Computer, method for distributing real estate sales property information, and program

Abstract

To provide an information processing device, an information processing method, and a program capable of effectively appealing to people living in rental properties to purchase real estate for relocation. [Solution] The information processing device of the present invention is an information processing device that extracts one or more properties from a property database that stores information on multiple properties for sale or a rental property database that stores information on multiple rental properties based on input rental property information, which is information on rental properties input by a first user (step S8), and outputs information indicating each of the one or more extracted properties to the first user (step S10).

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program, and more particularly to an information processing apparatus, an information processing method, and a program used for selling real estate properties such as condominiums.

Background Art

[0002] In recent years, in the sale of real estate properties such as condominiums, the use of sales sites installed on the Web has been increasing. According to the sales site, it becomes easier to reach potential customers compared to waiting at the real estate agency's storefront, so it is possible to expect an improvement in the contract rate.

[0003] Patent Document 1 discloses an example of a sales support apparatus used at a real estate sales site. The sales support apparatus according to this example has a function of having a purchaser input the desired monthly payment amount and extracting and displaying real estate properties for which the total monthly payment amount is less than or equal to the desired payment amount.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Incidentally, potential demand for real estate includes people living in rental properties. In other words, among those living in rental properties, there are people who think that it might be better to buy rather than continue renting, and such people can be said to be potential demand for real estate. Therefore, there is a need for technology that can effectively appeal to people living in rental properties about purchasing real estate for relocation. In this regard, it seems that the technology described in Patent Document 1 could present properties suitable for prospective buyers, but since the prospective buyer arbitrarily inputs the desired monthly payment amount which is the basis for selection, the properties presented were not necessarily suitable for the prospective buyer.

[0006] Therefore, one of the objectives of the present invention is to provide an information processing device, an information processing method, and a program that can effectively appeal to people living in rental properties to purchase real estate for relocation purposes.

[0007] Furthermore, for providers of sales websites, one of the key points for improving the conversion rate is to attract high-quality buyers who have a strong desire to purchase and the financial resources to do so. However, it has been difficult to attract high-quality buyers with conventional sales websites that treat all buyers equally.

[0008] Therefore, another object of the present invention is to provide an information processing device, an information processing method, and a program that can provide a sales site capable of attracting high-quality buyers. [Means for solving the problem]

[0009] An information processing device according to the first aspect of the present invention is an information processing device that extracts one or more properties from a database storing information on multiple properties based on input rental property information, which is information on rental properties entered by a first user, and outputs information indicating each of the extracted one or more properties to the first user.

[0010] An information processing device according to a second aspect of the present invention is an information processing device that, according to the selection of a second user who is a seller, registers information on a property for sale entered by the second user into either a property database that stores information on multiple properties for sale or a private property database that stores information on multiple private properties for sale; registers information indicating the desired conditions for a property to be purchased entered by a third user who is a buyer into a conditions database; and when a property for sale registered in the property database or the private property database matches the desired conditions registered in the conditions database, it distributes information on the property for sale to the third user.

[0011] An information processing method according to a first aspect of the present invention is an information processing method that includes the steps of: extracting one or more properties from a database storing information on multiple properties based on input rental property information, which is information on rental properties entered by a first user; and outputting information indicating each of the extracted one or more properties to the first user.

[0012] A second aspect of the present invention is an information processing method that includes the steps of: registering information on a property for sale entered by a second user, who is a seller, in either a property database that stores information on multiple properties for sale or a private property database that stores information on multiple private properties for sale, according to the selection of the second user, who is a seller; registering information indicating the desired conditions for a property to be purchased, entered by a third user, who is a buyer, in a conditions database; and distributing information on a property for sale to the third user when the property registered in the property database or the private property database matches the desired conditions registered in the conditions database.

[0013] A program according to the first aspect of the present invention is a program that causes a computer to perform the following steps: extract one or more properties from a database that stores information on multiple properties based on input rental property information, which is information on rental properties entered by a first user; and output information indicating each of the extracted one or more properties to the first user.

[0014] A program according to the first aspect of the present invention is a program that causes a computer to execute the following steps: register information on a property for sale entered by a second user, who is a seller, in either a property database that stores information on multiple properties for sale or a private property database that stores information on multiple private properties for sale, according to the second user's selection; register information indicating the desired conditions for a property to be purchased, entered by a third user, who is a buyer, in a conditions database; and, if a property for sale registered in the property database or the private property database matches the desired conditions registered in the conditions database, distribute information on the property to the third user. [Effects of the Invention]

[0015] According to the first aspect of the present invention, since properties for sale are extracted based on rental property information entered by the user, it becomes possible to effectively appeal to people living in rental properties about purchasing real estate for relocation.

[0016] According to a second aspect of the present invention, information on unlisted properties for sale is registered in a separate database of unlisted properties, and information on properties that match the desired conditions is distributed to buyers who are permitted to register their desired conditions in advance in a conditions database. This makes it possible to provide a sales site that can attract high-quality buyers. [Brief explanation of the drawing]

[0017] [Figure 1]This is a diagram showing the system configuration of the information processing system 1 according to an embodiment of the present invention. [Figure 2] This is a diagram showing the basic hardware configurations of the information processing device 10 and the user terminals 30 and 31 respectively. [Figure 3] This is a diagram showing the structures of the condominium database 20, the rental property database 21, and the property database 22. [Figure 4] This is a flowchart showing the processing flow of the sales property extraction process executed by the planner server 11. [Figure 5] This is a flowchart showing the details of the appropriate rent estimation process executed in step S4 of FIG. 4. [Figure 6] This is a flowchart showing the details of the appropriate purchase price estimation process executed in step S5 of FIG. 4. [Figure 7] This is a diagram showing an example of a screen displayed on the display of the user terminal 30 which is a smartphone. [Figure 8] This is a sequence diagram of the sales property extraction process executed by the planner server 11. [Figure 9] This is a diagram showing the structures of the non-public property database 23, the member database 24, the condition database 25, and the distribution database 26. [Figure 10] This is a flowchart showing the processing flow of the sales property registration process executed by the members market server 12. [Figure 11] This is a flowchart showing the processing flow of the desired condition registration process executed by the recommender server 13. [Figure 12] This is a flowchart showing the processing flow of the distribution process executed by the recommender server 13. [Figure 13] (a) is a diagram showing the public type selection page 50 which is an example of the public type selection page displayed in step S40 of FIG. 10, and (b) is a diagram showing the information input page 51 which is an example of the information input page displayed in step S46 of FIG. 10. [Figure 14]This figure shows an example of an information input page 52, which is displayed in step S52 of Figure 11. [Figure 15] This figure shows a delivery page 53, which is an example of a delivery page generated in step S66 of Figure 12. [Figure 16] This is a sequence diagram of a portion of the sales property registration and distribution process (up to registration in the distribution database 26) executed by the Members Market Server 12 and the Recommender Server 13. [Figure 17] This is a sequence diagram of the remaining steps of the member registration process, desired conditions registration process, and distribution process performed by the recommender server 13. [Modes for carrying out the invention]

[0018] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0019] Figure 1 is a diagram showing the system configuration of an information processing system 1 according to an embodiment of the present invention. As shown in the figure, the information processing system 1 has a configuration in which an information processing device 10 and user terminals 30 and 31 are interconnected via a network 3. The network 3 is, for example, the internet and is connected not only to the information processing device 10 and user terminals 30 and 31, but also to various websites, including a sales site 2 that sells real estate properties such as condominiums.

[0020] The information processing device 10 is a server computer and is functionally composed of three types of servers: a planner server 11, a members market server 12, and a recommender server 13, as well as seven types of databases: a condominium database 20, a rental property database 21, a property database 22, a private property database 23, a member database 24, a conditions database 25, and a distribution database 26.

[0021] Of these, the planner server 11 is a server that allows users residing in rental properties to input information about their current rental properties, and extracts and presents properties for sale to users based on the input information. It is connected to the apartment database 20, the rental property database 21, and the property database 22. The members market server 12 is a server that functions as a sales site for selling properties such as apartments on the network 3, and is connected to the recommender server 13 and the property database 22. The recommender server 13 is a server for selling properties uploaded to the members market server 12 privately, and is connected to the members market server 12, the property database 22, the private property database 23, the member database 24, the conditions database 25, and the distribution database 26. Details of each will be explained separately later. In this embodiment, each server and each database is described as being implemented within a single information processing device 10, but each server and each database may be distributed and implemented within multiple information processing devices 10.

[0022] User terminals 30 and 31 are computers used personally by the user. Figure 1 shows an example where user terminal 30 is a smartphone and user terminal 31 is a notebook-type personal computer, but the specific models of user terminals 30 and 31 are not limited to these. For example, user terminals 30 and 31 may be composed of tablet computers or desktop personal computers. In the following explanation, we will continue on the premise that user terminal 30 is a smartphone and user terminal 31 is a personal computer.

[0023] Figure 2 shows the basic hardware configuration of the information processing device 10 and user terminals 30 and 31, respectively. As shown in the figure, the information processing device 10 and user terminals 30 and 31 are configured such that a processor 101, a storage device 102, a communication device 103, an input device 104, and an output device 105 are interconnected via a bus 106.

[0024] The processor 101 is a central processing unit that reads and executes programs stored in the memory device 102. Each process described later, performed by the information processing device 10 and user terminals 30 and 31, is realized by each processor 101 reading and executing programs stored in its respective memory device 102. The processor 101 is configured to communicate with various parts of the server via the bus 106, and performs tasks such as controlling each part and processing data stored in the memory device 102, according to the description of the program to be executed.

[0025] The storage device 102 is a device that temporarily or permanently stores various programs and data. The storage device 102 is usually composed of a combination of multiple storage devices, such as a main memory device consisting of DRAM (Dynamic Random Access Memory) or SRAM (Static Random Access Memory), and an auxiliary storage device consisting of a hard disk or SSD (Solid State Drive).

[0026] The communication device 103 is a device that performs communication with an external communication device (including network 3 shown in Figure 1) in response to the control of the processor 101. The method of communication performed by the communication device 103 is not particularly limited, but examples include wired or wireless WAN (Wide Area Network) or LAN (Local Area Network).

[0027] The input device 104 is a device that receives input from the user and includes various input means such as a mouse, keyboard, and touch panel. The content of the user input received by the input device 104 is transmitted to the processor 101 via the bus 106. The output device 105 is a device that provides output to the user in accordance with the control of the processor 101 and includes various output means such as a display and speakers.

[0028] The following will explain in detail the processes performed by the information processing device 10. In the following explanation, we will first describe the processes performed by the planner server 11 with reference to Figures 3 to 8, and then describe the processes performed by the members market server 12 and the recommender server 13 with reference to Figures 9 to 17.

[0029] First, let's explain the processes performed by the planner server 11. Figure 3 shows the structure of the apartment database 20, rental property database 21, and property database 22 connected to the planner server 11. Of these, the apartment database 20 is a database that stores information about apartments as buildings, and is configured to store property name (name of the apartment), address, latitude and longitude, nearest station, walking time from nearest station (time required when walking from the nearest station), building structure (steel frame, reinforced concrete, steel-reinforced concrete, etc.), construction date (year and month when the apartment was built), total number of units, and yield (a numerical value representing the superficial profitability, such as how much rental income can be obtained relative to the property price).

[0030] The rental property database 21 is a database that stores information on rental properties, such as a single room in an apartment building (hereinafter referred to as "rental property"). It is configured to store information such as the apartment building ID, rent, floor area, layout (3LDK, 1R, etc.), number of rooms (number of rooms included in the property), floor level, balcony direction (south, east-southeast, etc.), corner room flag (information indicating whether or not it is a corner room), whether or not it has been renovated (information indicating whether or not it has been renovated), and type of rights (information indicating whether it is a condominium with a fixed-term leasehold or a condominium with ownership rights).

[0031] The property database 22 is a database that stores information on properties such as a single apartment unit that will be sold (hereinafter referred to as "the listed property"). It is configured to store information such as the apartment ID, price, price deviation score (value for money), floor area, floor plan (3LDK, 1R, etc.), number of rooms (number of rooms included in the property), floor level, balcony direction (south, east-southeast, etc.), and whether the property has been sold (information indicating whether it has been sold or not), associated with a sales property ID to identify the property for sale.

[0032] The data stored in the apartment database 20, the rental property database 21, and the property database 22 is registered in each database by the planner server 11 or the members market server 12. More specifically, the planner server 11 is configured to collect information on rental and sales properties by visiting one or more sales sites 2 on the network 3 and register it in each database. The members market server 12 is configured to collect information on rental and sales properties by accepting input from sellers or managers of rental properties and register it in each database.

[0033] Figure 4 is a flowchart showing the processing flow of the property extraction process performed by the planner server 11. The information processing device 10 plays a role in effectively appealing to people living in rental properties to purchase real estate for relocation purposes by executing this property extraction process by the planner server 11. The contents of the property extraction process will be explained in detail below with reference to Figure 4.

[0034] The planner server 11 first generates an input page for information on the rental property currently being occupied (hereinafter referred to as "occupied rental property") (hereinafter referred to as "input rental property information") and displays it on the displays of user terminals 30 and 31 (step S1).

[0035] Figure 7 shows an example of a screen displayed on the display of a user terminal 30, which is a smartphone. Although the figure shows an example of display on a smartphone, the content is the same for the screen displayed on a user terminal 31, which is a personal computer. The information input page 40 shown in Figure 7 is an example of an information input page displayed in step S1. As shown in the figure, the information input page 40 consists of an image and a description, as well as a property name input field 40a for entering the name of the apartment in which the user currently resides, a rent input field 40b for entering the rent, a size input field 40c for entering the size, a floor input field 40d for entering the floor, a corner room button 40e for selecting whether it is a corner room or not, a south-facing button 40f for selecting whether it faces south or not, a renovated button 40g for selecting whether it has been renovated or not, and a transition button 40h for proceeding with the process. Of these, the property name input field 40a is preferably composed of a combo box that allows the user to select the property name of each apartment stored in the apartment database 20.

[0036] Returning to Figure 4, the planner server 11, which has displayed the information input page 40, determines whether or not the transition button 40h on the information input page 40 has been pressed (step S2). If the planner server 11 determines in step S2 that the transition button 40h has been pressed, it obtains basic information of the occupied rental property (nearest station, distance from nearest station, year of construction, etc.) from the apartment database 20, which is indicated by the input rental property information entered on the information input page 40 (step S3), and then sequentially executes an appropriate rent estimation process to estimate an appropriate rent for the occupied rental property (step S4) and an appropriate purchase price estimation process to estimate an appropriate purchase price for the occupied rental property (step S5).

[0037] Figure 5 is a flowchart showing the details of the appropriate rent estimation process performed in step S4. As shown in the figure, the planner server 11, which has started the appropriate rent estimation process, first extracts apartments located near the currently occupied rental property from the apartment database 20 (step S20). Specifically, it may extract apartments located in the same ward as the currently occupied rental property based on the address in the apartment database 20, or it may extract apartments that share the same nearest station as the currently occupied rental property based on the nearest station in the apartment database 20, or it may extract apartments whose distance from the currently occupied rental property is less than or equal to a predetermined value based on the latitude and longitude in the apartment database 20.

[0038] Next, the planner server 11 extracts properties similar to the currently occupied rental property (hereinafter referred to as "nearby similar rental properties") from the apartment rental properties extracted in step S20 (stored in the rental property database 21) (step S21). Specifically, the planner server 11 first obtains the floor area, distance from the nearest station, and construction date of the currently occupied rental property. The floor area is the value entered in the area input field 40c (see Figure 7) on the information input page 40, while the distance from the nearest station and construction date are information included in the basic information obtained in step S3. The planner server 11 also obtains the apartment ID, distance from the nearest station, and construction date of each apartment extracted in step S20 from the apartment database 20, and extracts rental properties with the obtained apartment ID from the rental property database 21. Then, it obtains the floor area of ​​the extracted rental properties from the rental property database 21. Planner Server 11 extracts similar nearby rental properties based on the floor area, distance from the nearest station, and construction date of the occupied rental property and other rental properties obtained as described above. Specifically, properties whose floor area, distance from the nearest station, and construction date differ from the occupied rental property within predetermined values ​​should be extracted as similar nearby rental properties. Here, we have explained an example using floor area, distance from the nearest station, and construction date as criteria for similarity, but of course, other criteria can also be used.

[0039] Next, the planner server 11 adjusts the rent of the extracted nearby similar rental properties so that all conditions other than those used as criteria for similarity judgment in step S21 match the conditions of the occupied rental property (step S22). This adjustment is based on the information entered on the information input page 40 and the corresponding information of nearby similar rental properties. To give a specific example, if the occupied rental property is located on a lower floor and the nearby similar rental property is located on a higher floor, the rent of the nearby similar rental property is adjusted downwards. Also, if the occupied rental property is a corner unit and the nearby similar rental property is not a corner unit, the rent of the nearby similar rental property is adjusted upwards. Furthermore, if the occupied rental property faces south and the nearby similar rental property faces north, the rent of the nearby similar rental property is adjusted upwards. Also, if the occupied rental property has been renovated and the nearby similar rental property has not been renovated, the rent of the nearby similar rental property is adjusted upwards. The degree of increase and decrease may be determined by preparing a table containing various cases in advance and determining it according to the contents of that table, or by having an artificial intelligence learn various cases in advance and determining it based on the output of the artificial intelligence.

[0040] Finally, the planner server 11 calculates the average of the adjusted rents and obtains it as the appropriate rent for the occupied rental property (step S23), thereby terminating the appropriate rent estimation process.

[0041] Figure 6 is a flowchart showing the details of the appropriate purchase price estimation process performed in step S5. As shown in the figure, the planner server 11, which has started the appropriate purchase price estimation process, first extracts apartments located near the currently occupied rental property from the apartment database 20 (step S30). Specifically, similar to step S20 described above, it may extract apartments in the same ward as the currently occupied rental property based on the address in the apartment database 20, or it may extract apartments whose nearest station is the same as the currently occupied rental property based on the nearest station in the apartment database 20, or it may extract apartments whose distance from the currently occupied rental property is less than or equal to a predetermined value based on the latitude and longitude in the apartment database 20. However, in step S30, it is preferable to extract apartments over a geographically wider area compared to the extraction in step S20 described above and step S34 described later. By doing so, it becomes possible to extract more properties for calculating the yield in the subsequent step S31.

[0042] Next, the planner server 11 extracts the apartment properties (for sale and for rent) extracted in step S30 from at least one of the property database 22 and the rental property database 21, and extracts properties similar to the currently occupied rental property (hereinafter referred to as "neighborhood similar properties") from among the extracted properties (step S31). The criteria for determining similarity here may be the same as those in step S21 described above.

[0043] Next, the planner server 11 obtains the prices and rents of each similar property in the neighborhood that was extracted in step S31 (step S32). Specifically, it obtains or estimates the prices and rents of properties for sale and rental properties from the prices stored in the property database 22 and the rents stored in the rental property database 21.

[0044] Next, the planner server 11 uses the price and rent of each similar neighboring property obtained in step S32 to calculate the yield of each similar neighboring property using the following formula (1), and then finds the average value (step S33). In formula (1), A is the price of each similar neighboring property, B is the rent of each similar neighboring property, and C is the yield of each similar neighboring property. C = (B × 12) / A × 100 ... (1)

[0045] Next, the planner server 11 extracts apartments located near the currently occupied rental property from the apartment database 20 (step S34), and then extracts properties similar to the currently occupied rental property (neighborhood similar rental properties) from the extracted apartment rental properties (step S35). The details of the processing in steps S34 and S35 are the same as the processing in steps S20 and S21.

[0046] Next, the planner server 11 estimates the price of each similar rental property in the neighborhood using the following formula (2) (step S36), based on the average yield calculated in step S33 and the rent of each similar rental property in the neighborhood extracted in step S35 (as set in the rental property database 21). However, in formula (2), D is the rent of each similar rental property in the neighborhood, E is the average yield calculated in step S33, and F is the estimated price of each similar rental property in the neighborhood. F = (D × 12) × 100 / E ... (2)

[0047] Finally, the planner server 11 calculates the average of the prices estimated in step S36 and obtains it as the appropriate purchase price for the occupied rental property (step S37), thereby terminating the appropriate purchase price estimation process.

[0048] Return to Figure 4. After completing steps S4 and S5, the planner server 11 calculates the loan repayment amount for purchasing an occupied rental property based on the appropriate purchase price estimated in step S5 (step S6). In this calculation, it is preferable that the planner server 11 calculates the loan repayment amount using a predetermined repayment period and repayment method (fixed amount, fixed rate, etc.) and the latest interest rate. Alternatively, the information input page 40 shown in Figure 7 may be used to allow the user to input the repayment period and repayment method, and the loan repayment amount may be calculated using the entered repayment period and repayment method. Subsequently, the planner server 11 generates an information output page including the appropriate rent estimated in step S4 and the loan repayment amount calculated in step S6, and displays it on the displays of user terminals 30 and 31 (step S7).

[0049] The information output page 41 shown in Figure 7 is an example of an information output page displayed in step S7. The "current rent" included in the information output page 41 shown in the figure is the rent of the currently occupied rental property entered in the rent input field 40b of the information input page 40, the "appropriate rent" is the appropriate rent estimated in step S4, and the "loan repayment amount" is the loan repayment amount calculated in step S6. The information output page 41 also displays the difference between the "current rent" and the "appropriate rent," and the difference between the "current rent" and the "loan repayment amount." By looking at this information output page 41, the user can find out whether the current rent is appropriate and how the loan repayment amount would compare to the current rent if they were to purchase the currently occupied rental property.

[0050] Returning to Figure 4, the planner server 11 then extracts from the property database 22 properties that can be purchased at a price equivalent to the fair purchase price estimated in step S5, and that are more desirable (step S8). Here, "equivalent" means that the difference between the price stored in the property database 22 and the fair purchase price estimated in step S5 is within a predetermined value. Furthermore, "more desirable" means, for example, that the exclusive area is larger than the currently occupied rental property, that it is a newer building, or that it is located in a popular area (for example, in the case of Tokyo, Chuo Ward, Chiyoda Ward, Minato Ward, Bunkyo Ward, Shinjuku Ward, Shibuya Ward, etc.).

[0051] The planner server 11 determines whether or not a transition button on the information output page 41 (for example, the "Next" button included in the information output page 41 in Figure 7) has been pressed (step S9). If the planner server 11 determines in step S9 that a transition button has been pressed, it generates an information output page containing the list of properties for sale extracted in step S8 and displays it on the displays of the user terminals 30 and 31 (step S10).

[0052] The information output page 42 shown in Figure 7 is an example of an information output page displayed in step S10. As shown in the figure, the information output page 42 lists information on multiple properties for sale along with "View Property" and "Consult" buttons. The information input page 43 shown in Figure 7 is an example of a page displayed when the user clicks the "Consult" button. The information input page 43 is a page where the user enters their information and requests. When the user clicks the "Inquire" button included on this page, the planner server 11 generates an email containing the entered user information and requests, along with information on the corresponding occupied rental and for-sale properties, and sends it to the predetermined email address of the sales representative. This allows the sales representative to directly introduce properties for sale to the user who accessed the information input page 40.

[0053] Figure 8 is a sequence diagram of the sales property extraction process performed by the planner server 11. The following explanation of the process performed by the planner server 11 will refer to Figure 8 to describe it from a different perspective.

[0054] The planner server 11 first visits one or more sales sites 2 and collects information on rental and sales properties from each (step S100). Then, based on the collected information, it generates records for the apartment database 20, the rental property database 21, and the property database 22 and registers them in each (step S101).

[0055] Next, the planner server 11 receives input from user terminals 30 and 31 regarding the rental property in which the user is currently residing (step S102), and retrieves this basic information from the apartment database 20 (step S103). Subsequently, the planner server 11 extracts nearby similar rental properties from the apartment database 20 and the rental property database 21 that are located near the rental property in which the user is currently residing and are similar to the rental property in which the user is currently residing (step S104). Then, after adjusting the rent of the extracted nearby similar rental properties to match the conditions of the rental property in which the user is currently residing (step S105), the average of the adjusted rents is presented to the user as the appropriate rent for the rental property in which the user is currently residing (step S106).

[0056] Furthermore, the planner server 11 extracts similar nearby properties from the apartment database 20 and the property database 22 that are similar to the currently occupied rental property (step S107), and obtains their prices and rents from the data stored in the property database 22 and the rental property database 21 (step S108). Then, using the formula (1) described above, it calculates the average yield of each extracted nearby similar property (step S109).

[0057] The planner server 11 further extracts similar rental properties located near the currently occupied rental property and similar to the currently occupied rental property from the apartment database 20 and the rental property database 21 (step S110). Then, by substituting the average yield calculated in step S109 and the rent of each similar rental property extracted in step S110 into the above formula (2), it estimates the price of each similar rental property and estimates the appropriate purchase price of the currently occupied rental property from the results (step S111). Based on the appropriate purchase price thus estimated, the planner server 11 calculates the loan repayment amount if the user were to purchase the currently occupied rental property and presents it to the user (step S112).

[0058] Finally, the planner server 11 extracts from the property database 22 properties that can be purchased at a price equivalent to the appropriate purchase price estimated in step S111, and that are more desirable (step S113), and presents the information of the extracted properties to the user (step S114).

[0059] As explained above, the planner server 11 according to this embodiment extracts properties for sale based on information about the rental property currently occupied by the user, making it possible to effectively appeal to people living in rental properties about purchasing real estate for relocation.

[0060] Furthermore, according to the planner server 11 of this embodiment, the appropriate rent and purchase price for the rental property the user is currently living in are displayed, so the user can find out if their current rent is too high or if the loan repayment amount if they were to purchase the property is lower than their current rent. This can be an incentive to move, so according to the planner server 11 of this embodiment, it becomes possible to more effectively appeal to people living in rental properties to purchase real estate for relocation.

[0061] In this embodiment, the planner server 11 displays an information output page including a list of properties for sale in step S10, but it may also display an information output page including a list of rental properties, or an information output page including lists of both properties for sale and rental properties. In this case, the planner server 11 only needs to extract from the rental property database 21 rental properties that can be rented at a price equivalent to the rent entered by the user on the information input page displayed in step S1, and that are more desirable. Equivalent here means that the difference between the rent stored in the rental property database 21 and the rent entered by the user is within a predetermined value. Furthermore, "more desirable" means, for example, that the exclusive area is larger than the currently occupied rental property, that it is newly built, or that it is located in a popular area (for example, in the case of Tokyo, Chuo Ward, Chiyoda Ward, Minato Ward, Bunkyo Ward, Shinjuku Ward, Shibuya Ward, etc.).

[0062] Next, we will explain the processes performed by the Members Market Server 12 and the Recommender Server 13. Figure 9 shows the structure of the private property database 23, member database 24, condition database 25, and distribution database 26, which are connected to at least one of the Members Market Server 12 and the Recommender Server 13. The structure of the private property database 23 is the same as that of the property database 22 shown in Figure 3. However, the private property database 23 stores only information on properties for sale that the seller user has chosen to keep private. This point will be explained in more detail later.

[0063] The member database 24 is a database that stores information about members of the services provided by the recommender server 13. It is configured to store member IDs, names, address information, and other information in a mutually associated manner. Address information includes, for example, email addresses or social networking service (SNS) addresses. The member database 24 is pre-registered with information about users who may become buyers of properties for sale registered in the non-public property database 23.

[0064] The Condition Database 25 is a database that stores information indicating the desired conditions for a property to be purchased. It is configured to store information such as member ID, station, walking distance from the station (time required when walking from the station), price range, floor area, floor plan, number of rooms, and balcony direction, associated with the condition ID. As can be understood from the inclusion of the member ID, the information registered in the Condition Database 25 is information indicating the desired conditions of the user registered in the Member Database 24.

[0065] The distribution database 26 is a database that stores information on properties for sale that match the desired conditions, and is configured to store the date on which the distribution was performed, etc., in association with the combination of condition ID and property ID.

[0066] Figure 10 is a flowchart showing the processing flow of the sales property registration process executed by the Members Market Server 12. Figure 11 is a flowchart showing the processing flow of the desired conditions registration process executed by the Recommender Server 13, and Figure 12 is a flowchart showing the processing flow of the distribution process executed by the Recommender Server 13. The Information Processing Device 10 plays the role of providing a sales site that can attract high-quality buyers by having the Members Market Server 12 and the Recommender Server 13 execute these processes. The contents of each process will be explained in detail below, referring to Figures 10, 11, and 12 in order.

[0067] First, referring to Figure 10, the Members Market Server 12, which has started the property listing process, first generates a listing type selection page and displays it on the displays of user terminals 30 and 31 (step S40).

[0068] Figure 13(a) shows an example of the publication type selection page 50 displayed in step S40. This figure, as well as Figures 13(b), 14, and 15 shown later, show examples of how it is displayed on a personal computer, but the content is the same for the screen displayed on the user terminal 30, which is a smartphone. As shown in Figure 13(a), the publication type selection page 50 consists of a selection button 50a for selecting a sale by public and a selection button 50b for selecting a sale by private.

[0069] Returning to Figure 10, the Members Market Server 12 determines whether the user selected to sell publicly by obtaining the type of button pressed by the user on the displayed publication type selection page 50 (step S41). If it is determined that the user selected to sell publicly, the process proceeds to step S42; if it is determined that the user did not select to sell publicly (i.e., selected to sell privately), the process proceeds to step S46.

[0070] In step S42, the Members Market Server 12 generates an information input page for publicly listed properties and displays it on the displays of user terminals 30 and 31 (step S42). Although not shown in the diagram, the information input page displayed at this time may be the same as the information input page 51 shown in Figure 13(b) later. In step S42, the Members Market Server 12 determines whether the user has pressed the transition button on the information input page displayed (step S43), and if it determines that the user has pressed the button, it waits for confirmation from a sales representative as necessary (step S42). This waiting period can be terminated, for example, by displaying a page containing a confirmation button on the sales representative's terminal and having the sales representative press the confirmation button. By including step S42, it becomes possible for sales representatives to assess the properties for sale (including correcting the entered information) before the properties entered by the user are registered in the property database 22. When the waiting period ends due to the sales representative pressing the confirmation button, the Members Market Server 12 registers the property entered by the user in the property database 22 and terminates the property registration process for publicly listed properties.

[0071] Meanwhile, the Members Market Server 12, which proceeded to step S46, generates an information input page for non-publicly listed properties and displays it on the displays of user terminals 30 and 31 (step S46).

[0072] Figure 13(b) shows an example of an information input page 51 displayed in step S46. As shown in the figure, the information input page 51 consists of fields for entering or selecting information such as name, telephone number, email address, preferred contact method, postal code and address of the property for sale, apartment name, room number, floor area, desired sale period, desired sale price range, and other matters, as well as a transition button 51a.

[0073] Return to Figure 10. The Members Market Server 12 determines whether the user pressed the transition button 51a on the information input page 51 displayed in step S46 (step S47). If it determines that the user pressed the button, it waits for confirmation from a sales representative as necessary (step S48). The content and meaning of this wait are the same as in step S44. When the wait ends due to the sales representative pressing the confirmation button, the Members Market Server 12 sends the sales property entered by the user to the Recommender Server 13 and registers it in the Non-Public Property Database 23 via the Recommender Server 13. With these steps completed, the Members Market Server 12 finishes the sales property registration process for non-public sales properties.

[0074] Next, referring to Figure 11, the recommender server 13, which has started the desired conditions registration process, first performs member authentication to confirm whether the connected user is a member registered in the member database 24 (step S50). Then, based on the result of member authentication, it determines whether the user is a member or not (step S51). If the user is a member, it proceeds to step S52; otherwise, it terminates the desired conditions registration process. This prevents non-member users from registering desired conditions.

[0075] In step S52, the recommender server 13 generates an information input page for the desired conditions and displays it on the displays of the user terminals 30 and 31 (step S52).

[0076] Figure 14 shows an example of an information input page 52 displayed in step S52. As shown in the figure, the information input page 52 consists of fields for inputting or selecting the desired property area, property image, etc., and a transition button 52a. The field for inputting or selecting the area is configured so that the user can choose from several input methods, such as area of ​​interest, train line / station, or ward. The field for inputting or selecting the property image is configured so that the user can select from several predetermined images. In addition, it is of course possible to provide fields for inputting or selecting other information such as the desired property price range, floor area, layout, number of rooms, and balcony direction within the information input page 52.

[0077] Return to Figure 11. The recommender server 13 determines whether the user pressed the transition button 52a on the information input page 52 displayed in step S52 (step S53). If it determines that the user pressed the button, it registers the entered information in the condition database 25 (step S54) and terminates the desired condition registration process.

[0078] Finally, referring to Figure 12, the distribution process performed by the recommender server 13 will be explained. The recommender server 13 is configured to perform this distribution process in response to the registration of the non-public sales property in the non-public property database 23 in step S49 shown in Figure 10.

[0079] The recommender server 13, which has registered a new private property for sale in the private property database 23, first performs the processing in steps S61 and S62 for each record in the condition database 25 (step S60). Specifically, it determines whether the newly registered private property for sale matches the desired conditions indicated by the featured record (step S61), and if it determines that it matches, it registers the corresponding combination of condition ID and property ID in the distribution database 26 (step S62). At this point, the date in the newly registered record is left blank.

[0080] After completing the repetitive process in step S60, the recommender server 13 then performs the processes in steps S64 to S68 for each record in the distribution database 26 that does not have a date entered (step S63). Specifically, the recommender server 13 first reads the member ID corresponding to the condition ID from the condition database 25 (step S64), and then reads the address information corresponding to the read member ID from the member database 24 (step S65). Next, the recommender server 13 generates a distribution page that includes information on properties stored in the non-public property database 23, associated with the sales property ID (step S66). Then, it sends the distribution page generated in step S66 to the address information read in step S65 (step S67), sets the current date in the date in the distribution database 26 (step S68), and finishes processing for that record. After completing the repetitive process in step S63, the recommender server 13 finishes the distribution process.

[0081] Figure 15 shows an example of a distribution page 53 generated in step S66. As shown in the figure, distribution page 53 is a page containing various information about the sales property that has been distributed. The recommender server 13 generates this distribution page 53 by reading information from the non-public property database 23.

[0082] As shown in Figure 15, the distribution page 53 contains a graph area 53a that shows the value of the properties for sale. The recommender server 13 is configured to generate the information to be displayed in this graph area 53a based on the prices and standard scores stored in the property database 22 and the non-public property database 23. This will be explained in more detail below.

[0083] To begin with, regarding the standard score, when the recommender server 13 generates the distribution page 53, it extracts properties similar to the property for sale to be distributed (including at least one of the properties for sale stored in the property database 22, the properties for sale stored in the private property database 23, and the rental property database 22) from the property database 22 and the private property database 23. Criteria for determining similar properties can include, for example, the address (whether they are in the same ward or not), the nearest station (whether they are at the same nearest station or not), the floor area (whether the difference in floor area is within a predetermined value), the floor level (whether they are at the same floor or not), and the balcony direction (whether they face the same direction or not).

[0084] The recommender server 13, which has extracted similar properties, calculates the mean and variance of the prices of the extracted properties. The prices used here can be obtained or estimated in the same way as in step S32 of Figure 6. Using the calculated mean and variance, the recommender server 13 calculates the standard score of the price for each of the sales properties to be distributed and the extracted similar properties, and stores them in the property database 22 and the non-public property database 23. The standard scores thus calculated represent information indicating the value of each sales property within the similar property group.

[0085] The recommender server 13 generates a curve graph (for example, a normal distribution curve) based on the calculated mean and variance and plots it in the graph area 53a (the "price distribution" shown in the figure). It also plots the sales properties targeted for distribution and the extracted similar properties on the normal distribution curve based on their respective standard scores. At this time, as illustrated in Figure 15, the recommender server 13 uses markers of different shapes for the properties targeted for distribution, unsold similar properties, and sold similar properties. This allows users to intuitively understand the value of the properties targeted for distribution within the group of similar properties.

[0086] Figure 16 is a sequence diagram of part of the sales property registration and distribution process (up to registration in the distribution database 26) performed by the Members Market Server 12 and the Recommender Server 13. Figure 17 is a sequence diagram of the remaining parts of the member registration process, desired conditions registration process, and distribution process performed by the Recommender Server 13. The processes performed by the Members Market Server 12 and the Recommender Server 13 will be explained from a different perspective by referring to Figures 16 and 17 below.

[0087] First, referring to Figure 16, the members market server 12 first receives information about the property for sale from the user terminals 30 and 31 of the seller user (step S120). Then, it determines whether the user wishes to keep the listing private (step S121). If the user does not wish to keep the listing private, it registers the listing in the property database 22 (step S122). On the other hand, if the user wishes to keep the listing private, it transfers the information about the property for sale to the recommender server 13 (step S123).

[0088] The recommender server 13, upon receiving the transfer of the property information for sale, registers the information in the non-public property database 23 (step S124), extracts desired conditions that match the registered property information for sale from the condition database 25 (step S125), and registers the combination of the condition ID of the extracted desired conditions and the property ID of the registered property information for sale in the distribution database 26 (step S126).

[0089] Next, referring to Figure 17, in the stage of registering a member, the recommender server 13 first receives personal information from the user terminals 30,31 of the user who is a buyer of a non-publicly sold property (step S130), has the screening officer conduct a screening (step S131), and then registers it in the member database 24 (step S132). The screening in step S131 is to determine whether or not the user is worthy of being registered as a good buyer, and if the screening result is that the user is not worthy of being registered as a good buyer, step S132 is not executed.

[0090] Next, in the stage of registering desired conditions, the recommender server 13 first authenticates whether the user of user terminals 30,31 is a member registered in the member database 24 (step S133). If authenticated as a member, it receives information indicating the desired conditions from the user's user terminal 30,31 (step S134) and registers it in the conditions database 25 (step S135). Member authentication in step S133 can be, for example, authentication using a member ID and password.

[0091] Finally, in the stage of distributing the property information for sale, the recommender server 13 first obtains a combination of condition ID and property ID from the distribution database 26 (step S140). Then, it obtains the member ID corresponding to the condition ID from the condition database 25 (step S141), and further obtains the address information corresponding to the obtained member ID from the member database 24 (step S142).

[0092] Next, the recommender server 13 reads the sales property ID information obtained in step S140 from the non-public property database 23 (step S143) and generates a distribution page based on the read information (step S144). The specific contents of the distribution page are as described above. After that, the recommender server 13 sends the distribution page generated in step S144 to the address information obtained in step S142 (step S145) and terminates the process.

[0093] As described above, with the members market server 12 and recommender server 13 according to this embodiment, information on non-public properties for sale is registered in a non-public property database 23 separate from the property database 22, and information on properties for sale that match the desired conditions is distributed to buyers who are permitted to register their desired conditions in advance in the conditions database 25. This makes it possible to provide a sales site that gives preferential treatment to high-quality buyers and attracts high-quality buyers.

[0094] In this embodiment, the recommender server 13 starts the distribution process shown in Figure 12 in response to registering a non-public property in the non-public property database 23 in step S49 shown in Figure 10, and distributes a distribution page containing information on the non-public property stored in the non-public property database 23. However, in response to registering a public property in the property database 23 in step S45 shown in Figure 10, the recommender server 13 may also start the distribution process shown in Figure 12 and distribute a distribution page containing information on the public property stored in the property database 23, or it may perform both. When distributing a distribution page containing information on a public property, the recommender server 13 may, in step S61, determine whether the newly registered public property matches the desired conditions indicated by the featured record, and if it determines that it matches, register the corresponding combination of condition ID and sales property ID in the distribution database 26 in step S62. Furthermore, in step S66, the recommender server 13 may generate a distribution page containing information on the property stored in the property database 22 associated with the sales property ID.

[0095] Although preferred embodiments of the present invention have been described above, the present invention is not limited in any way to these embodiments, and it goes without saying that the present invention can be implemented in various forms without departing from its essence.

[0096] For example, in Figure 1, the property database 22 and the private property database 23 are depicted separately, but these may be integrated into a single database. In this case, it is preferable to set a public / private flag for each record in order to distinguish between publicly available and privately available properties.

[0097] Furthermore, although the above embodiment describes an example in which only high-quality buyers are registered in the member database 24, it is also possible to allow users other than high-quality buyers to register as members. In this case, by setting a flag in the member database 24 to identify whether or not a user is a high-quality buyer, registration of desired conditions can be permitted only to high-quality buyers, and information on non-publicly listed properties can be distributed to them. [Explanation of symbols]

[0098] 1. Information Processing System 2 Sales site 3 Network 10 Information Processing Devices 11 Planner Server 12 Members Market Server 13 Recommender Server 20 Apartment Database 21 Rental Property Database 22 Property Database 23 Non-public property database 24 Member Database 25 Condition Database 26 Distribution Database 30,31 User terminals Pages 40, 43, 51, 52: Information Input Pages 40a Property Name Input Field 40b Rent input field 40c Input field 40d Input field for floor location 40e Corner Room Button 40f South-facing button 40g Refurbished Buttons 40h, 51a, 52a Transition buttons 41,42 Information Output Page 50. Publication Type Selection Page 50a, 50b selection button 53 Distribution Page 53a Graph Area 101 Processors 102 Storage device 103 Communication equipment 104 Input device 105 Output device 106 Bus

Claims

1. A computer connected to a member database that stores address information and a flag that identifies whether or not a member is a good buyer when purchasing real estate, and a conditions database that stores information indicating the desired conditions for the property to be purchased for each member who is identified as a good buyer by the flag, When a new property for sale becomes available, it is determined for each record in the aforementioned conditions database whether the new property matches the desired conditions indicated in that record. Select one or more members corresponding to one or more records that are determined to match, Information on the newly listed properties for sale is distributed to the address information stored in the member database, which is associated with each of the one or more selected members. computer.

2. The aforementioned flag is set based on the personal information of the member. The computer according to claim 1.

3. The aforementioned personal information is obtained based on the content of the computer input operations performed by the member. The computer according to claim 2.

4. It is connected to at least one of the following: a property database that stores information on multiple properties for sale and a private property database that stores information on multiple private properties for sale. The aforementioned newly listed properties are properties newly registered in the aforementioned property database or the aforementioned non-public property database. The computer according to any one of claims 1 to 3.

5. The information on the aforementioned properties for sale that are subject to the aforementioned distribution is: The price of the aforementioned property for sale that is subject to the aforementioned distribution, This includes information indicating the price deviation of the sales property subject to distribution within a group of one or more similar properties that are similar to the sales property subject to distribution among the sales properties registered in the property database and the non-public property database, The computer according to claim 4.

6. A method for distributing real estate sales property information, executed by a computer connected to a member database that stores address information and a flag that identifies whether or not a member is a good buyer when purchasing real estate properties, and a conditions database that stores information indicating the desired conditions for properties to be purchased for each member who is identified as a good buyer by the flag, When a new property for sale becomes available, the computer determines, for each record in the conditions database, whether the new property for sale matches the desired conditions indicated by that record. The steps include: selecting one or more members corresponding to one or more records that the computer has determined to be a match; The computer distributes information about the newly listed properties for sale to address information stored in the member database, which is associated with each of the one or more selected members. A method for distributing real estate sales property information, including this method.

7. A program to be executed on a computer connected to a member database that stores address information and a flag that identifies whether or not a member is a good buyer when purchasing real estate, and a conditions database that stores information indicating the desired conditions for the property to be purchased for each member who is identified as a good buyer by the flag, When a new property for sale becomes available, the step of determining whether the new property matches the desired conditions indicated in each record of the conditions database is to be determined for each record. The steps include selecting one or more members corresponding to one or more records that have been determined to match, The steps include: distributing information on newly available properties to address information stored in the member database, associated with each of the one or more selected members; A program to cause the aforementioned computer to execute.

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