Our US Home Ownership Data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes various data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

Snapshot img

US Real Estate Brokerage Software Market Size 2024-2028

The US real estate brokerage software market size is forecast to increase by USD 989.1 million at a CAGR of 9.33% between 2023 and 2028.

The real estate brokerage software market In the US is witnessing significant growth due to several key trends. Residential real estate is continually seeking ways to enhance operational efficiency and client services. companies are responding by introducing innovative real estate software solutions, such as cloud-based deployment, omnichannel communications, and predictive analytics. Furthermore, the availability of open-source real estate brokerage software solutions is providing more options for brokers, enabling them to choose solutions that best fit their business requirements. These trends are driving the growth of the market and are expected to continue shaping its future trajectory.
Cloud-based brokerage software is a popular choice due to its flexibility, scalability, and cost-effectiveness. ROI is a key consideration for brokerages, making software technologies that offer blockchain technology, smart contracts, and contract management software attractive. Internet and smartphone usage continues to rise, driving the demand for user-friendly, mobile-responsive software. The market is expected to grow, offering significant opportunities for companies providing innovative, efficient, and secure solutions.

What will be the size of the US Real Estate Brokerage Software Market during the forecast period?

Request Free Sample

The real estate brokerage industry In the US is experiencing significant digital transformation, with an increasing adoption of software solutions to streamline operations and enhance customer experiences. Digital technologies, including CRM, transaction management, marketing automation, property listing management, and lead generation tools, are becoming essential for real estate brokerages to remain competitive. The complexity of real estate transactions necessitates smart solutions that offer centralized data management, security, and automation.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Customer relationship management
  Transaction management
  Lead generation
  Property management
  Others


Deployment

  Cloud based
  On-premises


Application

  Residential
  Commercial
  Industrial


Geography

  US

By Type Insights

The customer relationship management segment is estimated to witness significant growth during the forecast period.

Real Estate Customer Relationship Management (CRM) software In the US market is a vital tool for brokers and agents to manage client interactions and streamline business processes. CRM systems facilitate lead tracking, client data management, and automated communication workflows, allowing real estate professionals to analyze customer data, schedule follow-ups, and personalize engagement. The increasing importance of customer experience and personalized service In the competitive real estate sector is driving the growth of CRM software.

Additionally, remote work and cloud-based solutions, data analytics, integration with other tools, and emerging technologies like Augmented Reality (AR), Virtual Reality (VR), Machine Learning (ML), and Artificial Intelligence (AI) are enhancing the functionality and efficiency of CRM software In the real estate industry. Enhanced data security features are also crucial for protecting sensitive client information.

Get a glance at the market share of various segments Request Free Sample

The customer relationship management segment was valued at USD 401.70 million in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in the adoption of US Real Estate Brokerage Software Market?

The increasing focus of real estate brokers on enhancing operational efficiency and client services is the key driver of the market.

The Real Estate Brokerage Software Market In the US is witnessing significant growth due to the implementation of digital solutions that streamline operations and enhance customer service. These software solutions cater to the unique requirements of real estate brokerages by offering features such as Customer Relationship Management (CRM), Transaction Management, Marketing Automation, Property Listing Management, and Lead Generation. BoomTown offers an all-in-one plat

ListBuilder combines 600+ property data, MLS, home ownership data, mortgage data, demographic data, geographic data, and contact data points within the self-service ListBuilding tool.

Easily search filters and narrow your list results to identify the U.S. homeowners, distressed property owners, potential borrowers, commercial property owners, investors, or home service consumers that best fit your target profile. All your property data and home ownership data in one place!

ListBuilder is used by marketing agencies, real estate professionals, home service providers, and operations teams to improve operations and optimize sales effectiveness.

Backed by the industries most accurate and comprehensive property and skip tracing sources (BatchData APIs), ListBuilder offers more granular targeting capabilities, with top-tier contact data accuracy.

NZ Property Boundaries provides the best available representation of property boundaries in New Zealand.

Three data sources have been combined to create this property boundaries layer:

• Rating Units - These are the main data source, taken from the NZ Properties: Unit of Property layer. This layer uses District Valuation Roll data to identify rateable properties and aggregates all spatialised titles associated with a Rating Unit into a single property.
• Titles - Where there are no spatialised rating units, spatialised titles from the NZ Property Titles layer are included, if they are available.
• Parcels – Where there are no rating units or spatialised titles, parcels from the NZ Primary Parcels layer are used to fill the in the remaining gaps.

NZ Property Boundaries is updated on a weekly basis. For more information please refer to the NZ Property Boundaries Data Dictionary

Please note: NZ Property Boundaries is an interim solution while work continues to develop the NZ Properties: Unit of Property layer. For more information about NZ Properties: Unit of Property, please see the New Zealand Property Spine on the Toitū Te Whenua LINZ website.

APIs and web services This dataset is available via ArcGIS Online and ArcGIS REST services, as well as our standard APIs. LDS APIs and OGC web services ArcGIS Online map services

The Military Bases dataset was last updated on October 23, 2024 and are defined by Fiscal Year 2023 data, from the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas world-wide. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment. Only sites reported in the BSR or released in a map supplementing the Foreign Investment Risk Review Modernization Act of 2018 (FIRRMA) Real Estate Regulation (31 CFR Part 802) were considered for inclusion. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD. While every attempt has been made to provide the best available data quality, this data set is intended for use at mapping scales between 1:50,000 and 1:3,000,000. For this reason, boundaries in this data set may not perfectly align with DoD site boundaries depicted in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards, will remain compliant when this data is incorporated. Boundary data is most suitable for larger scale maps; point locations are better suited for mapping scales between 1:250,000 and 1:3,000,000. If a site is part of a Joint Base (effective/designated on 1 October, 2010) as established under the 2005 Base Realignment and Closure process, it is attributed with the name of the Joint Base. All sites comprising a Joint Base are also attributed to the responsible DoD Component, which is not necessarily the pre-2005 Component responsible for the site. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529039

The origin of terrestrial biota in Antarctica has been debated since the discovery of springtails on the first historic voyages to the southern continent more than 120 years ago. A plausible explanation for the long-term persistence of life requiring ice-free land on continental Antarctica has, however, remained elusive. The default glacial eradication scenario has dominated because hypotheses to date have failed to provide a mechanism for their widespread survival on the continent, particularly through the Last Glacial Maximum when geological evidence demonstrates that the ice sheet was more extensive than present. Here, we provide support for the alternative nunatak refuge hypothesis – that ice-free terrain with sufficient relief above the ice sheet provided refuges and was a source for terrestrial biota found today. This hypothesis is supported here by an increased understanding from the combination of biological and geological evidence, and we outline a mechanism for these refuges d...

This dataset contains Housing Affordability Supply and Demand Data broken down by very low, low and moderate income brackets.

This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled Housing Affordability Demand and Supply by Local Government Area.

Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census.

The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress.

Income bands are based on household income.

High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets.

Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

Field Definitions: LGA Name: 2011 Local Government Areas are an ABS approximation of officially gazetted LGAs as defined by each State and Territory Local Government Department. The boundaries produced for LGAs are constructed from allocations of whole Mesh Blocks and reviewed annually.

Tenure Type: This is a consolidation of the census tenure and landlord types. The following definitions have been used: Rented: Private and not stated, this is comprised of rented dwellings (excluding rent free) where the Landlord type is a Real Estate Agent, Person not in the same household or where the Landlord type is not stated Rented: Other, this is comprised of rented dwellings (excluding rent free) where the Landlord type is Employer (Govt or other), Housing cooperative,community,church group, or Residential park (incl caravan parks and marinas) Rented: TOTAL, this is comprised of the sum of Rented: Public, Rented: Private and not stated, and Rented: Other landlord. Please note that this field should be excluded when summing the total households Other tenure types: this is comprised of dwellings that are owned outright, occupied rent free, occupied under a life tenure scheme, other tenure types and tenure type not stated. Total Households: this is comprised of the sum of Being purchased (incl rent,buy), Rented: TOTAL and Other tenure types.

Total - Includes all South Australian households.

Source: The data was downloaded from data.sa.gov.au and spatialised by the Adelaide Data Hub using the ABS 2011 Local Government Areas dataset.

This dataset includes one file for each of the 51 counties that were collected, as well as a CA_Merged file with the parcels merged into a single file.Note – this data does not include attributes beyond the parcel ID number (PARNO) – that will be provided when available, most likely by the state of California.DownloadA 1.6 GB zipped file geodatabase is available for download - click here.DescriptionA geodatabase with parcel boundaries for 51 (out of 58) counties in the State of California. The original target was to collect data for the close of the 2013 fiscal year. As the collection progressed, it became clear that holding to that time standard was not practical. Out of expediency, the date requirement was relaxed, and the currently available dataset was collected for a majority of the counties. Most of these were distributed with minimal metadata.The table “ParcelInfo” includes the data that the data came into our possession, and our best estimate of the last time the parcel dataset was updated by the original source. Data sets listed as “Downloaded from” were downloaded from a publicly accessible web or FTP site from the county. Other data sets were provided directly to us by the county, though many of them may also be available for direct download. Â These data have been reprojected to California Albers NAD84, but have not been checked for topology, or aligned to county boundaries in any way. Tulare County’s dataset arrived with an undefined projection and was identified as being California State Plane NAD83 (US Feet) and was assigned by ICE as that projection prior to reprojection. Kings County’s dataset was delivered as individual shapefiles for each of the 50 assessor’s books maintained at the county. These were merged to a single feature class prior to importing to the database.The attribute tables were standardized and truncated to include only a PARNO (APN). The format of these fields has been left identical to the original dataset. The Data Interoperablity Extension ETL tool used in this process is included in the zip file. Where provided by the original data sources, metadata for the original data has been maintained. Please note that the attribute table structure changes were made at ICE, UC Davis, not at the original data sources.Parcel Source InformationCountyDateCollecDateCurrenNotesAlameda4/8/20142/13/2014Download from Alamenda CountyAlpine4/22/20141/26/2012Alpine County PlanningAmador5/21/20145/14/2014Amador County Transportation CommissionButte2/24/20141/6/2014Butte County Association of GovernmentsCalaveras5/13/2014Download from Calaveras County, exact date unknown, labelled 2013Contra Costa4/4/20144/4/2014Contra Costa Assessor’s OfficeDel Norte5/13/20145/8/2014Download from Del Norte CountyEl Dorado4/4/20144/3/2014El Dorado County AssessorFresno4/4/20144/4/2014Fresno County AssessorGlenn4/4/201410/13/2013Glenn County Public WorksHumboldt6/3/20144/25/2014Humbodt County AssessorImperial8/4/20147/18/2014Imperial County AssessorKern3/26/20143/16/2014Kern County AssessorKings4/21/20144/14/2014Kings CountyLake7/15/20147/19/2013Lake CountyLassen7/24/20147/24/2014Lassen CountyLos Angeles10/22/201410/9/2014Los Angeles CountyMadera7/28/2014Madera County, Date Current unclear likely 7/2014Marin5/13/20145/1/2014Marin County AssessorMendocino4/21/20143/27/2014Mendocino CountyMerced7/15/20141/16/2014Merced CountyMono4/7/20144/7/2014Mono CountyMonterey5/13/201410/31/2013Download from Monterey CountyNapa4/22/20144/22/2014Napa CountyNevada10/29/201410/26/2014Download from Nevada CountyOrange3/18/20143/18/2014Download from Orange CountyPlacer7/2/20147/2/2014Placer CountyRiverside3/17/20141/6/2014Download from Riverside CountySacramento4/2/20143/12/2014Sacramento CountySan Benito5/12/20144/30/2014San Benito CountySan Bernardino2/12/20142/12/2014Download from San Bernardino CountySan Diego4/18/20144/18/2014San Diego CountySan Francisco5/23/20145/23/2014Download from San Francisco CountySan Joaquin10/13/20147/1/2013San Joaquin County Fiscal year close dataSan Mateo2/12/20142/12/2014San Mateo CountySanta Barbara4/22/20149/17/2013Santa Barbara CountySanta Clara9/5/20143/24/2014Santa Clara County, Required a PRA requestSanta Cruz2/13/201411/13/2014Download from Santa Cruz CountyShasta4/23/20141/6/2014Download from Shasta CountySierra7/15/20141/20/2014Sierra CountySolano4/24/2014Download from Solano Couty, Boundaries appear to be from 2013Sonoma5/19/20144/3/2014Download from Sonoma CountyStanislaus4/23/20141/22/2014Download from Stanislaus CountySutter11/5/201410/14/2014Download from Sutter CountyTehama1/16/201512/9/2014Tehama CountyTrinity12/8/20141/20/2010Download from Trinity County, Note age of data 2010Tulare7/1/20146/24/2014Tulare CountyTuolumne5/13/201410/9/2013Download from Tuolumne CountyVentura11/4/20146/18/2014Download from Ventura CountyYolo11/4/20149/10/2014Download from Yolo CountyYuba11/12/201412/17/2013Download from Yuba County