Using data to uncover the best opportunity zones have to offer
- October 1, 2019: Vol. 6, Number 9

Using data to uncover the best opportunity zones have to offer

by Tamar Frumkin

Investors focused on making solid returns on opportunity zones have a number of factors to consider when examining qualified properties. There is more going on here than what meets the eye, even when it comes to official government designation reports. When evaluating the census data used to help determine areas that qualified as opportunity zones, one discovers several nuances which, if properly considered, could translate into significant advantages for investors.

When a data-driven investor aims to learn about an area, it is important to use fresh data, and back-test investment strategies over historical data points. Tracts chosen as opportunity zones according to the 2017 Tax Cuts and Jobs Act are not all created equally. Rapidly changing demographics and older data has impacted the potential value to be unlocked in each track.  To help shed some light on the chosen tracts that were designated as opportunity zones, it’s important to consider the following data challenges when evaluating each tract.

1) The usage of stale census data and why it is problematic. The latest document to reflect the final qualified opportunity zone designations for all states was published Dec. 14, 2018, on the U.S. Department of Treasury website. It turns out that 99 percent of the 8,764 tracts designated for the program were surveyed using outdated U.S. Census Bureau surveys from 2011-2015, meaning some of the data is as much as nine years old.

Let’s dig into the o-zone tracts designated according to 2014 census data, compared to the median income changes in 2017’s census data. The operative question is: What changed between 2014 and 2017? First, let’s measure the change by looking at the absolute percentage increase or decrease in median household income over time. Most tracts experienced a change of up to 20 percent in median household income — with 4,981 having a higher median household income in 2017 than in 2014, while 1,977 experienced a decline.

2) Historical data reveals rapidly changing trends. If, as we have seen, stale median household income data was used for o-zone tract selection, focusing on the tracts without an improvement trend could hold the biggest core opportunities for investors depending on other signals that indicate further gentrification.

Taken state-by-state, the biggest percentage of tracts that have depreciated are in Louisiana (44 percent), New Mexico (42 percent), and Nevada (39 percent). The best-performing tracts can be found in Nebraska with just 12 percent of tracts declining in value, Minnesota (17.2 percent), and Washington State (17.3 percent).

This historical data reveals that tracts may go through significant changes in just a handful of years. The available 2017 census data provides a few interesting insights, but it is highly likely that a 2019 version would show a significantly different picture.

Although dealing with stale data reaffirms that blind trust in the value of each tract is not wise, advanced technology provides many workarounds to discover the true value hidden in each investment opportunity in o-zones. Below are two disciplines we have employed to better evaluate tracts plagued with older data:

3) Use alternative data points and data sets for enhanced analysis. There are many ways of predicting socioeconomic indicators that could go well beyond the data available during the 2012-2015 period. A few interesting angles missing from the initial analysis that should be explored are:

  • Which metrics besides income best identify potentially distressed areas?
  • How can artificial intelligence be used to identify area growth trends without restricting the analysis to household income?
  • How could one predict socioeconomic indicators using alternative data sets to get a more current picture than 2015 census surveys?

Beyond income, which metrics best identify potentially distressed areas seem to be missing from the analysis?

  • Car ownership
  • Average commute time
  • Depending on the most common transportation modes in the MSA, what are the accessibility options for people living in the designated tract (i.e., distance from subway stations, availability of carpool services)?
  • Percentage of people with health insurance

 4) The value of using AI and big-data analysis. By using artificial intelligence and big-data analysis, it is possible to predict socioeconomic indicators almost in real time. For example, using anonymous mobile device locations, a fuller picture of factors such as how many members of the community attend college can be detected. This is a great example of how we can measure changing trends up to present day with no time limitations. Consider this data as compared to changing volume of Airbnb listings. This is a creative way to correlate points of interest data with year-over-year economic rent growth.

There are many related indicators that can signal economic growth in an area. A particularly compelling example is the opening of a Trader Joe’s or Whole Foods market. We have found that the  proximity of assets to Whole Foods branches is a good indicator of rent and value growth among commercial real estate properties. By automatically picking up on these types of correlations using AI, we can add another layer of data to o-zone tract analysis.

It turns out that there are no less than 20 different census tracts in the o-zone program with Whole Foods stores. If Amazon believes these are good locations for Whole Foods, how distressed can they actually be compared to other low-income cities? For the conservative investor, these tracts appear to be a safer investment over time, as they are already showing clear indications of being up-and-coming neighborhoods. Similar research has been done around Trader Joe’s locations, revealing similar correlations.

For those seeking the full tax deferral benefit, not all o-zones are the same. While it is possible to calculate what is investible in some states, in others it is not. To capture the tax deferral, an opportunity zone investment needs to exceed the amount of the asset being purchased. That is to say, if you made a $50 million investment on a property with retail space, you would have to invest at least an additional $50 million to be eligible for the tax benefit.

Remember, when looking to take advantage of o-zone tax deferral opportunities, the program is only relevant for heavy development. It is almost impossible to apply some of the most popular investment styles (value-add, for example) to the program. How would you spend a whopping $50 million on a multifamily property that you acquired for $50 million and still make a gain? It’s not likely.

Finding places that are fit for such heavy development is tricky, and those opportunities are sparse. For this reason, investors should first look for the available inventory that would make the tax deferral even possible. The number of eligible parcels is not clear. By using Federal Acquisition Regulations to report data, which is obtainable from some city hall websites, one could determine which o-zone projects have a good chance of being eligible for the full tax deferral benefit.


Tamar Frumkin is vice president of marketing at Skyline AI.

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