The median house price in 94027, Atherton, California, was about 8.3 million U.S. dollars. This made it the most expensive zip code in the United States in 2023. 11962 Sagaponack, N.Y., was the runner-up with a median house price of about 8.1 million U.S. dollars. Of the 10 most expensive zip codes in the United States in 2026, six were in California.

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.

blockgrouphomevalues # Context A home purchase is among the most import decisions, and potentially risk investments, in a person's life. Their choice can reflect interest in long term gains, housing costs and, in the U.S., part of the American Dream. Analytics of home values and rental costs, however, are commonly limited to highest level geographic aggregates and broad, even annual, periods of time. This publication produces a data file shared in the Block Groups Datasets dataverse hosted on https://dataverse.harvard.edu/dataverse/blockgroupdatasets. The data is shared under a Common Commons, open source license, without warranties, share alike, non commercial and by attribution. Method This publication attempts to cast home values down to U.S. Census block group geographies, by inheriting and averaging the measures from ZIP code level estimates. On the whole, block groups with a few hundred households are considerably smaller than ZIP code areas with several thousand. In addition, the two geographies are managed by separate Federal agencies, the U.S. Postal Service and the Census Bureau, so they are inherently dissimilar. The simplest method of projection involves overlaying the two geographies, having a block group inherit the estimates of the ZIP code level that covers it. When the block group spans ZIP code boundaries, an average is appropriate, weighted by land area lying in each parent. Data Zillow is recognized as an innovator in predicting home values, serving real estate agents, home buyers, and home sellers. Their research service publishes several estimates at a ZIP code level including measures of home value (Zillow Home Value Index ZHVI) and rental costs (Zillow Observed Rent Index ZORI). The ZHVI is broken down by housing type: single family homes and condominiums. And, each of their publications has monthly frequency dating, in some cases, to 2000. Block group geographic boundariess are maintained by the US Census' TIGER (Topologically Integrated Geographic Encoding and Referencing) publication. ZIP code boundaries are not generally published, but shared from a private company, Dotlas, in various retail marketing solutions. ZIP codes, also, have long been problematic for demographic analytics. Their boundaries span counties and states, so you cannot tiethem to familar geographies including Census tracts and block groups. The Census Bureau tries to address this by using ZIP Code Tabulation Areas (ZCTAs). These are coded very much like 5 digit ZIP codes and are equal to them most of the time. When A ZIP code geography crosses a county line, though, new ZCTAs are invented to represent each side of the split area. So, while ZIP codes cannot be aggregated, ZCTAs can total into counties, states, divisions and regions. The blockgrouphomevalues dataset offers the following columns: Column Data Type Description STATEFP string The 2-digit State FIPS code of the block group COUNTYFP string The 3-digit County FIPS code of the block group TRACTCE string The 6-digit Census Tract of the block group BLKGRPCE string The 1-digit Block Group of the block group GEOID string 12 digit concatenation of State, County, Tract and Block Group codes GEOIDFQ string The 'fully qualified' GEOID with US country prefix ALAND integer The land area if the block group in square meters AWATER integer The area if the block group, covered by water, in square meters INTPTLAT float Latitude of the block groups centroid point INTPTLON float Longitude of the block groups centroid point ZIP Codes Overlaying list List of the ZIP codes that overlay the block group ZHVI All Housing Types float Zillow Home Value Index, attributed to the block group, all housing types ZHVI Single Family Homes float Zillow Home Value Index, attributed to the block group, single family homes ZHVI Condos/Coops float Zillow Home Value Index, attributed to the block group, condominiums and cooperatively owned ZORI All Housing Types float Zillow Observed Rent Index, attributed to the block group Additional Notes When the Block Group Code BLKGRPCE is '0', that block group is under water. Block groups cover the Great Lakes, for example, making a confusing visual for chloropleth maps. To support visualization, the code also uses Census definitions of cities called Combined Statitical Areas, which group counties together. The CSA for New York includes 22 counties, distinguished as Central or Outlying. The Delineation Files publication includes the geographic IDs of state and county FIPS codes in each major city. Maps of these results may be visually biased. New York City and San Francisco Bay areas have extreme housing values, but they have small land areas. Denver by contrast has higher then median housing values with very large land areas. As a result, western Colorado looks like the dominating location of home values. When more than one ZIP code overlays a block group, values are attributed by the shared land area. This assumes that housing is uniform over...

In 2023, Sagaponack, NY (zip code 11962) was the zip code that witnessed the highest luxury house price increase in the United States. Year-on-year, prices in that zip code increased by 40 percent. Ross, CA (zip code 94957) stood at the other end of the scale, with a decline of 39 percent.

The median house prices in the most expensive zip codes in California reached as high as 8.3 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.

The median house prices in the most expensive zip codes in New England, United States ranged from 1.9 to 2.8 million U.S. dollars. Boston (zip code 02199) was the most expensive in New England with a median house price of 2.8 million U.S. dollars. Nevertheless, that was more affordable than in the ten zip codes with the highest median house price in the entire United States.

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

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.

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 datasets contain comprehensive information on current real estate listings in Washington, D.C., obtained from Zillow, and offer a detailed overview of the Washington, D.C. 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 full dataset includes all 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 over 5,000 current listings, this dataset is perfect for in-depth analysis of the Washington, D.C. housing market and the Washington, D.C. real estate scene. Potential applications include:

• Comparing listing prices and price per square foot across various neighborhoods and property types
• Mapping listings to visualize the spatial distribution of available inventory
• Analyzing the age of available housing stock using year-of-construction data
• Assessing typical HOA fees and property taxes for listings
• Identifying listings with desirable amenities
• Evaluating school quality near listings using GreatSchools ratings
• Contacting listing agents programmatically using the provided agent information

Whether you're a real estate professional, market analyst, data scientist, or simply interested in the Washington, D.C., housing market, this dataset offers a wealth of information to explore. You can begin investigating and discovering insights into Washington, D.C. real estate today.

The median house prices in the most expensive zip codes in New York reached as high as 8.1 billion U.S. in 2023. That was the median property price in dollars in 11962, Sagaponack, Suffolk County in that year. In 11976, Water Mill, the median price amounted to 4.5 million U.S. dollars. The two zip codes also ranked among the 10 zip codes with the highest median house price in the entire United States.

About this Dataset

Complete listing of U.S. real estate inventory by zip code. Edited data set sourced from www.realtor.com for better clarity and easier use.

Category

Sales & Transactions

Keywords

Housing,realestate,listings,zipcode

Row Count

65501

Price

Free

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

Fair Market Rents (FMRs) are used to determine payment standard amounts for the Housing Choice Voucher program, to determine initial renewal rents for some expiring project-based Section 8 contracts, to determine initial rents for housing assistance payment (HAP) contracts in the Moderate Rehabilitation Single Room Occupancy program (Mod Rehab), rent ceilings for rental units in both the HOME Investment Partnerships program and the Emergency Solution Grants program, calculation of maximum award amounts for Continuum of Care recipients and the maximum amount of rent a recipient may pay for property leased with Continuum of Care funds, and calculation of flat rents in Public Housing units. The U.S. Department of Housing and Urban Development (HUD) annually estimates FMRs for Office of Management and Budget (OMB) defined metropolitan areas, some HUD defined subdivisions of OMB metropolitan areas and each nonmetropolitan county. 42 USC 1437f requires FMRs be posted at least 30 days before they are effective and that they are effective at the start of the federal fiscal year (generally October 1).

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.

What is Rental Data?

Rental data encompasses detailed information about residential rental properties, including single-family homes, multifamily units, and large apartment complexes. This data often includes key metrics such as rental prices, occupancy rates, property amenities, and detailed property descriptions. Advanced rental datasets integrate listings directly sourced from property management software systems, ensuring real-time accuracy and eliminating reliance on outdated or scraped information.

Additional Rental Data Details

The rental data is sourced from over 20,000 property managers via direct feeds and property management platforms, covering over 30 percent of the national rental housing market for diverse and broad representation. Real-time updates ensure data remains current, while verified listings enhance accuracy, avoiding errors typical of survey-based or scraped datasets. The dataset includes 14+ million rental units with detailed descriptions, rich photography, and amenities, offering address-level granularity for precise market analysis. Its extensive coverage of small multifamily and single-family rentals sets it apart from competitors focused on premium multifamily properties.

Rental Data Includes:

• Property Types
• Single-Family Rentals
• Small Multi-family Units
• Premium Apartments
• 16,000+ ZIP Codes
• 800+ MSAs
• Pricing Trends
• Lease Terms Amenities

Dataset includes total residential sales by zip code for 2010-2022. When a zip code crosses a county boundary, it is split into two records by county.

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.