Executive Summary

Overview/ Research Question

The research question we will review in this paper is whether two large federal programs designed to revitalize distressed neighborhoods in US cities have been successful. This paper uses Median Home Value as one of the main variables to construct gentrification metrics in addition to measurment instruments created to measure latent constructs of neightborhood conditions and change over the 2000- 2010 study period.

The final question we wish to answer is has each federal program been successful?

Program Details

The two programs reviewed in this paper include the Low Income Housing Tax Credit and the New Market Tax Credits.

Low Income Housing Tax Credits (LIHTC) are one of the primary and largest policy instruments used to incentivize the construction of new affordable housing units in the United States. Through this program, the IRS gives billions in tax credits to the states, states are then able to award these credits to developers who are able to sell them for cash to investors. Developers then use cash to help build apartment buildings and are required to comply with the credit requirement of charging residents a lower rent rate.

The goals of this program is aimed at providing affordable housing while developers receive reasonable return for their investments.

There are 2 types of LIHTC provided:

• Type 1: Applies to new construction and consists of a 9 % tax credit that allows developers to claim about 9% of a projet's qualified cost basis every year for 10 years. This credit covers about 60-70% of new housing construction or renovation costs and can increase by up to 30% when located in areas categorized as being in dire need.
• Type 2: Consists of a 4 % tax credit. In addition to the 4% credit this tax credit type also allows investors to claim additional tax deductions based on depreciation and losses on projects that can be used to reduce the burden of their income taxes even further. Tax credit can be sold to investors such as banks for cash that they can then use to build housing.

In exchange for the LIHTC housing constructed must remain affordbale for at least 30 years and at least 40% of rental units must be reserved for housholds making 60% or less of the area's median income and 20% of units must be reserved for household making 50% or less of the area's median income.

New Market Tax Credits (NMTC) are mechanisms designed to catalyze economic development in distressed communities by attracting investments from private developers. Eligibility for this program is dependent on the project's location which is based on a census-based criteria.

The qualifying criteria as as follows:

• Qualified criteria: Census tracts must be within poverty rate of 20% or greater or median family income of 80% or less of that area's median family income.
• Qualified, Severely Distressed: Census tract must meet at least one of four criterias: poverty rate of 30% or greater, median family income of 60% or less of that area's median family income, unemployment rate of at least 1.5 times the national average, or non-metro area that meets "Qualified" criteria.

Data

We use 2000 to 2010 as the study period and look at broad trends in neighborhood change over the decade to determine whether neighborhoods targeted by the programs have achieved any more success than they would have without the billion of dollars in federal subsidies.

The raw data utilized in this paper includes the following census tract data: Harmonized Census Tract: Longitudinal Tracts Database. This data contains two subfolders containing csv data files: the first subfolder contains the data that is comprised of variables coming from the long-form version of the census (which is only administered to a sample of the population) or variables from the American Community Survey (the annual survey given to a subsample of citizens). The second subfolder contains the data which is comprised of variables that come from Dicennial Census short form, and thus they are population measures and not sample estimates.

The LTDB-codebook has details of variables from Longitudinal Tract Data Base Census Data for 1970-2010 and ACS 2008-2012. It defines the variables used at the tract and county levels for the Longitudinal Tract Database (LTDB) web-based map system.

As Census data files do not always have info about metro areas. Crosswalk file from the National Bureau of Economic Research has also been downloaded. Empty cbsanames are coded as "Rural". Only “countyname”,“state”,“fipscounty”, “msa”,“msaname”, “cbsa”,“cbsaname”, “urban” columns are kept in cbsa-crosswalk.rds.

The Census Tract data and the Crosswalk file were combined and filtered to combine set into a single table through loading data as character vectors, identifying missing values and replacing with variable mean or NAs, standardizing datasets across all of the years, and cleaning and tidying data from the same year, then combining sample and full data frames into a single file.

The LIHTC Data Dictionatry 2017 pdf contains details of the variables used in HUD National Low Income Housing Tax Credit (LIHTC) from 1987-2017, the LIHTCPUB dataset contains data for Low Income Housing Tax Credit Federal program, and the NMTC sheet dataset contians data for New Market Tax Credit Data Federal program. These two federal program datasets were combined with the census data sets through the matching of tracts for projects under NMTC and LIHTC and merging datasets directly by aggregating the project data first because there are multiple projects census tracts, dropping rural tracts, and creating new variables related to growth in Median Home Value.

Complete data wrangling steps can be located in the README file in the data folder.

The Rodeo Data folder contains datasets that have been cleaned and ready for analysis. These have been used in creating descriptive analysis for the project.

All data folders are located on the respository here.

Methods

Gentrification, physical and demographic changes to a neighborhood that brings in wealthier residents, new businesses, investment and development in the area, is an important topic in social science. Gentrification also bring concerns related social-justice due to displacement and dislocation of low-income residents from the neighborhood. Traditionally low-income neighborhood across the United States gentrify. Identifying neighborhood changes related to gentrification can help in planning for negative by-products of gentrification and urban development of the area. City can plan for low-income amenities and rent-controlled-housing in recently developed and developing neighborhoods.

To capture the neighborhood changes, gentrification metrices are constructed. Gentrification is not caused by a single variable, but it is the result of a pool of gentrifiers with cultural preference for urban living, better amenities, disposable income and urban housing. These variables can measure the trends in latent changes such as economic strength, community vulnerability, and neighborhood distress among many other factors, allowing the city to intervene before the low-income population is severely affected.

The objective of this paper is to capture initial picture of neighborhood change in the year 2000 using longitudinal tabulated database (LTDB) from census data containing data related to almost 72,000 unique census tracts. It contains almost 70 variables describing racial, socio-economic, housing, age, and marital status of the population. The key to observing changes is the conversion of Census tract data into useful ‘features’ or variables that help predict gentrification. In theory, neighborhood change suggests that low-priced neighborhoods adjacent to wealthy ones have the highest probability of gentrifying in the face of new housing demand.

This paper uses Median Home Value as one of the main variables to construct gentrification metrices. To measure the general dimension of community strength and vulnerability in the year 2000, three instruments are used to measure different latent constructs. For the purpose of this paper, neighborhood health is constructed from the entire available LTDB data.

For the purpose of predicting Median Home Value changes from 2000 to 2010 only urban tracts are considered. House Value less than $10,000 in 2000 and growth of over 200% are dropped. This is done to draw more meaningful comparison. Usually a property which is valued at less than $10,000 is vacant and growth of over 200% is marked by new developement. Including these outliers will skew the data.

The Descriptive Analysis portion of this paper utilizes census data to calculate change in Median Home Values between 2000-2010 as well as measuring gentrification occuring during during this period. Gentrification is operationalized and census tracts eligible for gentrification are identified.

The following section on Evaluation of Tax Credit Programs explores the impact of the two programs on areas vulnerable for gentrification and determines which of these approaches results in neighborhood growth through model evaluations after controlling for social status (professional employee population, homeowner population, and married population), diversity (non-Hispanic White population, population living in poverty, and population with household head living in home for less than 10 years), and neighborhood value (median home value, income per capita, and college-educated population).

Results

The New Market Tax Credit Program is effective at increasing growth.According to the regression model created under Evaluation of Tax Credit Programs neighborhood values increase by 10% under this program. In contrast, the LIHTC program does not display statistical significance in growth when changes in social status, diversity, and neighborhood value indexes are controlled for. Therefore, for our purposes, we find that the NMTC program is most effective.

However, it is important to also consider that census tracts in the LIHTC program may not be in need of as much growth since the goal of the program is primarily to provide lower-income residents with affordable housing in areas where they may have been displaced due to gentrification rather than to improve the surrounding neighborhood in the way that the NMTC program is designed to do. Both programs may be effective when examined through a lens other than home value growth.