the importance of demographic analyses in state- and local-level policy evaluations: a case study...

Population Research and Policy Review17: 167–196, 1998.© 1998Kluwer Academic Publishers. Printed in the Netherlands.

167

The importance of demographic analysesin state- and local-level policy evaluations:A case study analysis of property taxes in Texas, USA

STEVE WHITE & STEVE H. MURDOCKThe Center for Demographic and Socioeconomic Research and Education, Department ofRural Sociology, Texas A&M University, College Station, Texas, USA

Abstract. The reluctance of policy-makers to incorporate detailed demographic analyses inpolicy analyses often means that population composition is ignored in state and local pol-icy evaluations. This article uses standard demographic projection, standardization and ratedecomposition techniques to examine the implications of changing population compositionfor the property tax revenue base of Texas. The authors find that if current socioeconomicdifferentials persist into the future, projected compositional changes in the household pop-ulation of Texas will significantly impact property tax revenues. Thus revenue projectionsbased on aggregate growth and current average property value would seriously overestimatefuture property tax revenues in Texas because changes in the composition of the populationlead to disproportionate growth in households likely to live in lower valued housing unite.The results indicate that the continuing focus of state and local policy-makers on changes inpopulation size alone may be ill-advised and demonstrate the increasing importance of local-and state-level demographic analysis in a period of increasing Federal devolution of serviceprovision.

Keywords: Policy, Population composition, Taxes

Introduction

Tax, welfare, social security, and health-related reforms are among the mostdiscussed political issues in the USA. Concerns about such public and societalprograms are occurring not only at the national level but also at the state andlocal levels as a result of devolution, and the increases in state- and local-leveltaxes resulting from the devolution of service provision. Whether viewednationally or locally, projecting the implications of such reforms involves alarge number of political, economic and social considerations. Demographicfactors inclusive of changes in population composition have been consideredmost extensively in discussions of social security (Lee & Tuljapurkar 1997;Bennett & Olshansky 1996; Board of Trustees 1992), legislative redistricting(Morrison 1994; Serow et al. 1994; Clarke 1994; Terrie 1996), and health carereform (Manton et al. 1993; Burner et al. 1992; Pol & Thomas 1992) while in

168 STEVE WHITE & STEVE H. MURDOCK

many other policy areas demographic considerations have been largely lim-ited to an examination of the per capita effects of change in total populationsize. Similarly, most such analyses have been national in scope (Murdock1995; Robey 1985) with only limited attention having been given to the ef-fects of local-level demographic trends on state and local services and taxes(Tayman 1996; Caldwell 1983).

This is unfortunate because the demographic trends likely to impact theUSA in the coming decades (Day 1996; Murdock 1995; Gill et al. 1992;Robey 1985) are also likely to markedly impact the demand and costs for ser-vices and the revenues available to pay for such services. Population changecan be particularly important at the state and local levels because demo-graphic trends are likely to cause larger relative changes at these levels than atthe national level and thus be more significant sources of variation in servicedemand and supply.

In this paper, after examining some of the factors likely to lead to theneglect of demographic variables in policy analysis and planning, we presenta case study of the use of demographic projections to evaluate the impactsof change in population composition on property taxes in Texas. Using theresults of this analysis, we demonstrate that a failure to include more com-prehensive demographic analyses in the determination of the implications ofpolicy alternatives is likely to have serious implications for the evaluation ofthe equity and efficiency of state and local policy alternatives.

Demographic factors in service and tax policy analyses

Demographic factors have, of course, been examined as they impact numer-ous socioeconomic dimensions that affect major areas of policy concern.For example, substantial attention has been given to the examination of therelationships between demographic structure and such social phenomena aseducation and labor force participation (Howe 1988); poverty, segregation,and income inequality (Massey & Denton 1993; Massey & Eggers 1990);local labor market competition (Tigges & Tootle 1993); occupational achieve-ment (Stolzenberg 1990); urbanization and migration (Murdock et al. 1993),and numerous other factors. Relationships have been examined between theeconomy, public policy and changes in population structure (Tayman 1996;Lee et al. 1995; Denton & Spencer 1989; Easterlin & Crimmens 1985), de-mographic factors affecting welfare participation (Cao 1996), retirement andpension plans (Zedlewski 1990), and tax and wealth transference (Deaton& Paxton 1997; Lillard & Willis 1997; Giannarelli 1992) have also beenexamined. Even the effects of demographic factors on local taxes have beenselectively considered. Thus, the effects of immigration on local taxes (Clark

DEMOGRAPHIC ANALYSES IN POLICY EVALUATIONS 169

et al. 1994; Passel 1994; Los Angeles County Internal Services Division1992) and on public costs have been widely debated (Huddle 1993).

The implications of projected demographic patterns for future state andlocal revenues have seldom been extensively examined, however. There areseveral reasons for this neglect. First, there are substantial and legitimateconcerns about the accuracy and utility of long-term population projectionsfor small areas (Keilman & Keyfitz 1988; Keyfitz 1982; Ascher 1978). Givenall the uncertainties that underlie policy development, the introduction of anadditional source of uncertainty such as forecasted changes in demographiccomposition is often resisted. As a result, simply assuming that populationchange will be linear and that change in population size is the primary stimu-lus to growth or decline in public revenues and expenditures become the leastcontroversial assumptions for policy analysis (Barlow 1982; Cornia 1991;Ladd 1979). Secondly, since policy formation is unavoidably political, andthus subject to particular biases and constraints, policy analysts often seethe introduction of such additional considerations as those related to popula-tion composition as needlessly complicating an already complicated process(Portney 1988). Third, given that the normal planning horizon for policyevaluations, such as those related to state and local revenue and expenditureanalyses, rarely exceeds five years (Sawicki 1988), the long-term implicationsof demographic patterns can often be assumed to have little effect during theperiod of interest to policy makers.

Unfortunately, however, because political inertia tends to lead to generallyslow political processes of change, tax and other policies often continue forsubstantially longer periods of time than policy makers may have initiallyintended. This institutional inertia is the result of the political decision mak-er’s desire to avoid uncertainty and the controversies that arise when policychanges have unpopular distributional impacts (Portney 1988). Hence, evenwhen the viability of a policy is threatened by long-term social and demo-graphic change, political inaction often prevails. Perhaps the classic exampleof this phenomenon is the ‘pay-as-you-go’ funding mechanism of the USSocial Security System in which current payroll taxes pay current retirementbenefits. When the population or wage growth rate is sufficiently high, thisfunding mechanism works well, leading to a so-called ‘social insurance para-dox’ (Aaron 1966) in which each succeeding generation is made better off.This occurs because, under conditions of high population growth, the payrolltax revenues of each current generation of workers are larger than those ofthe previous generation of workers. However, when the population or wagegrowth rates of the working generation are low, the ‘pay-as-you-go’ fundingmechanism can “ . . . impoverisheach and every generation, relative to thesaving of the same funds” (Wolfe 1993: 42). The prospect of just such a size


reversal between the working and retired generations is why the retirementof the baby-boomers threatens the future solvency of the social security re-tirement system in the USA. In spite of this prospect, political inertia andinaction are such that the ‘pay-as-you-go’ funding mechanism continues toprevail.

The effects of differential demographic change on the social security sys-tem illustrate the need for more detailed analyses of population change in theevaluation of tax and spending policies. This is especially true in the case ofthe residential property tax which is characterized by various tax relief mech-anisms that are linked to the demographic characteristics of householders.

The property tax issue in Texas

During the latter part of the 1980s and the early 1990s, Texas experiencedsubstantial increases in public spending while its tax base remained stagnant.Because of a downturn in the Texas economy, immigration and real estatevalues declined and increases in tax rates were necessary to maintain basicrevenues streams. Declines in values were such that even increases in the totalnumber of households due to natural increase-based growth translated intovery little increase in total revenues. This trend was especially pronouncedin the area of public education where the property tax base, which providesabout half of all school revenue, did not keep pace with enrollment growthand education costs. Consequently, the school tax rate in Texas more thandoubled between 1984 and 1994 to keep pace with increasing costs and it isestimated (Staff Work Group 1996) that the residential property tax added 30percent to the average monthly mortgage payment in Texas by 1995.

As a result, property tax reform was at the top of the political agendain Texas as the Texas Legislature entered the 1997 Legislative Session. Theprimary objective of this reform effort was to reduce the burden of the currentproperty tax on homeowners. Far less attention was paid to factors that mightaffect the future outcomes of Texas property tax system. As such, there wassubstantial uncertainty concerning the impact of the various proposals forproperty tax reform that were considered.

Ultimately, the source of such uncertainty is the complex set of insti-tutional, social, and economic factors that interact to shape public financepolicy. One such factor is population change. Population change has impactedthe property tax base in Texas in at least two ways. First, rapid rates of popula-tion growth have altered the number of persons and households that contributeto the tax base. Thus, from 1980 to 1990 the population of Texas increasedby 19.8 percent while from 1990 to 1996, Texas population increased by 12.6percent showing the largest numerical increase of any state in the Nation from1990 to 1996 (Murdock et al. 1997). Similarly, its household base is estimated


to have increased by 36.2 percent from 1980 to 1995 (Texas State Data Center1996a). The second factor is the marked changes in population compositionthat are impacting Texas and other parts of the Nation. This second factorimpacts taxes by moving persons into categories (e.g. homeowners, elderly,etc.) that determine tax payment levels and liabilities. For example, Texaspopulation, although relatively young, has shown the same general demo-graphic trends relative to aging as the rest of the Nation with an increase inmedian age of nearly three years during the 1980s. Similarly, it has shownrapid rates of ethnic diversification such that Texas population was roughly40 percent non-Anglo by 1990 with one of every two net additions to thepopulation during the 1980s having involved a person of Hispanic Originand two of every three net additions involving a member of a non-Anglorace/ethnicity group (Murdock et al. 1997).

Demographic characteristics such as these are linked closely to housingtenure and housing resources (Stemlieb & Hughes 1986; Myers 1990) and,as the data in Table I suggest, age and race/ethnicity both individually andin combination affect tenure, the value of property owned, and rent paid.Table 2 illustrates the various residential property tax exemptions in Texas.For the most part, the amounts and types of available residential exemp-tions are determined by tenure, property value, and householder age, factorsthat either indirectly or directly reflect the population’s characteristics. Thus,demographic structure affects property tax revenues through its impacts onthe characteristics of the housing stock which establish residential value andthrough its relationships to residential tax exemptions which affect taxableresidential value.

The relationships between population change and the property tax base aresuch that even as a population increases in size, it is possible for per house-hold property value to decline because of selective changes in populationcomposition. As such, the assumption that property tax revenues will grow inproportion to changes in population size is not always correct.

Given such general relationships, the Center for Demographic and Socioe-conomic Research and Education at Texas A&M University was contractedby a major state agency working for the legislature of Texas to examine twoquestions:

1. How will projected change in the size, age and race/ethnicity compositionof the Texas population impact future residential property tax revenue inTexas?

2. Which demographic factors will have the most significant impacts on theresidential property tax base of Texas?

The analysis was completed during the latter part of 1996 and early 1997and was formally presented to the Texas Legislature in February of 1997. Its


Table 1. Average owner-occupied housing values, monthly rents,ownership rates, and renter rates by age, race/ethnicity, and tenurein Texas, 1990

Tenure/Age Anglo Black Hispanic Other

Average housing values and monthly rents (in $)

Owner

15–59 71,343 47,156 41,537 76,064

60–64 63,108 37,441 39,125 63,433

65–74 57,377 33,718 35,280 60,866

75+ 48,160 28,888 30,367 44,449

Renter

15–59 453 370 334 407

60–64 384 287 247 324

65–74 344 243 220 294

75+ 344 205 189 269

Ownership and renter rates (percents)

Owner

15–59 59.5 37.5 48.7 46.2

60–64 84.8 68.2 75.8 72.4

65–74 84.9 72.3 75.0 66.4

75+ 76.8 76.3 72.4 58.9

Renter

15–59 40.5 62.5 51.3 53.8

60–64 15.2 31.8 24.2 27.6

65–74 15.1 27.7 25.0 33.6

75+ 23.2 23.7 27.6 41.1

Source: US Census of Population and Housing (1990).

findings were included in the legislative deliberation regarding property taxesduring the 1997 session.

Methodology

Analysis was completed using a simulation model that projected property taxbase elements for 254 counties and 1,065 school districts in Texas. Citieswere not included in the projections because adequate data for the 1990 base-line calculations were not available for cities. Projections were completed forthe 40 year projection period from 1990–2030.


Table 2. Optional and mandatory partial residential exemptions by type of jurisdiction inTexas

Exemptions City County School

$5,000 Homestead – – M

$3,000 Homesteada – M –

20% Homesteadb O O O

Age 65, $10,000 – – M

Age 65, $3,000c O O O

Age 65, Ceiling – – M

Disabled, $10,000 – – M

Disabled, $3,000d O O O

Disabled Vete M M M

– = Not applicable to this jurisdiction; O = Optional; M = Mandatory.a Counties that collect a special tax for farm-to-market roads or flood control are required toexempt at least $3,000.b Any taxing entity can elect to offer an optional (and additional) homestead exemption up to20% but must be at least $5,000.c Any taxing entity can elect to offer an optional Age 65 exemption of at least $3,000.d Any taxing entity can elect to offer an optional disability exemption of at least $3,000.e The amount of the Disabled Vet Exemption varies with the severity of the disability. Thisexemption can be applied to any type of property and is, therefore, not limited to homesteads.

The model used the following data:the STF4B data from the 1990 Cen-sus (US Bureau of the Census 1993); the School District Data Book ver-sion of the Census Bureau’s STF3 data file (US Department of Education1995); the 1990 residential appraisal data from the property value reports forTexas counties and school districts (Office of the State Comptroller 1991);and projections of households by the age, race/ethnicity, and tenure of thehouseholder (Texas State Data Center 1996b).

The model was constructed to fulfil the following objectives:1. develop 1990 baseline age- and race/ethnicity-specific property values

for owner and renter households;2. project property tax values for the 1990–2030 projection period using the

baseline values and household projections;3. determine the effects of alternative demographic factors on changes in

the residential property tax base.The procedures used to attain these objectives are described below.


The development of 1990 baseline data

The completed baseline data set for each county and school district con-tained the following: the number of owner and renter households by the ageand race/ethnicity of the householder; residential property values for ownerand renter households by the age and race/ethnicity of the householder; theamounts and/or rates of residential property tax exemptions by type of ex-emption and taxing jurisdiction; and, the residential property tax rates bytaxing jurisdiction. While most of the baseline variables could be obtaineddirectly from the data sets, there were two exceptions: (1) residential propertyvalues for owner and renter households by the age and race/ethnicity of thehouseholder were not available at either the county or school district level,and (2) age and race/ethnicity specific ownership and rentership rates werenot available at the school district level.

The first limitation arose because a primary consideration was that the ageand race/ethnicity-specific owner and renter property values of the baselinebe identical to the property values reported in the county and school districtappraisal data. However, census housing values were not equivalent to theappraisal districts’ residential value categories and the appraisal data do notinclude detailed demographic information. As such, it was necessary to com-bine elements of the census and appraisal data. With the completion of thethree steps described below, 32 tenure, age, and race/ethnicity specific valuesfor householders were obtained (refer to Table 1). These value categorieswere computed at the county level. Details of the three steps and an exampleof their application are presented in the Appendix and additional details canbe obtained by contacting the authors. The steps involved the following:

1. Partitioning of the appraisal data into single-family and multifamily hous-ing groups that were equivalent, respectively, to the census data’s owner-occupied and renter-occupied tenure categories;

2. Conversion of the census data’s monthly rent into market value data thatwere equivalent to the appraisal data’s multifamily value category; and

3. Adjustment of the census data’s age-race/ethnicity specific owner valuesso that these values were equivalent to the appraisal district’s reports ofsingle family values.

The second limitation in the data was that the school district data do notcontain age and race/ethnicity specific ownership and rentership rates. Thislimitation was addressed using the following steps:

1. School districts were grouped by county;

2. Each school district’s proportionate share of the county’s number of per-sons by race/ethnicity category was calculated;


3. The county’s total number of owner and renter households by race/ethnicitywas allocated to each school district on the basis of the proportionateshare from step 2 above; and

4. The county’s household age structure by tenure and race/ethnicity wasapplied to the school districts’ number of owner and renter householdsby race/ethnicity from step 3 above to obtain school districts’ numbers ofhouseholds by the tenure, age, and race/ethnicity of the householder.

With the completion of the steps above, the owner and renter values forcounties and school districts could be multiplied by the appropriate numbersof households and summed to produce the total residential property valueas reported in the county and school district appraisal reports. These 1990baseline data were used to project changes in the state’s residential propertytax base.

Projecting property tax values for the 1990–2030 projection period

The primary objective of this step was to project changes in the residen-tial property tax base of Texas. Baseline fiscal variables were held constantthroughout the projection period. The projected changes in the residential taxbase were obtained using the following procedures:1. The baseline age and race/ethnicity-specific owner and rental values were

multiplied by the projected numbers of households by tenure, age, andrace/ethnicity of the householder to obtain projected residential propertyvalue;

2. Baseline values and/or rates of residential property tax exemptions werecalculated and subtracted from the projected residential property value toobtain projected residential taxable value; and

3. Baseline tax rates were applied to the projected taxable value to obtainprojected residential tax revenue.

Through the use of these procedures, projected changes in the size and com-position of households in Texas became the primary determinants of changesin the residential property tax base of Texas. As such, the methodology, as-sumptions, and limitations of the household projection technique are impor-tant and are described below.

The projections of property tax base values were completed using thebaseline values as described above and projections of households by tenure,age, and race/ethnicity. Household projections for the years 1990 to 2030were obtained from the Texas Population Estimates and Projections Program(Texas State Data Center 1996a, b).

Household projections are based on the use of the householder rate methodin which householder rates by tenure, age, and race/ethnicity are multipliedby the projected numbers of persons in each of the age and race/ethnicity


groups. Although space does not allow these projection methodologies to bedescribed in detail, a complete description is available from the Texas Pop-ulation Estimates and Projections Program (Texas State Data Center 1996a,b). The population projections were made using a standard cohort-componentmethod at the county and state level with the sum of county values for eachcohort being controlled to the state total (Shryock & Siegel 1976; Murdock& Ellis 1991). Single year of age cohorts for 76 age groups (0–1 to 75+),two sexes and four mutually exclusive race/ethnicity groups (Anglos [non-Hispanic Whites], Blacks [non-Hispanic Blacks], Hispanics [of all races] andOther persons [of all other racial groups, except Whites and Blacks, whoare not of Hispanic origin]) are used in the population projection model. Al-though the base projections contained several alternative projection scenarios,the analysis reported here used the scenario which population estimates for1990–95 suggested most closely approximated current patterns of change inTexas (this is referred to as the 1.0 scenario in the Texas Population Estimatesand Projections Program’s projections).

Determining the effects of alternative demographic factors on the propertybase and related property tax of Texas

The objective of this step was to evaluate the role that the assumed popu-lation characteristics will play in the determination of future property taxrevenues in Texas. Three techniques were used in this part of the data analysis– standardization, decomposition and value appreciation. Each of these threemethods of data analysis is described in greater detail below.

Standardization involved the use of two household populations with thenumber of households in each controlled to the total number of householdsprojected for each year from 1990 to 2030 under the projection scenariofrom the Texas Population Estimates and Projections Program. However, inone projection the tenure, age and race/ethnicity structure of 1990 was heldconstant throughout the projection period (this led to a projection hereafterreferred to as the Constant Projection) while in the second the tenure, age andrace/ethnicity structure projected by the Texas Population Estimates and Pro-jection Program (hereafter referred to as the Actual Projection) was used. Inthe Actual Projection, changes in fiscal variables reflect changes in the com-position of households as projected in the middle (1.0) scenario of the TexasPopulation Estimates and Projection Program’s projections. In the ConstantProjection, the tenure-age-race/ethnicity structure of 1990 was presumed toprevail throughout the projection period. As a result, any differences betweenthe Constant and Actual Projection in the sizes or rates of change in propertytax base variables can be assumed to be due to differences in the projectedpopulation composition.


The second technique used to evaluate demographic changes is rate de-composition. Decomposition is used to examine the differences in the cruderates for two or more populations with the objective of determining how muchof the overall rate difference is due to differences in specific compositionalcharacteristics. The crude rate decomposed here is the amount of residentialproperty tax revenue per household. Using Das Gupta’s method for the de-composition of rates (Das Gupta 1993), the tax revenue rates per householdwere partitioned into two compositional effects, age and race/ethnicity, anda rate effect which closely corresponds to a population growth effect in thepresent study. Decomposition of the property tax revenue per household wascompleted between two alternate sets of rates: (a) the Constant compared tothe Actual per household property tax rates for the years 1997, 2000, 2010,2020, and 2030, and (b) the 1990 and 2030 per household property tax ratesfor the Actual Projection.

The third method used to assess the impacts of demographic change wasthe inclusion of inflation or value appreciation scenarios in the school dis-tricts’ tax base projections. Four annual rates of property value increase, 0.0,2.3, 4.5, and 6.5 percent, were applied to the projected property values to ex-amine the effects of the elderly exemption. Respectively, these four scenariosrepresented a no inflation comparison base, and low, medium, and high seriesof property value appreciation. These rates were derived from a 16-year timeseries of residential property sales prices in Texas (Real Estate Center 1996).The inflation scenarios’ primary use was to evaluate the impacts of the schooldistricts’ mandatory tax limitation on homesteads with a householder 65 yearsof age or older. This exemption limits a homeowner’s property tax liability tothe amount paid in the year that the householder reaches 65 years of age. Sim-ulation was necessary because the model holds household property values andtax rates constant throughout the projection. Consequently, without the useof the inflation scenarios, the market values of properties owned by elderlyresidents do not change over time and it is not possible to discern the effectsof the elderly exemption on future tax revenue streams. Thus, without the useof the inflation scenarios, there would be no means by which to determine theimplications of demographic change for this significant element of the schooldistrict residential tax base.

The model used to project property tax revenues and the results of thestudy are thus based, as most complex projection models (Murdock & Ellis1991), on a number of assumptions and limitations. Among the major as-sumptions and limitations are:1. The householder and underlying population projections from the Texas

Population Estimates and Projections will correctly characterize house-hold growth in Texas for the projection period from 1990 through 2030.This entails additional assumptions about fertility, mortality and migra-


tion trends and about householder rates by age and race/ethnicity forthe counties and State of Texas (see Texas Population Estimates andProjections Program 1996a, b);

2. The relative relationships between age, race/ethnicity and tenure of house-holds and housing owner and renter values from 1990 will continuethroughout the projection period;

3. The analysis is limited to an examination of residential property valuesonly (i.e. it excludes industrial properties) and to those for counties andschool districts so that the analysis is not inclusive of all tax bases andjurisdictions for which property taxes are applicable. The analysis alsoexamines only revenues so that the effects of expenditures and total fiscalbalances are not examined;

4. The relationships assumed between census housing values and appraisaldistrict values were correctly measured in 1990 and will continue through-out the projection period;

5. The age and race/ethnicity characteristics of owner and renter householdsin counties correctly characterize the age and race/ethnicity characteris-tics of owner and renter households in school districts;

6. The relationships between owner and renter housing values and statusesand tax rates and exemptions will remain as they were in 1990; and

7. No significant changes in tax policy will occur during the projectionperiod which will alter tax rates and/or exemptions. Inherent in such pro-jections, as well, are implicit assumptions about the underlying economy,and social and political patterns.

In sum, the model used here, like any projection model, is based on numerousassumptions, the limitations of which must be recognized in examining theresults presented below.

Results

The projections of the tax base shown in this analysis include two futurelevels of residential property tax base values for the 1990 to 2030 time period.The Actual Projections present the expected levels of tax base values usingthe middle (1.0) scenario from the Texas Population Estimates and ProjectionProgram. The Constant Projections show the tax base values that would occurif the 1990 tenure-age-race/ethnicity structure of householders remained con-stant throughout the projection period. Differences between the Constant andActual projected values are due to the changes in household composition thatare projected to occur during the 1990 to 2030 time period. Because of spacelimitations the discussion presented here is largely limited to an examinationof the results for the total (school district plus county) values.


The baseline population and household projections underlying the ActualProjections of change in the tax base showed an aging and increasingly eth-nically diverse population (Murdock et al. 1997). These projections showed apopulation of 33.8 million and 13.3 million households by 2030. These repre-sent increases of 99 percent in the population and 119.9 percent in householdsfrom 1990 to 2030. In addition, the median age of householders was projectedto reach nearly 51 years by 2030 (compared to a median age of 43 in 1990)with nearly 27 percent of all householders being 65 years of age or olderin 2030 (compared to 18 percent in 1990). By 2030, nearly 58 percent ofall householders were projected to be nonAnglo (compared to 32 percent in1990) and the proportion of owner households was projected to be more than63 percent (compared to 61 percent in 1990).

Both the Constant and Actual Projections of changes in the tax base sug-gest that household growth in Texas will have significant impacts on theresidential property tax base. Table 3 presents the results of the 1990–2030Total Projection which combines county and school district data. Both theConstant (top panel) and Actual (bottom panel) Projections indicate that prop-erty values, taxable values, and tax revenues would all increase by more than100 percent during the 30-year projection period. For example, the ActualProjection shows that residential property value would increase by 113.7percent, taxable value by 113.0 percent, and tax revenues by 111.6 percent.Under the Constant Projection, property value would increase by 135.5 per-cent, taxable value by 139.1 percent, and tax revenues by 140.1 percent. Thus,even in the absence of an increase in tax rates or property value apprecia-tion, household growth would lead to a substantial increase in the amount ofresidential property tax revenue available.

Figure 1 presents a summary comparison of the 1990–2030 changes in theConstant and Actual Projections for elements of the total tax base under the0.0 inflation scenario. This chart shows that despite the fact that the overallpopulation and household growth rates are identical for both projections, theConstant Projection’s growth rates for fiscal variables exceed those for theActual Projection in all cases except one. This exception is the value lost tothe 65 years and older homestead exemption. Also of note is the negativegrowth rate of the Actual Projection’s tax revenue per household.

Differences in the two projections’ growth rates are due to changes inhousehold composition that are projected to occur in the Actual Projection.For example, the faster growth of the Actual Projection’s 65 years and olderhomestead exemption is due to the projected aging of the householder popula-tion. The slower rates of growth in the other fiscal variables in the Actual Pro-jection reflect the combined effects of aging and changes in the race/ethnicitycomposition of Texas householders. The Actual Projections show absolute


Table 3. Projection of total residential property tax base 1990–2030 in Texas assumingTexas population projection program’s projections and assuming 1990 age and race/ethnicitystructure throughout the projection period, under the 0.0 annual percent inflation scenario

Residential Exemptions

Years property value 65 & Over Homestead Other

Projected population assuming 1990 age, tenure and race/ethnicity characteristics

Numerical value (in $)

1990 558,396,756,784 12,258,011,000 54,492,9971071 323,538,408

1997 653,309,424,695 13,944,116,000 62,160,689,769 372,950,064

1998 667,834,802,728 14,194,947,000 63,317,569,389 380,356,091

1999 682,171,077,188 14,439,208,000 64,447,884,648 387,573,204

2000 696,887,663,559 14,688,566,000 65,601,140,550 394,952,288

2005 775,078,430,644 15,992,015,000 71,747,923,398 433,262,977

2010 866,590,601,526 17,503,692,000 79,010,114,876 477,495,005

2020 1,075,585,707,097 20,865,621,000 95,649,498,254 575,978,053

2030 1,315,122,833,023 24,557,181,000 114,989,115,924 680,966,567

Percent change

1990–1997 17.0 13.8 14.1 15.3

1997–1998 2.2 1.8 1.9 2.0

1998–1999 2.1 1.7 1.8 1.9

1999–2000 2.2 1.7 1.8 1.9

2000–2005 11.2 8.9 9.4 9.7

2005–2010 11.8 9.5 10.1 10.2

2010–2020 24.1 19.2 21.1 20.6

2020–2030 22.3 17.7 20.2 18.2

1990–2030 135.5 100.3 111.0 110.5

1997–2030 101.3 76.1 85.0 82.6

Projected population using Texas population projection program 1.0 scenario


1990 558,396,756,784 12,258,011,000 54,492,997,071 323,538,408

1997 647,386,073,874 13,843,557,000 62,154,104,623 372,244,781

1998 660,765,837,143 14,023,816,000 63,280,774,986 379,456,455

1999 673,948,653,477 14,186,851,000 64,382,096,553 386,488,299

2000 687,333,472,524 14,394,338,000 65,489,703,652 393,602,361

2005 757,992,578,278 15,482,883,000 71,290,137,950 430,688,579

2010 838,328,187,735 17,585,695,000 77,886,258,515 472,595,320

2020 1,011,283,684,252 26,256,632,000 92,428,695,713 560,609,758

2030 1,193,096,139,115 37,829,560,000 108,220,678,072 643,947,427

Percent change

1990–1997 15.9 12.9 14.1 15.1

1997–1998 2.1 1.3 1.8 1.9

1998–1999 2.0 1.2 1.7 1.9

1999–2000 2.0 1.5 1.7 1.8

2000–2005 10.3 7.6 8.9 9.4

2005–2010 10,6 13.6 9.3 9.7

2010–2020 20.6 49.3 18.7 18.6

2020–2030 18.0 44.1 17.1 14.9

1990–2030 113.7 208.6 98.6 99.0

1997–2030 84.3 173.3 74.1 73.0


Table 3 (continued)

Residential Tax revenue

Years taxable value Total Per household Per capita

Projected population assuming 1990 age, tenure and race/ethnicity characteristics


1990 491,359,333,151 3,473,297,637 572.1 204.5

1997 576,871,872,984 4,079,013,867 581.6 211.3

1998 589,982,605,738 4,171,878,789 583.0 212.3

1999 602,937,551,398 4,263,799,346 584.4 213.2

2000 616,244,696,810 4,358,118,710 585.9 214.2

2005 686,949,255,863 4,860,194,874 592.9 219.3

2010 769,646,139,296 5,449,157,346 600.0 225.8

2020 958,547,381,927 6,795,275,880 613.1 236.9

2030 1,174,954,759,968 8,340,419,257 624.6 245.9

Percent change

1990–1997 17.4 17.4 1.7 3.4

1997–1998 2.3 2.3 0.2 0.5

1998–1999 2.2 2.2 0.2 0.4

1999–2003 2.2 2.2 0.3 0.5

2000–2005 11.5 11.5 1.2 2.4

2005–2010 12.0 12.1 1.2 3.0

2010–2020 24.5 24.7 2.2 4.9

2020–2030 22.6 22.7 1.9 3.8

1990–2030 139.1 140.1 9.2 20.3

1997–2030 103.7 104.5 7.4 16.4

Projected population using Texas population projection program 1.0 scenario


1990 491,359,333,151 3,473,297,637 572.1 204.5

1997 571,061,271,645 4,035,092,365 575.4 209.0

1998 583,128,400,064 4,119,955,522 575.8 209.7

1999 595,041,415,654 4,203,962,068 576.2 210.2

2000 607,105,796,269 4,288,934,769 576.5 210.8

2005 670,846,389,353 4,738,380,847 578.1 213.8

2010 742,451,842,523 5,241,922,277 577.2 217.2

2020 892,148,981,118 6,282,297,528 566.8 219.0

2030 1,046,588,167,304 7,349,462,872 550.4 216.7

Percent change

1990–1997 16.2 16.2 0.6 2.3

1997–1998 2.1 2.1 0.1 0.3

1998–1999 2.0 2.0 0.1 0.3

1999–2000 2.0 2.0 0.1 0.2

2000–2005 10.5 10.5 0.3 1.4

2005–2010 10.7 10.6 −0.2 1.6

2010–2020 20.2 19.8 −1.8 0.9

2020–2030 17.3 17.0 −2.9 −1.1

1990–2030 113.0 111.6 −3.8 6.0

1997–2030 83.3 82.1 −4.3 3.7


Figure 1. Percent change in the number of households, total population and total tax base forthe constant (�) and actual ( ) projections under zero inflation scenario, 1990–2030.

and relative increases in the numbers of elderly and non-Anglo householderswho historically have occupied housing of lesser value than that of younger,Anglo households. The Actual Projections reflect the proportionate increasesin older and non-Anglo households that are expected to occur. By contrast,the Constant Projections hold the 1990 householder composition constantthroughout the 40-year projection period. Thus, the differences observed be-tween the Constant and Actual Projections are the result of compositionalchanges that occur apart from any changes in the total number of households.

Table 3 indicates that projected change in population composition willslow the growth rates of the residential property tax base. For example, underthe 0.0 inflation and Constant Projection scenarios, total tax revenues wouldgrow 140.1 percent from 1990-2030. Under the Actual Projection, tax rev-enues are expected to grow 111.6 percent. By contrast, the 65 years and olderhomestead exemption would grow 208.6 percent under the Actual Projectionbut by only 100.3 percent under the Constant Projection. Thus, the aging ofthe householder population will cause the growth rate of this exemption to


be more than twice as large as it would have been if the 1990 householdercomposition had prevailed.

Table 4 indicates that if the 1990 householder composition were to prevail,in 2030, county property value would be $60.6 billion more, school districtproperty value would be $61.4 billion more, and total property value wouldbe $122.0 billion more than under the Actual Projection. These data suggestthat demographic factors will cause residential property values in Texas to beabout 9.3 percent less in 2030 than they would be were the 1990 householdercomposition to continue throughout the projection period. Similar patternsoccur in the other property tax base values. Under the Actual Projection,demographic change would reduce county tax revenues by 9.5 percent, schooldistrict tax revenues by 12.5 percent, and total tax revenues by 11.9 percent.Again, the exception to this general pattern is the 65 years and older home-stead exemption. For example, by the year 2030, demographic change willcause this exemption to be 54.1 percent higher in the Total Actual Projection.

Clearly, then, changes in the demographic composition of Texas house-holders will impact the residential property tax base. This impact is mostapparent in the tax revenue per household variable. Data in Tables 3 and 4indicate that the growth in tax revenue per household will not keep pace withthe projected rate of household growth. Under the Total Actual Projection,tax revenues per household will decline from $572.10 in 1990 to $550.40in 2030. This 3.8 percent decline represents a revenue loss of $21.70 perhousehold. Under the Total Constant Projection, tax revenues per householdwould increase by 9.2 percent or $52.50 from 1990–2030. Table 4 showsthat, when added together, this deficit and excess yield a $74.20 differencebetween the Constant and Actual Projections that is attributable to changes inthe state’s household composition.

Table 4 shows that, by 2030, demographic change will reduce county taxrevenue per household by $11.90, school district revenue per household by$62.50, and total revenue per household by $74.20. The reason this occursis because, under the Actual Projections, the 1990–2030 growth rates fortaxable value are less than the projected 119.9 percent growth rate in thenumber of households. That is, under the Actual Projections, county taxablevalue is projected to increase 115.8 percent and school district taxable value isprojected to increase 110.0 percent. By contrast, the population growth rate,at about 99.0 percent, is projected to be less than that for households. Conse-quently, the Actual Projections show a positive growth rate in tax revenue percapita during the 1990-2030 projection period. However, for property taxes,household tax levels are more meaningful than per capita measures.

The school districts’ mandatory limitation on the school tax of the elderlyis a policy that is explicitly linked to demographic structure. This is because


Table 4. County, school district, and total 2030 constant and actual projection valuesfor residential property tax base in Texas under the 0.0 annual percent inflation

Taxing entity

Variable County (in $) School (in $) Total (in $)

Residential property value

Constant-2030 657,665,298,007 657,457,535,016 1,315,122,833,023

Actual-2030 596,993,539,237 596,102,599,878 1,193,096,139,115

Difference 60,671,758,770 61,354,935,138 122,026,693,908

Difference as a percent −9.2% −9.3% −9.3%

65 and over exemptions

Constant-2030 4,910,004,000 19,647,177,000 24,557,181,000

Actual-2030 7,624,518,000 30,205,042,000 37,829,560,000

Difference −2,714,514,000 −10,557,865,000 −13,272,379,000

Difference as a percent 55.3% 53.7% 54.1%

Homestead exemptions

Constant-2030 52,084,442,558 62,904,673,366 114,989,115,924

Actual-2030 46,382,881,048 61,837,797,024 108,220,678,072

Difference 5,701,561,510 1,066,876,342 6,768,437,852


Other exemptions

Constant-2030 207,010,845 473,955,722 680,966,567

Actual-2030 197,319,028 446,628,399 643,947,427

Difference 9,691,817 27,327,323 37,019,140


Residential taxable value

Constant-2030 600,465,686,419 574,489,073,549 1,174,954,759,968

Actual-2030 542,789,884,077 503,798,283,227 1,046,588,167,304

Difference 57,675,802,342 70,690,790,322 128,366,592,664


Total tax revenues

Constant-2030 1,682,823,776 6,657,595,481 8,340,419,257

Actual-2030 1,523,591,980 5,825,870,892 7,349,462,872

Difference 159,231,796 831,724,589 990,956,385


Tax revenues per household

Constant-2030 126.00 498.50 624.60

Actual-2030 114.10 436.30 550.40

Difference 11.90 62.20 74.20


Tax revenues per capita

Constant-2030 49.60 196.30 245.90

Actual-2030 44.90 171.80 216.70

Difference 4.70 24.50 29.20



it freezes property taxes at the amount paid during the year that a homeownerreaches 65 years of age. Table 5 shows the projected impacts of the tax limi-tation. Under the Actual Projection and the 6.5 inflation scenario, tax revenuelost to the freeze in 2030 would be $16.4 billion compared to $10.9 billionunder the Constant Projection. Similarly, between 1997 and 2030, under theConstant Household Projection and the 6.5 inflation scenario, the amount ofrevenues lost to the 65 years and older limitation would increase by 5,784percent compared to 3,729 percent under the Constant Projection.

The data on the elderly tax limitation clearly indicate the influence ofdemographic factors on the tax revenue base. Although the impacts of thelimitation are pervasive throughout the projection period, they are particularlydramatic after the year 2010. This is the time after which the baby-boomgeneration will begin to enter the 65 year-old age group. For example, inTable 5, using the 6.5 percent inflation scenario and the Actual Projection,the change in revenue lost to the limitation is 241.1 percent from 2010–2020but declines to 192.4 percent from 2020–2030. The $16.4 billion projectedto be lost due to the limitation in 2030 using the Actual Projection and the6.5 inflation scenario would represent 25.1 percent of the total tax revenuesprojected to be collected for school districts by 2030.

In the case of the elderly tax limitation described above, it is obvious thatthe aging of the population is a major force affecting the property tax base.However, with the exception of the age-related exemptions, it is difficultto ascertain how much of the revenue base change is age related and howmuch is related to the changing race/ethnicity composition of the householderpopulation.

Decomposition of the crude rate of tax revenue per household permitsthe effects of separate elements of demographic change to be examined indetail. Tables 6 and 7 present the results of two crude rate decompositions.Table 6 presents the results of decomposing the difference between the Con-stant and Actual Projections’ revenues per household in selected years fordifferent taxing entities using the 0.0 inflation scenario. The results in Ta-ble 6 suggest that, although age effects become more important after 2010,changes in the race/ethnicity of householders are the most significant sourcesof compositional changes that are expected to impact the residential tax base.

Table 7 applies the same decomposition technique to the difference be-tween the 1990 and 2030 crude revenue per household rates of the Actual Pro-jection under the 0.0 inflation scenario. As in the comparison of the Constantand Actual Projections in Table 7, age effects are significant after 2010 but itis the projected change in the race/ethnicity composition of Texas householdsthat has the largest impact on revenues per household. Growth, as seen inthe rate effect, would increase revenues in counties, school districts, and the


Table

5.P

roje

ctio

nof

taxa

ble

valu

ean

dta

xle

vylo

stdu

eto

the

limita

tion

onsc

hool

tax

onho

mes

tead

sof

the

elde

rlyin

Texa

s,19

97–2

030,

assu

min

g19

90ag

ean

dra

ce/e

thni

city

stru

ctur

eth

roug

hout

the

proj

ectio

npe

riod,

and

assu

min

gTe

xas

popu

latio

npr

ojec

tion

prog

ram

’spr

ojec

tions

unde

rth

e2.

3,4.

5,an

d6.

5in

flatio

nsc

enar

ios

Yea

rsIn

flatio

nsc

enar

io2.

3In

flatio

nsc

enar

io4.

5In

flatio

nsc

enar

io6.

5

Taxa

ble

valu

eR

even

ues

lost

Taxa

ble

valu

eR

even

ues

lost

Taxa

ble

valu

eR

even

ues

lost

65+

65+

65+

65+

65+

65+

Pro

ject

edpo

pula

tion

assu

min

g19

90ag

e,te

nure

and

race

/eth

nici

tych

arac

teris

tics

Num

eric

alva

lue

(in$)

1997

88,6

45,7

4888

,645

,748

17,3

43,8

43,4

5518

5,53

7,92

126

,625

,841

,362

284,

954,

858

1998

9,77

3,58

0,33

510

4,52

4,67

520

,677

,489

,379

221,

307,

498

32,0

86,3

45,3

2734

3,55

4,68

6

1999

11,3

30,3

37,7

2712

1,26

1,78

824

,251

,743

,200

259,

748,

738

38,0

45,3

53,1

6340

7,64

2,51

1

2000

12,9

74,2

64,8

1813

8,92

0,35

828

,098

,927

,971

301,

090,

322

44,5

71,4

02,1

5847

7,77

1,92

1

2005

22,6

28,4

85,8

9124

2,83

7,08

752

,066

,193

,340

559,

116,

569

87,5

02,7

41,3

3093

9,90

1,57

5

2010

35,3

14,7

97,2

0937

9,81

7,08

286

,576

,515

,107

931,

642,

609

154,

768,

169,

133

1,66

5,77

7,37

3

2020

72,4

45,1

26,5

6778

1,90

3,26

520

3,31

8,35

8,35

92,

195,

209,

024

415,

800,

157,

018

4,48

9,98

1,54

6

2030

130,

883,

942,

012

1,41

7,02

5,86

442

4,99

4,34

2,70

34,

602,

476,

436

1,00

7,35

0,47

2,48

010

,910

,006

,458

Per

cent

chan

ge

1997

–199

817

.917

.919

.219

.320

.520

.6

1998

–199

915

.916

.017

.317

.418

.618

.7

1999

–200

014

.514

.615

.915

.917

.217

.2

2000

–200

574

.474

.885

.385

.796

.396

.7

2005

–201

056

.156

.466

.366

.676

.977

.2

2010

–202

010

5.1

105.

913

4.8

135.

616

8.7

169.

5

2020

–203

080

.781

.210

9.0

109.

714

2.3

143.

0

1997

–203

01,

478.

31,

498.

52,

350.

42,

380.

63,

683.

43,

728.

7


Table

5(c

ontin

ued)

Yea

rsIn

flatio

nsc

enar

io2.

3In

flatio

nsc

enar

io4.

5In

flatio

nsc

enar

io6.

5

Taxa

ble

valu

eR

even

ues

lost

Taxa

ble

valu

eR

even

ues

lost

Taxa

ble

valu

eR

even

ues

lost

65+

65+

65+

65+

65+

65+

Pro

ject

edpo

pula

tion

usin

gTe

xas

popu

latio

npr

ojec

tion

prog

ram

1.0

scen

ario

Num

eric

alva

lue

(in$)

1997

8,11

6,82

5,35

686

,689

,796

16,9

75,6

07,8

3718

1,46

2,76

826

,060

,551

,626

278,

712,

760

1998

9,49

6,46

6,43

810

1,42

8,22

420

,091

,212

,946

214,

779,

071

31,1

76,5

89,4

5633

3,44

3,72

0

1999

10,9

23,4

95,0

1611

6,73

2,71

623

,380

,926

,639

250,

086,

906

36,6

79,2

44,2

6539

2,51

1,98

0

2000

12,4

48,6

04,7

0013

3,14

3,20

326

,960

,483

,116

288,

621,

095

42,7

65,5

65,2

2445

8,02

8,12

4

2005

21,3

38,3

87,2

0622

9,19

1,99

149

,097

,787

,697

527,

834,

195

82,5

14,0

21,8

8088

7,42

2,55

6

2010

34,7

07,0

24,1

9837

4,79

2,08

385

,086

,520

,038

919,

568,

023

152,

104,

585,

274

1,64

4,39

1,06

5

2020

89,6

06,4

53,8

1997

6,48

9,20

525

1,48

1,88

6,35

92,

742,

351,

468

514,

297,

915,

375

5,60

9,71

2,62

3

2030

194,

457,

804,

696

2,12

9,20

1,90

163

1,42

5,56

4,00

36,

918,

090,

538

1,49

6,64

7,78

1,76

516

,400

,764

,881

Per

cent

chan

ge

1997

–199

817

.017

.018

.418

.419

.619

.6

1998

–199

915

.015

.116

.416

.417

.617

.7

1999

–200

014

.014

.115

.315

.416

.616

.7

2000

–200

571

.472

.182

.182

.992

.993

.7

2005

–201

062

.763

.573

.374

.284

.385

.3

2010

–202

015

8.2

160.

519

5.6

198.

223

8.1

241.

1

2020

–203

011

7.0

118.

015

1.1

152.

319

1.0

192.

4

1997

–203

02,

295.

72,

356.

13,

619.

63,

712.

45,

643.

05,

784.

5


Table

6.D

ecom

posi

tion

ofth

epr

ojec

ted

diffe

renc

ein

per

hous

ehol

dre

side

ntia

lpro

pert

yta

xre

venu

esin

Texa

sfo

rth

esc

enar

ioas

sum

ing

aco

ntin

uatio

nof

1990

age

and

race

/eth

nici

typa

tter

nsan

dth

atfo

rth

eTe

xas

popu

latio

nes

timat

esan

dpr

ojec

tions

prog

ram

’s1.

0sc

enar

iofo

rse

lect

edye

ars

unde

rth

e0.

0in

flatio

nsc

enar

io,1

997

to20

30∗

Per

cent

age

ofch

ange

inP

erce

ntag

eof

abso

lute

chan

gein

Com

posi

tion

effe

ctdu

eto

:to

tale

ffect

due

to:

tota

leffe

ctdu

eto

:

Rac

e/R

ace/

Rac

e/

Juris

dict

ion/

Tota

lR

ate

ethn

icity

Rat

eet

hnic

ityR

ate

ethn

icity

year

effe

ctef

fect

Age

effe

ctef

fect

Age

effe

ctef

fect

Age

effe

ct

Tota

l

1997

6.2

−4.6

0.6

10.2

−74.

29.

716

4.5

29.9

3.9

66.2

2000

9.4

−6.4

0.7

15.1

−68.

07.

416

0.6

28.8

3.2

68.0

2010

22.8

−11.

22.

531

.5−4

9.2

11.0

138.

224

.85.

569

.7

2020

46.3

−14.

711

.549

.5−3

1.7

24.8

106.

919

.415

.265

.4

2030

74.2

−18.

223

.868

.6−2

4.6

32.1

92.5

16.5

21.5

62.0

Cou

nty

1997

1.2

−0.7

0.1

1.8

−58.

38.

315

0.0

27.0

3.8

69.2

2000

1.8

−1.0

0.1

2.7

−55.

65.

615

0.0

26.3

2.6

71.1

2010

4.0

−1.9

0.3

5.6

−47.

57.

514

0.0

24.4

3.8

71.8

2020

7.6

−2.3

1.1

8.8

−30.

314

.511

5.8

18.9

9.0

12.1

2030

11.9

−2.7

2.4

12.2

−22.

720

.210

2.5

15.6

13.9

70.5

Sch

ool

dist

rict

1997

5.1

−3.9

0.5

8.5

−76.

59.

816

6.7

30.2

3.9

65.6

2000

7.6

−5.4

0.6

12.4

−71.

17.

916

3.2

29.3

3.3

61.4

2010

18.8

−9.3

2.2

25.9

−49.

511

.113

7.8

24.8

5.9

69.3

2020

38.6

−12.

410

.440

.6−3

2.1

26.9

105.

219

.616

.464

.0

2030

62.2

−15.

521

.356

.4−2

4.9

34.2

90.7

16.6

22.9

60.5

∗ For

expl

anat

ion

see

Not

eTa

ble

7.


Table

7.D

ecom

posi

tion

ofch

ange

inpe

rho

useh

old

reve

nue

rate

sin

Texa

sfr

om19

90to

2030

for

the

Texa

spo

pula

tion

estim

ates

and

proj

ectio

nspr

ogra

m’s

1.0

scen

ario

unde

rth

e0.

0in

flatio

nsc

enar

io∗

Per

cent

age

ofch

ange

inP

erce

ntag

eof

abso

lute

chan

gein

Com

posi

tion

effe

ctdu

eto

:to

tale

ffect

due

to:

tota

leffe

ctdu

eto

:

Rac

e/R

ace/

Rac

e/

Tota

lR

ate

ethn

icity

Rat

eet

hnic

ityR

ate

ethn

icity

Juris

dict

ion

effe

ctef

fect

Age

effe

ctef

fect

Age

effe

ctef

fect

Age

effe

ct

Tota

l−2

1.7

61.7

−19.

7−6

3.7

−284

.390

.829

3.5

42.5

13.6

43.9

Cou

nty

−4.1

9.6

−1.9−1

1.8

−234

.146

.328

7.8

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state total. For example, in the total projection, growth would add $61.70 butchanges in the householder age structure would reduce this by $19.70 andchanges in the race/ethnicity composition would further reduce this growtheffect by $63.70. The net result, then, is that revenues per household decline$21.70 during the 1990–2030 projection period.

Conclusions

This study represents an applied analysis in which interrelationships betweenexisting institutional arrangements and demographic change were evaluated.The size of property tax revenues in Texas was found to be linked to changesin both the size and the composition of the household population. Althoughhousehold growth is projected to increase total property value by more than100 percent between 1990 and 2030, tax revenues per household woulddecline because the number of households is projected to increase by nearly120 percent. Compositional change affects residential property value becauseprojected increases in the relative and absolute numbers of non-Anglo andelderly households tend to decrease aggregate property values.

The relationships identified between institutional arrangements and de-mographic structure have implications for policies aimed at reforming theproperty tax system of Texas. For example, the present Texas tax systemlimits residential property tax exemptions to owner-occupied housing. Anyattempt to provide additional homestead property tax relief would affect thedistribution of property tax burdens between owners and renters. Similarly,changes in the exemptions for elderly householders would affect the distribu-tion of tax burdens between younger and older householders. Thus, due to thetax system’s differential treatment of different household types, the composi-tion of the householder population would affect the distributional outcomesof any tax relief that targets a particular sociodemographic group. Even moreimportant is the fact that the projections in this analysis illustrate that, in themedium to longterm, changes in household composition will serve to eitherexacerbate or mitigate the impacts of policy changes that target tax relief toparticular types of householders. For example, tax relief for owners wouldlead to relative reductions in tax revenues as the number of owner house-holds increased. Similarly, a tax program that increases older-age exemptionswould eliminate relatively smaller levels of tax revenue as householders age.

In fact, the results demonstrate the utility of demographic compositionalanalysis as a means of avoiding unanticipated equity and distributional prob-lems in policy formation. Thus, among the most important findings from theanalysis presented here is that using the constant projection that employsa 1990-based population composition would substantially overestimate the


level of future tax revenues. Thus, the actual rate of growth in total tax rev-enues is likely to decrease in Texas in the absence of any change in policy.Unless data on compositional related changes is used, policy makers couldplace undue reliance on levels of revenues that would never materialize.

In addition, an examination of the composition of households likely tobe impacted by future tax changes suggests that care will be necessary inthe steps taken to reduce taxes through reducing property or any other formof tax. Changes which reduce property taxes are likely to disproportionatelybenefit Anglos who are older and more likely to own their homes. Taxes tooffset lost property tax revenues, if they concentrate on more generally usedgoods, may disproportionately and negatively impact minority populations.Similarly, the provision of additional property tax relief for older residentsmight appear politically desirable given the aging of the population but wouldfurther accentuate difficulties likely to be created by existing exemptions thatwere implemented when the elderly population was numerically and propor-tionately much smaller. The use of simple per capita estimators that fail toutilize data on the characteristics of those paying and those households likelyto be paying different forms of taxes over time makes it more likely thatpolicies will be created that place tax burdens on those least able to pay suchtaxes and, therefore, threaten the viability of tax bases. In analyses of taxpolicy and other social policies, the absence of data on demographic charac-teristics is likely, as in general demographic analysis, to lead to mistakes inthe evaluation of causes and consequences.

In fact, this case study indicates that projections which include demo-graphic characteristics may be essential for adequate contingency planning.Simulations such as those performed in the present case study allow analyststo identify which groups would be most burdened by a particular change infiscal policy. Perhaps more importantly, the present simulations indicate that,even in the absence of political action, demographic change can interact withan existing policy structure to affect a redistribution of resources.

The distributional outcomes of state and local policies are likely to be ofincreasing importance as Federal policies of devolution and decentralizationexpand to impact more services. This case study suggests that demographicanalysis as reflected in such standard demographic procedures as composi-tional analysis will likewise become of increased importance. Demands forlocal and state-level demographic analyses are thus likely to also expand and


increasingly involve analyses of the distributional and equity characteristicsof public policies.

Appendix

Methods used to merge census and appraisal data sets to derive baselineowner and renter housing values by age and race/ethnicity of householder

Step One: Partition the appraisal data into single family and multifamilyhousing groups that are equivalent, respectively, to the census data’s owner-occupied and renter-occupied tenure categories. This was done at the countylevel as follows:

ASTM = CRR ×ASF × ASFVwhere

ASTM = Appraisal single family value allocated to the multifamily cate-gory,

CRR = Census ratio of single family rental units to total single familyunits,

ASF = Appraisal number of occupied single-family units, andASFV = Appraisal average value of single-family unit.For example, if:

CRR = 0.14286,ASF = 1,000, andASFV= $22,500, then:ASTM= $3,214,286 (i.e., 0.14286× 1,000× $22,500).

TheASTMamount of $3,214,286 is then deducted from the appraised valueof single family and added to the appraised value of multifamily units.

Step Two: Convert the census age-race/ethnicity specific monthly rental datato appraisal market value data. A derived rent multiplier was used to convertthe data as follows:

ARV ijk = RmK × RijkwhereARVijk= Appraisal rental market value for theith age group of thej th

race/ethnicity group in thekth county,RMk = Rent multiplier of the kth county derived from the ratio of the

appraisal adjusted multifamily total value to the census totalmonthly rent, and

Rijk = The census monthly rent for theith age group of thej thrace/ethnicity group in thekth county.


For example, if the appraisal adjusted multifamily value was $8,000,000 andthe census aggregate monthly rent was $80,000, then RMk would be equal to100. If Rijk were $265, then

ARV ijk = $26,500(i.e.,100× $265).

Total appraisal rental value is obtained by multiplying theARVijkof each ageand race/ethnicity group by the number of households in that group and thensumming the products for the sixteen renter age and race/ethnicity groups.

Step Three: Adjust the census age-race/ethnicity specific owner values to beequivalent to appraisal districts’ reports of single family values. This wasdone as follows:

AOV ijk = (V ijk/CMV k)×ASFV k × RACkwhere

AOV ijk = Appraisal owner market value for theith age group of thej thrace/ethnicity group in thekth county,

V ijk = The census owner value for theith age group of theithrace/ethnicity group in thekth county,

CMV k = The census mean owner value of thekth county,ASFV k = Appraisal average value of single family unit in thekth county.

andRACk = Ratio of appraisal adjusted single family total value to the Census

single-family owner-occupied total value in thekth county.

For example, if

V ijk = $26,500,CMV k = $23,000,ASFV k = $22,500, andRACk = 0.93168, then:AOV ijk = $24,153 (i.e., [$26,500/$23,000]× $22,500× 0.93168).

Total appraisal owner value is obtained by multiplying theAOVijkof each ageand race/ethnicity group by the number of households in that group and thensumming the products for the sixteen owner age and race/ethnicity groups.

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