California's Strengthened Housing Element Law: Early Evidence on Higher Housing Targets and Rezoning
Bibliographic record
Abstract
This article examines California's strengthened housing planning system as an example of land use reform impacts and intergovernmental conflict around housing policy.For the first time in its 50-year history, the state's plan mandate set local government housing targets for the 2021-through-2029 planning period higher than many municipalities' existing zoned capacity for new housing.Using administrative and census data, we describe changes in housing targets and changes in the housing plans cities have made in response.We analyze rezoning commitments in those plans, focusing on the 209 municipalities in southern California, especially the 93 housing plans deemed compliant by the state as of February 10, 2023.These municipalities, which represent less than one-third of the state's population, have already committed to over 10 times the amount of rezoning than in the previous planning period (in 2014).Using regressions with different measures of targets and rezonings, we find that larger increases in a city's housing target are associated with more rezoning and that increases in targets that require land zoned for multifamily housing have a stronger association.This assessment is important not only for the state's 40 million residents but also for national discussions about state-level intervention in local housing planning.Existing evidence suggests that state affordable housing appeals systems have been more effective than plan mandates, yet mandates have not yet been aggressively implemented until now.We also assess the actions by presumably exclusionary cities: those with more expensive housing, non-Hispanic White residents, homeowners, and elderly residents than the rest of the region.The results confirm that these cities had received relatively low targets previously but do not differ in their rates of rezoning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".