This dataset denotes HUD subsidized Multifamily Housing properties excluding insured hospitals with active loans. HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here.

The CoStar Commercial Repeat-Sales Index (CCRSI) for multifamily real estate in the United States started to decline in the second half of 2022, after more than a decade of steady growth. The index measures the development of sales prices of multifamily properties, with 2000 chosen as a base year. An index value of 200 means that sales prices have doubled since 2000. In March 2024, the value-weighed index, which is more representative of the high-value deals in core markets, hit 325 index points, down from a market peak of 416 in June 2022. The equal-weighed index is more influenced by the lower-priced deals that comprise the higher share of transactions. It stood at 438 index points in March 2024, down from a market peak of 503 in June 2022.

Multifamily Portfolio datasets (section 8 contracts) - The information has been compiled from multiple data sources within FHA or its contractors. HUD oversees more than 22,000 privately owned multifamily properties, and more than 1.4 million assisted housing units. These homes were originally financed with FHA-insured or Direct Loans and many are supported with Section 8 or other rental assistance contracts. Our existing stock of affordable rental housing is a critical resource for seniors and families who otherwise would not have access to safe, decent places to call home.

Housing Starts Multi Family in the United States decreased to 316 Thousand units in May from 454 Thousand units in April of 2025. This dataset includes a chart with historical data for the United States Housing Starts Multi Family.

Graph and download economic data for New Privately-Owned Housing Units Completed: Units in Buildings with 5 Units or More (COMPU5MUSA) from Jan 1968 to May 2025 about 5-unit structures +, new, private, housing, and USA.

The majority of the housing stock in the United States is single-family detached houses. Of the total ***** million housing units in 2023, about **** million were detached homes and *** million were attached single-family homes. In comparison, roughly **** million units were in multifamily buildings.

This dataset contains multifamily affordable and market-rate housing sites (typically 5+ units) in the City of Detroit that have been built or rehabbed since 2015, or are currently under construction. Most sites are rental housing, though some are for sale. The data are collected from developers, other government departments and agencies, and proprietary data sources in order to track new multifamily and affordable housing construction and rehabilitation occurring in throughout the city, in service of the City's multifamily affordable housing goals. Data are compiled by various teams within the Housing and Revitalization Department (HRD), led by the Preservation Team. This dataset reflects HRD's current knowledge of multifamily units under construction 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.

Graph and download economic data for New Privately-Owned Housing Units Started: Units in Buildings with 5 Units or More (HOUST5F) from Jan 1959 to May 2025 about 5-unit structures +, housing starts, privately owned, housing, and USA.

Graph and download economic data for New Privately-Owned Housing Units Under Construction: Units in Buildings with 5 Units or More (UNDCON5MUSA) from Jan 1970 to May 2025 about 5-unit structures +, construction, new, private, housing, and USA.

The Department of Finance (DOF) maintains records for all property sales in New York City, including sales of family homes in each borough. This list is a summary of all neighborhood sales for Class 1-, 2- and 3-Family homes Citywide in 2009. This list includes all sales of 1-, 2-, and 3-Family Homes' from January 1st, 2009 to December 31, 2009, whose sale price is equal to or more than $150,000. The Building Class Category for Sales is based on the Building Class at the time of the sale. Update Schedule: Annually

Residential Real Estate Market Outlook

The global residential real estate market size was valued at approximately $9.7 trillion in 2023 and is projected to reach an astounding $15.4 trillion by 2032, growing at a compound annual growth rate (CAGR) of 5.2%. This growth is driven by several factors, including increasing urbanization, rising disposable incomes, and the ongoing global shift towards homeownership as a stable investment. Demographic shifts, such as the growing number of nuclear families and millennials entering the housing market, also contribute significantly to this upward trend.

One of the primary growth factors for the residential real estate market is the increasing urbanization across the globe. As more people migrate to urban areas in search of better job opportunities and a higher standard of living, the demand for residential properties in cities continues to rise. This trend is particularly pronounced in developing countries, where rapid economic growth is accompanied by significant rural-to-urban migration. Additionally, the trend of urban redevelopment and the creation of smart cities are further fueling the demand for modern residential properties.

Another crucial growth factor is the rise in disposable incomes and improved access to financing options. With strong economic growth in many parts of the world, individual incomes have been rising, allowing more people to afford homeownership. Financial institutions are also playing a critical role by offering a variety of mortgage products with attractive interest rates and flexible repayment terms. This increased access to capital has enabled a broader section of the population to invest in residential real estate, thereby expanding the market.

Technological advancements and the digital transformation of the real estate sector are also contributing to market growth. The proliferation of online platforms and real estate technology (proptech) solutions has made the process of buying, selling, and renting properties more efficient and transparent. Virtual tours, online mortgage applications, and blockchain for property transactions are some of the innovations revolutionizing the industry. These technological advancements not only improve the customer experience but also attract tech-savvy millennials and Gen Z buyers.

Regionally, the Asia-Pacific region is experiencing significant growth in the residential real estate market. Countries like China and India, with their large populations and rapid urbanization, are at the forefront of this expansion. Government initiatives aimed at providing affordable housing and improving infrastructure are also playing a pivotal role. In contrast, mature markets like North America and Europe are witnessing steady growth driven by economic stability and continued investment in housing. Meanwhile, regions like Latin America and the Middle East & Africa are also showing promise, albeit at a slower pace, due to varying economic conditions and market maturity levels.

Property Type Analysis

The residential real estate market is segmented by property type, including single-family homes, multi-family homes, condominiums, townhouses, and others. Single-family homes are the most traditional and widespread type of residential property. They are particularly popular in suburban areas where space is more abundant. The demand for single-family homes continues to be driven by the desire for privacy, larger living spaces, and the ability to customize the property. These homes appeal especially to families with children and those looking to invest in a long-term residence.

Multi-family homes, which include duplexes, triplexes, and apartment buildings, are gaining traction, particularly in urban settings. These properties are attractive due to their potential for generating rental income and their ability to house multiple tenants. Investors find multi-family homes appealing as they offer a higher return on investment (ROI) compared to single-family homes. Additionally, the increasing trend of co-living and shared housing arrangements has bolstered the demand for multi-family properties in cities.

Condominiums, or condos, are another significant segment within the residential real estate market. Condos are particularly popular in urban areas where land is scarce and expensive. They offer a balance between affordability and amenities, making them an attractive option for young professionals and small families. Condominiums often come with added benefits such as maintenance services, security, and shared facilities like gyms and swimmin

Yearly Real Estate sales data by count and purchase price (median and average) from 2005 to 2018. All communities in the Keys to the Valley region are included.

Vermont Dataset Description

Purchase price - Average Sales Price based on listing price at time of purchase

Source – www.HousingData.org

NH Dataset Description

This data set provides an estimate of the median sale price of existing and new primary homes in New Hampshire. A primary home is defined as a single family home occupied by an owner household as their primary place of residence. Multi-family rental housing, seasonal or vacation homes and manufactured housing are not included in the analysis of this data.

Purchase price -
Median Sales Price

Data Collection Process - For the Period 1990 through 2014, the median purchase prices were calculated from data collected by the New Hampshire Department of Revenue Administration on the PA-34 Form through their vendor Real Data Corp. A PA-34 Form is filed by the buyer and seller at the time of sale of all real property in the State of New Hampshire. In 2015 this source of data was no longer available, and has been replaced by real estate transaction data supplied by The Warren Group and filtered and compiled by NHHFA. This change in data source is reflected in the charts by a break in the trend line.

Analysis - Median sale prices of all, new, existing, and condominium homes are calculated. The frequency of sales by $10,000 increment is also calculated for each of the above categories. Calculations based on sample sizes smaller than 50 are viewed as providing inconsistent and highly volatile results and are not typically released. Individual record level data is not released.

Limitations - The quality of this data at the higher geographic levels (statewide and counties) is consistent over the entire time series. For the larger LMAs and Municipalities the data is reasonably consistent with some holes in the data. For smaller LMAs and moderate sized municipalities the data is most consistent for existing homes since 1998. For the smallest municipalities this data set does not provide adequately consistent analysis.

Source - NHHFA Purchase Price Database; Source: 1990-2014 - NH Dept. of Revenue, PA-34 Dataset, Compiled by Real Data Corp. Filtered and analyzed by New Hampshire Housing.

https://www.nhhfa.org/publications-data/housing-and-demographic-data/

Yearly Real Estate sales data by count and purchase price (median and average) from 2005 to 2018. All communities in the Keys to the Valley region are included.

Vermont Dataset Description

Purchase price - Average Sales Price based on listing price at time of purchase

Source – www.HousingData.org

NH Dataset Description

This data set provides an estimate of the median sale price of existing and new primary homes in New Hampshire. A primary home is defined as a single family home occupied by an owner household as their primary place of residence. Multi-family rental housing, seasonal or vacation homes and manufactured housing are not included in the analysis of this data.

Purchase price -
Median Sales Price

Data Collection Process - For the Period 1990 through 2014, the median purchase prices were calculated from data collected by the New Hampshire Department of Revenue Administration on the PA-34 Form through their vendor Real Data Corp. A PA-34 Form is filed by the buyer and seller at the time of sale of all real property in the State of New Hampshire. In 2015 this source of data was no longer available, and has been replaced by real estate transaction data supplied by The Warren Group and filtered and compiled by NHHFA. This change in data source is reflected in the charts by a break in the trend line.

Analysis - Median sale prices of all, new, existing, and condominium homes are calculated. The frequency of sales by $10,000 increment is also calculated for each of the above categories. Calculations based on sample sizes smaller than 50 are viewed as providing inconsistent and highly volatile results and are not typically released. Individual record level data is not released.

Limitations - The quality of this data at the higher geographic levels (statewide and counties) is consistent over the entire time series. For the larger LMAs and Municipalities the data is reasonably consistent with some holes in the data. For smaller LMAs and moderate sized municipalities the data is most consistent for existing homes since 1998. For the smallest municipalities this data set does not provide adequately consistent analysis.

Source - NHHFA Purchase Price Database; Source: 1990-2014 - NH Dept. of Revenue, PA-34 Dataset, Compiled by Real Data Corp. Filtered and analyzed by New Hampshire Housing.

https://www.nhhfa.org/publications-data/housing-and-demographic-data/

The value of multifamily real estate investment in the United States has declined since 2021 when it peaked at 344 billion U.S. dollars. Some of the main reasons for the decline in investment included the tighter lending conditions, the increase in valuations over the past years, and the soaring construction costs. In 2024, the sector attracted nearly 143 billion U.S. dollars, accounting for more than one third of the total commercial market.

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.

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The Department of Finance (DOF) maintains records for all property sales in New York City, including sales of family homes in each borough. This list is a summary of neighborhood sales for Class 1-, 2- and 3-Family homes in Queens in 2008.

This list includes all sales of 1-, 2-, and 3-Family Homes' from January 1st, 2008 to December 31, 2008, whose sale price is equal to or more than $150,000. The Building Class Category for Sales is based on the Building Class at the time of the sale.

Update Schedule: Annually

This dataset provides loan-level information on when USDA Section 514 and 515 properties are projected to pay off their loans and exit USDA’s Multi-Family Housing program. Includes estimated property exit year, whether the loan is prepay eligible and when, loan amount, original loan term and remaining term days, borrower characteristics, property location and characteristics, and more.

The Department of Finance (DOF) maintains records for all property sales in New York City, including sales of family homes in each borough. This list is a summary of neighborhood sales for Class 1-, 2- and 3-Family homes in Manhattan in 2008. This list includes all sales of 1-, 2-, and 3-Family Homes' from January 1st, 2008 to December 31, 2008, whose sale price is equal to or more than $150,000. The Building Class Category for Sales is based on the Building Class at the time of the sale. Update Frequency: Annually

Real Estate Sales 730 Days

City of Hartford real estate sales for the past 2 years

By [source]

About this dataset

This dataset contains data on City of Hartford real estate sales for the last two years, with comprehensive records including property ID, parcel ID, sale date, sale price and more. This dataset is continuously updated each night and sourced from an official reliable source. The columns in this dataset include LocationStartNumber, ApartmentUnitNumber, StreetNameAndWay, LandSF TotalFinishedArea, LivingUnits ,OwnerLastName OwnerFirstName ,PrimaryGrantor ,SaleDate SalePrice ,TotalAppraisedValue and LegalReference - all valuable information to anyone wishing to understand the recent market trends and developments in the City of Hartford real estate industry. With this data providing detailed insights into what properties are selling at what time frame and for how much money – let’s see what secrets we can learn from examining the City of Hartford real estate activity!

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How to use the dataset

This dataset contains helpful information about homes sold in the Hartford area over the past two years. This data can be used to analyze trends in real estate markets, as well as monitor sales activity for various areas.

In order to use this dataset, you will need knowledge of EDA (Exploratory Data Analysis) such as data cleaning and data visualization techniques. You will also need a basic understanding of SQL queries and Python scripting language.

The first step is to familiarize yourself with the columns and information contained within the dataset by analyzing descriptive statistics like mean, min, max etc. Next you can filter or “slice” the data based on certain criteria or variables that interest you - such as sale date range, location (by street name or zip code), sale price range, type of dwelling unit etc. After using various filters for analysis it is important to take an error-check step by looking for outliers or any discrepancies that may exist - this will ensure more accuracy in results when plotting graphs and visualizing trends via software tools like Tableau and Power BI etc.

Next you can conduct exploratory analysis through plot visualizations of relationships between buyer characteristics (first & last name) vs prices over time; living units vs square footage stats; average price per bedroom/bathroom ratio comparisons etc – all while taking into account external factors such as seasonal changeovers that could affect pricing fluctuations during given intervals across multiple neighborhoods - use interactive maps if available ets. At this point it's easy to compile insightful reports containing commonalities amongst buyers and begin generalizing your findings with extrapolations which allow us gain a better understanding of current market conditions across different demographic spectrums being compared ie traditional Vs luxury properties – all made possible simply through dedicated research with datasets like these!

Research Ideas

• Analyzing market trends in the City of Hartford's real estate industry by tracking sale prices and appraised values over time to identify regions who are being under or over valued.
• Conducting a predictive analysis project to predict future sales prices, annual appreciation rates, and key features associated with residential properties such as total finished area and living units for investment purposes.
• Studying the impact of local zoning laws on property ownership and development by comparing sale dates, primary grantors, legal references, street names and ways in a given area over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: real-estate-sales-730-days-1.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------| | LocationStartNumber | The starting number of the location of the property. (Integer) | | ApartmentUnitNumber | The apartment unit number of the property. (Integer) | | StreetNameAndWay | The st...

Multi Family Property Management Software Market Outlook

The global multi family property management software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 6.2 billion by 2032, growing at a significant compound annual growth rate (CAGR) of 10.5% during the forecast period. The market growth is primarily driven by the rising demand for streamlined property management solutions and the increasing adoption of cloud-based technologies.

One of the primary growth factors contributing to the expansion of the multi family property management software market is the increasing urbanization and subsequent rise in the number of rental properties. As more people migrate to urban areas, the demand for multi-family housing units has surged, necessitating efficient management solutions. This trend is particularly noticeable in rapidly growing cities across Asia Pacific and North America, where property managers are seeking advanced software solutions to handle the complexities associated with managing numerous tenants and properties.

Additionally, the integration of advanced technologies such as artificial intelligence (AI) and the Internet of Things (IoT) into property management software is significantly enhancing the functionality and appeal of these solutions. AI-driven analytics provide property managers with predictive insights, enabling them to make data-driven decisions to improve tenant satisfaction and optimize operational efficiency. Similarly, IoT devices facilitate predictive maintenance, reducing downtime and repair costs, thus bolstering the overall value proposition of these software solutions.

The increasing emphasis on tenant experience and satisfaction is another critical factor fueling market growth. Modern tenants expect quick responses and seamless interactions with property managers, which has led to the adoption of software solutions that offer features like online rent payment, instant maintenance request submissions, and real-time communication channels. These capabilities not only enhance tenant satisfaction but also contribute to higher tenant retention rates, which is a key performance indicator for property managers.

Regionally, North America is expected to maintain its dominance in the multi family property management software market over the forecast period. This can be attributed to the high level of technological adoption, the presence of a large number of multi-family housing units, and the increasing investment in real estate technology. The Asia Pacific region, however, is anticipated to witness the highest growth rate, driven by rapid urbanization, increasing disposable incomes, and a burgeoning real estate sector. Europe, Latin America, and the Middle East & Africa are also expected to contribute significantly to the market, albeit at a slower pace compared to North America and Asia Pacific.

Component Analysis

The multi family property management software market can be segmented by component into software and services. The software segment is further divided into integrated software suites and standalone software, catering to different aspects of property management such as tenant management, lease tracking, and accounting. The services segment includes implementation, training, consulting, and support services that enhance the functionality and usability of the software solutions.

The software segment holds the largest market share and is expected to continue its dominance throughout the forecast period. This is due to the comprehensive functionalities offered by integrated software suites that address the diverse needs of property managers. These suites often include modules for accounting, tenant management, lease administration, and maintenance management, providing a one-stop solution for property management tasks. The increasing inclination towards integrated platforms that streamline operations and reduce the need for multiple disparate systems is a significant contributor to the growth of this segment.

Standalone software solutions, although witnessing slower growth compared to integrated suites, still hold substantial market value. These solutions are often preferred by property managers who require specific functionalities without the need for a full-fledged suite. For instance, a property manager might opt for a dedicated tenant management software or a specialized accounting tool based on their specific requirements. This flexibility and targeted approach make standalone solutions a viable option for smaller property management firms and