Graph and download economic data for Housing Inventory: Median Days on Market in California (MEDDAYONMARCA) from Jul 2016 to Jun 2025 about CA, median, and USA.

Median house prices for California districts derived from the 1990 census.

About Dataset

Context This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome.

The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.

Content The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows, their names are pretty self-explanatory: - longitude - latitude - housing_median_age - total_rooms - total_bedrooms - population - households - median_income - median_house_value - ocean_proximity

Acknowledgements This data was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.

and I encountered it in 'Hands-On Machine learning with Scikit-Learn and TensorFlow' by Aurélien Géron. Aurélien Géron wrote: This dataset is a modified version of the California Housing dataset available from: Luís Torgo's page (University of Porto)

Inspiration See my kernel on machine learning basics in R using this dataset, or venture over to the following link for a python based introductory tutorial: https://github.com/ageron/handson-ml/tree/master/datasets/housing

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

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 Jun 2025 about CA, listing, median, price, and USA.

Government Code section 65400 requires that each city, county, or city and county, including charter cities, prepare an annual progress report (APR) on the status of the housing element of its general plan and progress in its implementation. This dataset includes information reported to the Department of Housing and Community Development (HCD) by local jurisdictions on their APR form. Additional information about annual progress reports (APR), including the form, instructions, and definition can be found on HCD’s website here: https://www.hcd.ca.gov/planning-and-community-development/annual-progress-reports.

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.

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.

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

The California Housing dataset is based on 1990 US census and is widely used for machine learning and statistics. It was published in 1990 by Pace, R. Kelley and Ronald Barry, and can be found in the UCI Machine Learning Repository. The California Data set gives the information about Economic and Geographic values of the Houses,and also the economic status of the people present in the California.

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 July of 2025.

Dataset

This dataset was created by Rishabh Mediratta

Contents

Analysis of ‘California Housing Data (1990)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harrywang/housing on 12 November 2021.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

Graph and download economic data for Housing Inventory: Active Listing Count in California (ACTLISCOUCA) from Jul 2016 to Jun 2025 about active listing, CA, listing, and USA.

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.

VITAL SIGNS INDICATOR Housing Production (LU4)

FULL MEASURE NAME Produced housing units by unit type

LAST UPDATED October 2019

DESCRIPTION Housing production is measured in terms of the number of units that local jurisdictions produces throughout a given year. The annual production count captures housing units added by new construction and annexations, subtracts demolitions and destruction from natural disasters, and adjusts for units lost or gained by conversions.

DATA SOURCE California Department of Finance Form E-8 1990-2010 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/

California Department of Finance Form E-5 2011-2018 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/

U.S. Census Bureau Population Estimates 2000-2018 https://www.census.gov/programs-surveys/popest.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Single-family housing units include single detached units and single attached units. Multi-family housing includes two to four units and five plus or apartment units.

Housing production data for metropolitan areas for each year is the difference of annual housing unit estimates from the Census Bureau’s Population Estimates Program. Housing production data for the region, counties, and cities for each year is the difference of annual housing unit estimates from the California Department of Finance. Department of Finance data uses an annual cycle between January 1 and December 31, whereas U.S. Census Bureau data uses an annual cycle from April 1 to March 31 of the following year.

Housing production data shows how many housing units have been produced over time. Like housing permit statistics, housing production numbers are an indicator of where the region is growing. However, since permitted units are sometimes not constructed or there can be a long lag time between permit approval and the start of construction, production data also reflects the effects of barriers to housing production. These range from a lack of builder confidence to high construction costs and limited financing. Data also differentiates the trends in multi-family, single-family and mobile home production.

California requires that all local governments (cities and counties) adequately plan to meet the housing needs of everyone in the community. California’s local governments meet this requirement by adopting housing plans as part of their “general plan”. General plans serve as the local government’s "blueprint" for how the city and/or county will grow and develop and include eight elements: land use, transportation, conservation, noise, open space, safety, environmental justice, and housing.

The Housing Element Compliance Report provides current Housing Element Compliance Status. Additional information about Housing Elements can be found on HCD’s website here: https://www.hcd.ca.gov/planning-and-community-development/housing-elements.

Housing Inventory: New Listing Count in California was 36212.00000 Level in May of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: New Listing Count in California reached a record high of 49628.00000 in May of 2018 and a record low of 14892.00000 in December of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: New Listing Count in California - last updated from the United States Federal Reserve on July of 2025.

Financial overview and grant giving statistics of California Housing Consortium