Summary of Recommendations
Below is a list of all the recommendations we make in this guide.
- Understand Your Project Needs: Start by clarifying your vision or goal and understanding where and by whom the data will be used.
- Research Data Providers: Look into different real estate API providers, focusing on their completed projects, data sources, available data points, update frequency, usage examples, discrepancy resolution, granularity, data restrictions, API uptime, market-specific data quality, and cost variations.
- Consult API Documentation: Reference the data provider’s API documentation for detailed information on their offerings and capabilities.
- Examine Case Studies: Explore case studies to see how other projects have utilized real estate data APIs effectively.
- Initiate Conversations with Providers: Contact data providers to discuss your specific needs and how they can meet them.
- Use Prioritization Frameworks: Apply frameworks like RICE,MoSCoW Method, Kano Model, Weighted Scoring, or Opportunity Scoring to prioritize and select the right API based on your project requirements.
- Consider the Quality of Data: Focus on data accuracy, precision, transparency, and freshness to ensure it meets high standards and supports informed decision-making.
- Assess Update Frequency and Cost: Match the update frequency of the data to your project needs and budget.
- Evaluate Data Transparency: Investigate how transparent providers are about their data sources, which can be crucial for your application’s credibility.
- Leverage Emerging Technologies: Monitor how AI, VR, and AR might influence the use and integration of real estate data in future applications.
Overview of This Guide
Real estate data is essential to quality products, services, and applications, but it can be incredibly challenging to work with. For example:
- Data sources are vast and complicated. Sourcing, gathering, and preparing it for use is costly and resource-intensive.
- With the data in hand, quality then becomes the next hurdle. Even casual users have high standards for data quality, consistency, and freshness.
- Maintaining all the infrastructure needed to accomplish this is challenging and may be far beyond a team or organization’s capacity.
Real estate data APIs help solve these challenges but also introduce new ones, namely, choosing the right provider for your needs. That’s why we made this guide.
- We start by introducing ourselves.
- We then dive into estate data APIs generally, including a definition and the importance of data quality.
- Then we meet the major real estate data providers, including their strengths and differences.
- Next, we explore illustrate the power and potential of real estate data in the real world through use cases and case studies.
- Finally, we offer guidance on how to make the best decision and how we expect real estate data to change in the near future.
Why We Made This Guide
Real estate providers and their APIs solve many of the challenges inherent in working with real estate data, but they introduce the new challenge of choosing the best provider for your needs.
We created this guide to help you understand the landscape of real estate data APIs and equip you with the knowledge and tools to make the best decision for your vision and goals.
Who This Guide is For
The guide is designed for any individual or organization exploring real estate data APIs for any project, use, or application.
This guide is helpful for anyone at any stage of exploration, whether they’re just beginning or have been researching for months.
While this guide does feature LiveBy products, we went to great lengths to feature all real estate data providers while presenting ourselves neutrally.
At the very end of this article, we explain why we think you should consider LiveBy for your application.
Why Should You Trust Us?
For the past eight years, LiveBy has been a respected leader in local and hyperlocal real estate data.
Each day, hundreds of brokerages and tens of thousands of real estate agents interact with our data.
We at LiveBy have over eighty combined years in real estate, with much of that time focused on neighborhood and real estate data. For the past eight years, LiveBy has been a respected leader in local and hyperlocal real estate data. Each day, hundreds of brokerages and tens of thousands of real estate agents interact with our data.
We have advised dozens of organizations on strategies to leverage real estate data effectively in their solutions. Trusted by some of the most well-known brands in the industry, including CoreLogic, Berkshire Hathaway Home Services, and Anywhere Real Estate (formerly Realogy), we are a go-to source for reliable real estate data insights.
What Is a Real Estate Data API?
A real estate data API serves as a gateway, connecting a variety of real estate information to applications, platforms, and services.
This information typically includes property listings, neighborhood statistics, school data, location insights, market trends, and demographics.
To elaborate, let’s dissect the term “real estate data API.” An API, or Application Programming Interface, may sound technical, but it’s essentially a bridge facilitating communication between two applications. For instance, you utilized an API when you navigated to this page—your browser requested the necessary information from our website to display the content on your screen.
The term “real estate data” encompasses a broad spectrum of information, including properties, market trends, demographic details, neighborhoods, schools, and amenities. There isn’t a universally accepted definition of what constitutes “real estate data”—the inclusion of data such as commute times or event details relative to a residential listing can depend on the specific context, needs, and application.
What Are the Sources and Types of Information in Real Estate Data APIs?
- Property and Listing Data: Originates from Multiple Listing Services (MLS), with hundreds available across the United States.
- Market Trends: These are compiled by aggregating property listing data sourced from MLS and/or public record data real estate data.
- Demographic Data: Typically obtained from the US Census.
- School Data: Collected from various sources, including the National Center for Education Statistics (NCES), Schooldigger, and GreatSchools.
- Boundary Data: Details the geographic outlines of cities, neighborhoods, and school districts. Sources include neighborhood boundary providers like Precisely and LiveBy.
- Amenities and Points of Interest: Information about local services such as restaurants, coffee shops, and grocery stores is sourced from Yelp, Google, Foursquare, and Dun & Bradstreet.
🗒️ Note: It’s crucial to differentiate real estate data APIs from listing APIs. Listing APIs primarily transfer data from MLS to brokerage websites or applications, including details like price and photos. In contrast, real estate data APIs offer a broader range of data. This guide focuses on the unique aspects of real estate data APIs, distinct from listings APIs.
The Importance of Real Estate Data APIs:
- These APIs facilitate the complex tasks of researching, selecting, ingesting, standardizing, normalizing, and integrating data for use.
- They also involve contracting with necessary parties and ensuring the data is frequently updated whenever new raw data becomes available. This streamlines the development process considerably, enabling developers, teams, and organizations to efficiently incorporate real estate data into their products.
- The effectiveness of a real estate data API hinges critically on the provider’s ability to execute these technical processes proficiently.
The Imperative of Quality Data
Sourcing, ingesting, and standardizing real estate data from numerous sources is a formidable challenge.
Here’s why data consumers—ranging from buyers and sellers to agents—demand exceptionally high data quality:
The Importance of Data Accuracy in Building Trust: When home buyers or sellers first interact with your real estate data product, even minor data inaccuracies can undermine the trust-building process. Missing data for a specific area, slight inaccuracies in recent market statistics, or unclear explanations for data discrepancies can quickly erode confidence. For users like home buyers, sellers, agents, and brokerages, such issues can prompt a rapid and lasting departure from your product.
The Role of Data Quality in Decision-Making: Data quality is crucial because real estate data often guides user decisions. Inaccurate data can lead to misinformed decisions and dissatisfied users or customers.
We recognize the critical importance of data quality because our customers deeply value it. Achieving and maintaining industry-leading data quality is our continuous pursuit, reflecting our commitment to excellence and customer satisfaction.
What do we mean by data quality? Let’s start with a broad breakdown of data quality — accuracy, precision, transparency, and freshness.
- Data accuracy — How well does this data represent reality? For example, if a real estate app estimates that a home will sell for $300,000 and it sells for $305,000, that estimate is pretty accurate. In the context of real estate data APIs, accuracy also frequently refers to How well the data in the API reflects the data in the regional source.
- Data precision — How consistent are data points? For instance, if you have three homes that are very alike in size, location, and features, and the real estate app estimates all of them at around $300,000, then those estimates are precise.
- Data transparency—What are the real estate data provider’s sources? How transparent is the provider about their sources? Real estate data providers often source their data from other providers, so transparency matters. These sources are rarely publicly disclosed, so you may need to begin conversing with a data provider before understanding transparency.
- Data freshness — How frequently is data updated? More frequent updates may increase costs, so update frequency should be matched to your needs. Update frequency can also be limited by the data source (i.e., only updated as frequently as the source is updated.)
🗒️ Remember that this is only a general definition of data quality. We recommend considering what else data quality looks like for your specific needs and context. For example, your project may not require fresh data, but completeness is most important.
What to Look for in a Real Estate Data API
Investigating a real estate data API begins with the destination you have in mind:
What is your vision or goal? What are you trying to accomplish? Where will this data be used? By who? Why?
We recommend having a rough idea of your project needs and vision, especially before beginning conversations with real estate data providers.
Once you have a rough idea of your goal, you’re ready to research different real estate API providers, which we cover below.
👉 Use the following questions to guide your research and conversations with data providers:
- What projects has the data provider done that are similar to your needs?
- What are the data sources?
- What data points are available?
- How frequently is the data updated?
- What are some examples of where this data is currently being used?
- How are discrepancies reported and resolved?
- What level of granularity can data be displayed?
- What, if any, restrictions are there on the data (display, duration, location)?
- What is the expected uptime for the API?
- Does data quality shift in certain markets?
- How is data billed?
- How much does the cost change with the frequency of data updates?
Be sure to consult the data provider’s API documentation, as many of these questions can be answered by looking at that documentation. You can find links to data provider API documentation below.
We also recommend looking for case studies. Has the data provider in question completed similar projects to your needs and context?
Once you’ve gathered enough information online, it’s time to reach out to a real estate API vendor and begin conversations. Ideally, the data provider should help you through the process of deciding among multiple differences.
Current Real Estate Data API Providers
Selecting the right data provider that aligns with your needs and goals is critical to the success of your project. Let’s explore the notable real estate data providers, each offering unique datasets and specializing in different facets of the real estate data API landscape.
🗒️ This list is not exhaustive and only includes data providers that source and compile data into APIs, excluding individual data sources like the US Census, Google, SchoolDigger, or Yelp.
ATTOM Data
ATTOM is known for its robust property data, covering a wide array of data including ownership, mortgage, market trends, hazard information, school, and more. They aggregate data from over 3,000 counties, accounting for more than 99% of the U.S. population, and offer insights to 155 million properties. Their property data API and cloud solutions facilitate easy access and integration of data into user systems, making it a powerful tool for real-time data management and analytics.
CoreLogic
CoreLogic is a major player in real estate data and is known for its comprehensive and detailed property insights. Their extensive database covers more than 99.99% of U.S. property records, including 150+ million properties across over 3,000 counties. This vast coverage allows them to provide granular property details, such as ownership records, tax assessments, and property characteristics, as well as specialized analytics. They offer advanced analytics through bespoke platforms designed for in-depth property research, market analysis, and reports.
LiveBy
LiveBy specializes in enhancing real estate applications and websites with hyperlocal content. Our platform allows for the integration of detailed neighborhood insights, including market trends, demographics, school data, amenities, climate data, and lifestyle data — significantly boosting user engagement, time on site, and lead generation. Our real estate data and neighborhood APIs are all normalized to the neighborhood level, making it easy to deploy solutions at the neighborhood level. We also have one of the most comprehensive neighborhood boundary data sets in the world.
Local Logic
Local Logic’s granular location insights enhance the real estate decision-making process. Their key focus is on providing detailed neighborhood and property insights to improve livability and investment decisions in both residential and commercial real estate sectors in the United States and Canada. Unique to Local Logic is their approach to quantifying the vibe of a place using a data-driven method, offering over 17 different scores on various community characteristics.
Zillow
As one of the most recognized names in real estate, Zillow offers comprehensive and consumer-friendly APIs. They provide extensive data on over 110 million U.S. homes, including sale prices, listings, and Zestimate home values. Zillow is particularly known for its extensive suite of APIs that serve different aspects of real estate, from public data access to more specialized applications like Zestimates and transaction details. These APIs are designed to cater to developers looking to integrate Zillow’s rich datasets into their own applications, providing capabilities such as accessing current mortgage rates, lender reviews, neighborhood data, and real estate metrics.
Comparative Overview of Real Estate Data Providers
Below is a comparative table of the most notable real estate data providers highlighting their data specialization, key features, and their API documentation.
🗒️ We were unable to uncover the update frequency for each provider, so you will need to conduct your own research in order to better understand data update frequency.
Also note that this is a non-exhaustive list of the data types and categories each provider can supply. That was beyond the scope of this guide.
| Data Provider | Data Specialization | Key Features | Other Notable Info | Link to API documentation |
|---|---|---|---|---|
| ATTOM Data | Extensive property and neighborhood data | Detailed property reports, market trends, foreclosure data, environmental risk assessments | AI-ready solutions for advanced analytics | api.developer.attomdata.com |
| CoreLogic | Comprehensive property data | Property and ownership data, mortgage, risk evaluation, underwriting automation | Extensive database, industry benchmarks | zillowgroup.com/developers/ |
| LiveBy | Hyperlocal data for real estate websites | Detailed community-level data leveraging a proprietary set of community boundaries | Data normalized to the neighborhood, making it easy to deploy | docs.liveby.com/ |
| Local Logic | Location insights and analytics | Detailed neighborhood insights, property-specific data, maps, demographic data | Granular location insights | docs.locallogic.co/ |
| Zillow Group | Real estate market data | Zestimates, public records, neighborhood data, wide range of APIs for real estate-related data | Extensive market metrics and forecasts | developer.corelogic.com/ |
Real Estate Data API Use Cases
Applications for real estate data are vast, covering everything from environmental impact studies to public policy.
As such, this list of use cases focuses on commercial and residential real estate. Below are a list of the common use cases for real estate data APIs.
👉 We recommend clarifying your top 3-5 desired use cases for your specific project.
- Market Analysis and Forecasting: Analyzing current market conditions; forecasting future property values and market trends
- Real Estate Marketing: Enhancing property listings; generating marketing campaigns and leads
- Brokerage and Agent Tools: Providing agents and brokers with tools that provide property histories, comparative market analyses, and other critical sales data
- Advising Clients: Providing potential buyers with property and neighborhood information; advising clients on when to buy or sell
- Investment Analysis: Evaluating real estate investments; analyzing the performance of real estate portfolios
- Property Valuation: Estimate property values for listings, refinancing, or lending; providing data for more precise appraisals
- Real Estate Financing: Underwriting mortgages and other real estate financing products; Assessing and managing risk in lending by analyzing borrower and property details.
- Property Development and Management: Informing site selection and development planning with detailed insights; Managing properties more effectively by utilizing data on rental market trends, maintenance needs, and tenant preferences.
No matter your final use for real estate data, we also encourage you to conduct additional research, including using the recommendations featured throughout this guide.
Real Estate Data Stories and Case Studies
Up to this point, this guide has explored the need, challenge, and opportunity of real estate data, but we’ve not actually seen what real estate data providers and their APIs are capable of.
Below are a few true stories and case studies that show off the magic that is possible with real estate data APIs.
MAXA Designs
MAXA is a real estate design firm known for its ability to create stunning, on-brand marketing materials and templates — from flyers to posters to social media posts.
MAXA’s customers wanted to add market trends data (days on the market, months of inventory, etc) to their marketing assets, but acquiring this data directly from the source — the multiple listing service (MLS) — was prohibitive.
They first needed to set up data agreements for all the MLS’s their customers have, amounting to hundreds of separate contracts. Then, they would need a way to ingest that data, store it, and then add it to their products. After that up front work was done, they would need to maintain all the infrastructure required to provide this data.
Within weeks of beginning their conversations with LiveBy, they were experimenting with data in a test environment and a few weeks later were using data in their applications. A few weeks after that, the MAXA team had market data built into their products. It now takes only seconds to add or change different market statistics, at any community level, and their team and their customers are confident in knowing the data is always up to date and accurate.
Click here to learn more about MAXA Designs.
ActivePipe
ActivePipe is known for their ability to nurture real estate leads into customers. Their proprietary software is able to predict a lead’s intent, targeting them with the right message for their stage in the sales funnel.
The problem is that nurturing leads revolves around content, and creating content is time consuming. Beyond this, ActivePipe has long known the value of hyperlocal. People live in neighborhoods, not zip codes, and the ActivePipe team wanted to create content at the neighborhood level.
As with MAXA, in order to add this functionality to their product, they would have had to source their own data. In addition to contracting, ingesting, normalizing MLS data for market statistics, they also needed to handle neighborhood boundary data. They would also need to maintain all that data and the infrastructure that supports it, including keeping neighborhood and MLS data up to date.
With LiveBy, ActivePipe was able to quickly add neighborhood level marketing materials, enabling them to effortlessly create neighborhood-level content, and nurture hundreds of thousands of potential buyers.
Click here to learn more about ActivePipe.
The Future of Real Estate Data
It’s impossible to predict the future. However, following trends can serve as a reliable guide of what to expect in an industry. Keep watch for the coming trends that are affecting the future of real estate data:
Real Estate Lawsuits
Beginning in early 2024, multiple state and federal lawsuits and settlements involving the National Association of Realtors began to reach national audience. Those lawsuits have sparked other investigations into the real estate industry and will continue to shape and change the way the industry — and its data — is controlled. We expect federal investigations into multiple listing services to continue to progress and shape the real estate industry at large.
Artificial Intelligence and Generative AI
Since ChatGPT launched artificial intelligence (AI) into the mainstream in late 2022, technology companies and users have been experimenting with how this technology can improve our lives. Generative AI is affecting how data is processed, analyzed, understood. We expect AI will continue to transform real estate data in the following ways: Enabling users more direct interface with large data sets, for example using conversational interfaces, or create their own visuals and dashboards, often without technical knowledge.
Virtual and Augmented Reality
We are still at the beginning of the technology adoption curve for virtual reality (VR) and augmented reality (AR), however these forces will continue to shape the way we interact with applications and the data that underpin them. We’re already seeing the ability to virtually stage and tour homes, but we expect that real estate data will make it into these AR and VR worlds and applications. For example, with Realtor.com’s Street Peek app open on your smartphone, you can point your camera at any home, and it will show you the home’s price, number of beds and baths superimposed over the home.
Leveraging Prioritization Frameworks
Now that you have your goals and needs in hand, a strong handle of the other players in the space, questions to ask of these data providers, and have begun conversations with those data providers.
If you’re like most of our customers, organizing all this information is challenging. As such, we recommend leveraging prioritization frameworks to help clarify, capture, and organize key things you’re looking for in a real estate data API, and stack them up against real estate data providers.
🗒️ At LiveBy, the RICE framework is one of our favorite prioritization frameworks.
| Framework | Description | Pros | Cons |
|---|---|---|---|
| RICE Framework | Evaluates potential initiatives based on Reach, Impact, Confidence, and Effort to prioritize effectively. | Provides a quantitative way to assess and compare potential APIs based on their expected benefit. | Can be time-consuming and subjective, as it relies on accurate estimations of each criterion. |
| MoSCoW Method | Sorts features into Must-Have, Should-Have, Could-Have, and Won’t-Have categories for clear prioritization. | Simple and helps quickly identify essential features necessary for project viability. | Risk of over-prioritizing features as Must-Haves, which can lead to scope creep. |
| Kano Model | Categorizes features into Basic, Performance, and Delighters to prioritize based on customer satisfaction. | Focuses on customer satisfaction and helps identify features that are essential versus those that enhance or delight. | Requires detailed customer research to accurately categorize features, which can be time-consuming. |
| Weighted Scoring | Assigns scores to different features based on various criteria like impact and effort. | Flexible and allows for customization based on specific project needs. | Scoring can be arbitrary and may not always reflect true priorities if not based on solid data. |
| Opportunity Scoring | Evaluates features based on customer satisfaction and importance to identify high-impact opportunities. | Data-driven, focuses on customer needs and priorities. | Requires thorough market research and customer input, which can be resource-intensive. |
Why Choose LiveBy?
We hope that this guide has been a helpful guide you in your journey to find a real estate data API provider.
As you research, consider our real estate data APIs in your search. Here’s what makes us different from other real estate data providers:
- Our data is neighborhood-ready. Home buyers and sellers live in neighborhoods, not zip codes. Our data, including our neighborhood API, is normalized to the neighborhood, making it easy to display data at a neighborhood level.
- We have one of the largest geospatial data sets in the United States. At over thirty thousand communities — from micro neighborhoods to neighborhoods to cities — we have one of the largest boundary sets in the United States.
- Our data set is comprehensive. Our APIs include market statistics, boundaries, schools. We work with hundreds of MLS’s, across the United States.
- We are a small team. Around here, it’s easy to get in touch with a human.
In Conclusion
As mentioned earlier, real estate data is essential to quality products, services, and applications, but it can be incredibly challenging from a project’s start to finish.
Data sources are vast and complicated. Sourcing, gathering, and preparing it for use is costly and resource intensive.
With the data in hand, quality then becomes the next hurdle. Even casual users have high standards for data quality, consistency and freshness.
Maintaining all the infrastructure needed to accomplish this is challenging and may be far beyond your team’s current capacity.
Real estate data APIs help solve these challenges but also introduce new ones, namely, choosing the right provider for your needs.
We hope this guide has helped inform, guide, and support your decision you make a decision on a real estate data API.
🗒️ Thank you for reading! What can we do to make this guide better? Please email us at hello@liveby.co with any suggestions.
