Tierra Grande - April 2017

April 2017 â„¢

JOURNAL OF THE REAL ESTATE CENTER AT TEXAS A&M UNIVERSITY

NON-PROFIT ORG. U.S. POSTAGE PAID HOUSTON, TEXAS PERMIT No. 4126 COLLEGE STATION, TEXAS 77843-2115

In This Issue High-Speed Rail in Texas Real Estate Crowdfunding Household Debt East Austin Development Rural Land and Commodity Prices Condemnation and Tax Savings Home Price Discounts

Helping Texans make better real estate decisions since 1971

April 2017 â„¢

JOURNAL OF THE REAL ESTATE CENTER AT TEXAS A&M UNIVERSITY

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APRIL 2017

VOLUME 24, NUMBER 2 ™

TIERRA GRANDE JOURNAL OF THE REAL ESTATE CENTER AT TEXAS A&M UNIVERSITY

Director, GARY W. MALER Chief Economist, JAMES P. GAINES Senior Editor, DAVID S. JONES Managing Editor, NANCY MCQUISTION Associate Editor, BRYAN POPE Associate Editor, KAMMY BAUMANN Art Director, ROBERT P. BEALS II Graphic Specialist/Photographer, JP BEATO III Circulation Manager, MARK BAUMANN Lithography, RR DONNELLEY, HOUSTON

ADVISORY COMMITTEE: Doug Roberts, Austin, chairman; Doug Jennings, Fort Worth, vice chairman; Mario A. Arriaga, Conroe; Russell Cain, Port Lavaca; Alvin Collins, Andrews; Jacquelyn K. Hawkins, Austin; Besa Martin, Boerne; Walter F. “Ted” Nelson, Houston; C. Clark Welder, San Antonio; and Bill Jones, Temple, ex-officio repre­ senting the Texas Real Estate Commission.

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TIERRA GRANDE ™ (ISSN 1070-0234) is published quarterly by the Real Estate Center at Texas A&M University, College Station, Texas 77843-2115.

Telephone: 979-845-2031.

Center research reveals that an upswing in Texas commodity prices (oil, cattle, cotton, or corn) is generally followed by increases in land prices.

VIEWS EXPRESSED are those of the authors and do not imply endorsement by the Real Estate Center, Mays Business School, or Texas A&M University. The Texas A&M University System serves people of all ages, regardless of socioeconomic level, race, color, sex, religion, disability, or national origin.

PHOTOGRAPHY/ILLUSTRATIONS: JP Beato III, pp. 1, 14–15, 16, 20–21; Robert Beals II, pp.

2–3, 4–5, 15 (map), 24; Courtesy of Shinkansen, p. 6; Real Estate Center files, p. 10; Alden DeMoss, p. 25. © 2017, Real Estate Center. All rights reserved.

In Sync

Rural Land Prices Mirror Commodity Values

BY LUIS B. TORRES AND CHARLES E. GILLILAND

14 Change and Challenges

2 Courts, Trains,

and Eminent Domain High-speed rail prompts strong reactions on both the “Yes, let’s do it” and the “No, let’s not” sides. Here’s why.

BY RUSTY ADAMS

7 Real Estate Crowdfunding What’s the Buzz?

It’s the latest thing in investing, and it’s fascinating. But beware. Learn before you leap.

BY CHARLES E. GILLILAND

East Austin’s Affordable Housing Problem East Austin’s existing low-income population is shrinking as long-term residents are priced out of the market by rising land values, home prices, and property taxes.

BY HAROLD D. HUNT AND CLARE LOSEY

25 Losses and SelfEmployment Taxes

Changes in self-employment tax law prohibit married real estate professionals from offsetting one spouse’s income with the financial losses of the other spouse.

BY JERROLD J. STERN

10 Economic Edge ON THE COVER Dandelion seed head, spring

PHOTOGRAPHER JP Beato III

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Lower Debt Benefits Borrowers and Businesses Not everything is bigger in Texas. Home mortgages are smaller thanks to lower home prices, and Texans take advantage of that by heading to car dealerships.

BY ALI ANARI

26 Home Price Discounts in Texas Housing Markets

How healthy is your housing market? If sellers do not offer price discounts, it means demand is high. Big discounts mean it’s a buyers’ market.

BY ALI ANARI AND GERALD KLASSEN

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Land Markets

Courts, Trains, and Eminent Domain

By Rusty Adams

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f you live in Houston and Grandma lives in Dallas, visiting her might get a little easier if Texas Central gets its way.

Instead of driving three or four hours in traffic or fighting airport hassles, you’ll be able to hop on a train and travel there in about 90 minutes.

According to Texas Central, a private company hoping to develop and operate a high-speed railroad, the project would positively impact the economies of the state and local communities, relieve traffic congestion on I-45, and offer a convenient, clean, fun, and safe travel alternative. It would also provide infrastructure to meet the demands of the state’s explosive population growth. Texas Central expects to begin construction in 2018 and claims the project would be done in an

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environmentally sensitive manner without using government grants or subsidies. Sounds pretty neat, huh?

But to get to grandmother’s house, you have to go over the river and through the woods, and the people who live along the route are not exactly welcoming the train with open arms.

Opponents of the train make several arguments against it.

They say the project is underfunded and will not be able to raise the necessary money to build and operate the train. They claim the market will not support it, and it will eventually fail or require government subsidies. They contend that it’s a pretext for a land grab or that the land will actually be used for something else, such as pipeline or fiber optic cable. They are concerned about the effects of the train on existing transportation, TIERRA GRANDE

emergency vehicles, drainage, and on use for grazing, farming, and hay. They worry about safety, and they are not excited about the jingle, the rumble, and the roar.

Ain’t Got Time to Take a Fast Train Adding insult to injury, the train won’t stop and let people on, except for a potential stop in the tiny burg of Roans Prairie, about halfway between Huntsville and Bryan-College Station.

To ride the train, people would have to drive there. ore than anything, though, they don’t want their land to be taken for the train, and they claim Texas Central has no authority to take it. Texans Against High-Speed Rail was organized as a coordinated effort by landowners and their allies to protect property, property rights and values, and the landowners’ rural way of life from the negative impacts of the train.

This isn’t the first time high-speed rail proponents have looked at Texas. Texas lawmakers considered the possibility

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of high-speed rail in 1989 when a group of investors proposed a project. The legislature created the Texas High-Speed Rail Authority, making clear that the project, if it went forward, would pay its own way without the help of the state.

Then, as now, it was a tale of the city mouse and the country mouse. Rural landowners were up in arms; city dwellers had a ticket to ride and didn’t care.

The prevailing opinion was that the creation of the railway would not be viable. After all, private passenger rail travel has largely disappeared in the U.S., and it must have done so for a reason. The idea was that the railroads and the airlines competed for the same customers, and air travel was an alternative the market preferred. If rail became an alternative, it might result in higher airfares and fewer flights. On the other hand, if the train failed to attract passengers, it would require subsidies to survive. Some say the failure of the project was at least partially due to efforts by an airline to derail the project. Certainly many people preferred to keep their money

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and drive their personal vehicles rather than fight through the stations or airports and still need a rental car on arrival.

Ultimately, the project went off track after the company failed to put together the money. The High-Speed Rail Authority was abolished.

Power of Eminent Domain

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hen Santa brought my youngest a toy train this past Christmas, he moved the couch to set it up. Texas Central has to clear the way for its train, too. The big legal question in all of this is whether Texas Central has the right to take the property needed to build the railroad. The law calls this eminent domain.

Eminent domain is the power of the sovereign (the government) to take property for public use without the owner’s consent. In some cases, the power may be used by private persons or corporations who are authorized to exercise functions of public character. The process of actually taking the property is called condemnation. This is how we get roads, sidewalks, water supply systems, pipelines, and electrical transmission systems. The landowner, however, must be adequately compensated for the property taken.

The power of eminent domain is a holdover from English feudalism and has long been accepted as one of the inherent powers of government. The idea is recognized in the Magna Carta. The power was delegated by the government to sewer commissioners as early as 1427. The term originated in a 1625 work in Latin, in which Hugo Grotius wrote, I have said elsewhere that the property of subjects belongs to the state under the right of eminent domain; in consequence the state, or he who represents the state, can use the property of subjects, and even destroy it or alienate it . . . for the sake of the public advantage; and to the public advantage those very persons who formed the body politic should be considered as desiring that private advantage should yield. But in order that this may be done by the power of eminent domain the first requisite is public advantage; then, that compensation from the public funds be made, if possible, to the one who has lost his right.

If it sounds familiar, it should. The Fifth Amendment to the United States Constitution provides, “No person shall . . . be deprived of life, liberty, or property, without due process of law; nor shall private property be taken for public use, without just compensation.” A similar prohibition applies to the states through the Fourteenth Amendment. According to the U.S. Supreme Court, “the law of eminent domain is fashioned out of the conflict between the people’s interest in public projects and the principle of indemnity to the landowner.” Over the years, state and federal courts have tried to figure out exactly how it applies.

In the federal courts, the eminent domain power has been greatly expanded by broadening what is considered “public use.” The most recent landmark decision was in Kelo v. City of New London (2005). The City of New London, Connecticut, at public expense, condemned a home owned by a local nurse to transfer it to a private developer for a project that was to

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include a hotel, marina, restaurants, shopping, and residences.

The city claimed the new development would rejuvenate the economy of the distressed community. The U.S. Supreme Court prompted outrage on both sides of the aisle when it upheld the taking, saying that if the government believes it will generate more revenue, it may do so. Ms. Kelo’s house was moved off the property and the city took over. The land sits vacant. The development never materialized. It failed for lack of funding. nder state constitutions and laws, states may not give less protection than the federal government; but they can give more. Many states, including Texas, made changes in their laws in response to the Kelo decision.

The Texas Constitution (Article I, Section 17), after its most recent amendment in 2009, provides that property shall not be taken, damaged, or destroyed for or applied to public use without consent and without adequate compensation, and only for use by the state, a political subdivision of the state, or the public at large; or an entity granted the power of eminent domain under law.

The 2009 amendment also clarified that public use does not include the taking of property for transfer to a private entity for the primary purpose of economic development or enhancement of tax revenues, and provided that the legislature’s grant of eminent domain power requires a two-thirds vote of all the members elected to each house.

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WOULD HIGH-SPEED RAIL be successful in Texas? Opinions abound. Texas Central believes it would give people in the Houston area an appealing alternative to driving to Dallas. Landowners don’t want their land condemned under eminent domain to make way for the tracks. Counties are concerned about the impact on state and county roads.

first be a railroad company before it may condemn for those purposes.

Another potential route to eminent domain power is Section 131.011 of the Texas Transportation Code, which defines “interurban electric railway company” as a corporation chartered under Texas law to conduct and operate an electric railway between two municipalities in Texas. That section provides that such a company may exercise eminent domain powers the same as a railroad company and may condemn to acquire right-of-way on which to construct and operate rail lines, as well as sites for depots and power plants.

An entity created or that acquired eminent domain power before December 31, 2012, is required to send a letter on or before that date, reporting that fact to the Texas Comptroller for inclusion in the Comptroller’s Online Eminent Domain Database (COEDD) (Section 2206.101, Texas Government Code). The letter must identify each provision of law that grants eminent domain authority to the entity. If the entity did not submit the letter, the eminent domain power expired on September 1, 2013. he landowners point to the fact that Texas Central Railroad & Infrastructure Inc., the Texas Central entity seeking to exercise eminent domain power, was formed as TXHS Railroad Inc. on December 20, 2012, listing its purpose as conducting business activities related to the development, construction, financing, and operation of high-speed passenger rail service in the State of Texas. A letter complying with Section 2206.101 was sent to the comptroller on December 26, 2012. However, the letter only identified Chapter 112 of the Transportation Code; Chapter 131, applying to interurban electric railways, was not identified. In 2015, the company changed its name and its purpose, stating that its new purpose was to plan, build, maintain, and operate an interurban electric railroad.

Even with proper registration, landowners cite the Texas Supreme Court’s holding in Texas Rice Land Partners Ltd. v. Denbury Green Pipeline-Texas LLC, for its holding that mere registration was insufficient for acquiring eminent domain power. They also say Texas Central failed to comply with other strict requirements of the condemnation process, such as providing a copy of the Landowner’s Bill of Rights, prepared by the Attorney General. Importantly, the Texas Supreme Court has held that laws regarding eminent domain are strictly construed in favor of landowners, and that strict compliance is required. So if there is a close call, or if Texas Central fails to dot the i’s and cross the t’s, the landowners may have the upper hand.

While Texas Central wants to move ahead with acquiring the land and building the railroad, landowners have another plan. When some landowners refused to allow Texas Central on their land to survey the route, Texas Central took them to court, and there are currently close to 40 lawsuits filed in at least six counties. The cases turn on the same issues: If Texas Central has authority to condemn the property by the eminent domain power, then it also has the authority to go on the

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Additional statutory reforms prevent the use of eminent domain to confer a private benefit on a private party, even under a pretext of public use. In addition to the public use requirement, case law requires that condemnation be actually necessary to advance that use.

I Think I Can, I Think I Can Federal rules require prior approval for construction and operation of a railroad, and provide for ongoing regulation of passenger rail. In April 2016, Texas Central asked the federal Surface Transportation Board (STB) for an exemption from those requirements. In July, the STB ruled that it did not have jurisdiction over the project because it would be constructed and operated entirely within Texas. This puts the question in the hands of Texas courts, to be decided in accordance with Texas law.

There are two statutes by which Texas Central might claim to have eminent domain authority. The Texas Transportation Code provides that a “railroad company” may acquire property by condemnation if the property is required for certain purposes listed in the statute, such as the right-of-way, a roadbed, or the construction and operation of tracks (Section 112.053).

Texas Central claims to be performing some of the purposes listed in the statute.

Notably, however, performing those acts doesn’t magically transform them into a railroad company. Texas Central must APRIL 2017

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property to survey it. This all comes back to whether Texas Central is a railroad company.

Workin’ on the Railroad All the Livelong Day

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o what’s a railroad company? According to the Transportation Code, a railroad company includes (1) a railroad incorporated before September 1, 2007, or (2) any other legal entity operating a railroad (Section 81.002). The main inquiry, then, becomes whether Texas Central is “operating a railroad.” Texas Central says it is. The landowners say no.

The landowners say that just because a company calls itself a railroad doesn’t mean it is one. If that were the case, they claim, then anyone could declare themselves a railroad and start condemning land against the owners’ will.

They say that a company with no right-of-way, no tracks, and no trains—and not enough money to buy them— cannot possibly be operating a railroad. If a person can’t buy a ticket and ride a train, they contend, it’s not a railroad, and it certainly isn’t “operating.”

Texas Central’s response is, “We’ve been working on the railroad—all the livelong day.” According to Texas Central, it is doing all the things that anyone would expect a railroad company to be doing at this stage of its existence. After all, before it can buy trains or sell tickets, it has to put together the right-of-way. By engaging in preliminary and necessary steps, Texas Central contends, it is “operating a railroad.” If that were not the case, it says, then only existing railroads could exercise eminent domain power, and Texas has declared itself off-limits for any new railroads.

Texas Central has asked the courts to rule that it is a railroad company so that it may enter and survey the private lands it wants to use. It also contends that it must be allowed to conduct federally required environmental impact studies on the properties.

In one of the many pending cases, Texas Central asked a district court in Harris County to rule that it was a railroad company with eminent domain authority and to require landowners to allow entry for surveying. On December 16, 2016, the request was denied. The court refused to find, without further factual inquiry, that Texas Central is a railroad company.

It did not, however, rule that it is not a railroad company. That remains to be determined by a trial, which is currently scheduled for July. Even if Texas Central is found to be a railroad company, other legal battles loom. First, there would still be

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a required showing of public necessity. Second, in the absence of a certificate from the STB, who regulates high-speed rail in Texas? Texas Central says there is no regulatory body, so it does not need regulatory approval. Landowners say that can’t be the case, and the legislature would need to reinstate something similar to the High-Speed Rail Authority.

In the meantime, and in the absence of any further state action, local governments are getting in on the act. Texas Central may ultimately prevail, but it faces an uphill battle, especially at the county level. The condemnation process involves local landowners, local officials, and local juries, and landowners are getting help from their county commissioners. As an example, Grimes County will require a permit before the rails can cross a county road.

Getting the permit requires a showing of eminent domain authority from the state or federal government. This is deemed to be necessary because of the impact not only on the landowners but on the state and county road system. The costs to go over or under the train are said to be astronomical.

Train Bound for Glory?

Or Nowhere?

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he battle lines are clearly drawn. Supporters of high-speed rail envision thousands of riders making an easy trip to see grandma and driving Texas’ economic growth.

Opponents want to protect grandma’s farm and prevent government spending on a failed business venture. With numerous lawsuits and legal issues to be decided, only time will tell about highspeed rail in Texas. It may indeed get to the other side of the mountain. Or it may die with a hammer in its hand.

Adams (radams@mays.tamu.edu) is a member of the State Bar of Texas and a research attorney for the Real Estate Center at Texas A&M University.

THE TAKEAWAY In Texas, eminent domain power is delegated to certain private entities—such as railroads—that perform public functions. Texas Central says it is a railroad and as such may use the power of eminent domain to take land for a high-speed railroad between Dallas and Houston. A group of landowners says calling itself a railroad doesn’t make it one.

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Investment

Real Estate Crowdfunding What’s the Buzz?

By Charles E. Gilliland

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eady for the next big thing on the internet? Maybe it is real estate “crowdfunding.” In the past, investors interested in real estate faced a daunting mission as they embarked on the quest to place their funds. Direct investment in a project involved establishing special connections with developers or financial intermediaries and large sums. Small investors could not muster the funds nor identify projects to participate in lending to specific real estate projects.

Real estate investment trusts did allow small investors to access real estate but only indirectly through portfolios of properties. Crowdfunding aims to change that model to accommodate small investments by a broad array of individuals in either real estate lending or equity ownership for specific properties.

Title III of the JOBS Act became law in April 2012. Although the title sounds like a stimulus measure designed to create new jobs, the Jumpstart Our Business Startups (JOBS) Act actually sought to encourage funding for small businesses. Title III of the act addressed so-called crowdfunding and new Security and Exchange Commission (SEC) rules unleashed internetbased crowdfunding sites for business on May 16, 2016.

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These provisions promise to provide ventures access to funding from large numbers of ordinary investors. The idea is to allow a crowd of small investors access to transactions previously reserved to accredited investors (those with incomes exceeding $200,000 for several years or more than $1 million in net worth excluding equity in a home). Where traditional real estate investments normally require large amounts from each party to the deal, crowdfunding allows a large number of investors to fund a small part of the deal. Some platforms accept investments as small as $1,000.

Bringing Creativity to Life Crowdfunding traces its origins to Ireland where Jonathan Swift, the author of “A Modest Proposal,” founded a fund in the early 1700s to provide loans to low-income families.

Through the years, the approach has encompassed mass donations to various causes as well as loans and funding for public projects, even supporting a tour by the British rock group Marillion in 1997.

Perhaps inspired by that success, ArtistShare (https://www. artistshare.com/v4/) launched a platform to allow the public

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to fund projects for musicians in return for specified rewards. array of platforms, each focused on bringing prospective real That success in turn inspired a number of other sites, includestate project principals together with borrowers across the ing Kickstarter (https://www.kickstarter.com/), which focuses nation in an industry historically known for its local focus. on “bringing creative projects to life,” everything from movies to technology and food. The JOBS Act sought to open these cre- Sharing the Wealth Time-honored paths to real estate investing led through counative internet forces to business expansion. try clubs, alumni associations, and other social entities where LendingClub, a crowdfunding site that links borrowers with deals happened. These projects investors, began in 2007. normally required piles of cash Tier 1: Best of the Best Instead of real estate, Lendto finance. Borrowers faced ingClub focuses on consumer These sites have it all: High transparency, co-investment, prelimited possibilities when funding, high-volume, average to low fees, excellent bankruptcy protecloans to individuals. The tion and strong venture capital backing. seeking funds. Crowdfunding platform proved so successNone No site has all these features yet, but we hope that will change by strives to make access to funds ful that it launched an initial the time of our next review. and lucrative investments public offering in 2015 with a more accessible at lower cost. $15 share price. Shares immeTier 2: All-Stars arious online portals diately escalated, but then a These sites are all extremely strong in the majority of the fundamentals that focus on financing, shakeup resulted in the CEO’s are most important to investors. including debt and/ 1 Peer Street resignation, sparking SEC and *2 Real Crowd or equity investments for Justice Department investiga*2 Realty Mogul specific properties in scattions. Threatened lawsuits *2 Realty Shares tered locations. Currently, the initiated a roller-coaster ride 5 Acquire Real Estate crowdfunding industry is in to the current share prices in *6 LendingHome *6 Roofstock its infancy with limited histhe $5 to $6 range, highlight8 Patch of Land torical records. Nevertheless, ing the risky nature of this *These sites are tied, and tied sites are shown in alphabetical order. crowdfunding for real estate business.

Tier 3: Contenders has sparked interest among Undeterred by the Lendtech-savvy would-be investors ingClub problems, Goldman These sites are a step below the previous tier, but still have one or more key/strong features that makes them worthy of consideration. who can choose from a dizzySachs launched an online 9 FundThatFlip ing number of sites offering direct lending site named 10 1031 Crowdfunding opportunities. This explosion “Marcus by Goldman Sachs” 11 Crowd Street of initiatives may signal the as an online platform offering 12 City Funders beginning of a revolution in unsecured personal loans in 13 Early Shares how real estate deals happen October 2016. This foray into Tier 4: Up-And-Coming or they may foreshadow an internet lending, while not These sites may be missing certain features and/or are not quite as polimplosion. Those possibilifunded by the crowd, signals ished as competitors. However, each one has some sort of promise or poties suggest that uncertainty confidence in the approach to tential for the future. We look forward to watching how these sites evolve abounds. in this very young and exciting industry. making online personal loans 14 Equity Multiple One site that has attempted available on a broad basis.

15 Peer Realty to impose some order on the rowdfunding came to 16 DiversyFund abundance of platforms is The real estate following 17 Crowd Flipr Real-Estate Crowdfunding the financial melt18 Groundbreaker 19 Prodigy Network Review (http://www.therealdown in 2008 when some 20 Full Capital Stack estatecrowdfundingreview. sites began to provide capital 21 Carlton Crowdfund com/), which provides a free to borrowers locked out of 22 ShareStates ranking of the top 100 sites. It traditional lending sources.

23 TripleNetZeroDebt was founded by Ian Ippolito, 24 Money 360 However, since the SEC rule a self-described “serial tech change enacted in 2016, real Tier 5: The Challenged entrepreneur.” Tech-oriented estate crowdfunding is blosThese sites have or have had a legal or financial challenge to deal with. and skeptical of traditional soming into a bewildering *25 Fundrise

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C

*25

iFunding

*All Challenged are ranked equally.

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“The crowdfunding model aims to accommodate small investments by a broad array of individuals in either real estate lending or equity ownership for specific properties.” investment wisdom, he narrowly escaped disaster when advised to invest in stocks in 2008, just before the crash. More cautious after that experience, he looked for alternative investment opportunities.

Playing by SEC Rules

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he review is the result of his experience when the quest led him to crowdfunding. It offers his take on industry players.

It has information on each of the more than 100 listed platforms for the top 25 ranked firms with links to the websites. The review provides the most complete analysis of sites currently online. However, as Ippolito insists, “. . . I’m just an investor and not a financial, tax, or legal advisor. Everything I post on the site is purely my own opinion.”

Crowdfunding for real estate comes under the purview of the SEC. To protect individual investors from unacceptable levels of risk, SEC rules define the nature of this funding mechanism.

Specifically, an investor must use an intermediary to access this market. That intermediary must be a broker-dealer or funding portal that has registered with the SEC and is a member of the Financial Industry Regulatory Authority.

Risky Business Investing An investor must open an account with the intermediary to invest. The Peer Street platform (https://www.peerstreet.com/) touts returns ranging from 6 to 12 percent for investments ranging from six to 24 months at loan-to-value ratios of 75

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percent or less. Those generous returns in the recent economic environment with rock bottom interest rates suggest that although they are potentially lucrative, the loans are risky. In fact, the SEC notes that these are “early-stage ventures” that are speculative in nature and further warns “. . . there is no guarantee that crowdfunding investments will be immune from fraud.”

In Investor Bulletin: Crowdfunding for Investors, the SEC insists that, “You should be able to afford and be prepared to lose your entire investment” (https://www.sec.gov/oiea/ investor-alerts-bulletins/ib_crowdfunding-.html). In fact, the bulletin specifies limits to the amount an investor can invest depending on annual income and net worth levels.

A potential investor should be prepared to spend time investigating real estate crowdfunding in general, the particular portal chosen, and the properties offered before committing to any investment.

Dr. Gilliland (c-gilliland@tamu.edu) is a research economist with the Real Estate Center at Texas A&M University.

THE TAKEAWAY Touted as an innovation in efficiency in real estate finance, crowdfunding may become a big success. On the other hand, it may be an inglorious disaster. Only time will tell.

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Texas Economy

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When household incomes are not sufficient to pay cash for big-ticket items such as homes and cars, people borrow money and promise to repay the loans. On a macro level, household debt can contribute to economic growth by increasing consumer expenditures on goods and services. For instance, household expenditures financed by cashing in on home equity played a significant role in the U.S. economy’s growth rate in the early 2000s.

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Figure 1. Total Debt Balance Per Capita

Total Household Debt

Texas U.S. California

80 60 40 20 0 1999

2001 2003

2005

2007

2009 2011

2013

2015 2017

Source: Federal Reserve Bank of New York

Figure 2. Annual Growth Rates of Total Debt Balance Per Capita

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Texas U.S. California

Percent

20 10 0 –10 2000

2002

2004

2006

2008

2010

2012

2014

2016

Sources: Federal Reserve Bank of New York and Real Estate Center at Texas A&M University

70,010

Figure 3. Mortgage Loan Per Capita

60,010 Dollars Per Capita

However, the financial crisis of 2007–08, the U.S. subprime mortgage crisis of 2007–09, and the Great Recession (GR) revealed the danger of excessive household debt. Since the end of the GR, monitoring and controlling household debt levels have become important functions of central banks and regulatory agencies. To perform these functions, more data on household debt balances have been compiled and are now available.

The Real Estate Center at Texas A&M University has an ongoing research program to monitor Texas household loans with a focus on mortgage loans because these account for the majority of household loans. Here is what the research found.

• Texas per capita debt balances have been historically smaller than the nation’s mainly due to smaller mortgage loans, a consequence of lower average home prices in Texas.

• Mortgage debt per capita in both Texas and the U.S. have trended downward since the end of the GR, probably attributable to the Dodd-Frank Act. • Taking advantage of lower mortgage loans, Texans have been using their borrowing capacity on cars, resulting in higher-than-national-average car loans.

• Lower debt balances enabled Texas households to suffer less in the GR and weather the financial storm of 2007–09 better than the rest of the U.S.

Thousand Dollars Per Capita

100

50,010 40,010

Percent

30,010 Historically, Texas’ average levels of per capita total household 20,010 loans have been smaller than the nation’s since debt data series Texas U.S. 10,010 have been available (Figure 1).

California Texas had a per capita total debt balance of $18,010 in 10 2010 2011 2012 2013 2014 2015 2016 2017 first quarter 1999 compared with $21,470 for the nation Source: Federal Reserve Bank of New York and $31,760 for California. The state’s per capita total loans Figure 4. Shares of Mortgage Loans increased by 99.4 percent from first quarter 1999 to first quarin Total Household Loans ter 2008 when Texas, like the rest of the nation, took advan90 tage of low-cost loans engineered by the Fed to help the U.S. economy recover from the recession of 2001.

80 However, Texas’ borrowing growth rate was less than the 177.1 percent for California and the national average of 142.5 70 percent over the same period. Texas reached its pre-GR borrowTexas ing peak of $37,170 in fourth quarter 2008 compared with the 60 U.S. nationwide peak of $53,040 in third quarter 2008 and CaliforCalifornia nia’s peak of $88,010 in first quarter 2008.

50 In the aftermath of the GR, the state’s per capita total loans 2010 2011 2012 2013 2014 2015 2016 2017 Source: Federal Reserve Bank of New York changed little due to risk-averse lenders and borrowers and more stringent borrowing rules implemented by the Dodd-Frank Act (Figure 1). Texas per capita Measuring Debt total debt increased 4.4 percent ousehold loans are the amount of funds borrowed by all households from the from its peak level in fourth quarfinancial system. They fall into five categories: mortgage loans, car loans, credit ter 2008 to $38,800 in third quarter card loans, student loans, and home equity line of credit loans. Household loans 2016, the last quarter for which are commonly measured and expressed in terms of per capita and computed by dividing data is available. The nation’s per the aggregate dollar volume of loans in a period by the number of individuals with a credit capita total debt fell 12.1 percent report; that is, debt balance per individual with a credit report in a period. from its peak level in third quarter This research uses quarterly time series data on household debt compiled by the Federal 2008 to $46,600 in third quarter Reserve Bank of New York. The time series of Texas total household loans are from first 2016 while California’s per capita quarter 1999 while time series of the components of total loans are available from second total debt decreased by 25 percent quarter 2010. from its pre-GR peak to $66,020.

H

APRIL 2017

11

Figure 5. Average Home Prices with Conventional Mortgage

700

Texas U.S. California

Thousand Dollars

600 500 400 300 200 100 2000

2002

2004

2006

2008

2010

2012

2014

2015

Source: Federal Home Loan Banks

Figure 6. Auto Loans Per Capita

Thousand Dollars Per Capita

7

Mortgage Loans

T

Texas U.S. California

6 5 4 3

2 2010

2011

2012

2013

2014

2015

2016

2017

2016

2017

Source: Federal Reserve Bank of New York

Figure 7. Credit Card Loan Per Capita

Thousand Dollars Per Capita

4.0

Texas U.S. California

3.6 3.2 2.8 2.4 2010

2011

2012

2013

2014

2015

Source: Federal Reserve Bank of New York

Figure 8. Student Loan Per Borrower

Thousand Dollars Per Borrower

5.0

4.0 3.5 3.0 2011

2012

2013

2014

2015

exas’ per capita mortgage loan fell from $23,999 in second quarter 2010 to $23,300 in third quarter 2016 (Figure 3). Over the same period, the U.S. per capita mortgage loan fell from $36,339 to $31,500 while California’s per capita mortgage loan decreased from $62,529 to $51,160.

Mortgage loans account for the largest category of household loans in both Texas and the U.S., but Texas’ share of mortgage loans in total loans has been smaller than national averages. Texas’ share of mortgage loans as a percentage of total household loans fell from 66.4 percent of total loans in second quarter 2010 to 60.1 percent in third quarter 2016 (Figure 4).

The nation’s share of household mortgage loans decreased from 72.9 percent to 67.6 percent while the corresponding share for California fell from 79.8 percent to 77.5 percent over the same period.

Texas’ lower levels of mortgage loans and smaller shares of mortgage loans are mainly due to lower-than-national-average home prices in Texas. Historically, Texas home prices have been lower than national averages, and since 2000, the state’s average home price with a conventional mortgage loan has been 78 percent of national averages (Figure 5).

Car, Credit Card, Education Loans

Texas U.S. California

4.5

Higher growth rates of household loans were blamed for the financial crisis of 2008. But Texas experienced lower-thannational-average growth rates of per capita total loans before the GR as well as smaller decline rates during the GR (Figure 2).

2016

2017

Smaller mortgage loans have enabled Texans to allocate more of their borrowing capacities for purchasing cars.

Texas’ per capita auto loan in second quarter 2010 was $4,176.6 compared with $2,932.1 for the U.S. and $2,835.3 for California (Figure 6). After the U.S. economy recovered from the GR, Texas’ per capita auto loan rose to $6,250 in third quarter 2016, a 49.6 percent increase from second quarter 2010. Over the same period, per capita car loans for the

Source: Federal Reserve Bank of New York

M

ortgage accounts include all mortgage installment loans, including first mortgages and home equity installment loans (HEL), both of which are closed-end loans. Home Equity Revolving accounts (aka Home Equity Line of Credit or HELOC), unlike home equity installment loans, are home equity loans with a revolving line of credit

Loan Types that allows the borrower to choose when and how often to borrow up to an updated credit limit.

Auto loans are loans taken out to purchase a car, including auto bank loans provided by banking institutions (banks, credit unions, and savings and loan associations), and auto finance loans, provided by automobile dealers and automobile

financing companies. Bank card accounts (or credit card accounts) are revolving accounts for banks, bank card companies, national credit card companies, credit unions, and savings and loan associations. Student loans include loans to finance educational expenses provided by banks, credit unions, and other financial institutions as well as federal and state governments.

Source: Federal Reserve Bank of New York

12

TIERRA GRANDE

Figure 9. Home Equity Line of Credit Per Capita

Texans Kept Promises to Repay Loans

L

ower levels of loans can increase the ability of borrowers to repay their loans and weather financial storms. Not surprisingly, Texans suffered less than the rest of the nation in the GR because they had lower levels of debt.

Before the GR, Texans had higher-than-national-average percentage of debt balances 90-plus days late. During the GR, the highest percentage of late debt balances was 6.2 percent compared with 8.6 percent for the nation and 12.5 percent for California in first quarter 2010 (Figure 10). The maximum percentage of mortgage debt balances more than 90 days late in the same period for Texas was 4.4 percent compared with a national average of 8.9 percent and 12.9 percent for California (Figure 11).

In the GR, the maximum percentage of consumers with new foreclosures for Texas was 0.13 percent compared with 0.24 percent for the U.S. and 0.50 percent for California (Figure 12).

APRIL 2017

Texas U.S. California

5 4 3 2 1

0 2010

2011

2012

2013

2014

2015

2016

2017

Source: Federal Reserve Bank of New York

Figure 10. Percent Debt Balances 90-Plus Days Late

16

Texas U.S. California

Percent

12 8 4 0 1999

2001 2003

2005

2007

2009 2011

2013

2015 2017

Source: Federal Reserve Bank of New York

Figure 11 Percent of Mortgage Debt Balances 90-Plus Days Late

16

Texas U.S. California

Percent

12 8 4 0 1999

2001 2003

2005

2007

2009 2011

2013

2015 2017

Source: Federal Reserve Bank of New York

.6

Figure 12. Percent of Consumers with New Foreclosures Texas U.S. California

.5 .4 Percent

U.S. increased 46 percent to $4,280 while California’s rose 44.2 percent to $4,090.

Credit card loans have also been blamed for the 2008 financial crisis. However, Texas consumers managed to maintain lower-than-national-average levels of credit card debt (Figure 7).

While auto loan balances have been trending upward in both the state and the nation during the recovery from the GR, credit card balances took a downward trend that continued until early in 2014 and since then have trended upward.

Texas’ per capita credit card loan fell from $2,991.7 in second quarter 2010 to $2,490 in first quarter 2014 and since then has increased to $2,790 in third quarter 2016.

The nation’s per capita credit card loan decreased from $3,108.1 in second quarter 2010 to $2,580 in first quarter 2014 and since then has increased to $2,820 in third quarter 2016 while California’s fell from $3,647.4 to $2,930 and then rose to $3,070 over the same period.

About 59 percent of Texas students at four-year public and private institutions had student loans when they graduated in 2014. Student debt per borrower has been trending upward since data were available in 2011, but Texas students have managed to graduate with lower-than-national-average student loans (Figure 8). Average loan balance per Texas student in third quarter 2016 was $4,420, less than national average of $4,830 in the same quarter.

Texas voters approved two amendments to the state’s constitution in 2003 allowing financial institutions to offer home equity lines of credit (HELOC). The home equity loan is subject to several restrictions. The most important one is that the total of all loans secured on a home cannot exceed 80 percent of the home’s fair market value. Similar to mortgage debt balances, Texans have maintained lower levels of HELOC compared with national averages and California’s per capita HELOC (Figure 9).

Thousand Dollars Per Capita

6

.3 .2 .1 .0 1999

2001 2003

2005

2007

2009 2011

2013

2015 2017

Source: Federal Reserve Bank of New York

Lower home prices have allowed Texas households to maintain lower debt burdens. Because of this, Texas households weathered the financial storm of 2007–08 better than the rest of the U.S. Dr. Anari (m-anari@tamu.edu) is a research economist with the Real Estate Center at Texas A&M University.

THE TAKEAWAY Lower debt burdens have enabled Texas households to use their borrowing capacity to buy consumer goods, especially cars and trucks, thus contributing to Texas economic growth.

13

Residential

Change and Challenges East Austin’s Affordable Housing Problem

By Harold D. Hunt and Clare Losey

T

he rapidly rising price of single-family homes in East Austin has left homeownership out of reach for its existing low-income population. From 2011 to 2015, growth rates of home sales prices in East Austin were two to four times that of Travis County and the Austin-Round Rock MSA. While rooted in single-family housing, lack of affordability is also deepening in East Austin’s apartment sector. The widening divide between housing costs and household income has spelled disaster for East Austin’s affordability and forced many of its residents to relocate farther from downtown.

14

TIERRA GRANDE

THE TYNDALL AUSTIN condo development at North I-35 and East 8th St. (far left) will span an entire city block, with prices ranging from $260,000 to $850,000. Sixth and Brushy (left) includes two- to three-story townhomes as well as upscale condos with views of downtown Austin on the east side of 6th St. 275 183 1

78752 lvd .

35

78723

Lam ar B N.

290

The University of Texas Austin

78722

Downtown Austin

78721 78702

969

111

183

78741 The effect of explosive job and population growth on East Austin’s single-family housing 35 71 market was outlined in “East Side Story” (Center publication 2139). This article delves deeper into the area’s overall housing affordability challenges and how they will impact East Austin’s demographics, issues that more and more Texas cities will face.

East Austin’s Changing Demographic Dynamics East Austin has historically housed minority populations. A city plan published in 1928 segregated Austin’s Hispanic and African American populations to the area east of East Avenue (present-day I-35) by refusing city service to minorities living west of East Avenue. While this practice was prohibited in the 1940s, the population of East Austin remains primarily minority nearly a century after the original enactment of the plan. However, this trend is reversing.

Since 2000, the African American population in East Austin has declined in all but one ZIP code, 78741, which witnessed modest growth of 2.1 percent (see map). While the Hispanic population has grown in several ZIP codes, the growth levels are below Travis County with the exception of ZIP code 78741 (Table 1). From 2000 to 2015, the minority population Table 1. Racial Composition increased slightly in the two ZIP codes Zip Code (Percent) Travis County farthest north and 78702 78721 78722 78723 78741 78752 (Percent) south, 78752 and 78741. But the Hispanic or Latino (of any race) nonminority populaYear 2000 67.7 50.8 20.4 42.3 51.6 54.6 28.2 tion has experienced Year 2015 50.7 52.7 16.5 43.3 58.3 59.2 33.7 significant growth: Change from 2000–15 –17.0 1.9 –3.9 1.0 6.7 4.6 5.5 78702, which borders Black or African American downtown, observed Year 2000 23.7 45.2 22.1 31.8 8.8 13.2 9.3 a 25 percent increase Year 2015 13.6 30.7 12.2 20.8 10.9 11.6 8.3 in its nonminorChange from 2000–15 –10.1 –14.5 –9.9 –11.0 2.1 –1.6 –1.0 ity population. In (Not Hispanic or Latino) White Alone essence, while the minority population Year 2000 7.5 3.9 53.2 24.0 31.7 28.6 56.4 of Travis County has Year 2015 32.2 15.1 62.9 32.6 25.1 25.7 49.8 increased since 2000, Change from 2000–15 24.7 11.2 9.7 8.6 –6.6 –2.9 –6.6 the opposite is true Sources: American Community Survey (ACS) Demographic and Housing Estimates (2011–15); Profile of General Population and Housing Characteristics (2010); Profile of General Demographic Characteristics (2000). in East Austin.

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15

A MODEST EAST AUSTIN home from earlier times stands just a stone’s throw from high-priced new developments springing up east of I-35.

E

Table 2. Median Household Income

ast Austin has Zip Code Travis also historically County 78702 78721 78722 78723 78741 78752 housed a large lowincome population. In Year 2000 $23,348 $26,646 $35,794 $34,242 $25,369 $30,207 $46,761 2000, median household Year 2015 $41,016 $37,234 $64,929 $42,433 $31,657 $38,841 $61,451 Percent change from 2000–15 76 40 81 24 25 29 31 incomes across East Austin measured well below Source: ACS, Profile of General Demographic Characteristics (2000) Income in the Past 12 Months (2011–15) those of Travis County.

The median household income for three of the six ZIP codes was around half that of Travis County. By 2015, families in 78741 were earning less Travis County (Table 2). However, by 2015, the median housethan nonfamily households, which include a householder hold incomes of 78702 and 78722, the two ZIP codes closest to living alone or only with nonrelatives, such as a roommate downtown, had nearly doubled. or unmarried partner. Since 2000, the percentage of families An expanding income gap has contributed to the large in East Austin whose income falls below the poverty level growth in household income within certain ZIP codes. In 2014, has increased (Table 4). From 2000 to 2015, within four East median household income for 78722 was $54,526. A year later, Austin ZIP codes this statistic rose more quickly than that household income had jumped to $64,929, surpassing that of of Travis County. Rising unaffordability in East Austin has Travis County, a first for any East Austin ZIP code. From 2011 left families increasingly financially burdened. As median to 2015, the share of households in 78722 with median incomes household income continues to grow at a faster rate than of $100,000 or more increased nearly 10 percent (Table 3). median family income, more families will be forced to leave But disparate gains in household income in East Austin have the area. left certain parts of the area lagging the pace of income growth Housing Stock Constraints, Prices in Travis County. In 2015, the median household income for Drive Residents Out 78741 was still around half that of Travis County. Unsurprisingly, across the six East Austin ZIP codes, 78741 had the lowSince 2000, growth in the housing stock in Travis County has est percentage of households with median incomes of $100,000 outpaced that of East Austin. From 2000 to 2015, the housing or more. stock of East Austin grew 22 percent compared with Travis Families in East Austin have not fared well. From 2000 to County’s 38 percent increase. In 78722, the housing stock 2015, median household income recorded higher growth than increased by only 6 percent over 15 years, largely due to the median family income in East Austin. The reverse occurred in lack of land available for development.

16

TIERRA GRANDE

Table 3. Percentage of Households Earning $100,000 or More Zip Code (Percent) Year 2011 Year 2015

78702

78721

78722

78723

78741

78752

Travis County (Percent)

11.4 15.7

4.2 7.9

17.9 27.5

13.4 15.8

4.3 7.8

6.2 9.8

25.5 28.8

Source: ACS, Income in the Past 12 Months (2011–15)

Table 4. Percentage of Families with Income Below the Poverty Level Zip Code (Percent) 78702 Year 2000 Year 2015

25.5 24.0

78721 21.4 26.0

78722

78723

78741

78752

Travis County (Percent)

10.4 12.6

16.0 24.6

21.2 35.7

22.4 26.9

7.7 11.2

Source: ACS

Table 5. Number and Percent of Renter-Occupied Units Zip Code 78702

78721

78722

78723

78741

78752

East Austin

Travis County

52.8 3,823 7,242

42.2 1,307 3,099

55.1 1,589 2,886

55.8 5,818 10,430

85.5 14,609 17,080

73.8 5,062 6,862

67.7 32,208 47,599

48.6 155,791 320,766

53.5 4,464 8,337

48.5 2,027 4,176

57.2 1,702 2,973

58.1 6,969 11,989

86.0 17,355 20,177

75.6 5,728 7,579

69.2 38,245 55,231

48.3 206,795 428,220

Year 2000 % renter-occupied units Renter-occupied units Occupied housing units

Year 2015 % renter-occupied units Renter-occupied units Occupied housing units

Source: ACS, Selected Housing Characteristics (2011–15), General Housing Characteristics (2010 and 2000)

Table 6. Single-Family Housing Affordability Based on the Median Multiple

Year

Austin-Round Rock MSA (Dollars)

Travis County (Dollars)

Zip Code (Dollars) 78702

78721

78722

78723

78741

78752

55,452 55,452 226,500 301,000 4.08 5.43

35,350 35,350 171,000 349,000 4.84 9.87

30,591 30,591 100,385 235,000 3.28 7.68

44,798 44,798 245,500 394,250 5.48 8.80

41,839 41,839 161,000 312,800 3.85 7.48

30,021 30,021 137,500 225,000 4.58 7.49

33,173 33,173 166,000 281,000 5.00 8.47

55,452 57,528 226,500 301,000 4.08 5.23

35,350 36,674 171,000 349,000 4.84 9.52

30,591 31,736 100,385 235,000 3.28 7.40

44,798 46,475 245,500 394,250 5.48 8.48

41,839 43,406 161,000 312,800 3.85 7.21

30,021 31,145 137,500 225,000 4.58 7.22

33,173 34,415 166,000 281,000 5.00 8.17

55,452 58,829 226,500 301,000 4.08 5.12

35,350 37,503 171,000 349,000 4.84 9.31

30,591 32,454 100,385 235,000 3.28 7.24

44,798 47,527 245,500 394,250 5.48 8.30

41,839 44,387 161,000 312,800 3.85 7.05

30,021 31,850 137,500 225,000 4.58 7.06

33,173 35,194 166,000 281,000 5.00 7.98

Growth rate 1 (no growth in MHI) Median household income Median close price Median Multiple

2011 2015 2011 2015 2011 2015

59,795 59,795 187,000 255,000 3.13 4.26

Growth rate 2 (growth in MHI tied to DFW CPI) Median household income Median close price Median Multiple

2011 2015 2011 2015 2011 2015

59,795 62,034 187,000 255,000 3.13 4.11

Growth rate 3 (MHI growth MHI according to MSA MHI) Median household income Median close price Median Multiple

2011 2015 2011 2015 2011 2015

59,795 63,437 187,000 255,000 3.13 4.02

Source for income: ACS and Real Estate Center at Texas A&M University APRIL 2017

17

Much of the new construction in East Austin has been oriented toward renter-occupied housing as opposed to owneroccupied housing. In 2015, the share of renters in East Austin approached 70 percent, whereas in Travis County it fell below 50 percent (Table 5). From 2000 to 2015, approximately four times more renter-occupied housing units than owneroccupied units were added to East Austin. However, the share of renters in East Austin increased only slightly. With such a large pool of housing units, even a considerable addition of renter-occupied housing units will only incrementally increase the percentage of renter-occupied units.

In 2015, Austin was the least affordable of the four major Texas metros. From 2011 to 2015, growth in home sales prices far outpaced that of household income. Previously affordable areas are now unaffordable, leaving homeownership out of reach for much of the population, particularly the city’s lowincome residents. In 2011, the Austin-Round Rock MSA, with a median multiple just above 3.0, was still relatively affordable (see sidebar and Table 6).

Over the following five years, a near-40 percent increase in home sales prices pushed the median multiple for the MSA into “seriously unaffordable” territory. Inside Travis County, affordability was already an issue by 2011, which recorded a median multiple slightly above 4.0. In 2015, the multiple surpassed the “severely unaffordable” threshold. egardless of the considerable increases in home prices over the five-year period, all six East Austin ZIP codes were unaffordable in 2011 (Table 6). While the median multiple for each of the ZIP codes had surpassed that of the MSA, the multiples for two ZIP codes fell below that of Travis County. In 2011, certain areas of East Austin were more affordable than Travis County as a whole. But by 2015, within each income scenario, the median multiples for all six East Austin ZIP codes eclipsed the multiples for the county and the MSA. Many of the multiples for East Austin were double that of the MSA, itself classified as “seriously unaffordable.”

Without a doubt, single-family housing unaffordability for potential buyers in East Austin has skyrocketed over the past

R

Measuring Housing Affordability

I

n this article, low-income refers to households earning less than 80 percent of the median household income for Travis County. This article evaluates the affordability of existing single-family housing through the median multiple, a standardized method for comparing housing affordability across regions. The median multiple is merely the median home price divided by median household income. A median multiple of 3.1 or more is considered “unaffordable,” but the multiple can further be classified as moderately, seriously, or severely unaffordable (Table 6).

income, 2) growth in the 2011 median household income based on growth in the 2011–15 Dallas-Fort Worth (DFW) Consumer Price Index [CPI] and, 3) growth in the 2011 median household income based on growth in the 2011–15 Austin MSA’s median household income.

Texas metro-level CPI calculations are only available for DFW and Houston.

Income scenarios are meant to control for growth in the median household income from recent higher-earning residents relocating to East Austin. Within certain ZIP codes, large annual increases in median household income from a growing minority of higher-income earners has distorted the household Housing Affordability income. Using the 2015 median Rating Categories household income for 78722, Rating Median Multiple which increased over $10,000 from Severely Unaffordable 5.1 & Over 2014 to 2015, would be misrepSeriously Unaffordable 4.1 to 5.0 resentative of the population as Moderately Unaffordable 3.1 to 4.0 a whole and would deflate the Affordable 3.0 & Under median multiple. However, much Source: Demographia International Housing of the difference in the median Affordability Survey multiples amongst the three income scenarios derives from the exorbiUsing the Real Estate Center’s data, the tant growth of the median home sales median multiple was calculated for existprice, not income. ing single-family homes in the AustinSimilar to the median multiple, the ratio Round Rock MSA, Travis County, and the of annual rent to household income was six East Austin ZIP codes for 2011 and applied to three income scenarios to eval2015. Three scenarios for income growth uate apartment affordability. However, were applied to each geography: 1) no the income calculations for apartment growth in the 2011 median household affordability differ slightly from those of

18

single-family housing. The incomes for scenario 1, which assumes no growth, were not affected. For scenario 2, median household income was based on growth in the DFW CPI from 2011 to 2016. For scenario 3, as household income data for 2016 has not yet been published by the Census, an estimate of the 2016 median household income for the Austin MSA was calculated from the 2014–15 growth rate of the median household income for the MSA. This growth rate was applied to the 2015 median household income for the MSA, which was previously calculated for single-family housing affordability. hree important factors are of note.

First, instead of calculating an affordability index for apartments in each ZIP code, the affordability for East Austin was determined on an aggregate level. Secondly, the data, calculated from data collected by Enriched Data, reflects only apartments charging market rent.

Lastly, the median age of apartments differs significantly between East Austin and the MSA. Apartments in East Austin are older, with a median age of 43 compared with 30 years for the MSA. The difference in age partially explains the lower rents within East Austin, as older apartments are likely to charge less rent. A second measure of apartment affordability was calculated that only included apartments built since 2000. This is discussed in the section “Rents Also Rising.”

T

TIERRA GRANDE

Table 7. Apartment Affordability for All Existing Apartment Stock Austin-Round Rock MSA

Travis County

East Austin

$59,795

$55,452

$34,654

Growth rate 1 (no growth in MHI) Median household income, 2016 Median rent

1 bedroom 2 bedroom

$11,430 $14,508

$11,880 $15,144

$10,548 $13,428

Median Rent to Median Household Income

1 bedroom 2 bedroom

0.19 0.24

0.21 0.27

0.30 0.39

Growth rate 2 (growth in MHI tied to DFW CPI) Median household income, 2016

$63,522

$58,909

$36,814

Median rent

1 bedroom 2 bedroom

$11,430 $14,508

$11,880 $15,144

$10,548 $13,428

Median Rent to Median Household Income

1 bedroom 2 bedroom

0.18 0.23

0.20 0.26

0.29 0.36

$65,012

$60,290

$37,678

Growth rate 3 (MHI growth according to MSA MHI) Median household income, 2016 Median rent

1 bedroom 2 bedroom

$11,430 $14,508

$11,880 $15,144

$10,548 $13,428

Median Rent to Median Household Income

1 bedroom 2 bedroom

0.18 0.22

0.20 0.25

0.28 0.36

Source for income: ACS and and Real Estate Center at Texas A&M University

Table 8. Affordability for Apartments Completed Since 2000 Austin-Round Rock MSA

Travis County

East Austin

$59,795

$55,452

$34,654

Growth rate 1 (no growth in MHI) Median household income, 2016 Median rent

1 bedroom 2 bedroom

$13,680 $16,500

$15,420

$17,724

$19,764

$22,020

Median Rent to Median Household Income

1 bedroom 2 bedroom

0.23 0.28

0.28 0.36

0.51 0.64

$63,522

Growth rate 2 (growth in MHI tied to DFW CPI) Median household income, 2016

$58,909

$36,814

Median rent

1 bedroom 2 bedroom

$13,680

$15,420

$17,724

$16,500

$19,764

$22,020

Median Rent to Median Household Income

1 bedroom 2 bedroom

0.22 0.26

0.26 0.34

0.48 0.60

$65,012

Growth rate 3 (MHI growth according to MSA MHI) Median household income, 2016

$60,290

$37,678

Median rent

1 bedroom 2 bedroom

$13,680

$15,420

$17,724

$16,500

$19,764

$22,020

Median Rent to Median Household Income

1 bedroom 2 bedroom

0.21 0.25

0.26 0.33

0.47 0.58

Source for income: ACS and Real Estate Center at Texas A&M University

few years. The area was once considered affordable to the city’s low-income homeowners. It is now only affordable to buyers earning a median household income of at least $100,000 using a median multiple of 3.0. In 2015, the percentage of households in East Austin that surpassed this income threshold hovered between 8 and 28 percent, depending on the ZIP code. It is now virtually impossible for low-income residents to purchase a home in East Austin.

APRIL 2017

Rents Also Rising

A

partments in Travis County and the MSA offer an affordable solution to single-family housing. Overall, apartments in East Austin are also affordable.

But based on the general rule that renters should spend no more than one-third of their incomes on rent, unaffordability is already emerging in East Austin’s two-bedroom apartment sector.

Depending on the number of bedrooms and the income growth scenario, households in Travis County and the MSA spend between 18 and 27 percent of their annual incomes on apartment rent. Residents living in East Austin spend between 28 and 39 percent of their annual income on apartment rent (Table 7).

According to Julian Huerta, deputy executive director of Foundation Communities, apartment rents in East Austin have doubled over the past ten years. To portray this rise in apartment rents, a second measure of apartment affordability was developed. This measure includes only market-rate apartments built from 2000 onwards. While the annual rent-toincome ratios for Travis County and the MSA increase slightly, the ratios remain below the threshold for unaffordability (Table 8).

However, in East Austin, the ratios nearly double. While overall apartments in East Austin are affordable, apartments built since 2000 are unaffordable to the area’s existing population. The median rent for apartments built since 2000 in East Austin is higher than that of Travis County and the MSA. These apartments are primarily located in the ZIP code bordering downtown, 78702, and southeast of downtown, in 78741. Few affordable options are being created for low-income residents. The emergence of new apartment stock will drive up the rents of existing apartments, which will push more residents out of the area.

This research shows that the rapidly increasing cost of single-family homes and apartments will make East Austin increasingly unaffordable to low-income residents long housed in the area.

Dr. Hunt (hhunt@tamu.edu) is a research economist and Losey a research assistant with the Real Estate Center at Texas A&M University.

THE TAKEAWAY Low-income residents in East Austin are facing a housing affordability crisis. Both renters and homeowners are being impacted by the increasing cost of housing. This trend is expected to continue.

19

Land Markets

In Sync

Rural Land Prices Mirror Commodity Values

By Luis B. Torres and Charles E. Gilliland

T

his is the second article of two focusing on Texas commodities (most notably oil, cotton, cattle, and corn) and how the prices of those products affect land prices. Real Estate Center researchers found that commodity price differences between regions depended on whether oil, cotton, cattle, and corn were produced and how much acreage was devoted to each in the state’s seven rural land market areas.

To determine if commodity prices have a significant impact on land prices, Real Estate Center researchers tested the validity of several commonly held perceptions.

• Are commodity price fluctuations and their impact proportionate to the production (quantity) of a commodity in a region?

• Do the types of commodities produced lead to different effects between rural land regions?

• As commodity prices increase, is a commensurate positive impact felt in land prices?

• Will both short-term and long-term relationships between commodity prices and land prices emerge?

• Will divergence occur in the short-run from supply and demand factors but not be sustained over the long-run?

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TIERRA GRANDE

Land Market Regions

1 3

Do Commodity Prices Always Lead Land Price Changes?

At the state level, all four commodities exhibited significant leading indicator characteristics with respect to rural land prices (Table 1). The results imply that a change in the price of oil leads a change in the price of land by three months (one quarter), while a price change in cotton leads by 15 months (five quarters). A change in the price of beef leads by nine months (three quarters), and corn leads by six months (two quarters). The analysis shows that Texas rural land prices have a time-delayed response spread over different time periods, and the effects of each commodity are different. They also

demonstrated a positive relationship between them 2 collectively, meaning an increase in commodity prices generally leads to an increase in rural land prices.

Overall, the four commodity prices demonstrated a statistically significant leading indicator relationship with land prices for the seven rural land regions (Table 1). The length of the lead varies between regions and commodities. In region one, all four commodities exhibited a significant lead compared with region seven, where only beef and corn price changes were found to be statistically significant (Table 1). A commodity leads a change in the price of rural land

4 7 5 6

Table 1. Leading Statistical Relationship Between Commodity Prices and Texas Rural Land Prices Texas Region 1

Oil

Cotton

Beef

Corn

3 months (1 quarter)*

15 months (5 quarters)*

9 months (3 quarters)*

6 months (2 quarters)*

21 months (7 quarters)**

15 months (5 quarters)*

15 months (5 quarters)*

9 months (3 quarters)***

Region 2

12 months (4 quarters)**

15 months (5 quarters)*

3 months (1 quarter)*

–

Region 3

12 months (4 quarters)***

15 months (5 quarters)*

36 months (12 quarters)*

–

Region 4

3 months (1 quarter)*

33 months (11 quarters)*

–

27 months (9 quarters)***

Region 5

18 months (6 quarters)**

12 months (4 quarters)*

–

12 months (4 quarters)*

Region 6

12 months (4 quarters)***

21 months (7 quarters)***

27 months (9 quarters)*

–

Region 7

–

–

18 months (6 quarters)***

9 months (3 quarters)**

*1 percent significance, **5 percent significance, ***10 percent significance Linear Transfer Function (LTF) model results. The sign of the commodity coefficients are all positive.

Source: Real Estate Center at Texas A&M University APRIL 2017

21

by a minimum of three months to a maximum of 33 months. While the four commodities exhibited different lead times when compared to one another, the three historical Texas commodities— oil, cotton, and beef—exhibited stronger statistical leading relationships than corn (Table 1). In regions where commodities were nonexistent or minimal, no significant leading indicator status was observed.

160 140

Figure 1. Prices of Region 1 Land and Texas Related Commodities (Index 1980Q1 = 100) Rural Land

Oil

Cotton

Beef

Corn

120 100

1 Panhandle and South Plains

Short-Run, Long-Run Relationships The consistent significant results obtained from the estimates show commodity prices, especially oil and cotton, do have a significant direct short-run impact on Texas rural land prices for the 30-year sample period (Table 2). Not surprisingly, region seven had no short-run relationship because virtually none of the four commodities were produced in that region (Table 2).

No short-run relationship was exhibited between corn and any of the rural regions compared with the three commodity pillars of the Texas economy—oil, cattle, and cotton (Table 2). Changes in cotton prices seemed to exhibit a short-run relationship in six of (con’t on p. 24)

80 60 40 20 0 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 Note: Inflation adjusted by CPI-U: All Items, 1982–84 = 100 Sources: International Monetary Fund, U.S. Energy Information Administration, and Real Estate Center at Texas A&M University

300

Figure 2. Prices of Region 2 Land and Texas Related Commodities (Index 1980Q1 = 100) Rural Land

Oil

Cotton

Beef

Corn

250 200 2 Far West Texas

150 100 50

0 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 Note: Inflation adjusted by CPI-U: All Items, 1982–84 = 100 Sources: International Monetary Fund, U.S. Energy Information Administration, and Real Estate Center at Texas A&M University

180 160

Figure 3. Prices of Region 3 Land and Texas Related Commodities (Index 1980Q1 = 100) Rural Land

Oil

Cotton

Beef

Corn

3 West Texas

140 120 100 80 60 40 20 0 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 Note: Inflation adjusted by CPI-U: All Items, 1982–84 = 100 Sources: International Monetary Fund, U.S. Energy Information Administration, and Real Estate Center at Texas A&M University

22

Research Methodology

R

eal Estate Center researchers examined the relationship between oil, cotton, beef, and corn prices and rural land prices in the seven regional land markets in Texas from 1Q1980 to 3Q2016. The regional land markets were defined by the Center (see map). The analysis controlled for U.S. long-term interest rate effects. Quarterly land price data in dollars per acre were adjusted for inflation and seasonally adjusted; quarterly commodity prices were adjusted for inflation and seasonality; and the longterm rate of the ten-year Treasury note yield in percent per annum was adjusted based on the inflation expectations of the Federal Reserve Board of Governors.

Rural land prices seem to follow commodity price movements (Figures 1–7); oil prices especially seem TIERRA GRANDE

250

Figure 4. Prices of Region 4 Land and Texas Related Commodities (Index 1980Q1 = 100) Rural Land

Oil

Cotton

Beef

Corn

200 150

4 Northeast Texas

100 50 0 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 Note: Inflation adjusted by CPI-U: All Items, 1982–84 = 100 Sources: International Monetary Fund, U.S. Energy Information Administration, and Real Estate Center at Texas A&M University

250

Figure 5. Prices of Region 5 Land and Texas Related Commodities (Index 1980Q1 = 100) Rural Land

Oil

Cotton

Beef

Corn

200

5 Gulf CoastBrazos Bottom

150 100 50

Figure 6. Prices of Region 6 Land and Texas Related Commodities (Index 1980Q1 = 100)

0 200 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 Rural Land Oil Cotton 180 Note: Inflation adjusted by CPI-U: All Items, 1982–84 = 100 Beef Corn Sources: International Monetary Fund, U.S. Energy Information 160 Administration, and Real Estate Center at Texas A&M University 140 6 120 South Texas 100 80 60 40 20 0 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 Figure 7. Prices of Region 7 Land and Texas Note: Inflation adjusted by CPI-U: All Items, 1982–84 = 100 Sources: International Monetary Fund, U.S. Energy Information Administration, and Real Estate Center at Texas A&M University

250 200

to lead rural land prices. A linear transfer function (LTF) model approach was used to evaluate the leading relationship between each commodity and the price of rural land. This allowed identification of a leading statistical relationship between them and eliminated the possibility of any false relationship between them.

To examine both short-run and long-run impacts of commodity prices on rural land prices at the state and regional level, a vector autoregression (VAR) and a vector error correction model (VECM) were employed. The VAR model was used to investigate the short-run relationships, and the VECM will provide information on long-run relationships.

APRIL 2017

Related Commodities (Index 1980Q1 = 100) Rural Land Beef

Oil

Cotton

Corn

7 Austin-WacoHill Country

150 100 50

0 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 Note: Inflation adjusted by CPI-U: All Items, 1982–84 = 100 Sources: International Monetary Fund, U.S. Energy Information Administration, and Real Estate Center at Texas A&M University

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Table 2. Short-Run and Long-Run Relationship Between Commodity Prices and Texas Rural Land Prices Texas

Oil

Cotton

Beef

Corn

Short-run**

Short-run**

Short-run*

Short-run** and long-run*

Region 1

Short-run***

Short-run***

–

–

Region 2

Short-run* and long-run*

Short-run* and long-run*

–

–

Region 3

–

–

–

–

Region 4

Short-run** and long-run*

Short-run*** and long-run***

–

–

Region 5

Short-run* and long-run***

Short-run* and long-run*

–

–

Region 6

Short-run* and long-run*

Short-run* and long-run*

Short-run**

–

Region 7

–

–

–

–

*1 percent significance, **5 percent significance, ***10 percent significance Vector Autoregressive (VAR) and Vector Error Correction (VEC) model results.

For the VAR model the null hypothesis is that the real commodity price coefficients are jointly equal to zero in the real land price equation.

The Johansen Cointegration Test at the 5 percent critical value and the negative and significance of the error correction term in the VEC model.

Source: Real Estate Center at Texas A&M University

the seven regions followed by oil with five out of seven, and beef with one out of seven regions (Table 2).

The long-run estimates showed consistent significant results revealing a longrun relationship between oil and cotton in four out of seven rural land regions (Table 2), demonstrating the strong ties between these two commodities and rural land prices for the past 30 years. Unexpectedly, only a long-run relationship at the state level was found with corn prices (Table 2), meaning that by region there is no significant relationship, but an aggregate long-run relationship exists between rural land prices and corn prices.

Based on these findings, Center researchers proceeded to evaluate how an unpredictable change (shock) in the price of the four commodities affects

24

rural land prices in the short- and longrun. The analysis was done only for the regions where a significant relationship was found. This analysis reveals the isolated way an unexpected movement (shock) in the real price of commodities affects real rural land prices over time.

As the figures show, the response of land prices in the full sample to a positive change in the price of the commodities is statistically positive (Figures 9–33), meaning a positive unexpected change in the price of one of these commodities leads to a positive change in rural land prices. The inverse is true if commodity prices fall; that is, it would cause a negative change in rural land prices. The results mean that the effects of the commodity price changes are felt through time in the price of rural land.

For more information, see Center publication number 2151, “Oil, Cattle, Cotton: Commodities Affect Land Prices.”

For additional figures, see this article (2162) online at www.recenter.tamu.edu.

Dr. Torres (ltorres@mays.tamu.edu) and Dr. Gilliland (c-gilliland@tamu.edu) are research economists with the Real Estate Center at Texas A&M University.

THE TAKEAWAY Estimates show that changes in commodity prices lead changes in rural land prices. Of the four commodities, oil and cotton seem to have the strongest relationship with rural land prices, and their impact can be seen in rural land prices for more than two years.

TIERRA GRANDE

Taxes

Losses and Self-Employment

By Jerrold J. Stern

O

wners of profitable income-producing real estate In addition to back taxes and interest, the Fitches were cannot offset their net income by unrelated business assessed accuracy penalties from $67 to $584 for the years losses of a spouse when computing self-employment in question. The court determined that the penalties were (SE) taxes. This rule was underscored by a 2013 tax court case appropriate due to the Fitches’ “intentional disregard” for SE and is even more relevant today because SE tax rates increased tax rules. since the transactions in the court case.

While the dollar amounts in this case are not large, the manThe 2013 case pertains to Brenda and Donald Fitch, who ner in which the SE tax rules and penalties were applied is the resided in California during the period of the contested tax major concern. In addition, SE tax rates have increased since filings. Note that both 2013. Current computaCalifornia and Texas are tions follow. community-property The example illusCurrent SE Tax Computations states. SE taxes are trates (1) the SE tax Example – Assume a married taxpayer is filing jointly with their applied in the same base amount for 2017 spouse and has $280,000 of SE net income. The following SE tax manner in all 50 states is $127,200 (increased computations are required for 2017: in virtually all instances. annually for inflation); (2) Brenda was a licensed there are up to three tiers Adjusted SE net income (ASE): real estate agent under of tax depending on the SE net income................................................... $280,000 California law and a size of the taxpayer’s SE Multiply by .9235.............................................

0.9235 $258,580 member of the National income; (3) while there Tier 1 tax: Association of Realtors. is a limit for the Tier 1 Smaller of ASE or $127,200 “base”................... $127,200 Donald, a CPA, owned tax, there is no limit for Multiply by 15.3%............................................

0.153 19,462 and operated a CPA the Tier 2 or Tier 3 taxes, Tier 2 tax: practice. He spent four which are intended for ASE (if more than $127,200)............................. $258,580 hours per day working high-income taxpayers; Tier 1 base........................................................ $127,200 at his practice. (He was and (4) one-half of the SE Excess............................................................... $131,380 recovering from a meditax can be deducted when Multiply excess by 2.9%...................................

0.029 $ 3,810 Tier 3 tax: cal condition.) computing adjusted gross ASE (if more than $250,000)............................. $258,580 Apart from their income (AGI).

Tier 3 floor for married taxpayers filing jointly... $250,000 respective Realtor and As discussed and Excess............................................................... $ 8,580 CPA businesses, the coushown here, SE tax issues Multiply excess by .9%.....................................

0.009 $ 77 ple owned and managed can be complex from SE tax ...................................................................................... $ 23,349 eight rental properties. both a rules and com50% deduction for Adjusted Gross Income (AGI)..................... $ 11,675 They chose to own their putation perspective. properties separately.

Consultation with an Brenda owned three of accountant or attorney the properties. Donald owned five. They each performed dayknowledgeable about real estate tax matters is recommended. to-day tasks related to their respective rental properties. DonDr. Stern (stern@indiana.edu) is a research fellow with the Real Estate ald occasionally helped Brenda with advertising and repairs.

Center at Texas A&M University and a professor emeritus of accounting in During the three years in question, the taxable income assothe Kelley School of Business at Indiana University, Bloomington. ciated with Brenda’s properties ranged from $2,000 to $21,000.

In contrast, Donald’s CPA practice generated losses ranging from $59,000 to $69,000 during the same period. The couple THE TAKEAWAY reduced Brenda’s taxable rental income to zero by offsetting Owners of profitable income-producing real estate cannot her income by Donald’s losses. This approach was determined offset their net income by unrelated business losses of a to be acceptable for income tax purposes but not for SE tax spouse when computing self-employment (SE) taxes. Morepurposes. over, SE tax rates have increased in recent years.

APRIL 2017

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Residential

H

ome sellers and buyers usually face a number of uncertainties during the selling and buying processes. True market values of properties are always in question, as is the relative strength of supply and demand in local real estate markets in any period, sellers’ information about the properties versus buyers’ information, and many other issues.

These uncertainties stem from the fact that each property is unique because of location, structure, age, design, and a host of other details.

Housing markets are always in a state of flux. While sellers seek maximum selling prices and minimum days-on-market, buyers seek minimum prices and minimum search costs in terms of money and time.

List Price Discounts Center researchers found that the time series of list price discounts of homes sold in Texas is an important indicator of general market conditions and the market liquidity of homes sold in local housing markets. They also found that discounts to list prices in Texas residential markets trended downward after the state’s economy recovered from the Great Recession, but the downward trend has slowed recently and in some markets even reversed.

Because of price uncertainty, one selling strategy commonly practiced in U.S. real estate markets is to choose a list price higher than the average prices of comparable properties in local markets to leave “room for negotiations.” This is followed by a wait-and-see process testing the strength of demand by measuring the traffic of viewers to for-sale properties. Depending on the amount of traffic, the seller may revise the list price upward or downward. If there are no offers, listing prices

26

typically are lowered by offering discounts to listing prices or accepting offers below list prices. Conversely, in tight, strongdemand periods, buyers may offer more than the list price to obtain the property.

Deciding on the discount is difficult. The seller may want to offer a discount to attract buyers but at the same time wants to get the maximum price for the property. Sellers’ agents know local market conditions and can help the seller balance the need to get the best price with the need to reduce selling time.

List price asked by sellers is the counterpart of asking price in the stock market, while a price offer is similar to a bid price in the stock market. The bid-ask spread for a particular stock is the difference between bid and ask, and is a measure of the relative strength of the supply and demand for the stock as well as liquidity of the stock. Discounts to list prices in real estate markets are similar. Smaller (larger) discounts mean more (less) liquidity, higher (lower) demand relative to supply, and stronger (weaker) sellers’ bargaining power.

Computing Discount Rates

T

otal discount rates are computed from list and sale prices and can be expressed in terms of the ratios of 1) original list price to sale price, 2) sale price to original list price, 3) difference between original list price and sale price, or 4) difference between sale and original list price divided by list price.

Average total discount rates for real estate properties in local markets are computed as the difference between average list price and average sale price divided by the average list price. Discount rates can be computed for all properties sold in TIERRA GRANDE

regional markets and are commonly computed for residential, commercial, and industrial markets.

properties and increasing liquidity of homes sold in Texas housing markets, leading home sellers to offer lower list price discounts.

Local Market Discount Rates On the demand side of the housing market, the most imporTime series of the average list price discount rates in local real tant factors contributing to lower discount rates were more estate markets offer information about the strength of supply jobs, growing incomes, and lower mortgage rates. On the supand demand conditions. Increasing ply side, the shrinking inventories (decreasing) average discount rates of foreclosed homes left over from indicate increasing (decreasing) the Great Recession contributed ORIGINAL LIST PRICE is the original price supplies of properties relative to to smaller supplies of for-sale that a property was listed for in an MLS demand. homes. system. Sale Price is the final close These conditions are known The moving averages of disprice agreed to by the seller and buyer. as buyers’ markets when the list count rates to existing single-famTotal Discount Rate is the difference price discount rates are increasily homes sold in Texas fell from between Original List Price and Sale ing and sellers’ markets when the 8.3 percent in January 2012 to 3.7 discount rate declines. Prospecpercent in October 2015 (Figure 2).

Price expressed as a percentage of the tive sellers and buyers can look The downward trend was steep in Original List Price. at the trends in average list price 2012–13 but slowed since middiscount rates to better understand 2014. The moving average rates local market conditions, which are useful when selling or buyhave increased to 4.1 percent in December 2016, indicating the ing properties. end of the downward trend.

Sellers of existing condos had to offer discount rates of 10.4 Texas Housing Market Discount Rates percent in January 2012 to market condos (Figure 3). The rate ata used are monthly time series of average total trended downward, falling to 4.1 percent in October 2015 and discount rates for residential properties sold in Texas since then has trended upward to 4.4 percent in December markets from January 2012 to December 2016 for 2016, suggesting a mild cooling of the state’s condo markets in three markets of single-family homes, townhomes, and condolate 2016 and the end of falling discount rates. miniums (see table). Time series of discount rates are comThe moving averages of discount rates to existing townputed by dividing the difference between the average list price homes sold in Texas have fallen from 9 percent in January 2012 and average sale prices by average list price in a region (state, to 3.3 percent in August 2015 and since then have increased metropolitan, county) in a month and expressed as percentages. to 3.7 percent in December 2016, indicating the end of falling The resulting time series rates that began after of discount rates monitor the state’s economy the relative strengths of recovered from the Great the supply and demand Recession (Figure 4). sides of the state’s housDiscount Rates ing markets and the in Metro Housing liquidity of residential Markets properties in any given 2016M12 2012M01 DIFFERENCE period.

All five major Texas % % % Time series of list metropolitan areas expeprice discount rates for rienced downward trends Texas all types of residential in average list price properties (single-family, discount rates for singleAustin-Round Rock townhomes, condos) since family homes sold from 2012 is shown in Figure 1.

January 2012 to SeptemDallas-Plano-Irving The rates display seasonal ber 2016. However, the variations with peaks initial rates in January Fort Worth-Arlington in winter and troughs 2012 and the latest rates in summer. Sellers in in December 2016 vary Houston winter need to offer larger significantly across the discounts to list prices to metro areas (Figures 5 San Antonio-New Braunfels sell residential properties to 9).

SOURCE: REAL ESTATE CENTER AT TEXAS A&M UNIVERSITY when demand is weaker.

Houston-The WoodFigure 1 also shows the lands-Sugar Land had the 12-month moving averages of discount rates to adjust for sealargest list price discount rate for single-family homes in Janusonal variations and displays the trend in the time series. ary 2012 followed by Dallas-Plano-Irving, Fort Worth-ArlingThe moving average shows a downward trend in the rates ton, San Antonio-New Braunfels, and Austin-Round Rock (see after the Texas economy recovered from the Great Recession. table). The Houston discount rate fell steeply from 8.3 percent The rate fell from 8 percent in January 2012 to 3.6 in October in January 2012 to 3.8 percent in January 2014, then reached 2015 and increased to 4 percent in December 2016. The falling a trough of 3.2 percent in February 2015 (Figure 5). But the rates suggest the growing strength of demand for residential collapse of oil prices in 2014 led to losses in jobs and incomes

ICE D

LIST PRICE DISCOUNT RATES

Housing Markets

APRIL 2017

4.1 8.3 3.1 6.6

4.2 3.5

2.2 2.4 4.8 3.8

5.7 5.3 3.5 3.6

7.9 7.7 8.3 7.4

27

E C I R P LIST 9

Discount Rates Moving Average Discount Rates

8 7

8

2012

2013

2014

2015

Figure 4. Existing Townhomes Sold in Texas

9

2012

2013

2014

2015

2016

Figure 5. Existing Single-Family Homes Sold in Houston

7

6

2014

2015

2016

Figure 7. Existing Single-Family Homes Sold in Fort Worth-Arlington

7

7

5

2013

2014

2015

2016

Figure 8. Existing Single-Family Homes Sold in Austin-Round Rock

2012

2013

2014

2015

2016

2

Figure 6. Existing Single-Family Homes Sold in Dallas-Plano-Irving

2012

2013

2014

2015

2016

8 7

5

6

2

2016

Figure 9. Existing Single-Family Homes Sold in San Antonio-New Braunfels

6

5

3

3

2015

Percent 2012

4

4

2014

3

Percent

Percent

6

3

2013

4

4 2013

2012

5

5

2012

8 7

6

4

4

8 Percent

Percent

6

2

3

2016

8

8

6

4

3

2

Percent

Percent

Percent

5

4

10

8

6

5

12

Figure 3. Existing Condominiums Sold in Texas

10

7

6

2

Figure 2. Existing Single-Family Homes Sold in Texas

Percent

9

Figure 1. Texas Residential Properties Sold

DISCOUNT RATES

4 2012

2013

2014

2015

2016

3

2012

2013

2014

2015

2016

SOURCE: REAL ESTATE CENTER AT TEXAS A&M UNIVERSITY

SOURCE: REAL ESTATE CENTER AT TEXAS A&M UNIVERSITY that were reflected in growing list price discount rates.

Since February 2015, Houston home sellers have increased the discount rates, reaching 4.8 percent in December 2016 (Figure 5).

While the Houston metro area suffered from the oil price collapse, Dallas’ economy continued to expand after the Great Recession, mainly due to its strong link to the U.S. economy.

The impacts of the flourishing Dallas economy are reflected in the downward trend in discount rates. Dallas-Plano-Irving fell from 7.9 percent in January 2012 to 2.2 percent in December 2016, the lowest discount rate among the major state’s metro areas (see table and Figure 6).

The discount rates in Fort Worth-Arlington’s single-family housing market fell from 7.7 percent in January 2012 to 2.4 percent in December 2016, the second lowest rate among the state’s major metro areas (see table and Figure 7). The similarity of the area’s trend in discount rate with that of Dallas and its most recent low discount rates suggest that the expansion of the U.S. and Dallas economies may have positively impacted Fort Worth’s economy.

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Austin-Round Rock had the lowest list price discount rate in January 2012 at 6.6 percent (see table and Figure 8). The metro area’s discount rate fell to 2.2 percent in June 2014 but has since trended upward, reaching 3.1 percent in December 2016, suggesting a mild cooling off of the metro area’s single-family housing market (see table and Figure 8).

The downward trend in list price discount rates in the San Antonio-New Braunfels metro area from 7.4 percent in January 2012 to 3.8 percent in December 2016 has slowed since mid2015 (see table and Figure 9).

Dr. Anari (m-anari@tamu.edu) is a research economist and Klassen (gklassen@ mays.tamu.edu) is a research data scientist with the Real Estate Center at Texas A&M University.

THE TAKEAWAY Center research on home price discounts found that discounts are important indicators of general housing market conditions and liquidity of local markets.

TIERRA GRANDE

APRIL 2017

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