Projected Transportation Impacts of California Housing Plans

Authors:
Zack Subin, Associate Research Director, Housing and Climate
Aaron Barrall, Housing Data Analyst, UCLA Lewis Center
Quinn Underriner, Senior Data Scientist

California has ambitious climate targets set in state law: reducing carbon pollution by 40 percent below 1990 levels by 2030, and by 85 percent by 2045.^[1] To achieve these, the California Air Resources Board (CARB) develops Scoping Plans to coordinate pollution-reduction policies across state agencies. Its 2022 Scoping Plan counts on significantly reducing the need to drive—a 25 percent reduction, per capita, by 2030.^[2] However, bringing down people’s reliance on cars and vehicle miles travelled (VMT) has proven difficult, especially when most Californians continue to live in neighborhoods far from jobs and services.^[3]

This means that where new housing is built matters. California also has ambitious housing production targets: the 2022 Statewide Housing Plan envisions building 2.5 million new homes by 2030.^[4] This statewide target is aligned with the Regional Housing Needs Allocation (RHNA) process, which determines planning targets for local governments. Recent legislation has strengthened this process, raising the number of units and increasing penalties for non-compliance.^[5]

Previous Terner Center research has estimated that if those 2.5 million new homes were located solely to minimize VMT, it could reduce statewide VMT by 6 percent, per capita—a meaningful fraction of CARB’s 25 percent target.^[6] This research underlines the role of land use planning in meeting California’s climate targets, alongside other policy changes, such as shifting state funding toward public and active transportation. However, this idealized scenario was derived from a national study, which did not consider the details of California’s RHNA process.

To better understand the potential impact of new supply on VMT, we partnered with the UCLA Lewis Center for Regional Policy Studies to analyze where jurisdictions are actually planning for that housing to be built. We assessed the projected VMT impacts of local housing plans (Housing Elements) submitted to the California Department of Housing and Community Development (HCD) from 2021 through 2025.

We found that if housing were developed in accordance with these local plans by 2030, it would achieve a less than 1 percent (0.9 percent) statewide VMT reduction, per capita, relative to current conditions, leaving a large gap to the Scoping Plan target. This modest estimated reduction is due to the fact that regional governments allocated more housing to cities and counties with lower average per-capita VMT, but local governments did not propose sites in lower-VMT neighborhoods within their jurisdictions, on average. What this means is that regional governments demonstrated progress toward the state’s climate goals, but local governments may need better guidance to integrate VMT reduction strategies into their land use plans.

After a brief introduction to the RHNA and Housing Elements processes, we describe this research below, with additional methodological details, sensitivity experiments, and disaggregated results provided in a Technical Appendix.

RHNA and Housing Elements Processes

The Housing Element cycle proceeds in three steps (Figure 1):

1. Regional Housing Needs Determination. HCD coordinates with regional governments (i.e., Metropolitan Planning Organizations and Councils of Governments) to project needed housing growth in the Regional Housing Needs Determination (RHND).
2. Regional Housing Needs Allocation. Regional governments assign housing targets to local governments: these are incorporated cities, as well as counties that are responsible for land-use planning in their unincorporated areas. State law provides guidance for these allocations, including requiring regional governments to promote infill development and fair housing. The timeline is staggered for regions across the state, with the Sixth Cycle Housing Element (Sixth Cycle) beginning between 2018 and 2024 and lasting for eight years.
3. Housing Elements. Local governments assess capacity for housing development under their existing policy and commit to any additional rezoning or other planning measures they estimate will enable development consistent with their assigned targets. They report these assessments in site inventories provided to HCD in their Housing Element submissions.

Each step could distinctly affect per-capita VMT at a different scale by shifting planned housing development to locations where future residents are able to drive less. Step 1 determines how housing is allocated among regions within the state; Step 2 allocates this housing within each region to cities and counties; and Step 3 allocates this housing to neighborhoods within each city and county.

For example, if regional governments allocate more housing to urban core cities with low VMT, that would tend to reduce VMT in Step 2; if those cities, in turn, prioritize transit-accessible neighborhoods in their Housing Elements, that would tend to reduce VMT in Step 3.

Figure 1: The Regional Housing Needs Allocation and Housing Elements Processes

Source: Terner Center for Housing Innovation, UC Berkeley

Each step above could affect VMT:

1. State assignment of regional housing needs
2. Allocation of regional housing needs among jurisdictions
3. Assignment of capacity to housing element sites within jurisdictions

Summary of Methods

UCLA’s Lewis Center standardized Housing Element site inventories reported by 408 cities and counties.^[7] Each site inventory includes parcels’ potential capacity for housing units under current zoning and any planned rezoning.^[8] We aggregated total site capacity to census block groups. We omitted 42 cities and counties where geographic locations could not be reliably verified, leaving 366 jurisdictions, representing 77 percent of California’s population and 79 percent of housing allocations for the Sixth Cycle.^[9]

We estimate per-capita VMT impacts by looking at: the impact of new housing based on the regional allocation of housing needs to jurisdictions (RHND and RHNA allocations, abbreviated to “RHNA allocations” below);^[10] and the impact of new housing based on where within each jurisdiction housing has been planned (“Housing Elements”).^[11] Note that site inventories include jurisdictions’ commitments to future rezoning after developing their Housing Elements, but we did not analyze these actual rezonings, which differ substantially in some cities, such as Los Angeles.^[12] We assumed that new residents would drive the same, on average, as existing residents in their neighborhoods did in 2023,^[13] using Replica estimates previously analyzed and benchmarked by the Terner Center.^[14] We assumed that no other changes to driving patterns would occur during this time period,^[15] and we ignored variation in household size and vacancy rate.^[1] We analyzed results at three scales: jurisdictional, regional, and statewide. To estimate the scale of regional and statewide VMT impact, we weighted jurisdiction-level VMT findings by each jurisdiction’s RHNA allocation.^[16] At each scale, we compared weighted-average, per-capita VMT with that of existing populations in 2023.^[17]

We also adapted the previously published idealized scenario to one constrained by actual RHNA allocations. Whereas the idealized scenario assumed housing could be located in the lowest-VMT locations anywhere in the state, here we created a Lower-Bound VMT Scenario. This scenario assumes housing would be built in jurisdictions according to their RHNA allocations, but only in their relatively low-VMT neighborhoods.^[18] This Lower-Bound VMT Scenario would represent the maximum VMT reduction consistent with the RHNA allocations, before considering economic or political constraints—and before considering other housing policy goals (see Policy Considerations).

Finally, we compared the Sixth-Cycle RHNA allocations to those from the Fifth Cycle; geographic data from the Fifth-Cycle Housing Elements were unavailable to compare with the Sixth Cycle.

Results

Statewide results are summarized in Figure 2 and outlined below.

Figure 2: Average Projected Vehicle Miles Traveled (VMT) for Residents of Sixth-Cycle Housing DevelopmentsSource: Authors’ analysis of Housing Element submissions and Replica data.

Relative to California’s existing residents, regional RHNA allocations tend to modestly lower per-capita VMT. Local Housing Elements do not further reduce VMT. Local governments have potential for further VMT reduction prior to considering economic and political constraints. (Note that the existing population is much larger than the projected number of residents in new housing; see below for projected statewide VMT impact.)

Did regional housing allocations tend to reduce VMT?

Yes, in all four regions associated with large Metropolitan Planning Organizations (MPOs)^[19] and statewide, building housing according to RHNA allocations would reduce per-capita VMT relative to existing populations by an average of 6 percent. Regional governments allocated the majority of new housing to cities and counties with lower VMT,^[20] meaning that new residents would tend to have better opportunities to access daily destinations with less driving.

Did local government plans tend to reduce VMT?

No, in all four large MPO regions and statewide, building housing according to Housing Elements would not appreciably reduce VMT beyond the RHNA allocations scenario, on average. In other words, when jurisdictions identified specific parcels for new housing, these parcels were not more concentrated in lower-VMT areas.^[21]

Were there clear patterns explaining which local plans reduced VMT?

No, we did not find significant correlations with city size, population density, or median income.

Could local governments have hypothetically reduced VMT further?

Yes, had jurisdictions chosen more sites in their neighborhoods with relatively low VMT, housing plans could have reduced VMT much more than they did. The Lower-Bound VMT Scenario shows the potential to reduce per-capita VMT by an average of 35 percent, had jurisdictions planned for all new housing to be located in their very low-VMT neighborhoods.

Notably, the Lower-Bound VMT Scenario was nearly equivalent to the previous idealized scenario that would have directed housing growth to the lowest VMT locations across the whole state, rather than within each city—indicating that cities have ample variation in VMT within their borders that they could consider when planning land use.^[22] However, cities also consider other criteria when determining which neighborhoods are suitable for housing growth, such as intensity of existing land use, Affirmatively Furthering Fair Housing (AFFH) policies, and the economic and political feasibility of further housing development.

Did regional governments improve on the Fifth Housing Element Cycle?

Yes, each large MPO’s RHNA allocations tended to reduce VMT in the Sixth Cycle more than in the Fifth Cycle (based on 2023 VMT patterns). The Southern California Association of Governments (SCAG), spanning the Los Angeles and Inland Empire regions, showed the greatest improvement, with its 2021 allocations averaging 8 percent lower per-capita VMT than its 2012 allocations—even as the total allocated housing units more than tripled (Figure 3).

Figure 3: Change in RHNA Allocations for Incorporated Cities from the Fifth to the Sixth Planning Cycle, Overlaid with Average VMT Per Resident, Per Day

Source: Authors’ analysis of data from HCD and Replica.
Note: The figure excludes unincorporated areas of counties for legibility, but these areas were included in the analysis.

Many coastal cities saw relatively large increases in housing allocation. Regional governments allocated the majority of housing to lower-VMT cities.

How much would projected VMT reductions in new housing contribute to statewide VMT goals?

These plans average to a 6 percent reduction per new resident of the new housing compared with the existing population, statewide. But this new housing would only represent a fraction of the state’s housing stock at the end of this Housing Element Cycle. Combining existing and projected new populations to compare with the Scoping Plan target, this equates to a 0.9 percent reduction in statewide, per-capita VMT by the end of the Cycle.^[23] If local governments had purely optimized their Housing Elements to reduce VMT—as in the Lower-Bound VMT Scenario—up to a 5.1 percent statewide VMT reduction, per capita, could have been achieved.

Policy Considerations

These results underline the importance of continued policy reform to unlock housing development in walkable and/or transit-accessible neighborhoods. We identified progress toward state VMT goals when analyzing Steps 1 and 2 of the Housing Element Cycle: the RHND setting regional targets and the RHNA allocations assigning these to local governments. These steps showed improvements relative to the Fifth Housing Element Cycle, perhaps due to state policies such as AB 1771 (2018, Bloom) requiring regional governments to encourage infill housing development.

However, we did not find that cities and counties furthered this progress (on average) in Step 3 of the Housing Element Cycle, the local Housing Elements themselves. New housing laws enacted in 2025 may further facilitate housing development in lower-VMT locations and help coordinate future RHNA cycles with Sustainable Communities Strategies, including SB 79, which requires cities to allow mid-rise housing developments near some major transit stops.^[24] In some cities, SB 79 could enable more housing near transit than assumed in Sixth-Cycle Housing Elements, contingent on implementation and economic conditions.

Local governments may need refined state guidance to navigate competing policy goals, and additional research could help to inform these refinements for future Housing Element Cycles. For example, guidance could help balance VMT reduction from new housing with Assembly Bill (AB) 686 (Santiago, 2018) requirements for cities to advance AFFH by prioritizing affordable housing development in high-resource neighborhoods.^[25] Making better VMT maps accessible to policymakers could highlight overlooked opportunities to build housing in lower-VMT neighborhoods in diverse locations throughout the state, including some walkable and/or job-rich locations far from urban cores and lacking high-quality transit.

In addition, infill housing may yield broad climate benefits beyond reducing VMT, such as reducing climate pollution associated with home construction when building missing middle housing.^[26] Further research could improve understanding of these benefits in California.

However, these results also underscore that housing planning alone is unlikely to reduce VMT to the Scoping Plan’s target reduction of 25 percent by 2030. We project that if housing were built according to current local plans, this would only achieve about a one percentage point reduction—and even the Lower-Bound VMT Scenario, in which local governments optimize their Housing Elements for VMT reduction, would only achieve five percentage points.

Given the large gap remaining, complementary transportation-sector policies are especially critical for the state to get back on track toward its climate commitments. Recent transportation studies have recommended curtailing highway expansion, while expanding funding for transit and infrastructure supporting infill development.^[27] In some locations, coordinated transportation investment could synergize with new housing development by expanding sustainable transportation options and access to local destinations, allowing both existing residents and residents of new housing to drive less.

Successfully reducing VMT and expanding housing supply will require ambitious, coordinated approaches across levels of government and across sectors.^[28] CARB will be updating its climate plan (i.e., the Scoping Plan) within the next two years, at the same time that HCD updates its Statewide Housing Plan, concurrent with some regions beginning the Seventh Housing Element Cycle. Policymakers thus have an important opportunity to advance both housing and climate objectives together.

Acknowledgments

Several former graduate student researchers and postdoctoral scholars supported previous analyses facilitating this study, including Bala Balaganesan and Junsik Kim.

We would like to thank Sarah Karlinsky, Macy Leung, Nick Marantz, Ben Metcalf, Paavo Monkkonen, Carolina Reid, and Jamey Volker for providing comments on previous drafts.

We would like to thank Wells Fargo and Sobrato Philanthropies for supporting this research.

This research does not represent the institutional views of UC Berkeley or of the Terner Center’s funders. Funders do not determine research findings or recommendations in the Terner Center’s research and policy reports.

Endnotes

^[1] We used these assumptions for simplicity. Household sizes in California urban locations tend to be somewhat smaller, but this may be partly due to their acute housing shortages, which incentivize development of smaller units less suitable for larger households. This assumption may slightly bias the results toward finding larger VMT reductions from RHNA allocations and Housing Elements.

^[1] Senate Bill (SB) 32 (2016) codified the 2030 target, and Assembly Bill (AB) 1279 (2022) codified the 2045 target, which requires net zero economywide emissions, in addition to the 85 percent reduction in gross emissions.

^[2] Specifically, the Scoping Plan counts on a 25 percent reduction in vehicle miles traveled (VMT), per person, from 2019 levels by 2030. This pace of reduction exceeds targets in Sustainable Communities Strategies.

^[3] California Air Resources Board. (2023). 2022 Progress Report—California’s Sustainable Communities and Climate Protection Act. CARB. Retrieved from: https://ww2.arb.ca.gov/sites/default/files/2023-05/2022-SB150-MainReport-FINAL-ADA.pdf

^[4] California Department of Housing and Community Development. (2024, September 25). A Home for Every Californian: 2022 Statewide Housing Plan. Retrieved from: https://storymaps.arcgis.com/stories/94729ab1648d43b1811c1698a748c136; The Plan is required to be updated every four years. Its statutory requirements were modified after the 2022 Plan by Assembly Bill (AB) 1474 (Reyes, 2023) and AB 1508 (Ramos, 2023).

^[5] Monkkonen, P., et al. (2023). California’s Strengthened Housing Element Law: Early Evidence on Higher Housing Targets and Rezoning. Cityscape, 25(2), 119–142; Newsom, G. (2026). Housing and Homelessness (Governor’s Budget Summary: 2026-27, pp. 85–91). California Department of Finance. Retrieved from: https://ebudget.ca.gov/2026-27/pdf/BudgetSummary/HousingandHomelessness.pdf

^[6] This 6 percent reduction is in statewide per-capita VMT, combining both existing and new residents, allowing comparison with the Scoping Plan target. Subin, Z. (2024). How Much Can New Housing Contribute to State Climate Action? Terner Center for Housing Innovation, University of California, Berkeley. Retrieved from: https://ternercenter.berkeley.edu/blog/how-much-can-new-housing-contribute-to-state-climate-action/

^[7] Standardization (i.e., geocoding) was needed because Housing Element site inventories do not report a unique latitude- and longitude-identified parcel associated with each site, only site address and/or assessor’s parcel number. Some addresses cannot be uniquely associated with a parcel in the respective jurisdiction. Future site inventories could avoid this challenge by reporting a latitude and longitude with each site. These data extend the dataset previously developed to investigate progress toward AFFH policy goals. Barrall, A., & Monkkonen, P. (2024). The Fair Housing Land Use Score in California: An Evaluation of 199 Municipal Plans. Retrieved from: https://escholarship.org/uc/item/6fn5g105

^[8] Cities estimating sufficient capacity under existing zoning in their Housing Elements need not rezone.

^[9] RHNA cycles proceed over eight years, with deadlines staggered by region. The Sixth Cycle is approximately 2022–2030.

^[10] Step 1 resulted in very small projected impacts to VMT so we combined it with Step 2 for simplicity (VMT variation within regions is much larger than between regions); see the Appendix.

^[11] Specifically, for RHNA allocations, we assumed housing development would occur in each city or county proportional to its RHNA allocation (for weighting across jurisdictions up to regions and statewide) but following the geographic distribution of its existing population. For Housing Elements, we assume housing development would occur in each city or county proportional to its RHNA allocation, but following the geographic distribution of its Housing Element. The second estimates the combined impact of Housing Elements and RHNA allocations, while the first isolates the effect of RHNA allocations.

^[12] Barrall, A., & Phillips, S. (2024). CHIPing In: Evaluating the Effects of LA’s Citywide Housing Incentive Program on Neighborhood Development Potential. Lewis Center for Regional Policy Studies, University of California, Los Angeles. Retrieved from: https://escholarship.org/uc/item/7xf2b3j0

^[13] VMT is strongly predicted by a neighborhood’s characteristics, including its relationship to regional destinations. Resident age and household income also affect VMT, but it is important to remember that land use policy does not directly change the number of people with a given income but merely redistributes their home locations. Since we are interested in statewide VMT and ultimately greenhouse gas impacts, we did not include these demographic attributes. See, e.g., Chatman, D. G., et al. (2023). Policies to Improve Transportation Sustainability, Accessibility, and Housing Affordability in the State of California. Institute of Transportation Studies, UC Berkeley. Retrieved from: https://escholarship.org/uc/item/03z7t8r1; Marantz, N. J., et al. (2024). Evaluating the Potential for Housing Development in Transportation-Efficient and Healthy, High-Opportunity Areas in California (No. 20STC009; pp. 1–178). California Air Resources Board. Retrieved from: https://www.law.virginia.edu/node/2182176. Chatman, D. G. et al (2025). Assessing the Quantification Methodology for the Affordable Housing and Sustainable Communities Program. Institute of Transportation Studies, UC Berkeley. Retrieved from: https://escholarship.org/uc/item/99j4s0bp.

^[14] Subin, Z., et al. (2024). US Urban Land-Use Reform: A Strategy for Energy Sufficiency. Buildings & Cities, 5(1). Retrieved from: https://doi.org/10.5334/bc.434; Strangeway, R., & Subin, Z. (2025). Aligning Housing with Climate Goals: The Importance of Measuring VMT. Terner Center for Housing Innovation, UC Berkeley. Retrieved from: https://ternercenter.berkeley.edu/research-and-policy/aligning-housing-with-climate-goals-the-importance-of-measuring-vmt/

^[15] California per-capita VMT has been relatively flat over several decades; if future VMT were reduced consistent with policy goals, these results would be conservative. See, e.g. CARB. (2023). 2022 Progress Report—California’s Sustainable Communities and Climate Protection Act. California Air Resources Board. Retrieved from: https://ww2.arb.ca.gov/sites/default/files/2023-05/2022-SB150-MainReport-FINAL-ADA.pdf.
For estimates of recent trends in housing development as they relate to VMT, see Subin, Z., & Underriner, Q. (2025, May 22). Building Housing in Walkable Neighborhoods: Are U.S. Cities and States Making Progress? Terner Center for Housing Innovation, UC Berkeley. Retrieved from: https://ternercenter.berkeley.edu/blog/building-housing-in-walkable-neighborhoods-are-u-s-cities-and-states-making-progress/

^[16] Weighting by RHNA allocations minimizes any spurious effects due to disparate or unrealistic assumed probabilities of development across jurisdictions, or due to excluding jurisdictions where data were unavailable or there were high geographic error rates.

^[17] See the Appendix for further details, including equations.

^[18] Specifically, we used the VMT of the 10th-percentile census block group within each jurisdiction, using a “nearest” interpolation method so that this VMT matches at least one actual census block group.

^[19] These are the regions of Sacramento, the San Francisco Bay Area, Los Angeles plus Inland Empire, and San Diego: Sacramento Council of Governments (SaCOG), Metropolitan Transportation Commission (MTC)-Association of Bay Area Governments (ABAG), Southern California Association of Governments (SCAG), and San Diego Association of Governments (SANDAG), respectively.

^[20] Specifically, the weighted-average (and the weighted-median) per-capita VMT for cities or unincorporated counties was lower when weighted by their housing allocation than when weighted by their population.

^[21] Local Housing Elements’ estimated capacity for new homes was not any more concentrated in lower-VMT areas than existing residents (population density tends to be higher in lower-VMT neighborhoods; we assumed constant household size for planned housing). This finding appears to contrast with a Barrall and Monkkonen (2024) finding that, on average, cities planned for housing in their more transit-accessible neighborhoods. However, as explained further in the Appendix, we reconcile these findings as resulting from the somewhat different framing of the two studies: total statewide VMT impacts here vs. local distribution of sites for housing in the typical city in Barrall and Monkkonen.

^[22] Subin (2024).

^[23] The weighted-average VMT per resident among 14.6 million homes (California’s 2022 housing stock) driving 100 percent of current levels, plus residents of 2.5 million new homes driving 94 percent of current levels, is 99.1 percent of current levels (assuming constant household size and housing vacancy rates), or a 0.9 percent reduction from current levels. By comparison, the Lower-Bound VMT Scenario would have reduced VMT per new resident to 65 percent of current levels, reducing statewide VMT by 5.1 percent.

^[24] Other new housing laws enacted in 2025 that could accelerate infill housing development include AB 130, Senate Bill (SB) 233 (Seyarto), and AB 1275 (Elhawary). Fulton, W., & Aguilar, J. (2025, November 13). California Housing Supply and Land Use Legislative Round-Up 2025. Terner Center for Housing Innovation, UC Berkeley. Retrieved from: https://ternercenter.berkeley.edu/blog/california-housing-supply-and-land-use-legislative-round-up-2025/

^[25] Economy, C. (2024). Lessons from California’s Statewide Efforts to Affirmatively Further Fair Housing. Terner Center for Housing Innovation, UC Berkeley. Retrieved from: https://ternercenter.berkeley.edu/blog/lessons-from-californias-statewide-efforts-to-affirmatively-further-fair-housing/

^[26] Subin, Z. (2024, March 12). Understanding the Role of New Housing in Reducing Climate Pollution. Terner Center for Housing Innovation, UC Berkeley. Retrieved from: https://ternercenter.berkeley.edu/research-and-policy/role-of-new-housing-in-reducing-climate-pollution/; Rankin, K. H., et al. (2024). Embodied GHG of Missing Middle: Residential Building Form and Strategies for More Efficient Housing. Journal of Industrial Ecology, 28(3), 455–468. Retrieved from: https://doi.org/10.1111/jiec.13461

^[27] Tomer, A., & Swedberg, B. (2025). California’s Road to Climate Progress, Part 5: Transportation Policy Reforms to Deliver Climate and Development Goals (California’s Road to Climate Progress). Brookings Institution. Retrieved from: https://www.brookings.edu/articles/californias-road-to-climate-progress-part-5/

^[28] Reid, C., Subin, Z., & McCall, J. (2024). Housing + Climate Policy: Building Equitable Pathways to Sustainability and Affordability. Terner Center for Housing Innovation, UC Berkeley. Retrieved from: https://ternercenter.berkeley.edu/research-and-policy/climate-housing-overview/