Fixing Disparate Data in Commercial Real Estate

How do you know when a CRE software vendor is holding crossed fingers behind its back? When it says there’s no trouble pulling together all the disparate data you need to analyze.

There’s no reason why these companies should be any different than the thousands that preceded them throughout the entirety of the computer industry. Beyond the aggravation of making data connections between different systems, the challenge is, as Trepp puts it in a new white paper, “when good data is mixed with other good — but slightly different — data.”

The problem is a challenge in any industry and particularly thorny in commercial real estate. Such property data as boundary lines, unit size, and zoning come from municipal records and third parties. Debt records come from sources like Trepp and other specialized data brokers. Property comps, rental rates, vacancy rates, and demographic information come from a variety of sources, including data providers, first-hand collection, and reports. Then there’s such internal data as marketing records, forecasts, accounting tools, and analytic systems.

All the data needs to be correlated and accurately connected to specific properties, or else it will throw off an investor, lender, broker, asset manager, or other with a need for accurate information about a property and what might be done with it.

Trepp also points out something well-known in other industries: there are many places where things can go wrong, whether through additions of new data sources, modifications of existing ones, multiple ways data is brought into a system, multiple people in a company working together and all needing to use the proper formats, and CRE professionals lacking the understanding of data experts.

The firm suggests three strategies to start with. First, keep a tightly defined scope and approach. There will be limitations, but the more constrained you can keep the system and what you plan to do, the more likely you’ll be able to accomplish something quickly. This reduces complications down the road and costs at the outset. Don’t try to run before learning to walk.

Second, have a “proper embedded data function.” This is the next step and can be done within an organization or using a third-party consultancy. However, this gets complex and expensive, as you need specialized expertise and services.

Third, look for appropriate data tools. This is a case where bringing in a consultancy to help understand the options and choose the appropriate product can save money. Get the wrong product and you will spend a lot of money to accomplish little.