Final Report - HCAD€¦ · Almy, Gloudemans, Jacobs & Denne Final Report – Review of 2014...

Final Report

*****

Review of 2014 Commercial Values

For

Harris County Appraisal District

*****

Almy, Gloudemans, Jacobs & Denne

Property Taxation and Assessment Consultants

7630 North 10th

Avenue

Phoenix, Arizona 85021

June 2, 2014


Final Report – Review of 2014 Commercial Values for the Harris County Appraisal District ii

Contents

Acknowledgments .................................................................................................................................. v

List of Acronyms and Abbreviations .................................................................................................... vi

Executive Summary ............................................................................................................................. vii

Introduction ........................................................................................................................... vii

Evaluation .............................................................................................................................. vii

Recommendations ................................................................................................................ viii

1. Background and Setting ................................................................................................................. 1

1.1 Purpose of Study .............................................................................................................. 2

1.2 What We Did .................................................................................................................... 2

1.3 Valuation Setting and Environment ................................................................................. 3

1.4 HCAD Staffing and Operations ....................................................................................... 6

2. Commercial Valuation Procedures ................................................................................................. 7

2.1 Overview .......................................................................................................................... 7

2.2 Apartments ....................................................................................................................... 9

2.3 Offices ............................................................................................................................ 10

2.4 Retail Properties ............................................................................................................. 11

2.5 Warehouses .................................................................................................................... 11

3. Data Assembly ............................................................................................................................. 13

4. Time Trend Analysis .................................................................................................................... 14

4.1 Overview ........................................................................................................................ 14

4.2 Methodology .................................................................................................................. 14

4.3 Results and Adjustment Factors ..................................................................................... 17

5. Sales Ratio Analysis ..................................................................................................................... 18

5.1 Purpose and Objectives of Ratio Studies ....................................................................... 18

5.2 Data Screening ............................................................................................................... 19

5.3 Stratification ................................................................................................................... 19

5.4 Sales Ratio Statistics ...................................................................................................... 20

6. Sales Ratio Results ....................................................................................................................... 22

6.1 Sample Representativeness and Comparison of Sold and Unsold Properties ................ 22

6.2 Overall Results by Use Group ........................................................................................ 24

6.3 Apartments ..................................................................................................................... 25

6.4 Offices ............................................................................................................................ 27


Final Report – Review of 2014 Commercial Values for the Harris County Appraisal District iii

6.5. Retail Properties ............................................................................................................. 29

6.6 Warehouses .................................................................................................................... 32

6.7 Additional Stratification ................................................................................................. 34

7. Conclusions and Recommendations ............................................................................................. 39

Appendix 1 – Commercial Economic Area Maps ............................................................................... 42

Appendix 2 - Geographic Distribution of Sales By Use Group ........................................................... 46

Appendix 3A - Scatter Plots of Ratios by Value .................................................................................. 50

Appendix 3B - Scatter Plots of Percentage Differences from the Median by Value ........................... 53

Appendix 3C - Scatter Plot of Ratios by Value By Major Use Group ................................................. 56

Appendix 3D - Scatter Plot of Percentage Differences from the Median by Major Use Group .......... 58


Final Report – Review of 2014 Commercial Values for the Harris County Appraisal District iv

List of Tables and Figures

Tables

Table 1: Mann Whitney Tests for Differential Assessment of Sold & Unsold Property .................... 23

Table 2: Commercial Property Assessment Ratio Results Stratified by Use Group .......................... 25 Table 3: Assessment Ratios for Apartments Stratified by Sub-Type .................................................. 26 Table 4: Assessment Ratios for Apartments Stratified by Market Area ............................................. 27 Table 5: Assessment Ratio for Offices Stratified by Sub-Type .......................................................... 28 Table 6: Assessment Ratios for Offices Stratified by Market Area .................................................... 29

Table 7: Assessment Ratio for Retail Properties Stratified by Sub-Type ........................................... 29 Table 8: Assessment Ratios for Retail Properties Stratified by Market Area ..................................... 31 Table 9: Assessment Ratio for Warehouses Stratified by Sub-Type .................................................. 33 Table 10: Assessment Ratios for Warehouses Stratified by Market Area .......................................... 34 Table 11: Assessment Ratios for Independent School Districts ......................................................... 35

Table 12: Assessment Ratios for Size Groups (Net Rentable Area) ................................................... 35

Table 13: Assessment Ratios for Age Groups (Year Built) ................................................................ 36 Table 14: Assessment Ratios for Economic Building Classes ............................................................ 37

Table 15: Assessment Ratios by Value Range .................................................................................... 37

Figures

Figure 1: Graph of SAR and Months: Office Condos ......................................................................... 15 Figure 2: Graph of SAR and Months: Office Buildings ..................................................................... 16

Figure 3: Regression of Logarithms of SAR on Time: Office Buildings ........................................... 16 Figure 4: Commercial Time Adjustments ........................................................................................... 17

Figure 5: Q-Q Plot of Assessment Changes for Sold and Unsold Apartments ................................... 24 Figure 6: Box Plot of 2014 Apartment Ratios by Sub-Type ............................................................... 26

Figure 7: Box Plot of 2014 Office Ratios by Sub-Type ...................................................................... 28 Figure 8: Box Plot of 2014 Retail Ratios by Sub-Type ...................................................................... 30

Figure 9: Scatter Graph of Retail Ratios with Value in Market Area 4003 ........................................ 32 Figure 10: Box Plot of 2014 Warehouse Ratios by Sub-Type ............................................................ 33


Final Report – Review of 2014 Commercial Values for the Harris County Appraisal District v

The study team of Almy, Gloudemans, Jacobs & Denne (AGJD) was comprised of its three active

partners: Richard Almy, Robert Denne, and Robert Gloudemans, who served as project manager.

The Harris County Appraisal District (HCAD) provided full and timely support in providing all

information and data requested for our study. Sands Stiefer, Chief Appraiser, emphasized the desire

for an independent, objective review of HCAD’s commercial values and appraisal procedures. April

Holcomb, Director of Mass Appraisal Support, coordinated responses to all of our requests and was

outstanding in providing timely responses thereto. Sharon Boyd, former Chief of Appraisal Opera-

tions, served as our initial contact point and set the project off to strong start. We wish her well in her

retirement.

We thank Bobby Larry, Associate Chief Appraiser for Commercial Property, and Erica Nettles,

Commercial Valuation Manager, for sharing their first-hand knowledge and experience of HCAD’s

commercial valuation operations and procedures. Liz Hernandez, Appraisal Data Analyst, and Gene

Kotlyar, Appraisal Support Tech III, provided helpful explanations of HCAD’s time trend and sales

ratio procedures. We also acknowledge the input and support of Roland Altinger, Deputy Chief

Appraiser, and Jason Cunningham, Chief of Appraisal.


Final Report – Review of 2014 Commercial Values for the Harris County Appraisal District vi

AGJD Almy, Gloudemans, Jacobs & Denne

CAMA Computer Assisted Mass Appraisal

COD Coefficient of Dispersion

EBC Economic Building Class

EGI Effective gross income

GIS Geographic Information System

HCAD Harris County Appraisal District

IAAO International Association of Assessing Officers

IQR Interquartile Range

ISD Independent School District

LUC Land use code

MAP Methods and Assistance Program

MRA Multiple Regression Analysis

NOI Net operating income

PRB Coefficient of Price Related Bias

PRD Price Related Differential

PTAD Property Tax Assistance Division

Q-Q Plot Quantile-quantile plot

RECON Real Estate Center Online News

SAR Sales/Assessment Ratio

STRAP The parcel identifier used by HCAD

USPAP Uniform Standards of Professional Appraisal Practice


Final Report – Review of 2014 Commercial Values for the Harris County Appraisal District vii

Introduction

The Harris County Appraisal District (HCAD) asked Almy, Gloudemans, Jacobs & Denne (AGJD) to

make an independent sales ratio study to evaluate the accuracy of the District’s 2014 commercial

values. Our ratio study encompassed apartment, office, retail, and warehouse properties (Texas

property classes B and F1).

The main focus of our review was a ratio study in which we compared assessments against sales

prices using 2011 to 2013 sales. We evaluated calculated ratio study performance measures against

industry standards promulgated by the International Association of Assessing Officers (IAAO). We

also conducted a high-level review of the District’s appraisal procedures, which included staff

interviews and a review of relevant legislation, appraisal manuals, and documents.

Evaluation

HCAD has a sound commercial valuation program and achieves good results that generally meet

IAAO standards. Appraisal methods comply with industry standards. The staff uses all practical

means to assemble market data, which are then researched and screened preparatory to appraisal

analyses. The income approach is emphasized for the majority of commercial properties, including

garden and hi-rise apartments and office buildings. Appraisers target full market value and conduct

ratio studies to check assessment levels.

Unfortunately, Texas appraisal districts (including HCAD) face major statutory constraints and

challenges. Although the Texas Property Tax Code commendably requires districts to appraise real

property at 100 percent of market value and requires them to use generally accepted appraisal meth-

ods, the law erects obstacles that make achieving these goals difficult. First, there is no requirement

mandating the disclosure of sale prices and terms. Thus appraisal districts must purchase much of the

limited information available. In addition, appraisal districts have virtually no power to require

disclosure of needed rental income and operating expense data. Some data are voluntarily disclosed

during protests, but these data are received too late for use in the current year reappraisal and may

represent more troubled properties. Finally, sections 41.41 and 41.43 of the property tax code place

unusual impediments upon assessment administrators. Chief appraisers carry the burden of proof

when assessments are appealed and appellants need only hand-pick a limited number of comparables

to argue inequity, even though they along with the majority of properties in their class may be

appraised equitably and at market value. Compared to other assessment jurisdictions in the U.S,

HCAD (and other large county appraisal districts in Texas) faces an inordinate volume of appeals

each year, which further constrains resources.

In property tax administration, best practice requires that properties that were not recently sold be

valued comparably to similar properties that were recently sold. Besides raising obvious uniformity

issues, failure to do so can render sales ratio study statistics misleading. HCAD’s policy is to ap-

praise sold and unsold properties equally by applying the same valuation tables and rates to properties


Final Report – Review of 2014 Commercial Values for the Harris County Appraisal District viii

regardless of whether the properties had recently sold. Section 6.1 contains our statistical tests of

whether sold and unsold properties are appraised consistently. It appears that in some instances,

specifically for apartment properties, value changes from 2013 to 2014 are substantially higher for

sold than for unsold properties. Often 2014 values are very close to sales prices, more often than can

be explained by chance alone. A question that is beyond the scope of our study is the extent to which

this is the result of discriminatory appraisal practices or can be ascribed to causes related to the

protest system. At the same time that the system makes it comparatively easy to win a value reduc-

tion on arguably spurious inequity grounds, it constrains appraisal districts from changing such

lowered values in the next year. Furthermore, the system may encourage the “over-fitting” of availa-

ble sales to limit losses occasioned by future inequity appeals.

This issue aside, we judge that HCAD does a commendable job under such pressures and constraints.

We also conclude that the District can further improve its valuation procedures in specific ways

elaborated in our report.

Recommendations

Besides doing what it can to improve property tax legislation in Texas, we recommend that HCAD

work toward strengthening application of the income approach and extending it to other properties

for which adequate income information may be available. In particular, staff could test to see wheth-

er vacancy, expense, and capitalization rates vary by market areas.

We recommend that HCAD make greater use of statistical software and explore the development of

sales comparison models for properties with adequate sales over a multi-year period, most notably 4-

20 unit apartments and warehouse properties. Apartments with 20 or less units, along with most

retail properties, are currently appraised on the cost approach.

We would also like to see the District strengthen its sales ratio and time trend programs. Ratio

studies could examine additional categories of property, as illustrated by our analyses. As staff has

noted, time adjustment methods could be enhanced to handle nonlinear trends. Time-adjusted sales,

now used only in ratio studies, should be used in the development of capitalization rates and in other

appraisal applications.

The final section of our report develops these and other recommendations in more detail. Some may

require enhancements to present computer systems.

We conclude that HCAD does a solid, commendable job under difficult circumstances. As in most

major assessment jurisdictions, operations and procedures can be further improved. We commend

HCAD for initiating an independent review of its commercial values and appraisal practices.


Final Report – Review of 2014 Commercial Values for the Harris County Appraisal District 1

The Harris County Appraisal District (HCAD) asked Almy, Gloudemans, Jacobs & Denne

(AGJD) to make an independent sales ratio study to evaluate the accuracy of the District’s 2014

commercial appraisals. As background, an appraisal is an opinion (or estimate) of the value of a

property. The opinion is influenced by several factors, some of which will be addressed in this

report. One such factor is the definition of the value being sought—market value in this instance.

In essence, market value is not an objective fact, it is a hypothetical price.1 Open-market, arm’s-

length sales provide the most objective evidence of market values, and a sales ratio study sys-

tematically compares appraised values to sales prices. A sales ratio (R) is formed by dividing the

appraised value (A) by the sales price (S). For example, if a property was appraised for $350,000

and it was sold for $360,000, the ratio would be:

R = A / S = 350,000 / 360,000 = 0.972.

That is, the appraisal is 97 percent of the sale price. In a ratio study, such ratios are calculated for

all the sales that were deemed usable and patterns in those ratios are examined. There are three

areas of concern:

What is the general level of appraisal—how close is the typical ratio to 100 percent of

market value (the legal requirement in Texas)?

How uniform are the ratios—how close to the typical ratio are individual ratios?

Is there evidence of systematic errors or bias in appraisals, such as “sales chasing” or

low-value properties tending to have higher ratios than high-value properties?

Because actual sales prices are not infallible indicators of market value, mass appraisal methods

can neither perfectly replicate sales prices nor predict market values. Thus, sales ratios cannot

each be expected to equal 1.00 (or 100 percent). Thus judgment is required in evaluating sales

ratio data. However, the International Association of Assessing Officers (IAAO) has published a

Standard on Ratio Studies that represents a consensus on proper ratio study procedures and the

interpretation of results of ratio studies. We were guided by this standard.

Our ratio study encompassed apartment, office, retail, and warehouse properties (Texas property

classes B and F1). The results of the study are presented in sections 3 through 6.

1 See Texas Property Tax Code, Section 1.04(7).



1.1 Purpose of Study

External reviews of the accuracy and fairness of real estate values in appraisal districts are

regularly made by the Property Tax Assistance Department (PTAD) of the Texas Comptroller of

Public Accounts. HCAD appraisal staff also regularly use ratio studies to evaluate their work.

However, given the controversies that commonly surround the appraisal of commercial property,

the new chief appraiser thought that this was an opportune time for an independent, outside

review to ensure that the District was on the right track.

The accuracy of commercial real estate assessments vis-à-vis residential assessments can be an

issue for several reasons. News accounts of sales prices of commercial landmarks that are higher

than their assessed values often lead one to conclude that commercial property is undervalued.

This kind of impression is reinforced in many jurisdictions by statistics showing that the com-

mercial share of the total property tax base is declining (the residential real estate price bubble in

2000-2006 exacerbated this shift). The pressure exerted by commercial appeals on the valuation

system can fuel suspicions that the system can be tilted in favor of owners of major commercial

properties.

1.2 What We Did

As indicated above, our main activity was a statistical analysis of how 2014 commercial assess-

ments compare with recent sales of selected categories of commercial property. This study was

based on data supplied by HCAD; we collected no data independently. However, we took steps

to ensure that the data were sufficient and not biased (see Section 3 and 6.1). In particular, we

wanted to ensure that values for sold properties were similar to values for unsold properties, so

that findings based on the sales sample could be reliably used to characterize the overall quality

of commercial appraisals in Harris County.

A complementary activity was a high-level review of the Texas property tax system, the re-

sources available for valuing commercial real estate in Harris County, and the practices and

procedures used by HCAD in appraising commercial real estate in Harris County. The latter was

based on an on-site visit to HCAD offices and an inspection of relevant documents and systems.

Although a ratio study can reveal the existence of problems in appraisal practices, it cannot shed

much light on causes. For that, an examination of practices and procedures is needed. Our

procedural review included an examination of how HCAD assembles and screens sales data.

As discussed in Section 4, we also reviewed how sales were adjusted to reflect changes in market

conditions between the date of sale (any time after 1 January 2012) and the date of analysis (1

January 2014). If price levels over the span of the study were changing significantly, the market

values of the properties in the study would be different on the date of analysis. Generally accept-

ed mass appraisal practices require that actual prices be adjusted to the valuation date to reflect

price trends.



1.3 Valuation Setting and Environment

In Texas, property taxation is governed by the Texas Constitution and the Property Tax Code.

From the perspective of enlightened property tax policy and administration, as reflected in the

IAAO Standard on Property Tax Policy and the textbook, Fundamentals of Tax Policy, the

Texas property tax system contains provisions that are considered components of a model

system.

Most importantly, Article VIII, Section 1 of the Texas Constitution establishes a uniformity

standard and the Code contains a market value standard: Section 23.01 requires property to be

appraised at market value as of 1 January of the Tax Year, which is a calendar year (Section

23.01(a)). Reinforcing this standard is the requirement that generally accepted appraisal methods

and techniques be used (see section 6.05(i) and/or 23.01(b)). Furthermore, HCAD aspires to

comply with the Uniform Standards of Professional Appraisal Practice (USPAP). In addition, the

Code requires appraisal districts to develop and follow a plan of reappraisal, and reappraisals are

required at least once every three years (Section 25.18). HCAD currently is operating under its

2013-2014 plan.

Consistent with the requirement that generally accepted appraisal methods be used, Section

23.0101 requires chief appraisers to consider the three main appraisal methods and to use the

most appropriate method.2 We consider HCAD’s success in meeting this requirement in Section

2 of our report. We note however, that Section 23.013, which pertains to the market (or sales)

comparison approach, contains a troubling restriction on the period of sales that ordinarily can be

used. Specifically, Subsection (b) limits appraisers to two years of sales unless they can establish

that “enough comparable properties were not sold during [24 month] period to constitute a

representative sample.” The rationale for this restriction is not apparent, especially since Subsec-

tion (c) mandates adjustments to sales prices for changes in market conditions (time), a subject

that we address in Section 4. (The Standard on Ratio Studies recommends using as many as five

years’ of sales to obtain adequate samples.)

Undercutting the Code’s strong foundation for market value appraisals is a notable deficiency:

Property owners are not required to disclose sales prices and terms, which facts are essential to

credible appraisals and effective administration of a market value-based real property tax. Aside

from the disclosures needed to value personal property, disclosure requirements are notably

weak in Texas. At the same time, Texas appraisal districts face unusually onerous burdens of

proof in assessment appeals.

Not mandating the disclosure of sales prices and terms can be rationalized as protecting the

privacy of property owners. Sales information usually is possessed by other than property

owners, weakening the privacy argument. In fact, those that possess sales information have a

2 Section 23.011 outlines requirements for using the cost approach, and Section 23.012 pertains to the income

approach.



proprietary interest that the current legislative framework protects.3 Having sales information

freely available would lessen the value of their interests. Arguably, protecting these interests

makes property markets less efficient and prices more volatile, as well as making property tax

administration more difficult and costly. Only fourteen states do not mandate the disclosure of

sales prices according to IAAO’s most recent survey of property tax policies and administrative

practices.

At the same time that the legislative framework makes appraisal more difficult if not error prone,

the Code places unusual burdens of proof on chief appraisers. Section 41.41 provides two normal

valuation-related grounds for protest by taxpayers: the determination of value (usually overval-

uation) and inequality of appraisal. Unusually, Section 41.43 places the burden of proof on the

chief appraiser, not the protester. That is, if the chief appraiser cannot persuade an appraisal

review board (ARB) that her or his valuation is correct based on the preponderance of evidence,

the taxpayer’s contention regarding the value of the property prevails. In forty-six states, the

burden of proof initially is on the protester.

The burden of proof on the chief appraiser is raised to “clear and convincing evidence” in certain

circumstances: (1) if the protester’s property is valued at $1,000,000 or less, and the protester

submits a qualifying appraisal; (2) if previous year’s value was lowered by the ARB; and (3) if

the protester timely submits relevant information, such as an income and expense statements,

comparable sales, or evidence of inequality. The latter avenue to raising the burden of proof

imposed on the chief appraiser could impart a downward bias in appraisals, since submission of

sales information and operating data is completely voluntary. In any case, since most property

owner-supplied market information is received too late to be used in the initial valuation of

property in the current tax year; it can only be used in the next year.

Turning to protests based on inequality, although doubtless designed to make such protests

easier, the provisions of Section 41.43(b) are problematic. In most states, prevailing in an ine-

quality claim can be difficult because the appellant must establish the correct value of the

property in question and establish that the actual level of appraisal is other than the legal level—

usually that it is lower than the legal level. In contrast, Subsection (b) of the Code provides that:

“A protest on the ground of unequal appraisal of property shall be determined in favor of [em-

phasis supplied] the protesting party unless the appraisal district establishes that:

“(1) the appraisal ratio of the property is equal to or less than the median level of appraisal of a

reasonable and representative sample of other properties in the appraisal district;

“(2) the appraisal ratio of the property is equal to or less than the median level of appraisal of a

sample of properties in the appraisal district consisting of a reasonable number of other proper-

ties similarly situated to, or of the same general kind or character as, the property subject to the

protest; or “(3) the appraised value of the property is equal to or less than the median appraised

value of a reasonable number of comparable properties appropriately adjusted.”

3 For example, Section 22.27 of the Code requires appraisal districts to treat much of the property-specific infor-

mation they obtain confidentially.



The first two options require the district to establish that the property in question is undervalued

(or exactly equal to the median ratio), a curious requirement that places appraisal districts in a

difficult position vis-à-vis other interested parties. The third “test” almost guarantees that the

owners’ of high-value properties can prevail on “inequality” grounds as long as there are some

comparable properties with a lower value. The “reasonable” and “representative” requirements

are simply too vague. In addition, the Code seems to allow no allowance or margin for error in

the measurement of level of appraisal.

Finally, it is worth noting that tax districts can challenge the level of appraisal of a subset of

properties under Section 41.03 presumably on the ground that the level is below 100 percent (but

they fortunately cannot challenge individual property valuations). At the same time, the Property

Tax Assistance Division (PTAD) of the Texas Comptroller of Public Accounts oversees and

supports appraisal districts.4 In alternate years, it makes a property value (ratio) study and a

Methods and Assistance Program (MAP) study. PTAD made a MAP study of HCAD in 2012

and a property value study in 2013. HCAD passed both.

Nevertheless, HCAD (as with all other appraisal districts) is subject to countervailing forces in

an increasingly legalistic and adversarial appeal environment. As in other major urban assess-

ment districts, taxpayer representatives lodge many protests, some of which are protective and

are withdrawn or settled. In comparison to the last IAAO survey of appeal rates in 1999, the

level of protests in Harris County is quite high—nearly 20 percent versus about 5 percent in the

year of a reappraisal in other jurisdictions. The appeal rate among higher-value commercial

properties is even higher, exceeding 50% in 2013. There is a potential danger that value-defense

activities will crowd out appraisal activities, as has happened in a few assessment districts.

Mass appraisal in dynamic markets always presents challenges. In the wake of the Great Reces-

sion, HCAD’s reappraisal plan for 2013 and 2014 envisaged a continued recovery in the Coun-

ty’s commercial real estate market, and this appears to have been the case. According to HCAD

and industry reports, the office market began to trend upward in 2012 after stabilizing in 2011.

HCAD reported that capitalization rates declined in 2012 by about one percentage point. The

apartment market also recovered with rents increasing and vacancy rates decreasing. Capitaliza-

tion rates also declined. Of course, while capturing trends that affect entire sectors keeps assess-

ment levels close to market value, it does not necessary affect the uniformity of assessments. We

next consider the resources devoted to commercial property valuation and evaluate the flexibility

and robustness of the valuation system.

4 This year it was charged with oversight of ARBs.



1.4 HCAD Staffing and Operations

As background, the establishment of appraisal districts in 1982 placed Texas in the forefront of

efforts to modernize property tax appraisal operations in North America. In comparison to the

usual institutional arrangements for assessment administration (whether an elected assessor’s

office or an agency of a municipal or county government), the appraisal district concept has two

conceptual strengths: (1) a governing structure that encourages sound management and account-

ability and (2) a degree of independence that provides insulation from inappropriate political

interference in assessment practices.

The Harris County Appraisal District is one of the largest assessment districts in the United

States.5 Of the 1.6 million real estate parcels in the District, 106,000 are classified as commer-

cial, of which 65,000 are improved. HCAD has a total staff of 623, of which 285 are in appraisal

and analytical positions. The Commercial Appraisal Division (one of three appraisal divisions)

has a staff of 72. They specialize on various commercial sub-types. Although detailed and

current benchmark data are not available, the ratio of parcels per appraiser/analyst in Harris

County is 5,475, which is in line with an IAAO guideline of 1:5,000. Two additional offices,

Human Services and Review Appraisal, also report to the Chief Appraiser.

HCAD’s computer-assisted mass appraisal (CAMA) system supports all three approaches to

value. Among its greatest strengths is the ability to apply table-based cost and income models.

The chief appraiser places appropriate emphasis on professional development, designations, and

licensing.

5 Los Angeles County, California, is the largest. Harris County is third largest and may soon overtake Cook County

(Chicago), Illinois, as the second largest.



2.1 Overview

As previously noted, Texas Property Tax Code Section 6.06(i) requires appraisal districts to

adopt a written reappraisal plan every two years. The plan must provide for the routine review

and inspection of properties, delineation of market areas, development of market-based valuation

models, and related quality control. HCAD’s most recent plan, developed in 2012, covers the

2013 and 2014 appraisal years. Following best practice, it calls for annual revaluation with all

property owners receiving value notices regardless of whether their property value has changed.

The commercial appraisal process runs from approximately September through February of each

year and includes sales review, land valuation, and development and testing of valuation models.

Real property value notices are mailed in March and/or April of each year. The valuation date is

January 1, e.g., 1 January 2014 for the 2014 revaluation year. Informal hearings begin the first

week in May. There are three levels of appeal: HCAD, the appraisal review board, and the

courts. Although notices are required to report both land and building values, only total values

can be appealed.

All real properties are reviewed over a three-year inspection cycle. In addition to traditional field

inspections, the District uses orthographic and oblique imagery. Areas with strong growth,

changes in land use, relatively poor appraisal performance statistics, or other issues receive

priority. New construction and renovations are identified through building permits. The Com-

mercial Lister’s Manual, updated in 2013, provides detailed instructions for the proper coding

and listing of property data.

The District has delineated market areas for each major commercial property type. The areas are

reviewed annually and updated as appropriate. Currently there are 36 multi-family, 30 office, 30

retail, and 26 warehouse market areas. Separate valuation rates and factors are developed for

each area.

HCAD utilizes all three approaches to value for commercial properties. Cost values are devel-

oped for all commercial improvements and the cost approach serves as the primary valuation

method for smaller apartments (up to 20 units), single occupant and certain other retail proper-

ties, and new construction. Replacement costs are based on Marshall & Swift’s Marshall

Valuation Service adjusted for information obtained from local builders and contractors. The

CAMA system carries both actual and effective year built with effective year built being a

function of renovation level. Based on effective age, buildings are assigned to expected econom-

ic life intervals. Depreciation schedules reflect property type and remaining economic life and

may be adjusted based on ratio studies or other market analyses. Additional adjustments for

physical, function, and economic obsolescence can be applied to individual properties as appro-



priate, as can depreciation overrides. Land values are based on sales analysis and then tested and

refined through ratio studies.

The income approach is used for properties that commonly rent or lease. Net rents are used for

properties that usually rent on that basis, such as retail properties and class A offices. Gross

rents are used for most other properties with expenses often subject to a base year expense stop

in which the tenant pays for expenses beyond those incurred by the owner for the first year of the

lease. Typical vacancy rates are based on available data collected by the office, as well as local

industry guidelines. Allowable expenses include all appropriate items, including management

and leasing costs, tenant improvements, reserves for replacements, rent concessions, lease up

provisions, and excess vacancies. Capitalization rates are based on an analysis of available sales

supplemented by industry publications. In addition, for the last several years, including 2014, the

District has contracted with an independent consultant for a cap rate study of the local market.

This year the consultant was also asked to review the District’s work.

In all, over 4,000 income and expense filings were available to develop 20013 and 2014 income

rates and adjustments. Hearings provide the primary source of data. In addition, HCAD holds a

series of meetings with real estate managers each year to get the “pulse” of the market, including

typical rent rates, vacancies, and expenses including tenant improvement allowances for new

tenants and lease renewals. The District also uses industry publications such as Apartment Data

Services (ADS), REVAC, CoStar, REIS, the Institute of Real Estate Managers (IREM), and the

PwC (formerly Korpacz) Real Estate Investor Survey. Some of these are national in scope while

others (ADS and REVAC) focus on Texas metro areas.

Most income and expense analyses are performed with spreadsheets. However, staff has had

exposure to statistical software, which is currently used for sales ratio studies and time trend

analyses, and HCAD seems amenable to leveraging its added power in the development of

income and sales-based models.

As noted previously, Texas is not a full disclosure state and parties to a sale are not required to

declare the price paid in property transfers. HCAD contracts with a vendor for copies of deeds

recorded in the county for commercial sales. It mails a sales verification questionnaire to both

parties and, if a response is not received, a second mailing is made. If a response is still not

received, staff attempts to contact a party to the transfer by phone. Despite these efforts, re-

sponse rates for commercial sales are less than 5%, so that the District must rely on other sources

of sales data. One important source is sales information provided in hearing submissions, which

sometimes includes closing statements. Other sources include brokers, property managers, and

local appraisers. In addition, the District contracts with a national vendor for on-line access to

commercial sales. It also uses the Texas A&M Real Estate Center’s free, bi-weekly publication,

Real Estate Center Online News (RECON), which provides information on current real estate

activity in 48 Texas metropolitan areas. Sales analysts screen sale data received from these

various sources and forward their work to appraisers, who assign codes indicating the usability

of each sale for appraisal and ratio study purposes.



The sales comparison approach is used for land appraisal. Sales are also used in general market

analyses, including the development of time adjustment factors, depreciation tables, and capitali-

zation rates, as well as in ratio studies and, as such, play a critical role in all three approaches to

value. Time trend factors are used in sales ratio studies and could be used in other analyses,

including the development of capitalization rates.

In compliance with USPAP, the Chief Appraiser and staff prepare a “Mass Appraisal Report”

after each reappraisal. The report describes the work that was conducted and summarizes valua-

tion schedules and rates. The reports are typically prepared in May, e.g., May 2014 for the 2014

reappraisal. We reviewed the 2013 report.

2.2 Apartments

As mentioned, HCAD has defined 36 economic areas for apartments that are reviewed and

updated as appropriate each year. In addition, properties are divided into four building classes

based on construction quality, age, and amenities.

Four to 20 unit apartments (land use code 4209), for which land is a high percentage of total

value, are appraised on the cost approach. Garden and high-rise apartments (land use codes 4211

and 4212) are valued by the income approach.

In application of the income approach, staff determines typical rent rates for efficiency, one

bedroom, two bedroom, and 3+ bedroom units within each building class and economic area.

Thus, four rental rates (one for each building class) are determined for each unit type in each of

the 36 economic areas. For example, rent rates used in the 2013 reappraisal in economic area 1

(Montrose/Museum District) ranged from $6.92 per square foot for class D buildings to $21.11

for one bedroom units in class A buildings.

After an adjustment for lease concessions, a typical occupancy rate is applied to calculate

effective gross rent and secondary income is added to determine effective gross income. Allowa-

ble expenses are recognized for management, property insurance, reserves for replacements,

leasing commissions, maintenance, and other allowable expenses. Some expenses are applied on

a per square foot basis and others as a percentage of effective gross income.

Capitalization rates used in the 2014 reappraisal range from 5% to 6.5% for high-rise apartments

and from 6% to 10% for garden style apartments depending on building class. Rates were

decreased by 25 to 75 basis points from 2013. Since property taxes are not expensed, effective

tax rates are added to obtain final capitalization rates. As noted in the prior section, sales used in

the development of capitalization rates are not time-adjusted, which can lead to rates that are

somewhat high and values that are somewhat low in a rising market.

Although examining the development of income rates used for the 2014 reappraisal was beyond

the scope of our assignment, we are comfortable that the process used comports well with best

practice. We note, however, that vacancy rates, expense ratios, capitalization rates are not a



function of economic area and recommend that, to the extent that available data allow, HCAD

test for variations by economic area or groups thereof. One would suspect, for example, that

areas with stronger markets and perceived opportunities for future rent increases and apprecia-

tion may command lower cap rates than areas with higher vacancies and lower expectations.

In addition, we recommend that HCAD explore development of an MRA-based sales compari-

son model for 4-20 unit apartments, which are currently appraised by the cost approach. Alt-

hough sound in theory, the cost approach is usually the preferred valuation method only when

data are inadequate to apply the sales comparison or income approach. While we understand that

sales are limited, there may well be enough over a several year period to support a sales-based

multiple regression analysis (MRA) model. We note that Maricopa County, Arizona (metropoli-

tan Phoenix area) has successfully valued smaller apartments in this manner for many years.

2.3 Offices

Harris County’s office market has been strong in recent years. The District has delineated 30

economic areas for 2014. Office buildings are classified as A+, A, A-, B, C, D, or E based on

construction quality and building features. Only some economic areas have A+ buildings. Class

E buildings are typically less than 10,000 square feet and are valued on the cost approach.

Typical rent rates are developed for the other classes by economic area. Once obtained, potential

gross rent is of course adjusted for typical vacancies to arrive at effective gross income. Typical

vacancy rates range from 5% for class A+ buildings to more than 15% for class C and D build-

ings.

Allowable expenses are categorized as recoverable or non-recoverable. Recoverable expenses

are those that are normally passed on to the tenant, such as utility and maintenance fees. Non-

recoverable expenses are normally borne by the owner and include leasing commissions and

tenant improvement costs to ready space for new tenants or as agreed in lease extensions.

Expense recoveries (sometimes termed “pass troughs”) along with any miscellaneous income are

added to rental income to determine effective gross income (EGI). Allowable expenses are then

subtracted from EGI to determine NOI (net operating income).

As with apartments, vacancy, expense, and capitalization rates vary with land use code and

building class but, except perhaps for isolated exceptions, not by economic area. 2014 capitali-

zation rates range from 11% for class D space to 6.25% for class A+ space. An additional class

A++ capitalization rate of 6% was added in 2014 for trophy buildings.

Medical offices and other medical related properties are divided into 24 economic areas and four

building classes (A-D). Class A buildings are located in prime locations, typically near major

hospitals or medical centers, have the highest construction quality, attract the best tenants, and

generate the highest rental rates. Both A and B properties typically rent on a net basis, while C

and D properties usually have gross leases. Net operating income is, of course, calculated for all

four classes by recognizing vacancies and appropriate expenses. Capitalization rates range from

6.25% for class A properties to 10% for class D properties. Hospitals, surgical centers, and other



special-purpose medical properties are valued by the cost approach. Sales are considered when

available. Because of their limited number, complications related to the inclusion of built-ins

and medical equipment in sales, and the complexity of such properties, we did not include them

in our sales ratio analyses. From a valuation perspective, however, income tables for medical

properties are developed and organized in the same manner as for other offices and subject to the

same CAMA system limitations.

2.4 Retail Properties

Retail properties may be appraised by either the cost or income approach depending on land use

code. Properties valued on the cost approach include owner-occupied buildings, bars and

restaurants, auto sales and service, and most other properties dedicated to providing customer

service, sports, or entertainment. Properties valued by the income approach encompass most

anchored and multi-tenant properties, including shopping centers and malls, strip centers, and

department stores.

In addition to land use code, HCAD has categorized retail properties into four building classes

(A, B, C, and D) and 30 economic areas, up from 24 in 2013. Typical rent rates are developed

by building class, land use code, and economic area. Thus, there could be up to 120 rental rates

(4 building classes x 30 economic areas) for each land use code, although not all land uses and

building classes are found in all 30 economic areas. Needless to say, rental rates vary greatly

across the spectrum of property uses, building classes, and economic areas.

As with apartment and office buildings, potential gross income is reduced for vacancy allowanc-

es and allowable expenses and then capitalized into estimated value. 2014 capitalization rates

range from 7% to 11% depending on land use code and economic area.

While the income approach is soundly formulated, we note that many (perhaps a majority) of

retail properties fall into the categories appraised by the cost approach, in which we have less

confidence given relatively sparse land sales and the difficulty of depreciation estimates for older

buildings. Provided adequate rental data are available, consideration could be given to testing

income models for additional land use codes. Similarly, a sales comparison model could be

considered for owner-occupied properties, which would allow for testing building size, lot size

relative to building area, and other variables not explicitly recognized in present models. (In

Toronto an MRA-based sales comparison model has been successfully used for many years as

the principal valuation approach for smaller retail properties.)

2.5 Warehouses

Like the office market, Harris County’s warehouse market has been strong in recent years with

considerable new construction in prime areas. Warehouses represent a diverse group, ranging

from mini and office warehouses to flex, light manufacturing, and major distribution facilities.

HCAD finds property use 4399, which includes metal prefabricated buildings, particularly

diverse and difficult to appraise.



Similar to other properties, warehouses are classified by land use code, building class, and

economic area. There are 26 economic areas and five building classes (A-E), although class E

properties are valued based on the cost approach or assigned an override value. All other ware-

houses, including mini-warehouses, are appraised on the income approach.

The income approach is similar in structure to that used for other commercial categories. Capi-

talization rates range from 7% to 9.5% for mini-warehouses and from 7.25% to 8.75% for free-

standing warehouses.

As with the other property types discussed above, we find valuation procedures and income

tables to be well formulated and consistent with best practice, although we would like to see

vacancy, expense, and capitalization rates analyzed for possible variations among economic

areas or groups thereof. A related issue is that apparently the current CAMA system only

provides for varying these critical rates by grade, so that additional adjustments must be applied

manually. In addition, unlike the cost approach, appraisers cannot divide or section multi-use

(e.g., retail and storage) properties by use, so that a weighted average rent rate must be manually

calculated and applied. These are significant limitations that would need to be addressed if the

CAMA system is to apply valuation rates by other than primary use and grade.

A number of other jurisdictions with strong warehouse markets have developed sales comparison

models, sometimes as their primary valuation method. Given that there are approximately

18,000 warehouse accounts and that there have been several hundred sales over the past three

years, we recommend that the District consider testing a pilot sales comparison model for

warehouse properties. Variables could include land use codes, building classes, economic areas,

building size, and land/building ratios (or “site coverage”), the latter two of which are not

explicitly accounted for in current income models.



HCAD graciously provided all data we requested and timely responded to all questions we had

regarding the data.

Our sales ratio study and related analyses that follow are based on three primary files:

2013 Commercial Data Extract. This file contains property number (STRAP), address,

2013 notice and 2012 notice and final values, and property characteristics data for com-

mercial properties in classes B (apartments) and F1 (commercial).

2014 Commercial Data Extract. Same data as above but for 2014 notice values and 2013

notice and final values.

Commercial Sales Extract. This file contains all apartment and commercial sales availa-

ble at the time of extract for 2011 or later, including sales from January and February

2014. The file contains property number and address, property type and land use code at

time of sale, independent school district (ISD), economic area, key property characteris-

tics, assessed value, sale recording number), sale qualification and validation codes, sale

date and price, and tie back numbers for other accounts included in multiple-parcel sales.

With these files HCAD provided descriptions of the various data fields and codes. To facilitate

query and analysis, we merged data from the three files into a combined file.

The District also provided us with access to an ftp site with GIS files for apartment and commer-

cial properties. These files were used to produce the maps shown in the appendices.



4.1 Overview

When property values are changing meaningfully over the period of sales used in valuation or

ratio study analyses, it is important that sales prices be adjusted to the target valuation date,

which, in this case, is January 1, 2014.

HCAD conducts a time trend analysis each year to determine time-adjusted sales prices used in

ratio studies developed for its board of directors. The study uses three years of sales, e.g., 2011

to 2013 sales to evaluate values as of January 1, 2014. Separate studies are conducted for

apartment, office, retail, and warehouse properties. The studies use the sales ratio trend method

in which sales prices are divided by assessed values, graphed against time of sale, and analyzed

for significant movement after outliers are removed If sale-to-assessment ratios are moving

upward, inflation is indicated, and vice versa.

The 2014 study, dated February 3, 2014 reports trends of 0.2% per month for apartment and

retail properties and 0.3% per month for office and warehouse properties. The trends are linear

and compound monthly. The CAMA system does not provide support for differential adjust-

ments by geographic area.

4.2 Methodology

We conducted our own, independent time trend studies covering the 38 months for which sales

were available (January 2011 through February 2014) and adjusted all sales to the valuation date,

January 1, 2014. For this purpose, we considered all qualified sales (qualification flag = “Q”).

We removed outliers and other properties where it appeared that inclusion would more likely

confound that contribute to the analysis.

Similar to HCAD, we employed the sales ratio trend method promulgated in the IAAO litera-

ture6. We began by graphing sale-to-assessment ratios (SARs) against sale month coded 1 to 38

and determined whether the trend, if any, appeared reasonably linear or should be divided into

multiple segments, termed “splines”. We also looked at trends by land use code (LUC) and

market areas (also termed “economic” areas in HCAD) and developed separate trends as appro-

priate when there were adequate sales.

For office properties, for example, we determined that office condo behaved differently than low

and high-rise buildings. Figure 1 is a scatter graph of MONTHS (1-38) and SAR for office

condos. Inspection of the graph indicates that there are relatively few sales and no clear trend

6 See especially Mass Appraisal of Real Property (IAAO, 1999), pp 265-268, and Fundamentals of Mass Appraisal

(IAAO, 2011), pp 151-155.



over the study period. Figure 2 is a similar graph for low and high-rise office buildings with a

moving average fit to the data. The graph indicates that the trend can be divided into three

segments: one for approximately the first 12 months (2011), a second through approximately

month 30 (June 2013), and a third for the final months.

To fit the various trends we used the logarithm of SARs, which yields percentage trends and has

the side advantage of minimizing the effect of the lowest and highest ratios. The logarithms of

SAR were regressed on the appropriate time variables. As expected, the trend for office condos

was insignificant and so we applied no adjustment. For the low and high rise offices, consistent

with the graph and as shown in Figure 3, the first and third splines (MONTHS1 and MONTHS3)

were insignificant but the second spline (MONTHS2), representing sales from January 2012

through June 2013, was strongly significant with an indicated adjustment of 0.83% per month.

We followed a similar procedure for apartments, retail properties, and warehouses. Depending

on the graphical analysis, the regression analysis for each property group employed between one

and three time variables. In some cases properties were stratified by land use code and in some

cases by economic/market areas, depending on available sales and inspection of the graphs.

Figure 1: Graph of SAR and Months: Office Condos



Figure 2: Graph of SAR and Months: Office Buildings

Figure 3: Regression of Logarithms of SAR on Time: Office Buildings



4.3 Results and Adjustment Factors

Figure 4 contains final time trends developed for the various property groups. “Not Change” is

the indicated total change in prices from January 2011 to December 2013. No trends were

applied to January and February 2014 sales.

Trends for apartments and offices vary by specific property type and trends for retail and ware-

houses vary by market areas. Appendix 1 shows the geographic location of HCAD market or

“economic” areas (the maps display the last two digits of the codes shown in Figure 4). Like

low-rise and high-rise office buildings, garden and high-rise apartment buildings were combined

for the analysis because they appeared to have similar trends and also because of the small

number of usable sales for high-rise apartments.

Office buildings and warehouses in market areas 6007 and 6008 have the strongest trends, each

up in excess of 15% since January 2011. Still, none of the trends is reminiscent of the unsustain-

able run up in prices seen prior to the crash of 2008-2010 that affected much of the country and

from which Texas was largely spared. Although our trends were conducted independently of

HCAD and employed additional stratification and chronological segmentation, the results are

reasonably consistent with those reported by HCAD.

Figure 4: Commercial Time Adjustments

Net Sales

Market Area Spline 1 Rate 1 Spline 2 Rate 2 Spline 3 Rate 3 Change Used

Apartment 4-20 Unit (LUC 4209) Jan 11 - Dec 11 0 Jan 12 - Dec 13 0.0023 0.057 123

Garden (LUC 4211) Jan 11 - Dec 13 0.0034 0.126 70

Hi-Rise (LUC 4212) Jan 11 - Dec 13 0.0034 0.126 13

Office Office Low-Rise (LUC 4353) Jan 11 - Dec 11 0 Jan 12 - Jun 13 0.0083 Jul 13 - Dec 13 0 0.160 81

Office Hi-Rise (LUC 4354) Jan 11 - Dec 11 0 Jan 12 - Jun 13 0.0083 Jul 13 - Dec 13 0 0.160 48

Office Condo (LUC 4355) Jan 11 - Dec 13 0 0.000 19

Retail Mkt Areas 4002, 4011, 4015,

4017, 4020, 4024 Jan 11 - Dec 12 0 Jan 13 - Dec 13 0.0049 0.060 97

Mkt Areas 4004, 4005, 4006,

4010, 4012, 4014, 4016, 4018 Jan 11 - Mar 12 0 Apr 12 - Dec 13 0.0064 0.143 69

Other MktAreas Jan 11 - Dec 13 0.0026 0.126 156

Warehouses MktAreas 6007 and 6008 Jan 11 - Dec 11 0 Jan 12 - Jun 13 0.0073 0.191 65

All Other Areas Jan 11 - Dec 11 0 Jan 12 - Jun 13 0.0033 0.082 266



5.1 Purpose and Objectives of Ratio Studies

Sales ratio studies compare assessments against sales prices. A sales ratio is computed by

dividing the assessed (or appraised) value of a property sold in a given time period by its sale

price. If property values have changed significantly during the study period, sales prices should

be adjusted to the assessment date. In some cases, adjustments should also be made for the

inclusion of personal property or certain other features in the sale.

Provided that sold and unsold properties are similarly appraised, sales ratio studies provide the

most objective indicator of assessment performance. The typical or average ratio indicates the

overall or “common” level of appraisal. The degree of spread or dispersion in the ratios indi-

cates the consistency or uniformity of assessments relative to market values. The level of

assessment should be similar for various property types and economic areas, as well as across the

value range of properties.

The International Association of Assessing Officers (IAAO) has developed standards for

appraisal performance based on various sales ratio statistics. According to the organization’s

Standard on Ratio Studies (2013), the level of assessment as measured by the median ratio

should be between 0.90 and 1.10. For commercial properties, the average deviation from the

median ratio (a statistic termed the coefficient of dispersion) should not exceed 15% in large,

urban jurisdiction and 20% in other jurisdictions. Appraisal levels among major property classes

should be within 5% of that of the entire jurisdictions and appraisal levels should be similar

across the value range of properties. By adopting recommended practices and targeting market

value, most jurisdictions should be able to achieve IAAO’s standards for the level of assessment,

both overall and among major property groups. Achieving uniformity (COD) standards, howev-

er, is much more problematic because of limited market data, the complexity of commercial

markets, the variability of prices, and a multitude of factors that can affect the price paid for a

given property. Most jurisdictions do not meet IAAO’s commercial COD standards.

HCAD conducts sales ratio studies annually using both unadjusted sales and sales adjusted to the

valuation date. Three years of sales are used in the studies. Results are presented by property

class (B, C/D, and F), ISD, property group (apartment, office, medical office, retail, warehouse,

and other), and property groups within ISD. The Board uses the results by category and ISD in

the evaluation of assessment equity claims.

Section 5.2 below describes how we screened sales obtained from the District to separate those

that provide representative indicators of market value from those not valid for ratio studies.

Section 5.3 describes our stratification procedures. Section 5.4 explains reported ratio study

statistics.



5.2 Data Screening

Although we took note of HCAD’s codes indicating which sales were judged valid for sales ratio

and valuation purposes, we developed our own screening procedures. We began by including all

“qualified” sales but, in accordance with IAAO standards, added back unconfirmed and pending

sales for which there was no specific reason to reject the sale. We also added back sales marked

“IO” (Invalid Outlier) in favor of conducting our own outlier screening.

We removed extremely low sales prices (less than $45,000) and, to be even-handed, any assess-

ments below the same threshold. Such low prices usually relate to distressed properties or to

special considerations that render the price unrepresentative of other commercial properties. We

did not want them affecting our calculated ratio statistics.

We examined assessment ratios by use group (apartments, office buildings, retail, and ware-

houses) and applied a trimming procedure presented in the IAAO Standard on Ratio Studies to

identify outliers7. We trimmed any ratios that the method identified as “extremes” (approximate-

ly 1% of ratios), as well as some additional “outliers” that departed markedly from the rest of the

distribution.

5.3 Stratification

In addition to overall performance across a jurisdiction, a key aspect of ratio studies is examina-

tion of the degree of assessment uniformity among various groups of properties. We studied and

report assessment performance for each of the following property groups:

Property use groups: apartments, office buildings, retail, and warehouses

Independent school districts (ISDs)

Size ranges based on net rentable area

Age ranges

Economic building classes

Subclasses of apartment, office, retail, and warehouse properties for which there

were at least 10 usable sales

Market (economic) areas with at least 10 sales for the same use groups

Comparing results among these strata help to identify problems or inconsistencies in assessment

levels. In addition, as noted in section 2, some property groups are valued based on the cost

7 The technique, known as the inter-quartile range (IQR) method is based on the distance between the first and third

quartiles, which encompass half of the data. Extremes are values that fall 3 IQRs beyond the nearer quartile. The

objective of using them is to identify unusual, potentially unrepresentative values, much as is done using standard

deviations when the data can reasonably be assumed to be distributed according to a normal distribution, which

assessment ratios usually are not. Outliers are calculated in a manner similar to extremes, but are values lying 1.5

IQRs rather than 3IQRs from the nearer quartile.



approach and other based on the income approach and HCAD expressed interest in comparing

results for property groups appraised by the two methods.

5.4 Sales Ratio Statistics

We report the usual ratio study statistics, supplemented by one newer one. The median ratio of

assessment to sales prices (adjusted for time) is the pre-eminent measure of assessment level for

purposes of evaluating assessment performance. The coefficient of dispersion (COD) is the

average percentage difference from the median ratio and thus akin to the average error in lay-

man’s terms. It describes the extent to which properties are appraised at a uniform ratio, on the

one hand, or at widely varying ratios on the other hand. It reveals how closely the assessments

hit a target level, while the median reports whether the target seems to differ from the legally

mandated target (100% in Texas). A third traditional statistic, the price related differential

(PRD) attempts to measure price-related equity (or lack thereof), namely assessment regressivity

or progressivity. Assessment regressivity is the condition in which assessment levels decrease

with value and assessment progressivity is the opposite condition: assessment levels increase

with value. Both conditions are of course undesirable since all properties should be assessed at

the same percentage of market value.

Since statistics calculated from samples are subject to sampling error, it is important to consider

their reliability when forming judgments about indicated performance. Many factors influence

the reliability of sample statistics, including the representativeness of the sample (about which

little can be done) and the size and variability of the sample. The 95 percent confidence interval

for the median ratio is included to reveal the range within which the true median ratio can be

expected to fall given the size and variability of the sample of ratios actually studied. A mecha-

nism for similarly gauging the reliability of the COD and PRD is possible but seldom done and

not shown here. Instead, interest focuses on the magnitude of the statistics themselves.

Unlike the median and COD, the PRD does not provide an intuitively meaningful measure of the

bias it purports to describe and carries significant technical flaws as well. An alternative meas-

ure of price-related equity, the coefficient of price related bias (PRB), was recently incorporated

into the 2013 IAAO Standard on Ratio Studies. We report it, along with its significance and

confidence intervals. The statistic indicates the percentage by which assessment ratios fall (or

rise) with each doubling (or halving) of property value8 and thereby the extent to which

regressivity (or progressivity) is present in the ratios.

In this report the PRB coefficient is reported as a percentage, not as a fraction. Thus a PRB

coefficient of -1.410 percent indicates that assessment levels fall by 1.41% for each doubling of

value, for example, from $100,000 to $200,000, and fall by another 1.410 percent as values

double again from $200,000 to $400,000, and so on. The IAAO standard states that the PRB

8 For the purpose of minimizing a bias inherent in ratios, value for this purpose is defined as one half of the assess-

ment and one half of the sales price, thus giving equal weight to each. Using either alone would tend to bias the

measure in one direction or the other.



should generally fall between ±5% and those measures outside the range of ±10% with 95%

confidence are “unacceptable”. We regard this threshold as lax relative to other performance

standards. In any case, values closer to 0 indicate better price-related uniformity and confidence

intervals for the PRB will desirably overlap ±5%, meaning that one cannot conclude that assess-

ment levels change by more than ±5% as values double or are halved.

In addition, to help evaluate the adequacy of samples and the magnitude of assessed values

involved, we also report the number of sales, number of parcels, and total assessed value for each

property group studied. Finally, we report the minimum and maximum ratios to indicate the

range of ratios in the sample after applying the edit and trimming procedures described earlier.



6.1 Sample Representativeness and Comparison of Sold and Unsold Properties

In addition to satisfying ourselves that HCAD’s sales data-handling procedures gave no cause for

concern, discussed above, we examined the available sales data to check whether they appeared

to be representative in terms of both geographic distribution and the treatment of sold versus

unsold properties.

To check for geographic representativeness, we obtained files from HCAD’s geographic infor-

mation system (GIS) which provided information on the location of all commercial properties

within the jurisdiction, together with their identifiers and selected physical and economic charac-

teristics. We plotted the location of all properties of a given use type and overlaid the location of

sold properties of that use type to see if there tended to be any notably under- or over-represented

areas. We were satisfied that no notable problems arose in that regard. The locations of the sales

for each of the four use groups of properties examined in this study are mapped in Appendix 2.

Checking for the representativeness of the sample of sold properties in comparison to unsold

properties is a problematic issue. A given sample of sales is of very limited interest if it cannot

be taken as representative of the population of all taxable parcels as a whole. Happily, one

common problem, deficient sales of high-valued properties, is not pronounced in these data.

There does, however, appear to be a tendency for HCAD appraisals to be more reactive to sales

than is contemplated by the pre-eminent test for differential appraisal cited in the IAAO Stand-

ard on Ratio Studies. That test is the Mann-Whitney test for comparability of changes, on a

percentage basis, in assessments from the prior year to the current year. We used it to compare

sold properties to unsold properties, by use group, after excluding from the test any properties for

which there was evidence of physical change based on a comparison of use, size, year built, and

the like for the 2013 and 2014 assessment rolls. If there were no differential assessment of sold

properties, the average percentage change and the distribution of percentage changes in assess-

ments from 2013 to 2014 for sold and unsold parcels should be similar.

The Mann-Whitney test provides a means of calculating whether observed differences in the

pattern of observations from two samples is likely to have arisen by chance alone in the absence

of a real (non-random) difference between the two sets of data. In other fields it is common to

compare two means. For assessment ratios, where data are not reliably normal, medians are

more often compared. In any case, the differential-assessment issue and the Mann-Whitney test

bear on the entire distribution of ratios, not just its center.

Table 1 presents the statistical results. The largest difference in percentage changes in assess-

ments from 2013 to 2014 is observed for apartments, where the median change is 20.21% for

sold properties versus 5.83 for unsold properties, a difference of 14.38.9 The difference for

9 The issue of whether the various subsets of apartments were represented proportionately in the sold and unsold test

samples was explored. The distribution of sales does match the distribution of the population quite well. One of the



warehouses (5.16% versus 1.35%) is also notable. The final column of the table indicates that

there is less than one chance in a thousand that these differences could have arisen from sam-

pling variability as opposed to an underlying difference in the magnitude of changes in assess-

ment from 2013 to 2014. Differences in the medians for the other three use groups, while

generally statistically significant, are de minimis.

Table 1: Mann Whitney Tests for Differential Assessment of Sold & Unsold Property

Sold Parcels Unsold Parcels Difference in

Median %

Changes

Sig.*

Use Group

Median %

Change

Sold Parcels

Median %

Change

Unsold

Parcels

Apartments 20.21 94 5.83 4,943 14.38 0.000

Office Bldgs 3.12 59 2.28 3,690 0.84 0.015

Retail 3.06 205 2.17 26,519 0.89 0.009

Warehouse 5.16 149 1.35 15,780 3.81 0.000

*Likelihood that observed differences in percentage changes in value between sold and unsold

parcels is due to sampling error (chance).

The quantile-quantile (Q-Q) plot in Figure 5 provides more detail and perhaps better illustrates

the issue. The quantiles10

, plotted as circles, compare the magnitude of percentage changes for

sold parcels (vertical axis) and unsold apartments (horizontal axis). Just as the median gives the

value of the middle point of a distribution (and is the 50th

percentile), the first percentile (circle)

gives the value below which 1 percent of the distribution lies, the 99th

percentile gives the value

below which 99 percent of the data distribution lies, and so on. The solid diagonal line in the

plot gives the pattern that would be observed if the two distributions were identical. Instead we

see that the magnitude of changes for sold properties is more extreme than for unsold properties.

In particular, increases are larger for sold than for unsold properties. Thus, assessments for sold

apartments may not be representative of unsold apartments, which should be kept in mind when

reviewing the sales ratio results presented below.

economic building classes (EBC), however, was represented about four times more heavily among the apartment

sales than in the subset of unsold apartments. When proportionality was restored, by systematically thinning the

unsold data set for the other EBC classes, the results were essentially unchanged. The difference between the

medians dropped from 14.4 percentage points to 13.9 percentage points.

10

Quantile is the general term for values in a sorted set of data taken at regularly spaced intervals. Examples include

the median (one division into halves), quartiles (three divisions into quarters), quintiles (four divisions into five

equal pieces), and percentiles (99 divisions into 100 groups of approximately equal observations).



Figure 5: Q-Q Plot of Assessment Changes for Sold and Unsold Apartments

6.2 Overall Results by Use Group

Table 2 below presents the sales ratio statistics discussed previously for assessments by property

use group. Assessments for commercial property as a whole meet or approach IAAO level and

uniformity standards. The median ratio is 0.994, the COD is 15.6%, and the PRB is -0.06%,

indicating excellent uniformity between lower and higher value properties.

When examined by use groups, median ratios are highest for apartments (but see discussion in

6.1 above) and lowest for warehouses. Warehouses also have the highest COD and apartments

have the lowest. Two of the use groups, office buildings and warehouses, with CODs of 17.2%

and 19.0% respectively, have coefficients of dispersion that are higher than the 15% benchmark

set forth in the IAAO standard as appropriate for large urban jurisdictions. Three of the use

groups would appear to have issues with vertical equity according to the flawed PRD measure:

apartments and office buildings would appear regressive with PRDs in excess of 1.03, while

warehouses would appear progressive with a PRD under 0.98. The PRB, a more meaningful and

reliable measure of vertical inequity, provides a more sanguine verdict suggesting that assess-

ment ratios for apartments fall by 1.4 percent with each doubling of value, that the supposed bias

for office building s is not statistically significant at the 95 percent confidence level, and that the

assessment ratios for warehouses fall by 2.6 percent with each doubling of value. According to

the IAAO standard, such levels do not give rise to a cause for concern.



In order to understand the patterns of assessment equity more fully, we sub-stratified the various

use groups and conducted ratio studies by additional strata as further described in the sections

that follow.

Table 2: Commercial Property Assessment Ratio Results Stratified by Use Group

Parcel AV PRB PRB

Group Parcels ($millions) Sales Median Lower Upper Min Max COD PRD Coef. Sig. Lower Upper

Apartments 5,758 26,748 304 1.047 1.028 1.065 .792 1.686 .111 1.085 -1.410 0.000 -1.593 -0.489

Office Bldgs 4,042 28,606 167 .969 .947 1.021 .506 1.736 .172 1.062 -0.786 0.129 -1.802 0.231

Retail 29,215 29,048 382 .991 .979 1.006 .541 1.672 .139 1.024 0.522 0.314 0.495 1.540

Warehouse 17,672 19,996 360 .932 .906 .957 .381 1.777 .190 .973 2.570 0.000 1.245 3.895

Overall 56,687 104,398 1213 .994 .988 1.001 .381 1.777 .153 1.042 0.079 0.729 -0.367 0.525

95% Conf Interval 95% Conf Interval

6.3 Apartments

Aside from the differential assessment issue discussed above, apartments as a class appear to be

assessed in conformance with IAAO standards as they pertain to the median assessment ratio,

coefficient of dispersion, and PRB coefficient.

To examine assessment performance for apartments more thoroughly, we sub-stratified the

available data both by sub-type and by market areas with at least 10 sales. Table 3 reports results

by sub-types: 4-20 unit, garden, and high-rise building. The assessment level for 4-20 unit

buildings is somewhat higher than that for garden and high-rise buildings. The coefficients of

dispersion are well within IAAO’s standards.

Figure 6 below shows a box plot of the assessment ratios by use group. The boxes represent the

inter-quartile range (middle 50% of the data) and the horizontal dark lines toward the center of

the boxes indicate median ratios. Circles constitute “outlier” ratios and an asterisk indicates an

extreme. Ideally the boxes for each use group would align horizontally, indicating similar

assessment levels. Tighter, more compressed boxes indicate better assessment uniformity (as

measured by the COD). Taller boxes indicate poorer uniformity. In this case, the box plot

indicates that 4-20 unit buildings have generally higher ratios than other apartment buildings.



Table 3: Assessment Ratios for Apartments Stratified by Sub-Type

Parcel AV PRB PRB


4-20 Units 2,953 904 172 1.069 1.047 1.105 .792 1.636 .110 .984 3.368 0.002 1.246 5.490

Garden Low Rise 2,203 16,819 111 1.000 .989 1.048 .798 1.686 .113 1.050 -1.840 0.021 -3.394 -0.286

Hi-Rise 155 6,006 21 .976 .963 1.053 .888 1.208 .056 1.015 -1.795 0.329 -5.545 1.954

Overall 5,311 23,729 304 1.047 1.028 1.065 .792 1.686 .111 1.085 0.031 0.911 -0.519 0.581


Figure 6: Box Plot of 2014 Apartment Ratios by Sub-Type

Table 4 reports assessment ratios for market areas with at least 10 apartment sales. The data

suggest the possibility of some relative over-assessment of apartments in three market areas

where the confidence intervals do not overlap 1.00, although only in area 1701 does the stratum

median diverge from the overall median by five percent at a confidence level of 95 percent.

CODs are consistently good.



Table 4: Assessment Ratios for Apartments Stratified by Market Area

Parcel AV PRB PRB


1701 Montrose /

Museum District757 2,355 57 1.143 1.103 1.209 .929 1.589 .104 1.167

-2.203 0.011 -3.874 -0.533

1706 West Memorial

/ Briar Forest126 1,645 14 1.024 .965 1.117 .798 1.176 .071 1.079

-1.116 0.134 -2.628 0.397

1710 Medical Center

/ Bellaire216 1,815 19 1.015 .976 1.105 .827 1.626 .126 1.081

-0.869 0.597 -4.272 2.534

1711 Inner Loop East 870 606 33 1.048 1.011 1.153 .959 1.582 .112 .985 0.806 0.688 -3.255 4.867

1714 Pasadena / Deer

Park266 673 16 1.068 .995 1.118 .861 1.226 .074 .967

0.418 0.638 -1.446 2.282

1723 Inwood /

Northwest103 254 11 1.057 1.005 1.198 .936 1.276 .053 1.121

-2.755 0.051 -5.524 0.013

1724 FM 1960 /

Champions208 1,517 16 .999 .973 1.125 .846 1.215 .066 1.007

0.113 0.900 -1.778 2.003

Overall 2,546 8,865 166 1.060 1.034 1.103 .798 1.626 .107 1.108 -0.878 0.022 -1.631 -0.125


6.4 Offices

Table 5 presents results for low-rise, high-rise, and condo offices. The median ratio for condo

offices appears to be notably higher than the general median for offices and the 95% confidence

interval fails to overlap 1.00. Figure 7 shows the differences graphically. We understand that

the result for office condos may be due to the fact that some units sell as shells, so that their 2014

assessments may reflect a finished interior while sales prices reflect only the shell. In addition,

there are only 22 sales of office condos.

The COD for low-rise condos exceeds the IAAO standard of 15 and is higher than that of the

other two office categories. While the COD of 15.1 for high-rise offices nominally fails to

conform to the IAAO standard, it would easily do so when the issue of statistical reliability is

taken into account through the use of tolerance factors or confidence intervals.11

11

Tolerance factors provide a ready means of calculating whether a fixed standard, such as 15 percent at a confi-

dence level of 95 percent, has been met. Tolerance factors, like confidence intervals, depend on sample sizes and

COD magnitudes. The details are explained in Gloudemans “Confidence Intervals for the COD,” Assessment

Journal (2001).



Table 5: Assessment Ratio for Offices Stratified by Sub-Type

Parcel AV PRB PRB


low-rise 2,259 4,256 89 .941 .892 .970 .506 1.736 .179 1.034 0.228 0.861 -2.340 2.795

hi-rise 494 24,103 56 .963 .915 1.057 .589 1.601 .151 1.052 -3.126 0.076 -6.590 0.337

condo 1,032 175 22 1.121 1.056 1.254 .868 1.682 .113 .973 2.563 0.481 -4.876 10.001

Overall 3,785 28,534 167 .969 .947 1.021 .506 1.736 .172 1.062 -0.158 0.752 -1.142 0.827


Figure 7: Box Plot of 2014 Office Ratios by Sub-Type



Table 6 contains sales ratio statistics for market areas with at least 10 office sales. Median

confidence intervals all overlap 1.00. As with the high-rise category in the Table 5, the COD for

market area 4017 meets IAAO standards when sample reliability issues are taken into account.

Table 6: Assessment Ratios for Offices Stratified by Market Area

Parcel AV PRB PRB


4003 Greenway Plaza

Richmond Buffalo 1,062 3,448 11 .924 .736 1.057 .589 1.077 .117 1.000 -0.401 0.847 -4.978 4.177

4004 Galleria West Loop 568 6,194 13 1.106 .883 1.194 .665 1.408 .134 1.084 -5.330 0.080 -11.417 0.757

4012 Westchase 750 3,421 11 .957 .849 1.183 .845 1.209 .083 1.039 -2.665 0.052 -5.353 0.023

4015 Energy Corridor 1,696 4,108 16 1.081 .803 1.125 .683 1.214 .136 1.067 -1.202 0.263 -3.410 1.007

4017 FM 1960 West 6,962 5,242 23 1.056 .966 1.166 .554 1.736 .175 1.111 -1.597 0.467 -3.081 2.887

Overall Available 11,038 22,412 74 .988 .957 1.064 .554 1.736 .159 1.070 -1.280 0.033 -2.451 -0.109


6.5. Retail Properties

Results obtained from stratifying retail properties by sub-type, reported in Table 7 below, reveal

no problems. The slightly low median for auto sales/service is not materially different from the

remaining pattern, and the nominally excessive COD for the restaurant/fast-food category is not

statistically significant. PRB coefficients also reveal no problem areas. Figure 8 shows the

distribution of the ratios for each sub-type graphically.

Table 7: Assessment Ratio for Retail Properties Stratified by Sub-Type

PRB PRB

Group Sales Median Lower Upper Min Max COD PRD Coef. Sig. Lower Upper

Residential conversions 61 1.007 .966 1.095 .733 1.519 .140 1.028 0.093 0.961 -3.684 3.869

Restaurant/Fast Food 30 1.009 .904 1.070 .651 1.590 .151 1.045 -0.213 0.931 -5.197 4.771

Auto Sales/Service 69 .945 .899 .990 .541 1.672 .142 .962 1.590 0.330 -1.644 4.825Community, Nbhd &

Strip Centers 59 1.025 .969 1.058 .731 1.623 .146 1.070 -0.955 0.582 -4.406 2.497

Retail Stores 115 .994 .977 1.019 .555 1.598 .129 .986 0.883 0.386 -1.127 2.894

Retail Other 47 .988 .933 1.038 .471 1.395 .134 1.119 -3.199 0.055 -6.471 0.072

Overall 381 .991 .979 1.007 .471 1.672 .140 1.024 -0.039 0.863 -0.485 0.406




Figure 8: Box Plot of 2014 Retail Ratios by Sub-Type



Table 8 reports results for market areas with at least 10 retail sales and reveals several areas of

concern. Although the first two reported CODs in excess of 15 are not statistically significant,

those for market areas 4017 and 4027, with 62 and 30 sales respectively, are statistically signifi-

cant. Most strikingly, area 4003 presents a strong indication of assessment regressivity. The

calculated fall in the assessment ratio with each doubling of values is about 24 percent, with only

6 chances in 1,000 that the result could be an artifact of sampling variability despite the small

sample size of 11 sales. Figure 9: displays the PRB graph for these 11 sales. Notice how

assessment-to-sales ratios (ASRs) fall as value increases. Although there are only 11 sales and

no adjustments have been made to the statistical significance level to reflect the fact that multiple

comparisons are being made in this report using tests that assume single, not multiple, tests, such

an extreme result merits further investigation. A less extreme result in the other direction is seen

for area 4024, where ratios appear to increase rather than decrease with value, although here the

IAAO standard sees no cause for concern.

Table 8: Assessment Ratios for Retail Properties Stratified by Market Area

Parcel AV PRB PRB


4002 South Main 1,203 1,061 12 1.121 .934 1.226 .888 1.318 .110 .986 4.934 0.361 -6.538 16.405

4003 Greenway

Plaza Richmond

Buffalo Spdwy

1,062 3,448 11 1.034 .748 1.393 .748 1.524 .184 1.114-23.68 0.006 -38.475 -8.884

4006 American

General1,266 1,729 13 1.101 .877 1.395 .806 1.550 .173 1.057

-15.58 0.093 -34.224 3.058

4012 Westchase 750 3,421 10 .944 .893 1.086 .804 1.208 .085 1.010 0.212 0.922 -4.569 4.993

4016 Far

Northwest2,845 2,767 15 .953 .846 1.038 .794 1.446 .138 1.034

0.376 0.901 -6.061 6.814

4017 FM 1960

West6,962 5,242 62 .991 .932 1.045 .648 1.623 .174 1.023

1.998 0.18 -0.951 4.946

4018 FM 1960

Interstate 457,264 1,488 33 .948 .902 .988 .661 1.518 .113 1.007

0.913 0.658 -3.255 5.081

4019 North 5,963 1,515 45 .992 .976 1.028 .588 1.590 .102 1.015 -1.154 0.474 -4.373 2.065

4021 Kingwood 3,151 1,766 14 .966 .883 1.092 .651 1.180 .117 1.213 -2.973 0.166 -7.363 1.418

4024 Southwest

Freeway Far1,971 1,947 20 .960 .923 1.044 .746 1.407 .132 .881

6.093 0.008 1.786 10.400

4025 South 7,063 2,385 28 1.000 .924 1.043 .679 1.239 .104 .967 -0.996 0.503 -4.015 2.022

4027 Pasadena 4,316 1,603 30 .995 .904 1.097 .555 1.672 .190 .963 3.915 0.191 -2.073 9.904

4028 Clear Lake 1,656 2,090 10 .914 .884 1.042 .861 1.079 .063 1.037 -1.239 0.342 -4.065 1.587

Overall Available 45,472 30,462 303 .987 .974 1.005 .555 1.672 .142 1.025 0.723 0.198 -0.379 1.825




Figure 9: Scatter Graph of Retail Ratios with Value in Market Area 4003

6.6 Warehouses

As indicated in section 2, warehouses are a diverse, difficult group. This is borne out by the fact

that they have the highest COD of the four groups studied. Table 9 breaks out results by sub-

type and Figure 10 contains the corresponding box plot. As HCAD staff anticipated, use type

4399 (metallic buildings) has a low median ratio (0.869 based on 211 sales) and high COD at

18.9. The COD exceeds the IAAO threshold of 15.0 at the 95% confidence level. Mini-

warehouses, like condo offices, have a high median ratio (possibly related in part to the same

mismatch between interior finish in the property when assessed and when sold). In any case,

there are only 13 mini-warehouses properties and the sample is too small to conclude with 95%

confidence that the true assessment level does not exceed 100%. Due to the variability of the

ratios, no clear patterns of vertical inequity can be discerned.



Table 9: Assessment Ratio for Warehouses Stratified by Sub-Type

Parcel AV PRB PRB


4394 Service

Center 535 1,140 22 1.021 .889 1.083 .670 1.461 .120 .955 2.651 0.298 -2.522 7.823

4396 Mini-

Warehouse603 2,075 13 1.349 .991 1.529 .837 1.777 .171 1.039 -1.031 0.779 -8.903 6.841

4397 Office -

Warehouse588 1,654 28 .962 .894 1.020 .595 1.283 .104 1.060

-0.1560.937 -4.156 3.844

4398 Warehouse 2,673 6,977 54 1.018 .984 1.058 .466 1.634 .159 1.147 -2.344 0.117 -5.297 0.610

4399 Warehouse-

Metallic11,613 5,455 211 .869 .822 .906 .381 1.615 .189 .996 2.082 0.124 -0.574 4.738

Overall 16,012 17,301 328 .929 .899 .957 .381 1.777 .188 1.004 0.281 0.700 -1.155 1.718


Figure 10: Box Plot of 2014 Warehouse Ratios by Sub-Type



Table 10 contains ratio statistics for warehouses stratified by market area. Ratios vary widely as

seen in the minimum and maximum ratio columns. Medians differ among market areas and in

several cases the upper 95% confidence level falls short of 1.00. The CODs for areas 6008,

6009, 6014, and 6023 all fail the IAAO standard for CODs, as does the overall COD. Although

PRBs indicate problems, the variability of the ratios precludes any sub-stratum finding of signifi-

cant price related bias. The variations noted in Table 10 could presumably be assuaged if the

CAMA system gave appraisers the ability to vary market rents, expense ratios, and capitalization

rates by market area.

Table 10: Assessment Ratios for Warehouses Stratified by Market Area

PRB PRB

Group Parcels Parcel AV Sales Median

Lower

Bound

Upper

Bound Min Max COD PRD Coef. Sig.

Lower

Bound

Upper

Bound

6002 Inner Loop Southeast 1,992 1,049,935,219 20 .943 .851 1.038 .619 1.329 .142 1.062 0.084 0.979 -6.66 6.828

6005 Near Southwest 853 745,296,795 17 .861 .788 1.022 .489 1.200 .167 .918 5.763 0.106 -1.38 12.905

6007 Near Northwest 736 1,200,512,275 26 .913 .716 .968 .499 1.353 .163 .933 0.555 0.796 -3.82 4.93

6008 Near North 2,159 1,211,307,748 46 .876 .814 .959 .381 1.634 .190 1.011 4.751 0.091 -0.797 10.299

6009 Northeast 744 816,236,994 11 .791 .663 1.043 .638 1.422 .217 1.137 -6.788 0.133 -16.099 2.522

6010 Pasadena - LaPorte 1,206 1,369,179,899 23 .951 .798 1.006 .466 1.370 .168 .977 0.052 0.984 -5.282 5.386

6012 Southeast 1,151 706,876,776 13 .932 .786 1.045 .537 1.148 .138 1.462 -2.611 0.321 -8.141 2.919

6014 Far Northwest 713 970,262,559 12 .809 .687 1.349 .471 1.421 .333 .796 9.401 0.210 -6.233 25.036

6015 Far North 1,237 1,271,183,158 26 1.015 .799 1.054 .448 1.725 .173 .951 -2.813 0.301 -2.679 8.306

6016 Beltway North 631 1,166,397,276 16 .963 .791 1.053 .652 1.252 .128 .961 4.946 0.066 -0.382 10.275

6017 Beltway Northwest 1,093 2,313,216,822 30 .873 .818 .946 .442 1.302 .158 1.010 3.011 0.242 -2.143 8.164

6018 Beltway Southwest 306 614,200,369 14 .985 .889 1.083 .670 1.461 .127 1.005 1.445 0.734 -7.602 10.491

6022 East 1,190 1,112,218,968 20 1.030 .909 1.135 .669 1.529 .177 1.270 -0.409 0.879 -5.973 5.156

6023 Inner Loop Northwest 578 887,929,521 16 .790 .652 1.058 .466 1.777 .297 1.027 5.313 0.538 -12.734 23.359

Overall Available 14,589 15,434,754,379 290 .920 .890 .956 .381 1.777 .185 1.028 1.687 0.042 0.059 3.316

95% Confidence

Interval

95% Confidence

Interval

6.7 Additional Stratification

In addition breakdowns by sub-type and market area, we stratified properties by school district,

building size, year built, building grade, and value range.

Results by independent school district (ISD) where at least 10 sales were available are reported

in Table 11. Unlike stratification by use group, particularly metallic warehouses, the ISD detail

adds little to our understanding of systemic problem areas, reflecting instead the overall patterns

noted earlier. Two ISDs have non-compliant CODs (namely 4 and 20) but median and PRB

issues appear unexceptionable. The same is largely true with respect to size groups as reported

in Table 12. It might be noted there that the parcels in the 15,000 to 30,000 square feet category



do not fail IAAO’s COD standard. The PRB for this category is also poor, although not quite

one that the IAAO standard would characterize as a matter of concern.

Table 11: Assessment Ratios for Independent School Districts

PRB PRB


1 515 1.015 .999 1.033 .381 1.777 .155 1.075 0.164 0.619 -0.484 0.812

2 11 .927 .786 1.002 .768 1.081 .087 .954 1.794 0.214 -1.243 4.831

4 129 .955 .917 .979 .448 1.692 .187 .969 1.407 0.139 -0.462 3.276

8 75 .976 .947 1.016 .670 1.461 .109 1.035 0.013 0.984 -1.295 1.321

9 86 .974 .941 1.007 .387 1.634 .145 1.030 -0.557 0.592 -2.613 1.500

15 13 1.024 .982 1.166 .909 1.393 .091 .937 1.123 0.602 -3.486 5.732

16 22 .974 .909 1.023 .478 1.286 .128 .967 0.775 0.680 -3.090 4.640

17 44 .993 .932 1.010 .554 1.623 .129 1.000 -0.468 0.746 -3.363 2.427

18 30 1.010 .966 1.047 .651 1.422 .111 .993 -0.184 0.882 -2.696 2.327

19 51 1.003 .975 1.060 .471 1.446 .129 1.006 -0.587 0.507 -2.351 1.177

20 20 .999 .782 1.136 .602 1.672 .209 .993 0.868 0.791 -5.902 7.638

21 58 1.011 .977 1.048 .466 1.558 .143 .933 1.008 0.451 -1.651 3.668

24 46 1.020 .933 1.100 .739 1.736 .158 1.001 0.873 0.550 -2.049 3.795

25 51 .951 .886 .996 .442 1.590 .170 1.049 0.210 0.871 -2.274 2.676

26 24 .960 .870 1.079 .731 1.506 .166 1.083 0.828 0.638 -2.770 4.426

27 19 .919 .884 1.079 .707 1.686 .163 1.094 -1.679 0.588 -8.096 4.737

Overall (Available)1194 .995 .988 1.002 .381 1.777 .153 1.042 0.286 0.212 -0.164 0.736


Table 12: Assessment Ratios for Size Groups (Net Rentable Area)

Parcel AV PRB PRB


Lo -- 15,000 sq ft 1,865 1,796 52 .995 .968 1.069 .740 1.777 .140 1.016 -0.112 0.968 -5.708 5.483

15,000 -- 30,000 1,659 2,921 42 .972 .934 1.027 .707 1.590 .159 1.072 -10.354 0.005 -17.354 -3.354

30,000 -- 65,000 1,873 5,909 62 1.023 .985 1.079 .619 1.725 .148 1.030 -0.568 0.846 -6.410 5.273

65,000 -- 150,000 1,954 12,209 75 1.030 .991 1.074 .506 1.692 .149 1.065 -4.001 0.054 -8.070 0.068

150,000 -- Hi 1,885 48,936 141 .988 .971 1.010 .537 1.533 .115 1.053 -3.609 0.000 -5.520 1.698

Overall (avail) 9,236 71,771 391 .996 .988 1.022 .506 1.777 .138 1.077 0.091 0.692 -0.359 0.540




In Table 13 below there is some suggestion that median assessment ratios are highest for the

oldest properties, perhaps indicating that depreciation schedules, to the extent they’re used for

commercial properties, might be in need of review. Of the four CODs nominally out of compli-

ance, the only one that is statistically significant ironically is that for the newest properties (COD

of 16.4 based on 296 sales).

Table 13: Assessment Ratios for Age Groups (Year Built)

Parcel AV PRB PRB


Lo -- 1960 11278 5,394 138 1.050 1.015 1.106 .466 1.626 .158 .998 1.653 0.168 -0.695 4.000

1961 -- 1975 10015 14,519 153 .997 .988 1.021 .387 1.686 .131 1.099 0.939 0.151 -0.345 2.222

1976 -- 1985 12533 30,447 308 .995 .974 1.016 .442 1.777 .152 1.046 0.315 0.511 -0.628 1.258

1986 -- 2000 8016 19,401 317 .995 .980 1.003 .478 1.672 .147 1.004 1.040 0.025 0.128 1.951

2001 --Date 9735 32,852 296 .967 .951 .988 .381 1.725 .164 1.020 0.145 0.750 -0.746 1.035

Overall Available51577 102,614 1212 .995 .988 1.002 .381 1.777 .153 1.042 0.462 0.042 0.016 0.908


The analyses by economic building class reported in Table 14 below offer few surprises. The

highest quality (A) class has the lowest COD, suggesting it is the most easily appraised. Con-

versely the lowest quality (E) class has the highest COD (except for the uninformative “NA”

class) and fails to meet IAAO’s COD standard after possible sampling error is accounted for. In

addition, note the high number of “E” sales (782 of 1,213 total sales). The C class gives some

evidence of regressive price-related bias in assessments, although the IAAO standard would

dismiss it as de minimis.



Table 14: Assessment Ratios for Economic Building Classes

Parcel AV PRB PRB


A & +/- 2,514 47,455 162 .982 .969 1.010 .537 1.692 .122 1.049 -1.026 0.118 -2.317 0.264

B 3,173 17,053 117 .995 .970 1.022 .506 1.725 .148 1.073 -0.612 0.552 -2.640 1.417

C 3,007 8,835 64 1.040 .991 1.126 .707 1.777 .143 1.089 -3.759 0.029 -7.117 -0.400

D 982 1,794 20 1.043 .992 1.252 .837 1.529 .151 .986 -1.684 0.587 -8.073 4.706

E 58,227 35,363 782 .993 .982 1.002 .381 1.672 .158 1.012 -0.042 0.936 -1.075 0.990

NA 18,033 4,011 68 .999 .933 1.056 .585 1.736 .185 1.062 -0.708 0.613 -3.488 2.071

Overall 85,936 114,512 1213 .994 .988 1.002 .381 1.777 .153 1.042 0.215 0.346 -0.233 0.663


Table 15 presents the results of stratifying parcels by value, where “value” is defined as one-half

of assessed value plus one-half of sale price12

. The results reveal markedly similar medians, one

non-compliant COD for properties in the $450,000 to $1,000,000 value range, a number of

generally meaningless PRDs purporting to indicate regressivity, but no significant regressivity

per the much more reliable PRB statistics.

Table 15: Assessment Ratios by Value Range

PRB PRB


Lo -- $200,000 235 1.006 .991 1.024 .381 1.590 .125 1.015 0.590 0.786 3.697 4.877

200,000--450,000 262 .990 .966 1.001 .387 1.736 .158 1.023 1.337 0.739 -6.549 9.224

450,000--1,000,000 253 .997 .968 1.021 .442 1.777 .179 1.033 -7.892 0.077 -6.640 0.856

1,000,000--4,000,000 228 .996 .982 1.025 .466 1.725 .161 1.016 3.630 0.170 -1.572 8.831

$4,000,000--Hi 235 .984 .968 1.001 .506 1.692 .140 1.053 -2.062 0.014 -3.698 -0.426

Overall 1213 .994 .988 1.002 .381 1.777 .153 1.042 0.386 0.093 -0.064 0.836


Since the regressivity or progressivity of assessments is often a topic of specific interest, we have

analyzed it in further detail. Appendix 3A presents scatter plots of ratios for each of the five

value ranges used in Table 15 above and provides a means by which virtually every transaction

can be distinguished.

Appendix 3B is similar but, instead of plotting the ratios, it plots the difference between each

sale’s ratio and the median ratio. Both appendices 3A and 3B show excellent overall uniformity

between lower and higher value properties across Harris County.

12

As explained in section 5, this is done to minimize measurement bias in the direction of either regressivity or

progressivity.



Appendix 3C is similar to Appendix 3A except it plots the ratios by value for each of the four

major commercial use groups. Appendix 3D plots the differences between the ratios and their

median by value for each of the use groups, but does so using a logarithmic scale for value to

expand the lower value ranges and compress the upper value ranges to reveal more detail. The

first graph in Appendix 3C indicates some regressivity for apartments, which the first graph in

Appendix 3D makes clearer. The graphs are consistent with the PRBs shown for apartments in

Tables 2 and 3. The graphs for warehouses in the two appendices indicate wide dispersion and

modest progressivity, also consistent with ratio statistics noted earlier. The graphs for offices

and retail properties indicate excellent uniformity across value ranges.



Like other county appraisal districts in Texas, HCAD operates under uniquely difficult burdens

and constraints imposed by the Texas Property Tax Code that strongly favor the appellant in

appeal situations, particularly those appellants represented by parties specializing in valuation

protests. The inordinate ease of obtaining value reductions has resulted in a staggering amount

of protests each year, often debasing equity between those who protest and those who do not. It

also greatly increases staff workloads and the cost of property tax administration. While equity

and the right to appeal are cornerstones of sound assessment, the framework established by

changes to the Property Tax Code over the years appears to have resulted in out of control

appeals in Harris County and perhaps other large Texas counties as well.

Against this backdrop, HCAD had adopted sound valuation procedures that are consistent with

recommended best practices and overall achieve good results in comparison to IAAO standards

and results typically seen in other large jurisdictions. We do, however, find problematic areas

that merit attention and where valuation performance can be improved. One is the apparent

tendency, most notably for apartments, to change values for properties that recently sold more

than for unsold properties. As HCAD is well aware, warehouses present particular challenges in

Harris County. Values for metallic buildings, for which sales are numerous, appear relatively

low and uniformity of values as measured by the COD is comparatively poor compared to other

property types. While we find other isolated problems detailed in relevant sections of this report,

we also conclude that for the most part performance measures meet or come close to IAAO

standards. The overall level of assessment is close to market value, uniformity among major use

groups is generally good, and overall assessment levels are similar between lower and higher

value properties.

Below we enumerate specific recommendations for improving valuation performance. We

recognize that some of these require legislative change and thus are outside of HCAD’s control.

Others would appear to require changes to the present CAMA system.

1. Full disclosure of sales prices. As mentioned, Texas is one of a minority of states that do

not provide for disclosure of sale price at time of sale. Thus sales prices become proprie-

tary information and all current or prospective property owners find it harder to assemble

information on what properties are selling for. HCAD is no exception and must rely on

information obtained during protests, solicited from buyers and sellers, or obtained from

third parties. Sales data is crucial to accurate valuation and HCAD and those whom it

serves would benefit by disclosure of sales prices at time of sale.

2. Appeals system. All interested in a sound assessment system in Texas should work to

rebalance the protest system so that burdens of proof are more evenly distributed and that

inequality protests are based on stronger grounds than merely finding some properties

with a lower value in a class. While an evenhanded appeals system is crucial, we are

concerned that the current framework makes it possible for virtually any property owner

with the time and resources to rationalize a reduction, resulting in inequities between



comparable properties that protest and those that do not. If taken to its logical conclu-

sion, the system could spiral into infinite reductions as a property owner could always

point to a comparable property whose value had been reduced to justify a reduction to

their own value.

3. Land and building values. Although property owners can only appeal total value, cur-

rently county property appraisers are required to provide both land and building values on

valuation notices. We find this unnecessary and somewhat arbitrary (particularly for

condominiums). The requirement can also focus attention on one versus the other. Elim-

inating the unnecessary breakout of separate land and building values would make the as-

sessment system more straightforward and efficient.

4. Time frame of sales. Section 23.013 limits appraisers to using two years of sales unless

they can establish that “enough comparable” properties were not sold over the last two

years to constitute a “representative” sample. We find this unusual limitation particularly

troubling for commercial properties that sell far less frequently than residential proper-

ties. IAAO recommends using up to five years of sales as required for reliable samples,

and many if not most jurisdictions, both large and small, use more than two years of sales

in appraisal analyses. Older sales can be effectively adjusted to the assessment date and

larger samples produce more accurate, reliable values than smaller samples.

5. Use of time adjustments. Related to the above and consistent with best practice, HCAD

currently uses time-adjusted sales prices in its ratio studies. However, time-adjusted

prices are not used in appraisal analysis, including the development of capitalization

rates. In a rising market, this could lead to cap rates that are somewhat high, and vice

versa in a falling market. The accuracy of values could be further improved by using

time-adjusted prices in valuation models.

6. Flexibility of time adjustments. Although staff has tested and is knowledgeable of the

process of developing nonlinear adjustments, time adjustments used in ratio studies have

to date been strictly linear with a single rate per use type. We understand that this is due

to limitations imposed by the CAMA system. Hopefully such limitations can be correct-

ed so that staff can better reflect changing market conditions and differences observed

among property sub-types or market areas.

7. Statistical software and MRA. While spreadsheets are excellent for a wide array of anal-

yses germane to commercial properties, statistical software opens the door to more pow-

erful analyses. Multiple regression analysis (MRA) allows appraisers to build models

that consider a multitude of relevant factors, e.g., property type, location, grade, size, and

effective year built. Staff has had exposure to statistical software and it is currently using

it for time trend and sales ratio analyses. We recommend that staff explore leveraging the

same tools for valuation analysis.



8. Valuation parameters. The current CAMA system limits appraisers to a single vacancy

ratio, expense ratio, and capitalization rate for each property use and grade. While this

may be adequate for smaller jurisdiction, it is a significant constraint for a jurisdiction of

Harris County’s size and diversity. Appraisers should be able to test for and vary these

important valuation parameters by market area and potentially other variables (e.g., age).

We recommend that these apparent limitations be addressed and that staff test for and

make appropriate adjustments. Although staff can “work around” these constraints by

making parcel-by-parcel manual adjustments each year, this defeats the principle of mass

appraisal and may serve to encourage the differential appraisal of sold and unsold proper-

ties noted in our report.

9. Additional income approach applications. Although HCAD has extensive experience and

expertise in the income approach and the approach is currently emphasized for commer-

cial properties, additional applications could be considered. In particular, smaller retail

properties are appraised on the cost approach. If enough rental data are available, staff

could test use of the income approach for such properties.

10. MRA-based sales comparison models. HCAD should consider exploring the develop-

ment of MRA-based sales comparison models for 4-20 unit apartments and warehouses.

These smaller apartments, which frequently sell, are currently appraised on the cost ap-

proach. Warehouses are appraised by the income approach but without differentiation

based on building size, available land area, age, or other potentially relevant variables.

As noted, warehouses currently exhibit the highest CODs observed among the four prop-

erty groups studied. Sales comparison models offer an avenue for potentially improving

valuation accuracy for these two property groups.

11. Coefficient of price-related bias (PRB). The advantages of the PRB as a measure of

price-related bias have been well discussed. We recommend that HCAD consider adding

the PRB to its sales ratio studies to supplement or replace the PRD. Although the PRDs

that we have reported often fall outside of IAAO standards, in many or most of these cas-

es the results would not prove statistically significant. The PRB provides a more mean-

ingful and reliable measure of price-related bias.

12. Differential appraisal of sold properties. To ensure equal treatment and the reliability of

its reported ratio statistics, HCAD should test for differential appraisal of sold and unsold

properties. If found, the causes of such disparities should be uncovered and addressed.

While there is nothing wrong with changing assessments to follow trends observed in the

market as closely as practical, it is essential that unsold properties be changed similarly to

those that were recently sold.



Multi-Family – Apartment Areas



Appendix 1 – Commercial Economic Area Maps (Continued)

Office Areas




Retail Areas




Warehouse Areas



Apartment Sales



Appendix 2 (Continued)

Geographic Distribution of Sales By Use Group

Office Sales





Retail Sales





Warehouse Sales



(1 Graph for Each of 5 Value Ranges)







(1 Graph for Each of 5 Value Ranges)











(1 Graph for Each Major Use Group)



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