Method for estimating the value of real property6178406Abstract A method for estimating the price of real property such as a single family residence. A set of real estate properties comparable to the subject property is retrieved. The comparable properties and the subject property are characterized by a plurality of common attributes each having a respective value. Each attribute value from the comparable properties are evaluated to the same attribute value of the subject property on a fuzzy preference scale indicating desirable and tolerable deviations from an ideal match with the subject property. A measurement of similarity between each comparable property and the subject property is then determined. Next, the price of the comparable properties are adjusted to the value of the subject property and the best properties are extracted for further consideration. The extracted comparable properties are then aggregated into an estimate price of the subject property. Claims What is claimed is: Description FIELD OF THE INVENTION
TABLE 1
Preference Function for Number of Bedrooms
Comparable's
# Bedrooms 1 2 3 4 5 6+
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
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 of 0.1.
TABLE 2
Preference Function for Number of Bathrooms
Comparable
Subject 1 1.5 2 2.5 3 3.5 4 4.5 5+
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
After each attribute of the comparable 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 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 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, 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 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
Computation of the Measurement of Similarity
Weighted
Attribute Subject Comparable Comparison Preference Weight
Preference
Date of Sale x 6 months 6 months 0.67 0.2 0.134
Distance x 0.2 miles 0.2 miles 1 0.3 0.3
Living Area 2000 1800 90% 0.79 0.3 0.237
Lot Size 20000 35000 175% 0.33 0.1 0.033
# Bedrooms 3 3 3 .fwdarw. 3 1 0.05 0.05
# Bathrooms 2.5 2 2.5 .fwdarw. 2 0.75 0.05 0.0375
Similarity 0.7915
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+0.035+0.25)/8=0.6375 Referring again to FIG. 3, the comparable properties that have been selected for further review at 40 are then adjusted to reflect the value of the subject property at 42. 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 the plurality of adjustment rules stored in the adjustment rule database 26. The adjustment rules are generated from the plurality of attributes stored in the case base 24 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.
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 (015 = 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, waLI,
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)
GargageCarportSqFt#
latitude #
longitude #
Based on these attributes, the following adjustment rules are generated in the case base 24 and stored in the adjustment rule database 26.
RecordingDate none
SalePrice
SaleCode ?
SFRTotalRooms none
SFRTotalBaths see Table 4
In order to accommodate for even more or less bathrooms, Table 4 takes the difference between the subject property and the comparative 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 5
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].
Pool $10000 for a pool
LotArea (subject - comp)
BuildingArea (subject - comp) * (22 +
(sales_price_closing_of_comp * .00003))
NumberOfUnits ?
NumbeOfStories ?
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_comp_Age_subject) * (SalePrice_comp/1000)
if (Age_subject + Age_comp) / 2 < 5 then w = 3.2
else if (Age_subject + Age_comp) / 2 < 9 then w =
2.4 else if (Age_subject + Age_comp) / 2 < 12 then w = 1.6 else if
(Age_subject + Age_comp) / 2 < 20 then w = .8 else w = .4
max of 10% of salePrice
EffectiveYearBuilt w * (Age_comp-Age_subject) *
(SalePrice_comp/1000)
if (Age_subject + Age_comp) / 2 < 4 then w = 4
else if (Age_subject + Age_comp) / 2 < 6 then w = 3 else if
(Age_subject + Age_comp) / 2 < 8 then w = 2 else if
(Age_subject + Age_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 l evel 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
These adjustment rules are then applied to the comparable properties selected at 40 in order to adjust for the value of the subject 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
Example of an Adjustment
Attribute Subject Comparable Adjustment
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
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 44. In the illustrative embodiment, 4-8 comparables are selected at 44. 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. If it is not possible to find four comparables similar to the subject property, then no value estimate may be calculated for the subject property. However, if there are many comparables (i.e. about 100 hundred), then it is necessary to filter out the poorer comparables. In the illustrative embodiment, the best results are attained by keeping comparable properties that have no single adjustment larger than 10% of the sale price, a net adjustment that does not exceed 15% of the sale price, a gross adjustment that does not exceed 25% of the sale price, and a dollar per square foot that does not vary more than 15%. Basically, all of the adjusted comparable properties are excluded from further consideration if any comparable has a single adjustment larger than 10%, a net adjustment larger than 15% or a gross adjustment larger than 25%. The best (i.e. four to eight) of the remaining adjusted comparable properties are selected by sorting and ranking each of the comparables' measurement of similarity, the net adjustment, and the gross adjustment, in the manner as shown in Table 7. In particular, the comparables having the highest measurement of similarity score are placed at the top of the list and ranked in descending order. In Table 7, comparable property 113-012 has the highest measurement of similarity score and is ranked one, while comparable property 331-018 has the lowest measurement of similarity score and is ranked nine. Next to the measurement of similarity score and rank are the net and gross adjustment and respective rankings for the comparable properties. The rankings for the net and gross adjustment are attained in the same manner as the measurement of similarity. The rankings for the measurement of similarity, net adjustment, and gross adjustment, for each comparable are then summed across the board to produce a total ranking. The comparables with the lowest total rank are considered the best. In Table 7, comparables 113-012, 306-008, and 334-010, are the three best comparables.
TABLE 7
Selection of the best Comparables
Gross
Comparable Score rank Net Adjust rank Adjust rank total
113-012 0.95 1 1344 2 5924 4 7
306-018 0.88 2 3586 5 4186 1 8
093-011 0.78 3 5686 7 8191 7 17
305-006 0.67 4 6150 8 6160 6 18
685-046 0.64 5 3139 3 6099 5 13
847-984 0.58 6 -948 1 5670 3 10
873-005 0.53 7 -5261 6 9261 8 21
431-023 0.48 8 3546 4 4410 2 14
331-018 0.44 9 9310 9 11300 9 27
After the comparables have been ordered, it is necessary to determine how many of these comparables are to be used. Generally, the sales prices of the comparables should bound the sales price that will be estimated for the subject property. Therefore, it would be favorable to select comparables with both a negative and positive net adjustment. A comparable property with a negative net adjustment is likely to have an unadjusted price over the final estimate and a comparable with a positive net adjust is likely to have an unadjusted price under the final estimate. So, in order to do this, a temporary set of comparables is created by repeatedly adding the comparable with the best similarity score to the set until there are at least four comparables in the set and there is at least one comparable of each sign (negative and positive) net adjustment. In Table 7, the comparables with the top six similarity scores would be included in the set. All other comparables are discarded. Of the comparables in the set only four of each sign net adjustments are retained. The four retained are the four comparables with the lowest total rank. In the example, comparable 305-006 would be discarded since there are four comparables with a positive net adjustment and lower total rank. The five comparables selected form the final set of comparables. 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 46. 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 the comparables provided in Table 7 is shown 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
Comparable Aggregation
Comparable Adjusted Price Score Weighted Price
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
After producing the final estimate of the value of the subject property, a measurement of confidence indicating the reliability is generated. 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 subject. It is therefore apparent that there has been provided in accordance with this invention, a method for estimating the price of a real property 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.
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