JP2006085501A - Real estate price evaluation system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a real estate price evaluation system allowing a person having no special knowledge/experience about real estate to efficiently calculate an optimum evaluation price without asking investigation of an evaluation property from a surveyor. <P>SOLUTION: This real estate price evaluation system calculating an evaluation price of a real estate property at a specific address is provided with an evaluation price calculation means calculating the evaluation price of the real estate property at the inputted address by multiplying a reference price by a factor coefficient value (first error rate). <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention provides a real estate price evaluation system capable of calculating an efficient and optimal evaluation price without requesting an appraiser to investigate an evaluation property, even for a person who does not have specialized knowledge or experience of real estate. About.

  Conventionally, the price of a real estate property (for example, a detached house, a condominium, land) is calculated by an appraiser such as a real estate appraiser.

  The appraiser investigates the real estate property to be evaluated (hereinafter referred to as an evaluation property) and calculates a price expected to be actually sold (hereinafter referred to as an evaluation price). In addition, since the appraiser has specialized knowledge and experience of real estate, it is possible to calculate an optimum evaluation price with high reliability. For example, the appraiser can buy and sell the evaluation property, neighboring real estate properties, and conditions affecting the real estate property in the area (eg, municipality) where the evaluation property is included (for example, land area, nearest station distance, Evaluation price is calculated with reference to width, city planning area).

Meanwhile, the Ministry of Land, Infrastructure, Transport and Tourism, prefectures, etc. publish in the official gazette as an objective guideline a public price (a sale example etc.) indicating the price of a real estate property. The published price can also be viewed in newspapers, the Internet, mobile terminals (mobile phones), etc. (for example, Patent Document 1). As a result, even those who do not have specialized knowledge or experience in real estate can predict the evaluation price with reference to the published price.
JP 2002-278877 A

  However, the conventional method in which an appraiser surveys real estate properties and calculates the price of real estate properties is expensive because it requires the appraiser to investigate the evaluation properties and calculate the evaluation price. There was a problem that it took time to calculate the price. In addition, the published price published in the official gazette has a problem that it is not easy to analyze the individual elements of the published price and compare it with the valued property, so that the optimum price is not obtained.

 Therefore, the present invention has been made to solve the above-mentioned problems, and even if there is no expert knowledge or experience of real estate, the present invention can be efficiently performed without asking an appraiser to investigate the property to be evaluated. An object of the present invention is to provide a real estate price evaluation system capable of calculating an optimal evaluation price.

  A first feature of the present invention is a real estate price evaluation system that calculates an evaluation price of a real estate property at a specific address. The real estate price evaluation system associates an area including an address with a reference price of the area. First storage means for storing (for example, reference price DB 21d), first condition information indicating a condition affecting the evaluation price of the real estate property, and a first error rate indicating an error rate of the actual price of the real estate property with respect to the reference price And a second storage means (for example, common factor DB 21f or area factor DB 21g) and a reference value extraction means (for example, a standard price extraction means for extracting a reference price corresponding to the area including the input address from the first storage means) A reference price calculation unit 23) and an error rate extracting unit that extracts a first error rate corresponding to the first condition information relating to the real estate at the input address from the second storage means. (E.g., factor coefficient value calculation unit 24) and an evaluation price for calculating the evaluation price of the real estate at the input address by multiplying the extracted reference price by the extracted first error rate The gist is to include a calculation means (for example, an evaluation price calculation unit 25).

  According to such a feature, the evaluation price calculation unit multiplies the reference price based on the input address by the first error rate of the actual price of the real estate property relative to the reference price, thereby evaluating the real estate property by the reference price. Since conditions that affect the price can be reflected, it is possible to calculate the optimum evaluation price of the real estate property at the input address.

  According to a second feature of the present invention, in the first feature of the present invention, the real estate price evaluation system associates and stores the published price of the real estate property in the area and the first condition information relating to the real estate property. Storage means (for example, published price DB 21a), fourth storage means (for example, factor DB 21e) for storing second condition information indicating the first condition information applied in the area, and the area extracted from the third storage means An average published price calculation means that calculates the average published price that shows the average value of the published price of real estate properties in the area, and a published error rate that indicates the error rate of the published price with respect to the average published price for each real estate property in the area The real estate property is grouped based on the public error rate of the real estate property for each second condition information extracted from the fourth storage means in the notification error rate calculation means and the area A first grouping means, an average error rate calculating means for calculating an average error rate indicating an average value of an announcement error rate of the real estate property in each group for each second condition information, and an announced price of each real estate property in the area The property standard price calculation means for calculating the property standard price of each real estate property by dividing by the average error rate of the group to which the property property belongs, and the average property standard price calculated for the real estate property in the area as the standard price The gist is to include a reference price calculation means (for example, a reference price calculation unit 23) for calculating a value.

 According to this feature, it is possible to calculate a reference price from which conditions affecting real estate properties are removed.

  A third feature of the present invention is that, in the first feature of the present invention, the real estate price evaluation system stores fourth condition information indicating the first condition information applied in the area (for example, factor) DB 21e), fifth storage means (for example, actual price DB 21b) for associating and storing the actual price of the real estate property in the area and the first condition information relating to the real estate property, The actual error rate calculating means for calculating the actual error rate indicating the error rate of the reference price with respect to the actual price extracted from the storage means, and the real estate property for each second condition information extracted from the fourth storage means in the area The second grouping means for grouping the real estate properties based on the actual error rate, and the actual status of the real estate property in each group for each second condition information as the first error rate Error calculating means for calculating an average value of Saritsu (e.g., factor coefficient value calculating unit 24) to increase the and a.

 According to this feature, it is possible to calculate the first error rate for reflecting the condition affecting the real estate property on the reference price.

 According to a fourth feature of the present invention, in the second or third feature of the present invention, the first grouping means uses a sequential two-branch decision tree, which is a statistical method, or a regression analysis to store the fourth storage means. The gist is to group real estate properties based on the public error rate of the real estate properties for each second condition information extracted from.

 According to this feature, the first grouping means uses a sequential bifurcated decision tree that is a statistical method or a regression analysis to quantitatively clarify the trends and properties in the group in a unified manner. It is possible to classify each real estate property.

 According to a fifth feature of the present invention, in the second or third feature of the present invention, the second grouping means uses a sequential two-branch decision tree, which is a statistical method, or a regression analysis, and a fourth storage means. The gist is to group real estate properties based on the actual error rate of the real estate properties for each second condition information extracted from.

 According to such a feature, the second grouping means uses a sequential bifurcation decision tree that is a statistical method or a regression analysis to quantitatively clarify the trends and properties in the group in a unified manner. It is possible to classify each real estate property.

 A sixth feature of the present invention is the first to third features of the present invention, wherein the first condition information includes “land area”, “city planning area”, “width”, “gas / sewage”, “closest” "Station distance", "Shape", "Road orientation", "Contact road situation", "Aversion facility", "Road certification", "Road pavement situation", "Volume ratio", "Building age", "Floor area" It must be a condition for at least one of “Building Floors”, “Residence”, “Veranda Orientation”, “Floor Layout”, “Management Office”, “Northward”, “Auto Lock”, “Top Floor” The gist.

 According to this feature, conditions that affect real estate properties can be set finely for each land, detached house (with land) or apartment.

  According to the present invention, even if there is a person who has no specialized knowledge or experience of real estate, it is possible to calculate an efficient and optimal evaluation price without requesting an appraiser to investigate the evaluation property. An evaluation system can be provided.

[Embodiment 1]
(Configuration for realizing the real estate price evaluation system according to the present embodiment)
A configuration of the real estate system 1 according to the present embodiment will be described with reference to FIGS. 1 to 8. In the present embodiment, the evaluation server 15 constitutes a real estate price evaluation system that calculates an evaluation price of a real estate property at a specific address.

 FIG. 1 is a schematic configuration diagram of a real estate system 1 according to the present embodiment. As shown in the figure, the real estate system 1 includes a sales department terminal 10, a screening department terminal 11, published price data 12, actual price data 13, and an evaluation server 15.

 The sales department terminal 10 is a terminal device that inputs various information such as the address of the real estate property and the respective conditions affecting the real estate property in order for the sales staff member to calculate the evaluation price. The sales department terminal 10 is connected to the evaluation server 15 via a communication network (for example, the Internet or a telephone line).

 The examination unit terminal 11 is a terminal device that confirms various information such as the address of the real estate property and the respective conditions affecting the real estate property in order for the examination member to calculate the evaluation price. The examination unit terminal 11 is connected to the evaluation server 15 via a communication network.

 The published price data 12 includes the published land price of the real estate property that is published in the official gazette as an objective measure of the price of the real estate property by the Ministry of Land, Infrastructure, Transport and Tourism or prefectures. In addition, the official land price of real estate properties published in the official gazette can be viewed on newspapers and the Internet. The published price data 12 in this case is used when calculating the evaluation price of land and a detached real estate property (with land).

 Further, the published price data 12 may include a published price such as a price of a real estate property (a sale example etc.) actually bought and sold. The published price data 12 in this case is used when calculating the evaluation price of the real estate property of the apartment.

 The actual price data 13 includes actual land prices such as the prices of real estate properties actually sold (such as sales examples).

 The evaluation server 15 is a real estate price evaluation system that calculates an evaluation price of a real estate property at a specific address. The evaluation server 15 is connected to the sales department terminal 10 and the examination department terminal 11 via a communication network. Specifically, this will be described with reference to FIG.

 Below, the evaluation server 15 which concerns on this embodiment is demonstrated. FIG. 2 is a diagram showing details of the evaluation server 15 according to the present embodiment.

  As shown in the figure, the evaluation server 15 includes a storage unit 21, a reception unit 22, a reference price calculation unit 23, a factor coefficient value calculation unit 24, an evaluation price calculation unit 25, and a transmission unit 26. To do.

  The storage unit 21 includes a published price DB 21a (FIG. 3A), an actual price DB 21b (FIG. 3B), a base coefficient value DB 21c (FIG. 4), a base price DB 21d (FIG. 5), and a factor DB 21e. (FIG. 6), common factor DB 21f (FIG. 7), and area factor DB 21g (FIG. 8) are stored.

 As shown in FIG. 3 (a), the published price DB 21a is a third storage means for storing the published land price in the vicinity of the real estate property in the area including the specific address in association with the first condition information relating to the real estate property. Configure. Here, the first condition information indicates conditions regarding factors such as “land area”, “city planning area”, “width”, “gas / sewage”, “nearest station distance”, and the like. In other words, the condition indicates what value the factor takes.

 Specifically, as shown in FIG. 3 (a), the published price DB 21a includes a "property ID", "address", "published land price", "land area", "city planning area", " The “width”, “gas / sewage”, and “nearest station distance” are stored in association with each other.

 “Property ID” indicates information for identifying a real estate property such as land or building, and “Address” indicates the location or residence of the real estate property identified by the property ID.

The “public land price” indicates an objective property price announced by the Ministry of Land, Infrastructure, Transport and Tourism or prefecture. The “public land price” indicates the price per 1 m ^{2 of the} real estate at each address.

 “Land area” indicates the land area of the real estate property, and “City planning area” is not only whether or not the real estate property is the target area for city planning, If it is a target area, it indicates which area is an urbanization area, an urbanization adjustment area, a non-drawn city planning area, or an outside / semi-city planning area.

 “Width” indicates the width of the road in contact with the property (for example, on the front side), and “Gas / sewage” indicates that the property is provided with city gas and public sewage. It indicates whether or not there is.

 “Nearest station distance” indicates the distance from the property to the nearest station.

For example, as shown in the figure, for a real estate property with “Property ID = A1”, “Address” is A-ku, Sapporo City, Hokkaido, XXX1-1-1, and “Notified Land Price” is 360,000 yen. is in, "acreage" is 99m ^2, "city planning area" is the urban areas, "width" is 6m, "gas and sewage" is there is a gas and sewage, "the nearest station distance Is 1800m to Kita 24jo Station.

 As shown in FIG. 3B, the actual price DB 21b constitutes fifth storage means for storing the actual land price of the real estate property in the area including the specific address in association with the first condition information related to the real estate property. To do. Here, the first condition information indicates conditions regarding factors such as “land area”, “city planning area”, “width”, “gas / sewage”, “nearest station distance”, and the like.

 Specifically, as shown in FIG. 3 (b), the actual price DB 21 b includes the “property ID”, “address”, “real land price”, “land area”, “city planning area”, “ The “width”, “gas / sewage”, and “nearest station distance” are stored in association with each other. For “Property ID”, “Address”, “Land area”, “City Planning Area”, “Width”, “Gas / Sewage”, and “Nearest station distance”, see FIG. It is the same as the contents shown in.

The “real land price” indicates the price of a real estate property (sales example, etc.) actually bought and sold. The “real land price” indicates the price per 1 m ^{2 of the} real estate at each address.

For example, as shown in the figure, for a real estate property with “Property ID = A1”, “Address” is A-ku, Sapporo City, Hokkaido, 1-1-1 and “Real Land Price” is 340,000 yen. is in, "acreage" is 99m ^2, "city planning area" is the urban areas, "width" is 6m, "gas and sewage" is there is a gas and sewage, "the nearest station distance Is 1800m to Kita 24jo Station.

 As shown in FIG. 4, the reference coefficient value DB 21 c includes first condition information indicating conditions that affect the evaluation price of the real estate property for each group, and a reference coefficient value calculated by the reference price calculation unit 23 described later. It is related and memorized. Here, the first condition information indicates conditions regarding factors such as “land area”, “city planning area”, “width”, “nearest station distance”, and the like.

 FIG. 4A is a table that stores first condition information indicating a condition for “land area” and a reference coefficient value in association with each other.

Specifically, as shown in the figure, Group 1 includes real estate properties that satisfy the condition that “the land area is less than 100 m ² ”, and the reference coefficient value of the real estate properties is 0.85. . Group 2 includes a real estate property that satisfies the condition that “the land area is 100 m ² or more and less than 200 m ² ”, and the reference coefficient value of the real estate property is 0.95. Group 3 includes a real estate property that satisfies the condition that “the land area is 200 m ² or more and less than 300 m ² ”, and the reference coefficient value of the real estate property is 1.05. Group 4 includes real estate properties that satisfy the condition that “the land area is 300 m ² or more”, and the standard coefficient value of the real estate properties is 1.15.

FIG. 4B is a table for storing the first condition information indicating the condition for the “city planning area” and the reference coefficient value in association with each other.

 Specifically, as shown in the figure, Group 1 includes real estate properties that satisfy the condition that “the city planning area is an urbanized area”, and the standard coefficient value of the real estate property is 1.03. It is. Group 2 includes a real estate property that satisfies the condition that “the city planning area is an urbanization adjustment area”, and the standard coefficient value of the real estate property is 0.86. Group 3 includes a real estate property that satisfies the condition that “the city planning area is a non-drawn city planning area”, and the reference coefficient value of the real estate property is 1.01. Group 4 includes a real estate property that satisfies the condition that “the city planning area is an out-of-area / quasi-city planning area”, and the reference coefficient value of the real estate property is 0.82.

 FIG. 4C is a table for storing the first condition information indicating the condition for “width” and the reference coefficient value in association with each other.

 Specifically, as shown in the figure, Group 1 includes real estate properties that satisfy the condition that “the width is less than 4 m”, and the reference coefficient value of the real estate properties is 0.98. Group 2 includes a real estate property that satisfies the condition that “the width is 4 m or more and less than 8 m”, and the reference coefficient value of the real estate property is 0.95. Group 3 includes a real estate property that satisfies the condition that “the width is 8 m or more and less than 12 m”, and the reference coefficient value of the real estate property is 1.05. Group 4 includes a real estate property that satisfies the condition that “the width is 12 m or more”, and the reference coefficient value of the real estate property is 1.12.

 FIG. 4D is a table that stores the first condition information indicating the condition for the “nearest station distance” and the reference coefficient value in association with each other.

 Specifically, as shown in the figure, Group 1 includes a real estate property that satisfies the condition that “the nearest station distance is less than 1000 m”, and the standard coefficient value of the real estate property is 1.21. It is. Group 2 includes a real estate property that satisfies the condition that “the nearest station distance is 1000 m or more and less than 1500 m”, and the reference coefficient value of the real estate property is 1.02. Group 3 includes real estate properties that satisfy the condition that “the nearest station distance is 1500 m or more and less than 2000 m”, and the reference coefficient value of the real estate property is 1. Group 4 includes a real estate property that satisfies the condition that “the nearest station distance is 2000 m or more”, and the reference coefficient value of the real estate property is 0.98.

 The reference coefficient value DB 21c may be configured to include a table that associates conditions for other factors with reference coefficient values.

 As shown in FIG. 5, the reference price DB 21d constitutes a first storage unit that stores an area including a specific address in association with a reference price of the area. Here, the specific area including the address is “Hokkaido”, “Tohoku”, “Kita Kanto Koshinetsu”, “Metropolitan area”, “Tokai”, “Kinki”, “Hokuriku Sanin”, “Chu-Shikoku / Kyushu” , An area such as a municipality included in each of a wide area such as “Okinawa”.

 Specifically, the reference price DB 21d stores an area included in each of the wide area areas and a reference price calculated by a reference price calculation unit 23 described later in association with each other. As shown in the figure, the base price is common within each area.

 For example, as shown in the figure, the base price DB 21d stores Hakodate City XX town in the area of Hokkaido in the wide area 1 and 20 that is the base price in association with each other.

 As illustrated in FIG. 6, the factor DB 21 e configures a fourth storage unit that stores second condition information indicating first condition information applied in an area including a specific address. Here, the first condition information indicates conditions for “shape”, “road direction”, “close road condition”, “disgusting facility”, “road pavement condition”, and “volume ratio”. The two-condition information indicates whether each of “shape”, “road direction”, “closed road condition”, “disgusting facility”, “road pavement condition”, and “volume ratio” is applied in the wide area. Is. In addition, the factor DB 21e does not indicate “land area”, “city planning area”, “width”, “nearest station distance”, and “gas / sewage” that are applied in all wide areas.

  The “shape” indicates the shape of the land of the real estate property at the specific address, and the “road direction” indicates the direction of the road in contact with the real estate property (for example, on the front side).

 “Roadway status” indicates whether there is a convenient road (for example, a main road) around the real estate property, and the “disgusting facility” indicates that the residents are uncomfortable around the real estate property. This indicates whether there is a facility to think about.

 “Road pavement status” indicates whether the road surface is constructed of concrete or asphalt to increase the durability of the road surface. “Volume ratio” is the volume of the real estate property and the volume of the land. It shows the ratio ratio.

  For example, as shown in the figure, “shape”, “road direction”, “disgusting facility”, and “road pavement status” applied in Hokkaido in the wide area 1 are stored in association with each other.

 As shown in FIG. 7, the common factor DB 21f associates the first condition information indicating the condition affecting the evaluation price of the real estate property with the first error rate indicating the error rate of the actual price of the real estate property with respect to the reference price. The second storage means for storing is configured. Here, the first condition information indicates conditions regarding factors such as “land area”, “city planning area”, “width”, “nearest station distance”, and the like. The first error rate is a factor coefficient value calculated by a factor coefficient value calculation unit 24 described later.

 FIG. 7A is a table that stores first condition information indicating a condition for “land area” and factor coefficient values in association with each other.

Specifically, as shown in the figure, Group 1 includes real estate properties that satisfy the condition that “the land area is less than 100 m ² ”, and the factor coefficient value of the real estate property is 0.93. . Group 2 includes real estate properties that satisfy the condition that “the land area is 100 m ² or more and less than 200 m ² ”, and the factor coefficient value of the real estate properties is 0.98. Group 3 includes a real estate property that satisfies the condition that “the land area is 200 m ² or more and less than 300 m ² ”, and the factor coefficient value of the real estate property is 1.02. Group 4 includes a real estate property that satisfies the condition that “the land area is 300 m ² or more”, and the factor coefficient value of the real estate property is 1.09.

 FIG. 7B is a table for storing the first condition information indicating the condition for the “city planning area” and the factor coefficient value in association with each other.

 Specifically, as shown in the figure, Group 1 includes real estate properties that satisfy the condition that “the city planning area is an urbanized area”, and the factor coefficient value of the real estate property is 1.02. It is. Group 2 includes a real estate property that satisfies the condition that “the city planning area is an urbanization adjustment area”, and the factor coefficient value of the real estate property is 0.82. Group 3 includes a real estate property that satisfies the condition that “the city planning area is a non-drawn city planning area”, and the factor coefficient value of the real estate property is 1.02. Group 4 includes a real estate property that satisfies the condition that “the city planning area is an out-of-area / quasi-city planning area”, and the factor coefficient value of the real estate property is 0.81.

 FIG. 7C is a table for storing the first condition information indicating the condition for “width” and the factor coefficient value in association with each other.

 Specifically, as shown in the figure, group 1 includes a real estate property that satisfies the condition that “the width is less than 4 m”, and the factor coefficient value of the real estate property is 0.85. Group 2 includes a real estate property that satisfies the condition that “the width is 4 m or more and less than 8 m”, and the factor coefficient value of the real estate property is 1.03. Group 3 includes a real estate property that satisfies the condition that “the width is 8 m or more and less than 12 m”, and the factor coefficient value of the real estate property is 1.07. Group 4 includes a real estate property that satisfies the condition that “the width is 12 m or more”, and the factor coefficient value of the real estate property is 1.13.

 FIG. 7D is a table that stores the first condition information indicating the condition for “nearest station distance” and the factor coefficient value in association with each other.

 Specifically, as shown in the figure, Group 1 includes real estate properties that satisfy the condition that “the nearest station distance is less than 1000 m”, and the factor coefficient value of the real estate property is 1.11. It is. Group 2 includes a real estate property that satisfies the condition that “the nearest station distance is 1000 m or more and less than 1500 m”, and the factor coefficient value of the real estate property is 1.07. Group 3 includes a real estate property that satisfies the condition that “the nearest station distance is 1500 m or more and less than 2000 m”, and the factor coefficient value of the real estate property is 0.96. Group 4 includes a real estate property that satisfies the condition that “the nearest station distance is 2000 m or more”, and the factor coefficient value of the real estate property is 0.89.

 The common factor DB 21f may be configured to include a table associating conditions for other factors with factor coefficient values.

 As shown in FIG. 8, the area factor DB 21g associates the second condition information indicating the condition affecting the evaluation price of the real estate property and the first error rate indicating the error rate of the actual price of the real estate property with respect to the reference price. To remember. Here, the second condition information indicates conditions regarding factors such as “shape”, “road direction”, “disgusting facility”, “road pavement situation”, and the like applied in each wide area. The first error rate is a factor coefficient value calculated by a factor coefficient value calculation unit 24 described later.

 FIG. 8A is a table for storing the second condition information indicating the condition for the “shape” and the factor coefficient value in association with each other.

 Specifically, as shown in the figure, group 1 includes a real estate property that satisfies the condition “the shape is square”, and the factor coefficient value of the real estate property is 1. Group 2 includes a real estate property that satisfies the condition “the shape is L-shaped”, and the factor coefficient value of the real estate property is 0.98. Group 3 includes a real estate property that satisfies the condition that the shape is rectangular, and the factor coefficient value of the real estate property is 1. Group 4 includes real estate properties that satisfy the condition that “the shape is other (for example, non-molded)”, and the factor coefficient value of the real estate property is 0.91.

 FIG. 8B is a table for storing the second condition information indicating the condition for the “road direction” and the factor coefficient value in association with each other.

 Specifically, as shown in the figure, group 1 includes a real estate property that satisfies the condition that “the road direction is east”, and the factor coefficient value of the real estate property is 1.01. Group 2 includes a real estate property that satisfies the condition that “the road direction is west”, and the factor coefficient value of the real estate property is 0.99.

 FIG. 8C is a table for storing the second condition information indicating the condition for the “disgusting facility” and the factor coefficient value in association with each other.

 Specifically, as shown in the figure, group 1 includes a real estate property that satisfies the condition of “having disgusting facilities”, and the factor coefficient value of the real estate property is 0.91. Group 2 includes a real estate property that satisfies the condition “no disgusting facility”, and the factor coefficient value of the real estate property is 1.

 FIG. 8D is a table for storing the second condition information indicating the condition for the “road pavement situation” and the factor coefficient value in association with each other.

 Specifically, as shown in the figure, group 1 includes real estate properties that satisfy the condition “there is road pavement”, and the factor coefficient value of the real estate property is 1. Group 2 includes real estate properties that satisfy the condition “no road paving”, and the factor value of the real estate property is 0.92.

 In addition, the area factor DB 21g may be configured to include a table that associates conditions for other factors with factor coefficient values.

 The receiving unit 22 receives various types of information (for example, an address and conditions that affect real estate properties) from the sales department terminal 10 and the examination department terminal 12 via the communication network. The receiving unit 22 receives the published price data 12 and the actual price data 13 via the communication network. The receiving unit 22 can also acquire the published price data 12 and the actual price data 13 using a storage medium or the like (for example, a CDROM).

 The reference price calculation unit 23 calculates a reference price that is a price obtained by removing a condition affecting a real estate property from a public land price of the real estate property at a specific address.

Specifically, the reference price calculation unit 23 calculates an average price indicating an average value per 1 m ² of the published price of the real estate property in each area extracted from the published price DB 21a of the storage unit 21.

 Moreover, the reference price calculation unit 23 divides the calculated average price for each real estate property in each area by the published price of each real estate property in each area, so that the reference price calculation unit 23 is in each area with respect to the average price in each area. Calculate a notification error rate indicating the error rate of the notification price of each real estate property.

  In addition, in each area, the reference price calculation unit 23 sequentially divides the real estate property into two based on the notification error rate of the real estate property for each factor applied to each wide area extracted from the factor DB 21e of the storage unit 21. Grouping is performed using a decision tree (hereinafter, C & RT).

  Further, the reference price calculation unit 23 calculates an average error rate (hereinafter referred to as a reference coefficient value) indicating an average value of the notification error rate of the real estate property in each group for each factor, and stores the calculated reference coefficient value data. 21 reference coefficient value DB 21c.

  Further, the reference price calculation unit 23 calculates the property reference price of each real estate property by dividing the published price of each real estate property in each area by the reference coefficient value in the group to which the real estate property belongs.

  Furthermore, the reference price calculation unit 23 calculates a reference price indicating the average value of the property reference prices calculated for each real estate property in each area, and stores the calculated reference price data in the reference price DB 21d of the storage unit 21. Remember.

  The factor coefficient value calculation unit 24 calculates a factor coefficient value indicating the first error rate based on the actual error rate indicating the error rate of the standard price of each area with respect to the actual price of each real estate property in each area. It is.

  Specifically, the factor coefficient value calculation unit 24 divides the actual price of each real estate property extracted from the actual price DB 21b of the storage unit 21 by the reference price in each area for each real estate property in each area. The actual error rate indicating the error rate of the reference price in each area with respect to the actual price of each real estate property is calculated.

  Further, the factor coefficient value calculation unit 24 performs C & RT for each real estate property based on the actual error rate of the real estate property for each factor applied to each wide area extracted from the factor DB 21e of the storage unit 21 in each area. Use to group.

  Further, the factor coefficient value calculating unit 24 calculates a factor coefficient value indicating an average value of the actual error rate of the real estate property in each group for each factor, and the calculated factor coefficient value data is shared with the common factor DB 21f of the storage unit 21. It memorize | stores in area factor DB21g of the memory | storage part 21. FIG.

 The evaluation price calculation unit 25 calculates the evaluation price based on the reference price at the input address and the factor coefficient value of each factor applied by the wide area including the input address.

 Specifically, the evaluation price calculation unit 25 extracts the reference price from the reference price DB 21d of the storage unit 21 based on the input address, and the factor DB 21e of the storage unit 21 based on the wide area including the input address. The necessary factor is extracted from the common factor DB 21f of the storage unit 21 and the area factor DB 21g of the storage unit 21 with reference to necessary factors.

 Further, the evaluation price calculation unit 25 multiplies the extracted reference price by the factor coefficient value of each extracted factor and the land area of the input address to thereby calculate the real estate property at the input address. Calculate the evaluation price.

 The transmission unit 26 transmits the evaluation price calculated by the evaluation price calculation unit 25 to the sales unit terminal 10 or the examination unit terminal 11 via the communication network.

(Operation of the real estate price evaluation system according to this embodiment)
Hereinafter, the operation of the reference price calculation unit 23 for realizing the real estate price evaluation system according to the present embodiment will be described with reference to FIGS. 9 and 10.

 FIG. 9 is a flowchart of the operation of the reference price calculation unit 23 according to the present embodiment. FIG. 10 is a diagram showing C & RT in grouping according to the present embodiment.

 As shown in FIG. 9, in step 901, the reference price calculation unit 23 extracts the published price DB 21 a from the storage unit 21.

In step 902, the reference price calculation unit 23 calculates an average price indicating an average value per 1 m ² of the published price of the real estate property in each area.

 In step 903, the reference price calculation unit 23 divides the calculated average price for each real estate property in each area by the published price of each real estate property in each area, so that each area with respect to the average price in each area Calculate a notification error rate indicating the error rate of the notification price of each real estate property.

  In step 904, the reference price calculation unit 23 sets each real estate property in each area based on the notification error rate of each real estate property for each factor applied to each wide area extracted from the factor DB 21e of the storage unit 21. Grouping is performed using a sequential two-branch decision tree (hereinafter referred to as C & RT).

  Specifically, the reference price calculation unit 23 groups each real estate property by setting a condition A, a condition B, and the like for each factor applied to each wide area.

For example, with reference to FIG. 10A, description will be made using “land area” which is a factor of a real estate property. Reference price calculation unit 23, "acreage is 200m less than ^2", including the real estate of which is within the area are in group A, "acreage is 200m less than ^2" is a collection of real estate is, the reference price calculation unit 23, each real estate in the area "acreage is 200m ² or more" is, to set the conditions a, such as included in the group B is a collection of real estate is "acreage is 200m ² or more". The reference price calculation unit 23 groups the real estate properties in the area based on the notification error rate of the real estate properties in the area according to the condition A.

 As a result, Group A has “Property ID = A1”, “Property ID = A3”, “Property ID = B1”, “Property ID = B3”, “Property ID = C1”, “Property ID = C2 ”,“ Property ID = C3 ”,“ Property ID = D4 ”, etc., and Group B includes“ Property ID = A2 ”,“ Property ID = A4 ”,“ Property ID = “B2”, “Property ID = B4”, “Property ID = C4”, “Property ID = D1”, “Property ID = D2”, “Property ID = D3”, and the like are included.

 When referring to the notification error rate of each real estate property of Group A and Group B, it can be seen that there is a large gap in the notification error rate of each real estate property. Condition B is set.

Specifically, as shown in FIG. 10B, the base price calculation unit 23 has “the land area is less than 100 m ² ” for each real estate property included in the group A whose “land area is less than 100 m ² ”. included in the group 1, which is a set of real estate, the reference price calculation unit 23, each of the real estate properties that "acreage is 100m ² or more" included in the group a is a, set "acreage is 100m ² or more" of the real estate is A condition B that is included in the group 2 is set.

The reference price calculation unit 23, each real estate in the group B is "acreage is less than 300m ^2", included in the group 3 is a set of real estate "acreage is 300m less than ^2" is "acreage The condition B is set so that each real estate property included in the group B having “300 m ² or more” is included in the group 4 that is a set of real estate properties having “the land area is 300 m ² or more”.

 As a result, Group 1 includes “Property ID = A1”, “Property ID = B3”, “Property ID = C1”, “Property ID = C3”, etc. ID = A3 ”,“ Property ID = B1 ”,“ Property ID = C2 ”,“ Property ID = D4 ”, etc., and Group 3 includes“ Property ID = A4 ”,“ Property ID = B4 ”,“ Property ID = D1 ”,“ Property ID = D3 ”, etc. are included in Group 4,“ Property ID = A2 ”,“ Property ID = B2 ”,“ Property ID = C4 ”. Or “Property ID = D2” or the like.

 Referring to the notification error rate of each real estate property of Group 1 to Group 4, it can be seen that there is no significant difference in the notification error rate of each real estate property. The reference price calculation unit 23 performs grouping by repeating the above-described condition setting until there is no significant difference in the notification error rate of each real estate property in each group.

 In step 905, the reference price calculation unit 23 calculates a reference coefficient value indicating an average value of the public error rate of the real estate property by each group for each factor, and the calculated reference coefficient value data is the reference coefficient value of the storage unit 21. Store in the DB 21c.

 For example, as shown in FIG. 10B, the reference coefficient value for group 1 is 0.85, which is the average of the notification error rates for each property, and the reference coefficient value for group 2 is the notification error rate for each property. The standard coefficient value of Group 3 is 1.05, which is the average of the notification error rate of each real estate property, and the reference coefficient value of Group 4 is the average of the notification error rate of each real estate property. Which is 1.15.

  In step 906, the reference price calculation unit 23 divides the published price of each real estate property in each area by the reference coefficient value in the group to which the real estate property belongs to calculate the property reference price of each real estate property.

  In step 907, the reference price calculation unit 23 calculates a reference price indicating the average value of the property reference prices calculated for each real estate property in each area, and stores the calculated reference price data in the reference price DB 21d of the storage unit 21. To remember.

 Hereinafter, an operation of the factor coefficient value calculation unit 24 for realizing the real estate price evaluation system according to the present embodiment will be described with reference to FIG.

 As shown in the figure, in step 1101, the factor coefficient value output unit 24 extracts the actual price DB 21b of the storage unit 21 and the base price DB 21d of the storage unit 21.

  In step 1102, the factor coefficient value output unit 24 divides the actual price of each real estate property by the reference price in each area for each real estate property in each area, thereby assigning each real estate property to each area for the actual price. The actual error rate indicating the error rate of a certain standard price is calculated.

 In step 1103, the factor coefficient value calculation unit 24 determines, in each area, each real estate property based on the notification error rate of each real estate property for each factor applied to each wide area extracted from the factor DB 21 e of the storage unit 21. Are grouped using C & RT.

 Specifically, the factor coefficient value calculation unit 24 groups the real estate properties by setting condition A, condition B, and the like for each factor applied to each wide area. The grouping is the same as the contents of FIG. 10 described above.

 In step 1104, the factor coefficient value calculation unit 24 calculates a first error rate (hereinafter referred to as factor factor value) indicating an average value of the actual error rate of the real estate property by each group for each factor, and the calculated factor coefficient value Are stored in the common factor data 21f and the area factor data 21g of the storage unit 21.

 Hereinafter, an operation of the evaluation price calculation unit 25 for realizing the real estate price evaluation system according to the present embodiment will be described with reference to FIG.

 As shown in the figure, in step 1201, the evaluation price calculation unit 25 extracts the reference price from the reference price DB 21d of the storage unit 21 based on the input address.

 For example, when the input address is an address of “Property ID = A1”, the evaluation price calculation unit 25 extracts 320,000 yen that is the reference price from the reference price DB 21d of the storage unit 21 based on the input address. To do.

 In step 1202, the evaluation price calculation unit 25 refers to each necessary factor from the factor DB 21e of the storage unit 21 based on the wide area including the input address, and the common factor DB 21f of the storage unit 21 and the area of the storage unit 21 Each factor coefficient value is extracted from the factor DB 21g.

 For example, when the input address is the address of “Property ID = A1”, the evaluation price calculation unit 25 refers to the necessary factor from the factor DB 21e of the storage unit 21 based on the wide area 1 and Factor area values of “land area = 0.93”, “city planning area = 1.02”, “width = 1.03”, “gas / sewage = 1.11”, “distance to nearest station” = 0.96 "," Shape = 1 "," Road orientation = 0.99 "," Aversion facility = 1 ", and" Road pavement status = 1 "are extracted.

 In step 1203, the evaluation price calculation unit 25 multiplies the extracted reference price by the factor coefficient value of each extracted factor and the land area of the input address, thereby obtaining the real estate at the input address. Calculate the evaluation price of the property.

For example, when the input address is an address of “Property ID = A1”, the evaluation price calculation unit 25 sets 0.93 and 1.02 as the factor coefficient values for the base price of 320,000 yen. And 1.03, 1.11, 0.96, 1, 0.99, 1 and 1 and 99, which is the land area (m ² ) of the input address, The evaluation price of “property ID = A1” is “3265.4 million yen”.

 Below, operation | movement of the real estate system 1 which concerns on this embodiment is demonstrated with reference to the sequence diagram of FIG.

 In step 1301, the sales department terminal 10 transmits the address of the real estate property to be evaluated and the respective conditions affecting the real estate property (for example, land area, volume ratio, etc.) via a communication network (for example, the Internet or a telephone line). Various information such as building age, number of rooms, floor plan, etc.) are input and requested (transmitted) to the evaluation server 15.

 In step 1302, the evaluation server 15 calculates an approximate evaluation price based on the input address of the real estate property and various information such as each condition affecting the input real estate property.

 In step 1303, the evaluation server 15 transmits the estimated evaluation price calculated via the communication network to the sales department terminal 10.

 In step 1304, the sales department terminal 10 receives the address of the real estate property to be evaluated via the communication network, various types of information such as the respective conditions affecting the real estate property, and image data (for example, photos, (Digital camera image) is requested (transmitted) to the examination unit terminal 11.

 In step 1305, the examination unit terminal 11 refers to the image data of the real estate property to be evaluated, and confirms whether various information such as each condition affecting the input real estate property is appropriate.

 In step 1036, the examination unit terminal 11 transmits various information (hereinafter, confirmation information) such as each condition affecting the real estate property confirmed through the communication network to the sales department terminal 10.

 In step 1037, the sales department terminal 10 reconfirms the confirmation information confirmed by the examination department terminal 11 (hereinafter, reconfirmation information).

 In step 1308, the sales department terminal 10 requests (transmits) the address of the real estate property to be evaluated and the reconfirmation information to the evaluation server 15 via the communication network.

  In step 1309, the evaluation server 15 calculates an evaluation price based on the address of the real estate property to be evaluated and the reconfirmation information.

 In step 1310, the evaluation server 15 transmits the evaluation price calculated via the communication network to the examination unit terminal 11.

  In step 1311, the examination unit terminal 11 confirms whether or not it is the optimum evaluation price of the real estate property to be evaluated.

  In step 1312, the examination unit terminal 11 transmits the evaluation price of the real estate property at the address input via the communication network to the sales unit terminal 10.

 In this embodiment, when calculating the price of a detached house as a real estate property, “land area”, “city planning area”, “width”, “closest station distance”, “shape”, “road direction”, “disgust” Although the description has been made using conditions for “facility”, “road certification”, “road pavement status”, and “volume ratio”, the present invention is not limited to this.

 For example, when calculating the price of a condominium as a real estate property, “building age”, “nearest station distance”, “floor area”, “building floor”, “residential floor”, “veranda direction”, “room layout”, “ The conditions for “Management Office”, “Northward”, “Auto Lock”, “Top Floor”, etc. are used. Note that conditions for other factors may be used.

 In this embodiment, the wide-area area includes `` Hokkaido '', `` Tohoku '', `` Kita Kanto Koshinetsu '', `` Metropolitan area '', `` Tokai '', `` Kinki '', `` Hokuriku Sanyin '', `` Chu Shikoku / Kyushu '', Although described as nine wide areas of “Okinawa”, the present invention is not limited to this.

(Operation and effect of the real estate price evaluation system according to the present embodiment)
According to the real estate price evaluation system 15 according to the present embodiment, the evaluation price calculation unit 25 uses the reference price based on the input address and the factor coefficient value (first error rate) of the actual price of the real estate property with respect to the reference price. By multiplying with, it is possible to reflect the condition that affects the evaluation price of the real estate property in the reference price, so that the optimal evaluation price of the real estate property at the input address can be calculated.

 Further, according to the real estate price evaluation system 15 according to the present embodiment, it is possible to calculate a reference price from which conditions that affect real estate properties are removed.

 Further, according to the real estate price evaluation system 15 according to the present embodiment, it is possible to calculate a factor coefficient value (first error rate) for reflecting a condition that affects real estate properties in the reference price.

 Further, according to the real estate price evaluation system 15 according to the present embodiment, the reference price calculation unit 23 (first grouping means) uses the sequential two-branch decision tree that is a statistical technique, so that the trends and properties within the group are determined. It is possible to classify each real estate property into a group that has been clarified in a uniform manner.

 Further, according to the real estate price evaluation system 15 according to the present embodiment, the factor coefficient value calculation unit 24 (second grouping means) uses a sequential bifurcated decision tree that is a statistical method, so Each real estate property can be classified into a group whose properties are clarified quantitatively and uniformly.

 Furthermore, according to the real estate price evaluation system 15 according to the present embodiment, conditions that affect real estate properties can be set finely for each land, detached house (with land), or apartment.

[Embodiment 2]
The real estate price evaluation system according to the second embodiment will be described with reference to FIGS. Hereinafter, in the real estate price evaluation system according to the present embodiment, differences from the real estate price evaluation system of the first embodiment will be mainly described.

 9, operations other than step 904 of the reference price calculation unit 23 according to the present embodiment are the same as operations other than step 904 of the reference price calculation unit 23 according to the first embodiment.

 In the present embodiment, the reference price calculation unit 23 uses a regression analysis that is a statistical method instead of grouping real estate properties using a sequential two-branch decision tree that is a statistical method in Step 904 of FIG. 9. The reference price calculation unit 23 groups the real estate properties in each area based on the published error rate of each real estate property for each factor applied to each extracted wide area.

  Specifically, as shown in FIG. 14, the real estate properties in the area are grouped using a regression analysis table. In the figure, an example using “land area” will be described.

  As shown in FIG. 14 (a), the vertical axis of the regression analysis table indicates the notification error rate of each real estate property, and the horizontal axis of the regression analysis table indicates the condition for “land area”. The reference price calculation unit 23 arranges a black dot indicating each real estate property in the area at a specific coordinate position on the regression analysis table.

  As shown in FIG. 14 (b), the base price calculation unit 23 determines the “land area” of the group so that there is no significant difference in the notification error rate of the real estate property based on the notification error rate of the real estate property. Determine the conditions.

As a result, the base price calculation unit 23 sets the condition for “land area” in group 1 to “less than 100 m ² ”, sets the condition for “land area” in group 2 to “100 m ² or more and less than 200 m ² ”, and The condition for “land area” is set to “200 m ² or more and less than 300 m ² ”, and the condition for “land area” in group 4 is determined to be “300 m ² or more”.

  Note that the reference price calculation unit 23 may be configured to perform the above setting more finely than the conditions shown in FIG. 14B in order to improve the accuracy of grouping.

 In FIG. 11, the operations other than step 1103 of the factor coefficient value calculation unit 24 according to the present embodiment are the same as the operations other than step 1103 of the factor coefficient value calculation unit 24 according to the first embodiment described above.

 In this embodiment, the factor coefficient value calculation unit 24 uses the factor coefficient value calculation unit 24 in step 1103 of FIG. 11 instead of grouping real estate properties using a sequential two-branch decision tree that is a statistical technique. The coefficient value calculation unit 24 groups each real estate property based on the actual error rate of each real estate property for each factor applied to each extracted wide area area using regression analysis which is a statistical method. Turn into. Note that the grouping is the same as that shown in FIG.

 According to the real estate price evaluation system 15 according to the present embodiment, the reference price calculation unit 23 (first grouping means) quantitatively analyzes trends and properties in the group by using regression analysis that is a statistical method. Each real estate property can be classified into a group that is clarified uniformly.

 Further, according to the real estate price evaluation system 15 according to the present embodiment, the factor coefficient value calculation unit 24 (second grouping means) uses the regression analysis that is a statistical method to determine the trends and properties in the group. Each real estate property can be classified into groups that are clarified in a quantitatively unified manner.

 As mentioned above, although one Embodiment of this invention was described, it has only illustrated the specific example and does not specifically limit this invention. In addition, the operations and effects described in the present embodiment are merely the most preferable operations and effects resulting from the present invention, and the operations and effects according to the present invention are not limited to these descriptions.

1 is a schematic configuration diagram of a real estate system 1 according to Embodiment 1. FIG. It is a figure which shows the detail of the evaluation server 15 which concerns on Embodiment 1. FIG. It is a figure which shows an example of public price DB21a and actual price DB21b which concern on Embodiment 1. FIG. It is a figure which shows an example of reference | standard coefficient value DB21c which concerns on Embodiment 1. FIG. It is a figure which shows an example of standard price DB21d which concerns on Embodiment 1. FIG. It is a figure which shows an example of factor DB21e which concerns on Embodiment 1. FIG. It is a figure which shows an example of common factor DB21f which concerns on Embodiment 1. FIG. It is a figure which shows an example of area factor DB21g which concerns on Embodiment 1. FIG. 5 is a flowchart of the operation of a reference price calculation unit 23 according to the first embodiment. It is a figure which shows C & RT in the grouping which concerns on Embodiment 1. FIG. 5 is a flowchart of the operation of a factor coefficient value calculation unit 24 according to the first embodiment. 5 is a flowchart of the operation of an evaluation price calculation unit 25 according to the first embodiment. It is a sequence diagram which shows the real estate system which concerns on Embodiment 1. It is a figure which shows the regression analysis in the grouping based on Embodiment 2. FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Real estate system, 10 ... Sales department terminal, 11 ... Examination department terminal, 12 ... Published price data, 13 ... Actual price data, 15 ... Evaluation server, 21 ... Storage part, 21a ... Published price DB, 21b ... Actual price DB , 21c ... reference coefficient value DB, 21d ... reference price DB, 21e ... factor DB, 21f ... common factor DB, 21g ... area factor DB, 22 ... receiver, 23 ... reference price calculator, 24 ... factor coefficient value calculator 25 ... evaluation price calculation unit, 26 ... transmission unit

Claims (6)

A real estate price evaluation system that calculates the evaluation price of a real estate property at a specific address,
First storage means for storing an area including the address and a reference price of the area in association with each other;
Second storage means for associating and storing first condition information indicating a condition affecting the evaluation price of the real estate property and a first error rate indicating an error rate of the actual price of the real estate property with respect to the reference price;
Reference value extraction means for extracting the reference price corresponding to the area including the input address from the first storage means;
Error rate extraction means for extracting the first error rate corresponding to the first condition information relating to the real estate property at the input address from the second storage means;
Evaluation price calculation means for calculating an evaluation price of the real estate at the input address by multiplying the extracted first error rate by the extracted reference price. Real estate price evaluation system. Third storage means for storing the publicly announced price of the real estate property in the area and the first condition information relating to the real estate property in association with each other;
Fourth storage means for storing second condition information indicating the first condition information applied in the area;
Average published price calculation means for calculating an average published price indicating an average value of the published price of the real estate property in the area extracted from the third storage means;
For each real estate in the area, a notification error rate calculating means for calculating a notification error rate indicating an error rate of the notification price with respect to the average notification price;
First grouping means for grouping the real estate properties based on the notification error rate of the real estate properties for each of the second condition information extracted from the fourth storage means in the area;
Average error rate calculating means for calculating an average error rate indicating an average value of the notification error rate of the real estate property in each group for each second condition information;
A property standard price calculating means for calculating the property standard price of each real estate property by dividing the published price of each real estate property in the area by the average error rate in the group to which the real estate property belongs;
2. The real estate price evaluation system according to claim 1, further comprising reference price calculation means for calculating an average value of the property reference prices calculated for the real estate properties in the area as the reference price. The fourth storage means for storing the second condition information indicating the first condition information applied in the area;
Fifth storage means for storing the actual price of the real estate property in the area and the first condition information relating to the real estate property in association with each other;
A real error rate calculating means for calculating a real error rate indicating an error rate of the reference price with respect to the real price extracted from the fifth storage means for each real estate in the area;
Second grouping means for grouping the real estate properties based on the actual error rate of the real estate properties for each of the second condition information extracted from the fourth storage means in the area;
2. The property price according to claim 1, further comprising: an error rate calculation unit that calculates an average value of the actual error rate of the real estate property in each group for each second condition information as the first error rate. Evaluation system.   The first grouping means is based on the notification error rate of the real estate property for each of the second condition information extracted from the fourth storage means by using a sequential bifurcated decision tree that is a statistical technique or regression analysis. The real estate price evaluation system according to claim 2 or 3, wherein real estate properties are grouped together.   The second grouping means is based on the actual error rate of the real estate property for each of the second condition information extracted from the fourth storage means by using a sequential two-branch decision tree that is a statistical technique or regression analysis. The real estate price evaluation system according to claim 2 or 3, wherein real estate properties are grouped together. The first condition information includes “land area”, “city planning area”, “width”, “gas / sewage”, “nearest station distance”, “shape”, “road direction”, “close road condition”, “ Disgusting facilities, road certification, road pavement status, volume ratio, building age, floor area, building floors, residence, veranda orientation, floor plan, The property price according to any one of claims 1 to 3, wherein the property price is a condition for at least one of "Management Office", "Northward", "Auto Lock", and "Top Floor". Evaluation system.

Images