House prices grew year-on-year in most states in the U.S. in the first quarter of 2025. Hawaii was the only exception, with a decline of **** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Rhode Island—the state where homes appreciated the most—the increase was ******percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2025, 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 2025.

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.

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

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 2024. 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.

Reference: https://www.zillow.com/research/zhvi-methodology/

Official Background

In setting out to create a new home price index, a major problem Zillow sought to overcome in existing indices was their inability to deal with the changing composition of properties sold in one time period versus another time period. Both a median sale price index and a repeat sales index are vulnerable to such biases (see the analysis here for an example of how influential the bias can be). For example, if expensive homes sell at a disproportionately higher rate than less expensive homes in one time period, a median sale price index will characterize this market as experiencing price appreciation relative to the prior period of time even if the true value of homes is unchanged between the two periods.

The ideal home price index would be based off sale prices for the same set of homes in each time period so there was never an issue of the sales mix being different across periods. This approach of using a constant basket of goods is widely used, common examples being a commodity price index and a consumer price index. Unfortunately, unlike commodities and consumer goods, for which we can observe prices in all time periods, we can’t observe prices on the same set of homes in all time periods because not all homes are sold in every time period.

The innovation that Zillow developed in 2005 was a way of approximating this ideal home price index by leveraging the valuations Zillow creates on all homes (called Zestimates). Instead of actual sale prices on every home, the index is created from estimated sale prices on every home. While there is some estimation error associated with each estimated sale price (which we report here), this error is just as likely to be above the actual sale price of a home as below (in statistical terms, this is referred to as minimal systematic error). Because of this fact, the distribution of actual sale prices for homes sold in a given time period looks very similar to the distribution of estimated sale prices for this same set of homes. But, importantly, Zillow has estimated sale prices not just for the homes that sold, but for all homes even if they didn’t sell in that time period. From this data, a comprehensive and robust benchmark of home value trends can be computed which is immune to the changing mix of properties that sell in different periods of time (see Dorsey et al. (2010) for another recent discussion of this approach).

For an in-depth comparison of the Zillow Home Value Index to the Case Shiller Home Price Index, please refer to the Zillow Home Value Index Comparison to Case-Shiller

Each Zillow Home Value Index (ZHVI) is a time series tracking the monthly median home value in a particular geographical region. In general, each ZHVI time series begins in April 1996. We generate the ZHVI at seven geographic levels: neighborhood, ZIP code, city, congressional district, county, metropolitan area, state and the nation.

Underlying Data

Estimated sale prices (Zestimates) are computed based on proprietary statistical and machine learning models. These models begin the estimation process by subdividing all of the homes in United States into micro-regions, or subsets of homes either near one another or similar in physical attributes to one another. Within each micro-region, the models observe recent sale transactions and learn the relative contribution of various home attributes in predicting the sale price. These home attributes include physical facts about the home and land, prior sale transactions, tax assessment information and geographic location. Based on the patterns learned, these models can then estimate sale prices on homes that have not yet sold.

The sale transactions from which the models learn patterns include all full-value, arms-length sales that are not foreclosure resales. The purpose of the Zestimate is to give consumers an indication of the fair value of a home under the assumption that it is sold as a conventional, non-foreclosure sale. Similarly, the purpose of the Zillow Home Value Index is to give consumers insight into the home value trends for homes that are not being sold out of foreclosure status. Zillow research indicates that homes sold as foreclosures have typical discounts relative to non-foreclosure sales of between 20 and 40 percent, depending on the foreclosure saturation of the market. This is not to say that the Zestimate is not influenced by foreclosure resales. Zestimates are, in fact, influenced by foreclosure sales, but the pathway of this influence is through the downward pressure foreclosure sales put on non-foreclosure sale prices. It is the price signal observed in the latter that we are attempting to measure and, in turn, predict with the Zestimate.

Market Segments Within each region, we calculate the ZHVI for various subsets of homes (or mar...

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 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.

Graph and download economic data for All-Transactions House Price Index for Los Angeles County, CA (ATNHPIUS06037A) from 1975 to 2024 about Los Angeles County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.

https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg" alt="">

Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

In 2023, Sagaponack, NY (zip code *****) 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 ** percent. Ross, CA (zip code *****) stood at the other end of the scale, with a decline of ** percent.

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 map shows the average household income in the U.S. in 2022 in a multiscale map by country, state, county, ZIP Code, tract, and block group. Information for the average household income is an estimate of income for calendar year 2022. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases.The pop-up is configured to include the following information for each geography level:Average household incomeMedian household incomeCount of households by income groupAverage household income by householder age groupThe data shown is from Esri's 2022 Updated Demographic estimates using Census 2020 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's U.S. Updated Demographic (2022/2027) Data: Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2022/2027 Esri Updated DemographicsEssential demographic vocabularyThis item is for visualization purposes only and cannot be exported or used in analysis.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Graph and download economic data for All-Transactions House Price Index for Massachusetts (MASTHPI) from Q1 1975 to Q3 2025 about MA, appraisers, HPI, housing, price index, indexes, price, and USA.

Context

Zillow operates an industry-leading economics and analytics bureau led by Zillow’s Chief Economist, Dr. Stan Humphries. At Zillow, Dr. Humphries and his team of economists and data analysts produce extensive housing data and analysis covering more than 500 markets nationwide. Zillow Research produces various real estate, rental and mortgage-related metrics and publishes unique analyses on current topics and trends affecting the housing market.

At Zillow’s core is our living database of more than 100 million U.S. homes, featuring both public and user-generated information including number of bedrooms and bathrooms, tax assessments, home sales and listing data of homes for sale and for rent. This data allows us to calculate, among other indicators, the Zestimate, a highly accurate, automated, estimated value of almost every home in the country as well as the Zillow Home Value Index and Zillow Rent Index, leading measures of median home values and rents.

Content

The Zillow Rent Index is the median estimated monthly rental price for a given area, and covers multifamily, single family, condominium, and cooperative homes in Zillow’s database, regardless of whether they are currently listed for rent. It is expressed in dollars and is seasonally adjusted. The Zillow Rent Index is published at the national, state, metro, county, city, neighborhood, and zip code levels.

Zillow produces rent estimates (Rent Zestimates) based on proprietary statistical and machine learning models. Within each county or state, the models observe recent rental listings and learn the relative contribution of various home attributes in predicting prevailing rents. These home attributes include physical facts about the home, prior sale transactions, tax assessment information and geographic location as well as the estimated market value of the home (Zestimate). Based on the patterns learned, these models estimate rental prices on all homes, including those not presently for rent. Because of the availability of Zillow rental listing data used to train the models, Rent Zestimates are only available back to November 2010; therefore, each ZRI time series starts on the same date.

Acknowledgements

The rent index data was calculated from Zillow's proprietary Rent Zestimates and published on its website.

Inspiration

What city has the highest and lowest rental prices in the country? Which metropolitan area is the most expensive to live in? Where have rental prices increased in the past five years and where have they remained the same? What city or state has the lowest cost per square foot?

Graph and download economic data for All-Transactions House Price Index for Orange County, CA (ATNHPIUS06059A) from 1975 to 2024 about Orange County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.

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

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

Introduction:

This dataset, titled 'Airbnb Market Analysis and Real Estate Sales Data (2019),' comprises a comprehensive collection of information pertaining to the Airbnb rental market and property sales in two distinct areas within California: Big Bear and Joshua Tree, along with their associated zip codes (92314, 92315, 92284, and 92252). The dataset provides monthly aggregated data, allowing for an in-depth analysis of rental and real estate market trends in these regions. It includes the following files:

Datasets:

Market Analysis:

This file contains listing-level information from 2019, aggregated on a monthly basis. It encompasses various metrics, such as unique property codes (unified_id), generated revenue, availability (openness), occupancy ratios, nightly rates, lead times, and average length of stay for reservations made each month. Additionally, it provides insights into property amenities.

Amenities:

This file indicates whether a listing has specific amenities, denoting their presence with a value of 1 or their absence with a value of 0. Notably, it identifies the availability of a pool or hot tub in each listing.

Geolocation:

This file contains latitude and longitude coordinates for each listing, enabling precise spatial analysis and visualization.

Sales Properties:

This dataset provides information concerning properties available for sale within the study areas. In the Joshua Tree region (zip codes 92284 and 92252), there are two separate files—one presenting the overall information about sales properties and the other focusing on properties with pools.

This dataset is a valuable resource for researchers and analysts interested in gaining insights into the real estate and Airbnb rental markets in California, particularly within the specified regions."

Potential Applications:

This dataset provides a strong foundation for Power BI reporting, enabling the creation of insightful reports and dashboards. Analysts can utilize joins on unique IDs to extract key factors and KPIs, facilitating data-driven decision-making. Whether it's optimizing Airbnb listings, making informed real estate investments, or shaping policies, this dataset serves as a valuable resource for Power BI users seeking to gain deeper insights and drive data-driven strategies in the California real estate market

The table below showcases the total number of homes sold for each zip code in The Villages, Florida. It's important to understand that the number of homes sold can vary greatly and can change yearly.