Data on resident buyers who are persons that purchased a residential property in a market sale and filed their T1 tax return form: number of and incomes of residential property buyers, sale price, price-to-income ratio by the number of buyers as part of a sale, age groups, first-time home buyer status, buyer characteristics (sex, family type, immigration status, period of immigration, admission category).

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

Data on resident owners who are persons occupying one of their residential properties: sex, age, total income, the type and the assessment value of the owner-occupied property, as well as the number and the total assessment value of residential properties owned.

Real Estate Market size was valued at USD 79.7 Trillion in 2024 and is projected to reach USD 103.6 Trillion by 2031, growing at a CAGR of 5.1% during the forecasted period 2024 to 2031

Global Real Estate Market Drivers

Population Growth and Urbanization: In order to meet the demands of businesses, housing needs, and infrastructure development, there is a constant need for residential and commercial properties as populations and urban areas rise.

Low Interest Rates: By making borrowing more accessible, low interest rates encourage both individuals and businesses to make real estate investments. Reduced borrowing costs result in reduced mortgage rates, opening up homeownership and encouraging real estate investments and purchases.

Economic Growth: A thriving real estate market is a result of positive economic growth indicators like GDP growth, rising incomes, and low unemployment rates. Robust economies establish advantageous circumstances for real estate investment, growth, and customer assurance in the housing sector. Job growth and income increases: As more people look for rental or purchase close to their places of employment, housing demand is influenced by these factors. The housing market is driven by employment opportunities and rising salaries, which in turn drive home buying, renting, and property investment activity. Infrastructure Development: The demand and property values in the surrounding areas can be greatly impacted by investments made in infrastructure projects such as public facilities, utilities, and transportation networks. Accessibility, convenience, and beauty are all improved by improved infrastructure, which encourages real estate development and investment.

Government Policies and Incentives: Tax breaks, subsidies, and first-time homebuyer programs are a few examples of government policies and incentives that can boost the real estate market and homeownership. Market stability and growth are facilitated by regulatory actions that promote affordable housing, urban redevelopment, and real estate development.

Foreign Investment: Foreign capital can be used to stimulate demand, diversify property portfolios, and pump capital into the real estate market through direct property purchases or real estate investment funds. Foreign investors are drawn to the local real estate markets by favorable exchange rates, stable political environments, and appealing returns.

Demographic Trends: Shifting demographic trends affect housing preferences and demand for various property kinds. These trends include aging populations, household formation rates, and migration patterns. It is easier for real estate developers and investors to match supply with changing market demand when they are aware of demographic fluctuations.

Technological Innovations: New technologies that are revolutionizing the marketing, transactions, and management of properties include digital platforms, data analytics, and virtual reality applications. In the real estate industry, technology adoption increases market reach, boosts customer experiences, and increases operational efficiency.

Environmental Sustainability: Decisions about real estate development and investment are influenced by the growing knowledge of environmental sustainability and green building techniques. Market activity in environmentally aware real estate categories is driven by demand for eco-friendly neighborhoods, sustainable design elements, and energy-efficient buildings.

BatchData's Property Search API is trusted by organizations to power websites, applications, predictive models, and sales/marketing operations. The property search API boasts 300+ unique search filters enabling a granular and reliable property search experience.

Use Property Search API to identify properties within a certain buy-box, and combine with demographic data, MLS, mortgage data, and live events to surface motivated sellers and active buyers/borrowers. Or, search for properties who's homeowner information matches an ideal commercial or consumer profile.

Search by: - Owner Name - Property Assessed Value - Property Location - Building Characteristics - Household Demographics - Voluntary & Involuntary Liens - MLS Information - Sales, Transfer & Tax History - Owner Name

BatchData's Property Search API allows you to uncover the information you need.

1. Real Estate Market Data - this data helps users identify emerging trends, evaluate property values, and make informed investment decisions. Property Data
2. Detailed Property Data: The API offers in-depth details about individual properties, including square footage, number of bedrooms and bathrooms, lot size, and year built. It can also include information on property features such as pools, garages, and upgrades.
3. Demographic Data: The API can provide demographic information about the neighborhoods where properties are located, such as homeowner age, income, marital status and more. This helps users understand the community context and target market.

Residential Real Estate Market Outlook

The global residential real estate market size was valued at approximately $9.7 trillion in 2023 and is projected to reach an astounding $15.4 trillion by 2032, growing at a compound annual growth rate (CAGR) of 5.2%. This growth is driven by several factors, including increasing urbanization, rising disposable incomes, and the ongoing global shift towards homeownership as a stable investment. Demographic shifts, such as the growing number of nuclear families and millennials entering the housing market, also contribute significantly to this upward trend.

One of the primary growth factors for the residential real estate market is the increasing urbanization across the globe. As more people migrate to urban areas in search of better job opportunities and a higher standard of living, the demand for residential properties in cities continues to rise. This trend is particularly pronounced in developing countries, where rapid economic growth is accompanied by significant rural-to-urban migration. Additionally, the trend of urban redevelopment and the creation of smart cities are further fueling the demand for modern residential properties.

Another crucial growth factor is the rise in disposable incomes and improved access to financing options. With strong economic growth in many parts of the world, individual incomes have been rising, allowing more people to afford homeownership. Financial institutions are also playing a critical role by offering a variety of mortgage products with attractive interest rates and flexible repayment terms. This increased access to capital has enabled a broader section of the population to invest in residential real estate, thereby expanding the market.

Technological advancements and the digital transformation of the real estate sector are also contributing to market growth. The proliferation of online platforms and real estate technology (proptech) solutions has made the process of buying, selling, and renting properties more efficient and transparent. Virtual tours, online mortgage applications, and blockchain for property transactions are some of the innovations revolutionizing the industry. These technological advancements not only improve the customer experience but also attract tech-savvy millennials and Gen Z buyers.

Regionally, the Asia-Pacific region is experiencing significant growth in the residential real estate market. Countries like China and India, with their large populations and rapid urbanization, are at the forefront of this expansion. Government initiatives aimed at providing affordable housing and improving infrastructure are also playing a pivotal role. In contrast, mature markets like North America and Europe are witnessing steady growth driven by economic stability and continued investment in housing. Meanwhile, regions like Latin America and the Middle East & Africa are also showing promise, albeit at a slower pace, due to varying economic conditions and market maturity levels.

Property Type Analysis

The residential real estate market is segmented by property type, including single-family homes, multi-family homes, condominiums, townhouses, and others. Single-family homes are the most traditional and widespread type of residential property. They are particularly popular in suburban areas where space is more abundant. The demand for single-family homes continues to be driven by the desire for privacy, larger living spaces, and the ability to customize the property. These homes appeal especially to families with children and those looking to invest in a long-term residence.

Multi-family homes, which include duplexes, triplexes, and apartment buildings, are gaining traction, particularly in urban settings. These properties are attractive due to their potential for generating rental income and their ability to house multiple tenants. Investors find multi-family homes appealing as they offer a higher return on investment (ROI) compared to single-family homes. Additionally, the increasing trend of co-living and shared housing arrangements has bolstered the demand for multi-family properties in cities.

Condominiums, or condos, are another significant segment within the residential real estate market. Condos are particularly popular in urban areas where land is scarce and expensive. They offer a balance between affordability and amenities, making them an attractive option for young professionals and small families. Condominiums often come with added benefits such as maintenance services, security, and shared facilities like gyms and swimmin

Success.ai’s Commercial Real Estate Data and B2B Contact Data for Global Real Estate Professionals is a comprehensive dataset designed to connect businesses with industry leaders in real estate worldwide. With over 170M verified profiles, including work emails and direct phone numbers, this solution ensures precise outreach to agents, brokers, property developers, and key decision-makers in the real estate sector.

Utilizing advanced AI-driven validation, our data is continuously updated to maintain 99% accuracy, offering actionable insights that empower targeted marketing, streamlined sales strategies, and efficient recruitment efforts. Whether you’re engaging with top real estate executives or sourcing local property experts, Success.ai provides reliable and compliant data tailored to your needs.

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Success.ai’s B2B Contact Data for Global Real Estate Professionals delivers the tools you need to connect with the right people at the right time, driving efficiency and success in your business operations. From agents and brokers to property developers and executiv...

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of lessors of residential buildings and dwellings (except social housing projects) (NAICS 531111), annual, for five years of data.

Comprehensive demographic dataset for Young Property, Cary, NC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Frequency: Occasional Table: 46-10-0038-01 Release date: 2022-04-12 Geography: Province or territory, Census subdivision, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part

Symbol legend: .. / not available for a specific reference period x / suppressed to meet the confidentiality requirements of the Statistics Act A / data quality: excellent The footnotes appear in brackets in text

1) The universe of this table is restricted to individual resident owners who occupy a residential property. An owner's geographic location is determined by the location of the occupied property for both single- and multiple-property owners. A residential property refers to all land and structures intended for private occupancy whether on a permanent or a temporary basis.

2) The geographic boundaries used in this table are the 2016 census subdivisions boundaries.

3) Previous reference period estimates are subject to revision.

4) The Composite Quality Indicator (CQI) shown in this table is created by combining many individual quality indicators, each one representing the quality of different Canadian Housing Statistics Program (CHSP) data processing steps (for example: coding, geocoding, linkage and imputation) and includes the following values: A - Excellent: All domain variables and the variable of interest are of excellent quality. B - Very good: All domain variables and the variable of interest are of very good to excellent quality. C - Good: The quality of some of the domain variables or the variable of interest is considered good while all the other variables are of very good to excellent quality. D - Acceptable: The quality of some of the domain variables or the variable of interest is considered acceptable while all the other variables are of good to excellent quality. E - Use with caution: Several domain variables or the variable of interest are of poor quality. F - Too unreliable to be published. The CQIs are available starting with the reference period of 2020, except for the Northwest Territories where they are available from 2019 reference period.

5) Property type " refers to property characteristics and/or dwelling configuration on which there can be one or more residential structures. Property types include single-detached houses semi-detached houses condominium apartments mobile homes other property types properties with multiple residential units and vacant land."

6) Estimates by property type in Newfoundland and Labrador are only available in the census subdivision of St. John’s.

7) Estimates by property type in Northwest Territories are not available.

8) Estimates by property type in Nunavut are not available.

9) The number of properties owned by the property owner is limited to residential properties that are within a given province.

10) Newfoundland and Labrador estimates are not available at the provincial level and for the category “Outside of census metropolitan areas (CMAs) and census agglomerations (CAs)”.

11) Northwest Territories estimates are only available in the census agglomeration of Yellowknife.

12) A condominium apartment" refers to a set of living quarters that is owned individually while land and common elements are held in joint ownership with others."

13) Counts undergo random rounding, a process that transforms all raw counts into randomly rounded counts. This reduces the possibility of identifying individuals in the tabulations. All percentages are derived from rounded counts, subtotals and totals may not exactly equal the sum of components due to system rounding.

14) The number of property owners estimates are not available for the 2018 reference period.

15) The number of owners should be used with caution outside of census metropolitan areas (CMAs) and census agglomerations (CAs), as well as the proportion of owners by geography. This note does not apply to Nunavut.

16) Assessment value" refers to the assessed value of the property for the purposes of determining property taxes. It is important to note that the assessed value does not necessarily represent the market value. Given that different provinces and territories have their own assessment periods and duration of the valuation roll it is difficult to make accurate comparisons of similar properties from one province or territory to another. For properties that are being utilized for both residential and non-residential purposes only the residential portion's value has been taken into account. The reference years of the assessment values by province or territory are available here: Canadian Housing Statistics Program (CHSP)."

17) For Nunavut, the property use indicator is not available, the universe of this table includes all individual resident owners. For owners with multiple properties, the geographic location and type of property are from the residential property with the highest assessment value.

18) Averages and medians are calculated using values greater than zero for the variables of interest.

19) Total assessment value" represents the sum of the assessment values of all residential properties owned by an owner within a given province."

20) Total income of person" refers to the total income of an individual before deductions for income taxes during the previous year. This income measure is the sum of market income and government transfers. Market income includes employment income investment income private retirement income and other income from market sources during the previous year. Government transfers refer to all cash benefits received from federal provincial territorial or municipal governments during the previous year."

Cite: Statistics Canada. Table 46-10-0038-01 Single and multiple residential property owners: demographic data and value of properties owned https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=4610003801

In Omaha, NE, more than 25 GI projects have been completed to date, with several featuring GI practices in public parks. Using a repeat sales model , we examined the effect of GI on the value of nearby single-family homes, based on housing sales and characteristic data from 2000 to 2018. We evaluated the sales price for homes using a buffer zone of 0-0.5km, and three additional models: homes within 0-0.25km, 0.25-0.5km, and greater than 0.5km from parks where GI was installed for 25,472 sale pairs. In addition to the repeat sales model, we performed a hot spot analysis on several demographic characteristics to capture systematic differences at a smaller spatial scale and over a longer time period than the repeat sales model could capture. We used US Census data on race and household income to examine changing patterns over time and space, and a spatial lag Maximum Likelihood Estimation model to determine if the location of GI correlated with either of these demographics. This dataset is associated with the following publication: Hoover, F., J. Price, and M. Hopton. Examining the Effects of Green Infrastructure on Residential Sales Prices in Omaha, NE. Urban Forestry & Urban Greening. Elsevier B.V., Amsterdam, NETHERLANDS, 54: 126778, (2020).

Frequency: OccasionalTable: 46-10-0038-01Release date: 2022-04-12Geography: Province or territory, Census subdivision, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration partSymbol legend:.. / not available for a specific reference periodx / suppressed to meet the confidentiality requirements of the Statistics Act A / data quality: excellentThe footnotes in the table are represented in brackets.1) The universe of this table is restricted to individual resident owners who occupy a residential property. An owner's geographic location is determined by the location of the occupied property for both single- and multiple-property owners. A residential property refers to all land and structures intended for private occupancy whether on a permanent or a temporary basis.2) The geographic boundaries used in this table are the 2016 census subdivisions boundaries.3) Previous reference period estimates are subject to revision.4) The Composite Quality Indicator (CQI) shown in this table is created by combining many individual quality indicators, each one representing the quality of different Canadian Housing Statistics Program (CHSP) data processing steps (for example: coding, geocoding, linkage and imputation) and includes the following values: A - Excellent: All domain variables and the variable of interest are of excellent quality. B - Very good: All domain variables and the variable of interest are of very good to excellent quality. C - Good: The quality of some of the domain variables or the variable of interest is considered good while all the other variables are of very good to excellent quality. D - Acceptable: The quality of some of the domain variables or the variable of interest is considered acceptable while all the other variables are of good to excellent quality. E - Use with caution: Several domain variables or the variable of interest are of poor quality. F - Too unreliable to be published. The CQIs are available starting with the reference period of 2020, except for the Northwest Territories where they are available from 2019 reference period.5) Property type" refers to property characteristics and/or dwelling configuration on which there can be one or more residential structures. Property types include single-detached houses semi-detached houses condominium apartments mobile homes other property types properties with multiple residential units and vacant land."6) Estimates by property type in Newfoundland and Labrador are only available in the census subdivision of St. John’s.7) Estimates by property type in Northwest Territories are not available.8) Estimates by property type in Nunavut are not available.9) The number of properties owned by the property owner is limited to residential properties that are within a given province.10) Newfoundland and Labrador estimates are not available at the provincial level and for the category “Outside of census metropolitan areas (CMAs) and census agglomerations (CAs)”.11) Northwest Territories estimates are only available in the census agglomeration of Yellowknife.12) Counts undergo random rounding, a process that transforms all raw counts into randomly rounded counts. This reduces the possibility of identifying individuals in the tabulations. All percentages are derived from rounded counts, subtotals and totals may not exactly equal the sum of components due to system rounding.13) The number of property owners estimates are not available for the 2018 reference period.14) The number of owners should be used with caution outside of census metropolitan areas (CMAs) and census agglomerations (CAs), as well as the proportion of owners by geography. This note does not apply to Nunavut.15) Assessment value" refers to the assessed value of the property for the purposes of determining property taxes. It is important to note that the assessed value does not necessarily represent the market value. Given that different provinces and territories have their own assessment periods and duration of the valuation roll it is difficult to make accurate comparisons of similar properties from one province or territory to another. For properties that are being utilized for both residential and non-residential purposes only the residential portion's value has been taken into account. The reference years of the assessment values by province or territory are available here: Canadian Housing Statistics Program (CHSP)."16) For Nunavut, the property use indicator is not available, the universe of this table includes all individual resident owners. For owners with multiple properties, the geographic location and type of property are from the residential property with the highest assessment value.17) Averages and medians are calculated using values greater than zero for the variables of interest.18) Total assessment value" represents the sum of the assessment values of all residential properties owned by an owner within a given province."19) Total income of person" refers to the total income of an individual before deductions for income taxes during the previous year. This income measure is the sum of market income and government transfers. Market income includes employment income, investment income, private retirement income and other income from market sources during the previous year. Government transfers refer to all cash benefits received from federal, provincial, territorial or municipal governments during the previous year."Cite: Statistics Canada. Table 46-10-0038-01 Single and multiple residential property owners: demographic data and value of properties ownedhttps://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=4610003801

Residential property values by type of property for Canada, provinces and territories, annual data from 2005 to today.

These National Statistics provide monthly estimates of the number of residential and non-residential property transactions in the UK and its constituent countries. National Statistics are accredited official statistics.

England and Northern Ireland statistics are based on information submitted to the HM Revenue and Customs (HMRC) Stamp Duty Land Tax (SDLT) database by taxpayers on SDLT returns.

Land and Buildings Transaction Tax (LBTT) replaced SDLT in Scotland from 1 April 2015 and this data is provided to HMRC by https://www.revenue.scot/">Revenue Scotland to continue the time series.

Land Transaction Tax (LTT) replaced SDLT in Wales from 1 April 2018. To continue the time series, the https://gov.wales/welsh-revenue-authority">Welsh Revenue Authority (WRA) have provided HMRC with a monthly data feed of LTT transactions since July 2021.

LTT figures for the latest month are estimated using a grossing factor based on data for the most recent and complete financial year. Until June 2021, LTT transactions for the latest month were estimated by HMRC based upon year on year growth in line with other UK nations.

LTT transactions up to the penultimate month are aligned with LTT statistics.

Go to Stamp Duty Land Tax guidance for the latest rates and information.

Go to Stamp Duty Land Tax rates from 1 December 2003 to 22 September 2022 and Stamp Duty: rates on land transfers before December 2003 for historic rates.

The latest release was published 09:30 29 August 2025 and was updated with provisional data from completed transactions during July 2025.

The next release will be published 09:30 30 September 2025 and will be updated with provisional data from completed transactions during July 2025.

https://webarchive.nationalarchives.gov.uk/ukgwa/20240320184933/https://www.gov.uk/government/statistics/monthly-property-transactions-completed-in-the-uk-with-value-40000-or-above">Archive versions of the Monthly property transactions completed in the UK with value of £40,000 or above are available via the UK Government Web Archive, from the National Archives.

This table contains 168 series, with data for years 2007 - 2012 (not all combinations necessarily have data for all years) , and was last released on 2015-09-28. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...), North American Industry Classification System (NAICS) (3 items: Lessors of residential buildings and dwellings (except social housing projects); Non-residential leasing; Real estate property managers), Summary statistics (4 items: Operating revenue; Operating expenses; Salaries, wages and benefits; Operating profit margin).

Comprehensive demographic dataset for BKF Property, Russellville, AR, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Comprehensive demographic dataset for The Payne Property, Villa Rica, GA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Real Estate Market was valued at USD 9.8 trillion in 2023, and is slated to reach USD 14.54 trillion by 2030, due to the growing urbanization worldwide.

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.

The US Consumer Residential Property/Real Estate file has 120 million+ records which include home details, site details, and purchase details on residential properties. This file includes both owners and renters with linkages to Consumer Demographics.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used. This file contains over 120 million records.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html