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).

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.

Key Features of Success.ai’s Real Estate Professional Contact Data

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AI-Powered Validation: All profiles are verified using cutting-edge AI to ensure up-to-date accuracy. Real-Time Updates: Our database is refreshed continuously to reflect the most current information. Global Compliance: Fully aligned with GDPR, CCPA, and other regional regulations for ethical data use.

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• What Makes Us Stand Out? >> Unmatched Data Accuracy: Our AI-driven validation ensures 99% accuracy for all contact details. >> Comprehensive Global Reach: Covering professionals across diverse real estate markets worldwide. >> Flexible Delivery Options: Access data in formats that seamlessly fit your existing systems. >> Ethical and Compliant Data Practices: Adherence to global standards for secure and responsible data use.

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...

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.

The homeownership among White people in the United States was 74 percent, the highest out of all ethnicities, in 2022. American Dream Part of the “American Dream” is the idea of owning a home. It is seen as a status symbol and an indicator of wealth. People take a lot of pride in owning a home, and hope to do so at the earliest age possible. It is the idea of having a white picket fence with a nuclear family, a dog, and a car or two which is seen as the stereotypical “end goal”. However, in the aftermath of the 2008 recession, the rate of homeownership in the United States fell steadily until 2016. The recession hindered people’s chances of owning a home, due to less credit being available and their own fears about being stuck with a home in negative equity if another recession were to occur. As a result, the homeownership rate in the United States has barely increased in the past few years. Factors affecting homeownership Homeownership varies based on different factors. Married-couple families have the highest homeownership rates among different family statuses. Unsurprisingly, households with high incomes have the highest homeownership rates.

Following a period of stagnation over most of the 2010s, the number of owner occupied housing units in the United States started to grow in 2017. In 2023, there were over 86 million owner-occupied homes. Owner-occupied housing is where the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing. Homeownership sentiment in the U.S. Though homeownership is still a cornerstone of the American dream, an increasing share of people see themselves as lifelong renters. Millennials have been notoriously late to enter the housing market, with one in four reporting that they would probably continue to always rent in the future, a 2022 survey found. In 2017, just five years before that, this share stood at about 13 percent. How many renter households are there? Renter households are roughly half as few as owner-occupied households in the U.S. In 2023, the number of renter occupied housing units amounted to almost 45 million. Climbing on the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home.

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 periodA / 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

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

The property’s Flood Factor as well as data on economic loss.

The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name

Type

Description

fsid

int

First Street ID (FSID) is a unique identifier assigned to each location

long

float

Longitude

lat

float

Latitude

zcta

int

ZIP code tabulation area as provided by the US Census Bureau

blkgrp_fips

int

US Census Block Group FIPS Code

tract_fips

int

US Census Tract FIPS Code

county_fips

int

County FIPS Code

cd_fips

int

Congressional District FIPS Code for the 116th Congress

state_fips

int

State FIPS Code

floodfactor

int

The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)

CS_depth_RP_YY

int

Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00

CS_chance_flood_YY

float

Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00

aal_YY_CS

int

The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low

hist1_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist1_event

string

Short name of the modeled historic event

hist1_year

int

Year the modeled historic event occurred

hist1_depth

int

Depth (in cm) of flooding to the building from this historic event

hist2_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist2_event

string

Short name of the modeled historic event

hist2_year

int

Year the modeled historic event occurred

hist2_depth

int

Depth (in cm) of flooding to the building from this historic event

adapt_id

int

A unique First Street identifier assigned to each adaptation project

adapt_name

string

Name of adaptation project

adapt_rp

int

Return period of flood event structure provides protection for when applicable

adapt_type

string

Specific flood adaptation structure type (can be one of many structures associated with a project)

fema_zone

string

Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders

footprint_flag

int

Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

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 period x / 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 ires 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 price statistics from different countries. Contains property price indicators (real series are the nominal price series deflated by the consumer price index), both in levels and in growth rates. Can be used for property market analysis.

The dataset contains four different files with different metrics, including nominal index, nominal year-on-year changes, real index, and real year-on-year changes. Each file includes data in the format of date, country, and price.

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

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).

New Homeowner Data is a subset of our comprehensive property intelligence database that can be segmented by specific property criteria, household demographics, mortgage, and real estate portfolio information.

Companies in the home services, financial products, and consumer products industries use BatchService to identify new homeowners who have purchased a property in the last 90 days and uncover their direct phone number, email, and mailing address for timely marketing of products and services new homeowners need. New homeowner data can also be segmented property type (residential real estate or commercial real estate), length of ownership, owner occupancy status, and more!

New homeowner data is available in a variety of data delivery and data enrichment modes: API (you pull data from us using an API), webhook (we push data to you using an API), AWS S3 upload (we deliver the data to you), S3 download (you download the data from our S3 bucket), SFTP.

BatchService is both a data and technology solution helping companies in and around the real estate ecosystem achieve faster growth. BatchService specializes in providing accurate contact information for US property owners, including in-depth intelligence and actionable insights related to their property. Our portfolio of products, services, and go-to-market expertise help companies identify their target market, reach the right prospects, enrich their data, and power their products and services.

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

Zillow reigns supreme in the U.S. real estate website landscape, attracting a staggering 365.8 million monthly visits in 2024. This figure dwarfs its closest competitor, Realtor.com, which garnered less than half of Zillow's traffic. Online platforms are extremely popular, with the majority of homebuyers using a mobile device during the buying process. The rise of Zillow Founded in 2006, the Seattle-headquartered proptech Zillow has steadily grown over the years, establishing itself as the most popular U.S. real estate website. In 2023, the listing platform recorded about 214 million unique monthly users across its mobile applications and website. Despite holding an undisputed position as a market leader, Zillow's revenue has decreased since 2021. A probable cause for the decline is the plummeting of housing transactions and the negative housing sentiment. Performance and trends in the proptech market The proptech market has shown remarkable performance, with companies like Opendoor and Redfin experiencing significant stock price increase in 2023. This growth is particularly notable in the residential brokerage segment. Meanwhile, major players in proptech fundraising, such as Fifth Wall and Hidden Hill Capital, have raised billions in direct investment, further fueling the sector's development. As technology continues to reshape the real estate industry, online platforms like Zillow are likely to play an increasingly crucial role in how people search for and purchase homes. (1477916, 1251604)

CityPropStat provides aggregated property statistics for 795 cities and towns (i.e., Metropolitan and Micropolitan statistical areas) in the conterminous United States. These statistics include sum, mean, median, Gini index and entropy of residential floor space, cadastral parcel size, floor-area ratio, and property value, approximately for the reference year 2020, aggregated by building construction year in decadal steps (cumulative and incremental) from 1910 to 2020.Cumulative statistics: CBSA_Property_Statistics_1910-2020_cumulative.csvDecadal time slices statistics: CBSA_Property_Statistics_1910-2020_decadal_slices.csvData source: Zillow Transaction and Assessment Dataset (ZTRAX), provided to University of Colorado Boulder via a data share agreement (2016-2023).CityPropStats is a supplementary dataset to:Ortman S.G., et al. (accepted): "Changes in Agglomeration and Productivity are Poor Predictors of Inequality Across the Archaeological Record". Proceedings of the National Academy of Sciences (2025).Column description:cbsa_idCBSA GEOIDcbsa_nameFull namecbsa_typeCBSA type (metro vs micropolitan statistical area)year_fromEarliest year for selection interval of properties based on their construction yearyear_toLatest year for selection interval of properties based on their construction yearcbsa_popCBSA population or population change (US Census)tot_res_propsTotal residential propertiestot_res_area_sqkmTotal indoor area of residential properties in sqkmavg_res_area_sqmAverage indoor area of residential properties in sqmmedian_res_area_sqmMedian indoor area of residential properties in sqmq25_res_area_sqm25th percentile of indoor area of residential properties in sqmq75_res_area_sqm75th percentile of indoor area of residential properties in sqmgini_res_areaGini index of residential property indoor areatot_prop_value_usdTotal residential property value in USDmedian_prop_value_usdMedian residential property value in USDq25_prop_value_usd25th percentile of residential property values in USDq75_prop_value_usd75th percentile of residential property values in USDgini_prop_valueGini index of residential property valuestot_lot_area_sqkmTotal lot (cadastral parcel) area in sqkmavg_lot_area_sqmMean lot area in sqmmedian_lot_area_sqmMedian lot area in sqmq25_lot_area_sqm25th percentile of lot area in sqmq75_lot_area_sqm75th percentile of lot area in sqmgini_lot_areaGini index of lot areaavg_farMean floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesmedian_farMedian floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesq25_far25th percentile of floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesq75_far75th percentile of floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesentropy_res_areaShannon entropy of the indoor area of residential properties, based on propertiesentropy_prop_valueShannon entropy of the property value of residential properties, based on propertiesentropy_lot_areaShannon entropy of the lot size of residential properties, based on propertiesarea_completenessRatio of properties with a valid indoor area attribute [0,1]value_completenessRatio of properties with a valid property value attribute [0,1]lotsize_completenessRatio of properties with a valid indoor area, property value, and lot size attribute [0,1]area_value_completenessRatio of properties with a valid lot size attribute [0,1]area_value_lotsize_completenessRatio of properties with both a valid indoor area and property value attribute [0,1]

The median house price of residential real estate in California has increased notably since 2012. After a brief correction in property prices in 2022, the median price reached 756,200 U.S. dollars in December 2023.

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.

Median Property Value (Dollars) - B25077 County and State values are from the ACS 1 Year Survey Universe: Owner-occupied housing units

This dataset shows the Real Estate Across the United States (REXUS) is the primary tool used to track and manage the government's real property assets and to store inventory data, building data, customer data, and lease information. This dataset contains building inventory that consists of both owned and leased buildings with active and excess status.