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.

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 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.

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

Dataset

This dataset was created by Christian Noel Molina

Contents

Dataset

This dataset was created by Xray Sierra

Contents

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 August of 2025.

The California Housing Dataset is a dataset containing information about housing prices in California, with nine features and a target variable of median house price.

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.

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.

Dataset

This dataset was created by ProDanilVi

Released under Apache 2.0

Contents

Context

California House Pricing(1990)

Content

Prediction of House values in California in the year 1990.

Acknowledgements

Aurelien Geron

Inspiration

Will we see the fall in housing prices in the near future?

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.

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

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.

Dataset

This dataset was created by Nahian Rifaat

Contents

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

Context

Census data is essential in gaining insights into how the population is distributed within a certain geographic area.

Content

This census dataset contains details related to the housing of the population in California, including the number of people in a certain area, geographic area, their proximity to the bay and kind of house that the population is living in currently.

Acknowledgements

I have taken this data from the link mentioned below https://github.com/ageron/handson-ml/tree/master/datasets/housing

Inspiration

Through this data I want to gain insight in to the housing prices in California.

Graph and download economic data for All-Transactions House Price Index for Santa Clara County, CA (ATNHPIUS06085A) from 1975 to 2024 about Santa Clara County, CA; San Jose; CA; HPI; housing; price index; indexes; price; and USA.

The travel time data on this map is modeled from a 2005 transit network. The home values are as of 2000 and are expressed in year 2000 dollars. The home value estimates were created by the Association of Bay Area Governements by combining ParcelQuest real estate transaction data and real estate tax assessment data. This information can be generated for any address in the region using an interactive mapping tool available under Maps at onebayarea.org/maps.htm (Note - this tool is no longer available).