What is Real Estate Data? Examples, Datasets and Providers

Real Estate Use Cases in Detail

As we’ve seen, real estate data can be an asset whether you’re buying, selling or investing in property and land. It’s an especially versatile data type.

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Here’s some of the use cases which can inform your decisions and optimize your spending:

1. Predictive analytics

In real estate, companies can analyze the total condition of the building, its age, how solid it is, all reconstructions that were made before, and information about the current owner to get a correct property estimation. As reported by McKinsey & Company, machine learning was recently used to forecast the three-year rent per square foot for multifamily buildings in Seattle with an accuracy rate of over 90%.

2. Increased industry transparency

Real estate data provides transparency in business processes. As a result, real estate companies can make important decisions faster because they are provided with accurate, current and objective data.

3. Real time monitoring and communication

Agents can contact potential buyers at the right time when people are going to buy or sell real property. In turn, agents can also monitor trends and actual prices because real estate data provides them with the opportunity to offer clients more profitable variants.

4. Customised strategization

Real estate data tells real estate insurance companies what type of insurance people in their region need in advance, meaning they can then create custom plans.

These use cases aside, real estate data, like all data types, comes with its own set of challenges. Here is what you should look out for when investing in a third-party dataset.

• Does the provider have good reviews?
• Does the dataset cover the locations you’re interested in?
• Can the data be integrated readily into your company and software?
• Is the data priced in a way that’s feasible for your company?

How Is Real Estate Data Collected?

Real estate data is gathered from multiple sources, such as property listings, MLS aggregators, public records, and web scraping tools. Many providers also use advanced AI-driven technologies to extract and compile information from leading real estate websites. We recommend exploring datasets that provide coverage for your target region and use case to ensure relevance.

Categories of Real Estate Data

Real estate data can be divided into the following four categories:

Residential real estate data

Residential real estate data about areas that are designed for people to live in e.g. family homes, apartments, flats, lofts

Commercial real estate data

Commercial real estate data about properties which are designed to generate income for the owner e.g. shopping centres, hotels, offices

Industrial real estate data

Data about areas and buildings designed for company use, to research, design, produce and distribute physical goods e.g. warehouses, production facilities, logistical centres, laboratories

Land data

Data about ranches, farms and vacant land. Real estate investors buy vacant land with the view of the land being used for residential purposes in the future, which multiplies the value of the estate significantly.

So, whichever type of real estate you’re interested in, there’s data out there for you. Now let’s take a closer look at the attributes of this real estate data.

Attributes of Real Estate Data

There are a number of factors which real estate data providers consider when compiling a land and ownership database. These include:

Geolocation data. Where the property is situated. The information can be presented in the form of an address, or through coordinates.

Site or building coverage – The percentage of the lot area that is covered by the building area, which includes the total horizontal area when viewed in plan.

Plot density. A calculation which expresses the number of dwelling units per acre based on the gross lot area, factoring in thoroughfares, public parks or other public areas. City authorities often refer to plot density to express the minimum and maximum amount of land which is to be devoted to residential purposes.

Site area. The floor area ratio of the site in question. It’s calculated by dividing the total gross building floor area (square feet) by the land area of the lot. In cases where a project site encompasses several buildings on several lots, the floor area ratio may be combined and averaged over the entire project site.

Local authority. The local authority is the city or county in which the property is situated. Some land or assets are owned by federal, state, or local governments as well.

Tenure. Land tenure is the legal term for ‘ownership’: where land is owned by an individual, who is said to ‘hold’ the land. It determines who can use land, for how long, and under what conditions. Tenure can be based both on official laws and policies or on more informal arrangements.

Flood risk. Some properties are at greater risk than others of flooding. When a property is located in a confirmed floodplain, it can have a serious effect on the cost of property ownership, so it’s an important part of land and ownership data.

Property type. The property type refers to whether the property is intended to be used as a house, apartment, industrial facility, or for commercial real estate.

Financing. Property financing refers to the means used by the buyer when the property was purchased, including whether there is an outstanding mortgage.

Taxes. Property taxes are paid on property owned by an individual or corporation depending on the property’s value. It is calculated by the local government where the property is located and paid by the owner of the property.

How Can You Ensure the Quality of Real Estate Data?

Data quality is ensured through rigorous validation processes, such as cross-referencing with official records, monitoring data accuracy rates, and filtering out inconsistencies. High-quality datasets often report match rates and regular updates to maintain their reliability. Choose providers with a proven track record of accurate and up-to-date of Real estate data.

Is Real Estate Data Secure?

Real estate data security involves adhering to industry standards for data protection, including encryption and anonymization where needed. Delivery methods like SFTP, APIs, and secure storage ensure confidentiality. At Datarade, we are very focused on compliance with data privacy regulations like GDPR and CCPA, ensuring that all datasets listed on our platform meet the highest standards for security and ethical usage. We recommend working with providers who transparently demonstrate robust security certifications and protocols to safeguard sensitive information effectively.

How Is Real Estate Data Delivered?

Real estate data is delivered in formats like CSV, JSON, XML, or through APIs, allowing seamless integration into your systems. Delivery frequencies range from real-time updates to monthly or on-demand schedules. For ease of use, look for datasets that align with your preferred delivery format and system compatibility.

How Much Does Real Estate Data Cost?

The cost of real estate data depends on factors such as the dataset’s size, geographic coverage, update frequency, and customization. Pricing models may include one-off purchases, monthly subscriptions, or usage-based options. Many providers offer free samples to help you evaluate the data’s suitability for your needs.