What are some tips for evaluating data sources in commercial real estate?

It’s important to understand who owns your data and how the data will be used to make a business decision better. It’s not enough to buy the technology or gain access to the data.

Coming from the retail real estate world, I believe it's crucial to cross-check data from multiple sources to ensure consistency and accuracy. I frequently turn to resources like CoStar and AlphaMap.

What is it that you are looking for? First, you must know what it is that you seek and why you are seeking that information. Next, you must know where to source the information. And that's the crux of this post. Reliability of the source is absolutely paramount. Does the source know what they are talking about? Be wary of "brand names". Whilst these entities may ahve had the best people at some time, they may no longer have those people. It is the people that research the information that are the key to obtaining successful and valid information. With the rise of AI we are confronted with a computer telling us what's best. Are you willing to put your money where their "mouth" is? On top of this you must form your own view.

Depending on the required output of your data usage, it is important to remember the widespread issue of data scarcity (low publication frequency, non-equal comparisons, etc.) in the real estate sector. As a result, interpreting data for decision-making can sometimes be challenging. Always look at the data sets at hand, but also consider using alternative data sources or advanced analytics to navigate this scarcity.

Source Reputation: Start by evaluating the reputation and credibility of the data source. Established providers, government agencies, and reputable real estate organizations are often reliable choices. Data Accuracy: Verify the accuracy of the data. Look for sources that provide detailed, up-to-date information with minimal errors or discrepancies. Data Collection Methods: Understand how the data is collected. Reliable sources use rigorous methods, including surveys, site visits, and data validation, to ensure accuracy.

I don't think there's a better way to check the credibility of the data than spending time reviewing marketing packages from closed, under contract, and on-market deals. Look for similarities in key indicators like price/foot & unit, GRM, etc. If someone is using the price/foot of a building with a bunch of small 1-bedroom units to price their large all 2-bedroom building...that's a red flag!

Sometimes a conversation with a experienced person who's close to the processes that you are interested in, gives you better brief than reports from reputable, big market researchers. Especially if you are investigating in some niche part of the market.

"Garbage in, garbage out" has never been truer. Many companies rely heavily on data for decision-making, but sticking to a single data source can be a recipe for getting skewed insights. Also, bigger data providers are not always up to the task for all asset types or markets. You'll probably need to use multiple data sources and invest some time merging the records correctly.

ALWAYS cross-reference. For example, when considering REIT opportunities I start with @costar but cross reference with @nareit & stanger report

Granularity is a key factor. Only seeing a point on a map and activity measures can only go so far. Details specific to indiv property and the tenant activity brings context to shifts in visitor traffic metrics and consumer profiles. A shift in tenant mix whether it be the loss of an anchor or the gain of a destination tenant can speak volumes. Have access to comprehensive data from the start from consistently reliable sources. Bad data can waste a lot of time, effort and $.

Commercial real estate is all about the income. From a multifamily standpoint, I like to cross-reference the "market" rents listed in a competitor's marketing package by evaluating closed lease-deals as reported in the MLS by my residential colleagues.

Have team members or other professionals you can run the investment by who have varying degrees of risk tolerance and different backgrounds. This goes a long way in making sure that your blinders aren't on in any critical areas.

Data expert should also involve field experts. For example, when building a solution to monitor digital marketing spend, the head of marketing should be engaged throughout the process - from data ingestion to visualization. The marketing lead understands campaign nuances that data may miss. Their insights help spot anomalies in the data that could otherwise appear as errors. Involve marketing in metric design so the data captured provides meaningful, actionable insights into their programs. Technical-focused metrics like click-through rate may miss the mark. Work backwards from the decisions marketing needs to make to determine the right metrics to showcase.

Assess the credibility of your data sources by examining their track record & any industry endorsements. Ensure the data is current, as outdated information can compromise strategic moves. The scope should be comprehensive, covering geography, property types, and market trends. Granularity is key; more specific data enables nuanced analysis. Interface usability matters; it should be intuitive for efficient navigation. Customization options can add value, letting you tailor data to your needs. Check for data integrity to prevent errors that could impact your decisions. Conduct a cost-benefit analysis to ensure the data source is a good investment. Legal compliance is also a factor; make sure the provider adheres to relevant regulations.

In aggregating and evaluating data sources, don’t forget to utilize your network of industry professionals. Too often, we’re evaluating data that we have limited knowledge in, so why not reach out to those we know that have a lot more familiarity. Not only do you gain more assurance in what you’re seeking, but it helps to maintain those relationships.

Commercial real estate (CRE) firms should strongly consider open source technology for cost savings, efficiency, and transparency. First, open source platforms are free to use, saving significantly on licensing costs compared to expensive proprietary tools. Rather than paying high fees, firms can leverage quality open source solutions and invest the savings into other areas. Second, open source enables more efficient operations. Analysts can customize data and code to fit firm needs, allowing flexibility that proprietary systems lack. Analysts also avoid waiting on vendor deliverables or paying more for custom features. The ability to efficiently adapt open source in-house promotes efficiency. Follow me if you share the same interests

One thing that is always helpful across every domain, is utilizing domain experts' experience in your approach. They can help tremendously in ensuring the quality of your data, strengthen the validity of your methods and so much more. I believe having domain experts' input is crucial and can greatly help in better decision making in your work.

Evaluate based on bricks and dollars. Op-ex and Cap-ex are important considerations. Determined acquisition price is more important that the purchase price.