Recent Owners’ Equivalent Rent Inflation Is Probably Not a Blip

Recent Owners’ Equivalent Rent Inflation Is Probably Not a Blip
Amy Higgins and Randal Verbrugge



Recently, the overall rate of inflation has risen, owing partly to inflation in Owners’ Equivalent Rent (OER). But many wonder if the current rate of OER inflation, which is now at levels not seen since 2009, is simply a blip. We apply a forecasting approach to estimate whether OER inflation will continue to be elevated going forward, or whether it will revert back to the lower levels that have been more typical over the last several years. We find that OER inflation is likely to remain elevated over the next year.

OER is used in the US and in many other countries to estimate inflation in homeowner housing costs. At its core, OER captures the implicit rent that a homeowner would have to pay if he or she were to rent instead of own the same home (or equivalently, the funds that the homeowner is sacrificing by living in the home instead of renting it to someone else). The OER of a particular home is the rent that the home would command under current market conditions. In practice, statistical agencies estimate OER inflation for homes in a particular part of a city using inflation in the market rents of nearby rental units. (For more details on how inflation is estimated in the US, go to

OER plays a prominent role in both the Consumer Price Index (CPI) and the Personal Consumption Expenditures (PCE) price index because of how heavily it is weighted when all the individual components are aggregated into each index. In the CPI, it accounts for roughly 25 percent of the total index. In the PCE price index—the preferred inflation indicator of the Federal Open Market Committee—it accounts for approximately 12 percent. In core inflation measures, OER accounts for an even larger share. With such a large weight, the OER component can affect the overall rate of inflation significantly.

As for what is causing OER to rise, a number of factors have been proposed. Some financial writers have suggested that a shortage of rental housing is responsible, though not everyone agrees that such a shortage exists. Proponents of the rental-housing-shortage view point to historically low ratios of completed privately-owned housing units to population and a low ratio of private construction investment to GDP. However, if rental housing were in short supply, one would expect to see historically low rental vacancy rates. Yet these rates are not far from their levels in 1995, before the run-up in housing prices. Still, it is possible that declining vacancy rates could prompt some rent inflation. It is also possible that some cities could be experiencing historically low vacancy rates, though this is not true of the five cities we examine below.

Unemployment rates might also be expected to affect rent inflation, and they have been dropping steadily. High unemployment rates might be expected to dampen rent inflation, and declining unemployment rates might be expected to feed it.

One might also expect rents to rise when house prices rise, since higher home prices mean that real estate is more costly. Housing prices seem to have bottomed out in most regions of the country, and in some cities they have rebounded fairly briskly.

Finally, low interest rates obviously make it cheaper to buy a home, and we would expect that low rates would cause house prices to rise (since more buyers can now afford a given home), and rents to rise less than they would if interest rates were higher (since some households would decide to buy rather than rent). Low interest rates also reduce the costs that landlords face, hence might be expected to reduce market rents.

To forecast where OER inflation rates are headed over the next year, we construct a forecasting model that includes these four possible causes: vacancy rates, unemployment rates, house price changes, and interest rates. When we use data to estimate the model, we can also test whether OER inflation rates actually do respond to vacancy rates, unemployment rates, house prices, and interest rates. And then, as long as the relationships that have prevailed in the past continue to hold in the future, we can use current data to give us an idea about future rent developments.

In our forecasting model, we also include two other variables which might help forecast OER inflation. The first is previous OER inflation. The second is the price/rent ratio, a measure of the “gap” between housing prices and rents. Like the price/earnings ratio associated with stocks, housing assets are sometimes evaluated through the lens of the price/rent ratio. Over long horizons, this ratio should be stable—although the ratio should also depend upon the real interest rate, with a low real interest rate causing the ratio to rise. When the price/rent ratio is high, we would expect adjustment: either house prices should fall, or rents should rise, or both.

The specific data we look at for the first four factors are the regional vacancy rate, the local unemployment rate, the local rate of year-over-year house price appreciation, and the real mortgage interest rate (i.e., the average 30-year fixed mortgage rate, adjusted for inflation by subtracting the expected inflation rate as reported in the Survey of Professional Forecasters). We examine the relationship of these variables plus the price/rent ratio to year-over-year OER inflation.

We look at the four Census regions (Northeast, Midwest, South, and West) and five cities: Cleveland, Los Angeles, Miami, New York, and Philadelphia. These cities were chosen because they are among the handful of cities for which we have monthly OER data, and because each Census region is represented by at least one city. We use quarterly data from 1990:1-2014:2 to gauge the strength of the relationships, and we then use our model as a forecasting model to forecast OER inflation in each region over the next year (2014:3-2015:2). (For an in-depth investigation of OER inflation versus rent inflation, see “Explaining the Rent–OER Divergence, 1999–2007”.)

The estimation method used is a vector autoregression, estimated using Bayesian methods. This methodology often has excellent forecasting properties.

Our results are surprising. OER inflation does not appear to be influenced by vacancy rates, unemployment rates, the real interest rate, or our gap measure. Of the variables investigated, only lagged house price appreciation appears to have a statistically significant relationship to OER inflation (previous OER inflation is also statistically significant). In one sense, this is a conundrum, because it suggests that we “cannot explain” OER inflation using the “usual suspects.” High vacancy rates do not appear to slow OER inflation down appreciably; neither do high unemployment rates, low interest rates, or a low price/rent ratio. The only usual suspect which appears to feed into OER inflation is lagged house price appreciation—and even then, it appears to be statistically significant in only about half of the cases investigated. The unemployment rate appeared to be statistically significant at the 10 percent level in two of the Census regions.

OER inflation has a considerable “momentum” component; that is, high OER inflation tends to be followed by high OER inflation. It is this momentum that dominates the OER forecasts below.

Our forecasting models suggest that, barring large unforeseen shocks, OER inflation is likely to slow somewhat in the Northeast, rise to about 3 percent in the South, remain at about 2.9 percent in the West, and rebound to about 2 percent in the Midwest. However, there is considerable uncertainty surrounding these forecasts.

In our model, lagged house price appreciation and recent OER inflation are the most important predictors of future OER inflation. Other commonly suggested influences of OER inflation—vacancy rates, unemployment rates, the price/rent gap, and interest rates—are generally not useful predictors.


Via the Federal Reserve Bank of Cleveland


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