JP2002117275A - System for evaluating merchandise value - Google Patents
System for evaluating merchandise valueInfo
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- JP2002117275A JP2002117275A JP2000306469A JP2000306469A JP2002117275A JP 2002117275 A JP2002117275 A JP 2002117275A JP 2000306469 A JP2000306469 A JP 2000306469A JP 2000306469 A JP2000306469 A JP 2000306469A JP 2002117275 A JP2002117275 A JP 2002117275A
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- Prior art keywords
- residual value
- merchandise
- evaluation
- rate
- model function
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- 238000011156 evaluation Methods 0.000 claims abstract description 65
- 230000007704 transition Effects 0.000 claims abstract description 10
- 230000006870 function Effects 0.000 abstract description 46
- SAZUGELZHZOXHB-UHFFFAOYSA-N acecarbromal Chemical compound CCC(Br)(CC)C(=O)NC(=O)NC(C)=O SAZUGELZHZOXHB-UHFFFAOYSA-N 0.000 abstract 3
- 238000010586 diagram Methods 0.000 description 14
- 238000007689 inspection Methods 0.000 description 11
- 238000000034 method Methods 0.000 description 3
- 230000002238 attenuated effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
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Abstract
Description
【０００１】[0001]
【発明の属する技術分野】本発明は商品価値評価装置、
特に商品の将来における評価額の予測に関する。TECHNICAL FIELD The present invention relates to a commercial value evaluation device,
In particular, it relates to forecasting the valuation value of a product in the future.
【０００２】[0002]
【従来の技術】例えば、車両の販売計画を立案する際、
新車のみならずいわゆる中古車市場までも考慮に入れた
方がより正確な計画が立案できると考えられる。そのた
めには、中古車の価値を正確に評価し、査定時点におけ
る中古車の残価をより正確に予測できるようにすること
が望まれる。2. Description of the Related Art For example, when planning a vehicle sales plan,
Considering not only new cars but also the so-called used car market will make it possible to make more accurate plans. For that purpose, it is desired to accurately evaluate the value of the used car and to more accurately predict the residual value of the used car at the time of the assessment.
【０００３】ところで、現行車から新車に乗り換えると
き、現行車をいわゆる下取り車として新車の販売メーカ
に引き取ってもらうことが一般的である。下取り車の査
定価格は、登録年月日、車検満了日、メーカ、タイプ、
グレード、色等の使用状態に依存しない客観的なデータ
に基づいて基準査定価格を特定した後、中古車ボディの
傷や凹み、走行距離等中古車の使用状態によって基準査
定価格を補正して決定することが一般的である。車両の
査定価格は、通常、登録年月日から年月が経つに連れて
減衰していく。査定時点における中古車の残価の予測も
基本的にはこれと同じ方法で行うことができると考えら
れる。[0003] By the way, when switching from a current car to a new car, it is common to have the new car sold by a manufacturer of the new car as a so-called trade-in car. The assessment price of the trade-in car is the registration date, vehicle inspection expiration date, manufacturer, type,
After specifying the reference assessment price based on objective data that does not depend on the use condition such as grade, color, etc., the reference assessment price is corrected according to the use condition of the used car such as scratches and dents in the used car body, mileage, etc. It is common to do. The assessed price of a vehicle usually decays over time from the date of registration. It is considered that the estimation of the residual value of a used car at the time of the assessment can be basically performed in the same manner.
【０００４】本願と同一出願人は、市場動向を考慮して
商品の将来の査定時点における評価額をより正確に予測
する商品価値評価装置を出願している（特願２０００−
１５２４１号）。[0004] The same applicant as the present application has filed an application for a merchandise valuation apparatus that more accurately predicts the valuation value of a commodity at the time of future assessment in consideration of market trends (Japanese Patent Application No. 2000-2000).
No. 15241).
【０００５】[0005]
【発明が解決しようとする課題】しかしながら、従来例
では、商品の諸元、仕様等の商品特性を考慮していなか
ったこと、更に既販商品毎の基準評価率、すなわち残価
率を直線的な遷移で表すようにしていたことにより必ず
しも正確な残価予測を行うことができていなかった。However, in the conventional example, product characteristics such as product specifications and specifications were not taken into consideration, and the standard evaluation rate for each sold product, that is, the residual value rate was linearly calculated. However, accurate residual value prediction has not always been able to be performed.
【０００６】本発明は以上のような問題を解決するため
になされたものであり、その目的は、商品特性を考慮し
て商品の査定時点における評価をより正確に行う商品価
値評価装置を提供することにある。SUMMARY OF THE INVENTION The present invention has been made to solve the above problems, and an object of the present invention is to provide a merchandise value evaluation apparatus for more accurately evaluating merchandise at the time of appraisal in consideration of merchandise characteristics. It is in.
【０００７】[0007]
【課題を解決するための手段】以上のような目的を達成
するために、本発明に係る商品価値評価装置は、既販商
品の販売実績及び販売実績から査定要因を除去する補正
用データに基づいて既販商品の査定要因に影響されない
基準評価率を算出する既販商品評価率算出処理手段と、
販売が開始されてからの時間経過に伴い変化する既販商
品毎の基準評価率の遷移パターンを残価減衰モデルの関
数として生成する残価減衰モデル生成処理手段と、評価
対象商品の残価減衰モデル関数に基づき将来の査定時点
における評価を行う商品評価処理手段とを有することを
特徴とする。この発明によれば、商品特性を考慮して将
来の査定時点における商品の評価を行うので、その予測
精度を向上させることができる。In order to achieve the above object, a merchandise value evaluation apparatus according to the present invention is based on sales results of sold products and correction data for removing assessment factors from sales results. Selling product evaluation rate calculation processing means for calculating a reference evaluation rate that is not affected by assessment factors of sold products,
A residual value decay model generation processing means for generating a transition pattern of a reference valuation rate for each sold product that changes with time since the start of sales as a function of the residual value decay model, and a residual value decay of the evaluation target product Product evaluation processing means for evaluating at the time of future assessment based on the model function. According to the present invention, since the product is evaluated at the time of the future assessment in consideration of the product characteristics, the prediction accuracy can be improved.
【０００８】また、残価減衰モデル関数の係数と既販商
品の商品特性との関係をモデル化する関係モデル関数生
成処理手段を有し、前記商品評価処理手段は、前記関係
モデル関数生成処理手段が生成したモデル関数により現
時点においてまだ販売されていない新商品の残価減衰モ
デル関数の係数を算出し、当該新商品の将来の査定時点
における評価を行うことを特徴とする。この発明によれ
ば、まだ販売されていない新商品に対しても将来におけ
る評価を行うことができる。[0008] Further, there is provided a relation model function generation processing means for modeling a relation between a coefficient of a residual value decay model function and a product characteristic of a sold product, and the product evaluation processing means includes a relation model function generation processing means. Is used to calculate a coefficient of a residual value decay model function of a new product that is not yet sold at the present time by using the generated model function, and to evaluate the new product at a future assessment time. According to the present invention, it is possible to evaluate a new product that has not been sold yet in the future.
【０００９】また、残価率が減衰する遷移パターンを関
数で表そうとすると指数関数になることに着目して残価
減衰モデル関数を指数関数で表し、この残価減衰モデル
関数に基づき将来時おける評価を行うようにしたので、
その予測精度を向上させることができる。[0009] In addition, if a transition pattern in which the residual value rate is attenuated is represented by an exponential function, the residual value decay model function is represented by an exponential function. So that the evaluation of
The prediction accuracy can be improved.
【００１０】更に、前記商品評価処理手段は、商品が新
規に購入されたときの価格に対する当該商品が中古とし
て購入されたときの価格の比率である残価率によって当
該商品を評価することを特徴とする。Further, the product evaluation processing means evaluates the product by a residual value rate which is a ratio of a price when the product is purchased as used to a price when the product is newly purchased. And
【００１１】[0011]
【発明の実施の形態】以下、図面に基づいて、本発明の
好適な実施の形態について説明する。Preferred embodiments of the present invention will be described below with reference to the drawings.
【００１２】図１は、本発明に係る商品価値評価装置を
適用した中古車残価予測支援装置の一実施の形態を示し
たブロック構成図である。中古車残価予測支援装置２
は、基本残価率算出処理部４、残価減衰モデル生成処理
部６、関係モデル関数生成処理部８及び残価率予測処理
部１０を有している。基本残価率算出処理部４は、中古
車データベース１２に蓄積された売買データに基づき各
中古車の残価率を算出し、それを残価率補正用データベ
ース１４を参照して補正することで基準評価率として得
られる基本残価率を基本残価率データベース１６に格納
する。残価減衰モデル生成処理部６は、販売が開始され
てからの時間経過に伴い変化する中古車毎の基準評価率
の遷移パターンを残価減衰モデルの関数として生成し、
残価減衰モデルデータベース１８に保存する。関係モデ
ル関数生成処理部８は、残価減衰モデル関数の係数と中
古車毎の商品特性との関係をモデル化する。中古車毎の
商品特性は、現行車特性データベース２０に格納されて
いる。残価率予測処理部１０は、評価対象車の残価減衰
モデル関数に基づき当該評価対象車の将来の査定時点に
おける残価率を算出することで評価を行う。新車の残価
率を算出する際に用いる新車の商品特性は、新車特性デ
ータベース２２に格納されている。FIG. 1 is a block diagram showing an embodiment of a used vehicle residual value prediction support device to which a commercial value evaluation device according to the present invention is applied. Used car residual value prediction support device 2
Has a basic residual value calculation processing unit 4, a residual value attenuation model generation processing unit 6, a relational model function generation processing unit 8, and a residual value ratio prediction processing unit 10. The basic residual value calculation processor 4 calculates the residual value of each used vehicle based on the sales data accumulated in the used vehicle database 12 and corrects the residual value by referring to the residual value correction database 14. The basic residual value rate obtained as the reference evaluation rate is stored in the basic residual value rate database 16. The residual value attenuation model generation processing unit 6 generates, as a function of the residual value attenuation model, a transition pattern of the reference evaluation rate for each used vehicle, which changes with the lapse of time since the sale was started,
It is stored in the residual value decay model database 18. The relation model function generation processing unit 8 models the relation between the coefficient of the residual value attenuation model function and the product characteristics of each used car. The product characteristics of each used car are stored in the current vehicle characteristics database 20. The residual value rate prediction processing unit 10 performs an evaluation by calculating a residual value rate at the time of a future assessment of the evaluation target vehicle based on a residual value attenuation model function of the evaluation target vehicle. The product characteristics of the new vehicle used when calculating the residual value ratio of the new vehicle are stored in the new vehicle characteristics database 22.
【００１３】なお、現在においてまだ販売されていない
新車も将来においては中古車となるので、現行車のみな
らず新車も評価対象車となる。本実施の形態において用
いる現行車、新車あるいは中古車というのは、特に断ら
ない限り現時点における車両の状態をいう。[0013] It should be noted that new vehicles that are not yet sold at present will be used vehicles in the future, so that not only current vehicles but also new vehicles will be evaluated vehicles. A current vehicle, a new vehicle, or a used vehicle used in the present embodiment refers to the current state of the vehicle unless otherwise specified.
【００１４】図２は、本実施の形態における中古車デー
タベース１２のデータ構成例を示した図である。中古車
データベース１２には、排気量、グレード等の基本仕様
に関する仕様情報、当該中古車が新車として販売された
ときの登録年月日、新車価格等の新車時の売買データ、
中古車として販売されたときの中古車価格、走行距離、
評価点等の中古車売買データ、更に次車検年月日、当該
車両が新車として市場に投入されてから登録されるまで
に要した月数（経過月数）及び基本残価率算出処理部４
が算出した残価率が中古車毎に蓄積される。FIG. 2 is a diagram showing an example of the data structure of the used car database 12 in the present embodiment. The used car database 12 includes specification information on basic specifications such as displacement, grade, etc., registration date when the used car is sold as a new car, sales data of a new car such as a new car price,
Used car price, mileage when sold as a used car,
Used car sales data such as evaluation points, the next vehicle inspection date, the number of months (elapsed months) required for the vehicle to be registered after being introduced to the market as a new vehicle, and a basic residual value rate calculation processing unit 4
Is calculated for each used car.
【００１５】図３及び図４は、本実施の形態における残
価率補正用データベース１４に格納されている査定成分
に関する元情報の例を示した概念図である。本実施の形
態における基本残価率算出処理部４は、査定成分を除去
して基本残価率を算出するが、この除去すべき査定成分
に関する元情報として走行距離に関する走行距離査定テ
ーブルが図３に、車検残月数に関する車検残テーブルが
図４にそれぞれ示されている。走行距離査定テーブルに
は、登録されるまでに要した経過月数に対し走行距離に
よって加減される評価価格が示されている。テーブル内
の数字にはさまれた空白欄からなる帯の上側はプラスの
評価価格、下側はマイナスの評価価格である。空白欄の
ところは、プラスもマイナスもされないところである。
車検残テーブルには、車検の残月数に対してプラスされ
る評価価格が示されている。図４では、乗用車系のテー
ブルのみを示したが、トラック系など車検制度にあわせ
て複数用意することができる。FIG. 3 and FIG. 4 are conceptual diagrams showing examples of the original information on the assessment components stored in the residual value rate correction database 14 in the present embodiment. The basic residual value rate calculation processing section 4 in the present embodiment calculates the basic residual value rate by removing the assessment component. The mileage assessment table for the mileage is used as the original information on the assessment component to be removed, as shown in FIG. FIG. 4 shows a vehicle inspection remaining table relating to the number of months remaining for vehicle inspection. The mileage assessment table shows an evaluation price that is adjusted according to the mileage with respect to the number of elapsed months required for registration. The upper side of the band consisting of blank columns sandwiched by numbers in the table is a positive evaluation price, and the lower side is a negative evaluation price. The blank space is where neither plus nor minus is done.
The vehicle inspection remaining table shows an evaluation price added to the remaining months of vehicle inspection. FIG. 4 shows only a table for a passenger car, but a plurality of tables such as a truck can be prepared according to a vehicle inspection system.
【００１６】その他にも、本実施の形態では、現行車特
性データベース２０、新車特性データベース２２を予め
用意しておくが、各データベース２０，２２は、それぞ
れ後述する処理において利用されるときに説明する。In addition, in the present embodiment, the current vehicle characteristic database 20 and the new vehicle characteristic database 22 are prepared in advance, but the respective databases 20 and 22 will be described when they are used in processing described later. .
【００１７】次に、本実施の形態における中古車の残価
予測処理を図５に示したフローチャートを用いて説明す
る。Next, a residual value prediction process for a used car according to the present embodiment will be described with reference to a flowchart shown in FIG.
【００１８】まず、基本残価率算出処理部４は、中古車
データベース１２及び残価率補正用データベース１４を
参照にして基本残価率を算出する。そのために、基本残
価率算出処理部４は、中古車に関する中古車価格、新車
価格、登録されるまでに要した経過月数、走行距離及び
次車検年月日を中古車データベース１２から全て取り出
し、更に図３に示した走行距離査定テーブルに基づいて
経過月数及び走行距離の各データ値から各中古車の評価
価格を取得する。例えば、登録日から２３月経過した中
古車の走行距離が１２０００Ｋｍの場合の評価価格はプ
ラス１０万円である。また、車検までの月数が７月ある
Ａクラスの評価価格はプラス５千円である。各テーブル
から抽出した評価価格を加算することで査定要因除去値
を算出する（ステップ１０１）。そして、中古車の各デ
ータにつき、 基本残価率＝１００×（中古車価格−査定要因除去値）／新車価格 ・・・（１ ） の式により査定要因を除去した基本残価率を計算し（ス
テップ１０２）、この算出結果を基本残価率データベー
ス１６に格納する。First, the basic residual value rate calculation processing section 4 calculates the basic residual value rate with reference to the used car database 12 and the residual value rate correction database 14. For this purpose, the basic residual value rate calculation processing unit 4 extracts from the used car database 12 all used car prices, new car prices, elapsed months required for registration, mileage, and next car inspection dates for used cars. Then, based on the travel distance assessment table shown in FIG. 3, the evaluation price of each used car is acquired from each data value of the elapsed months and the travel distance. For example, when the travel distance of a used car after 23 months from the registration date is 12,000 km, the evaluation price is plus 100,000 yen. In addition, the evaluation price of the A class, which has a month until the vehicle inspection is July, is plus 5,000 yen. An evaluation factor removal value is calculated by adding the evaluation prices extracted from each table (step 101). Then, for each data of the used car, the basic residual value rate is calculated by the following formula: basic residual value rate = 100 × (used vehicle price−removed value of assessment factor) / new vehicle price (1) (Step 102), the calculation result is stored in the basic residual value rate database 16.
【００１９】以上のようにして、本実施の形態では、車
検残月数、走行距離等の要因によって千差万別になって
いる各中古車の評価すなわち中古車価格に対して当該要
因を除去する補正を行って査定要因に影響されない残価
率の基本モデルを得るようにしている。図６には、車種
Ａの全中古車の基本残価率を販売月毎に表示した例が示
されている。なお、図６における販売始期からの経過月
数というのは、当該車両が新車として購入された後、中
古車として登録されるまでに要した月数のことをいう。
他の図においても同様である。As described above, in the present embodiment, the evaluation of each used car, which varies widely depending on factors such as the number of months remaining for vehicle inspection and the mileage, that is, the correction for removing the factor from the used car price. To obtain a basic model of the residual value rate that is not affected by assessment factors. FIG. 6 shows an example in which the basic residual value rate of all used vehicles of the vehicle type A is displayed for each sales month. Note that the number of months elapsed from the start of sales in FIG. 6 refers to the number of months required after the vehicle is purchased as a new car and before it is registered as a used car.
The same applies to other figures.
【００２０】続いて、残価減衰モデル生成処理部６は、
車種毎の基準評価率の遷移パターンを残価減衰モデルと
して表す。つまり、残価減衰モデルの関数を以下のよう
にして生成する（ステップ１０３）。上述した特願２０
００−１５２４１号に開示した商品価値評価装置では、
基準評価率の遷移パターンを時間の経過に伴い下降する
直線と平行する直線で表した。しかし、本発明者は、残
価率の実績を分析してみると、実際は新車として購入さ
れてから中古車として再度購入される３０ヶ月くらいは
落ち具合が相対的に急であり、それを過ぎるとある程度
安定してくることに着目した。すなわち、残価率が減衰
する遷移パターンを関数で表そうとすると指数関数にな
ることに着目した。そこで、本実施の形態では、車種毎
の基準評価率の遷移パターンを表すために指数関数モデ
ル式を適用することにした。つまり、残価率は、 残価率＝ｅｘｐ（αｘα＋βｘβ＋ｃ）＋ε ・・・（２） という指数関数のモデル式で表すことができる。ここ
で、ｘαは経過月数、ｘβは発売年式（購入年）、α，
βはそれぞれの係数、ｃは実績によって求まる定数項、
εは誤差項であり、予測する際には０とする。なお、後
述するようにα，βと共に計算により求める値なので、
ｃも便宜的に係数と称することにする。Subsequently, the residual value decay model generation processing unit 6
The transition pattern of the reference evaluation rate for each vehicle type is represented as a residual value attenuation model. That is, a function of the residual value decay model is generated as follows (step 103). Japanese Patent Application No. 20 mentioned above
In the merchandise value evaluation device disclosed in 00-15241,
The transition pattern of the reference evaluation rate was represented by a straight line parallel to a straight line descending with time. However, when the present inventor analyzes the results of the residual value rate, the fall is relatively steep for about 30 months when the vehicle is actually purchased as a new car and then re-purchased as a used car. I noticed that it became stable to some extent. In other words, we focused on the fact that an attempt to represent a transition pattern in which the residual value rate is attenuated becomes an exponential function. Therefore, in the present embodiment, an exponential function model formula is applied to represent the transition pattern of the reference evaluation rate for each vehicle type. That is, the residual value rate can be represented by an exponential function model expression of residual value rate = exp (αxα + βxβ + c) + ε (2). Here, xα is the number of elapsed months, xβ is the release year (purchase year), α,
β is each coefficient, c is a constant term obtained from actual results,
ε is an error term, which is set to 0 at the time of prediction. In addition, since it is a value obtained by calculation together with α and β as described later,
c is also referred to as a coefficient for convenience.
【００２１】新車を購入してから月日が経つにつれ残価
率は通常落ちてくるはずである。式（２）におけるｘα
はこれを表している。また、発売年式が変わると、同じ
車種であっても市場に投入されてから価値自体は落ちて
くる。例えば、１９９８年に初めて市場に投入された車
両を１９９８年に購入した場合と２０００年に購入した
場合、購入価格は同じでもそれぞれ５年後、すなわち１
９９８年に購入した車両の２００３年における価値と２
０００年に購入した車両の２００５年における価値と
は、同じ５年後であっても同じではなく、通常、後に購
入した方が価値は多少落ちてくる。（２）におけるｘβ
はこれを表している。[0021] As the date of purchase of a new car passes, the residual value rate should normally fall. Xα in equation (2)
Represents this. Also, if the release year changes, the value itself will drop after it is launched into the market, even for the same model. For example, when a vehicle first introduced to the market in 1998 is purchased in 1998 and a vehicle purchased in 2000, the purchase price is the same even after 5 years, that is, 1
2003 Value of Vehicles Purchased in 998 and 2
The value of a vehicle purchased in 000 in 2005 is not the same even in the same five years from now, and the value of the vehicle purchased later is usually somewhat lower. Xβ in (2)
Represents this.
【００２２】式（２）において、当該車種の合計台数が
Ｎ台だとすると、In equation (2), if the total number of the vehicle types is N,
【数１】 が最小となるα，β，ｃを求めればよい。ここで、（実
績残価率）ｉは、式（１）で算出される当該車種の各車
両の基本残価率であり、（モデル残価率）ｉは、式
（２）で表される残価率である。この計算結果を図６に
当てはめた例を図７に示す。(Equation 1) Α, β, and c at which the minimum is obtained. Here, (residual residual value rate) i is the basic residual value rate of each vehicle of the vehicle type calculated by equation (1), and (model residual value rate) i is represented by equation (2). The residual value rate. FIG. 7 shows an example in which this calculation result is applied to FIG.
【００２３】以上の処理を全ての車種に対して行うこと
で車種毎に残価減衰モデル関数が生成される。この処理
の結果、得られた残価減衰モデル関数の係数を残価減衰
モデルデータベース１８に格納する。残価減衰モデルデ
ータベース１８のデータ構成例を図８に示す。By performing the above processing for all vehicle types, a residual value attenuation model function is generated for each vehicle type. As a result of this processing, the obtained coefficients of the residual value attenuation model function are stored in the residual value attenuation model database 18. FIG. 8 shows a data configuration example of the residual value decay model database 18.
【００２４】次に、関係モデル関数生成処理部８は、残
価減衰モデルデータベース１８に格納された残価減衰モ
デル関数の係数と現行車毎の商品特性との関係をモデル
化する（ステップ１０４）。現行車毎の商品特性は、現
行車特性データベース２０に予め登録されている。現行
車特性データベース２０のデータ構成例を図９に示す。
商品特性というのは、商品である車両の特徴を表す値で
ある。本実施の形態では、新車を購入した顧客に購入し
た新車に対する評価を各項目それぞれ５点満点でしても
らい、それを車種毎に平均した値を各車種の評価点とし
て求める。その評価点を各車種の商品特性として用いて
いる。Next, the relational model function generation processing unit 8 models the relation between the coefficients of the residual value attenuation model function stored in the residual value attenuation model database 18 and the product characteristics of each current vehicle (step 104). . The product characteristics for each current vehicle are registered in the current vehicle characteristics database 20 in advance. FIG. 9 shows a data configuration example of the current vehicle characteristic database 20.
The merchandise characteristics are values representing characteristics of a vehicle as a merchandise. In the present embodiment, a customer who has purchased a new car is asked to give an evaluation of the new car that has been purchased, with a maximum of 5 points for each item, and an average value for each model is determined as an evaluation point for each model. The evaluation points are used as product characteristics of each model.
【００２５】本実施の形態では、モデル化するために以
下の関数を用いる。In the present embodiment, the following functions are used for modeling.
【００２６】[0026]
【数２】 但し、Ｕはモデルセンター数（隠れ層の個数）、λi jは
ＲＡＤ係数（隠れ層と出力層の重み係数）、λ0 jは定
数、ｘは入力ベクトル（動態評価点の値）、ｃiはセン
ター重みベクトル、φは隠れ層の出力関数、yjは出力値
（残価率減衰パターンモデルのパラメータ値）、ｉはセ
ンター番号、ｊはレンジ次数（出力数）である。(Equation 2) However, (a value of kinetic evaluation point) U model center number (the number of hidden layers), lambda i j is RAD coefficient (weight coefficient of the hidden layer and the output layer), lambda 0 j is a constant, x is the input vector, c i is the center weight vector, φ is the output function of the hidden layer, y j is the output value (parameter value of the residual value decay pattern model), i is the center number, and j is the range order (the number of outputs).
【００２７】つまり、本実施の形態では、ニューロの学
習の仕組みを利用してモデル化する。具体的には、式
（４）のyj（ｊ＝１，２，３）に車種別の各係数α，
β，ｃを、ｘに現行車の商品特性を表すスタイル、加速
性能等の各項目をそれぞれ代入し学習させる。この式
（４）に全現行車の商品特性（評価点）を代入し学習さ
せることによりＵ、λi j、λ0 j、ｃiを計算する。That is, in the present embodiment, modeling is performed using the mechanism of learning of neurons. More specifically, y j (j = 1, 2, 3) in equation (4) is used to calculate each coefficient α,
β and c are substituted by x for each item such as a style representing the product characteristics of the current vehicle, acceleration performance, and the like, and are learned. U By substituting learn all current cars product characteristics (evaluation point) in the equation (4), λ i j, λ 0 j, calculates the c i.
【００２８】学習により関数モデル式が求まると、残価
予測処理部１０は、評価対象車の残価減衰モデル関数に
基づき当該評価対象車の将来の残価率を算出する（ステ
ップ１０５）。残価率の計算は、新車と現行車とで多少
異なる。現在においてまだ販売されていない新車の残価
率は、次のようにして計算する。When the function model formula is obtained by learning, the residual value prediction processing unit 10 calculates the future residual value rate of the evaluation target vehicle based on the residual value decay model function of the evaluation target vehicle (step 105). The calculation of the residual value ratio differs slightly between new and current vehicles. The residual value rate of a new car that is not yet sold at present is calculated as follows.
【００２９】図１０は、本実施の形態における新車特性
データベース２２のデータ構成例を示した図であるが、
新車の残価率は、新車の評価点に基づき求める。現行車
毎の評価点は、実際に現行車を購入した顧客により計算
されるのに対し、新車の評価点は、新車の開発者により
指定される。新車は、まだ販売されていないからであ
る。残価予測処理部１０は、指定された新車の評価点を
式（４）に代入することで係数α，β，ｃを算出する。
そして、各係数を式（２）に代入した残価減衰モデル関
数の変数である経過月数ｘα及び発売年式ｘβを入力す
る。例えば、２０００年に購入した車両の残価率を１年
毎に求めることにすると、ｘβを＝２０００、ｘα＝１
２，２４，３６，４８，６０をそれぞれ代入して各年経
過後における残価率を算出する。このようにして、残価
率を求めたい将来の査定時点における残価率を算出する
と、この算出結果を予測残価率データベース２４に格納
する。図１１に本実施の形態における予測残価率データ
ベース２４のデータ構成例を示す。FIG. 10 is a diagram showing an example of the data structure of the new vehicle characteristic database 22 in the present embodiment.
The residual value rate of a new car is determined based on the evaluation points of the new car. The evaluation score for each current vehicle is calculated by the customer who actually purchased the current vehicle, while the evaluation score for a new vehicle is specified by the developer of the new vehicle. New cars have not been sold yet. The residual value prediction processing unit 10 calculates coefficients α, β, and c by substituting the designated new vehicle evaluation points into the equation (4).
Then, the number of elapsed months xα and the release year formula xβ, which are variables of the residual value decay model function in which each coefficient is substituted into the equation (2), are input. For example, when the residual value rate of a vehicle purchased in 2000 is determined every year, xβ = 2000, xα = 1
2, 24, 36, 48, and 60 are substituted to calculate the residual value rate after each year. When the residual value rate at the time of the future assessment for which the residual value rate is to be obtained is calculated in this way, the calculation result is stored in the predicted residual value rate database 24. FIG. 11 shows a data configuration example of the predicted residual value rate database 24 in the present embodiment.
【００３０】なお、式（４）の残価減衰モデル関数は車
種毎に生成されているので、新車の残価率予測の計算に
用いる際には複数ある残価減衰モデル関数の中から一つ
の関数を特定する必要があるが、本実施の形態では、新
車の商品特性に最も類似する商品特性を持つ現行車種の
残価減衰モデル関数を用いることにする。より具体的に
は、同一出願人による特願平１１−２４０１８号に記載
された方法を用い、新車と同一車両分類に属する車種の
関数を用いればよい。Since the residual value decay model function of the equation (4) is generated for each vehicle type, one of the plurality of residual value decay model functions is used when calculating the residual value decay prediction of a new vehicle. Although it is necessary to specify the function, in the present embodiment, a residual value decay model function of the current vehicle type having the product characteristics most similar to the product characteristics of the new vehicle is used. More specifically, a method described in Japanese Patent Application No. 11-24018 filed by the same applicant may be used, and a function of a vehicle type belonging to the same vehicle classification as a new vehicle may be used.
【００３１】一方、現行車の場合はステップ１０３にお
いて各車種の係数α，β，ｃは算出されているので、残
価率を求めるべき車種（例えば車種Ａ）の係数を式
（２）に代入した残価減衰モデル関数の各変数ｘα，ｘ
βに適当な値を入力することで将来の査定時点における
車種Ａの残価率を算出する。On the other hand, in the case of the current vehicle, since the coefficients α, β, and c of each vehicle type have been calculated in step 103, the coefficient of the vehicle type (for example, vehicle type A) for which the residual value rate is to be obtained is substituted into the equation (2). Variables xα, x of the residual value decay model function
By inputting an appropriate value to β, the residual value rate of the vehicle type A at the time of the future assessment is calculated.
【００３２】本実施の形態によれば、各車種の商品特性
（評価点）を用いて評価点と残価率で表した各車両の評
価価値の落ち具合との関係を指数関数によりモデル化
し、そのモデル化した残価減衰モデル関数に基づき残価
率を算出するようにしたので、残価率の予測精度をより
向上させることができる。また、現行車のみならず現時
点においてまだ販売されていない新車に対しても将来に
おける価値を精度良く予測することができる。According to the present embodiment, the relationship between the evaluation points and the degree of decrease in the evaluation value of each vehicle expressed by the residual value rate using the product characteristics (evaluation points) of each vehicle model is modeled by an exponential function. Since the residual value rate is calculated based on the modeled residual value decay model function, the prediction accuracy of the residual value rate can be further improved. Further, it is possible to accurately predict the future value of not only the current vehicle but also a new vehicle that has not yet been sold at the present time.
【００３３】なお、本実施の形態では、車両の評価を残
価率によって表すようにしたが、残価率に基づき算出で
きる販売価格等によっても行うことができる。In the present embodiment, the evaluation of the vehicle is represented by the residual value rate. However, the evaluation can be performed based on the sales price or the like which can be calculated based on the residual value rate.
【００３４】また、上記各実施の形態では、商品として
車両を例にして説明したが、他の商品にも適用できるこ
とはいうまでもない。Further, in each of the above-described embodiments, the vehicle has been described as an example of a product, but it is needless to say that the present invention can be applied to other products.
【００３５】[0035]
【発明の効果】本発明によれば、商品特性を考慮するよ
うにしたので、将来の査定時点における商品の評価をよ
り正確に行うことができる。また、既販商品のみなら
ず、現時点においてまだ販売されていない商品の将来の
査定時点における評価をも行うことができる。According to the present invention, since the characteristics of the product are taken into consideration, the product can be more accurately evaluated at the time of future assessment. In addition, it is possible to evaluate not only products already sold but also products not yet sold at the present time at the time of future assessment.
【００３６】また、既販商品毎の基準評価率の遷移パタ
ーンを表す残価減衰モデルを指数関数で表すようにした
ので、将来の査定時点における評価をより正確に行うこ
とができる。Further, since the residual value decay model representing the transition pattern of the reference evaluation rate for each sold product is represented by an exponential function, the evaluation at the time of future assessment can be performed more accurately.
【００３７】また、各商品の評価として算出する残価率
の予測精度をより向上させることができる。Further, the accuracy of predicting the residual value calculated as the evaluation of each product can be further improved.
【図１】 本発明に係る商品価値評価装置を適用した中
古車残価予測支援装置の一実施の形態を示したブロック
構成図である。FIG. 1 is a block diagram showing an embodiment of a used vehicle residual value prediction support device to which a commercial value evaluation device according to the present invention is applied.
【図２】 本実施の形態における中古車データベースの
データ構成例を示した図である。FIG. 2 is a diagram illustrating a data configuration example of a used car database according to the present embodiment.
【図３】 本実施の形態における残価率補正用データベ
ースに格納されている走行距離査定テーブルの例を示し
た図である。FIG. 3 is a diagram showing an example of a traveling distance assessment table stored in a residual value rate correction database according to the present embodiment.
【図４】 本実施の形態における残価率補正用データベ
ースに格納されている車検残テーブルの例を示した図で
ある。FIG. 4 is a diagram illustrating an example of a vehicle inspection residual table stored in a residual value rate correction database according to the present embodiment.
【図５】 本実施の形態における残価予測処理を示した
フローチャートである。FIG. 5 is a flowchart showing a residual value prediction process in the present embodiment.
【図６】 本実施の形態において中古車の基本残価率を
販売月毎に表示したときの例を示した図である。FIG. 6 is a diagram showing an example when a basic residual value rate of a used car is displayed for each sales month in the present embodiment.
【図７】 図６に残価減衰モデル関数を付加して表示し
たときの例を示した図である。FIG. 7 is a diagram showing an example when a residual value decay model function is added to FIG. 6 and displayed.
【図８】 本実施の形態における残価減衰モデルデータ
ベースのデータ構成例を示した図である。FIG. 8 is a diagram illustrating a data configuration example of a residual value decay model database according to the present embodiment.
【図９】 本実施の形態における現行車特性データベー
スのデータ構成例を示した図である。FIG. 9 is a diagram showing a data configuration example of a current vehicle characteristic database according to the present embodiment.
【図１０】 本実施の形態における新車特性データベー
スのデータ構成例を示した図である。FIG. 10 is a diagram illustrating a data configuration example of a new vehicle characteristic database according to the present embodiment.
【図１１】 本実施の形態における予測残価率データベ
ースのデータ構成例を示した図である。FIG. 11 is a diagram illustrating a data configuration example of a predicted residual value database according to the present embodiment.
２ 中古車残価予測支援装置、４ 基本残価率算出処理
部、６ 残価減衰モデル生成処理部、８ 関係モデル関
数生成処理部、１０ 残価率予測処理部、１２中古車デ
ータベース、１４ 残価率補正用データベース、１６
基本残価率データベース、１８ 残価減衰モデルデータ
ベース、２０ 現行車特性データベース、２２ 新車特
性データベース、２４ 予測残価率データベース。2 used vehicle residual value prediction support device, 4 basic residual value rate calculation processing unit, 6 residual value attenuation model generation processing unit, 8 relational model function generation processing unit, 10 residual value rate prediction processing unit, 12 used vehicle database, 14 residual Database for rate correction, 16
Basic residual value database, 18 residual value decay model database, 20 current vehicle characteristic database, 22 new vehicle characteristic database, 24 predicted residual value database.
フロントページの続き (72)発明者 茅根 康夫 愛知県豊田市トヨタ町１番地 トヨタ自動 車株式会社内 Ｆターム(参考） 5B049 BB16 CC08 CC11 CC36 DD01 EE03 EE12 EE14 EE41 FF03 FF04 Continued on the front page (72) Inventor Yasuo Chine 1 Toyota Town, Toyota City, Aichi Prefecture Toyota Motor Corporation F-term (reference) 5B049 BB16 CC08 CC11 CC36 DD01 EE03 EE12 EE14 EE41 FF03 FF04
Claims (4)
定要因を除去する補正用データに基づいて既販商品の査
定要因に影響されない基準評価率を算出する既販商品評
価率算出処理手段と、 販売が開始されてからの時間経過に伴い変化する既販商
品毎の基準評価率の遷移パターンを残価減衰モデルの関
数として生成する残価減衰モデル生成処理手段と、 評価対象商品の残価減衰モデル関数に基づき将来の査定
時点における評価を行う商品評価処理手段と、 を有することを特徴とする商品価値評価装置。1. A sold product evaluation rate calculation processing means for calculating a reference evaluation rate which is not influenced by an evaluation factor of a sold product based on a sales result of the sold product and correction data for removing an evaluation factor from the sales result, and A residual value decay model generation processing means for generating a transition pattern of a reference valuation rate for each sold product that changes with time since the start of sales as a function of the residual value decay model; Commodity evaluation processing means for evaluating at the time of a future assessment based on the attenuation model function.
て、 残価減衰モデル関数の係数と既販商品の商品特性との関
係をモデル化する関係モデル関数生成処理手段を有し、 前記商品評価処理手段は、前記関係モデル関数生成処理
手段が生成したモデル関数により現時点においてまだ販
売されていない新商品の残価減衰モデル関数の係数を算
出し、当該新商品の将来の査定時点における評価を行う
ことを特徴とする商品価値評価装置。2. The merchandise value evaluation device according to claim 1, further comprising a relation model function generation processing means for modeling a relation between a coefficient of a residual value decay model function and merchandise characteristics of a sold merchandise, The processing means calculates a coefficient of a residual value decay model function of a new product which is not yet sold at the present time by the model function generated by the relation model function generation processing means, and evaluates the new product at a future assessment time. A merchandise value evaluation device characterized in that:
て、 残価減衰モデル関数は指数関数であることを特徴とする
商品価値評価装置。3. The merchandise value evaluation device according to claim 1, wherein the residual value decay model function is an exponential function.
値評価装置において、 前記商品評価処理手段は、商品が新規に購入されたとき
の価格に対する当該商品が中古として購入されたときの
価格の比率である残価率によって当該商品を評価するこ
とを特徴とする商品価値評価装置。4. The merchandise value evaluation device according to claim 1, wherein the merchandise evaluation processing means is configured such that a price when the merchandise is newly purchased is compared with a price when the merchandise is newly purchased. A merchandise value evaluation device for evaluating the merchandise according to a residual value rate that is a ratio of
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