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).

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to Aug 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Fair Market Rents (FMRs) represent the estimated amount (base rent + essential utilities) that a property in a given area typically rents for. The data are primarily used to determine payment standard amounts for the Housing Choice Voucher program. However, FMRs are also used to determine initial renewal rents for expiring project-based Section 8 contracts, 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 (ESG) program, calculate 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 calculate flat rent amounts in Public Housing Units.

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

The dataset "aus_real_estate.csv" encapsulates comprehensive real estate information pertaining to Australia, showcasing diverse attributes essential for property assessment and market analysis. This dataset, comprising 5000 entries across 10 distinct columns, offers a detailed portrayal of various residential properties in cities across Australia.

The dataset encompasses crucial factors influencing property valuation and purchase decisions. The 'Price' column represents the property's cost, spanning a range between $100,000 and $2,000,000. Attributes such as 'Bedrooms' and 'Bathrooms' highlight the accommodation specifics, varying from one to five bedrooms and one to three bathrooms, respectively. 'SqFt' denotes the square footage of the properties, varying between 800 and 4000 square feet, elucidating their size and spatial dimensions.

The 'City' column encompasses major Australian urban centers, including Sydney, Melbourne, Brisbane, Perth, and Adelaide, delineating the geographical distribution of the properties. 'State' further categorizes the locations into New South Wales (NSW), Victoria (VIC), Queensland (QLD), Western Australia (WA), and South Australia (SA).

The dataset encapsulates temporal information through the 'Year_Built' attribute, spanning from 1950 to 2023, providing insights into the age and vintage of the properties. Moreover, property types are delineated within the 'Type' column, encompassing variations such as 'Apartment,' 'House,' and 'Townhouse.' The binary 'Garage' column signifies the presence (1) or absence (0) of a garage, while 'Lot_Area' provides an understanding of the land area, ranging from 1000 to 10,000 square feet.

This dataset offers a comprehensive outlook into the Australian real estate landscape, facilitating multifaceted analyses encompassing property valuation, market trends, and regional preferences. Its diverse attributes make it a valuable resource for researchers, analysts, and stakeholders within the real estate domain, enabling robust investigations and informed decision-making processes regarding property investments and market dynamics in Australia.

Fair Market RentsThis National Geospatial Data Asset (NGDA) dataset, shared as a Department of Housing and Urban Development (HUD) feature layer, displays fair market rents (FMR) in the United States. According to HUD, "Fair Market Rents (FMRs) represent the estimated amount (base rent + essential utilities) that a property in a given area typically rents for. The data are primarily used to determine payment standard amounts for the Housing Choice Voucher program. However, FMRs are also used to determine initial renewal rents for expiring project-based Section 8 contracts, 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 (ESG) program, calculate 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 calculate flat rent amounts in Public Housing Units."Milwaukee-Waukesha-West Allis, WI Metropolitan Statistical Area (MSA)Data currency: current Federal service (Fair Market Rents)NGDAID: 122 (Fair Market Rents (Fair Market Rents For The Section 8 Housing Assistance Payments Program) - National Geospatial Data Asset (NGDA))For more information, please visit: Fair Market RentsSupport documentation: Fair Market Rents (FMRs)For feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

Median monthly rental prices for the private rental market in England by bedroom category, region and administrative area, calculated using data from the Valuation Office Agency and Office for National Statistics.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of lessors of residential buildings and dwellings (except social housing projects) (NAICS 531111), annual, for five years of data.

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 2025Date Update: 01/2025

Broad Rental Market Area (or BRMA) boundaries are used to determine Local Housing Allowance (LHA) rates. Empowered by the Welfare Reform Act (2007), the Rent Officer has defined the current boundaries in accordance with the Rent Officers (Housing Benefit Functions) (Amendment) Order 2008, which came into force on January 5th, 2009. The Order defines a BRMA as an area (a) comprising two or more distinct areas of residential accommodation, each distinct area of residential accommodation adjoining at least one other in the area; (b) within which a person could reasonably be expected to live having regard to facilities and services for the purposes of health, education, recreation, personal banking and shopping, taking account of the distance of travel, by public and private transport, to and from facilities and services of the same type and similar standard; and (c) containing residential premises of a variety of types and including such premises held on a variety of tenancies.

The rental housing developments listed below are among the thousands of affordable units that are supported by City of Chicago programs to maintain affordability in local neighborhoods. The list is updated periodically when construction is completed for new projects or when the compliance period for older projects expire, typically after 30 years. The list is provided as a courtesy to the public. It does not include every City-assisted affordable housing unit that may be available for rent, nor does it include the hundreds of thousands of naturally occurring affordable housing units located throughout Chicago without City subsidies. For information on rents, income requirements and availability for the projects listed, contact each property directly. For information on other affordable rental properties in Chicago and Illinois, call (877) 428-8844, or visit www.ILHousingSearch.org.

AI-Powered Rental Price Index Market Outlook

According to our latest research, the global AI-Powered Rental Price Index market size reached USD 1.84 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.2% projected through the forecast period. By 2033, the market is anticipated to achieve a value of USD 8.19 billion, driven by increasing demand for data-driven pricing strategies, rapid digital transformation in real estate, and the growing adoption of artificial intelligence across property valuation and management. As per our comprehensive analysis, the market is witnessing exponential growth due to the need for accurate, real-time rental price insights, supporting both property owners and tenants in making informed decisions.

One of the primary growth factors fueling the AI-Powered Rental Price Index market is the escalating need for transparency and precision in rental pricing, especially in highly dynamic urban real estate environments. Traditional pricing methodologies often fall short in accounting for rapidly shifting market variables, such as sudden changes in demand, local economic trends, or emerging neighborhood developments. AI-powered solutions leverage advanced algorithms and machine learning models to process vast datasets, including historical rental prices, property attributes, neighborhood analytics, and even social sentiment. This enables real estate stakeholders to arrive at more accurate and competitive rental prices, minimizing vacancies and maximizing returns. Further, the integration of AI with Internet of Things (IoT) and smart city initiatives is enhancing the granularity and timeliness of rental data, solidifying the value proposition of AI-powered rental indices.

Another significant growth driver is the increasing adoption of digital platforms by real estate agencies, property managers, and institutional investors. The transformation from manual, spreadsheet-based assessments to automated, AI-driven platforms is streamlining operations, reducing human error, and enabling scalable portfolio management. Financial institutions are also leveraging AI-powered rental indices for risk assessment, loan underwriting, and investment analysis, further expanding the addressable market. Additionally, the proliferation of proptech startups and increased venture capital investments in real estate technology are accelerating the innovation cycle, resulting in more sophisticated and customizable AI-powered pricing solutions. The rising consumer expectation for transparency and fairness in rental pricing, particularly among younger, tech-savvy renters, is further catalyzing market growth.

Furthermore, regulatory developments and government initiatives aimed at improving housing affordability and market efficiency are positively impacting the AI-Powered Rental Price Index market. In many regions, public sector agencies are collaborating with technology providers to develop standardized rental indices, which support policy-making, rent control measures, and urban planning. These collaborations are fostering an environment where AI-powered analytics are not only a competitive advantage for private enterprises but also a tool for public good. However, market expansion is somewhat tempered by challenges related to data privacy, algorithmic transparency, and the need for standardized data formats across jurisdictions. Addressing these issues will be crucial for sustained growth and broader adoption in the coming years.

Regionally, North America continues to dominate the AI-Powered Rental Price Index market, accounting for the largest share in 2024, owing to its mature real estate sector, high digital adoption, and strong presence of leading proptech firms. Europe is experiencing rapid growth, particularly in countries with high urbanization rates and regulatory support for digital transformation in real estate. Asia Pacific is emerging as a high-growth region, driven by urban expansion, smart city projects, and a burgeoning middle class seeking reliable rental information. While Latin America and Middle East & Africa are currently smaller markets, they present significant long-term potential as digital infrastructure and real estate investment accelerate. Overall, regional dynamics are shaped by varying levels of technological maturity, regulatory frameworks, and the pace of urbanization.

The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. These dataset give the community of housing analysts the opportunity to use a consistent set of affordability measures. The most recent year HADS is available as a Public Use File (PUF) is 2013. For 2015 and beyond, HADS is only available as an IUF and can no longer be released on a PUF. Those seeking access to more recent data should reach to the listed point of contact.

Abstract copyright UK Data Service and data collection copyright owner.The COntinuous REcording of Lettings and Sales (CORE) is a national information source that provides annual official statistics on new lettings and sales of social housing stock. All datasets are based on administrative data collected via the government's CORE system. The CORE lettings data include information on the characteristics of both private registered providers and local authority new social housing tenants and the homes they rent. For each year, data is structured into four datasets based on type of letting (social rent general needs and supported needs, and affordable rent general needs and supported needs). It is a regulatory requirement for providers registered with the Homes and Communities Agency to supply the data. For those who are not registered, submissions are voluntary. Local authorities have participated in CORE since 2004-5 on a voluntary basis. Weighting is applied to adjust for non-response by local authorities for social rent datasets, and imputation is also carried out to address item-level non-response of key data on tenant characteristics for both local authorities and privately registered providers. The three datasets for affordable rent are not weighted or imputed. The CORE sales data include information on sales of local authority dwellings and some summary details on sales of registered provider stock (previously known as Registered Social Landlords or housing associations). Collecting these data allows for a better understanding of the socio-economic and demographic make-up of affordable housing customers and local housing markets and products. The sales dataset is imputed, with more information on the imputations within the data dictionary. The CORE data are used by central government to inform national housing policy and by local government to inform their Strategic Housing Market Assessments. The data are also used by academics, researchers, charities and the wider public to understand social housing issues. Further information may be found on the GOV.UK Social housing lettings and Social housing sales webpages. Users should note that the Lettings and Sales data are now held in separate datasets at each access level (see below). Previously, they were held in combined studies, SNs 7603, 7604 and 7686, which have now been withdrawn. End User Licence, Special Licence and Secure Access datasets The CORE datasets are available at three access levels, depending on the level of detail in the data. For the standard End User Licence (EUL) version (SNs 9237 and 9238), the geographic level of the data is set at Government Office Region (GOR). Letting and voiding dates are provided at month and year only; age variables are top-coded at 90 years; income, benefits, earnings, charge and shortfall variables are banded to disguise unique values; landlords are grouped into coded categories. For the Special Licence access (SL) version (SNs 9239 and 9240), geographic level is set at Local Authority. The SL data have more restrictive access conditions than those made available under the standard EUL. Prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. For Secure Access (SNs 9241 and 9242), the full CORE datasets are available, with some key variables recoded. Prospective users of the Secure Access version will need to fulfil additional requirements, including completion of face-to-face training and agreement to further stringent access conditions. SN 9240: Continuous Recording of Social Housing Sales (CORE):This study contains the SL-level CORE Sales data only. The SL CORE Lettings data are held under SN 9239. Main Topics:The following topics are covered:Lettings data: tenant income; tenant benefits; household demographics (including economic status, nationality, etc.); number of affordable or social lettings; reason for requiring social housing; void periods/ number of times offered; rent and other charges; Reasonable Preference Group (including homelessness status); size and type of property.Sales data: number of private registered providers of social housing (PRP) sales; type of sale; household demographics; size of property; type of property; tenant income; financial characteristics of sale (mortgage, % discount, equity, etc.); reason for leaving last home; location of new housing; whether served in the Armed Forces.

This dataset contains existing multifamily rental sites in the City of Detroit with housing units that have been preserved as affordable since 2018 with assistance from the public sector.

Over time, affordable units are at risk of falling off line, either due to obsolescence or conversion to market-rate rents. This dataset contains occupied multifamily rental housing sites (typically 5+ units) in the City of Detroit, including those that have units that have been preserved as affordable since 2015 through public funding, regulatory agreements, and other means of assistance from the public sector. Data are collected from developers, other governmental departments and agencies, and proprietary data sources by various teams within the Housing and Revitalization Department, led by the Preservation Team. Data have been tracked since 2018 in service of citywide housing preservation goals. This reflects HRD's current knowledge of multifamily units in the city and will be updated as the department's knowledge changes. For more information about the City's multifamily affordable housing policies and goals, visit here.

Affordability level for affordable units are measured by the percentage of the Area Median Income (AMI) that a household could earn for that unit to be considered affordable for them. For example, a unit that rents at a 60% AMI threshold would be affordable to a household earning 60% or less of the median income for the area. Rent affordability is typically defined as housing costs consuming 30% or less of monthly income. Regulated housing programs are designed to serve households based on certain income benchmarks relative to AMI, and these income benchmarks vary based on household size. Detroit city's AMI levels are set by the Department of Housing and Urban Development (HUD) for the Detroit-Warren-Livonia, MI Metro Fair Market Rent (FMR) area. For more information on AMI in Detroit, visit here.

Fair Market Rents (FMRs) represent the estimated amount (base rent + essential utilities) that a property in a given area typically rents for. The data is primarily used to determine payment standard amounts for the Housing Choice Voucher program; however, FMRs are also used to:

Determine initial renewal rents for expiring project-based Section 8 contracts;

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 (ESG) program;

Calculate 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;

Calculate flat rent amounts in Public Housing Units.

Data is updated annualy in accordance with 42 USC 1437f which 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, October 1st.In order to calculate rents for units with more than four bedrooms, an extra 15% cost is added to the four bedroom unit value. The formula is to multiply the four bedroom rent by 1.15. For example, in FY21 the rent for a four bedroom unit in the El Centro, California Micropolitan Statistical Area is $1,444. The rent for a five bedroom unit would be $1,444 * 1.15 or $1,661. Each subsequent bedroom is an additional 15%. A six bedroom unit would be $1,444 * 1.3 or $1,877. These values are not included in the feature service.

To learn more about Fair Market Rents visit: https://www.huduser.gov/portal/datasets/fmr.html/

Data Dictionary: DD_Fair Market Rents

Date of Coverage: FY2022 Data Updated: Annuallyhttps://catalog.data.gov/dataset/fair-market-rents-fair-market-rents-for-the-section-8-housing-assistance-payments-program-

Housing Index in Hong Kong increased to 139.39 points in September 14 from 138.74 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Housing Index in Germany decreased to 218.87 points in August from 219.06 points in July of 2025. This dataset provides the latest reported value for - Germany House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

A. SUMMARY Beginning in 2022, the law requires owners of residential housing units in San Francisco to report certain information about their units to the San Francisco Rent Board on an annual basis. For units (other than condominium units) in buildings of 10 residential units or more, owners were required to begin reporting this information to the Rent Board by July 1, 2022, with updates due on March 1, 2023 and every March 1 thereafter. For condominium units and units in buildings with less than 10 residential units, reporting began on March 1, 2023 with updates due every March 1 thereafter. Owners are also required to inform the Rent Board within 30 days of any change in the name or business contact information of the owner or designated property manager. The Rent Board uses this information to create and maintain a “housing inventory” of all units in San Francisco that are subject to the Rent Ordinance.

B. HOW THE DATASET IS CREATED The Rent Board has developed a secure website portal that provides an interface for owners to submit the required information (The Housing Inventory). The Rent Board uses the information provided to generate reports and surveys, to investigate and analyze rents and vacancies, to monitor compliance with the Rent Ordinance, and to assist landlords and tenants and other City departments as needed. The Rent Board may not use the information to operate a “rental registry” within the meaning of California Civil Code Sections 1947.7 – 1947.8.

C. UPDATE PROCESS The Housing Inventory is continuously updated as it receives submissions from the public. The portal is available to the public 24/7. The Rent Board Staff also makes regular updates to the data during regular business hours, and the data is shared to DataSF every 24 hours.

D. HOW TO USE THIS DATASET It is important to note that this dataset contains information submitted by residential property owners and tenants. The Rent Board does not review or verify the accuracy of the data submitted. Please note that historical data is subject to change.

Notes for Analysis - Addresses have been anonymized to the block level - Latitude & Longitude are the closest mid-block point to the unit - Each row is a unit. To count total units, first select a year then count unique ids. Do not sum unit count.