Recently we worked on a real estate dashboard for one of our clients which consisted of various statistics including YTD production, progress to Goal, the prior year’s production, and pending production. Ultimately, the client wanted to use business analytics to predict their agents' production for the current year.
Although most of these statistics can be easily gathered with a few simple queries, the end-of-the-year production projection proved to be a beastly matter. While the projection itself is a simple formula, the various levels of conditional logic proved too complex to hold true on a consistent basis.
The original formulas based on filter criteria were sufficient, but as we increased the logic and its dependencies, this proved to be a headache for the following reasons:
1. Some agents didn't have any production in the last year.
2. Some agents have no production in the current year.
3. Agent schedules range from below part-time to full-time.
4. Some agents have teams, while others are solo.
The initial formulas featured a myriad of conditions and, while they worked well towards the end of the year, the formulas proved imperfect at the beginning of the year. Furthermore, when we filtered the dashboard a) by the office and, b) then by an agent, the projections were inconsistent.
The root of the problem was that not all agents started at the same time. Some agents started just two weeks ago, and some agents 2+ years ago. Thus, their respective dashboards would display inaccurate and inconsistent projections even if, in reality, they were pacing towards the same goal.
As you can see, the scenarios are endless and each month that passes left us maintaining more and more formulas for each scenario. The company's business model was built on data, but understanding the data required machine learning algorithms.
Toying with ML, we decided to delegate the heavy lifting to AI. We started by using an open source machine learning platform and training datasets with a minimum of two years of production transactions. Grouping production by week, we were able to ask the machine to predict this year's week 52nd production. Using this projection, we added the percent of potential closes of pending production. This looks something like:
AI projected outcome + (Pending Sales * Sell Rate) = Year-end projection
How it works:
1. A linear model that uses least-squares to approximate current year-end total production.
2. Samples for modeling were based on the weekly production from a database, going back two years.
After deployment, we found that implementation was simpler, projections were now accurate and stable, and maintaining the code was also easier. As we train the datasets to consider account trends, our projections will mature with more accuracy. The more data we have, the more accurate the result.
Deciding to invest in custom-built software is a key digital transformation strategy for companies ready to ditch their old business processes. In the real estate world, companies that rely on data to make strategic decisions have an edge over their competitors. Our client used predictive analytics to fairly compensate their employees, increasing trust and authority with their agents.
If your digital transformation initiative for this year includes overhauling how your company collects, uses, and applies data, connect with Ventive. We help clients every day transform into the next stage of their business.