The median home sales price is the middle value of the prices for which homes are sold (both market and private transactions) within a calendar year. The median value is used as opposed to the average so that both extremely high and extremely low prices do not distort the prices for which homes are sold. This measure does not take into account the assessed value of a property.Source: First American Real Estate Solutions (FARES) and RBIntel (2022-forward)Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2022, 2023

The travel time data on this map is modeled from a 2005 transit network. The home values are as of 2000 and are expressed in year 2000 dollars. The home value estimates were created by the Association of Bay Area Governements by combining ParcelQuest real estate transaction data and real estate tax assessment data. This information can be generated for any address in the region using an interactive mapping tool available under Maps at onebayarea.org/maps.htm (Note - this tool is no longer available).

House prices grew year-on-year in most states in the U.S. in the third quarter of 2024. The District of Columbia was the only exception, with a decline of ***** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Hawaii—the state where homes appreciated the most—the increase exceeded ** percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2024, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2024.

This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: - Price in dollars - Address of the property - Latitude and Longitude of the address obtained by using Google Geocoding service - Area Name of the property obtained by using Google Geocoding service

This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas)

However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes).

This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/

I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction

Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variability in the joint space–time continuum. We then use geostatistics to predict and map monthly housing prices across the area of interest over a period of 4 years. For this analysis, we introduce the Bayesian maximum entropy (BME) method into real estate research. We use BME because it rigorously integrates uncertain or secondary soft data, which are needed to build the price indices. The soft data in our analysis are property tax values, which are plentiful, publicly available, and highly correlated with transaction prices. The results demonstrate how the use of the soft data provides the ability to map house prices within a small areal unit such as a subdivision or neighborhood.

Graph and download economic data for Real Residential Property Prices for China (QCNR628BIS) from Q2 2005 to Q1 2025 about China, residential, HPI, housing, real, price index, indexes, and price.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

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

The sales price and the floor area of ​​each house sold in Amsterdam is known to the Land Registry for each address and has been supplied to the Spatial Planning and Sustainability Department via the Department of Research, Information and Statistics of the Municipality of Amsterdam for the purpose of creating the Housing Value Map. In a Geographic Information System (GIS) all transaction addresses are shown as points on the map and the price per m2 of each point is calculated (= sales price / m2 floor area). Extreme values ​​have been removed. An interpolation method, in which there must be at least 2 transaction addresses within a radius of 300 metres, creates the Property Value Cards. On this Housing Value Map, the blue areas mean that you get a lot of housing for your money there. The houses in the red areas are apparently (very) popular for aspects other than the floor space of the house: the level of facilities, the proximity of the historic centre, the public space, the building type or the living environment. The Housing Value Map is therefore an exceptionally good indication of the valuation of a neighbourhood.

The average transaction price of new housing in Europe was the highest in Norway, whereas existing homes were the most expensive in Austria. Since there is no central body that collects and tracks transaction activity or house prices across the whole continent or the European Union, not all countries are included. To compile the ranking, the source weighed the transaction prices of residential properties in the most important cities in each country based on data from their national offices. For example, in Germany, the cities included were Munich, Hamburg, Frankfurt, and Berlin. House prices have been soaring, with Sweden topping the ranking Considering the RHPI of houses in Europe (the price index in real terms, which measures price changes of single-family properties adjusted for the impact of inflation), however, the picture changes. Sweden, Luxembourg and Norway top this ranking, meaning residential property prices have surged the most in these countries. Real values were calculated using the so-called Personal Consumption Expenditure Deflator (PCE), This PCE uses both consumer prices as well as consumer expenditures, like medical and health care expenses paid by employers. It is meant to show how expensive housing is compared to the way of living in a country. Home ownership highest in Eastern Europe The home ownership rate in Europe varied from country to country. In 2020, roughly half of all homes in Germany were owner-occupied whereas home ownership was at nearly ** percent in Romania or around ** percent in Slovakia and Lithuania. These numbers were considerably higher than in France or Italy, where homeowners made up ** percent and ** percent of their respective populations.For more information on the topic of property in Europe, visit the following pages as a starting point for your research: real estate investments in Europe and residential real estate in Europe.

The Historic Preservation Office (HPO) works to protect and enhance historic neighborhoods, buildings and sites in Phoenix. HPO works closely with the Historic Preservation Commission to identify and designate eligible properties and districts for listing on the Phoenix Historic Property Register. Protection is provided to designated properties through city review and approval of exterior alterations to buildings and demolition requests. HPO also administers the Historic Preservation Bond fund that supports a number of financial assistance programs for historic properties. Rehabilitation training and educational activities are offered to heighten public awareness and appreciation for the community's historic resources.Contact Information: historic@phoenix.gov

Zoopla Properties Listing dataset to explore detailed property information, including pricing, location, and features. Popular use cases include real estate market analysis, property valuation, and investment research.

Use our Zoopla Properties Listing Information dataset to explore detailed property listings, including property details, pricing, location, and market trends across various regions. This dataset provides valuable insights into property valuations, consumer preferences, and real estate dynamics, enabling businesses and researchers to make data-driven decisions.

Tailored for real estate professionals, investors, and market analysts, this dataset supports market trend analysis, property valuation assessments, and investment strategy development. Whether you're evaluating property investments, tracking market conditions, or conducting competitive analysis, the Zoopla Properties Listing Information dataset is a key resource for navigating the real estate landscape.

Dataset Features

• url: The original listing URL on Zoopla.
• property_type: Type of property (e.g., Flat, Detached, Terraced).
• property_title: Title or headline of the listing.
• address: Full postal address of the property.
• google_map_location: Geographical coordinates (latitude, longitude).
• virtual_tour: Link to a virtual walkthrough or 360° tour.
• street_view: Link to the Google Street View of the property.
• url_property: Zoopla-specific property page URL.
• currency: Currency in which the property is priced.
• deposit: Security deposit required (typically for rentals).
• letting_arrangements: Letting details (e.g., short-term, long-term).
• breadcrumbs: Category breadcrumbs for location and type navigation.
• availability: Availability status (e.g., Available now, Under offer).
• commonhold_details: Information about commonhold ownership.
• service_charge: Annual service charge (for leasehold properties).
• ground_rent: Annual ground rent cost.
• time_remaining_on_lease: Lease duration remaining in years.
• ecp_rating: Energy Performance Certificate rating.
• council_tax_band: Council tax band.
• price_per_size: Price per square meter or foot.
• tenure: Tenure type (Freehold, Leasehold, etc.).
• tags: Descriptive tags (e.g., New build, Chain-free).
• features: List of property features (e.g., garden, garage, en-suite).
• property_images: URLs to property photos.
• additional_links: Other related links (e.g., brochures, agents).
• listing_history: Changes in price, listing dates, and status over time.
• agent_details: Information about the listing agent or agency.
• points_ofInterest: Nearby landmarks or facilities (schools, transport).
• bedrooms Number of bedrooms.
• price: Listed price of the property.
• bathrooms: Number of bathrooms.
• receptions: Number of reception rooms (living, dining, etc.).
• country_code: Country code of the listing (e.g., GB for UK).
• energy_performance_certificate: Detailed EPC documentation or summary.
• floor_plans: URL or data related to property floor plans.
• description: Detailed property description from the listing.
• price_per_time: Price frequency for rentals (e.g., per week, per month).
• property_size: Area of the property (in sq ft or sq m).
• market_stats_last_12_months: Market stats for the area over the past year.
• market_stats_renta_opportunities: Data on rental yields and opportunities.
• market_stats_recent_sales_nearby: Sales history for nearby properties.
• market_stats_rental_activity: Local rental activity trends.
• uprn: Unique Property Reference Number for UK properties.
• listing_label: Label/category of the listing.

Distribution

• Data Volume: 44 Columns and 95.92K Rows
• Format: CSV

Usage

This dataset is ideal for a variety of high-impact applications:

• Property Valuation Models: Train ML models to estimate market value using features like size, location, and amenities.
• Real Estate Market Analysis: Identify pricing trends, demand patterns, and neighbourhood growth over time.
• Investment Research: Analyse rental yields, price per square foot, and historical price changes for investment opportunities.
• Recommendation Systems: Develop intelligent recommendation engines for property buyers and renters.
• Urban Planning & Policy Making: Use location and infrastructure data to guide city development.
• Sentiment & Description Analysis: NLP-driven insights from listing descriptions and agent narratives.

Coverage

• Geographic Coverage: Global
• Time Range: Ongoing collection; historical data may span multiple years

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   -

Median price paid for residential property in England and Wales by property type and electoral ward. Annual data.

This data set is a listing of all properties sold by NORA through the following disposition channels.-Auction: Properties put up for auction and sold to the highest bidder.-Development: Properties offered via request for proposals to create affordable housing.-Lot Next Door: Properties sold to adjacent owners.-Alternate Land Use: Properties sold for purposes of creating green space and used for activities such as community gardens.

VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

Only a small fraction of vacant housing units are actually considered available. Only vacant units for rent or for sale make up the available housing stock. Vacant housing that is not on the market, such as homes for seasonal, recreational, or occasional use & housing for migrant workers, are not part of the available housing stock.The housing availability rate is an indicator that economists and housing policy analysts often track. A low housing availability rate indicates a "tight" housing market (a seller's market or landlord's market) whereas a high housing availability rate indicates a buyer's or renter's market.This map shows the housing availability rate depicted by the color: pink indicates a low housing availability rate, and green indicates a high housing availability rate. The count of available housing units is depicted by the size of the symbol.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

The average price of detached and duplex houses in the biggest cities in Germany varied between approximately ***** euros and 10,000 euros per square meter in 2024. Housing was most expensive in Munich, where the square meter price of houses amounted to ***** euros. Conversely, Berlin was most affordable, with the square meter price at ***** euros. How have German house prices evolved? House prices maintained an upward trend for more than a decade, with 2020 and 2021 experiencing exceptionally high growth rates. In 2021, the nominal year-on-year change exceeded 10 percent. Nevertheless, the second half of 2022 saw the market slowing, with the annual percentage change turning negative for the first time in 12 years. Another way to examine the price growth is through the house price index, which uses 2015 as a base. At its peak in 2022, the German house price index measured about *** percent, which means that a house bought in 2015 would have appreciated by ** percent. Is housing affordable in Germany? Housing affordability depends greatly on income: High-income areas often tend to have more expensive housing, which does not necessarily make them unaffordable. The house price to income index measures the development of the cost of housing relative to income. In the first quarter of 2024, the index value stood at ***, meaning that since 2015, house price growth has outpaced income growth by about ** percent. Compared with the average for the euro area, this value was lower.

This data set provides the calculated annual average price of residential homes sold, by home type, within Peel and the area municipalities since 2005. Data is compiled from monthly data released by the Toronto Real Estate Board’s Market Watch reports.NoteAverage annual home price by type for Peel and each of the area municipalities has been calculated using monthly sales and dollar volume. For years 2005 to 2011, data was first aggregated based on TREB districts.

In 2021, Allegheny County Economic Development (ACED), in partnership with Urban Redevelopment Authority of Pittsburgh(URA), completed the a Market Value Analysis (MVA) for Allegheny County. This analysis services as both an update to previous MVA’s commissioned separately by ACED and the URA and combines the MVA for the whole of Allegheny County (inclusive of the City of Pittsburgh). The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies.

This MVA utilized data that helps to define the local real estate market. The data used covers the 2017-2019 period, and data used in the analysis includes:

• Residential Real Estate Sales
• Mortgage Foreclosures
• Residential Vacancy
• Parcel Year Built
• Parcel Condition
• Building Violations
• Owner Occupancy
• Subsidized Housing Units

The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources.

Please refer to the presentation and executive summary for more information about the data, methodology, and findings.

1950 Hoffman-La Roche World Maps 1600s Repro Lithographs M1 M2 M3 M4 M5 M6 M7 - Sold on eBay June 6th, 2024 for $111.00 - Historical sales data for collectible reference.