The median house price in *****, Atherton, California, was about *** million U.S. dollars. This made it the most expensive zip code in the United States in 2023. ***** Sagaponack, N.Y., was the runner-up with a median house price of about *** million U.S. dollars. Of the ** most expensive zip codes in the United States in 2026, six were in California.

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.

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

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.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

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 was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Our dataset features comprehensive housing market data, extracted from 250,000 records sourced directly from Redfin USA. Our Crawl Feeds team utilized proprietary in-house tools to meticulously scrape and compile this valuable data.

Key Benefits of Our Housing Market Data:

• In-Depth Market Analysis: Gain insights into the real estate market with up-to-date data on recently sold properties.
  
  
• Price Trend Identification: Track and analyze price trends across different cities.
  
  
• Accurate Price Estimation: Estimate property values based on key factors such as area, number of beds and baths, square footage, and more.
  
  
• Detailed Real Estate Statistics: Access detailed statistics segmented by zip code, area, and state.

Unlock the Power of Redfin Data for Real Estate Professionals

Leveraging our Redfin properties dataset allows real estate professionals to make data-driven decisions. With detailed insights into property listings, sales history, and pricing trends, agents and investors can identify opportunities in the market more effectively. The data is particularly useful for comparing neighborhood trends, understanding market demand, and making informed investment decisions.

Enhance Your Real Estate Research with Custom Filters and Analysis

Our Redfin dataset is not only extensive but also customizable, allowing users to apply filters based on specific criteria such as property type, listing status, and geographic location. This flexibility enables researchers and analysts to drill down into the data, uncovering patterns and insights that can guide strategic planning and market entry decisions. Whether you're tracking the performance of single-family homes or exploring multi-family property trends, this dataset offers the depth and accuracy needed for thorough analysis.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

Zillow Properties Listing dataset to access detailed real estate listings, including property prices, locations, and features. Popular use cases include market analysis, property valuation, and investment decision-making in the real estate sector.

Use our Zillow Properties Listing Information dataset to access detailed real estate listings, including property features, pricing trends, and location insights. This dataset is perfect for real estate agents, investors, market analysts, and property developers looking to analyze housing markets, identify investment opportunities, and assess property values.

Leverage this dataset to track pricing patterns, compare property features, and forecast market trends across different regions. Whether you're evaluating investment prospects or optimizing property listings, the Zillow Properties dataset offers essential information for making data-driven real estate decisions.

Dataset Features

• zpid: Unique property identifier assigned by Zillow.
• city: The name of the city where the property is located.
• state: The state in which the property is located.
• homeStatus: Indicates the current status of the property
• address: The full address of the property, including street, city, and state.
• isListingClaimedByCurrentSignedInUser: This field shows if the current Zillow user has claimed ownership of the listing.
• isCurrentSignedInAgentResponsible: This field indicates whether the currently signed-in real estate agent is responsible for the listing.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• price: Current asking price of the property.
• yearBuilt: The year the home was originally constructed.
• streetAddress: Specific street address (usually excludes city/state/zip).
• zipcode: The postal ZIP code of the property.
• isCurrentSignedInUserVerifiedOwner: This field indicates if the signed-in user has verified ownership of the property on Zillow.
• isVerifiedClaimedByCurrentSignedInUser: Indicates whether the user has claimed and verified the listing as the current owner.
• listingDataSource: The original source of the listing. Important for data lineage and trustworthiness.
• longitude: The longitudinal geographic coordinate of the property.
• latitude: The latitudinal geographic coordinate of the property.
• hasBadGeocode: This indicates whether the geolocation data is incorrect or problematic.
• streetViewMetadataUrlMediaWallLatLong: A URL or reference to the Street View media wall based on latitude and longitude.
• streetViewMetadataUrlMediaWallAddress: A similar URL reference to the Street View, but based on the property’s address.
• streetViewServiceUrl: The base URL to Google Street View or similar services. Enables interactive visuals of the property’s surroundings.
• livingArea: Total internal living area of the home, typically in square feet.
• homeType: The category/type of the home.
• lotSize: The size of the entire lot or land the home is situated on.
• lotAreaValue: The numerical value representing the lot area is usually tied to a measurement unit.
• lotAreaUnits: Units in which the lot area is measured (e.g., sqft, acres).
• livingAreaValue: The numeric value of the property's interior living space.
• livingAreaUnitsShort: Abbreviated unit for living area (e.g., sqft), useful for compact displays.
• isUndisclosedAddress: Boolean indicating if the full property address is hidden, typically used for privacy reasons.
• zestimate: Zillow’s estimated market value of the home, generated via its proprietary model.
• rentZestimate: Zillow’s estimated rental price per month, is helpful for rental market analysis.
• currency: Currency used for price, Zestimate, and rent estimate (e.g., USD).
• hideZestimate: Indicates whether the Zestimate is hidden from public view.
• dateSoldString: The date when the property was last sold, in string format (e.g., 2022-06-15).
• taxAssessedValue: The most recent assessed value of the property for tax purposes.
• taxAssessedYear: The year in which the property was last assessed.
• country: The country where the property is located.
• propertyTaxRate: The most recent tax rate.
• photocount: This column provides a photo count of the property.
• isPremierBuilder: Boolean indicating whether the builder is listed as a premier (trusted) builder on Zillow.
• isZillowOwned: Indicates whether the property is owned or managed directly by Zillow.
• ssid: A unique internal Zillow identifier for the listing (not to be confused with network SSID).
• hdpUrl: URL to the home’s detail page on Zillow (Home Details Page).
• tourViewCount: Number of times users have viewed the property tour.
• hasPublicVideo: This

This map uses a two-color thematic shading to emphasize where areas experience the least to the most affordable housing across the US. This web map is part of the How Affordable is the American Dream story map.

Esri’s Housing Affordability Index (HAI) is a powerful tool to analyze local real estate markets. Esri’s housing affordability index measures the financial ability of a typical household to purchase an existing home in an area. A HAI of 100 represents an area that on average has sufficient household income to qualify for a loan on a home valued at the median home price. An index greater than 100 suggests homes are easily afforded by the average area resident. A HAI less than 100 suggests that homes are less affordable. The housing affordability index is not applicable in areas with no households or in predominantly rental markets . Esri’s home value estimates cover owner-occupied homes only. For a full demographic analysis of US growth refer to Esri's Trending in 2017: The Selectivity of Growth.

The pop-up is configured to show the following 2017 demographics for each County and ZIP Code:

Total Households 2010-17 Annual Pop Change Median Age Percent Owner-Occupied Housing Units Median Household Income Median Home Value Housing Affordability Index Share of Income to Mortgage

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.

This feature service outlines relationships between Zip Code Tabulation Areas (ZCTAs) used to denote Small Area Fair Market Rents (SAFMRs) and the Fair Market Rents (FMRs) calculated for Metropolitan Statistical Areas (MSAs) and County geographies. Small Area Fair Market Rents (SAFMRs) are FMRs calculated for ZIP Codes within Metropolitan Areas. Small Area FMRs are required to be used to set Section 8 Housing Choice Voucher payment standards in areas designated by HUD (available here). Other Housing Agencies operating in non-designated metropolitan areas may opt-in to the use of Small Area FMRs. Furthermore, Small Area FMRs may be used as the basis for setting Exception Payment Standards – PHAs may set exception payment standards up to 110 percent of the Small Area FMR. PHAs administering Public Housing units may use Small Area FMRs as an alternative to metropolitan area-wide FMRs when calculating Flat Rents.

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.

House prices grew year-on-year in most states in the U.S. in the third quarter of 2024. The District of Columbia was the only exception, with a decline of ***** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Hawaii—the state where homes appreciated the most—the increase exceeded ** percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2024, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2024.

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

The median house prices in the most expensive zip codes in California reached as high as *** million U.S dollars. Atherton (94027), had the most expensive median house price, followed by Santa Barbara (93108), and Beverly Hills (90210). Six of the ranked zip codes were among the top ten most expensive zip codes in the United States in 2023.

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

The Utrecht Housing Dataset is a synthetic dataset designed for students and practitioners to learn about data science and machine learning. Derived from the Dutch housing market, it is high-quality and noise-free, making it suitable for multiple algorithms such as decision trees, linear regression, logistic regression, and neural networks. This dataset was specifically created for educational purposes and emphasises responsible AI by being accessible to learners with diverse academic backgrounds.

Dataset Features:

• id: Unique identifier for each house, ranging from 0 to 100,000 (not used in algorithms).
• zipcode: Zip code of the house's location, indicating its area. Possible values: 3520, 3525, 3800.
• lot-len: Length of the house plot in meters, ranging from 5.0 to 100.0.
• lot-width: Width of the house plot in meters, ranging from 5.0 to 100.0.
• lot-area: Total area of the house plot in square meters, derived from lot-len * lot-width.
• house-area: The living area of the house in square meters (e.g., 30.0 for small houses, 200.0 for mansions).
• garden-size: The size of the garden in square meters, with larger gardens being desirable.
• balcony: Number of balconies (common values: 0, 1, 3). x-coor: X-coordinate of the house's location (range: 2000 to 3000).
• y-coor: Y-coordinate of the house's location (range: 5000 to 6000).
• buildyear: The year the house was built (from as early as 1100 to modern times).
• bathrooms: Number of bathrooms (common values: 1, 2, or 3). Output/Target Features
• tax value: Estimated value of the house for taxation, ranging from 50,000 to 1,000,000 euros.
• Retail value: The market value of the house, also ranges from 50,000 to 1,000,000 euros.
• energy-eff: Binary indicator (0 or 1) of whether the house is energy-efficient.
• monument: Binary indicator (0 or 1) of whether the house has architectural or historical monumental value.

Usage:

The dataset is ideal for: - Machine Learning Applications: Training and testing predictive models for tax valuation, market value, and energy efficiency. - Feature Analysis: Exploring the relationships between housing attributes and target values. - Educational Purposes: Teaching students about regression, classification, and feature engineering. - Visualisation: Creating plots and graphs due to the well-structured and interpretable data.

Coverage:

The dataset provides a comprehensive representation of housing features relevant to the Dutch market, ensuring high usability for educational and experimental projects.

License:

CC0 (Public Domain)

Who Can Use It:

This dataset is designed for students, researchers, data scientists, and machine learning practitioners seeking to explore real-world applications of AI in housing markets.

How to Use It:

• Develop predictive models for tax and retail value estimation.
• Evaluate housing energy efficiency or monumental status using classification techniques.
• Explore feature importance to understand what drives housing value.
• Benchmark machine learning algorithms on a synthetic, high-quality dataset.

The average price per square foot of floor space in new single-family housing in the United States decreased after the great financial crisis, followed by several years of stagnation. Since 2012, the price has continuously risen, hitting *** U.S. dollars per square foot in 2022. In 2024, the average sales price of a new home exceeded ******* U.S. dollars. Development of house sales in the U.S. One of the reasons for rising property prices is the gradual growth of house sales between 2011 and 2020. This period was marked by the gradual recovery following the subprime mortgage crisis and a growing housing sentiment. Another significant factor for the housing demand was the growing number of new household formations each year. Despite this trend, housing transactions plummeted in 2021, amid soaring prices and borrowing costs. In 2021, the average construction cost for single-family housing rose by nearly ** percent year-on-year, and in 2022, the increase was even higher, at close to ** percent. Financing a house purchase Mortgage interest rates in the U.S. rose dramatically in 2022 and remained elevated until 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under ***** percent, whereas in 2024, the average rate for the same mortgage type was more than twice higher. That has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about