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.

Total Housing Inventory in the United States increased to 1540 Thousands in May from 1450 Thousands in April of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and parcels nationally.

Over 60M parcels reflecting over 330M permits over the past 20 years.

This comprehensive dataset contains building permits issued in the United States, providing valuable insights into residential and commercial construction activities. With over millions of records covering millions of homes, this dataset offers a vast opportunity for analysis and business growth.

Includes permits from various states across the US

Covers residential and commercial construction activities

Insights:

Residential vs. Commercial: Analyze the distribution of permits by type (residential, commercial) to understand local market trends.

Construction Activity: Track permit issuance over time to identify patterns and fluctuations in construction activity.

Geographic Patterns: Examine the concentration of permits by state, county, or city to reveal regional development opportunities.

Potential Applications:

Contractors and Builders: Utilize this dataset to identify potential projects, estimate job values, and stay up-to-date on permit requirements.

Local Governments: Analyze building permit data to inform land-use planning, zoning regulations, and infrastructure development.

Investors and Developers: Explore the types of construction projects being undertaken in specific areas, enabling informed investment decisions.

Value Propositions:

Understand Current Home Condition: Gain insights into the current state of homes by analyzing building permit data, allowing you to:

Identify areas with high concentrations of permits

Determine the scope and type of work being performed

Infer the potential for improved home values

Lender Lead Generation: Use this dataset to identify potential refinance candidates based on improved homes, enabling lenders to:

Target homeowners who have invested in their properties

Offer tailored financial solutions to capitalize on increased property value

Contractor Lead Generation:

Solar installers can target neighbors of solar customers, increasing the chances of successful referrals and upselling opportunities.

Pool cleaners can target new pools, identifying potential customers for maintenance and cleaning services.

Roofing contractors can target homes with recent roofing permits, offering replacement or repair services to homeowners.

Home Service Providers:

Handyman services can target homes with permit records, offering a range of maintenance and repair services.

Appliance installers can target new kitchens and bathrooms, identifying potential customers for appliance installation and integration.

Real Estate Professionals:

Realtors can analyze permit data to understand local market trends, adjusting their sales strategies to capitalize on areas with high construction activity.

Property managers can identify potential investment opportunities, using permit data to evaluate the feasibility of investment projects.

Data Analysis Ideas:

Trend Analysis: Identify trends in permit issuance by type (residential, commercial), project size, or location to forecast future demand.

Geospatial Analysis: Visualize permit data on a map to analyze the concentration of construction activity and identify areas with high growth potential.

Correlation Analysis: Examine the relationship between permit issuance and local economic indicators (e.g., GDP, unemployment rates) to understand the impact of construction on the local economy.

Business Use Cases:

Market Research: Analyze permit data to inform business decisions about market trends, competition, and growth opportunities.

Risk Assessment: Identify areas with high concentrations of permits and potential risks (e.g., building code non-compliance) to adjust business strategies accordingly.

Investment Analysis: Use permit data to evaluate the feasibility of investment projects in specific regions or markets.

Data Visualization Ideas:

Interactive Maps: Create interactive maps to visualize permit concentration by location, type, and project size.

Permit Issuance Charts: Plot permit issuance over time to illustrate trends and fluctuations in construction activity.

Bar Charts by Category: Display the distribution of permits by category (e.g., residential, commercial) to highlight market trends.

Additional Ideas:

Combine with other datasets: Integrate building permit data with other sources (e.g., crime statistics, weather patterns) to gain a more comprehensive understanding of local conditions.

Analyze by demographic factors: Examine how permit issuance varies across different demographics (e.g., age, income level) to understand market preferences and behaviors.

Develop predictive models: Create statistical mo...

Xverum’s Location Data is a highly structured dataset of 230M+ verified locations, covering businesses, landmarks, and points of interest (POI) across 5000 industry categories. With accurate geographic coordinates, business metadata, and mapping attributes, our dataset is optimized for GIS applications, real estate analysis, market research, and urban planning.

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The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building. This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by Tract Date of Coverage: 2016-2020

Graph and download economic data for Housing Inventory: Active Listing Count in Georgia (ACTLISCOUGA) from Jul 2016 to May 2025 about active listing, GA, listing, and USA.