3 Ways Real Estate Investors Can Get Ahead With Property Data APIs

If it’s true that “data is the new oil” in today’s digital economy, property data-centric real estate APIs are quickly becoming key to striking it rich for modern realtors, investors, underwriters, traders and others.

Have you been hearing a lot of buzz about APIs and wondering how mortgage lenders, real estate professionals, investors, and traders can use them to their competitive advantage? In this post, we’ll answer these questions to give you a clear understanding of why real estate APIs can make the difference between success and failure in a real estate market where decisions are increasingly made by the nanosecond.

What, Exactly, Is a Real Estate API?

Formally known as an application programming interface, an API is a set of programming code that queries data, parses responses, and sends instructions between one software platform to another. Property data APIs, part of a broader category of real estate APIs, are designed to deliver actionable insight on a property, or multiple properties, derived from any mix of visual computing, artificial intelligence, and big data that is made available on-demand.

Updated on a continuous basis real estate APIs provide instant, accurate insights on different facets of a property. Any given property data API might focus on details such as property type, year built, number of bedrooms and bathrooms, price per square foot, Airbnb and traditional rental income, demographics, neighborhood analytics. Another might zero in on property-centric deed, tax, mortgage, foreclosure and other market data. Still others might provide instant, accurate insights on current physical attributes of a property—type and condition of roof, the presence of swimming pools, fencing, yard debris, and other characteristics that may impact valuation and trends that can affect it over time.

Real Estate’s API Economy

In truth, data has always been the lifeblood of real estate investing, and for the last three decades, the Internet has played a central role in making that data ever more accessible. But in the age of Zillow, iBuyers, and single family rental property (SFR) investment sites like Roofstock, individual and corporate investors now purchase thousands of properties per month. As a result, companies involved in transacting, investing, and trading rental properties have been racing to leverage technology to help them make faster, smarter investment decisions to keep ahead of the competition.

APIs, which are built using XML or JSON open standard data exchange formats, provide a ready means to integrate key data points into user workflows. At their most essential, APIs act as messengers that take requests from a user and then tell the associated backend system to return the appropriate intel related to the user’s query. Typically available by subscription, APIs come in different flavors. Some deliver basic datasets—MLS-based real estate listings retrieved via API don’t entail the use of analytics to provide value-added intelligence, for instance.

But many data providers employ various forms of AI or machine learning to interpret raw data to create useful information relative to their area of specialty. Underwriters, investors, and traders interpolate this data into their own models to make acquisition or divestment decisions quickly.

Real Estate Data On-Demand

There are countless real estate industry APIs available, spanning any number of user needs. But any list of the best real estate APIs could easily include a few prominent names. Zillow’s API and the Multiple Listing Service (MLS) IDX, for instance, help turn real estate agents and brokerages turn their sites into mini real estate portals using the organizations’ property and mortgage content. ATTOM Data’s APIs provide lead gen and predictive market data to real estate professionals. Meanwhile, the Estated API filters data based on current and historical property owners, deeds and most recent tax assessments. And the Walk Score API, meanwhile, calculates the walkability of an address based on a property’s walking distance from nearby amenities to help in valuation models.

At CAPE, our own API leverages high-resolution geospatial imagery, computer vision-driven neutral networks to deliver current and precise data on property condition—the size of the property, type of roof construction and condition, the presence of solar panels, whether there are overhanging trees, a pool, yard debris and other information to increase the accuracy of risk and valuation models. For organizations that transact and trade real estate, APIs deliver three major benefits—so long as the right APIs for the job are utilized.

Speed & Efficiency

With home values in some markets soaring 22.4% year-over-year, interest rates still hovering at near lows, and more buyers than ever competing to win the home of their dreams or the cornerstone of their investment portfolios, real estate brokerages, lenders, and investors need to act fast. Especially with most homes attracting multiple offers and selling above asking price—often as-is and sight unseen.

The problem: Many traditional forms of property information are either incomplete or out-of-date. In an environment marked by low inventories and sky-high prices, 40% of mortgages are now completed appraisal wavers. Broker Price Opinions (BPOs) can be pricy and slow—and can miss up to 60% of exterior condition issues. But automated valuation models (AVMs) offer property intelligence on-demand, in many cases drawing from relevant APIs encompassing rich data on numerous property attributes.

Fair warning, however: As a growing number of these stakeholders are finding, when it comes to enhancing AVMs, only APIs delivering the highest-quality geospatial property condition intelligence will do if you hope to assess risk and valuation with a level of accuracy that would otherwise require a costly and time-consuming on-site inspection.

Better Investment Choices

In addition to speed and cost savings, instant property condition assessments delivered via API and integrated with existing workflows help fill in gaps and errors in other data sources with additional condition variables that can deliver significant lift to valuation and pricing models.

Which is one reason real estate APIs are of particular interest to iBuyers and single family rental property (SFR) investors. Whether they’re exploring a single property, or shopping citywide, regionally, or even nationally, accurate, on-demand condition data available via APIs can be leveraged to zero in on promising properties and avoid risky ones. CAPE’s API, for instance, even enables them to assess aggregate changes at the neighborhood, zip code or CBSA level to identify promising investments and uncover trends that may otherwise go unnoticed—or get plucked up by savvier competitors.

Better Buy-Sell Decisions

To optimize earnings, mortgage loan traders must make buy-and-sell decisions quickly while choosing the best disposition path for each loan. Obviously, the greater the credit risk of a given mortgage, the more critical the collateral value behind a loan. When it comes to non-performing assets, property condition is a critical consideration best weighed with high-quality property intel.

This kind of intel is also critical to real estate investment trusts (REITs) and other organizations managing real estate portfolios across multiple properties or regions. For these businesses, access to accurate condition data represents a strategic advantage in not only acquiring new properties, but also in determining when and where it might be a good time to sell.

APIs and the Bottom Line

Whether it’s working more efficiently, saving money, landing the best investment or optimizing a multi-property portfolio, real estate APIs are more than just beneficial. In the fast-paced world of real estate investment, they’re now key to beating out the competition and boosting bottom-line results.

To learn more about how CAPE Analytics and its API can make better, faster real estate investment decisions and reduce operational expenses, request a demo now.

Aggregate Statistics Created Using Data Produced from Nearmap Imagery