KR20220009791A - Real estate direct transaction system and method using artificial intelligence - Google Patents

Abstract

A real estate direct transaction system and method using AI are provided. The real estate direct transaction system using AI includes an input unit that receives information about an apartment to be sold from a seller, an information storage unit that stores the information entered in the input unit, receives information from the information storage unit, and calculates a fee reduction rate. , an analysis unit that transmits the brokerage fee to which the fee reduction rate is applied to the information storage unit, and a display unit that delivers the brokerage fee to which the fee reduction rate and the fee reduction rate are applied from the information storage unit to the seller, and the analysis unit uses the information in deep learning It includes an information reliability analysis unit that analyzes using artificial intelligence based on the information to determine the authenticity of the information and the reliability level of the information.

Description

Real estate direct transaction system and method using AI {Real estate direct transaction system and method using artificial intelligence}

The present invention relates to a real estate direct transaction system and method using AI, in which a seller directly inputs related product information to receive a discount on brokerage fees and a buyer can receive reliable information from the seller.

The existing real estate brokerage service is a method in which an authorized real estate agent pays a fee to the service system and advertises goods, and the vicious cycle of high commissions, false sales, and exaggerated information and advertisements leads to a vicious cycle of declining service quality and awareness of the real estate brokerage business, and high brokerage fees. have.

In response to this, an online real estate direct transaction service appeared, but the online real estate direct transaction service (Peter Pan, etc.) checks the product information posted by the seller (lessor) directly on internet cafes and homepages, and attempts to contact the buyer (lessee) directly. There is a problem in that direct trading is difficult to activate due to circumstances such as careful access and difficult standardization of product information.

In addition, there is a risk of exposure of personal information (address, contact information, etc.) from the standpoint of the seller (lessor) without significant restrictions, and there is a factor reluctant to register the goods due to difficulties such as identification of the contacting buyer (lessee). There is a problem in that the input of missing information frequently occurs, and the inputted object and the actual object are different.

An object of the present invention is to provide a real estate direct transaction system and method using AI that can solve the conventional problems by reducing the brokerage fee of real estate brokerage and providing accurate information of the seller.

In addition, the seller receives a discount on brokerage fees through information provision, and the provided information is to provide a real estate direct transaction system and method using AI that can increase the simplification and efficiency of brokerage work by verifying the accuracy of the information through AI. do it with

The problems of the present invention are not limited to the problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

A real estate direct transaction system using AI according to an embodiment for solving the above problem includes an input unit for receiving information about an apartment to be sold from a seller, an information storage unit for storing the information input to the input unit, and the An analysis unit that receives the information from the information storage unit, calculates the fee reduction rate, and delivers the brokerage fee to which the fee reduction rate is applied to the information storage unit, and the brokerage fee to which the fee reduction rate and fee reduction rate are applied from the information storage unit and a display unit for delivering to the seller, and the analysis unit includes a reliability analysis unit for analyzing the information using deep learning-based artificial intelligence to determine the authenticity of the information and a reliability level of the information.

The analysis unit may further include a seller's reliability analysis unit for determining the reliability level of the seller by analyzing the information previously input by the seller using a deep learning-based artificial intelligence.

The analysis unit may further include a selling price adequacy analysis unit that analyzes the selling price information input by the seller by using a deep learning-based artificial intelligence to determine the appropriateness level of the selling price.

The analysis unit may further include a fee reduction rate calculator configured to calculate a fee reduction rate by analyzing a reliability level of the information, a reliability level of the seller, and an appropriateness level of the selling price.

The information storage unit consists of a block chain network, and when the block chain network generates a block chain confirmation that is a request signal to record the acquired information in the block chain network, the block chain node responds to the block chain confirmation request. Therefore, block chain storage can be achieved by propagating block chain transactions to adjacent nodes.

In a real estate direct transaction method using AI according to an embodiment for solving the above problems, inputting first information of a seller and second information of a buyer into an input unit, the input unit receiving the first information and the second information transmitting to an information storage unit, and storing the first information and the second information by the information storage unit, wherein the information storage unit transmits the first information to an analysis unit, and the analysis unit analyzes the first information to calculate a fee reduction rate, delivering the fee reduction rate and brokerage fee to the seller, selecting a suitable certified real estate agent from a certified real estate agent POOL, and a first matching with the second information input by the buyer Selecting information and delivering it to the buyer, wherein the analysis unit analyzes the first information using deep learning-based artificial intelligence to determine the authenticity of the first information and the reliability level of the first information It may include a reliability analysis unit of information.

The analysis unit includes a seller's reliability analysis unit that determines the reliability level of the seller by analyzing the first information previously input by the seller by using a deep learning-based artificial intelligence, and deep learning the selling price information input by the seller A selling price adequacy analysis unit that determines the appropriateness level of the selling price by analyzing it using a learning-based AI, and the fee reduction rate by analyzing the reliability level of the first information, the reliability level of the seller, and the appropriateness level of the selling price It may further include a fee reduction rate calculation unit for calculating .

The step of inputting the seller's first information and the buyer's second information into the input unit includes the name of the householder, address, contact information, email, apartment acquisition date, apartment photo, exclusive area, room/toilet/ The method may further include inputting information on the number of parkings and the desired selling price to the input unit.

In the step of inputting the seller's first information and the buyer's second information into the input unit, the buyer's information on the desired purchase area, information on price, information on the number of rooms/toilets/parking, exclusive area/supply area for The method may further include inputting information, information on a date of approval of use, information on a preferred floor/scent, information on the number of households, and contact information into the input unit.

The information storage unit consists of a block chain network, and when the block chain network generates a block chain confirmation that is a request signal to record the acquired information in the block chain network, the block chain node responds to the block chain confirmation request. Therefore, block chain storage can be achieved by propagating block chain transactions to adjacent nodes.

According to the real estate direct transaction system and the method using AI according to an embodiment, the seller can directly input the relevant product information and receive a discount on the brokerage fee to induce the seller to input reliable information. Furthermore, real estate agents can easily close transactions based on reliable information.

Effects according to the embodiments are not limited by the contents exemplified above, and more various effects are included in the present specification.

1 is a diagram schematically illustrating a real estate direct transaction system using AI according to an embodiment.
2 is a diagram schematically illustrating an AI-based real estate direct transaction platform according to an embodiment.
3 is a diagram schematically illustrating a second analysis unit according to an exemplary embodiment.
4 is a diagram schematically showing a seller's information input and a fee discounted accordingly in a real estate direct transaction system using AI according to an embodiment.
5 is a diagram schematically illustrating a buyer's information input in a real estate direct transaction system using AI according to an embodiment.
6 is a diagram schematically illustrating a real estate direct transaction method using AI according to an embodiment.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in a variety of different forms, and only these embodiments allow the disclosure of the present invention to be complete, and common knowledge in the technical field to which the present invention belongs It is provided to fully inform the possessor of the scope of the invention, and the present invention is only defined by the scope of the claims.

It should be understood that all flowcharts, state transition diagrams, pseudo code, and the like may be tangibly embodied on computer-readable media and represent various processes performed by a computer or processor, whether or not a computer or processor is explicitly shown.

The functions of the various elements shown in the drawings including a processor or functional blocks represented by similar concepts may be provided by the use of dedicated hardware as well as hardware having the ability to execute software in association with appropriate software. When provided by a processor, the functionality may be provided by a single dedicated processor, a single shared processor, or a plurality of separate processors, some of which may be shared. Like reference numerals refer to like elements throughout.

Hereinafter, specific embodiments will be described with reference to the accompanying drawings.

1 is a diagram schematically illustrating a real estate direct transaction system using AI according to an embodiment.

Referring to FIG. 1 , the real estate direct transaction system 100 using AI may include an AI-based real estate direct transaction platform 10 , a seller 20 , a buyer 30 , and a real estate agent POOL 40 .

The seller 20 may include a plurality of sellers 20-1, 20-2, ..20-N. The plurality of sellers (20-1, 20-2, ..20-N) may be formed in various forms, such as individuals or corporations who wish to sell goods as individual sellers, respectively.

The purchaser 30 may include a plurality of purchasers 30-1, 30-2, ..30-N. The plurality of purchasers (30-1, 30-2, ..30-N) may be formed in various forms, such as individuals or corporations who wish to purchase goods as individual purchasers, respectively.

Here, the object may mean an apartment for sale. However, the present invention is not limited thereto, and may include jeonse and monthly rent for an apartment. In some embodiments, the object may refer to only objects within Seoul and six metropolitan cities.

The real estate agent POOL 40 may be composed of a plurality of real estate agents. For example, a plurality of real estate agents may be classified according to various factors such as age, experience, gender, and place of residence. In some embodiments, the real estate agent may be composed of less than 40 years old, and in some embodiments, the real estate agent may be configured mainly in metropolitan cities.

The AI-based real estate direct transaction platform 10 applies a fee discount rate based on the information of the goods input by a plurality of sellers (20-1, 20-2, ..20-N), It can be provided to buyers (30-1, 30-2, ..30-N). In addition, in some embodiments, a certified real estate agent suitable for the product may be selected from the real estate agent POOL ( 40 ). The AI-based real estate direct transaction platform 10 will be looked at in more detail later.

In some embodiments, an AI-based real estate direct transaction platform 10 and a plurality of sellers (20-1, 20-2, ..20-N), a plurality of buyers (30-1, 30-2, ..30) -N), the connection of the real estate agent POOL 40 may be made through a network.

Here, the network refers to a connection structure in which information exchange is possible between each node, such as a plurality of terminals and servers, and examples of such networks include RF, 3rd Generation Partnership Project (3GPP) network, and Long Term (LTE). Evolution) network, 5th Generation Partnership Project (5GPP) network, WIMAX (World Interoperability for Microwave Access) network, Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network) , PAN (Personal Area Network), Bluetooth (Bluetooth) network, NFC network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc. include, but are not limited thereto.

In some embodiments, a plurality of sellers (20-1, 20-2, ..20-N), a plurality of buyers (30-1, 30-2, ..30-N) and a real estate agent POOL (40) The included real estate agent may be connected to the AI-based real estate direct transaction platform 10 through the terminal. In some embodiments, the terminal may be connected to a smart phone, a tablet computer, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a wearable device, and the like. The wearable device may be implemented as smart glasses or a smart watch that can be worn on a user's body.

A plurality of sellers (20-1, 20-2, ..20-N) may be connected to the AI-based real estate direct transaction platform 10 through a terminal, and a plurality of sellers (20-1, 20-2, ..20-N) can directly input the information of its own product into the AI-based real estate direct transaction platform 10 . Here, the information of one's own property is, as described above, information about the apartment to be sold (name of householder, address, contact information, email, apartment acquisition date, apartment photo, exclusive area, number of rooms/toilets/parking, desired sale A) or information about the apartment you want to rent or rent (name of householder, address, contact number, email, apartment acquisition date, apartment photo, exclusive area, number of rooms/toilets/parking, jeonse/deposit/monthly rent, etc.) it means.

Here, the AI-based real estate direct transaction platform 10 may be implemented through a web page, an app page, a program or an application, and a plurality of sellers (20-1, 20-2, ..20-N) can easily You can enter your own product information and get a discount on brokerage fees.

The plurality of buyers (30-1, 30-2, ..30-N) may be connected to the AI-based real estate direct transaction platform 10 through the terminal, and the plurality of buyers (30-1, 30-2, ..30-N) can directly input the information of the desired object into the AI-based real estate direct transaction platform 10 .

Here, the information of the desired item means information about the apartment to be purchased (desired area, price range, contact information, e-mail, exclusive area, number of rooms/toilets/parking, desired purchase price, etc.), as described above. .

2 is a diagram schematically showing an AI-based real estate direct transaction platform according to an embodiment, FIG. 3 is a diagram schematically showing a second analysis unit according to an embodiment, and FIG. 4 is an AI-based view using AI according to an embodiment It is a diagram schematically showing the seller's information input in the real estate direct transaction system and the fee discounted accordingly, and FIG. 5 is a diagram schematically showing the buyer's information input in the real estate direct transaction system using AI according to an embodiment.

2 to 4 , the AI-based real estate direct transaction platform 10 may include an input unit 11 , a first analysis unit 13 , a second analysis unit 15 , and a display unit 17 .

In some embodiments, the input unit 11 provides categories of inputable information to the plurality of sellers 20-1, 20-2, ..20-N, and the plurality of sellers 20-1, 20-2 ,..20-N) can collect the object information entered. For example, the input unit 11 enters the household name, address, contact information, e-mail, apartment acquisition date, apartment photo, exclusive area, number of rooms/toilets/parking, desired selling price, etc. The categories of inputable information such as household name, address, contact number, e-mail, apartment acquisition date, apartment photo, exclusive area, number of rooms/toilets/parking, and jeonse/deposit/monthly rent for the apartment you want to purchase are set to multiple sellers. (20-1, 20-2, ..20-N) is presented to each, and each of the plurality of sellers (20-1, 20-2, ..20-N) is selected from among the categories of information available for input to collect the entered information.

In some embodiments, the input unit 11 may be connected to a device provided by each of the plurality of sellers 20-1, 20-2, ..20-N. For example, it may be connected to a smart phone, a tablet computer, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a wearable device, and the like.

In some embodiments, the input unit 11 may receive information such as names, genders, addresses, etc. of each of the plurality of sellers 20-1, 20-2, ..20-N. In addition, the input unit 11 may provide a member authentication service. The member authentication service receives the user's ID and password, collects them, and can use it as login information. In some embodiments, the input unit 11 may use SNS information as login information in connection with SNS such as Facebook or Twitter.

In some embodiments, the input unit 11 may receive information such as names, genders, addresses, etc. of each of the plurality of purchasers 30-1, 30-2, ..30-N. In addition, the input unit 11 may provide a member authentication service. The member authentication service receives the user's ID and password, collects them, and can use it as login information. In some embodiments, the input unit 11 may use SNS information as login information in connection with SNS such as Facebook or Twitter.

Although not shown, the AI-based real estate direct transaction platform 10 may include an information storage unit.

The information storage unit may receive and store object information and analysis information from the input unit 11 , the first analysis unit 13 , and the second analysis unit 15 . In addition, the information storage unit may transmit the stored object information and analysis information to the first analysis unit 13 , the second analysis unit 15 , and the display unit 17 .

The information storage unit is built-in such as random access memory (RAM), flash memory, read only memory (ROM), erasable programmable ROM (EPROM), electronically erasable and programmable ROM (EEPROM), registers, hard disk, removable disk, memory card, etc. It may be implemented as a removable type storage device, such as a USB memory, of course.

In some embodiments, the information storage unit may be formed of a blockchain network. As is known, a blockchain network is a distributed data storage technology that stores data in blocks, connects them in a chain, and replicates and stores them on numerous computers at the same time. Also called public ledger. Transaction history is sent to all users participating in a transaction without keeping transaction records on a centralized server, and every transaction has the advantage of not being able to forge or falsify data by sharing and collating information among all transaction participants.

The information storage unit obtains unique information from the input unit 11 , the first analysis unit 13 , and the second analysis unit 15 , and may store the obtained information in a block chain manner through a block chain network. And, when an update event for information stored in the block chain method occurs, the block chain update can be stored on the block chain network to match the update event.

When the information storage unit consists of a block chain network, the information storage unit may perform a block chain storage process, a block chain update process, and an information utilization process. However, this is only an example, and is not limited thereto.

The block chain storage process is a process in which the information storage unit stores the acquired information in a block chain manner through a block chain network. For example, when the information storage unit generates a block chain confirmation, which is a request signal to record the information obtained from the input unit 11, the first analysis unit 13, and the second analysis unit 15, in the block chain network, the block chain The node propagates the block chain transaction to the adjacent node according to the corresponding block chain confirmation request so that the block chain is stored. Here, the information obtained from the input unit 11 , the first analysis unit 13 , and the second analysis unit 15 may include product information, customer information, and analysis result information. However, the present invention is not limited thereto, and only some of the above-described information may be stored in a block chain manner. For example, only customer information can be stored in a blockchain manner. In addition, in some embodiments, it is also possible to store additional information other than the above-described information about the product, customer information, and analysis result information in a block chain manner.

The block chain update process is a process of storing block chain updates on a block chain network to match the update event when an update event for information stored in a block chain method occurs. To this end, the block chain update process includes the block chain update confirmation request process of requesting the block chain update confirmation for the information updated by the update event to the block chain node when an update event for information stored in the block chain method occurs; , when a blockchain update confirmation is requested, the blockchain node propagates the blockchain transaction based on the blockchain update confirmation to neighboring nodes to complete the blockchain update confirmation and notify the completion of the blockchain update confirmation There will be a process of notification of the completion of the confirmation. In some embodiments, the information storage unit may further include a block chain management unit instructing updater request, updater confirmation, and updater confirmation completion.

The first analysis unit 13 and the second analysis unit 15 receive information stored in the input unit 11 by each of the plurality of sellers 20-1, 20-2, ..20-N, and By analyzing it, it is possible to calculate the brokerage fee discount rate.

In some embodiments, any one of the first analysis unit 13 and the second analysis unit 15 is a plurality of sellers (20-1, 20-2, ..20-N) by a person, each of the input unit 11 ) can be analyzed, and the other is information stored in the input unit 11 by each of the plurality of sellers (20-1, 20-2,..20-N) by deep learning-based artificial intelligence. can be analyzed. Hereinafter, a case in which the first analysis unit 13 analyzes information by a person and the second analysis unit 15 analyzes the information by deep learning-based artificial intelligence will be described as an example.

In some embodiments, the information stored in the input unit 11 by each of the plurality of sellers 20-1, 20-2, ..20-N is transmitted to the first analysis unit 13 and the second analysis unit 15 . It can be divided and analyzed by type of information. In addition, in some embodiments, the information stored in the input unit 11 by each of the plurality of sellers 20-1, 20-2, ..20-N is primarily analyzed by the first analysis unit 13. and the analysis result of the first analysis unit 13 may be re-analyzed by the second analysis unit 15 . In addition, in some embodiments, the information stored in the input unit 11 by each of the plurality of sellers 20-1, 20-2, ..20-N is the first analysis unit 13 and the second analysis unit ( 15) It is also possible to separately analyze each and calculate the analysis results for each.

As shown in FIG. 3 , the second analysis unit 15 includes the information reliability analysis unit 15A, the seller 20 reliability analysis unit 15B, the selling price adequacy analysis unit 15C, and the fee reduction rate calculation. It may include a part 15D and the like. However, this is only an example, and is not limited thereto.

The reliability analysis unit 15A of the information may determine the authenticity of the information and the reliability level of the information input by each of the plurality of sellers (20-1, 20-2, ..20-N).

If the input information is picture information of an object as an example, the reliability analysis unit 15A of the information steals whether the photographed picture was taken directly by the deep learning-based artificial intelligence through the data learned in advance. You can judge the authenticity of a photograph. As an example, TensorFlow may be used as the deep learning-based artificial intelligence engine, and an Inception v3 model may be used. Supervised learning, a method of learning a machine learning model using training data that knows the correct answer, can be used to determine the authenticity of the input photo information. can be set to Information on the photo input by the seller 20 may be stored in the information storage unit, and learning may be performed by various source codes.

The information reliability analysis unit 15A first compares the photo input by the seller 20 with the photo accumulated in the information storage unit. It can be judged that the photograph was taken. On the other hand, by comparing the photo input by the seller 20 with the photo accumulated in the information storage unit, if the similarity is 90% or more, it can be determined that the photo input by the seller 20 is a stolen photo. The information reliability analysis unit 15A may set a predetermined similarity reference value, and may compare the primarily determined result with a predetermined similarity reference value to re-determine whether the information is authentic or not. The predetermined similarity reference value may be variously changed according to a type of a picture, a photographing location, and the like.

The information reliability analysis unit 15A compares the photo input by the seller 20 with the photo accumulated in the information storage unit when it is primarily determined that the photo input by the seller 20 is a photo taken directly Thus, it is possible to determine the reliability level. For example, the reliability rating can be determined by analyzing the number of photos input by the seller, the resolution of the input photos, information displayed on the input photos, and the like. The factor determining the reliability level may be variously changed. The information reliability analysis unit 15A may analyze the reliability of various information, such as an input address, contact information, and e-mail, in addition to pictures. In some embodiments, the reliability level may be divided into high, medium, and low, and in some embodiments, the reliability level may be divided into 1 to 10 grades. However, the present invention is not limited thereto.

The reliability analysis unit 15B of the seller 20 may determine the reliability level of the seller 20 by collecting the information previously input by the seller 20 . The reliability analysis unit 15B of the seller 20 can determine the reliability level of the seller by deep learning-based artificial intelligence based on the data from which the information previously input by the seller 20 is learned. In some embodiments, the reliability grades may be divided into high, medium, and low, and in some embodiments, the reliability grades may be divided into 1 to 10 grades. However, the present invention is not limited thereto.

In some embodiments, the selling price adequacy analysis unit 15C may determine whether the selling price is appropriate for the goods input by each of the plurality of sellers 20-1, 20-2, ..20-N. The selling price adequacy analysis unit 15C compares the seller 20's desired selling price with the actual transaction price, etc. through the learned data on the goods of the seller 20 by deep learning-based artificial intelligence to determine whether it is appropriate. In some embodiments, the adequacy analysis unit 15C of the selling price is a plurality of sellers (20-1, 20-2,... By additionally using the information of the selling price, it is possible to calculate the appropriateness of the selling price as a grade. In some embodiments, the adequacy level of the selling price may be divided into high, medium, and low, and in some embodiments, the adequacy level of the selling price may be divided into 1 to 10 grades. However, the present invention is not limited thereto.

The fee reduction rate calculation unit 15D may calculate the fee reduction rate according to the reliability level of the information, the reliability level of the seller 20, and the appropriateness level of the selling price. Furthermore, the fee reduction rate calculator 15D may calculate the brokerage fee before applying the cut rate, and calculate the discounted brokerage fee by applying the fee reduction rate. The fee reduction rate calculation unit 15D and the calculated fee reduction rate and the discounted brokerage fee may be transmitted to the information storage unit and the display unit 17 .

For example, as shown in FIG. 4 , the seller 20 provides information on the name of the head of household, information on address, information on contact information, information on e-mail, information on acquisition date, information on acquisition price, and photo. In the case of inputting information on information on, information on exclusive area, information on the number of rooms/toilets/parking, and information on the desired selling price into the input unit 11, the information is stored in the information storage unit, and the first analysis The unit 13 and the second analysis unit 15 may receive the information from the information storage unit.

In some embodiments, the first analysis unit 13 may check identity authentication, brokerage request confirmation, registration copy confirmation, seller currency confirmation, transaction case confirmation, number of entered photos, resolution, area expression, complex information, sale request, etc. have.

In some embodiments, the second analysis unit 15 analyzes the reliability rating of the seller by examining the prior data of the seller 20, analyzes the reliability rating of the information based on the input information, and determines the desired selling price information. Based on this, the appropriateness of the selling price can be analyzed.

The fee reduction rate calculation unit 15D is based on the analysis results of the first analysis unit 13 and the second analysis unit 15, and the reduction rate is 0.1% for the information on the name and address of the householder input by the seller. can be calculated.

The fee reduction rate calculation unit 15D cuts the information on the name of the householder and the information on the address input by the seller 20 based on the analysis results of the first analysis unit 13 and the second analysis unit 15 . A rate of 0.1% can be calculated.

The fee reduction rate calculation unit 15D calculates a reduction rate of 0.1% for the information on the photo input by the seller 20 based on the analysis results of the first analysis unit 13 and the second analysis unit 15 . can

The fee reduction rate calculation unit 15D calculates a reduction rate of 0.2% for the information on the photo input by the seller 20 based on the analysis results of the first analysis unit 13 and the second analysis unit 15 . can

The fee reduction rate calculation unit 15D is based on the analysis results of the first analysis unit 13 and the second analysis unit 15, the information on the exclusive area input by the seller 20, the number of rooms/toilets/parking A reduction rate of 0.1% can be calculated for the information on

The fee reduction rate calculation unit 15D calculates a reduction rate of 0.1% with respect to the information on the desired selling price input by the seller 20 based on the analysis results of the first analysis unit 13 and the second analysis unit 15 . can do.

Accordingly, the total reduction rate becomes 0.7%, and the existing brokerage fee of 0.9% can be discounted to 0.2%. Accordingly, the brokerage fee can be discounted from 10,800,000 won to 2,400,000 won, and this brokerage fee can be delivered to the seller 20 who directly entered information, so that the seller 20 can actively input information do. In addition, since the brokerage fee discounted in this way is equally applied to the buyer 30, the seller 20's active input of information not only reduces the brokerage fee of the seller 20, but also the brokerage fee of the buyer 30 is also discounted. Therefore, it can be a factor in which transactions can be concluded quickly.

The display unit 17 may display information to be provided to the buyer 30 . For example, information on photos of goods, information on exclusive area, information on the number of rooms/toilets/parking, information on the desired selling price, and information on brokerage fees can be received from the information storage unit and displayed. have. For example, the display unit 17 may be connected to a device provided by the purchaser 30 . For example, it may be connected to a smart phone, a tablet computer, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a wearable device, and the like.

In some embodiments, the display unit 17 may select information similar to the information input by the purchaser 30 and provide it to the purchaser 30 . Specifically, the plurality of purchasers (30-1, 30-2, ..30-N) may be connected to the AI-based real estate direct transaction platform 10 through the terminal, and the plurality of purchasers 30-1, 30 -2,..30-N) can directly input the information of the desired object into the AI-based real estate direct transaction platform 10 . For example, as shown in FIG. 5 , the plurality of buyers (30-1, 30-2, ..30-N) has information on the desired purchase area, information on price, and information on the number of rooms/toilets/parking. , information on exclusive area/supply area, information on the date of approval for use, information on preferred floor/slope, information on the number of households, and contact information can be entered, and the information can be stored in the information storage unit. have. The display unit matches information input by each of the plurality of purchasers 30-1, 30-2, ..30-N from the information storage unit, or selects similar information to the plurality of purchasers 30-1, 30- 2,..30-N) can be displayed on each terminal.

The display unit 17 may display the discount brokerage fee to which the fee reduction rate and the fee reduction rate are applied to the seller 20 . For example, information on a fee reduction rate and a discount brokerage fee to which the fee reduction rate is applied may be received from the information storage unit and displayed. For example, the display unit 17 may be connected to a device provided by the seller 20 . For example, it may be connected to a smart phone, a tablet computer, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a wearable device, and the like.

6 is a diagram schematically illustrating a real estate direct transaction method using AI according to an embodiment.

Referring to FIG. 6 , the real estate direct transaction method using AI includes a step of distinguishing a seller and a buyer (S10), a step of inputting information between the seller and the buyer (S20), and additional information to the AI-based real estate direct transaction platform. The step of inputting (S30), the step of calculating the fee reduction rate by analyzing the information in the analysis unit (S40), the step of delivering the fee reduction rate and the brokerage fee to the seller (S50), and the steps of matching the buyer ( S60), selecting a suitable accredited realtor from the accredited realtor POOL (S70), concluding a contract (S80), receiving a fee (S90), and confirming the completion of registration (S100) may include .

The step of distinguishing the seller from the buyer (S10) and the step of the seller 20 inputting information into the input unit 11 (S10) include the steps of selecting a category for the seller 20 or the buyer 30, and the seller If the category for (20) is selected, the seller (20) enters the apartment to be sold with the household name, address, contact information, e-mail, apartment acquisition date, apartment photo, exclusive area, number of rooms/toilets/parking, and the desired selling price. In the input section (11 ), and when the category for the buyer 30 is selected, the buyer 30 asks for information on the desired purchase area, information on price, information on the number of rooms/toilets/parking, exclusive area/supply area It may include the step of inputting information, information on the date of approval of use, information on a preferred floor/fragrance, information on the number of households, and information on contact information into the input unit 11 . Information input by the seller 20 and the buyer 30 into the input unit 11 may be transmitted to the information storage unit.

The step of inputting additional information into the AI-based real estate direct transaction platform (S30) includes inputting information other than the information about the seller 20 and the buyer 30. In some embodiments, additional information may include map view, nearby schools/academies, shopping/restaurants/convenience facilities, hospitals/sports facilities, public transportation information, complex overview, transaction details, utility/administrative fee level, holding tax, monthly cost, acquisition cost, etc. may include However, the present invention is not limited thereto.

In the step (S40) of analyzing the information in the analysis unit to calculate the fee reduction rate, the information input by the seller 20 and the conventional data about the seller 20 are transmitted from the information storage unit, and the reliability analysis unit ( The step of 15A) determining the authenticity of the information about the product and the reliability level of the information using deep learning-based artificial intelligence based on the information on the goods input by the seller, and the reliability analysis unit 15B of the seller 20 The step of determining the reliability level of the seller 20 using deep learning-based artificial intelligence based on the data learned from the information previously input by the seller, and the adequacy analysis unit 15C of the selling price input by the seller 20 The step of calculating the appropriateness of the selling price using deep learning-based artificial intelligence for information on the desired selling price of the product as a grade, and the fee reduction rate calculation unit 15D determines the reliability of the information, the reliability grade of the seller 20, The method may include calculating a fee reduction rate according to the appropriateness level of the selling price.

The step of delivering the fee reduction rate and the brokerage fee to the seller 20 (S50) may include transmitting the brokerage fee calculated by applying the existing brokerage fee, the fee reduction rate, and the fee reduction rate to the seller.

In the step (S60) of matching the buyer 30, the information input by the buyer 30 is received from the information storage unit, and item information matching the information input by the buyer 30 is selected and the buyer 30 It may include the step of delivering to

The step (S70) of selecting a suitable accredited realtor from the accredited realtor POOL means a step of selecting an accredited realtor suitable for the product in the accredited realtor POOL, and the suitable accredited realtor may be automatically placed based on the information of the accredited realtor inputted into the accredited realtor POOL .

The step of concluding the contract (S80) and the step of receiving a fee (S90) includes the step of signing a contract and receiving a fee between the authorized broker, the seller 20, and the buyer 30 selected for the product. For example, it may include a step in which the selected real estate agent inquires about the registration of the product, and a step in which the selected real estate agent and the buyer 30 conduct a field tour.

Furthermore, the real estate direct transaction method using AI may further include a step (S100) of additionally confirming the registration completion.

In this way, the seller 20 can induce the input of reliable information by the seller 20 by directly inputting the relevant product information to receive a discount on the brokerage fee. Furthermore, real estate agents can easily close transactions based on reliable information.

Although embodiments of the present invention have been described above with reference to the accompanying drawings, those of ordinary skill in the art to which the present invention pertains may be embodied in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

10: Real estate direct transaction platform using AI
20: Seller
30: Buyer
40: Realtor POOL
100: Real estate direct transaction system using AI

Claims (10)

an input unit for receiving information about an apartment to be sold from the seller;
an information storage unit for storing the information input to the input unit;
an analysis unit that receives the information from the information storage unit, calculates a fee reduction rate, and transmits a brokerage fee to which the fee reduction rate is applied to the information storage unit; and
and a display unit that transmits, from the information storage unit, the brokerage fee to which the fee reduction rate and the fee reduction rate are applied to the seller,
The real estate direct transaction system using AI, wherein the analysis unit includes a reliability analysis unit that analyzes the information using deep learning-based artificial intelligence to determine the authenticity of the information and the reliability level of the information.
According to claim 1,
The real estate direct transaction system using AI, wherein the analysis unit further comprises a seller's reliability analysis unit that analyzes the information previously input by the seller by using deep learning-based artificial intelligence to determine the reliability level of the seller.
3. The method of claim 2,
The real estate direct transaction system using AI, wherein the analysis unit further comprises an adequacy analysis unit for determining the appropriateness level of the selling price by analyzing the selling price information input by the seller by deep learning-based artificial intelligence.
4. The method of claim 3,
The real estate direct transaction system using AI, wherein the analysis unit further comprises a fee reduction rate calculation unit for calculating a fee reduction rate by analyzing the reliability level of the information, the reliability level of the seller, and the appropriateness level of the selling price.
5. The method of claim 4,
The information storage unit consists of a blockchain network,
When the block chain network generates a block chain confirmation, which is a request signal to record the acquired information in the block chain network, the block chain node propagates the block chain transaction to adjacent nodes according to the block chain confirmation request to block A real estate direct transaction system using AI that stores chains.
inputting the seller's first information and the buyer's second information into an input unit;
transmitting, by the input unit, the first information and the second information to an information storage unit, and storing the first information and the second information by the information storage unit;
transmitting, by the information storage unit, the first information to an analysis unit, and calculating a fee reduction rate by the analysis unit analyzing the first information;
transmitting the fee reduction rate and brokerage fee to the seller;
Selecting a suitable real estate agent from the real estate agent POOL; and
Selecting first information matching the second information input by the buyer and transmitting the selected first information to the buyer,
The analysis unit analyzes the first information using deep learning-based artificial intelligence to determine the authenticity of the first information and a reliability analysis unit for determining the reliability level of the first information. A real estate direct transaction method using AI.
7. The method of claim 6,
The analysis unit,
A reliability analysis unit of the seller that determines the reliability level of the seller by analyzing the first information previously input by the seller using a deep learning-based artificial intelligence, and deep learning-based artificial intelligence based on the selling price information input by the seller A price adequacy analysis unit that analyzes and determines the appropriateness level of the selling price by analyzing it, and a fee for calculating the fee reduction rate by analyzing the reliability level of the first information, the reliability level of the seller, and the appropriateness level of the selling price Real estate direct transaction method using AI further including a reduction rate calculation unit.
8. The method of claim 7,
The step of inputting the seller's first information and the buyer's second information into the input unit,
The seller further includes the step of entering the household name, address, contact information, email, apartment acquisition date, apartment photo, exclusive area, number of rooms/toilets/parking, and desired selling price information into the input unit to the apartment that the seller wants to sell Real estate direct transaction method using AI.
9. The method of claim 8,
The step of inputting the seller's first information and the buyer's second information into the input unit,
Information on the area where the buyer wants to purchase, information on price, information on the number of rooms/toilets/parking, information on exclusive area/supply area, information on the date of approval for use, information on preferred floor/direction, number of households Real estate direct transaction method using AI further comprising the step of inputting information on the information and contact information into the input unit.
10. The method of claim 9,
The information storage unit consists of a blockchain network,
When the block chain network generates a block chain confirmation, which is a request signal to record the acquired information in the block chain network, the block chain node propagates the block chain transaction to adjacent nodes according to the block chain confirmation request to block A direct real estate transaction method using AI with chain storage.

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