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.

a detailed dataset covering multiple features in the housing environment, created by the US Census Bureau for years covering 2007-2011.

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.

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directly sourced from property management software systems, ensuring real-time accuracy and eliminating reliance on outdated or scraped information.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

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 three percent. The annual appreciation for single-family housing in the U.S. was 0.71 percent, while in Hawaii—the state where homes appreciated the most—the increase exceeded 10 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 413,000 U.S. dollars, up from 277,000 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 2.3 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 20 percent in 2024.

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.

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.

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.

Graph and download economic data for All-Transactions House Price Index for Michigan (MISTHPI) from Q1 1975 to Q4 2024 about MI, appraisers, HPI, housing, price index, indexes, price, and USA.

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.

Geospatial data on real estate prices and housing in the USA is a strategic asset for informed decision-making in the property market. This data - at census block level - reveals regional price and construction trends, allowing real estate professionals and investors to optimize their strategies.

What is included?

At the Census Block level, this dataset includes some of the following key features:

Buildings

• Number Of Bedrooms: 0, 1, 2, 3, 4, 5+
• Year Of Construction: 1939 or before, 1940-1949...... 2010-2013, 2014 or later
• Home Occupation: Vacant, Occupied

Price and Rental Data

• Monthly Mortgage: Under $200, $200-$499...... $3000-$3999, $4000 or over
• Monthly Rent: Under $100, $100-$249...... $1500-$2499, $2500 or over
• Real-Estate Value: Under $25K, $25K-$50K...... $500K-$1M, Over $1M
• Home Ownership: Rented, Owned

Vehicles

• Number Of Vehicles In Household: 1, 2...... 4, 5+

• • Buildings: Marketers can use the number of bedrooms to target specific household sizes, tailoring campaigns for families with more members or individuals seeking different accommodations. The year of construction provides insights into the age and potential condition of homes, guiding marketers in promoting renovation or modernization services. The home occupation variable allows for targeted messaging based on the status of the property, such as addressing the needs of vacant properties versus occupied ones.
  • Price and Rental Data: These data variables provide crucial insights for marketers in the real estate and financial sectors. For example, businesses offering mortgage services can tailor their promotions based on the financial capacity of potential clients, while real estate agencies may focus on specific price ranges or ownership statuses to match their property listings with the preferences of their target audience.
  • Vehicles: Marketers can use the number of vehicles in a household to target automotive-related products and services. For instance, companies offering auto insurance or vehicle accessories may customize their campaigns based on the number of vehicles in a household, tailoring their offerings to the specific needs of potential customers.

• This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.

  Still looking for demographic data at the postal code level? Contact sales.

• There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:

  • Commuting
  • Educational Attainment
  • Employment
  • Families
  • Finance
  • Housing

Graph and download economic data for Housing Inventory: Active Listing Count in the United States (ACTLISCOUUS) from Jul 2016 to Feb 2025 about active listing, listing, and USA.

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. Please See HUD’s Small Area FMR Final Rule for additional information regarding the uses of Small Area FMRs.Note that this service does not denote precise SAFMR geographies. Instead, the service utilizes a relationship class to associate the information for each SAFMR with the FMR areas that its ZCTA overlaps. For example, ZCTA 94558 overlaps the Santa Rosa, Napa, and Vallejo-Fairfield MSAs. Selecting that ZCTA will reveal the SAFMR information associated with each FMR area.

  To learn more about the Small Area Fair Market Rents visit: https://www.huduser.gov/portal/datasets/fmr/smallarea/index.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: Fiscal Year 2024Date Update: 10/2023

Summary File 1 (SF1) Urban/Rural Update contains summary statistics on population and housing subjects derived from the responses to the 2010 Census questionnaire. Population items include sex, age, race, Hispanic or Latino origin, household relationship, household type, household size, family type, family size, and group quarters. Housing items include occupancy status, vacancy status, and tenure (whether a housing unit is owner-occupied or renter-occupied). The summary statistics are presented in 333 tables, which are tabulated for multiple levels of observation (called "summary levels" in the Census Bureau's nomenclature), including, but not limited to, regions, divisions, states, metropolitan/micropolitan statistical areas, counties, county subdivisions, places, congressional districts, American Indian Areas, Alaska Native Areas, Hawaiian Home Lands, ZIP Code tabulation areas, census tracts, block groups, and blocks. There are 177 population tables and 58 housing tables shown down to the block level; 84 population tables and 4 housing tables shown down to the census tract level; and 10 population tables shown down to the county level. Some of the summary areas are iterated for "geographic components" or portions of geographic areas, e.g., the principal city of a metropolitan statistical area (MSA) or the urban and rural portions of a MSA. With one variable per table cell and additional variables with geographic information, the collection comprises 2,597 data files, 49 per state, the District of Columbia, Puerto Rico, and the National File. The Census Bureau released SF1 in three stages: initial release, National Update, and Urban/Rural Update. The National Update added summary levels for the United States, regions, divisions, and geographic areas that cross state lines such as Combined Statistical Areas. This update adds urban and rural population and housing unit counts, summary levels for urban areas and the urban/rural components of census tracts and block groups, geographic components involving urbanized areas and urban clusters, and two new tables (household type by relationship for the population 65 years and over and a new tabulation of the total population by race). The initial release and National Update is available as ICPSR 33461. ICPSR supplies this data collection in 54 ZIP archives. There is a separate archive for each state, the District of Columbia, Puerto Rico, and the National File. The last archive contains a Microsoft Access database shell and additional documentation files besides the codebook.

In the third quarter of 2019, the most expensive properties in Budapest were found in District I, under the postal code 1014. The second most expensive neighborhood was in District V, where the property price per square meter amounted to 1.04 million forints.

Searchable online database of homes for sale, rent, and not currently on the market, with value estimator, market report, and real-estate trend tool. Users search by location (neighborhood, city, zip code, address) and parameters, such as property specifications, pricing, and keyword. Registration allows for favorite listing saving, customized property e-mail alerts, and other privileges. Users can also access real-estate listing data through an API.

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.

Graph and download economic data for Housing Inventory: Active Listing Count in Florida (ACTLISCOUFL) from Jul 2016 to Feb 2025 about active listing, FL, listing, and USA.

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 168 U.S. dollars per square foot in 2022. In 2024, the average sales price of a new home exceeded 500,000 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 12 percent year-on-year, and in 2022, the increase was even higher, at close to 17 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 three 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.

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.