When the top innovators and leaders from the real estate and proptech gather, the conversation often turns to real estate data and how to optimize a data and analytics strategy to gain a competitive advantage.
Chris Flynn, head of product and strategy at First American Data & Analytics, joined a panel of leading real estate and proptech executives at Blueprint Las Vegas in September for a deep dive discussion that explored real estate data trends and approaches and best practices for the development of a successful data and analytics strategy. He shared some of the highlights, key takeaways and best practices for companies to follow.
What makes a successful data platform?
Certainly, data infrastructure is a critical component, but the data that is fed into the infrastructure must be high quality to produce high quality insights. For example, when you look at the types of data that play meaningful roles in the fintech and proptech spaces, they are very often aggregated from disparate sources and manufactured, so understanding how the data from those disparate data sources are linked is a foundational step.
The data provider needs deep expertise in connecting and manufacturing the data. How is the data maintained to assure it is current? What quality control (QC) processes have been established to ensure the quality of the data output? Then, finally, how is the data delivered and consumed into the platform, whether it is a third-party platform or one built through internal resources. These are all important questions that should be considered when thinking about the data needed to fuel a successful platform.
“It’s not uncommon to assume that more data equals better outcomes. That might ultimately be the case, but it can also be a cautionary tale of getting too much, too quick, producing an ineffective strategy that does not yield the desired results. So, to that end, it is important to establish a data strategy plan, layering in the appropriate content at the appropriate time, so you can focus on the most meaningful problems.”
What is the foundation of an advanced analytics strategy?
There are two schools of thought about advanced analytics strategy. The first focuses on specific pain points and/or functionality that your target market or customer base requires. And the second looks more inward, focusing on continually enhancing the raw materials. A successful strategy needs both.
At First American Data & Analytics, we use artificial intelligence to extract every ounce of information from our data sources. For example, there are millions of documents containing property, mortgage, and lien information, and we extract a number of data attributes from each document, generating millions more data attributes that fuel our proprietary and industry-leading datasets. Understanding how to use these millions of images or data attributes and how to QC them, link them, and normalize them into a standardized, easily consumable format is a top priority in establishing a successful advanced data analytics strategy. You must have a relentless and ongoing focus on data science and challenge yourself and your teams to incorporate new methods, continually push for metrics-driven outcomes, and deliver your content to your clients faster.
The concept of big data has existed in real estate for years, but it still seems we are in the early stages of deriving utility. How can companies ensure they derive impactful insights from the data they are purchasing?
This may sound straightforward, but success starts with a clear understanding of the problem you are trying solve and then identifying the data that will have the greatest impact in fueling a solution. There is a lot of data out there, so it’s not uncommon to assume that more data equals better outcomes. That might ultimately be the case, but it can also be a cautionary tale of getting too much, too quick, producing an ineffective strategy that does not yield the desired results.
So, to that end, it is important to establish a data strategy plan, layering in the appropriate content at the appropriate time, so you can focus on the most meaningful problems. The best data providers will play a consultative and collaborative role that extends well beyond just the purchase of the data. This type of relationship is especially important for earlier stage companies as they find their footing.
Geolocation data is a relatively new concept in enhancing real estate investments and operations. What do you think is the opportunity writ large for new data categories to enhance signal in the real estate industry?
The increased availability of geolocation data, spatial data, and other unique data attributes will play a significant role in advancing companies’ ability to gain deeper and more actionable insights. The marriage of property data, which is historically a tabular dataset, with a more spatial component will open a lot of opportunity. Bringing both types of data together within a comprehensive data strategy should produce faster and more specific real estate searches, accelerate the time needed to identify and vet investment opportunities, as well as provide a more comprehensive view of a subject property – all of which should help companies make more informed business decisions.
What advice would you give to real estate companies who want to go to the next level in their data strategy?
You don’t need to do it all yourself. There are companies that focus their time and attention on real estate data, infrastructure, and other related datasets. Market entrants often look at real estate and property data and underestimate the amount of work it takes to manufacture and support advanced data analytics. My advice is to focus on the market and, very specifically, identify the problems you want to solve, and then work with data partners that can enable that effectively and efficiently. Work with firms that offer a support structure that simplifies the data processes through advanced professional services and a white glove approach.