What is property data?
Property data provides intelligence on properties, including ownership, property address, property tax records, transactions, commercial and residential property, rental property, investment in real estate, land use, building permits, legal descriptions, owner name, and property records. Real estate investors use property information to make data-driven investment decisions. Personal property owners, commercial property owners, tenants, leaseholders, occupants, brokers, lawyers, and others in the real estate and construction industries utilize property information for a variety of use cases.
Where does the data come from?
Property information comes from both commercial and public sources. Government records, public records, archives, local authority records, broker or realtor websites, tax maps, and databases built over a long time provide the data.
Most of the data is still in non-digital format. Vendors collect physical documents, curate data from multiple listing services, refer to online commercial records, and use other related references such as foreclosure reports and assessment data.
What types of attributes should I expect?
Property information attributes have three different types:
- Land and ownership data: It describes ownership details, tenure, local authority, and plot density. It also details the physical attributes of the property. The ownership data lists name and contact information, ownership type (private, collective, or common), boundaries, location, value, zone, postal code, county, number and types of buildings on the property, map, points of interest dispersion, and points of interest quality measures.
Additional attributes that affect the valuation may also be part of this data, including climate, geospatial data, as well as soil quality and water bodies in the case of agricultural land. The demographic profile of the area, along with the crime rate, politics, and environmental issues can also get included.
- Real estate listing data: It covers the financial attributes such as the average property value, average property lot size, property characteristics, mortgages and loans in the area, and rental statistics. Real estate listing data provides intelligence on the patterns of buying, investment, and development.
- Real estate demographic data: This data provides information on the property sellers and the location of the properties.
Realtors and buyers are also interested in the property history information, specifically for buildings, to help make informed decisions about the required property upkeep and renovations.
How should I test the quality of the data?
As the data is curated from diverse sources, assessing the accuracy is the primary concern while testing for quality. The information provided by local authorities and government records is typically complete and accurate. However, data from other sources may be inconsistent, inaccurate, or incomplete. Some of the property-related information is in the non-digital format, and the accuracy can get lost in the conversion.
Testing the quality of the data involves cross-checking with multiple sources to find missing data, inconsistencies, and other quality issues. The standardization of various data attributes is also a key concern as different regions may not have a common terminology or official measurement metrics.
To test the quality of the data:
- Validate data from one source with other sources for accuracy, consistency, and completeness.
- Confirm that the information is currently valid and recently updated.
- Verify that the data is standardized and the format is relevant to your use case.
Who uses property data?
Property information is used widely by a number of different industries.
Buyers, developers, and real estate investors use it to make buying decisions. Various analytic cases use this data for predicting trends, gauging the transparency in the industry, and monitoring the property market.
Retail and CPG industries use this data for deciding on their expansion strategy and store location planning.
Property rights lawyers use it routinely as a part of their work. Other lawyers may also use this data for specific cases such as divorce proceedings.
Governments use this data to make decisions on zoning laws, infrastructure development projects, and efforts to improve the local economies.
What are the common challenges when buying the data?
The biggest challenge in the case of property data is assuring its accuracy. Information collected from different sources may not be accurate or consistent. Some of the information can also be outdated, and timeliness is another challenge. Information such as the ownership history needs to be verified with multiple sources to ensure that the data presented is correct.
- Data accuracy: While primary government sources provide accurate data, some secondary sources deliver inconsistent or inaccurate data. Information derived from property-related physical documents can also have a loss of accuracy during digitization. Property information contains the assessment of the current state of the property and the required renovations. These assessments can be very subjective, depending on the proficiency of the assessor, and may paint a widely inaccurate picture.
- Data timeliness: Property information attributes vary in nature, and factors such as local demographic data keep regularly changing. For reliable investment decisions, the analysis must use recently updated property information. Testing data timeliness is critical and challenging, as the information from different sources may not be immediately available.
- Standardization: Standardization of various data attributes of property information is also a challenge. Measurement metrics, acceptable thresholds, terminology, addresses, and legal constraints change depending on the region. Ensuring that the metrics match the requirement is essential to arrive at the right buying or renting decisions.
What are similar data types to property information?
Property information is similar to commercial real estate data, construction projects data, real estate market data, and mortgage data. These data categories are commonly used for property valuation and property investment.
You can find a variety of examples of geospatial data in the Explorium Data Gallery.
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What are the most common use cases?
The most common use cases include property valuation, property investment, real estate intelligence, and store location planning.
- Property Valuation: Data-driven valuation of property is a complex process, leveraging various categories of data from the real estate market. Valuation of a specific property drives the buying, investing, or renting decisions. Realtors, investors, renters, and property developers use this data to identify the best properties for investment.
- Property Investment: Real estate buyers and agents make their investment decisions based on the attributes of a property, including valuation, market conditions, and ownership information. Good pricing models provide accurate price predictions to achieve a higher ROI. Property information contributes to the data powering the analysis.
- Real Estate Intelligence: It provides deep insights on the real estate market, with actionable insights on buying, selling, holding, or renting. Real estate intelligence uses technology and various data categories, including property information, to drive timely decisions and get a better ROI.
- Store Location Planning: The location of a store directly impacts its performance and profits. Retailers use a wide variety of data to decide the best store locations and maximize their profits. A good model uses historical data of locations and profits of the stores, and augments it with other external data. The complex modeling leverages demographic data, points of interest data, geospatial data, climate data, and various other categories of real estate data to decide on the location.
Which industries commonly use this type of data?
A lot of government agencies commonly track this data for assessing the property market and the local economy. Several organizations in the property business use this data extensively for powering analytical cases. Other industries that benefit from using this data in their analysis include retail, CPG, home improvement, travel, hospitality, leisure, entertainment, healthcare, manufacturing, and hi-tech.
How can you judge the quality of your vendors?
The accuracy and timeliness of data are directly dependent on the sources used, and a good quality vendor will work with only trusted sources. You can evaluate the vendor quality quickly with case studies and demos, as they present data in action. On the other hand, customer reviews, testimonials, and ratings offer insights into the vendor relationships with the customers. All this information is usually available on vendor websites. Interaction with vendor reps is the next step, which helps you understand the full capabilities and resolve your queries.
- Case studies: They demonstrate how the vendor data resolved the challenges particular projects faced. They also provide information on the quality of data and range attributes the vendor delivers. You can get a good idea from case studies if the datasets match your requirements and if custom datasets are available.
- Demo: Demos provide insights into the integration capabilities and quality of datasets offered by the vendor. Many vendors offer sample datasets, which can also give you a good assessment of how they match your requirements.
- Customer testimonials and ratings: Mostly available on vendor websites, these ratings give a glimpse into the strengths and weaknesses of the vendor. Customer reviews provide information about the scale of datasets, availability of custom datasets on request, vendor involvement in implementation, and vendor commitment to projects.
- Interacting with vendor reps: This is a genuine opportunity to resolve all your queries and evaluate if the vendor can deliver what you need. The knowledgeability of the vendor reps is also a good measure of vendor competence.