ForeScore: Measuring the Place Behind Every Mortgage

Credit score measures the borrower.
ForeScore measures the place.

We define place as the evolving economic, housing, and demographic environment that shapes both collateral value and borrower behavior throughout the life of a loan. ForeScore data provides two forward-looking metrics at the ZIP code level for each origination month: one hundred million observations updated quarterly. Scenarios like the "severely adverse" CCAR scenario are also available.

ForeScore: Default

Quantifies how place affects the probability that loans in a given location will default in the future.

ForeScore: Prepayment

Measures how place influences the likelihood that borrowers will repay their loans ahead of schedule in the future.

Both scores are indexed around 100. A ForeScore Default of 80 in a given ZIP indicates conditions 20% more favorable than the historical average; a score of 130 indicates conditions 30% less favorable. The adjustment is straightforward: apply the ForeScore deviation from par to your existing risk estimates, and your model immediately reflects the future location risks your portfolios actually face. Or, incorporate the data directly into your model for even better risk assessment.

What distinguishes ForeScore data from other geographic risk tools is its orientation in time. Most available tools are descriptive, telling you where defaults and losses have historically been concentrated. ForeScore data are predictive. They forecast local economic conditions forward over the full remaining life of a mortgage loan, capturing not where risk has been, but where it is headed.

MORE FAVORABLE CONDITIONS

50 100 150

80 20% more favorable than baseline

LESS FAVORABLE CONDITIONS

50 100 150

130 30% less favorable than baseline

The result is model-ready scores per ZIP code and origination date that integrates directly into existing default, prepayment, and pricing models. No software to license, no model rebuild required.

Independent Dimensions of Mortgage Risk

The most important question about any risk variable is whether it adds genuine information beyond what you already measure. ForeScore does, and the evidence is unambiguous.

The table below cross-classifies realized default rates by borrower credit score and ForeScore across 47,000 mortgage originations, with defaults observed over seven years. Neither variable subsumes the other; each explains variation the other leaves unexplained.

The practical implication is significant. Sensitivity to local conditions results in changes in default rate of over 4-fold across credit score quintiles. For the weakest-credit borrowers in the sample, there is a 21% increase in default rate based on place. Even among superprime borrowers who rarely default, place is a powerful discriminator in managing risk. This is precisely where additional information is most valuable and where ignoring place leads to the most consequential mispricing.

The commercial impact is measurable. In UFA's analysis of more than 30 million mortgage loans, incorporating ForeScore improved pool valuation:

130 bps

baseline integration

380 bps

fuller model application

ForeScore in Practice

Local economic risk touches every stage of the mortgage lifecycle. ForeScore is built for the institutions that manage it.

Portfolio Concentration Risk Management

Risk managers and chief credit officers use ForeScore to map geographic exposure across their mortgage holdings – identifying concentrations in deteriorating markets before delinquencies surface and before regulators ask.

Secondary Market Investment & MBS Valuation

Investors use ForeScore to assess the geographic risk distribution of loan pools at the ZIP code level, adjusting default and prepayment assumptions by location for more accurate pool pricing and capital allocation.

Loan Servicing & Portfolio Surveillance

Servicers use ForeScore's updates to identify which parts of their portfolio are in markets where conditions are weakening – enabling proactive intervention before borrowers miss payments.

Mortgage Insurance

Insurers use ForeScore to assess the geographic concentration of their insured portfolio, supporting more accurate reserve estimation and more disciplined underwriting in markets where local conditions are deteriorating.

How ForeScore is Built

ForeScore is built on a proprietary econometric model of local economies, updated regularly. The process integrates economic, housing, and demographic data at the ZIP code level into forward-looking risk scores across the full 30-year mortgage horizon.

Economic, Housing & Demographic Data

Proprietary Econometric Model

Regular Update

ForeScores

Getting Started with ForeScore

ForeScore is delivered as a quarterly data file — ZIP-code-level scores that integrate directly into your existing models and systems. No software to license, no integration project.

First engagements typically begin with a methodology briefing and a sample analysis run against a representative portfolio or loan pool. All engagements are confidential.

Request a Briefing