Considerations on Rent Control

(On November 13, I was invited to testify before the Jersey City city council on rent control. Below is an edited version of my testimony.)

My name is J. W. Mason. I have a Ph.D. in Economics from the University of Massachusetts at Amherst, I am an assistant professor of economics at John Jay College of the City University of New York, and I am a Fellow at the Rosevelt Institute.

My goal today is to present some general observations on rent regulation from the perspective of an economist.

Among economists, rent regulation seems be in similar situation as the minimum wage was 20 years ago. At that time, most economists  took it for granted that raising the minimum wage would reduce employment. Textbooks said that it was simple supply and demand — if you raise the price of something, people will buy less of it. But as more state and local governments raised minimum wages, it turned out to be very hard to find any negative effect on employment. This was confirmed by more and more careful empirical studies. Today, it is clear that minimum wages do not reduce employment. And as economists have worked to understand why not, this has improved our theories of the labor market.

Rent regulation may be going through a similar evolution today. You may still see textbooks saying that as a price control, rent regulation will reduce the supply of housing. But as the share of Americans renting their homes has increased, more and more jurisdictions are considering or implementing rent regulation. This has brought new attention from economists, and as with the minimum wage, we are finding that the simple supply-and-demand story doesn’t capture what happens in the real world.

As of 2019, there are approximately 200 cities in the US with some type of rent regulation. Most of them are in three states — New York, New Jersey, and California. Other areas where rent control was once widespread, such as Massachusetts, have seen it eliminated by state law.

A number of recent studies have looked at the effects of rent regulations on housing supply, focusing on changes in rent regulations in New Jersey and California and the elimination of rent control in Massachusetts. Contrary to the predictions of the simple supply-and-demand model, none of these studies have found evidence that introducing or strengthening rent regulations reduces new housing construction, or that eliminating rent regulation increases construction. Most of these studies do, however, find that rent control is effective at holding down rents.

A 2007 study by David Sims and a 2014 study by Autor, Palmer, and Pathak both look at the effects of the end of rent control in Massachusetts, after the passage of Question 9 by Massachusetts ballot referendum in 1994. Sims found that the end of rent control had little effect on the construction of new housing. He did however find evidence that rent control decreased the number of available rental units, by encouraging condo conversions. In other words, rent control seemed to affect the quantity of rental housing, but not the total quantity of the housing stock. Unsurprisingly, Sims also found significant increases in rent charged after decontrol, suggesting that rent control was effective in limiting rent increases. Finally, he found that rent controlled units had much longer tenure times, supporting the idea that rent control promotes neighborhood stability. Autor and coauthors reached similar conclusions. They also found that eliminating rent control also raised rents in homes in the same area that were never subject to the controls, reinforcing the idea that rent control contributes to neighborhood stability.

A 2007 study by Gilderbloom and Ye of more recent rent control laws here in New Jersey finds evidence that rent controls actually increase the supply of rental housing, by incentivizing landlords to subdivide larger rental units.

A 2015 study by Ambrosius, Glderbloom and coauthors also looks at changes in New Jersey rent regulations. As with the previous study, they find that rent control in New Jersey has not produced any detectable reduction in new housing supply. However, they also find that many of these laws,  because of their relatively generous provisions, in particular vacancy decontrol, only limit rent increases on a relatively small number of housing units. 

The most recent major study of rent control, by Diamond McQuade, and Qian in 2018, uses detailed data on San Francisco housing market to look at the effect of the mid-1990s change in rent control rules there. They suggest that while the law did effectively limit rent increases, and had no effect on new housing construction, it did have a negative effect n the supply of rental housing by encouraging condo conversions. 

The main conclusions from this literature are, first, that rent regulation is effective in limiting rent increases, although how effective it is depends on the specifics of the law. Vacancy decontrol in particular may significantly weaken rent control. Second, there is no evidence that rent regulations reduce the overall supply of housing. They, may, however, reduce the supply of rental housing if it is easy for landlords to convert apartments to condominiums or other non-rental uses. This suggests that limitations on these kinds of conversions may be worth exploring. Third, in addition to their effect on the overall level of rents, rent regulations also play an important role in promoting neighborhood stability and protecting long-term tenants.

Let me now turn to the question of why the textbook story is wrong. There are several features of housing markets and of rent control that help explain why the simple supply-and-demand model is inapplicable.

First, these arguments misunderstand the goal of rent regulation. In part, it is to preserve the supply of affordable housing. But it also recognizes the legitimate interest of long-term tenants in remaining in their homes. A rented house or apartment is still a family’s home, which they have a reasonable expectation of remaining in on terms similar to those they have enjoyed in the past. Just as we have a legal principle that people cannot be arbitrarily deprived of their property, and just as many local governments put limits on how rapidly property taxes can increase, a goal of rent control is to give people similar protection from being forced out of their homes by rent increases. 

Second, and related to this, there is a social interest in income diversity and stable neighborhoods. In the absence of rent control or other measures to control housing costs, an area that sees rising productivity or improved amenities may see a sharp rise in rents and become affordable only for higher-income households. Besides the questions of equity this raises, there are economic costs here, as it becomes difficult for people holding lower paid jobs to live within commuting distance; an area that becomes more homogenous may also lose the social and cultural dynamism that caused the improvement in the first place. Similarly, the evidence seems clear that in the absence of rent regulation, turnover among tenants will be higher, leading to less stable communities and discouraging investment by renters in their neighborhoods. The absence of rent regulation may also create political obstacles to efforts to increase housing supply, attract new employers, or otherwise improve urban areas, since current residents correctly perceive that the result of any improvement may be higher rents and displacement. Rent regulation removes these conflicts between the social interest in thriving, high-wage cities and the interests of current residents. This makes it an important component of any broader urban development program.

Third, rent regulations in general affect only increases in rents. When a new property comes on the market, landlords can charge whatever the market will bear. And when they make major improvements, again, most existing rent regulations, including the current Jersey City law, allow them to recapture those costs via higher rents. So what rent control is limiting are the rent increases that are not the result of anything the landlord has done — the rent increases that result from the increased desirability of a particular area, or of a broader regional shortage of housing relative to demand. There is no reason that limiting these windfall gains should affect the supply of housing.

Fourth, in many high-cost areas, housing supply is relatively fixed. The reason that existing homes in many large cities cost multiple times more than the costs of construction, is that the ability to add new housing in these areas is very limited, by some mix of regulatory barriers like zoning, and physical or economic barriers. In economists’ terms, the supply of housing in these areas is inelastic  – it doesn’t respond very much to changes in price. This fact is widely recognized, but its implications for rent regulation are not. In a setting where the supply of new housing is already limited by other factors  – whether land-use policy or the capacity of existing infrastructure or sheer physical limits on construction –  rent regulation will have little or no additional effect on housing supply. Instead, it will simply reduce the monopoly profits enjoyed by owners of existing housing.

Fifth, housing is very long-lived. According to the Bureau of Economic Analysis, the average age of a tenant-occupied residential structure in the US is 42 years. In much of the northeast and in older cities, the average age will be greater. The fact that housing lasts this long has important implications. No one constructing new housing is thinking about returns that far out. Most business investment is expected to repay its costs in less than 10 years. Housing construction may have a longer payback period — as we know, much construction is financed with 30-year mortgages. But the rents 40 or more years in the future are simply not a factor in the construction of new housing.  This means that there is a great deal of space to regulate the rents on existing housing without affecting the decision to build or not build

The bottom line is that rents in the everyday sense are often also economic rents. When economists use the term rent, they mean a payment that someone receives from some economic activity because of an exclusive right over it, as opposed to contributing some productive resource. When a landlord gets an income because they are lucky enough to own land in an area where demand is growing and new supply is limited, or an income from an older building that has already fully paid back its construction costs, these are rents in the economic sense. They come from a kind of monopoly, not from contributing real resources to production of housing. And one thing that almost all economists agree on is that removing economic rents does not have costs in terms of reduced output or efficiency. 

Finally, I would like to offer a few design principles for rent regulation, based on my read of the literature.

First, rent control needs to be combined with other measures to create more affordable housing. The main goals of rent regulation are to protect renters’ legitimate interest in remaining in their homes; to advance the social interest in stable, mixed-income neighborhoods; and to curb the market power of landlords. Other measures, including subsidies and incentives, reforms to land-use rules, and public investment in social housing, are needed to increase the supply of affordable housing. These two approaches should be seen as complements.

Second, there are good reasons that most existing rent control focuses on rent increases rather than the absolute level of rents. Rent control structured this way allows new housing to claim the market rent, giving the developer a chance to recover the costs of construction. Rent increases many years after the building is finished are more likely to reflect changes in the value of the location, rather than the costs of production. From the point of view of allowing existing tenants to remain in their homes, it is also makes sense to focus on increases, rather than the absolute level of rents.

Third, since rent regulation is aimed at the monopoly rents claimed by landlords, it should allow for reasonable rent increases to reflect increased costs of maintaining a building. At the same time, there is a danger that landlords will engage in unneeded improvements if this allows them to raise rents more than they would otherwise be allowed to. A natural way to balance this is to adjust the allowable rent increase each year based on some measure of average costs or a broader price index, as in the current Jersey City law.

Fourth, for rent control to be effective, tenants also need to be protected from the threat of eviction or other pressure from landlords. To give renters genuine security in their homes, they need an automatic right to renew their lease, unless the landlord can demonstrate nonpayment of rent or other good cause.

Fifth rent control is more likely to have perverse effects when the controls are incomplete. When rent regulations do reduce the supply of affordable rental housing, this is typically because they have loopholes allowing landlords to escape the regulations. In particular, vacancy decontrol or allowing larger rent increases on vacancy significantly reduces the impact of rent control and may encourage landlords to push out existing tenants. There is also some evidence that landlords seek to avoid rent regulation by converting rental units into units for sale. To avoid these kinds of unintended consequences, rent regulations should be as comprehensive as possible, and options to remove units from the regulated market need to be closed off wherever possible. 

Thank you.

CBO Interest Rate Forecasts, 2011-2019

This is just a brief addition to the previous post. I should have included this figure, which shows the CBO’s 10-year forecasts for the interest rate on the 10-year Treasury bond, compared with the actual interest rate.

Forecasts by year made. Source: CBO 10-Year Economic Projections, various years

One obvious point here is that, for most of the past decade, the CBO has been projecting a return of interest rates to “normal” levels, which has stubbornly failed to take place. If we compare the interest rate on Treasury bonds at any point since 2010 to the CBO’s forecasts from a couple years before, the actual interest rate is lower than the forecast. This is especially true in the earlier years.

Another point, more relevant to my post, is the latest adjustment really is a big deal. While there have been comparable downward adjustments, there haven’t been any in a while; in fact for the past four years the long-run forecast has been fixed around 3.7 percent.1 This is also the first interest rate forecast since the recession that predicts that interest rates will remain near current levels indefinitely. Of course, it may still end up being an overestimate, if the recent decline in rates continues.

One takeaway is that when trying to guess what interest rates will be in the future, you probably can’t do better than assuming that they’ll be more or less where current rates are. There have been many, many confident predictions over the past decade that interest rates will soon rise — the CBO is far from the worst offender here — and they have consistently been proven wrong. If you want to talk about the future path of government debt, or some similar question where interest rates matter, you need a very good reason to assume interest rates much higher than what we see today. A strong feeling that interest rates just have to go up someday, isn’t enough. And as long as interest rates remain close to current levels, the debt ratio is not going to go up very much, even with deficits significantly larger than today’s.

I should note that while I think pictures like this are clarifying, I don’t find that they’re always effective rhetorically. People who are committed to some variety of hard-money view find it easy to say, “well sure, predictions of rising rates have been wrong for many years. But how do you know they won’t be right this time?”

* * *

If you’re just interested in the policy debate, you can stop reading here. But I can’t help pointing to another takeaway, from a more theoretical perspective: This picture is clearly not the result of a process where expected value of a variable is just an unbiased estimate of its true future value. In that case the errors should be distributed at random around the actual path, instead of all way off to one side.2

To be sure, the CBO’s numbers are not forecasts in a strict sense, but inputs into its legally mandated projections of the future path of the debt. The CBO needs to make forecasts in a way that minimizes not just ex post errors, but challenges to is credibility and neutrality. The relevant question is not whether the forecasts are as accurate as they can be, but whether they are “reasonable” in some broader sense. And this is as it should be! If I were dictator of the CBO, I would not insist on using forecast values that I myself think will be closest to the true values, but would balance this against the need for a consistent and transparent methodology and the costs of getting too far from the views of the relevant community of experts. The CBO is not simply a machine for generating forecasts, it plays a specific role in a concrete political process.

But of course, this isn’t just the CBO. Any institution operates on the basis of a set of shared beliefs about the world, and the process by which those beliefs are generated needs to be compatible with the other activities and reproduction of the institution. In any setting where people have to act collectively, getting as accurate as possible a picture of the relevant facts needs to be weighed against the need for some picture that everyone can agree on. Which, from the point of view of economics, suggests we need to think more carefully about expectations. We need to distinguish between the “expected” value as the central tendency of a given probability distribution; the subjective belief in someone’s head about the likely outcome; and the implicit belief about the outcome that is the basis of the relevant behavior. The essence of the rational expectations revolution was to collapse these three senses into the first one, effectively removing expectations as an independent object of inquiry.


For anyone interested, here is the R code that generates the above figure. I think including the relevant code whenever you present quantitative results is best practice, for blogs as much as anywhere else.

# can update this as new projections become available
files <- c('https://www.cbo.gov/system/files/2018-06/51135-2011-08-economicprojections.xlsx',
'https://www.cbo.gov/system/files/2018-06/51135-2012-08-economicprojections.xlsx',
'https://www.cbo.gov/sites/default/files/recurringdata/51135-2013-02-economicprojections.xls',
'https://www.cbo.gov/sites/default/files/recurringdata/51135-2014-08-economicprojections.xlsx',
'https://www.cbo.gov/sites/default/files/recurringdata/51135-2015-08-economicprojections.xlsx',
'https://www.cbo.gov/sites/default/files/recurringdata/51135-2016-08-economicprojections-2.xlsx',
'https://www.cbo.gov/sites/default/files/recurringdata/51135-2017-06-economicprojections2.xlsx',
'https://www.cbo.gov/system/files/2018-08/51135-2018-08-economicprojections.xlsx',
'https://www.cbo.gov/system/files/2019-08/51135-2019-08-economicprojections_1.xlsx')
# using the August reports where available. For some reason there's none in summer 2013.

n <- length(files)
cbo.projections <- list()

for (i in 1:n) {
temp <- tempfile()
download.file(files[i], temp)
x <- read.xlsx(temp, sheetIndex = 3)
unlink(temp)
cbo.projections[[i]] <- x
}

names(cbo.projections) <- 2011:2019

cbo.interest <- as.data.frame(matrix(nrow=n*2, ncol=12))
names(cbo.interest) <- c('forecast.year', paste0('y', 1:11))
cbo.interest[,1] <- rep(2011:2019, each=2)

s <- c(7, 7, 8, rep(7, n-3))
# for some reason in 2013 the data starts one column further over.

for (i in 1:n){
x <- cbo.projections[[i]]
yearrow <- subset(x, x[,4]=='Units', select=s[i]:(s[i]+10))
interestrow <- subset(x, x[,2]=='10-Year Treasury Note', select=s[i]:(s[i]+10))
for (j in 1:11){
cbo.interest[i*2-1, j+1] <- levels(yearrow[1,j])[yearrow[1,j]]
cbo.interest[i*2, j+1] <- levels(interestrow[1,j])[interestrow[1,j]]
}

}

interest <- read.delim('https://fred.stlouisfed.org/data/GS10.txt', skip=16, sep =' ')[,-2:-3]
names(interest) <- c('date', 'GS10')
interest$year <- substr(interest$date, 1, 4)
interest.ann <- aggregate(interest$GS10, by=list(interest$year), FUN=mean)

y1 <- 2010
y2 <- 2029

plot(x=y1:y2, y =y1:y2, ylim=c(0,6), xlab='', ylab='Projected Interest Rate')
for (i in seq(1, n*2, by=2)){
lines(x=cbo.interest[i,-1], y=cbo.interest[i+1,-1], col=rainbow(n*2)[i])
}
lines(x=2010:2019, y=interest.ann[58:67,2], lwd=2)
legend(x='bottomright', legend = 2011:2019, col=rainbow(n*2)[seq(1, n*2, by=2)], bty='n', lty=1, ncol=2)
title(main='CBO forecasts for the 10-Year Treasury Bond, 2011-2019')
# the correct thing to do here would be to convert the data to long format and produce the plot with ggplot.
# would be simpler and give prettier results. But this works and I am too lazy to redo it.