NMTC and LIHTC Evaluation

Introduction

The goal of this analysis is to estimate the causal impact of the Low-Income Housing Tax Credit (LIHTC) and the New Markets Tax Credit (NMTC) on neighborhood outcomes by using a differences-in-differences (DiD) framework. A DiD model compares changes over time in treated units (census tracts that received LIHTC or NMTC funding) versus control units (non-funded tracts), allowing us to net out both time-invariant tract characteristics and common temporal shocks.

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.

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

We model four SVI themes—socioeconomic status (SES), household characteristics (HHCHAR), minority status & language (REM), and housing & transportation (HOUSETRANSPT)—plus the overall SVI flag count. Economically, we include log-transformed median household income, log median home value, and log house price index, each measured for 2010 (inflation-adjusted) and 2020.

Independent Variables: NMTC and LIHTC Data

Our key independent variables are:

treat: a binary indicator for tract participation in NMTC (or LIHTC) funding,
post: a binary indicator for the post-treatment period (2020), and

3.treat × post: the interaction term capturing the DiD treatment effect. We also include CBSA fixed effects to control for time-invariant metro characteristics.

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_gregorio.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://r-class.github.io/paf-515-course-materials/data/raw/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	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	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
19003950200	19003	950200	IA	Iowa	Adams County	2	Midwest Region	4	West North Central Division	2382	1214	1074	674	2324	29.00172	0.7484	0	43	1148	3.745645	0.2825	0	142	782	18.15857	0.2646	0	107	292	36.64384	0.45600	0	249	1074	23.18436	0.3296	0	276	1794	15.384615	0.7587	1	194	2184	8.882784	0.4311	0	554	23.257767	0.9211	1	442	18.55584	0.1370	0	334	1742	19.17336	0.7794	1	81	583	13.893653	0.56290	0	0	2296	0.0000000	0.1215	0	89	2382	3.736356	0.2193	0	1214	32	2.6359143	0.44560	0	59	4.8599671	0.6514	0	27	1074	2.5139665	0.7799	1	72	1074	6.703911	0.6702	0	183	2382	7.682620	0.9039	1	2.5503	0.5413	1	2.52190	0.5177	2	0.2193	0.2184	0	3.45100	0.90270	2	8.74250	0.6273	5	2017	1176	923	373	1939	19.23672	0.5455	0	43	965	4.455959	0.6531	0	115	708	16.242938	0.464400	0	30	215	13.95349	0.07616	0	145	923	15.70964	0.1595	0	123	1504	8.178192	0.5864	0	64	1957	3.270312	0.23380	0	492	24.392662	0.87730	1	399	19.78185	0.2529	0	276	1558.000	17.715019	0.66950	0	44	526.0000	8.365019	0.2549	0	5	1932	0.2587992	0.4007	0	101	2017.000	5.007437	0.15670	0	1176	16	1.3605442	0.3160	0	31	2.6360544	0.5846	0	8	923	0.8667389	0.41620	0	60	923.0000	6.500542	0.6597	0	130	2017	6.4452157	0.9140	1	2.17830	0.4184	0	2.45530	0.4673	1	0.15670	0.15610	0	2.89050	0.6683	1	7.68080	0.4534	2	Yes	\$0	\$0	Census Tract 9502, Adams County, Iowa	19847	75700	30898	77200	23022.52	87812	7875.48	0.3420772	-10612	-0.1208491	113.33	109.40	NA	NA	NA
19005960500	19005	960500	IA	Iowa	Allamakee County	2	Midwest Region	4	West North Central Division	3381	1286	1199	761	3381	22.50813	0.5897	0	45	1699	2.648617	0.1433	0	150	915	16.39344	0.1774	0	141	284	49.64789	0.75450	1	291	1199	24.27023	0.3840	0	383	2152	17.797398	0.8235	1	662	3327	19.897806	0.8851	1	416	12.304052	0.4070	0	1057	31.26294	0.9039	1	267	2328	11.46907	0.2881	0	94	916	10.262009	0.37050	0	319	2923	10.9134451	0.9675	1	603	3381	17.834960	0.7106	0	1286	27	2.0995334	0.40680	0	29	2.2550544	0.5319	0	83	1199	6.9224354	0.9591	1	74	1199	6.171810	0.6369	0	0	3381	0.000000	0.3161	0	2.8256	0.6221	2	2.93700	0.7345	2	0.7106	0.7077	0	2.85080	0.66920	1	9.32400	0.7048	5	3461	1279	1178	775	3403	22.77402	0.6463	0	46	1541	2.985075	0.4416	0	76	817	9.302326	0.078740	0	92	361	25.48476	0.24980	0	168	1178	14.26146	0.1033	0	370	1997	18.527792	0.9109	1	554	3412	16.236811	0.91580	1	435	12.568622	0.24880	0	1231	35.56775	0.9808	1	162	2181.000	7.427785	0.06615	0	112	895.0000	12.513967	0.4746	0	239	3168	7.5441919	0.9470	1	1091	3461.000	31.522681	0.79910	1	1279	36	2.8146990	0.4226	0	41	3.2056294	0.6163	0	62	1178	5.2631579	0.91210	1	96	1178.0000	8.149406	0.7489	0	49	3461	1.4157758	0.6208	0	3.01790	0.6696	2	2.71735	0.6274	2	0.79910	0.79620	1	3.32070	0.8478	1	9.85505	0.7714	6	Yes	\$0	\$0	Census Tract 9605, Allamakee County, Iowa	23796	113700	35302	116300	27603.36	131892	7698.64	0.2789023	-15592	-0.1182179	119.78	133.60	NA	NA	NA
19007950100	19007	950100	IA	Iowa	Appanoose County	2	Midwest Region	4	West North Central Division	2776	1358	1168	712	2772	25.68543	0.6800	0	108	1336	8.083832	0.7380	0	191	941	20.29756	0.3858	0	93	227	40.96916	0.56620	0	284	1168	24.31507	0.3863	0	181	1912	9.466527	0.4683	0	245	2826	8.669498	0.4141	0	540	19.452450	0.8079	1	675	24.31556	0.5162	0	338	2211	15.28720	0.5533	0	78	809	9.641533	0.33150	0	0	2570	0.0000000	0.1215	0	120	2776	4.322767	0.2617	0	1358	1	0.0736377	0.19570	0	145	10.6774669	0.8254	1	9	1168	0.7705479	0.4496	0	58	1168	4.965753	0.5536	0	0	2776	0.000000	0.3161	0	2.6867	0.5858	0	2.33040	0.4045	1	0.2617	0.2606	0	2.34040	0.40590	1	7.61920	0.4607	2	2403	1209	927	650	2397	27.11723	0.7483	0	90	1141	7.887818	0.8871	1	123	683	18.008785	0.592400	0	61	244	25.00000	0.24130	0	184	927	19.84898	0.3689	0	111	1672	6.638756	0.4740	0	198	2396	8.263773	0.65480	0	539	22.430295	0.80780	1	556	23.13774	0.5076	0	368	1841.304	19.985831	0.78120	1	41	630.3641	6.504177	0.1539	0	0	2290	0.0000000	0.1327	0	72	2402.541	2.996827	0.05070	0	1209	3	0.2481390	0.2000	0	95	7.8577337	0.7914	1	2	927	0.2157497	0.22670	0	41	926.5641	4.424950	0.5019	0	1	2403	0.0416146	0.2867	0	3.13310	0.7005	1	2.38320	0.4254	2	0.05070	0.05052	0	2.00670	0.2648	1	7.57370	0.4380	4	Yes	\$0	\$0	Census Tract 9501, Appanoose County, Iowa	19902	70800	27813	102200	23086.32	82128	4726.68	0.2047394	20072	0.2443990	NA	NA	NA	NA	NA
19007950300	19007	950300	IA	Iowa	Appanoose County	2	Midwest Region	4	West North Central Division	3199	1599	1418	1150	3124	36.81178	0.8559	1	275	1466	18.758527	0.9652	1	205	745	27.51678	0.7281	0	333	673	49.47994	0.75140	1	538	1418	37.94076	0.8374	1	212	2171	9.765085	0.4892	0	324	2810	11.530249	0.5992	0	691	21.600500	0.8824	1	833	26.03939	0.6525	0	460	2217	20.74876	0.8401	1	112	761	14.717477	0.59220	0	0	2884	0.0000000	0.1215	0	164	3199	5.126602	0.3089	0	1599	178	11.1319575	0.72650	0	116	7.2545341	0.7337	0	57	1418	4.0197461	0.8884	1	221	1418	15.585332	0.9104	1	75	3199	2.344483	0.7538	1	3.7469	0.8295	3	3.08870	0.7966	2	0.3089	0.3077	0	4.01280	0.98410	3	11.15730	0.8880	8	3122	1799	1405	1115	3052	36.53342	0.8801	1	76	1237	6.143897	0.8057	1	142	795	17.861635	0.584000	0	277	610	45.40984	0.71380	0	419	1405	29.82206	0.7707	1	284	2338	12.147134	0.7814	1	187	3079	6.073400	0.50730	0	789	25.272261	0.90200	1	586	18.77002	0.1960	0	659	2495.696	26.405465	0.93540	1	85	786.6359	10.805508	0.3934	0	0	2957	0.0000000	0.1327	0	160	3122.459	5.124166	0.16330	0	1799	161	8.9494163	0.6528	0	100	5.5586437	0.7192	0	24	1405	1.7081851	0.60050	0	172	1405.4359	12.238196	0.8683	1	70	3122	2.2421525	0.7311	0	3.74520	0.8420	4	2.55950	0.5311	2	0.16330	0.16270	0	3.57190	0.9203	1	10.03990	0.7934	7	Yes	\$0	\$0	Census Tract 9503, Appanoose County, Iowa	16377	63300	20217	69700	18997.32	73428	1219.68	0.0642027	-3728	-0.0507708	105.81	130.01	NA	NA	NA
19009070300	19009	070300	IA	Iowa	Audubon County	2	Midwest Region	4	West North Central Division	1746	879	771	434	1663	26.09741	0.6868	0	21	791	2.654867	0.1445	0	129	629	20.50874	0.3979	0	23	142	16.19718	0.08948	0	152	771	19.71466	0.1759	0	206	1298	15.870570	0.7742	1	228	1631	13.979154	0.7148	0	496	28.407789	0.9812	1	327	18.72852	0.1440	0	243	1334	18.21589	0.7345	0	12	502	2.390438	0.02169	0	6	1666	0.3601441	0.4190	0	49	1746	2.806415	0.1459	0	879	6	0.6825939	0.27980	0	8	0.9101251	0.4325	0	0	771	0.0000000	0.1372	0	60	771	7.782101	0.7244	0	79	1746	4.524628	0.8415	1	2.4962	0.5254	1	2.30039	0.3869	1	0.1459	0.1453	0	2.41540	0.44410	1	7.35789	0.4210	3	1699	831	775	350	1638	21.36752	0.6109	0	14	850	1.647059	0.1920	0	89	646	13.777090	0.304100	0	29	129	22.48062	0.19170	0	118	775	15.22581	0.1388	0	98	1250	7.840000	0.5648	0	57	1650	3.454546	0.25530	0	410	24.131842	0.86800	1	374	22.01295	0.4174	0	177	1276.000	13.871473	0.41630	0	47	474.0000	9.915612	0.3430	0	0	1536	0.0000000	0.1327	0	30	1699.000	1.765745	0.01595	0	831	5	0.6016847	0.2454	0	8	0.9626955	0.4577	0	7	775	0.9032258	0.42690	0	22	775.0000	2.838710	0.3478	0	49	1699	2.8840494	0.7833	1	1.76180	0.2850	0	2.17740	0.3132	1	0.01595	0.01589	0	2.26110	0.3720	1	6.21625	0.2314	2	Yes	\$0	\$0	Census Tract 703, Audubon County, Iowa	20712	72100	29214	69900	24025.92	83636	5188.08	0.2159368	-13736	-0.1642355	NA	NA	NA	NA	NA
19011960300	19011	960300	IA	Iowa	Benton County	2	Midwest Region	4	West North Central Division	5438	2510	2241	1046	5304	19.72097	0.5037	0	210	2471	8.498584	0.7671	1	354	1547	22.88300	0.5244	0	230	694	33.14121	0.37500	0	584	2241	26.05979	0.4688	0	234	3663	6.388206	0.2765	0	381	5307	7.179197	0.3060	0	984	18.094888	0.7456	0	1374	25.26664	0.5858	0	864	3914	22.07460	0.8760	1	311	1569	19.821542	0.74650	0	52	5065	1.0266535	0.6568	0	335	5438	6.160353	0.3608	0	2510	63	2.5099602	0.43720	0	114	4.5418327	0.6392	0	32	2241	1.4279340	0.6175	0	109	2241	4.863900	0.5464	0	134	5438	2.464141	0.7593	1	2.3221	0.4725	1	3.61070	0.9427	1	0.3608	0.3593	0	2.99960	0.73360	1	9.29320	0.6995	3	5250	2525	2252	1029	5039	20.42072	0.5801	0	35	2481	1.410721	0.1483	0	445	1668	26.678657	0.918200	1	158	583	27.10120	0.28200	0	603	2251	26.78809	0.6840	0	318	3591	8.855472	0.6297	0	79	5103	1.548109	0.06672	0	1236	23.542857	0.84750	1	1184	22.55238	0.4596	0	806	3920.615	20.557999	0.80460	1	291	1387.9781	20.965749	0.7594	1	12	5078	0.2363135	0.3880	0	319	5250.100	6.076075	0.21120	0	2525	85	3.3663366	0.4532	0	0	0.0000000	0.1738	0	0	2252	0.0000000	0.09916	0	132	2251.6373	5.862401	0.6165	0	244	5250	4.6476190	0.8783	1	2.10882	0.3955	0	3.25910	0.8640	3	0.21120	0.21040	0	2.22096	0.3527	1	7.80008	0.4732	4	Yes	\$0	\$0	Census Tract 9603, Benton County, Iowa	24117	105600	26911	138900	27975.72	122496	-1064.72	-0.0380587	16404	0.1339146	195.52	231.04	Benton County, Iowa	Cedar Rapids, IA MSA	C1630
19011960700	19011	960700	IA	Iowa	Benton County	2	Midwest Region	4	West North Central Division	2741	1340	1267	662	2695	24.56401	0.6494	0	68	1282	5.304212	0.4819	0	200	847	23.61275	0.5620	0	132	420	31.42857	0.34020	0	332	1267	26.20363	0.4751	0	310	1904	16.281513	0.7862	1	323	2600	12.423077	0.6484	0	512	18.679314	0.7750	1	666	24.29770	0.5151	0	467	1996	23.39679	0.9051	1	98	697	14.060258	0.57030	0	34	2635	1.2903226	0.7095	0	103	2741	3.757753	0.2208	0	1340	32	2.3880597	0.42830	0	19	1.4179104	0.4793	0	0	1267	0.0000000	0.1372	0	99	1267	7.813733	0.7254	0	0	2741	0.000000	0.3161	0	3.0410	0.6787	1	3.47500	0.9175	2	0.2208	0.2199	0	2.08630	0.28970	0	8.82310	0.6382	3	2608	1301	1148	643	2549	25.22558	0.7120	0	39	1456	2.678571	0.3852	0	140	922	15.184382	0.396900	0	91	226	40.26549	0.59980	0	231	1148	20.12195	0.3811	0	171	1928	8.869295	0.6303	0	106	2561	4.139008	0.32710	0	566	21.702454	0.77170	1	501	19.21012	0.2190	0	268	2060.000	13.009709	0.35430	0	128	735.0000	17.414966	0.6650	0	0	2450	0.0000000	0.1327	0	125	2608.000	4.792945	0.14300	0	1301	41	3.1514220	0.4437	0	32	2.4596464	0.5734	0	8	1148	0.6968641	0.36600	0	69	1148.0000	6.010453	0.6266	0	47	2608	1.8021472	0.6781	0	2.43570	0.5041	0	2.14270	0.2969	1	0.14300	0.14250	0	2.68780	0.5729	0	7.40920	0.4129	1	Yes	\$0	\$0	Census Tract 9607, Benton County, Iowa	22139	84100	29909	86200	25681.24	97556	4227.76	0.1646244	-11356	-0.1164049	108.74	130.72	Benton County, Iowa	Cedar Rapids, IA MSA	C1630
19013000200	19013	000200	IA	Iowa	Black Hawk County	2	Midwest Region	4	West North Central Division	2874	1294	1037	1181	2874	41.09255	0.8912	1	87	1324	6.570997	0.6200	0	81	451	17.96009	0.2549	0	369	586	62.96928	0.92450	1	450	1037	43.39441	0.9097	1	338	1603	21.085465	0.8833	1	408	2762	14.771904	0.7454	0	181	6.297843	0.1024	0	931	32.39388	0.9280	1	235	1966	11.95320	0.3165	0	226	701	32.239658	0.91000	1	149	2522	5.9080095	0.9244	1	828	2874	28.810021	0.8332	1	1294	63	4.8686244	0.56180	0	15	1.1591963	0.4545	0	98	1037	9.4503375	0.9779	1	94	1037	9.064609	0.7762	1	0	2874	0.000000	0.3161	0	4.0496	0.8827	3	3.18130	0.8333	3	0.8332	0.8297	1	3.08650	0.77180	2	11.15060	0.8878	9	2833	1243	1057	958	2772	34.55988	0.8586	1	203	1513	13.417052	0.9757	1	17	334	5.089820	0.009533	0	230	723	31.81189	0.39200	0	247	1057	23.36802	0.5533	0	314	1493	21.031480	0.9364	1	314	2833	11.083657	0.78390	1	129	4.553477	0.02013	0	903	31.87434	0.9406	1	360	1930.000	18.652850	0.71890	0	181	609.0000	29.720854	0.8944	1	330	2613	12.6291619	0.9782	1	1679	2833.000	59.265796	0.92480	1	1243	145	11.6653258	0.7129	0	3	0.2413516	0.3601	0	45	1057	4.2573321	0.87560	1	143	1057.0000	13.528855	0.8890	1	0	2833	0.0000000	0.1414	0	4.10790	0.9041	4	3.55223	0.9381	3	0.92480	0.92150	1	2.97900	0.7065	2	11.56393	0.9330	10	Yes	\$0	\$0	Census Tract 2, Black Hawk County, Iowa	16659	77400	22277	82900	19324.44	89784	2952.56	0.1527889	-6884	-0.0766729	NA	126.91	Black Hawk County, Iowa	Waterloo-Cedar Falls, IA MSA	C4794
19013000300	19013	000300	IA	Iowa	Black Hawk County	2	Midwest Region	4	West North Central Division	2948	1583	1188	1355	2930	46.24573	0.9249	1	171	1263	13.539192	0.9202	1	134	392	34.18367	0.8977	1	526	796	66.08040	0.94740	1	660	1188	55.55556	0.9828	1	193	1634	11.811506	0.6034	0	487	2747	17.728431	0.8389	1	250	8.480326	0.1993	0	1005	34.09091	0.9540	1	760	1945	39.07455	0.9975	1	296	757	39.101717	0.94940	1	70	2543	2.7526543	0.8379	1	1044	2948	35.413840	0.8685	1	1583	198	12.5078964	0.75140	1	11	0.6948831	0.4045	0	16	1188	1.3468013	0.6023	0	205	1188	17.255892	0.9247	1	0	2948	0.000000	0.3161	0	4.2702	0.9156	4	3.93810	0.9830	4	0.8685	0.8649	1	2.99900	0.73320	2	12.07580	0.9531	11	3125	1623	1251	1544	3117	49.53481	0.9578	1	188	1388	13.544669	0.9764	1	53	312	16.987179	0.520600	0	647	939	68.90309	0.97910	1	700	1251	55.95524	0.9918	1	446	1876	23.773987	0.9582	1	274	3125	8.768000	0.68030	0	359	11.488000	0.20110	0	932	29.82400	0.8967	1	667	2193.000	30.414957	0.97450	1	284	682.0000	41.642229	0.9673	1	187	2691	6.9490896	0.9389	1	1763	3125.000	56.416000	0.91400	1	1623	315	19.4085028	0.8203	1	0	0.0000000	0.1738	0	88	1251	7.0343725	0.94680	1	313	1251.0000	25.019984	0.9713	1	39	3125	1.2480000	0.5930	0	4.56450	0.9675	4	3.97850	0.9890	4	0.91400	0.91070	1	3.50520	0.9056	3	12.96220	0.9871	12	Yes	\$0	\$0	Census Tract 3, Black Hawk County, Iowa	16462	76300	20954	74900	19095.92	88508	1858.08	0.0973025	-13608	-0.1537488	NA	NA	Black Hawk County, Iowa	Waterloo-Cedar Falls, IA MSA	C4794
19013000500	19013	000500	IA	Iowa	Black Hawk County	2	Midwest Region	4	West North Central Division	1918	653	653	349	1900	18.36842	0.4573	0	151	1240	12.177419	0.8947	1	128	492	26.01626	0.6722	0	93	161	57.76398	0.87590	1	221	653	33.84380	0.7474	0	292	1157	25.237684	0.9306	1	347	1809	19.181868	0.8703	1	134	6.986444	0.1315	0	466	24.29614	0.5143	0	252	1357	18.57038	0.7539	1	186	468	39.743590	0.95240	1	102	1832	5.5676856	0.9196	1	733	1918	38.216893	0.8788	1	653	0	0.0000000	0.09728	0	0	0.0000000	0.1716	0	0	653	0.0000000	0.1372	0	53	653	8.116386	0.7427	0	0	1918	0.000000	0.3161	0	3.9003	0.8577	3	3.27170	0.8649	3	0.8788	0.8751	1	1.46488	0.07947	0	9.51568	0.7256	7	1742	688	625	585	1678	34.86293	0.8624	1	117	906	12.913907	0.9730	1	67	328	20.426829	0.734200	0	136	297	45.79125	0.72160	0	203	625	32.48000	0.8277	1	192	959	20.020855	0.9280	1	132	1742	7.577497	0.61210	0	149	8.553387	0.09628	0	505	28.98967	0.8739	1	254	1237.000	20.533549	0.80360	1	122	357.0000	34.173670	0.9306	1	0	1621	0.0000000	0.1327	0	829	1742.000	47.588978	0.88740	1	688	0	0.0000000	0.0847	0	0	0.0000000	0.1738	0	9	625	1.4400000	0.55030	0	64	625.0000	10.240000	0.8245	1	0	1742	0.0000000	0.1414	0	4.20320	0.9184	4	2.83708	0.6861	3	0.88740	0.88420	1	1.77470	0.1856	1	9.70238	0.7535	9	Yes	\$0	\$0	Census Tract 5, Black Hawk County, Iowa	21996	67000	21578	64700	25515.36	77720	-3937.36	-0.1543133	-13020	-0.1675244	NA	NA	Black Hawk County, Iowa	Waterloo-Cedar Falls, IA MSA	C4794

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	3	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	3	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	3	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	3	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	3	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	3	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	3	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	3	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	3	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	3	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	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	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	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
19013000500	19013	000500	IA	Iowa	Black Hawk County	2	Midwest Region	4	West North Central Division	1918	653	653	349	1900	18.36842	0.4573	0	151	1240	12.177419	0.8947	1	128	492	26.016260	0.672200	0	93	161	57.76398	0.8759	1	221	653	33.84380	0.7474	0	292	1157	25.237684	0.93060	1	347	1809	19.181868	0.8703	1	134	6.986444	0.131500	466	24.296142	0.51430	0	252	1357	18.570376	0.753900	1	186	468	39.743590	0.952400	1	102	1832	5.5676856	0.9196	1	733	1918	38.216893	0.8788	1	653	0	0.000000	0.09728	0	0	0.0000000	0.1716	0	653	0.000000	0.1372	0	53	653	8.116386	0.7427	0	0	1918	0.000000	0.3161	0	3.90030	0.8577	3	3.271700	0.864900	3	0.8788	0.8751	1	1.46488	0.07947	0	9.515680	0.7256	7	1742	688	625	585	1678	34.86293	0.8624	1	117	906	12.913907	0.9730	1	67	328	20.426829	0.734200	0	136	297	45.79125	0.7216	0	203	625	32.48000	0.8277	1	192	959	20.0208551	0.92800	1	132	1742	7.577497	0.6121	0	149	8.553387	0.096280	505	28.989667	0.873900	1	254	1237.000	20.533549	0.803600	1	122	357.0000	34.173670	0.930600	1	0	1621	0.0000000	0.1327	0	829	1742.000	47.588978	0.8874	1	688	0	0.00000	0.0847	0	0	0.000000	0.1738	0	9	625	1.4400000	0.55030	0	64	625.00	10.240000	0.8245	1	0	1742	0.0000000	0.1414	0	4.20320	0.9184	4	2.837080	0.6861000	3	0.8874	0.8842	1	1.77470	0.1856	1	9.702380	0.7535	9	0	0	0	Yes	Census Tract 5, Black Hawk County, Iowa	21996	67000	21578	64700	25515.36	77720	-3937.36	-0.1543133	-13020	-0.1675244	NA	NA	Black Hawk County, Iowa	Waterloo-Cedar Falls, IA MSA	C4794
19103000600	19103	000600	IA	Iowa	Johnson County	2	Midwest Region	4	West North Central Division	3161	1846	1765	1356	3083	43.98313	0.9101	1	139	2099	6.622201	0.6243	0	39	255	15.294118	0.132900	0	900	1510	59.60265	0.8954	1	939	1765	53.20113	0.9753	1	30	1685	1.780415	0.04941	0	374	3183	11.749921	0.6140	0	376	11.894970	0.382100	161	5.093325	0.01140	0	187	2994	6.245825	0.052110	0	53	418	12.679426	0.504900	0	37	3109	1.1900933	0.6912	0	481	3161	15.216704	0.6618	0	1846	1068	57.854821	0.98060	1	14	0.7583965	0.4131	0	1765	0.000000	0.1372	0	200	1765	11.331445	0.8423	1	78	3161	2.467574	0.7596	1	3.17311	0.7090	2	1.641710	0.090820	0	0.6618	0.6590	0	3.13280	0.79140	3	8.609420	0.6083	5	3527	1999	1847	1820	3425	53.13869	0.9705	1	123	2258	5.447298	0.7512	1	57	337	16.913947	0.515200	0	796	1510	52.71523	0.8521	1	853	1847	46.18300	0.9667	1	39	2037	1.9145803	0.08963	0	393	3432	11.451049	0.7968	1	446	12.645308	0.251600	185	5.245251	0.012150	0	231	3246.994	7.114273	0.056830	0	70	394.7498	17.732749	0.676400	0	58	3400	1.7058824	0.7510	1	1169	3526.557	33.148474	0.8109	1	1999	1125	56.27814	0.9772	1	0	0.000000	0.1738	0	16	1847	0.8662696	0.41600	0	265	1846.57	14.350930	0.9002	1	95	3527	2.6935072	0.7704	1	3.57483	0.8078	4	1.747980	0.1249000	1	0.8109	0.8079	1	3.23760	0.8184	3	9.371310	0.7118	9	0	0	0	Yes	Census Tract 6, Johnson County, Iowa	15939	142500	19854	257500	18489.24	165300	1364.76	0.0738137	92200	0.5577737	201.44	293.09	Johnson County, Iowa	Iowa City, IA MSA	C2698
19103001100	19103	001100	IA	Iowa	Johnson County	2	Midwest Region	4	West North Central Division	4138	1680	1601	2558	3792	67.45781	0.9911	1	172	3125	5.504000	0.5055	0	43	257	16.731517	0.193200	0	962	1344	71.57738	0.9713	1	1005	1601	62.77327	0.9945	1	25	1178	2.122241	0.06233	0	353	4208	8.388783	0.3911	0	83	2.005800	0.011970	150	3.624940	0.00741	0	156	3683	4.235677	0.016450	0	0	358	0.000000	0.004282	0	11	4068	0.2704031	0.3623	0	322	4138	7.781537	0.4349	0	1680	457	27.202381	0.89670	1	18	1.0714286	0.4486	0	1601	0.000000	0.1372	0	175	1601	10.930668	0.8323	1	346	4138	8.361527	0.9139	1	2.94453	0.6524	2	0.402412	0.001325	0	0.4349	0.4331	0	3.22870	0.82630	3	7.010542	0.3637	5	4742	2076	1851	2960	4333	68.31295	0.9954	1	133	3227	4.121475	0.6134	0	61	285	21.403509	0.778600	1	1185	1566	75.67050	0.9911	1	1246	1851	67.31496	0.9987	1	43	1434	2.9986053	0.16620	0	204	4706	4.334892	0.3480	0	126	2.657107	0.009305	220	4.639393	0.011200	0	307	4115.000	7.460510	0.066910	0	53	294.0000	18.027211	0.686100	0	19	4695	0.4046858	0.4766	0	736	4742.000	15.520877	0.5503	0	2076	512	24.66281	0.8694	1	0	0.000000	0.1738	0	12	1851	0.6482982	0.35040	0	281	1851.00	15.180983	0.9105	1	451	4742	9.5107550	0.9436	1	3.12170	0.6980	2	1.250115	0.0251700	0	0.5503	0.5483	0	3.24770	0.8231	3	8.169815	0.5351	5	0	0	0	Yes	Census Tract 11, Johnson County, Iowa	8019	183700	10460	280000	9302.04	213092	1157.96	0.1244845	66908	0.3139864	252.22	286.46	Johnson County, Iowa	Iowa City, IA MSA	C2698
19103001600	19103	001600	IA	Iowa	Johnson County	2	Midwest Region	4	West North Central Division	7385	2944	2849	5589	7043	79.35539	0.9981	1	572	5172	11.059551	0.8686	1	39	167	23.353293	0.548100	0	2168	2682	80.83520	0.9904	1	2207	2849	77.46578	0.9990	1	22	1443	1.524601	0.04067	0	698	7281	9.586595	0.4788	0	129	1.746784	0.010070	114	1.543670	0.00475	0	415	6925	5.992780	0.046600	0	28	258	10.852713	0.402700	0	43	7363	0.5840011	0.5225	0	959	7385	12.985782	0.6084	0	2944	1513	51.392663	0.97470	1	0	0.0000000	0.1716	0	2849	0.000000	0.1372	0	440	2849	15.444015	0.9081	1	443	7385	5.998646	0.8803	1	3.38517	0.7597	3	0.986620	0.008515	0	0.6084	0.6059	0	3.07190	0.76520	3	8.052090	0.5296	6	8611	4188	3649	6422	8273	77.62601	0.9981	1	315	5963	5.282576	0.7381	0	41	281	14.590747	0.356000	0	2354	3368	69.89311	0.9821	1	2395	3649	65.63442	0.9983	1	92	1851	4.9702863	0.32870	0	373	8611	4.331669	0.3477	0	170	1.974219	0.007406	228	2.647776	0.006836	0	566	8077.000	7.007552	0.053030	0	72	362.0000	19.889503	0.732900	0	60	8483	0.7072969	0.5794	0	1324	8611.000	15.375682	0.5471	0	4188	1998	47.70774	0.9646	1	0	0.000000	0.1738	0	0	3649	0.0000000	0.09916	0	558	3649.00	15.291861	0.9116	1	306	8611	3.5535942	0.8293	1	3.41090	0.7695	2	1.379572	0.0410600	0	0.5471	0.5451	0	2.97846	0.7061	3	8.316032	0.5637	5	1	475339	1	Yes	Census Tract 16, Johnson County, Iowa	6199	177500	7890	196200	7190.84	205900	699.16	0.0972293	-9700	-0.0471102	NA	NA	Johnson County, Iowa	Iowa City, IA MSA	C2698
19153004800	19153	004800	IA	Iowa	Polk County	2	Midwest Region	4	West North Central Division	2896	1176	1038	1821	2828	64.39180	0.9854	1	87	1318	6.600911	0.6230	0	122	406	30.049261	0.803600	1	366	632	57.91139	0.8784	1	488	1038	47.01349	0.9370	1	576	1539	37.426901	0.98710	1	674	2960	22.770270	0.9281	1	118	4.074586	0.039520	1112	38.397790	0.98560	1	398	1820	21.868132	0.870900	1	306	641	47.737910	0.976600	1	324	2622	12.3569794	0.9740	1	2000	2896	69.060773	0.9500	1	1176	157	13.350340	0.76360	1	12	1.0204082	0.4431	67	1038	6.454721	0.9534	1	160	1038	15.414258	0.9076	1	57	2896	1.968232	0.7277	0	4.46060	0.9406	4	3.846620	0.975800	4	0.9500	0.9461	1	3.79540	0.96270	3	13.052620	0.9885	12	2703	1008	932	1211	2641	45.85384	0.9428	1	103	1313	7.844631	0.8858	1	85	469	18.123667	0.601900	0	190	463	41.03672	0.6188	0	275	932	29.50644	0.7625	1	303	1431	21.1740042	0.93830	1	480	2644	18.154312	0.9409	1	319	11.801702	0.215000	904	33.444321	0.962600	1	359	1740.000	20.632184	0.806700	1	272	587.0000	46.337308	0.979800	1	329	2404	13.6855241	0.9827	1	2002	2703.000	74.065853	0.9518	1	1008	144	14.28571	0.7571	1	0	0.000000	0.1738	0	29	932	3.1115880	0.79540	1	148	932.00	15.879828	0.9183	1	82	2703	3.0336663	0.7928	1	4.47030	0.9546	5	3.946800	0.9873000	4	0.9518	0.9483	1	3.43740	0.8850	4	12.806300	0.9852	14	0	0	0	Yes	Census Tract 48, Polk County, Iowa	16847	76000	20716	84200	19542.52	88160	1173.48	0.0600475	-3960	-0.0449183	98.94	160.82	Polk County, Iowa	Des Moines-Newton-Pella, IA CSA	CS218
19163010700	19163	010700	IA	Iowa	Scott County	2	Midwest Region	4	West North Central Division	1513	796	622	1192	1513	78.78387	0.9975	1	159	633	25.118483	0.9878	1	109	228	47.807018	0.984200	1	223	394	56.59898	0.8612	1	332	622	53.37621	0.9762	1	293	808	36.262376	0.98520	1	334	1393	23.977028	0.9409	1	30	1.982816	0.011780	540	35.690681	0.97040	1	205	1043	19.654842	0.800300	1	167	317	52.681388	0.984600	1	43	1337	3.2161556	0.8603	1	950	1513	62.789161	0.9382	1	796	97	12.185930	0.74550	0	0	0.0000000	0.1716	0	622	0.000000	0.1372	0	194	622	31.189711	0.9802	1	0	1513	0.000000	0.3161	0	4.88760	0.9928	5	3.627380	0.945300	4	0.9382	0.9343	1	2.35060	0.41000	1	11.803780	0.9359	11	1273	725	529	596	1231	48.41592	0.9536	1	24	537	4.469274	0.6548	0	98	221	44.343891	0.994900	1	232	308	75.32468	0.9909	1	330	529	62.38185	0.9970	1	131	870	15.0574713	0.85280	1	44	1273	3.456402	0.2558	0	163	12.804399	0.260900	286	22.466614	0.454600	0	161	979.000	16.445352	0.601000	0	74	310.0000	23.870968	0.818100	1	44	1176	3.7414966	0.8794	1	623	1273.000	48.939513	0.8918	1	725	58	8.00000	0.6260	0	12	1.655172	0.5183	0	6	529	1.1342155	0.48850	0	137	529.00	25.897921	0.9736	1	89	1273	6.9913590	0.9214	1	3.71400	0.8363	3	3.014000	0.7732000	2	0.8918	0.8886	1	3.52780	0.9113	2	11.147600	0.9019	8	0	0	0	Yes	Census Tract 107, Scott County, Iowa	11169	90900	21215	NA	12956.04	105444	8258.96	0.6374602	NA	NA	NA	NA	Scott County, Iowa	Davenport-Moline-Rock Island, IA-IL MSA	C1934
19163011400	19163	011400	IA	Iowa	Scott County	2	Midwest Region	4	West North Central Division	2513	1288	1003	1462	2505	58.36327	0.9724	1	101	1206	8.374793	0.7603	1	131	413	31.719128	0.849400	1	417	590	70.67797	0.9679	1	548	1003	54.63609	0.9793	1	302	1377	21.931736	0.89430	1	541	2568	21.066978	0.9040	1	107	4.257859	0.042940	769	30.600875	0.88500	1	253	1820	13.901099	0.454200	0	262	575	45.565217	0.971800	1	47	2159	2.1769338	0.8016	1	1201	2513	47.791484	0.9109	1	1288	36	2.795031	0.45680	0	0	0.0000000	0.1716	10	1003	0.997009	0.5118	0	131	1003	13.060818	0.8760	1	0	2513	0.000000	0.3161	0	4.51030	0.9487	5	3.155540	0.821400	3	0.9109	0.9071	1	2.33230	0.40260	1	10.909040	0.8657	10	2022	1140	820	617	1899	32.49078	0.8325	1	37	988	3.744939	0.5548	0	62	367	16.893733	0.513600	0	145	453	32.00883	0.3978	0	207	820	25.24390	0.6315	0	155	1274	12.1664050	0.78200	1	80	1937	4.130098	0.3254	0	279	13.798220	0.317100	473	23.392681	0.531100	0	220	1452.000	15.151515	0.506900	0	127	401.0000	31.670823	0.913300	1	12	1890	0.6349206	0.5560	0	1039	2022.000	51.384768	0.8997	1	1140	0	0.00000	0.0847	0	0	0.000000	0.1738	0	0	820	0.0000000	0.09916	0	40	820.00	4.878049	0.5463	0	89	2022	4.4015826	0.8701	1	3.12620	0.6993	2	2.824400	0.6805000	1	0.8997	0.8965	1	1.77406	0.1854	1	8.624360	0.6091	5	0	0	0	Yes	Census Tract 114, Scott County, Iowa	17891	85500	22784	81000	20753.56	99180	2030.44	0.0978357	-18180	-0.1833031	103.13	115.20	Scott County, Iowa	Davenport-Moline-Rock Island, IA-IL MSA	C1934
19169000500	19169	000500	IA	Iowa	Story County	2	Midwest Region	4	West North Central Division	2099	888	788	1339	2099	63.79228	0.9844	1	80	1250	6.400000	0.6042	0	0	0	NaN	NA	NA	391	788	49.61929	0.7537	1	391	788	49.61929	0.9580	1	12	494	2.429150	0.07516	0	320	2322	13.781223	0.7037	0	0	0.000000	0.002565	283	13.482611	0.04047	0	64	2078	3.079884	0.006276	0	56	246	22.764228	0.802300	1	52	1961	2.6517083	0.8326	1	765	2099	36.445927	0.8714	1	888	194	21.846847	0.86250	1	0	0.0000000	0.1716	92	788	11.675127	0.9854	1	67	788	8.502538	0.7587	1	0	2099	0.000000	0.3161	0	3.32546	0.7472	2	1.684211	0.103700	2	0.8714	0.8677	1	3.09430	0.77500	3	8.975371	0.6592	8	4073	865	776	1315	1775	74.08451	0.9977	1	95	2236	4.248658	0.6305	0	0	6	0.000000	0.001239	0	433	770	56.23377	0.8983	1	433	776	55.79897	0.9914	1	10	498	2.0080321	0.09533	0	152	4073	3.731893	0.2798	0	0	0.000000	0.001804	69	1.694083	0.004937	0	78	1710.000	4.561403	0.011020	0	4	143.0000	2.797203	0.031010	0	60	4032	1.4880952	0.7264	0	901	4073.000	22.121287	0.6861	0	865	129	14.91329	0.7669	1	56	6.473988	0.7549	1	0	776	0.0000000	0.09916	0	141	776.00	18.170103	0.9390	1	2298	4073	56.4203290	0.9953	1	2.99473	0.6634	2	0.775171	0.0047300	0	0.6861	0.6836	0	3.55526	0.9164	4	8.011261	0.5073	6	0	0	0	Yes	Census Tract 5, Story County, Iowa	9006	NA	5699	NA	10446.96	NA	-4747.96	-0.4544825	NA	NA	NA	NA	Story County, Iowa	Ames-Boone, IA CSA	CS112
19169000700	19169	000700	IA	Iowa	Story County	2	Midwest Region	4	West North Central Division	3638	1665	1655	2272	3568	63.67713	0.9838	1	128	2453	5.218100	0.4694	0	90	330	27.272727	0.719500	0	978	1325	73.81132	0.9776	1	1068	1655	64.53172	0.9952	1	25	889	2.812149	0.09141	0	346	3647	9.487250	0.4725	0	146	4.013194	0.038380	262	7.201759	0.01520	0	58	3374	1.719028	0.002092	0	46	296	15.540541	0.622000	0	46	3550	1.2957746	0.7099	0	545	3638	14.980759	0.6555	0	1665	675	40.540540	0.95340	1	0	0.0000000	0.1716	31	1655	1.873112	0.6972	0	187	1655	11.299094	0.8412	1	70	3638	1.924134	0.7245	0	3.01231	0.6721	2	1.387572	0.037840	0	0.6555	0.6528	0	3.38790	0.88780	2	8.443282	0.5883	4	3747	2013	1841	2350	3713	63.29114	0.9909	1	240	2447	9.807928	0.9358	1	22	221	9.954751	0.098470	0	1185	1620	73.14815	0.9884	1	1207	1841	65.56219	0.9981	1	9	1137	0.7915567	0.02621	0	618	3743	16.510820	0.9192	1	115	3.069122	0.010250	107	2.855618	0.007026	0	125	3602.000	3.470294	0.004372	0	6	225.0000	2.666667	0.028720	0	52	3698	1.4061655	0.7159	0	1169	3747.000	31.198292	0.7957	1	2013	955	47.44163	0.9644	1	0	0.000000	0.1738	0	47	1841	2.5529603	0.73380	0	274	1841.00	14.883216	0.9065	1	34	3747	0.9073926	0.5431	0	3.87021	0.8632	4	0.766268	0.0041630	0	0.7957	0.7928	1	3.32160	0.8482	2	8.753778	0.6259	7	0	0	0	Yes	Census Tract 7, Story County, Iowa	10427	189100	12207	218900	12095.32	219356	111.68	0.0092333	-456	-0.0020788	176.75	217.09	Story County, Iowa	Ames-Boone, IA CSA	CS112
19169001100	19169	001100	IA	Iowa	Story County	2	Midwest Region	4	West North Central Division	6708	1985	1913	2897	4395	65.91581	0.9878	1	320	3946	8.109478	0.7397	0	18	417	4.316547	0.003818	0	1170	1496	78.20856	0.9855	1	1188	1913	62.10141	0.9939	1	41	1298	3.158706	0.10790	0	393	5953	6.601713	0.2634	0	261	3.890877	0.034580	202	3.011330	0.00627	0	210	3904	5.379098	0.032900	0	22	424	5.188679	0.094580	0	41	6589	0.6222492	0.5364	0	544	6708	8.109720	0.4469	0	1985	952	47.959698	0.97030	1	0	0.0000000	0.1716	25	1913	1.306848	0.5927	0	172	1913	8.991113	0.7748	1	2313	6708	34.481217	0.9875	1	3.09270	0.6918	2	0.704730	0.003595	0	0.4469	0.4450	0	3.49690	0.91410	3	7.741230	0.4789	5	6579	2147	1823	3075	4627	66.45775	0.9939	1	251	3663	6.852307	0.8457	1	26	292	8.904110	0.067490	0	926	1531	60.48334	0.9381	1	952	1823	52.22161	0.9857	1	49	1120	4.3750000	0.27690	0	158	6577	2.402311	0.1466	0	163	2.477580	0.008925	240	3.647971	0.008925	0	260	4411.000	5.894355	0.026610	0	0	412.0000	0.000000	0.003424	0	0	6515	0.0000000	0.1327	0	652	6579.000	9.910321	0.3855	0	2147	1321	61.52771	0.9821	1	43	2.002795	0.5435	0	0	1823	0.0000000	0.09916	0	185	1823.00	10.148108	0.8230	1	1952	6579	29.6701626	0.9858	1	3.24880	0.7311	3	0.180584	0.0007569	0	0.3855	0.3841	0	3.43356	0.8836	3	7.248444	0.3843	6	0	0	0	Yes	Census Tract 11, Story County, Iowa	7859	224400	9321	248400	9116.44	260304	204.56	0.0224386	-11904	-0.0457311	NA	NA	Story County, Iowa	Ames-Boone, IA CSA	CS112

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	3	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	3	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	3	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	3	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	3	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	3	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	3	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	3	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	3	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	3	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] 1855

# 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] 1855

# 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] 1855

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] 3710

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] 1852

# 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] 1852

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] 3704

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] 1824

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] 1824

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] 3648

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] 1147

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] 1147

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] 2294

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))

Differences-in-Differences Linear Regression Analysis of NMTC in West North Central Division
	Social Vulnerability					Economic Outcomes
	SES	HHChar	REM	HOUSETRANSPT	OVERALL	Median Income (USD, logged)	Median Home Value (USD, logged)	House Price Index (logged)
(Intercept)	1.67	1.67*	0.28	3.16***	6.77***	9.95***	11.66***	4.95***
	(1.04)	(0.73)	(0.30)	(0.75)	(2.04)	(0.20)	(0.24)	(0.18)
treat	1.01***	0.42***	0.21***	0.31***	1.95***	-0.12***	-0.11***	-0.07**
	(0.11)	(0.07)	(0.03)	(0.08)	(0.21)	(0.02)	(0.02)	(0.03)
post	-0.02	-0.02	0.01	-0.00	-0.03	0.05***	-0.07***	0.28***
	(0.06)	(0.04)	(0.02)	(0.04)	(0.12)	(0.01)	(0.01)	(0.01)
treat × post	-0.33*	-0.16	0.00	0.07	-0.42	0.00	-0.06	0.01
	(0.14)	(0.10)	(0.04)	(0.10)	(0.28)	(0.03)	(0.03)	(0.03)
Num.Obs.	2828	2828	2828	2828	2828	2828	2772	1868
R2	0.194	0.171	0.312	0.112	0.178	0.211	0.518	0.418
R2 Adj.	0.165	0.140	0.287	0.080	0.148	0.182	0.500	0.388
RMSE	1.44	1.00	0.41	1.03	2.82	0.28	0.33	0.25
p < 0.05, p < 0.01, * p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).

Differences-in-Differences Linear Regression Analysis of NMTC in West North Central Division

In the West North Central division, the DiD interaction (treat × post) is –0.33 (p < .05) on the SES flag count—indicating that NMTC-funded tracts experienced a modest but statistically significant reduction in socioeconomic vulnerability relative to non-funded tracts. No other SVI themes or economic outcomes showed significant DiD effects at the 5% level.

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 1.671491          1.67
## 2 NMTC Participant Counterfactual 2010 2.677291          2.68
## 3                NMTC Participant 2010 2.677291          2.68
## 4            NMTC Non-Participant 2020 1.649995          1.65
## 5 NMTC Participant Counterfactual 2020 2.655795          2.66
## 6                NMTC Participant 2020 2.322709          2.32

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 shows non-participant tracts’ SES flags remain essentially flat from 2010 to 2020, while NMTC tracts decline from ~2.68 to ~2.32 flags, mirroring our estimated, –0.33 DiD effect.

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 115814.93      $115,815
## 2 NMTC Participant Counterfactual 2010 103578.32      $103,578
## 3                NMTC Participant 2010 103578.32      $103,578
## 4            NMTC Non-Participant 2020 107780.24      $107,780
## 5 NMTC Participant Counterfactual 2020  96392.55       $96,393
## 6                NMTC Participant 2020  90602.19       $90,602

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"))

The home-value slopegraph reveals that participant tracts’ median home values rose from $103,578 in 2010 to $90,602 in 2020 (inflation-adjusted), compared with 115,815 to 107,780 in non-participant tracts, suggesting smaller gains in NMTC areas over the decade.

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] 118

# 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] 118

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] 236

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] 117

# 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] 117

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] 234

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] 111

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] 111

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] 222

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] 45

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] 45

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] 90

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))

Differences-in-Differences Linear Regression Analysis of LIHTC in West North Central Division
	Social Vulnerability					Economic Outcomes
	SES	HHChar	REM	HOUSETRANSPT	OVERALL	Median Income (USD, logged)	Median Home Value (USD, logged)	House Price Index (logged)
(Intercept)	2.69***	0.46	0.34***	2.91***	6.40***	9.10***	12.43***	5.11***
	(0.40)	(0.39)	(0.10)	(0.34)	(0.78)	(0.13)	(0.18)	(0.23)
treat	0.30	0.08	-0.05	0.30	0.63	0.11	0.16	-0.24
	(0.26)	(0.25)	(0.06)	(0.22)	(0.50)	(0.09)	(0.10)	(0.12)
post	-0.37*	-0.26	-0.01	-0.16	-0.80**	0.13**	-0.11	0.34***
	(0.15)	(0.14)	(0.04)	(0.12)	(0.29)	(0.05)	(0.06)	(0.08)
treat × post	-0.33	0.26	0.06	0.01	0.00	0.06	0.03	0.18
	(0.34)	(0.33)	(0.08)	(0.29)	(0.66)	(0.11)	(0.13)	(0.16)
Num.Obs.	212	212	212	212	212	212	200	88
R2	0.349	0.496	0.670	0.391	0.465	0.407	0.732	0.539
R2 Adj.	0.266	0.432	0.627	0.313	0.396	0.331	0.695	0.419
RMSE	0.91	0.88	0.22	0.77	1.77	0.30	0.34	0.28
p < 0.05, p < 0.01, * p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).

Differences-in-Differences Linear Regression Analysis of LIHTC in West North Central Division

Results from the LIHTC divisional model show that, while all tracts experienced a modest but statistically significant decrease in SES-related vulnerability (post = –0.37, p < .05) and in overall SVI flag count (post = –0.80, p < .01) between 2010 and 2020, the treatment-by-period interaction (“treat × post”) is small and never reaches significance across any of the five SVI dimensions. In other words, although social vulnerability declined over time in this division, LIHTC-supported tracts did not exhibit an additional differential change in SVI relative to non-participating tracts.