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.

Graph and download economic data for Housing Inventory: Median Listing Price in California (MEDLISPRICA) from Jul 2016 to May 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.

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 756,200 U.S. dollars in December 2023.

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

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.

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 ---

Similar Datasets:

• Boston House Prices: LINK
• Gender Pay Gap Dataset: LINK
• Company Bankruptcy Prediction: LINK
• Spanish Wine Quality Dataset: LINK

Context

This is the dataset is a modified version of the California Housing Data used in the paper Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.. 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 too 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.

Modifications with respect to the original data

This dataset includes 5 extra features defined by me: "Distance to coast", "Distance to Los Angeles", "Distance to San Diego", "Distance to San Jose", and "Distance to San Francisco". These extra features try to account for the distance to the nearest coast and the distance to the centre of the largest cities in California.

The distances were calculated using the Haversine formula with the Longitude and Latitude:

https://wikimedia.org/api/rest_v1/media/math/render/svg/a65dbbde43ff45bacd2505fcf32b44fc7dcd8cc0" alt="">

where:

• phi_1 and phi_2 are the Latitudes of point 1 and point 2, respectively
• lambda_1 and lambda_2 are the Longitudes of point 1 and point 2, respectively
• r is the radius of the Earth (6371km)

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. The columns are as follows, their names are pretty self-explanatory:

1) Median House Value: Median house value for households within a block (measured in US Dollars) [$] 2) Median Income: Median income for households within a block of houses (measured in tens of thousands of US Dollars) [10k$] 3) Median Age: Median age of a house within a block; a lower number is a newer building [years] 4) Total Rooms: Total number of rooms within a block 5) Total Bedrooms: Total number of bedrooms within a block 6) Population: Total number of people residing within a block 7) Households: Total number of households, a group of people residing within a home unit, for a block 8) Latitude: A measure of how far north a house is; a higher value is farther north [°] 9) Longitude: A measure of how far west a house is; a higher value is farther west [°] 10) Distance to coast: Distance to the nearest coast point [m] 11) Distance to Los Angeles: Distance to the centre of Los Angeles [m] 12) Distance to San Diego: Distance to the centre of San Diego [m] 13) Distance to San Jose: Distance to the centre of San Jose [m] 14) Distance to San Francisco: Distance to the centre of San Francisco [m]

Source

This data was entirely modified and cleaned by me. The original data (without the distance features) was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.

The original dataset can be found under the following link: https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html

Graph and download economic data for Housing Inventory: Active Listing Count Year-Over-Year in California (ACTLISCOUYYCA) from Jul 2017 to May 2025 about active listing, CA, listing, and USA.

Housing Inventory: Median Days on Market Year-Over-Year in California was 16.22% in April of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Days on Market Year-Over-Year in California reached a record high of 82.76 in February of 2023 and a record low of -43.86 in May of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Days on Market Year-Over-Year in California - last updated from the United States Federal Reserve on June of 2025.

New Private Housing Authorized by Building Permits for California was 7672.63448 Units in March of 2025, according to the United States Federal Reserve. Historically, New Private Housing Authorized by Building Permits for California reached a record high of 27485.24167 in November of 1988 and a record low of 2305.94976 in July of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for New Private Housing Authorized by Building Permits for California - last updated from the United States Federal Reserve on June of 2025.

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.

Housing Inventory: Median Listing Price Month-Over-Month in California was 2.28% in April of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Listing Price Month-Over-Month in California reached a record high of 4.75 in March of 2022 and a record low of -4.54 in December of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Listing Price Month-Over-Month in California - last updated from the United States Federal Reserve on May of 2025.

In October 2024, the median sales price of an existing single-family home in San Mateo, California was two million 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 205,000 U.S. dollars.

In October 2024, the median sales price of an existing single-family home in Del Norte, California saw an increase of nearly 30 percent, compared to the 5.8 percent increase in the state. 20 counties saw prices fall during this period. In Trinity, the median sales price of single-family homes decreased by approximately 36 percent.

Show sum of units reported on the annual progress report, Tables A and A3 (2013-2017) and Table A2 (since 2018). HCD is transitioning to a new database system; the bi-weekly updates for this dataset will continue in late 1st quarter 2025.

No of Housing Unit: California data was reported at 14,176,670.000 Unit in 2017. This records an increase from the previous number of 14,078,685.000 Unit for 2016. No of Housing Unit: California data is updated yearly, averaging 13,416,348.500 Unit from Jun 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 14,176,670.000 Unit in 2017 and a record low of 12,245,260.000 Unit in 2000. No of Housing Unit: California data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB012: Number of Housing Units: By States.

Graph and download economic data for New Private Housing Units Authorized by Building Permits for California (CABPPRIVSA) from Jan 1988 to Apr 2025 about permits, buildings, CA, new, private, housing, and USA.

Housing Inventory: Price Reduced Count Year-Over-Year in California was 73.35% in April of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Price Reduced Count Year-Over-Year in California reached a record high of 191.13 in June of 2022 and a record low of -59.36 in July of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Price Reduced Count Year-Over-Year in California - last updated from the United States Federal Reserve on May of 2025.