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Glossary of AVM-related Terms

Term Definition (source)
AAAVM (Appraiser Assisted AVM) An appraisal product where an appraiser guides the AVM program prior to the determination of value. The appraiser makes any selections, corrections, rankings, or adjustments to the data to be submitted to the AVM for valuation. (d)
Absolute Value The value of a number or expression regardless of its sign. For example, 3 and 3 (minus 3) both have an absolute value of 3. The mathematical symbol for absolute value is one vertical bar on each side of the number in question, for example, |3|. (c)
Accuracy The extent to which an AVM’s valuations approximate actual market values. Analysts usually examine a model’s overall accuracy through two different types of metrics – measures of AVM bias and measures of AVM precision. (b) The closeness of a measurement, computation, or estimate to the true, exact, or accepted value. Accuracy also can be expressed as a range about the true value. See precision and statistical accuracy. (c)
Actual Age The actual number of years that have elapsed since the completed construction of an improvement; also referred to as historical age or chronological age. (c)
Ad Valorem According to value. (c)
Ad Valorem Tax A tax levied in proportion to the value of the thing(s) being taxed. Exclusive of exemptions, use-value assessment provisions, and the like, the property tax is an ad valorem tax. (c)
Adaptive Estimation Procedure (AEP) A computerized, iterative, self-referential procedure using properties for which sale prices are known to produce a model that can be used to value properties for which sale prices are not known. Also called feedback. (c)
Additive models Models in which the dependent variable is estimated by multiplying each independent variable by its coefficient and adding each product to the constant. (e)
Address Normalization System A component at the front-end of many AVMs which identifies and re- pairs a variety of minor errors that commonly occur in user-supplied property addresses including both misspellings and incorrect addresses and zip codes. (b)
Adjusted Hit Rate The percentage of valuations delivered relative to a modified sample population. To gain a better understanding of an AVM’s performance, analysts may remove outliers from the test results or valuations where the model may have had access to the benchmark value. (b)
Adjustments Modifications in the reported value of a variable, such as sale price. For example, adjustments can be used to estimate market value in the sales comparison approach by modifications for differences between comparable and subject properties. Note: Adjustments are applied to the characteristics of the comparable properties in a particular sequence that depends on the method of adjustment selected. (c)
Adverse Land Use A land use that decreases the value of nearby properties, usually because the adverse use is incompatible with the uses of the neighboring properties. A garbage dump near a residential neighborhood is an example of adverse land use. (c)
Age See chronological age; total economic life; effective age; and historical age. (c)
Age/Life Method A method of estimating accrued depreciation founded on the premise that, in the aggregate, a neat mathematical function can be used to infer accrued depreciation from the age of a property and its economic life. Another term is “straight-line depreciation” (see depreciation). (c)
Aggregate Mean See weighted mean. (c)
Agricultural Property Improved or unimproved land that is devoted to or available for the production of crops and/or other agricultural products, livestock, and agricultural support buildings. (c)
Algorithms Computer-oriented, precisely defined set of steps that, if followed exactly, will produce a prespecified result, for example, the solution to a problem. (e)
Allocation by Abstraction A method of separating a whole property value into land and improvement components. The appraiser estimates replacement cost new, subtracts an appropriate amount for depreciation, and subtracts the remainder from the whole property value to estimate the land value. (c)
Allocation by Ratio A method of separating a whole property value into land and improvement components, in which the appraiser develops proportions of land and improvement values for comparable properties and applies those proportions to the subject’s whole property value. (c)
Allocation Method A method used to value land, in the absence of vacant land sales, by using a typical ratio of land to improvement value. Also called land ratio method. (c)
Amenity A feature of an improvement that enhances its suitability for its basic use. A fireplace in a single – Family residence is an amenity, as is covered parking at an apartment complex. By definition, amenities always increase value. (c)
Analog (1) The form of data display in which values are shown in graphic form, such as curves.

(2) A form of computing in which values are represented by directly measurable quantities, such as voltages or resistances. Note: Analog computing methods contrast with digital methods, in which values are treated numerically. (c)

Analyst-Assisted AVM (AAAVM) A hybrid valuation product that relies on the experience of a valuation professional, but not necessarily a state-licensed or state-certified appraiser, to validate and supplement an AVM’s value conclusions. (b)
Ancillary Data Subsidiary data used to define the area of interest, for example, topographic, administrative, or geologic data. Ancillary data may be digitized and merged with the primary image data to facilitate analysis. (c)
Anticipated Use Method A method used to appraise under developed land. Expected improvements to the land are specified, and total development costs are estimated and subtracted from the projected selling price to give an estimate of the value of the un developed land. (c)
Appraisal (1) The act of estimating the money value of property.

(2) The money value of property as estimated by an appraiser.

(3) Of or pertaining to appraising and related functions, for example, appraisal practice, appraisal services. Compare assessment. (c)

Appraisal Emulation Model The appraisal emulation model that follows the steps that an appraiser might follow in forming a value estimate (although not with the same insight or flexibility that a qualified appraiser brings to the assignment). The model selects comparable sales using some standard criteria. It then rates those comparable sales by suitability, based on the physical and sales characteristics of each comparable sale, by adjusting the varying elements (much as is done on an appraisal form); the model then calculates an estimate of value. (c)
Appraisal Methods The three methods of appraisal, that is, the cost approach, income approach, and sales comparison approach. (c)
Appraisal Principles The economic concepts underlying appraisal. See under principle of: anticipation, change, conformity, contribution, progression and substitution. See highest and best use; and variable proportions, law of. (c)
Appraisal Ratio (1) The ratio of the appraised value to an indicator of market value.

(2) By extension, an estimated fractional relationship between the appraisals and market values of a group of properties. See level of appraisal. (c)

Appraisal Report The oral or written communication of a completed appraisal. (c)
Appraisal, Acquisition An appraisal to determine the market value of a property that is to be taken by eminent domain, so that the owner might be justly compensated. (c)
Appraisal, Complete The act or process of estimating value or an estimate of value performed without invoking the departure provision (USPAP). (c)
Appraisal, Limited The act or process of estimating value or an estimate of value performed under and resulting from invoking the departure provision (USPAP). (c)
Appraisal-Sale Price Ratio The ratio of the appraised value to the sale price (or adjusted sale price) of a property; a simple indication of appraisal accuracy. (c)
Appraise To make an estimate of value, particularly of the value of property. (c)
Appraised Value The estimate of the value of a property before application of any fractional assessment ratio, partial exemption, or other adjustments. (c)
Appraiser Emulation Model See Comparable Sales Model. (b)
Appreciation Increase in value of a property, in terms of money, from causes other than additions and betterments. For example, a farm may appreciate if a shopping center is built nearby, and property of any sort may appreciate as a result of inflation. Contrast depreciation. (c)
Area, Floor See floor area of building. (c)
Area, Ground See ground area of building. (c)
Arm’s-Length Transaction (1) A sale between a willing buyer and a willing seller that are unrelated and are not acting under duress, abnormal pressure or undue influences.

(2) A sale between two unrelated parties, both seeking to maximize their positions from the transaction. (c)

Array An ordered arrangement of data, such as a listing of sales ratios, in order of magnitude. (c)
Assessed Value (1) A value set on real estate and personal property by a government as a basis for levying taxes.

(2) The monetary amount for a property as officially entered on the assessment roll for purposes of computing the tax levy. Assessed values differ from the assessor’s estimate of actual (market) value for three major reasons: fractional assessment ratios, partial exemptions, and decisions by assessing officials to override market value. The process of gathering and interpreting economic data to provide information that can be used by policymakers to formulate tax policy. (c)

Assessment (1) In general, the official act of determining the amount of the tax base.

(2) As applied to property taxes, the official act of discovering, listing, and appraising property, whether per formed by an assessor, a board of review, or a court.

(3) The value placed on property in the course of such act. (c)

Assessment Date The status date for tax purposes. Appraised values reflect the status of the property and any partially completed construction as of this date. (c)
Assessment District The administrative area in which the officer or public body responsible for making the original assessment has jurisdiction. Note: The local assessment district is usually coterminous with a county, township, or city, but the state itself may be the assessment district for some types of property. (c)
Assessment Equity The degree to which assessments bear a consistent relationship to market value. (c)
Assessment Jurisdiction A geographical area within which an assessing officer has the responsibility to determine the assessed value of property for ad valorem taxation. (c)
Assessment Level The common or overall ratio of assessed values to market values. (c)
Assessment Period (1) The period beginning with the assessment date and ending with the date on which the assessor is required to complete the original assessment.

(2) Sometimes used synonymously with assessment year. (c)

Assessment Progressivity (Regressivity) An appraisal bias such that high-value properties are appraised higher (or lower) than low-value properties in relation to market values. See price-related differential. (c)
Assessment Ratio (1) The fractional relationship an assessed value bears to the market value of the property in question.

(2) By extension, the fractional relationship the total of the assessment roll bears to the total market value of all taxable property in a juris diction. See level of assessment. (c)

Assessment Ratio Study An investigation intended to determine the assessment ratio and assessment equity. (c)
Assessment Roll The basis on which the property tax levy is al located among the property owners in a jurisdiction with taxing powers. The assessment roll usually lists an identifier for each tax able parcel in the jurisdiction, the name of the owner of record, the address of the parcel or the owner, the assessed value of the land, the assessed value of the improvements, applicable exemption codes, and the total assessed value. Synonyms include “cadastre,” “list,” “grand list,” “abstract of ratables,” and “rendition.” (c)
Assessment Year (1) A year beginning on the day after the assessment date and ending on the assessment date in the calendar year next following.

(2) The 365 days beginning with the appraisal date. (c)

Assessment, Acquisition-Based One of a small number of non-market assessment valuation standards. A property is placed on the tax roll at its acquisition cost; that value is not changed until the next qualifying sale, as, for example, under California’s Proposition (c)
Assessment, Arbitrary (1) Assessment without consideration of such information as is reasonably available to the assessor.

(2) Assessment according to the “best knowledge and belief” of the assessor when a person fails to list property in accordance with law (c)

Assessment, Area-Based One of a small number of nonmarket assessment valuation standards. A property is placed on the tax roll at a value reflecting the land area or the surface area (floor area) of an improvement. (c)
Assessment, Central An original assessment made by an officer or body whose jurisdiction with respect to the property so assessed extends over two or more local assessment districts. (c)
Assessment, Doomage An assessment made without adequate information when a taxpayer fails to comply with laws requiring him or her to list his or her property for taxation. Compare assessment, arbitrary; assessment, penalty. (c)
Assessment, Fractional Assessment at a fraction of full value, or of such other standard as may be fixed by law. Note: Fractional assessment may constitute underassessment, or it may be sanctioned by law as, for example, in Alabama, where all property is legally assessable at 60 percent of full value, and in Minnesota, where various classes of property are assessable at various percentages of full value. (c)
Assessment, Jeopardy An assessment made out of the regular routine when there is reason to believe that adherence to such routine will jeopardize the collection of taxes on the property so assessed. (c)
Assessment, Original (1) The official act of the assessor in discovering, listing, and appraising property for tax purposes up to the point at which the assessment roll is first filed for public inspection.

(2) The act of assessment which first places a property on the assessment roll for the particular assessment date; the agency that performs this act is the original assessing agency (or jurisdiction).

(3) The valuation at which property is listed in the assessment roll at the time it is first filed for public inspection. (c)

Assessment, Retrospective An assessment made after the close of a tax year on property that was legally assessable, but was not listed for taxation during such year. (c)
Assessment, Standardized A system of property tax assessments based largely on the uniform application of established rules and principles. Note: Standardized assessment is usually manifested by the use of land value maps, depth and corner influence tables, building classification and cost schedules, reproduction cost and depreciation schedules, and the like. (c)
Assessment, Statutory Valuation of a property in a specified use at an amount specified by law (for example, a golf course will be assessed at ten dollars per acre); most often used for mineral properties and open space. (c)
Assessment-Appraisal Ratio The ratio of the assessed value of a property to an independent appraisal. (c)
Assessment-Sale Price Ratio The ratio of the assessed value to the sale price (or adjusted sale price) of a property. (c)
Assessor (1) The head of an assessment agency; sometimes used collectively to refer to all administrators of the assessment function.

(2) The public officer or member of a public body whose duty it is to make the original assessment. (c)

Attribute Characteristic of a property. (c)
Audit A systematic investigation or appraisal of procedures or operations for the purpose of determining conformity with specifically prescribed criteria. (c)
Audit Program The procedures undertaken or particular work done by an accountant in conducting an examination. (c)
Audit Trail A set of records of the changes made to another set of records. (c)
Automated Valuation Model An automated valuation model (AVM) is a mathematically based computer software program that produces an estimate of market value based on market analysis of location, market conditions, and real estate characteristics from information that was previously and separately collected. The distinguishing feature of an AVM is that it is a market appraisal produced through mathematical modeling. Credibility of an AVM is dependent on the data used and the skills of the modeler producing the AVM. (e)
Average The arithmetic mean. (c)
Average Absolute Deviation See average deviation. (c)
Average Deviation The arithmetic means of the absolute deviations of a set of numbers from a measure of central tendency, such as the median. Taking absolute values is generally understood without being stated. The average deviation of the numbers 4, 6, and 10 about their median (6) is (2 + 0 + 4) ÷ 3 = 2. The average deviation is used in computing the coefficient of dispersion (COD). (c)
AVM Cascade A process that uses a ranking of AVMs (aka “Model Preference Table” ®) in conjunction with user-defined logic to determine when to accept an AVM value or go to the next model in the ranking. (a)
AVM-Assisted Appraisal (AVMAA) With an AVM-assisted appraisal, an experienced state-licensed or state-certified appraiser develops a USPAP-compliant appraisal based on the results of a reliable AVM. (b)
Back-testing The process of comparing an AVM’s outputs against benchmark values to gauge the model’s performance. (b)
Base-Home Approach A method of appraising single – Family residential properties whereby each residence to be appraised is compared with one having common or typical characteristics and of known value, called the base home, and differences between the two in terms of condition, size, number of garages, and the like are weighted by the appraiser in the determination of the value of the property to be appraised. (c)
Base-Lot Method A method of appraising land parcels whereby each parcel to be appraised is compared with a parcel having common or typical characteristics and of known value, called the base lot, and differences between the two in terms of location, size, shape, topography, and the like are analyzed by the appraiser in estimating the value of the lot to be appraised. (c)
Benchmark (1) A term used in land surveying to mean a known point of reference.

(2) In property appraisal, a property of known value and of known effective age and replacement cost.

(3) By extension, a model property to be used in determining by comparison the grade or quality class of other properties. (c)

Benchmark Value The market value against an AVM’s value predictions will be compared for performance testing. Recent, arm’s length transactions are preferred. (b)
Bias The degree to which an AVM or cascade tends to overvalue or undervalue subject properties. Analysts quantify an AVM’s bias by calculating the mean or median valuation error. (b)
Binary (Dummy) Variable  (1) Binary variables are qualitative data items that have only two possibilities – yes or no (for example, corner location).

(2) A variable for which only two values are possible, such as results from a yes-or-no question; for example, does this building have any fireplaces? Used in some models to separate the influence of categorical variables. Also called a dichotomous variable or dummy variable. (e)

Blended Model An AVM that incorporates a combination of valuation methods including but not limited to: Hedonic, home price Index, Hybrid, Tax Assessed value multipliers, statistical regression or an Appraisal Emulation approach. (d)
Blind Testing AVM performance testing where analysts remove any valuations for which the model may have had access to the benchmark value. Blind testing provides users with a better understanding of how a particular AVM or cascade will perform in the production setting. (b)
Bootstrap A computer-intensive method of statistical inference that is based on a repeated resampling of data to provide more information about the population characteristics. The bootstrap is a data-driven procedure that is particularly useful for confidence interval approximation when no traditional formulas are available or the sample has been drawn from a population that does not conform to the normal distribution. (c)
Bump Logic User-defined methods or criteria to select from or combine various classes of valuation services, e.g., AVM, BPO, hybrid valuation, drive-by or full appraisal. (a)
Bundle of Rights The six basic rights associated with the private ownership of property: right to use; sell; rent or lease; enter or leave; give away; and refuse to do any of these. (c)
Calibration The process of estimating the coefficients in a mass appraisal model. (e)
CAMA See computer-assisted mass appraisal. (c)
Cascade A decision-process for using multiple AVMs to evaluate a subject property. The logic underlying an AVM cascade consists of two components – a model preference table and usage rules (for instance, confidence score thresholds) for each model included in the cascade. Cascades cycle through a predetermined progression of AVMs until one of these models produces a sufficiently accurate valuation. (b)
Cash-equivalent Sale Price An indicator of market value that is a refinement over the raw sale price, in that the effects of unusual financing arrangements and extraneous transfers of personal property have been removed. See price, adjusted sale. (c)
Categorical Variable A variable summarizing more complex qualitative judgments by assigning each to a category or giving each a rating. For example, quality of construction might be categorized as poor, fair, average, or good, or assigned a rating from 1 to 4. The categories might then be scaled by assigning a value of (c)
Caveat Emptor “Let the buyer beware.” A common maxim stating that the buyer purchases at his or her own risk. (c)
Central Tendency, Measure of A single point in a range of observations around which the observations tend to cluster. The three most commonly used measures of central tendency are the mean, median, and mode. (c)
Chi-square A particular statistic, and a particular frequency distribution associated with it, of interest in inferential statistics. (c)
Chronological Age The number of years elapsed since an original structure was built. Synonyms are actual age and historical age. Contrast with effective age. (c)
Cluster Analysis A statistical technique for grouping cases (for example, properties) based on specified variables, for example, size, age, and construction quality. The objective of cluster analysis is to generate groupings that are internally homogenous and highly different from one another. Various cluster algorithms can be employed. (e)
Coding (1) The act of reducing a description of a unique object, such as a parcel of real estate, to a set of one or more measures or counts of certain of its characteristics, such as square footage, number of bathrooms, and the like.

(2) Encoding, a related term, is usually used to refer to the act of translating coded descriptions useful to human beings into a form that can be processed by computers.

(3) Coding is sometimes also used to refer to the writing of instructions that direct the processing done by computers. (c)

Coefficient  (1) In a mathematical expression, a number or letter preceding and multiplying another quantity. For example, in the expression, “5X”, 5 is the coefficient of X, and in the expression “aY”, a is the coefficient of Y.

(2) A dimensional statistic, useful as a measure of change or relationship. (e)

Coefficient of Concentration The percentage of observations falling within a specified percentage (say, 15 percent) of a measure of central tendency. (c)
Coefficient of Determination (R2) A statistic that characterizes two or more sets of numbers. The coefficient of determination, when multiplied by 100, gives the percentage strength of the (linear) relationship between or among the sets of numbers. (See correlation.) For two variables, the coefficient of determination and the square of the correlation coefficient are identical; for three or more variables, the coefficient of determination measures the strength of the relationship between the dependent variable and all the independent variables combined. (c)
Coefficient of Variation (COV) A standard statistical measure of the relative dispersion of the sample data about the mean of the data; the standard deviation expressed as a percentage of the mean. (c)
Comp Set The set of properties that have been selected for valuation purposes of the Subject Property. (d)
Comparable (or “Comp”) A sale in the area of the Subject Property that has characteristics and attributes that are similar to those of the Subject Property and is considered for valuation purposes. (d)
Comparable Sales Model Whether performed by an appraiser or an ‘appraiser emulation model,’ comparable sales analysis seeks to identify properties that resemble the subject in terms of location and building attributes, and then adjust these ‘comparables’ to compensate for any dissimilarities. These adjustments form the basis for an estimate of market value. (b)
Comparable Sales; Comparables (1) Recently sold properties that are similar in important respects to a property being appraised. The sale price and the physical, functional, and locational characteristics of each of the properties are compared to those of the property being appraised in order to arrive at an estimate of value.

(2) By extension, the term “comparables” is sometimes used to refer to properties with rent or income patterns comparable to those of a property being appraised. (c)

Computer-Assisted Mass Appraisal (CAMA) A system of appraising property, usually only certain types of real property, that incorporates computer – Supported statistical analyses such as multiple regression analysis and adaptive estimation procedure to assist the appraiser in estimating value. (c)
Condition A judgment of the depreciation of an improvement. Note: This is a difficult area of comparison because although the condition of the subject is known, it is difficult to know the condition of the comparable. Differences in condition may justify variances in selling prices of similar assets. An investigation of the condition of the comparables should be done, if possible. (c)
Confidence Interval A range of values, calculated from the sample observations, that are believed, with a particular probability, to contain the true population parameter (mean, median, COD). The confidence interval is not a measure of precision for the sample statistic or point estimate, but a measure of the precision of the sampling process. See reliability. (c)
Confidence Level The required degree of confidence in a statistical test or confidence interval, commonly 90, 95, or 99 percent. A 95 percent confidence interval would mean, for example, that one can be 95 percent confident that the population measure (such as the median or mean appraisal ratio) falls in the indicated range. (c)
Confidence Score A metric that seeks to identify how ‘close’ an estimate of value will be to the Reference Value. (d)
Confidence Score A score that an AVM returns along with its outputs that indicates the relative reliability of that value estimate or, the degree of confidence that the user should place in that particular valuation. (b)
Conformity See principle of conformity. (c)
Contribution See principle of contribution. (c)
Contributory Value The amount a component of a property contributes to the total market value. For improvements, contributory value must be distinguished from cost. (c)
Control Sample Part of a set of data set aside for testing the results of analysis. (c)
Correlation A statistical phenomenon (and a technique for estimating its strength) whereby knowledge of one fact about a thing implies some knowledge of a second fact about that thing. For example, because the volume and weight of water are correlated, knowing that a quantity of water is one gallon also means knowing that its weight is eight and one-third pounds. Linear correlation, the kind most often encountered, means that an increase in one factor in some proportion (say, a doubling) changes the other in the same proportion. With curvilinear correlation, as between the radius and the area of a circle, this is not true, despite the fact that the correlation may be very strong in the sense that knowledge of one fact tells you precisely the other fact. These are examples of variables perfectly correlated or nearly so; more often, correlation is only partial – For example, the correlation between the age and height of a child. The correlation coefficient gives the strength of the linear relationship between the two variables. (c)
Correlation Coefficient (r) A statistic that characterizes two or more sets of numbers and, when squared and multiplied by 100, gives the percentage strength of the (linear) relationship between the two sets of numbers. For example, if the coefficient of correlation between measures of the height and weight of a group of people were 0.9, then one would deduce that knowing the height of someone (loosely speaking) would explain (or account for) 81 percent of the weight. (c)
Correlation Matrix The table of numbers used to display the correlation coefficients for each pair of variables when three or more variables are thought to be correlated. (c)
Cost Approach (1) One of the three approaches to value, the cost approach is based on the principle of substitution – that a rational, informed purchaser would pay no more for a property than the cost of building an acceptable substitute, with like utility. The cost approach seeks to determine the replacement cost new of an improvement less depreciation plus land value.

(2) The method of estimating the value of property by: (a) estimating the cost of construction based on replacement or reproduction cost new or trended historical cost (often adjusted by a local multiplier); (b) subtracting depreciation; and (c) adding the estimated land value. The land value is most frequently determined by the sales comparison approach. (e)

Coverage The geographic counties and municipalities in which an AVM is functional or performs acceptably. (b)
Current-Market-Value Appraisals Appraisals that reflect contemporary market values rather than market values at some point in the past. Currency is commonly taken to be implicit in the term market value. (c)
Data Information expressed in any of a number of ways. ” Data” is the general term for masses of numbers, codes, and symbols generally, and “information” is the term for meaningful data. “Data” is the plural of datum, one element of data. (c)
Data Edit The process of examining recorded data to ensure that each element of data is reasonable and is consistent with others recorded for the same object, such as a parcel of real estate. Data editing, which may be done by human beings or by computer, is essentially a mechanical process, distinct from verifying the correctness of the recorded information by calling or writing property owners. (c)
Data Management The human (and sometimes computer) procedures employed to ensure that no information is lost through negligent handling of records from a file, that all information is properly supplemented and up-to-date, and that all information is easily accessible. (e)
Date of Acquisition The effective purchase date of an asset. From the date of acquisition, the asset must appear in the accounts and in financial statements, and depreciation, if any, must be recorded. (c)
Date of Sale (date of transfer) The date on which the sale is agreed. This is considered to be the date the deed, or other instrument of transfer, is signed. The date of recording can be used as a proxy if it is not unduly delayed as in a land contract. (c)
Date, Assessment See assessment date. (c)
De Minimis Threshold The minimum loan amount for which the federal banking agencies require a real estate appraisal. As of mid 2019, the de minimis threshold is $250,000. All real estate-related transactions originated or purchased by a federally regulated lending institution where the loan amount exceeds $250,000 require a USPAP-compliant real estate appraisal performed by a state-licensed or state-certified appraiser. (b)
Deferred Maintenance Repairs and similar improvements that normally would have been made to a property but were not made to the property in question, thus increasing the amount of its depreciation. (c)
Denominator In a fraction, the number by which another number (the numerator) is divided. For example, the denominator of 3/4 is 4. (c)
Dependent Variable A variable, such as sale price, the value of which is predicted by the values of other variables, such as location and finished living area. Such a variable may be said to depend on the other (independent) variables. (c)
Depreciation Loss in value of an object, relative to its replacement cost new, reproduction cost new, or original cost, whatever the cause of the loss in value. Depreciation is sometimes subdivided into three types: physical deterioration (wear and tear), functional obsolescence (suboptimal design in light of current technologies or tastes), and economic obsolescence (poor location or radically diminished demand for the product). (c)
Descriptive Statistics (1) The branch of the science of statistics that is concerned only with characterizing or describing a set of data (numbers).

(2) By extension, the measures used to characterize a particular set of data. Compare inferential statistics. (c)

Dichotomous Variable See binary (dummy) variable. (c)
Digital Data Data displayed, recorded, or stored in binary notation. (c)
Digital Image An image having numeric values representing tones. Each numeric value represents a different tone. (c)
Direct Market Method/Analysis One of two formats of the sales comparison approach to value (the other being the Comparable Sales Method). In the direct market method, the market analyst specifies and calibrates a single model used to estimate market value directly using multiple regression analysis or another statistical algorithm. (e)
Direct Market Models Direct market models analyze how various housing characteristics influence sales prices in a particular period and geographic area. These models typically describe market value as a function of a property’s location and physical attributes. Direct market models are noted for their superior accuracy compared to other approaches; however, their substantial data requirements often result in a lower percentage of usable valuations, as many states lack sufficiently detailed property records to fuel this type of AVM. Hedonic models are location-specific and therefore difficult to generalize across different markets. (b)
Discrete Data Discrete data are qualitative items that have three or more predefined values (for example, topography: level, rolling, or steep). (c)
Discrete Variable A variable for which it is not conceivable that, given any two observed values, a value lying between them may occur. For example, the number of rooms in a house is a discrete variable, but the living area of the house is not. See binary (dummy) variable and continuous variable. (c)
Dispersion The degree to which data are distributed either tightly or loosely around a measure of central tendency. Measures of dispersion include the average deviation, coefficient of dispersion, coefficient of variation, range, and standard deviation. (c)
Distribution-free Statistics A set of robust nonparametric methods whose interpretation or reliability does not depend on stringent assumptions about the distribution of the underlying population from which the sample has been drawn. See parametric statistics. (c)
Economic Area A geographic area, typically encompassing a group of neighborhoods, defined on the basis that the properties within its boundaries are more or less equally subject to a set of one or more economic forces that largely determine the value of the properties in question. (e)
Economic (External) Obsolescence (1) A cause of depreciation that is a loss in value as a result of impairment in utility and desirability caused by factors outside the property’s boundaries.

(2) Loss in value of a property (relative to the cost of replacing it with a property of equal utility) that stems from factors external to the property. For example, a buggy-whip factory, to the extent that it could not be used economically for anything else, suffered substantial economic obsolescence when automobiles replaced horse-drawn buggies. (c)

Effective Age The typical age of a structure equivalent to the one in question with respect to its utility and condition, as of the appraisal date. Knowing the effective age of an old, rehabilitated structure or a building with substantial deferred maintenance is generally more important in establishing value than knowing the chronological age. (c)
Efficiency The extent to which an organization will be able to use a particular AVM or AVM cascade for a specific application. Analysts frequently measure efficiency or ‘usability’ through various hit rates, especially usable hit rates. (b)
Elasticity (1) The responsiveness of supply and demand to changes in price. Supply or demand that changes rapidly in response to price changes is “elastic.” Supply or demand that changes slowly in response to price changes is “inelastic.”

(2) A measure of the responsiveness of tax yields to changes in economic conditions. The yield of an elastic tax increases rapidly in a growing economy. The yield of an inelastic tax increases slowly. Often measured by the formula: percent change in tax ÷ percent change in personal income. (c)

Error The difference between the actual value of a variable and the expected value of the variable exclusive of sampling problems. Errors may be positive or negative, although in common speech taking the absolute value of the errors is sometimes implied. In multiple regression analysis, the term “error” is often used loosely to mean residual. (c)
Estimated Value The estimated Market Value as of a specified date. (d)
Euclidean Distance Metric A measure of distance between two points “as the crow flies”. In property valuation it is used to find the nearest neighbor, or similar property based on an index of dissimilarity between property location, or attributes. When using multi-variate selection, the squared difference is divided by the standard deviation of the variable so as to normalize the differences. Also see Minkowski Metric (e)
Exploratory Data Analysis That part of statistical practice concerned with reviewing the data set to isolate structures, uncover patterns, or reveal features that may improve the confirmatory analysis. (c)
Exponent A symbol usually written to the right and above an expression to indicate particular mathematical operations. For example, 62 means 6 × 6, or six squared. Fractional exponents indicate inverse operations; for example, an exponent of 1/2 signifies a square root. Exponents are also called powers. Valuation models make use of the following properties of exponents: A number raised to the exponent 0 is always 1.00; zero raised to any power is zero; any number raised to the power 1 is itself. Negative numbers cannot have exponents less than 1. (c)
Factor (1) An underlying characteristic of something (such as a house) that may contribute to the value of a variable (such as its sale price), but is observable only indirectly. For example, construction quality is a factor defined by workmanship, spacing of joists, and materials used. Factor definition and measurement may be done subjectively or by a computer-assisted statistical algorithm known as factor analysis.

(2) Loosely, any characteristic used in adjusting the sale prices of comparables.

(3) The reciprocal of a rate. Assessments may be equalized by multiplying them by a factor equal to the reciprocal of the assessment ratio, and value can be estimated using the income approach by multiplying income by a factor equal to the reciprocal of the discount rate. (c)

Federally Related Transaction According to the federal banking agencies’ appraisal regulations, any real estate-related financial transaction that requires a real estate appraisal. (b)
Fee Appraisal Appraisal of properties one at a time for pay. (c)
Fee Simple In land ownership, complete interest in a property, subject only to governmental powers such as eminent domain. Also fee simple absolute. See ownership. (c)
Feedback See adaptive estimation procedure. (c)
Field Review The practice of reviewing the reasonableness of assessments by viewing the properties in question, sometimes by examining their interiors but more often by looking at their exteriors. (c)
Flowchart Any of a number of kinds of graphic descriptions of an algorithm, showing the operations, data flow, equipment, and so on. (c)
Forecast Standard Deviation A measure of the expected error range of an estimate by an AVM, expressed as a percentage or a decimal. The lower the FSD, the higher the confidence in the precision of the estimate. Example: if the FSD for an estimate is 10%, then the expectation is that 68% of the time the actual market value will fall within +/- 10% of the model estimate. (b)
Forecasted Valuation Estimate of value for subject property at a point in time in the future. (d)
Forecasting Models An AVM that forecasts future changes in property values based on a wide range of macroeconomic variables. (b)
Foreclosure The legal process by which a lien on a property is enforced. (c)
Frequency Distribution A table showing the number or percentage of observations falling in the boundaries of a given set of classes. Used in ratio studies to summarize the distribution of the individual ratios. See histogram; and mode. (c)
Functional Obsolescence Loss in value of a property resulting from changes in tastes, preferences, technical innovations, or market standards. (c)
Functional Utility The ability of improvements to satisfy market standards and demands. (c)
Furlong A land measure of 1/8 mile or ten chains or forty rods. (c)
Gantt Chart A form of bar chart used in project management. Each element (task) of a project is represented by a horizontal bar. The bars are placed on the chart according to a time scale. The left end of each bar indicates when the task is to begin. The length of each bar indicates the duration of the task. The right end of each bar indicates when the task is to be completed. (c)
Generally Accepted Given authoritative recognition by professional bodies such as the Financial Accounting Standards Board and its Generally Accepted Accounting Principles (GAAP). (c)
Geocode A code used to locate or identify a point, such as the center of a parcel of real estate, geographically. The code is composed of the east – West and north – South coordinates of the point relative to some standard point of reference. (c)
Geocoding Geographical referencing or coding of data. (c)
Geographic Information System (1) A database management system used to store, retrieve, manipulate, analyze, and display spatial information.

(2) One type of computerized mapping system capable of integrating spatial data (land information) and attribute data among different layers on a base map. (e)

Geometric Mean A measure of central tendency computed by multiplying the values of all of the observations by one another and then taking the result to an exponent equal to one divided by the number of observations. The geometric mean is particularly appropriate when a typical rate of change is being calculated, such as an inflation rate or a cost index. (c)
Goodness-of-fit A statistical estimate of the amount, and hence the importance, of the errors or residuals for all the predicted and actual values of a variable. In regression analysis, for example, goodness of fit indicates how much of the variation between independent variables (property characteristics) and the dependent variable (sales prices) is explained by the independent variables chosen for the AVM. (e)
Gross Hit Rate The number of valuations delivered relative to the total number of property addresses in the test sample; usually expressed as a percentage (e.g., 1,000 properties were submitted to an AVM and 600 properties were returned for a 60% gross hit rate). (b)
Harmonic Mean Ratio The reciprocal of the arithmetic mean of the reciprocals of each value in the data set. The harmonic mean ratio is less affected by extreme values in the data set than the arithmetic mean or the geometric mean. (c)
Hedonic Model 1) The term hedonic model broadly refers to a class of property-specific AVMs that examine housing characteristics (both physical and locational) to estimate a subject’s market value. These models operate under the assumption that a house is a composite good comprising many traits that appeal to consumers in varying degrees. Through the application of direct market and comparable sales analyses, hedonic AVMs attempt to quantify the extent to which certain attributes contribute to a property’s overall value, and then develop value conclusions based on these findings. (b)

2) Hedonic pricing attempts to take observations on the overall good or service and obtain implicit prices for the goods and services. Prices are measured in terms of quantity and quality. When valuing real property, the spatial attributes and property specific attributes are valued in a single model. Calibration of the attribute components is performed statistically by regressing the overall price onto the characteristics. (e)

Hedonic Price Index An analytical model used to quantify the pricing tradeoff between measurable product capacity and intangible attributes such as an amenity feature, design, or reputation. (f)
Hedonic Regression A multivariate analysis that predicts and explains the value of individual characteristics bundled together to form a good or service; based on the marginal utility of good character and the desirability of that good. (f)
Heterogeneous Unlike; without interrelation. The opposite of homogeneous. (c)
Heteroscedasticity  Non-constant variance, specifically, in regression analysis a tendency for the absolute errors to increase (fan out) as the dependent variable increases. (e)
Highest and Best Use A principle of appraisal and assessment requiring that each property be appraised as though it were being put to its most profitable use (highest possible present net worth), given probable legal, physical, and financial constraints. The principle entails first identifying the most appropriate market and, second, the most profitable use within that market. The concept is most commonly discussed in connection with underutilized land. (c)
Histogram A bar chart or graph of a frequency distribution in which the frequencies of the various classes are indicated by horizontal or vertical bars whose lengths are proportional to the number or percentage of observations in each class. (c)
Historical Age The number of years elapsed since an original structure was built. Synonyms are actual age and chronological age. See cost, original. (c)
Hold-out sample Part of a set of data set aside for testing the results of analysis. (e)
Homogeneous  Possessing the quality of being alike in nature and therefore comparable with respect to the parts or elements; said of data if two or more sets of data seem drawn from the same population; also said of data if the data are of the same type (that is, if counts, ranks, and measures are not all mixed together). (e)
House Price Index A model that provides an estimate of a property’s value as a function of time. HPIs find the house price path most consistent with observed appreciation or depreciation rates in a region. Typically referred to as an “index of home price appreciation,” indexes are mathematical algorithms that ad- just property values up or down to reflect price changes over time. These models are price-specific and not property-specific. (b)
Hybrid models 1) Models that incorporate both additive and multiplicative components. See also additive model, hedonic models and multiplicative model. (e)

2) Models that blend index and hedonic methods to evaluate a property’s market value. (b)

Hypothesis A statement in inferential statistics the truth of which one is interested in determining. The usual procedure is to state what one chooses to accept in the absence of sufficient evidence to the contrary (the statement is called the null hypothesis), specify the relationship or statement to be proved (the alternative hypothesis), and analyze the available data to determine whether the null hypothesis can be rejected (and hence the alternative hypothesis accepted) at some confidence level. (c)
IAAO International Association of Assessing Officers. (c)
Improvement Anything done to raw land with the intention of increasing its value. A structure erected on the property constitutes one very common type of improvement, although other actions, such as those taken to improve drainage, are also improvements. Although such cases are rarely intentional, “improvements” can conceivably diminish the value of the land; note, however, that easements restricting the use and value of land are not considered improvements. (c)
Improvements Buildings, other structures, and attachments or annexations to land that are intended to remain so attached or annexed, such as sidewalks, trees, drives, tunnels, drains, and sewers. Note: Sidewalks, curbing, sewers, and highways are sometimes referred to as “betterment,” but the term “improvements” is preferred. (c)
Income Approach One of the three approaches to value, based on the concept that current value is the present worth of future benefits to be derived through income production by an asset over the remainder of its economic life. The income approach uses capitalization to convert the anticipated benefits of the ownership of property into an estimate of present value. (e)
Incurable A part of depreciation for which it is not economical to correct the condition, and if corrected, the cost of correcting the condition exceeds the value added. (c)
Independent Appraisal An estimate of value using a model different from that used for assessment purposes. Independent appraisals are used to supplement sales in sales ratio studies or in appraisal ratio studies. (c)
Independent Validation Process/Model validation designed and executed by parties that are separate (independent) from those that parties that build, sell, resell or are using or running the system being validated. The validating parties may be internal to the organization (e.g. credit risk managers) or independent third-parties (e.g. consultants). Validation should include conceptual soundness as well as back-testing against out-of-sample benchmarks. It should be done by staff with appropriate qualifications and incentives. (a)
Independent Variable A variable whose value is not determined by other (dependent) variables. (c)
Index A Valuation Method that provides an estimate of value as a function of time. An index Method finds the house price path most consistent with observed appreciation/depreciation rates in a region. Typically referred to as an “index of home price appreciation,” indexes are mathematical algorithms that adjust home price values (up or down) for the passing of time. (d)
Inelastic See elasticity. (c)
Inferential Statistics The branch of statistical studies concerned with making predictions about the values of a large number of observations of a variable on the basis of a small number of observations of that variable and related facts.

(2) By extension, the statistics calculated in such predictions. (c)

Insured Valuation These products safeguard clients against losses incurred due to an inaccurate assessment of real estate collateral – for instance, if a loan goes into default or enters foreclosure. Vendors warrant the reliability of their valuations and work with top-rated insurers to offer coverage on a range of AVMs and derivative products. (b)
Iteration One repetition or repeated cycle in a process of estimating values as close as possible to actual values by repeated approximations. The results of each approximation are used in the next one. (c)
Kilometer Unit of length equal to 1,000 meters. Equals 0.6214 statute miles. (c)
Kruskal-Wallis Test A test in inferential statistics, valid for all types of numerical data, that seeks to determine whether the observations in a sample came from one population as opposed to three or more distinct, homogeneous subpopulations. This test is used in assessment to analyze assessment ratios from three or more classes of property to determine whether significant assessment biases are present among the classes of property. When only two classes are being compared, the appropriate test is the Mann – Whitney test. (c)
Land, Improved Land that has been made more valuable by the application of labor or labor and capital to it or public property adjacent to it. (c)
Law of Variable Proportions Often called law of decreasing returns or the law of proportionality. States that when the quantity of one productive service is increased by equal increments, the quantities of other productive services remaining fixed, the resulting increment of product will decrease after a certain point. (c)
Lease A written contract by which the lessor (owner) transfers the rights to occupy and use real or personal property to another (lessee) for a specified time in return for a specified payment (rent). (c)
Legal Description A delineation of dimensions, boundaries, and relevant attributes of a real property parcel that serve to identify the parcel for all purposes of law. The description may be in words or codes, such as metes and bounds or coordinates (see coordinate system). For a subdivided lot, the legal description would probably include lot and block numbers and subdivision name. (c)
Lessee The person receiving a possessory interest in property by lease, that is, the owner of a leasehold estate. (c)
Lessor The person granting a possessory interest in property by lease, that is, the conveyor of a leasehold estate, the holder of a leased fee estate. (c)
Level of Appraisal The common, or overall, ratio of appraised values to market values. Three concepts are usually of interest: the level required by law, the true or actual level, and the computed level, based on a ratio study. (c)
Level of Assessment; Assessment Ratio The common or overall ratio of assessed values to market values. Compare level of appraisal. Note: The two terms are sometimes distinguished, but there is no convention determining their meanings when they are. Three concepts are commonly of interest: what the assessment ratio is legally required to be, what the assessment ratio actually is, and what the assessment ratio seems to be, on the basis of a sample and the application of inferential statistics. When level of assessment is distinguished from assessment ratio, “level of assessment” usually means either the legal requirement or the true ratio, and “assessment ratio” usually means the true ratio or the sample statistic. (c)
Life, Economic See total economic life. (c)
Life, Physical The period over which a physical property is capable of functioning without being scrapped or reconstructed. (c)
Linear Regression A kind of statistical analysis used to investigate whether a dependent variable and a set of one or more independent variables share a linear correlation and, if they do, to predict the value of the dependent variable on the basis of the values of the other variables. Regression analysis of one dependent variable and only one independent variable is called simple linear regression, but it is the word simple (not linear) that distinguishes it from multiple regression analysis with its multiple independent variables. (c)
Loan-To-Value Ratio (M) The relationship (usually as a percentage) between the amount of a mortgage and the value of the security pledged as security for the mortgage. (c)
Location Value Response Surface Analysis A mass appraisal technique that involves creating value influence centers, computing variables to represent distances (or transformations thereof) from such points and using the variables in a multiple regression or other model to capture location influences. Implementation of the technique is enhanced by the use of a geographic information system. Some geographic information systems permit the value influence centers to be displayed and measured as a three-dimensional grid surface, the results of which can be likewise used in calibration techniques to arrive at the contribution of location based on the model specification. (e)
Location Variable A variable, such as the distance to the nearest commercial district or the traffic count on an adjoining street, that seeks to measure the contribution of locational factors to the total property value. (e)
Locational Obsolescence A component of economic obsolescence; loss in value due to suboptimal siting of an improvement. (c)
Logarithm; Log The number that, when used as an exponent for another number (called the base), results in a third number of some practical interest (called the antilogarithm). There are two bases that are used with any frequency; the base 10 produces what are called common logarithms, and the base 2.71828 (e) produces what are called natural logarithms. For example, log(10)100 = 2; 10 =100. Logarithms were originally used to simplify complex calculations involving multiplications inasmuch as two numbers can be multiplied by adding their logarithms and taking the antilog of the result. (c)
Log-Linear Relationship A correlation between two variables such that if the value of one variable changes by a certain percentage, the value of the other changes by a certain amount. (Recall that logarithms permit multiplication to be done by means of adding logs.) For example, there is a log – Linear relationship between x and y in the following sequence: (c)
Long-Lived Items Items that are the basic structure of a building and are not usually replaced during economic life. For example: foundation, roof structure, and framing. (c)
Macroeconomics The economics of the economy as a whole the forces causing recession, depression, and inflation together with the forces resulting in economic growth. (c)
Mann-Whitney Test A test in inferential statistics, similar to the Kruskal – Wallis test, that seeks to determine whether the differences in values between two sets of observations from any population are statistically significant. (c)
Map A conventional representation, usually on a plane surface and at an established scale, of the physical features (natural, artificial, or both) of a part or the whole of the earth’s surface. Features are identified by means of signs and symbols, and geographical orientation is indicated. (c)
Margin of Error A measure of the uncertainty associated with statistical estimates of a parameter. It is typically linked to consumer surveys or political poll questions. A margin of error is a key component of a confidence interval. It reports a “plus or minus” percentage or proportion quantity in a confidence interval at a specified level of probability (typically 95 percent). See confidence interval. (c)
Market  (1) the topical area of common interest in which buyers and sellers interact.

(2) The collective body of buyers and sellers for a particular product. (e)

Market Adjustment Factors Market adjustment factors, reflecting supply and demand preferences, are often required to adjust values obtained from the cost approach to the market. These adjustments should be applied by type of property and area and are based on sales ratio studies or other market analyses. Accurate cost schedules, condition ratings, and depreciation schedules will minimize the need for market adjustment factors. (c)
Market Analysis A study of real estate market conditions for a specific type of property. (e)
Market Analyst An appraiser who studies real estate market conditions and develops mathematical formulas that represent those market conditions. (e)
Market Approach A valuation term with several meanings. In its broadest use, it might denote any valuation procedure intended to produce an estimate of market value, or any valuation procedure that incorporates market – Derived data, such as the stock and debt technique, gross rent multiplier method, and allocation by ratio. In its narrowest use, it might denote the sales comparison approach. (c)
Market Area  (See Economic Area) (e)
Market Price The price a particular buyer and seller agree to in a particular transaction; the amount actually paid. Compare market value. (c)
Market Value  Market value is the major focus of most real property appraisal assignments. Both economic and legal definitions of market value have been developed and refined. A current economic definition agreed upon by agencies that regulate federal financial institutions in the United States is: The most probable price (in terms of money) which a property should bring in a competitive and open market under all conditions requisite to a fair sale, the buyer and seller each acting prudently and knowledgeably, and assuming the price is not affected by undue stimulus. Implicit in this definition is the consummation of a sale as of a specified date and the passing title from seller to buyer under conditions whereby: 1) The buyer and seller are typically motivated; 2) Both parties are well informed or well advised, and acting in what they consider to be their best interests; 3) A reasonable time is allowed for exposure in the open market; 4) Payment is made in terms of cash in United States dollars or in terms of financial arrangements comparable thereto; 5) The price represents the normal consideration for the property sold unaffected by special or creative financing or sales concessions granted by anyone associated with the sale. (e)
Marketability The salability of a property at a specific time, price, and terms. (c)
Market-Related Adjustment Accounts for changes in market conditions between the time a comparable sold and the effective date of the appraisal. See market adjustment factors. (c)
Mass Appraisal The process of valuing a group of properties as of a given date, using standard methods, employing common data, and allowing for statistical testing. (c)
Mass Appraisal Model A mathematical expression of how supply and demand factors interact in a market. (c)
Mean A measure of central tendency. The result of adding all the values of a variable and dividing by the number of values. For example, the (arithmetic mean) of 3, 5, and 10 is 18 divided by 3, or 6. (e)
Mean Absolute Error A measure of an AVM’s precision, mean absolute error is the average percentage error that the model exhibits when evaluating the properties in the test sample. However, analysts take the absolute value of the percentage error (i.e., the magnitude or size of the variance) instead of distinguishing between positive and negative error. For example, a mean absolute error of 8.75 percent indicates that, on average, the AVM’s valuations differ from the reference value by 8.75 percent. (b)
Mean Error The average valuation error. Analysts calculate an AVM’s mean error by totaling the percentage of variance for each value estimate in the sample and dividing by the number of returned valuations. This metric indicates the extent to which an AVM tends to overvalue or undervalue subject properties. For example, a mean error of 2.5 percent suggests that, on average, the model’s valuations overestimate properties’ market values by 2.5 percent. (b)
Median A measure of central tendency. The value of the middle item of an uneven number of items arranged or arrayed according to size; the arithmetic average of the two central items in an even number of items similarly arranged. (e)
Median A measure of central tendency. The value of the middle item in an uneven number of items arranged or arrayed according to size; the arithmetic average of the two central items in an even number of items similarly arranged; a positional average that is not affected by the size of extreme values. (c)
Median Absolute Error A measure of an AVM’s precision, median absolute error enables testers to determine the ‘middle’ degree of variation between an AVM’s outputs and the corresponding benchmarks. To calculate this metric, analysts take the absolute value of the percentage error (i.e., the magnitude or size of the variance) instead of distinguishing between positive and negative error. For example, a median absolute error of the 8.75 percent suggests that half of the model’s predictions are within 8.75 percent of the sale price (whether above or below) and half are outside of that range. As with measures of AVM bias, the majority of users prefer median absolute error to mean absolute error because the former is less sensitive to a few anomalous outliers. (b)
Median Absolute Deviation The median of the absolute deviations from the median. In a symmetrical distribution, the measure approximates one – Half the interquartile range. (c)
Median Error A measure of AVM bias, or the extent to which an AVM tends to overvalue or undervalue subject properties. Analysts determine the median variance by sorting the percentage error for each valuation from lowest to highest. For samples with an odd number of results, the ‘middle’ percentage error represents the median error. In instances where there is an even number of results, the median is the average of the middle two percentage errors. A median error of 2.5 percent suggests that 50 percent of the AVM’s valuation errors will be greater than 2.5 percent and 50 percent of the model’s value predictions will be at least 2.5 percent lower than the benchmark value. In general, the median error is preferable to the mean because a small number of unusually low or high value estimates will not distort the results. (b)
Median Percent Deviation The median of the absolute percent deviations from the median; calculated by dividing the median absolute deviation by one – Hundredth of the median. (c)
Microeconomics The economics of units, such as firms and neighborhoods, of an economic system (as opposed to macroeconomics, which studies the economy as a whole). (c)
Minkowski Metric  Any of a family of possible ways of measuring distance. Euclidean distance, a member of this family, computes straight line distances (as the crow flies) by squaring differences in like coordinates, summing them, and taking the square root of the sum. In mass appraisal model building, Minkowski metric usually refers to the sum of absolute differences (not squared) in each dimension, and resembles a “taxicab’ or city block pattern. Other alternatives are possible, including the distance as calculated only for the dimension of greatest difference, but the city block distance is most common. (e)
Minkowski Metric Any of a family of possible ways of measuring distance. Euclidean distance, a member of this family, computes straight – Line distances by squaring differences in like coordinates, summing them, and taking the square root of the sum. In mass appraisal model building, Minkowski metric usually refers to the sum of absolute differences (not squared) in each dimension, and resembles a “taxicab” or city block pattern. Other alternatives are possible, including the distance as calculated only for the dimension of greatest difference, but the city block distance is most common. (c)
Mode A measure of central tendency. (1) In an array of the values of a variable, the most frequently occurring value.

(2) By extension for grouped data, the class with the greatest number of observations. (c)

Model (1) A representation of how something works.

(2) For purposes of appraisal, a representation (in words or an equation) that explains the relationship between value or estimated sale price and variables representing factors of supply and demand. (e)

Model Representation or description of a system, theory, process, workflow or other phenomenon (e.g. model of an atom, an economic model). A model may attempt to represent how a system or other phenomenon works or functions. For example, computer models are representations of the various relationships among events, processes or values. A model is made up of one or more Methods. (d)
Model (1) A representation of how something works.

(2) For purposes of appraisal, a representation (in words or an equation) that explains the relationship between value or estimated sale price and variables representing factors of supply and demand. (c)

Model Calibration The development of the adjustments or coefficients from market analysis of the variables to be used in an automated valuation model. (e)
Model Preference Table® A Model Preference Table® ranks AVMs approved for use in a particular geographic area based on specific performance criteria. The sequence determines how an automated platform will order valuations from these sequenced models. An MPT® can be an element of a cascade. (a)
Model specification The formal development of a model in a statement or equation, based on data analysis and appraisal theory. (e)
Model Validation The process of demonstrating that an AVM or other econometric model produces results that are valid for a given application. This process involves both evaluating the AVM’s theoretical underpinnings as well as the sufficiency of the vendor’s data management and model calibration efforts. Back-testing the AVM’s outputs against benchmark values is also an important component of model validation. (b)
Moving Average A statistic used to smooth the values of a variable when those values are erratic over distance or time, as in the case of land values and mortgage commitments. For example, a five – Block simple moving average of land values along a major street would assign to block 16 the average of the values for blocks (c)
Multicollinearity Correlation among two or more variables. In regression analysis, high multicollinearity among the independent variables complicates modeling and will compromise the reliability of the resulting coefficients. If the multicollinearity is perfect, the multiple regression algorithms simply will not work and either an error message may result or the software may purge one or more of the problem variables. (e)
Multiple Listing Service (MLS) A computerized database subscription service used by real estate brokers and agents to share information about properties for sale. Hundreds of systems are located throughout the U.S. and Canada to serve local market areas. (c)
Multiple Regression Analysis (MRA) A particular statistical technique, similar to correlation, used to analyze data in order to predict the value of one variable (the dependent variable), such as market value, from the known values of other variables (called “independent variables’), such as lot size, number of rooms, and so on. If only one independent variable is used, the procedure is called simple regression analysis and differs from correlation analysis only in that correlation measures the strength of the relationship, whereas regression predicts the value of one variable from the value of the other. When two or more variables are used, the procedure is called multiple regression analysis. (e)
Multiplicative models A mathematical model in which the coefficients of independent variables serves as powers (exponents) to which the independent variables are raised or in which independent variables themselves serve as exponents; the results are then multiplied to estimate the value of the dependent variable. (e)
Multiplicative Transformation A transformation of a set of variables accomplished by multiplying a variable by one or more other variables. For example, room area is a multiplicative transformation of length and width. (c)
Multivariate Statistical Technique Any of a number of statistical analyses in which data (such as the information on a single property record card) containing a number of variables (such as lot size, number of rooms, and construction type) are analyzed to predict the value of some other variable. See multiple regression analysis. (c)
Natural Logarithm See logarithm. (c)
Neighborhood  (1) The environment of a subject property that has a direct and immediate effect on value.

(2) A geographic area (in which there are typically fewer than several thousand properties) defined for some useful purpose, such as to ensure for later multiple regression modeling that the properties are homogenous and share important locational characteristics. (e)

Neighborhood The geographic area used to evaluate the Subject Property. (d)
Neighborhood Analysis  A study of the relevant forces that influence property values within the boundaries of a homogenous area. (e)
Net Assessed Value The official dollar value remaining on an assessment roll after deducting the amount of any applicable partial exemptions from the gross assessed value. (c)
Neural Network (ANN) A collection of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. An artificial neural network has several key elements: input, processing (calibration), and output. Other names associated with neural networks include connectionism, parallel distributed processing, neuro – Computing, natural intelligent systems, and machine learning algorithms. (c)
Nonconforming Use (1) A use of property that does not comply with the applicable zoning ordinance.

(2) Uses that differ noticeably from prevailing uses in a neighborhood are sometimes also referred to as “nonconforming.” (c)

Nondisclosure States States that do not disclose sales prices when county clerks record real estate transactions. Unless a vendor has access to market data from an alternative source, AVM performance generally suffers in nondisclosure states. There are currently twelve nondisclosure states – Alaska, Idaho, Kansas, Louisiana, Mississippi, Missouri, Montana, New Mexico, North Dakota, Texas, Utah and Wyoming. (b)
Nonparametric Statistic A statistic whose interpretation or reliability does not depend on the distribution of the underlying data. (c)
Nonsampling Error The error reflected in ratio study statistics from all sources other than sampling error. While nonsampling error is unavoidable due to the inefficiencies inherent in real property markets, the imperfections of the appraisal process, and the imperfections of conducting ratio studies, all practicable steps must be taken to minimize nonsampling error in ratio studies. (c)
Normal Distribution A theoretical distribution often approximated in real-world situations. It is symmetrical and bell-shaped; 68 percent of the observations occur within one standard deviation of the mean, and 95 percent within two standard deviations. (c)
Null Hypothesis A hypothesis that one chooses to accept in the absence of sufficient evidence to the contrary. (c)
Numerator In a fractional expression, the number that is divided by some other number (the denominator). For example, 3 is the numerator of the fraction 3/4, 4 is the denominator. (c)
Object of Tax (1) The objective element on which a tax is imposed.

(2) The aims or purposes for which the tax is designed. Note: This term, as defined in (1) above, does not seem to be distinguished adequately from “measure of tax,” “subject of tax,” and “tax base.” Such distinction as can be drawn is pedantic, and the use of the term in this sense is not recommended. It is also recommended that the term “purpose of tax” be substituted for the second usage given above. Compare measure of tax; subject of tax; tax base. (c)

Objective Data Objective data are variables for which the correct value can be verified. Examples include zoning, corner lot (yes or no), land area, and front feet. (c)
Observation One recording or occurrence of the value of a variable, for example, one sale ratio among a sample of sales ratios. (c)
Obsolescence A decrease in the value of a property occasioned solely by shifts in demand from properties of this type to other types of property and/or to personal services. Some of the principal causes of obsolescence are (1) changes in the esthetic arts;

(2) changes in the industrial arts, such as new inventions and new processes;

(3) legislative enactments;

(4) change in consumer demand for products that results in inadequacy or over-adequacy;

(5) migration of markets that results in misplacement of the property. Contrast depreciation, physical; depreciation, economic. (c)

Occupancy The act of taking or holding possession of property. (c)
Opportunity Cost The principle that the cost of a resource for one use is the value of the resource in its best alternative use. (c)
Outlier Instances of extreme undervaluation or overvaluation compared against a known value. (a)
Outliers Observations that have unusual values, that is, they differ markedly from a measure of central tendency. Some outliers occur naturally; others are due to data or modeling errors. (a)
Outputs (1) Goods produced by a firm.

(2) The information returned by a computer to its user. (c)

Overimprovement An improvement whose cost exceeds the cost of an alternative improvement by more than the excess of the present worth of the given improvement and the land over the present worth of the alternative improvement and the land, often because a structure is too large or too costly for the most profitable use of the site. Contrast underimprovement. (c)
Ownership The rights to the use of property, to the exclusion of others. (c)
Parallax The apparent displacement of position of a body with respect to a reference point or system of coordinates, caused by moving the point of observation. (c)
Parameter Numerical descriptive measure of the population, for example, the arithmetic mean or standard deviation. Parameters are generally unknown and estimated from statistics calculated from a sample of the population. (c)
Parametric Statistic A statistic whose interpretation or reliability depends on the distribution of the underlying data. (c)
Parcel A contiguous area of land described in a single legal description or as one of a number of lots on a plat; separately owned, either publicly or privately; and capable of being separately conveyed. (c)
Parcel Identification Number A numeric or alphanumeric description of a parcel that identifies it uniquely. Assessors use various systems, many with common features. A growing number of these systems include geocoding. In the thirty states where it exists, the Public Land Survey System, authorized by the United States Government in 1785, is often a basis for parcel identification. (c)
Partial Equilibrium Analysis An analysis of one unit of the economy in light of constraints imposed by economic forces outside the unit. An example would be a highest and best use analysis of vacant land where the land use is limited by zoning. (c)
Partial Interest An interest (in property) that is less complete than a fee simple interest. (c)
Percentage Error The building block of all accuracy-related performance metrics. Analysts calculate percentage error in the following manner: Percentage Error = (AVM Value – Benchmark Value x 100%) / Benchmark Value (b)
Percentile The values that divide a set of data into specified percentages when the data are arrayed in ascending order. The tenth percentile includes the lowest 10 percent of the values, the twentieth percentile includes the lowest 20 percent of the values, and so forth. (c)
Physical Deterioration A cause of depreciation that is a loss in value due to ordinary wear and tear and the forces of nature. (c)
Platform A software solution that automates an organization’s AVM selection rules and valuation acceptance criteria. This technology is particularly useful for implementing a cascade and ensuring enterprise-wide compliance with collateral valuation policies. (b)
Point Estimate A single numerical value that can be used to estimate a population parameter. It is calculated on the basis of information collected from a sample. Point estimates are generally constructed to provide the best unbiased estimate of the population parameter consistent with the sample data. However, the point estimate is only an estimate and is unlikely to have the same value as the population parameter. (See confidence interval and reliability for discussion of precision of the sampling process.) (c)
Polygon A line chart. (c)
Pooled Regression Combining two or more strata to form one regression model. (c)
Population All the items of interest, for example, all the properties in a jurisdiction or neighborhood; all the observations in a data set from which a sample may be drawn. (c)
Position Hit Rate The hit rate for an AVM that is in a second or subsequent position within an AVM cascade expressed as the percentage of AVM valuations returned relative to the number of properties submitted to that AVM (i.e., those properties where the preceding AVM(s) did not return a usable value). (b)
PRB See coefficient of price-related bias. (c)
PRD See price-related differential. (c)
Precision The dispersion of a model’s valuation errors, with greater dispersion being considered less precise, and lower dispersion being considered more precise. Mean and median absolute error, standard deviation and forecast standard deviation are all measures of an AVM’s precision. (b)
Precision The level of detail in which a quantity or value is expressed or represented. It can be characterized as the number of digits used to record a measurement. A high level of represented precision may be used to imply a greater level of accuracy; however, this relationship may not be true. Precision also relates to the quality of an operation or degree of refinement by which results are obtained. A method of measurement is considered precise if repeated measurements yield the same or nearly the same numeric value. See accuracy and statistical precision. (c)
Price The amount asked, offered, or paid for a property. (See USPAP for additional comments.) (c)
Price, Adjusted Sale The sale price that results from adjustments made to the stated sale price to account for the effects of time, personal property, atypical financing, and the like. (c)
Price, Sale (1) The actual amount of money exchanged for a unit of goods or services, whether or not established in a free and open market. An indicator of market value.

(2) Loosely used synonymously with “offering” or “asked” price. Note: The sale price is the “selling price” to the vendor and the “cost price” to the vendee. (c)

Price-Related Differential (PRD) The mean divided by the weighted mean. The statistic has a slight bias upward. Price-related differentials above 1.03 tend to indicate assessment regressivity; Price-related differentials below 0.98 tend to indicate assessment progressivity. (c)
Principle of Change The principle of change asserts that all markets are in a continual state of change. According to this principle, properties generally go through the three stages of integration (development), equilibrium (stasis), and disintegration (decline). (c)
Principle of Conformity The principle of conformity states that the value of a group of properties will rise to its highest possible level in an area where architectural styles are reasonably homogenous and surrounding land uses are compatible with the use of the specified properties. (c)
Principle of Contribution The principle of contribution requires an appraiser to measure the value of any improvement to a property by the amount it contributes to market value, not by its cost. (c)
Principle of Progression The principle of progression holds that the worth of an inferior property is increased by its proximity to better properties of the same use class. (c)
Principle of Substitution The principle of substitution states that no buyer will pay more for a good than he or she would have to pay to acquire an acceptable substitute of equal utility in an equivalent amount of time. (c)
Progressivity See assessment progressivity (regressivity). (c)
Property (1) An aggregate of things or rights to things. These rights are protected by law. There are two basic types of property: real and personal.

(2) The legal interest of an owner in a parcel or thing (see bundle of rights). (c)

Property Residual Technique A technique used to estimate the value of a property from a knowledge of its net operating income, discount rate, remaining economic life, the amount of the reversion, and the income path attributable to the property over the holding period (generally the remaining economic life of the property). The technique estimates total value by discounting anticipated income and adding the result to the present worth of the reversion. (c)
Property Rights See bundle of rights. (c)
Property Tax Base See assessment base. (c)
Property Tax Policy Any purposeful course of action by governmental bodies that affects or determines the way property taxes are created, levied, collected, or spent. Property tax policy issues can include determining the division of responsibility between local and state/provincial governments, equalization, appeals, public relations, reappraisal systems, the market value standard, exemptions and abatements, fractional assessments (ratios), and limits on taxes and assessed values. (c)
Property Taxes See tax, property. (c)
Property Use Category A United States Census Bureau classification scheme based on actual utilization of real property. There are seven classes of real property:

1)      Residential (Nonfarm) Single-Family—Includes each detached, semidetached, or attached house, if separately assessed and not on a farm, that is a residence for one family only. For detached houses, this would include one-family rural properties or suburban estates not used primarily for farming, and mobile homes assessed as real property. This category includes each condominium unit in a multiunit dwelling structure, plus each condominium’s share of the common area, unless the common area is separately assessed.

2)      Residential (Nonfarm), Multifamily—Includes each residential property that contains two or more living units, including duplexes, apartment houses, and cooperatives that are assessed as a single entity. The category encompasses street level stores and doctors’ offices in apartment buildings, but excludes motels or hotels.

3)      Acreage (or “Acreage and Farms”)—Includes farms, timberland, recreational acreage, idle land, and waste land in rural locations. Excludes vacant platted lots that lie within or adjacent to a municipality and that usually carry a lot/block system designation rather than acreage. Separately assessed timber or mineral rights are omitted from this category.

4)      Vacant Platted Lots—Unimproved parcels described in terms other than acreage, usually by a convention using lot, block, and subdivision name. Vacant platted lots are often located either within a municipality or in areas of higher population density than the surrounding territory.

5)      Commercial Property—Generally any nonindustrial, nonresidential realty of a commercial enterprise. Includes realty used as a retail or wholesale establishment, retail establishment with living quarters, office building, hotel or motel, gasoline service station, commercial garage, parking lot, warehouse, theater, bank, clinic, nursing home, proprietary school, and the like.

6)      Industrial Property—Generally any property used in a manufacturing activity, including a factory, wholesale bakery, dairy plant, food processing plant, mill, mine, quarry, all locally assessed utility property, and the like.

7)      Other and Unallocable—Includes any property not classified within any of the preceding groups. Examples are mineral rights, timber rights, and oil rights, if they are separately assessed as real estate. (c)

Proposition 13 States States where the tax assessment value is based on the acquisition price rather than the property’s current market value. Tax assessment models do not work in these states. (b)
Qualified Sale A property transfer that satisfies the conditions of a valid sale and meets all other technical criteria for inclusion in a ratio study sample. If a property has undergone significant changes in physical characteristics, use, or condition in the period between the assessment date and sale date, it would not technically qualify for use in ratio study. (c)
Qualitative Data Pertaining to the subjective nature of some variable of interest. For example, view, fire protection, quality, or site/location. (c)
Qualitative Variable Something that can be appreciated but not objectively reduced to an unambiguous scale. For example, view is a qualitative variable. (c)
Quality Class A subjective classification of a structure by an appraiser, intended to describe materials used, workmanship, architectural attractiveness, functional design, and the like. Quality class, or its synonym “grade,” is the key variable in most cost schedules. (c)
Quantitative Variable Pertaining to the objective nature of some variable of interest, that is, something that can be measured or counted with little ambiguity. For example, number of bathrooms is a quantitative variable. (c)
Quartiles The values that divide a set of data into four equal parts when the data are arrayed in ascending order. The first quartile includes the lowest quarter of the data; the second quartile, the second lowest quarter, and so forth. (c)
Quotient Transformation A transformation of two or more variables accomplished by dividing one by the other. For example, the length of a room is a quotient transformation of its area and its width. Like the multiplicative transformation, many useful quotient transformations are less intuitively obvious than the one in the example given. (c)
Radius The radius of the circle that encompasses all of the Comps to indicate the relative distance of the Comps to one another and the Subject Property. (d)
Random Sample A sample for which each item of the population has an equal chance of being included and, by extension, each possible combination of n items has an equal chance of occurrence. (c)
Range (1) The maximum value of a sample, minus the minimum value.

(2) The difference between the maximum and minimum values that a variable may assume. (c)

Rank (1) The position of an item relative to others in a set ordered according to the value of each member of the set in relation to the others.

(2) The act of ordering the members of a set according to the value of each member in relation to the others. For example, the numbers 0.95, 0.87, 1.09, and 0.83 have ranks of 3, 2, 4, 1. See Spearman rank test. (c)

Ratio Study  A study of the relationship between appraised or assessed values and market values. Indicators of market values may either be sales (sales ratio study) or independent “expert” appraisals (appraisal ratio study). Of common interest in ration studies are the level and uniformity of the appraisals and assessments. (e)
RCN Replacement cost new or reproduction cost new. (c)
RCNLD Replacement cost new less depreciation or reproduction cost new less depreciation. (c)
Real Estate The physical parcel of land and all improvements permanently attached. Compare real property. (c)
Real Estate Appraisal According to the federal banking agencies’ appraisal regulations, a real estate appraisal must comply with USPAP (unless safety and soundness requires adherence to stricter standards); be written and contain sufficient information for the lender to engage in the transaction; contain an estimate of market value; and be performed by a state-licensed or state-certified appraiser. (b)
Real Estate Evaluation For certain transactions (e.g., those that are below the de minimis), the federal banking regulators allow lending institutions under their jurisdiction to obtain a real estate evaluation in lieu of an appraisal. According to the “Interagency Appraisal and Evaluation Guidelines,” evaluations must include the effective date of the valuation; describe the real estate collateral, its condition, as well as its current and projected use; describe the information source(s) used in the analysis; describe the analysis and supporting information; and provide an estimate of the real estate’s market value, with any limiting conditions. Provided the lender properly validates the model and develops appropriate usage rules, an AVM’s output usually qualifies as a real estate evaluation. (b)
Real Estate Transfer Documents The various kinds of deeds whereby real property is conveyed. Compare conveyances. (c)
Real Property See property. (c)
Real Time To make images or data available for inspection simultaneously with their acquisition. (c)
Realty (1) Any tangible thing whose fee ownership constitutes real property, that is, land or improvements.

(2) A synonym for real estate. (c)

Reciprocal The result obtained when 1 is divided by a given number. For example, the reciprocal of 4 is 0.25. Factors are reciprocals of rates. (c)
Reconciliation The final step in the valuation process wherein consideration is given to the relative strengths and weaknesses of the three approaches to value, the nature of the property appraised, and the quantity and quality of available data in formation of an overall opinion of value (either a single point estimate or a range of value). Also termed “correlation” in some texts. (c)
Reference Type/Benchmark Type The source of the Reference Value (e.g. 1004, 2055, sales price). (d)
Reference Value The value against which the AVM predicted value will be compared against. (d)
Regression Analysis See multiple regression analysis. (c)
Regression Coefficient The coefficient calculated by the regression algorithm for the data supplied that, when multiplied by the value of the variable with which it is associated, will predict (for simple regression) or help to predict (for multiple regression) the value of the dependent variable. For example, in the equation, Value = $10,000 + $5,000 × number of rooms, $5,000 is a regression coefficient. (c)
Regression Line The line on a graph that represents the relationship defined by the regression coefficients. For example, the line from the relationship given in the definition of regression coefficient would cross the y – Axis at the value $10,000 and would go up $5,000 for each movement of 1 to the right. This example illustrates one of the subtleties required in understanding regression analysis: in fact, there is no line, because the independent variable is not a continuous variable, but it is easier to talk about the relationship by pretending that the variable is continuous and represent the relationship by a line rather than the more nearly correct series of vertical bars on a bar chart. (c)
Regressivity See assessment progressivity (regressivity). (c)
Regressivity Index See Price-related differential. (c)
REIT Real estate investment trust; combines capital of many investors to acquire or finance real estate through formation of a corporation whose shares are traded in a market. (c)
Relationship The phenomenon whereby knowledge of the value of one variable tells you something about the probable value of another. (See correlation.) Relationships may be positive (an increase in the value of one variable implies an increase in the value of the other variable) or negative (a change in the value of one variable implies a change in the other direction for the value of the other variable). Independence of two variables means that there is no relationship between them. (c)
Reliability In a sampling process, the extent to which the process yields consistent population estimates. Ratio studies typically are based on samples. Statistics derived from these samples may be more or less likely to reflect the true condition in the population depending on the reliability of the sample. Representativeness, sample size, and sample uniformity all contribute to reliability. Formally, reliability is measured by sampling error or the width of the confidence interval at a specific confidence level relative to the central tendency measure. (c)
Remaining Economic Life (REL) As of the appraisal date, the number of years in the future over which the operation of an asset is anticipated to be economically feasible; often expressed as a percentage of the total economic life (REL %). (c)
Remodel To improve a structure by changing its floor plan, functions, or characteristics. (c)
Repeat Sales Analysis Model Repeat Sales analysis aggregates changes in value and statistical means for properties sold more than once during a specified period of time in a given geographic area. For example, in a zip or postal code area, estimate market-level housing price changes. If an individual property has not been substantially changed since its last sale, this analysis matches each pair of sales transactions (thus the name “repeat sales”). The amount of appreciation (or depreciation) is calculated from the time of the first sale to the second and so on, providing an estimate of the overall appreciation of that local housing market during that time period. The larger the number of available sales pairs, the more statistically reliable the estimate of overall housing price trends will be. Because this analysis is based on identifying properties where more than one sale has occurred, the challenge is to identify enough observations to provide a meaningful index of housing values, while keeping to as small a geographic area as possible. A repeat sales index may also overestimate market appreciation if the data contains pairs of sales in which the second sales price reflects substantial improvements (or other alterations) made to the property after the first sale. On the other hand, repeat sales indices can and do provide very useful valuation estimates in jurisdictions where the data is insufficient to support hedonic models. In addition, they may prove more accurate in tracking housing values for the houses that a hedonic model may struggle with (especially those subject to extreme positive or negative influences) when a prior sale is known on the property. (e)
Repeat Sales Index Repeat sales models examine the sales prices of the same property at two (or more) points in time, thus accounting for the affects that spatial factors and individual property characteristics have on market value. Examples include the OFHEO’s House Price Index and the S&P/Case-Shiller® Home Price Indices. (b)
Replacement Cost New Less Depreciation (RCNLD) In the cost approach, replacement cost new less physical incurable depreciation. (c)
Replacement Cost; Replacement Cost New (RCN) The cost, including material, labor, and overhead, that would be incurred in constructing an improvement having the same utility to its owner as a subject improvement, without necessarily reproducing exactly any particular characteristics of the subject. The replacement cost concept implicitly eliminates all functional obsolescence from the value given; thus, only physical depreciation and economic obsolescence need to be subtracted to obtain replacement cost new less depreciation (RCNLD). (c)
Representative Sample A sample of observations from a larger population of observations, such that statistics calculated from the sample can be expected to represent the characteristics of the population being studied. (c)
Residence (1) A domicile.

(2) A domicile at which a person is actually dwelling.

(3) A dwelling place, whether or not it constitutes a domicile (preferred). Note: Ordinarily, in the law of taxation, “residence” means “domicile” unless a contrary meaning is specified or is indicated by the context. (c)

Residential (nonfarm) Multifamily See Property. (c)
Residential (nonfarm) Single Family See Property. (c)
Residential Property Real property that might be vacant land or an improved parcel of land devoted to or available for residential use. See property use category. (c)
Residual The difference between an observed value and a predicted value for a dependent variable. (c)
Residual Technique A method of arriving at the unknown value of a property component by subtracting the known values of other components from a known overall value. (c)
Residual Value The value of the property after cleanup of environmental contamination. This may be more or less than the original value depending on counterbalancing effects of stigma and improvements to plant efficiency. (c)
Result Code Most AVMs return a result code that explains the outcome of each submission. For instance, result codes should identify when a successful valuation was returned, why a valuation was obtained but not returned (e.g., the value fell below the model’s minimum confidence score thresholds), or why a valuation was not obtained (e.g., ‘unable to verify address’ or ‘invalid property type’). (b)
Retrospective (“Retro”) Valuation Estimate of Value for subject property at a point in time in the past utilizing data prior to a specified date of stated value. (d)
Root Mean Square (RMS) The square root of the average value of the sum of the squares of the differences between values in a set and the corresponding values that have been accepted as correct or standard. Used to measure map accuracy. (c)
Sale of Convenience A sale designed to correct defects in a title, create a joint or common tenancy, or serve some similar purpose. Such sales generally are transacted at only a nominal price. (c)
Sale Price See price, sale; price, adjusted sale. (c)
Sale Price-Assessment Ratio The reciprocal of the assessment sale price ratio. (c)
Sale Terms The amount of down payment, the interest on the mortgage, and information on points and other fees involved in a real estate sale. Also called “terms of financing” or “financing terms.” (c)
Sale, Arm’s-Length A sale in the open market between two unrelated parties, each of whom is reasonably knowledgeable of market conditions and under no undue pressure to buy or sell. (c)
Sale, Conditional A sale, especially of chattels, in which the transfer of title is made to depend on the performance of a condition subsequent to the making of the sales contract and delivery of goods. Note: The most common condition is that the remainder of the purchase price be paid. Property held under a conditional sales contract may be repossessed without foreclosure proceeding, and the former holder has no equity of redemption. Compare mortgage, chattel. (c)
Sale, Distress A sale made to meet the immediate and pressing needs of the seller at whatever price the property will bring. (c)
Sale, Forced A sale made pursuant to law; usually an auction sale that is involuntary on the part of the owner. (c)
Sale, Normal A sale in which neither the buyer nor the seller acts under legal or economic compulsion, in which both parties are reasonably well informed, and in which both are primarily actuated by economic motives. Compare value, market and sale, arm’s length. (c)
Sale-Leaseback A sale and subsequent lease given by the buyer back to the seller as part of the same transaction. (c)
Sales Comparison One of the three approaches to value, the sales comparison approach estimates a property’s value (or some other characteristic, such as its depreciation) by reference to comparable sales. (e)
Sales Data (1) Information about the nature of the transaction, the sale price, and the characteristics of a property as of the date of sale.

(2) The elements of information needed from each property for some purpose, such as appraising properties by the direct sales comparison approach. (c)

Sales Ratio Study A ratio study that uses sale prices as proxies for market values. (c)
Sales Ratio/Assessment Ratio The ratio of an appraised (or assessed) value to the sale price or adjusted sale price of a property. See Assessment-Sale price ratio. (c)
Sample Actual properties submitted to an AVM Vendor for evaluation. Subset of Population. (d)
Sample Size The number of properties submitted to the AVM Vendor for valuation. (d)
Sampling Error The error reflected in ratio study statistics that results solely from the fact that a sample of the population is used rather than a census of the population. (c)
Scatter Diagram or Scatterplot A graphic means of depicting the relationship or correlation between two variables by plotting one variable on the horizontal axis and one variable on the vertical axis. Often in ratio studies it is informative to determine how ratios are related to other variables. A variable of interest is plotted on the horizontal axis, and ratios are plotted on the vertical axis. (c)
Short-Lived Items Items of a structure that have a shorter life than the basic structure. For example, roofing, water heaters, floor covering, and interior finish. (c)
Significance A measure of the probability that an event is attributable to a relationship rather than merely the result of chance. (c)
Site The location of a person, thing, or event. (c)
Site Amenities The specific location-related positive attributes of a property: topography, utilities, street traffic, view, and so on. (c)
Site Analysis A study that determines the suitability of a specific parcel of land for a specific purpose. (c)
Site Characteristics (1) Characteristics of (and data that describe) a particular property, especially land size, shape, topography, drainage, and so on, as opposed to location and external economic forces.

(2) By extension, any characteristics of either the site or the improvement. (c)

Site Development Improvements made to a land site (for example, grading, utility installation, roadways, and curbs) before a building is constructed. (c)
Situs The actual or assumed location of a property for purposes of taxation. In personal property, situs may be the physical location of the property or, in the instance of highly mobile property, the more – Or – Less permanent location of the property owner. (c)
Skewed The quality of a frequency distribution that makes it asymmetrical. Distributions with longer tails on the right than on the left are said to be skewed to the right or to be positively skewed; distributions with longer tails to the left are said to be skewed to the left or to be negatively skewed. (c)
Slope The change in the dependent variable associated with a change of one in the independent variable of interest. The slope is given by the coefficient of the independent variable. (c)
Soft Benchmark Values Benchmark values used to monitor an AVM or AVM cascade’s performance in the production environment. Because actual sales prices may not be available for these properties, lenders use ‘soft’ value such as pre-closing sales prices, borrowers’ estimates of value, an appraisal, or the results of a desktop or field review to track general trends in an AVM’s predictive accuracy. (b)
Software Anything that is stored electronically on a computer is software. The storage device is hardware. There are two general categories of software, operating systems and the utilities that allow the computer to function, and applications, programs that allow users to work with the computer, e.g. word processing, spreadsheets, databases, AVMs. (e)
Spatial Refers to the location of, proximity to, or orientation of objects with respect to one another in N – Dimensional space. Generally, refers to phenomena that can be mapped in two or three dimensions on or near the earth’s surface. (c)
Spearman Rank Test A standard nonparametric test useful in examining assessment bias, among other things. It is based on the correlation of two sets of ranks. (c)
Square Foot A unit of area equal to a square one foot in length on each side. (c)
Standard 6 See Uniform Standards of Professional Appraisal (c)
Standard Deviation Analysts frequently measure dispersion of valuation error in terms of a standard deviation, a common statistical formula which measures how tightly these variances are clustered around the mean percentage error. If the distribution of an AVM’s valuation errors follows a normal bell-shaped curve, then roughly 68 percent of its value estimates will fall within plus or minus one standard deviation of the mean variance. A lower standard deviation indicates a tight concentration around the mean variance; an elevated standard deviation suggests that the model performs inconsistently. (b)
Standard Deviation The statistic calculated from a set of numbers by subtracting the mean from each value and squaring the remainders, adding together all the squares, dividing by the size of the sample less one, and taking the square root of the result. When the data are normally distributed, one can calculate the percentage of observations within any number of standard deviations of the mean from normal probability tables. When the data are not normally distributed, the standard deviation is less meaningful, and one should proceed cautiously. (c)
Standard Error A measure of the precision of a measure of central tendency; the smaller the standard error, the more reliable the measure of central tendency. Standard errors are used in calculating a confidence interval about the arithmetic mean and the weighted mean. (c)
Standard Error of the Estimate (SEE) An expression for the standard deviation of the observed values about the regression line; thus, it provides an estimate of the variation likely to be encountered in making predictions from the regression equation. (c)
Standardize (1) To transform a variable to standard form; that is, to make the mean of the frequency distribution equal to zero and the standard deviation equal to one.

(2) To adjust, for appraisal purposes, reported data such as income and expenses, so as to remove the effects of non – Real estate factors, such as abnormally good or bad management, weather, and the like. The more common term for this adjustment process is “normalization.” (c)

Statistical Accuracy The closeness between the statistical estimate and the true (but unknown) population parameter value it was designed to measure. It is usually characterized in terms of error or the potential significance of error and can be decomposed into sampling error and nonsampling error components. Accuracy can be specified by the level of confidence selected for a statistical test. See accuracy. (c)
Statistical Precision A reference to how closely the survey results from a sample can reproduce the results that would be obtained from the entire population (a complete census). The amount by which a sample statistic can vary from the true population parameter is due to error. Even if all the sample data are perfectly accurate, random (sampling) error affects statistical precision (measured by the standard error or standard deviation). The dispersion of ratios in the population and the sample size have a controlling influence over the precision of any statistical estimate. When the reliability of a statistical measure is being evaluated, narrower confidence intervals have greater precision. See precision. (c)
Statistics (1) Numerical descriptions calculated from a sample, for example, the median, mean, or coefficient of dispersion. Statistics are used to estimate corresponding measures, termed parameters, for the population.

(2) The science of studying numerical data systematically and of presenting the results usefully. Two main branches exist: descriptive statistics and inferential statistics. (c)

Stepwise Regression A kind of multiple regression analysis in which the independent variables enter the model, and leave it if appropriate, one by one according to their ability to improve the equation’s power to predict the value of the dependent variable. (e)
Stepwise Regression Analysis A kind of multiple regression analysis in which the independent variables enter the model, and leave it if appropriate, one by one according to their ability to improve the equation’s power to predict the value of the dependent variable. (c)
Stigma A perception that a property continues to be contaminated even though it has been cleaned up. Stigma may affect property value. (c)
Stratification The division of a sample of observations into two or more subsets according to some criterion or set of criteria. Such a division may be made to analyze disparate property types, locations, or characteristics, for example. (e)
Stratify To divide, for purposes of analysis, a sample of observations into two or more subsets according to some criterion or set of criteria. (c)
Subject Property The property being valued. (d)
Subjective Having the quality of requiring judgment in arriving at an appropriate answer or value for a variable (such as the quality class of a structure). See qualitative variable; quantitative variable. (c)
Subjective Data Subjective data are items for which the proper value is a matter of judgment and more difficult to verify. Examples include construction class, condition, effective year built, neighborhood desirability, and view. (c)
Subset A group of properties within a sample, smaller than the sample, usually although not necessarily defined by stratification rather than by sampling. (c)
Substitution The appraisal principle that states that a potential owner will pay no more for a property than the amount for which a property of like utility may be purchased; that a property’s value tends to be set by the cost of acquiring an equally desirable substitute. (c)
Sum of Squared Errors The sum of the squared deviations from the predicted values (rather than the mean value). (c)
Sum of Squares The result obtained by adding all the squares of the individual deviations from some given value. Usually it is the sum of the squares of the deviations of the individual values of a variable from the mean value. (c)
Superadequacy A feature of a property exceeding in quality or amount the corresponding feature in a typical property of the same use. Superinsulation is one example. Superadequacies fall into the larger category of functional obsolescence. (c)
Superpositioning The capability of overlaying, normally an aerial image and a line map, for the purpose of data collection or data maintenance. (c)
Tangible Property; Tangibles See property. (c)
Tax A compulsory charge levied by a government unit against the income or property of a person, natural or corporate, for the common benefit of all citizens. The term does not include specific charges made against particular persons or property for current or permanent benefits and privileges accruing only to those paying such charges, such as licenses, permits, and specific assessments. (c)
Tax Assessed A Valuation Method that provides an estimate of value as a function of the tax assessed value by applying a multiplier or other adjustment to the tax assessed value. (d)
Tax Assessed Value Model Derive an estimate of value by examining market values attributed to properties by the local taxing authorities. As a matter of local law and custom, the values reported by the taxing authorities often (but not always) vary from the current market value in some reasonably predictable manner. For example, some jurisdictions require the taxing authority to report the value at 25 percent of estimated market value. In others, values are reassessed only on an infrequent basis. Some jurisdictions report multiple values – Assessed, appraised, and market values. By examining local laws and customs with respect to how that value is derived, it is often possible to provide a general adjustment to values reported by taxing authorities to better approximate current market value. (c)
Tax Assessment Models Tax assessment models estimate market value based on local taxing authorities’ annual (often less than annual) evaluations of properties located within their jurisdictions. These models do not function in Proposition 13 states. (a)
Tax Burden Economic costs or losses resulting from the imposition of a tax. Burden can be determined only by detailed economic analysis of all economic changes resulting from the tax. In popular usage, the term often refers to the initial incidence rather than to ultimate economic costs. (c)
Tax District (1) In general, a state or any political subdivision thereof having or exercising the power to levy taxes.

(2) As applied to property taxes, any area, whether coterminous with or within a state or a political subdivision thereof, within which the tax rate levied by such state or political subdivision is required by law to be uniform on properties of the same class. (c)

Tax Rate (1) The amount of tax stated in terms of a unit of the tax base, for example, 30 mills per dollar, 2 percent, 2 cents per gallon.

(2) For the property tax, the percentage of assessed value at which each property is taxed in a given district. Distinguish between effective tax rate and nominal tax rate. (c)

Tax Roll An official list showing the amount of taxes charged against each taxpayer and/or each property within the jurisdiction of a tax district. Note: In property taxation, the tax roll is sometimes combined with the assessment roll into a single document. (c)
Tax, Ad Valorem A tax levied on a base that is measured by value. Note: This term is often used to refer only to property taxes or to general property taxes, although technically it is applicable to income taxes, ad valorem tariffs, special property taxes, and so on. Contrast tax, specific. (c)
Tax, Betterment See assessment, special. (c)
Tax, Property Any tax that is imposed on persons on account of their ownership or possession of property and is measured by the number of units, the value, or some presumptive evidence of number of units or value, of such property. Note: This tax is generally, but not necessarily, intended to be a direct, proportional ad valorem tax. Compare tax, special property. (c)
Tax, Proportional A tax in which the effective tax rate is the same for all taxpayers regardless of the sizes of the tax bases on which they are subject to taxation. Contrast tax, progressive; tax, regressive. (c)
Tax, Regressive (1) A tax in which the effective rate is higher for a taxpayer subject to taxation on a small tax base than for a taxpayer subject to taxation on a large tax base.

(2) Loosely used to refer to any tax that absorbs a smaller proportion of the wealth or income of the well – To – Do classes than of the poorer classes. Note: A tax is said to be regressive in administration, though not legally regressive, when the ratio of the actual base to the statutory base declines as the statutory base increases, in such manner as to nullify a proportional statutory rate or to make a progressive statutory rate actually regressive. The same usage is conversely applicable to the terms “progressive tax” and “proportional tax,” but is less commonly associated with them. Contrast tax, progressive; tax, proportional. (c)

Three Approaches to Value A convenient way to group the various methods of appraising a property. The cost approach encompasses several methods for estimating replacement cost new of an improvement less depreciation plus land value. The sales comparison approach estimates values by comparison with similar properties for which sale prices are known. The methods included in the income approach are based on the assumption that value equals the present worth of the rights to future income. (c)
Time Series Analysis A family of techniques that can be used to measure the cyclical movements, random variations, seasonal variations, and secular trends observed over a period of time. (e)
Time-Adjusted Sale Price The price at which a property sold, adjusted for the effects of price changes reflected in the market between the date of sale and the date of analysis. (c)
Tolerance An acceptable margin of error or inaccuracy. (c)
Topographic Map Refers to the basic description and elevation of a piece of land. (c)
Topography The contour of land surface, for example, gently rolling, mountainous, or flat. (c)
Total Economic Life The period of time or units of production over which the operation of an asset is economically feasible, not necessarily the same as its physical life. (c)
Transaction Type The transaction associated with the Reference Value (e.g. purchase, cash-out refinance, rate-term refinance). (d)
Trending Adjusting the values of a variable for the effects of time. Usually used to refer to adjustments of assessments intended to reflect the effects of inflation and deflation and sometimes also, but not necessarily, the effects of changes in the demand for microlocational goods and services. (c)
Trending Factor A figure representing the increase in cost or selling price over a period of time. Trending accounts for the relative difference in the value of a dollar between two periods. (c)
Trimmed Mean The arithmetic mean of a data set identified by the proportion of the sample that is trimmed from each end of the ordered array. For example, a 10 percent trimmed mean of a sample of size ten is the average of the eight observations remaining after the largest and smallest observations have been removed. (c)
t-Statistic A particular statistic important in inferential statistics for certain kinds of hypothesis testing of certain kinds of data. (c)
t-Test A particular parametric statistical test useful, among other things, in testing the level of assessment. (c)
Two-Tailed Test A test in which the alternative hypothesis does not specify the direction of the relationship, as opposed to a one-tailed test, in which the direction of relationship is specified. For example, the alternative hypothesis that “a does not equal b” implies a two – Tailed test, whereas “a is greater than b” implies a one-tailed test. See null hypothesis. (c)
Underimprovement An improvement that does not develop a site to its highest and best use; usually a violation of the principle of conformity. Contrast overimprovement. (c)
Uniform Standards of Professional Appraisal Practice Annual publication of the Appraisal Standards Board of The Appraisal Foundation: “These Standards deal with the procedures to be followed in performing an appraisal, review or consulting service and the manner in which an appraisal, review or consulting service is communicated. . . .Standard 6 sets forth criteria for the development and reporting of mass appraisals for ad valorem tax purposes or any other universe of properties” (p. 1). (c)
Unweighted Mean A mean in which each value is considered only once. See weighted mean. (c)
Usable Hit Rate The percentage of valuations returned by an AVM that meet the organization’s predetermined acceptance criteria – usually related to predictive accuracy or confidence score. For example, suppose a user defines a successful valuation as a value estimate that is within plus or minus 15% of its corresponding benchmark. If 500 of the 600 returned values from a total sample of 1000 properties met the user’s definition of an acceptable valuation, then the usable hit rate would be expressed as 500/1000 or 50%. (b)
Usage Rules Rules that establish when a particular AVM is appropriate for use. These policies frequently include property and transaction-related restrictions (e.g., FICO score, LTV, etc.). (b)
Use Class (1) A grouping of properties based on their use rather than, for example, their acreage or construction.

(2) One of the following classes of property: single-family residential, multifamily residential, agricultural, commercial, industrial, vacant land, and institutional/exempt.

(3) Any subclass refinement of the above for example, townhouse, detached single-family, condominium, house on farm, and so on. See property use category. (c)

Use Code A code (used on a property record form) to indicate a property’s use class or, less often, potential use. (c)
Use Value (1) The value of property in a specific use.

(2) Property entirely used for a specific purpose or use that may entitle the property to be assessed at a different level than others in the jurisdiction. Examples of properties that may be assessed at use value under the statutes include agricultural land, timberland, and historical sites. Compare value in use. (c)

Use, Highest and Best See highest and best use. (c)
Useful Life Estimated normal operating life in terms of utility to the owner of a fixed asset or group of assets. (c)
USPAP See Uniform Standards of Professional Appraisal Practice (c)
U-Test See Mann-Whitney test. (c)
Utility (1) The quality of a property or service that enables it to satisfy human wants.

(2) The satisfaction obtained from the goods and services that a consumer consumes. (c)

Validity The quality of a data element or procedure being what it should be in terms of some ultimate purpose or use. See integrity. Compare accuracy, precision. (c)
Valuation (1) The process of estimating the value — market, investment, insured, or other properly defined value — of a specific parcel or parcels of real estate or of an item or items of personal property as of a given date.

(2) The process or business of appraising, of making estimates of the value of something. The value usually required to be estimated is market value. (c)

Valuation Acceptance Criteria Criteria for determining the acceptability of a value estimate generated by a particular AVM. These policies are model-specific and usually consider confidence score thresholds or forecast standard deviations (FSD), as well as the organization’s risk appetite. (a)
Valuation Date The specific date as of which assessed values are set for purposes of property taxation. This date may also be known as the “date of finality.” See assessment date. (c)
Valuation Method A specific process used in a Model that estimates a value for a property. (d)
Valuation, Assessed See assessed value. (c)
Value in Exchange (1) The amount an informed purchaser would offer for property under given market conditions.

(2) The concept that states value is based on the ability of property to command another asset, such as money, in trade. (c)

Value in Use The value of property for a specific use. The concept that holds value to be inherent in property itself, that is, the value is based on the ability of the asset to produce revenue through ownership. (c)
Value Increment The amount by which a property has increased in value. (c)
Value Shopping Ordering multiple valuations of the same property and using the one with the highest value estimate to underwrite the loan—an unsafe and unsound practice. (b)
Value, Actual Market value, especially as distinguished from so-called book, par, or face values. (c)
Value, Cash Market value in terms of cash. (c)
Value, Exchange Synonymous with the preferred term “market value.” (c)
Value, Full Synonymous with the preferred term market value. (c)
Value, Going The value of an entire property in active service and with an established clientele, as distinguished from its value immediately before being put into service or upon retirement from service. (c)
Value, Improved A loose term generally defined as that portion of the present worth of a property that represents the resale factor. (Term not recommended for use.) (c)
Value, Intrinsic (1) The value of an article due to its own physical qualities rather than to the rights, privileges, or immunities with respect to other properties or persons which its possession confers.

(2) A term used to designate “value” that is supposed to reside within an article rather than within the minds of its actual or would – Be possessors. Note: This is a term that is much abused and that might well be discarded. Although it is proper to say that the intrinsic value of a stock certificate is the value, if any, of the paper, it is not correct to say that real estate has an intrinsic value in excess of, or less than, its market value. (c)

Value, Market See market value. (c)
Value, Money Synonymous with the preferred term “cash value.” (c)
Value, Normal A loose term used to denote some sort of mean between high and low market prices obtaining over a period of time or in different markets at any moment of time. (Term not recommended for use.) (c)
Variable An item of observation that can assume various values, for example, square feet, sales prices, or sales ratios. Variables are commonly described using measures of central tendency and dispersion. (e)
Variable Costs The costs of the variable resources used by a firm in either the short run or the long run. (c)
Variable Proportions, Law of Also called the “law of decreasing returns,” this states that as quantities of one productive factor in crease, the quantities of other productive factors remaining fixed, the resulting additional increments of product or output will de crease after a certain point. (c)
Variance A measure of dispersion equal to the standard deviation squared. (c)
Variation (1) A general term meaning dispersion.

(2) A reference to a particular statistic called the coefficient of variation. (c)

Verify To check the accuracy of something. For example, sales data may be verified by interviewing the purchaser of the property, and data entries may be verified by check digits. (c)
Vertical Inequity Differences in the levels of assessment of properties related to the value ranges of the properties. That is, proper ties of higher value have assessment levels different from proper ties of lower value. See horizontal inequity. (c)
Weighted Average Method In personal property appraisal, a method of inventory cost accounting whereby inventory is valued according to the unit price of all units owned throughout the year, calculated by dividing total acquisition cost of all inventory by the number of units owned. (c)
Weighted Coefficient of Dispersion The coefficient of dispersion when the absolute differences between individual assessment ratios and the measure of central tendency (for example, median ratio) are weighted on the basis of sale price. (c)
Weighted Coefficient of Variation The coefficient of variation when the squared differences between individual assessment ratios and the arithmetic mean ratio are weighted on the basis of sale price. (c)
Weighted Mean An average in which each value is adjusted by a factor reflecting its relative importance in the whole before the values are summed and divided by their number. (e)
Weighted Mean Ratio Sum of the appraised values divided by the sum of the sale prices (or independent estimates of market value), which weights each ratio in proportion to the sale price (or independent estimate of market value). (c)
Weighted Mean; Weighted Average An average in which each value is adjusted by a factor reflecting its relative importance in the whole before the values are summed and divided by their number. (c)
Written Estimate of Market Value For certain transactions (e.g., those that are below the de minimis), the NCUA allows credit unions under its jurisdiction to obtain a written estimate of market value in lieu of an appraisal. According to the agency’s regulations, individuals who prepare ‘written estimates of market value’ must be sufficiently qualified; however, the agency has also opined, “an AVM can be used to meet the valuation requirement in conjunction with review by a loan officer or an individual with knowledge, training, and experience in the real estate market where the loan is being made.” (b)
Zoning The exercise of the police power to restrict land owners as to the use of their land and/or the type, size, and location of structures to be erected thereon. (c)
z-Statistic The number calculated in a z-Test; whose significance is evaluated by reference to a z-Table. (c)
z-Table A table of critical values associated with the z-Test. (c)
z-Test A test of any of a number of hypotheses in inferential statistics that has validity if sample size is sufficiently large and the underlying data are normally distributed. (c)

 

a) AVMetrics

b) AVMs 201: A Practical Guide to the Implementation of Automated Valuation Models, Jim Kirchmeyer, 2008.

c) IAAO 2015, Glossary for Property Appraisal and Assessment, 2015. (2013 online: https://www.iaao.org/media/Pubs/IAAO_GLOSSARY.pdf )

d) Collateral Assessment & Technologies Committee, Summary of Definitions & Terms, 2006.

e) Joint Industry Task Force on AVMs, IAAO Standard on AVM Glossary, September 2003. https://www.iaao.org/media/standards/AVM_STANDARD.pdf

f) Appraisal Institute, Joint Industry Task Force on Automated Valuation Models, Work Group Terminology, 2005.

 

The Proper Way to Select an AVM

After determining that a transaction or property is suitable for valuation by an Automated Valuation Model (AVM), the first decision one must make is “Which AVM to use?” There are many options – over 20 commercially available AVMs – significantly more than just a few years ago.  While cost and hit rate may be considerations, model accuracy is the ultimate goal. A few additional estimates that are off by more than 20 percent can seriously increase costs. Inaccuracy can increase second-looks, cause loans not to close at all or even stimulate defaults down the road.

Which is the best AVM?

We test the majority of residential models currently available, and in the nationwide test in Figure #1 below, Model AM-39 (not its real name) was the top of the heap. It has the lowest average (absolute) error (MAE) by .1 over the 2nd place model.  Model AM-39 is a full percentage point better than the 5th ranked model, which is good, but that’s not everything. Model AM-39 has the highest percentage of estimates within +/- 10% (PPE10%). Model AM-39 has the 2nd lowest percentage of extreme overvaluations (>=20%, or RT20 Rate), an especially bad type of error indicating a significant overvaluation or Right Tailed error.

Figure 1: National AVM Ranking

If you were shopping for an AVM, you might think that Model AM-39 is the obvious choice. This model performs at the top of the list in just about every measure, right? Well, not so fast. Consider that those measurements are based on testing AVM’s across the entire nation, and if you are only doing business in certain geographies, you might only care about which model or AVM is most accurate in those areas. Figure 2 shows a ranking of models in Nevada, and if your heart was set on Model AM-39, then you would be relieved to see that it is still in the top 5. And, in fact, it performs even better when limited to the State of Nevada. However, three models outperform Model AM-39, with Model X-24 leading the pack in accuracy (albeit with a lower Hit Rate).

Figure 2 Nevada AVM Rankings

So, now you might be sold on Model X-24, but you might still look a little deeper. If, for example, you were a credit union in Clark County, you might focus on performance there. While Clark County is pretty diverse, it’s quite different from most other counties in Nevada. In this case, Figure 3 shows that the best model is still, Model X-24, and it performs very well at avoiding extreme overvaluations.

Figure 3 Clark County AVM Rankings

However, if your Clark County Credit Union is focused on entry level home loans with properties values below $100K, you might want to check just that segment of the market. Figure 4 shows that Model X-24 continues to be the best performer in Clark County for this price tier. Note that the other top models, including Model AM-39, show significant weaknesses as their overvaluation tendency climbs into the teens. This is not a slight difference, and it could be important. Model AM-39 is seven times more likely than Model X-24 to overvalue a property by 20%, and those are high-risk errors.

Figure 4 Clark County AVM Rankings, <$100K Price Tier

Look carefully at the model results in Figure 4 and you’ll see that Model X-24, while being the most accurate and precise, has the lowest hit rate. That means that about 40% of the time, it does not return a value estimate. The implication is: you really want a second and a third AVM option.

 

Now let’s consider a different lending pattern for the Clark County credit union. Consider a high value property lending program and look at figure 5, which is an analysis of the over-$650K properties and how the models perform in that price tier. Figure 5 shows that Model X-24 is no longer in the top five models. The best performer in Clark County for this price tier is Model AM-39, with 92% within +/-10% and zero overvaluation error in excess of 20%. The other models in the top five also do a good job of valuing properties in this tier.

Figure 5 Clark County AVM Ranking, >$650K Price Tier

Figure 6 summarizes this exercise, which demonstrates the proper thinking when selecting models. First, focus on the market segment that you do business in – don’t use the model that performs best outside your service area. Second, rather than using a single model, you should use several models prioritized into what we call a “Model Preference Table®” in which models are ranked #1, #2, #3 for every segment of your market. Then, as you need to request an evaluation, the system should call the AVM in the #1 spot, and if it doesn’t get an answer, try the next model(s) if available.

Figure 6 Summary of AVM Rankings

In this way, you get the most competent model for the job. Even though one model will test better overall, it won’t be the best model everywhere and for every property type and price range.  In our example, the #1 model in the nation was not the preferred model in every market segment we focused on. If we had focused on another geography or market segment, we almost certainly would have seen a reordering of the rankings and possibly even different models showing up in the top 5. The next quarter’s results might be different as well, because all the models’ developers are constantly recalibrating their algorithms; inputs and conditions are changing, and no one can afford to stand still.

AVMs Keep Getting Better, Craig Gilbert Noticed

For more than 12 years we’ve been testing AVMs and watching them improve over time. More model builders have developed better techniques, and with the falling cost of processing and storage, and with the improving availability of data, AVMs just continue to get better and better.

We aren’t the only ones noticing. We recently read with pleasure Craig Gilbert’s observations of the same phenomenon (Craig is an expert appraiser and co-founder of RAC – Relocation Appraisers and Consultants).

Since co-developing the AVM for Veros in 1999+, I’ve been predicting that AVMs would eventually morph over from Mortgage Origination & Portfolio Valuations, the primary intended uses, into Relocation buyouts. The question has been “when”, not “if”.  Relocation represents a microcosm sub-market of the overall residential appraisal business – maybe 5% of the total?

Back in the early days, AVMs were not as accurate as they are today. This has changed. I was thinking about this very thing this morning before opening the current issue of Mobility Magazine, and there it was. The time has arrived.

Read Mobility Magazine December 2018 article “TECHNOLOGY TODAY – What’s Hot for Mobility” written by Steven M. John and Mary-Grace Ellington of HomeServices Relocation.

Here are a few excerpts from the article:

– Recent experiments to test reliability of AVMs show the results to be comparable to formal, in-person appraisals.”

– These valuation tools can save significant time and money while offering convenience.”

– A typical FAVM can be obtained for a fraction of the cost of a traditional appraisal.”  [“F” = Forecasting]

– Target values are not fed into the models, and they are not subject to obvious human bias, so theirs perceived impartiality”

– Fidelity Residential Solutions has been at the forefront of testing these new tools.”

Other Resources

Some of you may know Lee Kennedy, an Independent AVM Expert, of AVMetrics, started by Lee in 2005. Lee is a really great guy, has been an appraiser since the mid-80’s, has testified as an expert witness on cases involving use of AVMs and the Financial Crisis and has spoken at recent A.I. Symposium. He’s like the AVM gate-keeper. In his blog titled “The Wild, Wild West of Automated Valuations”, there is a graph showing that the mean absolute error of tested AVMs decreased from 14.7% in 2009 to 5.8% in 2017 and 2018. This is for all AVMs in entire U.S..  Some of course are more accurate than a +-5.7% error rate, when drilling down to specific neighborhoods and AVMs, on a case-by-case basis.

The Wild, Wild West of Automated Valuations

Recently the OCC, FDIC and the Federal Reserve proposed raising the de minimis threshold for residential properties below which appraisals are not required to complete a home loan. Currently, most homes transacting at $250K and above require an appraisal, but Federal regulators propose to raise that level to $400K. A November 30th Wall Street Journal article raises some interesting issues about the topic. They reported that the number of appraisers is down 21% since the housing crisis, but more homes require an appraiser, since more and more homes exceed the threshold each year. The article also states that these factors open the door for cheaper, faster and “largely untested” property valuations based on computer algorithms, also known as Automated Valuation Models (AVMS).

At AVMetrics, we have been continuously testing AVMs for over 15 years, so we’ve seen how they’ve performed over time. As an example, the accompanying chart shows model performance accuracy as measured by mean absolute error, a statistical metric of valuation error.  We utilize many statistical measures of evaluating model accuracy and precision, and they all show significant improvement in AVMs over time. And, as these automated tools get better and the workforce of appraisers continues to shrink, the FFIEC members’ proposed change seems warranted, but that doesn’t mean they don’t have their critics.

Mean Absolute Error of all tested AVM models for the last 10 years

Ratish Bansal of Appraisal Inc was quoted in The Journal describing the state of AVMs as “a wild, wild West,” inviting, “abuse of all kind.” Furthermore, he contrasts that with the voluminous regulatory standards covering the use of appraisals.

We note much of those voluminous standards represent nearly the same quality control that was in place before the Credit Crisis.  In other words, appraisals are not a guarantee against collateral risk.  They are simply one tool in the toolbox – an effective, but comparatively time consuming and expensive tool. Also of note, far from being the “wild, wild west,” AVMs are also governed by regulators, most notably, Appendix B of the Appraisal and Evaluation Guidelines (OOC 2010-42) and Model Risk Management guidance (OCC 2011-12). These regulatory guidelines require that AVM developers be qualified, users of AVMs use robust controls, incentives be appropriate, and models be tested regularly and thoroughly with out-of-sample benchmarks. They require documentation of risk assessments and stipulate that a Board of Directors must oversee the use of all models. In other words, if AVMs were the “the wild, wild west” they would be rooted in a town with oversight of the legendary Wyatt Earp.

My strong feeling is that appraisals should not be a sole and exclusive tool when evaluations can be effectively employed in appropriate, lower-risk scenarios. Appraisers are a valuable and limited resource, and they should be employed at (to use appraisal terminology) their highest and best use.  Trying to be a “manual AVM” is not the highest and best use of a highly qualified appraiser.  Their expertise should be focused on the qualitative aspects of property valuation such as the property condition and market and locational influences. They should also be focused on performing complex valuation assignments in non-homogeneous markets.  AVMs do not capture and analyze the qualitative aspects of a property very well, and they still stumble in markets with highly diverse house stock or houses with less quantifiable attributes such as view properties.

However, several companies are developing ways of merging the robust data processing capabilities of an AVM with the qualitative assessment skills of appraisers.  Today, these products typically use an AVM at their core and then satisfy additionally required evaluation criteria (physical property condition, market and location influences) with an additional service.  For example, a lender can wrap a Property Condition Report (PCR) around the AVM and reconcile that data in support of a lending decision.  This type of “Hybrid valuation” is on the track we’re headed down.  Many companies have already created these types of products for commercial and proprietary use.

We at AVMetrics believe in using the right tool for the job, and we believe there is a place for automated valuations in prudent lending practices. We think the smarter approach would be to marginally raise the de minimis threshold, but simultaneously to provide additional guidance for considering other aspects of a lending decision, specifically, collateral considerations and eligibility criteria for appraisal exemptions such neighborhood homogeneity, property conformity, market conditions and more.

Cascade vs Model Preference Table® – What’s the Difference?

In the AVM world, there is a bit of confusion about what exactly is a “cascade.” It’s time to clear that up.  Over the years, the terms “cascade” and “Model Preference Table®” have been used interchangeably, but at AVMetrics, we draw an important distinction that the industry would do well to adopt as a standard.

In the beginning, as AVM users contemplated which of several available models to use, they hit on the idea of starting with the preferred model, and if it failed to return a result, trying a second model, and then a third, etc.  This rather obvious sequential logic required a ranking, which was available from testing, and was designed to avoid “value shopping.”[1]  More sophisticated users ranked AVMs across many different niches, starting with geographical regions, typically counties.  Using a table, models were ranked across all regions, providing the necessary tool to allow a progression from primary AVM to secondary AVM and so on.

We use the term “Model Preference Table” for this straightforward ranking of AVMs, which can actually be fairly sophisticated if they are ranked within niches that include geography, property type and price range.

More sophisticated users realized that just because a model returned a value does not mean that they should use it.  Models typically deliver some form of confidence in the estimate, either in the form of a confidence score, reliability grade, a “forecasted standard deviation” (FSD) or similar measure derived through testing processes.  Based on these self-measuring outputs from the model, an AVM result can be accepted or rejected (based on testing results) in favor of the next AVM in the Model Preference Table.  This application reflects the merger of MPT rankings with decision logic, which in our terminology makes it a “cascade.”

Criteria AVM MPT® Cascade “Custom” Cascade
Value Estimate X X X X
AVM Ranking X X X
Logic + Ranking X X
Risk Tolerance + Logic + Ranking X

 

The final nuance is between a simple cascade and a “custom” cascade.  The former simply sets across-the-board risk/confidence limits and rejects value estimates when they fail to meet the standard.  For example, the builder of a simple cascade could choose to reject any value estimate with an FSD > 25%.  A “custom cascade” integrates the risk tolerances of the organization into the decision logic.  That might include lower FSD limits in certain regions or above certain property values, or it might reflect changing appetites for risk based on the application, e.g., HELOC lending decisions vs portfolio marketing applications.

We think that these terms represent significant differences that shouldn’t be ignored or conflated when discussing the application of AVMs.

 

Lee Kennedy, principal and founder of AVMetrics in 2005, has specialized in collateral valuation, AVM testing and related regulation for over three decades.  Over the years, AVMetrics has guided companies through regulatory challenges, helped them meet their AVM validation requirements, and commented on pending regulations. Lee is an author, speaker and expert witness on the testing and use of AVMs. Lee’s conviction is that independent, rigorous validation is the healthiest way to ensure that models serve their business purposes.

[1] OCC 2005-22 (and the 2010 Interagency Appraisal and Evaluation Guidelines) warn against “value shopping” by advising, “If several different valuation tools or AVMs are used for the same property, the institution should adhere to a policy for selecting the most reliable method, rather than the highest value.”

How AVMetrics Tests AVMs

Testing an AVM’s accuracy can actually be quite tricky.  It is easy to get an AVM estimate of value, and you can certainly accept that a fair sale on the open market is the benchmark against which to compare the AVM estimate, but that is really just the starting point.

There are four keys to fair and effective AVM testing, and applying all four can be challenging for many organizations.

  1. Your raw data must be cleaned up, to ensure that there aren’t any “unusable” or “discrepant” characters in the data; differences such as “No.” “#” and “Num,” must be normalized.
  2. Once your test data is “scrubbed clean” it must be assembled in a universal format and it must be large enough to provide reliable test results, even down to the segment level for each property type within each price level within each county, etc. and this might require hundreds of thousands of records. 
  3. Timing must be managed so that each model receives the same sample data at the same time with the same response deadline.
  4. Last, and most difficult, the benchmark sales data must not be available to the models being tested.  In other words, if the model has access to the very recent sales price, it will be able to provide a near-perfect estimate by simply estimating that the value hasn’t changed (or changed very little) in the days or weeks since the sale. 

AVMetrics tests every commercially available AVM continuously and aggregates this testing into a report quarterly; AVMetrics’ testing process meets these criteria and many more, providing a truly objective measure of AVM performance. 

The process starts with the identification of an appropriate sample of properties for which benchmark values have very recently been established.  These are the actual sales prices for arm’s-length transactions between willing buyers and sellers—the best and most reliable indicator of market value.  To properly conduct a “blind” test, these benchmark values must be unavailable or “unknown” to the vendors testing their model(s).  AVMetrics provides in excess of a half million test records annually to AVM vendors (without information as to their benchmark values).  The AVM vendors receive the records simultaneously, run these properties through their model(s) and return the predicted value of each property within 48 hours, along with a number of other model-specific outputs.  These outputs are received by AVMetrics, where the results are evaluated against the benchmark values.  A number of controls are used to ensure fairness, including the following:

  • ensuring that each AVM vendor receives the exact same property list (so no model has any advantage)
  • ensuring that each AVM is given the exact same parameters (since many allow input parameters that can affect the final valuation)
  • ensuring through multiple checks that no model had access the recent sale data, which would provide an unfair advantage

In addition to quantitative testing, AVMetrics circulates a comprehensive vendor questionnaire twice annually.  Vendors that wish to participate in the testing process complete, for each model being tested, roughly 100 parameter, data, methodology, staffing and internal testing questions.  These enable AVMetrics, and more importantly our clients, to understand model differences within both testing and production contexts, and it enables us and our clients to satisfy certain regulatory requirements describing the evaluation and selection of models (see OCC 2010-42).

AVMetrics next performs a variety of statistical analyses on the results, breaking down each individual market, each price range, and each property type, and develops results which characterize each model’s success in terms of precision, usability and accuracy.  AVMetrics analyzes trends at the global, market and individual model levels, identifying where there are strengths and weaknesses, and improvements or declines in performance.

The last step in the process is for AVMetrics to provide an anonymized comprehensive comparative analysis for each model vendor, showing where their models stack up against all of the models in the test; this invaluable information facilitates the continuous improvement of each vendor’s model offerings.

Raising the De Minimis Threshold – Fear Not!

Background

There is a lot of controversy about appraisals and Appraisers these days, and the FFIEC proposed rule change – increasing the de minimis threshold to $500,000 – allowing for an appraisal exemption and the use of an evaluation in lieu of an appraisal – has sparked anxiety in the world of collateral risk.  Our colleagues at the Collateral Risk Network (CRN) expressed their opposition to the proposal. Not surprisingly for a group of its size, there are diverse opinions at the individual membership level of the group.  Our opinion is that the change – far from being the catastrophe imagined – will in fact have some important benefits.

A Place for De Minimis

While the CRN and certain appraiser blogs expressed skepticism – to put it mildly – we believe that there is a place for an appropriate de minimis level, even the $500,000 level now being considered.  On low risk transactions, evaluations (as opposed to full appraisals) can be appropriate and even beneficial for risk management of the overall lending system.

Here’s why.  Lending volumes tend to scale up and down faster than the supply of appraisers.  As a result, boom cycles in the lending business can place extreme pressure on appraisers.  This scenario makes quality control extremely challenging.  The option to leverage efficient evaluations on low risk transactions can improve the risk management of the entire system by devoting limited appraisal resources to their highest and best use.  In other words, when you place strain on a system, something has to give, and raising the de minimis threshold enables lenders to focus scarce resources on the riskier transactions.

Evaluations and the Credit Crisis

The CRN expressed concern about allowing the mistakes of the recent Credit Crisis to be repeated, and we could not be in more agreement.  However, their letter insinuated that evaluations (specifically BPOs and AVMs) were to blame for inflated valuations.  Of the vast number and type of quality problems experienced during the credit crisis, evaluations were not a major contributing factor.  In fact, we are not aware of any reported cases of AVMs being blamed for the quality problems experienced during the credit crisis.

Appraisals as a Source of Market Analysis

Strangely, the CRN comments suggested that reviewing individual appraisals is an important source of market trend analysis for investors during overheated markets.  We find this highly improbable.  The typical single-family appraisal may contain microanalysis of neighborhoods or small markets that lenders may find informative, but most Investors already access market and economic trend data via other sources, including their own or 3rd party economic analyses and risk management tools.

Existing Quality Control Infrastructure for Appraisals

The CRN letter makes the case that appraisals benefit from an extensive regulatory framework and quality control infrastructure surrounding their use, making them inherently safer for the industry to rely upon.  We note that much of the same quality control infrastructure and practices were in place before the last crisis.  Much of that appraisal quality control depends on the same people and practices – e.g., “desk appraisals” performed by other appraisers – making them subject to similar risk factors.  In other words, appraisals are not a guarantee against risk.  They are simply one tool in the toolbox – an effective and comparatively expensive tool – but they should not be an exclusive tool when evaluations can be effectively employed in lower-risk scenarios. .

Application of Evaluations

We believe in using the right tool for the job, and we believe that there is a place for evaluations in prudent lending practices. Relying on additional risk measurements, rather than just focusing on a one size fits all de minimis level can provide a formula for better risk management.  For example: A $350,000 transaction at a 40% LTV for a pay stub borrower has less need for an appraisal; an evaluation might be able to suffice.  Better to allocate that valuable appraisal resource to a $225,000 transaction at 90% LTV.  Raising the de minimis, while providing additional guidance for other measures, provides lenders and investors more flexibility to make smarter risk management decisions, and it releases valuable appraisal resources to be used where they can have the most benefit.

Now that the FFIEC has recently closed its commentary period regarding the proposed de minimis lending threshold of $500,000, we expect to receive final communication from the FFIEC during 2016.
We anticipate that lenders will adapt to the new regulations incrementally, with quality controls designed for the new thresholds, not discarded with the bathwater.

Lee Kennedy & Mike Coyne,

AVMetrics, LLC.

Same Scandal, New Perpetrator

It seems like only yesterday we were lamenting the hubris of Volkswagen, loading software into their TDI models to fake out emissions tests on tens of millions of vehicles.  Here we are again, this time with Mitsubishi.  The only real surprise is that these companies don’t learn.

Hyundai in 2012, Ford in 2014, Volkswagen in 2015, and now Mitsubishi, although this is not even their first scandal.  In the early 2000s, Mitsubishi was embarrassed by defects that were covered up.

It’s surprising that these companies cannot identify the root cause is faulty business processes.  Instead, they root out the responsible parties and do a mea culpa, or the CEOs resign in shame for their leadership failures (as in the case of Volkswagen last year).  Why doesn’t anyone realize that if your system is to self-test for emissions and mileage, eventually you are going to have a problem, because that is not a foolproof system?

The faulty business process is their lack of independent testing.  These emissions and mileage results are vital business inputs, and the integrity of those results is mission critical.  Where are their controls?

Our industry is financial services, where federal regulations have long required independent testing in many areas.  Our specific segment of the industry is the Automated Valuation Model (AVM) business, which has a regulatory mandate for independent validation.  Financial institutions use many different kinds of computer models to improve decision making, and AVMs are one kind of model.  They estimate property values, and for banks that makes loans on property, that comes in handy in dozens of ways.

But, if there are systematic problems with AVMs, for example, if they over-valued everything by 20%, it could cause a huge problem for banks and credit unions.  This is where we come in.  We independently test and validate every commercially available residential AVM on a continuous basis, thoroughly, rigorously and impartially.  And, the beneficiaries are everyone.  Banks and credit unions benefit, borrowers benefit, and even the AVM developers benefit because of the feedback we provide to them as well as the broader consumer confidence in their products.

Certainly it is incumbent upon leaders to create a culture of integrity.  One way of doing that is to do more than admonish people to be honest.  Instead, create a system where there is independent testing, and make sure that everyone knows that their results will be tested.  Voila!  When people know they are being checked, integrity soars, and everyone wins.  Don’t just demand integrity; build it into the process!

Lee Kennedy, principal and founder of AVMetrics in 2005, has specialized in collateral valuation, AVM testing and related regulation for over three decades.  Over the years, AVMetrics has guided companies through regulatory challenges, helped them meet their AVM validation requirements, and commented on pending regulations to help bring clarity and sanity to the situation.  Lee is an author, speaker and expert witness on the testing and use of AVMs.  Lee’s conviction is that independent, rigorous validation is the healthiest way to ensure that models serve their business purposes.  Every commercially available AVM vendor trusts AVMetrics to provide feedback to them on their models, facilitating each model’s continuous improvement.

Cascade vs Model Preference Table – What’s the Difference?

In the AVM world, there is a bit of confusion about what exactly is a “cascade.” It’s time to clear that up.  Over the years, the terms “cascade” and “Model Preference Table”TM have been used interchangeably, but at AVMetrics, we draw an important distinction that the industry would do well to adopt as a standard.

In the beginning, as AVM users contemplated which of several available models to use, they hit on the idea of starting with the preferred model, and if it failed to return a result, trying a second model, and then a third, etc.  This rather obvious sequential logic required a ranking, which was available from testing, and was designed to avoid “value shopping.”[1]  More sophisticated users ranked AVMs across many different niches, starting with geographical regions, typically counties.  Using a table, models were ranked across all regions, providing the necessary tool to allow a progression from primary AVM to secondary AVM and so on.

We use the term “Model Preference Table” for this straightforward ranking of AVMs, which can actually be fairly sophisticated if they are ranked within niches that include geography, property type and price range.

More sophisticated users realized that just because a model returned a value does not mean that they should use it.  Models typically deliver some form of confidence in the estimate, either in the form of a confidence score, reliability grade, a “forecasted standard deviation” (FSD) or similar measure derived through testing processes.  Based on these self-measuring outputs from the model, an AVM result can be accepted or rejected (based on testing results) in favor of the next AVM in the Model Preference Table.  This application reflects the merger of MPT rankings with decision logic, which in our terminology makes it a “cascade.”
MPT vs Cascade vs Custom Cascade

The final nuance is between a simple cascade and a “custom” cascade.  The former simply sets across-the-board risk/confidence limits and rejects value estimates when they fail to meet the standard.  For example, the builder of a simple cascade could choose to reject any value estimate with an FSD > 25%.  A “custom cascade” integrates the risk tolerances of the organization into the decision logic.  That might include lower FSD limits in certain regions or above certain property values, or it might reflect changing appetites for risk based on the application, e.g., HELOC lending decisions vs portfolio marketing applications.

We think that these terms represent significant differences that shouldn’t be ignored or conflated when discussing the application of AVMs.

 

Lee Kennedy, principal and founder of AVMetrics in 2005, has specialized in collateral valuation, AVM testing and related regulation for over three decades.  Over the years, AVMetrics has guided companies through regulatory challenges, helped them meet their AVM validation requirements, and commented on pending regulations. Lee is an author, speaker and expert witness on the testing and use of AVMs. Lee’s conviction is that independent, rigorous validation is the healthiest way to ensure that models serve their business purposes.

[1] OCC 2005-22 (and the 2010 Interagency Appraisal and Evaluation Guidelines) warn against “value shopping” by advising, “If several different valuation tools or AVMs are used for the same property, the institution should adhere to a policy for selecting the most reliable method, rather than the highest value.”

What Volkswagen Needs Most

2000px-Volkswagen_logo_2012.svgRight now, what Volkswagen needs most is probably not what you think.  Yes, they need lawyers and public relations help, and probably more lawyers.  And, they need some internal investigations and a new mission statement.  But, what they need most is that magic thing that money simply cannot buy: Volkswagen needs to regain the consumer’s (and the world’s) trust, since the damage to their reputation will take years to repair; this was not simply a faulty product to recall and repair, as staggering as those reparations can also be, this was a direct assault on our belief systems, and that gets personal.

So what shocked you about this deception?  Was it the tons of emissions pumped into the air by drivers who thought that they were driving “clean diesel?” Was it the brazen advertising of “clean diesel” by VW that (someone at) VW knew was not clean?  Was it the deliberate creation of code loaded into every single TDI 2.0 liter engine that was designed to foil emissions tests?  Not for me.  The most shocking detail was that the car companies self-certify their emissions and mileage stats.

The reality is that we should never expect that people (or industries or corporations) will always be ethical.  In almost all arenas of society we put systems of oversight in place to detect malicious acts in order to deter them.  The police are trusted to enforce the laws and use lethal force if necessary; yes, they have an Internal Affairs division, but their oversight is ultimately civilian.  The military has a civilian Commander in Chief.  The federal government’s three branches are designed to provide mutual oversight.

Federal regulations in banking have long since recognized that banks and credit unions cannot self-regulate either. And as technology and social changes advance far more quickly than the regulators can keep up, they put controls in place to allow carefully vetted entities to serve as their proxy for oversight, most commonly in the form of independent divisions within an organization or third party service providers.

As a case in point, the financial industry depends heavily on sophisticated models to facilitate their business decisions. Specifically within the property valuation segment of the industry, Automated Valuation Models (AVMs) are the most common models used.

Banks use AVMs to estimate property values, quickly, cheaply and accurately.  AVMs aren’t as accurate as an appraisal, but they cost a few dollars instead of a few hundred dollars.  Banks use them for all sorts of things: portfolio valuation, sales, marketing, servicing, appraisal quality control, even equity lending decisions.  And if lenders base their decisions on systematically erroneous data, or improperly built models, they can run into big problems not only to their bottom line, but also to their reputation. Understanding the risk that this creates, regulators have required all such models to be independently validated.  There are two critical aspects to this precaution.

First, validation must be independent.  The validation must be done by a person or team that is separate from the developers, users and buyers (most AVMs are built by independent companies that specialize in their development).  Very specifically, in the regulatory guidance, the model’s builders cannot be relied upon as an objective source of validation.

Second, the validation must be conducted by staff qualified in modeling or analytics, with the adequate authority to blow the whistle if they find issues.  The validation must be performed in real-world conditions, it should be ongoing, and it should be reported on at least annually.  When there are changes to the models, the business environment or the marketplace, the models need to be re-validated.

Banks and credit unions can do their own AVM validation, but most find it generally difficult to meet all of the requirements, including the authority to blow the whistle if they find issues. These validation caveats are the best justifications for looking to a company like AVMetrics to provide this service for you.

AVMetrics is in no way beholden to banks and credit unions, the AVM developers, or the resellers in any way; we draw no income from selling, developing or using AVM products.  AVMetrics conducts extensive ongoing AVM validation and has done so for over a decade.  Independence is essential, because self-certification can be an invitation to abuse, and as Volkswagen just found out, it can be devastating.  Our industry implemented independent testing years ago.  It is rigorous, exhaustive and frequent, but that is appropriate because there is a lot at stake.

If Volkswagen ever hopes to get the public’s trust back, they need to open the curtains and put on all the lights. In addition, every result and claim will need to be independently validated, since no one is likely to believe them otherwise.

These are common-sense controls, and while the application to the auto industry is not perfectly analogous to the testing of AVMs, the basic lesson is clear: we should not trust companies to test and certify themselves.

Lee Kennedy, principal and founder of AVMetrics in 2005, has specialized in collateral valuation, AVM testing and related regulation for over three decades.  Over the years, AVMetrics has guided companies through regulatory challenges, helped them meet their AVM validation requirements, and commented on pending regulations to help bring clarity and sanity to the situation.  Lee is an author, speaker and expert witness on the testing and use of AVMs.  Lee’s conviction is that independent, rigorous validation is the healthiest way to ensure that models serve their business purposes.  Every commercially available AVM vendor trusts AVMetrics to provide feedback to them on their models, facilitating each model’s continuous improvement.