Patent Application
20150154663
1. Field of the Invention
The present invention relates generally to computer analysis of real estate appraisal data, with special attention paid to identifying and evaluating data errors.
2. Description of the Related Art
A property appraisal is an opinion of the value of a given property based on certain facts. Property appraisals are commonly made by a residential appraiser based on facts ascertained by the appraiser. Generally, the appraiser estimates the value of the property that is the subject of appraisal (hereinafter the “subject property”) by considering the sale price of properties that are similar to the subject property and that have recently been sold (hereinafter “comparable(s)” or “comp(s)”). Most appraisals include forms (whether printed or electronic) that include data fields in which the appraiser represents the various facts about the subject property and the comps upon which the appraisal is based.
Some appraisers may enter false values into the data fields of the appraisal form. This entry of false data may be an intentional misrepresentation by the appraiser in order to change an appraised value of the subject property. For example, there may be an incentive for some appraisers to over-estimate the value of a subject property, perhaps in order to please a real estate agent who refers business to the appraiser. For example, if a comp used in an appraisal sold for $300,000, all other things being equal the subject property is likely also worth around $300,000 (actual details of such an evaluation are discussed further below); however if an appraiser wanted to increase the appraised value of the subject property, the appraiser could misrepresent the sales price of the comp as $320,000, which would correlatively increase the apparent value of the subject property. Such intentional misrepresentations are referred to hereinafter as fraud or fraudulent errors.
The false value entered into the data field may alternatively represent an error made by the appraiser, rather than fraud. For example, when the appraiser is ascertaining the various facts about the subject property or comps, the appraiser may make an error in measurement or identification. For example, the appraiser may accidentally measure the Lot Size of the subject property to be 10,000 sq. ft. when it is in fact 10,500 sq. ft., or the appraiser may accidentally misidentify as a bedroom a room that does not qualify as a bedroom. Such accidental misrepresentations are referred to hereinafter as accidental or negligent errors.
False data field entries, whether accidental or fraudulent, result in the estimated value of the subject property being inaccurate—i.e., the property is either over- or under-valued. Such inaccurate valuation of the subject property can be a source of collateral risk for those that rely upon the appraised value of a property, such as institutions involved in providing a mortgage for the subject property or creating/trading instruments backed by the subject property.
An appraisal reviewer attempts to determine the acceptability of an appraised value, generally by manually verifying that the comparable selections, adjustments, and reconciliations made by the appraiser meet standards and are mathematically correct. However, an appraisal reviewer generally cannot determine whether the appraiser's representations about the characteristics of the subject property and the characteristics of the comps are accurate without making a physical visit to each property used in an appraisal, which is clearly not feasible. At best, appraisal reviewers generally can only detect palpable errors such as data field entries 130 without any value entered at all.
According to an aspect of one exemplary illustration of the present disclosure, a method may include causing a processor to: access appraisal-data-field entries from a plurality of property appraisals, each of the appraisal-data-field entries indicating a value assigned to a property characteristic of a property included in the respective property appraisal; perform an error detection operation for each of the accessed appraisal-data-field entries as a target entry, the error detection operation comprising detecting a discrepancy between the target entry and an appraisal-data-field entry corresponding to the target entry; and flag as erroneous each appraisal-data-field entry determined by the error detection operation to be erroneous. Appraisal-data-field entries correspond to one another when they indicate respective values assigned to a same property characteristic of a same property.
According to another aspect of the above-mentioned exemplary illustration, the method may further include causing the processor to assign respective numerical discrepancy values to flagged appraisal-data-field entries. A magnitude of the discrepancy value assigned to at least one of the flagged appraisal-data-field entries may be different than a magnitude of the discrepancy value assigned to at least one other of the flagged appraisal-data-field entries.
According to another aspect of the above-mentioned exemplary illustration, the method may further include causing the processor to assign a total discrepancy score to at least one of the plurality of property appraisals that depends upon a sum of any numerical discrepancy values assigned to those appraisal-data-field entries that are included in the property appraisal being assigned the total discrepancy score.
According to another aspect of the above-mentioned exemplary illustration, the method may further include causing the processor to display data corresponding to at least some of the plurality of property appraisals, the displayed data including the respective total discrepancy scores assigned thereto, receive input specifying one of the displayed property appraisals, and display in response to the received input at least any flagged appraisal-data-field entries of the specified property appraisal in association with respective deemed-correct values for the displayed flagged appraisal-data-field entries.
According to another aspect of the above-mentioned exemplary illustration, respective magnitudes of the assigned discrepancy values may depend at least in part upon how much the flagged appraisal-data-field being assigned the discrepancy value affects valuation of a subject property in the property appraisal that includes the flagged appraisal-data-field entry being assigned the discrepancy value.
According to another aspect of the above-mentioned exemplary illustration, respective magnitudes of the assigned discrepancy values may depend on a type of property characteristic indicated by the flagged appraisal-data-field entry being assigned a discrepancy value, and said types of property characteristics 140 may include sales price, gross living area, and lot size.
According to another aspect of the above-mentioned exemplary illustration, respective magnitudes of the assigned discrepancy values may depend on at least one of: a type of property characteristic indicated by the flagged appraisal-data-field entry being assigned the discrepancy value, a discrepancy type of the discrepancy detected for the flagged appraisal-data-field entry being assigned the discrepancy value, and a magnitude of the discrepancy detected for the flagged appraisal-data-field entry being assigned the discrepancy value.
According to another aspect of the above-mentioned exemplary illustration, the magnitude of the numerical discrepancy value may further depend upon whether the target entry corresponds to a subject property of the respective property appraisal that includes the target entry.
According to another aspect of the above-mentioned exemplary illustration, respective magnitudes of the assigned discrepancy values may depend on a discrepancy type of the discrepancy detected for the flagged appraisal-data-field entry being assigned the discrepancy value, and said discrepancy types may include self-discrepancies and peer-discrepancies.
According to another aspect of the above-mentioned exemplary illustration, a target entry for which a self-discrepancy is detected may be flagged as erroneous when at least one of the following is true: a value different from the value of the target entry is agreed upon by at least a predetermined number of appraisal-data-field entries that correspond to the target entry, and the target entry inflates a valuation of a subject property of the appraisal that includes the target entry.
According to another aspect of the above-mentioned exemplary illustration, a target entry for which a peer-discrepancy is detected may be flagged as erroneous when a value different from the value of the target entry is agreed upon by at least a predetermined number of appraisal-data-field entries that correspond to the target entry.
According to another aspect of the above-mentioned exemplary illustration, the discrepancy types may include outlier discrepancies, and a flagged appraisal-data-field entry may have an outlier discrepancy when: a magnitude of the discrepancy detected for the target entry exceeds a predetermined threshold, and the target entry inflates a valuation of a subject property of the appraisal that includes the target entry.
According to another aspect of the above-mentioned exemplary illustration, for at least one type of property characteristic, a higher discrepancy value may be assigned when a detected discrepancy is both a self-discrepancy and a peer-discrepancy than when an otherwise identical detected discrepancy is only one of a peer-discrepancy and a self-discrepancy.
According to another aspect of the above-mentioned exemplary illustration, for at least one type of property characteristic, a higher discrepancy value may assigned when a self-discrepancy is detected than when an otherwise identical peer-discrepancy is detected.
According to an aspect of another exemplary illustration of the present disclosure, a method may include causing a processor to: access appraisal-data-field entries from a plurality of property appraisals, each of the appraisal-data-field entries indicating a value assigned to a property characteristic in the respective property appraisal; associate with one another those appraisal-data-field entries that correspond to a same property as one another, correspond to a same property characteristic as one another, and have transaction dates separated by less than a predetermined time from of one another; perform an error detection operation for each of the accessed appraisal-data-field entries as a target entry, the error detection operation comprising detecting a discrepancy between the target entry and an appraisal-data-field entry associated therewith; flag as erroneous each appraisal-data-field entry determined by the error detection operation to be erroneous, said flag indicating a type and magnitude of the detected discrepancy; and identify at least one property appraisal of the plurality of property appraisals as suspect based upon flagged appraisal-data-field entries.
According to an aspect of another exemplary illustration of the present disclosure, a computer program product may comprise a non-transitory computer readable medium having program code stored thereon, the program code being executable by a processor to perform the method of any of the above-mentioned exemplary illustrations.
According to an aspect of another exemplary illustration of the present disclosure, a computing device may include at least one processor, and a memory unit, having stored thereon program code executable by the at least one processor to perform the method of any of the above-mentioned exemplary illustrations.
According to an aspect of another exemplary illustration of the present disclosure, a system may include at least one processor; a database including a plurality of appraisal-data-field entries from a plurality of property appraisals, each of the appraisal-data-field entries indicating a value assigned to a property characteristic in the respective property appraisal; and a non-transitory computer readable medium having program code stored thereon, the program code being executable by the at least one processor to perform the following operations: access the appraisal-data-field entries from the database, perform an error detection operation for each of the accessed appraisal-data-field entries as a target entry, the error detection operation comprising detecting a discrepancy between the target entry and an appraisal-data-field entry corresponding to the target entry, flag as erroneous each appraisal-data-field entry determined by the error detection operation to be erroneous, said flag indicating a type and magnitude of the discrepancy, and identify at least one property appraisal of the plurality of property appraisals as suspect based upon flagged appraisal-data-field entries. Appraisal-data-field entries may correspond to one another when they indicate respective values assigned to a same property characteristic of a same property.
The present invention can be embodied in various forms, including business processes, computer implemented methods, computer program products, computer systems and networks, user interfaces, application programming interfaces, and the like. The foregoing summary is intended merely to give a general idea of various aspects of exemplary illustrations of the invention, and does not limit the invention in any way.
These and other more detailed and specific features of the present invention are more fully disclosed in the following specification, reference being had to the accompanying drawings, in which:
In the following description, for purposes of explanation, numerous details are set forth, such as flowcharts and system configurations, in order to provide an understanding of one or more embodiments of the present invention. However, it is and will be apparent to one skilled in the art that these specific details are not required in order to practice the present invention.
Appraisal Data Field Entries:
As noted above, an appraiser estimates the value of the subject property by considering the sale price of comps. However, because no comp is exactly the same as the subject property, appraisers generally “adjust” the sale price of the comps to reflect the differences between the comp and the subject property. The appraiser attempts to determine how certain property characteristics (such as gross living area (“GLA”), number of bathrooms, etc.) affect the sale price of a property, and establishes adjustment factors for adjusting the sales prices of comps based on this determination. For example, suppose that an appraiser believes that in a particular market each bathroom contributes $10,000 to the price of a property. In such an example, if a given comp is practically identical to the subject property except that the comp has 3 bathrooms while the subject property has 2 bathrooms, then the appraiser would “adjust” the comp price down $10,000 to reflect this difference. Thus, if the exemplary comp sold for $200,000, then the appraiser might estimate the value of the subject property to be $190,000 ($200,000 comp sales price minus $10,000 for having one less bathroom). In practice more than one comp is used in each appraisal in order to increase accuracy (generally at least three), in which case each comp is “adjusted” and an estimated value of the subject property is determined based on the adjusted comps' prices (for example, averaging the adjusted comps' prices).
Various price-affecting characteristics of properties can be used in appraisals, and there are various means for estimating how much such characteristics affect value. For example, appraisers might rely upon their own subjective experience to estimate how much each property characteristic contributes to total value. Alternatively, mathematical techniques may be used to estimate adjustment factors. For example, automated valuation models (AVMs) may derive adjustment factors from a pool of property data by performing a regression on a hedonic equation. Once such adjustment factors are derived, an appraiser may be able to simply enter characteristics of the subject property and characteristics of selected comparable properties into data fields of the AVM, and the AVM can automatically make the appropriate adjustments to the comps and estimate a value of the subject property.
Most appraisal systems (including AVM systems and others) include numerous data fields for each appraisal (sometimes hundreds of such fields), with each data field corresponding to a characteristic of the subject property or comps. The appraiser assesses each property characteristic and enters a value into the corresponding data field.
A given property is often used in multiple appraisals. Moreover, a single appraiser may use the same property in multiple appraisals. For example, as shown in
Regardless of the appraisal system that is used, the result of the appraisal will depend upon the appraiser's representations about the characteristics of the subject property and the characteristics of the comps. If an appraiser erroneously or fraudulently misrepresents a characteristic of the subject property or a characteristic of a comp (i.e., enters an inaccurate value in a data entry field), then the estimated value of the subject property resulting from the appraisal will be, at best, inaccurate and at worst, a source of collateral risk.
In one illustrative example of the present disclosure, an appraisal evaluation module analyzes a pool of property appraisals and automatically determines a score for each appraisal (a “total discrepancy score”). The total discrepancy score indicates an amount of risk that the appraisal over or under valued the subject property, and gives an indication of overall quality of the appraisal. The total discrepancy score allows appraisal reviewers to easily identify those appraisals that need the most scrutiny, and to focus manual review on these appraisals. The total discrepancy score also facilitates analysis of the reliability of appraisers and detection of potentially fraudulent behavior.
In the illustrative example, the appraisal evaluation module searches the data field entries of property appraisals that are stored in a database (“appraisal data field entries”) and determines whether there are any erroneous values. The appraisal evaluation module identifies erroneous values by detecting discrepancies between corresponding data field entries and determining for each discrepancy if one of the discrepant values is erroneous. A discrepancy score may be assigned to each erroneous data field that is identified, the magnitude of the score reflecting the likely amount of risk the particular error creates. The total discrepancy score may represent a scaled score based on the sum of discrepancy scores assigned to erroneous data fields used in the appraisal.
The magnitude of the discrepancy score assigned to an erroneous data field entry 130 may represent the likely amount of risk created by the error. For example, the discrepancy score may depend on characteristics of the error that are correlated with risk, including: the type of the discrepancy, the nature of the property characteristic 140 represented by the erroneous data field, the magnitude of the error, and whether the error tends to inflate or deflate the estimated value of the subject property, to name a few examples. Some specific examples of how the discrepancy score may be assigned are discussed in greater detail below.
Exemplary Processes of the Appraisal Evaluation Module:
In process step 1620, corresponding data field entries are determined. For example, the appraisal evaluation module may identify corresponding data field entries by assigning a universal identification number (hereinafter “UID”) to each instance of property data that has a same property address, and then treat those data field entries that have a same UID and that correspond to a same property characteristic as corresponding data field entries. For example,
The appraisal evaluation module may also be configured to assign a same UID to only instances of property data having transaction dates that are relatively close in time (i.e., sale/appraisal dates that are separated by less than a predetermined amount of time). This is because the characteristics of the property may change over time and thus data field entries from different appraisals occurring far apart in time may be legitimately discrepant without necessarily indicating error. If a property characteristic 140 changes between two appraisals, there would be a discrepancy in data field entries 130 of the two appraisals, but both data field entries 130 would be correct. Accordingly, the predetermined amount of time may be set low enough to minimize the likelihood that property characteristics 140 will change between appraisals, while still being high enough that each set of corresponding data field entries still includes enough entries for a meaningful comparison. The predetermined amount of time may be advantageously set, merely as an example, to around three months.
In process step 1630, the appraisal evaluation module may detect discrepancies between corresponding data field entries. A discrepancy is a not-insignificant difference between two or more corresponding data field entries.
The appraisal evaluation module may be configured to detect as discrepancies only those differences between data field entries that are larger than a predetermined significance threshold (i.e., insignificant differences are ignored). A different significance threshold value may be set for each type of property characteristic 140. The significance threshold may be based on considerations such as how much the property characteristic 140 tends to effect the valuation of the subject property in the appraisal containing the error and/or on acceptable margins of human error. Errors in some property characteristics 140 (such as GLA) affect valuation more than others, and these types of errors therefor may desirably have a comparatively lower significance threshold. Moreover, a certain margin of error in measuring some property characteristics 140 (such as Lot Size) is expected, while other property characteristics 140 (such as Bedrooms) may have very low or even no acceptable margin of error.
In process step 1640, the appraisal evaluation module may determine for a given discrepancy detected in process step 1630 which of the discrepant values (if any) is the erroneous value. The mere fact that two values are different does not immediately indicate which of the two different values is the correct one. However, the appraisal evaluation module may apply various selection rules to determine which of the discrepant values is most likely the correct value. The appraisal evaluation module may determine a deemed-correct value for each discrepancy, and flag as an error the data field entry 130 that is discrepant from the deemed-correct value. For example, the appraisal evaluation module may set a consensus value of the set of corresponding data field entries as the deemed-correct value for the discrepancy. The consensus value may be a value agreed upon by a certain proportion (e.g., a majority) of the corresponding data field entries.
Preferably, the appraisal evaluation module may determine a deemed-correct value to be used for a particular discrepancy based upon a type of the discrepancy, and may apply different criteria for determining a deemed-correct value for different types of discrepancies (discussed in greater detail below). Types of discrepancies may include, for example, self-discrepancies, peer-discrepancies, outlier-discrepancies, and typographical errors. Accordingly, process step 1640 may preferably include therein decision block 1645 in which it is determined whether the discrepancy is of a self-discrepancy type or a peer-discrepancy type. A self-discrepancy is a discrepancy between corresponding data field entries entered by the same appraiser. A peer-discrepancy is a discrepancy between corresponding data field entries entered by different appraisers. If the discrepancy is a self-discrepancy type, then the process continues to sub-process A, illustrated in
In process step 1650, a discrepancy score is assigned to data field entries flagged in process step 1640 as erroneous. Details regarding the discrepancy score are discussed further below.
In process step 1660, a total discrepancy score is assigned to each appraisal based on the discrepancy scores assigned to data field entries included in the respective appraisal. Details regarding the total discrepancy score are discussed further below.
Self-Discrepancies:
As discussed above, whether or not a discrepancy is determined to be an error, and if determined to be an error what discrepancy score should be assigned thereto, may depend upon a type of the discrepancy. For example, as discussed above, in the preferred configuration of the process step 1640 illustrated in
In decision block 1710, it is determined whether or not there is a self-consensus. A self-consensus exists if there is a value in the set of corresponding data field entries that was used by the appraiser in question more often than any other value.
If there is a self-consensus (i.e., decision block 1710 result=YES), then the deemed-correct value for the discrepancy in question may be the value used most often by that appraiser. Thus, in process step 1705, the data field entry 130 entered by the appraiser in question that differs from this deemed-correct value is determined to be the erroneous value. For example, there is a self-discrepancy in the set [GLA]26 illustrated in
If there is a tie in the number of times a value is used by the same appraiser in a set of corresponding data field entries, then various tie-breaking procedures may be used. For example, if there is no self-consensus (i.e., decision block 1710 result=NO), then the process proceeds to decision block 1715, in which it is determined whether there is a peer consensus.
A peer-consensus exists if a value is used by a predetermined proportion of peer data field entries 130 (for simplicity, hereinafter it will be assumed that the predetermined proportion is a simple majority, although this need not be the case). If there is a peer consensus (i.e., decision block 1715 result=YES), then the deemed-correct value for the discrepancy in question may be the peer-consensus value. Thus, in process step 1725 the data field that differs from this deemed-correct value is determined to be the erroneous value. In
If there is no peer consensus (i.e., decision block 1715 result=NO), then the value that most decreases (or least increases) the valuation of the subject property in the respective appraisal in which the property data appears is set as the deemed-correct value for the discrepancy. Thus, in process step 1720, the value that differs from the deemed correct value (i.e., the value that most inflates valuation) is determined to be the erroneous value. Generally, when the discrepancy is between data field entries 130 for comps, then the deemed-correct value is the better value of the two (discussed further below). Conversely, when the discrepancy is between data field entries 130 including at least one data field entry 130 from a subject property, then, generally, the deemed-correct value is the worse value of the two. The exception to the forging general rules is when the discrepancy is between Sales Price data field entries 130 for comps, in which case the lower value will always be the deemed-correct value. (subject properties do not have Sales Price data field entries 130, and thus a discrepancy in Sale Price will never include a data field entry 130 from a subject property).
A value is “worse” than another value if it would contribute less to the valuation of a hypothetical property than the other value would, and “better” if it would contribute more. For many property characteristics 140 (including GLA, Lot Size, number of Bathrooms, number of Bedrooms, etc.) the “worse” value is the lower value (and correlatively, the “better” value is the higher value), since having less of these characteristics in a hypothetical property would cause the hypothetical property to be less valuable. Such property characteristics 140 are positively correlated with property value. However, for some property characteristics 140 (such as Age), the higher value is the “worse” value (and correlatively, the “better” value is the lower value). Such property characteristics 140 are negatively correlated with property value. Whether or not certain characteristics are positively or negatively correlated with property value may depend upon the appraisal system being used (for example, if a scaled numerical score is used for “condition”, whether a low numerical value represents the best condition and a high numerical value represents the worst condition, or vice-versa, may be arbitrarily defined by the appraisal system).
The above-noted general rules for how to determine the value that most decreases (least increases) valuation are explained further as follows. As shown in
Thus, when the discrepancy is between two comps, the worse value will always increase the subject property valuation and the better value will always decrease the subject property valuation. According to the tie breaking rule noted above, the value that most decreases or least increases valuation is the deemed-correct value, and therefore when the discrepancy is between two comps the better value will always be the deemed-correct value (except for the case of sales price, as noted above).
The situation is slightly more complicated when the discrepancy is between a subject property and a comp, since either both values will decrease the valuation or both values will increase the valuation. For example, if the subject property value is the better value, then it will inflate valuation; but if the subject property value is better, then this implies that the comp value is worse, and therefore the comp value would also inflate valuation. Accordingly, since both values will increase of decrease valuation, the one that decreases the valuation the most or increases the valuation the least will be the deemed correct value. The subject property value will always affect the valuation—whether positively or negatively—more than the comp value, because appraisal systems are generally more sensitive to a change in the subject property than to a similar change to one comp. Thus, in the case when both values will decrease the valuation (i.e., when the comp value is better and the subject property value is worse), the value that decreases the valuation the most will be the deemed correct value, which will be the subject property value (i.e., the worse value). Further, in the cause when both values will increase the valuation (i.e., when the comp value is worse and the subject property is better), the value that increases the valuation the least will be the deemed correct value, which will be the comp value (i.e., the worse value). Thus, when the discrepancy involves a subject property data field entry, the worse value is always the deemed-correct value.
The above-noted results are summarized in
For example, in
Each of process steps 1705, 1725, and 1720 result in the determination of an erroneous data field entry, and after any of these process steps the process proceeds to decision block 1730, in which it is determined whether or not the erroneous data field entry 130 is a typographical error (discussed further below).
If the erroneous data field entry 130 is a typographical error (i.e., decision block 1730 result=YES), then the process proceeds to process step 1745 and the erroneous data entry field is not flagged as an error. Alternatively, the erroneous data field entry 130 may be flagged with a specific typographical error flag that is different from the other error flags discussed further below. Sub-process A ends if process step 1745 is reached.
If the erroneous data field entry 130 is not a typographical error (i.e., decision block 1730 result=NO), then the process proceeds to decision block 1735, in which it is determined whether or not the erroneous data field entry 130 is an outlier-discrepancy (discussed further below).
If the erroneous data field entry 130 is an outlier-discrepancy (i.e., decision block 1735 result=YES), then the process proceeds to process step 1750 in which the erroneous data field entry 130 is flagged as both a self-discrepancy type error and an outlier-discrepancy type error. Sub-process A ends if process step 1750 is reached.
If the erroneous data field entry 130 is not an outlier-discrepancy (i.e., decision block 1735 result=NO), then the process proceeds to process step 1740 in which the erroneous data field entry 130 is flagged as a self-discrepancy type error. Sub-process A ends if process step 1740 is reached.
Peer-Discrepancies:
In the preferred configuration of the process step 1640 illustrated in
In decision block 1755, it is determined whether or not a peer-consensus exists. A peer-consensus is a value agreed upon by a certain predetermined proportion of peer data field entries 130 (for simplicity, hereinafter it will be assumed that the predetermined proportion is a simple majority, although this need not be the case).
If there is a peer-consensus value (i.e., decision block 1755 result=YES), then the process proceeds to decision block 1760, in which it is determined whether or not at least a predetermined number of different peer appraisers agree on the peer-consensus value (for simplicity, hereinafter it will be assumed that the predetermined number is three, although this need not be the case). If three different peer appraisers agree on the peer-consensus value (i.e., decision block 1760 result=YES), then the peer-consensus value is set as the deemed-correct value. Thus, the process continues to process step 1770, and the data field entry 130 that differs from this deemed-correct value is determined to be the erroneous value. In
In decision block 1775 it is determined whether or not the erroneous data field entry 130 is a typographical error (discussed further below).
If the erroneous data field entry 130 is a typographical error (i.e., decision block 1775 result=YES), then the process proceeds to process step 1765. Further, the process also proceeds to process step 1765 when the result of either of decisions blocks 1755 or 1760 is NO. In process step 1765 the erroneous data entry field is not flagged as an error. Alternatively, the erroneous data field entry 130 may be flagged with a specific typographical error flag that is different from the other error flags discussed further below. Sub-process B ends if process step 1765 is reached.
If the erroneous data field entry 130 is not a typographical error (i.e., decision block 1775 result=NO), then the process proceeds to decision block 1780, in which it is determined whether or not the erroneous data field entry 130 is an outlier-discrepancy (discussed further below).
If the erroneous data field entry 130 is an outlier-discrepancy (i.e., decision block 1780 result=YES), then the process proceeds to process step 1785 in which the erroneous data field entry 130 is flagged as both a peer-discrepancy type error and an outlier-discrepancy type error. Sub-process B ends if process step 1785 is reached.
If the erroneous data field entry 130 is not an outlier-discrepancy (i.e., decision block 1780 result=NO), then the process proceeds to process step 1790 in which the erroneous data field entry 130 is flagged as a peer-discrepancy type error. Sub-process B ends if process step 1790 is reached.
Outlier-Discrepancies:
An outlier-discrepancy is a self-discrepancy or a peer-discrepancy that additionally meets the following criteria: (1) the discrepancy is of large magnitude, and (2) the erroneous value tends to inflate the appraisal valuation of a subject property. Outlier-discrepancies may also be restricted to only certain property characteristics.
For the first criterion identified above, a predetermined outlier threshold may be set, and when the magnitude of the discrepancy exceeds the outlier threshold the first criterion is satisfied. A different outlier threshold value may be set for each type of property characteristic. Each outlier threshold is larger (generally much larger) than the significance threshold for the same type of property characteristic. For example, for property characteristics 140 such as sales price, GLA, and lot size, the outlier threshold may be set to 15% of the deemed-correct value.
For the second criterion, one may determine whether the erroneous value tends to inflate valuation by considering whether it is better or worse than the deemed-correct value and applying the general rules discussed above with respect to self-discrepancies, which are summarized, in
In determining a type of discrepancy, the appraisal evaluation module may consider values with very small differences as being the same. For example, the appraisal evaluation module may round values before determining whether or not they agree with each other. For example, Sale Price data field entries 130 may be rounded to the nearest $1000, and GLA, Lot Size, and Basement size may be rounded to the nearest 10 sq. ft. The rounding threshold may preferably be less than the above described significance threshold. However, rounding may alternatively be used in lieu of the significance threshold.
Typographical Errors:
When a discrepancy is extremely large then it is likely that the erroneous data field entry 130 is the result of a simple typographical error. For example, such extremely large discrepancies may occur by accidentally adding or dropping a zero when entering a number (e.g., 10,000 instead of 1,000), transposing two numbers (9,100 instead of 1,900), or simply entering the wrong number because of an errant key stroke or because they look confusingly similar in the appraiser's notes (e.g., 7,000 instead of 1,000). These types of errors are very unlikely to be indicative of fraud, since a person intent on misrepresenting a value in an appraisal would be unlikely to misrepresent the number by a very large amount, since very large discrepancies are more likely to stand out and draw suspicion. Instead, a person intent on fraudulently misrepresenting a value generally attempts to keep the fraudulent value somewhat close to the correct value so as to avoid raising red-flags. For example, an appraiser trying to increase the appraised value of the subject property might change a GLA data field entry 130 of one of the comps from 2,500 to 2,000, but the appraiser would be very unlikely to change the data field entry 130 to 250 sq. ft. Similarly, these types of errors are unlikely to be indicative of an appraiser's negligence in ascertaining the property characteristics, since it is highly unlikely that even a negligent appraiser would err by such a large amount. For example, it is possible that an appraiser may incorrectly—although unintentionally—measure the square footage of a property's basement as 950 sq. ft. when it is actually 920 sq. ft., but it is highly unlikely that an appraiser would incorrectly measure it to be 92 sq. ft.
While, these types of errors do indicate a certain amount of negligence on the part of the appraiser—namely, lack of due care in entering values into data fields—fraud and/or negligence in ascertaining property characteristics 140 are generally more likely to go undetected by conventional appraisal review than sloppy data entry. Accordingly, those discrepancies that are extremely large may be identified by the appraisal evaluation module as typographical errors, and may be treated differently than other identified errors. For example, the appraisal evaluation module may refrain from flagging typographical errors as errors, or may flag typographical errors differently than other errors. The appraisal evaluation module may refrain from assigning a discrepancy score (discussed further below) to typographical errors, assign a smaller discrepancy score to typographical errors than to other types of errors, or may assign a normal discrepancy score to typographical errors but include an indication in the error flag that the error is likely a typographical error.
Threshold values for determining typographical errors may be predetermined constant values, may be variable values (such as a percentage of the higher value), or a combination of predetermined constant values and variable values. For example, a discrepancy whose magnitude is greater than a predetermined percentage of the higher of the two discrepant values may be identified as a typographical error. For example, when a discrepancy's magnitude is 75% or more of the higher value, the erroneous value may be identified as a typographical error. Alternatively, a discrepancy may be identified as a typographical error when the value of either of the discrepant data field entries 130 (as opposed to the magnitude of the discrepancy) is below a minimum value or above a maximum value. For example, certain predetermined values for minimum and maximum acceptable data field entry 130 values may be established, such as $1,001 minimum and $9,999,999 maximum for Sale Price. Moreover, any data field entry 130 with values falling outside the min/max range may be identified as typographical errors even when the is no discrepancy detected, such as when there are not yet any other corresponding data field entries 130 that could cause a discrepancy with the given data field entry.
Discrepancy Score:
The appraisal evaluation module determines a discrepancy score to assign to each data field flagged as an error. As mentioned above, the magnitude of the discrepancy score will depend on how much risk the error creates. “Risk” in this context means a risk of over- or under-valuation of the subject property. The more that an error affects an estimated valuation of a subject property, the more risky it is.
Various ways in which the magnitude of the discrepancy score depend on how much risk the error creates are discussed below. In particular, specific examples of discrepancy scores will be discussed with respect to
The appraisal evaluation module may determine a type of the discrepancy associated with the error, and assign a discrepancy score based on the type of discrepancy. An error of the self-discrepancy type may be assigned a higher discrepancy score than a peer-discrepancy error, and an error of the outlier-discrepancy type may be assigned a higher discrepancy score than other types of errors.
The appraisal evaluation module may determine a type of property characteristic 140 associated with the erroneous data field entry, and assign a discrepancy score based on the type of property characteristic 140. Errors for certain types of property characteristics 140 are more risky than errors for other types of property characteristics 140. This is because some types of property characteristics 140 tend to contribute more to the valuation of the subject property than other types of property characteristics 140, and thus an error therein is more likely to result in an under- or over-valuation. Moreover, for the very reason that these types of property characteristics 140 affect the valuation more, an appraiser attempting to fraudulently increase the valuation of the subject property is more likely to misrepresent one of these types of property characteristics 140 than others, and thus errors for these property characteristics 140 are more likely to be indicative of fraud.
The appraisal evaluation module may determine a magnitude of the discrepancy (i.e., an absolute value of the difference between the erroneous data field entry 130 and the deemed-correct value), and assign a discrepancy score based on the magnitude. Errors of comparatively higher magnitude are more risky than other errors.
The appraisal evaluation module may assign a discrepancy score based on whether the erroneous data field entry 130 tends to inflate the valuation of the subject property of the appraisal in which the error occurs. The module may determine whether the erroneous data field entry 130 tends to inflate the valuation of the subject property of the appraisal in which the error occurs, and assign higher discrepancy scores when it does so. Errors that tend to inflate the valuation of the subject property of the appraisal in which the error occurs are more risky than other errors (in this case, risk means risk to those relying on the appraisal such as financial intuitions, rather than risk of over- or under-valuation). Moreover, errors that tend to inflate the valuation of the subject property tend to be more indicative of fraud, since the incentives to misrepresent property characteristics 140 generally push for over-valuation more than for under-valuation.
The appraisal evaluation module may assign a discrepancy score based on whether the erroneous data field entry 130 is for a subject property. The module may determine whether the erroneous data field entry 130 is for a subject property, and assign a higher discrepancy score when it is. Errors made in data field entries 130 for a subject property affect the valuation of the subject property more than errors in data field entries for comps.
In addition to the specific examples discussed above, the magnitude of the discrepancy score may be considered to “depend at least in part upon how much the flagged appraisal-data-field being assigned the discrepancy value affects a valuation of a subject property in the property appraisal that includes the flagged appraisal-data-field entry” when the discrepancy score assigned to at least some errors is higher than that assigned to other errors, where at least some of the errors assigned higher discrepancy scores tend to affect an estimated valuation of a subject property more than those errors assigned a lower discrepancy value.
Property Discrepancy Score:
Each instance of property data in an appraisal may be assigned a property discrepancy score 125, which reflects all of the individual discrepancy scores for each data field entry 130 of the property data. The property discrepancy score 125 may simply be the sum of the individual discrepancy scores for each data field entry 130 of the property data, or it may be a scaled score. For example,
The flagged error 135 in [GLA]26 of appraisal #12 is both a self-discrepancy and a peer-discrepancy and is for a subject property, and therefore the discrepancy score for this error is three points (one point for being an error, one additional point for being a self-discrepancy in GLA, and one subject-property penalty point). There are no other flagged errors 135 in the data field entries 130 of appraisal #12 with respect to UID 26, and therefore the property discrepancy score for the property data for UID 26 of appraisal #12 is simply the same as the discrepancy score of its only flagged error 135—three points.
No flagged errors 135 occur in the property data for UID 26 of appraisals #18, #25, #29, #31, #33, or #35, and therefore the property discrepancy scores for these instances of property data are all zero points, since the discrepancy score of each of their data field entries 130 is zero points.
The flagged error 135 in [Sale Price]26 of appraisal #27 is an outlier-discrepancy of a peer type, and therefore the discrepancy score for this error is two points (one point for being an error, and one additional point for being a Sale Price Outlier). The flagged error 135 in [Lot Size]26 of appraisal #27 is a peer-discrepancy, and therefore the discrepancy score for this error is one point (one point for being an error, and no additional points).
Thus, the property discrepancy score 125 for the property data for UID 26 of appraisal #27 is the discrepancy score for the first flagged error 135 (two points) plus the discrepancy score for the second flagged error 135 (one point), which equals three points.
Total Discrepancy Score:
Each appraisal is assigned a total discrepancy score 145 by the appraisal evaluation module. The total discrepancy score 145 reflects the cumulative risk posed by all of the flagged errors 135 contained in data field entries 130 of the appraisal. For example, the total discrepancy score 145 may equal a sum of the property discrepancy scores 125 for all of the properties used in the appraisal. The total discrepancy score 145 may also be scaled to make review thereof by appraisal reviewers easier. For example, the total discrepancy score 145 may be on a scale from 1 to 5, with 1 indicating no discrepancies (and hence little risk) and 5 indicating severe discrepancies (and hence great risk).
The appraisal evaluation module may also allow an appraisal reviewer to select displayed appraisals, in which case information relating to the specific property data used in the selected appraisal may be displayed.
Upon selecting a specific appraisal, a new display may be generated focused upon the selected appraisal. For example, each instance of property data that is included in the selected appraisal may be displayed along with its associated property discrepancy score 125. Moreover, the data field entries 130 for the instances of property data may be displayed in comparative form, so as to facilitate easy review by the appraisal reviewer. The data field entries 130 that have been flagged as erroneous may be displayed in a distinctive manner so as to set them apart from the other data field entries (in
The appraisal evaluation module may allow the appraisal reviewer to select one of the instances of property data shown in the display of the selected appraisal. Upon selection of an instance of property data, the appraisal evaluation module may generate a new display in which all instances of property data that have the same UID as the selected instance of property data are displayed. The display of the instances of property data may include displaying the data field entries 130 of the various instances of property data in a comparative manner. The data field entries 130 that have been flagged as erroneous (flagged errors 135) may be displayed in a distinctive manner so as to set them apart from the other data field entries. Information about the flagged errors may also be displayed, such as the discrepancy points awarded for the error, the type of error, and/or the magnitude of the error. For example, the display of the selected appraisal may resemble the table shown in
Any of the aforementioned displays may also include an indication of the appraiser who made the appraisal, for example as shown in
In the illustrative example discussed above, various thresholds were described. It will be understood that not all of the thresholds need to be implemented, and that additional threshold may be implemented. If all of the above-noted thresholds are implemented, then preferably they have the following relationship: [rounding threshold]<[significance threshold]<[outlier threshold]<[aggravated-outlier threshold]<[typographical error threshold]. Merely as one illustrative example, the following thresholds may be implemented for Sale Price: rounding threshold=$1000; significance threshold=$4000 or 2%, whichever is greater; outlier threshold=15%; aggravated-outlier threshold=35%; typographical error threshold=75%.
The above-described illustrative example includes an appraisal evaluation module. In some examples, the appraisal evaluation module may be implemented as computer-readable instructions (e.g., software) on one or more computing devices (e.g., servers, personal computers, etc.), stored on computer readable media associated therewith (e.g., disks, memories, etc.). A computer program product may comprise such instructions stored on computer readable media for carrying out the functions described herein.
The appraisal evaluation module may be stored entirely in a memory one of the computing devices 1410/1420/1430 (for example the central computing device 1420), and may be accessed by the other computing devices 1410/1430 via the network connections. In such a configuration, the computing devices 1410/1430 execute the appraisal evaluation module by accessing the program code stored on the central computing device 1420.
Alternatively, the appraisal evaluation module may be stored in a distributed manner across more than one of the computing devices 1410/1420/1430, and may be accessed by a given one of the computing devices via the network connections. For example, the computing devices 1410/1430 may have stored in their respective memories a user interface portion of the appraisal evaluation module, while the central computing device 1420 stores in a memory thereof a database portion and/or an evaluation process performing portion of the appraisal evaluation module. In such a configuration, a user may execute the user interface portion of the appraisal evaluation module stored on a computing device 1410, causing the computing device 1410 to communicate with the central computing device 1420. In response, the computing device 1420 may execute the portions of the appraisal evaluation module stored therein and communicate data generated thereby to the computing device 1410. The computing device 1410 may then, via the continued execution of the user interface portion of the appraisal evaluation module stored therein, display the data obtained from the central computing device 1420.
While the example of
Computing devices such as the computing devices 1300, 1410/1420/1430, and 1510/1530/1540 generally include computer-executable instructions such as the instructions of the appraisal evaluation module, where the instructions may be executable by one or more computing devices such as those listed above. Computer-executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, C#, Objective C, Visual Basic, Java Script, Perl, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer-readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer-readable media.
It is understood that as used herein and in the appended claims a processor may “perform” a particular function by issuing the appropriate commands to other units (e.g., other components of the computing device, peripheral devices linked to the computing device, other computing devices, etc.), the commands being such as would cause the other units to take certain actions related to the function. For example, although a processor obviously does not display an image in the sense of the processor itself physically emitting light in a pattern, the processor may nonetheless “perform” the function of “displaying” an image in the sense of issuing the appropriate commands that would cause a display device to emit light in the pattern. To continue the example, the display device that the processor causes to display the image may be part of the computing device that includes the processor, or may be connected remotely to the computing device that includes the processor, for example through a network. Thus, for example, a processor included in a server hosting a webpage and may “display” an image by issuing commands via the internet to another computing device, the commands being such as would cause the remote computing device to display the image. Moreover, for the processor to have “performed” the particular function, the generation of a command that would cause another unit to perform the various actions of the function is sufficient—it is irrelevant whether the other unit actually completes the actions or not.
A computer-readable medium (also referred to as a processor-readable medium) includes any non-transitory (e.g., tangible) medium that participates in providing data (e.g., instructions) that may be read by a computer (e.g., by a processor of a computer). Such a medium may take many forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include, for example, dynamic random access memory (DRAM), which typically constitutes a main memory. Such instructions may be transmitted by one or more transmission media, including coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to a processor of a computer. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read.
Databases, data repositories or other data stores described herein may include various kinds of mechanisms for storing, accessing, and retrieving various kinds of data, including a hierarchical database, a set of files in a file system, an application database in a proprietary format, a relational database management system (RDBMS), etc. Each such data store is generally included within a computing device employing a computer operating system such as one of those mentioned above, and are accessed via a network in any one or more of a variety of manners. A file system may be accessible from a computer operating system, and may include files stored in various formats. An RDBMS generally employs the Structured Query Language (SQL) in addition to a language for creating, storing, editing, and executing stored procedures, such as the PL/SQL language mentioned above.
With regard to the processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the claims.
Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Although the present invention has been described in considerable detail with reference to certain embodiments thereof, the invention may be variously embodied without departing from the spirit or scope of the invention. Therefore, the following claims should not be limited to the description of the embodiments contained herein in any way. Many embodiments and applications other than the examples provided would be apparent upon reading the above description. The scope should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the technologies discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the application is capable of modification and variation.
All terms used in the claims are intended to be given their broadest reasonable constructions and their ordinary meanings as understood by those knowledgeable in the technologies described herein unless an explicit indication to the contrary in made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.
The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.
