Mortgage Rate Intelligence & Market Updates

The ongoing advancement and adoption of artificial intelligence continues to raise concerns about widespread job losses. Over the past three years, our regional business surveys have asked firms about their AI adoption and its effects on their workforces. This year, we found that AI use among regional businesses has continued to rise sharply, with more than 60 percent of service firms and about half of manufacturers now using AI—a notable increase from 40 percent and 26 percent, respectively, reported in 2025. Despite this rapid adoption, regional firms’ investments in AI are generally modest, usage tends to be concentrated among a small share of workers within firms, and layoffs have remained uncommon. And, while some firms have scaled back hiring due to AI, others have added workers to help them use it. Retraining employees in response to AI remains the primary way firms are adjusting their workforces.

Stock market participation in the U.S. has changed dramatically over the past four decades. In the mid-1980s, fewer than 30 percent of households held equity. By the early 2000s, more than half of U.S. households owned equity, either directly or through mutual funds, 401(k)s, and IRAs. As participation widened, the way stock market fluctuations passed through to household spending may have changed, with potential implications for how the broader economy behaves. An argument can be made that the rise in equity market participation has dampened the response of output to interest rate changes as stock market fluctuations are now spread across a larger share of households, moderating movements in consumer spending, asset prices, and investment spending.

A long-standing body of research, inspired by Bernanke and Kuttner (2005), has documented the effects of Fed interest rate surprises on stock markets. While stock markets provide valuable information about the investor risk premium and dividend growth expectations, researchers have only recently developed more comprehensive tools to estimate the term structure of equity risk premia and dividend growth expectations across a broad range of maturities. In this post, we investigate the impact of monetary policy surprises (or shocks) on short- and long-term estimates of risk premia and growth expectations through the lens of the Giglio, Kelly, and Kozak (2024) model.

Total debt balances declined slightly by $13 billion in the second quarter of 2026, according to the latest Quarterly Report on Household Debt and Credit from the New York Fed’s Center for Microeconomic Data. Mortgage and student loan balances saw a small decline, while there were increases across other debt products. Delinquency rates across most products remained fairly stable. Still, between 2022:Q3 and 2026:Q1, the percentage of credit card balances 90+ days delinquent rose from 7.6 percent to 12.8 percent, prompting concerns that Americans are falling behind on their debt payments at rates not seen since the Great Recession. Yet the flow delinquency rate—which captures the rate of new delinquencies—has remained relatively stable for almost two years. In this post, we use data from the New York Fed Consumer Credit Panel (CCP) to better understand the state of the consumer, and to explain the difference between our two measures of delinquency. We find that the stock delinquency rate is rising because of a pool of stale, charged-off debts that lenders have been reporting for longer durations, rather than a fundamental worsening in the incidence of delinquency.

In March 2020, the Financial Industry Regulatory Authority (FINRA) began reporting aggregate trading volume for securities issued by the U.S. Treasury Department. The public data do not, however, include information about the trading activity of Separate Trading of Registered Interest and Principal of Securities (STRIPS). STRIPS are created from existing Treasury securities and offer risk […]

Americans are moving less than they used to. Moving rates have declined steadily for decades, falling from close to 20 percent annually in the mid-1980s to below 10 percent by 2019. This decline has persisted through business cycles and has been evident across all regions, and has affected a broad range of demographic groups. Falling mobility matters because moving helps households access job opportunities, adjust to changing circumstances, and improve their housing situations. In this post, we show that the decline in mobility also holds for renters, with growing challenges to owning a home being an important contributing factor.  We use data from the annual New York Fed SCE Housing Survey to study renters’ expected mobility and the factors that shape it. Renter mobility is important as renters account for roughly a third of U.S. households and, unlike homeowners, are not subject to mortgage rate lock-in. Since expected mobility predicts actual moving behavior, it provides an early signal of where residential mobility is headed before moves occur.

Welcome to Street Level, my new series on Liberty Street Economics. As research director, I try to keep track of the wide range of work that the economists at the New York Fed produce. My goal for this series is to periodically offer some thematic discussion of that work, highlighting recent analysis by Research staff and adding my own observations on the issue at hand. In this inaugural post, I’ll focus on perhaps the hottest topic going: How artificial intelligence is changing the labor market and hiring behaviors.

Monetary policymakers closely monitor the term structure of sovereign bond yields to uncover market participants’ beliefs about the future monetary policy stance, inflation, and activity. A particular object of interest is the natural real rate of return, or “r-star,” which acts as a guide for monetary policy decisions. Numerous papers have questioned how much information investors possess, and how precisely they know r-star. In this post based on a recent Staff Report, we explore what the term structure of interest rates can teach us about r-star and its perception by investors.

Stablecoins are digital assets whose value is pegged to that of a fiat currency, typically the U.S. dollar at a peg of $1.00 per token.  In a previous blog post, we described the rapid growth of stablecoins through early 2025, highlighted changes in stablecoins’ reserve-asset composition, and examined their reactions to Bitcoin price shocks. In this post, we document the growth of stablecoins since our last post. Then, we examine how shocks from outside the crypto industry can impact the composition of stablecoins’ reserve assets. For our case study, we use the 2023 failure of Silicon Valley Bank (SVB) and its impact on USD Coin (USDC, issued by Circle), the second-largest stablecoin by market capitalization.