Method for validating specified prices on real property6115694Abstract A computer-implemented method for validating specified prices on real property. A set of real estate properties comparable to the subject property are retrieved. A measurement of similarity between each comparable property and the subject property is then determined. A plurality of adjustment rules are then applied to adjust the price of the comparable properties. The adjusted comparable properties are then extracted, sorted, and ranked, according to the specified sale price. The extracted comparable properties are then aggregated into an estimate price of the subject property. After aggregation, the estimate price of the subject property is compared to the specified price and a measurement of confidence validating the reliability of the specified price is then generated. Claims What is claimed is: Description FIELD OF THE INVENTION
TABLE 1
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Preference Functino for Number of Bedrooms
Comparable's # Bedrooms
1 2 3 4 5 6+
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Subject's
1 1.00 0.50 0.05 0.00 0.00 0.00
# Bedrooms 2 0.20 1.00 0.50 0.05 0.00 0.00
3 0.05 0.30 1.00 0.60 0.05 0.00
4 0.00 0.05 0.50 1.00 0.60 0.20
5 0.00 0.00 0.05 0.60 1.00 0.80
6+ 0.00 0.00 0.00 0.20 0.80 1.00
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Table 2 can be used in a similar manner to generate preference functions for the number of bathrooms attribute. For example, if the subject property has 2 bathrooms, then Table 2 will provide a preference value of 1 for comparable properties having two bathrooms. However, if the comparable property has two and a half bathrooms, then the comparable will be given a preference value of 0.70. Also, Table 2 indicates that a comparable property having three bathrooms will have a preference value of 0.25, three and half bathrooms will have a preference value of 0.05, four or more bathrooms will have a preference value of zero. In addition, Table 2 indicates that a comparable property having one and a half bathrooms will have a preference value of 0.70 and one bathroom will have a preference value of 0.1.
TABLE 2
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Preference Function for Number of Bathrooms
Comparable
Subject
1 1.5 2 2.5 3 3.5 4 4.5 5+
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1 1.00 0.75 0.20 0.05 0.01 0.00 0.00 0.00 0.00
1.5 0.60 1.00 0.60 0.25 0.10 0.05 0.00 0.00 0.00
2 0.10 0.70 1.00 0.70 0.25 0.05 0.00 0.00 0.00
2.5 0.05 0.20 0.75 1.00 0.75 0.20 0.05 0.00 0.00
3 0.01 0.10 0.40 0.80 1.00 0.80 0.40 0.10 0.05
3.5 0.00 0.05 0.15 0.45 0.85 1.00 0.85 0.45 0.30
4 0.00 0.00 0.05 0.20 0.50 0.90 1.00 0.90 0.70
4.5 0.00 0.00 0.00 0.10 0.30 0.70 0.95 1.00 0.95
5+ 0.00 0.00 0.00 0.05 0.15 0.35 0.75 0.95 1.00
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After each attribute of the comparable real estate properties has been evaluated against the subject property and a preference vector has been generated, the measurement of similarity between each comparable and the subject property is determined. The measurement of similarity is a function of the preference vector computed above and of the priorities of the attributes, which are reflected by a set of predetermined weights. The predetermined weights for the illustrative embodiment are shown in Table 3 under the weight column. In the illustrative embodiment, the living area attribute has a weight of 0.3, the date of sale and distance attributes both have a weight of 0.2, the price and lot size attributes have a weight of 0.1, while the number of bedrooms and bathrooms attributes have a weight of 0.05. The measurement of similarity for a comparable property is determined by multiplying the predetermined weight by the preference vector generated for each attribute. This product results in a weighted preference value. After all of the weighted preference values have been determined, the weighted preferences are summed together to generate the measurement of similarity. An example of a measurement of similarity computation between a comparable property and a subject property is provided in Table 3. In the example provided in Table 3, the subject property has a price of $200,000, a living area of 2000 square feet, a lot size of 20,000 square feet, three bedrooms, and two and a half bathrooms. The comparable property was sold six months ago, is located 0.2 miles from the subject property, sold for $175,000, has a living area of 1800 square feet, a lot size of 35,000 square feet, three bedrooms and two bathrooms. A comparison between the subject property and the comparable property is provided in the fourth column for each attribute. In Table 3, the price comparison is 87.50%, the living area comparison is 90%, the lot size comparison is 175%, and the number of bedroom comparison is 0%. As described above, each comparison results in a preference which is multiplied by the predetermined weight. The weighted preferences for each attribute for the comparable property are listed in the weighted preference column and the measurement of similarity is the sum of the weighted preferences. In Table 3, the measurement of similarity for this particular comparable property is 0.7915.
TABLE 3
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Similarity Measurement Computation
Weighted
Attribute Subject Comparable Comparison Preference Weight Preference
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Date of Sale
x 6 months
6 months
0.67 0.2 0.134
Distance x 0.2 miles.sup. 0.2 miles.sup. 1 0.2 0.2
Living Area 2000 1800 90% 0.79 0.3 0.237
Lot Size 20000 35000 175% 0.33 0.1 0.033
Sale Price 200000 175000 88% 1 0.1 0.1
# Bedrooms 3 3 3->3 1 0.05 0.05
# Bathrooms 2.5 2 2.5->2 0.75 0.05 0.0375
Similarity 0.7915
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After the measurement of similarities have been computed for all of the comparable properties, the comparables are then sorted in decreasing order of similarity. After sorting, the comparables are arranged in a preference distribution as shown in FIG. 5, with the comparable property having the highest measurement of similarity placed at one end of the distribution and the comparable property having the lowest measurement of similarity placed at the opposite end of the distribution. The comparable properties are then compared against a predetermined threshold that reflects desirable and tolerable deviations of an ideal match with the subject property. More specifically, the comparable properties that have a measurement of similarity above the predetermined threshold will be extracted for further review, while the comparable properties below the threshold are removed from further consideration. FIG. 5 shows two possible similarity distributions for two different retrievals. In these distributions, a value of 0.5 is used as the predetermined threshold. Therefore, comparable properties having a measurement of similarity above 0.5 are extracted for further review, while the comparables with measurements of similarities less than 0.5 are removed and no longer considered. In FIG. 5, retrieval number one has 11 comparable properties having a measurement of similarity above 0.5, while retrieval number two has five comparable properties with a measurement of similarity above 0.5. Instead of using a predetermined threshold to determine which retrieval provides the best results, an alternative approach is to take the average of the similarity values of the retrieved comparables. This corresponds to the area under the curve of the distributions and is determined by taking the average measurement of similarity. For example, the average similarity measure for retrievals one and two in FIG. 5 would be determined as follows: Average Similarity Measure Subject 1 (from best 8 comps): (1+1+0.85+0.8+0.7+0.7+0.7+0.5)/8=0.78125 Average Similarity Measure Subject 2 (from best 8 comps): (1+0.9+0.8+0.7+0.7+0.4+.035+0.25)/8=0.6375 Referring again to FIG. 3, the comparable properties that have been selected for further review at 44 are then adjusted to reflect the value of the subject property at 46. In particular, any difference between the subject property and the comparable properties that would cause the comparables to be more or less valuable than the subject property will require an adjustment. Thus, if a comparable property is superior to the subject property, then an adjustment is needed to decrease the price of the comparable. However, if the comparable property is inferior to the subject property, then an adjustment is needed to increase the price of the comparable. The adjustments to the price of the comparable properties are performed by using a plurality of adjustment rules stored in the adjustment rule database 30. The adjustment rules are generated from the plurality of attributes stored in the case base 28 for all of the comparable properties. As mentioned earlier, there are approximately 166 attributes available for the subject property and the comparable properties in the illustrative embodiment. A illustrative listing of the attributes are presented below. The attributes described with a # are numeric and the remaining attributes are textual. The numeric attributes are described with a number and the textual attributes are described with text. For example, the attribute total room is described with a number such as three, four, or the like, and the pool attribute is described with a text format such as indoor, spa, etc.
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Recording Date
YYMMDD
SalePrice # in hundreds
SaleCode (Verified, Full, Unconfirmed, Approximate,
Partial, Confirmed, Non-valid)
SFRTotalRooms #
SFRFullBaths #
SFRHalfBaths # (number of half baths)
SearchableBaths # Full + Half Baths (1 full + 1 half = 2 baths)
SFRFireplaces #
SFRStyle (coloniAl, Bungalow, Cape, D--contemporary,
E--ranch, F--tudor, G--mediterranian,
H--georgian, I--high ranch, J--victorian,
K--conventional, L--a frame)
SFRBedrooms #
Pool (C--pool/spa, E--enclosed, Z--solar, H--heated,
I--indoor, P--pool, S--spa, V--vinal)
LotArea # (sq ft)
BuildingArea #
NumberOfUnits #
NumberOfStories #/10 (0.15 = 1.5 stories)
ParkingSpaces #
LocationInfluence (A--positive view, B--ocean, C--bay front,
D--canal, E--river, F--lake/pond, G--wooded,
H--golf, I--corner lot/sound, J--corner,
K--cul-de-sac, L--greenbelt, N--negative)
TypeOfConstruction (A--frame, B--concrete, C--masonry, D--brick,
E--stone, F--concrete block, G--manufact,
H--metal, I--others, J--adobe, K--dome, L--log,
M--special, N--heavy, O--light, S--steel)
Foundation (C--concrete, S--slab, L--mud sill, M--masonry,
P--piers, R--crawl/raised)
YearBuilt # 19XX
EffectiveYearBuilt # 19XX
Quality (Average, Excellent, Fair, Good, Poor, Luxury)
Condition (Average, Excellent, Fair, Good, Poor, None)
AirCondition (Central, Evaporative, Heat pump, WaLl, None,
Office only, Partial, Window, Yes, Z-chill water)
Heating (A--gravity, B--forced air, C--floor furnace,
D--wall furnace, E--hot water, F--ele bboard,
G--heat pump, H--steam, I--radiant, J--space
heater, K--solar, L--none, P--partial, Y--yes,
Z--Central)
ParkingType (A--Attached, B--built in, C--carport,
D--detached, E--basement, F--off-site, G--open,
H--none, J--finished, K--covered, P--paved,
Q--adequate, R--roof, S--subterranean,
U--unimproved, Y--yes, Z--garage)
BasementArea #
RoofType (A--arched, F--flat, G--gable, H--hip,
M--mansard, T--truss-jois)
RoofCover (A--mood shingles, B--mood shake, C--composite
shingle, D--asbestos, E--built up, F--tar+gravel,
G--slate, H--rock+gravel, I--tile, J--other,
R--roll, S--steel, Y--concrete)
Frame (C--concrete, S--steel, M--masonry, W--wood)
GarageCarportSqFt #
latitude #
longitude #
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Based on these attributes, the following adjustment rules are generated in the case base 28 and stored in the adjustment rule database 30. RecordingDate none SalePrice SaleCode ? SFRTotalRooms none SFRTotalBaths see Table 4
TABLE 4
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Adjustment Function for Number of Bathrooms
Comp
Subject
1 1.5
2 2.5 3 3.5 4 4 5+
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1 0.00
-1.50
-3.00
-5.00
-8.00
N/A N/A N/A N/A
1.5 1.00 0.00 -1.00 -3.50 -6.00 -9.00 N/A N/A N/A
2 4.00 1.50 0.00 -2.25 -4.00 -6.50 N/A N/A N/A
2.5 7.00 4.50 2.00 0.00 -2.00 -4.50 -7.00 N/A N/A
3 9.00 6.50 3.00 2.00 0.00 -2.50 -5.00 -7.50 '@*-5
3.5 N/A 8.50 6.50 4.50 2.50 0.00 -3.00 -5.50 '@*-5
4 N/A N/A 8.50 7.00 5.50 3.00 0.00 -3.00 '@*-5
4.5 N/A N/A N/A 10.00 8.00 6.00 3.00 0.00 '@*-5
5+ N/A N/A N/A '@*-5 '@*-5 '@*-5 '@*-5 '@*-5 0.00
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In order to accommodate for even more or less bathrooms, Table 4 takes the difference between the subject property and the comparable property (i.e., @) and multiplies the difference by five. For example, if the subject property has seven bathrooms and the comparable has three, then the adjustment would be 20 ([7-3]*5). If the subject property has three bathrooms and the comparable has seven, then the adjustment would be -20 ([7-3]*-5). SFRFireplaces (subject-comp)*2000 SFRStyle ? SFRBedrooms see Table
TABLE 5
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Adjustment Function for Number of Bedrooms
Sub- Comp
ject 1 2 3 4 5 6+
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1 0.00 0.00 -3.50
N/A N/A N/A
2 0.00 0.00 0.00 -2.50 N/A N/A
3 4.00 0.00 0.00 0.00 -4.00 N/A
4 N/A 4.00 0.00 0.00 0.00 '(@-1)*3.5
5 N/A N/A 4.00 0.00 0.00 '(@-1)*3.5
6+ N/A N/A N/A '(@-1)*3.5 '(@-1)*3.5 0.00
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In order to accommodate for even more or less bedrooms, Table 5 takes the difference between the subject property and the comparable property (i.e., @) and subtracts the difference by one and multiplies the difference by 3.5. For example, if the subject property has six bedrooms and the comparable has four, then the adjustment would be 3.5[[(6-4)-1]*3.5]. If the subject property has four bedrooms and the comparable has six, then the adjustment would be -3.5[[(6-4)-1]*-3.5].
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Pool $10000 for a pool
LotArea (subject - comp)
BuildingArea (subject - comp) * (22 +
(sales.sub.-- price.sub.-- closing.sub.-- of.sub.-- comp * .00003))
NumberOfUnits ?
NumberOfStories ?
ParkingSpaces ?
LocationInfluence no adjustment between comps in same level
(B--ocean, F--lake/pond, A--positive view,
C--bay front = +10%, D--canal, E--river,
G--wooded, H--golf, L--greenbelt = +5%
K--cul-de-sac, J--corner = no adjust
I--corner lot/sound, N--negative = -5%)
TypeOfConstruction ?
Foundation ?
YearBuilt use only if no effective year built w *
(Age.sub.-- comp-Age.sub.-- subject) * (SalePrice.sub.-- comp/
1000)
if (Age.sub.-- subject + Age.sub.-- comp)/2 < 5 then w = 3.2
else if (Age.sub.-- subject + Age.sub.-- comp)/2 < 9 then w =
2.4 else if (Age.sub.-- subject + Age.sub.-- comp)/2 < 12
then w = 1.6 else if (Age.sub.-- subject + Age.sub.-- comp)/
2 < 20 then w = .8 else w = .4
max of 10% of salePrice
EffectiveYearBuilt w * (Age.sub.-- comp-Age.sub.-- subject) *
(SalePrice.sub.-- comp/1000)
if (Age.sub.-- subject + Age.sub.-- comp)/2 < 4 then w = 4
else if (Age.sub.-- subject + Age.sub.-- comp)/2 < 6 then w =
3 else if (Age.sub.-- subject + Age.sub.-- comp)/2 < 8 then
w = 2 else if (Age.sub.-- subject + Age.sub.-- comp)/2 < 15
then w = 1 else w = .5 max of 10% of salePrice
Quality(.02 * sale price) for each level of difference
(Luxury > Excellent > Good > Average > Fair >
Poor) Condition (.02 * sale price) for each level of
difference (Excellent > Good > Average > Fair >
Poor)
AirCondition (.01 * sale price) for each level of difference
(Central > Evaporative, Heat pump, waLl, Yes, Z--
chill water > None, Office only, Partial, Window,)
Heating (.01 * sale price) for each level of difference
(Z--Central, B--forced air > A--gravity, C--
floor furnace, D--wall furnace, E--hot water,
F--ele bboard, G--heat pump, H--steam,
I--radiant, J--space heater, K--solar, Y--yes >
L--none, P--partial)
ParkingType ?
BasementArea if not finished 1/4 to 1/2 value of living area
if finished 1/2 to 1 value of living area
RoofType ?
RoofCover ?
Frame ?
GargageCarportSqFt ?
latitude none
longitude none
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These adjustment rules are then applied to the comparable properties selected at 40 in order to adjust for the value of the subject property. Instead of applying all of the above adjustment rules, time can be saved by only applying several of the adjustment rules deemed to be more important than the others, such as the adjustment rules for the attributes of fireplaces, a pool, the effective age of the property, quality of the property, and condition of the property. An example of an adjustment for a comparable property is provided in Table 6. In the example provided in Table 6, the comparable property has a sale price of $175,000 dollars. However, the comparable property has a building area of 1800 square feet, while the subject property has a building area of 2000. Using the adjustment rules for the attribute building area, the price of the comparable is adjusted by $5450 (i.e., 22+(175000 * 0.00003)=$27.25 per square foot which is (200* $27.25=$5450)). Also, the price of the comparable is adjusted for the lot area since the comparable has a larger lot size. In Table 6, the lot area attribute is adjusted by $1/sq ft for a total of -$5000. Since the comparable has two bathrooms and the subject property has two and a half bathrooms, the price needs to be adjusted by using the rules provided in Table 4, which turns out to be $2000. There are no adjustments necessary for the bedroom attribute because both the subject property and the comparable property have the same number of bedrooms. Since the comparable does not have a fireplace and the subject property has one, the price needs to be adjusted accordingly. Using the adjustment rule for fireplaces, the price is adjusted $2000. If the adjustment rules are used for the effective year, quality, condition, and pool attributes for the subject and comparable property, the rules will generate an adjustment of $2800, $3500, $0, and $10,000, respectively. All of the adjustments are then summed with the sale price of the comparable property to arrive at the adjusted price. In Table 6, the adjusted price of the comparable property is $195,750.
TABLE 6
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Example of an Adjustment
Attribute Subject Comparable
Adjustment
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SalePrice ? 175000 175000
BuildingArea 2000 1800 5450
LotArea 20000 25000 -5000
SFRTotalBaths 2.5 2 2000
SFRBedrooms 3 3
SFRFireplaces 1 0 2000
EffYearBuilt 93 89 2800
Quality Good Average 3500
Condition Average Average
Pool Yes No 10000
195750
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Referring again to FIG. 3, after all of the adjustments are applied to the sales price of the comparable properties, another set of comparable properties that more closely match the subject property are extracted at 48. In the illustrative embodiment, 4-8 comparables are selected at 48. If less than four comparables are selected, then the comparables may not correctly reflect the market and if more than eight comparables are used, then some of the comparables may not be similar enough to the subject property. The best (i.e., four to eight) of the remaining adjusted comparable properties are selected by sorting the comparables according to their adjusted prices in the manner as shown in Table 7. The comparables with the highest adjusted price are placed at the top and ranked in descending order. The third column in table 7 represents the difference between the adjusted price of the comparable and the specified price of the subject. The four to eight properties with the smallest difference are selected as the final set of comparables.
TABLE 7
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Selection of the best Comparables
Property Adjusted Price
Difference
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342-837 214400 14400
113-012 204500 4500
306-018 201400 1400
093-018 200600 600
305-006 200400 400
685-046 200200 200
847-984 199750 250
873-005 199600 400
431-023 199000 1000
331-018 197000 3000
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Referring again to FIG. 3, after the best of the adjusted comparables have been selected, the adjusted prices of the selected comparables are aggregated into an estimate price of the subject property at 50. The aggregated estimated price is determined by multiplying the adjusted price of the comparable properties to their respective measurement of similarity and summed together to generate a total weighted price. Next, the total weighted price is divided by the total of the similarity measurements for the comparable properties. The result is an estimate price of the subject property. An example of the aggregation for comparables is provided in Table 8. In this example, the total weighted price is $757,640 and the total similarity score is 3.83. Thus, dividing $757,640 by 3.83 results in an estimate price of 199,900 for the subject property.
TABLE 8
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Comparable Aggregation
Comparable
Adjusted Price
Score
Weighted Price
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113-012 197000 0.95 187150
306-008 202000 0.88 177760
093-011 196500 0.78 153270
685-046 192000 0.64 122880
847-984 201000 0.58 116580
total 3.83 757640
final estimate = 757640/3.83 =
199900
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After producing the final estimate of the value of the subject property, the estimate is compared against the specified price for validation at 52. A measurement of confidence indicating the reliability of the validation is then generated at 54. In particular, the confidence measurement in the estimate can be obtained by averaging the similarity scores of the comparables in the final selection, or by averaging the number of comparables over a threshold in the primary retrieval. The estimate is justified by displaying the comparables in enough detail so that they can be shown to be similar to the specified price of the subject. It is therefore apparent that there has been provided in accordance with this invention, a method for validating the price of a real estate subject property based on a specified price that fully satisfy the aims and advantages and objectives hereinbefore set forth. The invention has been described with reference to several embodiments, however, it will be appreciated that variations and modifications can be effected by a person of ordinary skill in the art without departing from the scope of the invention. For example, the present invention can be used to validate the estimated price generated by a conventional estimator.
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