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.

Our Realtor.com (Multiple Listing Service) dataset represents one of the most exhaustive collections of real estate data available to the industry. It consolidates data from over 500 MLS aggregators across various regions, providing an unparalleled view of the property market.

Features:

Property Listings: Each listing provides comprehensive details about a property. This includes its physical address, number of bedrooms and bathrooms, square footage, lot size, type of property (e.g., single-family home, condo, townhome), and more.

Photographs and Virtual Tours: Visuals are crucial in the property market. Most listings are accompanied by high-quality photographs and, in many cases, virtual or 3D tours that allow potential buyers to explore properties remotely.

Pricing Information: Listings provide asking prices, and the dataset frequently updates to reflect price changes. Historical price data, which includes initial listing prices and any subsequent reductions or increases, is also available.

Transaction Histories: For sold properties, the dataset provides information about the date of sale, the sale price, and any discrepancies between the listing and sale prices.

Agent and Broker Information: Each listing typically has associated details about the property's real estate professional. This might include their name, contact details, and affiliated brokerage.

Open House Schedules: Open house dates and times are listed for properties that are actively being shown to potential buyers.

1. Analytical Insights:

Market Trends: By analyzing the dataset over time, one can glean insights into market dynamics, such as the rate of price appreciation or depreciation in certain areas, the average time properties stay on the market, and seasonality effects.

Neighborhood Data: With comprehensive geographical data, it becomes possible to understand neighborhood-specific trends. This is invaluable for potential buyers or real estate investors looking to identify burgeoning markets.

Price Comparisons: Realtors and potential buyers can benchmark properties against similar listings in the same area to determine if a property is priced appropriately.

1. Utility:

For Industry Professionals and Analysts: Beyond buyers and sellers, the dataset is a trove of information for real estate agents, brokers, analysts, and investors. They can harness this data to craft strategies, predict market movements, and serve their clients better.

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 Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q1 2025 about sales, median, housing, and USA.

This dataset uses data provided from Washington State’s Housing Market, a publication of the Washington Center for Real Estate Research (WCRER) at the University of Washington.

Median sales prices represent that price at which half the sales in a county (or the state) took place at higher prices, and half at lower prices. Since WCRER does not receive sales data on individual transactions (only aggregated statistics), the median is determined by the proportion of sales in a given range of prices required to reach the midway point in the distribution. While average prices are not reported, they tend to be 15-20 percent above the median.

Movements in sales prices should not be interpreted as appreciation rates. Prices are influenced by changes in cost and changes in the characteristics of homes actually sold. The table on prices by number of bedrooms provides a better measure of appreciation of types of homes than the overall median, but it is still subject to composition issues (such as square footage of home, quality of finishes and size of lot, among others).

There is a degree of seasonal variation in reported selling prices. Prices tend to hit a seasonal peak in summer, then decline through the winter before turning upward again, but home sales prices are not seasonally adjusted. Users are encouraged to limit price comparisons to the same time period in previous years.

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.

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.

Housing Index in Saudi Arabia increased to 104.90 points in the first quarter of 2025 from 104.20 points in the fourth quarter of 2024. This dataset provides - Saudi Arabia Housing Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Commercial Real Estate Prices for United States (COMREPUSQ159N) from Q1 2005 to Q3 2024 about real estate, commercial, rate, and USA.

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.

Key information about House Prices Growth

• Mexico house prices grew 8.8% YoY in Dec 2024, following an increase of 9.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Dec 2024, with an average growth rate of 7.4%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

Housing Index in Sweden decreased to 936 points in the first quarter of 2025 from 937 points in the fourth quarter of 2024. This dataset provides - Sweden House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The dataset shows the real estate price for the first half of 2008, recorded through the Real Estate Market Observatory (OMI) managed by the Revenue Agency, which specifically processes and analyses all the technical and economic data and information concerning the value of a property and the rental market recorded in the Municipality of Milan.

The data shown in the Dataset are divided into:

• The conservative state indicated by capital letters refers to the most frequent one of ZONE
• The Market Value is expressed in Euro/sqm referring to the Net Area (N) or Gross Area (L)
• The presence of the asterisk (*) next to the typology indicates that the relative Market or Rental Values have been corrected.
• For the Box, Parking spaces and Garage types, the different appreciation of the market according to the conservation status is not significant
• For the type Shops the judgment O / N / S is to be understood as referring to the commercial position and not to the conservation status of the real estate unit

The IMO is to be considered as a full-fledged tool, through which the Revenue Agency ensures the transparency of the real estate market.

The information provided shall be the sole property of the Agency. The customer is not allowed to sell, rent, rent, transfer, transfer the contents of the Database or assume some other obligations to third parties.

The information contained in the database can be used by the customer, also for the purpose of their processing provided that, in the case of publication, the relevant source is cited. For any further contractual conditions and use it is possible to connect to the link https://www.agenziaentrate.gov.it/portale/web/guest/fiche/fabbricatiterreni/omi/banche-data/quotas-real estate/conditions-contractual-qi.

The dataset shows the real estate price for the second half of 2018, recorded through the Real Estate Market Observatory (OMI) managed by the Revenue Agency, which specifically processes and analyses all the technical and economic data and information concerning the value of a property and the rental market recorded in the Municipality of Milan. The data shown in the Dataset are divided into: * The conservative state indicated by capital letters refers to the most frequent one of ZONE * The Market Value is expressed in Euro/sqm referring to the Net Area (N) or Gross Area (L) * The presence of the asterisk (*) next to the typology indicates that the relative Market or Rental Values have been corrected. * For the Box, Parking spaces and Garage types, the different appreciation of the market according to the conservation status is not significant * For the type Shops the judgment O / N / S is to be understood as referring to the commercial position and not to the conservation status of the real estate unit The IMO is to be considered as a full-fledged tool, through which the Revenue Agency ensures the transparency of the real estate market. The information provided shall be the sole property of the Agency. The customer is not allowed to sell, rent, rent, transfer, transfer the contents of the Database or assume some other obligations to third parties. The information contained in the database can be used by the customer, also for the purpose of their processing provided that, in the case of publication, the relevant source is cited. For any further contractual conditions and use it is possible to connect to the link https://www.agenziaentrate.gov.it/portale/web/guest/fiche/fabbricatiterreni/omi/banche-data/quotas-real estate/conditions-contractual-qi.

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.

The dataset shows the real estate price for the second half of 2005, recorded through the Real Estate Market Observatory (OMI) managed by the Revenue Agency, which specifically processes and analyses all the technical and economic data and information concerning the value of a property and the rental market recorded in the Municipality of Milan. The data shown in the Dataset are divided into: * The conservative state indicated by capital letters refers to the most frequent one of ZONE * The Market Value is expressed in Euro/sqm referring to the Net Area (N) or Gross Area (L) * The presence of the asterisk (*) next to the typology indicates that the relative Market or Rental Values have been corrected. * For the Box, Parking spaces and Garage types, the different appreciation of the market according to the conservation status is not significant * For the type Shops the judgment O / N / S is to be understood as referring to the commercial position and not to the conservation status of the real estate unit The IMO is to be considered as a full-fledged tool, through which the Revenue Agency ensures the transparency of the real estate market. The information provided shall be the sole property of the Agency. The customer is not allowed to sell, rent, rent, transfer, transfer the contents of the Database or assume some other obligations to third parties. The information contained in the database can be used by the customer, also for the purpose of their processing provided that, in the case of publication, the relevant source is cited. For any further contractual conditions and use it is possible to connect to the link https://www.agenziaentrate.gov.it/portale/web/guest/fiche/fabbricatiterreni/omi/banche-data/quotas-real estate/conditions-contractual-qi.

In 2022, house price growth in the UK slowed, after a period of decade-long increase. Nevertheless, in March 2025, prices reached a new peak, with the average home costing ******* British pounds. This figure refers to all property types, including detached, semi-detached, terraced houses, and flats and maisonettes. Compared to other European countries, the UK had some of the highest house prices. How have UK house prices increased over the last 10 years? Property prices have risen dramatically over the past decade. According to the UK house price index, the average house price has grown by over ** percent since 2015. This price development has led to the gap between the cost of buying and renting a property to close. In 2023, buying a three-bedroom house in the UK was no longer more affordable than renting one. Consequently, Brits have become more likely to rent longer and push off making a house purchase until they have saved up enough for a down payment and achieved the financial stability required to make the step. What caused the recent fluctuations in house prices? House prices are affected by multiple factors, such as mortgage rates, supply, and demand on the market. For nearly a decade, the UK experienced uninterrupted house price growth as a result of strong demand and a chronic undersupply. Homebuyers who purchased a property at the peak of the housing boom in July 2022 paid ** percent more compared to what they would have paid a year before. Additionally, 2022 saw the most dramatic increase in mortgage rates in recent history. Between December 2021 and December 2022, the **-year fixed mortgage rate doubled, adding further strain to prospective homebuyers. As a result, the market cooled, leading to a correction in pricing.

Abstract This paper estimates quantile hedonic price indexes for apartments in Belo Horizonte, Brazil, 1995-2012. From an urban economic point of view, the real estate is one example of a segmented market and for this reason we choose the quantile regression approach. The several results suggest that before 2004, when there was a lack of institutional mark for the real estate mortgages and macroeconomics environment was too uncertain, there a little appreciation in apartments prices. In this period there wasn’t a regular pattern in quantile appreciation. Since 2005, there was a great appreciation of apartments in all segments of the market, since the real estate mortgage increases due to the reformulation of the real estate mortgage institutional market. Before 2009, the appreciation was more pronounced in the highest segments. Since 2009, there was a reversion of the quantile appreciation pattern. The appreciation in lowest segments was higher than in the highest. Partly, this change can be attributed to countercyclical policies implemented by Brazilian Government which focus on families with medium and low incomes.

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.

The dataset shows the real estate price for the second half of 2023, recorded through the Observatory of the Real Estate Market (OMI) managed by the Revenue Agency, which specifically processes and analyses all the technical and economic data and information concerning the value of a property and the rental market recorded in the Municipality of Milan. The data shown in the Dataset are divided into: The conservative state indicated by capital letters refers to the most frequent one of ZONE The Market Value is expressed in Euro/sqm referring to the Net Area (N) or Gross Area (L) The presence of the asterisk (*) next to the typology indicates that the relative Market or Rental Values have been corrected. For the Box, Parking spaces and Garage types, the different appreciation of the market according to the conservation status is not significant For the Shops type, the O/ N /S judgment is to be understood as referring to the commercial position and not to the conservation status of the real estate unit The IMO is to be considered as a full-fledged tool, through which the Revenue Agency ensures the transparency of the real estate market. The information provided shall be the sole property of the Agency. The customer is not allowed to sell, rent, rent, transfer, transfer the contents of the Database or assume some other obligations to third parties. The information contained in the database can be used by the customer, also for the purpose of their processing provided that, in the case of publication, the relevant source is cited. For any further contractual conditions and use it is possible to connect to the link https://www.agenziaentrate.gov.it/portale/web/guest/fiche/fabbricatiterreni/omi/banche-data/quotas-real estate/conditions-contractual-qi.