Webmap of Allegheny municipalities and parcel data. Zoom for a clickable parcel map with owner name, property photograph, and link to the County Real Estate website for property sales information.

Property map viewer for the State of Tennessee that covers 85 of the 95 counties in Tennessee.This application allows for searching and displaying property ownership and location information for 87 counties in Tennessee. It is designed to work in concert with the Real Estate Assessment Data site operated by the Comptroller of the Treasury. The following counties are not available in this application but can be found on their own internet sites: Bradley, Davidson (Metro Nashville), Hamilton (Chattanooga), Knox (Knoxville), Montgomery (Clarksville), Rutherford(Murfreesboro), Shelby (Memphis), Sumner, Unicoi, and Williamson.

Web App. Use the tabs provided to discover information about map features and capabilities. Link to Metadata. A variety of searches can be performed to find the parcel of interest. Use the Query Tool to build searches. Click Apply button at the bottom of the tool.Query by Name (Last First) (e.g. Bond James)Query by Address (e.g. 41 S Central)Query by Locator number (e.g. 21J411046)Search results will be listed under the Results tab. Click on a parcel in the list to zoom to that parcel. Click on the parcel in the map and scroll through the pop-up to see more information about the parcel. Click the ellipse in the Results tab or in the pop-up to view information in a table. Attribute information can be exported to CSV file. Build a custom Filter to select and map properties by opening the Parcels attribute table:1. Click the arrow tab at the bottom middle of the map to expand the attribute table window2. Click on the Parcels tab3. Check off Filter by map extent4. Open Options>Filter5. Build expressions as needed to filter by owner name or other variables6. Select the needed records from the returned list7. Click Zoom to which will zoom to the selected recordsPlease note that as the map zooms out detailed layers, such as the parcel boundaries will not display.In addition to Search capabilities, the following tools are provided:MeasureThe measure tool provides the capabilities to draw a point, line, or polygon on the map and specify the unit of measurement.DrawThe draw tool provides the capabilities to draw a point, line, or polygon on the map as graphics. PrintThe print tool exports the map to either a PDF or image file. Click Settings button to configure map or remove legend.Map navigation using mouse and keyboard:Drag to panSHIFT + CTRL + Drag to zoom outMouse Scroll Forward to zoom inMouse Scroll Backward to zoom outUse Arrow keys to pan+ key to zoom in a level- key to zoom out a levelDouble Click to Zoom inFAQsHow to select a parcel: Click on a parcel in the map, or use Query Tool to search for parcel by owner, address or parcel id.How to select more than one parcel: Go to Select Tool and choose options on Select button.How to clear selected parcel(s): Go to Select Tool and click Clear.

Zillow Properties Listing dataset to access detailed real estate listings, including property prices, locations, and features. Popular use cases include market analysis, property valuation, and investment decision-making in the real estate sector.

Use our Zillow Properties Listing Information dataset to access detailed real estate listings, including property features, pricing trends, and location insights. This dataset is perfect for real estate agents, investors, market analysts, and property developers looking to analyze housing markets, identify investment opportunities, and assess property values.

Leverage this dataset to track pricing patterns, compare property features, and forecast market trends across different regions. Whether you're evaluating investment prospects or optimizing property listings, the Zillow Properties dataset offers essential information for making data-driven real estate decisions.

Dataset Features

• zpid: Unique property identifier assigned by Zillow.
• city: The name of the city where the property is located.
• state: The state in which the property is located.
• homeStatus: Indicates the current status of the property
• address: The full address of the property, including street, city, and state.
• isListingClaimedByCurrentSignedInUser: This field shows if the current Zillow user has claimed ownership of the listing.
• isCurrentSignedInAgentResponsible: This field indicates whether the currently signed-in real estate agent is responsible for the listing.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• price: Current asking price of the property.
• yearBuilt: The year the home was originally constructed.
• streetAddress: Specific street address (usually excludes city/state/zip).
• zipcode: The postal ZIP code of the property.
• isCurrentSignedInUserVerifiedOwner: This field indicates if the signed-in user has verified ownership of the property on Zillow.
• isVerifiedClaimedByCurrentSignedInUser: Indicates whether the user has claimed and verified the listing as the current owner.
• listingDataSource: The original source of the listing. Important for data lineage and trustworthiness.
• longitude: The longitudinal geographic coordinate of the property.
• latitude: The latitudinal geographic coordinate of the property.
• hasBadGeocode: This indicates whether the geolocation data is incorrect or problematic.
• streetViewMetadataUrlMediaWallLatLong: A URL or reference to the Street View media wall based on latitude and longitude.
• streetViewMetadataUrlMediaWallAddress: A similar URL reference to the Street View, but based on the property’s address.
• streetViewServiceUrl: The base URL to Google Street View or similar services. Enables interactive visuals of the property’s surroundings.
• livingArea: Total internal living area of the home, typically in square feet.
• homeType: The category/type of the home.
• lotSize: The size of the entire lot or land the home is situated on.
• lotAreaValue: The numerical value representing the lot area is usually tied to a measurement unit.
• lotAreaUnits: Units in which the lot area is measured (e.g., sqft, acres).
• livingAreaValue: The numeric value of the property's interior living space.
• livingAreaUnitsShort: Abbreviated unit for living area (e.g., sqft), useful for compact displays.
• isUndisclosedAddress: Boolean indicating if the full property address is hidden, typically used for privacy reasons.
• zestimate: Zillow’s estimated market value of the home, generated via its proprietary model.
• rentZestimate: Zillow’s estimated rental price per month, is helpful for rental market analysis.
• currency: Currency used for price, Zestimate, and rent estimate (e.g., USD).
• hideZestimate: Indicates whether the Zestimate is hidden from public view.
• dateSoldString: The date when the property was last sold, in string format (e.g., 2022-06-15).
• taxAssessedValue: The most recent assessed value of the property for tax purposes.
• taxAssessedYear: The year in which the property was last assessed.
• country: The country where the property is located.
• propertyTaxRate: The most recent tax rate.
• photocount: This column provides a photo count of the property.
• isPremierBuilder: Boolean indicating whether the builder is listed as a premier (trusted) builder on Zillow.
• isZillowOwned: Indicates whether the property is owned or managed directly by Zillow.
• ssid: A unique internal Zillow identifier for the listing (not to be confused with network SSID).
• hdpUrl: URL to the home’s detail page on Zillow (Home Details Page).
• tourViewCount: Number of times users have viewed the property tour.
• hasPublicVideo: This

This app is for viewing parcel data in the state of Tennessee

Interactive property web map viewer for Dedham, MA. View maps with the latest parcel information. Overlay zoning, special districts and other property related information.Addresses, Parcels, and other layers will be updated twice a year, on January 1 and July 1.

Property Viewing Software Market Outlook

The global property viewing software market size was valued at approximately USD 2.1 billion in 2023 and is expected to reach around USD 5.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.2% during the forecast period. This significant growth is driven by the increasing demand for innovative real estate solutions that enhance customer experience and streamline property management. The rise of digital transformation in the real estate industry, coupled with advancements in virtual reality (VR) and augmented reality (AR), is significantly contributing to the market's expansion.

One of the primary growth factors of the property viewing software market is the increasing adoption of digital tools among real estate agents and property managers. As the real estate sector becomes more competitive, professionals are leveraging advanced software solutions to offer virtual tours, schedule viewings, and provide detailed property information to potential buyers and tenants. This shift towards digitalization not only improves operational efficiency but also enhances customer engagement and satisfaction, thereby driving market growth.

Additionally, the COVID-19 pandemic has acted as a catalyst for the adoption of property viewing software. Social distancing measures and travel restrictions have led to a surge in demand for virtual property viewings. Homebuyers and tenants are increasingly relying on virtual tours to explore properties from the comfort and safety of their homes. This trend is expected to continue post-pandemic, as the convenience and efficiency of virtual viewings become a standard practice in the real estate industry.

Technological advancements in VR and AR are also playing a crucial role in the growth of the property viewing software market. These technologies enable realistic and immersive virtual tours, providing potential buyers with a comprehensive understanding of the property layout and features. As these technologies become more sophisticated and accessible, the adoption of property viewing software is expected to increase further, driving market growth during the forecast period.

From a regional perspective, the North American market is anticipated to hold a significant share due to the early adoption of advanced technologies and the presence of major market players. Europe is also expected to witness substantial growth, driven by increasing digitalization initiatives in the real estate sector. The Asia Pacific region is projected to experience the highest growth rate, supported by rapid urbanization and the growing real estate market in countries such as China and India.

Component Analysis

The property viewing software market is segmented by component into software and services. The software segment is expected to dominate the market, driven by the increasing demand for advanced and user-friendly property viewing solutions. These software solutions enable real estate agents and property managers to create virtual tours, schedule viewings, and manage property information efficiently. The integration of AI and machine learning in property viewing software is further enhancing its capabilities, making it an indispensable tool for real estate professionals.

Within the software segment, 3D virtual tour software is gaining significant traction. This type of software allows for the creation of immersive and interactive property tours, providing potential buyers with a detailed view of the property without the need for physical visits. The rising popularity of VR and AR technologies is further boosting the demand for 3D virtual tour software, contributing to the growth of the software segment.

On the other hand, the services segment includes implementation, consulting, and support services. These services are essential for the successful deployment and maintenance of property viewing software. As the adoption of these software solutions grows, the demand for professional services to ensure seamless integration and optimization is also expected to increase. This segment is likely to witness steady growth during the forecast period, driven by the need for technical support and consultancy services.

Moreover, the trend towards Software as a Service (SaaS) models is gaining momentum in the property viewing software market. SaaS-based solutions offer several benefits, including lower upfront costs, scalability, and ease of access. These advantages are encouraging real estate professionals to opt for cloud-based pr

Discover the power of McGRAW’s comprehensive data solutions, the industry's largest and most complete property and ownership database in the nation. Additionally, the mortgage industry's most sought-after analytics solutions for loan quality, risk management, compliance, and collateral valuation. These data sets are built to empower businesses with reliable, accurate, and actionable insights across the mortgage, real estate, and title sectors. With access to over 150 million records and 200 attributes, our expansive data repository enables you to streamline decision-making, optimize marketing, and enhance customer targeting across industries. Take a look at the comprehensive data sets below:

Mortgage Data Our mortgage data encompasses loan origination, borrower profiles, mortgage terms, and payment statuses, providing a complete view of borrowers and mortgage landscapes. We deliver details on active and historical mortgages, including lender information, loan types, interest rates, and mortgage maturity. This empowers financial institutions and analysts to predict market trends, assess creditworthiness, and personalize customer outreach with accuracy.

Property Data McGRAW’s property data includes detailed attributes on residential and commercial properties, spanning property characteristics, square footage, zoning information, construction dates, and much more. Our data empowers real estate professionals, property appraisers, and investors to make well-informed decisions based on current and historical property details.

Title Data Our title data service provides a clear view of ownership history and title status, ensuring comprehensive information on property chain-of-title, lien positions, encumbrances, and transaction history. This invaluable data assists title companies, legal professionals, and financial institutions in validating title claims, mitigating risks, and reducing time-to-close.

Ownership Data McGRAW ownership data supplies in-depth insights into individual and corporate property ownership, offering information on property owners, purchase prices, and ownership duration. This dataset is crucial for due diligence, investment planning, and market analysis, giving businesses the competitive edge to identify opportunities and assess ownership patterns in the marketplace.

Unmatched Data Quality & Coverage Our data covers the full spectrum of residential and commercial properties in the United States, with attributes verified for accuracy and updated regularly. From state-of-the-art technology to rigorous data validation practices, McGRAW’s data quality stands out, providing the confidence that businesses need to make strategic decisions.

Why Choose McGRAW Data?

Extensive Reach: Over 150 million records provide unparalleled depth and breadth of data coverage across all 50 states.

Diverse Attributes: With 200 attributes across mortgage, property, title, and ownership data, businesses can customize data views for specific needs.

Actionable Insights: Our data analytics tools and customizable reports translate raw data into valuable insights, helping you stay ahead in the competitive landscape.

Leverage McGRAW’s data solutions to unlock a holistic view of the mortgage, property, title, and ownership landscapes. For real estate professionals, lenders, and investors seeking data-driven growth, McGRAW provides the tools to elevate decision-making, enhance operational efficiency, and drive business success in today’s data-centric market.

Search Monmouth County tax parcel data by shape, block and lot, or buffer. The Monmouth County Property Viewer contains a complete set of tools used to query, select properties, and review property information. Each tool can be collapsed and expanded to provide more room for viewing the map at the user preference. Please review the Help ( i ) section for instructions on how to use this tool.

{{description}}

To access parcel information:Enter an address or zoom in by using the +/- tools or your mouse scroll wheel. Parcels will draw when zoomed in.Click on a parcel to display a popup with information about that parcel.Click the "Basemap" button to display background aerial imagery.From the "Layers" button you can turn map features on and off.Complete Help (PDF)Parcel Legend:Full Map LegendAbout this ViewerThis viewer displays land property boundaries from assessor parcel maps across Massachusetts. Each parcel is linked to selected descriptive information from assessor databases. Data for all 351 cities and towns are the standardized "Level 3" tax parcels served by MassGIS. More details ...Read about and download parcel dataUpdatesV 1.1: Added 'Layers' tab. (2018)V 1.2: Reformatted popup to use HTML table for columns and made address larger. (Jan 2019)V 1.3: Added 'Download Parcel Data by City/Town' option to list of layers. This box is checked off by default but when activated a user can identify anywhere and download data for that entire city/town, except Boston. (March 14, 2019)V 1.4: Data for Boston is included in the "Level 3" standardized parcels layer. (August 10, 2020)V 1.4 MassGIS, EOTSS 2021

This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.

A polygon feature class of all properties within Miami-Dade County that includes property appraiser data.Updated: Weekly-Sat The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

AT_2004_BACO

   File Geodatabase Feature Class


   Thumbnail Not Available

   Tags

   Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, A&T, Database, Assessors, Taxation




   Summary


   Serves as a basis for performing various analyses based on parcel data.


   Description


   Assessments & Taxation (A&T) Database from MD Property View 2004 for Baltimore County. The A&T Database contains parcel data from the State Department of Assessments and Taxation; it incorporates parcel ownership and address information, parcel valuation information and basic information about the land and structure(s) associated with a given parcel. These data form the basis for the 2004 Database, which also includes selected Computer Assisted Mass Appraisal (CAMA) characteristics, text descriptions to make parcel code field data more readily accessible and logical True/False fields which identify parcels with certain characteristics. Documentation for A&T, including a thorough definition for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab.


   It should be noted that the A&T Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the A&T Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields).


   A cursory review of the 2004 version of the A&T Database indicates that it has more accurate data when compared with the 2003 version, particularly with respect to dwelling types. However, for a given record it is not uncommon for numerous fields to be missing attributes. Based on previous version of the A&T Database it is also not unlikely that some of the information is inaccurate. This layer was edited to remove points that did not have a valid location because they failed to geocode. There were 5870 such points. A listing of the deleted points is in the table with the suffix "DeletedRecords."


   Credits


   Maryland Department of Planning


   Use limitations


   BES use only.


   Extent



   West -76.897802  East -76.335214 

   North 39.726520  South 39.192552 




   Scale Range

   There is no scale range for this item.

Our Realtor.com (Multiple Listing Service) dataset represents one of the most exhaustive collections of real estate data available to the industry. It consolidates data from over 500 MLS aggregators across various regions, providing an unparalleled view of the property market.

Features:

Property Listings: Each listing provides comprehensive details about a property. This includes its physical address, number of bedrooms and bathrooms, square footage, lot size, type of property (e.g., single-family home, condo, townhome), and more.

Photographs and Virtual Tours: Visuals are crucial in the property market. Most listings are accompanied by high-quality photographs and, in many cases, virtual or 3D tours that allow potential buyers to explore properties remotely.

Pricing Information: Listings provide asking prices, and the dataset frequently updates to reflect price changes. Historical price data, which includes initial listing prices and any subsequent reductions or increases, is also available.

Transaction Histories: For sold properties, the dataset provides information about the date of sale, the sale price, and any discrepancies between the listing and sale prices.

Agent and Broker Information: Each listing typically has associated details about the property's real estate professional. This might include their name, contact details, and affiliated brokerage.

Open House Schedules: Open house dates and times are listed for properties that are actively being shown to potential buyers.

1. Analytical Insights:

Market Trends: By analyzing the dataset over time, one can glean insights into market dynamics, such as the rate of price appreciation or depreciation in certain areas, the average time properties stay on the market, and seasonality effects.

Neighborhood Data: With comprehensive geographical data, it becomes possible to understand neighborhood-specific trends. This is invaluable for potential buyers or real estate investors looking to identify burgeoning markets.

Price Comparisons: Realtors and potential buyers can benchmark properties against similar listings in the same area to determine if a property is priced appropriately.

1. Utility:

For Industry Professionals and Analysts: Beyond buyers and sellers, the dataset is a trove of information for real estate agents, brokers, analysts, and investors. They can harness this data to craft strategies, predict market movements, and serve their clients better.

Gain an in-depth view of property characteristics for more than 157 million properties across the United States (also available at the state- and county-level).

The property viewing software market is experiencing robust growth, driven by increasing demand for efficient property management solutions and the widespread adoption of technology within the real estate sector. The market's expansion is fueled by several key factors. Firstly, the shift towards online property viewings, accelerated by the recent pandemic, has created a significant demand for user-friendly software that streamlines the entire process, from scheduling appointments to collecting feedback. Secondly, the rising popularity of cloud-based solutions offers accessibility and scalability, making them attractive to businesses of all sizes. This is further complemented by the increasing integration of smart home technologies, which enhances the overall property viewing experience and facilitates data collection for improved decision-making. While the on-premises segment still holds a considerable market share, cloud-based solutions are projected to witness faster growth due to their cost-effectiveness and flexibility. Competition within the market is fierce, with established players and emerging startups continuously innovating to offer enhanced features and functionalities. This includes features like virtual tours, 3D modeling, and automated communication tools. Geographical expansion, particularly within developing economies with burgeoning real estate markets, presents significant opportunities for market growth. However, challenges remain, including the need for robust cybersecurity measures to protect sensitive data and the ongoing requirement for user training and technical support to ensure seamless adoption and usage. Despite potential restraints such as initial investment costs and the need for continuous updates to remain competitive, the long-term outlook for the property viewing software market remains positive. The continued rise of e-commerce in the real estate industry, coupled with the growing preference for digital solutions, points to a consistently expanding market. The segmentation by application (residential, tenant) further showcases the versatility and wide applicability of this software, catering to both individual property owners and large-scale property management companies. As technological advancements continue, we can anticipate the integration of even more sophisticated features, like AI-powered analytics and predictive modeling, which will further optimize the property viewing process and enhance the overall user experience. This will ultimately drive further market expansion and adoption in the coming years.

AT_2004_HARF

   File Geodatabase Feature Class


   Thumbnail Not Available

   Tags

   Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, A&T, Database, Assessors, Taxation




   Summary


   Serves as a basis for performing various analyses based on parcel data.


   Description


   Assessments & Taxation (A&T) Database from MD Property View 2004 for Harford County. The A&T Database contains parcel data from the State Department of Assessments and Taxation; it incorporates parcel ownership and address information, parcel valuation information and basic information about the land and structure(s) associated with a given parcel. These data form the basis for the 2004 Database, which also includes selected Computer Assisted Mass Appraisal (CAMA) characteristics, text descriptions to make parcel code field data more readily accessible and logical True/False fields which identify parcels with certain characteristics. Documentation for A&T, including a thorough definition for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab.


   It should be noted that the A&T Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the A&T Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields).

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Please Note: Due to the extensive size of the parcel points file - download is recommended from the REST endpoint (https://geodata.md.gov/imap/rest/services/ PlanningCadastre/MD_PropertyData/MapServer/exts/MDiMapDataDownload/customLayers/0)This is a comprehensive point theme that incorporates parcel ownership and address information - parcel valuation information and basic information about the land and structure(s) associated with a given parcel. Data for the Parcel dataset are obtained from the State Department of Assessments and Taxation (SDAT) for all jurisdictions on monthly basis. For more information on the attribute definitions - please see the MDProperty View Subscriber's guide. Attribute definitions start on page 42. Last Updated: Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_PropertyData/MapServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This Image Service of Maryland Property Data allows for the manipulation of the display properties of the Statewide Tax Maps dataset. This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_PropertyData/ImageServer