House Price Index YoY in the United States remained unchanged at 4.80 percent in January. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Sep 2024, following an increase of 6.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2024, with an average growth rate of 5.5%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

CEIC calculates House Prices Growth from quarterly House Price Index. Federal Housing Finance Agency provides House Price Index with base January 1991=100.

Existing Home Sales in the United States increased to 4260 Thousand in February from 4090 Thousand in January of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

What Makes Our Data Unique?

Inmuebles24’s Mexico Real Estate Listings Data offers an unparalleled level of detail and accuracy in the real estate sector. With over 100,000 meticulously curated property listings, this dataset is designed to provide users with the most comprehensive view of the Mexican real estate market. Each listing includes detailed metadata such as property type, location, pricing, and contact information, along with additional attributes like the number of bedrooms, bathrooms, and available amenities. Our data is enriched with precise geolocation coordinates, allowing for advanced spatial analysis and mapping applications.

Our dataset stands out for its up-to-date nature, with listings scraped and refreshed regularly to ensure that buyers and analysts always have access to the latest market trends. This dynamic approach to data curation means that users can trust the data for making informed decisions, whether they are monitoring market trends, conducting investment research, or developing real estate strategies.

How Is the Data Generally Sourced?

The data is sourced directly from Inmuebles24, one of Mexico's leading real estate marketplaces. We employ a robust web scraping infrastructure that captures the full breadth of listings available on the platform. Our scraping technology is designed to extract data efficiently, ensuring that we capture every relevant detail from the listings, including images, descriptions, pricing, and metadata. Each entry is validated and cleaned to remove any duplicates or outdated information, ensuring that the dataset is both comprehensive and reliable.

Primary Use-Cases and Verticals

This Data Product is particularly valuable across several key verticals:

Real Estate Investment Analysis: Investors can leverage this dataset to identify lucrative opportunities by analyzing property prices, location attributes, and market trends.

Market Research and Trends: Researchers can use the data to track the evolution of the real estate market in Mexico, identifying shifts in pricing, demand, and supply across various regions.

Property Development: Developers can assess the market landscape, understanding where new developments might meet the most demand based on the attributes and locations of current listings.

Urban Planning: Government and city planners can utilize the geolocation data to analyze urban sprawl, housing density, and other critical metrics for sustainable development.

Real Estate Marketing: Marketers and real estate agents can tailor their strategies based on detailed insights into the types of properties available, pricing trends, and consumer preferences.

How Does This Data Product Fit into Our Broader Data Offering?

This Mexico Real Estate Listings Data Product is part of our broader commitment to providing high-quality, actionable data across various sectors and geographies. Inmuebles24’s real estate data complements our extensive portfolio of data products that cater to industries such as financial services, marketing, and location-based services. By integrating this dataset with other data offerings, users can derive even deeper insights. For example, combining real estate data with consumer behavior data could unlock new dimensions of market research, enabling a more holistic approach to understanding market dynamics.

Our broader data offering is built around the principle of providing end-to-end data solutions that empower businesses to make data-driven decisions with confidence. Whether you’re a real estate investor, a market researcher, or a developer, our data products are designed to meet your needs with precision and reliability

New Home Sales in the United States increased to 676 Thousand units in February from 664 Thousand units in January of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Index in the United States increased to 436.50 points in January from 435.80 points in December of 2024. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Nahb Housing Market Index in the United States decreased to 39 points in March from 42 points in February of 2025. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Starts in the United States increased to 1501 Thousand units in February from 1350 Thousand units in January of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q4 2024 about sales, median, housing, and USA.

Housing Index in Spain increased to 1972.10 EUR/SQ. METRE in the fourth quarter of 2024 from 1921 EUR/SQ. METRE in the third quarter of 2024. This dataset provides the latest reported value for - Spain House Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The average resale house price in Canada was forecast to reach nearly 836,000 Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach 1.2 million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was 1.9 million Canadian dollars in 2024.

Housing Index in Portugal increased to 228.89 points in the third quarter of 2024 from 220.74 points in the second quarter of 2024. This dataset provides - Portugal House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Housing Index in Ireland increased to 191.30 points in January from 191.20 points in December of 2024. This dataset provides the latest reported value for - Ireland Residential Property Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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 9238: Continuous Recording of Social Housing Sales (CORE):

This study contains the EUL-level CORE Sales data only. The EUL CORE Lettings data are held under SN 9238.

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.

For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This database was created to accompany a report prepared by Joe Daniels, PhD, and Martine August, PhD, entitled “Acquisitions Programs for Affordable Housing: Creating non-market supply and preserving affordability with existing multi-family housing.” The database and report form part of the work performed under the HART project, and the report can be found at HART’s website: HART.ubc.ca. The database is a single table that summarizes 11 key elements, plus notes and references, of a growing list of policies from governments across the world. There are currently 108 policies included in the database. The authors expect to update this database with additional policies from time to time. The authors hope this database will serve as a resource for governments looking to become familiar with a variety of policies in order to help them evaluate what policies might be most applicable in their communities. Data Fields: List of data fields (15 total): 1. Government Order 2. Government Jurisdiction 3. Policy Name/Action 4. Acquisition Target 5. Years Active 6. Funder/Funding 7. Funding Amount (Program) 8. Funding Form 9. Affordability Standard 10. Affordability Term 11. Features/Requirements 12. Comments 13. Reference link 1 14. Reference link 2 15. Reference link 3 Description of data fields (15) 1. Government Order: - Categorizes the relative political authority in terms of one of three categories: Municipal (responsible for a city or small region), Provincial (responsible for multiple municipalities), or Country (responsible for multiple provinces; highest political authority). - This field may be used to help identify those policies most relevant to the reader. 2. Government Jurisdiction: - Indicates the name of the government. - For example, a country might be named “Canada,” a province might be named “Quebec,” and a municipality might be named “Calgary.” 3. Policy Name/Action: - Indicates the name of the policy. - This generally serves as the unique identifier for the record. However, there may be some programs that are only known by a common term; for example, “Right of First Refusal.” 4. Acquisition Target: - Describes the type of housing asset that the policy is concerned with. For example, acquiring land, acquiring existing rental buildings, renovating existing supportive housing. 5. Years Active: - The time period that the policy has been active. - Typically formatted as “[Year started] - [Year ended]”. If just a single year is listed (e.g. “2009”) that means the policy was only active that one year. - If the policy is active with no end date, then the format will be “[Year started] - ongoing.” If the policy has a specified end date in the future, that year will be listed instead: “[Year started] – [Expected final year].” 6. Funder/Funding: - The government, government agency, or organization responsible for the use of those funds made available through the policy. 7. Funding Amount (Program): - The dollar value of funds connected to the policy. - Sometimes this is the total value of funds available to the policy, and sometimes it is the actual value of funds that were used. - The funds indicated here do not necessarily correspond to the time period indicated in the ‘Years Active’ field. Additional detail will be added to clarify whenever possible. - If policy has “N/A” listed here, see ‘Features/Requirements’ for more information. 8. Funding Form: - Indicates the type of financial tools available to the policy. For example, “capital funding,” “forgivable loans,” or “rent supplements.” - If policy has “N/A” listed here, see ‘Features/Requirements’ for more information. 9. Affordability Standard: - Indicates whether the policy includes an explicit standard or benchmark of affordability that is used to guide or otherwise inform the policy’s goals. 10. Affordability Term: - Indicates whether the affordability standard applies to a specific time period. - This field may also contain other information on time periods that are relevant to the policy; for example, an operating loan guaranteed to be active for a specific number of years. 11. Features/Requirements: - Describes the broad objectives of the policy as well as any specific guidelines that the policy must follow. 12. Comments: - Author’s commentary on the policy. 13. Reference link 1: - A web address (URL) or citation indicating the source of the details on the policy. 14. Reference link 2: - A second web address (URL) or citation indicating the source of the details on the policy. 15. Reference link 3: - A third web address (URL) or citation indicating the source of the details on the policy. File list (1): 1. Property Acquisition Policy Database.xlsx

Long term dataset showing the 30 year fixed rate mortgage average in the United States since 1971.

Comprehensive Federal Tax Lien Data by CompCurve Unlock unparalleled insights into tax lien records with CompCurve Federal Tax Lien Data, a robust dataset sourced directly from IRS records. This dataset is meticulously curated to provide detailed information on federal tax liens, unsecured liens, and tax-delinquent properties across the United States. Whether you're a real estate investor, financial analyst, legal professional, or data scientist, this dataset offers a treasure trove of actionable data to fuel your research, decision-making, and business strategies. Available in flexible formats like .json, .csv, and .xls, it’s designed for seamless integration via bulk downloads or API access, ensuring you can harness its power in the way that suits you best.

IRS Tax Lien Data: Unsecured Liens in Focus At the heart of this offering is the IRS Tax Lien Data, capturing critical details about unsecured federal tax liens. Each record includes key fields such as taxpayer full name, taxpayer address (broken down into street number, street name, city, state, and ZIP), tax type (e.g., payroll taxes under Form 941), unpaid balance, date of assessment, and last day for refiling. Additional fields like serial number, document ID, and lien unit phone provide further granularity, making this dataset a goldmine for tracking tax liabilities. With a history spanning 5 years, this data offers a longitudinal view of tax lien trends, enabling users to identify patterns, assess risk, and uncover opportunities in the tax lien market.

Detailed Field Breakdown for Precision Analysis The Federal Tax Lien Data is structured with precision in mind. Every record includes a document_id (e.g., 2025200700126004) as a unique identifier, alongside the IRS-assigned serial_number (e.g., 510034325). Taxpayer details are comprehensive, featuring full name (e.g., CASTLE HILL DRUGS INC), and, where applicable, parsed components like first name, middle name, last name, and suffix. Address fields are equally detailed, with street number, street name, unit, city, state, ZIP, and ZIP+4 providing pinpoint location accuracy. Financial fields such as unpaid balance (e.g., $15,704.43) and tax period ending (e.g., 09/30/2024) offer a clear picture of tax debt, while place of filing and prepared_at_location tie the data to specific jurisdictions and IRS offices.

National Coverage and Historical Depth Spanning the entire United States, this dataset ensures national coverage, making it an essential resource for anyone needing a coast-to-coast perspective on federal tax liens. With 5 years of historical data, users can delve into past tax lien activity, track refiling deadlines (e.g., 01/08/2035), and analyze how tax debts evolve over time. This historical depth is ideal for longitudinal studies, predictive modeling, or identifying chronic tax delinquents—key use cases for real estate professionals, lien investors, and compliance experts.

Expanded Offerings: Secured Real Property Tax Liens Beyond unsecured IRS liens, CompCurve enhances its portfolio with the Real Property Tax Lien File, focusing on secured liens tied to real estate. This dataset includes detailed records of property tax liens, featuring fields like tax year, lien year, lien number, sale date, interest rate, and total due. Property-specific data such as property address, APN (Assessor’s Parcel Number), FIPS code, and property type ties liens directly to physical assets. Ownership details—including owner first name, last name, mailing address, and owner-occupied status—add further context, while financial metrics like face value, tax amount, and estimated equity empower users to assess investment potential.

Tax Delinquent Properties: A Wealth of Insights The Real Property Tax Delinquency File rounds out this offering, delivering a deep dive into tax-delinquent properties. With fields like tax delinquent flag, total due, years delinquent, and delinquent years, this dataset identifies properties at risk of lien escalation or foreclosure. Additional indicators such as bankruptcy flag, foreclosure flag, tax deed status, and payment plan flag provide a multi-dimensional view of delinquency status. Property details—property class, building sqft, bedrooms, bathrooms, and estimated value—combined with ownership and loan data (e.g., total open loans, estimated LTV) make this a powerhouse for real estate analysis, foreclosure tracking, and tax lien investment.

Versatile Formats and Delivery Options CompCurve ensures accessibility with data delivered in .json, .csv, and .xls formats, catering to a wide range of technical needs. Whether you prefer bulk downloads for offline analysis or real-time API access for dynamic applications, this dataset adapts to your workflow. The structured fields and consistent data types—such as varchar, decimal, date, and boolean—ensure compatibility with databases, spreadsheets, and programming environments, making it easy to integrate into your ...

Case Shiller Home Price Index in the United States increased to 332.56 points in January from 332.33 points in December of 2024. This dataset provides the latest reported value for - United States S&P Case-Shiller Home Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building. This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by Tract Date of Coverage: 2016-2020

Graph and download economic data for All-Transactions House Price Index for Los Angeles County, CA (ATNHPIUS06037A) from 1975 to 2024 about Los Angeles County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.