Month MBS Rate PMMS 30 Difference between Actual Mortgage MBS and PMMS Jul 2024 6.72% 6.85% -0.13% Aug 2024 6.51% 6.50% 0.01% Sep 2024 6.19% 6.18% 0.01% Oct 2024 6.05% 6.43% -0.38% Nov 2024 6.32% 6.81% -0.49% Dec 2024 6.46% 6.72% -0.25% Jan 2025 6.55% 6.96% -0.41% Feb 2025 6.61% 6.84% -0.24% Mar 2025 6.49% 6.65% -0.16% Apr 2025 6.42% 6.73% -0.30% May 2025 6.50% 6.82% -0.32% Jun 2025 6.794% 6.818% -0.024%

Content

The Federal Reserve Board has discontinued this series as of October 11, 2016. More information, including possible alternative series, can be found at http://www.federalreserve.gov/feeds/h15.html.

Contract interest rates on commitments for fixed-rate first mortgages. Source: Primary Mortgage Market Survey data provided by Freddie Mac.

Copyright, 2016, Freddie Mac. Reprinted with permission.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1971-04-01

• Observation End : 2016-09-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Ian Schneider on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The 30-year fixed rate mortgage is the most-common type of loan for home purchases in the United States. The data for this report is sourced from Freddie Mac's Primary Mortgage Market Survey. The values presented in this report are annual figures, derived from equally weighted monthly averages.

The HUD monthly interest rate survey provides information on interest rates, loan terms, and house prices. The survey is conducted by property type, loan type, and lender type. How the survey is conducted The survey provides information on all properties, new properties, and previously occupied properties.The survey provides information on fixed-rate and adjustable-rate loans.The survey provides information on lenders such as savings associations, mortgage companies, commercial banks, and savings banks.What the survey includes The survey provides information on interest rates, loan terms, and house prices.The survey provides information on property type, loan type, and lender type.Update on the Discontinuation of FHFA's Monthly Interest Rate Survey (MIRS)On May 29, 2019, FHFA published its final Monthly Interest Rate Survey (MIRS), due to dwindling participation by financial institutions. MIRS had provided information on a monthly basis on interest rates, loan terms, and house prices by property type (all, new, previously occupied); by loan type (fixed- or adjustable-rate), and by lender type (savings associations, mortgage companies, commercial banks and savings banks); as well as information on 15-year and 30-year, fixed-rate loans. Additionally, MIRS provided quarterly information on conventional loans by major metropolitan area and by Federal Home Loan Bank district, and was used to compile FHFA’s monthly adjustable-rate mortgage index entitled the “National Average Contract Mortgage Rate for the Purchase of Previously Occupied Homes by Combined Lenders,” also known as the ARM Index.As some banks use the ARM Index as the basis for adjusting the interest rates on adjustable-rate mortgages, FHFA created and designated as the replacement for the ARM Index a version of Freddie Mac’s 30-year Primary Mortgage Market Survey® (PMMS®) that adjusts for differences between the two. This new index is called “MIRS Transition Index” and will be published on fhfa.gov on the final Thursday of every month. June 2019 was the first MIRS Transition index value to be published. The MIRS Transition index is intended to be used in lieu of the discontinued index for currently outstanding loans, and not as a reference rate on newly-originated adjustable-rate mortgages. The MIRS Transition Index was briefly referred to as PMMS+. It is not a replacement for PMMS.

View weekly updates and historical trends for 30 Year Mortgage Rate. from United States. Source: Freddie Mac. Track economic data with YCharts analytics.

Hong Kong HK: Residential Mortgage: Value: NL: Primary Market data was reported at 7,987.000 HKD mn in Oct 2018. This records an increase from the previous number of 6,233.000 HKD mn for Sep 2018. Hong Kong HK: Residential Mortgage: Value: NL: Primary Market data is updated monthly, averaging 3,803.000 HKD mn from Dec 2000 (Median) to Oct 2018, with 215 observations. The data reached an all-time high of 10,163.000 HKD mn in Jun 2017 and a record low of 846.000 HKD mn in Aug 2008. Hong Kong HK: Residential Mortgage: Value: NL: Primary Market data remains active status in CEIC and is reported by Hong Kong Monetary Authority. The data is categorized under Global Database’s Hong Kong SAR – Table HK.KB006: Residential Property Loans: Residential Mortgage Survey: Value.

Hong Kong HK: Residential Mortgage: New Loans Approved: Primary Market data was reported at 19.738 % in Sep 2018. This records a decrease from the previous number of 19.795 % for Aug 2018. Hong Kong HK: Residential Mortgage: New Loans Approved: Primary Market data is updated monthly, averaging 20.444 % from Dec 2000 (Median) to Sep 2018, with 214 observations. The data reached an all-time high of 60.719 % in Sep 2002 and a record low of 6.008 % in Feb 2010. Hong Kong HK: Residential Mortgage: New Loans Approved: Primary Market data remains active status in CEIC and is reported by Hong Kong Monetary Authority. The data is categorized under Global Database’s Hong Kong SAR – Table HK.KB008: Residential Property Loans: Residential Mortgage Survey: Ratios.

30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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 | |:----------------|:----------------------------------------------------...

Hong Kong HK: Residential Mortgage: Case: NL: Primary Market data was reported at 1,270.000 Unit in Sep 2018. This records a decrease from the previous number of 1,925.000 Unit for Aug 2018. Hong Kong HK: Residential Mortgage: Case: NL: Primary Market data is updated monthly, averaging 1,117.000 Unit from Jan 2003 (Median) to Sep 2018, with 189 observations. The data reached an all-time high of 3,503.000 Unit in Jul 2003 and a record low of 241.000 Unit in Aug 2008. Hong Kong HK: Residential Mortgage: Case: NL: Primary Market data remains active status in CEIC and is reported by Hong Kong Monetary Authority. The data is categorized under Global Database’s Hong Kong SAR – Table HK.KB007: Residential Property Loans: Residential Mortgage Survey: Cases.

Table B.3.1 presents quarterly mortgage rate data specific to the Irish market. These data include all euro and non-euro denominated mortgage lending in the Republic of Ireland only. New business refers to new mortgage lending drawdowns during the quarter, broken down by type of interest rate product (i.e. fixed, tracker and SVR). The data also provide further breakdown of mortgages for principal dwelling house (PDH) and buy-to-let (BTL) properties. Renegotiations of existing loans are not included. .hidden { display: none }

The Survey of Mortgage Lenders (SML) was launched on 1st April 1992 to succeed the 5% Sample Survey of Building Society Mortgage Completions (BSM) (See GN:33191). The aims were to improve the survey in three principal ways:
a) to broaden the range of institutions surveyed to incorporate other mortgage lenders in addition to building societies and Abbey National. With the entry of the high street banks and then the centralised lenders into the mortgage market, information provided by the building societies no longer represented the whole market in the way it did when the BSM was set up in the 1960s.
b) to extend its coverage to include further advances, remortgages and top-up loans in addition to first mortgages.
c) to increase the level of detail on the questionnaire especially with respect to the characteristics of the mortgage loan.
An important consideration for users of the data is that the SML figures allow continuity with the BSM survey results to be maintained for a reasonable period.

Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q3 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

Statistics Norway carries out the rental market survey every year in order to prepare statistics on rental prices for different types of housing in different parts of the country.

The survey is called Housing and Living Conditions (BOB) when we contact tenants. The name Housing and housing conditions are used to simplify the communication about the survey as tenants must report on their own housing and not on the rental market in general.

The population in BOB is all rental housing resident of private households in Norway. Since there is no complete register of rental housing, one must use a combination of various central administrative registers in Statistics Norway as a basis for establishing a sample framework with the largest possible proportion of rental housing. The sample in 2018 was established by drawing 37,000 addresses from the established drawing frame of assumed rental housing. The selection unit is the address of the assumed rental property, and the response unit is the person who lives at the address.

BOB is carried out as a pure web survey. The survey was conducted in 2018 over three weeks, starting on October 1.

Following a period of stagnation over most of the 2010s, the number of owner-occupied housing units in the United States started to grow in 2017. In 2024, there were over 86.9 million owner-occupied homes. Owner-occupied housing is where the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing, and social housing. Homeownership sentiment in the U.S. Though homeownership is still a cornerstone of the American dream, an increasing share of people see themselves as lifelong renters. Millennials have been notoriously late to enter the housing market, with one in four reporting that they would probably continue to always rent in the future, a 2022 survey found. In 2017, just five years before that, this share stood at about 13 percent. How many renter households are there? Renter households are roughly half as few as owner-occupied households in the U.S. In 2024, the number of renter-occupied housing units amounted to over 45 million. Climbing on the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.

Digital Closing Disclosure Review Market Outlook

According to our latest research, the global Digital Closing Disclosure Review market size reached USD 1.42 billion in 2024, reflecting rapid technological adoption across the real estate and mortgage sectors. The market is projected to grow at an impressive CAGR of 14.7% from 2025 to 2033, reaching a forecasted value of USD 4.68 billion by 2033. This robust growth is primarily fueled by increasing demand for streamlined, secure, and compliant digital mortgage processes, as well as evolving regulatory frameworks and consumer expectations for transparency and efficiency in real estate transactions.

One of the primary growth factors driving the Digital Closing Disclosure Review market is the ongoing digital transformation within the financial and real estate industries. As organizations seek to enhance operational efficiency, minimize manual errors, and comply with stringent regulatory requirements, the adoption of digital solutions for closing disclosure reviews has surged. These platforms automate the review and verification of closing documents, significantly reducing turnaround times and human error. The push towards paperless operations, accelerated by the COVID-19 pandemic, has further reinforced the necessity for digital solutions, making them a critical component of modern mortgage and real estate workflows.

Another significant driver is the increasing regulatory scrutiny and the need for compliance with consumer protection laws, such as the TILA-RESPA Integrated Disclosure (TRID) rule in the United States. Digital Closing Disclosure Review platforms offer robust audit trails, automated compliance checks, and real-time updates, ensuring that all parties involved adhere to legal requirements. This not only reduces the risk of penalties and litigation but also builds trust among consumers and stakeholders. The ability to provide transparent, accurate, and timely disclosures is becoming a competitive differentiator for financial institutions, further propelling the market’s expansion.

The growing demand for enhanced customer experience is also contributing to the market’s upward trajectory. Homebuyers and borrowers now expect seamless, digital-first interactions throughout the mortgage process. Digital Closing Disclosure Review solutions enable lenders, title companies, and other stakeholders to deliver faster, more transparent, and user-friendly services. Features such as e-signatures, mobile accessibility, and automated notifications simplify the review process for all parties, reducing friction and improving overall satisfaction. This shift towards customer-centricity is prompting organizations to invest heavily in advanced digital platforms, supporting sustained market growth.

Regionally, North America dominates the Digital Closing Disclosure Review market, accounting for the largest share in 2024 due to the early adoption of digital mortgage technologies, a robust regulatory environment, and the presence of leading industry players. Europe and Asia Pacific are also witnessing rapid growth, driven by increasing digitalization in financial services and evolving real estate markets. Latin America and the Middle East & Africa are emerging as promising markets, albeit at a slower pace, as digital infrastructure improves and regulatory frameworks evolve. The regional landscape is expected to continue shifting as global digital adoption accelerates and cross-border investments increase.

Component Analysis

The Component segment of the Digital Closing Disclosure Review market is divided into Software and Services, each playing a pivotal role in shaping the industry landscape. Software solutions form the backbone of digital closing disclosure processes, providing automated document generation, compliance checking, and workflow management. These platforms are designed to integrate seamlessly with existing mortgage and real estate systems, offering scalability, security, a

Statistics Norway has carried out its own surveys on Housing Conditions in 1967, 1973, 1981, 1988 and 1995. The purpose is to give a broad description of the living conditions in Norway, based on information about the size and standard of homes compared with household size, composition and attitudes. Other objectives were to collect data to evaluate the impact of changes in housing policy and the change in living conditions over time. Particularly important was to study the changes in the housing market after the great housing policy shift that began in Norway in 1982. Data on residential environment and housing standard already found in living condition surveys. From 1997, the collection of housing data fully integrated in the new system for the collection of living data. Principal is the Ministry of Local Goverment and Regional Development, data collected by Statistics Norway and analyzed largely by the Norwegian Building Research Institute. The survey is documented in two files, household and individual levels. Housing Conditions Survey is mainly a household survey, but also contains information about each person belonging to the household (this file). The individual file of the investigation contains a device for every person, regardless of age, with individual information about the person, and also all information about housing conditions, etc. for the household to which the person belongs.

Evaluate Canada’s best mortgage rates in one place. RATESDOTCA’s Rate Matrix lets you compare pricing for all key mortgage types and terms. Rates are based on an average mortgage of $300,000

What is this data?The Local Housing Profiles are a curated set of data on the housing market. The Chicago Metropolitan Agency for Planning (CMAP) provides these profiles for each of the 7 counties, 284 municipalities, and Chicago community area (CCA) in northeastern Illinois.How can this data be used? Are there any use cases?The Local Housing Profiles can be used by residents, practitioners, planners, and policymakers to understand the latest data on a community’s housing demand, supply, and affordability relative to regional trends.Who created this data? How and when?Developed in partnership with the Institute for Housing Studies at DePaul University (IHS), these reports include data from a number of sources, including socioeconomic, demographic, and housing unit data from the American Community Survey (ACS), and key housing market indicators generated from parcel-level administrative data and collected by the IHS via its Data Clearinghouse.Additional information on field names, data sources, and other metadata can be found in the Data Dictionary. More comprehensive background on the data tables summarized in the profiles can be found in the Technical Documentation.Where can I find the latest data? How frequently is it updated?The primary source is data from the U.S. Census Bureau’s 2023 American Community Survey program. It is expected that this product will be updated annually. However, as this item was developed in partnership with the IHS at DePaul University, please reach out the Data Specialist if you need additional information about plans for future updates.Questions?Are you looking for the PDF versions? Find and download the print-friendly Local Housing Data Profiles from the agency website.