Property Data API for Real Estate Analytics
Meta: ATTOM’s Property Data API is the ideal solution for real-time analytics applications.
Contents:
How ATTOM’s Property API Facilitates Real Estate Analytics
- Facing the Challenge of Data Fragmentation
How ATTOM Eliminates Data Fragmentation
- Facing the Challenge of Data Interruption
How ATTOM Eliminates Data Interruption
- Managing Scale Limitations
How ATTOM Manages Scale Limitations
A Dive into the Core Data Sets for Property Platforms
Property Datasets
Geospatial Datasets
Hyper-Local Neighborhood and Environmental Datasets
ATTOM’s Property Data API is Designed for Proptech
FAQs
Product teams and real estate developers work hard to make sure their user interfaces (UIs) and systems work smoothly and with minimal friction. But if the data they are providing through those systems and UIs are unreliable, their technology and infrastructure prowess may be mute.
Scalable, quality data are the building blocks of core analytics offered by proptech platforms, and ATTOM has a corner on that market. ATTOM’s Property Data API is the ideal solution for powering real-time analytics because it provides nationwide property, neighborhood, valuation, and geospatial data all from one source.
How ATTOM’s Property Data API Facilitates Real Estate Analytics
A responsive proptech or real estate data platform for analytics application requires infrastructure powerful enough to process massive amounts of reliable data quickly. If the data sourced by the platform lacks quality, it doesn’t matter how sophisticated or efficient the infrastructure is, the results for clients using the platform will be disappointing.
The problems faced by developers in providing quality data include data fragmentation caused by pulling data from multiple real estate data sources, unreliable supply chains linking the sources to the receiving platform, and limitations regarding the amount of data they can process and receive.
- Facing the Challenge of Data Fragmentation
Because many developers collect data from various sources, they suffer from data fragmentation. The disparate sources tapped could include county records, different geocoders, or various valuation models.
The fragmentation occurs when developers who use different data sources experience delays between when a data point changes in the real world and when it is actually available in their system or application (high latency delayed data). For example, a deed transfer registered by a county clerk might take hours or even weeks to show up on a proptech platform.
For a property investor who relies on a data provider, this delay could mean missing out on time-sensitive deals and losing out to a competitor who has access to real-time, standardized data.
How ATTOM Eliminates Data Fragmentation
ATTOM’s Property Data API acts as a “delivery” layer for other data platforms. ATTOM’s platform is a central point for property data aggregation and is a one-stop-shop for real estate data. Developers only need to source data from one place, and the data they receive are already normalized and standardized. Developers can avoid chaotic data and receive “AI-ready” products.
ATTOM smooths data in many ways. IDs are used to cross-reference records, such as property characteristics, boundary lines, and local community data (like school and crime metrics). This avoids messy guesswork like address matching for records.
ATTOM uses a multi-step data processing program to manage conflicting information. For example, if multiple local sources provide different information for the same property, a program algorithmically prioritizes the most reliable source to construct a single, complete view. ATTOM also standardizes terminology. For example, it can understand different county tax schemas.
Regarding delivery, ATTOM’s delivery systems remove much of the fragmentation that can occur when data teams download data and stitch files together from different sources. In addition to its API, ATTOM uses cloud-sharing platforms like Snowflake and Databricks to avoid traditional file-transfer bottlenecks.
- Facing the Challenge of Data Interruption
Data providers have their own supply chain. For example, a platform might pull tax data from a county assessor’s website, clean it so it is readable, and load it into a database. But if the county assessor changes its infrastructure, website, or API, the supply of that data can break. This can create missing data and gaps in historical records, frustrating end-users.
How ATTOM Eliminates Data Interruption
For organizations with different architectural needs, ATTOM also delivers data through Snowflake, Databricks, bulk delivery, and other enterprise options.
Traditionally, companies built their own “extract, transform, and load” systems that pulled data from vendors like ATTOM. These systems often failed due to system timeouts or infrastructure changes.
Cloud-based solutions like Snowflake and Databricks remove the need for an extraction pipeline. A client queries ATTOM directly, and if a change occurs in a county assessor’s data, it is handled by ATTOM. The change does not obstruct cloud data delivery.
Additionally, if a data source for property data goes offline for some reason, ATTOM uses an algorithm to automatically rely on alternatively sourced data to keep the data flow going.
- Managing Scale Limitations
Not all proptech platforms are built to manage the sheer bulk of data that they might be sent by data providers. A platform might be able to handle a small amount of data, but it could crash when deliveries exceed a certain threshold.
For example, tracking a few hundred properties in a single zip code requires minimal computing power, but scaling to 150+ million parcels across the United States can totally paralyze a system. For geodata, a single map search could take minutes to load, and geolocation queries could go unanswered
How ATTOM Manages Scale Limitations
ATTOM delivers data on a massive scale and for over 160+ million U.S. property parcels. With billions of historical rows, traditional data delivery methods can crawl to a halt.
ATTOM’s Property Data API powers real-time apps, but ATTOM also aligns with broader architectural needs if teams scale into bulk cloud data shares. Snowflake and Databricks require no movement of data. The datasets live in ATTOM’s cloud environment, and clients run queries against it using their own Snowflake or Databricks computing power.
If a client needs to scale up from analyzing a single county to the entire country, they simply scale up their virtual warehouse on demand, with no data storage limitations.
Columnar big data formats, like Parquet and GeoParquet, are options for clients needing bulk file transfers (e.g., via SFTP) for in-house databases. Standard formats like CSV or Excel break down at scale because they are row-oriented and massive in file size.
A Dive into the Core Data Sets for Property Platforms
ATTOM’s Property Data API is a leading solution for real estate analytics. The data provided is not just reliable in terms of their quality, accuracy, and easy delivery, they are also comprehensive.
Property Datasets
These datasets comprise the core of a property data platform, and ATTOM provides them all through its Property Data API. They include:
- real estate analytics datasets, such as detailed property characteristics
- historical deed and recorder data
- land registry datasets
- mortgages
- foreclosures
- tax assessments
Predictive Analytic Datasets
- AVMS (automatic valuation models)
- Rental AVMS
AVMs show historical valuation trends, rental yields, and equity data. With this information, property data platforms can build accurate risk assessment tools, investor dashboards, or CRM integrations for loan origination and portfolio tracking tools.
Geospatial Datasets
These data sets are the foundations for mapping and location data. Data points through ATTOM’s Property Data API include:
- parcel boundaries
- latitude/longitude coordinates
- spatial footprints
For companies and research institutions, these data serve spatial queries and show mapped property lines without needing separate geocoding services.
Hyper-Local Neighborhood and Environmental Data
The hyperlocal data from ATTOM’s solution include:
- neighborhood boundaries
- school districts
- crime statistics
- local amenities
- environmental risks, such as flood zones and wildfire risk areas
These hyper-local datapoints are the often-overlooked factors regarding property values and are critical to understanding the potential of local markets.
ATTOM’s Property Data API is Designed for Proptech
ATTOM’s Property Data API is idea for a unified, nationwide, and scalable foundation for property data platform developers. It delivers property, neighborhood, valuation, and geospatial data through a single gateway, allowing product teams to focus on core real estate analytics.
If your business is property information systems, real estate app development, SaaS, or a platform for real estate investment analytics, your core requirement is to source quality, uninterrupted, fast data.
ATTOM provides one of the industry’s most comprehensive nationwide property datasets, available through its Property Data API and other enterprise delivery options.
Contact an ATTOM representative to learn how ATTOM can make your platform stand out.
FAQs
- What are the main challenges real estate platform developers face when sourcing data?
Developers typically encounter three primary obstacles: data fragmentation due to sourcing data from a mix of providers that are not standardized; data interruption due to supply chain shifts and outages; and scale limitations where systems can crash due to extreme data volume.
- How does ATTOM’s Property Data API eliminate data fragmentation for its clients?
ATTOM’s platform acts as a centralized data aggregation and “delivery” layer. It normalizes chaotic data into “AI-ready” national datasets. It does this by assigning a unique ATTOM ID to each property to cross-reference records and eliminate address-matching guesswork; using multi-step algorithmic processing to resolve conflicting information from multiple local sources into a single, reliable view; and standardizing terminology. Users receive clean and structured data from ATTOM’s Property Data API.
- Does ATTOM’s infrastructure accommodate large-scale data analytics?
ATTOM’s API is not designed for bulk data downloads, but there are other options. ATTOM houses datasets covering over 155 million U.S. property parcels directly in the cloud. Data are accessed through Snowflake and Databricks. Also, instead of moving massive, traditional row-oriented files (like Excel or CSV) that break down at scale, clients can scale up their virtual warehouse on demand. For clients requiring bulk file transfers for in-house databases, ATTOM offers highly efficient columnar big data formats, such as Parquet and GeoParquet.
- What core data sets are available through ATTOM’s Property Data API?
ATTOM’s API provides a unified gateway to several comprehensive datasets. Three prominent datasets are property data (property profiles, historical deed/recorder data, mortgages, foreclosures, tax assessments); predictive valuation models (AVM and rental AVM); geospatial datasets for mapping (parcel boundaries, latitude/longitude coordinates, and spatial footprints); and hyper-local neighborhood and environmental data (school districts, neighborhood boundaries, crime statistics, local amenities, and environmental hazard risks like flood zones and wildfire risks).
