Welcoming Our New Head of Data Science

We’re extremely excited to welcome Zach Wade to our team as our new Head of Research & Data Science. He is an accomplished data scientist with practical experience spanning both academic research and applied commercial product development. Zach will lead the efforts on our Indices business, and also interface with our set of early customers. He brings a unique perspective on the commercial real estate industry to the table and truly understands the challenges commercial real estate investors face. Most importantly, he shares our vision to create a new information economy for the Real world.

In his words “I’m excited about bringing underutilized data resources to the forefront of CRE decision making and applying a disciplined ensemble of machine learning, computer vision, NLP and agent-based learning methods to enable computer-assisted decision making in the CRE industry.”

Prior to Real Factors, Zach founded Blue River Quant Labs, a data science think tank focused on experimental research applications combining machine learning, game theory, real estate and quantitative economics. Zach served as a data science consultant to a variety of real estate-focused software and data businesses. Most recently, he was the Head of Data Science at BuildFax, a leading provider of real estate software and data.

Before working as a consultant, Zach served as a Quantitative Researcher for Deutsche Bank’s Real Assets Group. Prior to Deutsche Bank, Zach worked as a researcher at Columbia University’s Paul Milstein Center for Real Estate. Zach holds a MSc in Economics & Management from the London School of Economics with distinction and a B.S. in Economics from the University of British Columbia where he graduated with 1st class honors. When he is not solving complex Real Estate data problems, Zach is a Mountain Rescue volunteer in Breckenridge, CO, where he lives.

Quantifying Gentrification With Public Data

Wouldn’t it be great to have invested in Brooklyn 15 years ago? How about SoMa in San Francisco or Wynwood in Miami? In the past, investing in up and coming neighborhoods occurred as a result of gut feel and “knowing the market”. However, with advances in machine learning and user based location data (Yelp, Foursquare, Google, Twitter, etc.), it has become increasingly possible to understand the composition, quality, and evolution of a given neighborhood.

Researchers at Harvard Business School recently fused Yelp data with Census, Federal Housing data, and Streetscore (a Google product) to understand the composition of different neighborhoods. They were able to clearly identify a relationship between increased housing prices and growing numbers of local groceries, cafes, restaurants, and bars.

In a different study done at the University of Cambridge, researchers fused Twitter and Foursqaure data with the UK Index of Multiple Deprivation (IMD) (a statistical exercise conducted by the the British government which measures the relative prosperity of neighborhoods across England). They found a correlation of a “deprived” area — meaning low economic status — with “high social diversity” meant that a neighborhood was in the process of being gentrified.

Even focusing on a single type of business can highlight the changes in a given neighborhood. In the HBS study, it was discovered that the entry of Starbucks (or other similar coffee shops) were indicative of housing price growth. They also found that Yelp establishments from 2007-2011 predict changes in educations levels over the next five years.

Looking at the work done by and organic grocers like Whole Foods, the pattern is equally as visible. Whole Foods recently announced two new locations in Washington D.C., which was viewed by locals as a sign that their neighborhood was on the up and up. One location, two blocks from Howard University, “was interpreted as proof that the neighborhood’s recent gentrification is no ephermal phenomenom”.

This makes sense, though, as Whole Foods relies on “heavily on micro-demographics and complicated algorithms” to determine prime locations. On aggregate, when taking into account locations of Whole Foods and competitors like Trader Joes and Harris Teeter, you can quickly get a sense for the quality of neighborhood. These locations, paired with other types of businesses like Barry’s Bootcamp and Blue Bottle Coffee, can create a beautiful picture of neighborhoods you want to invest in.

Commercial Real Estate deals have come a long way over the past decade. With growing institutional allocations and increased foreign investment, there is a need to be more data-driven to have an edge – open data provides that.

Sources: HBS, University of Cambridge, The WashingtonianInverse.com

The Case for CRE in Seattle

Seattle is on fire and presents a fantastic option for institutional investors that typically spend their time in Tier 1s like NYC, SF, and LA. Multifamily development has continued to grow and rents continue to rise. According to RealPage Chief Economist Greg Willett, “Lots of places register very solid expansion of downtown jobs, but Seattle’s urban core growth rate is in a whole different category”.

The housing market in Seattle has been number one in the country for the past 14 months, breaking its own pre-recession record. The only longer held record is by San Francisco during the dot com boom from 1999-2001. An argument can always be made that there needs to be a correction in the market (similar to Miami and Las Vegas over the past decade), however underlying socioeconomic characteristics and market fundamentals seem to make Seattle a great long term investment opportunity.

Although housing costs are on the rise, they are still lower than San Francisco. The average rent, as an example, is nearly half of the equivalent unit in San Francisco ($1,654 per units vs. $2,990 per unit). The city also presents similar options to San Francisco when it comes to cultural attractions: great restaurants and cafes, an indie art and music scene, and outdoor options within driving distance of the city.  

Seattle’s urban core is what truly leads the market. In areas like Downtown, South Lake Union/Queen Anne, and Capitol Hill, 21,707 new apartments have opened since 2010. Despite all of the new supply though, 96.7% are occupied and rents are rising 6.6% per year.

When it comes to the economy, a case can be made that Seattle is on par with San Francisco, arguably an even better option. Unemployment in Seattle is now down to 4% according to the Bureau of Labor Statistics, compared to 3.6% for San Francisco. Moreover, the minimum wage in Seattle will rise to $15 effective in 2018, increasing disposable income. Venture funding has grown and technology giants like Amazon and Microsoft continue to open job recs quicker than they can fill them.

The influence of technology companies has had a strong effect on the office market and Seattle is no different. Lease activity spiked 11% nationally last according to JLL. Nearly 30% of the growth came from six markets where technology or knowledge-intensive businesses dominate: Silicon Valley, Raleigh-Durham, Brooklyn, San Diego and Seattle.

Drilling down into fundamentals, it’s very clear that the Seattle market is strong. For Office, net absorption and new construction is up, total vacancy has trended down over the past five years to 9.2%, and average asking rents have trended up since 2013 to $36/sq. ft. In industrial, there is barely enough space to keep up with demand – vacancy has dropped to a historical low of 2.5% Even in a niche sub-vertical like Life Sciences, vacancy is at 1.3%, with little new construction. Some may argue it’s too late, but it seems like the best is yet to come.

Sources: JLL, NREI, Seattle.gov, Seattle Business Times, BLS.gov

The Challenges Faced By Institutional Commercial Real Estate Professionals

As commercial real estate becomes more mainstream, price fluctuations have a broader impact on investors, markets and economies at large. While metrics and analytical tools are maturing, they continue to be imperfect for managing an asset class with heterogeneous assets, fragmentation, agency issues, and a lack of data transparency. Compared to other asset classes, even the U.S. property market lacks some key historic data to support extremely advanced modeling and decision making.

Lack of accurate, timely, consistent, and unified data from across the enterprise compromises CRE firms’ and analysts’ ability to benchmark performance against the market via reliable indexes. This is an increasingly critical capability as competition grows.

The challenge facing organizations today is not finding data to analyze – it’s having a capability flexible enough to analyze all-of the disparate data through a single lens. Further, spread-sheet based processes and unintegrated function-specific applications result in data silos. As a consequence:

  • Significant time and resources are required to aggregate data to obtain a full understanding of a market or portfolio.
  • Out of necessity, highly compensated and very capable individuals are typically compiling data from multiple sources in a spreadsheet in a labor-intensive way, then working to manually scrub the data for inconsistencies or errors.
  • Substantial time and resources are required to aggregate data in order to obtain a granular, portfolio-wide view or meet reporting transparency requirements.

Many institutions have multiple, specialized investment teams focused on specific markets or verticals. Each of these teams possess valuable data in their systems and files. However, this valuable data is often siloed and not easily available to colleagues from other teams.

Existing data from real estate fundamentals or the U.S. Census Bureau have been immensely useful in understanding and forecasting trends in real estate performance. However, much of this data is released with a time lag, making it difficult to be ahead of market-moving trends.

Today, ICRE investors and lenders have recognized the strategic importance of data assets to achieve business results.  To realize the full benefits of data assets, firms need to create, manage, share, and leverage these assets in their daily processes. Therein lies an opportunity for technology to optimize processes and improve alpha.