Real estate development planning support device, real estate development planning support program, and real estate development planning support method

Abstract

To accurately predict, in a development planning stage of a real estate, an evaluation index depending on a region where a real estate being a planning object is located at an operation stage.SOLUTION: A real estate development planning support device 100 includes: a similar real estate retrieval unit 106 which extracts, by using a plan variable of a real estate under development planning, a similar real estate having a higher similarity level with a real estate being under development than a threshold; and an evaluation index prediction unit 107 which predicts an evaluation index of a real estate under development planning stage at an operation stage by using an adaptive value of the evaluation index obtained by correcting a reference value of the evaluation index of the similar real estate based on a characteristic of the planning object place of the development plan, and the similarity level of the similar real estate.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a real estate development plan support device, a real estate development plan support program, and a real estate development plan support method.

Background Art

[0002] For real estate developers, there is an increasing need to consider the impact on the surrounding area during the operation of real estate as the government regulations and the tolerance of the surrounding residents. Therefore, at the real estate development planning stage, a technology for predicting the impact of the planned real estate on the surrounding area during operation is important.

[0003] As a related technology of this technology, there is an invention according to Patent Document 1. Patent Document 1 describes that by utilizing the past performance data of each of the construction cost of an apartment building, the income amount obtained from the operation of rental properties, and the expenditure amount required for the operation of rental properties, and generating the estimated result data necessary for formulating a business plan for constructing an apartment building on the land designated by the real estate information and operating it for rent, it is possible to support the formulation of a business plan.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the invention according to Patent Document 1 described above, the influence of the characteristics of the planned target area on the real estate to be predicted is not considered, and the evaluation index predicted for the real estate at the planning stage may deviate from the actual evaluation index. That is, the invention according to Patent Document 1 has a problem that the prediction accuracy regarding the evaluation index depending on the location area is low.

[0006] Therefore, the present invention aims to accurately predict operational evaluation indicators that depend on the area in which the planned property is located, even at the stage of real estate development planning. [Means for solving the problem]

[0007] To solve the aforementioned problems, the real estate development plan support device of the present invention is characterized by comprising: a similar real estate search unit that extracts similar real estate whose similarity to the real estate under development plan is higher than a threshold using the planning variables of the real estate under development plan; a suitable value of the evaluation index obtained by correcting the reference value of the evaluation index of the similar real estate based on the characteristics of the planned site of the development plan; and an evaluation index prediction unit that predicts the evaluation index during operation of the real estate under development plan using the similarity.

[0008] The present invention provides a real estate development planning support program that causes a computer to perform the following steps: extract similar real estate properties whose similarity to the real estate under development is higher than a threshold, using the planning variables of the real estate under development; and predict the evaluation indicators for the real estate under development during operation, using the reference values ​​of the evaluation indicators for the similar real estate properties corrected based on the characteristics of the planned site for the development, and the similarity.

[0009] The present invention provides a method for supporting real estate development plans, comprising the steps of: a similar real estate search unit extracting similar real estate whose similarity to the real estate under development plans is higher than a threshold, using the planning variables of the real estate under development plans; and an evaluation index prediction unit predicting the evaluation index for the real estate under development plans during operation, using a suitable value of the evaluation index obtained by correcting the reference value of the evaluation index of the similar real estate based on the characteristics of the planned site of the development plan, and the similarity. Other means will be described within the section on embodiments for carrying out the invention. [Effects of the Invention]

[0010] According to the present invention, even at the stage of real estate development planning, it becomes possible to accurately predict operational evaluation indicators that depend on the area in which the planned real estate is located. [Brief explanation of the drawing]

[0011] [Figure 1] This is a block diagram showing the overall configuration of the real estate development planning support device according to this embodiment. [Figure 2] This is a block diagram showing the configuration of the pedestrian flow prediction unit of a real estate development planning support device. [Figure 3] This is a block diagram showing the configuration of the power consumption prediction unit of a real estate development planning support device. [Figure 4] Block diagram showing the hardware configuration of a real estate development planning support device. [Figure 5] This is an explanatory diagram of the structure and data examples of a real estate information database. [Figure 6] This is a diagram illustrating the configuration and data examples of a network database. [Figure 7] This is a diagram illustrating the structure and data examples of the human flow database. [Figure 8] This graph shows actual data on electricity consumption. [Figure 9] This is an overall flowchart of the operations performed by the real estate development planning support system. [Figure 10] This is a flowchart of the similar property search process performed by the real estate development planning support system. [Figure 11] This is a flowchart of the pedestrian flow prediction process performed by a real estate development planning support system. [Figure 12] This is a flowchart of the power consumption prediction process performed by a real estate development planning support system. [Figure 13] This is an explanatory diagram of the real estate development plan evaluation settings screen displayed by the real estate development plan support system. [Figure 14] This is an explanatory diagram of the warning dialog box for insufficient planning variables displayed by the real estate development planning support system. [Figure 15]It is an explanatory diagram of a real estate comparison evaluation result display screen displayed by a real estate development plan support device.

Embodiments for Carrying Out the Invention

[0012] Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to the respective drawings. This embodiment is for accurately predicting evaluation indexes depending on regional characteristics during the operation of the planned real estate and supporting the formulation of a real estate development plan.

[0013] FIG. 1 is a block diagram showing the overall configuration of a real estate development plan support device 100 according to this embodiment. The actual hardware configuration of the real estate development plan support device 100 will be described later using FIG. 4. The real estate development plan support device 100 of this embodiment includes an input device 101, a display device 102, and a server computer 103. The real estate development plan support device 100 receives information about the planned real estate from a user at the stage of the real estate development plan, refers to similar real estate information, and performs correction in consideration of the regional characteristics where the planned real estate is located. Thereby, the real estate development plan support device 100 accurately predicts evaluation indexes depending on regional characteristics during the operation of the planned real estate and supports the formulation of a real estate development plan.

[0014] The input device 101 is an input interface for transmitting a user's operation, such as a mouse, a keyboard, a touch device, etc., to the server computer 103. The display device 102 is an output interface such as a liquid crystal display, and is used for displaying data output by the server computer 103, interactive operations with the user, etc. The input device 101 and the display device 102 may be integrated, such as a touch panel display.

[0015] The real estate development plan evaluation setting screen 1301 (Figure 13), the real estate development plan evaluation setting screen 1401 (Figure 14), and the real estate comparison evaluation result display screen 1501 (Figure 15), which will be described later, are displayed by the display device 102, and operations on the input fields and buttons in each screen are performed by the user operating the input device 101.

[0016] The server computer 103 includes, as a functional configuration, a plan evaluation setting input unit 104, a similar real estate search unit 106, an evaluation index prediction unit 107, and an output generation unit 110. The server computer 103 further includes a real estate information database 111, a network database 112, a pedestrian flow database 113, a power consumption actuals database 114, and a network interface 115. The network interface 115 is connected to a network 116 and communicates with external devices via this network 116.

[0017] The planning evaluation setting input unit 104 acquires user input of planning variables, site information, and prediction condition information for the real estate subject to the development plan via the input device 101. The planning evaluation setting input unit 104 passes the planning variables to the similar real estate search unit 106, the site information to the land condition acquisition unit 105, and the prediction condition information to the evaluation index prediction unit 107. The planning variables include at least one piece of information from the following: height, total floor area, building area, number of floors, number of tenants, number of air conditioning units, total window area, number of floors for each use (commercial, residential, office, logistics, etc.), and type of BEMS (Building and Energy Management System).

[0018] Furthermore, the planned site information consists of a set of coordinates representing the polygonal shape of the property site. This information may also be vector data containing, for example, the coordinate information of the endpoints of all line segments that make up the site surface. The forecast conditions information consists of the climate conditions and calendar conditions at the time of the forecast. Climate conditions include information that includes one or more of the following: weather, temperature, humidity, sunshine duration, solar radiation, wind direction, wind speed, atmospheric pressure, precipitation, snowfall, and snow depth. Calendar conditions include information that includes at least one of the following: year, month, day, day of the week, and time. When a representative date from the past is selected, the forecast conditions information is the climate information or calendar information conditions for that representative date. However, the forecast conditions information may also be climate conditions or calendar conditions specified independently by the user.

[0019] The land condition acquisition unit 105 acquires information on the target site from the plan evaluation setting input unit 104, converts it into land condition information for the target site, and then passes the land condition information to the similar real estate search unit 106. Here, land condition information includes, for example, at least one of the following: land use zone, floor area ratio, and building coverage ratio. Here, the method for converting the target site information into land condition information may be, for example, by referring to external urban planning decision information via the network 116, or by referring to urban planning decision information stored internally.

[0020] The similar property search unit 106 obtains planning variables for properties subject to real estate development plans from the planning evaluation setting input unit 104 and land condition information from the land condition acquisition unit 105. The similar property search unit 106 then calculates the similarity score for properties that meet the land conditions of the target site from the existing real estate information stored in the real estate information database 111. The similarity score D is calculated, for example, by equation (1). The similarity score D is calculated using the planning variable vector x and the design variable vector y of existing real estate extracted from the existing real estate information, with only the corresponding terms being used. The difference in magnitude between the planning variable vector x and the design variable vector y contributes to a decrease in the similarity score.

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[0021] The similar property search unit 106 may also use the output values ​​of a machine learning model that has been trained in advance based on the relationship between actual power consumption values, pedestrian traffic, and design variables. Here, the existing property information includes at least one of the following for each existing property: location, height, total floor area, building area, number of floors, number of tenants, number of air conditioning units, total window area, number of floors for each use (commercial, residential, office, logistics, etc.), and type of BEMS. In addition, the information included in the input planning variables must be included in the existing property information.

[0022] If the variance of all similarities falls below a predetermined threshold, the similar real estate search unit 106 determines that there are insufficient planning variables and displays the real estate development plan evaluation setting screen 1301 on the display device 102.

[0023] Subsequently, the similar property search unit 106 extracts properties whose similarity exceeds a predetermined threshold and passes the information of the corresponding similar properties and their similarity information to the evaluation index prediction unit 107 and the output generation unit 110, respectively. The specific calculation flow of the similar property search process performed by the similar property search unit 106 is described in detail in the explanation of Figure 9. The evaluation index prediction unit 107 includes the pedestrian flow prediction unit 108 and the power consumption prediction unit 109.

[0024] The evaluation index prediction unit 107 predicts the evaluation index for the operation of the property during the development plan by using the appropriate value of the evaluation index obtained by correcting the reference value of the evaluation index of similar properties in light of the characteristics of the planned site for the development plan, and the similarity of similar properties. Furthermore, the evaluation index prediction unit 107 predicts the evaluation index for the operation of the property during the development plan by using the appropriate value obtained by correcting the value of the evaluation index (e.g., electricity consumption) obtained by correcting the reference value of the evaluation index of similar properties in light of the characteristics of the planned site for the development plan, and if there are other evaluation indicators that are correlated with the evaluation index (e.g., surrounding traffic volume), by the appropriate value of the other evaluation indicator, and the similarity of similar properties.

[0025] As shown in Figure 13, the output generation unit 110 displays map information in a display mode corresponding to the scale displayed on the screen, and also displays it on the display device 102 in a display mode that allows input of the target site, planning variables, and prediction conditions for the evaluation index. As shown in Figure 14, the output generation unit 110 displays information on similar properties that have high conformance values ​​for the evaluation index among similar properties, but which are not included in the planning variables. As shown in Figure 15, the output generation unit 110 displays the prediction results screen on a graph in a display mode corresponding to the predicted value of the evaluation index, and displays the conformance values ​​of the evaluation index for similar properties on the graph. Furthermore, the output generation unit 110 displays map information of the area surrounding the target site, and also displays the exterior of similar properties on the target site. The exterior of similar properties is displayed, for example, by three-dimensional model data of the building of the similar property, but is not limited to this.

[0026] Figure 2 is a block diagram showing the configuration of the pedestrian flow prediction unit 108 of the real estate development planning support device 100. The pedestrian flow prediction unit 108 includes a network feature setting unit 201, a traffic volume extraction unit 202, a pedestrian flow suitability value calculation unit 203, and a pedestrian flow prediction value calculation unit 204. Based on information about the planned site and the planned sites of properties that generate the same travel purposes as the planned sites located in the surrounding area, the pedestrian flow prediction unit 108 predicts the traffic volume around similar properties when similar properties are located on the planned site. The evaluation index prediction unit 107 uses the predicted traffic volume around similar properties to correct the reference value of the power consumption of similar properties.

[0027] The network feature setting unit 201 obtains network data from the network database 112 regarding the area surrounding the planned site and obtains similar property information from the similar property search unit 106. The network feature setting unit 201 reflects the changes in the node features and link features (hereinafter referred to as network features) of the acquired network data when a similar property is constructed on the planned site, and passes the reflected network features to the pedestrian flow suitability value calculation unit 203.

[0028] Here, network data refers to graph data where road intersections within the prediction target area are represented as nodes and roads as links. Network data includes node coordinate information, node-specific features (hereinafter referred to as node features), link data connecting nodes, and link-specific features (hereinafter referred to as link features). Node features consist of quantitative data on the urban environment surrounding each node. Examples of node features include the presence or absence of traffic lights at intersections corresponding to each node, the number of connecting roads, and the presence or absence of pedestrian crossings.

[0029] The first row of node feature field 604 shown in Figure 6, [0, 3, 0], indicates that node ID 1 has no traffic lights, is connected to a three-way intersection with three roads, and does not have a pedestrian crossing. Link features consist of quantitative information about the urban environment surrounding each link, such as the width and length of the road section corresponding to each link, and the number of shops and parks adjacent to the road section. The first row of link feature field 614, [10, 150, 15, 1], indicates that link ID 1 has a width of 10m, a road length of 150m, 15 adjacent shops, and 1 adjacent park.

[0030] The generated traffic volume extraction unit 202 uses the prediction condition information obtained from the plan evaluation setting input unit 104 to obtain climate information and calendar information for past representative days of the target area via the network interface 115 through the network 116 from an external source, and uses this as prediction conditions. The generated traffic volume extraction unit 202 may also use the climate conditions and calendar conditions selected by the user as prediction conditions. Next, the generated traffic volume extraction unit 202 extracts the generated traffic volume around the target area for the days corresponding to the prediction conditions from the pedestrian flow database 113 and passes it to the pedestrian flow suitability value calculation unit 203.

[0031] The pedestrian flow rate fitting value calculation unit 203 obtains the modified network features from the network feature setting unit 201 and obtains the traffic volume generated around the relevant planned site from the traffic volume extraction unit 202. The pedestrian flow rate fitting value calculation unit 203 then predicts the number of people traveling to the similar property as their destination, the proportion of transportation used during travel, and the travel routes, respectively, in the event that a similar property is constructed on the planned site. Based on these predictions, the pedestrian flow rate around the planned site in the event that a similar property is constructed on the planned site is calculated as the pedestrian flow rate fitting value.

[0032] The number of people traveling to similar properties, the distribution of transportation methods during travel, and the travel routes can be calculated, for example, using logit-type destination selection models, transportation method selection models, and route selection models. These logit-type destination selection models, transportation method selection models, and route selection models are trained in advance using pedestrian flow data observed via GPS (Global Positioning System) or Wi-Fi (registered trademark) positioning, as well as Origin Destination (OD) data obtained from PT (Person Trip) surveys.

[0033] The pedestrian traffic prediction calculation unit 204 obtains the similarity score of each similar property from the similar property search unit 106 and the pedestrian traffic suitability value of each similar property from the pedestrian traffic suitability calculation unit 203. The pedestrian traffic prediction calculation unit 204 then calculates the predicted pedestrian traffic around the planned property during operation from the similarity score and the pedestrian traffic suitability value. After that, the pedestrian traffic prediction calculation unit 204 passes the predicted pedestrian traffic value and the pedestrian traffic suitability value of each similar property to the output generation unit 110. The predicted pedestrian traffic value can be calculated, for example, by taking a weighted average of the pedestrian traffic suitability values ​​of each similar property, using the similarity score of each similar property as a weight, as shown in equation (2).

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[0034] Here, B(x) represents the set of similar properties found based on the planning variable vector x of the property under planning. p(y) represents the pedestrian flow fit value calculated from the design variable vector y of similar properties. P (x) is the predicted pedestrian flow value calculated from the planning variable vector x of the property under consideration. The specific pedestrian flow prediction calculation process and flow will be described in detail in the explanation of Figure 10.

[0035] Figure 3 is a block diagram showing the configuration of the power consumption prediction unit 109 of the real estate development planning support device 100. The power consumption forecasting unit 109 includes a power consumption reference value calculation unit 301, a power consumption suitable value calculation unit 302, and a power consumption forecast value calculation unit 303. When one or more predetermined weather conditions are specified by the user, the power consumption forecasting unit 109 uses the actual power consumption values ​​of similar properties on days when the weather conditions and past weather information for the area where similar properties are located match as reference values ​​to forecast the power consumption under representative weather conditions.

[0036] The power consumption reference value calculation unit 301 obtains similar property information from the similar property search unit 106. Next, the power consumption reference value calculation unit 301 uses the forecast condition information obtained from the plan evaluation setting input unit 104 as forecast conditions, and obtains climate information and calendar information for past representative days of the target area via the network interface 115 through the network 116 from an external source. Alternatively, the power consumption reference value calculation unit 301 uses the climate conditions and calendar conditions selected by the user as they are.

[0037] Subsequently, the power consumption reference value calculation unit 301 obtains the actual power consumption value for a day that matches the prediction conditions of a similar property. The power consumption reference value calculation unit 301 uses this actual power consumption value as the power consumption reference value for the similar property. In addition to the method described above, the power consumption reference value may also be calculated using, for example, the output of a machine learning model that has learned the correspondence between climate conditions, calendar conditions, and power consumption for each similar property, when those weather conditions and calendar conditions are input.

[0038] The power consumption suitability calculation unit 302 obtains a suitable value for the flow of people for each similar property from the flow of people prediction unit 108 and a reference value for power consumption from the power consumption reference value calculation unit 301. Then, the power consumption reference value calculation unit 301 estimates a correction amount for power consumption by inputting the suitable value for the flow of people around the similar property into a learning model that has been pre-trained on the relationship between fluctuations in flow of people and fluctuations in power consumption, for example, as shown in equation (3), and then adds or subtracts the correction amount to the suitable value to calculate the corrected power consumption S. e Calculate.

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[0039] Here, c e (y) represents the reference value of power consumption calculated from the design variable vector y of similar properties. p (y) is the climate and calendar conditions from which reference values ​​for the power consumption of similar properties y were obtained. And f is a learning model that has been pre-trained on the relationship between climate and calendar conditions and the fluctuations in power consumption. In other words, the power consumption suitability calculation unit 302 uses the climate or calendar conditions selected by the user to correct the reference values ​​for the power consumption of similar properties.

[0040] Note that c p (y) may represent the actual value of pedestrian traffic on a day with similar conditions. And f may be a learning model that has been pre-trained on the relationship between fluctuations in pedestrian traffic and fluctuations in power consumption. In other words, the power consumption suitability calculation unit 302 uses the predicted traffic volume around similar properties to correct the reference value of power consumption for similar properties. This learning model f could be, for example, a linear regression model. Furthermore, if multiple features are used as pedestrian flow information, such as the number of visitors and the pedestrian traffic volume in the surrounding streets, a multiple regression model or a neural network may be used as the learning model f.

[0041] The power consumption prediction calculation unit 303 obtains the similarity score for each similar property from the similar property search unit 106 and the power consumption suitability value for each similar property from the power consumption suitability calculation unit 302, and calculates the power consumption prediction value from the similarity score and the power consumption suitability value.

[0042] Subsequently, the power consumption reference value calculation unit 301 passes the power consumption suitability value and the similarity-power consumption prediction value for each similar property to the output generation unit 110. The power consumption prediction value can be calculated, for example, by taking a weighted average of the power consumption prediction values ​​for each similar property, using the similarity of each similar property as a weight, as shown in equation (4). A flowchart of the specific power consumption prediction calculation process will be described in detail in the explanation of Figure 11.

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[0043] The output generation unit 110 receives the suitability values, similarity scores, and predicted values ​​for the evaluation indicators of each similar property, as well as the evaluation indicators of the property under planning, from the evaluation indicator prediction unit 107. Furthermore, the output generation unit 110 extracts the exterior data of each similar property from the property information database 111 and displays the property comparison evaluation result display screen 1501 (Figure 15) on the display device 102. The specific screen content will be described in detail in the explanation of Figure 15.

[0044] The real estate information database 111 is a database for storing existing real estate information. Access to the real estate information database 111 is performed during the search process from the similar real estate search unit 106. The structure of the real estate information database 111 will be described in detail later in Figure 5.

[0045] The network database 112 is a database that stores network features of the area surrounding the planned site. Access to the network database 112 is performed during the prediction process from the pedestrian flow prediction unit 108. The network features and the structure of the network database 112 will be explained in detail later in Figure 6.

[0046] The pedestrian flow database 113 is a database that stores the traffic volume at the starting and ending points around the planned area. Access to the pedestrian flow database 113 is performed during the prediction processing by the pedestrian flow prediction unit 108. The structure of the pedestrian flow database 113 will be described in detail later in the explanation of Figure 7.

[0047] The electricity consumption data database 114 is a database that stores actual electricity consumption data for existing properties. Access to the electricity consumption data database 114 is performed during the prediction process of the electricity consumption forecasting unit 109. Examples of actual electricity consumption data and data stored in the electricity consumption data database 114 will be explained in detail later in Figure 8.

[0048] Figure 4 is a block diagram showing the hardware configuration of the real estate development planning support device 100. The server computer 103 is, for example, a general-purpose computer having interconnected processors 401 and storage devices 402 and a network interface 115. The processor 401 is a central processing unit that implements each functional unit in Figure 1 by executing the processing program 403. The storage device 402 is composed of any type of storage medium. The storage device 402 may include, for example, semiconductor memory or a hard disk drive. The network interface 115 is an interface for the processor 401 to communicate with external devices via the network 116, and is, for example, a NIC (Network Interface Card).

[0049] The storage device 402 stores the processing program 403, the real estate information database 405 shown in Figure 1, the network database 112, the pedestrian flow database 113, and the power consumption performance database 114. The processing program 403 is a computer program consisting of a combination of instructions executed by the processor 401, and is a real estate development planning support program that predicts evaluation indicators for the operation of real estate under development planning. In this example, the functional units shown in Figure 1, such as the plan evaluation setting input unit 104, land condition acquisition unit 105, similar real estate search unit 106, pedestrian flow prediction unit 108, power consumption prediction unit 109, and output generation unit 110, are realized when the processor 401 executes the processing program 403 stored in the memory device 402.

[0050] In other words, in this embodiment, the processing performed by each of the above-mentioned functional units is actually carried out by the processor 401 executing instructions described in the processing program 403. Note that the real estate information database 405, network database 112, pedestrian flow database 113, and power consumption data database 114 may be stored outside the server computer 103 and accessible via the network interface 115.

[0051] Display by the display device 102 is achieved when the processor 401 generates data for display and outputs it to the display device 102, and the display device 102 displays the data according to that data.

[0052] Figure 5 is an explanatory diagram illustrating the structure and data examples of the real estate information database 111. The real estate information database 111 includes the real estate information 500 shown in Figure 5. The table configuration and the field configuration of each table in Figure 5 are the configuration necessary for carrying out the present invention, and tables and fields may be added depending on the application.

[0053] The real estate information 500 consists of a real estate ID field 501, a real estate name field 502, a usage field 503, a site information field 504, and a real estate feature field 505. The Real Estate ID field 501 stores identification information (hereinafter referred to as Real Estate ID) associated with each property. The Real Estate ID stored in the Real Estate ID field 501 may be a number used for processing procedures with the government, such as a building permit number.

[0054] The property name field 502 stores the name of the property associated with the aforementioned property ID. The Use field 503 stores the use information of the property associated with the aforementioned property ID. The Use field 503 may store one representative use information from, for example, commercial facilities, office facilities, accommodation facilities, residential facilities, and logistics bases, and the number of floors for each use may be stored as vector data to represent a mixed-use facility.

[0055] The site information field 504 stores the shape of the property site associated with the aforementioned property ID and its coordinates. The information stored in the site information field 504 may be, for example, vector data containing the coordinate information of the endpoints of all line segments that make up the site surface.

[0056] The Real Estate Feature Field 505 stores feature data or vector data related to the property that is associated with the aforementioned Real Estate ID. The Real Estate features stored in the Real Estate Feature Field 505 may be shape information such as height, site area, and building floor area, or information on installed equipment and IT systems such as air conditioning and energy management systems. In addition, when representing type information, a one-hot vector such as [0,1,0,0] may be used. Here, a one-hot vector is a discrete vector whose value can only be 0 or 1. The first row of the Real Estate Feature Field 505 shown in Figure 5, [180, 4000, 150,…], indicates that the height is 180m, the site area is 4000 square meters, and 150 air conditioning units are installed.

[0057] Figure 6 is an explanatory diagram of the configuration and data example of the network database 112. While a table-format configuration example is shown here, the data format is not limited to tables and can be any format. The network database 112 is configured to include node information 600 and link information 610 as shown in Figure 6. The table configuration and field configuration of each table in Figure 6 are the configuration necessary for carrying out the present invention, and tables and fields may be added depending on the application.

[0058] Node information 600 consists of a node ID field 601, a latitude field 602, a longitude field 603, and a node feature field 604. The node ID field 601 stores the identification information (hereinafter referred to as node ID) for each node.

[0059] The latitude field 602 stores the latitude information of the node's position coordinates associated with the aforementioned node ID. The longitude field 603 stores the longitude information of the node's position coordinates associated with the aforementioned node ID.

[0060] The node feature field 604 stores the features associated with the node corresponding to the node ID mentioned above (hereinafter referred to as node features). For example, if the node features differ depending on the time of day, such as when a street becomes a pedestrian-only street from 12:00, fields for each time of day may be added to the node feature field 604, such as the 12:00 node feature field 605.

[0061] The link information 610 consists of a link ID field 611, a starting node field 612, a destination node field 613, and a link feature field 614. The link ID field 611 stores the identification information (hereinafter referred to as the link ID) for each link.

[0062] The starting node field 612 stores the node ID of the starting node of the link associated with the aforementioned link ID. The ending node field 613 stores the node ID of the ending node of the link associated with the aforementioned link ID.

[0063] The link feature field 614 stores the features associated with the link corresponding to the aforementioned link ID (hereinafter referred to as link features). For example, if the number of stores increases from 12 o'clock, and the link features differ depending on the time of day, you may add fields to the link feature field 614 for each time of day, such as the 12 o'clock link feature field 615.

[0064] Figure 7 is an explanatory diagram of the structure and data example of the human flow database 113. While a table-formatted structure is shown here, the data format is not limited to tables and can be any format. The passenger flow database 113 includes trip information 700 as shown in Figure 7. The table configuration and field configuration of each table in Figure 7 are the configuration necessary for carrying out the present invention, and tables and fields may be added depending on the application.

[0065] The trip information 700 consists of an ODID (Origin Destination IDentifier) ​​field 701, a departure node field 702, a destination node field 703, a trip count field 704, a destination field 705, and a movement feature field 706. The ODID field 701 stores identification information (hereinafter referred to as ODID) associated with each type of movement.

[0066] The departure node field 702 stores the node ID of the departure node associated with the aforementioned ODID. The destination node field 703 stores the node ID of the destination node associated with the aforementioned ODID.

[0067] The Trip Count field 704 stores the number of people making the trip, which is associated with the aforementioned ODID. The Purpose field 705 stores the purpose of the trip, which is associated with the aforementioned ODID. Here, the purpose of the trip may consist of text data representing one of the following: commuting, going to school, going home, work, or personal, which is obtained from a PT survey or similar. For example, the first line of the Purpose field 705 in Figure 7 is "Personal."

[0068] The movement feature field 706 stores vector data representing movement-related features associated with the aforementioned ODID. For example, movement features may include the date and time of movement, mode of transport, and attributes of the person moving.

[0069] Figure 8 is a graph showing actual electricity consumption data. The graph in Figure 8 has power consumption on the vertical axis (801) and power consumption at each time of day (802). The graph discretely plots actual values ​​(803) obtained at each data acquisition time interval.

[0070] Figure 9 is an overall flowchart of the operations performed by the real estate development planning support device 100. First, the user inputs planning variables, planning site information, and prediction condition information into the planning evaluation setting input unit 104 via the input device 101 (step S901). Next, the plan evaluation setting input unit 104 transmits the plan target site information to the land condition acquisition unit 105 (step S902), transmits the plan variables to the similar real estate search unit 106 (step S903), and transmits the prediction conditions to the evaluation index prediction unit 107 (step S904).

[0071] Subsequently, the land condition acquisition unit 105 converts the planned site information into land conditions and transmits it to the similar real estate search unit 106 (step S905). The similar property search unit 106 uses land conditions and planning variables to calculate the similarity between existing properties and planned properties stored in the property information database 111, and transmits the similar property information that exceeds a threshold, along with the similarity of those properties, to the evaluation index prediction unit 107 (step S906).

[0072] Next, the pedestrian flow prediction unit 108 calculates pedestrian flow prediction values ​​and pedestrian flow adjustment values ​​from similar real estate information and its similarity, network data around the planned site stored in the network database 112, generated traffic volume data stored in the pedestrian flow database 113, and weather information and calendar information obtained from external sources, and transmits them to the power consumption prediction unit 109 and the output generation unit 110 (step S907).

[0073] The power consumption forecasting unit 109 then calculates a predicted power consumption value and a suitable power consumption value from similar real estate information, its similarity and actual power consumption values, and weather and calendar information obtained from external sources, and transmits them to the output generation unit 110 (step S908). The actual power consumption values ​​of similar real estate are stored in the power consumption actual database 114.

[0074] Finally, when the output generation unit 110 displays the real estate comparison and evaluation results screen (Figure 15) on the display device 102 (step S909), the process shown in Figure 9 is terminated. The user can then check the real estate comparison and evaluation results on this screen.

[0075] Figure 10 is a flowchart of the similar property search process performed by the real estate development planning support device 100. First, the similar property search unit 106 obtains the planning variables of the property subject to the real estate development plan from the planning evaluation setting input unit 104 (step S1001), and obtains land condition information from the land condition acquisition unit 105 (step S1002). Next, the similar property search unit 106 executes a loop process from steps S1004 to S1007 for each existing property in the property information database 111 (steps S1003, S1008).

[0076] In the loop process, the similar property search unit 106 first determines whether existing properties meet the land conditions of the planned site. The similar property search unit 106 determines, for example, whether the building coverage ratio, floor area ratio, and building use of the existing property if it were constructed on the planned site meet the land conditions of the planned site (step S1004). In step S1004, if it is determined that the existing property meets the land conditions of the planned site (Yes), the similar property search unit 106 calculates the similarity to the property that is the subject of the real estate development plan (step S1005). Next, the similar property search unit 106 determines whether the similarity of the existing property exceeds a threshold (step S1006). Here, the threshold may be, for example, a predetermined value set in advance, or a value that changes to satisfy the number of candidate properties to be extracted that has been set in advance.

[0077] If, in step S1006, it is determined that the similarity of this existing property exceeds the threshold (Yes), the similar property search unit 106 determines that this existing property is a similar property, extracts similar property information (step S1007), and proceeds to step S1008. If, in step S1006, it is determined that the similarity of this existing property does not exceed the threshold (No), the similar property search unit 106 proceeds to step S1008.

[0078] If, in step S1004, it is determined that the existing property does not meet the land conditions of the planned site (No), the similar property search unit 106 proceeds to step S1008. In step S1008, if the similar property search unit 106 finds any unprocessed existing properties in the property information database 111, it returns to step S1003. If the similar property search unit 106 has processed all existing properties in the property information database 111, it proceeds to step S1009.

[0079] In step S1009, the similar property search unit 106 calculates the variance for the similarity calculated for all extracted similar properties. The similar property search unit 106 determines whether the variance exceeds a predetermined threshold (step S1010). If it determines that the variance exceeds the predetermined threshold (Yes), the similar property search unit 106 outputs the similar property information and its similarity information to the evaluation index prediction unit 107 and the output generation unit 110, respectively (step S1013), and terminates the process shown in Figure 10.

[0080] In step S1010, if it is determined that the variance is below a predetermined threshold (No), the real estate development plan evaluation setting screen 1301 shown in Figure 13 is displayed on the display device 102 as an alert screen (step S1011). At this point, the planning variables that contribute to the variance may be calculated from past evaluation results and displayed on the real estate development plan evaluation setting screen 1301. The similar real estate search unit 106 then determines whether the user has selected to re-enter the planning variables or has selected to resume the evaluation without selecting to re-enter the planning variables (step S1012).

[0081] If the user chooses to re-enter the design variables in step S1012 (Yes), the process returns to step S1001. If the user does not choose to re-enter the design variables and instead chooses to resume the evaluation (No), the process proceeds to step S1013. In step S1013, the similar property search unit 106 outputs similar property information and its similarity information to the evaluation index prediction unit 107 and the output generation unit 110, respectively, and the process shown in Figure 10 is completed.

[0082] Figure 11 is a flowchart of the pedestrian flow prediction process performed by the real estate development planning support device 100. The pedestrian flow prediction unit 108 executes the loop processing steps S1102 to S1106 (steps S1101, S1107) for each similar property output from the similar property search unit 106.

[0083] At the beginning of the loop processing, the network feature setting unit 201 obtains similar property information from the similar property search unit 106 (step S1102). Next, the network feature setting unit 201 obtains network features around the planned site from the network database 112 and reflects the changes in features when a similar property is constructed on the planned site in the obtained network features (step S1103).

[0084] Next, the generated traffic volume extraction unit 202 extracts generated traffic from the pedestrian flow database 113 that matches the prediction conditions and has a purpose of movement that matches the use of similar properties in each area surrounding the planned site (step S1104). At this time, the generated traffic volume extraction unit 202 obtains prediction condition information from the plan evaluation setting input unit 104, and using this as a condition, extracts past representative days of the planned site from an external source via the network 116 and network interface 115, and uses the climate information and calendar information of the representative days as prediction conditions. Alternatively, the generated traffic volume extraction unit 202 may use the climate conditions and calendar conditions selected by the user as they are. Furthermore, if the generated traffic volume extraction unit 202 uses pedestrian flow data observed by, for example, GPS or Wi-Fi® positioning, it extracts generated traffic on days that match the aforementioned weather conditions. Furthermore, if it is determined that the traffic volume generated by the construction of similar real estate will be significant, the traffic volume extraction unit 202 may predict the traffic volume using a learning model that has previously learned the relationship between the traffic volume generated and the characteristics of real estate in the surrounding area, rather than extracting the traffic volume from the pedestrian flow database 113.

[0085] The human traffic flow adjustment value calculation unit 203 considers the network features after the feature changes have been reflected and estimates the probability that residents around the planned site will travel to the similar property as their destination when the similar property is constructed on the planned site. By multiplying this by the traffic volume generated in each area around the planned site, the unit calculates the number of people who will visit the similar property (step S1105). The probability of traveling to the similar property as a destination is calculated using a pre-trained destination selection model, for example, as shown in equation (5).

number

[0086] Here, y represents similar properties. r represents the surrounding area of ​​the planned site. A(r) represents the set of properties with the same use as similar properties that can be visited from the surrounding area r of the planned site. u(y) represents the utility of the destination, which depends only on destination y. v(y|r) represents the utility of travel when moving from the surrounding area r of the planned site to destination y.

[0087] Next, the pedestrian flow fitment calculation unit 203 calculates the number of people traveling by each mode of transport by multiplying the estimated number of visitors to each property by the mode of transport share ratio, and calculates the traffic volume for each mode of transport and each street by multiplying the number of people traveling by each mode of transport by each route selection probability (step S1106). The pedestrian flow fitment calculation unit 203 calculates the mode of transport share ratio and route selection probability using, for example, a pre-trained mode of transport selection model and route selection model. If the pedestrian traffic suitability calculation unit 203 has any unprocessed similar properties output from the similar property search unit 106, it returns to step S1101. If the pedestrian traffic suitability calculation unit 203 has processed all of the similar properties output from the similar property search unit 106, it proceeds to step S1108.

[0088] In step S1108, the pedestrian flow prediction value calculation unit 204 obtains the similarity score for each similar property from the similar property search unit 106 and the pedestrian flow suitability value for each similar property from the pedestrian flow suitability value calculation unit 203, and calculates the pedestrian flow prediction value from the similarity score and the pedestrian flow suitability value. Finally, the pedestrian flow prediction value calculation unit 204 passes the pedestrian flow prediction value and the pedestrian flow suitability value for each similar property to the output generation unit 110 (step S1109), and the process shown in Figure 11 is completed.

[0089] Figure 12 is a flowchart of the power consumption prediction process performed by the real estate development planning support device 100. The power consumption reference value calculation unit 301 obtains similar property information from the similar property search unit 106 (step S1201). Next, the power consumption reference value calculation unit 301 obtains prediction condition information from the plan evaluation setting input unit 104, and using this as a condition, extracts past representative dates for the target area via the network interface 115 through the network 116 from an external source, and uses the climate information and calendar information for the representative dates as prediction conditions (step S1202). The power consumption reference value calculation unit 301 may also use the climate conditions and calendar conditions selected by the user as prediction conditions.

[0090] The power consumption prediction unit 109 executes the loop processing steps S1204 to S1205 (steps S1203, S1206) for each similar property output from the similar property search unit 106.

[0091] In the loop process, the power consumption reference value calculation unit 301 calculates a power consumption reference value from past power consumption data of similar properties in the power consumption performance database 114, based on the acquired climate conditions and calendar conditions (step S1204).

[0092] Next, the power consumption suitability calculation unit 302 obtains the pedestrian flow suitability value for each similar property from the pedestrian flow prediction unit 108 and the power consumption reference value from the power consumption reference value calculation unit 301 to calculate the power consumption suitability value that takes into account the change in pedestrian flow when each similar property is constructed on the planned site (step S1205). In step S1206, if there are any unprocessed similar properties output from the similar property search unit 106, the power consumption prediction unit 109 returns to step S1203. If the power consumption prediction unit 109 has processed all of the similar properties output from the similar property search unit 106, it proceeds to step S1207.

[0093] In step S1207, the power consumption prediction calculation unit 303 obtains the similarity score for each similar property from the similar property search unit 106 and the power consumption suitability value for each similar property from the power consumption suitability calculation unit 302, and calculates the power consumption prediction value from the similarity score and the power consumption suitability value.

[0094] Finally, the power consumption prediction calculation unit 303 passes the power consumption prediction value and the power consumption suitability value for each similar property to the output generation unit 110 (step S1208), and then terminates the process shown in Figure 12.

[0095] Figure 13 is an explanatory diagram of the real estate development plan evaluation setting screen 1301 displayed on the display device 102 by the real estate development plan support device 100.

[0096] The real estate development plan evaluation settings screen 1301 shown in Figure 13 includes a planned site input pane 1302, a plan variable input pane 1303, a prediction condition setting pane 1304, an evaluation indicator selection pane 1305, an evaluation execution pane 1306, and a map display management area 1307. The planned site input pane 1302 displays a planned site marker 1302A and a compass screen 1302B.

[0097] When a user selects a site under real estate development planning on the map within the planned site input pane 1302 by clicking or tapping, a planned site marker 1302A surrounding the perimeter of the selected site is displayed. Once the display of the planned site marker 1302A is complete, the planned site information is registered in the planned evaluation setting input unit 104.

[0098] The user can change the map's scale and display position by specifying and dragging a site on the map within the planned site input pane 1302 using a method different from the keys or mouse clicks used when selecting a site for real estate development. At this time, by assigning a key other than the aforementioned keys, a mouse click, or two-finger touch panel rotation as an event, the user can change the map's display angle by performing the above operations on the planned site input pane 1302. The compass screen 1302B displays the orientation of the map displayed in the planned site input pane 1302 at the time of display.

[0099] This makes it possible to display sites under real estate development in conjunction with the locations of key facilities and distinctive features such as rivers, making it easier to find sites under real estate development on a map.

[0100] The planning variable input pane 1303 is a user-input pane that accepts the planning variables currently determined for the property being planned. Here, a selection field may be provided for variables with predetermined options, a free-form input field for other variables, and an attachment field may be provided for items that require the attachment of files, such as exterior data from the design stage. It is not necessary for all variables to be entered.

[0101] The prediction condition setting pane 1304 displays the candidate date condition setting field 1304A and the evaluation condition setting field 1304B. The user first selects either the candidate date condition setting field 1304A or the evaluation condition setting field 1304B. If the user selects the candidate date condition setting field 1304A, they can input weather and calendar conditions for a representative date to be used as the forecast condition, based on past weather and calendar information for the target area. If the user selects the evaluation condition setting field 1304B, they can choose to set the weather and calendar conditions themselves. In this case, only one of the candidate date condition setting field 1304A or evaluation condition setting field 1304B can be used for input, and if one field is selected, the other field will be displayed as invalid.

[0102] The evaluation metric selection pane 1305 accepts the evaluation metrics that the user wants to check. Here, the user can select at least one or more arbitrary evaluation metrics simultaneously. The evaluation execution pane 1306 displays an input field 1306A for the number of comparison targets and an evaluation start button 1306B.

[0103] In the comparison target input field 1306A, the user can select the number of similar properties to the entered planning variable, or an upper or lower limit for the number of properties to extract. After the user has entered at least one item in the planning variable input pane 1303, the prediction condition setting pane 1304, and the evaluation indicator selection pane 1305, the evaluation start button 1306B becomes operable, and when the evaluation start button 1306B is clicked, the similar property search unit 106 is executed.

[0104] The map display management area 1307 displays a zoom-out button 1307A, a zoom-in button 1307B, a zoom screen 1307C, and a home position transition button 1307D. The user can reduce the scale of the map displayed on the planned site input pane 1302 by pressing the scale reduction button 1307A using the cursor or touch in the map display management area 1307. The user can increase the scale of the map by pressing the scale increase button 1307B. The scale screen 1307C displays the scale of the map displayed on the planned site input pane 1302 at the time of display. The user can also return the planned site input pane 1302 to the initial state of position, scale, and orientation by pressing the home position transition button 1307D using the cursor or touch.

[0105] Figure 14 is an explanatory diagram of the real estate development plan evaluation setting screen 1401 displayed by the real estate development plan support device 100. In front of the real estate development plan evaluation settings screen 1401 shown in Figure 14, a warning dialog 1402 about insufficient plan variables is displayed.

[0106] The "Deficient Plan Variables" warning dialog 1402 displays a deficient plan variable text box 1402A, a "Restart Evaluation" button 1402B, and a "Re-enter" button 1402C. The deficient plan variable text box 1402A displays the names of variables that, if entered, are likely to increase the variance of the similarity scores of similar properties.

[0107] If the user clicks the restart evaluation button 1402B using the cursor or touch, the result is determined to be "No" in step S1012, and the system transitions to the real estate development plan evaluation settings screen 1401. If the user clicks the re-input button 1402C using the cursor or touch, the result is determined to be "Yes" in step S1012, and the system transitions to the real estate development plan evaluation settings screen 1301. This allows the user to know about any missing planning variables and the planning variables that should be considered in advance before the evaluation, thus enabling them to understand the reliability of the evaluation results and the planning variables that should be considered before using the real estate development plan support device 100.

[0108] Figure 15 is an explanatory diagram of the real estate comparison and evaluation result display screen 1501 displayed by the real estate development plan support device 100. The real estate comparison and valuation results display screen 1501 shown in Figure 15 includes an appearance display pane 1503 and a valuation results display pane 1504, with a similar real estate display floating window 1502 and a reference variable display floating window 1505 displayed in front of it.

[0109] The floating window 1502 for displaying similar properties displays the names of similar properties extracted by the similar property search unit 106, along with their similarity scores. The appearance display pane 1503 displays the appearances of the similar properties extracted by the similar property search unit 106. When the user selects a similar property they wish to compare, the output generation unit 110 displays the appearance of the selected similar property in the appearance display pane 1503 using its three-dimensional model data or the like.

[0110] This allows users to view a reference exterior even when there is no 3D model data showing the exterior of the planned property. Similarly, the reference variable display floating window 1505 displays the appropriate values ​​for each evaluation index of the similar property selected in the evaluation result display pane 1504, which are calculated by the evaluation index prediction unit 107. This allows even non-experts, who may not understand the impact of the predicted values ​​of the planned property in isolation, to judge the impact of the predicted values ​​of the planned property by comparing the evaluation index with that of similar properties.

[0111] The exterior display pane 1503 displays the exterior of a similar property selected by the user in the similar property display floating window 1502, superimposed onto the target site. The exterior of the similar property displayed in Figure 15 is a 3D model displayed on the target site, but it may also be a group of image data displayed in a separate pane, and is not limited to that.

[0112] The evaluation results display pane 1504 shows graphs of the average traffic volume on the main street for each season and graphs of the average power consumption for each season. The horizontal axis of the graph of the average traffic volume on the main street represents the season (time elapsed), and the vertical axis represents the average traffic volume. The dashed line shows the fitted value of the average traffic volume for similar properties, and the solid line shows the predicted value of the average traffic volume for the property under planning. The horizontal axis of the average power consumption graph shows the season (time elapsed), and the vertical axis shows the average power consumption. The dashed line shows the appropriate value of the average power consumption of similar properties, and the solid line shows the predicted value of the average power consumption of the property under planning. In other words, this evaluation result display pane 1504 is a prediction results screen that displays the predicted values ​​of the evaluation indicators for the property under planning on a time-series graph, and the appropriate values ​​of the evaluation indicators for similar properties on a time-series graph. The graphs shown in this evaluation result display pane 1504 allow for an objective assessment of the validity of the predicted values ​​of the evaluation indicators for the property under planning. Furthermore, the evaluation results displayed in the evaluation results display pane 1504 are not limited to the graphs described above, but may be in any format.

[0113] The Reference Variable Display Floating Window 1505 displays design variables related to similar properties that have a superior fit value for evaluation indicators, but which are not entered as planning variables. For example, a user can refer to the design variable values ​​of a similar property that has the lowest and most advantageous fit value for the evaluation indicator, power consumption, for elements that have not yet been decided in a real estate development plan. By referring to similar properties, users can more easily consider a more advantageous plan from the perspective of evaluation indicators such as power consumption.

[0114] As described above, the real estate development planning support device 100 of this embodiment can accurately predict evaluation indicators that depend on the location of the planned real estate during its operation, even at the real estate development planning stage, and can support the formulation of a real estate development plan. Furthermore, the real estate development planning support device 100 of this embodiment can predict the amount of electricity consumed during real estate operation for properties in the real estate development planning stage, for which no data exists from past operations because the properties are not yet in operation.

[0115] Furthermore, the real estate development planning support device 100 of this embodiment can estimate approximate predicted values ​​for evaluation indicators such as power consumption and pedestrian traffic, which require architectural design information and specific real estate development plans to predict, even in phases where the plan has not yet been finalized. As a result, the real estate development planning support device 100 can prevent significant changes after the plan and design have been finalized, thereby reducing planning and design costs.

[0116] Variant form The present invention is not limited to the embodiments described above, and includes various modifications. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. It is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0117] Each of the above configurations, functions, processing units, and processing means may be implemented in part or in whole by hardware, such as an integrated circuit. Each of the above configurations and functions may also be implemented in software by a processor interpreting and executing a program that implements each function. Information such as programs, tables, and files that implement each function can be stored in a recording device such as memory, a hard disk, or an SSD (Solid State Drive), or on a recording medium such as a flash memory card or a DVD (Digital Versatile Disk).

[0118] In each embodiment, the control lines and information lines shown are those deemed necessary for explanation and do not necessarily represent all control lines and information lines in the actual product. In practice, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0119] 100 Real Estate Development Planning Support Device 101 Input Device 102 Display device 103 Server Computer 104 Planning and Evaluation Setting Input Section 105 Land Conditions Acquisition Department 106 Similar Property Search Department 107 Evaluation Metrics Prediction Department 108 People flow prediction department 109 Power Consumption Prediction Unit 110 Output generation unit 111 Real Estate Information Database 112 Network Database 113 Human Flow Database 114 Electricity Consumption Data Database 115 Network Interfaces 116 Network 201 Network Feature Setting Unit 202 Traffic Volume Extraction Unit 203 Human Flow Rate Calculation Unit 204-person flow rate prediction calculation unit 301 Power Consumption Reference Value Calculation Unit 302 Power Consumption Optimal Value Calculation Unit 303 Power Consumption Prediction Calculation Unit 401 Processor 402 Storage device 403 Processing Program (Real Estate Development Planning Support Program)

Claims

1. A similar property search unit extracts similar properties whose similarity to the property under development is higher than a threshold, using the planning variables of the property under development. An evaluation index prediction unit that uses the reference value of the evaluation index of the aforementioned similar real estate, corrected in light of the characteristics of the planned site of the development plan, to predict the evaluation index during the operation of the real estate in the development plan, A real estate development planning support device characterized by being equipped with the following features.

2. The evaluation index prediction unit uses the values ​​of the evaluation index obtained by correcting the reference values ​​of the evaluation index of similar real estate based on the characteristics of the planned site of the development plan, and the similarity to the corrected values ​​obtained by adjusting the reference values ​​of other evaluation indexes that are correlated with the evaluation index, when such other evaluation indexes exist, to predict the evaluation index for the operation of the real estate in the development plan. The real estate development planning support device according to feature 1.

3. The aforementioned evaluation index refers to electricity consumption. The real estate development planning support device according to feature 1.

4. The evaluation index prediction unit specifies one or more predetermined weather conditions and uses the actual power consumption values ​​of the similar property on days when the weather conditions and past weather information for the area where the similar property is located match as reference values ​​to predict the power consumption of the property under the development plan during operation under representative weather conditions. The real estate development planning support device according to feature 3.

5. The aforementioned evaluation indicator refers to the surrounding traffic volume. The real estate development planning support device according to feature 1.

6. The evaluation index prediction unit predicts the traffic volume around the similar property when the similar property is located on the planned site, based on information about the planned site and the planned sites of similar properties located in the vicinity that generate the same travel purposes as the property under development. The real estate development planning support device according to feature 5.

7. An output generation unit that displays map information on the screen and displays it on the display device in a display mode that allows input of the planned target area, the planned variables, and the prediction conditions for the evaluation indicators on the screen. The real estate development planning support device according to claim 1, further comprising the above.

8. The output generation unit displays a prediction results screen in which the predicted values ​​of the evaluation indicators are displayed on a graph. The real estate development planning support device according to feature 7.

9. The output generation unit displays a prediction results screen that shows the matching values ​​of the valuation indicators for similar properties on a graph. The real estate development planning support device according to feature 7.

10. The output generation unit displays map information of the area surrounding the planned site, and also displays the exterior view of the similar property on the planned site. The real estate development planning support device according to feature 8.

11. The output generation unit displays on the screen the values ​​of design variables that are not included in the planning variables among the design variables of a predetermined similar property in which the conformance value of the evaluation index is superior. The real estate development planning support device according to feature 10.

12. On the computer, A procedure for extracting similar properties whose similarity to the property under development is higher than a threshold, using the planning variables of the property under development. A procedure for predicting the evaluation indicators for the operation of real estate in the development plan, using the reference values ​​of the evaluation indicators for similar real estate, corrected based on the characteristics of the target site of the development plan, and the similarity, A real estate development plan support program to enable its implementation.

13. The similar property search unit extracts similar properties whose similarity to the property under development is higher than a threshold, using the planning variables of the property under development. The evaluation index prediction unit predicts the evaluation index for the operation of the real estate in the development plan using the reference value of the evaluation index of the similar real estate, which has been corrected based on the characteristics of the planned site of the development plan, and the similarity. A method for supporting real estate development plans, characterized by comprising the following features

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