The Statewide Property Inventory (SPI) is a detailed inventory of the State's real property assets including land, structures/improvements, leased space and State-owned space leased to others. This website provides summary-level information from the SPI.Included in the information provided are properties which have been declared surplus by the California State Legislature. Some of these properties are currently for sale by the Department of General Services.The Department of General Services - Real Estate Services Division makes every effort to ensure the accuracy and completeness of the information presented, but disclaims liability for omissions or errors in the contents of this data set.Original AGOL Item owned by DGS is located here.

Graph and download economic data for State Tax Collections: T01 Property Taxes for California (QTAXT01QTAXCAT3CANO) from Q1 1994 to Q1 2025 about collection, tax, CA, and USA.

In 2024, about 55 percent of foreign buyers of residential real estate in California were from the Asia/Oceania region. Buyers of Latin American/Caribbean origin were the second largest group with 02 percent of buyers.

14 sheets of data, each sheet representing a table from the database that stores the information. The order of the sheets is based on the sequence of reporting forms for the Financial Transactions Report.

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

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 20 data sources in California
3. 14,500,000 properties in California
4. 4 billion records (listed in the "data types" block above) in total
5. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

Graph and download economic data for State Government Tax Collections, Property Taxes in California (CAPROPTAX) from 1942 to 2024 about tax, CA, government, and USA.

The number of homes sold in California declined for the second year in a row in 2023, reaching roughly *******. The most homes were sold in 2021, amounting to over ******* transactions. Despite the slowdown of the market, the median home price in California peaked in 2023.

This dataset was updated April, 2024.

This ownership dataset was generated primarily from CPAD data, which already tracks the majority of ownership information in California. CPAD is utilized without any snapping or clipping to FRA/SRA/LRA. CPAD has some important data gaps, so additional data sources are used to supplement the CPAD data. Currently this includes the most currently available data from BIA, DOD, and FWS. Additional sources may be added in subsequent versions. Decision rules were developed to identify priority layers in areas of overlap.

Starting in 2022, the ownership dataset was compiled using a new methodology. Previous versions attempted to match federal ownership boundaries to the FRA footprint, and used a manual process for checking and tracking Federal ownership changes within the FRA, with CPAD ownership information only being used for SRA and LRA lands. The manual portion of that process was proving difficult to maintain, and the new method (described below) was developed in order to decrease the manual workload, and increase accountability by using an automated process by which any final ownership designation could be traced back to a specific dataset.

The current process for compiling the data sources includes:

* Clipping input datasets to the California boundary

* Filtering the FWS data on the Primary Interest field to exclude lands that are managed by but not owned by FWS (ex: Leases, Easements, etc)

* Supplementing the BIA Pacific Region Surface Trust lands data with the Western Region portion of the LAR dataset which extends into California.

* Filtering the BIA data on the Trust Status field to exclude areas that represent mineral rights only.

* Filtering the CPAD data on the Ownership Level field to exclude areas that are Privately owned (ex: HOAs)

* In the case of overlap, sources were prioritized as follows: FWS > BIA > CPAD > DOD

* As an exception to the above, DOD lands on FRA which overlapped with CPAD lands that were incorrectly coded as non-Federal were treated as an override, such that the DOD designation could win out over CPAD.

In addition to this ownership dataset, a supplemental _source dataset is available which designates the source that was used to determine the ownership in this dataset.

Data Sources:

* GreenInfo Network's California Protected Areas Database (CPAD2023a). https://www.calands.org/cpad/; https://www.calands.org/wp-content/uploads/2023/06/CPAD-2023a-Database-Manual.pdf

* US Fish and Wildlife Service FWSInterest dataset (updated December, 2023). https://gis-fws.opendata.arcgis.com/datasets/9c49bd03b8dc4b9188a8c84062792cff_0/explore

* Department of Defense Military Bases dataset (updated September 2023) https://catalog.data.gov/dataset/military-bases

* Bureau of Indian Affairs, Pacific Region, Surface Trust and Pacific Region Office (PRO) land boundaries data (2023) via John Mosley John.Mosley@bia.gov

* Bureau of Indian Affairs, Land Area Representations (LAR) and BIA Regions datasets (updated Oct 2019) https://biamaps.doi.gov/bogs/datadownload.html

Data Gaps & Changes:

Known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. Additionally, any feedback received about missing or inaccurate data can be taken back to the appropriate source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.

24_1: Input datasets this year included numerous changes since the previous version, particularly the CPAD and DOD inputs. Of particular note was the re-addition of Camp Pendleton to the DOD input dataset, which is reflected in this version of the ownership

State Government Tax Collections, Property Taxes in California was 3558041.00000 Thous. of $ in January of 2024, according to the United States Federal Reserve. Historically, State Government Tax Collections, Property Taxes in California reached a record high of 3870605.00000 in January of 1998 and a record low of 14858.00000 in January of 1942. Trading Economics provides the current actual value, an historical data chart and related indicators for State Government Tax Collections, Property Taxes in California - last updated from the United States Federal Reserve on July of 2025.

The table below showcases the total number of homes sold for each zip code in Long Beach, California. It's important to understand that the number of homes sold can vary greatly and can change yearly.

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.

The table below showcases the total number of homes sold for each zip code in Burbank, California. It's important to understand that the number of homes sold can vary greatly and can change yearly.

2002-03 to 2023-24 Property Taxes Annual Report - Allocations, for the finance application.

The table below showcases the total number of homes sold for each zip code in Palmdale, California. It's important to understand that the number of homes sold can vary greatly and can change yearly.

The table below showcases the 10th, 25th, 50th, 75th, and 90th percentiles of assessed property values for each zip code in Los Angeles, California. It's important to understand that assessed property values can vary greatly and can change yearly.

The table below showcases the 10th, 25th, 50th, 75th, and 90th percentiles of assessed property values for each zip code in Burbank, California. It's important to understand that assessed property values can vary greatly and can change yearly.

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 Housing Inventory: Median Listing Price Year-Over-Year in California (MEDLISPRIYYCA) from Jul 2017 to Jun 2025 about CA, listing, median, price, 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.