WO2020218838A1 - Method of managing real property investment, system and computer program thereof - Google Patents

Method of managing real property investment, system and computer program thereof Download PDF

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Publication number
WO2020218838A1
WO2020218838A1 PCT/KR2020/005356 KR2020005356W WO2020218838A1 WO 2020218838 A1 WO2020218838 A1 WO 2020218838A1 KR 2020005356 W KR2020005356 W KR 2020005356W WO 2020218838 A1 WO2020218838 A1 WO 2020218838A1
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real property
property
real
information
market
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PCT/KR2020/005356
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English (en)
French (fr)
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Kyoung Don JEAN
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Repan Co., Ltd.
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/16—Real estate
    • G06Q50/163—Real estate management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/16—Real estate

Definitions

  • the present disclosure relates to real property investment method, system, and a related computer program, which allow institutional investors or funds to invest in properties that would not have been readily accessible due to its size and overhead costs.
  • REITs Real estate investment trusts
  • REITs or funds usually invest in large size properties such as office buildings, hotels, storage buildings, and commercial buildings. However, investing in an individual apartment or single house is not typical because costs and management fees involved in each transaction are high relative to the transaction's prospected profits. Since such a transaction involves similar due diligence and legal costs as larger buildings, REITS or funds tend to avoid investing such small size properties. To reduce such due diligence and transaction costs as well as management costs, a computer-assisted method for analyzing potential real property investment targets, predicting potential investment return of such property, and effectively managing after acquiring the property is needed.
  • Embodiments of the present invention provide a method of managing real property investment, system, and computer program thereof that facilitates communication among related entities, mediates conflicts of interest, and reduces management costs when conducting a small-scale distributed investment.
  • One embodiment provides a computer-assisted method for acquiring and managing a plurality of small real property asset, by receiving an offer for sale of a real property asset from an agent over a network, logging the real property in a multi-node ledger, determining a right agent of the real property, assigning the right agent to the real property in the multi-node ledger, and determining a potential return by analyzing a value of the real property utilizing a deep learning algorithm where the algorithm uses at least a first database containing market information regarding a market to which the real property belongs to, where the real property market information comprises one or more information selected from the sales price, rent price, rental period, appraisal report information, a number of similar real property on the market for rent or sale, school district, the population of the market or other information that may affect the value of the property or real property market and a second database containing information of the real property.
  • the deep learning algorithm utilizes artificial neural networks having nodes which are connected to form a directed graph along a temporal sequence and calculate inputs in parallel to obtain intermediates.
  • the deep learning algorithm may utilize the quasi-recurrent neural network or recurrent neural network, which are collectively called a recurrent neural network herein.
  • the quasi-recurrent neural network may use time-based historical data. When such historical data has a wide time gap between two adjacent data, a data point can be predicted by the quasi-recurrent neural network, and the predicted interim data can fill the gap.
  • the method obtains one or more predetermined investment factors from one or more preselected funds. It matches the real property with said one or more preselected funds based on the potential return and the predetermined investment factors.
  • the determination of the right agent is conducted based on a self-executed smart contact.
  • the self-executed smart contract may include a clause identifying an exclusive agency arrangement of the real property.
  • Each of the predetermined investment factors may have an assigned point, which is awarded based on the various weighed values of the elements in determining the profitability of the real property.
  • the property After matching a fund with the real property, the property may be purchased and need to be managed.
  • the determined agent may be designated as the managing agent for the real property.
  • a list of managing items can be provided to the managing agent allowing the agent to manage the property systemically.
  • the managing items can be provided to the managing agent via his/her mobile phone through a dedicate app.
  • the app may use GPS information to match the managing items with the real property.
  • a network-enabled computer system to acquire and manage a plurality of real property assets.
  • the system includes a network input interface accessible by an agent over a network where the agent is to input an offer for sale of a real property asset; a ledger where the ledger logs the offer for sales allowed and information of the real property asset; a judge engine determining a right agent of the real property where the determined right agent is assigned the real property in the ledger; a fund data containing one or more predetermined investment factors identified by one or more investment funds; a deep learning engine for determining a potential return by analyzing a value of the real property utilizing a deep learning algorithm where the algorithm utilizes at least a first database containing market information regarding a market to which the real property belongs to, where the real property market information comprises one or more information selected from sales price, rent price, rent period, appraisal report information, a number of real property on the market for rent or sale, school district, or population of the market; and a second database containing information of the real property; wherein the
  • the system may have access to a multi-node ledger system, including those utilizing blockchain technology, and its deep learning algorithm may utilize a recurrent neural network.
  • the judge engine may use a self-executed smart contact in determining the right agent.
  • Such a contract may have a clause identifying an exclusive agency arrangement of the real property.
  • a system for acquiring and managing a plurality of real property asset utilizes a deep learning engine for determining a potential return of a target real property having a size by predicting a future value of the target real property through a deep learning algorithm where the algorithm utilizes recurrent neural network.
  • the prediction may involve multiplying the future value with a weighing factor.
  • the weighting factor may be obtained by dividing a public assessment value of the target real property by an average public assessment value of a plurality of real properties having the same size.
  • the same size may be determined by square meters or based on how many bedrooms the properties have.
  • the plurality of real properties is within a single market, such as an apartment complex or condominium building in which the real property is located or maybe a single town or providence.
  • the system may include an agent determination engine, which determines an agent who will receive a brokerage fee and be assigned to a management agent for the target real property.
  • the determined agent could be more than one person.
  • the method of managing real property investment, system and computer program according to the present invention can reduce due diligence and transaction costs as well as management costs and enable effective management after acquiring the property by analyzing potential real property investment targets, predicting potential investment return of such property.
  • Fig. 1 illustrates a real property management system according to one embodiment and various interactive entities.
  • Fig. 2 illustrates a real property management system according to one embodiment and its network communication environment.
  • Fig. 3 illustrates a real property management system according to one embodiment.
  • Fig. 4 is a flow chart illustrating a process of real property management according to one embodiment.
  • Fig. 5 is a flow chart relating to a process of determining a right agent of real property.
  • Fig. 6 is a flow chart relating to a process of determining a right agent of real property.
  • Fig. 7 is a flow chart relating to a process of determining a right agent of real property.
  • Fig. 8 illustrates a process of collecting data based on an open API database.
  • Fig. 9 illustrates a process of collecting data based on a website connection.
  • Fig. 10 illustrates a process of data treatment according to one embodiment.
  • Fig. 11 illustrates a deep learning model according to one embodiment.
  • Fig. 12 illustrates a prediction model using a deep learning model according to one embodiment.
  • Fig. 13 illustrates an attention layer according to one embodiment.
  • Fig. 14 illustrates an example of an apartment, data of which can be used in training the neural network.
  • Fig.1 generally illustrates a real property management system according to one embodiment and various interactive entities that may be involved in related transactions.
  • a platform server 10 may interact with consulting group 3, investor 1, financial institution 5 and property supplier 7.
  • the property supplier can be either the owner of a property to be sold or a real property agent hired by the owner.
  • the consulting group can be a private equity firm that invests on behalf of private investors.
  • the investor may be a fund or REITs working with the platform system.
  • the platform identifies a property that meets the investment criteria of the fund or REITS, the investor can decide to purchase the property using financial leverage obtained from a financial institution such as short-term or long-term loans. Banks or security firms may be examples of the financial institution.
  • Investor, consulting group, financial institution, and property supplier can be a plurality of entities or single entity, respectively.
  • Real estate investment management system involves interactions among Investor 1, Consulting group 3, Financial institution 5, and Supplier 7 by a communication network.
  • the platform server 10 has a server system 10.
  • Consulting group 3 uses consultant terminal 23 to communicate with other entities.
  • Investor 1 uses an investor terminal 21, financial institution 5 uses a financial institution terminal 25, and the supplier 7 uses a real estate manager terminal 27.
  • Each terminal may include a terminal computer capable of communicating, such as a PC, a notebook computer, and a mobile device.
  • a plurality of consultant terminals 23, investor terminals 21, and financial group terminals 25, and manager terminals 27 may be connected to the server system 10 through a communication network, respectively.
  • Consultant terminal 23, investor terminal 21, financial group terminal 25, real estate manager terminal 27 may transmit information to each other through the server 10, or not through the server 10. They can also communicate with each other directly.
  • Figs. 3 and 4 show the server system 10 having the information collecting unit 11, real estate property information DB 13, management unit 12, AI return prediction engine (14), agency determination engine 15, distributed recording engine 17, communication unit 18 and the like.
  • the information collection unit 11 collects external real estate related data, which is stored in the property DB 13.
  • Real estate data may include macroeconomic information and past prices for property, market quotes, government property policy information, regional development information, etc.
  • Sources of real estate-related information may be from public data and information services of various government departments or companies.
  • the real estate agent network such as a multi-listing service may provide property information.
  • the information collector 11 may access external information servers providing data. The information collecting unit 11 may periodically access the information source and acquire the information.
  • the information can be provided by an information collection authority to the information providing server.
  • AI revenue prediction engine 14 can be trained to predict the expected return on a particular investment using a variety of information stored in the real estate information DB 13.
  • the AI revenue prediction engine 14 predicts expected returns, degree of risk, payback period, etc.
  • the trial engine 15 may determine a true agent who has the brokerage authority, According to the determination, the investment management authority of the property can be granted to the true agent.
  • the trial engine is used to determine the beneficiary of the real estate brokerage fee as well as the manager who will have real estate management rights one the property is acquired.
  • Distributed recording engine 17 may save investment and transaction-related information in a server and/or various terminals.
  • the management unit 12 transmits data to each terminal according to the procedure and manages the server system 10 by operating the overall investment and transaction procedures.
  • a real estate investment management method may include collecting real estate information S11, mounting the investment guideline S12, registering the property information S13, running the trial engine S14, driving the revenue prediction engine S15, searching for database and property information S16, matching a predetermined yield with a property S17, real estate investment analysis and securing the purchase conditions for the matched property S18.
  • the server 10 may collect real estate information.
  • the collection of information may be made in unit 11, and the collected information is stored in the real estate information DB 13.
  • the consultant terminal 23 or investor terminal 21 may transmit investment guideline information to the server 10.
  • the guideline means the investment condition that each investor predetermines. Multiple investors are each transferring investment conditions suitable for their investment objectives and investment tendencies to server 10 and can be stored. If there is a listing matching with this information, investing in such suitable properties can be made.
  • the real estate broker terminal 27 presents the sale information of the real property to the server 10 and such information can be registered in the server.
  • a property owner hires a real estate agent to sell the property, the agent can register the property information through the real estate broker terminal 27 in the server 10.
  • the server 10 drives the trial engine 15.
  • the real estate agent enters real estate information through the real estate broker terminal 27. This information is sent to the server 10.
  • Server 10 may distribute brokerage or management authorities as appropriate.
  • the trial engine 15 may be driven again at S30. It may be optionally performed in either of S14 or S30.
  • the server 10 drives a revenue prediction engine.
  • the prediction engine 14 is trained using the data stored in the real estate information DB 13.
  • a deep learning engine is utilized in the prediction.
  • the revenue prediction engine 14 evaluates the value of the property, having a predetermined characteristic to determine future returns.
  • the server 10 searches for existing information and for sale information.
  • the property information recorded in the real estate information DB 13 is searched to secure a list of investment objects.
  • the server 10 matches the predetermined yield and real estate for sale.
  • the server 10 provides real estate matching information and potential loan amount to the finance group terminal 25.
  • the person in charge of the financial group 5 confirms the loan through the terminal.
  • the server 10 analyzes the real estate investment and secures the purchase conditions.
  • the real estate broker terminal 27 transmits the seller confirmation information to the server 10.
  • a real estate investment, and management method may include providing a matching real estate information S20, S22, approving the start of investment S20, S22, notifying the start of investment S24, driving the trial engine S30, selecting a real estate manager (S31), notifying the purchase order and completing the purchase S33, setting fund / REIT's S40, make agent fee payment S42, updating an asset list S44, providing update information S46, and storing information in a server or multi-node ledger S48.
  • the server 10 transmits the matched real estate information to the investor terminal 21.
  • Investor (1) checks the real estate information sent to the investor terminal (21) and approves investment initiation.
  • the investor terminal (21) which has received the approval to commence investment, shall provide this information to the server 10. As a result, investment is started.
  • the server 10 transfers the matched real estate information to the consultant terminal 23.
  • consulting group (3) checks the real estate information sent to the consultant terminal (23).
  • the Consultant approves the commencement of investment in the property as confirming information sent through Consultant's terminal 23.
  • the approval is transmitted to the server 10.
  • the Consultant's investment is initiated.
  • the server 10 sends the investment start notification information to the financial group terminal and the investor terminal.
  • the server 10 selects a real estate manager through a trial engine or agent determination engine at S14 and S30, and the selection is stored in the real estate information DB 13.
  • the person in charge of the consulting group may issue a purchase order through the consultant terminal to which the purchase confirmation is also sent.
  • the method may include setting up funds / REIT's in S40.
  • the funds or REITS manage the property purchased, and the management of the property is authorized by the investor through a management contract.
  • the agent determined by the trial engine is assigned to manage the property and receives a management fee from the funds or REITs inS42.
  • the server 10 updates the list of assets for which investment is completed. This updated list and all information and records relating to investments are available to the financial groups terminal 25, the investor terminal 21, and the consultant terminal 23. The same information may be stored in a plurality of terminals. Blockchain technology can be used in this process.
  • examples of the trial engine S14, S30 are shown in detail in Figs. 5 - 7.
  • the real estate investment management method based on the system described herein will send an inquiry to confirm whether for the registrant to have secured the exclusive right in step S51.
  • an agent can register the information of real estate for sale in the real estate broker terminal 27 in the step S13 of Fig. 4.
  • the server 10 transmits a signal inquiring whether it has received exclusive brokerage authority from the current owner.
  • the property owner may have listed the property with several realtors or a single realtor.
  • the agent may have non-exclusive representation or may be granted exclusive representation. This step is to check the type of the representation.
  • the real estate broker who registered a real property for sale can enter whether or not he or she has secured the exclusive rights in the agent terminal 27.
  • the information is transmitted to the server 10 (S53).
  • Server 10 that received input of whether or not to secure the exclusive right sets a period for an objection that other agents can raise an objection for the brokerage right of the registered property.
  • a person who does not have the right of representation can be recognized by the system as a property information registrant and has the authority to sell the property.
  • an opposition window is provided before the invention is completed.
  • the server 10 determines whether there was an objection within the period in S61.
  • the server 10 may receive a signal related to the objection from the real estate broker terminal 27. If the server 10 determines that there is an objection within the period, the server 10 determines in turn that the objection was made properly in S63. That is, in S61, the objection is filed.
  • the server 10 informs the information registrant as the sole agent who will receive 100% of the brokerage commission in S65.
  • Criteria for judging whether to file an objection as a property manager may vary.
  • Fig. 8 and Fig. 9 illustrates two different information collection processes.
  • Fig. 8 relates to an API connection method, whether a database is directly connected with an API connection.
  • Fig. 9 is a website connection method, in which a website is connected, and relevant information is extracted after analyzing the website structure and database.
  • the collected data is cleaned and properly formatted and compiled so that the deep learning process 204 can utilize the data to predict a property value 205.
  • an embodiment according to the present invention utilizes a recurrent neural network for a deep learning engine.
  • various data inputs are processed based on temporal sequence ad the output from a step is fed as input to the following step, unlike the traditional neural networks where all the inputs and outputs are independent of each other.
  • the same type of data can be used to minimize the complexity associated with the calculation.
  • one set of two different types of data can be used.
  • the first type of data supplied to the engine is numeric data 301, and the other type of data is non-numeric characteristics 302.
  • the numeric data may include size, price, transaction date, GPS data, floor information, built date, or any other data characterized by a numeric value.
  • Each type of data can be composed of a plurality of data which can be sorted and organized suitably for the calculation.
  • the characteristic data may include school name, apartment brand name, landmark name, or any other characteristics that may affect the property value and market trend.
  • information 401 regarding a target property is compiled and supplied to the network for prediction.
  • the process may include consulting with actual property price database 402, which contains past transaction information, including the past transaction price of the target property.
  • past transaction history data 403 Using the past transaction history data 403, a future value may be calculated using the trained network.
  • the network may utilize an intermedium market data where past transaction data of similar properties within the same property market.
  • Fig. 13 illustrates how historical data is applied to later data.
  • Historical data 307a is used not only for the calculation of the first data 307d but also for the next calculation 307g. Because historical data affects the training and prediction processes, having meaning historical data would affect the accuracy of the prediction value. When such historical data does not exist, or only a few data points exist, interim data may be estimated using the same network. For example, a property was sold 10 years ago and is proposed for sale. There may not be sufficient data to predict the current or future value of the property. In such a case, two different methods may be adopted. One way is creating interim data using the network and creating sequential data for the particular property. To do so, the network may be trained with sequential data of properties that are similar to the target property. Similar property may mean that those properties have the same size and are located in the same property market. The same property market may mean a regional area such as township or providence or a complex or building such as a single condominium building or single apartment or townhome complex.
  • Another way is to predict only as a building or class of properties.
  • building 500 in Fig. 14 has many apartments 511, 521, 531, 541, 542, 543, etc. Even though these apartments are in a single building, their respective prices may be different depending on the floor and the location within the building. However, if there is no sufficient data as to individual apartment, the network can be trained at the level of the building, and then resulting prediction would not be specific to a particular apartment. In such a case, a further adjustment can be made using a weight factor.
  • the weight factor is calculated using a public assessment price of each apartment of the building. For example, there are sixteen apartments in building of Fig. 14. Each apartment may have different public assessment price. Then, the weight factor may be calculated by the public assessment price of the target apartment divided by the average public assessment price of the sixteen apartments. The level of the prediction may be adjusted depending on relevant data availability.
  • An embodiment, according to the present invention is tested using known historical data, as shown in Table 1.
  • the historical data are grouped in four different sizes: pico, small, medium, and large. The smallest is six-month data with about 41,000 data points. The next size contains 226,000 data points. The next one has 2 million data, and the largest has 5.8 data.
  • the verification data collection relates to actual data that are compared with predicted results by the engine.

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