1. Questions
Governor Arnold Schwarzenegger signed the Sustainable Communities and Climate Protection Act (SB 375) in 2008. SB 375 mandates that the California Air Resources Board set greenhouse gas (GHG) emissions reduction targets for each of the state’s 18 metropolitan planning organizations (MPO), and requires each MPO to adopt a plan (“sustainable communities strategy”) to achieve those targets through coordinated transportation and land use planning (California Government Code § 65080(b)(2)). SB 375 incentivizes housing development near transit hubs to achieve the targets by streamlining the environmental review process for transit-oriented development (TOD) projects under the California Environmental Quality Act (SACOG 2023). However, while TOD might reduce GHG emissions, it might also foster gentrification by attracting wealthier residents and businesses to the area, potentially displacing existing residents and businesses (Chapple and Loukaitou-Sideris 2019; Robert F. Smith Staff writers 2023). This study investigates whether three TOD projects in Sacramento, CA, that utilized SB 375 streamlining led to gentrification.
2. Methods
We focus on Sacramento because it is one of California’s largest and fastest-growing cities and because it is one of the only cities where multiple TOD projects have utilized SB 375 streamlining (Affolter et al. 2024). We examine three Sacramento TOD projects shown in Figure 1: Sacramento Commons, Stockton & T, and 19J. Sacramento Commons redeveloped the Capitol Tower’s Garden apartments – previously “the second largest source of affordable housing serving moderate to middle-income households in the city’s core” – to create denser apartments, hotels, and retail spaces starting in 2019 (Planning and Design Commission 2015). Stockton & T repurposed an empty AT&T office building into apartments, single-family housing, and retail space with construction beginning in 2018. 19J converted a vacant building into affordable-by-design apartments and retail space starting in 2017.
We explore the projects’ effects on gentrification using the five indicators shown in Table 1 – three sociodemographic indicators, a measure of business change, and a measure of residents’ concerns about gentrification.
For the sociodemographic indicators, we examine the projects’ respective Census tracts before and after each project’s construction. We also compare the tract-level changes with corresponding trends across the entire city – one indication of project-generated gentrification is where a project’s neighborhood exhibits greater gentrification trends than the city as a whole after construction. This “multilevel” approach – both analyzing tract-level changes and comparing them to citywide trends – is common in gentrification studies (Cohen and Pettit 2019, 8). The dates of the baseline (2016-2018) and after-construction (2021) data are listed in Table 3. We use tract-level data from the year before each project started construction because the knowledge of forthcoming development can itself spur gentrification. All post-construction data is from 2021, which was the latest data available at the time of our analysis. One limitation of using ACS 5-year data is that the data is averaged over a 5-year period, which can mask year-to-year changes. However, we used sufficiently long pre-post periods for each project (between four and six years) to reduce data overlap.
As in prior studies, we assess the statistical significance of tract-level sociodemographic shifts before and after the development using the Census Bureau Statistical Analysis Tool, which provides a Z-score analysis with a standard 90% confidence level (Cohen and Pettit 2019; United States Census Bureau 2022). For the third indicator, our analysis is restricted to percentage point changes because the Longitudinal Employer-Household Dynamics (LEHD) data lacks margins of error (US Census Bureau Center for Economic Studies 2024).
We categorize evidence of gentrification for the sociodemographic indicators as none, slight, medium, or significant, based on the statistical significance (where possible), magnitude, and direction of the changes in the tract, and (where possible) comparison to citywide changes. Table 2 outlines the grading rules for race ethnicity and financial characteristics indicators. For example, in Sacramento Commons, although the White population in the project tract rose, the contrast with a citywide decline in the White population indicates medium evidence of gentrification. We use a similar process for grading the third indicator (worker sociodemographics), albeit without the statistical significance criteria.
For the fourth indicator (business change), we use a simple decision rule – slight evidence of gentrification is indicated where more high-end businesses (≥2 dollar signs on Google Maps) existed at the time of analysis than before project construction. We also use a simple decision rule for the fifth indicator (existing resident concerns) – slight evidence of gentrification is indicated where existing residents voiced fears of gentrification in the transcripts of the city’s Planning and Design Commission meetings for each development.
We assign to each project the highest gentrification level estimated across all indicators (and their subcategories).
3. Findings
The results of our indicator analysis are displayed in Table 3 and Figure 1 (worker sociodemographics).
Sacramento Commons
We find medium evidence of gentrification from the Sacramento Commons project, with the race and ethnicity indicator showing the most evidence of gentrification. For race and ethnicity, the share of White residents increased in the project’s Census tract while decreasing citywide. Concurrently, the share of Black residents, and those identifying as either an “other” race or two or more races increased less or decreased more in the tract than across the city as a whole. From a community perspective, existing community members expressed concern about displacement, especially of low- and middle-income residents, since Capitol Tower (which the Sacramento Commons project replaced) provided middle-income housing. Another indicator of possible gentrification is the increase in the share of workers with bachelor’s degrees while citywide decreases. That also coincided with three new high-end businesses opening, which can indicate gentrification.
Stockton and T
We also find medium evidence of gentrification from the Stockton and T project, with the race and ethnicity indicator again showing the most evidence of gentrification. Tract 17.01 shows a gentrification pattern for people identifying as two or more races and Asian, meanwhile tract 17.02 shows a gentrification pattern for people identifying as White, “other” race, and Hispanic or Latino. The financial characteristics in tract 17.01 also show slight evidence of gentrification with the median gross rent increasing more than citywide. The sociodemographics of workers had mixed results with the percentage of White workers decreasing (not indicative) while Asian workers increased (indicative).
19J
We similarly find medium evidence of gentrification from the 19J project, with the race and ethnicity indicator again showing the most evidence of gentrification. In tract 11.02 there was a greater decline in Black residents than citywide. Meanwhile, tract 11.03 shows a gentrification pattern for Native Americans, “other” races, and two or more races. Financial characteristics also show slight evidence of gentrification in tract 11.02, with the median gross rent and income increasing more than citywide.
Our analysis suggests that all three projects resulted in some level of gentrification, with a medium degree of evidence. Sacramento Commons has the most indicators of gentrification, possibly due to the project requiring the demolition of existing housing, which could have led to higher rent and consequently displacement, a concern of community members. Overall, our findings suggest that TOD can correlate with gentrification, but the effects can be mixed and difficult to identify. Given the varied influence of TOD projects on gentrification, planners and policymakers should consider these implications when developing TOD projects.
Acknowledgments
We would like to acknowledge and thank the NCST for their support and the NCST Undergraduate Fellowship Summer of 2023 which provided the funding and opportunity to conduct this research project.