Real Estate Research provides analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog include the Atlanta Fed's Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.

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November 24, 2015

The Pass-Through of Monetary Policy

In the wake of the Great Recession, the Federal Reserve instituted three rounds of large-scale asset purchases (LSAPs) in 2008, 2010 and 2012, more commonly known as "quantitative easing 1" (QE1), "QE2" and "QE3." The objective of these interventions was to keep interest rates low in an attempt to stimulate household consumption and business investment.1

In the United States, housing is the single largest asset on the household balance sheet, accounting for 73 percent of nonfinancial assets for the average U.S. household and an even higher share for homeowners.2 Mortgage payments represent the largest class of household debt obligation.

Evidence of the effectiveness of the asset purchase programs on real economic activity has until recently been limited due to the lack of data and a credible identification strategy (by which we mean a way to separate the causal impact of the LSAPs on the economy from other government programs and market factors that were occurring at the same time). When we chart a timeline of the three LSAPs against the primary mortgage rate, we can see that the primary mortgage market rate effectively dropped below 6 percent when the Fed began buying $600 billion in mortgage-backed securities during QE1. Indeed, the rate dropped following each of the subsequent LSAPs.


Since 2009, a number of papers have been published that evaluate the effectiveness of the policy interventions through different transmission channels. One such paper (Keys, Piskorski, Seru, and Yao, 2014) reports on borrowers with adjustable-rate mortgages (ARMs) who automatically receive the benefits of lower interest rates with no frictions or transaction costs, unlike borrowers with fixed-rate mortgages (FRMs) who must refinance in order to take advantage of lower interest rates. The paper provides new evidence on the effectiveness of the LSAPs.

Our strategy is to compare the change in the household balance sheets of 7/1 ARM borrowers to those of 5/1 ARM borrowers, using credit bureau data linked to mortgages. These two ARMs are the most popular ARM products among prime borrowers with very similar credit quality and risk preferences, yet they differ only in years 6 and 7, when the 5/1 ARM is eligible for a rate reset and the 7/1 is still locked (that is, the rate is still fixed). This creates a natural experiment that allows us to isolate other factors that might affect the mortgage rate.

By controlling for borrower characteristics and economic environments, we estimate that mortgage rates in the treatment group (5/1 ARMs) dropped in the first year by 1.14 percentage points, from 5.1 percent, and that payments dropped by $150 per month, or about a 20 percent reduction on average. The average borrower had a cumulative two-year savings of $3,456.3 We also subsequently found that borrowers spent 18 percent of the money saved on paying off credit card balances and that there was an 11 percent increase in new car purchases for the group. As a result, the leverage of U.S. households' dropped considerably from its peak during the financial crisis.4

We also find significant heterogeneity for these effects across different populations. Less creditworthy and more liquidity-constrained borrowers appear to have benefited the most from LSAPs as they experienced significantly greater reductions in mortgage rates and payments and larger improvements in mortgage and credit card performance. In terms of how they spent the extra liquidity received, highly leveraged borrowers (high credit utilization) spent 40 to 50 percent of the extra liquidity received during the first year, or $814 out of $1,740, to repay their revolving debts, then spent 20 percent of the extra liquidity received during the second year. Borrowers in the top quartile of credit utilization rates allocated about 70 percent of the extra liquidity toward repaying their credit card debt. We found similar effects among borrowers in the bottom quartile of credit scores. (The low-wealth borrowers with low credit utilization experienced a much larger increase in auto debt or new car purchases.) In other words, the LSAP programs effectively stimulated household investment and consumption.

We also find, as a result of the estimated effects at the micro level, a significant impact on local (nontradable) employment growth, consumer spending, and house price recovery in regions that were more exposed to ARMs. For example, a 10 percentage point increase in the ARM share, which is associated with about a 20-basis-point average reduction in ZIP code mortgage rates, is associated with about a 0.25 percentage point increase in quarterly home price growth, or about 1 percent annual appreciation, a very meaningful increase.

By Vincent Yao, visiting scholar at the Federal Reserve Bank of Atlanta and associate professor in the Real Estate Department in the J. Mack Robinson College of Business at Georgia State University.


Di Maggio, Marco; Amir Kermani; and Rodney Ramcharan. 2014. "Monetary Pass-Through: Household Consumption and Voluntary Deleveraging," Working Paper.

Hancock, Diana and Wayne Passmore. 2014. "How the Federal Reserve's Large-Scale Asset Purchases (LSAPs) Influence Mortgage-Backed Securities (MBS) Yields and U.S. Mortgage Rates," Finance and Economics Discussion Series 2014–12. Board of Governors of the Federal Reserve System.

Keys, Benjamin J.; Tomasz Piskorski; Amit Seru; and Vincent Yao. 2014. "Mortgage Rates, Household Balance Sheets, and the Real Economy," NBER Working Paper No. 20561.


1 The LSAPs involved purchases of long-term securities issued by the U.S. Treasury, agency debts, and agency mortgage-backed securities (MBS). They ultimately affected the yields of the MBS as well as the mortgage rates offered to borrowers in the primary mortgage market through several potential transmission channels: (1) the signaling of the Fed's commitment to keeping rates low, (2) a portfolio rebalance between assets and deposits and among different durations, and (3) increasing the liquidity value of MBS (Hancock and Passmore, 2014).

2 Survey for Consumer Finance, Federal Reserve Board of Governors, 2013.

3 Di Maggio, Kermani. and Ramcharan (2014) found much bigger savings for subprime and Alt-A borrowers based on a similar approach.

4 It is notable that in the United States the majority of prime borrowers take out fixed rate mortgages while most subprime borrowers take out adjustable rate mortgages.

November 24, 2015 in Financial crisis, Mortgage crisis | Permalink


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November 03, 2015

Keeping an Eye on the Housing Market

In a recent speech, Federal Reserve Bank of San Francisco President John Williams suggested that signs of imbalances were starting to emerge in the form of high asset prices, particularly in real estate. He pointed out that the house price-to-rent ratio had returned to its 2003 level and that, while it may not be at a tipping point yet, it would be important to keep an eye on the situation and act before the imbalance grows too large. President Williams is not the only one monitoring this situation. Many across the industry are keeping a watchful eye on the rapid price appreciation (see here, here, and here), including my colleagues and me at the Atlanta Fed.

While it is too soon to definitively know if a bubble is forming, the house price-to-rent ratio seems like a relevant measure to track. Why? Basically, because households have the option to rent or own their home, equilibrium in the housing market is characterized by a strong link between prices and rents. When prices deviate substantially from rents (or vice versa), the cost-benefit calculus in the rent-versus-own equation changes, inducing some households to make a transition. In effect, these transitions stabilize the ratio.

In an effort to better understand house price trends, we chart the house price-to-rent ratio at an annual frequency on top of a stacked bar chart depicting year-over-year house price growth (see chart below). Each stacked bar reflects the share of ZIP codes in each range of house price change. Shades of green indicate house price appreciation from the year-earlier level, and shades of red indicate house price decline. The benefit of considering house price trends through the lens of this stacked bar chart is, of course, that it provides a better sense for the distribution of house price change that is often masked by the headline statistic.

Looking at these two measures in concert paints an interesting picture, one that doesn't appear to be a repeat of the early 2000s. While the house price-to-rent ratio indicates that house prices on a national basis have been increasing relative to rents, the distribution of house price change looks a bit different. In 2003, roughly 20 percent of ZIP codes across the nation were experiencing house price appreciation of 15 percent or more on a year-over-year basis. In 2014 and 2015, less than 5 percent of ZIP codes experienced this degree of appreciation.


To better understand the regional variation, we repeated this exercise at a metro level using the Case-Shiller 20 MSAs (see charts below). (House price-to-rent ratios for Las Vegas and Charlotte were not calculated because the Bureau of Labor Statistics does not provide an owners' equivalent rent for primary residence series for these markets.) This more detailed approach reveals that elevated price-rent ratio readings were only present in a few, perhaps supply-constrained, metropolitan areas (see top right corner of each chart for the Saiz supply elasticity measure). Moreover, current home price appreciation across ZIP codes does not have the breadth that was present during the early 2000s.

Notes: (1) All price-to-rent ratios are indexed to 1998, except Dallas and Phoenix, which are indexed to 2002. (2) SE = Saiz's Supply Elasticities. Pertains to city boundaries, not metropolitan areas. For more information, see Albert Saiz, "The Geographic Determinants of Housing Supply," The Quarterly Journal of Economics (August 2010) 125

As John Krainer, an economist at the San Francisco Fed, pointed out in a 2004 Economic Letter, "it is tempting to identify a bubble as a long-lasting deviation in the price-rent ratio from its average value. But knowing how large and long-lasting a deviation must be to resemble a bubble is not obvious." We will continue digging and report back when we think we know something more.

Photo of Jessica DillJessica Dill, economic policy analysis specialist in the Atlanta Fed's research department

November 3, 2015 in Homeownership, Housing boom, Housing crisis, Housing prices, Rental homes | Permalink


Thank You for giving us another tool to monitor the real estate market. As an investor it's always good to keep an eye on the risks vs rewards scale.

Posted by: Mike | January 04, 2016 at 11:33 AM

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