Tenant brokers are a critical asset in enabling corporate organizations to build a strategically located workforce. They act as trusted partners to understand their clients’ business needs and how these organizations think about their workforce today. They must be strategic advisors, offering ways for their clients to execute on their workforce strategies, whether that calls for relocating or renewing a lease, site selection for their talent or growth needs, or optimizing talent costs. Last but certainly not least, tenant brokers must be well poised as credible analysts, educating their clients with sound analysis of real estate market conditions and workforce availability.
Wearing multiple hats in one job takes grit and resourcefulness that only the most savvy tenant brokers learn to master. Demonstrating competency with a sound analysis of job and real estate markets is absolutely critical in getting clients onboard. Educating clients and having a solution for their workforce needs is easier said than done when the analysis relies upon a variety of data sets which are siloed, inconsistent, and often unstructured. Time is of the essence in order to win deals.
Terrain’s Single Location Analysis product
At Terrain, we understand that speed, unique insight, and influence are the most important competencies of a trusted advisor, and we’re ready to help. We’re using analytics and machine learning to democratize data so that you can be among the savviest of tenant brokers, privy to the knowledge and resources it takes to accelerate decision-making and the execution of deals.
This is where Terrain’s core product, Single Location Analysis, plugs in. Once you have an understanding of your client’s workforce needs and how they’re thinking about the workforce today, you can leverage Single Location Analysis to pull reports that uncover growth or trends that will help you identify metropolitan areas, counties, cities, and neighborhoods in North America with the highest quality and largest talent pools. This data will help you consult your clients by helping you compare employer characteristics such as employee skill type, quality, headcount, talent growth or contraction, and salary. You might even uncover neighborhoods with surprising talent characteristics. For example, Terrain’s data has helped uncover characteristics such as lower average salary rates and higher diversity populations that were different from what the client expected Not only will this data help you better understand the talent composition of different areas, it will also equip you with the actionable insights your client needs for their decision-making on which markets to pursue. All while avoiding massive search costs.
Single Location Analysis in Action
We recently had the opportunity to work with Jane, a savvy broker who came to us with an urgent request for supporting a client that needed direction. This client knew it was interested in tapping into the talent markets in Atlanta, but wasn’t sure whether to do so remotely or build an office. In general, the client’s workforce strategy was to prioritize hiring in talent markets that were growing quickly and were skewed towards a younger, tech-oriented workforce.
When the tenant broker came to us, Jane was flustered by all the research she was conducting to no avail. There was plenty of public knowledge pointing to Atlanta offering a younger talent base due to the abundance of schools and higher education in the area, but this observation was already inferred by the client. Jane was determined to add more insight to support the decision-making. It was challenging if not near impossible for her to build a decisive analysis with salient recommendations when she had to pull data from so many sources that were inconsistent with each other. How could she act more as an advisor rather than a mere facilitator, adding value to her client’s workforce priorities? One of the biggest challenges we hear from tenant rep brokers like Jane is educating their clients on why some rent prices are higher than others. While sometimes it has to do with real estate factors such as the quality of office space or proximity to public transportation, other times it can be related to the associated talent pool.
By using Terrain's Single Location Analysis data, Jane was able to provide her client with bespoke, data-driven insights on different neighborhoods in Atlanta, highlighting details surrounding the abundance of great talent in the area, the high growth rate, the average salary, diversity composition, age distribution, as well as other aspects including Terrain’s proprietary TalentRank* metric. These metrics captured more than just costs but also quality of talent.
Terrain’s data uncovered that instead of merely hiring remotely, it would be more worthwhile to build a center of excellence to establish the client’s presence in Atlanta. The SLA report enabled Jane to help her client think about different neighborhoods in Atlanta by providing a breakdown of high growth and strong talent associated with different areas in Atlanta and more importantly, helped her client get comfortable with potentially paying a higher rent price to have an office there in order to pursue high-value engineering talent. The client became convinced that Northeast Atlanta was their desired submarket and more specifically, the Atlantic Station neighborhood offered them the best quality talent. Essentially, the tenant broker transformed the client’s business decision from an initiative to minimize costs to one that is strategically pursuing a talent market with high-value talent.
Interested in seeing how Single Location Analysis can support your client’s needs and speed insight to action to accelerate tenant deals? Terrain offers a variety of pricing options for you to select the best subscription plan for your business. Let us equip you with the insights you need to influence with confidence.
Footnotes:
* To offer a metric that gauges technical talent quality, Terrain’s TalentRank tool acts similarly to a resume by pulling data sets from the web to take into account things such as where employees have worked, how long they’ve worked at these companies, schools they’ve attended, open-source contributions, and other employee-specific online data.
