Residential Property Prices in the United States increased 3.44 percent in December of 2024 over the same month in the previous year. This dataset includes a chart with historical data for the United States Residential Property Prices.

Abstract

Historical tax assessment data for all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C. Each table represents a previous edition of CoreLogic's tax assessment data.

The CoreLogic Smart Data Platform (SDP) Historical Property data was formerly known as the CoreLogic Tax History data. The CoreLogic SDP Historical Property data is an enhanced version of the CoreLogic Tax History data. The CoreLogic SDP Historical Property data contains almost all of the variables that were included in the CoreLogic Tax History data, as well as additional property-level characteristics.

Methodology

In the United States, parcel data is public record information that describes a division of land (also referred to as "property" or "real estate"). Each parcel is given a unique identifier called an Assessor’s Parcel Number or APN. The two principal types of records maintained by county government agencies for each parcel of land are deed and property tax records. When a real estate transaction takes place (e.g. a change in ownership), a property deed must be signed by both the buyer and seller. The deed will then be filed with the County Recorder’s offices, sometimes called the County Clerk-Recorder or other similar title. Property tax records are maintained by County Tax Assessor’s offices; they show the amount of taxes assessed on a parcel and include a detailed description of any structures or buildings on the parcel, including year built, square footages, building type, amenities like a pool, etc. There is not a uniform format for storing parcel data across the thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from CoreLogic’s parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see CoreLogic 2024 GitLab.

Usage

Each table contains an archived snapshot of the property data, roughly corresponding to the following assessed years:

• Historical Property 1 = 2022-2023
• Historical Property 2 = 2021-2022
• Historical Property 3 = 2020-2021
• Historical Property 4 = 2019-2020
• Historical Property 5 = 2018-2019
• Historical Property 6 = 2017-2018
• Historical Property 7 = 2016-2017
• Historical Property 8 = 2015-2016
• Historical Property 9 = 2014-2015
• Historical Property 10 = 2013-2014
• Historical Property 11 = 2012-2013
• Historical Property 12 = 2011-2012
• Historical Property 13 = 2010-2011
• Historical Property 14 = 2009-2010
• Historical Property 15 = 2008-2009

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Users can check theASSESSED_YEAR variable to confirm the year of assessment.

Roughly speaking, the tables use the following census geographies:

• 2020 Census Tract: Historical Property 1-2
• 2010 Census Tract: Historical Property 3 – 12
• 2000 Census Tract: Historical Property 13 – 15

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The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.

For more information about included variables, please see **core_logic_sdp_historical_property_data_dictionary_2024.txt **and Historical Property_v3.xlsx.

Under Supporting files, users can also find record counts per FIPS code for each edition of the Historical Property data.

For more information about how the CoreLogic Smart Data Platform: Historical Property data compares to legacy data, please see core_logic_legacy_content_mapping.pdf.

Bulk Data Access

Data access is required to view this section.

For every real estate property in Arlington which has been sold, this dataset includes property sales information and can be associated with other Real Estate datasets by the RPC (RealEstatePropertyCode).

Single Family Home Prices in the United States increased to 422800 USD in May from 414000 USD in April of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q1 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

House Price Index YoY in the United States decreased to 3 percent in April from 3.90 percent in March of 2025. 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 Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Discover the power of McGRAW’s comprehensive data solutions, the industry's largest and most complete property and ownership database in the nation. Additionally, the mortgage industry's most sought-after analytics solutions for loan quality, risk management, compliance, and collateral valuation. These data sets are built to empower businesses with reliable, accurate, and actionable insights across the mortgage, real estate, and title sectors. With access to over 150 million records and 200 attributes, our expansive data repository enables you to streamline decision-making, optimize marketing, and enhance customer targeting across industries. Take a look at the comprehensive data sets below:

Mortgage Data Our mortgage data encompasses loan origination, borrower profiles, mortgage terms, and payment statuses, providing a complete view of borrowers and mortgage landscapes. We deliver details on active and historical mortgages, including lender information, loan types, interest rates, and mortgage maturity. This empowers financial institutions and analysts to predict market trends, assess creditworthiness, and personalize customer outreach with accuracy.

Property Data McGRAW’s property data includes detailed attributes on residential and commercial properties, spanning property characteristics, square footage, zoning information, construction dates, and much more. Our data empowers real estate professionals, property appraisers, and investors to make well-informed decisions based on current and historical property details.

Title Data Our title data service provides a clear view of ownership history and title status, ensuring comprehensive information on property chain-of-title, lien positions, encumbrances, and transaction history. This invaluable data assists title companies, legal professionals, and financial institutions in validating title claims, mitigating risks, and reducing time-to-close.

Ownership Data McGRAW ownership data supplies in-depth insights into individual and corporate property ownership, offering information on property owners, purchase prices, and ownership duration. This dataset is crucial for due diligence, investment planning, and market analysis, giving businesses the competitive edge to identify opportunities and assess ownership patterns in the marketplace.

Unmatched Data Quality & Coverage Our data covers the full spectrum of residential and commercial properties in the United States, with attributes verified for accuracy and updated regularly. From state-of-the-art technology to rigorous data validation practices, McGRAW’s data quality stands out, providing the confidence that businesses need to make strategic decisions.

Why Choose McGRAW Data?

Extensive Reach: Over 150 million records provide unparalleled depth and breadth of data coverage across all 50 states.

Diverse Attributes: With 200 attributes across mortgage, property, title, and ownership data, businesses can customize data views for specific needs.

Actionable Insights: Our data analytics tools and customizable reports translate raw data into valuable insights, helping you stay ahead in the competitive landscape.

Leverage McGRAW’s data solutions to unlock a holistic view of the mortgage, property, title, and ownership landscapes. For real estate professionals, lenders, and investors seeking data-driven growth, McGRAW provides the tools to elevate decision-making, enhance operational efficiency, and drive business success in today’s data-centric market.

This dataset documents the history of permits, licenses, violations, and appeals for each property in the City.

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Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q1 2025 about sales, housing, and USA.

Department of Records (DOR) Boundaries of real estate property parcels derived from legal recorded deed documents.

Unlock the Potential of U.S. National MLS Real Estate Data

Discover the wealth of information encapsulated in licensing bulk MLS (Multiple Listing Service) data, a cornerstone of the real estate realm. From property particulars to market trends, delve into the significance and multifaceted utility of MLS data across diverse industries.

MLS Real Estate Data includes:

• Property Information: Address, size, layout, condition, amenities, and more.
• Price History: Historical price changes, listing dates, and sales dates.
• Geographic Insights: Location, neighborhood information, school districts, and proximity to amenities.
• Property Photos: MLS images of properties (see the condition of a property inside and out.)
• Agent/Broker Information: Certain details about the listing agent or broker as well as their notes on properties.
• Market Dynamics: Data on local real estate market conditions, including inventory levels, price trends, and days on the market.

Housing Index in the United States decreased to 434.90 points in April from 436.70 points in March of 2025. 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.

Gain an in-depth view of property characteristics for more than 157 million properties across the United States (also available at the state- and county-level).

Key information about US Nominal Residential Property Price Index

• United States Nominal Residential Property Price Index was reported at 232.573 2010=100 in Sep 2024.
• This records an increase from the previous number of 230.393 2010=100 for Jun 2024.
• US Nominal Residential Property Price Index data is updated quarterly, averaging 61.010 2010=100 from Mar 1970 to Sep 2024, with 219 observations.
• The data reached an all-time high of 232.573 2010=100 in Sep 2024 and a record low of 10.610 2010=100 in Mar 1970.
• US Nominal Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Nominal Residential Property Price Index: 2010=100: Quarterly.

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 155,000,000 properties in total
3. 4 billion records (listed in the "data types" block above) in total
4. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

House Price Index YoY in Indonesia decreased to 1.07 percent in the first quarter of 2025 from 1.39 percent in the fourth quarter of 2024. This dataset includes a chart with historical data for Indonesia House Price Index YoY.