Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

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.

VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

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 Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. 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 you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where 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. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

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 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 CPI itself.

The FHFA House Price Index (FHFA HPI®) is the nation’s only collection of public, freely available house price indexes that measure changes in single-family home values based on data from all 50 states and over 400 American cities that extend back to the mid-1970s. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data. ​ What does the FHFA HPI represent? The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas. U.S. Federal Housing Finance Agency, All-Transactions House Price Index for Connecticut [CTSTHPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CTSTHPI, August 2, 2023.

Graph and download economic data for S&P CoreLogic Case-Shiller IL-Chicago Home Price Index (CHXRSA) from Jan 1987 to Jul 2025 about Chicago, WI, IN, IL, HPI, housing, price index, indexes, price, and USA.

FHFA House Price IndexThe FHFA House Price Index (FHFA HPI®) is a comprehensive​ collection of publicly available house price indexes that measure changes in single-family home values based on data that extend back to the mid-1970s from all 50 states and over 400 American cities. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data.What does the FHFA HPI represent?The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975.The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas.

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

Graph and download economic data for S&P CoreLogic Case-Shiller CA-Los Angeles Home Price Index (LXXRSA) from Jan 1987 to Jul 2025 about Los Angeles, CA, HPI, housing, price index, indexes, price, and USA.

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

This dataset contains detailed information on current real estate listings in Houston, Texas, sourced from Zillow, and provides a comprehensive snapshot of the Houston housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The data includes key details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With 25,900 current listings, this dataset is ideal for in-depth analysis of the Houston housing market and the Houston real estate market. Potential use cases include:

• Comparing listing prices, price per square foot across different neighborhoods, property types
• Mapping listings to visualize the spatial distribution of for-sale inventory
• Analyzing the age of for-sale housing stock from year-built data
• Evaluating typical HOA fees, and property taxes for listings
• Identifying listings with sought-after amenities
• Assessing school quality near listings from GreatSchools ratings
• Contacting listing agents programmatically using the included agent info

Whether you're a real estate professional, market researcher, data scientist, or just curious about the Houston housing market, this dataset provides a wealth of information to explore. You can start investigating Houston real estate today.

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.

This dataset is an engineered version of the original Ames Housing dataset from the "House Prices: Advanced Regression Techniques" Kaggle competition. The goal of this engineering was to clean the data, handle missing values, encode categorical features, scale numeric features, manage outliers, reduce skewness, select useful features, and create new features to improve model performance for house price prediction.

The original dataset contains information on 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, with the target variable being SalePrice. This engineered version has undergone several preprocessing steps to make it ready for machine learning models.

Preprocessing Steps Applied

1. Missing Value Handling: Missing values in categorical columns with meaningful absence (e.g., no pool for PoolQC) were filled with "None". Numeric columns were filled with median, and other categorical columns with mode.
2. Correlation-based Feature Selection: Numeric features with absolute correlation < 0.1 with SalePrice were removed.
3. Encoding Categorical Variables: Ordinal features (e.g., quality ratings) were encoded using OrdinalEncoder, and nominal features (e.g., neighborhoods) using OneHotEncoder.
4. Outlier Handling: Outliers in numeric features were detected using IQR and capped (Winsorized) to IQR bounds to preserve data while reducing extreme values.
5. Skewness Handling: Highly skewed numeric features (|skew| > 1) were transformed using Yeo-Johnson to make distributions more normal-like.
6. Additional Feature Selection: Low-variance one-hot features (variance < 0.01) and highly collinear features (|corr| > 0.8) were removed.
7. Feature Scaling: Numeric features were scaled using RobustScaler to handle outliers.
8. Duplicate Removal: Duplicate rows were checked and removed if found (none in this dataset).

The final dataset has fewer columns than the original (reduced from 81 to approximately 250 after one-hot encoding, then further reduced by feature selection), with improved quality for modeling.

New Features Created

To add more predictive power, the following new features were created based on domain knowledge: 1. HouseAge: Age of the house at the time of sale. Calculated as YrSold - YearBuilt. This captures how old the house is, which can negatively affect price due to depreciation. - Example: A house built in 2000 and sold in 2008 has HouseAge = 8. 2. Quality_x_Size: Interaction term between overall quality and living area. Calculated as OverallQual * GrLivArea. This combines quality and size to capture the value of high-quality large homes. - Example: A house with OverallQual = 7 and GrLivArea = 1500 has Quality_x_Size = 10500. 3. TotalSF: Total square footage of the house. Calculated as GrLivArea + TotalBsmtSF + 1stFlrSF + 2ndFlrSF (if available). This aggregates area features into a single metric for better price prediction. - Example: If GrLivArea = 1500 and TotalBsmtSF = 1000, TotalSF = 2500. 4. Log_LotArea: Log-transformed lot area to reduce skewness. Calculated as np.log1p(LotArea). This makes the distribution of lot sizes more normal, helping models handle extreme values. - Example: A lot area of 10000 becomes Log_LotArea ≈ 9.21.

These new features were created using the original (unscaled) values to maintain interpretability, then scaled with RobustScaler to match the rest of the dataset.

Data Dictionary

• Original Numeric Features: Kept features with |corr| > 0.1 with SalePrice, such as:
  • OverallQual: Material and finish quality (scaled, 1-10).
  • GrLivArea: Above grade (ground) living area square feet (scaled).
  • GarageCars: Size of garage in car capacity (scaled).
  • TotalBsmtSF: Total square feet of basement area (scaled).
  • And others like FullBath, YearBuilt, etc. (see the code for the full list).
• Ordinal Encoded Features: Quality and condition ratings, e.g.:
  • ExterQual: Exterior material quality (encoded as 0=Po to 4=Ex).
  • BsmtQual: Basement quality (encoded as 0=None to 5=Ex).
• One-Hot Encoded Features: Nominal categorical features, e.g.:
  • MSZoning_RL: 1 if residential low density, 0 otherwise.
  • Neighborhood_NAmes: 1 if in NAmes neighborhood, 0 otherwise.
• New Engineered Features (as described above):
  • HouseAge: Age of the house (scaled).
  • Quality_x_Size: Overall quality times living area (scaled).
  • TotalSF: Total square footage (scaled).
  • Log_LotArea: Log-transformed lot area (scaled).
• Target: SalePrice - The property's sale price in dollars (not scaled, as it's the target).

Total columns: Approximately 200-250 (after one-hot encoding and feature selection).

License

This dataset is derived from the Ames Housing...

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

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

Graph and download economic data for S&P CoreLogic Case-Shiller TX-Dallas Home Price Index (DAXRNSA) from Jan 2000 to Jul 2025 about Dallas, TX, HPI, housing, price index, indexes, price, and USA.

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

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.

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