Insights
Market reports, forecast data, industry insights, and more from iEmergent.
iEmergent has expanded our technology and tools to help lenders use all types of data in their lending strategies, but our foundation is our proprietary forecasts. We provide forward-looking data that drills down to the neighborhood level and quantifies future mortgage opportunity in markets in the U.S.
In other words, we use data to show lenders where loans are going to be. We can see, in each neighborhood in the U.S., how many purchase loans will occur this year, next year, and the year after that.
Here, we’ll share insight into our forecasting approach, the fundamental concepts of our purchase forecast model, our process for validation, and our consistently high accuracy.
iEmergent forecasts mortgage opportunity for markets at various levels of geography—census tracts, counties, MSAs, regions, states—across the 50 U.S. states and the District of Columbia.
What is mortgage opportunity?
A forecast of mortgage loan volume in units and dollars that will occur over a specific period of time within a specific geography.
The purpose of our forecasts is to help organizations make better business decisions that lead to a successful, sustainable future. By knowing what’s next in a market, lenders can anticipate change and capture opportunity—as efficiently as possible. Amid economic uncertainty, shifting homebuyer behavior, and regulatory changes, leaders need more than expertise and experience to maintain and improve performance. They need reliable data that show what’s going to happen in their markets.
Our insights help mortgage lenders:
National mortgage opportunity forecasts are nice, but what can an individual MLO or lender do with that information? Nothing much actionable.
That’s why our forecasts are built from the bottom up: from the census tract level, which roll up into county, MSA, region, state, and national forecasts.
A lender in California shouldn’t have to—and we’d argue couldn’t effectively—use state or national forecasts to make strategic decisions for their local markets. Each and every census tract expands, contracts, and changes in unique ways, creating micro markets across the country. Understanding what’s expected for every market each year for the next five years is the only way to build strategic, localized lending approaches with confidence.
After the 2020 Census, there are 84,414 census tracts in the U.S.
Geography isn’t the only way to narrow down forecasted lending activity. Borrowers are not a monolith, and your forecasts shouldn’t be either. In addition to market breakdowns, iEmergent forecasts can be segmented by:
iEmergent clients can view forecasted loans and dollars for, as an example, moderate-income Black borrowers down to the neighborhood (census tract) level. That’s powerful insight into the future that’s not possible with any other data or tool.
Understanding the nuances of each market gives lenders a data-backed path to building forward-looking lending strategies—an imperative in today’s lending environment.
The iEmergent forecasting method is a hybrid of traditional demand forecast models, and it has evolved significantly since 2004, when we issued our first forecast. From the beginning, our model did not attempt to explain why each mortgage market behaves as it does; instead, we focused on market outcomes to identify how many and what type of loans will be originated over the next one to five years.
A lot of variables go into our forecasts, but the following concepts are fundamental to our approach.
The behavior of mortgage markets is complex—there are hundreds of indicators, trends, patterns, and events that impact how and why markets behave as they do.
After analyzing millions of loan application records over decades, iEmergent recognized specific patterns of behavior that captured the complexity of these multiple factors. What emerged was the Purchase Mortgage Generation Rate (PMGR), or the rate at which an individual market produces purchase mortgages. Not only is the PMGR of each market unique, but it is predictable by our model. Thus, it is the primary driver of our forecasts. The PMGR inherently captures the impact of broad-scale economics, decades of history from the HMDA Loan Application Record, homebuyer and housing behavior patterns, and other prominent indicators.
Through the PMGR, we simplify the projection of loans and dollars through a single indicator.
Many traditional mortgage industry volume forecasts are calculated using top-down, optimal-utility methodologies. Assumptions are made based on broad supply-side market behaviors that follow Say’s Law of Markets: “Aggregate supply creates its own aggregate demand.”
In contrast, iEmergent applies a “demand-driven” approach that captures the changing household-buyer patterns that define housing patterns over time. Here’s why, as explained more than a decade ago by our founder, Dennis Hedlund:
"Houses can’t buy themselves. Low interest rates can’t shop for homes to buy. Available credit won’t spontaneously buy homes. Low housing prices don’t buy homes. Secondary markets by themselves don’t incent people to buy homes. Big inventories can’t write a check for the mortgage. Households buy homes. And if households don’t buy homes, then mortgages aren’t originated."
Households that could potentially finance a home in a given year constitute the homebuyer pool.
The homebuyer pool is the number of households that are ready, willing, and able to buy a home.
These potential buyers are the foundation of homeownership demand and the second pillar of the iEmergent forecasting methodology.
Per the U.S. Census Bureau, there are an estimated 129.4 million households in 2023. Each year, iEmergent partitions households into three groups:
Using probability theory, adjustments are made to each of these three groups as new households are formed, and households convert from one group to another (e.g., non-homeowners become homeowners, and homeowners without a mortgage convert to homeowners with a mortgage).
As of January 2023, the U.S. Census Bureau estimates an overall homeownership rate of 65.9%.
Most important to our forecast methodology, however, is determining the size of the homebuyer pools for upcoming years. Despite 129.4 million households in 2023, not all of them will be ready, willing, and able to purchase a home in the next twelve months. Therefore, we account for that portion of the pool by creating a fourth partition that removes those households that are least likely to originate a mortgage. Households that have recently purchased or refinanced a home are taken out of the pool, as are those households who are unemployed, are struggling with balance sheet/credit issues, or starting foreclosure.
These segments tell us about the demand side of mortgage origination by understanding who’s in and out of the homebuyer pool. In 2023, only 78.9 million households (61% of U.S. households) are part of the homebuyer pool.
Housing markets are complex ecosystems, and homeownership behaviors are constantly evolving, ebbing, and flowing. The supply-demand dichotomy will eventually establish new equilibriums—at different points in time for different communities. The iEmergent forecasting methodology is built on the reality that homeownership demand is a critical driver of mortgage opportunity.
The relationship between each market’s Homebuyer Pool and PMGR determines the final outcome of our forecasts: the number of purchase mortgage loans and dollars that will be originated over the next one to five years.
Telling the future isn’t easy. Thankfully, our data-driven models—refined year after year—give us insight into what’s ahead. We pride ourselves on maintaining well above the industry minimum accuracy standard of 70% for predictive analytics. Each year, we compare our forecasts to actuals and remain well above average for accuracy.
We calculate the accuracy of our forecast results by comparing it to the data that is released through HMDA, which we consider to be the “gold standard” in the industry and the closest approximation of “actual” that is available.
Our accuracy analysis focuses on iEmergent’ all-occupancy, purchase, 1-4 unit forecast model—it is the cornerstone of our methodology and the foundation upon which we build our segment forecasts. Separate validation studies are available for our market segment forecasts on loan type, borrower race/ethnicity, loan size (conforming vs. jumbo), and borrower income level (low, moderate, middle, and upper).
Each year, we have validated our forecast results against the actual HMDA data for core-based statistical areas (CBSAs), counties, and the nation.The table below captures the volume accuracy percentage—((forecast dollars - actual dollars)/actual dollars)—for recent forecast years at the 18-month, 12-month, and 6-month time horizons for counties in the U.S.
Volume accuracy percentages for various levels of geography and time horizons.
The chart below shows our 2022 purchase loan accuracy for the Top 30 Metropolitan Statistical Areas (MSAs), using the average of our various time horizons and comparing them to the HMDA actuals.
In our validation studies, we also examine the Absolute Error (AE) by individual census tract. The chart below summarizes the census tract-level AE for 2022 purchase loans, comparing iEmergent’s 2022 forecast to the 2022 HMDA actuals.
In more than 55.9% of the 84,414 nationwide census tracts, the iEmergent forecast error was under 10 loans. At an AE of 15 loans, iEmergent’s census tract-level accuracy increases to 69.2% of all tracts.
We also forecast various segments, including race/ethnicity. Our accuracy rates for these breakdowns continually exceed industry expectations. For 2022, our Q4 forecast was more than 93% accurate at the national level for each borrower race/ethnicity group:
Our overall national accuracy also remains in line with—and often higher than—other major national forecasts, despite (or likely thanks to) our very different bottom-up approach. However, our focus remains on market-level, segmented forecasts that help lenders take action to grow originations and meet sales goals.
Today’s mortgage lending market is challenging—lenders are having to do more with less, and competition is sky high. Knowing where loans will be in your markets—and which borrowers are expected to take out those loans—is like having a crystal ball.
When you understand your market—its past, present, and future—you can deploy strategies and resources with confidence, knowing they’re based on what’s next in that market.
Lenders use iEmergent’s data and tools to:
See how you can use iEmergent to meet your lending goals: Request a demo
Since our founding, iEmergent has been committed to producing and offering accurate forecasts of mortgage opportunity to organizations of all sizes and types. We help these lending institutions make decisions that improve their long-term performance and profitability. The forecasting approach, models, and methods we use were carefully chosen according to how well they help serve that purpose. Experience has taught us that the best models and methods are built on strong and proven fundamentals but are constantly being tested and modified to ensure ongoing efficacy.
See how Lake Michigan Credit Union (LMCU) boosts originations, grows its referral network, and recruits ideal talent with iEmergent market intelligence.
See how iEmergent’s forecasts and tools come together in Mortgage MarketSmart to help lenders set and reach enterprise, regional, branch, and MLO goals. Request a demo