Graph and download economic data for Rental Vacancy Rate for Texas (TXRVAC) from 1986 to 2024 about vacancy, rent, TX, rate, and USA.

South Dakota was the U.S. state with the highest vacancy rate index in January 2025. Conversely, New Jersey, New York, and Illinois had the lowest vacancy rate index during that period. All three states had an index value of under five percent. Overall, apartment vacancies in the U.S. have increased since 2021, due to the increase in new supply.

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directly sourced from property management software systems, ensuring real-time accuracy and eliminating reliance on outdated or scraped information.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

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show age, type, vacancy rates, and owner/renter tenure of housing units by Zip Code Tabulation Area in the Atlanta region.

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 2013-2017). 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.

Naming conventions:

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)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here).

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

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).

Fair Market Rents (FMRs) are primarily 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), and to serve as a rent ceiling in the HOME rental assistance program. The U.S. Department of Housing and Urban Development (HUD) annually estimates FMRs for 530 metropolitan areas and 2,045 nonmetropolitan county FMR areas. By law the final FMRs for use in any fiscal year must be published and available for use at the start of that fiscal year, on October 1. 2014.

What makes your data unique? - We have our proprietary AI to clean outliers and to calculate occupancy rate accurately.

How is the data generally sourced? - Web scraped data from Airbnb. Scraped on a weekly basis.

What are the primary use-cases or verticals of this Data Product? - Tourism & DMO: A one-page CSV will give you a clear picture of the private lodging sector in your entire country. - Property Management: Understand your market to expand your business strategically. - Short-term rental investor: Identify profitable areas.

Do you cover country X or city Y?

We have data coverage from the entire world. Therefore, if you can't find the exact dataset you need, feel free to drop us a message. Our clients have bought datasets like 1) Airbnb data by US zipcode 2) Airbnb data by European cities 3) Airbnb data by African countries.

Rents for industrial real estate in the U.S. have increased since 2017, with flexible/service space reaching the highest price per square foot in 2024. In just a year, the cost of, flex/service space rose by nearly *****U.S. dollars per square foot. Manufacturing facilities, warehouses, and distribution centers had lower rents and experienced milder growth. Los Angeles, Orange County, and Inland Empire, California, are some of the most expensive markets in the country. Office real estate is pricier Industrial real estate is far from being the most expensive commercial property type. For instance, average rental rates in major U.S. metros for office space are much higher than those for industrial space. This is most likely because office units are generally located in urban areas where there is limited space and thus higher demand, whereas industrial units are more suited to the outskirts of such urban areas. Industrial units, such as warehouses or factories, require much more space because they need to house large, heavy equipment or serve as a storage unit for future shipments. Big-box distribution space is gaining in importance Warehouses and distribution may currently command the lowest average rent per square foot among industrial space types, but the growing popularity of the asset class has earned it considerable gains over the past years. In 2021 and 2022, high occupier demand and insufficient supply led to soaring taking rent of big-box buildings. During that time, the vacancy rate of distribution centers fell below ****percent. The development of industrial and logistics facilities has accelerated since then, with the new supply coming to market, causing the vacancy rate to increase and the pressures on rent to ease.

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.

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?
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This USA Housing Market Dataset (Synthetic) contains 300 rows and 10 columns of real estate-related data designed for housing price prediction, trend analysis, and investment insights. It includes key property details such as price, number of bedrooms and bathrooms, square footage, year built, garage spaces, lot size, zip code, crime rate, and school ratings.

This dataset is ideal for: ✅ Machine Learning Models for predicting housing prices ✅ Market Research & Investment Analysis ✅ Exploring Property Trends in the USA ✅ Educational Purposes for Data Science and Analytics

This dataset provides a realistic yet synthetic view of the real estate market, making it useful for data-driven decision-making in the housing industry.

Let me know if you need any modifications!

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.

In District of Columbia, the average rent per square foot was **** U.S. dollars in 2018, whereas renters in Oregon were expected to pay half as much in rent per square foot. DC was the most expensive state for renters, followed by New York, Hawaii, Massachusetts and California. Why is DC so expensive? District of Columbia is the center of the U.S. political system with all three branches of federal government sitting there: Congress (legislative), President (executive) and the Supreme Court (judicial). The above average household incomes of its residents mean that high rents are still sustainable for the rental market. Limited space in DC DC has the largest share of apartment dwellers in the country. This is most likely due to limited space, as the federal district has a much higher population density than the states. The political importance of DC and the high population density suggest that the federal district is likely to retain its spot as the most expensive rental market in the future.

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 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.

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