Using Data To Support Stakeholder Expectations

#Negotiation Tip Number 4: Gather and Leverage the Data.   In his book “Moneyball,” Michael Lewis quotes John Henry, renowned investment manager and owner of the Boston Red Sox, in reference to a comparison between professional baseball and the financial markets, “People in both fields operate with beliefs and biases. To the extent you can eliminate both and replace them with data, you gain a clear advantage.” Since that book was published, data analytics has become a vital part of how almost every major professional sports team makes decisions. Data is equally important in commercial real estate negotiations. Most CRE professionals realize the importance of obtaining data, but few understand how to fully use it to achieve a successful outcome. In a negotiation while representing a buyer of a low-rise office building in a submarket with dozens of similar-sized office buildings, my team cherry-picked comparable sales and sent them to the seller’s representative, making a case for a purchase price around $90 per square foot. On the contrary, the seller’s representative made the case that the purchase price should be closer to $100 per square foot — submitting their own version of comparable sales as justification. At this point, our team was certainly tempted to accept the invitation from the seller’s broker to play the high-low game. Instead, we evaluated the seller’s comp set to determine how we could either work toward bridging the gap or defend our original position all while trying to achieve our client’s goals. As we dissected both data sets, we were able to see that many of the seller’s comparable sales had already been renovated, while the property being bought still needed cosmetic renovation. That was telling from a qualitative analysis, but the most convincing case came when we put both sets of sales comps on a line graph to show the trend in sale price per square foot over time. This line graph was very helpful for both the buyer and the seller to understand the current value of the property as the next data point in a trendline. Ultimately, they agreed on a purchase price that equated to $87 per square foot. Both sides had data, but it wasn’t until it was dissected and brought to life that anyone truly understood how it brought relevance to the negotiation. #CapitalMarkets, #InvestmentSales, #CRE, #CommercialRealEstate

Several times M&A negotiations stall not because of unrealistic demands from the other side but because of ❗️uncommunicated assumptions about business performance and risks and ❗️lack of a data-driven negotiation process. Two examples from the past six months: 1️⃣ We represented a buyer team, involving a complex earnout scenario in an acquisition. Negotiations around valuation and deal structure began to stall because the target felt we were pushing for a structure that would penalize the target unfairly for post-close performance. 2️⃣ We represented a seller that believed in their forward performance. However the top few potential buyers were skeptical and didn't believe our client could hit the numbers. ✅ Solutions to both scenarios: 🎯Instead of negotiating with the other side in a vacuum, we built and shared a more detailed model with them with our drivers and our assumptions based on historical data and the customer pipeline. 🎯 The discussions moved from the abstract to collaboration on a shared set of numbers and business assumptions. 🎯 We were able to step through specific scenarios and have a much more nuanced, granular discussion, with each side using the same framework and model structure. 🎯And we were able to drive to a successful resolution in both deals. (This is NOT saying that you open everything to the other side. Knowing the parts of the model that should be shared for negotiation purposes and those that shouldn't be is critical). But sometimes the best path forward in "stuck deals" is throwing light on the data. #mergersandacquisitions