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2020 Nobel Econ Prize Reveals Negotiating & Price Discovery Methods

The Swedish Academy of Sciences announced the winner of the 2020 Nobel Prize in Economics this week. The research behind it has applications in post-pandemic commercial real estate negotiations.

This year the prize went to a couple of U.S. Game Theorists, Paul Milgrom and Robert Wilson, both of Stanford University.

Milgrom and Wilson created new price discovery models that help optimize outcomes for buyers and sellers [lessees and lessors]. Their focus was government auctions (i.e., e spectrum-frequency bands, electricity, batches of troubled debt, and the like). Yet, the research is applicable in an array of competitive market transaction negotiations. Commercial real estate is prime. I was interested (and amused) that their research supports my beatdown of typical RFPs and the concept of “Valutility” I’ve introduced.

Their research in a nutshell:

Milgrom and Wilson’s new transaction models acknowledge utility inherent in products or services to be bought, sold, or leased that is common to all bidders (offerors), but that net future values derived from those products and services differ from user to user. Building on that, their research proved that the more visibility each user had into their net future benefits (value), the greater the accuracy of their offers without the noise attributed to discounts for anticipated errors in assumptions or added premiums to counter perceived competition. That’s no surprise. What is unique about their models is that the bargaining timeline is not static. The visibility into future benefits is made more clear because each participant can be made aware of new information as it becomes available and the behavior of the other bidders and information derived from other offers.

The researchers found that when bidders are made aware of the other bidders’ estimates of their future value, each bidder becomes more confident in their bid process thus making offers more aligned with their estimate of future value without discounting or hedging because of price and info obfuscation, or paying blind premiums due to competition. It doesn’t eliminate information disadvantage but certainly lessens its impact.

In the words of The Committee for the Prize in Economic Sciences in Memory of Alfred Nobel, “These insights do not just enable sellers to raise higher revenues or buyers to procure at lower costs. They also facilitate sales to the most appropriate buyer or procurement from the most appropriate seller.”

So how would this work in practice for Post-Pandemic Real Estate Negotiations?

Consider a tenant looking to lease retail space in a shopping center with several vacancies. The future value of the space being leased by the tenant is impacted by the future resolution of those vacant spaces, as well as the ongoing viability of existing co-tenancies. Who ultimately leases the vacant space in the center, and for what use? Will the existing tenants stay, and for how long? What if the unemployment rate in the area rises or the car count drops. What will the sales per square foot trend be for all the tenants in the center? How many convection ovens are bought or gym memberships purchased (or canceled) in the area? What if the restaurants go dark or stay dark. What if one or more of the major employers in the area closes, automates, or relocates. What if electricity or insurance costs spike? What are the key variables to the new tenant’s success or failure, and how can those metrics be indexed? It’s those indexes that can provide visibility into future benefit (or drag). How about the next pandemic, the infection rate, and lethality? Index it.

Like many of you, I’ve inserted into loss-prevention measures in leases such as termination rights and scheduled rent changes for loss of important co-tenancies or spikes in unemployment. But these will become more commonplace, and the number and type of indexes to be used as factors should grow. Occupancy rates, product demand, income level, education level, product supply/demand gap, anything pertinent, each can be used as an index for future rents. Regression Analysis of causal factors of existing facilities can be used to establish base cases.

There has been a lot of talk about percentage rents as a structure that could get traction post-Covid. I laugh because I’ve spent the last 15 years vanquishing percentage rents from my retail clients’ leases. The breakpoints (where percentage rent equals scheduled rent) were always a factor of rents at lease origination; it was a means for the landlord to piggyback on the tenants’ success. I would only encourage bringing back percentage rents if the breakpoint drops to near zero and remove the asymmetry for all indexes (yes, including CPI) so landlords participate in the downside as well as the upside.

Expanding the concept of Indexed Rents can be used for all property types to provide greater future benefit visibility, but retail is an obvious fit post-pandemic.

Please let me know what other variables you’ve used to index rents or that you think might be appropriate.

Cooperation and competition are fundamental aspects of economics. No doubt introducing greater use of indexed rents will generate pushback. But incorporating key metrics as indexes to factor rents could provide more stability for both tenant and landlord, more success for both, and with less turnover.


If you’d like to see the research behind this year’s Nobel Prize in Economic Sciences, go here:

If you’d like to see my jawdropping, groundbreaking, utterly brilliant concept of “Valutility,” you can get the book, Confessions of a Corporate Real Estate Hitman: Killer Negotiating in Business and Life – Creating my Unfair Advantage at Amazon here:

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