Equity
Higher-income and lower-income neighborhoods lost inspections at the same rate.
This analysis asked whether the 82% inspection decline fell harder based on how much households earn by ZIP code, the share of renters stretched by housing costs, or racial and ethnic composition. It did not. Across 254 ZIP codes, there is no meaningful relationship between these characteristics and how sharply inspections declined. Higher-income and lower-income ZIP codes, as well as Latino, Black non-Hispanic, Asian non-Hispanic, and White non-Hispanic communities, all lost inspections at roughly equal rates. This points to a systemic and uniform decline rather than a decline concentrated by income, the share of renters stretched by housing costs, or the Hispanic, White, Black, or Asian share of a ZIP code. The statistical measure used to test this is R², which is explained below.
If a disparity existed: what we would expect to see
Lower-income neighborhoods would show steeper declines. Dots would slope downward left to right.
R² well above zero would mean the decline tracked with income, the share of renters stretched by housing costs, or the Hispanic, White, Black, or Asian share of a ZIP code.
✓ What we actually found
No pattern. Dots are scattered randomly regardless of income, housing costs, or racial and ethnic composition.
R² near 0.0 means demographics do not explain inspection decline. The decline was equally distributed.
R² measures how much of the variation in inspection decline is explained by each variable. Values near zero indicate no meaningful relationship.
| Variable tested | Category | R² value | Variation explained |
|---|---|---|---|
| Median Household Income | Economic | 0.016 | 1.6%. No meaningful relationship. |
| Renters Stretched by Housing Costs | Economic | 0.004 | 0.4%. No meaningful relationship. |
| Hispanic / Latino Population (%) | Race / Ethnicity | 0.015 | 1.5%. No meaningful relationship. |
| White Non-Hispanic Population (%) | Race / Ethnicity | 0.020 | 2.0%. No meaningful relationship. |
| Black Non-Hispanic Population (%) | Race / Ethnicity | 0.020 | 2.0%. No meaningful relationship. |
| Asian Non-Hispanic Population (%) | Race / Ethnicity | 0.005 | 0.5%. No relationship detected. |
This analysis used R² ≥ 0.10 as a conservative threshold for a meaningful relationship, meaning at least 10% of variation explained. All values here are under 0.03, confirming the inspection decline was not demographically targeted.
Why only four racial and ethnic groups were tested: The demographic analysis focused on Hispanic or Latino, White non-Hispanic, Black non-Hispanic, and Asian non-Hispanic populations because these four groups had large enough populations at the ZIP code level to produce statistically reliable results. Other groups, including American Indian or Alaska Native, Native Hawaiian or Pacific Islander, and Two or More Races, were present in the Census data but represented very small populations within individual ZIP codes. Including them would have produced unreliable results that could misrepresent those communities.
Interactive Tool
Where do you live? Look up your ZIP code.
See how much housing inspections changed in your neighborhood compared to the rest of LA County.
Each dot represents one ZIP code. 254 ZIP codes with valid demographic data and inspections in the January-September 2023 baseline window. Select a variable using the tabs below, then hover over any dot for details.
Trend line shown for reference. A flat or near-flat slope confirms no meaningful relationship between demographics and inspection change.
Negative values indicate fewer inspections than 2023. Positive values indicate more inspections than 2023.
A small number of ZIP codes exceed 100% because they had very few inspections in 2023 compared to later years. Three ZIP codes exceed this chart's 175% range and are not plotted: 91381 (500%), 91020 (212.5%), and 91214 (192.3%). All three are included in the R² calculation above.
The expected story is that when public services decline, disadvantaged communities lose the most. Across 254 ZIP codes, this data refuses to tell that story: how much households earn by ZIP code barely matters, how many renters are stretched thin by housing costs barely matters, and whether a ZIP code is majority Hispanic, White, Black, or Asian barely matters. None comes close to explaining where inspections were lost.
Since none of the six variables explain the decline, the only explanation left is systemwide: the decline hit everywhere at roughly comparable rates, regardless of who lives there. That changes what kind of fix makes sense. A targeted decline would call for targeted reforms, like closer oversight of where inspectors are sent and why. A uniform collapse calls for something different: more budget, more staff, and a rule requiring that inspections actually happen. The Board has already written that rule once. RHHP, in effect since October 2024, gives unincorporated areas a mandated four-year inspection cycle. The 85 contracted cities, which account for more than 99% of the inspections in this dataset, have no equivalent.
An equal decline is not the same as equal harm. These six variables rule out a ZIP-level demographic pattern in where inspections were lost, not in what the loss costs. Conditions were not uniform when inspectors did go: among the 176 ZIP codes with 50 or more inspections, violation rates ranged from 0% to 77%, with a median of 38%. The same missed inspection means more in a building that was already failing.