Tierra Grande - July 2016 by Texas Real Estate Research Center

JULY 2016 â&#x201E;¢

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 Real Estate Salaries Exuberant Price Behavior Property Tax Caps Surveys Employment and Home Prices Texas Personal Income Part-Time Profits a Must

Helping Texans make better real estate decisions since 1971

JULY 2016 â&#x201E;¢

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

September! The Real Estate Center staff is looking forward to the sea breeze and lovely sunset in Galveston on September 9th. Stop by booth 309 and visit with us at the Texas Association of Realtors Expo. www.texasrealtorsconference.com

iii

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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

JULY 2016

VOLUME 23, NUMBER 3 ™

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

Sizing Up Surveys

Surveyors these days have cutting-edge technology that makes their surveys more accurate than ever. Check out this refresher course on types of surveys and the purposes they fulfill. BY RUSTY ADAMS

Graphic Specialist/Photographer, JP BEATO III Circulation Manager, MARK BAUMANN Lithography, RR DONNELLEY, HOUSTON ADVISORY COMMITTEE: Russell Cain, Port Lavaca, chairman; Doug Jennings, Fort Worth, vice chairman; Mario A. Arriaga, Conroe; Jacquelyn K. Hawkins, Austin; Walter F. “Ted” Nelson, Houston; Doug Roberts, Austin; Kimberly Shambley, Dallas; Ronald C. Wakefield, San Antonio; C. Clark Welder, San Antonio; and Bill Jones, Temple, ex-officio repre senting the Texas Real Estate Commission. 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. SUBSCRIPTIONS free to Texas real estate licen sees. Others can download articles free at www. recenter.tamu.edu. 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 (top left, right); Real Estate Center files, pp. 2, 6, 17, 23, 28; Robert Beals II, pp. 9, 16 (bottom left), 26. © 2016, Real Estate Center. All rights reserved.

2 Real Estate Payday

Texans’ Compensation Competitive How does your compensation stack up against real estate professionals in other parts of the U.S.? A recent study has the answers you’re looking for. BY ALI ANARI

6 Curb Your Enthusiasm

Keeping an Eye on Exuberant Home Prices Home prices sometimes rise rapidly based on buyers’ belief that they will make money down the road when they sell. These “exuberant” or “explosive” prices can cause disappointment for buyers whose future expectations are not met. BY LUIS B. TORRES, ENRIQUE MARTÍNEZ-GARCÍA AND VALERIE GROSSMAN

ON THE COVER A drone’s eye view of afternoon traffic, Houston

PHOTOGRAPHER Robert Beals II

JULY 2016

10 Tax or Consequences

Griping about property taxes is a favorite pastime in Texas. But one measure under consideration— capping appraisal increases—had unexpected negative consequences in California. BY CHARLES E. GILLILAND

18 Not Back Yet

Some States Still Lagging After Great Recession Some states haven’t returned to their prerecession employment levels. Why did Texas come through the Great Recession so much better than other states? BY HAROLD D. HUNT AND LUIS B. TORRES

23 What’s in Your Wallet?

Texans’ Income Fares Well During Oil Busts and Booms The energy industry is the lifeblood of Texas, but economic diversification has helped personal incomes stay close to the national average despite the ups and downs of oil prices. BY LUIS B. TORRES AND WAYNE DAY

28 Profit Motive Key for Part-Timers

Fair warning, part-timers. If you can’t prove you’re working to make a profit, the IRS will deny your deductions. BY JERROLD J. STERN

Brokerage

Real Estate Payday Texans’ Compensation Competitive By Ali Anari

Compensation of the state’s real estate professionals is competitive with the nation’s, and there is plenty of room for the number of Texas brokers and managers to grow. And, people who use real estate professional services are willing to pay more for experienced real estate professionals.

TIERRA GRANDE

Real Estate Professionals According to the Bureau of Labor Statistics (BLS), real estate professionals fall into four groups: real estate brokers; property, real estate and community association managers; appraisers and assessors of real estate; and real estate sales agents (see sidebar). BLS occupational employment data are collected from reporting firms excluding self-employed. The statistics are samples and actual numbers are higher than the number responding to the BLS survey.

Brokers

Bureau of Labor Statistics Definitions

his research uses the U.S. Bureau of Labor Statistics’ Occupational Employment Statistics for 2015 and compares several important metrics for the nation and Texas real estate professionals, such as average compensations, location quotients, and employment per 1,000 jobs. Employment per 1,000 jobs is the number of jobs (employment) in the given occupation per 1,000 jobs in the given area and is a measure of job concentration. Another measure of job concentration is location quotient, defined as the ratio of an occupation’s share of employment in a given area to that occupation’s share of employment in the U.S. as a whole. For example, an occupation that makes up 8 percent of jobs in a specific area compared with 10 percent of U.S. employment would have a location quotient of 0.8 for the area in question. A location quotient of more (less) than one for an occupation in a region shows more (less) concentration of the occupation in the region.

Real Estate Brokers

Appraisers and Assessors

Texas was one of the states with the Brokers operate real estate offices, or of Real Estate highest brokerage employment in work for commercial real estate firms, These professionals appraise real prop2015 (Table 1). The number of brooverseeing real estate transactions. Other erty and estimate its fair value. They kers per 1,000 Texas jobs was 0.21, duties usually include selling real estate or may assess taxes in accordance with prelowest among states with the highest renting properties and arranging loans. scribed schedules. employment level in this occupation. Property, Real Estate, and Community Real Estate Sales Agents A location quotient of 0.75 for broAssociation Managers Sales agents rent, buy, or sell property kers in Texas in 2015 indicates less Managers plan, direct, or coordinate the for clients. They perform duties such as than the national average concentraselling, buying, leasing, or governance studying property listings, interviewing tion in the broker occupation. activities of commercial, industrial, or prospective clients, accompanying cliWhile employment per 1,000 jobs residential real estate properties. This ents to property sites, discussing condiand location quotient were lower group includes managers of homeowner tions of sale, and drawing up real estate than the national average, the average and condominium associations, rented contracts. This group includes agents who annual compensation of $106,120 or leased housing units, buildings, or represent buyers. for Texas brokers was higher than land (including rights-of-way). the national average as well as other states with higher employment. Dallas-Plano-Irving and HoustonTable 1. U.S. and States with Highest Broker Employment, 2015 The Woodlands-Sugar Land are among the nation’s metropolitan areas with the highest employment and Employment per Location Annual highest compensations for brokers (Table 2). State Employment 1,000 Jobs Quotient Wage, $

Property, Real Estate, and Community Association Managers

U.S.

38,810

0.28

1.00

80,210

North Carolina

6,020

1.46

5.19

60,010

Texas ranked third in the number of real estate professionals in the property, real estate, and community association managers class in 2015 after California and Florida (Table 3). Employment per 1,000 jobs and location quotient for Texas for this occupation in

California

5,040

0.33

1.16

87,470

Florida

2,840

0.36

1.27

87,460

Illinois

2,460

0.42

1.49

78,950

Texas

2,430

0.21

0.75

106,120

Source: Bureau of Labor Statistics

Table 2. Metropolitan Areas with Highest Broker Employment, 2015 Metropolitan Area

Employment

Employment per 1,000 Jobs

Location Quotient

Annual Wage, $

Dallas-Plano-Irving, TX Houston-The Woodlands-Sugar Land, TX New York-Jersey City-White Plains, NY-NJ Washington-Arlington-Alexandria, DC-VA-MD-WV Los Angeles-Long Beach-Glendale, CA Chicago-Naperville-Arlington Heights, IL Charlotte-Concord-Gastonia, NC-SC Denver-Aurora-Lakewood, CO Phoenix-Mesa-Scottsdale, AZ Raleigh, NC

840 800 1,430 910 1,400 1,890 1,920 880 1,430 960

0.36 0.27 0.22 0.37 0.34 0.53 1.73 0.64 0.76 1.71

1.28 0.98 0.78 1.33 1.21 1.88 6.13 2.28 2.71 6.06

133,840 106,890 104,240 94,340 82,670 79,950 69,870 68,650 68,030 60,830

Source: Bureau of Labor Statistics JULY 2016

Table 3. States with Highest Property, Real Estate, and Community Association Managers Employment, 2015 State

Employment

Employment per 1,000 Jobs

Location Quotient

Annual Wage, $

U.S. California Florida Texas Illinois New York

174,410 27,280 19,820 11,420 7,200 6,670

1.26 1.76 2.50 0.99 1.23 0.74

1.00 1.39 1.98 0.78 0.97 0.59

68,240 74,450 56,020 89,250 66,670 107,920

Source: Bureau of Labor Statistics

2015 were lower than the national average (Table 3). Average annual compensation for the state’s property, real estate, and community association managers was $89,250, higher than the $68,240 national average and topped only by New York. Dallas and Houston were among the U.S. metro areas with the highest employment in this category (Table 4). Dallas ranked first in annual average compensation for property, real estate, and community association managers, followed by New York and Houston (Table 4).

Appraisers and Assessors

Texas had the largest number of appraisers of all U.S. states in 2015 (Table 5). Employment per 1,000 Table 4. Metropolitan Areas with Highest Property, jobs and the location Real Estate, and Community Association Managers Employment, 2015 quotient for this class of real estate professionals Employment per Location Annual both were higher than Metropolitan Area Employment 1,000 Jobs Quotient Wage, $ the national average Dallas-Plano-Irving, TX 3,010 1.29 1.02 108,430 (Table 5). The average New York-Jersey City-White Plains, NY-NJ 6,770 1.04 0.83 101,430 annual compensation Houston-The Woodlands-Sugar Land, TX 3,390 1.16 0.92 95,450 of $65,340 for Texas Washington-Arlington-Alexandria, DC-VA-MD-WV 3,240 1.34 1.06 83,390 appraisers and assesAtlanta-Sandy Springs-Roswell, GA 4,280 1.72 1.36 77,280 sors of real estate was Anaheim-Santa Ana-Irvine, CA 3,620 2.37 1.88 76,480 higher than the national Los Angeles-Long Beach-Glendale, CA 7,700 1.88 1.48 71,580 average of $58,400 and Chicago-Naperville-Arlington Heights, IL 5,370 1.5 1.19 70,640 second only to CaliforMinneapolis-St. Paul-Bloomington, MN-WI 3,220 1.71 1.35 64,870 nia. Dallas ranked secPhoenix-Mesa-Scottsdale, AZ 3,460 1.84 1.46 63,350 ond after Los Angeles in Miami-Miami Beach-Kendall, FL 3,380 3.1 2.45 52,730 average annual compenSource: Bureau of Labor Statistics sation for appraisers and assessors among the nation’s metro areas with the highest number of jobs in this category (Table Table 5. States with the Highest Appraisers and Assessors 6). Dallas had more appraisers than of Real Estate Employment, 2015 Houston as well as a higher location quotient and more appraisers per 1,000 Employment per Location Annual jobs (Table 6). State Employment 1,000 Jobs Quotient Wage, $ U.S. Texas California Florida New York Georgia

60,290 5,650 4,380 3,880 3,420 2,650

Source: Bureau of Labor Statistics

0.44 0.49 0.28 0.49 0.38 0.65

1.00 1.12 0.65 1.12 0.87 1.48

58,400 65,340 77,740 49,990 60,550 46,770

Sales Agents Texas had the second largest number of sales agents after Florida among the states with the highest employment in this class of real estate professionals (Table 7). The number of real estate sales agents per 1,000 jobs and the location quotient for Texas for this category in 2015 were higher than the corresponding national averages. The average annual compensation of $68,410 for the state’s sales agents was higher than the national TIERRA GRANDE

Table 6. Metropolitan Areas with Highest Appraisers and Assessors of Real Estate Employment, 2015 Metropolitan Area average as well as averages of other states with high employment in this group (Table 7). The Houston metro area had the highest number of sales agents after New York and Atlanta. Houston ranked third in annual compensation for real estate sales agents after New York and Chicago (Table 8).

Los Angeles-Long Beach-Glendale, CA Dallas-Plano-Irving, TX Minneapolis-St. Paul-Bloomington, MN-WI Houston-The Woodlands-Sugar Land, TX St. Louis, MO-IL Denver-Aurora-Lakewood, CO New York-Jersey City-White Plains, NY-NJ Chicago-Naperville-Arlington Heights, IL Phoenix-Mesa-Scottsdale, AZ Atlanta-Sandy Springs-Roswell, GA

Employment

Employment per 1,000 Jobs

Location Quotient

Annual Wage, $

1,200 1,210 1,610 1,090 890 1,000 1,800 1,420 950 1,600

0.29 0.52 0.85 0.37 0.67 0.73 0.28 0.4 0.51 0.65

0.67 1.18 1.95 0.85 1.53 1.66 0.65 0.91 1.16 1.48

81,620 78,890 71,730 71,550 71,500 62,050 60,520 58,050 57,060 50,060

Source: Bureau of Labor Statistics

Value of Experience

Table 7. U.S. and States with Highest Real Estate Sales Agents scar Wilde said, “Experience is Employment, 2015 simply the name we give our mistakes.” In real estate, profesEmployment per Location Annual sionals’ experience can help minimize State Employment 1,000 Jobs Quotient Wage, $ costly mistakes in terms of money and U.S. 151,700 1.10 1.00 58,410 time. Prospective buyers, sellers, and Florida 20,360 2.57 2.34 54,090 renters search for properties to be purTexas 14,350 1.24 1.13 68,410 chased, sold, or rented. These searches California 12,200 0.79 0.72 62,330 are costly and time consuming, but real Georgia 9,410 2.29 2.08 45,620 estate professionals can minimize costs Washington 7,540 2.53 2.30 53,640 and time by collecting, compiling, and Source: Bureau of Labor Statistics organizing property information. People who use them value the Table 8. Metropolitan Areas with Highest Real Estate Sales Agents Employment, 2015 knowledge and experience of real estate profesEmployment per Location Annual sionals and are willing to Metropolitan Area Employment 1,000 Jobs Quotient Wage, $ pay more for them. New York-Jersey City-White Plains, NY-NJ 5,240 0.81 0.73 104,150 Research shows the Chicago-Naperville-Arlington Heights, IL 3,910 1.1 1 76,950 value of experience can Houston-The Woodlands-Sugar Land, TX 4,840 1.65 1.5 67,200 be measured by the Washington-Arlington-Alexandria, DC-VA-MD-WV 3,470 1.44 1.31 66,150 ratio of experienced Dallas-Plano-Irving, TX 3,300 1.42 1.29 64,560 professional compensaLos Angeles-Long Beach-Glendale, CA 3,410 0.83 0.75 60,180 tion to that of entry Seattle-Bellevue-Everett, WA 4,630 3.02 2.74 55,130 level. Users of real Orlando-Kissimmee-Sanford, FL 3,820 3.42 3.11 54,560 estate professionals Miami-Miami Beach-Kendall, FL 2,980 2.73 2.48 53,930 pay as much as two to Atlanta-Sandy Springs-Roswell, GA 6,570 2.65 2.41 46,730 three times more for Source: Bureau of Labor Statistics experienced professionals (Table 9). Dr. Anari (m-anari@tamu.edu) is a research economist with the Real Estate Center at Texas A&M University.

THE TAKEAWAY Bureau of Labor Statistics research focusing on real estate professionals compared Texas’ compensation with other states and the United States as a whole. Results indicate that compensation in Texas is competitive with other states and the nation. JULY 2016

Table 9. Annual Compensation for Texas Real Estate Occupations, 2014 Occupational Title

Entry

Experienced

Ratio

Real Estate Brokers Property, Real Estate and Community Association Managers Appraisers and Assessors of Real Estate Real Estate Sales Agents

38,369

144,573

3.8

36,777

99,111

2.7

35,132 23,866

76,831 78,268

2.2 3.3

Source: Texas Workforce Commission

Residential

CURB YOUR ENTHUSIASM Keeping an Eye on Exuberant Home Prices

By Luis B. Torres, Enrique Martínez-García, and Valerie Grossman

ears after the largest housing bust in United States history, could another boom be developing? Texas avoided much of the 1998–2006 housing boom and subsequent collapse in real (inflation-adjusted) house prices during the Great Recession (Figure 1). However, since the housing rebound took hold in 2012, Texas has registered strong price appreciation similar to that of the U.S. Could this mean another housing boom is underway? If so, should we worry about it? Many housing analysts and scholars share the view that the 1998–2006 housing boom resulted from a departure of house prices from their intrinsic values, which are determined by economic fundamentals. This led to

a misallocation of resources in the economy and distorted investment patterns. Eventually, it precipitated the Great Recession in the U.S., which spilled over into many developed countries. Learning from such experiences may help avoid a repeat of the trauma caused by the latest boom-bust housing cycle. A rapid pace of real house price appreciation during a boom by itself does not necessarily imply that house prices are becoming out of step with the fundamentals of the housing market. However, real house price increases can result from a misalignment with fundamentals if, for example, recent trends of high-growth performance drive market participants to expect higher prices in the future. An expectations-driven misalignment of this sort is often called a bubble. In a situation like this, buyers are willing to invest in houses above their fundamental value if they expect to be compensated through future price increases. If enough buyers share the same beliefs about the market, they will drive current house prices up. The robust gains achieved through price appreciation then sustain a self-fulfilling prophecy, providing validation to the buyers’ expectation of rapid house price growth. This keeps the market misaligned from its fundamentals until enough investors become leery of a bust, the flow of money into housing dries out, and eventually the feared collapse occurs. Detecting a misalignment in house prices from their fundamental-based intrinsic values, let alone preventing one, is not a simple task. The difficulty arises because actual house prices can be observed and measured, but the intrinsic value of housing cannot. Economists instead look at TIERRA GRANDE

Table 1. Chronology of Real House Price Explosive Behavior in U.S. and Texas Area

Start Date

End Date

Duration (Quarters)

Depth (Percent)

U.S.

4Q2001

4Q2005

32.72

Texas

3Q1987

1Q1991

–15.96

3Q1999

4Q2002

6.84

Source: Federal Reserve Bank of Dallas

Explosive Behavior in Texas Real House Prices In Texas, two periods of explosive behavior were identified before and during periods of recession in Texas and the U.S. The first occurred during the late 1980s oil bust, pushing the Texas economy into a recession. House prices in the state broadly collapsed while those in the rest of the country did not (Table 1 and Figure 1). The second period was before and during the shallow 2001 national and state recessions. This coincided with a prolonged period of exuberance with significant real appreciation of housing. For the state, the episode ended much earlier, allowing Texas to avoid the worst of the housing fallout JULY 2016

Figure 1. Historical Evolution of Real House Prices in U.S. and Texas 200 Index, 1Q1975 = 100

home prices that would have otherwise prevailed in the market under similar conditions, ruling out expectations-driven bubbles. This entails discriminating across different existing theories that disagree on the main drivers of the housing market demand and supply and working with often incomplete data. On the demand side, fundamental factors could include demographics, income and employment growth, access to credit, and interest rates. The supply side factors are construction costs (the price of lumber, drywall, and labor wages), the housing stock, and land availability. esearch has opened up new avenues to tackle the detection of misalignments using new statistical tools and techniques to monitor the behavior of prices over time and make inferences without being tied to a particular fundamentals-based model. One such approach revealed that house prices change in a predictable way when an expectationsdriven bubble occurs—they become explosive (or exuberant). Therefore, new techniques to detect exuberance may detect or signal the formation of housing bubbles. This is especially useful if it can identify a boom arising from a fundamental misalignment before the housing bust occurs. It is best to use it in conjunction with economic fundamentals and other available signals tracking house price increases since no one indicator has the ability to identify such misalignments with certainty and timeliness.

160

U.S. Real House Prices Texas Real House Prices Alternative U.S. Real House Prices

120 80 40 0 1880 1900 1920 1940 1960 1980 2000 2020 Notes: Texas real house prices (green line) are constructed with Freddie Mac data and Dallas Fed estimates of Texas CPI data. The historical U.S. real house price series (blue line) is based on the Case-Shiller index after 1975. An alternative rendering based on Freddie Mac data for the post-1975 period (red line) is provided for comparability. Sources: Freddie Mac; Robert J. Shiller (2005): Irrational Exuberance. Broadway Books, 2nd edition, 2005 (http://www.econ.yale.edu/~shiller/data.htm); Authors' calculations.

that caused so much damage elsewhere in the U.S. (Table 1 and Figure 1). The U.S. and Texas housing markets have had dissimilar experiences over the past 40 years. They share common factors that could contribute to the spread of house price exuberance, such as access to mortgages, federal housing and tax policy, and the decline in U.S. interest rates since the end of the 1990s. But the documented differences (Table 1) could be related to the importance of the oil industry in Texas (Figure 2), the high supply elasticity of housing in the state, and differences in home-equity lending (for example, total mortgage debt cannot exceed 80 percent of the fair market value of the home) among other factors. This research found that differences across regional housing markets in Texas result in differences in the patterns of exuberant behavior in the 25 Metropolitan Statistical Areas (MSAs). The results indicate that episodes of exuberant behavior were widespread. Ripple effects from oil boom and bust periods, especially during the 1980s, and from the continuous decline in U.S. interest rates since the 1990s, underpin what occurred in most MSAs, but regional factors also play a role in the likelihood that a period of exuberance will occur. The chronology of exuberant house price behavior in the state’s four major MSAs (Austin, Dallas-Fort Worth, Houston, and San Antonio) is shown in Table 2. Commonalities and

some unusual price movements and are amber in 3Q2015 after experiencing at least one green period since 3Q2014.

Figure 2. Texas Real House Prices and Rig Count 110 Index 1Q1983 = 100

Explosive Behavior Same as a Bubble?

1,100 900

700

500

300

Number of Rigs

100

Housing Prices Rig Count

100 60 1983 1987 1991 1995 1999 2003 2007 2011 2015 Note: Inflation adjusted Sources: Federal Housing Finance Agency and Baker Hughes

differences were found, notably that Austin and DFW appear largely insulated from the rest of the state and the U.S. Interestingly, San Antonio follows the pattern of the U.S. housing market more closely than that of Texas, registering a prolonged period of exuberant behavior during the national housing boom in the early 2000s. Houston’s housing has more similarities with the state and, in particular, with the chronology of the main energy-related MSAs in Texas (Table 3). Table 2 also includes the same statistics for the four major Border MSAs (Brownsville, El Paso, Laredo, and McAllen). El Paso and McAllen in particular experienced periods of exuberance during the late 1980s and early 1990s. El Paso, Laredo, and McAllen shared a period of exuberant behavior later than the state as a whole. This appears to coincide with the tail of the U.S. housing boom. The energy-related MSAs had explosive price behavior in housing as well, but with notable differences from the rest of the state. Upstream energy MSAs Midland and Odessa did not register a period of exuberance in the 1980s. However, Longview, Tyler, the downstream energy MSAs including Corpus Christi and Victoria (Table 3), and the state as a whole did. During the last part of the U.S. period of exuberance (mainly from 2004–07), both upstream and downstream energy regions registered periods of exuberant behavior that set them apart from the rest of the state (Table 3).

Current Developments in Texas Housing Markets

either the state of Texas nor any of its 25 MSAs currently are having episodes of explosive behavior in house prices. The map (Figure 3) illustrates how the different housing markets look with the data available up to 3Q2015. The map also provides detail about the performance of selected MSAs over the past five quarters. Pie charts have been superimposed over a few MSAs. DFW appears in green with no pie chart because it has shown no hint of explosiveness since at least 3Q2014. In contrast, Midland appears with an amber and red pie chart because—while currently amber—it has spent one period in the red zone since 3Q2014. Three of the major MSAs (Austin, Houston, and San Antonio) plus College Station-Bryan experienced

A word of caution is warranted. Are these periods of explosive behavior the same as housing bubbles? The answer is they are not. Ultimately, to determine whether a period of explosive behavior is expectations-driven and nonfundamental, researchers need to overcome the problem of how to properly measure the fundamentals and through them the intrinsic value of housing. The approach used here adds a powerful new tool for monitoring housing markets, though. The Federal Reserve Bank of Dallas aims to monitor Texas local housing markets and those of the rest of the U.S. They plan to make this information available on the Fed’s website to help inform market participants and scholars alike about

Table 2. Episodes of Explosive Behavior in Selected Metropolitan Statistical Areas (MSAs) in Texas Area

Start Date

End Date

Duration (Quarters)

Depth (Percent)

– – 4Q1986 2Q1999 1Q1990 2Q2002

– – 4Q1988 2Q2003 4Q1990 Q32007

– – 9 13 4 22

– – –10.94 11.83 –4.45 19.50

– 1Q1990 4Q2005 4Q2005 2Q1987 1Q2006

– 3Q1992 3Q2006 2Q2007 4Q1990 2Q2007

– 11 4 7 15 6

– –4.62 8.41 4.91 –15.01 2.82

Largest MSAs Austin DFW Houston San Antonio Border MSAs Brownsville El Paso Laredo McAllen

Source: Federal Reserve Bank of Dallas

Table 3. Episodes of Exuberance in Energy-Related Metropolitan Statistical Areas (MSAs) in Texas Area

Start Date

End Date

Duration (Quarters)

Depth (Percent)

2Q2005 2Q2012 4Q2005 1Q1989 2Q2006 1Q1989 2Q2004

3Q2007 3Q2014 3Q2007 4Q1990 3Q2007 4Q1991 3Q2007

10 10 8 8 6 12 14

36.08 15.81 29.07 –4.83 6.75 –11.17 7.26

2Q1987 2Q2002 4Q1983 2Q1987 3Q2006

4Q1991 2Q2007 1Q1986 4Q1990 4Q2008

19 21 10 15 10

–19.84 15.17 –8.62 –16.74 5.74

Upstream Energy Midland Odessa Longview Tyler Downstream Energy Corpus Christi Victoria

Source: Federal Reserve Bank of Dallas TIERRA GRANDE

housing. The Real Estate Center at Texas A&M University will continue to monitor this statistic once available to the public. The information will appear in its monthly publication, Texas Housing Insight, which follows the state’s housing market. Dr. Torres (ltorres@mays.tamu.edu) is a research economist with the Real Estate Center at Texas A&M University. Dr. Martínez-García (enrique. martinez-garcia@dal.frb.org) is a senior research economist and advisor and Ms. Grossman (valerie.grossman@dal.frb.org) is a senior research analyst in the Research Department of the Federal Reserve Bank of Dallas. We thank Mark Wynne, vice president of the Federal Reserve Bank of Dallas, for helpful feedback. All remaining errors are ours alone. The views expressed in this article are those of the authors and do not necessarily reﬂect the views of the Federal Reserve Bank of Dallas or the Federal Reserve System. JULY 2016

Notes: Red 95 percent critical value, Amber 95–80 percent critical value, and Green below 80 percent over the five-quarter period between 3Q2014 to 3Q2015. Pie charts show developments over the past five quarters. Source: Federal Reserve Bank of Dallas

THE TAKEAWAY A powerful new tool for monitoring housing markets can be used to measure exuberant housing price behavior. Exuberant price behavior may occur when home prices are not based on housing market fundamentals such as demand and supply. The results show that neither the state of Texas nor any of its 25 MSAs currently are experiencing episodes of explosive behavior in house prices.

Taxes

eginning in the 1970s, a series of lawsuits challenging Texas state public school financing produced a system that inexorably links Texas property tax policy with school funding issues. Failing to generate support for a personal income tax to equalize available resources across school districts, the Texas Legislature came to rely on local property taxes to meet those needs. This dependence dramatically inflated tax burdens for Texas property owners. Effective tax rates increased from approximately 1 percent of market value in the early 1980s to rates approaching 3 percent in some areas of the state. In addition to rate increases, administrative reorganization created a single appraisal district in each county except for Randall and Potter, which share a single district. That move necessitated a comprehensive overhaul of the entire property tax system. Because home values tend to increase more rapidly than those of other property types, this new system shifted a growing proportion of total property taxes to homeowners. Frequent reappraisals caused sizable increases in taxes as rising values coupled with steady or rising tax rates increased tax liabilities. To counteract that tendency, the legislature crafted a number of measures designed to soften rising tax burdens. Concentrating the efforts on homeowners, various measures addressed the problem on two fronts. First, to ease homeowners’ tax burdens, some measures exempt part or all of the taxable value of qualified homes. Second, to cushion homeowners from unanticipated tax increases, appraised value increases were

limited to 10 percent each year for qualified homes. Third, so called “truth in taxation” provisions created a process to empower taxpayers to roll back proposed tax rate increases by taxing units. Despite these measures, property tax increases have propelled Texas 2014 effective tax rates for homeowners to seventh highest in the nation according to research published by the Government of the District of Columbia, indicating a substantial tax burden for Texans compared with other states (Table 1). Texas sales tax rates in Houston were also among the highest, ranking 13th nationally. The report also analyzes the overall burden of state and local taxes for each state based on taxes in its largest city (Table 2). Houston ranks 44th among the 50 states and the District of Columbia with an overall 6.1 percent tax burden for a family with a $50,000 household income. That amounts to less than half the burden in the top three states: Michigan (Detroit), Connecticut (Bridgeport) and New Jersey (Newark). Texas’ burden is well below the average of 8.4 percent and the median Utah (Salt Lake City) burden of 8.2 percent. Despite Texas’ relatively low overall tax burden, the effective rate of $2.57 per $100 of value indicated in Table 1 continues to prompt outcries from taxpayers for further relief. This in turn has policy makers casting about for a measure that would provide significant tax relief through more restrictive caps on appraisal increases. Limits of 1 percent or 5 percent have routinely surfaced in proposals in the past. Similar pressures in California inspired the famous Proposition 13 tax measure in the late 1970s, which limited annual TIERRA GRANDE

Table 1. 2014 Residential Property Tax Rates in Largest City in Each State

increases on appraisals to 2 percent so long as ownership continued in the same hands. This provision led to predictions that assessed values would lag market values in areas with rapidly rising prices. Decades later, circumstances confirmed that forecast. Further, the gap between market values and assessed values ensured that assessed values continued to rise even when home prices fell in California because the actual market value of the homes continued to exceed the restricted assessed value. Homeowners, faced with a diminished home value, angrily demanded to know how their taxable value had risen, only to learn that the passage of time had produced unintended consequences from Proposition 13. urther unanticipated consequences of the limits continued to roil taxpayers. Newer homebuyers began to notice substantially lower taxes applied to long-term homeowners, with properties of equal market values incurring vastly different tax liabilities. This horizontal inequality tended to inhibit sales by those with longstanding tenure and impose higher taxes JULY 2016

Rank

City

State

Nominal Rate Per $100

Assessment Level (Percent)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

Detroit Milwaukee Bridgeport Indianapolis Newark Des Moines Houston Manchester Columbus Burlington Omaha Columbia Baltimore Memphis Portland Providence Jacksonville Atlanta Boise Billings Fargo Sioux Falls New Orleans Minneapolis Albuquerque Little Rock Portland Wichita Philadelphia Wilmington Anchorage Louisville Phoenix Boston Oklahoma City Los Angeles Charlotte Kansas City Las Vegas Virginia Beach Salt Lake City New York Seattle WASHINGTON Charleston Birmingham Denver Cheyenne Chicago Honolulu Jackson

MI WI CT IN NJ IA TX NH OH VT NE SC MD TN ME RI FL GA ID MT ND SD LA MN NM AR OR KS PA DE AK KY AZ MA OK CA NC MO NV VA UT NY WA DC WV AL CO WY IL HI MS

6.88 3.00* 4.22 2.92 3.10 4.71 2.57 2.23 6.89 2.43 2.19 52.36 2.25 7.78 1.94 1.93 1.91 4.54 1.72 65.40 32.54 1.78 14.87 1.59 4.25 7.01 2.06 11.74 1.34 4.20 1.32 1.26 12.65 1.26 11.38 1.22 1.28 6.12 3.28 0.95 1.60 19.11 0.95 0.85 1.37 6.95 8.70 7.17 6.83 0.35 0.18

50.00 100.00 70.00 100.00 93.34 55.73 100.00 101.00 31.88 88.15 96.00 4.00 93.00 25.00 100.00 100.00 97.30 38.04 96.54 2.47 4.09 85.00 10.00 92.60 33.33 20.00 69.70 11.50 100.00 31.81 100.00 100.00 10.00 100.00 11.00 100.00 94.18 19.00 35.00 92.90 55.00 4.60 93.60 100.00 60.00 10.00 7.96 9.50 10.00 100.00 10.00

3.44 3.00 2.95 2.92 2.74 2.62 2.57 2.27 2.20 2.14 2.10 2.09 2.09 1.95 1.94 1.93 1.86 1.73 1.66 1.62 1.59 1.51 1.49 1.47 1.42 1.40 1.39 1.35 1.34 1.34 1.32 1.26 1.26 1.26 1.25 1.22 1.21 1.16 1.15 0.88 0.88 0.88 0.87 0.85 0.82 0.70 0.69 0.68 0.68 0.35 0.02

7.08 2.75

56.00 56.00

1.56 1.40

UNWEIGHTED AVERAGE MEDIAN

Effective Rate Per $100

NOTE: All rates and percentages in this table are rounded and include state and local property taxes levied by multiple taxing authorities as identified by state survey respondents. Effective tax rates listed here are net of assessment value and do not reflect any exemptions or credits, or any other property tax credits, deductions, or exemptions offered by the state or locality. Source: Data collected from surveys to State Revenue Department officials, and state websites.

Figure 1. Texas Property Tax Levies by Taxing Unit

Consider the index of tax levies adjusted for inflation to 1994 dollars shown in Figure 2. Values greater than 45 1 indicate a real increase in tax revenues. At 1.9, school 40 tax levies have nearly doubled in real terms since 1994. 35 Other units’ levies have more than doubled since that 30 time. However, prior to 1994, school levies grew more School Levy rapidly than the other units. From 1994 through 2005, 25 school levies also increased faster than other units. Tax 20 relief measures taking hold in 2007 halted that trend. City Levy 15 Special district levies, fueled in part by the addition 10 of numerous groundwater conservation districts after County Levy 5 1997, have expanded most rapidly since 2005. However, Special District Levy both city and county total levies also expanded rapidly 0 1994 1998 2002 2006 2010 2013 from 2005 through 2008. From 2009 through 2011, levies did not grow for cities, counties, and schools. Source: Texas Comptroller of Public Accounts However, in 2012, cities, counties, and schools began to on newcomers and younger homeowners. These conditions expand their total levies once more. led a taxpayer to take the matter to the U.S. Supreme Court These expanding numbers reflect the combined influence of contending that such a scheme violated the Equal Protection local growth and local decisions to provide more revenue to Clause of the U.S. Constitution (Nordlinger v. Hahn). The the various taxing units. Some, perhaps a major share, of the Court ruled for the assessor, affirming the Proposition 13 limiexpansion of special district levies can be traced to the impletations. Thus, the unequal treatment of homeowners persists mentation of statewide water policy provisions in response to in California. regional water planning. Proposition 13, combined with legislation responding to Arguably, supporting this planning effort involves prudent litigation also had a negative impact on California’s public outlays designed to provide water for future generations of schools (Serrano v. Priest). Texans. Increasing city and county levies reflect individual local governmental decisions to pursue activities requiring The Serrano ruling combined with Proposilocal public expenditures. These locally focused actions pretion 13 to suppress school district revenue sumably address concerns of the local citizenry. growth and virtually eliminate local control over most school funding. In the years since, Figure 2. Trends in Texas Tax Levies By Taxing Unit California’s investment in education, rela1994 = 1.0 tive to the national average, has declined. In 2.4 2005–06, the per-pupil expenditure was $614 School Levy below the national average (Local Revenue 2.2 City Levy for Schools, EdSource, 2009). County Levy 2.0 his history suggests that those advocatSpecial District Levy ing tax policy changes should examine 1.8 anticipated outcomes before adopting 1.6 particular measures to avoid unintended consequences. A review of economic stud1.4 ies suggests that evaluation of alternative 1.2 tax policies should consider the following issues: 1.0 • Will it provide an adequate tax base to support the 0.8 budgeted activity at an acceptable rate? 1994 1998 2002 2006 2010 2013 • Will the tax inflict a minimal distortion to the sigSource: Real Estate Center Texas A&M University nals guiding economic decision making? • Will the tax system be readily understandable? Texans would do well to keep these criteria in mind • Will the tax policy be regarded as “fair”? when weighing the merits of proposed policy changes. Currently, some citizens argue that the property tax base See Real Estate Center publication 2037: Property Taxes: The as it is configured does not provide adequate funding at Bad, The Good, and the Ugly for a discussion of these criteria. a reasonable tax rate. Further restricting tax caps would Total tax levies by the various taxing entities in Texas from aggravate that situation. A restrictive cap, such as the one 1994 through 2013 are shown in Figure 1. In 2013, the $24.85 California has adopted, could foster noticeable and growing billion school tax levy represented 54.9 percent of the total, distortions to the efficient operation of housing markets down from 60.3 percent in 2005. Obviously, school taxes comover time. Reducing tax liabilities by capping appraisal pose the major portion of property taxes statewide. Index of Property Tax Levy

Billions of Dollars

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Table 2. 2014 Estimated Burdens of Major Taxes for Hypothetical Family Earning $50,000 Yearly Taxes rates multiplies inconsistencies in the tax system as time passes. Finally, although Californians have decided that unequal treatment producing substantial variations between homeowners is justified, Texans would do well to carefully weigh these long-term effects resulting from tightened caps. n addition to these factors, imposing the cost of supplying public goods on those enjoying them through higher taxes causes taxpayers to weigh cost and benefits before supporting spending measures. Reducing tax burdens for homeowners, arguably the main beneficiaries of local government expenditure, could bias them in favor of more spending because they bear a lesser burden than they would face without the caps. As the debate over high property tax burdens progresses, Texans should be cautious to avoid even larger problems for the future. Dr. Gilliland (c-gilliland@tamu. edu) is a research economist with the Real Estate Center at Texas A&M University.

THE TAKEAWAY Texas citizens have been clamoring for tax relief. In California, Proposition 13 was supposed to keep taxes down by limiting appraisal growth rates. But the results have been far from beneficial. Texans can learn from the changes California made and what ultimately went wrong. JULY 2016

Burden

Rank

City

State

Income2

Property

Sales3

Auto

Amount

Percent

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

Detroit Bridgeport Newark Philadelphia Baltimore Milwaukee New York Chicago Kansas City Boston Columbus Providence Los Angeles Des Moines Burlington Charlotte Portland Louisville Atlanta Jackson Portland Birmingham Columbia Indianapolis Minneapolis Salt Lake City Little Rock Omaha Wichita Phoenix Virginia Beach Charleston Memphis Boise Oklahoma City New Orleans WASHINGTON Denver Wilmington Albuquerque Manchester Billings Honolulu Houston Las Vegas Seattle Fargo Sioux Falls Jacksonville Anchorage Cheyenne

MI CT NJ PA MD WI NY IL MO MA OH RI CA IA VT NC ME KY GA MS OR AL SC IN MN UT AR NE KS AZ VA WV TN ID OK LA DC CO DE NM NH MT HI TX NV WA ND SD FL AK WY

2,679 523 564 3,442 1,965 1,051 2,157 2,066 1,532 2,017 1,988 815 201 1,355 680 1,895 908 2,068 1,897 974 2,012 1,900 1,168 2,347 1,346 1,499 1,267 866 1,056 619 1,467 1,778 – 1,366 877 1,075 1,557 1,099 1,531 742 – 1,627 1,320 – – – 197 – – – –

3,316 4,953 4,928 1,329 3,008 3,449 1,309 1,693 1,831 2,022 1,839 2,733 2,988 2,278 2,876 1,373 2,541 1,334 1,323 1,785 2,202 755 1,416 966 1,407 1,271 1,060 1,939 1,152 1,718 1,251 762 1,891 1,096 1,175 819 759 1,034 1,724 1,776 2,774 1,186 672 1,642 1,546 1,445 1,708 1,548 1,318 1,543 841

826 886 950 936 603 794 1,673 1,046 1,462 822 963 621 1,239 712 891 979 797 851 1,052 1,621 – 1,610 1,387 825 1,176 1,031 1,428 993 1,330 1,269 628 887 1,679 1,047 1,413 1,618 946 1,121 – 766 290 21 837 1,164 1,157 1,321 923 923 919 – 597

175 393 135 290 282 266 229 368 333 293 210 761 348 424 257 402 382 292 257 145 284 172 440 211 286 307 288 235 386 288 500 411 654 213 191 136 219 188 184 151 288 390 254 240 338 274 159 198 223 377 271

6,996 6,755 6,577 5,997 5,857 5,559 5,368 5,173 5,158 5,153 5,000 4,930 4,775 4,769 4,704 4,649 4,627 4,546 4,529 4,524 4,498 4,436 4,412 4,349 4,216 4,109 4,043 4,033 3,925 3,894 3,846 3,839 3,833 3,722 3,655 3,648 3,481 3,442 3,439 3,435 3,352 3,224 3,083 3,046 3,041 3,041 2,988 2,669 2,461 1,920 1,708

14.0 13.5 13.2 12.0 11.7 11.1 10.7 10.3 10.3 10.3 10.0 9.9 9.5 9.5 9.4 9.3 9.3 9.1 9.1 9.0 9.0 8.9 8.8 8,7 8.4 8.2 8.1 8.1 7.8 7.8 7.7 7.7 7.7 7.4 7.3 7.3 7.0 6.9 6.9 6.9 6.7 6.4 6.2 6.1 6.1 6.1 6.0 5.3 4.9 3.8 3.4

$1,416 1,361

$1,790 1,546

$1,021 957

$286 274

$4,205 4,109

8.4 8.2

AVERAGE 1 MEDIAN

Based on jurisdictions actually levying tax. States with dashes do not have an income tax. 3 States with dashes do not have a sales tax. NH does not have a general sales tax, but some selective sales taxes apply to consumption items included. Source: Data collected from surveys to State Revenue Department officials, and state websites. 1 2

Development

Sizing Up By Rusty Adams

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p Surveys H

ow can a Texas farm buyer make sure he is getting all the land he is paying for and that he has access to and from the property? How does a home purchaser know where her lot ends and her neighbor’s begins? Is her home in the flood plain? How does a commercial developer know that a planned shopping center won’t cross a property line or setback line and that utilities will be available once it is built? The answer? A survey. A survey is a measurement of a tract of land and its boundaries and contents. Situations in which a survey is advisable include when land is bought, sold, cleared or divided; when planning construction projects or subdivisions; when harvesting timber; or when building a fence. A survey is often required by lenders and is necessary to obtain title insurance or flood insurance. Participants in real estate transactions, and their sales agents, brokers, and attorneys, should be familiar with the types of surveys available.

Types of Surveys Land surveyors are licensed by the Texas Board of Professional Land Surveying. The types of surveys available are set forth in the Manual of Practice for Land Surveying in Texas, published

JULY 2016

by the Texas Society of Professional Surveyors. The professional services of a Registered Professional Land Surveyor are divided into categories, each of which is defined in the manual and has specific requirements. Categories are further divided into four “conditions,” determined by the location of the site to be surveyed (urban business district, urban, suburban, and rural).

Category 1A: Land Title Survey This is the survey real estate professionals will encounter most often. Whenever a transaction requires a title policy, such as when property is purchased or refinanced, this survey is required. A Land Title Survey is a comprehensive investigation and evaluation of factors affecting boundary locations, ownership lines, rights of way, and easements within or immediately surrounding a property. The distinguishing characteristic of a Category 1A survey is that it provides what the title company needs to insure the title. It includes greater detail than a standard survey. It shows recorded easements, as well as any evidence indicating the possibility of prescription or limitation rights, and visible improvements.

NATHAN KERR of Kerr Surveying LLC in Bryan, Texas, conducting a survey (previous page). This page, clockwise from top left: Brad and Nathan Kerr, father and son, set up and calibrate a GPS device. Another device is robotic and can survey independently or be operated by the surveyor. Survey marker in Bryan, Texas. Survey monument on a Grand Canyon walking path was placed 79 years ago and shows the elevation above sea level (page 17).

As part of a Land Title Survey, the surveyor will produce: • monuments for corners, points of curves, or references to property lines of adjacent properties; • a signed, sealed, and dated written description; • a signed, sealed, dated, and certified map or plat clearly depicting the survey as made on the ground; and • if required, a written report of the surveyor’s findings and determinations. Sometimes this will include opinions, particularly if the surveyor finds conflicting evidence. While the survey must be accurate and complete for the purposes of title insurance, in practice, the degree of detail will depend a lot on the nature of the transaction. The sale of a residence will likely require substantially less detail than a large commercial transaction. Because survey matters are standard exceptions in a title policy, buyers want the most accurate survey possible to protect themselves from unpleasant and costly surprises. A surveyor is impartial and simply records what is found in the records and on the ground. The survey should be reviewed by an attorney.

Category 1B: Standard Land Survey This is a traditional boundary survey. While similar to a Land Title Survey, it is not for title-insuring purposes. It locates the boundaries and determines the area, and it may include rights of way and easements within or surrounding the parcel. However, it normally does not locate improvements, rights of way, or easements within the surveyed site unless requested by the client, or in cases in which the surveyor in his professional judgment observes something that might indicate an encumbrance. Like a Land Title Survey, the surveyor sets monuments for all corners, points of curves, or references to property lines; provides a signed, sealed, and dated written description and a signed, sealed, dated, and certified map or plat clearly depicting the survey as made on the ground; and, as required, a written report of the surveyor’s findings and determinations.

Category 2: Route Survey A Route Survey locates the planned path of a “linear project” or right of way that crosses a piece of property from one point to another. A common use of a route survey is for planning a right of way or for acquiring an easement (or fee title) for a road, canal, pipeline, electric line, or the like. Commonly, but not always, the route is described by defining a center line and then defining the route as a certain distance on either side of the center line. The surveyor will provide: • a signed, sealed, dated, and certified map; • signed and sealed written descriptions of each segment of the route (as it crosses tracts of separate owners); and • references to monuments and specific locations for use in planning construction.

Category 3: Locative Survey A Locative Survey is often called a layout or stake-out survey or a site plan, and is usually done as a preliminary step in a construction project. It establishes the location and position of various structures in relation to the boundaries of the site. The surveyor may install reference stakes, markers (or monuments), construction baselines, and benchmarks. The surveyor also prepares a plan or drawing showing the perimeter of the property and the location of the stakes or monuments and data sufficient to identify them. The survey also includes a signed, sealed, and dated written description. A Locative Survey is similar to a Construction Survey and may become part of a Construction Survey. However, it only involves locating the proposed structures. It does not necessarily include continuous or periodic observation of the site as construction progresses. TIERRA GRANDE

Category 4: Reclassified Category 4 was once for mortgage loan inspections but has been reclassified as Category 1B. For continuity, the remaining categories retained their original numbers.

sea level and are used for surveys of tidal boundaries, locative and construction surveys, route surveys, topographic surveys, investigative surveys, and making measurements from photographs.

Category 5: Construction Survey

Category 9: Investigative Survey

A Construction Survey usually follows or includes a Locative Survey. It is performed before, during, and/or at the end of a construction project. The surveyor makes measurements while construction is in progress to control elevation, horizontal position, dimensions and configuration, and, following construction, to obtain essential dimensions for computing construction pay quantities and establishing “as built” conditions. In other words, the surveyor makes sure the building is built according to the engineering design. A Construction Survey might include a “slab survey” after pouring the slab but prior to framing, to make sure it’s in the right place, as well as an “as built” survey after construction is complete.

Investigative Surveys are used when it’s necessary to determine the location of certain features or improvements, and their locations relative to each other, to determine the effects of an actual, impending, or planned occurrence. Examples include investigation of serious accidents for litigation purposes, mitigating natural disasters, determining whether a structure or other object is an encroachment on a property, and measuring ground subsidence or erosion.

Category 6: Topographic Survey Topographic Surveys gather information to be depicted on topographic maps, which are used by landowners, engineers, architects, planners, and developers to design the development of a site, landscaping, flood control, runoff of surface water, and various other purposes. The surveyor determines contours of the land and may determine volumes or quantities (of water, for instance). The surveyor’s product includes: • a control survey network, with horizontal and vertical positions noted; • major control points monumented and referenced; • a signed, sealed, dated, and certified plat showing elevations or relief by contour lines or grid plotted elevations; and • locative descriptions of the control points.

Category 7: Horizontal Control Survey A Horizontal Control Survey is used as a framework to which other surveys are referenced and adjusted and, therefore, must be extremely accurate. It ties a particular location to the National Geodetic Survey and the Texas Plane Coordinate System, which are consistent coordinate systems used to establish latitude and longitude. These surveys establish a network of points on the ground sufficiently accurate to provide control for any surveying project. They are needed for accurate mapping and charting projects and in construction of underground utilities, power lines, highways, bridges, tunnels, and dams, and are particularly adaptable to property and subdivision surveys. By statute (Natural Resources Code, Ch. 21, Subchapter D), state plane coordinates may be referenced in deed descriptions.

Category 8: Vertical Control Survey Vertical Control Surveys also are tied into the National Geodetic Survey and used as benchmarks for other surveys. They must be very accurate. They determine elevation relative to JULY 2016

Category 10: GIS/LIS Surveys and Products A Geographic Information System (GIS) Land Information System (LIS) for surveying is the creation of maps and databases representing boundaries, manmade objects, natural features, or topography. The maps and databases are used as elements of GIS/LIS mapping applications. Surveys are a key part of any real estate transaction. Knowing the different types of surveys and the purpose each serves is valuable knowledge for real estate professionals. E.V. “Rusty” Adams III (rusty@brazoslawyers.com) is an attorney with the Peterson Law Group in Bryan-College Station.

THE TAKEAWAY Land surveys come in different types, including land title surveys, construction surveys, and topographic surveys, among others. Each type of survey serves a particular purpose, such as documenting details needed to insure title to the property, defining boundaries, and ensuring that construction projects are built according to the engineering design.

U.S. Economy

It should come as no surprise that the Texas economy has outperformed most states and the United States as a whole over the last decade. However, it may be surprising to learn that by the end of 2015, 11 states still had not returned to their prerecession employment levels. 18

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Table 1. Percent Change in Nonfarm Employment vs. Housing Prices Peak to Trough Percent Change

Trough to 4Q2015 Percent Change

Peak to 4Q2015 Percent Change

Nonfarm Employment

Housing Price

Nonfarm Employment

Housing Price

Nonfarm Employment

Housing Price

U.S. Texas

–7.9 –4.6

–20.7 –4.1

12.1 17.7

26.5 33.3

3.3 12.3

0.3 27.8

Alabama Connecticut Maine Michigan Mississippi Missouri Nevada New Jersey New Mexico Rhode Island Wyoming

–8.5 –8.8 –8.8 –15.0 –7.3 –7.5 –14.9 –8.4 –7.0 –10.9 –10.7

–15.5 –20.9 –11.6 –32.2 –13.9 –15.6 –59.6 –21.3 –18.1 –28.2 –10.7

6.3 7.9 7.2 14.5 6.4 7.4 15.5 8.5 4.9 10.0 5.6

14.0 4.9 11.8 38.3 13.9 18.3 78.5 9.0 8.9 16.1 18.2

–2.8 –1.6 –2.3 –2.8 –1.3 –0.7 –1.7 –0.5 –2.4 –2.0 –5.7

–3.6 –17.1 –1.2 –6.2 –1.9 –0.1 –27.9 –14.3 –10.8 –16.7 5.6

State

Quarterly data from 1Q2005 thru 4Q2015 for Texas and 11 states not yet recovered from prerecession employment peak. Source: Haver Analytics

Texas had matched its prerecession nonfarm employment peak by the end of 2011, a period of only three years. The U.S. took seven years, attaining parity in 2014. Based on the Federal Housing Finance Agency’s (FHFA) home price index, home prices in ten of those 11 states also had not returned to their prerecession highs by the end of 2015. Housing prices in the U.S. ended 2015 barely above their prerecession peak as well. While Texas recorded a small dip in home prices during the recession, it was nothing like the double-digit drops in many states. Why did Texas employment and home prices perform so much better than the 11 hardest hit states and the U.S. overall during one of the worst economic downturns in decades (Table1)? The general thought is that a boom in oil and gas activity occurred at just the right time to carry Texas through the recession. That is indeed part of the story, but head-to-head

data comparisons reveal that Texas has been at a comparative advantage for some time.

Migration Has Favored Texas Eight of the states that had not recovered experienced net outmigration over the entire 11-year analysis period (Table 2). Connecticut, Michigan, New Jersey, and Rhode Island have seen net domestic outmigration every year since 2005. By contrast, Texas experienced fairly consistent positive domestic migration, indicating long-term economic attractiveness relative to other states. Relocations occur from being “pulled to” a new location, “pushed from” an existing one, or both. Individuals may be pulled to other states by the prospect of better jobs, more affordable housing, or better climate. They may be pushed to leave a state by such things as job loss or a particularly burdensome tax structure.

Data Used for Comparisons

he analysis period spans 11 years, from the beginning of 2005 to the end of 2015. The starting point was chosen because the run-up to housing bubbles and prerecession employment peaks was still in its early stages. Employment data were acquired from the U.S. Bureau of Labor Statistics (BLS). Quarterly data were calculated by using average monthly employment during each quarter. This was necessary

JULY 2016

to match the FHFA’s housing quarterly index. Haver Analytics is the source for FHFA’s index data. Although several variations are available, the “purchase only” House Price Index (HPI) was used for this analysis. The HPI is a broad measure of changes in single-family home prices. It is a “weighted, repeat-sales index,” meaning that it measures average price changes in repeat sales of the same properties

over time. The “purchase only” option excludes any refinancings. U.S. Census Bureau data from the Real Estate Center’s website were used to showcase annual net domestic migration patterns between the states over the study period. No examination of births over deaths or international migration was done. The goal was to use changes in domestic migration as a proxy for a state’s economic attractiveness.

Table 2. Net Annual Domestic Migration by State 2005–15 Year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Cumulative Gain or Loss 2015 Population Estimate

53,210 232,616 129,966 131,171 143,423 29,950 115,222 141,906 116,921 162,390 170,103

16,256 33,752 16,826 16,927 11,044 537 11 –929 1,838 2,816 –2,268

–17,446 –15,075 –20,678 –9,257 –7,824 –273 –13,351 –19,076 –17,007 –27,211 –27,619

2,713 1,543 –172 411 –2,937 –1,259 91 –586 –1,317 646 –1,718

–57,347 –70,056 –87,176 –103,637 –87,339 –18,052 –43,108 –33,437 –28,825 –28,797 –38,911

590 –19,588 3,968 –1,576 –5,529 –1,048 –5,779 –5,659 –4,776 –9,007 –12,230

7,761 14,264 6,383 –2,920 –124 –276 –13,426 –13,454 –7,792 –8,126 –8,744

52,464 54,069 40,769 15,622 –3,801 –2,731 –7,410 14,524 13,437 23,452 27,959

–67,216 –76,853 –67,751 –51,234 –31,690 –8,503 –44,996 –49,289 –45,678 –55,474 –65,254

6,958 7,822 5,940 –2,139 3,366 2,093 92 –7,117 –10,658 –14,099 –13,352

–10,940 –10,502 –11,151 –7,498 –6,172 –1,132 –6,469 –5,137 –5,049 –3,263 –4,693

315 3,252 6,678 5,302 7,192 –94 –216 5,516 2,603 –2,795 –1,885

1,426,878

96,810

–174,817

–2,585

–596,685

–60,634

–26,454

228,354

–563,938

–21,094

–72,006

25,868

27,469,114 4,858,979 3,590,886 1,329,328 9,922,576 2,992,333 6,083,672 2,890,845 8,958,013 2,085,109 1,056,298 586,107

Source: Real Estate Center at Texas A&M University

All domestic migration, either positive or negative, was generally restrained during the height of the recession in 2010 and 2011. This would be expected as individuals often find it difficult to relocate during recessions. Possible reasons include perceived lack of better economic opportunities elsewhere, decline in home values below a homeowner’s mortgage payoff, or simply fear of change in uncertain times.

Peaks and Troughs Offer Insights

he percentage of decline in both total nonfarm employment and home prices reveals how hard some states were hit during the last recession (Table 1). While Texas only suffered 4 percent declines in both categories, Nevada was saddled with an almost 60 percent decline in home prices. Nevada’s total nonfarm employment dropped 14.9 percent, but employment in the construction sector dropped a devastating 67 percent peak to trough. A consistent pattern emerged when comparing the magnitude of employment and housing price declines in the U.S. and every state except Texas, where it is about the same (Table 1). The

160 150 Index 1Q2005 = 100

140 130 120 110 100 90

Comparing Changes in Housing Prices and Nonfarm Employment for Texas and United States

$ $

United States Home Price Texas Home Price United States Nonfarm Employment Texas Nonfarm Employment

housing price troughs were always greater than the employment declines. One possible explanation is that Texas avoided the residential home overbuilding that other states did not, which caused housing bubbles that collapsed and led into the recession. The same pattern is observed in the recovery that occurred from the troughs to the end of the analysis period, except in Connecticut. Housing price rebounds were always greater than the rebounds in nonfarm employment. The trend toward greater change in home prices than in employment for Texas and the U.S. overall is shown in the figure. The data are indexed to a starting base of 100 in 1Q2005, which creates a level playing field for examining the changes in magnitude during the analysis period. The U.S. home price bubble is obvious in the early years (see figure), a trend not present in the Texas housing prices. Over the analysis period, nonfarm employment peaks always occurred after housing prices had peaked. The range of lag times varied from one to six quarters (Figure and Table 3). Furthermore, nonfarm employment always reached a trough before housing prices, except in New Mexico where it occurred simultaneously. Lead times ranged from four to 12 quarters. Employment reaching a bottom before home prices would be expected. Declining job growth would typically lead to an expectation of further job losses. This continue to negatively affect home prices until $ should a turnaround in employment changes market expectations to future job growth. Prerecession housing prices peaking before employment was not expected. Logically, employment would peak and then begin a decline before home prices break $ over and follow employment down. After seasonally adjusting the data, employment did in fact decline before home prices, as would be expected.

80 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Note: Housing Index Series is derived from "FHFA House Price Index, Purchase Only" not seasonally adjusted data Source: Haver Analytics

Importance of Goods-Producing Jobs Nonfarm employment is divided into two categories: service-providing and goods-producing employment. The percentage of jobs in each at the beginning and the end of the study period is shown in Table 4. TIERRA GRANDE

Table 3. Chronology of Prerecession Peaks and Postrecession Troughs for Nonfarm Employment and Housing Prices

State

Peak Date

Employment Lead (+)/Lag (–)

Trough Date

Employment Lead (+)/Lag (–)

Employment vs. Housing Price

Quarters

Employment vs. Housing Price

Quarters

U.S. Texas

4Q2007 – 1Q2007 4Q2008 – 3Q2008

(–) 3 (–) 1

1Q2010 – 2Q2011 1Q2010 – 1Q2011

(+) 5 (+) 4

Alabama Connecticut Maine Michigan Mississippi Missouri Nevada New Jersey New Mexico Rhode Island Wyoming

4Q2007 4Q2007 3Q2007 4Q2005 4Q2007 2Q2008 2Q2007 4Q2007 4Q2007 4Q2006 3Q2008

(–) 1 (–) 6 (–) 1 (–) 1 (–) 2 (–) 3 (–) 5 (–) 6 (–) 2 (–) 2 (–) 4

1Q2010 1Q2010 1Q2010 1Q2010 1Q2010 1Q2010 1Q2010 1Q2011 1Q2012 1Q2010 1Q2010

(+) 4 (+) 8 (+) 8 (+) 4 (+) 4 (+) 4 (+) 7 (+) 4 0 (+) 12 (+) 7

– – – – – – – – – – –

3Q2007 2Q2006 2Q2007 3Q2005 2Q2007 3Q2007 1Q2006 2Q2006 2Q2007 2Q2006 3Q2007

– – – – – – – – – – –

1Q2011 1Q2012 1Q2012 1Q2011 1Q2011 1Q2011 4Q2011 1Q2012 1Q2012 1Q2013 4Q2011

Quarterly data from Q12005 thru Q42015 for Texas and 11 states not yet recovered from prerecession employment peak. Source: Haver Analytics

The change in these percentages shows a shrinking percentage of jobs in the goods-producing sector. The shift to more service jobs, which has been occurring for several decades, is observed in the U.S. as a whole and every state except Wyoming. By 4Q2015 eight to nine out of every ten jobs were serviceproviding jobs, depending on the state. Texas had an absolute increase of 221,300 in goods-producing jobs between 2005 and 2015. However, the much larger increase in service sector jobs still caused the goods-producing percentage to shrink. Of the 11 nonrecovered states, only Wyoming had more goods-producing jobs at the end of 2015 than at the beginning of 2005. The increase is primarily a result of significant increases in construction employment. How much larger the job losses were peak to trough in the goods-producing sector compared with the service sector is shown in Table 5. Texas had the smallest percentage loss in goods-producing jobs at –15.4 percent, while Nevada experienced the largest loss at –51.8 percent. Although the recovery from the troughs was strong in some cases, every state and the U.S. overall ended up with fewer goods-producing jobs at the end of 2015 compared with their prerecession peaks. However, the net decline was significantly smaller in Texas (–1.3%). All other states and the U.S. had double-digit declines. ervice-providing employment is beyond or close to the prerecession peaks for the nonrecovered states and the U.S. overall (Table 5). One service sector subcategory, education and healthcare services, never reached a trough during the study period in any region. The anchor holding back the lagging states is insufficient return of jobs in the goodsproducing sector compared with Texas. Three subcategories make up the goods-producing sector: manufacturing, construction, and mining and logging. Data shows that manufacturing provided the bulk of goods-producing

JULY 2016

Table 4. Changes in Service-Providing Jobs, Goods-Producing Jobs From 1Q2005 to 4Q2015 Change from 1Q2005 Service Percent Change

Goods Percent Change

U.S. Texas

2.6 1.5

–2.6 –1.5

Michigan New Jersey Missouri Alabama Connecticut Nevada Mississippi New Mexico Maine Rhode Island Wyoming

2.1 2.6 3.0 3.5 2.8 5.3 3.7 1.4 2.1 3.3 –0.3

–2.1 –2.6 –3.0 –3.5 –2.8 –5.3 –3.7 –1.4 –2.1 –3.3 0.3

14,210,667 2,203,400 1,057,867

–1,776,667 221,300 –455,367

State

U.S. Total Texas Total 11 States w/o Texas

Ranked by largest to smallest nonfarm employment. Source: Haver Analytics

jobs in the U.S. overall and most of the nonrecovered states (Table 6). The exceptions are Nevada, New Mexico, and Wyoming. In Texas, about half the goods-producing jobs have been in manufacturing. The ratio of manufacturing jobs from 2005 to 2015 has declined in the U.S. overall and every state except Nevada. However, the Nevada increase is due more to a loss in the construction sector than a gain in the manufacturing sector.

Table 5. Percent Changes in Service-Providing Employment vs. Goods-Producing Employment Peak to Trough Percent Change State U.S. Texas Alabama Connecticut Maine Michigan Mississippi Missouri Nevada New Jersey New Mexico Rhode Island Wyoming

Trough to 4Q2015 Percent Change

Peak to 4Q2015 Percent Change

ServiceProviding

GoodsProducing

ServiceProviding

GoodsProducing

ServiceProviding

GoodsProducing

–5.1 –2.9

–25.1 –15.4

11.7 18.3

14.8 16.7

6.0 14.9

–13.9 –1.3

–5.3 –6.7 –6.5 –10.0 –4.1 –4.6 –9.9 –6.2 –4.8 –7.6 –6.2

–24.0 –21.6 –24.7 –36.2 –22.2 –26.5 –51.8 –28.0 –25.1 –31.4 –25.9

6.5 8.3 6.7 10.9 6.9 6.8 15.0 8.6 5.0 9.8 5.4

7.4 5.3 10.8 35.2 4.3 11.1 27.3 8.0 8.0 12.7 6.4

0.9 1.0 –0.2 –0.2 2.5 1.9 3.6 1.8 0.0 1.5 –1.1

–18.3 –17.5 –16.6 –13.8 –18.8 –18.4 –38.6 –22.2 –19.1 –22.7 –21.2

Quarterly data from Q12005 thru Q42015 for Texas and 11 states not yet recovered from prerecession employment peak. Source: Haver Analytics

The home price and employment numbers show Texas has consistently outperformed most of the country, especially the 11 unrecovered states, for more than a decade. lthough the service-providing sector now dominates employment, the value of goods-producing jobs cannot be underestimated. Most of the goods-producing jobs are in “basic” industries that bring in revenue from outside the state while also promoting increased local service-sector job Texas Not One-Trick Pony Anymore growth. A number of factors have contributed to the state’s success, They also typically pay higher salaries than the service secincluding a favorable tax and regulatory environment, affordtor. Wages paid per employee in goods-producing jobs averaged able housing, and a good quality of life. These positive attributes approximately 20 percent higher than service-producing wages have given businesses incentive to locate here, resulting in from 1Q2005 to 3Q2015 at the national level. This difference is years of solid employment opportunities. present in all the unrecovered states and in Texas, where wages per employee in goods-producing jobs are on average 47 percent higher, one of the highest registered differences. Table 6. Ratio of Goods-Producing Jobs by Subsector Unlike most other states, Texas has offered a more even mix of manufacturing, construction, and mining Ratio Change from 1Q2005 to 4Q2015 and logging jobs. While the manufacturing subsector Manufacturing Construction Mining and Logging continues its gradual decline, construction and mining State Percent Change Percent Change Percent Change and logging jobs tend to fluctuate both up and down U.S. –3.4 2.2 1.2 over the long-term. As a result, Texas has been better Texas –8.2 4.0 4.2 positioned for future economic growth and less volatile home prices. Michigan –0.6 0.5 0.1

Nevada lost 4,767 jobs in manufacturing since the beginning of 2005. The construction subsector lost 51,767 jobs. The higher percentage of mining and logging jobs in Texas, primarily upstream oil and gas jobs, is evident in Table 6 as well. Even with the 2015 downturn in oil and gas activity, the sector still represented 14.1 percent of all goods-producing jobs in Texas.

New Jersey Missouri Alabama Connecticut Nevada Mississippi New Mexico Maine Rhode Island Wyoming

–7.2 –1.9 1.8 –3.5 6.9 –3.9 –5.9 –4.3 –5.6 –1.7

7.1 1.9 –1.7 3.5 –12.3 3.6 –3.0 4.3 5.5 5.5

States are ranked by largest to smallest nonfarm employment. Source: Haver Analytics

0.0 0.0 0.0 0.0 5.4 0.2 8.9 0.0 0.1 –3.7

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

THE TAKEAWAY A relationship exists between employment and housing prices. In states where employment did not recover to pre-Great Recession levels, housing prices have yet to recover as well. Both reflect the economic performance of the region. TIERRA GRANDE

Texas Economy

What’s in Your Wallet? Texans’ Income Fares Well During Oil Busts and Booms By Luis B. Torres and Wayne Day

Expansions and contractions in the energy industry drive both booms and busts in Texas. The uneven growth caused by the ups and downs in oil prices is especially present in oil-dependent regions such as Houston, Midland, and Odessa, which benefit greatly in boom periods and suffer when the price of oil falls. In today’s adverse oil price environment, the oil-dependent regions are feeling the hardship in employment and income growth.

Definitions and Methodology Personal income per capita (PIPC) can be used to measure the historical performance of a region. It can serve as a proxy for standard of living since it contains multiple sources of income received and represents the amount of money individuals have for consumption and saving purposes. Improvements in the economy correlate with increasing levels of income from wages and salaries. Alternatively, downturns in the economy hinder growth in personal income, making it possible to examine how personal income has performed over time both within the state and relative to the United States. Comparisons can be made between different regions that are concentrated or diversified in similar or different industries. To measure the historical performance of Texas and its Metropolitan Statistical Areas (MSAs) compared with other JULY 2016

regions and the U.S., PIPC levels were estimated for all the nation’s MSAs. PIPC estimation by U.S. region provides a dynamic picture through time of how income levels in the economies that are more energy concentrated have performed compared with other nonenergy regions within and outside the state. This is particularly relevant in the current “bust” in the energy industry and helps measure its impact on the state’s and region’s standards of living.

Personal Income Per Capita and Components Personal income is a measure of an individual’s total earnings. When divided by population, it represents the average income of the people in a region. PIPC is also used to measure a region’s standard of living. PIPC consists of wages and salaries, proprietor’s income, property

income, transfer payments (such as Medicare) and other income. Wages and salaries are the monetary remuneration of employees. Proprietor’s income consists of earnings from sole proprietorships, partnerships, and tax-exempt corporations. Property income is derived from dividends, rent, and interest. Transfer payments are payments for which no current services are performed, usually government social benefits. The other income category is a residual of beneﬁts paid to wage and salary workers and a residence adjustment for workers who live and work in different areas. This research examined PIPC levels and growth across the major metros and their changes from 1969 to 2014. The source of the information is the Bureau of Economic Analysis (BEA), which includes various types of income, and earnings and employment by industry for the period.

420 380 340 300 260

Texas Living Standards are Better

220

Texas personal income per capita (PIPC) has maintained levels similar to the national average, and growth rates exceeding the nation (Figures 1 and 2), meaning the state’s living standards have been raised (Table 1) while outperforming the

180

240

140 100 1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 1. Personal Income Per Capita (Net of Transfers) 1969 to 2014 Index U.S. = 100

United States Texas Austin-Round Rock Dallas-Fort Worth-Arlington

220

180 Index

2014

Houston-The Woodlands-Sugar Land Midland Odessa San Antonio-New Braunfels

Notes: Inflation adjusted. The data are indexed each year relative to the U.S. from 1969 to 2014 to measure the region’s performance compared with the U.S. PIPC for a region is divided by the PIPC for the U.S. which compares if a region’s PIPC is greater than (>100), less than (<100) or approaches (=100) the U.S. Source: Bureau of Economic Analysis

200

160 140

most in periods of economic decline. Our interest is income paid out for employment and property ownership in the prevailing regional economic activities. To determine the degree of industrial specialization, location quotients (LQ) were employed to compare a region of interest with a benchmark region. An LQ is the proportion of employment in an industry for a region divided by the proportion of employment in the same industry for the benchmark region. A value greater than 1.0 suggests that the region of interest has a greater proportion of employment in that industry relative to the benchmark region, which in this case is the U.S. The data on employment from the BEA is used to estimate the LQs by state and MSA.

120 100 80 60 1970 1975 1980 1985 1990 1995 2000 2005 2010 United States Texas Austin-Round Rock Dallas-Fort Worth-Arlington

2014

Houston-The Woodlands-Sugar Land Midland Odessa San Antonio-New Braunfels

Notes: Inflation adjusted. The data are indexed to a starting base of 100 in 1969, which creates a level playing field for examining the changes in magnitude during the analysis period. Source: Bureau of Economic Analysis

U.S. Over time, oil boom periods have pushed Texas PIPC upward and subsequent downturns have dragged levels downward (Figure 3). Overall, the presence of the oil industry has benefited Texas, largely because wages in the oil sector are among the highest of any sector. This mitigates the downward impact during busts. For the rest of this discussion, any reference to PIPC is net of transfers (such as Social Security and welfare payments) as they do not constitute earnings currently worked for. Transfer payments, which include payments to the unemployed, rise

Figure 2. Growth of Personal Income Per Capita (Net of Transfers) Index 1969 = 100

Index

The economic performance of a region relies on its industrial makeup and degree of specialization or diversification. Employment and income levels are directly correlated to the industrial makeup and performance of a region. Since the 1980s, the Texas economy has become more diversified, reducing its dependence on the energy industry and making it more resilient when oil prices collapse. Still, the oil industry remains one of the state’s leading drivers of economic growth. Texas is an example of the economic costs of concentration and of the benefits of economic diversification.

Table 1. Personal Income Per Capita Components Relative to the National Average Per Capita Income

Wages and Salaries

Proprietor’s Income

Transfers

(U.S. = 100) Region

Year

(1)

(2)

(3)

(4)

Texas

2014 2010 2001 2000 1990 1980 1969

99.2 95.0 94.2 92.7 88.6 98.0 88.6

107.5 101.4 102.0 99.5 92.7 104.4 90.4

85.2 83.8 75.8 76.5 82.1 90.3 85.0

83.3 83.5 79.5 80.3 77.5 72.1 77.3

Note: Inflation adjusted Source: Bureau of Economic Analysis TIERRA GRANDE

1,500

Figure 3. Production of Crude Oil (Thousands of Barrels) and Number of Operating Rigs 1969 to Jan 2016

150 130

1,100

110

Number of Operating Rigs

1,300

900

700

500

300

Production of Crude Oil

anking U.S. MSAs by the average annual inflation-adjusted growth rate in PIPC from 1969 to 2014, Texas major oil MSAs (Houston, Midland, and Odessa) rank 16th, 1st, and 21st out of 381 metros, respectively (Tables 2 and 3). The Austin MSA, a region based more in technology, education, and state government, is also near the top of the rankings at 15th. The DFW and San Antonio MSAs are further down the list at 99th and 157th, respectively. The DFW MSA is an important transportation, business and financial services, and technology region that is highly related to the U.S. economy. San Antonio, in comparison, traditionally has been a tourism and military area. Using the same comparison by state, Texas ranks 11th out of the 50 states. The other four MSAs in the top five are Fayetteville-Springdale-Rogers (AR-MO), Lafayette, LA, and Houma-Thibodaux, LA, followed by Bridgeport-

100 10 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Sources: Baker Hughes and Energy Information Administration

Table 2. Location Quotients for SigniďŹ cant Industries, Top 5 MSAs, Texas MSAs, and Bottom 10 MSAs Region

Rank

Average

Texas Midland, Texas

3.62

Fayetteville-Springdale-Rogers, Ark.-Mo.

3.05

Lafayette, La.

2.83

Houma-Thibodaux, La.

2.82

Bridgeport-Stamford-Norwalk, Conn.

2.70

Austin-Round Rock, Texas

2.25

Houston-The Woodlands-Sugar Land, Texas

2.25

Odessa, Texas Dallas-Fort Worth-Arlington, Texas

21 99

2.17 1.80

Dallas-Plano-Irving, Texas

1.87

Fort Worth-Arlington, Texas San Antonio-New Braunfels, Texas Battle Creek, Mich. Mansfield, Ohio Jackson, Mich.

157 372 373 374

1.67 1.64 0.79 0.77 0.76

Sierra Vista-Douglas, Ariz.

375

0.75

Yuma, Ariz.

376

0.72

Elizabethtown-Fort Knox, Ky.

377

0.72

Palm Bay-Melbourne-Titusville, Fla. Flint, Mich.

378 379

0.70 0.67

Sebring, Fla.

380

0.60

Lake Havasu City-Kingman, Ariz.

381

0.14

Location Quotients (Number of Jobs) Mining (3.92) Mining (35.68) Transportation and warehousing (2.12); Management of companies and enterprises (5.63) Mining (14.85) Forestry, fishing, and related activities (4.71); Mining (9.62); Transportation and warehousing (2.87) Finance and insurance (2.16); Management of companies and enterprises (2.14) State government (2.16); Mining (1.50); Professional, scientific, and technology (1.39); Information (1.35) Mining (5.18); Utilities (1.53); Construction (1.39); Transportation and warehousing (1.31) Mining (15.02); Wholesale trade (1.84); Construction (1.61) Mining (2.16); Finance and insurance (1.37); Mining (1.82); Finance and insurance (1.50); Information (1.56); Wholesale trade (1.47) Mining (3.01); Transportation and warehousing (1.92) Federal, civilian (1.70); Military (2.69); Mining (1.40); Finance and insurance (1.33) Manufacturing (2.29); Federal, civilian (2.82) Manufacturing (2.22) Manufacturing (1.63) Utilities (2.71); Government and government enterprises (2.28); Federal, civilian (5.67); Military (8.24) Forestry, fishing, and related activities (25.53); Government and government enterprises (1.69); Federal, civilian (2.65); Military (4.58); Local government (1.51) Government and government enterprises (2.26); Federal, civilian (4.50); Military (10.78) Administrative and waste management services (1.68); Federal, civilian (1.40) Health care and social assistance (1.34) Forestry, fishing, and related activities (11.62); Administrative and waste management services (1.75) Utilities (1.56); Construction (1.60); Retail trade (1.49); Real estate and rental and leasing (1.59)

Note: Inflation adjusted. Rankings are for average annual inflation-adjusted growth rate in PIPC. Out of 381 MSAs. Dallas-Plano-Irving and Fort Worth-Arlington are microdivisions. LQs are the average for 2001 to 2014 when data is available. Significant LQs are those above 1.25. The average column is the 1969 to 2014 average growth rate of PIPC less transfers. Source: Bureau of Economic Analysis JULY 2016

Stamford-Norwalk (CT). The Fayetteville-Springdale-Rogers (AR-MO) MSA is specialized in management of companies and enterprises, and in transportation and warehousing services, all related to Walmart operations (Table 2). The other, BridgeportStamford-Norwalk (CT) is concentrated in finance and insurance, and in the management of companies and enterprises (Table 2). Texasâ&#x20AC;&#x2122; four major MSAs (Austin, DFW, Houston, and San Antonio) match or exceed the U.S. annual inflation-adjusted average growth from 1969 to 2014 (Figure 2). Wages and salaries per capita is a more accurate picture of income paid

for work completed. It does not include earnings from selfemployment, business investments, other passive investments or transfer payments from the government as does personal income. The Houston, Midland, and Odessa MSAs slide to 20th, 7th, and 17th, respectively. The Austin MSA jumps to eighth while the DFW and San Antonio MSAs increase to 59th and 63rd, respectively. Both personal income growth and wage and salary growth suggest that it pays to be in oil and gas, at least in Texas (Table 3). The average LQs from 2001 to 2014 are estimated for the rest of the Texas MSAs. The Austin MSA is concentrated in state government; professional, scientific, and technology Table 3. Rankings of Average Growth Rate from 1969 to 2014 services; and information Per Capita services. Interestingly, Personal the Austin MSA registers Personal Income Net Wage Property an LQ greater than 1.0 Income of Transfers and Salary Income Transfers in mining, indicating Texas 14 11 6 29 24 some specialization in Austin-Round Rock 23 15 8 344 344 the oil industry (Table 2). Dallas-Fort Worth-Arlington 156 99 59 264 264 The Houston MSA, not Houston-The Woodlands-Sugar Land 16 16 20 155 155 surprisingly, specializes Midland 1 1 7 115 115 in mining and construcOdessa 26 21 17 51 51 tion. The DFW MSA is San Antonio-New Braunfels 175 157 63 159 159 concentrated in finance and insurance, and also Note: Inflation adjusted. Out of 50 states. Out of 381 MSAs. Source: Bureau of Economic Analysis in mining. The other

TIERRA GRANDE

the majority of the state’s personal income is generated in this region (Figure 4). The oil MSAs have benefited from the booms that come with increasing oil prices. The Midland MSA has reached PIPC levels that place it at the top of the nation. On the other hand, in times of bust, PIPC levels take considerable hits. Houston has fared exceptionally well as a world energy hub. A more diversified base has allowed the Houston MSA to take advantage of the booming oil sector while mitigating the downturns Concentration Leads to Uneven Growth with specialization in other sectors such as health care. ust as diversification of a retirement account balances In contrast, because the Midland and Odessa MSAs are growth with acceptable levels of risk, diversification of highly concentrated in the oil industry, both register greater industries in a regional economy has a bearing on the varideclines in income levels during oil bust periods. A distinction ability of income levels. A high concentration in one industry should be made between white-collar and blue-collar energyleaves the regional economy susceptible to economic shocks dependent regions. Houston and Midland are considered whitein that industry. While the oil MSAs have experienced high collar regions and have registered greater economic progress levels of PIPC growth and high levels of PIPC relative to other despite the ups and downs in the oil industry compared with metros, they also experience more variations as oil booms and Odessa, a blue-collar region. busts occur. The Austin MSA has performed exceptionally well given The Midland MSA registered the highest average inflationits ties to the technology sector even though it registered a adjusted growth rate from 1969 to 2014 for all MSAs but fall in income levels after the tech bust. The DFW MSA has the largest variation in terms of growth rates in the state, a more diversified economy that includes a technology secand second highest in the nation (Table 4). This variability tor, as well as transportation and financial services, which have allowed it to maintain consistent personal income Table 4. Personal Income Share 2014 (Thousands of Dollars) levels higher than the U.S. over Personal Income time. The San Antonio MSA Personal Income Less Transfers income levels are consistently (Dollars) (Percent) (Dollars) (Percent) below the nation’s although its 14,683,147,000 12,154,008,000 United States growth rate has matched pace Texas Total 1,231,084,591 8.4 1,052,969,574 8.7 with the country’s thanks to the strong military presence Austin-Round Rock 91,385,667 7.4 81,707,183 7.8 and tourism services. Dallas-Fort Worth-Arlington 344,279,922 28.0 304,711,143 28.9 major oil region in Texas, the Odessa MSA, is concentrated in mining, followed by wholesale trade and construction. The relatively high LQs for Austin and Dallas in mining reflect the importance of the oil industry in Texas even in regions not considered part of the state’s energy industry. Texas has a high LQ in mining, reflecting the state’s specialization in the sector. When ranking U.S. states by LQs in mining, Texas registers the seventh-highest LQ.

Houston-The Woodlands-Sugar Land San Antonio-New Braunfels Major MSAs Total

355,790,380 96,341,038 887,797,007

28.9 7.8 72.1

Midland Odessa Midland and Odessa MSAs Total

15,300,461 7,244,097 22,544,558

1.2 0.6 1.8

Brownsville-Harlingen El Paso Laredo McAllen-Edinburg-Mission Border MSAs Total

10,598,668 26,606,169 7,561,382 19,740,566 64,506,785

0.9 2.2 0.6 1.6 5.2

Rest of Texas

256,236,241

20.8

Source: Bureau of Economic Analysis

is a result of the boom and bust periods in the oil industry and is also present in the other oil MSAs. The Odessa and Houston MSAs register a standard deviation that ranks them second and third in the state and fifth and 62nd in the nation, respectively. The Houston MSA weathers the storm better than its oil counterparts through increased diversification in other industries, such as health care services and trade through its port. The increases in the state’s living standards have been geographically uneven. Texas MSAs have registered different income levels and growth of PIPC as a result of the particular characteristics in the makeup of the regional economies. The economic progress in the state has mostly come from the Texas Triangle MSAs of Austin, DFW, Houston, and San Antonio as JULY 2016

318,564,899 79,680,233 784,663,458

30.3 7.6 74.5

Where is Texas Income Going?

The current bust period in the oil industry will drive down the state’s personal income levels in 2016, but its economic 7,183,814 0.7 progress should persist given 20,611,592 2.0 5,795,075 0.6 the state’s industrial makeup, 13,886,550 1.3 degree of specialization in the 47,477,031 4.5 energy and technology sectors, and diversification in service 200,090,009 19.0 industries such as transportation and warehousing. The state’s favorable business climate translates into cost advantages, perhaps raising the state’s living standard in the foreseeable future. 14,407,872 6,331,204 20,739,076

1.4 0.6 2.0

Dr. Torres (ltorres@mays.tamu.edu) is a research economist and Wayne Day a research assistant with the Real Estate Center at Texas A&M University.

THE TAKEAWAY MSAs that specialize in oil or technology on average have registered higher growth in income per capita over time. Income growth differs between oil regions, depending on whether the area has primarily high-paying white-collar jobs or blue-collar jobs.

Taxes

nformation about selling real estate on a part-time basis is abundant on the web. Part-timers can find out about specific part-time real estate positions and get advice about selling on a part-time basis, among other topics. The article “Can Part-time Real Estate Agents Succeed at Selling Houses?” by InvestFourMore (investfourmore.com), is an example. In general, all is well as far as the IRS is concerned if the part-timer generates a taxable net profit and has reasonable documented expenses. However, net losses incurred by parttime real estate salespersons draw IRS scrutiny and may be disallowed if the part-timer cannot demonstrate and document a “profit motive.” The primary motive of the salesperson must be to earn a profit. If so, the activity is treated by the IRS and courts as a trade or business (for which losses are allowed) rather than a hobby (for which losses are restricted or denied). The determination of whether the primary motive is for profit is based on the “facts and circumstances” of each particular situation. There is no mechanical test for demonstrating a primary profit motive, but a number of factors are typically considered by the IRS and the courts. While no single factor is determinative, documentation is key. According to Treasury regulations (which have the force and effect of law), the most commonly used factors relevant for real estate part-timers are: • manner in which activity is conducted (for example, salespersons should keep records in a business-like manner, using a recordkeeping system such as QuickBooks); • expertise of the salesperson; • time and effort spent on the activity; • salesperson’s success in similar or dissimilar real estate activities; • salesperson’s history of income or losses with respect to the activity; and • salesperson’s finances. A recent court case illustrates how the factors are applied. In this case, over $30,000 of deductions related to part-time real estate sales activities were disallowed. From 2007 through 2009, Mr. Pouemi earned a living as a full-time technician for Verizon. Sometime during 2009, he lost his job. In court,

he stated that he “did real estate on the side.” He was a licensed real estate broker. Mr. Pouemi did not maintain a formal set of accounting records and did not have a bank account dedicated to his real estate activities. He did not obtain any listings or make any sales during 2008 and 2009. In 2007, he listed and sold one property from which he netted $9,457. The house was one block from Mr. Pouemi’s personal residence. umerous expenses included car and truck expenses, parking and tolls, tools, office expenses, and “personal marketing.” In addition, 26 other types of expenses were claimed. All were disallowed by the IRS. The tax court upheld the IRS position. Mr. Pouemi lost the case for several reasons. In general, there was no “convincing substantiation” for any of his expenses. He did not maintain a contemporaneous log of automobile expenses even though he claimed over 28,000 (yes, over 28,000) business travel miles. The court found the business mileage to be “completely implausible.” Mr. Pouemi created a table during the IRS audit, but many of the

entries were vague. Moreover, he had no documentation of the percentage of his cell phone, text messaging, or email expenses that was related to business. He offered no plausible explanation of how his real estate activity required the expenditure of $3,552 for “tools.” He did not have a staff, and he offered no plausible explanation of his claimed deductions for expenses of “staff meetings” and “payroll processing.” Nor did he explain what the claimed expense of $850 for “personal marketing” entailed. Although he testified that he took continuing education classes, he demonstrated no serious effort to advance his career as a real estate professional. His testimony that he devoted 30 hours per week to his real estate activity was “not credible.” Mr. Pouemi’s fact pattern is admittedly extreme. A tax accountant or tax attorney knowledgeable about real estate transactions might have suggested that he conduct his real estate activities in a more business-like manner, including adequate recordkeeping, reasonable, realistic deductions, and IRSapproved documentation. Dr. Stern (stern@indiana.edu) is a research fellow with the Real Estate Center at Texas A&M University and a professor of accounting in the Kelley School of Business at Indiana University.

THE TAKEAWAY To deduct expenses and net losses, part-time real estate salespersons must keep business-like records demonstrating and documenting that their primary motive is to make a profit. TIERRA GRANDE

Hot water in my apartment is only 100 degrees. Am I being unreasonable to ask my landlord to turn up the heat?

Our apartment building roof leaks because of recent hail damage, but we still don’t have the money from the insurance company. What’s our obligation to our tenants?

It’s been two days since I lost water in my apartment. My landlord says he’s working on it. Can I haul him into court?

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