UC Irvine

Problem, research strategy, and findings: Several U.S. states with high housing costs have recently adopted laws intended to promote infill development. These new laws expand state agencies’ supervisory responsibilities to ensure that local governments comply with state mandates. Effective administration of these laws will require state agencies to accurately estimate the amount of new housing that might be created and to target review to the jurisdictions that are failing to meet the relevant requirements. Here we present quantitative tools both for prioritizing review of local plans and zoning ordinances and for estimating future housing development. We applied the tools to the implementation of California laws requiring local governments to amend their zoning ordinances to allow accessory dwelling units on parcels zoned for detached single-family housing development. We provide computer code, written in the open-source statistical computing language R, that implements these tools. Although we present off-the-shelf tools, our proposed tools should supplement other regulatory techniques rather than serving as a substitute. Takeaway for practice: Requirements for local governments to allow infill development should be accompanied by mandates for data collection. With good data, state agencies can use open-source statistical software to create quantitative measures that can help estimate future housing production and set priorities for reviewing local plans and zoning ordinances.