Real estate score calculation system, real estate score calculation method, and program

Abstract

To provide a system for proposing a measurement model that measures stability of rent of investment real estate.SOLUTION: A system is configured to include a user terminal 20 of a user, and a real estate score calculation device 10 that is connected with the user terminal 20 in a data transmittable / receivable manner and calculates a P score as a measurement model that measures stability of rent of investment real estate. The real estate score calculation device 10 is configured to: input real estate information collected from the outside to a machine learning model; extract a plurality of feature quantities greatly affecting a rent index by analyzing the machine learning model; define the feature quantities as a plurality of parameters; set the maximum point and a point calculation reference for each parameter based on the rent index and a correlation level; calculate points for each parameter based on the point calculation reference, when parameter information is input as the real estate information from the user terminal 20; and calculate the P score from the calculated points for respective parameters.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a system, method, and program for proposing an econometric model for measuring rental stability of investment real estate. [Background technology]

[0002] Generally known methods for valuing investment properties include the income capitalization method and the comparable transaction method. However, in Japan, actual transaction data for real estate transactions is not publicly disclosed, and there are no generally available statistical models for determining prices. This has made it difficult for individual investors to determine the appropriateness of investment property prices.

[0003] For example, Patent Document 1 discloses a real estate investment analysis support device that supports the analysis of the performance of investment real estate from various perspectives. Also, MFS Corporation offers a real estate investment service (INVASE) that includes a "voucher service" that determines the amount of real estate investment loan that can be borrowed, and a "refinancing service" that introduces financial institutions to which real estate investment loans can be refinanced.

[0004] However, there are no services available that quantitatively evaluate the appropriateness of the price of investment real estate. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-126522 Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention aims to provide a real estate score calculation system that calculates a P score (a numerical value that quantitatively indicates the degree of possibility that the expected rent will not be obtained in the future) as a quantitative model to measure the stability of rental income for investment real estate. [Means for solving the problem]

[0007] The real estate score calculation system according to the first invention is A system comprising a user terminal of a user, and a real estate score calculation device connected to the user terminal so as to be able to transmit and receive data, and which calculates a P score as a quantitative model for measuring the stability of rental income of investment real estate, The real estate score calculation device A machine learning unit processes information collected from real estate information sources (external or internal real estate information sites, real estate companies, and other sources) into real estate information targeted at rent indicators that indicate the stability of rent (for example, price per square meter, vacancy rate, average period from when a vacancy occurs until a tenant is decided, etc.), and inputs the processed real estate information into a machine learning model; and a P-score model creation unit that analyzes the machine learning model to extract multiple feature amounts that have a high impact on the rent index, defines these feature amounts as multiple parameters, sets a weight for each of the parameters based on the degree of correlation with the rent index, and sets the maximum points and point calculation criteria for each of the parameters; a P-score calculation unit that, when the parameter information is input as real estate information from the user terminal, calculates points for each parameter based on point calculation standards for each parameter set in the P-score model creation unit, and calculates the sum of the calculated points for each parameter as the P-score; The present invention is characterized by comprising:

[0008] The real estate score calculation system according to the second invention is the first invention, In the real estate score calculation system according to claim 1, The system is characterized by having a base cap rate calculation unit that calculates a base cap rate as an appropriate yield using an approximation formula obtained from the P score calculated by the P score calculation unit and an actual yield distribution chart.

[0009] The real estate score calculation system according to the third invention is the first or second invention, The P score model creation unit is characterized in that it sets the maximum points for each parameter based on the rent index and the degree of correlation so that the total value of the maximum points for each parameter is a constant value, and sets point calculation standards so as to calculate points according to the parameter information input from the user terminal.

[0010] The real estate score calculation system according to the fourth aspect of the present invention is the first or second aspect of the present invention, The P score model creation unit analyzes the relative influence of the parameters and sets the proportion of each parameter to the whole as a weight so that the total value of the weights for each parameter becomes 100%.

[0011] A real estate score calculation system according to a fifth aspect of the present invention is the first or second aspect of the present invention, The parameters set by the P score model creation unit are characterized by including all or part of the following: "location," "nearest station," "distance from station," "year built," "exclusive floor area," "rental status," "floor location," and "brand."

[0012] The real estate score calculation method according to the sixth aspect of the present invention is A method for calculating a P-score as a quantitative model for measuring the stability of rental income for investment real estate based on real estate information input from a user's user terminal, A machine learning process involves processing information collected from real estate information sources (external or internal real estate information sites, real estate companies, and other sources) into real estate information targeted at rent indices, which are indicators of rent stability, and inputting the processed real estate information into a machine learning model. a P-score model creation process in which the machine learning model is analyzed to extract multiple feature amounts that have a high impact on the rent index, these feature amounts are defined as multiple parameters, and the proportion of each parameter to the total parameters is set as a weight based on the degree of correlation with the rent index, and the maximum points and point calculation criteria for each parameter are set; a P-score calculation step of calculating points for each parameter based on the point calculation standard for each parameter set in the P-score model creation step when the parameter information is input as real estate information from the user terminal, and calculating the sum of the calculated points for each parameter as the P-score; The present invention is characterized by comprising:

[0013] The real estate score calculation program according to the seventh invention is A program that calculates a P-score as a quantitative model to measure the stability of rental income for investment real estate based on real estate information entered from a user's user terminal, On the computer, A machine learning means that uses information collected from real estate information sources (external or internal real estate information sites, real estate companies, and other sources) to process real estate information targeting rent indices, which are indicators of rent stability, and inputs the processed real estate information into a machine learning model; a P-score model creation means for analyzing the machine learning model to extract multiple feature amounts that have a high influence on the rent index, defining these feature amounts as multiple parameters, setting a weight for each of the parameters based on the degree of correlation with the rent index, and setting a maximum point and point calculation standard for each of the parameters; a P-score calculation means for calculating points for each parameter based on point calculation standards for each parameter set by the P-score model creation means when the parameter information is input as real estate information from the user terminal, and for calculating the sum of the calculated points for each parameter as the P-score; The present invention is characterized in that the following is executed. [Effects of the Invention]

[0014] According to the present invention, the P-score is used as a quantitative model for measuring the stability of investment real estate rental rates, enabling the quantitative evaluation of the appropriateness of investment real estate prices. For example, by selecting eight items of information about a property a user wishes to purchase and entering the rent, the user can ascertain the appropriate price of the property. Furthermore, by changing conditions such as location, age, and rental status, the user can understand the extent to which these changes affect the price. Since the yield relative to the P-score is updated monthly based on market trends, price information is always up-to-date, ensuring transparency in investment real estate prices and promoting buying and selling, which is expected to ultimately lead to improved liquidity of investment real estate. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a system configuration diagram showing an example of the configuration of a real estate score calculation system according to an embodiment of the present invention. [Figure 2] 1 is a functional block diagram illustrating a real estate score calculation device according to an embodiment of the present invention. [Figure 3] FIG. 2 is a flowchart illustrating a P-score calculation process procedure in the real estate score calculation device according to the embodiment of the present invention. [Figure 4] FIG. 10 is a diagram showing an example of definition of each parameter required for P score calculation. [Figure 5-1] FIG. 10 is a diagram showing an example of point calculation criteria set for each parameter required for P score calculation. [Figure 5-2] FIG. 10 is a diagram showing an example of point calculation criteria set for each parameter required for P score calculation. [Figure 5-3] FIG. 10 is a diagram showing an example of point calculation criteria set for each parameter required for P score calculation. [Figure 5-4] FIG. 10 is a diagram showing an example of point calculation criteria set for each parameter required for P score calculation. [Figure 5-5]FIG. 10 is a diagram showing an example of point calculation criteria set for each parameter required for P score calculation. [Figure 5-6] FIG. 10 is a diagram showing an example of point calculation criteria set for each parameter required for P score calculation. [Figure 5-7] FIG. 10 is a diagram showing an example of point calculation criteria set for each parameter required for P score calculation. [Figure 5-8] FIG. 10 is a diagram showing an example of point calculation criteria set for each parameter required for P score calculation. [Figure 6] FIG. 10 is a diagram showing an example of calculating a base cap rate. [Figure 7] FIG. 1 is a diagram showing an example of calculation criteria for a base cap rate. [Figure 8] This is a diagram showing an example of calculating the P score and base cap rate. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Note that the embodiments described below are merely examples given to facilitate understanding of the present invention and are not intended to limit the present invention. In other words, the present invention may be modified or improved from the embodiments described below without departing from the spirit of the present invention.

[0017] The usage environment of a real estate score calculation system according to an embodiment of the present invention will be described with reference to FIG. 1. FIG. 1 is a system configuration diagram showing an example of the configuration of a real estate score calculation system. As shown in FIG. 1, a real estate score calculation device 10 is a device that calculates a P score as a quantitative model for measuring the stability of rent for investment real estate, and is connected to a user terminal 20 via a communication network. Here, the P score is a numerical value that quantitatively indicates the degree of possibility that the expected rent will not be obtained in the future. In this embodiment, the numerical range of the P score is set to a range of 0 to 5, and the lower the P score, the higher the risk of not being able to obtain the rent.

[0018] For example, a user who wants to obtain information on investment real estate accesses the real estate score calculation device 10 from the user terminal 20 and inputs the real estate information of the investment real estate that is the target property from the user terminal 20. Then, the real estate score calculation device 10 calculates the P score of the target property based on the real estate information and displays the result on the user terminal 20. Here, the above real estate information includes information such as "location", "nearest station", "distance from the station", "age of construction", "floor area", "rental situation", "floor number", and "condominium brand".

[0019] Also, the real estate score calculation device 10 can calculate the P score of the target property based on the above real estate information, and can also calculate the appropriate rate of return and appropriate price of the investment real estate that is the target property.

[0020] Next, the functional configuration of the real estate score calculation device according to the embodiment of the present invention will be described while referring to FIG. 2. FIG. 2 is a functional block diagram for explaining the real estate score calculation device. As shown in FIG. 2, the real estate score calculation device 10 includes a machine learning unit 11, a P score model creation unit 12, a P score calculation unit 13, and a base cap rate calculation unit 14. Each functional unit will be described in detail below.

[0021] <Machine learning unit> The machine learning unit 11 performs processing to process the information collected from an external real estate company (real estate information source) into real estate information targeted at rent indicators, and inputs the processed real estate information into a machine learning model (hereinafter, the explanation will be made using the unit price per square meter, which is one of the representative examples of rent indicators).

[0022] The P score model creation unit 12 analyzes the machine learning model to extract a plurality of feature quantities that have a high degree of influence on the unit price per square meter, defines these feature quantities as a plurality of parameters, sets the ratio of each parameter to the total parameter as the specific gravity based on the degree of correlation with the unit price per square meter, and sets the maximum point and point calculation criteria for each parameter.

[0023] In addition, the P score model creation unit 12 sets the maximum points for each parameter based on the square meter price and the degree of correlation so that the total value of the maximum points for each parameter becomes a constant value, and sets the point calculation standard so that the points are calculated according to the parameter information input from the user terminal.

[0024] The P-score model creation unit 12 analyzes the relative influence of the parameters and sets the percentage of each parameter to the whole as a weight so that the total sum of the weights for each parameter is 100%. The larger the weight value, the greater the influence that parameter has on the price per square meter. The maximum points for each parameter are set in accordance with the weight value.

[0025] Next, the maximum points, point calculation criteria, and weights set by the P score model creating unit 12 will be described with reference to FIG. 4 and FIGS. 5-1 to 5-8.

[0026] Fig. 4 shows an example of the parameter definition. In the example of Fig. 4, eight types of parameters are defined: "Location," "Nearest station," "Distance from station," "Age of building," "Exclusive floor area," "Rental status," "Floor location," and "Apartment brand."

[0027] In the example of Figure 4, a "maximum point" and a "weight" are set for each parameter, and the total value of the "maximum points" is "5.0," and the total value of the "weights" is "100%." ​​Furthermore, for the "location" parameter, "maximum point = 0.94" is set, and furthermore, "weight = 19%" is set as the percentage that this parameter accounts for overall. The other parameters are also as shown in Figure 4.

[0028] Figs. 5-1 to 5-8 are diagrams showing an example of point calculation criteria set for each parameter. Here, for each parameter of "location", "nearest station", "distance from the station", "number of years since construction", "floor area", "rental situation", "floor number", and "condominium brand", the point calculation criteria are set to calculate points according to the parameter information input from the user terminal 20.

[0029] In the example of Figs. 5-1 to 5-8, for the "location" parameter, according to the parameter information input from the user terminal 20, for example, when it is Chiyoda Ward, "point = 0.87" is calculated, and when it is Minato Ward, "point = 0.94" (maximum point) is calculated, and the point calculation criteria are set.

[0030] Also, for the "nearest station" parameter, according to the parameter information input from the user terminal 20, for example, when it is Shinjuku, the point calculation criteria are set so that "point = 0.64" is calculated.

[0031] The point calculation criteria are set so that the points for other parameters are calculated as shown in Figs. 5-1 to 5-8.

[0032] When the parameter information is input as real estate information from the user terminal 20, the P score calculation unit 13 calculates the points for each parameter based on the point calculation criteria for each parameter set by the P score model creation unit 12, and calculates the sum of the calculated points for each parameter as the P score.

[0033] Next, we will explain how to calculate the P score using a specific example. For example, when the parameter information "Location = Shinjuku Ward," "Nearest station = Shinjuku," "Distance from station = 10 minutes," "Age of building = 2 years," "Exclusive floor area = 20 m," "Rental status = Vacant," "Location floor = 3rd floor," and "Apartment brand = Gala" are entered, the points for each parameter are calculated as follows based on the point calculation criteria for each parameter. Location points = 0.73 "Nearest station" points = 0.64 "Distance from station" points = 0.18 "Age of building" points = 1.41 "Exclusive Area" points = 0.21 "Rental Status" points = 0.25 "Location Floor" points = 0.06 "Condominium brand" points = 0.16

[0034] Next, the points calculated for each parameter are added together to calculate the P score. Therefore, the P score is calculated as follows: P-score = 0.73 + 0.64 + 0.18 + 1.41 + 0.21 + 0.25 + 0.06 + 0.16 = 3.64

[0035] In this embodiment, the P score ranges from 0 to 5, and in this example, a P score of 3.64 was obtained. The P score allows users to quantitatively evaluate the appropriateness of the price of an investment property. As mentioned above, the P score is a numerical value that quantitatively indicates the degree of possibility that the expected rent will not be obtained in the future, and the lower the P score, the higher the risk of a rent decrease or no rent being obtained.

[0036] <Base Cap Rate Calculation Department> The base cap rate calculation unit 14 calculates the base cap rate as an appropriate yield using an approximation formula obtained from the P score calculated by the P score calculation unit and an actual yield distribution chart.

[0037] Figure 6 shows an example of calculating the base cap rate. The base cap rate (BCR) is a value that is reviewed monthly. For example, if the P score is 5.0, the BCR will be calculated as 3.28% (October 2022).

[0038] An example of the calculation criteria for the base cap rate is shown in Figure 7. As shown in Figure 7, the P score is calculated from real estate information collected monthly, and the P score and actual yield are plotted on a scatter plot, and the approximation formula is used as the calculation formula for the base cap rate.

[0039] Next, a data processing procedure when real estate information is input from a user terminal in the real estate score calculation system according to the embodiment of the present invention will be described with reference to the flowchart of FIG.

[0040] <Data processing procedure> In step S10, real estate information on the investment property that is the target property is input from the user terminal 20. Here, the parameter information of this real estate information includes information such as "location," "nearest station," "distance from station," "age of building," "exclusive floor area," "rental status," "floor location," and "apartment brand."

[0041] Next, in step S20, points for each parameter are calculated from the parameter information input as real estate information. That is, points for each parameter are calculated from the parameter information input as real estate information from the user terminal 20 based on the point calculation standard for each parameter set in the P score model creation unit 12.

[0042] Next, in step S30, the P score is calculated using the points for each parameter calculated in step S20. In other words, the P score is calculated by adding up the points for each parameter. In addition, although not shown in this flowchart, it is also possible to calculate the fair yield (base cap rate) and fair price of the investment property that is the target property.

[0043] Next, in step S40, the calculation result calculated in step S30 is transmitted to the user terminal 20. In addition to the P score of the target property, this calculation result may also include the fair yield (base cap rate) and fair price of the target property.

[0044] The data processing procedure described above may be realized as a method executed by a computer, or may be realized as a program to be executed by a computer.

[0045] Finally, we will explain an example of calculating the P score and base cap rate (BCR) as verification results of this system. Figure 8 shows the example of the MFS Otemachi Mansion as the target property, and the calculated P score was 3.31 and BCR was 3.89%.

[0046] From the above, it can be seen that using the P-score and base cap rate (BCR) as quantitative models to measure the stability of rental rates for investment properties is effective, and it is expected that this will ensure the transparency of investment property prices, promote buying and selling, and ultimately lead to improved liquidity of investment properties.

[0047] In this embodiment, the rent index, which indicates the stability of rent, was explained using the price per square meter, but this is not limited to this. The above explanation is based on the hypothesis that "a stable property that can continue to command high rents, based on the formula = rent / yield = price, should have a high real estate price = a correlation with the price per square meter." A certain correlation was found between the P score generated by targeting the price per square meter and the actual yield, and so price per square meter was used as an example. Therefore, in addition to or instead of price per square meter, specific data (items, features) that are considered to be (most) indicative of rent stability, such as vacancy rate or the average time from when a vacancy occurs until a tenant is found, may be targeted. [Explanation of symbols]

[0048] 10...Real estate score calculation device 11...Machine Learning Department 12...P score model creation section 13...P score calculation section 14...Base cap rate calculation section 20...User terminal

Claims

1. A system comprising a user terminal of a user, and a real estate score calculation device connected to the user terminal so as to be able to transmit and receive data, and which calculates a P-score as a quantitative model for measuring the stability of rental income of investment real estate, The real estate score calculation device a machine learning unit that processes information collected from real estate information sources into real estate information targeted at rent indices, which are indicators of rent stability, and inputs the processed real estate information into a machine learning model; a P-score model creation unit that analyzes the machine learning model to extract multiple feature amounts that have a high influence on the rent index, defines these feature amounts as multiple parameters, sets a weight for each of the parameters based on the degree of correlation with the rent index, and sets a maximum point and a point calculation standard for each of the parameters; a P-score calculation unit that, when the parameter information is input as real estate information from the user terminal, calculates points for each parameter based on point calculation standards for each parameter set in the P-score model creation unit, and calculates the P-score by adding up the calculated points for each parameter; A real estate score calculation system comprising:

2. The real estate score calculation system according to claim 1, A real estate score calculation system characterized by having a base cap rate calculation unit that calculates a base cap rate as an appropriate yield using an approximation formula obtained from the P score calculated by the P score calculation unit and an actual yield distribution chart.

3. 3. The real estate score calculation system according to claim 1, The P score model creation unit sets the maximum points for each parameter based on the rent index and the degree of correlation so that the total value of the maximum points for each parameter is a constant value, and sets point calculation standards so as to calculate points according to the parameter information input from the user terminal.

4. 3. The real estate score calculation system according to claim 1, The P score model creation unit analyzes the relative influence of the parameters and sets the proportion of each parameter to the whole as a weight so that the total value of the weights for each parameter is 100%. This is a real estate score calculation system characterized by the above.

5. 3. The real estate score calculation system according to claim 1, A real estate score calculation system characterized in that the parameters set by the P score model creation unit include all or part of the following: "location," "nearest station," "distance from station," "year built," "exclusive floor area," "rental status," "floor location," and "brand."

6. A method for calculating a P-score as a quantitative model for measuring the stability of rental income for investment real estate based on real estate information input from a user's user terminal, comprising: A machine learning process in which information collected from real estate information sources is processed into real estate information targeting rent indices, which are indicators of rent stability, and the processed real estate information is input into a machine learning model; a P-score model creation process in which the machine learning model is analyzed to extract multiple feature amounts that have a high influence on the rent index, these feature amounts are defined as multiple parameters, and the proportion of each parameter to the total parameters is set as a weight based on the degree of correlation with the rent index, and the maximum points and point calculation criteria for each parameter are set; a P-score calculation step of calculating points for each parameter based on the point calculation standard for each parameter set in the P-score model creation step when the parameter information is input as real estate information from the user terminal, and calculating the sum of the calculated points for each parameter as the P-score; A real estate score calculation method comprising:

7. A program that calculates a P-score as a quantitative model for measuring the stability of rental income for investment real estate based on real estate information input from a user's user terminal, On the computer, A machine learning means for processing information collected from real estate information sources into real estate information targeted at rent indices, which are indicators of rent stability, and inputting the processed real estate information into a machine learning model; a P-score model creation means for analyzing the machine learning model to extract multiple feature amounts that have a high influence on the rent index, defining these feature amounts as multiple parameters, setting a weight for each of the parameters based on the degree of correlation with the rent index, and setting a maximum point and point calculation standard for each of the parameters; a P-score calculation means for calculating points for each parameter based on point calculation standards for each parameter set by the P-score model creation means when the parameter information is input as real estate information from the user terminal, and for calculating the P-score by adding up the calculated points for each parameter; A real estate score calculation program characterized by executing the above.

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