The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

This statistic shows the share of commercial real estate which was flexible worldwide in 2018 and a projection for 2030. Flexible real estate is space which can be easily adapted for different property uses, e.g. warehouse converted into office space or a showroom. In 2018, only 2.5. percent of global commercial real estate was flexible, but this was set to increase to ** percent by 2030.

This dataset comes from Zillow and provides a comprehensive look at U.S. housing market trends from 2018 to May 2024. It includes detailed data on median home values, average days outstanding for property sales, and their impact on reducing prices in several cities. This dataset is ideal for analyzing the correlation between home values, time to market, and price adjustments, offering valuable insights for real estate professionals, economists, and data analysts interested in the dynamics of the U.S. housing market.

About the license, taken from the Zillow website:

“For research and academic projects, we provide the following metrics that have more flexible Terms of Use regarding data storage and manipulation – https://www.zillow.com/research/data/”

In 2024, the value of commercial real estate in Germany was estimated at nearly two trillion U.S. dollars, up from approximately *** trillion U.S. dollars in 2018. Germany was the largest commercial real estate market in Europe and one of the few that grew during the coronavirus (COVID-19) crisis.

The commercial real estate market in Mexico has grown since 2018, despite contracting during the coronavirus pandemic. As of the last month of 2024, the value of commercial real estate in Mexico was estimated at close to *** billion U.S. dollars, up from *** billion U.S. dollars the year before.

The Market Value Analysis (MVA) is a tool designed to assist the private market and government officials to identify and comprehend the various elements of local real estate markets. It is based fundamentally on local administrative data sources. By using an MVA, public sector officials and private market actors can more precisely craft intervention strategies in weak markets and support sustainable growth in stronger market segments.

This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

This dataset, maintained by the Office of Policy and Management, includes real estate sales in Connecticut from 2001 to 2022, with a sales price of $2,000 or more. Each record provides details such as the town, property address, date of sale, property type (residential, apartment, commercial, industrial, or vacant land), sales price, and property assessment.

Data are collected annually for each Grand List (GL) year, spanning October 1 through September 30. For example, the 2018 GL covers sales from October 1, 2018, to September 30, 2019, in compliance with Connecticut General Statutes (sections 10-261a and 10-261b).

Note: Municipalities undergoing property revaluation are not required to submit sales data for the 12 months following implementation. Covering over two decades of data, this dataset is a vital resource for tracking real estate trends and property valuations across Connecticut.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment.

Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019.

This statistic shows the volume of real estate sold worldwide in 2018, by region. In that year, the value of real estate sales in the Americas amounted to ***** billion U.S. dollars.

This data set represents real estate market announcements monitoring data in Latvia in 2023. The data was collected from online ads site ss.com. The database contains 209,9 thousand ads and consists of 24 groups of data (type of deal, price, characteristics and address of real estate, etc.). The data reflects the dynamics of price changes by months (at the beginning of the month) in 2023. Monitoring continued in 2023 was started in 2018. In 2023 the new impuls of data application was found. It is associated with the possibility of planning the urban environment taking into account the transition of transport to environment friendly fuel types.

United States Existing Home Sales: US data was reported at 420,000.000 Unit in Sep 2018. This records a decrease from the previous number of 539,000.000 Unit for Aug 2018. United States Existing Home Sales: US data is updated monthly, averaging 436,000.000 Unit from Jan 1999 (Median) to Sep 2018, with 237 observations. The data reached an all-time high of 753,000.000 Unit in Jun 2005 and a record low of 218,000.000 Unit in Jan 2009. United States Existing Home Sales: US data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s USA – Table US.EB005: Existing Home Sales.

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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?

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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.

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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

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

This statistic shows the annual growth rate of the real estate advertising market in China from 2011 to 2018. In 2018, the real estate advertising in China decreased by ** percent compared to the previous year.

Graph and download economic data for Market Hotness: Page View Count per Property in Middlesex County, MA (LDPEPRYYCOUNTY25017) from Aug 2018 to Oct 2025 about Middlesex County, MA; Boston; MA; listing; and USA.

The Market Value Analysis is a comprehensive study of the residential real estate using data from 2016 through 2018. Research led by the New Orleans Redevelopment Authority and The Reinvestment Fund