All industries are making increasing use of big data – and the real estate industry is no exception.
Perhaps you’ve heard the term “big data” kicked around in various contexts and have formed a hazy idea of what it means. That’s not surprising because there are several ways to interpret the term. We like this definition, from Forbes:
“Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.”
Data is big when there is a lot of it (high-volume) and/or when it flows rapidly into your organization (high-velocity). Big data is generally available in two forms:
- Unstructured Data: Data available in an unorganized form that hampers interpretation unless specific technology is applied. Unstructured data is usually text, and can include blogs, articles and social media posts.
- Multi-Structured Data: Data in a variety of formats, such as statistical, tabular, column-delimited, spreadsheets, images, transactions, forms, questionnaires and application text.
Of course, data, big or small, is valuable only if it is accurate. As they say, garbage in, garbage out.
Technology for Big Data
Raw big data (often called data lakes) must be mined, interpreted and analyzed in order to be useful. Many data lakes are maintained in the cloud, which is cheaper and safer than buying internal storage to manage it yourself. Many cloud-based analytic toolkits are available to tame big data and bend it to your needs. Examples include IBM’s BlueMix and Google’s BigQuery. In addition, many companies develop their own technologies, tailored to their specific requirements. Special operating systems, such as Hadoop, have evolved into data distribution systems that optimize access to big data for analysis, queries and operations. In-memory analytics (often called hybrid transaction/analytical processing, or HTAP) increases processing speed and power in dynamic environments.
Big Data in Real Estate and Lending
Big data analytics in the lending industry is used to support decision-making and to increase transparency. This is seen in a growing number of contexts, such as mortgage lending and peer-to-peer lending for commercial real estate:
- Mortgage lenders use big data to qualify loan applicants, evaluate properties, quantify risks and set a loan’s interest rate. The data includes listing and sales data in a property’s immediate neighborhood, local/regional/national trends, interest rate forecasts and a whole slew of other information that helps to decide mortgage parameters.
- Peer-to-peer lenders, such as Patch of Land, Lending Club and Prosper, use many thousands of data points to help evaluate borrowers. They too need to determine the grade of individual or business borrowers (A, B, C), assess risks and formulate an appropriate interest rate. Lenders depend on the due diligence performed by P2P lending portals. Portals that do not crunch big data effectively enough to make good decisions may lose market share as loans sour.
Big Data in Real Estate Crowdfunding
A new area where big data is bound to grow is real estate crowdfunding. Portals will benefit from big data analytics used to perform due diligence in order for properties to be listed on the platform. While still in its infancy, the real estate crowdfunding industry will increasingly rely on big data to perform due diligence on listed properties and sponsors, as well as to qualify investors as accredited.
Analysis of big data from credit reporting agencies and other sources proceeds via proprietary information models to assess risk, pre-approve loan requests, extend automatic terms to repeat borrowers and formulate an internally-developed score. Let’s be clear – the big data analysis supplements (rather than replaces) traditional underwriting procedures, such as arms-length third-party appraisals, pulling comparables, assessing risk profiles and evaluating local market conditions. The ultimate goal is an integrated, data-driven underwriting process that is fair, transparent and efficient.
Transparency comes about through full data sharing between the portal and investors. At PeerRealty, we strive to provide investors with all relevant information about investments, so that investors can augment our due diligence process (and that of our sponsors) with their own. Harnessing the power of big data is the next frontier in our due diligence process, which will give investors confidence and facilitate the successful completion of deals.
