Forest Meadows, CA

Data Snapshot: Forest Meadows, CA

Forest Meadows is a city in California with a population of 1,485 residents based on U.S. Census Bureau ACS 2023 estimates. Median household income here reaches $88,250 per year, while the poverty rate sits at 5.5%. Typical homes in Forest Meadows are valued around $484,100, and the median resident age is 50.5 years. About 39.8% of adults hold a bachelor's degree or higher, while 49.9% of working-age residents are in the labor force.

On a cost-of-living index where 100 equals the national average, California registers 110.7 (Above Average) per BEA Regional Price Parities 2024. HUD Fair Market Rent for a typical two-bedroom in this state is $2,053 per month. Average monthly utility bills run around $275 based on EIA 2023 residential energy data. FEMA's National Risk Index rates natural-hazard exposure at 88.7 out of 100 (Very High).

According to the U.S. Census Bureau's 2023 American Community Survey (ACS) 5-year estimates, PlainCities renders standardized city-level demographic, income, education, employment, and housing indicators across more than 19,000 incorporated places nationwide. Population counts and median household income figures are sourced directly from Census Table B01003 and B19013 respectively, with poverty rates from B17001 and cost-of-living adjustments from the Bureau of Economic Analysis (BEA) Regional Price Parities (RPP) 2024 release. Every value displayed in this snapshot links back to the official upstream tables and is refreshed on the ACS annual release cadence (typically December of the survey year + 1).

Methodology: city boundaries follow the Census Bureau's place-level geography (incorporated places, census-designated places, and consolidated city-county governments), with FIPS codes ensuring consistent identifiers across decennial and annual surveys. Cost-of-living comparisons use BEA's Regional Price Parities (RPP) indexed to the national average (100), enabling cross-city purchasing-power comparisons. See our methodology page for full source attribution, refresh cadence, and known data quality limitations including margin-of-error disclosure and sample-size thresholds.