The purpose of the RHFS is to provide current and continuous measure of the financial health and property characteristics of single-family and multifamily rental housing properties in the United States. The survey provides information on the financing of single-family and multifamily rental housing properties with emphasis on new originations for purchase, refinancing, and loan terms associated with these originations. In addition, the survey includes information on property characteristics, such as number of units, amenities available, rental income and expenditure information. This survey was conducted in 2012 and will be conducted in 2015.

The Rental Housing Finance Survey provides a current and continuous measure of financial, mortgage, and property characteristics of rental housing properties in the United States. The survey focuses on the financing of rental housing properties, with emphasis on new mortgages, refinanced mortgages, or similar devices such as deeds of trust or land contracts, and the characteristics of debt originations. The 2018 RHFS included single-family residential and multifamily residential properties with at least one housing unit intended for rent. Data collection was conducted from June 2018 through November 2018.

The 2001 Residential Finance Survey (RFS) was sponsored by the Department of Housing and Urban Development and conducted by the Census Bureau. The RFS is a follow-on survey to the 2000 decennial census designed to collect, process, and produce information about the financing of all nonfarm, residential properties. The 1991 data is also available.

The 2001 Residential Finance Survey (RFS) was sponsored by the Department of Housing and Urban Development and conducted by the Census Bureau. The RFS is a follow-on survey to the 2000 decennial census designed to collect, process, and produce information about the financing of all nonfarm, residential properties. The 1991 data is also available.

Please see RHFS Table Creator for easier access to RHFS tables.1: Mean and median estimates are in units as stated and are not rounded to the nearest thousand..2: The Census Bureau classifies nursing homes as Group Quarters. The RHFS sample frame does not include nursing homes, because all Group Quarters are excluded from the AHS sample from which the RHFS sample is drawn. Nursing units, however, may be located on properties in the RHFS sample. Properties containing only nursing units were excluded from the RHFS sample.

These data contain data on the characteristics of the financing of homeowner and rental properties, including characteristics of the mortgages, properties, and property owners of approximately 66,000,000 properties securing about 38,000,000 mortgages. About 70,000 properties were in the sample. Data for homeowner properties and rental and vacant properties are provided on both a property record and a mortgage record.

Please see RHFS Table Creator for easier access to RHFS tables.1: Mean and median estimates are in units as stated and are not rounded to the nearest thousand.

PI-provided abstract: The Census Bureau took the Residential Finance Survey (RFS) as part of the decennial census from 1950-2000. The RFS is the only survey designed to collect and produce data about the financing of nonfarm, privately-owned residential properties. The RFS is a unique survey for several reasons: It collects, tabulates, and presents data for properties, the standard unit of reference for financial transactions related to housing. In most other demographic surveys, the unit of reference is the person, household, or housing unit. It is the only source of information on property, mortgage, and financial characteristics for multi-unit rental properties. Information on multi-family loans and properties is particularly difficult to obtain, but is important to understand if progress is to be made in the development of standards for underwriting multi-family mortgages. It conducts interviews of property owners and mortgage lenders, resulting in more accurate information on property and mortgage characteristics. The RFS is the only survey which is able to provide a comprehensive view of mortgage finance in the USA, by providing information not only about the loan itself from the lender, but also information about the property owner's demographic characteristics. As part of the decennial census, it is mandatory. This is important in collecting information from mortgage lenders. The RFS is exempt from statutes prohibiting release of financial records by financial institutions. It is able to subdivide the industry into relevant components. Different parts of the industry have excellent information on their own loans and clients, but not that of the industry as a whole. Information on lending by individual investors or small groups of investors such as pension funds is collected only by the RFS.

Graph and download economic data for All Employees, Real Estate and Rental and Leasing (CEU5553000001) from Jan 1990 to Sep 2025 about leases, real estate, rent, establishment survey, financial, employment, and USA.

These data consist of a sample of approximately 62,000 housing units taken form housing units included in the 1980 Census of Housing. Rental and vacant units, although included in the survey, are not contained in the files. Detailed information is shown on the financing of the "homeowner" and "condominium" residential properties, including characteristics of the mortgages (type, status, origin, source, amount, etc.), properties (value, year structure built, number of rooms, etc.), and property owner(s) (age, sex, race, Spanish origin, income, etc.).

This report presents statistical summaries of data from the Residential Finance Survey conducted in 2001 as part of Census 2000. Detailed information is shown on the financing of homeowner and rental properties, including characteristics of the mortgages, properties, and property owners. The data shown relate to the geographic boundaries as they existed for Census 2000 and are presented for the entire United States and for the four census regions.

Real Estate Breakeven Analysis for U.S. Home Types

Buy vs Rent Comparison Across Markets

By Zillow Data [source]

About this dataset

This dataset provides a comprehensive analysis of the current real estate situation in the United States. It includes breakeven analysis charts that compare buying vs renting across major U.S. markets. This dataset contains various metrics such as home types, housing stock, price-to-income ratio, cash buyers, mortgage affordability and rental affordability to name a few. This data has been compiled using Zillow's own data along with TransUnion financing survey data and the Freddie Mac Primary Mortgage Market Survey to provide an accurate understanding of each metro area’s market health and purchasing power for buyers and renters alike. By downloading this information you can compare different regions based on size rank and other factors to get full insights regarding their potential fit for your needs or investments strategies as well as any potential risks associated with each region's housing market health

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How to use the dataset

This dataset is for real estate professionals, owner-occupants, potential buyers and renters who are interested in understanding which U.S. markets offer the most favorable home buying or rental opportunities from a financial perspective over the long term.

The “Real Estate Breakeven Analysis for U.S Home Types” dataset contains data pulled from Zillow's current and forecasted housing market metrics across many different real estate regions in the United States including cities, counties, states, metro areas and combined statistical areas (CSAs). The data includes several measures of affordability such as median price-to-rent ratio (MedPR), median breakeven horizon (MedBE) - which refers to how long it takes to make up purchase costs when compared with renting; cash purchaser share; mortgage rate; mortgage affordability indices; rental affordability rates etc.

In order to analyze and compare buying vs renting decisions across various regions in the US this dataset provides breakeven analysis at various levels of geographies i.e., state names, region types (city/metro area/county) and show how long it will take homeowners to break even on their purchase costs when compared with renting in that region over a longer period of time using discounted cash flow methodology. This information helps people understand what type of transaction is a better fit for them by weighing short term vs long term goals accordingly by evaluating these different factors related to housing metrics carefully before making financial decisions about purchasing or renting properties in desired location(s).

To use this dataset one can use either basic filters like RegionType or RegionName or more detailed filter criteria like CountyName, City name , Metro area name , State Name etc . For example if someone wanted to look at properties available for rent only then they can apply filters based on Province Type =‘Rental’ Also one can further refine searches based on filtering them with defined SampleRate , Median Price – To – Rent Ratio …..etc . This could be useful if seekers would want only specific type of property like Condominium/Coop /Multifamily 5+ Units /Duplex Triplex listing etc …and then apply other parameters like Cash Buyers percent , Mortgage Affordability Rate….etc ..in order narrow down search results while looking at Breakeven scores /horizons in their target locations . One should take advantages of all relevant parameters while searching through data before making any decision related with owning rental properties so that they can make sure best possible investment decision given

Research Ideas

• Visualizing changes in real estate trends across regions by comparing price to rent ratios, mortgage affordability indices and cash buyers over time.
• Market segmentation analysis based on region-level market characteristics such as negative equity data, rental affordability, median house values and population size.
• Predicting housing demand within a particular region based on its breakeven horizon or price to rent ratio

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: BreakEven_2017-03.csv | Column name | Description | |:----------------|:----------------------------------------------------...

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

RE: Fixed Costs to Sales data was reported at 45.120 % in 2016. This records an increase from the previous number of 44.880 % for 2015. RE: Fixed Costs to Sales data is updated yearly, averaging 45.000 % from Dec 2015 (Median) to 2016, with 2 observations. The data reached an all-time high of 45.120 % in 2016 and a record low of 44.880 % in 2015. RE: Fixed Costs to Sales data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s Korea – Table KR.S046: Financial Statement Analysis: 2015 Survey: Complete Enumeration: Real Estate, Renting and Leasing (RE).

This statistic shows how much financial sacrifice single-family renters in the United States would have to make to own their home. In 2011, ***** percent of respondents answered this would mean no financial sacrifice at all to them.

RE: Non-Current Liabilities Ratio data was reported at 195.150 % in 2016. This records a decrease from the previous number of 216.970 % for 2015. RE: Non-Current Liabilities Ratio data is updated yearly, averaging 206.060 % from Dec 2015 (Median) to 2016, with 2 observations. The data reached an all-time high of 216.970 % in 2015 and a record low of 195.150 % in 2016. RE: Non-Current Liabilities Ratio data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s Korea – Table KR.S046: Financial Statement Analysis: 2015 Survey: Complete Enumeration: Real Estate, Renting and Leasing (RE).

Graph and download economic data for Other Financial Information: Estimated Monthly Rental Value of Owned Home by Housing Tenure: Renter (CXU910050LB1705M) from 1984 to 2021 about owned, information, rent, estimate, financial, housing, and USA.

RE: Gross Value Added to Machinery And Equipment data was reported at 3,898.000 % in 2016. This records an increase from the previous number of 2,702.730 % for 2015. RE: Gross Value Added to Machinery And Equipment data is updated yearly, averaging 3,300.365 % from Dec 2015 (Median) to 2016, with 2 observations. The data reached an all-time high of 3,898.000 % in 2016 and a record low of 2,702.730 % in 2015. RE: Gross Value Added to Machinery And Equipment data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s Korea – Table KR.S046: Financial Statement Analysis: 2015 Survey: Complete Enumeration: Real Estate, Renting and Leasing (RE).

Graph and download economic data for Other Financial Information: Estimated Monthly Rental Value of Owned Home: All Consumer Units (CXU910050LB0101M) from 1984 to 2023 about owned, consumer unit, information, rent, estimate, financial, housing, and USA.

Indonesia Business Survey: Business Activity: Realization: Weighted Net Balance: Financial, Corporate Leassing & Services: Building Rental data was reported at 0.422 % in Dec 2022. This records a decrease from the previous number of 0.459 % for Sep 2022. Indonesia Business Survey: Business Activity: Realization: Weighted Net Balance: Financial, Corporate Leassing & Services: Building Rental data is updated quarterly, averaging 0.143 % from Mar 2005 (Median) to Dec 2022, with 72 observations. The data reached an all-time high of 0.890 % in Sep 2008 and a record low of -1.127 % in Jun 2020. Indonesia Business Survey: Business Activity: Realization: Weighted Net Balance: Financial, Corporate Leassing & Services: Building Rental data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SD002: Business Survey: Business Activity. [COVID-19-IMPACT]