JP7748342B2 - Real estate simulation device, real estate simulation method, and real estate simulation program - Google Patents

Description

The present invention relates to a real estate simulation device, a real estate simulation method, and a real estate simulation program.

For example, real estate companies may simulate the income and expenditure of rental income for rental properties. A conventional system for simulating rental properties is disclosed, for example, in Patent Document 1.

Japanese Patent Application Laid-Open No. 2005-173957

However, Patent Document 1 does not disclose any information regarding how to easily and accurately create simulation data for new contract data.

The present invention was made in consideration of the above, and aims to provide a real estate simulation device, a real estate simulation method, and a real estate simulation program that can easily and accurately create simulation data for new contract data.

In order to solve the above-mentioned problems and achieve the objectives, a real estate simulation device equipped with a control unit is provided, wherein the control unit stores contract data including the contract number, business partner, property, plot, contract start date, contract end date, monthly rent, rent start date, and cancellation date; a plot master registering the property, floor, plot, area, and use in association with each other; a contract rule master registering the property, area, area size, use, number of months from cancellation to contract conclusion, number of months from contract conclusion to delivery, and number of months from delivery to rent invoice; and a contract rule master registering the property, floor, use, plot, area, area size, monthly rent per tsubo (unit of square meters). and a forecast data creation means configured to be able to access an estimated rent master in which values are associated and registered, and which acquires floor, area, and use from the section master using the property and section of the target contract data as keys, identifies a record of the estimated rent master using the property, section, floor, and use as keys, identifies a record of the contract rule master using the area, area size, and use of the identified estimated rent master record as keys, and creates new contract data, which is simulation data, by referencing the identified records of the estimated rent master and contract rule master.

Furthermore, according to one aspect of the present invention, the forecast data creation means may create billing data including rent for each month based on the created new contract data.

According to another aspect of the present invention, the forecast data creation means calculates the monthly rent for the new contract data by multiplying the acquired area by the monthly unit price per tsubo in the estimated rent master, and calculates the contract start date by adding the number of months from the cancellation date in the contract rule master to the contract conclusion,
The rent due date may be calculated as the contract start date + the number of months from the conclusion of the contract in the contract rule master to delivery + the number of months from delivery to when the rent is billed.

Furthermore, according to one aspect of the present invention, the forecast data creation means may obtain the floor, area, and use from the section master using the property and section of the target contract data as keys, identify a record in the estimated rent master using the property, section, floor, and use as keys, and create updated contract data, which is simulation data, by referencing the identified record in the estimated rent master.

Furthermore, according to one aspect of the present invention, the forecast data creation means may create billing data including each month's rent based on the created renewal contract data.

Furthermore, according to one aspect of the present invention, the forecast data creation means may calculate the monthly rent for the updated contract data by multiplying the acquired area by the monthly price per tsubo in the estimated rent master.

Furthermore, in order to solve the above-mentioned problems and achieve the objectives, the present invention provides a real estate simulation method executed by an information processing device having a control unit, wherein the control unit executes a real estate simulation method by using a real estate simulation method that includes contract data including a contract number, business partner, property, plot, contract start date, contract end date, monthly rent, rent start date, and cancellation date; a plot master that associates and registers the property, floor, plot, area, and use; a contract rule master that associates and registers the property, area, area size, use, number of months from cancellation to contract conclusion, number of months from contract conclusion to delivery, and number of months from delivery to rent invoice; and a contract rule master that associates and registers the property, floor, use, plot, area, area size, monthly rent, The system is configured to be able to access an estimated rent master in which the price per square meter is associated and registered, and includes a forecast data creation process executed by the control unit, which acquires the floor, area, and use from the section master using the property and section of the target contract data as keys, identifies a record of the estimated rent master using the property, section, floor, and use as keys, identifies a record of the contract rule master using the area, area size, and use of the identified record of the estimated rent master as keys, and references the identified records of the estimated rent master and contract rule master to create new contract data, which is simulation data.

In order to solve the above-mentioned problems and achieve the objectives, the present invention provides a real estate simulation program to be executed by an information processing device having a control unit, wherein the control unit stores contract data including the contract number, business partner, property, plot, contract start date, contract end date, monthly rent, rent start date, and cancellation date; a plot master registering the property, floor, plot, area, and use in association with each other; a contract rule master registering the property, area, area size, use, number of months from contract cancellation to contract conclusion, number of months from contract conclusion to delivery, and number of months from delivery to rent invoice; and a contract rule master registering the property, floor, use, plot, area, area size, and monthly rent per tsubo in association with each other. The real estate simulation program is configured to access an estimated rent master registered in association with the property and plot of the target contract data, and the control unit executes a forecast data creation process in which the control unit acquires the floor, area, and use from the plot master using the property, plot, floor, and use as keys to identify a record of the estimated rent master, identifies a record of the contract rule master using the area, area size, and use of the identified estimated rent master record as keys, and creates new contract data, which is simulation data, by referencing the identified estimated rent master and contract rule master records.

The present invention has the advantage that by automatically generating simulation data for new contract data, it is possible to create simulation data for new contract data easily and with high accuracy.

FIG. 1 is a diagram for explaining examples of three patterns of withdrawal methods: cumulative billing, single-month billing, and single-month multiple billing. FIG. 2 is a diagram illustrating an example of the configuration of the supplier master. FIG. 3 is a diagram illustrating an example of the configuration of the property master. FIG. 4 is a diagram illustrating an example of the configuration of the floor master. FIG. 5 is a diagram showing an example of the configuration of a section master. FIG. 6 is a diagram showing an example of the configuration of the rental contract master. FIG. 7 is a diagram illustrating an example of the configuration of the contract rule master. FIG. 8 is a diagram illustrating an example of the configuration of the estimated rent master. FIG. 9 is a diagram showing an example of the configuration of billing data. FIG. 10 is a flowchart for explaining an outline of the overall processing of the control unit of the information processing device according to this embodiment. FIG. 11 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to the present embodiment. FIG. 12 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to this embodiment. FIG. 13 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to this embodiment. FIG. 14 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to this embodiment. FIG. 15 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to the present embodiment. FIG. 16 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to this embodiment. FIG. 17 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to this embodiment. FIG. 18 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to this embodiment. FIG. 19 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to this embodiment. FIG. 20 is a diagram for explaining a specific example of processing by the control unit of the information processing device according to the present embodiment.

An embodiment of the present invention will be described in detail with reference to the drawings. However, the present invention is not limited to this embodiment.

[1. Overview]
For example, major real estate companies sometimes create income and expenditure simulations for rental properties, but without any established company rules, simulations are often performed based on the experience and intuition of each individual employee, preventing the creation of unified simulation data across the company. Furthermore, conducting simulations while referencing actual contract data is time-consuming, and there are often large discrepancies between the simulation results and actual results. As a result, not only the company's own forecasts but also the forecast income and expenditure figures reported to owners by the AM company often deviate from reality.

In this embodiment, while each person in charge has their own rules of thumb, we focused on the fact that if company rules and market evaluations can be mastered and contract change predictions can be made, more precise simulations can be performed in a shorter period of time.

More specifically, in this embodiment, simulation data for new contract data is automatically generated based on contract data based on actual performance, for example, by referencing a contract rule master that specifies in-house rules for contracts by size and use, and an estimated rent master that reflects market evaluations, thereby easily and accurately creating simulation data for new contract data.

The real estate simulation device of this embodiment can be widely applied to the real estate management industry (including companies that own their own properties, PM companies, etc.).

2. Configuration
Fig. 1 is a block diagram showing an example of the configuration of a real estate simulation device 100 according to this embodiment. In Fig. 1, the real estate simulation device 100 includes a control unit 102, a communication interface unit 104, a storage unit 106, and an input/output interface unit 108. The units included in the real estate simulation device 100 are connected to each other so as to be able to communicate with each other via any communication path.

The communication interface unit 104 communicatively connects the real estate simulation device 100 to the network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 has the function of communicating data with other devices via the communication line. Here, the network 300 has the function of connecting the real estate simulation device 100 to the server 200 and the bank system 400 so that they can communicate with each other, and is, for example, the Internet or a LAN (Local Area Network).

An input device 112 and an output device 114 are connected to the input/output interface unit 108. The output device 114 may be a monitor (including a home television), a speaker, or a printer. The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that functions as a pointing device in conjunction with a mouse. Note that, below, the output device 114 may be referred to as the monitor 114.

The storage unit 106 stores various databases, tables, files, and the like. The storage unit 106 also stores computer programs that work in conjunction with the OS (Operating System) to issue commands to the CPU (Central Processing Unit) to perform various processes. The storage unit 106 can be, for example, a memory device such as RAM (Random Access Memory) or ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, or an optical disk.

The memory unit 106 includes a business partner master 106a, a property master 106b, a floor master 106c, a section master 106d, a rental contract master 106e, a contract rule master 106f, an estimated rent master 106g, and a data table 106h. Figure 2 is a diagram showing an example of the configuration of the business partner master 106a. Figure 3 is a diagram showing an example of the configuration of the property master 106b. Figure 4 is a diagram showing an example of the configuration of the floor master 106c. Figure 5 is a diagram showing an example of the configuration of the section master 106d. Figure 6 is a diagram showing an example of the configuration of the rental contract master 106e. Figure 7 is a diagram showing an example of the configuration of the contract rule master 106f. Figure 8 is a diagram showing an example of the configuration of the estimated rent master 106g. Figure 9 is a diagram showing an example of the configuration of billing data in data table 106h. In the following explanation, all amounts are expressed in yen, and this notation may be omitted.

The customer master 106a is a master for storing information about customers (tenants, vendors, etc.). For example, as shown in Figure 2, the customer master 106a can be configured as a table in which customer CDs and customer names are registered in association with each other. In the example shown in the figure, the first row contains customer CD "10000001" and customer name "Tenant A."

The property master 106b is a master for storing property information. For example, as shown in Figure 3, the property master 106b can be configured as a table in which property CDs and property names are registered in association with each other. In the example shown in the figure, the first line contains the property CD "00000001" and the property name "Kyobashi Building."

The floor master 106c is a master for storing floor information. For example, as shown in Figure 4, the floor master 106c can be configured as a table that associates and registers property CDs, property names, floor CDs, and floor names. In the example shown in the figure, the first row contains property CD "00000001", property name "Kyobashi Building", floor CD "A", and floor name "1st Floor".

The partition master 106d is a master for storing information about partitions. For example, as shown in FIG. 5, the partition master 106d can be configured as a table that associates and registers property CD, property name, floor CD, partition CD, partition name, area, and use. In the example shown in the figure, the first row contains property CD "00000001", property name "Kyobashi Building", floor CD "A", partition CD "1F-1", partition name "1st floor-1", area "100 tsubo", and use "office".

The rental contract master 106e is a master for storing contract data with tenants.
Billing data is created based on the rental contract master 106e. When a contract is renewed or canceled, the contract history number is incremented and data is stored in the rental contract master 106e. For example, as shown in Figure 6, the rental contract master 106e can be configured as a table or the like that registers contract data including the contract number, business partner CD, business partner name, property CD, property name, section CD, section name, contract history number, contract start date, contract end date, billing items, monthly amount, rent start date, and cancellation date.

In the example shown in the figure, the first line contains the contract number "A0000001", client CD "10000001", client name "Tenant A", property CD "00000001", property name "Kyobashi Building", section CD "1F-1", section name "1st floor-1", contract history number "1", contract start date "2021/6/1", contract end date "2022/5/31", billing item "Rent", monthly amount "50,000", and rent due date "2021/6/1".

The contract rule master 106f is a master that defines internal rules for contracts by size, purpose, etc. As shown in Figure 7, the contract rule master 106f can be composed of tables that associate and register the property CD, area, area size, purpose, cancellation-contract, contract-delivery, and delivery-rent.

"Area" is the area that manages the region to which the property belongs, and is classified, for example, as Kanto Area, Kansai Area, Kyushu Area, Chubu Area, Tohoku Area, etc. "Area size" is used to manage the size of the property, and is classified, for example, as large, medium, small, etc., based on the total floor area of the building. "Use" is used to manage the main use of the property, and is classified, for example, as office, commercial, hotel, complex facility, etc. "Cancellation - Contract" is the number of months from cancellation to contract conclusion. "Contract - Delivery" is the number of months from contract conclusion to delivery. "Delivery - Rent" is the number of months from delivery to when rent is billed.

In the example shown in the figure, the first line has property CD "00000001", area "Kanto area", area size "medium", use "office", termination-contract "2.0", contract-delivery "1.0", and delivery-rental "1.0".

The estimated rent master 106g is a master that specifies rents that reflect market valuation. For example, as shown in Figure 8, the estimated rent master 106g can be composed of a table that associates and registers the following: property CD, floor CD, use, section CD, area, area size, one monthly item, monthly unit price per 3.3 m2, one monthly occurrence date, two monthly items, monthly unit price per 6.5 m2, and two monthly occurrence dates. The estimated rent master 106g is updated with the latest information every time the market valuation changes.

In the example shown in the figure, the first line is property "CD00000001", floor CD "A", use "office", section CD "1F-1", area "Kanto area", area size "medium", monthly item 1 "rent", monthly unit price per 3.3 sq. m "170", monthly 1 occurrence date "0: rent occurrence date", monthly item 2 "common area fee", monthly unit price per 6.6 sq. m "30", monthly 2 occurrence date "0: rent occurrence date".

Data table 106h is a file for storing various data such as billing data. Billing data is created based on contract data. As shown in Figure 9 (A), billing data may include the contract number, contract history number, client CD, property CD, relevant month, billing item, and amount. In the example shown in the figure, the first line contains the contract number "A0000001," the contract history number "1," the client CD "10000001," the property CD "00000001," the relevant month "2021/6," the billing item "rent," and the amount "50,000." Figure 9 (B) is a diagram showing the monthly balance (income) of the billing data. The monthly balance of the billing data can be confirmed on the balance confirmation screen (not shown).

Returning to Figure 1, the control unit 102 is a CPU or the like that provides overall control of the real estate simulation device 100. The control unit 102 has internal memory for storing control programs such as an OS, programs that define various processing procedures, and required data, and executes various information processing operations based on these stored programs.

The control unit 102 is configured to be able to access the business partner master 106a, property master 106b, floor master 106c, section master 106d, rental contract master 106e, contract rule master 106f, estimated rent master 106g, and data table 106h, which are stored in the memory unit 106. The business partner master 106a, property master 106b, floor master 106c, section master 106d, rental contract master 106e, contract rule master 106f, estimated rent master 106g, and data table 106h may be stored in another location (e.g., server 200) as long as the control unit 102 is able to access them.

The control unit 102 conceptually includes a registration unit 102a, a simulation development environment creation unit 102b, a prediction data creation unit (simulation unit) 102c, and a screen display control unit 102d.

The registration unit 102a inputs, adds, changes, updates, etc. data into the customer master data 106a, property master data 106b, floor master data 106c, section master data 106d, rental contract master data 106e, contract rule master data 106f, and estimated rent master data 106g in response to operator operations on a master maintenance screen (not shown). The registration unit 102a also creates billing data based on the contract data in the rental contract master data 106e and stores it in the data table 106h.

When running a simulation, the simulation development environment creation unit 102b imports billing data from the customer master 106a, property master 106b, floor master 106c, section master 106d, rental contract master 106e, and data table 106h, as well as the contract rule master 106f and estimated rent master 106g, to provide a simulation development environment.

The forecast data creation unit 102c obtains the floor, area, and use from the section master 106d using the property and section of the target contract data in the rental contract master 106e as keys, identifies a record in the estimated rent master 106g using the property, section, floor, and use as keys, identifies a record in the contract rule master 106f using the area, area size, and use of the identified record in the estimated rent master 106g as keys, and creates new contract data, which is simulation data, by referencing the identified records in the estimated rent master 106g and contract rule master 106f.

The forecast data creation unit 102c may also create billing data including the rent for each month based on the new contract data created.

In addition, for new contract data, the forecast data creation unit 102c may calculate the monthly rent by multiplying the acquired area by the monthly unit price per tsubo in the estimated rent master 106g, the contract start date by the cancellation date + the number of months from the cancellation in the contract rule master 106f until the contract is concluded, and the rent due date by the contract start date + the number of months from the contract conclusion in the contract rule master 106f until delivery + the number of months from delivery until the rent is billed.

The forecast data creation unit 102c obtains the floor, area, and use from the section master 106d using the property and section of the target contract data in the rental contract master 106e as keys, identifies a record in the estimated rent master 106g using the property, section, floor, and use as keys, and creates updated contract data, which is simulation data, by referencing the identified record in the estimated rent master 106g.

The forecast data creation unit 102c may create billing data including the rent for each month based on the created renewal contract data.

The forecast data creation unit 102c may calculate the monthly rent for updated contract data by multiplying the acquired area by the monthly price per tsubo (unit of land) from the estimated rent master 106g.

The screen display control unit 102d controls the display and input reception of various screens (e.g., master maintenance screen, simulation environment planning screen, prediction data creation screen, etc.) displayed on the monitor 114.

3. Specific Examples
A specific example of the processing performed by the control unit 102 of the real estate simulation device 100 according to this embodiment will be described with reference to FIGS.

(3-1. Overall processing)
FIG. 10 is a flowchart for explaining an outline of the overall processing of the control unit 102 of the real estate simulation apparatus 100 according to this embodiment.

With reference to Figure 10, an overview of the overall processing of the control unit 102 of the real estate simulation device 100 in this embodiment will be described. In Figure 10, the registration unit 102a executes registration processing for the performance environment (step S1). Specifically, in the registration processing, the registration unit 102a registers business partners and sets data in the business partner master 106a in response to operations by the operator on a master maintenance screen (not shown).

The registration unit 102a also registers properties and sets data in the property master 106b, floor master 106c, and section master 106d in response to operator operations on a master maintenance screen (not shown).

The registration unit 102a also registers rental contracts, sets contract data in the rental contract master 106e in response to operator operations on a master maintenance screen (not shown), and creates billing data based on the contract data and stores it in the data table 106h.

The registration unit 102a also performs contract rule master maintenance and sets data in the contract rule master 106f in response to operator operations on a master maintenance screen (not shown).

The registration unit 102a also performs rental contract master maintenance and sets data in the estimated rent master 106g in response to operator operations on a master maintenance screen (not shown).

The simulation development environment creation unit 102b executes the simulation development environment creation process (step S2). Specifically, in the simulation development environment creation process, when executing a simulation, the simulation development environment creation unit 102b imports billing data from the customer master 106a, property master 106b, floor master 106c, section master 106d, rental contract master 106e, and data table 106h, as well as the contract rule master 106f and estimated rent master 106g, and expands (copies) them into the work area of the memory unit 106 to create a simulation environment.

If changes are required, the contract rule master 106f and estimated rent master 106g can also be made in the simulation environment.

The forecast data creation unit 102c executes the forecast data creation process (step S3) to create new and renewed contract data (also referred to as "simulation data" or "forecast data") based on the contract rule master 106f and the estimated rent master 106g, and creates billing data (also referred to as "simulation data" or "forecast data") including monthly rent based on the new and renewed contract data.

Specifically, in the forecast data creation process, the forecast data creation unit 102c obtains the floor, area, and use from the section master 106d using the property and section of the target contract data in the rental contract master 106e as keys, identifies a record in the estimated rent master 106g using the property, section, floor, and use as keys, identifies a record in the contract rule master 106f using the area, area size, and use of the identified record in the estimated rent master 106g as keys, and creates new contract data, which is simulation data, by referencing the identified record in the estimated rent master 106g and the contract rule master 106f.

The forecast data creation unit 102c may also create billing data including the rent for each month based on the new contract data created.

In addition, for new contract data, the forecast data creation unit 102c may calculate the monthly rent by multiplying the acquired area by the monthly unit price per tsubo in the estimated rent master 106g, the contract start date by the cancellation date + the number of months from the cancellation in the contract rule master 106f until the contract is concluded, and the rent due date by the contract start date + the number of months from the contract conclusion in the contract rule master 106f until delivery + the number of months from delivery until the rent is billed.

In addition, the forecast data creation unit 102c obtains the floor, area, and use from the section master 106d using the property and section of the target contract data in the rental contract master 106e as keys, identifies a record in the estimated rent master 106g using the property, section, floor, and use as keys, and creates updated contract data, which is simulation data, by referencing the identified record in the estimated rent master 106g.

The forecast data creation unit 102c may also create billing data including the rent for each month based on the created updated contract data.

In addition, the forecast data creation unit 102c may calculate the monthly rent for updated contract data by multiplying the acquired area by the monthly price per tsubo in the estimated rent master 106g.

(3-2. Sample data)
2 to 9 and 11 to 20 are diagrams for explaining a specific example of the processing by the control unit 102 of the real estate simulation device 100 in this embodiment. A specific example of the processing by the control unit 102 of the real estate simulation device 100 in this embodiment will be explained with reference to the above Figs. 2 to 9 and 11 to 20.

Figure 11 is a diagram showing a screen image of the simulation development environment creation screen. The simulation development environment creation screen has a field for specifying the target fiscal year and an execute button (not shown). When the operator specifies the fiscal year on the simulation development environment creation screen and presses the execute button (not shown), the forecast data creation unit 102c imports the master and data of the actual environment (customer master 106a, property master 106b, floor master 106c, section master 106d, rental contract master 106e, billing data in data table 106h, contract rule master 106f, and estimated rent master 106g) and copies them all at once to the work area (simulation environment) of the memory unit 106.

Figure 12 is a diagram showing an example of the forecast data creation screen. The forecast data creation screen has a field for specifying the target fiscal year, a field for specifying the simulation period, and an execute button (not shown). The fiscal year is automatically set to the year specified on the simulation development environment creation screen. When the operator specifies the simulation period on the forecast data creation screen and presses the execute button (not shown), the forecast data creation unit (simulation unit) 102c creates forecast data (simulation data) for new and updated contract data for the specified simulation period based on the contract rule master 106f and estimated rent master 106g for the contract data in the rental contract master 106e. The simulation period is specified to limit the period due to processing load considerations.

(Logic for automatically creating new contract data)
The logic for automatically creating new contract data will be described with reference to Figures 13 to 16. Figure 13 shows a detailed processing flow of the new contract data creation process executed by the forecast data creation unit 102c. In Figure 13, the floor CD, area, and purpose are obtained from the section master 106d using the property CD and section CD of the target contract data in the rental contract master 106e as keys (Step S11). A record in the estimated rent master 106g is identified using the property CD, section CD, floor CD, and purpose as keys (Step S12). A record in the contract rule master 106f is identified using the area, area size, and purpose of the identified record in the estimated rent master 106g as keys (Step S13). New contract data, which is simulation data, is created by referencing the identified records in the estimated rent master 106g and the contract rule master 106f (Step S14).

In this case, the monthly rent is calculated by multiplying the acquired area by the monthly unit price per tsubo in the estimated rent master 106g, the contract start date is calculated by adding the cancellation date plus the number of months from the cancellation in the contract rule master 106f until the contract is concluded, and the rent due date is calculated by adding the contract start date plus the number of months from the contract conclusion in the contract rule master 106f until delivery plus the number of months from delivery until the rent is billed.

Based on the new contract data created, billing data including each month's rent is created (step S15).

Figure 14 (A) shows an example of contract data that is the subject of the rental contract master 106e in Figure 6. In the example shown in the figure, the contract number is "A0000002", business partner CD is "10000002", business partner name is "Tenant B", property CD is "00000002", property name is "Ginza Building", section CD is "1F-2", section name is "1st Floor-2", contract history number is "1", contract start date is "2021/7/1", contract end date is "2022/6/30", billing item is "Rent", monthly amount is "90,000", and rent start date is "2021/7/1".

Figure 14 (B) shows an example of billing data created based on the above contract data (data copied from the actual environment). The rent amounts for each month from the contract start date "2021/7/1" to the contract end date "2022/6/30" are listed.

Figure 14 (C) shows the income and expenditure of the above billing data. Income and expenditure can only be predicted up to the month of cancellation.

Using the property CD "00000002" and section CD "1F-2" of the contract data as keys, floor CD "A", area "500 tsubo", and use "office building" are obtained from the section master 106d of Figure 5. Furthermore, using property "00000002", section CD "1F-2", floor CD "A", and use "office" as keys, a record in the estimated rent master 106g of Figure 8 is identified, and using the area, area size, and use of the identified record in the estimated rent master 106g as keys, a record in the contract rule master 106f is identified.

Figure 15 (A) shows the identified records in the contract rule master 106f. Figure 15 (B) shows the identified records in the estimated rent master 106g.

The contract cancellation date in the contract data is "5/31/2022," so by referencing the contract rule master 106f, the cancellation - contract is "3.0," so the contract start date is three months later, "9/1/2022." The contract end date is determined to be the same as the contract period of the contract being canceled (one year in this example), so it is "8/31/2023." Furthermore, the rent due date is "1.5," so it is +1.5 months until delivery, and "Delivery - Rent" is "1.5," so it is +1.5 months from delivery until rent is due, which is three months later, so it is "12/1/2022."

Regarding the rent amount, the contract data section obtained from the section master 106d is for an area of "500 tsubo," so when the estimated rent master 106g is referenced, the monthly rent per tsubo is "200," so the rent amount is 500 x 200 = 100,000.

Figure 15 (C) shows an example of new contract data automatically created in a simulation environment. In the example shown in the figure, the contract number is "A9000001," the client CD is "90000001," the client name is "dummy tenant," the property CD is "00000002," the property name is "Ginza Building," the section CD is "1F-2," the section name is "1st Floor-2," the contract history number is "1," the contract start date is "2022/9/1," the contract end date is "2023/8/31," the billing item is "rent," the monthly amount is "100,000," and the rent start date is "2022/12/1." The rent has been increased from "90,000" to "100,000."

Figure 16 (A) shows an example of billing data created based on the new contract data. Figure 16 (B) is a diagram showing the monthly balance of the billing data, which is written below the original contract data. The monthly balance of the billing data can be confirmed on a balance confirmation screen (not shown).

(Automatic contract data update and creation logic)
The logic for automatically updating and creating contract data will be described with reference to Figures 17 to 20. Figure 17 is a diagram showing a detailed flow of the process for creating updated contract data executed by the forecast data creation unit 102c. In Figure 17, the floor CD, area, and purpose are obtained from the section master 106d using the property CD and section CD of the target contract data in the rental contract master 106e as keys (step S21). A record in the estimated rent master 106g is identified using the property CD, section CD, floor CD, and purpose as keys (step S22). The identified record in the estimated rent master 106g is referenced to create updated contract data, which is simulation data (step S23).

In this case, the monthly rent may be calculated by multiplying the acquired area by the monthly price per tsubo in the estimated rent master 106g.

Based on the created renewal contract data, billing data including each month's rent is created (step S24).

Figure 18 (A) shows an example of contract data that is the subject of the rental contract master 106e in Figure 6. In the example shown in the figure, the contract number is "A0000003", business partner CD is "10000003", business partner name is "Tenant C", property CD is "00000003", property name is "Shinbashi Building", section CD is "1F-1", section name is "1st Floor-1", contract history number is "1", contract start date is "2021/6/1", contract end date is "2022/5/31", billing item is "Rent", monthly amount is "165,000", and rent due date is "2021/6/1".

Figure 18 (B) shows an example of billing data created based on the above contract data (data copied from the actual environment). The rent amounts for each month from the contract start date "2021/6/1" to the contract end date "2022/5/31" are listed.

Figure 18 (C) shows the monthly income and expenditure of the above billing data. Income and expenditure can only be predicted up to the end of the contract month.

Using the property CD "00000003" and section CD "1F-1" from the contract data as keys, floor CD "A", area "2,600 sq. m", and use "office building" are obtained from the section master 106d in Figure 5, and the property "00000003", section CD "1F-1", floor CD "A", and use "office" are used as keys to identify the record in the estimated rent master 106g in Figure 8.

Figure 19 (A) shows the identified records in the estimated rent master 106g.

Since the contract end date in the contract data is "5/31/2022," the "contract start date" of the updated contract will be +1 day, "6/1/2022." The "rent start date" will also be "6/1/2022." The "contract end date" will be determined to be the same as the contract period in the contract data (1 year in this example), and will be set to "5/31/2023." As for the "rent amount," the area in the contract data is "800 tsubo," so when the estimated rent master 106g is referenced, the monthly price per tsubo is "220," so 800 x 220 = 176,000.

Figure 19 (B) shows an example of updated contract data automatically created in a simulation environment. The second line in the figure shows an example of updated data, including contract number "A0000003", client CD "10000003", client name "Tenant C", property CD "00000003", property name "Shinbashi Building", section CD "1F-1", section name "1st Floor-1", contract history number "2", contract start date "6/1/2022", contract end date "5/31/2023", billing item "Rent", monthly amount "176,000", and rent due date "6/1/2022". The rent has been increased from "165,000" to "176,000".

Figure 20 (A) shows an example of billing data created based on the update data for the contract data. Figure 20 (B) shows the monthly balance of the billing data. The monthly balance of the billing data can be confirmed on a balance confirmation screen (not shown).

As explained above, according to this embodiment, the rental contract master 106e registers contract data including the contract number, business partner, property, plot, contract start date, contract end date, monthly rent, rent start date, and cancellation date; the plot master 106d registers the property, floor, plot, area, and use in association with each other; the contract rule master 106f registers the property, area, area size, use, number of months from contract termination to contract conclusion, number of months from contract conclusion to delivery, and number of months from delivery to rent invoice in association with each other; and the estimated rent master 106e registers the property, floor, use, plot, area, area size, and monthly rent per tsubo in association with each other. 06g, and a forecast data creation unit 102c that acquires the floor, area, and use from the section master using the property and section of the target contract data as keys, identifies the estimated rent master record using the property, section, floor, and use as keys, identifies the contract rule master record using the area, area size, and use of the identified estimated rent master record as keys, and creates new contract data that is simulation data by referencing the identified estimated rent master and contract rule master records, making it possible to create simulation data for new contract data easily and with high accuracy.

[4. Contribution to the United Nations-led Sustainable Development Goals (SDGs)]
This embodiment can contribute to improving business efficiency and promoting appropriate management decisions by companies, thereby contributing to the achievement of SDGs Goals 8 and 9.

In addition, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to SDGs goals 12, 13, and 15.

Furthermore, this embodiment can contribute to strengthening control and governance, thereby contributing to Goal 16 of the SDGs.

5. Other Embodiments
The present invention may be implemented in various different embodiments other than those described above within the scope of the technical concept set forth in the claims.

For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

In addition, the processing procedures, control procedures, specific names, registered data for each process, information including parameters such as search conditions, screen examples, and database configurations shown in this specification and drawings may be changed as desired unless otherwise specified.

Furthermore, with regard to the real estate simulation device 100, the components shown are functional concepts and do not necessarily have to be physically configured as shown.

For example, all or any part of the processing functions of the real estate simulation device 100, particularly the processing functions performed by the control unit, may be implemented by a CPU and a program interpreted and executed by the CPU, or may be implemented as hardware using wired logic. The program is recorded on a non-transitory, computer-readable recording medium containing programmed instructions for causing the information processing device to execute the processes described in this embodiment, and is mechanically read by the real estate simulation device 100 as needed. In other words, a computer program that works in conjunction with the OS to issue commands to the CPU and perform various processes is recorded in a storage unit such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM, and works in conjunction with the CPU to form the control unit.

In addition, this computer program may be stored on an application program server connected to the real estate simulation device 100 via any network, and all or part of it may be downloaded as needed.

In addition, the program for executing the processing described in this embodiment may be stored on a non-temporary computer-readable recording medium, or may be configured as a program product. Here, this "recording medium" includes memory cards, USB (Universal Serial Bus) memories, SD (Secure Digital) cards, flexible disks, magneto-optical disks, ROMs, EPROMs (Erasable Programmable Read Only Memory), EEPROMs (registered trademark) (Electrically Erasable and Programmable Read Only Memory), CD-ROMs (Compact Disk Read Only Memory), MOs (Magneto-Optical disks), DVDs (Digital Versatile Disks), and more. This includes any "portable physical media" such as a Blu-ray Disc (registered trademark) and a Blu-ray (registered trademark) Disc.

A "program" is a data processing method written in any language or description method, and may be in any format, such as source code or binary code. It should be noted that a "program" is not necessarily limited to a single structure, but also includes a distributed structure consisting of multiple modules or libraries, or a structure that achieves its function by working in conjunction with a separate program, such as an OS. The specific structure and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, can use well-known structures and procedures.

The various databases stored in the memory unit are storage devices such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and website provision.

The real estate simulation device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as such an information processing device to which any peripheral device is connected. The real estate simulation device 100 may also be realized by installing software (including programs or data, etc.) that causes the device to perform the processing described in this embodiment.

Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the figures, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various additions or functional loads. In other words, the above-mentioned embodiments may be implemented in any combination, or embodiments may be implemented selectively.

REFERENCE SIGNS LIST 100 Real estate simulation device 102 Control unit 102a Registration unit 102b Simulation formulation environment creation unit 102c Estimated data creation unit 102d Screen display control unit 104 Communication interface unit 106 Storage unit 106a Client master 106b Property master 106c Floor master 106d Section master 106e Rental contract master 106f Contract rule master 106g Estimated rent master 106h Data table 108 Input/output interface unit 112 Input device 114 Output device 300 Network

Claims (8)

A real estate simulation device including a control unit,
The control unit
Contract data including contract number, customer, property, lot, contract start date, contract end date, monthly rent, rent start date, and termination date;
A plot master that associates and registers property, floor, plot, area, and use,
A contract rule master that associates and registers the property, area, area size, use, number of months from contract termination to contract conclusion, number of months from contract conclusion to delivery, and number of months from delivery to when rent is billed;
The estimated rent master is a database that associates the property, floor, use, plot, area, floor size, and monthly rent per tsubo.
It is configured to be accessible to
a prediction data creation means for acquiring floor, area, and use from the section master using the property and section of the target contract data as keys, identifying a record of the estimated rent master using the property, section, floor, and use as keys, identifying a record of the contract rule master using the area, area size, and use of the identified estimated rent master record as keys, and creating new contract data, which is simulation data, by referring to the identified records of the estimated rent master and contract rule master;
A real estate simulation device comprising: The real estate simulation device described in claim 1, wherein the forecast data creation means creates billing data including monthly rent based on the created new contract data. The forecast data creation means performs the following on the new contract data:
The monthly rent is calculated by multiplying the acquired area by the monthly unit price per tsubo in the estimated rent master.
The contract start date is calculated by adding the cancellation date to the number of months from the cancellation of the contract rule master to the conclusion of the contract.
The real estate simulation device according to claim 1 or 2, characterized in that the rent due date is calculated as the contract start date + the number of months from the contract conclusion in the contract rule master to delivery + the number of months from delivery to when rent is billed. The real estate simulation device described in claim 1, characterized in that the forecast data creation means obtains the floor, area, and use from the section master using the property and section of the target contract data as keys, identifies the record in the estimated rent master using the property, section, floor, and use as keys, and creates updated contract data, which is simulation data, by referencing the identified estimated rent master record. The real estate simulation device described in claim 4, wherein the forecast data creation means creates billing data including monthly rent based on the created renewal contract data. The real estate simulation device of claim 4 or 5, characterized in that the forecast data creation means calculates the monthly rent for the updated contract data by multiplying the acquired area by the monthly price per tsubo (square meter) in the estimated rent master. A real estate simulation method executed by an information processing device having a control unit,
The control unit
Contract data including contract number, customer, property, lot, contract start date, contract end date, monthly rent, rent start date, and termination date;
A plot master that associates and registers property, floor, plot, area, and use,
A contract rule master that associates and registers the property, area, area size, use, number of months from contract termination to contract conclusion, number of months from contract conclusion to delivery, and number of months from delivery to when rent is billed;
The estimated rent master is a database that associates the property, floor, use, plot, area, floor size, and monthly rent per tsubo.
It is configured to be accessible to
Executed in the control unit:
a forecast data creation process in which the property and plot of the target contract data are used as keys to obtain the floor, area, and use from the plot master, the property, plot, floor, and use are used as keys to identify the record of the estimated rent master, the area, area size, and use of the identified estimated rent master record are used as keys to identify the record of the contract rule master, and new contract data, which is simulation data, is created by referencing the identified records of the estimated rent master and contract rule master;
A real estate simulation method comprising: A real estate simulation program to be executed by an information processing device having a control unit,
The control unit
Contract data including contract number, customer, property, lot, contract start date, contract end date, monthly rent, rent start date, and termination date;
A plot master that associates and registers property, floor, plot, area, and use,
A contract rule master that associates and registers the property, area, area size, use, number of months from contract termination to contract conclusion, number of months from contract conclusion to delivery, and number of months from delivery to when rent is billed;
The estimated rent master is a database that associates the property, floor, use, plot, area, floor size, and monthly rent per tsubo.
It is configured to be accessible to
In the control unit,
a forecast data creation process in which the property and plot of the target contract data are used as keys to obtain the floor, area, and use from the plot master, the property, plot, floor, and use are used as keys to identify the record of the estimated rent master, the area, area size, and use of the identified estimated rent master record are used as keys to identify the record of the contract rule master, and new contract data, which is simulation data, is created by referencing the identified records of the estimated rent master and contract rule master;
A real estate simulation program for running the following: