Every mortgage investor faces the same question: Twoborrowers have identical creditscores. Which loan is safer?

Answering that question well means looking at ForeScore,credit score, and mortgage risk together, rather than treating credit historyas the whole story

Conventional mortgage valuation has no satisfactory answer.A credit score summarizes a borrower’s financial history, but it saysremarkably little about the economic environment in which that borrower mustrepay a mortgage. It cannot tell whether local employment is strengthening orweakening, whether housing markets are becoming more resilient or more fragile,or whether neighborhood conditions make mortgage distress more—or less—likely.

That omission matters because mortgages do not default inisolation. They default in places where people work, buy homes, experiencerecessions, and confront unexpected economic shocks.

ForeScore was developed to measure those missing dimensions.

The question, therefore, is not whether ForeScore predictsmortgage performance. It does. The more important question is whether itcaptures information that the credit score does not already contain.

The answer appears in the data.

Looking at Mortgage Risk in Two Dimensions

To examine the relationship between borrower credit qualityand local economic conditions, we grouped a random sample of 12,500 mortgageoriginations into twenty-five categories based on two variables: average creditscore and ForeScore. We then observed mortgage performance over the followingseven years.

The resulting pattern is remarkably clear.

Table 1 reveals an almost textbook example of complementaryinformation. Read down any column and borrowers with similar credit scoresexperience dramatically different default rates as ForeScore changes. Readacross any row and higher credit scores consistently reduce defaults withinevery ForeScore category.

Neither variable subsumes the other. Each explains variationthat the other leaves unexplained.

This table illustrates how credit scores and ForeScorejointly separate mortgage loans into well-defined risk categories. Rows compareborrowers with similar ForeScores across different credit scores. Columnscompare borrowers with similar credit scores across different ForeScores. Thedata are based on a random sample of 47,000 mortgage originations with realizeddefaults observed over the subsequent seven years.

The table is worth studying carefully because it illustratesa simple but powerful idea: mortgage risk has two dimensions. One reflects theborrower’s financial history. The other reflects the economic resilience of theplace where that borrower lives.

Ignoring either dimension leaves important information onthe table.

Borrowers with the Same Credit Score Are Not EquallyRisky

The clearest illustration comes from borrowers whose averagecredit score is 631.

Traditional valuation treats these borrowers as broadlycomparable. Yet their realized default rates range from only 4 percent in thestrongest ForeScore locations to 19 percent in the weakest—a nearly five-folddifference.

Their credit histories are essentially the same.

Their local economies are not.

Those differences cannot be explained by credit scoresbecause the credit scores are already held constant. They arise from localeconomic conditions that influence whether households can continue makingmortgage payments when adversity strikes.

Strong labor markets, resilient housing demand, and stableneighborhoods provide borrowers with more opportunities to avoid default. Weaklocal economies do the opposite. ForeScore measures those forward-lookingconditions.

This is precisely what one hopes to see from a complementaryrisk measure. If two variables always rank loans identically, one adds littlevalue. But when one meaningfully reorders loans that the other treats asequivalent, it contributes genuinely new information.

ForeScore does exactly that.

Credit Scores Still Matter

The relationship also works in the opposite direction.

Holding ForeScore constant, mortgage defaults declinesteadily as credit scores improve. Even within the highest-risk ForeScoregroup, realized defaults fall from 28 percent among the weakest-creditborrowers to only 3 percent among borrowers with the strongest credit histories.

Credit quality remains one of the most powerful predictorsof mortgage performance ever developed.

ForeScore does not replace the credit score. It complementsit.

The two measures capture different dimensions of mortgagerisk. Credit scores summarize the borrower’s demonstrated willingness andability to repay based on past behavior. ForeScore summarizes the economicenvironment in which that repayment must occur.

Together they produce a more complete assessment than eithercan alone.

The Most Interesting Result Is Their Interaction

The most important insight does not come from any single rowor column.

It comes from how the two measures interact. Across allcredit score tiers defaults in the ForeScore quintiles increase aboutfive-fold.

Borrowers with excellent credit remain relatively resilientacross a wide range of local economic environments. Changes in ForeScorecertainly matter, although the relative differences are aboutfive-fold, the absolute differences are modest, from near zero to 3-4%.

Borrowers with weaker credit tell a differentstory. For these loans, the same change in local economic conditions canalso increase observed default rates by a factor of five. But now the absolutedifference in the second credit decile is from 4% to 19%, an increase of 15%!

This is a classic interaction effect. Credit qualityinfluences how sensitive borrowers are to local economic conditions, whilelocal economic conditions influence how much credit quality ultimately matters.

As borrower quality declines, place becomes increasinglyimportant.

That is precisely where valuation decisions become mostdifficult—and where additional information becomes most valuable.

The implications extend beyond default probabilities.Expected losses are substantially larger among lower-credit borrowers, makingloan values increasingly sensitive to local economic conditions. Two mortgageswith similar credit scores can therefore have materially different economicvalues simply because they are originated in different markets.

Investors who ignore place risk inevitably miss thesedifferences.

Why This Matters

These findings have practical implications for every stageof mortgage investing.

When investors rely exclusively on credit scores, theyinevitably group together loans whose realized risks differ substantially. Theresult is systematic mispricing. Safer loans subsidize riskier ones. Lossreserves become less accurate. Capital is allocated less efficiently.Creditworthy borrowers may even be declined because they belong to broad creditcategories that conceal important differences.

Adding ForeScore improves risk segmentation bydistinguishing borrowers who appear identical through the narrow lens of credithistory but face very different economic futures.

Better segmentation leads directly to better pricing, moreaccurate reserve estimates, stronger portfolio surveillance, and moredisciplined risk management.

Perhaps most important, it allows investors to identifyprofitable lending opportunities that conventional models overlook whileavoiding risks that traditional credit measures underestimate.

Finance advances by measuring risk more accurately.

Credit scores represented one of the great innovations inconsumer finance because they transformed millions of individual borrowinghistories into reliable forecasts of repayment.

ForeScore extends that same logic.

Rather than replacing the credit score, it measuressomething the credit score was never designed to capture: the economicresilience of the places where mortgages are repaid.

The evidence suggests that both signals matter.

Together they provide a substantially more complete pictureof mortgage risk than either can provide alone.

‍