The median house price of residential real estate in California has increased notably since 2012. After a brief correction in property prices in 2022, the median price reached ******* U.S. dollars in December 2023.

Graph and download economic data for Housing Inventory: Median Listing Price in California (MEDLISPRICA) from Jul 2016 to Aug 2025 about CA, listing, median, price, and USA.

Graph and download economic data for All-Transactions House Price Index for California (CASTHPI) from Q1 1975 to Q2 2025 about appraisers, CA, HPI, housing, price index, indexes, price, and USA.

In October 2024, the median sales price of an existing single-family home in Del Norte, California saw an increase of nearly ** percent, compared to the *** percent increase in the state. ** counties saw prices fall during this period. In Trinity, the median sales price of single-family homes decreased by approximately ** percent.

In October 2024, the median sales price of an existing single-family home in San Mateo, California was *********** U.S. dollars. This was more than double the median sales price in the state of California. The most affordable county was Trinity, where an existing single family home sold for approximately ******* U.S. dollars.

All-Transactions House Price Index for California was 967.68000 Index 1980 Q1=100 in January of 2025, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for California reached a record high of 967.81000 in October of 2024 and a record low of 41.67000 in January of 1975. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for California - last updated from the United States Federal Reserve on September of 2025.

Graph and download economic data for All-Transactions House Price Index for Los Angeles County, CA (ATNHPIUS06037A) from 1975 to 2024 about Los Angeles County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.

In October 2024, the median square footage sales price of a single-family home in the San Francisco Bay Area was approximately *** U.S. dollars. Condos, on the other hand, sold for *** U.S. dollars per square foot. The Far North was the most affordable region with sales price of *** and *** U.S. dollars for single family homes and condos, respectively.

United States House Price Index: FHFA: California data was reported at 655.910 Mar1980=100 in Jun 2018. This records an increase from the previous number of 639.250 Mar1980=100 for Mar 2018. United States House Price Index: FHFA: California data is updated quarterly, averaging 227.460 Mar1980=100 from Mar 1975 (Median) to Jun 2018, with 174 observations. The data reached an all-time high of 655.910 Mar1980=100 in Jun 2018 and a record low of 41.630 Mar1980=100 in Mar 1975. United States House Price Index: FHFA: California data remains active status in CEIC and is reported by Federal Housing Finance Agency. The data is categorized under Global Database’s United States – Table US.EB014: House Price Index.

Median Home Sale Price: All Residential: Chico, CA data was reported at 375.000 USD th in Jul 2020. This records an increase from the previous number of 363.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Chico, CA data is updated monthly, averaging 256.500 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 375.000 USD th in Jul 2020 and a record low of 160.000 USD th in May 2012. Median Home Sale Price: All Residential: Chico, CA data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

The median house prices in the most expensive zip codes in California reached as high as *** million U.S dollars. Atherton (94027), had the most expensive median house price, followed by Santa Barbara (93108), and Beverly Hills (90210). Six of the ranked zip codes were among the top ten most expensive zip codes in the United States in 2023.

New housing price index (NHPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (201612=100).

Mexico Average House Price: Baja California data was reported at 903,598.000 MXN in Mar 2019. This records an increase from the previous number of 720,437.000 MXN for Dec 2018. Mexico Average House Price: Baja California data is updated quarterly, averaging 658,724.000 MXN from Dec 2014 (Median) to Mar 2019, with 18 observations. The data reached an all-time high of 903,598.000 MXN in Mar 2019 and a record low of 562,050.000 MXN in Dec 2014. Mexico Average House Price: Baja California data remains active status in CEIC and is reported by Federal Mortgage Society. The data is categorized under Global Database’s Mexico – Table MX.EB005: Average House Price.

Graph and download economic data for All-Transactions House Price Index for San Diego-Chula Vista-Carlsbad, CA (MSA) (ATNHPIUS41740Q) from Q4 1975 to Q2 2025 about San Diego, appraisers, CA, HPI, housing, price index, indexes, price, and USA.

The median house price in *****, Atherton, California, was about *** million U.S. dollars. This made it the most expensive zip code in the United States in 2023. ***** Sagaponack, N.Y., was the runner-up with a median house price of about *** million U.S. dollars. Of the ** most expensive zip codes in the United States in 2026, six were in California.

Existing single family homes in California have been selling faster than ever in the past year. As of March 2022, the median time between a new listing and a purchase in the major counties in California was below ** days, whereas in August 2019, it was between ** and ** days. The robust demand for properties in California has led to substantial house price growth in almost all counties in California in the past year.

A comprehensive latest dataset of Los Angeles, California’S housing market. This dataset includes key metrics such as median sale price, number of homes sold, and inventory levels, updated monthly.

VITAL SIGNS INDICATOR
Home Prices (EC7)

FULL MEASURE NAME
Home Prices

LAST UPDATED
December 2022

DESCRIPTION
Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE
Zillow: Zillow Home Value Index (ZHVI) - http://www.zillow.com/research/data/
2000-2021

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2000-2021

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2000-2021

Bureau of Labor Statistics: Consumer Price Index - http://data.bls.gov
2000-2021

US Census ZIP Code Tabulation Areas (ZCTAs) - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html
2020 Census Blocks

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Housing price estimates at the regional-, county-, city- and zip code-level come from analysis of individual home sales by Zillow based upon transaction records. Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. ZHVI is computed from public record transaction data as reported by counties. All standard real estate transactions are included in this metric, including REO sales and auctions. Zillow makes a substantial effort to remove transactions not typically considered a standard sale. Examples of these include bank takeovers of foreclosed properties, title transfers after a death or divorce and non arms-length transactions. Zillow defines all homes as single-family residential, condominium and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that can be owned in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums in that the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Data is adjusted for inflation using Bureau of Labor Statistics metropolitan statistical area (MSA)-specific series. Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index (CPI) does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of the CPI itself.

Graph and download economic data for All-Transactions House Price Index for Orange County, CA (ATNHPIUS06059A) from 1975 to 2024 about Orange County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.

Source

This is the dataset used in this book: https://github.com/ageron/handson-ml/tree/master/datasets/housing to illustrate a sample end-to-end ML project workflow (pipeline). This is a great book - I highly recommend!

The data is based on California Census in 1990.

About the Data (from the book):

"This dataset is a modified version of the California Housing dataset available from Luís Torgo's page (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors.

The following is the description from the book author:

This dataset appeared in a 1997 paper titled Sparse Spatial Autoregressions by Pace, R. Kelley and Ronald Barry, published in the Statistics and Probability Letters journal. They built it using the 1990 California census data. It contains one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).

The dataset in this directory is almost identical to the original, with two differences: 207 values were randomly removed from the total_bedrooms column, so we can discuss what to do with missing data. An additional categorical attribute called ocean_proximity was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data. Note that the block groups are called "districts" in the Jupyter notebooks, simply because in some contexts the name "block group" was confusing."

About the Data (From Luís Torgo page):

http://www.dcc.fc.up.pt/%7Eltorgo/Regression/cal_housing.html

This is a dataset obtained from the StatLib repository. Here is the included description:

"We collected information on the variables using all the block groups in California from the 1990 Cens us. In this sample a block group on average includes 1425.5 individuals living in a geographically co mpact area. Naturally, the geographical area included varies inversely with the population density. W e computed distances among the centroids of each block group as measured in latitude and longitude. W e excluded all the block groups reporting zero entries for the independent and dependent variables. T he final data contained 20,640 observations on 9 variables. The dependent variable is ln(median house value)."

End-to-End ML Project Steps (Chapter 2 of the book)

1. Look at the big picture
2. Get the data
3. Discover and visualize the data to gain insights
4. Prepare the data for Machine Learning algorithms
5. Select a model and train it
6. Fine-tune your model
7. Present your solution
8. Launch, monitor, and maintain your system

The 10-Step Machine Learning Project Workflow (My Version)

1. Define business object
2. Make sense of the data from a high level
   • data types (number, text, object, etc.)
   • continuous/discrete
   • basic stats (min, max, std, median, etc.) using boxplot
   • frequency via histogram
   • scales and distributions of different features
3. Create the traning and test sets using proper sampling methods, e.g., random vs. stratified
4. Correlation analysis (pair-wise and attribute combinations)
5. Data cleaning (missing data, outliers, data errors)
6. Data transformation via pipelines (categorical text to number using one hot encoding, feature scaling via normalization/standardization, feature combinations)
7. Train and cross validate different models and select the most promising one (Linear Regression, Decision Tree, and Random Forest were tried in this tutorial)
8. Fine tune the model using trying different combinations of hyperparameters
9. Evaluate the model with best estimators in the test set
10. Launch, monitor, and refresh the model and system