Real estate transaction support device, real estate transaction support method, and program

Abstract

To promote a real estate transaction between individuals.SOLUTION: A real estate transaction support device 10 comprises: an input receiving unit 11 that receives input of a desired condition for a property from a consumer and information on the attribute of the consumer; a matching processing unit 12 that specifies a property that matches the desired condition, by using the desired condition and attribute information of the consumer input to the input receiving unit and property information of properties and information on the attribute of a supplier registered in advance, and determines the possibility that the consumer whose desired condition matches the specified property purchases the property.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a real estate transaction support device and a real estate transaction support method for supporting real estate transactions, and further to a program for realizing these. [Background technology]

[0002] Generally, real estate transactions are conducted through a real estate agent acting as an intermediary between the seller and the potential buyer. Therefore, the seller has the problem that the only way to learn of the existence of potential buyers, their attributes, and the possibility that the potential buyers will actually purchase the property is through the real estate agent.

[0003] To solve this problem, for example, Patent Document 1 proposes a system that supports direct real estate transactions between sellers and prospective buyers. The system disclosed in Patent Document 1 first associates and registers the seller's personal information (name, contact information, etc.) with property information, and then associates and registers the buyer's personal information (name, contact information, etc.) with desired information indicating the conditions of the desired property.

[0004] Next, when a prospective buyer requests to view property information, the system disclosed in Patent Document 1 outputs the seller's contact information to the prospective buyer who requested the view. The system disclosed in Patent Document 1 allows sellers and prospective buyers to find each other and communicate directly with each other without going through a real estate agent. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-083031 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the system disclosed in Patent Document 1 does not present the possibility of a potential buyer purchasing the property. Therefore, the seller must personally investigate the possibility of a potential buyer purchasing the property, which places a heavy burden on sellers who are unfamiliar with real estate transactions.

[0007] One example of a purpose of the present disclosure is to facilitate real estate transactions between individuals. [Means for solving the problem]

[0008] In order to achieve the above object, a real estate transaction support device according to one aspect of the present disclosure includes: an input receiving unit that receives input of desired conditions for a property from a demander and attribute information of the demander; a matching processing unit that uses the desired conditions and attribute information of the demander that have been input, and property information of each pre-registered property and attribute information of the supplier, to identify a property that matches the desired conditions, and further determines the possibility that the demander whose desired conditions match will purchase the identified property; The present invention is characterized in that it is provided with:

[0009] In order to achieve the above object, a real estate transaction support method according to one aspect of the present disclosure includes: an input receiving step of receiving input of desired conditions for a property and attribute information of the demander from the demander; a matching processing step of identifying a property that matches the desired conditions using the desired conditions and attribute information of the demander whose input has been accepted, and property information of each pre-registered property and attribute information of the supplier, and further determining the possibility that the demander whose desired conditions match will purchase the identified property; The present invention is characterized by having the following:

[0010] Furthermore, in order to achieve the above object, a program according to one aspect of the present disclosure includes: On the computer, an input receiving step of receiving input of desired conditions for a property and attribute information of the demander from the demander; a matching processing step of identifying a property that matches the desired conditions using the desired conditions and attribute information of the demander whose input has been accepted, and property information of each pre-registered property and attribute information of the supplier, and further determining the possibility that the demander whose desired conditions match will purchase the identified property; The method is characterized in that: [Effects of the Invention]

[0011] As described above, according to the present disclosure, it is possible to promote real estate transactions between individuals. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of an example of a real estate transaction support device. [Figure 2] FIG. 2 is a configuration diagram specifically showing an example of a real estate transaction support device. [Figure 3] FIG. 3 is a diagram showing an example of desired conditions of a consumer and consumer attribute information. [Figure 4] FIG. 4 is a diagram showing an example of property information and supplier attribute information. [Figure 5] FIG. 5 is a diagram showing an example of information displayed on the terminal device of the consumer. [Figure 6] FIG. 6 is a diagram showing an example of information displayed on the terminal device of the supplier. [Figure 7] FIG. 7 is a flow diagram showing an example of the operation of the real estate transaction support device. [Figure 8] FIG. 8 is a diagram illustrating an example of encrypted attribute information. [Figure 9] FIG. 9 is a block diagram showing an example of a computer that realizes a real estate transaction support device. DETAILED DESCRIPTION OF THE INVENTION

[0013] (Embodiment) Hereinafter, in the embodiments, a real estate transaction support device, a real estate transaction support method, and a program will be described with reference to FIGS.

[0014] [Device configuration] First, the schematic configuration of an example of a real estate transaction support device will be described with reference to Fig. 1. Fig. 1 is a configuration diagram showing the schematic configuration of an example of a real estate transaction support device.

[0015] 1 is a device for supporting real estate transactions. As shown in FIG. 1, real estate transaction supporting device 10 includes input receiving unit 11 and matching processing unit 12.

[0016] The input receiving unit 11 receives input from a demander of desired conditions for a property and the demander's attribute information (hereinafter referred to as "demander attribute information"). The matching processing unit 12 uses the received demander's desired conditions and demander attribute information, as well as property information for each pre-registered property and supplier attribute information (hereinafter referred to as "supplier attribute information") to identify a property that matches the desired conditions. Furthermore, the matching processing unit 12 uses the above information to determine the possibility that a demander with matching desired conditions will purchase the identified property.

[0017] In this way, the real estate transaction support apparatus 10 determines the likelihood that a demander will purchase a property, allowing the property supplier to know the likelihood of reaching a contract with the demander. Furthermore, the demander can know the degree of suitability between the property and themselves. Therefore, the real estate transaction support apparatus 10 can promote real estate transactions between individuals.

[0018] Next, the configuration and functions of an example of the real estate transaction support apparatus 10 will be specifically explained with reference to Figures 2 to 5. Figure 2 is a configuration diagram specifically showing an example of the real estate transaction support apparatus.

[0019] As shown in FIG. 2, in an embodiment, the real estate transaction support device 10 is connected to a terminal device 41 of a property supplier 40 and a terminal device 51 of a property demander 50 via a network 30 such as the Internet, so as to be capable of data communication.

[0020] 2, the real estate transaction support apparatus 10 includes an output unit 13 and a storage unit 14 in addition to the input receiving unit 11 and the matching processing unit 12. Furthermore, the real estate transaction support apparatus 10 is equipped with a machine learning model 20.

[0021] When a demander 50 transmits desired conditions and demander attribute information for a desired property from the terminal device 51, the input reception unit 11 receives the transmitted desired conditions and demander attribute information. The input reception unit 11 then stores the received input desired conditions and demander attribute information in the memory unit 14 in association with each other, thereby registering the demander.

[0022] Furthermore, when the supplier 40 transmits property information and supplier attribute information from the terminal device 41, the input reception unit 11 receives the transmitted property information and supplier attribute information. The input reception unit 11 then stores the received property information and supplier attribute information in the storage unit 14 in association with each other, thereby registering the property.

[0023] Here, the demanders 50 include those who wish to purchase real estate properties and those who wish to rent real estate properties. The suppliers 40 include those who wish to sell real estate properties and those who provide real estate properties for rent (landlords).

[0024] 2 shows an example in which there is one supplier 40 and multiple consumers 50, but the embodiment is not limited to this. There may be multiple suppliers 40 and multiple consumers 50, or there may be multiple suppliers 40 and one consumer 50.

[0025] Fig. 3 is a diagram showing an example of a consumer's desired conditions and consumer attribute information. As shown in Fig. 3, the desired conditions include the floor plan, desired exclusive area, cost, time to the station, whether or not pets are kept, etc. Furthermore, the consumer attribute information includes the name, age, family composition, annual income, place of work, etc. of the consumer 50.

[0026] Fig. 4 is a diagram showing an example of property information and supplier attribute information. As shown in Fig. 4, the property information includes information such as the price, floor area, and management fee of the property, as well as information explaining the advantages unique to the property. The supplier attribute information includes the name, age, family composition, annual income, place of work, etc. of the supplier 40.

[0027] In the embodiment, the matching processing unit 12 uses a machine learning model 20 to identify properties that match the desired conditions. The machine learning model 20 is, for example, a neural network, and is actually implemented by a machine learning program executed on a computer. The machine learning model 20 may be implemented by an external computer, not by the real estate transaction support apparatus 10.

[0028] The machine learning model 20 is constructed by machine learning using, for example, data on past properties as training data. The training data may be training data in which the desired conditions of the consumer 50, consumer attribute information, property information, and attribute information of the supplier 40 for past properties are used as explanatory variables, and the success or failure of the transaction is used as the objective variable. The success or failure of the transaction is a label indicating whether the transaction was successful or not.

[0029] Therefore, when the machine learning model 20 receives a set of desired conditions and consumer attribute information for the consumer 50 and a set of property information and supplier attribute information for the supplier 40, it outputs whether the transaction is successful or not.

[0030] Specifically, the matching processing unit 12 retrieves property information and supplier attribute information of one supplier 40, and desired conditions and demander attribute information of one demander 50 from the storage unit 14, and inputs each of the retrieved information into the machine learning model 20. Then, the matching processing unit 12 receives the results output by the machine learning model 20, and if a transaction is concluded, determines that the property and the desired conditions match.

[0031] Furthermore, the matching processing unit 12 can identify properties that match the desired conditions on a property-by-property or demand-by-demand basis. In the case of property-by-property basis, the matching processing unit 12 identifies all desired conditions (demandors) that match one property. In the case of demand-by-demand basis, the matching processing unit 12 identifies all properties that match the desired conditions of one demander 50.

[0032] Specifically, in the case of a property basis, the matching processing unit 12 sequentially combines property information and supplier attribute information for one property with the desired conditions and consumer information of each of multiple consumers 50, inputs the combined information into the machine learning model 20, and obtains the results for each consumer. Then, the matching processing unit 12 identifies the consumer with whom a transaction has been concluded.

[0033] In the case of a demander 50 basis, the matching processing unit 12 sequentially combines the desired conditions and demander attribute information of one demander 50 with the property information and supplier information of each of a plurality of properties, inputs the combined information into the machine learning model, and obtains the results for each property. Then, the matching processing unit 12 identifies the properties for which a transaction has been concluded.

[0034] In addition, the matching processing unit 12 uses the demand attribute information of the demander 50 whose desired conditions match and the supplier attribute information of the supplier 40 of the corresponding property to calculate an index indicating the likelihood that the demander 50 whose desired conditions match the property will purchase the property.

[0035] Specifically, the matching processing unit 12 vectorizes each of the consumer attribute information and the supplier attribute information, and calculates the cosine similarity between the vectorized consumer attribute information and the vectorized supplier attribute information as an index. The calculated index is hereinafter also referred to as "match rate." In addition, the matching processing unit 12 can rank the properties or consumers in descending order of the calculated match rate.

[0036] Furthermore, the matching processing unit 12 can also cause the generation AI to generate reasons why a property matches the desired conditions. In this case, the matching processing unit 12 inputs the desired conditions and demander information, and property information and supplier attribute information of a property that matches the desired conditions to the generation AI.

[0037] As shown in Fig. 5, the output unit 13 transmits the property information (all or part) and the matching rate of the property identified by the matching processing unit to the terminal device 51 of the demander 50. Also, as shown in Fig. 6, the output unit 13 transmits the attribute information (all or part) and the matching rate of the demander 50 who has input desired conditions that match the property to the terminal device 41 of the supplier 40.

[0038] Fig. 5 is a diagram showing an example of information displayed on a terminal device of a consumer. As shown in the example of Fig. 5, the screen of terminal device 51 of consumer 50 displays suitable properties, their ranking, and suitability rate. In addition, the example of Fig. 5 also displays the reason why the property meets the desired conditions.

[0039] Fig. 6 is a diagram showing an example of information displayed on a supplier's terminal device. As shown in the example of Fig. 6, the screen of terminal device 41 of supplier 40 displays consumers 50 whose desired conditions match the property and the matching rate. In addition, the example of Fig. 6 also displays the reason why the property matches the desired conditions.

[0040] [Device operation] Next, the operation of the real estate transaction support apparatus 10 will be explained using FIG. 7. FIG. 7 is a flow diagram showing the operation of an example of the real estate transaction support apparatus. In the following explanation, reference will be made to FIGS. 1 to 6 as appropriate. In addition, in the embodiment, a real estate transaction support method is implemented by operating the real estate transaction support apparatus 10. Therefore, the explanation of the real estate transaction support method will be replaced by the explanation of the operation of the real estate transaction support apparatus 10 below.

[0041] 7, first, when a supplier 40 transmits property information and supplier attribute information from a terminal device 41, the input receiving unit 11 receives the transmitted property information and supplier attribute information (step A1). In step A1, the input receiving unit 11 further stores the received input property information and supplier attribute information in the storage unit 14, and registers them in association with each other.

[0042] Next, when the demander 50 transmits the desired conditions and demander attribute information for the desired property from the terminal device 51, the input receiving unit 11 receives the transmitted input of the desired conditions and demander attribute information (step A2). In step A2, the input receiving unit 11 further stores the received input of the desired conditions and demander attribute information in the memory unit 14, and registers them in association with each other.

[0043] The order of execution of steps A1 and A2 is not particularly limited, and both may be executed simultaneously. Furthermore, step A1 is repeatedly executed the same number of times as the number of property information and supplier attribute information transmitted. Step A2 is repeatedly executed the same number of times as the number of desired conditions and demander attribute information transmitted.

[0044] After executing steps A1 and A2, the matching processing unit 12 inputs the property information and supplier attribute information of the supplier 40 and the desired conditions and demander attribute information of the demander 50 into the machine learning model 20 to identify a property that matches the desired conditions (step A3).

[0045] Specifically, in step A3, in the case of a property basis, the matching processing unit 12 sequentially combines property information and supplier attribute information for one property with the desired conditions and consumer information of each of multiple consumers 50, inputs the combined information into the machine learning model 20, and obtains results for each consumer.

[0046] Also, in step A3, in the case of a demand-based system, the matching processing unit 12 sequentially combines the desired conditions and demand attribute information for one demander 50 with the property information and supplier information for each of multiple properties, inputs the combined information into the machine learning model, and obtains results for each property.

[0047] Next, the matching processing unit 12 uses the consumer attribute information of the consumer 50 whose desired conditions match and the supplier attribute information of the supplier 40 of the relevant property to calculate an index (matching rate) indicating the likelihood that the consumer 50 whose desired conditions match the property will purchase the property (step A4).

[0048] Next, the output unit 13 transmits the property identified in step A3 and the matching rate calculated in step A4 to the terminal device 51 of the demander 50 (step A5).

[0049] Furthermore, the output unit 13 transmits the demander 50 who input the desired conditions that match the property identified in step A3 and the matching rate calculated in step A4 to the terminal device 41 of the supplier 40 (step A6).

[0050] [Effects of the embodiment] Generally, when a property purchase and sale contract or rental contract is concluded between individuals, the demander attribute information and the supplier attribute information tend to be similar. Therefore, according to the embodiment, the demander attribute information and the supplier attribute information, in addition to the desired conditions and property information, are used to match the property with the demander 50, thereby improving the accuracy of the match.

[0051] Furthermore, in the embodiment, an index (matching rate) indicating the likelihood that the demander 50 will purchase the property is calculated. Therefore, for example, the property supplier 40 can know the likelihood of reaching a contract with the demander 50. Also, the demander 50 can know the degree of match between the property and himself / herself. Therefore, according to the embodiment, it is possible to promote real estate transactions between individuals.

[0052] [Variations] Next, a modified example of the real estate transaction supporting apparatus 10 will be described below. In this modified example, the machine learning model 20 is constructed by machine learning using encrypted demander attribute information and supplier attribute information as training data. The demander attribute information and supplier attribute information are encrypted by replacing part of the information with other information. The encryption will be described with reference to FIG. 8. FIG. 8 is a diagram showing an example of encrypted attribute information.

[0053] The example in Fig. 8 shows consumer attribute information before encryption and consumer attribute information after encryption. As shown in Fig. 8, words included in the consumer attribute information before encryption are replaced with words having different meanings in the consumer attribute information after encryption.

[0054] Furthermore, word replacement may be performed using, for example, a dictionary for word replacement prepared in advance. Furthermore, word replacement may be performed based on set rules. One route is to write the entire word in alphabets and convert each character to the character immediately following it in the alphabet.

[0055] In the modified example, the input receiving unit 11 performs encryption by replacing a part of the information in the supplier attribute information and the consumer attribute information that have been received with another information. Specifically, the input receiving unit 11 uses the above-mentioned dictionary to replace words in the supplier attribute information and the consumer attribute information with another word.

[0056] The matching processing unit 12 uses the encrypted supplier attribute information and consumer attribute information to identify properties that match the desired conditions and determine the possibility of purchasing the identified properties. If the information to be output contains words that have been replaced by encryption, the output unit 13 uses the above-mentioned dictionary to restore the words to their original state before outputting the information.

[0057] By using the modified example, it is possible to reduce the chance of personal information being leaked through the output from the machine learning model 20, thereby promoting the confidentiality of personal information. Furthermore, in the above example, encryption is performed on supplier attribute information and consumer attribute information, but encryption may also be performed on property information and desired conditions in addition to these attribute information.

[0058] [program] The program in the embodiment may be any program that causes a computer to execute steps A1 to A6 shown in Fig. 7. By installing and executing this program on a computer, the real estate transaction support apparatus 10 and the real estate transaction support method can be realized. In this case, the processor of the computer functions as an input receiving unit 11, a matching processing unit 12, and an output unit 13 and performs processing.

[0059] In the embodiment, the storage unit 14 may be realized by a storage device such as a hard disk provided in the computer, or may be realized by a storage device of another computer. Examples of the computer include a general-purpose PC and a server computer, as well as a smartphone and a tablet terminal device.

[0060] The program in the embodiment may be executed by a computer system constructed by a plurality of computers. In this case, for example, each computer may function as one of the input receiving unit 11, the matching processing unit 12, and the output unit 13.

[0061] [Physical configuration] Here, a computer that realizes the real estate transaction support apparatus 10 by executing a program in the embodiment will be described with reference to Fig. 9. Fig. 9 is a block diagram showing an example of a computer that realizes the real estate transaction support apparatus.

[0062] 9, the computer 110 includes a CPU (Central Processing Unit) 111, a main memory 112, a storage device 113, an input interface 114, a display controller 115, a data reader / writer 116, and a communication interface 117. These components are connected to each other via a bus 121 so as to be able to communicate data with each other.

[0063] Furthermore, the computer 110 may include a GPU (Graphics Processing Unit) or an FPGA (Field-Programmable Gate Array) in addition to or instead of the CPU 111. In this aspect, the GPU or FPGA can execute the programs in the embodiments.

[0064] The CPU 111 loads a program in the embodiment, which is composed of a group of codes and stored in the storage device 113, into the main memory 112 and executes each code in a predetermined order to perform various calculations. The main memory 112 is typically a volatile storage device such as a DRAM (Dynamic Random Access Memory).

[0065] The program in the embodiment is provided in a state stored in a computer-readable recording medium 120. The program in the embodiment may be distributed over the Internet connected via the communication interface 117.

[0066] Specific examples of the storage device 113 include a hard disk drive and a semiconductor storage device such as a flash memory. The input interface 114 mediates data transmission between the CPU 111 and input devices 118 such as a keyboard and a mouse. The display controller 115 is connected to a display device 119 and controls the display on the display device 119.

[0067] The data reader / writer 116 mediates data transmission between the CPU 111 and the recording medium 120, reads programs from the recording medium 120, and writes processing results from the computer 110 to the recording medium 120. The communication interface 117 mediates data transmission between the CPU 111 and other computers.

[0068] Specific examples of the recording medium 120 include general-purpose semiconductor storage devices such as CF (Compact Flash (registered trademark)) and SD (Secure Digital), magnetic recording media such as flexible disks, or optical recording media such as CD-ROMs (Compact Disk Read Only Memory).

[0069] The real estate transaction support apparatus 10 in this embodiment can be realized not by a computer with a program installed, but by hardware corresponding to each unit, for example, an electronic circuit. Furthermore, the real estate transaction support apparatus 10 may be partially realized by a program and the remaining unit by hardware. In the embodiment, the computer is not limited to the computer shown in FIG. 7.

[0070] Some or all of the above-described embodiments can be expressed by (Supplementary Note 1) to (Supplementary Note 12) described below, but are not limited to the following descriptions.

[0071] (Appendix 1) an input receiving unit that receives input of desired conditions for a property from a demander and attribute information of the demander; a matching processing unit that uses the desired conditions and attribute information of the demander that have been input, and property information of each pre-registered property and attribute information of the supplier, to identify a property that matches the desired conditions, and further determines the possibility that the demander whose desired conditions match will purchase the identified property; A real estate transaction support device comprising:

[0072] (Appendix 2) the matching processing unit determines the possibility by calculating an index indicating the possibility using the attribute information of the demander and the attribute information of the supplier; 2. A real estate transaction support device according to claim 1.

[0073] (Appendix 3) the input receiving unit performs encryption by replacing a part of the attribute information of the supplier and the attribute information of the consumer with another part of the attribute information of the supplier and the attribute information of the consumer; the matching processing unit uses the encrypted attribute information of the supplier and the attribute information of the demander to identify a property that matches the desired conditions and determine the possibility of purchasing the identified property; 2. A real estate transaction support device according to claim 1.

[0074] (Appendix 4) The matching processing unit identifies a property that matches the desired conditions using a machine learning model constructed by machine learning using training data of the desired conditions of the demander, attribute information of the demander, property information, attribute information of the supplier, and sales results of past properties. 2. A real estate transaction support device according to claim 1.

[0075] (Appendix 5) an input receiving step of receiving input of desired conditions for a property and attribute information of the demander from the demander; a matching processing step of identifying a property that matches the desired conditions using the desired conditions and attribute information of the demander whose input has been accepted, and property information of each pre-registered property and attribute information of the supplier, and further determining the possibility that the demander whose desired conditions match will purchase the identified property; A real estate transaction support method comprising:

[0076] (Appendix 6) In the matching processing step, the possibility is determined by calculating an index indicating the possibility using the attribute information of the demander and the attribute information of the supplier. A method for assisting real estate transactions as set forth in Appendix 5.

[0077] (Appendix 7) In the input receiving step, encryption is performed by replacing part of the attribute information of the supplier and the attribute information of the consumer with different information; In the matching processing step, the encrypted attribute information of the supplier and the attribute information of the demander are used to identify a property that matches the desired conditions, and determine the possibility of purchasing the identified property. A method for assisting real estate transactions as set forth in Appendix 5.

[0078] (Appendix 8) In the matching processing step, a machine learning model is constructed by machine learning using training data of the demander's desired conditions, the demander's attribute information, the property information, the supplier's attribute information, and sales results for past properties, to identify properties that match the desired conditions. A method for assisting real estate transactions as set forth in Appendix 5.

[0079] (Appendix 9) On the computer, an input receiving step of receiving input of desired conditions for a property and attribute information of the demander from the demander; a matching processing step of identifying a property that matches the desired conditions using the desired conditions and attribute information of the demander whose input has been accepted, and property information of each pre-registered property and attribute information of the supplier, and further determining the possibility that the demander whose desired conditions match will purchase the identified property; A program that executes.

[0080] (Appendix 10) In the matching processing step, the possibility is determined by calculating an index indicating the possibility using the attribute information of the demander and the attribute information of the supplier. 10. The program described in Appendix 9.

[0081] (Appendix 11) In the input receiving step, encryption is performed by replacing part of the attribute information of the supplier and the attribute information of the consumer with different information; In the matching processing step, the encrypted attribute information of the supplier and the attribute information of the demander are used to identify a property that matches the desired conditions, and determine the possibility of purchasing the identified property. 10. The program described in Appendix 9.

[0082] (Appendix 12) In the matching processing step, a machine learning model is constructed by machine learning using training data of the demander's desired conditions, the demander's attribute information, the property information, the supplier's attribute information, and sales results for past properties, to identify properties that match the desired conditions. 10. The program described in Appendix 9. [Industrial Applicability]

[0083] As described above, according to the present disclosure, it is possible to promote real estate transactions between individuals. The present disclosure is useful in the field of real estate transactions. [Explanation of symbols]

[0084] 10 Real estate transaction support device 11 Input reception section 12 Matching processing section 13 Output section 14 Storage section 20 Machine Learning Models 30 Network 40 Supplier 41 Supplier's terminal equipment 50 Consumer 51 Consumer terminal equipment 110 Computer 111 CPU 112 main memory 113 Storage device 114 Input Interface 115 Display Controller 116 Data Reader / Writer 117 Communication Interface 118 Input Devices 119 Display Device 120 Recording Media 121 Bus

Claims

1. an input receiving unit that receives input of desired conditions for a property from a demander and attribute information of the demander; a matching processing unit that uses the desired conditions and attribute information of the demander that have been input, and property information of each pre-registered property and attribute information of the supplier, to identify a property that matches the desired conditions, and further determines the possibility that the demander whose desired conditions match will purchase the identified property; A real estate transaction support device comprising:

2. the matching processing unit determines the possibility by calculating an index indicating the possibility using the attribute information of the demander and the attribute information of the supplier; The real estate transaction support device according to claim 1.

3. the input receiving unit performs encryption by replacing a part of the attribute information of the supplier and the attribute information of the consumer with another part of the attribute information of the supplier and the attribute information of the consumer; the matching processing unit uses the encrypted attribute information of the supplier and the attribute information of the demander to identify a property that matches the desired conditions and determine the possibility of purchasing the identified property; The real estate transaction support device according to claim 1.

4. The matching processing unit identifies a property that matches the desired conditions using a machine learning model constructed by machine learning using training data of the desired conditions of the demander, attribute information of the demander, property information, attribute information of the supplier, and sales results of past properties. The real estate transaction support device according to claim 1.

5. an input receiving step of receiving input of desired conditions for a property and attribute information of the demander from the demander; a matching processing step of identifying a property that matches the desired conditions using the desired conditions and attribute information of the demander whose input has been accepted, and property information of each pre-registered property and attribute information of the supplier, and further determining the possibility that the demander whose desired conditions match will purchase the identified property; A real estate transaction support method comprising:

6. On the computer, an input receiving step of receiving input of desired conditions for a property and attribute information of the demander from the demander; a matching processing step of identifying a property that matches the desired conditions using the desired conditions and attribute information of the demander whose input has been accepted, and property information of each pre-registered property and attribute information of the supplier, and further determining the possibility that the demander whose desired conditions match will purchase the identified property; A program that executes.

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