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Real Estate Research

March 27, 2014

Limiting Property Tax Assessments to Slow Gentrification

A recent New York Times article on gentrification discussed a number of cities—including Boston, Philadelphia, and Washington, D.C.—that are planning to freeze property tax assessments for long-time homeowners in gentrifying neighborhoods. The concern is that rising house prices will also raise property assessments, forcing low-income residents to move to escape the greater tax burden and thereby accelerating the pace of gentrification. Although the desire to protect existing residents from gentrification appears to be new, laws capping assessment growth for all property or all primary homes ("homesteads") have been around since Californians passed Proposition 13 in 1978. After California, a number of additional states passed laws limiting how quickly an individual property's assessed value could increase. The bulk of these laws passed in the early eighties to the mid-nineties, and advocates for the law were concerned, at least in part, with limiting the size of local government. If this tax backlash of the previous decades is uncorrelated with more recent gentrification pressures, this may be a good test of statewide assessments caps.

Using a data set of low-income central-city neighborhoods that Dan Hartley of the Cleveland Fed assembled from the 2000 census and the 2007 American Community Survey, we can look at the share of neighborhoods that gentrified in capped and uncapped states. Hartley shows that a central city moving from below-median-MSA house price to above-median house price is a good indicator of gentrification. Relying on the table of statewide assessment caps that Haveman and Sexton compiled, we identify 10 states and the District of Columbia (plus the city of New York) with the strictest limits. In these states, assessed value can increase only at the rate of inflation or by a fixed percentage ranging from 2 percent (California) to 10 percent (Texas). Table 1 presents the share of neighborhoods that gentrified in capped and uncapped states.

Table 1: Share of Neighborhoods that Gentrified between 2000 and 2007

Note that neighborhoods protected by assessment caps actually gentrified faster than those in states without them.

However, we might worry that the decision to impose statewide assessment caps was not random. In the case of Prop 13, rising home prices was certainly a factor in rising property taxes. It is possible that some underlying factor may drive statewide price up but also cause poor inner-city neighborhoods to appreciate faster than other homes in the metro area. One candidate is restrictive zoning laws that limit densification of already desirable neighborhoods. Such laws could both drive up aggregate house prices and push homebuyers into more marginal neighborhoods, causing them to appreciate relatively faster. However, assessment caps are only one possible response to rising property taxes. If voters wish to limit the growth in property taxes, they don't need capped assessments—they can restrict the growth in property tax revenues directly. At the same time, assessment caps that don't also cap the property tax rate don't actually constrain property taxes, but instead shift the tax burden from longtime owners to new buyers. In Table 2, we limit the sample to states that have a binding revenue growth cap or that jointly cap assessments and municipal tax rates. In this case, we assume that, conditional on imposing a tax expenditure limit, the decision to cap assessments rather than property tax revenue is random. We rely on the work by Hoyt, Coomes, and Biehl (2011) to identify various statewide tax expenditure limits.

Table 2: Share of Neighborhoods that Gentrified between 2000 and 2007 with some form of binding property tax limit

Limiting the sample to states that have chosen to constrain the property tax in some way, we still observe assessment caps seeming to accelerate gentrification rather than slow it. How can that be? One possibility is that because these are state-wide limits, the caps have reduced the turnover in more desirable neighborhoods, driving new homebuyers to marginal central-city neighborhoods. In that case, targeted assessment caps that apply only to currently low-priced neighborhoods could still be efficacious. On the other hand, the existence of an assessment cap may increase the long-run return from "pioneering" in a low-priced neighborhood.

So far, we have been using change in relative house prices as our definition of gentrification. However, advocates for assessment caps are plainly concerned about the ability of homeowners to stay in their home in the face of rising home values. While the in-migration of higher-income residents and house prices are highly correlated, we do not observe the duration of time that existing residents remain in their home. Unfortunately, there are few individual-level data sets with sufficiently granular geography to allow such an analysis. As an alternative, we can look at the change in median income of residents. This value is available at the census-tract level in the 2000 census and the 2007 American Community Survey. Table 3 presents change in median income for all census tracts and for gentrifying tracts with and without assessment caps. While median incomes rose in gentrifying neighborhoods (even as they declined nationally), they rose faster in tracts subject to an assessment cap. However, this difference is not statistically different from zero (p value 0.303).

Change in real income for gentrifying neighborhoods with and without assessment caps

Finally, assessment caps do nothing for renters, who may be impacted much more immediately by rising neighborhood quality than homeowners. It is possible that assessment caps could still allow a small share of long-time owners to stay, and the observed effects are just dominated by the movement of renters. If we had access to administrative data with finer geographic identifiers, we could look at whether neighborhoods that gentrified with assessment caps now exhibit more income or racial heterogeneity than neighborhoods without. However, looking only at aggregate data, property taxes do not appear to be a primary driver of neighborhood change, and concerns about gentrification do not appear to warrant interfering with the assessment process.

Photo of Chris CunninghamChris Cunningham, research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta

March 27, 2014 in Housing demand , Housing prices | Permalink | Comments ( 0 )

March 05, 2014

Government Involvement in Residential Mortgage Markets

With the federal funds rate effectively at the zero lower bound, the Federal Reserve has used unconventional forms of monetary policy. Specifically, the central bank has issued forward guidance about the policy path and purchased large amounts of U.S. Treasury bonds and agency mortgage-backed securities (MBS) in an effort to lower long-term interest rates. In the case of agency MBS purchases, a goal was to stimulate the housing market by lowering mortgage rates. Two papers presented at the recent Atlanta Fed/University of North Carolina—Charlotte conference, "Government Involvement in Residential Mortgage Markets," examine the extent to which the Federal Reserve has been successful.

Unanticipated announcements of new large-scale asset purchase programs (LSAPs), or changes in these programs, should have an immediate impact on interest rates under the assumption that the total stock of purchases is what matters. On the other hand, the flow of purchases may independently influence markets through portfolio rebalancing—that is, investors reacting to the removal of duration and convexity from the market—and liquidity effects—that is, ease of reselling assets in the future. Diana Hancock and Wayne Passmore conduct an empirical analysis of the differing effects of the LSAPs in their paper, "How the Federal Reserve's Large-Scale Asset Purchases Influence MBS Yields and Mortgage Rates." Using weekly data from July 2000 to June 2013, the authors estimate a model of MBS yields that controls for market expectations about future interest rates and find that the Federal Reserve's market share of MBSs and Treasuries are negatively related to MBS yields. Under their model, the Fed's holdings of MBSs has lowered MBS yields by 54 basis points and the Treasury holdings have pushed down the MBS yields another 70 basis points. This finding is consistent with portfolio rebalancing and liquidity effects being important determinants of MBS yields.

The finding is important because it suggests that agency MBS yields and mortgage rates will rise when the Federal Reserve reduces its MBS purchases—even if the Fed successfully signals that it intends to keep rates low for an extended time. On the margin, this could serve to dampen housing market activity, including refinancing. Since the beginning of the third phase of quantitative easing (QE3), the Fed's MBS market share has grown from around 17 percent to nearly 24 percent. Given the estimate that each percentage point increase in market share pushes MBS yields down by 2.3 basis points, reducing the Fed’s MBS market share back to a pre-QE3 level would push MBS yields up by around 16 basis points, which is unlikely to be economically meaningful.

While the cost of mortgage refinancing is borne by MBS investors, most of the policy attention is placed on the benefit to borrowers through an increase in their disposable income. In cases where borrowers are underwater and having difficulty making mortgage payments, refinancing can ease borrowers’ financial distress. In "The Effect of Mortgage Payment Reduction on Default: Evidence from the Home Affordable Refinance Program," Jun Zhu, Jared Janowiak, Lu Ji, Kadiri Karamon, and Douglas McManus estimate that during the 2009 to 2012 period, a 10 percent reduction in monthly mortgage payments for participants in Freddie Mac’s Home Affordable Refinance Program (HARP) resulted in a 12 percent reduction in the monthly default hazard for 30-year fixed rate conventional-conforming mortgages. This likely helped slow the flow of mortgages entering the foreclosure pipeline and gave neighborhoods time to stabilize.

Government involvement in residential mortgage markets takes many forms (see the conference website for papers that examine other forms of intervention). Taken together, the papers discussed here provide evidence that the Federal Reserve's LSAPs and Freddie Mac's HARP did put downward pressure on longer-term interest rates and facilitated refinancing activity that helped to support housing and mortgage markets. The tapering of the MBS LSAPs should not be a cause for concern. The Fed’s strong forward guidance combined with the slow, judicious pace of the taper imply that stagnation of the housing market is unlikely.

Photo of Carl HudsonBy Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department, and

March 5, 2014 in Government-sponsored enterprises , Loan modifications , Monetary policy | Permalink | Comments ( 0 )

February 19, 2014

Asymmetric Information and the Financial Crisis

In describing the $13 billion settlement reached between JPMorgan and the Department of Justice last November, Attorney General Eric Holder said,

Without a doubt, the conduct uncovered in this investigation helped sow the seeds of the mortgage meltdown. JPMorgan was not the only financial institution during this period to knowingly bundle toxic loans and sell them to unsuspecting investors, but that is no excuse for the firm's behavior.

What Holder describes sounds like a textbook example of what economists call asymmetric information: JPMorgan knew something about the loans it was selling (that they were toxic) that they didn't reveal to investors. Specifically, the government alleged that JPMorgan reported facts to the investors that turned out to be wrong. For example, JPMorgan may have said that it made only 10 percent of the loans in a pool to investors (as opposed to owner-occupants) when the actual percentage was 20 percent. So it would seem as if economic theory, which has a lot to say about asymmetric information, should help us understand the crisis. Indeed, to many, asymmetric information and "bad incentives" are the leading explanations of the financial crisis. For example, a Reuters article that described the settlement made the following claim:

The behavior that the largest U.S. bank admitted to, authorities said, is at the heart of what inflated the housing bubble: lenders making bad mortgages and selling them to investors who thought they were relatively safe. When the loans started turning bad, investors lost faith in the banking system, and a housing crisis turned into a financial crisis.

In future posts, we will consider this seemingly intuitive idea, and argue that the economic theory of asymmetric information, in fact, provides very little aid in understanding the central questions of the crisis.

Let's focus on Holder's quote. The standard theory of asymmetric information implies that JPMorgan's misrepresentations could not cause significant losses to investors. That may seem surprising. Many may think that either we don't understand the economics of asymmetric information or it's just another example of the naïveté of economists regarding how the real world actually works. While there is certainly no shortage of examples of economists holding naïve opinions about the real world, in this case, we will argue that we are correctly characterizing the economist's view and that it is based on a common-sense argument.

Let's start with the economics. Let's assume that JPMorgan is selling a pool of loans, about which it knows the true quality, to a group of buyers who can't observe the true quality. What does economic theory say will happen?

A. Investors will overpay for the assets and lose money.
B. Investors will underpay for the assets and make money.
C. Investors will infer the true quality of the loans and pay accordingly.

The answer is C. To many, that may sound shocking, but the basic logic is simple: investors know that they cannot observe the true quality of the loans and they know that JPMorgan has an incentive to dump bad loans in the pool. Thus, they correctly infer that JPMorgan will dump bad loans in the pool. In other words, investors form correct beliefs about the quality of a loan,1 despite not being able to observe quality directly.2

"Knowingly bundl[ing] toxic loans" may be unethical or even illegal, but according to the economic theory of asymmetric information, it shouldn't cause unexpected financial losses to investors. The key to understanding the gap between Holder and economics is the word "unsuspecting." Economists assume that all market participants are inherently suspicious. Market participants understand that the people with whom they are doing business have an incentive to cheat them if those people know more about the products that they are selling.

Are economists naïve to think that market participants can figure out the incentives of their adversaries? We would argue that common sense says people are pretty suspicious. Take, for example, real estate agents. A cursory search on the internet yields the following table of "translations" of real estate listings:

Loaded with Potential: means loaded with problems the seller didn't want to tackle.
Cute: means they couldn't think of any other possible way to describe it.
Great Bones: means you're going to have to gut it and rebuild.
Wooded/Shaded Lot: means surrounded by trees and leaves on the ground.
Charming: means they couldn't think of a more appropriate word.
Needs a Little TLC: means it needs about $45,000 dollars or more in renovations and repairs.
Won't Last Long at This Price: means the price is so low it will compel you to see it but it will take a miracle for you to want to buy it.
No Disclosures: means you're going to have to find out all the problems with the home on your own.

Most people read this and chuckle, but no one is surprised that real estate agents stretch the truth. After all, it's their job to convince you to buy. And, in general, people view salespeople as among the least ethical of all occupations, only slightly above members of Congress. Perhaps the most egregious example of this, and in fact the example that motivated the seminal paper on the economics of asymmetric information, is used-car salespeople. Do used-car salespeople try to misrepresent the quality of the cars that they are trying to sell? Most people would likely answer this question with a resounding "Yes, of course." Does this cause injury to most used-car buyers? Not so much. Since the general public recognizes that "used-car salesman" is basically American slang for a fraudster, nobody really believes what they say.

In subsequent posts, we will answer questions about the crisis that turn on asymmetric information problems:

  1. Theory says investors should have guessed the quality of the loans. Did they?
  2. If investors knew the quality of the loans they were buying, why did JPMorgan pay $13 billion to settle accusations that it misrepresented the quality of the loans it was selling?
  3. Can't policymakers fix some of these incentive problems? Doesn't forcing issuers such as JPMorgan to retain a portion of the securities they issue align incentives and mitigate the asymmetric information problem?
  4. If asymmetric information didn't cause investor losses, does that mean it doesn't affect economic outcomes? (Spoiler: The answer is an emphatic no.)
  5. What about rating agencies? Didn't they know that deals were bad but lie to investors and say they were good?

Photo of Paul WillenBy Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, and

 

Photo of Kris GerardiKris Gerardi, associate economist and policy adviser at the Federal Reserve Bank of Atlanta.



1 In some situations, investors will hold beliefs that may be wrong on an individual asset-by-asset basis, but that are right on average. For example, they might not know which loans are the most likely to default, but their beliefs about the performance of the pool of loans will be, on average, right.

2 More generally, the revelation principle says that in any equilibrium of an asymmetric information game, we can confine our attention to equilibria in which all private information is fully revealed. For example, in Akerlof's (1970) example of equilibrium in the used car market, the seller knows whether the car is a peach or a lemon but only the lemons trade. Everyone knows which car is good (the one that the dealer doesn't sell), but the buyer doesn't buy it because he knows that the dealer would have an incentive to substitute a bad car.

February 19, 2014 in Financial crisis , Mortgage crisis , Subprime MBS , Subprime mortgages | Permalink | Comments ( 0 )

February 10, 2014

Good News/Bad News/Good News: Affordability and Rising House Prices

First the good news: overall U.S. house sales in 2013 were the highest since 2006, according to the online Financial Times and, at least for current homeowners, house prices had double-digit increases during the year. The bad news, at least for potential buyers, is that house prices had double-digit increases. Combined with higher mortgage interest rates and relatively stagnant income growth, there have been a lot of concerns lately about housing affordability as the economy continues to recover.

In 2013, home prices increased by 14 percent, according to the S&P/Case-Shiller 20-City Composite Home Price Index. In June, talk that the Fed may begin scaling back its bond purchases led to mortgage rates rising quickly from 3.5 percent to 4.4 percent. As a result, buyers faced higher monthly mortgage payments. How concerned should we be that lower levels of housing affordability will make housing less attainable as the economy improves? Is there a possibility that rising prices and interest rates will limit the pace of the housing recovery? One way to put attainability in perspective is to examine an affordability index such as the one that the National Association of Realtors (NAR) produces.

Though not without its flaws, the NAR Affordability Index (AI) is widely cited and can give a sense of the magnitude of the change in housing affordability. The NAR index is designed to estimate the extent to which the ability of the median household to purchase the median home has changed over time. In addition, it also lets us determine the extent to which changes in mortgage rates, home prices, and income contribute to changes in affordability.

The NAR AI incorporates median incomes, mortgage rates, and median home prices. It is computed as the ratio of median family income to the income required to qualify for a mortgage on the median-priced single-family home, assuming a 20 percent down payment and a monthly payment-to-income ratio of 25 percent.

The index equals 100 when the median-income family just qualifies for a mortgage on the median-priced home. A value above 100 implies that the median-income household has more than enough income to qualify for a mortgage on the median-priced home, and a value below 100 means that the median-income household does not have sufficient income to qualify. During most of 2012, the NAR AI was at or near 200. At this level, the median-income family can afford two mortgage payments with 25 percent of its income. Or, to put it another way, that family had to spend only 12.5 percent of its income on a mortgage payment on the median-priced house. (Note that there’s a powerful regional component to housing affordability as well. Even taking regional incomes into account, homes are considerably more expensive in the West and Northeast than in the South and Midwest.)

Armed with the formula for the AI, we can decompose it into its three parts—home prices, mortgage rates, and incomes—to see which factor is creating the strongest declines in affordability. Looking at the decomposition through time, house prices are typically a drag to the AI, and nominal income growth supports the AI (see chart 1). Historically, interest rates play the role of affordability swing factor.

Decomposition of the NAR Affordibility Index 1969-2012

The record levels of affordability since the onset of the Great Recession were due to the combination of falling house prices and declining interest rates. The recent decline in the NAR AI is primarily due to the rebound in housing prices, which in 2012 returned to the role of a drag to affordability. After six years of contributing to affordability, mortgage interest rates became a drag in 2013 (see chart 2). Sluggish income growth has had little effect on the index.

Decomposition of the NAR Affordibility Index 2013

Looking ahead, many forecasters are predicting that house price growth will be in the positive single digits, slower than the past couple of years. Should we be concerned that continued home price and mortgage rate increases may decrease affordability and thus lower construction and house sales? For several reasons, the answer is: probably not. First, sales and affordability, as measured by the AI, have had little correlation historically (see chart 3). A lack of correlation is not surprising since the decision to buy is a function of access to credit in addition to what is measured by the affordability index, which has no measure of access to credit. That is, attainability is a function of both affordability and access to credit.

Affordability Index versus New House Sales 1971–2013

Second, the increases in house prices and mortgage rates may signal a growing economy and, subsequently, recovering credit markets. Given this context, as the NAR affordability declines, what we may see is an expansion of credit and increasing access to credit. Finally, as banks continue their search for profitable opportunities, we may see their eagerness to lend increase (that is, the availability of credit could increase).

We will cover access to and availability of credit in future posts, but for now I think we can safely say that affordability as measured by indices such as NAR's should not be a primary concern.

Photo of Carl HudsonBy Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department, and


Elora Raymond, graduate research assistant, Center for Real Estate Analytics in the Atlanta Fed's research department/PhD student, School of City and Regional Planning, Georgia Institute of Technology

February 10, 2014 | Permalink | Comments ( 0 )