Does the Low-Income Housing Tax Credit Program Expand Access to Opportunity Neighborhoods? Tracking Movements of Low-Income Tenants in California

Abstract

The Low-Income Housing Tax Credit (LIHTC) program can potentially help expand access to neighborhoods with low poverty and economic opportunities for low-income households. Prior studies described that LIHTC units are in neighborhoods with relatively high poverty, but with improvements in recent years. Beyond cross-sectional analyses, scholars have not extensively looked at the movements of tenants. It remains unclear whether the program creates opportunities for low-income households to move into better neighborhoods than they previously lived in or reinforces segregation by encouraging moves to similarly or more disadvantaged neighborhoods. Using an extensive consumer database, I am tracking the movements of households who move into new LIHTC properties in California. The experimental findings show that residents experience, on average, increases in poverty exposure by up to six percentage points over other moved low-income renters. Tenants see lower levels of neighborhood amenities than at their previous addresses. The construction of LIHTC housing can increase the chance of households moving into minority-concentrated areas.

Keywords

affordable housing low income tax credit residential mobility segregation

Rental production subsidies, such as the Low-Income Housing Tax Credit (LIHTC) program, can directly influence where low-income households live and their livelihood. A large body of literature supports the importance of neighborhood context for individual and family outcomes (Chetty, Hendren and Katz 2016; Chetty and Hendren 2018; Sharkey and Faber 2014). As the largest affordable housing production program in the U.S., the creation of LIHTC housing can potentially help expand access to neighborhoods with more opportunities than many low-income households currently live. Prior research, however, described that LIHTC properties are often located in less desirable neighborhoods than market-rate rental units in terms of poverty exposure, minority concentration, employment activity, crime, and school quality (Dawkins 2013; Ellen, O’Regan and Voicu 2009; Ellen, Horn and Kuai 2018; Horn, Ellen and Schwartz 2014; Lens 2014; Lens, Ellen and O’Regan 2011; McClure and Johnson 2015; McClure and Schwartz 2023). There are some hopeful signs. As more low- income households flow into suburban neighborhoods (Schouten 2021), the LIHTC program is also entering higher-opportunity suburban neighborhoods (Ellen et al. 2015; McClure and Johnson 2015). Many state housing agencies, which evaluate LIHTC applications and make funding decisions, also start to explicitly encourage siting properties in “areas of opportunity” (Ellen et al. 2015; Kuai 2023).

There is a lack of data on LIHTC tenants and their experiences. A handful of studies with limited tenant information are cross-sectional and unable to follow tenant movements (such as Ellen et al. 2018, Ellen et al. 2016, and O’Regan and Horn 2013.) Having data on residential movements is particularly important to fully understand whether the LIHTC program provides low-income tenants with better access to opportunities than they previously experienced. To fill this gap, I experiment with an innovative method to locate low-income households in California with a proprietary consumer database. This paper presents empirical evidence on whether low-income households can leverage low rents offered by the LIHTC program and reach higher-opportunity neighborhoods at the same time. The results indicate that a low-income household, on average, experiences an increase in their neighborhood poverty exposure by two to six percentage points and decreases in many neighborhood amenities after moving into a unit enabled by the tax credit program. There are two types of credits in the LIHTC program with different allocation mechanisms: the 9% credit and the 4% credit. Despite being noncompetitive among applicants and “lightly” regulated,¹ the 4% program is able to place households into slightly better neighborhoods than those of the competitive 9% program. More research is needed to understand the exact drivers of residential mobility among low-income households. However, moving into a LIHTC unit may reduce a low-income household’s experiences with some neighborhood amenities and potentially undermines the ability to translate stable rents into economic mobility.

Background

The LIHTC program was established by the Tax Reform Act of 1986 to subsidize housing productions that serve low-income Americans. This subsidy program has financed over 3.1 million low-income units.² The U.S. Internal Revenue Service administers the program and allocates credits to state housing finance agencies based on a formula set by legislation. The U.S. Department of Housing and Urban Development (HUD), the primary agency in charge of federal housing programs in the U.S., only collects data and provides technical assistance with regard to the LIHTC program. Under the federal tax credit program, an affordable housing development can qualify for tax credits if at least 40% of households have incomes below 60% of the Area Median Income (AMI) or if at least 20% of tenants have incomes below 50% of the AMI. In 2018, income averaging was added to allow LIHTC properties to serve households with incomes of up to 80% of AMI as long as the average income in the project remains eligible. In practice, most properties contain only low-income units (Collinson, Ellen and Ludwig 2016). Rents are capped at 30% of the AMI tied to the unit. Properties must meet affordability requirements for a minimum of 30 years. California extends this requirement and requires a 55-year affordability use period for properties enabled by the 9% credit.

Each state must issue a Qualified Allocation Plan (QAP), typically updated annually or every other year. A QAP outlines basic criteria that are required by federal regulation. States also adopt individually tailored priorities in QAPs, such as setting aside credits for properties in areas with the greatest need. Some states incentivize siting in neighborhoods with low poverty rates, whereas others prioritize revitalization in communities with higher poverty (Ellen et al. 2015; Kuai 2023). California first started to encourage LIHTC developments to be sited near growth or high-income areas and near public amenities in 2003 (California Tax Credit Allocation Committee 2003). California revised its QAP in late 2016 to explicitly encourage more LIHTC units to be sited in high-opportunity neighborhoods to address segregation and improve residential mobility for low-income households (Brodfuehrer and Williams 2018; Reid 2019).

There are two types of credits: the 9% credit (70% subsidy) and the 4% credit (30% subsidy), which refer to the percentage of qualified development costs that may be used to lower the federal income tax liability of developers or investors. Each development receives a ten-year stream of credits. The 9% credit is awarded on a highly competitive basis to primarily subsidize new construction and substantial rehabilitation projects. Proposals are often scored and ranked in terms of their strength in financial proforma, location, community approval, tenant composition, and other state-formulated criteria. The 4% credit is typically claimed for rehabilitation and new construction on a noncompetitive basis. The 4% credit is awarded “automatically” to projects where 50% or more of the development costs are funded by a tax-exempt bond (Keightley 2019).³ As a 9% deal represents a significant percentage of equity, reducing the need for debt and other subsidies, a 4% deal requires significantly heavier financing layering (Reid and Kneebone 2021).

Researchers have not extensively examined the 4% credit. Given the programmatic difference, the distributions of 4% developments may differ from those of 9% developments and where low-income tenants are able to live. It is important to highlight this distinction in this study even though it is not a focus of this paper to evaluate differences in policy levers. Policies set for the competitive 9% allocation process are often more meaningful in determining the fate of a project than for the “non-competitive” 4% proposals even as some states, including California, start to implement scoring requirements for the 4% projects. Thus locational incentives based on poverty deconcentration, racial integration, and siting in high-opportunity neighborhoods, if any, may vary by program. Recent research has shown different spatial siting patterns between the two types (Kuai 2023).

Literature Review

Neighborhoods are the immediate social context in which individuals and families interact with the institutions and social agents. Neighborhoods also control one’s access to community opportunity structures and resources (Gephart 1997). Extensive research concluded that the quality of a neighborhood affects a wide range of life outcomes (see reviews: Freddie Mac Multifamily and National Housing Trust 2018; Winkler, Varn and Lee 2019). Results from the Moving to Opportunity experiment in the U.S. showed that moving to lower-poverty neighborhoods can improve the well-being of low-income individuals, mostly in physical and mental health (Clampet-Lundquist et al. 2011). Neighborhood quality can affect the long-run earning trajectory of children from low-income households as well (Chetty, Hendren and Katz 2016).

One argument for a housing production subsidy is that the private market does not produce enough affordable housing for low-income households in desirable neighborhoods (Been, Ellen and O’Regan 2019; Khadduri and Wilkins 2008). Low-income households face discrimination (Faber and Mercier 2022; Nguyen 2005; Tighe 2012; Tighe, Hatch and Mead 2017) and a lack of information (Kleit and Galvez 2011) which prevent them from accessing opportunity-rich neighborhoods. A production subsidy can increase the prevalence of affordable housing in relatively high-cost neighborhoods, increasing the chances that low-income households can live there (Khadduri and Wilkins 2008). Although the LIHTC program was established before the federal government specifically promoted poverty deconcentration and racial desegregation, housing advocates have long charged it with these goals (Hollar and Usowski 2007; Shamsuddin and Cross 2020). The oversight is, however, devolved to state agencies. In recent years, some states started to encourage siting more developments in “areas of opportunity” with criteria defined by each state (Ellen et al. 2015; Kuai 2023).⁴ As many focus on expanding housing choices, there is an ongoing debate about the limits of the neighborhood opportunity concept. Research showed that mobility to opportunity neighborhoods provided by subsidized housing programs does not always increase subjective well-being, earning, and neighborhood satisfaction (Blumenberg and Pierce 2014; Lens and Gabbe 2017). Neighborhood benefits are also likely to be dependent on household type.

Research examining spatial distributions of LIHTC properties showed that units are built in neighborhoods with poverty rates that are higher than the U.S. average (Ellen, O’Regan and Voicu 2009; Freeman 2004; McClure and Johnson 2015; McClure and Schwartz 2023). Although LIHTC units are about three times more likely to be sited in high-poverty neighborhoods compared to all rental units, tenants are more likely to reach lower-poverty neighborhoods than recipients of other housing assistance (Ellen, O’Regan and Voicu 2009). Many studies included neighborhood measures to examine the distributions of LIHTC units, such as concentration of subsidized units, employment activity, crime, school quality, and climate risks (Brennan, Mehta and Steil 2022; Dawkins 2013; Ellen, Horn and Kuai 2018; Horn, Ellen and Schwartz 2014; Lens 2014; Lens, Ellen and O’Regan 2011; McClure and Johnson 2015; Mehta, Brennan and Steil 2020; Shamsuddin and Leib 2022). These studies generally showed that LIHTC properties are sited in less desirable neighborhoods.

The California Tax Credit Allocation Committee (CTCAC) is in charge of allocating the tax credit in California. It started to encourage siting developments near growth or high-income areas and near amenities in 2003 (California Tax Credit Allocation Committee 2003). CTCAC revised its QAP in 2017 to include a fair housing goal (CTCAC, 2017). A development ranks higher if it is located in a “Highest or High Resource” tract (California Tax Credit Allocation Committee 2017). In general, these tracts tend to be lower-poverty and more suburban with a lower share of minorities. California Tax Credit Allocation Committee also uses a tie-breaker system based on factors such as fulfilling housing goals, gaining administrative support, and acquiring external funds. In 2012, only 17 of the 236 new construction proposals did not receive the maximum number of points (Lang 2015). While this indicates that there is substantial demand, the system may undermine the efficacy of locational prioritization.

Existing literature on LIHTC tenants and their experiences is quite limited. Housing and Urban Development has only provided state-level aggregates on tenants since 2018. There are only a handful of cross-sectional studies with tenant data. O’Regan and Horn (2013) found that 45% of tenants have extremely low incomes in 18 states. Extremely low-income households still experience high levels of rent burden even with rental assistance (O’Regan and Horn 2013). Ellen, Horn and O’Regan (2016) found that poor tenants are more likely to live in high-poverty neighborhoods than nonpoor LIHTC tenants. In terms of accessing neighborhood opportunities, Ellen, Horn and Kuai (2018) found that poor and minority tenants live in neighborhoods that are significantly more disadvantaged than white tenants do. Reid (2019) revealed a strong attachment to the neighborhoods where LIHTC residents moved from. This work also suggested that tenants face significant obstacles in improving labor and housing positions rather than a lack of access to labor markets.

Tracking Residential Movements Using a Consumer Database

This paper examines the movements of low-income households living in LIHTC properties with an experimental data source. To estimate whether an affordable housing production subsidy improves neighborhood access, tenant characteristics and movement data are required. Nevertheless, such information rarely exists. I experiment with an alternative method to track tenant movements in California using a longitudinal consumer database by InfoUSA⁵. InfoUSA aggregates raw household data from property and tax assessments, voter registrations, utility connects, postal data, and other public sources. This database provides yearly address information of households with additional demographic variables on predicted income, estimated wealth, predicted owner/renter status, presence of children, age, and imputed race and ethnicity of the householder.⁶ Some variables are in limited use due to data quality concerns in this paper. In 2017 alone, there are 16 million records.⁷

I first extract all building-level addresses of LIHTC properties, including newly constructed and substantially rehabilitated, placed in service between 2006 and 2015 from HUD’s LIHTC Project Database and data published by the CTCAC.⁸ I then locate households with these addresses in InfoUSA’s consumer database between 2006 and 2017 and flag those who moved into LIHTC units within three years after a development is open to new occupancy. I link households across years using an identifier provided by InfoUSA. I record the origin of each move. I define a household as low-income if the predicted income is less than 60% of AMI in the Multifamily Tax Subsidy Projects Income Limits.⁹ The renter status must be “most likely” or “reported.” I also track the movements of other low-income renters as counterfactuals. Not every building address yielded a matched search result. However, the sample includes a representative spatial distribution of units and developments.

Neighborhood demographic variables are from the Census (2010) and the American Community Survey (2014–2018). Additional tract-level measures are from HUD’s Affirmatively Furthering Fair Housing (AFFH) dataset.¹⁰ I use the following indicators to measure neighborhood opportunities, which are explained later: poverty rate, the chance of being located in a high-poverty neighborhood, the chance of being located in a minority-concentrated neighborhood, school proficiency index, jobs proximity index, low-wage jobs proximity index, low transportation cost index, transit trips index, and environmental health index. To facilitate the comparison between LIHTC tenants with renters receiving other subsidies, I add counts of Housing Choice Voucher holders from the 2017 Picture of Subsidized Households and counts of public housing units in 2016 from the National Geospatial Data Asset. I use a Census Tract to approximate a neighborhood due to the limitation of neighborhood data in the U.S.

Table 1 summarizes tracked households with units placed in service between 2006 and 2015 in California. I am able to track 31,129 low-income households who moved into LIHTC units, which accounts for 23.6% of units placed in service during the study period.¹¹ The consumer database is no way a perfect source of residential movements, but it can provide some patterns of residential flows and has been used in other studies (Chapple et al. 2022; Greenlee 2019). One major limitation of a consumer database is that it may undercount certain populations while overcount other populations (Chapple et al. 2022). It is likely to lose track of a household when such a household maintains no formal financial ties, does not use postal services, splits from another household, or has informal living arrangements. The tracked sample has higher shares of households from newly constructed units.¹² A mismatch is also possible when addresses for LIHTC buildings may differ from household addresses captured by the InfoUSA data (such as the mailing addresses vs. living addresses.) A comparison of the sample is described in Table 2 in the next section.

Table 1.

Sample of Tracked Households in New LIHTC Developments, 2006 to 2015.

	All	Tracked
No. of developments	1,653	1,233
No. of low-income units	132,243	31,129
Share of 4% units	64.8%	65.4%
Share of 9% units	35.1%	34.6%
Share of units in new constructions	57.7%	64.3%
Share of new units among 4% units	47.6%	54.4%
Share of new units among 9% units	76.7%	83.1%

Sources: Updated HUD LIHTC Project Database and InfoUSA U.S. Consumer Database 2005–2017.

Note: Projects placed in service between 2006 and 2015 in California. HUD = Department of Housing and Urban Development; LIHTC = Low-Income Housing Tax Credit.

Table 2.

Neighborhood Characteristics, Opportunity Indicators by Renter Type.

	Units in tracked sample		Low-income units in all developments in CA		Public housing	HCV	Poor renters	All renter
	4% LIHTC	9% LIHTC	4% LIHTC	9% LIHTC	Public housing	HCV	Poor renters	All renter
Neighborhood Poverty Rate	20.1	25.5	22.1	27.6	33.6	22.1	23.8	17.9
Neighborhood Minority Share	65.3	72.2	68.0	72.6	79.3	71.2	72.9	60.3
% in High-Poverty Neighborhoods	18.6	34.6	24.7	39.3	60.4	24.6	30.0	16.1
% in Minority-Concentrated Neighborhoods	24.4	36.5	35.7	45.2	61.4	41.4	45.5	26.9
School Proficiency Index (+)	42.2	37.5	43.0	35.7	29.4	39.5	37.7	49.8
Environmental Health Index (+)	32.2	26.8	28.4	26.0	20.3	26.5	24.8	28.1
Jobs Proximity Index (+)	49.7	49.2	52.2	55.0	50.7	46.5	47.0	54.5
Low-Wage Jobs Proximity Index (+)	48.6	43.6	46.5	42.0	38.6	46.0	43.4	48.3
Low Transportation Cost Index (+)	70.5	65.0	75.5	68.6	72.4	74.9	65.8	73.1
Transit Trips Index (+)	67.6	68.1	71.7	69.2	74.0	74.8	69.8	73.3
N	10,756	20,373	185,571	130,105	29,252	327,638	696,078	5,542,127

Sources: Updated HUD LIHTC Project Database, HUD AFFH datasets (2018), American Community Survey (2014–2018), InfoUSA U.S. Consumer Database, Picture of Subsidized Households (2017), Census (2010), and National Geospatial Data Asset (2016).

Note: All active LIHTC developments include all LIHTC properties in California which are still active. Tabulations for public housing, HCV, poor renter, and all renters include all tracts in California. (+) means a positive correlation between the index and neighborhood opportunity. (-) means a negative relationship. AFFH = Affirmatively Furthering Fair Housing; LIHTC = Low-Income Housing Tax Credit; HCV = Housing Choice Vouchers; HUD = Department of Housing and Urban Development.

Selecting Neighborhood Opportunity Indicators

There is mounting evidence that neighborhood contexts may shape life outcomes. However, scholars have little agreement over which opportunity indicators matter and which dimensions matter most for a particular type of opportunity (Galster 2008; Lung-Amam et al. 2018; Sharkey and Faber 2014). A few studies have raised questions about how opportunity should be defined and by whom. Broadly, Lung-Amam and colleagues (2018) showed that several factors appear to matter across various groups, such as “safety, access to employment, school quality, and sense of community.” However, differences exist in how residents perceive opportunities by race, income, and geography. As many states start to encourage the siting of units in areas of opportunity (Ellen et al. 2015; Freddie Mac Multifamily and National Housing Trust 2018), Reid (2019) shed some light on the potential disconnect among opportunity measures in QAPs, tenants’ experiences and decision-making processes, and the intent of improving economic mobility by government officials.

With these caveats in mind, this paper selects opportunity measures based on policy goals and locational criteria outlined in QAPs. Poverty and racial segregation measures are the most popular indicators of how scholars characterize neighborhood opportunity (Lens and Reina 2016; McClure 2006). States frequently use area poverty rate to define an “area of opportunity” (Ellen et al. 2015). I calculate three measures: the tract poverty rate for a unit, whether a unit is located in a high-poverty neighborhood (poverty rate > 30%), and whether a unit is located in a minority-concentrated neighborhood (tract minority share – CBSA minority share > 20%).¹³

Scholars characterize spatial opportunity using social and spatial factors (Galster and Sharkey 2017; Lung-Amam et al. 2018). These measures, such as unemployment rate, education equality, or health outcomes, are seen in the literature on neighborhood effects (see Freddie Mac Multifamily and National Housing Trust 2018; Winkler, Varn and Lee 2019). I pick factors from the AFFH assessment toolkit, which HUD grantees could use to conduct their mandatory Assessments of Fair Housing. These measures are intended to match metrics states use such as “low poverty rates,” “away from environmental hazards,” “quality education institutions,” and “access to transportation and employment” in QAPs (Ellen et al. 2015; Kuai 2023). The five indicators selected are the school proficiency index (for education quality), the jobs proximity index (for economic opportunities), the low transportation cost index (for locational affordability), the transit trips index (for transportation access), and the environmental health index (for a healthy environment).¹⁴ A higher score on each measure means better opportunities in that neighborhood.

The quality and availability of neighborhood services may significantly impact an individual’s well-being. While some measures promoted by policy-making have a clear linkage with life outcomes, others do not have empirical support. First of all, schools serve as an important mediator of neighborhood context (Popkin, Harris and Cunningham 2002). Research showed a strong correlation between educational resources and student performance (Jargowsky and El Komi 2009). If a school lacks basic resources, students are unlikely to receive a quality education. Many parents also believe that fewer resources can negatively impact their children’s experiences (Galster and Santiago 2006) and resort to seeking resources outside their neighborhoods (Jarrett 1997).

Neighborhoods matter in the access to economic opportunities. Residents that are a long distance from jobs may be unable to get decent jobs. Many welfare policies—built upon the conceptualization of the spatial mismatch hypothesis (Ihlanfeldt and Sjoquist 1991; Kain 1968)—focus on the long commutes needed to connect welfare participants in central-city residential locations and those expanding job opportunities in the suburbs. Many QAPs include criteria like “good job access.” I use AFFH’s jobs proximity index to quantify the accessibility of a neighborhood to all job locations within a CBSA.

Nevertheless, recent evidence indicates that social-interactive dimensions of neighborhoods matter more than spatial mismatch (Rothstein 2017; Zenou 2013). Lens (2014) found that subsidized households are often near employment centers. These households also live among the greatest concentration of low-skilled unemployed individuals who compete for the same low-wage jobs. I thus derive an additional index to measure the accessibility to low-wage jobs accounting for competition among low-wage workers.

Almost two-thirds of the states included locational affordability and transit elements in their 2016 QAPs.¹⁵ California offers points if a site has a transit amenity (California Tax Credit Allocation Committee 2017). I include the AFFH’s low transportation cost and transit trips indices. A fundamental justification for access to low-cost transportation, largely transit, is to provide a basic level of mobility. In theory, living in a neighborhood with low transportation costs may enable low-income families to access jobs, education, and other services. However, we need to interpret these indicators with caution. Low transportation cost in a neighborhood often correlates with sufficient access to transit. The neighborhood effects of ensuring transit access to low-income households are often unclear (Blumenberg, Pierce and Smart 2015; Shen 2001). Access to automobiles, which is not a neighborhood factor, shows stronger positive relationships with employment outcomes among low-income households (Blumenberg, Pierce and Smart 2015; Shen 2001). Families who move to areas with low transportation costs do not reduce their transportation expenditures (Smart and Klein 2018). The locational affordability theory may have overstated the benefit of cost savings in transit-rich neighborhoods (Smart and Klein 2018). Many scholars probed the impacts of the environment on health (Ellen and Turner 1997; Freddie Mac Multifamily and National Housing Trust 2018). There are clear links between adverse health outcomes with pollution and noise (Schell & Denham, 2003; Os, 2004). Prior research found that lower-income and minority-occupied neighborhoods are exposed to higher concentrations of air-, water-, and soil-borne pollutants (Ash and Fetter 2004; Hynes and Lopez 2008). I use AFFH’s environmental health index to capture the neighborhood-level exposure to harmful toxins.

Neighborhoods of LIHTC Developments

I start by describing the spatial distributions of all active LIHTC units in California. Low-income households living in LIHTC properties experience elevated levels of neighborhood poverty and minority concentration. Table 2 compares the distributions of LIHTC tenants with those with other subsidies and all renters. LIHTC tenants are living in high-poverty and high-minority neighborhoods on average. Consistent with prior studies, neighborhoods of LIHTC tenants are similar to those of voucher holders¹⁶ in terms of poverty and racial composition. Neighborhood poverty rates and minority shares of where a LIHTC tenant lives are lower than where a poor renter lives, but still far higher than an average renter lives. The 4% program is able to place tenants into slightly better neighborhoods with lower poverty exposure than the 9% program in California.

The bottom half of Table 2 indicates that LIHTC households live in neighborhoods with worse education quality, job accessibility, and transportation access than renters as a whole. Differences do exist between the two types of credits. 4% units are located in neighborhoods with slightly better opportunity measures than 9% units. Overall, this snapshot reveals that the LIHTC program has not yet created meaningful access for low-income households to neighborhood opportunities.

Spatial Distributions of Movers

While active LIHTC units are still located in more disadvantaged neighborhoods in California, does a low-income tenant improve their access to neighborhood opportunities by moving into a LIHTC unit from their previous location? With InfoUSA’s consumer database, I can identify both the neighborhood where a household moves from and the neighborhood of their current LIHTC unit if the household appears in the sample more than once. Among this sample, most tenants moved within the same county: 79% among 4% tenants and 86% among 9% tenants. 83% and 88%, respectively, moved within the same core-based statistical area.

Table 3 displays summary statistics of the sample. Table 3A shows that tracked LIHTC tenant sample is more impoverished than other low-income renters. Distributions of LIHTC tenants in sample are similar to the distributions of LIHTC tenants in California from HUD.¹⁷ Table 3B shows the unadjusted mean differences of neighborhood measures before and after a move. A tenant faces an increase in poverty rate by four percentage points after moving into a 9% unit. In comparison, a low-income renter faces almost no change in poverty exposure after a move. Low-Income Housing Tax Credit tenants also see decreased opportunities, except for transportation indices.

Table 3A.

Average Changes in Opportunity Indicators and Household Characteristics of Tracked Movers into LIHTC Units.

	Poverty rate	School proficiency index	Environmental health index	Jobs proximity index	Low-wage jobs proximity index	Low transportation cost index	Transit trips index
Movers into 4% units	2.8	−0.8	−1.8	3.7	−1.6	2.5	1.6
Movers into 9% units	4.1	−4.3	−0.1	2.4	−0.6	0.6	0.2
Low-income renter movers	0.2	0.1	0.4	0.0	−0.2	−0.1	−0.6

Sources: Updated HUD LIHTC Project Database, InfoUSA U.S. Consumer Database, American Community Survey (2014–2018), and HUD AFFH datasets (2018).

Note: HUD = Department of Housing and Urban Development, LIHTC = Low-Income Housing Tax Credit.

Table 3B.

Characteristics of Tracked Movers into LIHTC Units.

	Average estimated income ($)	% Children presence	% Married	% Single	% Age: 25–44	% Age: 25–44	% African American	% Hispanic
Movers into 4% units	16,000	15.4	17.4	74.2	48.4	28.7	16.7	25.4
Movers into 9% units	13,500	18.2	19.2	71.8	51.6	29.5	13.1	38.2
Low-income renter movers	20,700	14.0	8.6	74.1	66.6	20.8	16.0	23.6

Sources: InfoUSA U.S. Consumer Database.

Note: LIHTC = Low-Income Housing Tax Credit. Estimated incomes are rounded to the nearest 100s. The income immediately before a household moves is analyzed in this paper. Race and ethnicity variable is in limited use in this paper.

Almost 40% of low-income tenants who moved into LIHTC units are from neighborhoods with 20% or more poverty rate prior to their moves as shown in Figure 1. An even higher percentage of LIHTC 9% movers are from extremely poor neighborhoods with a poverty rate greater than 40%. A quarter of low-income households lived in low-poverty neighborhoods with less than 10% poverty before their moves. Fewer tenants now live in neighborhoods with less than 10% poverty rate. Low-Income Housing Tax Credit developments in California expanded the portion of low-income tenants living in neighborhoods with high poverty rates. 18.6% of 4% tenants and 34.6% of 9% tenants live in neighborhoods with a poverty rate more than 30%, up from 12.6% and 23.0% before their moves.

The LIHTC program may have helped some households move away from some extreme-poverty neighborhoods while relocating others from lower-poverty neighborhoods to higher-poverty ones. Figure 2A shows the destination neighborhood poverty rate for tenants who move from neighborhoods with a poverty rate more than 30%. About 40% and 60% of low-income households are able to leave high-poverty neighborhoods and move into 4% and 9% units with lower neighborhood poverty rates. Figure 2B indicates that incoming tenants to poor neighborhoods are from all different types of neighborhoods in terms of the poverty rate. However, a significant portion of tenants is from low-poverty neighborhoods. Figure 3 shows movements to or from low-poverty neighborhoods. Those from low-poverty neighborhoods are more likely to remain in a low-poverty neighborhood, although there is still quite a bit of shuffling.

A state arguably has more leverage on new construction over rehabilitation on the siting location of that housing. Rehabilitation projects generally have existing tenants who may be temporarily relocated (or not) during the rehabilitation. Tenants of these newly rehabilitated properties could generally be households not newly moved into affordable housing. Figures 4 and 5 show the distribution of movers by destination or origin poverty rates. They follow a similar pattern as the one discussed above, even with new developments targeted for families. However, 4% developments redistribute more shares of tenants into the lowest-poverty neighborhoods.

Measuring Changes in Neighborhood Opportunity

I then estimate an exploratory regression model to examine the full extent of change in tenants’ neighborhood conditions:

P o v_{i m t} = P o v_{i m t - 1} + β_{1} C r e d i t 9_{i} + β_{2} C r e d i t 4_{i} + μ_{t} + θ_{m} + ε_{i m t}

where

P o v_{i m t}

is the destination neighborhood poverty rate for a household i in CBSA m after they move into a LIHTC development in year t.

P o v_{i m t - 1}

is the origin poverty rate for a household i in CBSA m before they move into a LIHTC unit in year t-1. I include credit type dummies for the 9% and the 4% credits

(C r e d i t T_{i})

μ_{t}

represents the CBSA fixed-effect, and

θ_{m}

represents the year fixed-effect. I also add household characteristics: the presence of children, age group, marital status change, and predicted income. Since the imputed race and ethnicity variable does not cover every household and the coding is based on certain criteria as described above, this variable is added separately to an additional model to provide suggestive results. I replicate the same models with minority share at the destination as the dependent variable. I report these full results in Table 4. I further estimate a similar model as above for each of the neighborhood opportunity measures. I apply covariates in the second column from Table 4. I report abridged regression results of opportunity measures in Table 5.

Table 4.

Regression Results for Changes in Neighborhood Poverty Rates and Minority Shares among Tenants in Tax Credit Units and Other Low-Income Movers.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Poverty rate at destination			Minority share at destination
4% Unit	3.303***	3.013***	3.018***	4.201***	3.844***	3.745***
4% Unit	(0.143)	(0.143)	(0.112)	(0.286)	(0.331)	(0.338)
9% Unit	5.848***	5.409***	5.107***	7.788***	7.122***	6.616***
9% Unit	(0.340)	(0.315)	(0.280)	(0.348)	(0.386)	(0.416)
Children Presence		0.147**	0.142**		0.700***	0.676***
Children Presence		(0.0541)	(0.0623)		(0.125)	(0.169)
Age: 25–44		0.453***	0.404***		1.217***	0.799***
Age: 25–44		(0.101)	(0.0969)		(0.119)	(0.120)
Age: 45–64		0.455**	0.491***		1.568***	1.456***
Age: 45–64		(0.173)	(0.137)		(0.296)	(0.216)
Age: 65+		−1.255***	−0.990***		−1.880***	−1.264***
Age: 65+		(0.111)	(0.0396)		(0.226)	(0.0948)
Remain single		−1.288***	−1.149***		−5.769***	−5.292***
Remain single		(0.150)	(0.146)		(0.491)	(0.482)
Become single		0.464	0.475		−2.855***	−2.498**
Become single		(0.280)	(0.285)		(0.833)	(0.847)
Become married		−0.990***	−0.858***		0.239	0.381
Become married		(0.140)	(0.139)		(0.248)	(0.287)
Predicted income (in $1,000)		−0.235***	−0.236***		−0.348***	−0.352***
Predicted income (in $1,000)		(0.00806)	(0.00700)		(0.0159)	(0.0126)
African American			2.551***			9.131***
African American			(0.0381)			(0.228)
Hispanic			1.558***			7.349***
Hispanic			(0.0805)			(0.132)
Other			0.681***			4.017***
Other			(0.0565)			(0.171)
Poverty rate at origin	0.260***	0.164***	0.151***
Poverty rate at origin	(0.00739)	(0.0100)	(0.00717)
Minority share at origin				0.500***	0.442***	0.395***
Minority share at origin				(0.0105)	(0.0115)	(0.00812)
Constant	10.85***	16.18***	15.97***	9.041***	19.77***	19.98***
Constant	(0.276)	(0.460)	(0.424)	(0.521)	(1.272)	(1.112)
Observations	1,015,970	1,015,970	854,964	1,015,970	1,015,970	854,964
R²	0.162	0.232	0.240	0.354	0.383	0.401
CBSA FE & Year FE	Yes	Yes	Yes	Yes	Yes	Yes

Sources: Updated HUD LIHTC Project Database, InfoUSA Consumer Database, and American Community Survey (2014–2018).

Note: Robust standard errors in parentheses. *** p < .01, ** p < .05, * p < .1. Omitted categories are age: <25 and remain married, and non-Hispanic white. CBSA = Core-Based Statistical Area; FE = Fixed Effect; LIHTC = Low-Income Housing Tax Credit.

Table 5.

Abridged Regression Results for Changes in Neighborhood Indicators among Tenants in Tax Credit Units and Other Low-Income Movers.

	School proficiency index	Environmental health index	Jobs proximity index	Low-wage jobs proximity index	Low transportation cost index	Transit trips index
4% unit	−1.687***	0.471**	−0.944**	−1.553***	−1.557***	−1.393***
4% unit	(0.355)	(0.191)	(0.338)	(0.337)	(0.107)	(0.278)
9% unit	−9.124***	2.864***	−3.534***	−2.982***	−2.592***	−2.859***
9% unit	(0.344)	(0.301)	(0.535)	(0.323)	(0.248)	(0.382)
Constant	13.11***	24.66***	31.85***	31.89***	21.19***	7.134***
Constant	(0.709)	(0.965)	(1.012)	(1.077)	(0.909)	(0.902)

Observations	1,016,018	1,016,018	1,016,018	1,016,018	1,016,018	1,016,018
R²	0.250	0.586	0.194	0.149	0.770	0.507
CBSA FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes

Sources: Updated HUD LIHTC Project Database, InfoUSA Consumer Database, and American Community Survey (2014–2018).

Note: Robust standard errors in parentheses. *** p < .01, ** p < .05, * p < .1. Omitted categories are age: <25 and remain married. Coefficients for indices at origin, presence of children, age groups, marital status, and predicted income are not reported. CBSA = Core-Based Statistical Area; FE = Fixed Effect; HUD = Department of Housing and Urban Development; LIHTC = Low-Income Housing Tax Credit.

The counterfactual group is those low-income renter households who also moved during the same period. However, this group is not perfect. Low-income households with different types of assistance and unsubsidized low-income households may have to go through different decision-making processes and jump through different hurdles to move.¹⁸ A low-income household may also move for other reasons, such as job relocation, family dissolution, change in income, or receiving rental assistance. Unfortunately, it is not possible to exactly categorize every low-income household and their moves. This exercise also uses one group, those households making less than 60% of AMI, although there is a huge spectrum of income among tenants living in LIHTC properties.

Low-Income Housing Tax Credit households in California experience significant increases in their neighborhood poverty exposure after they moved into LIHTC units. The neighborhood poverty rate for a 4% household increases by about 3.3 percentage points over other low-income renter movers (Table 4). A LIHTC 9% household faces an even larger increase in poverty rate by about six points over other low-income renter movers. The overall results in Table 4 paint a concerning picture for LIHTC households. Table 5 displays abridged results for changes in the neighborhood opportunity measures; 9% tenants experience more pronounced drops in opportunities than 4% tenants; 9% households experience larger decreases in the school proficiency index, the low-wage jobs proximity, the jobs proximity, and the transit trips indices than 4% households. 9% households see a decrease of nine points in the school proficiency index, while there is a small significant change for 4% households. This index decreases less, by 3.7 points, among 9% households with children, reflecting a potential trade-off a household has to make. 9% households experience slightly improved environmental health and slightly lowered transportation costs than 4% households and other low-income renter households after the relocation.

Table 6 shows the results among a subsample of LIHTC tenants who moved into newly constructed LIHTC developments in California.¹⁹ New units perform better than rehabilitated ones in expanding access to neighborhood opportunities. However, newly constructed 9% units still overwhelmingly worsen access to opportunities for low-income households. While 4% tenants experience an increase in school proficiency index over other low-income renters by 1.9 percentage points, 9% households see a nine-percentage-point decrease again. The environmental health index is the only improved indicator for a relocated 9% tenant. Low-income households who moved into 4% units have the best chance of gaining access to neighborhoods with better opportunities when compared with households who moved into 9% units and other low-income renters.

Table 6.

Abridged Regression Results for Changes in Neighborhood Indicators among Tenants in Newly Constructed Tax Credit Units and Other Low-Income Movers.

	Poverty rate	Minority share	School proficiency index	Environmental health index	Jobs proximity index	Low-wage jobs proximity index	Low transportation cost index	Transit trips index
4% unit	3.053***	4.858***	1.861***	1.619***	0.786**	−1.990**	−1.688***	−2.722***
4% unit	(0.224)	(0.470)	(0.576)	(0.265)	(0.299)	(0.696)	(0.106)	(0.461)
9% unit	5.484***	7.421***	−9.467***	3.027***	−3.236***	−3.628***	−2.711***	−3.239***
9% unit	(0.351)	(0.425)	(0.364)	(0.301)	(0.639)	(0.381)	(0.259)	(0.431)
Constant	16.51***	20.91***	13.48***	24.71***	31.93***	32.00***	21.15***	7.508***
Constant	(0.452)	(1.354)	(0.758)	(1.020)	(1.139)	(1.100)	(0.980)	(1.078)
Observations	1,004,865	1,004,865	1,004,865	1,004,865	1,004,865	1,004,865	1,004,865	1,004,865
R²	0.231	0.382	0.249	0.586	0.193	0.149	0.769	0.504
CBSA FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Sources: Updated HUD LIHTC Project Database, InfoUSA Consumer Database, and American Community Survey (2014–2018).

Note: Robust standard errors in parentheses. *** p < .01, ** p < .05, * p < .1. Omitted categories are age: <25 and remain married. Coefficients for indices at origin, age groups, marital status, and predicted income are not reported. CBSA = Core-Based Statistical Area; FE = Fixed Effect; HUD = Department of Housing and Urban Development; LIHTC = Low-Income Housing Tax Credit.

Lastly, Table 7 looks at the likelihood of relocation to a high-poverty neighborhood with a logistic regression framework. These models use the same covariates as column 2 in Table 4. A 4% household is 1.7 times and a 9% household is 2.7 times more likely to move into a high-poverty neighborhood than other low-income households who moved during the same time. A 9% household is also about two times more likely to move into a minority-concentrated neighborhood. Overall, LIHTC households in 4% units are less likely to be in high-poverty and minority-concentrated neighborhoods than those in 9% units in California, but still at elevated levels.

Table 7.

Logistic Regression Results for Changes in Neighborhood Indicators among Tenants in Tax Credit Units and Other Low-Income Movers.

	All tax credit units		New construction units
	In high-poverty neighborhood	In minority-concentrated neighborhood	In high-poverty neighborhood	In minority-concentrated neighborhood
4% unit	1.707***	1.077***	1.865***	1.203***
4% unit	(0.0395)	(0.0233)	(0.0578)	(0.0339)
9% unit	2.660***	1.917***	2.729***	1.824***
9% unit	(0.0696)	(0.0503)	(0.0790)	(0.0533)
Constant	0.00646***	0.0194***	0.00637***	0.0216***
Constant	(0.00293)	(0.00492)	(0.00292)	(0.00562)
Observations	1,006,279	1,006,279	995,495	995,495
Pseudo R²	0.165	0.202	0.165	0.202
CBSA FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes

Sources: Updated HUD LIHTC Project Database, InfoUSA Consumer Database, and American Community Survey (2014–2018).

Note: Coefficients in odds ratios, exponentiated standard errors in parentheses. *** p < .01, ** p < .05, * p < .1. Omitted categories are age: <25 and remain married. Coefficients for poverty rate at the origin, minority share at the origin, presence of children, age groups, marital status, and predicted income are not reported. CBSA = Core-Based Statistical Area; FE = Fixed Effect; Department of Housing and Urban Development; LIHTC = Low-Income Housing Tax Credit.

Conclusion

During the study period, the LIHTC program has not meaningfully placed low-income households into neighborhoods of opportunity in California. Cross-sectionally, LIHTC tenants have access to neighborhoods similar to where voucher holders live. However, tenants still considerably lack access to opportunity areas compared to the distribution of all renters. The 4% and the 9% tax credits, two subprograms with different allocation mechanisms, have distinct outcomes. The gaps in opportunity measures between the two programs are small. This result indicates that the noncompetitive 4% program is able to place low-income households into neighborhoods similar to those of the competitive 9% program. The 4% program even increases the chance of accessing quality schools and low-cost transportation for low-income tenants. Recent prioritizations toward opportunity in the 9% program remain limited in reducing the historical pattern of economic and racial segregation of low-income households. In some cases, the construction of LIHTC housing may even increase minority inflows to certain neighborhoods.

Overall, California may have missed the mark in meaningfully improving access to neighborhood opportunities for low-income households by leveraging the LIHTC investment. State regulators have to take a closer look at the intersection among the movements of low-income households, siting outcomes, and QAP locational incentives. Low-Income Housing Tax Credit is a vital financing source for creating new affordable housing and preserving existing housing stock in the U.S. If the sometimes competing fair housing and neighborhood revitalization goals are both necessary, deliberation is needed in balancing these goals with tenant experiences in mind.

Although residents may have a fair amount of agency in choosing their neighborhoods, it is uncertain whether the LIHTC program can help residents leverage stable rents into greater economic mobility. Results from tracking movements of low-income renter in California paint a more troublesome picture for the LIHTC program, especially for the 9% program. Low-Income Housing Tax Credit residents, on average, experience an increase, rather than a decrease, in their neighborhood poverty exposure while facing decreases in many other neighborhood amenities and resources. That means a low-income household may have to trade better neighborhood amenities for low rent offered by a LIHTC unit. More research is needed to understand the drivers of residential mobility among LIHTC households.

It is also vital to collect quality data on project attributes and the characteristics of households living in LIHTC properties as well as to monitor and assess tenant selections, residential moves, and community services. To fill the void in administrative data, this paper provides an innovative method, although experimental in nature, to study residential mobility with a consumer database. The methodology can also be replicated on a larger scale and in other studies of residential mobility. Future research should also investigate if the tenant movement patterns vary by sponsor type or project targeted population.

Footnotes

Acknowledgments

The author would like to thank Michael Lens, Paavo Monkkonen, Evelyn Blumenberg, Edward Kung, participants of the 2022 Fall Research Conference of the Association for Public Policy Analysis and Management, and three anonymous reviewers for their insightful feedback. The author also thank Jaclene Begley for her continued support and guidance. All errors or omissions are my own.

Authors’ Note

Disclaimer: Yiwen (Xavier) Kuai’s contributions to this article occurred prior to his position at Fannie Mae. The views expressed are those of the author and not those of Fannie Mae or the Federal Housing Finance Agency.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: UCLA Graduate Division and UCLA Luskin School of Public Affairs provided funding for this project.

ORCID iD

Yiwen Kuai

Notes

Author Biography

Yiwen Kuai is a research economist at Fannie Mae and a research affiliate at the University of California, Los Angeles.

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