By the time the World Health Organization declared COVID-19 a pandemic in March 2020, Los Angeles was already showing troubling signs of what was to come. Cities with international airports, significant tourist economies, and ample jet-setting populations quickly ascended to the top of case and morbidity counts. Los Angeles County, with its exposure to international tourism and massive population, easily outpaced all counterparts in the U.S. for both cases and deaths. Los Angeles County's vast spatial extent, enormous population, complex ethnic diversity, and deep economic disparities make it an ideal laboratory for the study of human behaviors. The ample and trustworthy COVID-19 data make it an excellent location for statistical modeling of infection rates. Our neighborhood-level analysis offers a powerful perspective into the causal associations that county- or state-level analyses cannot. Using COVID-19 case rate data from the LA Public Health Department and 2018 Census block group data, we constructed a series of statistical models measuring the association between COVID-19 infection rates, ethnicity, income, housing, household density, and a host of socioeconomic variables. Our exceptionally robust data model (Adjusted R2 = 0.93) demonstrates that neighborhood housing characteristics were the most statistically significant factor associated with elevated neighborhood case rates, followed by income and ethnicity (percent Hispanic and Asian) characteristics. Implications for both public policy and methodology are discussed.