WTW's Geospatial Mortality Model Resets PRT Bid Pricing

WTW's Geospatial Mortality Model (GMM), extended on July 14, 2026 from plan-sponsor funding use to insurer bid pricing, applies zip-code-level socioeconomic adjustment to base mortality tables in U.S. pension risk transfer transactions, producing liability present-value displacement of 3-8% on geographically concentrated plans relative to a standard RP-2014 projection (WTW, July 2026). That range is large enough to move a competitive bid outcome.

What WTW Actually Shipped

WTW's July 14 release repositions a tool that pension plan sponsors have used since 2021 to set funding and accounting mortality assumptions, extending it explicitly to insurers and reinsurers pricing PRT buyouts and managing in-force longevity exposure (WTW Geospatial Mortality Model launch, GlobeNewswire, July 2026). The current version is calibrated against nearly four million life-years of pension plan experience, including 2020 through 2024 data that captures the post-COVID mortality recovery, and was built by screening more than 200 socioeconomic factors by geography before retaining the strongest predictors: income quartile, educational attainment, local healthcare access density, and environmental exposure proxies. The output is a zip-code multiplier applied against a standard mortality table, not a replacement table in its own right, which is the detail that determines how it slots into an existing PRT pricing workflow rather than requiring a wholesale actuarial rebuild.

The timing is not incidental. Annuity purchase interest rates sit at their highest levels since early 2024, with the duration-7 rate at 5.05% and the duration-15 rate at 5.26% as of October Three's June 2026 pricing update, and insurer capacity in the U.S. PRT market remains at peak levels even as that rate tailwind compresses the premium plan sponsors must pay to transfer risk. In a market this competitive, a 3-8% swing in projected liability present value is not a rounding error in an actuarial memo; it is the difference between a winning bid and a bid that leaves a few hundred basis points of spread on the table.

The Two-Stage Adjustment Mechanic

A standard U.S. PRT bid starts with an RP-2014 base table, typically the White Collar or Blue Collar variant selected to match the plan's industry and benefit structure, projected forward with a mortality improvement scale such as the SOA's MIM-2026 or the older MP-2021. The GMM inserts a geospatial multiplier g(z) at the participant level, keyed to zip code z, so the adjusted mortality rate becomes q_x,adjusted = q_x,standard × g(z). A multiplier below 1.0 signals a geographic cluster with higher-than-average mortality; a multiplier above 1.0 signals a cluster with longer expected longevity. Rolling each participant's adjusted q_x through a present-value annuity calculation, discounted at a rate anchored to A/AA corporate bond yields matching the insurer's asset strategy, produces the aggregate liability displacement.

A worked example shows the magnitude in dollar terms. A $500 million PRT transaction whose participants concentrate in high-education, high-income northeastern zip clusters might carry an RP-2014 standard-table liability present value of $480 million. Layer in the geospatial adjustment along with a dynamically differentiated improvement scale, discussed below, and that present value can rise to $498 million to $505 million, a 4-5% variance that either compresses the winning insurer's target spread or forces a higher bid to hold margin. That is the entire pricing problem in one number: an insurer bidding blind to geography on a plan like this is quoting a price roughly $20 million light relative to what the true expected liability requires.

Why the Improvement Scale Has to Move With the Base Table

The static version of this adjustment, a one-time base-table shift applied at bid date and then projected forward using an aggregate improvement scale, understates the real problem. The SOA's MIM-2026 supplies improvement rates at the population level, but the GMM's post-COVID experience data through 2024 shows high-SES geographic clusters recovering faster from excess pandemic-era mortality than low-SES clusters, meaning the improvement trajectory itself diverges by geography rather than converging back to a single national rate. An actuary who applies a static geospatial discount to current q_x rates without also differentiating the forward improvement scale will understate a high-longevity plan's true liability duration by an estimated 0.5 to 1.5 years. In a business where assets and liabilities are meant to be closely duration-matched, an error of that size is not a modeling nuance; it changes how much long corporate bond or private credit allocation the deal actually requires to back the guarantee.

That duration effect runs in both directions. A downward mortality adjustment for a low-longevity plan shortens expected liability cash-flow duration, pulling the required asset match toward shorter maturities and freeing capital that would otherwise sit in long-duration paper. A high-longevity geospatial adjustment does the opposite: it lengthens duration, increases the required allocation to long corporate bonds or private credit, and directly compresses the credited-rate spread embedded in the bid unless the insurer prices for the extra duration risk up front. Asset-liability management is not a downstream consequence of the mortality pricing decision here; it is part of the same calculation.

Winner's Curse: What Happens When Only Some Insurers Adjust

A PRT market where one insurer deploys geospatial adjustment and its competitors continue pricing off standard tables does not settle into a stable equilibrium; it segments the market by geography in a way that punishes the standard-table bidders specifically on the deals that matter most. Plans whose participants cluster in high-mortality zip codes become artificially cheap targets for the geospatial-adjusted insurer, who can bid a lower premium than standard-table competitors and still hold margin, because its model correctly prices the shorter expected liability duration. Plans in high-longevity clusters run the opposite risk: a standard-table insurer that wins one of these plans has, without realizing it, embedded a longevity shortfall into its margin from the moment the deal closes, because its bid never priced the extra years of annuity payments the geospatial data would have flagged.

This is the textbook adverse-selection dynamic that geodemographic mortality pricing has already worked through in the UK bulk annuity market, where carriers have used postcode-level longevity models since the early 2000s. UK insurers including Legal & General built material, durable competitive advantages from proprietary postcode-level models, with mortality loading factors spanning roughly a 16-point spread across postcode-level longevity bands in that market's calibration data, a gap wide enough to separate profitable and unprofitable bids on the same plan. The U.S. market is arriving 15-plus years behind that precedent, but at $30 billion to $40 billion in annual PRT transaction volume, the pricing edge from even a 1% aggregate liability displacement is worth hundreds of millions of dollars a year across the insurer fleet (WTW, U.S. Pension Risk Transfer Market Overview). Insurers that do not adopt some form of geographic mortality adjustment are not choosing to stay neutral; they are choosing to lose the low-mortality-cluster deals to sharper bidders while accumulating exposure on the high-longevity clusters those same bidders are happy to let them keep.

Standard Table vs. Geospatial-Adjusted Pricing: Illustrative $500M PRT Bid

Pricing basis	Implied liability PV	Driver
RP-2014 standard table, static improvement	$480M	No geographic mortality differentiation
GMM-adjusted, static improvement scale	~$492M	Base-table displacement only, improvement scale held aggregate
GMM-adjusted, geospatially differentiated improvement	$498M-$505M	Base-table displacement plus diverging post-COVID improvement trajectory by SES cluster

Source: illustrative worked example built on WTW's disclosed 3-8% liability displacement range and the post-COVID improvement divergence described in the July 2026 GMM release. Figures are indicative, not a specific transaction.

The ASOP No. 25 Credibility Question

ASOP No. 25, Credibility Procedures, and ASOP No. 35, Selection of Demographic and Other Noneconomic Assumptions, both bear directly on how a pricing actuary can lean on the GMM's output rather than a standard table. ASOP No. 25 requires the actuary to document the credibility of the geospatial data source and confirm that each geographic segment carries sufficient exposure to support the mortality displacement factor being applied to it; a zip code with a handful of plan participants and no independent claims history cannot carry a full-credibility adjustment on its own; the actuary needs a blending approach that weights the model's segment-level output against the prior standard-table assumption in proportion to the segment's credibility, converging toward full model weight only as the underlying exposure base grows. ASOP No. 35 adds a second requirement layered on top: because a geospatial mortality basis is a departure from a standard, broadly used demographic assumption, the actuarial opinion supporting the bid must disclose that departure and the rationale for it, not merely footnote that a proprietary model was used somewhere in the pricing process. Actuarial opinions for the GMM's first PRT bid cycle should expect to spell out the credibility standard applied at the segment level, the blending methodology between model and prior, and the specific rationale for treating zip-code-level socioeconomic data as more predictive than the plan's own experience where the two diverge.

Why This Matters

Pricing actuaries working PRT bids this cycle face a binary choice with no comfortable middle ground: adopt some version of geographic mortality differentiation now, while the U.S. market is early enough that being second is still viable, or accept that standard-table bids will systematically win the wrong deals, the high-mortality-cluster plans priced too rich to win and the high-longevity-cluster plans priced too cheap to win safely. Reserving and ALM teams downstream of a PRT close inherit the consequence either way; a mispriced longevity assumption does not surface as an obvious loss for years, by which point the asset portfolio built to match a mis-estimated liability duration is already locked in. The UK market's 20-year head start on postcode-level pricing did not stay a niche advantage; it became the baseline expectation for a competitive bulk annuity book. The U.S. PRT market, now at peak insurer capacity and record interest rates pulling plan sponsors to the table, is the environment in which that same transition compresses into a much shorter window.

Further Reading on actuary.info

• SOA's MIM-2026 Mortality Improvement Model and the VM-20 Pension De-Risking Link - The improvement-scale mechanics behind Stage 2 of the GMM adjustment discussed here.
• Mortality Improvement and the VM-20 Life Insurance Pricing Reset - How improvement-scale changes flow through statutory life pricing more broadly.
• Brookfield, Just Group, and Milliman's PFI Approach to PRT Pricing - A separate proprietary framework already active in the PRT bid market.
• IRS 2027 DB Mortality Tables and SECURE Act Pension Pricing Caps - The regulatory mortality-table side of pension pricing that interacts with insurer PRT bid assumptions.
• ASOP No. 12 and Unintended Bias in Pricing Model Risk Classification - The governance framework question that geospatial socioeconomic pricing inputs will eventually have to answer.