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4-26-2017 draft exposure document

DRAFT STANDARD ON AUTOMATED VALUATION MODELS (AVMS)1. SCOPEThis standard provides guidance for public and private sector mass appraisal activities, private sector single property appraisals, and programs for the review of appraisals, real estate held in portfolio, and mortgage backed securities that depend on Automated Valuation Model (AVM) systems. The standard provides recommendations, guidelines, and best practices for the design, preparation, and interpretation of results when developing and using AVMs for the valuation of real property. AVMs can be used when sufficient economic data exists to permit development of representative and valid statistical samples. The standard’s layout follows the recognized steps used in the development and operation of an AVM. Each section includes descriptions of governing principles. This is followed by rules related to minimum and preferred requirements, as well as best practices. In addition, the appendixes present the methodologies used in developing and implementing various types of AVMs and related topics. As presented in this standard, the development of an AVM conforms to USPAP Standard 6 (Appraisal Foundation 2016–2017, 46–56).

2. INTRODUCTION2.1 Definition of an AVM

An automated valuation model (AVM) is a mathematically based computer application that assists in the development of a market value estimate by selecting the best statistically generated simulation of market activity by the analysis of location, market conditions, and property characteristics from information that was previously collected. An AVM has the ability to generate value estimates of the subject at multiple specified points in time. Most AVM’s emphasize the use of statistical models and procedures.

2.1.1 Appraiser-Assisted AVMs

Appraiser assisted AVMs are statistically based models that provide a range of comparable sales and possibly other value indicators. This process assumes that the appraiser will develop the final estimate of value. Related technical issues are found in Appendix A.4.1.

2.1.2 Fully Automated Valuation Model

A fully automated valuation model is developed by an analyst who performs market analysis to create an application formula, which directly generates a final estimate of value. A fully automated valuation model emphasizes statistical analysis. Related technical issues are found in Appendix A.4.2.

Types of AVMs related to the fully automated valuation model include:

sales comparison approach with or without estimates from the statistically derived model;

cost approach;

hedonic regression;

income approach using either income capitalization or income multipliers; and

price indices

2.2 Purpose of an AVM

The purpose of an AVM is to provide an accurate, uniform, equitable, and reliable estimate of market value at a given point in time. AVM values that are reviewed for reliability and generated in compliance with regulations of the governing bodies (for example, USPAP Standard 6) are considered appraisals provided the modeler follows the procedures for AVM development and any required reporting of results. Models that adhere to the best practices for data verification, data analysis, market analysis, and ongoing quality control present the widest opportunity for general acceptance.

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4-26-2017 draft exposure documentAVMs are applicable to any real property for which adequate market information and property data are available.

2.3 Development and Application

The development of an AVM uses mass appraisal principles and techniques, in which data are acquired and analyzed to develop a sample based on valid sold properties to develop a market valuation model that can be applied to similar properties in the same market area. These may be either individual properties of interest or all properties that meet the requirements of the model. The two major components of valuation modeling are specification and calibration. Model specification is the process of developing the proposed model structure. Model calibration is the process of deriving coefficients or the variables previously specified. Specification and calibration techniques vary with the purpose of the AVM, type of property, available data, and the experience and knowledge of the market analyst. The basic steps to develop an AVM are:

Creation of a scope of work for the intended purpose

Identification and acquisition of property data

Exploratory data analysis and filtering of data

Stratification

Determination of data representativeness

Model specification

Model calibration

Quality assurance

Model application and value review

2.4 Model Documentation

2.4.1 Scope of Work

The scope of work defines the type of property and geographic area in which the AVM will be applied, and the steps required to implement the AVM. The AVM supporting documentation should state all assumptions, special limiting conditions, extraordinary assumptions, and hypothetical conditions.

A key assumption in many AVM applications concerns the assumed use of the property. Most real estate databases contain the current use of property. Therefore, most AVMs assume that the current use is the highest and best use.

2.4.2 Identification of Property Characteristics and Data Acquisition

Prior to developing an AVM the property data needs to be identified and acquired. In some cases the property data is acquired before the AVM process begins. In other cases, the market analyst can determine what property data is necessary. Identification of the required property data is controlled by the action performed by the model. Property data elements that have a relationship to value dictate the data collection methods.

Data falls into two general categories: property data and market data. Property data are composed of elements that represent physical attributes of the property. Typical items for real property are location, land characteristics, and building features. Market data include sales, income, and replacement cost information. Data can be acquired from multiple sources:

Internal resources

Government sources

Third party sources

Public (news) sources

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4-26-2017 draft exposure document Client and customer

2.4.3 Assumptions

After data and market analysis have been performed, a primary assumption is that data, collected by physical site inspection or observed through various electronic systems, is considered reliable for use in the AVM final formula application. This assumption is dependent on performing adequate quality assurance. It is also assumed that checks and edits will be made as new data is introduced into the system to ensure compatibility with model specifications.

2.4.4 Exploratory Data Analysis

Data characteristic or attribute analyses include:

Data quality review—consistency of data entry, outlier identification and removal;

Data distributions—frequency of value-related characteristics;

Market patterns—trends in property characteristics, locational analysis using sales data;

Time trends—sales frequency and sales dates;

Determination of reasonableness of data elements

Determination of the age or collection dates of the data elements; and

Determination of reasonableness of data sources.

2.4.5 Stratification

Property data can be stratified to improve model interpretation, quality review and results. Stratification may be done by type of property, location, or other property characteristics.

In stratification, parcels are sorted into homogeneous groups based on data such as use, physical characteristics, or location. Properties are first stratified by use such as agricultural, apartments, commercial, industrial, and residential. Additional stratification by physical characteristics or value ranges may be performed to minimize the differences within strata and maximize differences among strata. Geographic stratification may be useful wherever the value of various property attributes varies significantly among areas and is particularly effective when housing types and styles are relatively uniform within areas (IAAO 2011, 139–143). Location stratification reduces the need for complex models. However, excessive stratification may provide too little variation in the data. It may be possible to create a global valuation model without stratification, if location adjustments are included as part of the model specification and calibration processes.

2.4.6 Representativeness

To produce quality results, data used in developing an AVM model must be representative of the subject property or population of properties. The available sales may limit applicability of the AVM model.

2.4.7 Model Specification

Model specification is the process of determining the format (mathematical form) of the AVM. The market analyst must determine the model to be employed and specify the variables to be used.

2.4.8 Model Calibration

Calibration is the process of determining the coefficients in an AVM. Several statistical tools can be used to calibrate AVM models (see Section 3 Specification and Calibration of AVM Models). Model development and calibration are iterative processes that are repeated until statistical diagnostics are satisfactory.

2.4.9 Quality Assurance

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4-26-2017 draft exposure documentAn AVM must be tested to determine if it meets required accuracy standards before being deployed. This is accomplished through statistical diagnostics and ratio studies in which value estimates (e.g., estimated sale price or estimated rent) are compared to actual values (e.g., sale price or reported rent) for the same properties. GIS can be used to analyze the spatial trends of the value estimates. Before it is implemented, the AVM also should be tested using sales that were not used in the calibration process (e.g., holdout sample or other cross-validation techniques). Properties with unusually large residuals, atypical characteristics, or extreme ratios of model estimates to sale prices, termed “outliers," should be reviewed. It is likely that the sale price (or other value serving as the dependent variable in the model) is not representative, the data are partially incorrect, or the property exhibits atypical features that cannot be adequately accounted for in the model. If the data cannot be corrected, the property should be removed from the sample. (See Section 7)

2.4.10 Model Application and Value Review

Once tested and validated, the AVM can be applied to properties of the same type in the area or region where the model applies. These values should be reviewed for reasonableness and consistency. It is also good practice to systematically review the generated values for reasonableness and consistency with similar properties in the same locational area.

3. SPECIFICATION AND CALIBRATION OF AVM MODELS In practice specification and calibration are performed in an iterative process, which includes the following steps:

1. Specify a model2. Calibrate the model3. Test the model4. Make adjustments to model specification5. Recalibrate the model6. Test the model7. Continue to repeat the process until statistically significant improvement is minimized

The AVM specification and calibration iterative process assumes that data are collected and verified in a consistent and professional manner (Standard on Verification and Adjustment of Sales 2010).

3.1 Data Quality Assurance

Data quality assurance includes evaluation of data availability and accuracy of all physical and market data including property identification and location.

3.1.1 Data Availability

The model specification process begins with an evaluation of the data availability. The availability of data will influence the specification of the model and may indicate the need for revisions in the specification and/or limit the usefulness of the resulting value estimates. Publicly available data from sources, such as assessors and commercial sector third-party information services are the basis for most statistical models. As AVM use increases, new commercial data brokers are emerging to service model developers and operators. These new data warehouses aggregate large amounts of data from all available sources, including data mining and screen scraping. When using second and third generation data, it is good practice to verify the accuracy of elements found in the date prior to inclusion in the model. The AVM market analyst must use statistical data analysis to confirm the assumption that the quality of the data will provide reasonable support for the modeling process. The AVM developer and user should use methods found in the Standard on Mass Appraisal of Real Property to collect data and to confirm the accuracy of data collection and review. AVM models are based on a sample of the universe of data. The specification process must review the sample data used to develop the model as well as the population to which the model will be applied. The sample should be representative of the population (see Section 7.2) in all key elements of value including the types of properties, market conditions, value range, and land and building sizes, and building ages. Property types for which market information is not available

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4-26-2017 draft exposure documentshould be excluded from both the sample and total population files as the model specification will not be representative of these properties.

Knowledge of key property characteristics is crucial to model specification. Limitations in the integrity and availability of the data are important determinants of the model specification. Models should be specified after a thorough understanding of the data in the sample and population.

3.1.2 Data Verification

Data verification is common in assessors’ AVM development, but not in commercial AVM development. Market analysts rely on the accuracy of the data provided to them, and verify data quality by its relationship to sale prices. When data items that appraisers would consider highly correlated to value do not prove to have that relationship (correlation matrix or regression t or F values, etc.), this may be an indication of inconsistent data collection or scarcity of data. Data that are not consistently collected or that are mostly missing from the sales file should not be used in the model specification or calibration phases; as such data can be insignificant and may produce misleading results.

3.1.3 Qualitative and Quantitative data

Data may be qualitative or quantitative. Quantitative data are objective and can be counted or measured. Qualitative data are descriptive and subjective and contribute to the overall value components of land, buildings or overall general values.

3.1.4 Property Identification and Location

Geographic information systems (GIS) can be used to match AVM system property addresses to addresses in national address files. GIS files identify and locate properties by latitude and longitude. For more information on parcel identification see Standard on Digital Cadastral Maps and Parcel Identifiers.

3.2 Model Specification and Calibration Methods

AVM models are based upon one or more of the three approaches to value (cost, sales comparison, and income). Model Specification starts with review of the data to determine which valuation model or models are likely to yield the best results. Model specification is based on data analysis and appraisal theory. Specification techniques using the three approaches to value are outlined in Appendix A: Model Specification. Model calibration develops coefficients applied to data elements used in an AVM application. Model calibration techniques are outlined in Appendix F. Calibration Techniques.

3.3 Stratification

In stratification, parcels are sorted into relatively homogeneous groups based on use, physical characteristics, or location. Properties are first stratified by use such as agricultural, apartments, commercial, industrial, and residential. Additional stratification by geographic areas, physical characteristics or value ranges may be performed to minimize the differences within strata and maximize differences among strata. An expanded discussion of the application of stratification principles to AVM model development is found in Appendix C: Stratification in AVM Development.

3.4 Time Series Analysis

Time series analyses are a family of techniques that can be used to measure the cyclical movements, random variations, seasonal variations, and secular trends observed over a period. In property valuation, these analyses can be used to develop a multiplier or index factor to update existing appraised values or to adjust sale prices for individual properties to a valuation date. Since values can change at different rates in different markets, separate factors should be tested for each property type and market area. LVRSA may partially adjust for local inflation and deflation. Specific methods are outlined in Appendix E: Time Series Analysis.

3.5 Calibration Techniques

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4-26-2017 draft exposure documentModel calibration is the development of adjustments or coefficients through market analysis of the variables to be used in an AVM. Most AVMs in use today rely strictly on statistical models as the method of calibration. Specific techniques are discussed in Appendix F: Calibration Techniques.

3.6 Calibration Summary

The various methods and procedures used to calibrate the AVM are the engines that drive accuracy and credibility of the estimate made. Data integrity and the skill level of the analyst define the accuracy of one calibration technique as compared to others. Users of AVM products must be aware of the interdependence between skills and technologies of calibration when deciding how well the AVM will perform. However, the ultimate determinant of performance quality is statistical results and meeting of statistical standards as provided in Section 7. See also Appendix F: Calibration Techniques.

3.7 Variable Selection for Direct Market Models

It is important to include significant variables. Given a regression-based system, independent variables (i.e., property characteristics) that are highly correlated with other variables or have t-scores less than 2 should be used with caution in the model.

3.8 Location

There are many methods for analyzing the influence of location. Two examples are grouping similar properties by economic area and analyzing location at the individual property level. Methods for analyzing locational influence are explored in Appendix D: Statistical Methods for Developing Location Adjustments.

4. RESIDENTIAL AVMSThe residential property class has the longest history of being valued by AVMs. AVM developers (or users) must understand the intended use of the AVM. AVMs lend themselves to estimating the value of residential property. This results from the abundance of data and having most indicators of contributory value being physical.

The overall ability of the AVM to determine value accurately can be evaluated using the quality assurance measures found in Section 7. Additional information on residential AVMs is found in Appendix G: Residential AVMs.

5. COMMERCIAL AND INDUSTRIAL AVMSCommercial and industrial properties provide unique AVM challenges. In some markets there are relatively few sales of commercial and industrial properties. More detail on application of the three approaches to value is found in Appendix H: Valuation Models for Commercial and Industrial AVMs. The market analyst may have the additional problem of needing to provide separate land and building values that most income models are not designed to deliver. Commercial and industrial sales often contain significant amounts of intangible items which contribute to the reported final settlement price, but are not part of the underlying real property market value. Adjustments may be required to compensate.

5.1 Influences on Value

Property use, location, physical characteristics and site influences, and income affect the value of commercial properties.

5.1.1 Property Use

Property use is important as a comparison characteristic. Use of broad categories will increase the number of sales available for each category.

5.1.2 Location

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4-26-2017 draft exposure documentFor commercial and industrial properties, location analysis relates largely to identifying zones or groups of properties subject to similar influences. Nuisances for residential properties, such as a railroad track or heavy traffic patterns, could be an important amenity for commercial and industrial properties.

5.1.3 Physical Characteristics and Site Influences

Commercial and industrial properties require a number of physical characteristics for comparison and modeling. These variables may be quantitative or qualitative. The most significant quantitative characteristic is building area. Other significant quantitative and qualitative variables are similar to those used for residential AVMs. Some examples include building quality and lot size. Others, such as traffic patterns or ceiling height may be important to specific property uses or occupancies.

AVM developers should consider the property characteristics that are traditionally provided to clients and determine how these characteristics will be presented even if they do not relate to the existing AVM model development.

5.1.4 Income Data

A value determined using the income approach is essentially a calculation of the present worth of the future benefits to an income stream. Income data includes revenues, expenses, net income, and capitalization rates or income multipliers, which are then used to develop a projection of an income stream to estimate the market value.

5.2 Limited Information

Sales and income data can be expanded by using multiple years of data and making time adjustments. When economic or sales information is limited, benchmarks can be created by applying the income approach to sales data and by applying sales data to the properties with income. See Appendix H: Valuation Models for Commercial and Industrial AVMs.

6. LAND MODELSLand values can be derived from other improved property models. Land specific models may be based on income, sales comparison, and unit-in-place methods. Land value estimation provides a set of unique problems for AVMs, primarily because there are relatively few land sales. (See Appendix I: Land Valuation Model Specification)

7. MODEL QUALITY ASSURANCEAVM testing is critical for quality assurance, to determine the applicability of the model, and the need for further specification. The process of developing and deploying an automated valuation model must include safeguards to insure the accuracy of data used and the integrity of results produced. Those safeguards are similar in kind and effect to those employed in evaluating the performance of any mass appraisal project. In addition, because AVM results may reflect values at any given point of time, it is important for the user to be provided sufficient information to evaluate the quality of these results.

7.1 Data Quality Assurance

Data used in model specification and calibration should pass the following screening tests:

1. Data should be sufficient to produce reasonable predictive models with regard to the property characteristics utilized. As a rule, the number of sales should be at least five times (fifteen times is desirable) the number of independent variables (Gloudemans 1999, 127).

2. Sales used should be valid transactions that reflect market value. Data should be consistent across the population of properties to be valued using the model. Examples include quality, physical condition, and effective age.

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Geographic information systems (GIS) and various types of remote sensing (e.g., video/aerial photography, LiDAR) may also help in reviews. GIS software is used to maintain digital maps and provide geographic representations of property attributes and features. It can be used to highlight properties with impossible, unlikely, or inconsistent data. For example, properties coded as being waterfront can be color-coded, displayed on a map, and reviewed for accuracy. (Standard on Digital Cadastral Maps and Parcel Identifiers 2015)

4-26-2017 draft exposure document3. Property characteristic data should be accurate. Sales data and characteristics should be representative of the

underlying population or the subset of properties that may be subject to valuation using the AVM.

(Standard on Verification and Adjustment of Sales 2010)

Data quality assurance should provide a means of evaluating the application of the developed AVM formula to a specific population of properties. The product of that evaluation may include the acceptable ranges of specific property characteristics and ranges of estimated market values to which the model can be applied.

In addition to the quality assurance statistics discussed below, it is good practice to provide the user with a measure or index of the relative confidence in individual value estimates, especially at the extremes of the data ranges. This can be done by using stratified ratio studies to examine the extreme low and high ends of various property characteristics in the modeling and holdout data sets.

In some circumstances sales used in developing AVM models may not be representative of subject properties for which value estimates are sought. This could be the case, for example, if the model contained few sales with prices or characteristics similar to those in a high end residential community under development. In this instance, no estimate of value should be provided at those points where the estimates become unreliable due to the data falling outside of acceptable parameters (see Section 7.4 on Ratio Studies). Alternate confidence score methods may be found in vendor applications and are referred to in United States federal guidelines (see: Interagency Guidance. 75 Federal Register at 77,469). However, parameters and methodologies for these techniques have not been standardized and it may be difficult to compare similarly named measures produced by different vendor-provided models. This standard takes no position on the validity or usefulness of such guidance.

7.2 Data Representativeness

Samples used in developing models and those used to test AVM results should be representative of the total population of properties that may be valued. In many kinds of statistical studies, samples are selected randomly from the population of sales to ensure that the type and range of properties in the population to which results may be applicable are adequately represented in the sample.

When markets are extremely volatile, samples may be less representative. In this case, AVM estimates may suffer the effects of lag induced errors. This can be minimized with runtime quality control.

(IAAO 2013, 11)

7.3 Model Diagnostics

The specific diagnostic tools available to market analysts and users of automated valuation models will vary with the model methodology employed. Multiple regression analysis provides the market analyst and user with a wide range of diagnostic statistics that may not be available with other calibration methodologies. Other valuation techniques should generate data quality, ratio and final quality statistics from the economic data available and the AVM model estimates of value. In any event, the market analyst must make effective use of the diagnostic tools available during model calibration. The market analyst should be able to explain how statistics were used and how they relate to the predictive quality of a specific model in relation to the sales data available for calibration.

7.4 Ratio Studies

Ratio studies are statistical studies based on mathematical comparisons between estimated values and market values as indicated by sale prices. The ratios thus calculated are subjected to statistical analysis to determine central tendency (level), and vertical (value related) and horizontal uniformity or variability. Variability statistics provide information about the degree to which model-determined values for individual properties are uniform and consistent. While ratio study statistics provide valuable guidance as to the overall quality of the AVM value estimates, statistical measures tend to be more applicable to groups or multiple estimates than to individual property estimates. For example, a model may

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4-26-2017 draft exposure documenthave a ratio study showing a median level of 100%. This means that, typically, value estimates equal market value; it does not mean that every value estimate equals market value. It is more likely, however, that, given low variability (i.e., good uniformity statistics) and good vertical equity, individual property value estimates will be closer to market value.

Sales based ratio studies are among the most objective methods for testing the performance and quality of any valuation system. Much of the information in this section has been reprinted from Part 1 of the Standard on Ratio Studies (IAAO 2013). Guidance on calculating the statistics recommended in this section is found in the Standard on Ratio Studies as well as Mass Appraisal of Real Property (IAAO, 1999).

7.4.1 Measures of Level

Measures of central tendency of the ratio of estimated values to sale prices provide an indication of the overall level, with respect to market value, of value estimates for any group of properties represented by a particular sales sample. Point estimates of these measures are calculated as shown in table 1, found in Appendix J: Statistical Tables. Reliability statistics (also shown in table 1), such as confidence intervals, should also be calculated around each of these measures to better understand whether the calculated measures of level are likely to reflect the level of the underlying population (see Section 7.4.3 on Measures of Reliability). The median ratio, considered in conjunction with its confidence interval, should be used to evaluate whether AVM results attain market value standards.

7.4.2 Measures of Variability

Several statistical tests are available and should be used to determine the degree of variability (uniformity) in the results of any AVM model. Common measures of variability include the coefficient of dispersion (COD) and coefficient of variation (COV). While the COV, based on the standard deviation, is more standard in general statistical testing, because of potential greater distortion of COVs due to outlier inclusion, the COD is more strongly recommended as the uniformity statistic of choice.

7.4.2.1 Coefficient of Dispersion (COD)

The most useful measure of variability is the COD, which measures the average percentage deviation of the ratios from the median ratio. The COD has the desirable feature that its interpretation does not depend on the assumption that the ratios are normally distributed. Standards for interpreting CODs are contained in Section 9.2 of Part 1 of the Standard on Ratio Studies (IAAO 2013). In general, more than half the ratios will fall within one COD of the median.

(IAAO 2013, 13)

7.4.2.2 Coefficient of Variation (COV)

The COV is another common measure of variability. Although it is shown in table 1, it is not the preferred measure of uniformity for ratios due to outlier effects. Note that the COV is calculated only about the mean—not the median or weighted mean. The standard deviation and COV enable more concise predictions about the occurrence of various ratios in the population when the ratios are normally distributed.

7.4.3 Measures of Reliability

Reliability, in a statistical sense, concerns the degree of confidence one can place in a calculated statistic for a sample (for example, how accurately does the sample median ratio approximate the true population median ratio?). Measures of reliability explicitly consider the errors inherent in a sampling process and should be taken into account when evaluating an AVM. There are two related measures of reliability: confidence intervals and standard errors. A confidence interval consists of two numbers that bracket a calculated measure of central tendency for the sample; one can have a specified degree of confidence that the true measure of central tendency for the population falls between the two numbers. Standard errors relate to the distance one must add to and subtract from certain measures of central tendency to compute the confidence interval.

Confidence intervals can be calculated about various measures of level and variability; standard errors can be properly calculated about the mean and weighted mean. The level of the value estimate indicated by model results should be

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4-26-2017 draft exposure documentwithin 10% of market value. This standard should be considered met if a 90% two-tailed confidence interval around the median overlaps the range of 90% to 110% of market value. Confidence intervals for the applicable measures of appraisal level are demonstrated in table 1 in Appendix J: Statistical. (See IAAO [1990, 515–546] and Gloudemans [1999, 257–339] for information on performing these calculations.) There are also procedures for developing confidence intervals around the COD and PRB.

7.4.4 Vertical Inequities

The COD and COV relate to dispersion among the ratios in a stratum, regardless of the value of individual parcels. Another form of inequity may be systematic differences in the AVM assigned values of low-value and high-value properties, termed “vertical” inequities. An index statistic for measuring vertical equity is the PRD (Price-Related Differential). This statistic should be close to 1.00. Measures significantly above 1.00 tend to indicate regressivity (i.e., low values on high priced properties); measures below 1.00 suggest progressivity. The PRD may not be a reliable measure of vertical inequities when samples are small or the weighted mean is heavily influenced by several extreme sale prices. If not representative, extreme sale prices may be excluded from the model prior to the calculation of the PRD. Similarly, when samples are very large, the PRD may be too insensitive to show small pockets of properties in the population where there is significant vertical inequity. Another measure of vertical equity has been developed to help validate and better understand findings of inequity. This is the coefficient of price-related bias (PRB), which is found by regressing percentage differences from the median ratio on percentage differences in value. The PRB provides a percentage by which model-derived value estimates rise or fall as values double. As a general matter, the PRB coefficient should fall between –0.05 and 0.05. PRBs for which 95% confidence intervals fall outside of this range indicate that one can reasonably conclude that assessment levels change by more than 5% when values are halved or doubled. PRBs for which 95% confidence intervals fall outside the range of – 0.10 to 0.10, indicate very serious vertical inequities.

(IAAO 2013, 14-15 and Appendix D: Statistical Methods for Developing Location Adjustments)

7.4.5 Guidelines for Evaluation of Quality

Because the development and utilization of automated valuation models are ongoing, without definitive beginning or end dates, sales ratio studies should be performed on a regular, periodic basis to establish the current performance status of the model. Particularly when models are first developed or undergo significant updates with infusion of substantial new data, such ratio studies should include utilizing holdout samples accumulated according to Section 7.7. Model accuracy should be measured against the Standard on Ratio Studies (IAAO 2013, Part 1) for the particular property type valued by the model. Table 2, in Appendix J: Statistical Tables is taken from the IAAO Standard on Ratio Studies (IAAO 2013) and provides guidelines for evaluating the quality of appraisal level and variability based on statistical measures previously discussed.

7.4.6 Importance of Sample Size

There is a general relationship between statistical precision and the number of observations in a sample drawn from a given population: the larger the sample, the greater the precision. The required sample size for any given degree of precision depends primarily on acceptable sampling error and the variability in the population. When there are insufficient sales to achieve target levels of precision, all valid sales should be used unless this results in non-representativeness. If an abundance of sales is available, it is permissible to randomly include sufficient sales to obtain reasonably small margins of error. See also Section 7.5.

7.4.7 Outliers

In ratio studies, outlier ratios are extremely low or high ratios as compared with other ratios in the sample. When the sample is small, outlier ratios may distort calculated ratio study statistics. Some statistical measures, such as the median ratio, are resistant to the influence of outliers. However, the mean and COV, and, to a lesser extent, the COD, are sensitive to extreme ratios. Documentation accompanying any AVM should describe the methodology used to identify outliers and the procedures followed once outliers are identified.

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4-26-2017 draft exposure document7.5 Holdout Samples

Holdout samples represent groups of valid sales selected in a manner that their characteristics approximate those of the population of properties covered by the automated valuation model. Statistical analysis of such samples can be used to verify results based on sales used in model development. Inherent in the definition of holdout samples is the premise that the sales not be used in developing the original model. Sales that occur after model calibration can also be used in testing and validating the model. This method may be preferable when few sales are available. Alternatively, cross validation techniques can be used in lieu of traditional holdout samples.

The use of holdout samples to test and verify model provided value estimates is a critical step. It provides an analysis independent from the data used in developing the model in the first place. As such, statistical measures of level and uniformity on value estimates produced by the model, but compared to the holdout sales, would be expected to differ from those directly used in developing the model. The critical issue is the degree of difference in these measures. If there is substantially worse level or (more commonly) uniformity in the hold out sample, recalibration of the AVM model may be necessary. At the very least, representativeness of the data used in the model should be reviewed and price and characteristic ranges compared with those in the holdout sample.

7.6 Value Reconciliation

When more than one value estimate is produced for a subject property, model documentation must contain a thorough explanation of the procedures followed to reconcile those candidate estimates into a final estimate of value. Those procedures must include analysis of the relative strengths and weaknesses of the candidate estimates, and explanation of how that analysis results in a final value estimate.

7.7 Frequency of Updates

AVM estimates of value are based on formulas derived from market analysis of a specific geo-economic area during a specified period. Because AVM value estimates are dated, AVM developers must be prepared to update those estimates for significant changes in market conditions and periodically update the underlying models. Movements in the market and the availability of market information should dictate the frequency of this process.

8. DOCUMENTATION AND REPORTS Documentation needs to be available to support the results of the AVM.

There should be detailed documentation to support the market analysis process and the final valuation formula. This documentation should consist of a report that defines the process and methods used to arrive at a final estimate of value and should be available to clients if requested.

There are several reports associated with the development of an AVM and the reporting of an individual property’s estimate of value.

8.1 Restricted Use Report

A restricted use report is limited to the immediate intended user (client) of the AVM report. Restricted use reports are typically limited to such basic information as street address, indication of value, report date and may include some basic property descriptive characteristics, known additional indicators of value (such as last sale price/date and property tax assessment). Restricted use reports show the application of an AVM model formula to an individual property. These reports do not contain the supporting documentation for the appraisal process performed to create the formula. Such supporting documentation should be in a separate documentation report.

8.2 Appraiser-Assisted AVM (AAAVM) Report

Appraiser-Assisted AVM reports are documents prepared by an AVM provider and reviewed and/ changed by an appraiser. This type of report combines AVM formulas with appraisers’ review and verification of valuations. The appraiser performs a review at one or more levels of inspection, and corrects or confirms the report and value estimate.

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4-26-2017 draft exposure documentWhen appraisers alter the value conclusion provided by the AVM model, the responsibility for the quality of the result shifts from the model developer to the appraiser. Quality assurance statistics may need to be rerun. In addition, if the developed results fall outside the original confidence intervals of the model the appraiser should report this to the client.

8.3 Documentation Report

A documentation report is a detailed narrative report of the modeling process that can be prepared upon request by a client. This report explains the process of developing the model.

8.4 Uses of AVM Reports

AVM reports must be sufficient to be understood by clients and intended users. Some AVM reports are limited to a property identifier and AVM value. Other reports will provide additional information as requested by the clients. AVMs may use additional documentation in separate reports that support the entire AVM model process with individual reports per property that are considered the AVM report. Examples and a list of uses are found in Appendix L: Uses of AVM Reports.

8.5 Mapping Issues

Reports should specify a location identifier for the property being valued to ensure that this description is consistent with the physical nature of what is expected to be valued. Maps and historical information about the parcel(s) may be useful and worth including in the report.

9. GLOSSARYAlgorithm—Computer-oriented, precisely defined set of steps that, if followed exactly, will produce a prespecified result (for example, the solution to a problem).

Additive Model—A model in which the dependent variable is estimated by multiplying each independent variable by its coefficient and adding each product to the constant.

Automated Valuation Model—An automated valuation model (AVM), a mathematically based computer software program that market analyst use to 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.

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.

Blended Model—A blended model (see Section 7.8: Value Reconciliation) is one where more than one modeling technique is used in deriving the estimate of value. Typically, the technique involves running a hedonic model and a repeat sales index. The results are then compared and evaluated. Based on each result, the blended model reports a final estimate of value. In addition to the hedonic model and repeat sales index, many blended models also include the results of a tax-assessed value model.

Calibration—The process of estimating the coefficients in a mass appraisal model.

Coefficient—(1) In a mathematical expression, a number or letter preceding and multiplying another quantity.

Cluster Analysis—A statistical technique for grouping cases (for example, properties) based on specified variables such as size, age, and construction quality. The objective of cluster analysis is to generate groupings that are internally homogeneous and highly different from one another.

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4-26-2017 draft exposure documentComparable Sales Model–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.

Confidence Interval–A range of values, calculated from the sample observations, which are believed, with a particular probability, to contain the true population parameter (mean, median, COD, etc.) The confidence interval is not a measure of precision for the sample statistic of point estimate, but a measure of the precision of the sampling process.

Confidence Score -- The AVM Confidence Score is a value that indicates the level to which each of multiple AVM models “agrees” with the other estimated values for a given property.

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 minus 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.

Data Management—The human (and sometimes computer) procedures employed to ensure that no information is lost through negligent handling of records from a file, all information is properly supplemented and up-to-date, and all information is easily accessible.

Direct Capitalization— The conversion of expected income and rate of return into an estimated present value when using the income approach to determine value.

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 model used to estimate market value directly using multiple regression analysis or another statistical algorithm.

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.

Euclidean Distance Metric—A measure of straight-line distance between two points. In property valuation, it is used to find the nearest neighbor or similar property based on an index of dissimilarity between property location and attributes. When using multivariate selection, the squared difference is divided by the standard deviation of the variable to normalize the differences. (Also see Minkowski Metric.)

Geoeconomic Area—(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 homogeneous and share important locational characteristics.

Geoeconomic Area Analysis—A study of the relevant forces that influence property values within the boundaries of a homogeneous area.

Hedonic Model—Hedonic pricing attempts to take observations of the overall goods or services 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.

Heteroscedasticity—Nonconstant variance; specifically, in regression analysis, a tendency for the absolute errors to increase (fan out) as the dependent variable increases.

Holdout Sample—Part of a set of data set aside for testing the results of analysis.

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4-26-2017 draft exposure documentHomogeneous—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).

Nonlinear Model—Model that incorporates both additive and multiplicative components. (See also Additive Model, Hedonic Model, and Multiplicative Model.)

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.

Geographic Information System (GIS)—(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.

Goodness-of-Fit—A statistical estimate of the amount, and hence the importance, of 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.

Location Value Response Surface Analysis (LVRSA)—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.

Location Variable—A variable that seeks to measure the contribution of locational factors to the total property value, such as the distance to the nearest commercial district or the traffic count on an adjoining street.

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.

Market Analysis—A study of real estate market conditions for a specific type of property.

Market Analyst—An appraiser who studies real estate market conditions and develops mathematical formulas that represent those market conditions.

Market Area—(See Economic Area.)

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:

The buyer and seller are typically motivated;

Both parties are well informed or well advised, and acting in what they consider their best interests;

A reasonable time is allowed for exposure in the open market;

Payment is made in terms of currency or in terms of financial arrangements comparable thereto.

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4-26-2017 draft exposure documentThe 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.

Mean—A measure of central tendency. The result of adding all the values of a variable and dividing by the number of values.

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.

Minkowski Metric—Any of a family of possible ways of measuring distances. 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. However, the Minkowski Metric is based on the sum of the absolute value of the differences, without squaring.

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.

Model Specification—The formal development of a model in a statement or equation, based on data analysis and appraisal theory.

Model Calibration—The development of the adjustments or coefficients from market analysis of the variables to be used in an automated valuation model.

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.

Multiplicative Model—A mathematical model in which the coefficients of independent variables serve 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.

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.

Neural Network—An artificial neural network (ANN) is 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.

Outlier—An observation that has unusual values, that is, it differs markedly from a measure of central tendency. Some outliers occur naturally; others are due to data errors.

Ratio Study—In the context of AVM output, a study of the relationship between model-produced values and market values. of common interest in ratio studies are the level and uniformity of the ratios.

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 the sales of similar/comparable properties.

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4-26-2017 draft exposure documentStepwise 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.

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.

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.

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.

Variable—An item of observation that can assume various values, such as square feet, sales prices, or sales ratios. Variables are commonly described using measures of central tendency and dispersion.

REFERENCES [ADD MORE RECENT REFERENCES]American Planning Association. 2003. “Land Based Classification Standards.” Available from www.planning.org/LBCS/GeneralInfo.

Appraisal Foundation. 2016–2017. Uniform standards of professional appraisal practice (USPAP). Washington, D.C.: Appraisal Foundation.

Appraisal Institute. 2002. The dictionary of real estate appraisal. 4th ed. Chicago: Appraisal Institute.

Carbone, R. 1976. The design of an automated mass appraisal system using feedback. PhD diss., Carnegie-Mellon University.

Collateral Risk Management Consortium (CRC). 2003. The CRC guide to automated valuation model (AVM) performance testing. Paper presented at Fidelity National Information Solutions (FNIS) Valuation Innovation and Leadership Summit, CRC, May 28, in Laguna Beach, CA.

D’Agostino, R.B., and Stephens, M.A. 1986. Goodness-of-fit techniques. New York: Marcel Dekker.

Gloudemans, R. J. 1999. Mass appraisal of real property. Chicago: IAAO.

Gloudemans, R., and R. Almy. 2011. Fundamentals of Mass Appraisal. Kansas City, MO: IAAO.

Gloudemans, R.J. 2001. Confidence intervals for the coefficient of dispersion: Limitations and solutions. Assessment Journal 8 (6):23–27.

Guerin, B.G. 2000. MRA model development using vacant land and improved property in a single valuation model. Assessment Journal 7 (4):27–34.

Hoaglin, D.C., Mosteller, F., and Tukey, J.W. 1983. Understanding robust and exploratory data analysis. New York: John Wiley & Sons.

United States federal guidelines Interagency Guidance. 75 Federal Register at 77,469).

IAAO. 1990. Property appraisal and assessment administration. Chicago: IAAO.

IAAO. 1997. Glossary for property appraisal and assessment. Chicago: IAAO.

IAAO.2013. Standard on ratio studies. Chicago: IAAO.

IAAO. 2002. Automated valuation modeling: A primer. Typescript.

Mendenhall, W., and Sincich, T. 1996. A second course in statistics: Regression analysis. 5th ed. Upper Saddle River, NJ: Prentice Hall.

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4-26-2017 draft exposure documentTomberlin, N. 1997. “Trimming outlier ratios in small samples.” Paper presented at IAAO International Conference on Assessment Administration, September 14–17, at Toronto, ON, Canada.

Waller, B.D. 1999. The impact of AVMs on the appraisal industry. The Appraisal Journal 67 (3):287–292.

Ward, R.D., and Steiner, L.C. 1988. A comparison of feedback and multivariate nonlinear regression analysis in computer-assisted mass appraisal. Property Tax Journal 7 (1):43–7.

Wollery, A., and Shea, S. 1985. Introduction to computer assisted valuation. Boston, MA: Oelgeschlager, Gunn & Hain, Publishers, Inc.

ADDITIONAL SUGGESTED READINGSGloudemans, R.J. 2002. Comparison of three residential regression models: Additive, multiplicative, and nonlinear. Assessment Journal 9 (4):25–36.

O’Connor, P.M. 2002. Comparison of three residential regression models: Additive, multiplicative, and nonlinear. Assessment Journal 9 (4):37–44.

Maloney, J., Ripperger, R., and O’Connor, P.M. 2001. “The first application of modern location adjustments to cost approach and its impact.” Linking Our Horizons, 67th International Conference on Assessment Administration. IAAO, September 9–12, at Miami Beach, FL.

Colwell, P.F., J.A. Heller, and J.W. Trefzger. 2009. Expert testimony: Regression analysis and other systematic methodologies. The Appraisal Journal, Summer 2009, Vol. 77, Issue 3, p253–262.

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APPENDIXES

APPENDIX A. MODEL SPECIFICATION Model specification is the review of the data to determine which valuation model yields the best possible results. Model specification is based on the data analysis and appraisal theory. Model quality is dependent on the experience of the market analysts.

A.1 Cost Approach

The cost approach is an indirect method of arriving at market value, based on specification of replacement cost new less depreciation plus market derived land value. The cost approach is calibrated by review of construction costs and sales. This approach generally produces more acceptable results for newer properties, and it’s very important for properties with insufficient sales. Model specification for the cost approach requires the estimation of separate land and building values. The cost approach formula converts to a model specification:

MV=πGQ×[(1−BQD )×RCN+LV ]

Where,

MV is the market value estimate.

πGQ represents the general qualitative variables such as location, economic adjustments, and time of sale.

BQD is a building qualitative variable representing depreciation.

RCN is the replacement/reproduction cost new.

LV is the land value.

(Gloudemans 1999, 124)

MV=πGQ×[(πLQ×∑LA )+(π BQ×∑BA )+∑OA ]

Where,

• MV is the estimated market value;

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• πGQ is the product of general qualitative variables;

• πLQ is the product of land qualitative variables;

• ΣLA is the sum of land additive variables;

• πBQ is the product of building qualitative variables;

• ΣBA is the sum of building additive variables; and

• ΣOA is the sum of other additive variables.

(Gloudemans and Almy 2011, 178)

If a third party provides the cost tables, it is the responsibility of the AVM market analyst to calibrate the cost tables to the local market to provide a valid indicator of value by the cost approach.

A.2 Sales Comparison Approach

A.2.1. General Model

The Sales Comparison Approach is a direct market approach. In an AVM, there may be the traditional three comparable sale grid, that may or may not be backed up by statistics or a statistically based direct market model.

The first model will include data items important in determining comparability and may involve the calculation of a dissimilarity measure, such as the Minkowski or Euclidean distance metrics. A second model will include data items significant in directly estimating value from the market and is used to adjust the selected comparable sales to the subject. Model specification for the comparable sales method can be summarized as follows:

MV S=S PC+ADJC MVs represents the market value estimate;

SPC represents the selling price of comparable sale property; and

ADJC represents adjustments to the comparable sale.

(Gloudemans 1999, 124)

A.2.2 Direct Market Model Forms

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Direct market approach models may take one of three forms: additive (also termed “linear”), multiplicative, or hybrid (also termed “nonlinear”). These three model specifications and their calibration (regression) techniques will be referred to as multiple regression analysis (MRA). In an additive model, the contribution of each variable in the model is added together. In a multiplicative model, the contributions are multiplied. Hybrid models can accommodate both additive and multiplicative components. All three types of models will provide quality statistics. Additive models are the most prevalent of the three, based on tradition and wide availability of software programs. Multiplicative models are based on linear regression platforms as methods to provide for curvilinear relationships between the data and value. Multiplicative models require additional training and knowledge of the analyst. Some software with linear programs will permit scientific terms to be used in multiplicative formulas. Nonlinear (hybrid) models are used the least due to limited software availability, but these models more accurately reflect professional appraisal principles.

A.2.2.1 Additive models

Additive models have the form:

MV=B0+B1× X1+B2×X 2+…

Where,

MV is the dependent variable;

B0 is a constant;

Xi represents the independent variables in the model; and

Bi are corresponding rates or coefficients.

In a direct market model, the dependent variable “MV” is either sale price or sale price per unit. Additive models are relatively easy to calibrate and many have automated calibration software.

A.2.2.2 Multiplicative models

In a multiplicative model the contribution of the variables is multiplied rather than added:

MV=B0× X1B1×X2

B2×…

MV is the dependent variable;

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B0 is a constant;

X1B1 where X represents the independent variables in the model; and

X1B1 where B1 represents the corresponding rates or coefficients.

In this example, each variable is raised to a corresponding power.

Multiplicative models consist of a constant B0 and percentage adjustments. They have several advantages, including the ability to capture curvilinear relationships more effectively and the ability to adjust proportionately to the value of the property being appraised. Multiplicative models are usually calibrated using linear regression packages. This requires some of the variables to be converted to logarithmic format for calibration, which can complicate model development and application.

A.2.2.3 Hybrid (Nonlinear) models

Hybrid (nonlinear) models are a combination of additive and multiplicative models. As such, they are theoretically the best alternative of the three, but software is relatively limited to generic statistical software.

A general hybrid (nonlinear) model specification that separates value into building, land, and “other” components (e.g., outbuildings) is:

MV=πGQ× [π BQ×∑BA )+πLQ×∑LA ¿+∑OA ¿

Where,

MV is the estimated market value;

πGQ is the product of general qualitative variables;

πBQ is the product of building qualitative variables;

ΣBA is the sum of building additive variables;

πLQ is the product of land qualitative variables;

ΣLA is the sum of land additive variables; and

ΣOA is the sum of other additive variables.

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(IAAO 1990, 351; Gloudemans and Almy 2011, 178)

A.3 Income Approach

The income approach is an indirect method for determining market value. The appraiser evaluates income for quantity, quality, direction, duration, and the expense incurred to earn this income, and then converts it by means of a capitalization rate into an estimate of market value.

In an income model, the dependent variable may be income, income per unit, expenses, and expenses per unit or capitalization rate. The steps in this approach are:

1. Estimate gross income, expenses, and net income from market data.2. Select the appropriate capitalization method (model specification).3. Derive a capitalization rate or income multiplier from the market (model calibration).4. Compute value by capitalization. (IAAO 2002)

While there are many model specifications of the income approach, the basic overall direct capitalization formula is:

MV=NOI /R

Where, MV is the price examined in the calibration and the resulting estimate of market value;

NOI is the net operating income; and

R is the overall capitalization rate.

Another set of income approach methodologies uses the relationship between gross income and rent. These are commonly known as Gross Income Multipliers (GIM) and Gross Rental Multipliers (GRM).

The model takes the form of:

MV=GI A×GIM

Or

MV=GIM×GRM

Where, MV is the price examined in the calibration and resulting estimate of market value;

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GIA is the gross annual income;

GIM is the gross income multiplier;

Or

MV is the price examined in the calibration and resulting estimate of market value;

GIM is the gross monthly income;

GRM is the Gross Monthly Rent

A.4 Uses of AVM Model Types

A.4.1 Appraiser Assisted AVMs

Appraiser assisted AVMs are models that provide a range of comparable sales to the appraisers or other partial values to be finalized by the appraiser. This process assumes that the appraiser will develop the final estimate of value.

A.4.2 Fully Automated Valuation Model

A fully automated valuation model is developed by an analyst who performs market analysis to create an application formula, which directly generates a final estimate of value. A fully automated valuation model emphasizes the use of statistical models and procedures.

Types of AVMs related to the fully automated valuation model include: sales comparison approach; cost approach; direct market model income capitalization approach; and price indexation (Time trending, See Appendix G: Residential AVMs, G.2 Time

Series Models for Residential Property)

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Bennett, 04/17/17,

Wadsworth: 54 – Again I am not familiar with price indexation as a type of AVM. Should “price index” or “price indexing” be in the glossary? Change to to “price indexing by time” to be more clear?


APPENDIX B. VALUE DEFENSE Market analysts must be prepared to review and defend values developed through AVMs. The review process begins with checking the statistical accuracy of the data at the 95% confidence level. If no problem is found, the estimated value should be evaluated for consistency with similar properties within the locational area and with any recent sales of the subject property or similar properties. AVM generated ranges/values should be accurate based on statistical quality reviews and consistent from property to property. The fact that a property sold for a price different from the AVM estimate does not mean that the AVM estimate is wrong. The sale date may differ significantly from the appraisal date, and the property may have sold for a relatively low or high price, depending on the peculiarities of the situation and motivations of the buyer and seller. The best support for an AVM value is recent sales of comparable properties . Provided there is a proven consistent relationship between sale prices and listing prices, listings can also be used. For income properties, it may be possible to support value estimates derived from one AVM (e.g., a sales comparison model) with estimates derived from alternative methods (e.g., an income model). The consistency of the value estimates with others produced by the AVM model, as well as the overall reliability of the AVM model as evidenced by a ratio study of a holdout sample or other statistical measures, can also be evaluated and used to defend the value.

AVM developers should prepare documentation that will allow clients and other intended users the ability to understand in nontechnical terms how the model was developed and applied. If the user modifies the AVM produced values, they are responsible for the results and any changes in quality assurance measures. In this instance, original quality assurance measures may not be applicable for value defense.The first step in preparation of a value defense program is the certification that the data sources are accurate and consistently collected using a published manual. Aggregated data sources can be considered as suspect when the linking fields are defined by broad regions and not point locations. Examples include use of delivery or services areas to track population changes, quality of improvements, space utilization and detailed market trends.

The use of point based data identification allows locational factors to be determined down to a parcel level including items like orientation to views and solar, wind potential, and transportation

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Bennett, 04/13/17,

Wadsworth: I would include a discussion of stratified (e.g. within neighborhood or a distance from the subject, or by type (e.g. 1 story, split level, 2 story or within a value or size range, etc.) level, COD, and confidence interval as part of the value defense to show that regulatory value standards have been met and similar treatment of like properties


patterns. This allows for a model response that is closer to the thought process of the typical buyer and seller within a sub-market and market area.

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APPENDIX C. STRATIFICATION IN AVM DEVELOPMENT In stratification, parcels are sorted into relatively homogeneous groups based on use, physical characteristics, or location. Properties are first stratified by use such as agricultural, apartments, commercial, industrial, and residential. Additional stratification by physical characteristics or value ranges may be performed to minimize the differences within strata and maximize differences among strata, when the complexity of the model is limited or additive regression is used . Unless geographic coordinates are used to develop a response surface, geographic stratification is appropriate wherever the value of various property attributes varies significantly among areas and is particularly effective when housing types and styles are relatively uniform within areas. Location stratification reduces the need for complex models. However, excessive stratification may provide too little variation in the data. This may cause problems because market based calibration techniques require measurable differences within data variables in order to detect a correlation with market value. When the market for a given type of property is regional or national in scope, it may be possible to create valuation models without stratification if location adjustments are included as part of the model specification and calibration processes.

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APPENDIX D. STATISTICAL METHODS FOR DEVELOPING LOCATION ADJUSTMENTSTwo methods for developing location adjustments are the creation or use of existing neighborhoods and use of geocoordinates to develop some form of location value response surface analysis (LVRSA). Neighborhoods are the traditional and most common form of location analysis. In AVMs, neighborhoods may be based upon streets and natural boundaries, government assessor-designated areas, census tracts, or postal delivery codes. LVRSA techniques relate relative prices as measured, for example, by the ratio of each sale price to the median price, to each property’s unique location, as represented by its geographical coordinates.

Software that provides the ability to perform analysis such as LVRSA uses a variety of smoothing techniques to compute a unique location adjustment, termed the relative location value (RLV), for each property. By use of residuals from a first model developed without location, variables can be plotted and analyzed to create the RLV grid. This variable is then included, along with other variables, in a multiple regression or other model to capture location influences.

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Alan Dornfest, 04/25/17,

We need to decide on whether to use neighborhoods, geoeconomic areas, or both. Reviewers please comment.


APPENDIX E. TIME SERIES ANALYSISTime series analyses are a family of techniques that can be used to measure the cyclical movements, random variations, seasonal variations, and secular trends observed over a period. In property valuation, these analyses can be used to develop a multiplier or index factor to update existing appraised values or to adjust sale prices for individual properties to a valuation date. Since values can change at different rates in different markets, separate factors should be tested for each property type and market area.

Five time series analysis methods used to develop time trend factors in the appraisal and assessment industries are:

Value per-unit analysis,

Re-sales analysis,

Sales/assessment or sales/AVM value ratio trend analysis

Median/mean value per period in the form of a moving average/median sale price

Inclusion of time sale variables in MRA/AVM models

These methods are summarized below (for a more detailed explanation and discussion, see Mass Appraisal of Real Property (Gloudemans 1999, 263–270).

Value per-unit analyses track changes in sale price per unit (e.g., per square foot for residential properties or per unit for apartments) over time. The method is easily understood and lends itself well to graphical representation, as well as to statistical modeling to extract the average rate of change. A downside is that the method does not account for the myriad of other value influences, such as age and construction quality, that affect per-unit values.

Re-sales analysis uses repeat sales occurring over a given time period. Price changes between sales are converted to monthly rates and an average (or median) rate of change is extracted. As can be imagined, the larger the number of repeat sales, the more reliable the estimated rate of change. The method can overestimate rates of change if repeat sales reflect substantial improvements (or other alterations) made to the property since the first sale. Repeat sales may be too selective and not represent the subject or population of properties to be valued by an AVM.

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Sales/assessment (or sales/AVM value) ratio trend analysis involves tracking changes in the ratio of sales prices to existing assessments (or AVM values) made as of a common base date. Increases in the ratios indicate inflation and vice versa. The ratio also provides the index factor required to convert an AVM value to a full value estimate. Like value per-unit analysis, the method lends itself well to graphical and statistical analysis. An advantage of the method is that AVM values account for most value determinants and thus can isolate time trends better than the value per-unit method. The method assumes that the AVM values share a common basis, and its reliability depends partly on the accuracy or uniformity of the AVM values.

Median/mean value per period is taught in many of the courses offered to single property appraisal specialists and real estate sales personnel as a quick method to arrive at a time adjustment to allow the use of older sales in special cases. The modeler collects all sale prices from a region for a specific type of property under study. The sales are grouped into periods of one or three months and a simple average or median price per period is calculated. The average or median prices per period are regressed to establish a trend line. The trend value is then used to adjust the sale price to the effective date of the valuation. This has become popular with spreadsheet valuation models used for market analysis and price opinions found on local real estate sites. The method has the same limitation as value per-unit analysis, in that it does not account for the myriad of other factors that affect sales prices.

Once a time trend is established, it can be used to adjust values to any point within the sales period.

Trend factors can be extrapolated for a short period beyond the sales period, but this must be done with caution and grows increasingly unreliable as the period is lengthened. If more than several months are involved, the first three methods can be used to calibrate the trend (one would not ordinarily develop time adjustments through use of a modeling approach without recalibrating the entire AVM model).

(The Appraisal Institute 2002, 291)

The most common error in developing a time adjustment factor is the failure to look for other items within a sale which are responsible for a distorted shift in per unit values. Forces of nature are an example, after flood event property in low areas may be slow to sell and require price reductions while that on higher ground remains steady in the market. Stratification of the sales used in the study will aid in preventing the skewed assumptions.

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APPENDIX F. CALIBRATION TECHNIQUESModel calibration is the development of adjustments or coefficients through market analysis of the variables to be used in an AVM. Many AVMs in use today rely strictly on statistical models as the method of calibration. Some techniques may be based on regression, adaptive estimation, or neural networks. All regression programs are based in statistics, while adaptive estimation procedure is based on a tracking method from the engineering sciences. Neural networks emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. Artificial neural networks are collections of mathematical models that can emulate some of the observed properties found in the real estate market.

F.1 Calibration Using Statistically Based Methods

MRA is a statistically based analysis that evaluates the linear and/or curvilinear relationship between a dependent (response) variable and several independent (predictor) variables, and extracts parameter estimates for independent variables used collectively to estimate value in a mathematical model. Models produced using MRA come with a rich set of diagnostic statistics that provide evaluation tools for the market analyst to compare results between and among specified models. These goodness-of-fit statistics provide information about each variable’s significance in predicting values, and how well the variables in the model work together to produce creditable results overall.

F.1.1 MRA Assumptions

The accuracy and credibility of an MRA model depend on the degree to which certain assumptions are met. The most important assumptions are complete, accurate and representative data, normal distribution of errors, constant variance of the errors and uncorrelated independent variables.

Complete and accurate data are required if MRA is to achieve predictive accuracy. The assumption is that sold properties’ data from which models are constructed are representative of the properties to which they are applied. It is important that both lower and higher valued properties be represented in the model. Data should also be divided into training samples used to develop the model, and holdout samples (control samples) used to test model results.

Because of its robust character, minor violation of the representative data assumption will not dramatically affect results. Poor data quality or samples not representative of the population will

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produce poor performing models. The market analyst must be able to present the MRA results in an understandable and defensible format for appraisers and AVM clients.

Linearity assumes the marginal contribution to value by an independent variable is constant over the entire range of the variable. When additive models are used, this assumption may not be supported in the market place, requiring a transformation of the variable. Additivity continues with the concept of marginal contribution in that any one independent variable is unaffected by the other variables in the model. In other words, linear additive models do not possess the ability to measure nonlinear effects or interactive effects of market conditions, without transforming raw variables. In such cases, one must consider using hybrid (nonlinear) models.

Normal distribution of errors follows the assumption that the data are normally distributed, and therefore, any error in predictions is normally distributed. Without the assumption of normally distributed errors, the inferences for using the standard error of the estimate and coefficient of variation (COV) as measures for goodness of fit are meaningless. Constant variance of the error term implies that the residuals are uncorrelated with the dependent variable, which is the sale price. In other words, as the price level changes, the error term remains constant or homoscedastic; when unequal variances occur at different price ranges, it is heteroscedastic.

A term known as multicollinearity describes the condition where independent variables are correlated (measure the same thing) with each other. Depending on the method used, regression may reject one variable as insignificant or exaggerate coefficients for both variables, if multicollinearity is introduced into the model. A correlation matrix is a good tool when testing for multicollinearity.

To avoid seriously violating assumptions of linearity, additivity, and constant variance of the error term, the market analyst must consider the use of transforming variables or other calibration methods described in the standard. A multiplicative, hybrid (nonlinear), model structure is best for measuring interactive effects.

F.1.2 Diagnostic Measures of Goodness-of-Fit

The market analyst using regression must be aware of and understand how the various key statistical measures used in regression relate to the reliability of results. These statistics fall into two categories: overall measures that aid in the interpretation of model performance and individual variable measures that assist in the understanding of how well an individual variable performs in helping to estimate value, as well as keeping the standard error term to a minimum.

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Primary measures of goodness-of-fit for overall model performance are the coefficient of determination (R2), standard error of the estimate (SEE), COV, and average percent error.

Goodness-of-fit measures for individual variables in a model are produced by most MRA software packages and include the coefficient of correlation (R), t-statistic, f-statistic, and beta coefficients. Each of these measures will provide information about an individual variable’s linearity or importance of contribution toward improving predictive success, and relative importance, as variables are compared to each other.

(D’Agostino and Stephens 1986)

When all the measures are used collectively, along with an understanding of data quality issues, those skilled in developing and using MRA can fully evaluate the credibility of the AVM estimates. Appraisers asked to review AVM results must understand the role that goodness-of fit statistics play in evaluating AVM results and should review the Appraisal Standards Board’s USPAP Standard 6 and AO-18.

(Appraisal Foundation 2016–2017, 39–49, 121–127)

F.1.3 MRA Software, Options, and Techniques

MRA is the most widely used method for calibrating models. As such, the availability of MRA software provides users many choices. No one software package is deemed superior to another, as success using MRA is a combination of modeling skills and software familiarity. Variations of a selected MRA technique can be a decisive factor in selecting an MRA and statistical application package. Many MRA techniques have been adopted over the years to help regression take better advantage of its predictive powers. Stepwise, constrained, robust, ridge regression, and others are acceptable techniques used to improve predictive success. Many of the statistical software packages include variable selection routines that aid the market analyst in selection of significant variables.

F.1.4 MRA Strengths

Goodness-of-fit statistics—gives credence to the validity of results.

Software availability—many regression software products are available.

Widely accepted calibration method.

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Broad education network—MRA is taught at most colleges and universities around the world.

Credible values—in the hands of a skilled market analyst, MRA is proven to produce results that meet the test of model performance.

F.1.5 MRA Weaknesses

Requires a level of statistical knowledge—market analysts must possess significant background in data analysis and statistical methods.

Predictive accuracy is constrained by assumptions.

Requires data sets that have sufficient sample size.

Interactive and nonlinear market trends are difficult to measure without transforming data.

F.2 Calibrating Using Adaptive Estimation Procedure (AEP)

Adaptive Estimation Procedure (AEP) is a calibration technique that was adapted to real estate value in the early 1980s. Also known as feedback, AEP is based on an engineering concept that relies on continual adjustment to coefficients as the calibration engine passes, or tracks, back and forth through the data until convergence is achieved (minimum error is achieved); thus the term feedback. For property valuation, the algorithm tracks the sale price as a moving target through time. It compares property characteristics as variables that measure the change in sale price, and calibrates a coefficient for each variable. The coefficients are used to estimate value that is then compared to sale price. A running tally is kept on the error term as the process continues. Figure 1 depicts the feedback loop.

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AEP will make multiple passes through the sales file, constantly adjusting coefficients before a final solution is reached. Success using AEP is dependent upon the market analyst’s ability to properly specify a model with characteristics that measure and evaluate local market conditions. Market analysts using AEP have considerable control over the variables used in the model and the coefficient amounts. AEP uses whatever variables are introduced into the model. No variable is excluded because of insignificance. As part of the specification phase, the model can be pre-calibrated with starting, minimum, and maximum coefficients to help it converge sooner, and to help ensure that rational coefficients will be produced. Setting the starting, minimum, and maximum coefficients is analogous to constraining coefficients used in constrained regression.

F.2.1 AEP Model Structure

A hybrid (nonlinear) model structure has the ability to directly deal with interactive and nonlinear effects found in the market place. The structure closely resembles a cost model; however, the calibrations give it the benefit of a direct market model. The flexibility of the hybrid (nonlinear) model built into AEP allows the qualitative variables to be calibrated in two different ways: as

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multiplicative, that is XiB1 (rates), or binaries B1Xi. Deployment of a feedback model in an AVM format allows for flexibility without the added complexity of transformations found with additive models.

F.2.2 Variable Control in AEP

Calibration of individual variables in AEP differs significantly from the fitting of a straight line or curve in linear or nonlinear regression. Controlling for extremes in the coefficient amounts is a concern when using feedback. The use of smoothing and damping factors will help provide model stability during the calibration phase. Smoothing is applied to only the quantitative variables. Using an algorithm, smoothing keeps track of each variable’s exponentially smoothed mean (moving average) as a way of learning until a final solution is reached. Smoothing factors are used in conjunction with damping factors. The market analyst provides the settings for the smoothing factor. Additionally, damping factors control the amount of movement each coefficient (quantitative and qualitative) will have as each new case is introduced into the model while calibrating. Some feedback systems will dynamically adjust damping and smoothing for optimized results. Locking or constraining coefficient movement forces residuals onto another variable. With so much control over the model, even similarly specified models may produce different final answers.

F.2.3 Results and Goodness-of-Fit Measures

Final results using AEP are measured first by the comparison of how close the estimated price comes to the actual price. Another measure, the reasonableness of coefficient amounts, is based on the skill and knowledge of the analyst in pre-defining the model prior to calibration. AEP does not care if a model uses square foot of living area at a price or the window count at a price. If either can logically predict accurate value estimates, AEP will generate a coefficient that produces the lowest error term. Feedback understands that grouped patterns of property characteristics are the determinants of price and the individual characteristics do not necessarily produce marginal contributions to price.

The AEP is not reliant on statistical measures of the model, or variable significance. Convergence occurs when the average absolute error does not change appreciably from one iteration to another. Some software allows other criteria to be set by the user (e.g., maximum iterations, pre-defined absolute error). There is no statistical measure that accounts for significance of a variable. Pseudo-R2 statistics can be generated after the feedback model is complete. Output should include the accounting for calibration of each variable by giving information on the number of

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observations, starting, minimum and maximum ranges, and the low, high, and final coefficient of the variable.

F.2.4 AEP Advantages

Produces separate estimates for land and improvements.

Based on reducing the absolute error term, not just minimizing the squared error term.

Outliers’ influence can be diminished during variable calibration cycle.

Requires fewer observations than regression.

Individual variable movement can be easily constrained.

Cost system attributes can be directly calibrated.

Allows for flexibility without the added complexity of transformations.

F.2.5 AEP Disadvantages

Software availability is limited, and there is no standardized algorithm.

Does not contain standard goodness-of-fit statistics found in regression software.

Requires initial model to be specified carefully.

Similarly specified models may produce different answers

o This results from the rate of convergence and the timing of the entry of observations

F.3 Artificial Neural Networks

A newer system for calibrating real estate valuation models is Artificial Neural Networks (ANNs). The concept is borrowed from the biological sciences and functions of the human brain. The key element of the ANN paradigm is the novel structure of the information processing system. An ANN is composed of many highly interconnected processing elements that are analogous to neurons and are tied together with weighted connections that are analogous to synapses. As the name implies, ANN comes as close to producing artificial intelligence models as any calibration method. Like nonlinear regression and feedback, neural networks can calibrate models that consist of both linear and nonlinear terms simultaneously. The user inputs each variable with assigned weights (coefficients). The software exposes the data using an algorithm in a hidden layer where the

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weights are adjusted (calibrated) in a manner that reduces the squared error. This is an iterative process much like those found with feedback and hybrid (nonlinear) regression. The final output results in a single estimate of value with the exact formula remaining hidden from the market analyst.

F.3.1 The Artificial Neuron

The basic unit of a neural network, the artificial neuron, simulates the four basic functions of natural neurons. Those functions are represented by inputs, the processing of inputs (summation), transfer (linear, sigmoid, sine, and so on), and outputs an answer. Artificial neurons are much simpler than the biological neuron; Figure 2 shows the basics of an artificial neuron.

Inputs to the network are represented by the mathematical symbol xn. Each of these inputs is multiplied by a connection weight that is represented by wn. In the simplest case, these products are simply summed, fed through a transfer function to generate a result, and then output.

Even though all artificial neural networks are constructed from this basic building block, the fundamentals may vary in these building blocks.

F.3.2 Strengths of Neural Networks

The ability of the neural network to “learn” as it goes and to take new information and process it as the network has been trained.

Neural networks can recognize and match complicated, vague, or incomplete patterns in data.

Options that provide analysts confidence about future use of neural network applications, such as helping to improve data quality.

Accuracy of neural networks is comparable to other calibration methods found in the standard.

F.3.3 Weaknesses of Neural Networks

The complexity of how the process actually works is in the hidden layer.

Lack of a definable model structure at the output stage makes explanation of value and support of the value more difficult.

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Requires considerable background in data analysis, data structure, and mathematical concepts.

Limited research links pertaining to use in real property valuation.

Requires considerable investment in computer power and software.

(Gloudemans 1999, 329 [FMA does not cover this in detail])

F.4 Calibration Summary

The various methods and procedures used to calibrate the AVM are the engines that drive accuracy and credibility of the estimate made. By itself, no single calibration method is better than another. Data quality and the skill level of the analyst define the accuracy of one calibration technique as compared to others. Users of AVM products must be aware of the interdependence between skills and technologies of calibration when deciding how well the AVM will perform.

The use of MRA has been the longstanding choice for calibration and has a proven record. Feedback, hybrid (nonlinear) regression, and neural networks are emerging technologies that require different levels of skill and knowledge concerning modeling real property values. Understanding calibration in relation to this standard encourages the AVM market analysts and clients to understand that AVM development is not a black box process; instead, it is based on

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well-defined concepts surrounding the appraisal process. Details for learning and understanding the skills and technical aspects of calibration are found in the references throughout this section of the standard. AVM clients must understand that developers of AVM products are not limited to using a single method of calibration. Product market analysts often base their value estimates on multiple technologies. Included in these technologies are simple sales listings of automated sales comparison selections, with adjustments derived from the modeling process. Appraisers asked to use or review an AVM should read Advisory Opinion 18—published as part of USPAP Standard 6 by the Appraisal Foundation (2003, 46–56, 180–187).

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APPENDIX G. RESIDENTIAL AVMS G.1 Single-Family

When adequate sales data are available, the direct sales comparison approach is the preferred method of valuing residential property. The approach may take two forms: direct market models and comparable sales. When adequate sales data are not available, cost approach models may apply to newer residential buildings.

Direct market models developed from sales analyses use various model structures, with coefficients derived via a mathematical calibration method. The comparable sales method is a two-part method in which comparable sales are found and then adjusted to the subject property. Whether automated or appraiser derived, the comparable sales method relies on the quality and experience real estate appraisers.

G.1.1 Cost Models

The cost approach works best when applied to newer properties that do not exhibit a great deal of measurable depreciation, and where the land value can be reasonably estimated from recent land sales. Replacement cost models are anchored in tables developed by studying local building cost data. Many replacement cost new tables (RCN) are calibrated with known sales transactions to market calibrate the cost tables. These RCN estimates need further calibration for actual property condition (depreciation), location (macro and micro), and a supportable estimate of vacant land value, to arrive at estimates of market value. These items represent the demand side of the market. A strength of the cost model is that it can be applied to any improvement regardless of size, quality, age, condition, or style.

G.1.2. Comparable Sales Models

Comparable sales models use sale prices of properties with attributes similar to the subject property to establish estimates of market value. It essentially requires two models. The first one is a comparable selection model. The advantage of this model is that all recent sales with proximity to the subject are considered. This method may work well in homogeneous areas with a high sales volume. If the comparable sales have significant attribute differences, the confidence of the adjustments being made begins to suffer. For selecting quality comparable sales, an AVM routine may consider using a weighted selection model (e.g., regression coefficients, Minkowski or Euclidian distance metrics). Once the best comparable sales are selected, they must be adjusted

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for attributes that are dissimilar to the subject. Mathematically, the adjustments can be derived from just two sales; one sale possesses the attribute, while the other does not. The difference in sale price measures the value of the missing attribute. Sales comparison methods that are supported by direct market models that use quantitative methods to derive the adjustments are more stable, reliable and statistically supported than simple matched pairs analysis.

In its formatted form, the comparable sales approach should display how each attribute adjustment in the AVM contributes to the overall value estimate. Users of AVMs are cautioned that matched pairs analysis is not a statistical calibration method.

G.1.3 Direct Market Models

The basic premise of direct market models (also termed hedonic models) is that the price of a marketed good is related to its characteristics or the services it provides. In real estate appraisal, three direct models based on regression, MRA, are additive, multiplicative and IAAO general hybrid model. For example, the price of a home reflects the characteristics of that home (e.g., size, construction quality, style, location). Therefore, we can value the individual attributes of a home by looking at the prices people are willing to pay for them. Direct market models lend themselves well to the calibration methods and techniques discussed throughout this standard. Properly designed direct market models will produce AVMs capable of very accurate and credible value estimates. Therefore the knowledge and experience of the modeler are important to the statistical quality of the direct market model. All three model structures discussed in this standard are well suited to the valuation of single-family residences. Additive models have been most popular in the government assessment offices using CAMA systems and work very well in most cases. Multiplicative models carry certain advantages (See A2.2.2) and can be effectively adopted. Because they accommodate currency and percentage adjustments, hybrid (nonlinear) models provide the most flexibility. Where an additive model will add the same lump sum amount to all property having air conditioning, multiplicative and hybrid (nonlinear) models will attribute different amounts depending on the style, quality, and location of the property. Multiplicative and hybrid (nonlinear) model structures also lend themselves well to the valuation of spatially dispersed or highly heterogeneous residences with sufficient sales and data representativeness.

G.2 Time Series Models for Residential Property

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Indexed models relate to time-series analysis (see Section 4.4 on Time Series Analysis) as described earlier. Use of these models represents a common method of delivering quick automated value estimates. These models simply measure the average change in value over time and factor the value forward from a benchmark starting place, such as the average value in a census block or market area. The accuracy of indexed models is inconsistent and less reliable than fully specified models. These models work best in areas of homogeneity where the range of value is close to the average value.

Indexing is a common method used to update cost tables to reflect current cost. As with market models, a benchmark in time is required as a starting point. Cost coefficients are then updated, using a single index factor representing the measurable change since the original cost coefficients were generated. One current method of indexing is to use an economic indicator such as the consumer price index (CPI). In the cost approach, indexed models have no way of adjusting values at the micro level for location and other market influences that affect value. Time adjustments may be developed from the analysis of known sale prices within a geographic area, such as a neighborhood or postal code, and over a specified time reference.

Users and consumers of index models must understand how the index factor is created and how the accuracy of the original value was derived before giving a lot of credibility to an AVM using an index model.

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APPENDIX H. VALUATION MODELS FOR COMMERCIAL AND INDUSTRIAL AVMSCommercial and industrial property can be valued by sales comparison, income, and cost AVMs.

H.1 Cost Models

While commercial and industrial cost models are like residential cost models, they typically comprise different structural components. The commercial and industrial cost model requires several extra features or miscellaneous items. Cost models are most appropriate for commercial, industrial, and special purpose properties where there is insufficient sales and income information.

H.2 Sales Comparison Models

It is often difficult to get sufficient qualified sales to develop commercial and industrial comparable sales and statistical models. However, where sufficient sales can be found, direct market models can be developed using variables for location, use, size, construction quality, age or condition, land size or frontage, and relevant amenities or nuisances. Additive, multiplicative, and hybrid (nonlinear) models can all be used; yet proper model specification is critical.

H.3 Income Models

The income approach can be used to develop commercial and industrial AVMs. Because these properties are frequently sold based on their income streams, the income approach can be the most desirable. The two most popular approaches are direct capitalization and gross income multipliers (GIM). Discounted cash flow (DCF) analysis can also be used; however, the data requirements for developing yield capitalization estimates from DCF analysis make the method more challenging than direct capitalization. In addition, a number of the assumptions required for DCF analysis, including anticipated yield, holding period, and value at the end of that period, can be difficult to derive from the market and, therefore, may be subjective.

H.3.1 Modeling Gross Income

Gross incomes may be analyzed from local market surveys or questionnaires or they may be obtained from industry publications. Typically the gross rent per unit (e.g., square feet/square meters, rental unit, or room rate) is the dependent variable in the model. Gross income models are ordinarily easier to develop than net income models because the data is easier to obtain and

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less subject to manipulation. Gross income models can be developed for either potential or effective gross incomes. The independent variables are those that affect the expected gross income, including location, use, age or condition, amenities and nuisances, etc. Where data is limited to develop separate models for each use or occupancy, a single model may be developed by determining a reference use or occupancy group and using binary or categorical variables for the other use or occupancy groups.

H.3.2 Vacancy and Collection Losses

Vacancy and collection losses are deducted from the Potential Gross Income (PGI) to account for typical losses due to vacancy and bad debts based on local market conditions. The vacancy and collection losses usually vary by property use and are expressed as a percentage of the annual PGI. The percentage may be determined by a market analysis of PGIs compared with actual income, or from information supplied by local lenders and industry trade publications.

H.3.3 Modeling Expenses

Expense data may be obtained from the same sources as the revenue data. Expense ratios can be developed by either stratification or a modeling approach. The expense ratio is the dependent variable, and the independent variables are similar to (but typically fewer than) those used to determine the gross income per unit. Like gross income models, a single expense ratio model may be developed, where insufficient data are available for multiple models, by determining a reference use or occupancy group and creating binary variables for the other use or occupancy groups.

H.3.4 Direct Capitalization

Direct capitalization involves developing an overall rate (OAR) directly from the market place. The OAR is then used with the estimated net income to estimate the value by income capitalization. Like expense ratios, capitalization rates can be developed using either stratification or a modeling approach. The advantage of OARs is that they use the NOI that includes both gross incomes and expenses, and thus may specifically reflect a typical investor analysis of commercial properties. The dependent variable in developing a direct capitalization rate is the indicated OAR (estimated net income divided by the sale price). In developing an OAR model, a single model can be developed by determining a reference use or occupancy group, and creating binary or categorical variables for the other uses or occupancy groups. As in the revenue and expense models, this permits more data to be used in the model. In addition to variables for location, use, age or

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condition, and amenities and nuisances, the OAR model should include an adjustment for attributes that affect the recapture portion of the OAR (such as land/building value ratios, REL estimates, and expense ratios).

H.3.5 Gross Income Multiplier

GIM models involve developing multipliers directly from the market place for either the potential gross income or the effective gross income, depending on the data collected. Effective Gross Income Multiplier models are generally more stable. GIMs have the advantage of not requiring expense data that may be missing, unreliable, difficult to interpret, or incomplete. The GIM is the dependent variable while the independent variables are typically the same as those previously described for an OAR model. However, it is important to ensure that variables related to differences in expense ratios are included because gross incomes are unadjusted for expenses.

H.3.6 Property Taxes

Care should be taken to treat property taxes consistently in the development and application of AVMs. Property taxes may be included as an expense or as a component of the OAR.

H.4 Quality Assurance

Commercial and industrial quality assurance is particularly critical due to the limited amount of sales and income data available for analysis and modeling. Commercial and industrial quality assurance is accomplished in much the same manner as with any other type of property. Valuation research and appraisal procedures are subject to review, and the values tested and statistically analyzed for accuracy and consistency. See also Section 7 of this standard.

In addition, quality assurance must be extended to the income data collected. Estimated gross incomes, expense ratios, OARs, and GIMs should all be reviewed for consistency. Gross income and expense data should be compared with like properties to identify outliers that may need to be removed from the modeling process unless the data can be corrected.

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APPENDIX I: LAND MODELSLand provides a set of unique problems for AVMs. Land is highly speculative and there frequently are relatively few sales for analysis and modeling.

Land values are highly affected by location, which is one of the reasons why land values appear to be more speculative. Other factors affecting land values include national, state, and local regulations affecting development and what stage the geoeconomic area is in its life cycle. Developed land will command a significant premium over undeveloped land, especially when there is no guarantee that the purchaser or potential developer will be successful. In addition, geoeconomic areas evolve from growth to stability, to decline, and potentially to being a land-driven market where the improvements have no value.

GIS is extremely valuable as an aid in establishing the effect of location on land. Where a GIS is not available, geoeconomic areas can be developed based on appraisal judgment, or grids can be developed and x, y coordinates manually derived from the grids to better handle location.

Land that is significantly distant from urban areas may be best valued based on its income potential for the production of agricultural products.

I.1 Land Valuation Model Specification

Market land valuation modeling requires data on use, potential change in uses, location, and physical characteristics. Land models, like improved models, require qualitative and quantitative variables, as well as data transformations.

I.1.1 Property Use

The analyst should use the existing use of the property. The market will indicate in the location analysis will determine the highest and best use value and indicate where use is changing.

The analyst must estimate the proper use of a parcel of land for any AVM. In addition, the analyst must consider the possible changes occurring in land use and the effects on assemblage and reconfiguration of existing infrastructure and occupancies. This will serve to determine how it should be appraised as well as provide a key variable for comparison and to determine what sales are best suited for building the model. Although many states and Canadian provinces provide use codes for reporting, currently there are no generally accepted standards for classifying land uses.

I.1.2 Location

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Location and parcel size are arguably the most important pieces of land data. The most common method of modeling location is through the delineation of economic (or submarket) areas or geoeconomic areas. Variations of location value response surface analysis (LVRSA) have been developed to determine location adjustments both with and without delineating economic areas.

Appraisers, using maps and their judgment (based on knowledge of market conditions), generally decide geoeconomic areas or submarket boundaries. All parcels in a geoeconomic areas or submarket receive the same location adjustment. There are two factors to be aware of when using this approach. First, boundaries may be drawn to coincide with major streets, natural barriers, and/or political subdivision boundaries. and, secondly, the market analyst should be aware that location adjustments can change abruptly from one submarket or geoeconomic areas to another. LVRSA uses a geographic grid to display value residuals or sales ratios based on values derived from a model lacking a location variable, to develop factors that quantify the relative locational advantage or disadvantage of the property. This process may include identifying positive and negative value influence centers (VICs). Distance variables from all of the VICs are computed for each parcel. If VICs are used, the distance variables are then included in the model to calculate the location adjustment for each parcel. The location adjustments determined in this manner may be developed for cost, sales comparison, and income AVM models. Due to the method LVRSA uses to develop the location adjustment, it will include anything that is not accounted for elsewhere in its estimate of the location adjustment.

Geographic grids for LVRSAs are best obtained from a GIS. However, where one does not exist, a geographic grid can be manually developed by using maps and arbitrary grids, such as every 100 feet. The x, y coordinates can then be determined for each parcel and entered into the database. Although not as accurate and effective as a GIS, this approach can be used where one does not exist or is not yet available to the market analyst.

When using geoeconomic areas or submarkets for location adjustment, care must be taken not to create too many geoeconomic areas or submarkets because this may result in too few sales for effective analysis and modeling. Central Business Districts, cities/towns, natural features, and major streets can be used to define geoeconomic area boundaries. Because the single property appraiser generally values only a single, or few properties, and the AVM market analyst often values many parcels and sometimes deal with adjacent parcel review by the public, the AVM market analyst might prefer to use blocks, subdivisions, or neighborhoods for location adjustments so that adjacent and nearby parcels receive the same adjustment.

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I.1.3 Physical Characteristics and Site Influences

In addition to land use and location variables, AVMs require a number of physical characteristics and site influences for comparison and modeling. These may be quantitative or qualitative variables.

The most significant quantitative characteristic is land size. Land size is determined by the number of land units by type, such as lot, site, front feet or meter, square feet or meter, acre, hectare, etc. Therefore, it is usually necessary to develop some form of land size adjustments to reflect the changing rate per unit based on the total parcel size.

Most of the other important characteristics and influences are qualitative. These include topography, site amenities (such as government services), property access, water and sewer, proximity to negative influences (like railroads or treatment plants), and proximity to positive influences (like view, golf courses, water frontage, or recreational areas). A negative influence under one condition might be considered positive in another situation. An example might be high traffic volume that could be positive for commercial properties and negative for residential properties.

I.2 Land Data Collection

Sales and income data for land are collected, verified, and maintained in the same manner as improved parcels. Maps and aerial photographs are used to supplement field reviews to effectively collect, maintain, and review land data.

Land use/soil productivity data for income modeling of agricultural property may be obtained from national and state/province agricultural agencies, universities, and agricultural cooperatives and associations. When insufficient arm’s-length sales are available, data to develop capitalization rates for agricultural properties may be obtained from farm lenders such as the Federal Land Bank and Farm Credit Bank, as well as local lenders.

I.3 Development of the Model(s)

Sales comparison is the primary approach for estimating the market value of land. The valuation of land by sales comparison shares many of the same analyses and modeling processes with improved valuation models. The dependent variable in a sales comparison model should be sales price or sales price per unit. For example, if land sales in an area are based on square feet of land area, then the dependent variable should be sale price per square foot. Typical independent

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variables including property use, zoning, size, or location and site characteristics including physical characteristics, amenities (positive influences), and negative influences.

For leased land, and agricultural and rural properties, where insufficient sales are available, a capitalized income stream is commonly used to estimate the market value. Income land appraisal relies on capitalized income analysis.

I.3.1 Land Valuation Modeling by Sales Comparison

Land values may be modeled separately from improved values, or vacant and improved property may be modeled in a single combined valuation model (Guerin 2000). The primary benefit of a combined model is that both vacant and improved sales are used, which significantly increases the sales sample size for analysis and modeling. When developing a combined model, a binary variable should be used to separate vacant and improved sales. In addition, separate time and size adjustments should be tested for vacant and improved sales.

I.3.2 Land Valuation Modeling by Income

Income data can be used to value rented or leased land. Income capitalization for land follows the same general principles as commercial and industrial properties.

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APPENDIX J: STATISTICAL TABLESTable 1. Example of Ratio Study Statistical Analysis Data AnalyzedRank of ratio Appraised value Market value Ratioof observation (AV in $) (MV in $) (AV/MV)

1 48,000 138,000 0.348

2 28,800 59,250 0.486

3 78,400 157,500 0.498

4 39,840 74,400 0.535

5 68,160 114,900 0.593

6 94,400 159,000 0.594

7 67,200 111,900 0.601

8 56,960 93,000 0.612

9 87,200 138,720 0.629

10 38,240 59,700 0.641

11 96,320 146,400 0.658

12 67,680 99,000 0.684

13 32,960 47,400 0.695

14 50,560 70,500 0.717

15 61,360 78,000 0.787

16 47,360 60,000 0.789

17 58,080 69,000 0.842

18 47,040 55,500 0.848

19 136,000 154,500 0.880

20 103,200 109,500 0.942

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21 59,040 60,000 0.984

22 168,000 168,000 1.000

23 128,000 124,500 1.028

24 132,000 127,500 1.035

25 160,000 150,000 1.067

26 160,000 141,000 1.135

27 200,000 171,900 1.163

28 184,000 157,500 1.168

29 160,000 129,600 1.235

30 157,200 126,000 1.248

31 99,200 77,700 1.277

32 200,000 153,000 1.307

33 64,000 48,750 1.313

34 192,000 144,000 1.333

35 190,400 141,000 1.350

36 65,440 48,000 1.363

Results of statistical analysisStatistic Result calculated on preceding dataNumber of observations in sample 36Total appraised value $3,627,040Total market value $3,964,620Average appraised value $100,751Average market value $110,128Mean ratio 0.900Median ratio 0.864Geometric mean ratio 0.849

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Weighted mean ratio 0.915Price-related differential (PRD) 0.98Price-related bias (PRB) coefficient (t-value) 0.233 (3.02)PRB 95% two-tailed confidence interval (0.081 – 0.384)Coefficient of dispersion (COD) 29.8%Standard deviation 0.297Coefficient of variation (COV) 33.0%Probability that population mean ratiois between 90% and 110% 49.7%95% mean two-tailed confidence interval 0.799–1.00095% median two-tailed confidence interval 0.684–1.06795% weighted mean two-tailed confidence interval 0.806–1.024Shape of distribution of ratios Normal (based on binomial distribution)Date of analysis 9/99/9999Category or class being analyzed Residential

Table 2. Ratio Study Performance Standards

Type of property—General

Type of property—Specific COD Range**

Single-family residential (including residential condominiums)

Newer or more homogeneous areas

5.0 to 10.0

Single-family residential

Older or more heterogeneous areas

5.0 to 15.0

Other residential Rural, seasonal, recreational, manufactured

5.0 to 20.0

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housing, 2–4 unit family housing

Income-producing properties

Larger areas represented by large samples

5.0 to 15.0

Income-producing properties

Smaller areas represented by smaller samples

5.0 to 20.0

Vacant land 5.0 to 25.0

Other real and personal property

Varies with local conditions

These types of property are provided for guidance only and may not represent jurisdictional requirements.

Appraisal level for each type of property shown should be between 0.90 and 1.10, unless stricter local standards are required.

PRD's for each type of property should be between 0.98 and 1.03 to demonstrate vertical equity.

PRD standards are not absolute and may be less meaningful when samples are small or when wide variation in prices exist. In such cases, statistical tests of vertical equity hypotheses should be substituted (see table 1-2).

Alternatively, assessing officials can rely on the PRB, which is less sensitive to atypical prices and ratios. PRB coefficients should generally fall between −.05 and .05. PRBs that are statistically significant and less than −0.10 or greater than 0.10 indicate significant vertical inequities.

** CODs lower than 5.0 may indicate sales chasing or non-representative samples. (See Standard on Ratio Studies

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APPENDIX K: MAPPING EXAMPLES RELATED TO AVMSAVM produced values may have implications related to parcels and their specific valuation and mapping issues. These issues include:

Use of parcel descriptions generated for the needs of tax assessment or property taxes in lieu of legal descriptions used for the conveyance of the parcel

Identification validity of sales used for the assessment of real property

Multiple tax parcels that may need to be aggregated to represent the total value of the sale or eliminated from the analysis.

Lags in updating assessed values and property characteristics in relation to updating mapping records.

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APPENDIX L: USES OF AVM REPORTSAVM reports may have many uses. This standard will only list some of the typical uses.

L.1 Real Estate Lenders

Reduce time to approve real estate loan applications

Provide unbiased estimate of value for loan underwriting

Provide real estate value/scores to compliment borrower’s credit scoring

Standard estimates for annual review of individual appraiser’s performance

Quality assurance for selling pooled loans

Review of loan portfolios

Support for lending decisions and geographic distribution required by the Community Reinvestment Act

Statistical support for litigation

Updates current valuation of portfolio properties

Support in purchase of loan portfolios or lending institutions

Portfolio valuation reviews by secondary mortgage markets and bond rating firms

Systematic review of mortgage loan transaction to assist in the discovery of potential systematic fraud

L.2 Real Estate Professionals

Support in setting listing price

Support in negotiation between sellers and buyers

Central database for appraisers

Selection of appropriate economic information to support subject property

Support for appraiser’s opinions of value

Support for appraiser’s review and desktop appraisal assignments

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Support for appraisal consulting assignments that involve large numbers of properties

Statistical support for litigation

L.3 Government

Planning and land use decisions

Development of value estimates for review by assessment staff appraisers

Standardized estimates of value to annually review field appraisers’ performances

Valuation substitutes for appraisals in ratio study reports

Screening of sale prices for valid market sales transactions

Audits of lenders by state and federal regulators

Assist states with standardized values to review property assessments in school funding formulas

Fraud identification and prevention by enforcement, taxation, customs, and oversight agencies (such as GSE, HUD, IRS, Canada Mortgage and Housing Corporation, Statistics Canada, and state and national bank regulators)

Fraud prosecution by comparing transactions to standardized values

Assist in valuation for right-of-way and property condemnation cases

Support for prior values in disaster situations to provide efficiencies is economic rebound of communities

L.4 General Public

Support for various business development and economic decisions

Assistance in determining best listing price

Assistance in determining best offering price

Review of local government tax assessments

Estate estimates of real estate value by attorneys and estate administrators

L.5 Property Tax Assessors

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Determining mass appraisal values

Support of values in appeals and litigation

Appraisal quality control

Valuation of contaminated properties

Studies used in the analysis of fiscal planning

AVM reports may be sufficient as stand-alone products, or they may lead to a request for a more detailed appraisal report based on the needs and usage of the intended user. This listing is only a portion of the potential uses of AVMs. When clients request AVMs for a limited and specific use, the AVM report will provide quality information to the intended user quickly and inexpensively.

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Documents

DRAFT - IAAO Home · Web viewsales comparison approach with or without estimates from the statistically derived model; cost approach; ... Sales based ratio studies are among the most