479	898	940.3866	95.49264	0.9489	1	907	0	0.0000000	0.1079	0	2	0.2205072	0.4456	0	2	488	0.4098361	0.23670	146	487.6463	29.939736	0.9404	1	0	940	0.0000000	0.1831	0	4.23680	0.9379	4	2.61712	0.5593	2	0.9489	0.9409	1	1.91370	0.2196	1	9.71652	0.7468	8	Yes	Census Tract 5, Calhoun County, Alabama	11742	38800	13571	38800	13620.72	45008	-49.72	-0.0036503	-6208	-0.1379310	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015000600	01015	000600	AL	Alabama	Calhoun County	1	South Region	6	East South Central Division	2571	992	796	1394	2133	65.35396	0.9789	1	263	905	29.060773	0.98990	1	121	306	39.54248	0.75940	1	209	490	42.65306	0.44810	0	330	796	41.45729	0.68030	0	641	1556	41.19537	0.9554	1	416	1760	23.636364	0.8383	1	220	8.556982	0.24910	0	584	22.714897	0.41610	539	1353	39.83740	0.9955	1	243	466	52.14592	0.9783	1	30	2366	1.2679628	0.48990	1944	2571	75.61260	0.8440	1	992	164	16.5322581	0.7673	1	8	0.8064516	0.5110	0	46	796	5.7788945	0.8329	1	184	796	23.115578	0.9049	1	614	2571	23.88176	0.9734	1	4.44280	0.9548	4	3.12890	0.8088	2	0.8440	0.8362	1	3.9895	0.9792	4	12.40520	0.9696	11	1950	964	719	837	1621	51.63479	0.9467	1	157	652	24.079755	0.9922	1	22	364	6.043956	0.01547	0	129	355	36.338028	0.34200	0	151	719	21.00139	0.23030	0	363	1387	26.17159	0.9048	1	351	1613	21.760694	0.9435	1	249	12.769231	0.32090	0	356	18.256410	0.27140	332	1259.7041	26.35540	0.9135	1	136	435.6156	31.22018	0.8775	1	0	1891	0.0000000	0.09479	1463	1949.9821	75.02633	0.8219	1	964	14	1.4522822	0.3459	0	8	0.8298755	0.5269	0	19	719	2.6425591	0.61120	197	719.0542	27.397100	0.9316	1	329	1950	16.8717949	0.9655	1	4.01750	0.9001	4	2.47809	0.4764	2	0.8219	0.8149	1	3.38110	0.8712	2	10.69859	0.8583	9	Yes	Census Tract 6, Calhoun County, Alabama	10958	48000	14036	43300	12711.28	55680	1324.72	0.1042161	-12380	-0.2223420	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015002101	01015	002101	AL	Alabama	Calhoun County	1	South Region	6	East South Central Division	3872	1454	1207	1729	2356	73.38710	0.9916	1	489	2020	24.207921	0.97860	1	20	168	11.90476	0.02541	0	718	1039	69.10491	0.93320	1	738	1207	61.14333	0.96900	1	113	725	15.58621	0.6035	0	664	3943	16.839970	0.6495	0	167	4.313016	0.05978	0	238	6.146694	0.02255	264	2359	11.19118	0.3027	0	94	263	35.74144	0.9050	1	46	3769	1.2204829	0.48250	1601	3872	41.34814	0.6572	0	1454	761	52.3383769	0.9504	1	65	4.4704264	0.6738	0	5	1207	0.4142502	0.2791	0	113	1207	9.362055	0.7004	0	1516	3872	39.15289	0.9860	1	4.19220	0.9133	3	1.77253	0.1304	1	0.6572	0.6511	0	3.5897	0.9337	2	10.21163	0.7885	6	3238	1459	1014	1082	1836	58.93246	0.9735	1	251	1403	17.890235	0.9767	1	31	155	20.000000	0.44920	0	515	859	59.953434	0.85540	1	546	1014	53.84615	0.95350	1	134	916	14.62882	0.7033	0	251	3238	7.751699	0.5588	0	167	5.157505	0.03597	0	169	5.219271	0.02111	323	1667.0000	19.37612	0.7205	0	94	277.0000	33.93502	0.9040	1	0	3164	0.0000000	0.09479	1045	3238.0000	32.27301	0.5125	0	1459	607	41.6038382	0.9185	1	65	4.4551062	0.6949	0	24	1014	2.3668639	0.57900	85	1014.0000	8.382643	0.6775	0	1402	3238	43.2983323	0.9876	1	4.16580	0.9263	3	1.77637	0.1225	1	0.5125	0.5082	0	3.85750	0.9661	2	10.31217	0.8160	6	Yes	Census Tract 21.01, Calhoun County, Alabama	4968	92000	9312	153500	5762.88	106720	3549.12	0.6158587	46780	0.4383433	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015002300	01015	002300	AL	Alabama	Calhoun County	1	South Region	6	East South Central Division	3882	1861	1608	1366	3882	35.18805	0.7753	1	186	1539	12.085770	0.80740	1	284	1109	25.60866	0.35530	0	202	499	40.48096	0.39670	0	486	1608	30.22388	0.34700	0	727	2610	27.85441	0.8534	1	547	3706	14.759849	0.5669	0	716	18.444101	0.82530	1	904	23.286966	0.45720	719	2919	24.63172	0.8986	1	207	1191	17.38035	0.5923	0	0	3720	0.0000000	0.09298	490	3882	12.62236	0.3118	0	1861	38	2.0419130	0.4070	0	199	10.6931757	0.7836	1	52	1608	3.2338308	0.6986	0	166	1608	10.323383	0.7304	0	0	3882	0.00000	0.3640	0	3.35000	0.7384	3	2.86638	0.6919	2	0.3118	0.3089	0	2.9836	0.7289	1	9.51178	0.7100	6	3265	1774	1329	1103	3265	33.78254	0.7880	1	122	1422	8.579465	0.8131	1	101	844	11.966825	0.10960	0	126	485	25.979381	0.15930	0	227	1329	17.08051	0.11070	0	267	2122	12.58247	0.6388	0	328	3265	10.045942	0.6808	0	440	13.476263	0.36070	0	843	25.819296	0.74470	530	2422.0000	21.88274	0.8097	1	254	861.0000	29.50058	0.8574	1	0	3026	0.0000000	0.09479	811	3265.0000	24.83920	0.4221	0	1774	7	0.3945885	0.2444	0	338	19.0529876	0.8924	1	19	1329	1.4296464	0.44520	120	1329.0000	9.029345	0.7016	0	0	3265	0.0000000	0.1831	0	3.03140	0.6608	2	2.86729	0.7016	2	0.4221	0.4185	0	2.46670	0.4669	1	8.78749	0.6230	5	Yes	Census Tract 23, Calhoun County, Alabama	15086	77500	21540	78500	17499.76	89900	4040.24	0.2308740	-11400	-0.1268076	120.54	131.82	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01023956700	01023	956700	AL	Alabama	Choctaw County	1	South Region	6	East South Central Division	3011	1772	1179	1715	3011	56.95782	0.9531	1	266	890	29.887640	0.99100	1	267	1035	25.79710	0.36240	0	79	144	54.86111	0.73440	0	346	1179	29.34690	0.31850	0	738	2053	35.94739	0.9287	1	543	2904	18.698347	0.7133	0	569	18.897376	0.84040	1	648	21.521089	0.33840	813	2273	35.76771	0.9901	1	252	771	32.68482	0.8778	1	0	2880	0.0000000	0.09298	2455	3011	81.53437	0.8712	1	1772	38	2.1444695	0.4136	0	485	27.3702032	0.9349	1	72	1179	6.1068702	0.8435	1	109	1179	9.245123	0.6964	0	0	3011	0.00000	0.3640	0	3.90460	0.8597	3	3.13968	0.8131	3	0.8712	0.8631	1	3.2524	0.8387	2	11.16788	0.8840	9	3335	1912	1362	1135	3313	34.25898	0.7948	1	188	1147	16.390584	0.9686	1	212	1058	20.037807	0.45090	0	27	304	8.881579	0.02679	0	239	1362	17.54772	0.12350	0	466	2537	18.36815	0.7948	1	495	3335	14.842579	0.8413	1	791	23.718141	0.85250	1	613	18.380810	0.27840	884	2714.0000	32.57185	0.9752	1	230	918.0000	25.05447	0.7925	1	25	3103	0.8056719	0.41920	2637	3335.0000	79.07046	0.8436	1	1912	0	0.0000000	0.1079	0	758	39.6443515	0.9799	1	16	1362	1.1747430	0.40060	75	1362.0000	5.506608	0.5316	0	8	3335	0.2398801	0.4965	0	3.52300	0.7901	4	3.31780	0.8870	3	0.8436	0.8365	1	2.51650	0.4924	1	10.20090	0.8033	9	Yes	Census Tract 9567, Choctaw County, Alabama	12737	60900	16852	63400	14774.92	70644	2077.08	0.1405815	-7244	-0.1025423	NA	NA	NA	NA	NA
01023957000	01023	957000	AL	Alabama	Choctaw County	1	South Region	6	East South Central Division	2567	1187	916	767	2567	29.87924	0.6933	0	145	1060	13.679245	0.86050	1	101	719	14.04729	0.04540	0	43	197	21.82741	0.09791	0	144	916	15.72052	0.02333	0	355	1704	20.83333	0.7366	0	289	2296	12.587108	0.4736	0	324	12.621737	0.51120	0	688	26.801714	0.68810	572	1746	32.76060	0.9809	1	121	636	19.02516	0.6414	0	5	2283	0.2190101	0.22520	1314	2567	51.18816	0.7225	0	1187	0	0.0000000	0.1224	0	335	28.2224094	0.9394	1	13	916	1.4192140	0.4834	0	70	916	7.641921	0.6353	0	0	2567	0.00000	0.3640	0	2.78733	0.5903	1	3.04680	0.7745	1	0.7225	0.7158	0	2.5445	0.5114	1	9.10113	0.6601	3	2077	1158	866	759	2072	36.63127	0.8256	1	61	780	7.820513	0.7726	1	106	735	14.421769	0.19760	0	11	131	8.396947	0.02525	0	117	866	13.51039	0.04053	0	351	1464	23.97541	0.8815	1	205	2077	9.870005	0.6729	0	402	19.354839	0.68820	0	496	23.880597	0.63430	466	1576.0000	29.56853	0.9544	1	154	612.0000	25.16340	0.7942	1	0	2002	0.0000000	0.09479	1018	2077.0000	49.01300	0.6638	0	1158	0	0.0000000	0.1079	0	439	37.9101900	0.9766	1	0	866	0.0000000	0.09796	42	866.0000	4.849884	0.4884	0	5	2077	0.2407318	0.4971	0	3.19313	0.7061	3	3.16589	0.8369	2	0.6638	0.6582	0	2.16796	0.3247	1	9.19078	0.6792	6	Yes	Census Tract 9570, Choctaw County, Alabama	16224	51600	21740	74000	18819.84	59856	2920.16	0.1551639	14144	0.2363005	NA	NA	NA	NA	NA
01031010500	01031	010500	AL	Alabama	Coffee County	1	South Region	6	East South Central Division	4529	1950	1664	1649	4022	40.99950	0.8432	1	114	1424	8.005618	0.56260	0	309	1057	29.23368	0.48130	0	251	607	41.35091	0.41690	0	560	1664	33.65385	0.45740	0	1269	3370	37.65579	0.9387	1	516	4279	12.058892	0.4492	0	832	18.370501	0.82310	1	894	19.739457	0.23950	1023	3404	30.05288	0.9666	1	303	1112	27.24820	0.8108	1	43	4270	1.0070258	0.44510	1761	4529	38.88276	0.6383	0	1950	6	0.3076923	0.2576	0	276	14.1538462	0.8279	1	8	1664	0.4807692	0.2925	0	125	1664	7.512019	0.6289	0	507	4529	11.19452	0.9441	1	3.25110	0.7138	2	3.28510	0.8639	3	0.6383	0.6324	0	2.9510	0.7136	2	10.12550	0.7794	7	4815	2118	1731	1329	4470	29.73154	0.7256	0	147	1903	7.724645	0.7670	1	209	1256	16.640127	0.29310	0	208	475	43.789474	0.51620	0	417	1731	24.09012	0.33700	0	953	3728	25.56330	0.8985	1	668	4485	14.894091	0.8425	1	1053	21.869159	0.79500	1	766	15.908619	0.16760	1010	3719.0000	27.15784	0.9262	1	243	1133.0000	21.44748	0.7184	0	1	4577	0.0218484	0.19150	1643	4815.0000	34.12253	0.5321	0	2118	0	0.0000000	0.1079	0	475	22.4268178	0.9157	1	37	1731	2.1374928	0.55080	144	1731.0000	8.318891	0.6750	0	330	4815	6.8535826	0.9282	1	3.57060	0.8018	3	2.79870	0.6649	2	0.5321	0.5276	0	3.17760	0.7990	2	10.07900	0.7892	7	Yes	Census Tract 105, Coffee County, Alabama	14641	88000	21367	78100	16983.56	102080	4383.44	0.2580990	-23980	-0.2349138	128.88	137.26	Coffee County, Alabama	Dothan-Enterprise-Ozark, AL CSA	CS222

Log NMTC and LIHTC Variables

svi_national_nmtc_df$Median_Income_10adj_log <- log(svi_national_nmtc_df$Median_Income_10adj)
svi_national_nmtc_df$Median_Income_19_log <- log(svi_national_nmtc_df$Median_Income_19)

svi_national_nmtc_df$Median_Home_Value_10adj_log = log(svi_national_nmtc_df$Median_Home_Value_10adj)
svi_national_nmtc_df$Median_Home_Value_19_log = log(svi_national_nmtc_df$Median_Home_Value_19)

svi_national_nmtc_df$housing_price_index10_log = log(svi_national_nmtc_df$housing_price_index10)
svi_national_nmtc_df$housing_price_index20_log = log(svi_national_nmtc_df$housing_price_index20)

svi_divisional_nmtc_df$Median_Income_10adj_log <- log(svi_divisional_nmtc_df$Median_Income_10adj)
svi_divisional_nmtc_df$Median_Income_19_log <- log(svi_divisional_nmtc_df$Median_Income_19)

svi_divisional_nmtc_df$Median_Home_Value_10adj_log = log(svi_divisional_nmtc_df$Median_Home_Value_10adj)
svi_divisional_nmtc_df$Median_Home_Value_19_log = log(svi_divisional_nmtc_df$Median_Home_Value_19)

svi_divisional_nmtc_df$housing_price_index10_log = log(svi_divisional_nmtc_df$housing_price_index10)
svi_divisional_nmtc_df$housing_price_index20_log = log(svi_divisional_nmtc_df$housing_price_index20)

svi_national_lihtc_df$Median_Income_10adj_log <- log(svi_national_lihtc_df$Median_Income_10adj)
svi_national_lihtc_df$Median_Income_19_log <- log(svi_national_lihtc_df$Median_Income_19)

svi_national_lihtc_df$Median_Home_Value_10adj_log = log(svi_national_lihtc_df$Median_Home_Value_10adj)
svi_national_lihtc_df$Median_Home_Value_19_log = log(svi_national_lihtc_df$Median_Home_Value_19)

svi_national_lihtc_df$housing_price_index10_log = log(svi_national_lihtc_df$housing_price_index10)
svi_national_lihtc_df$housing_price_index20_log = log(svi_national_lihtc_df$housing_price_index20)

svi_divisional_lihtc_df$Median_Income_10adj_log <- log(svi_divisional_lihtc_df$Median_Income_10adj)
svi_divisional_lihtc_df$Median_Income_19_log <- log(svi_divisional_lihtc_df$Median_Income_19)

svi_divisional_lihtc_df$Median_Home_Value_10adj_log = log(svi_divisional_lihtc_df$Median_Home_Value_10adj)
svi_divisional_lihtc_df$Median_Home_Value_19_log = log(svi_divisional_lihtc_df$Median_Home_Value_19)

svi_divisional_lihtc_df$housing_price_index10_log = log(svi_divisional_lihtc_df$housing_price_index10)
svi_divisional_lihtc_df$housing_price_index20_log = log(svi_divisional_lihtc_df$housing_price_index20)

Diff-in-Diff Models

NMTC Evaluation

Divisional SVI

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
nmtc_did10_div_svi <- svi_divisional_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(nmtc_did10_div_svi)

## [1] 3704

# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
nmtc_did20_div_svi <- svi_divisional_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
  )


nrow(nmtc_did20_div_svi)

## [1] 3704

nmtc_diff_in_diff_div_svi <- bind_rows(nmtc_did10_div_svi, nmtc_did20_div_svi)

nmtc_diff_in_diff_div_svi <- nmtc_diff_in_diff_div_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_svi)

## [1] 7408

Divisional Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
nmtc_did10_div_inc <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(nmtc_did10_div_inc)

## [1] 3703

# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
nmtc_did19_div_inc <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(nmtc_did19_div_inc)

## [1] 3703

nmtc_diff_in_diff_div_inc <- bind_rows(nmtc_did10_div_inc, nmtc_did19_div_inc)

nmtc_diff_in_diff_div_inc <- nmtc_diff_in_diff_div_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_inc)

## [1] 7406

Divisional Home Value

nmtc_did10_div_mhv <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(nmtc_did10_div_mhv)

## [1] 3479

nmtc_did19_div_mhv <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(nmtc_did19_div_mhv)

## [1] 3479

nmtc_diff_in_diff_div_mhv <- bind_rows(nmtc_did10_div_mhv, nmtc_did19_div_mhv)

nmtc_diff_in_diff_div_mhv <- nmtc_diff_in_diff_div_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_mhv)

## [1] 6958

Divisional House Price Index

nmtc_did10_div_hpi <- svi_divisional_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(nmtc_did10_div_hpi)

## [1] 1026

nmtc_did20_div_hpi <- svi_divisional_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(nmtc_did20_div_hpi)

## [1] 1026

nmtc_diff_in_diff_div_hpi <- bind_rows(nmtc_did10_div_hpi, nmtc_did20_div_hpi)

nmtc_diff_in_diff_div_hpi <- nmtc_diff_in_diff_div_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_hpi)

## [1] 2052

NMTC Divisional Model

# SVI & Economic Models

m1_nmtc_div <- lm( SVI_FLAG_COUNT_SES ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m2_nmtc_div <- lm( SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m3_nmtc_div <- lm( SVI_FLAG_COUNT_REM ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m4_nmtc_div <- lm( SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m5_nmtc_div <- lm( SVI_FLAG_COUNT_OVERALL  ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi)

m6_nmtc_div <- lm( MEDIAN_INCOME ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_inc )

m7_nmtc_div <- lm( MEDIAN_HOME_VALUE ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_mhv )

m8_nmtc_div <- lm( HOUSE_PRICE_INDEX ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_hpi )

# Add all models to a list
models <- list(
  
  "SES" = m1_nmtc_div,
  "HHChar"  = m2_nmtc_div,
  "REM" = m3_nmtc_div,
  "HOUSETRANSPT" = m4_nmtc_div,
  "OVERALL" = m5_nmtc_div,
  "Median Income (USD, logged)" = m6_nmtc_div,
  "Median Home Value (USD, logged)" = m7_nmtc_div,
  "House Price Index (logged)" = m8_nmtc_div
)


# Display model results
modelsummary(models,  fmt = 2, stars = c('*' = .05, '**' = .01, '***' = .001), coef_omit = "cbsa", gof_omit = "IC|Log",
             notes = list('All models include metro-level fixed effects by core-based statistical area (cbsa).'),
             title = paste0("Differences-in-Differences Linear Regression Analysis of NMTC in ", census_division)) %>%
  group_tt(j = list("Social Vulnerability" = 2:6, "Economic Outcomes" = 7:9))

tinytable_27eevuu92ofnoif5b40w

Differences-in-Differences Linear Regression Analysis of NMTC in Middle Atlantic Division
	Social Vulnerability					Economic Outcomes
	SES	HHChar	REM	HOUSETRANSPT	OVERALL	Median Income (USD, logged)	Median Home Value (USD, logged)	House Price Index (logged)
* p < 0.05, p < 0.01, * p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).
(Intercept)	1.59***	1.62***	0.08*	1.16***	4.44***	10.12***	11.79***	4.90***
	(0.11)	(0.08)	(0.03)	(0.07)	(0.21)	(0.02)	(0.03)	(0.04)
treat	0.94***	0.45***	0.21***	0.59***	2.18***	-0.19***	-0.14***	0.03
	(0.11)	(0.08)	(0.03)	(0.08)	(0.22)	(0.03)	(0.04)	(0.07)
post	-0.04	-0.04	0.00	0.02	-0.06	0.03***	-0.07***	0.24***
	(0.03)	(0.03)	(0.01)	(0.02)	(0.07)	(0.01)	(0.01)	(0.01)
treat × post	-0.42**	-0.06	-0.05	-0.03	-0.56	0.06	0.11*	0.05
	(0.16)	(0.12)	(0.05)	(0.11)	(0.31)	(0.04)	(0.05)	(0.09)
Num.Obs.	7238	7238	7238	7238	7238	7236	6788	2020
R2	0.184	0.060	0.342	0.313	0.269	0.088	0.729	0.317
R2 Adj.	0.178	0.053	0.337	0.308	0.263	0.081	0.727	0.302
RMSE	1.40	1.07	0.41	0.98	2.80	0.33	0.46	0.32

Across the SVI flag models for the Middle Atlantic Division, the category that experienced statistically significant changes based on NMTC program enrollment on a divisional basis is Socioeconomic Status. The Socioeconomic Status category consists of measures of poverty, unemployment, housing burdens, educational levels, and lack of health insurance. Thus, the NMTC’s goal of effecting changes in SES vulnerability appears to be working in the Middle Atlantic Division. Looking at the SES model:

The average SES Flag Count across census tracts eligible for the NMTC program was 1.59 (intercept) flags in 2010. In 2020 this decreased to 1.55 (intercept + post) flags.
For tracts that received tax credits from the NMTC program, the average SES flag count was 2.53 (intercept + treat) flags in 2010. In 2020, this decreased to 2.07 (intercept + treat + post + treat*post) flags.
Although 2.07 flags in 2020 for tracts in the NMTC program is greater than the average of 1.55 flags in non-treated tracts, the decrease in flag count was greater for tracts enrolled in the NMTC program as indicated by the treat*post coefficient. The counterfactual for NMTC-treated tracts was 2.49 flags (intercept + treat + post).

Across the indicators for changes in Economic Conditions, Median Home Value was statistically significantly greater in tracts that received NMTC dollars versus those that did not:

Specifically looking at the logged treat x post coefficient, there was an increase of 11% (.11*100) for each 1-unit increase.

Visualize SES

status <- c("NMTC Non-Participant", 
             "NMTC Participant Counterfactual", 
             "NMTC Participant", 
             "NMTC Non-Participant", 
             "NMTC Participant Counterfactual", 
             "NMTC Participant")
year <- c(2010, 
          2010, 
          2010, 
          2020, 
          2020, 
          2020)
outcome <- c(m1_nmtc_div$coefficients[1], 
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[2], 
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[2],
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[3], 
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[2] + m1_nmtc_div$coefficients[3],
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[2] + m1_nmtc_div$coefficients[3] + m1_nmtc_div$coefficients[length(m1_nmtc_div$coefficients)])

svidiv_viz_ses_nmtc <- data.frame(status, year, outcome)
svidiv_viz_ses_nmtc$outcome_label <- round(svidiv_viz_ses_nmtc$outcome, 2)
svidiv_viz_ses_nmtc

##                            status year  outcome outcome_label
## 1            NMTC Non-Participant 2010 1.585556          1.59
## 2 NMTC Participant Counterfactual 2010 2.523727          2.52
## 3                NMTC Participant 2010 2.523727          2.52
## 4            NMTC Non-Participant 2020 1.542052          1.54
## 5 NMTC Participant Counterfactual 2020 2.480224          2.48
## 6                NMTC Participant 2020 2.055891          2.06

slopegraph_plot(svidiv_viz_ses_nmtc, "NMTC Participant", "NMTC Non-Participant","Impact of NMTC Program on SVI SES Flag Count", paste0(census_division, " | 2010 - 2020"))

The slopegraph for SES SVI flags for the NMTC program indicates that in the Middle Atlantic Division our NMTC Participant Tracts had a notable decrease in socioeconomic social vulnerability flags in 2020 from the expected count of 2.48 for the counterfactual to 2.06 for the actual outcome.

Visualize Median Home Value

status <- c("NMTC Non-Participant", 
             "NMTC Participant Counterfactual", 
             "NMTC Participant", 
             "NMTC Non-Participant", 
             "NMTC Participant Counterfactual", 
             "NMTC Participant")
year <- c(2010, 
          2010, 
          2010, 
          2020, 
          2020, 
          2020)
outcome <- c(exp(m7_nmtc_div$coefficients[1]), 
           exp(m7_nmtc_div$coefficients[1])*exp(m7_nmtc_div$coefficients[2]), 
           exp(m7_nmtc_div$coefficients[1])*exp(m7_nmtc_div$coefficients[2]),
           exp(m7_nmtc_div$coefficients[1])*exp(m7_nmtc_div$coefficients[3]), 
           exp(m7_nmtc_div$coefficients[1])*exp(m7_nmtc_div$coefficients[2])*exp(m7_nmtc_div$coefficients[3]),
           exp(m7_nmtc_div$coefficients[1])*exp(m7_nmtc_div$coefficients[2])*exp(m7_nmtc_div$coefficients[3])*exp(m7_nmtc_div$coefficients[length(m7_nmtc_div$coefficients)])
)

svidiv_viz_medhmv_nmtc <- data.frame(status, year, outcome)

### Note that instead of rounding like we did for SVI variables, we will be formatting our outcome as US dollars
svidiv_viz_medhmv_nmtc$outcome_label <- scales::dollar_format()(svidiv_viz_medhmv_nmtc$outcome)

svidiv_viz_medhmv_nmtc

##                            status year  outcome outcome_label
## 1            NMTC Non-Participant 2010 131767.2      $131,767
## 2 NMTC Participant Counterfactual 2010 114941.7      $114,942
## 3                NMTC Participant 2010 114941.7      $114,942
## 4            NMTC Non-Participant 2020 123150.8      $123,151
## 5 NMTC Participant Counterfactual 2020 107425.6      $107,426
## 6                NMTC Participant 2020 120052.5      $120,053

slopegraph_plot(svidiv_viz_medhmv_nmtc, "NMTC Participant", "NMTC Non-Participant", "Impact of NMTC Program on Average Median Home Value", paste0(census_division, " | 2010 - 2020"))

When we control for metro-level effects, our average Median Home Value for tracts that did not receive NMTC dollars was ＄131,767.20 in 2010 when adjusted for inflation. This decreased to ＄123,150.8 in 2020, a -6.54% decrease in home values according to US Census data. When we control for metro-level effects, our NMTC treated tracts had an average Median Home Value of ＄114,941.70 in 2010. The average Median Income in these tracts increased to ＄120,052.50 in 2020. This is an ~11% increase over what we would have expected according to general trends. As a counterfactual, we can see that we would have expected the average Median Home Value of ＄107,425.60 in 2020 if it had decreased by -6.5%. The slopegraph for Average Home Value for the NMTC program indicates that in the Middle Atlantic Division our NMTC Participant Tracts had a notable increase in 2020 from the expected home value of ＄107,426 for the counterfactual to ＄120,053 for the actual outcome. This increase is also in contrast to the general trend of decreasing average median home values within NMTC-eligible tracts in the division from 2010 to 2020.

LIHTC Evaluation

Divisional SVI

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
lihtc_did10_div_svi <- svi_divisional_lihtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(lihtc_did10_div_svi)

## [1] 658

# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
lihtc_did20_div_svi <- svi_divisional_lihtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "lihtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
  )


nrow(lihtc_did20_div_svi)

## [1] 658

lihtc_diff_in_diff_div_svi <- bind_rows(lihtc_did10_div_svi, lihtc_did20_div_svi)

lihtc_diff_in_diff_div_svi <- lihtc_diff_in_diff_div_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_svi)

## [1] 1316

Divisional Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
lihtc_did10_div_inc <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(lihtc_did10_div_inc)

## [1] 657

# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
lihtc_did19_div_inc <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(lihtc_did19_div_inc)

## [1] 657

lihtc_diff_in_diff_div_inc <- bind_rows(lihtc_did10_div_inc, lihtc_did19_div_inc)

lihtc_diff_in_diff_div_inc <- lihtc_diff_in_diff_div_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_inc)

## [1] 1314

Divisional Median Home Value

lihtc_did10_div_mhv <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(lihtc_did10_div_mhv)

## [1] 577

lihtc_did19_div_mhv <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(lihtc_did19_div_mhv)

## [1] 577

lihtc_diff_in_diff_div_mhv <- bind_rows(lihtc_did10_div_mhv, lihtc_did19_div_mhv)

lihtc_diff_in_diff_div_mhv <- lihtc_diff_in_diff_div_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_mhv)

## [1] 1154

Divisional House Price Index

lihtc_did10_div_hpi <- svi_divisional_lihtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(lihtc_did10_div_hpi)

## [1] 77

lihtc_did20_div_hpi <- svi_divisional_lihtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "lihtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(lihtc_did20_div_hpi)

## [1] 77

lihtc_diff_in_diff_div_hpi <- bind_rows(lihtc_did10_div_hpi, lihtc_did20_div_hpi)

lihtc_diff_in_diff_div_hpi <- lihtc_diff_in_diff_div_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_hpi)

## [1] 154

LIHTC Divisional Model

# SVI & Economic Models

m1_lihtc_div <- lm( SVI_FLAG_COUNT_SES ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m2_lihtc_div <- lm( SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m3_lihtc_div <- lm( SVI_FLAG_COUNT_REM ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m4_lihtc_div <- lm( SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m5_lihtc_div <- lm( SVI_FLAG_COUNT_OVERALL  ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi)

m6_lihtc_div <- lm( MEDIAN_INCOME ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_inc )

m7_lihtc_div <- lm( MEDIAN_HOME_VALUE ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_mhv )

m8_lihtc_div <- lm( HOUSE_PRICE_INDEX ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_hpi )

# Add all models to a list
models <- list(
  
  "SES" = m1_lihtc_div,
  "HHChar"  = m2_lihtc_div,
  "REM" = m3_lihtc_div,
  "HOUSETRANSPT" = m4_lihtc_div,
  "OVERALL" = m5_lihtc_div,
  "Median Income (USD, logged)" = m6_lihtc_div,
  "Median Home Value (USD, logged)" = m7_lihtc_div,
  "House Price Index (logged)" = m8_lihtc_div
)


# Display model results
modelsummary(models,  fmt = 2, stars = c('*' = .05, '**' = .01, '***' = .001), coef_omit = "cbsa", gof_omit = "IC|Log",
             notes = list('All models include metro-level fixed effects by core-based statistical area (cbsa).'),
             title = paste0("Differences-in-Differences Linear Regression Analysis of LIHTC in ", census_division)) %>%
  group_tt(j = list("Social Vulnerability" = 2:6, "Economic Outcomes" = 7:9))

tinytable_doqtccus39v5l2a0086e

Differences-in-Differences Linear Regression Analysis of LIHTC in Middle Atlantic Division
	Social Vulnerability					Economic Outcomes
	SES	HHChar	REM	HOUSETRANSPT	OVERALL	Median Income (USD, logged)	Median Home Value (USD, logged)	House Price Index (logged)
* p < 0.05, p < 0.01, * p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).
(Intercept)	2.94***	1.82***	0.24***	1.33***	6.33***	9.86***	11.68***	4.86***
	(0.19)	(0.16)	(0.07)	(0.16)	(0.39)	(0.06)	(0.09)	(0.28)
treat	0.23	0.30*	0.16**	0.34**	1.03***	-0.03	-0.13	0.11
	(0.14)	(0.12)	(0.05)	(0.12)	(0.28)	(0.04)	(0.07)	(0.14)
post	-0.09	-0.09	-0.02	0.05	-0.15	0.04	-0.07*	0.27***
	(0.07)	(0.06)	(0.02)	(0.06)	(0.14)	(0.02)	(0.03)	(0.07)
treat × post	0.10	0.20	-0.04	-0.05	0.21	0.00	0.12	0.00
	(0.19)	(0.17)	(0.07)	(0.16)	(0.39)	(0.06)	(0.10)	(0.19)
Num.Obs.	1314	1314	1314	1314	1314	1312	1152	154
R2	0.145	0.137	0.271	0.220	0.236	0.209	0.698	0.255
R2 Adj.	0.123	0.114	0.251	0.199	0.216	0.188	0.689	0.162
RMSE	1.15	1.01	0.41	0.98	2.37	0.35	0.53	0.37

The diff-in-diff regression models for the LIHTC program in the Middle Atlantic Division do not indicate any statistically significant changes in social vulnerability or economic outcomes. Therefore we cannot conclude that the program had a measurable impact on our SVI and economic outcomes.

However,the Middle Atlantic Division tracts that received LIHTC dollars were statistically more vulnerable based on Household Characteristics, Racial and Ethnic diversity, Housing and Transportation, and Overall Social Vulnerability.

Since the LIHTC program focuses on increasing availability of housing for low income individuals in a variety of communities, these results may suggest it is effective at targeting more vulnerable areas and accomplishing this goal. However, further research is needed.

Lab 05 Middle Atlantic Division

Introduction

Diff-in-Diff models

Dependent Variables: SVI Variables, House Price Index, Median Home Values, and Median Income

Independent Variables: NMTC and LIHTC Data

Library

Load Functions

Data

House Price Index Data

CBSA Crosswalk Data

Census Data

NMTC Data

LIHTC Data

Log NMTC and LIHTC Variables

Diff-in-Diff Models

NMTC Evaluation

Divisional SVI

Divisional Median Income

Divisional Home Value

Divisional House Price Index

NMTC Divisional Model

Visualize SES

Visualize Median Home Value

LIHTC Evaluation

Divisional SVI

Divisional Median Income

Divisional Median Home Value

Divisional House Price Index

LIHTC Divisional Model