Program, information processing apparatus, method, and system

Abstract

To provide a novel technique for calculating floor-by-location indexes in the used condominium market.SOLUTION: A program for operating a computer 20 having a processor 29, causes the processor to perform the steps of: receiving an input of a price list of a used apartment building, for which a floor-by-location index is to be calculated, at the time of its new construction; calculating a first floor-by-location index for the used apartment building at the time of its new construction from the price list at the time of its new construction; accepting two or more inputs of unit prices of the used apartment building; calculating two or more second floor-by-location indexes for each unit of the used apartment building from the two or more unit prices of the used apartment building; calculating a correlation coefficient between the second floor-by-location index and the first floor-by-location index corresponding to the second floor-by-location index; and calculating a third floor-by-location index for any unit of the used apartment building based on the correlation coefficient.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a program, an information processing apparatus, a method, and a system.

Background Art

[0002] As a technique for estimating the price of each unit in a used condominium, there is a technique for estimating the selling price of a condominium property using the selling price list at the time of new construction of the condominium, disclosed in Patent Documents 1 and 2. Further, as disclosed in Non-Patent Document 1, the price of each unit in a used condominium is obtained by manual calculation by a real estate appraiser or the like based on empirical rules or the like.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the techniques described in Patent Documents 1 and 2, even when estimating the used selling price of another property using a predetermined relational expression from the used selling price of a single property (room), or estimating the used selling price of another property from the used selling prices of a plurality of properties, the estimation of the used selling price only stops at using the average value of the attribute values of the plurality of properties.

[0006] Even within the same condominium, individual properties are intricately related to factors that influence their selling prices, such as direction, floor number (story), and even the size and orientation of the balcony. Therefore, it is difficult to make a price estimation that fully reflects such factors using the techniques described in Patent Documents 1 and 2.

[0007] Also, in the technique described in Non-Patent Document 1, the methods used by real estate appraisers and the like are based solely on empirical rules, which require extensive experience and knowledge, are highly specialized, and cannot be said to be highly usable methods that can be utilized in general price appraisals.

[0008] The price list at the time of new construction is created according to the characteristics of the new condominium sales market (full-house sales in a relatively short period, differences in the attributes of market participants), which is different from the price differences by location and floor in the used housing market. On the other hand, although the price differences are different, their superiority and inferiority are determined based on the general principles of real estate prices. Note that the degree of difference in the utility price differences by floor and location between the used housing time and the new construction time varies based on regionality and the individuality of the condominium.

[0009] Therefore, the present disclosure has been made to solve the above problems, and its object is to provide a technique for calculating a new index by floor and location in the used condominium market.

Means for Solving the Problems

[0010] A program for operating a computer equipped with a processor, the program causing the processor to execute: a first step of receiving an input of a price list at the time of new construction of an old apartment building for which a hierarchical and location-specific index is to be determined; a second step of calculating a first hierarchical and location-specific index at the time of new construction of the old apartment building from the price list at the time of new construction; a third step of receiving inputs of the prices of two or more individual apartments in the old apartment building; a fourth step of calculating two or more second hierarchical and location-specific indexes for the individual apartments in the old apartment building from the prices of two or more individual apartments in the old apartment building; a fifth step of calculating a correlation coefficient between the second hierarchical and location-specific index and the first hierarchical and location-specific index corresponding to the second hierarchical and location-specific index; and a sixth step of calculating a third hierarchical and location-specific index for an arbitrary individual apartment in the old apartment building based on the correlation coefficient.

Effect of the Invention

[0011] According to the present disclosure, it is possible to provide a technique for calculating a new hierarchical and location-specific index in the used apartment market.

Brief Description of the Drawings

[0012]

Figure 1

Figure 2

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Figure 4

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Figure 7

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Figure 10

Figure 11

Embodiments for Carrying Out the Invention

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all the drawings for explaining the embodiments, the same reference numerals are given to common components, and repeated explanations are omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Also, not all of the components shown in the embodiments are essential components of the present disclosure. Also, each figure is a schematic diagram and is not necessarily drawn precisely.

[0014] Also, in the following description, a "processor" is one or more processors. At least one processor is typically a microprocessor such as a CPU (Central Processing Unit), but may also be another type of processor such as a GPU (Graphics Processing Unit). At least one processor may be single-core or multi-core.

[0015] Also, at least one processor may be a processor in a broad sense, such as a hardware circuit (e.g., FPGA (Field-Programmable Gate Array) or ASIC (Application Specific Integrated Circuit)) that performs part or all of the processing.

[0016] In the following description, the expression "xxx table" may be used to describe information from which an output can be obtained for an input. This information may be data of any structure or a learning model such as a neural network that generates an output for an input. Therefore, "xxx table" can be referred to as "xxx information".

[0017] In the following description, the configuration of each table is an example. One table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0018] In the following description, the "program" may be used as the subject to describe a process. However, since the program is executed by a processor to perform a defined process while appropriately using a storage unit and / or an interface unit, etc., the subject of the process may be the processor (or a device such as a controller having the processor).

[0019] The program may be installed in a device such as a computer, or may be in, for example, a program distribution server or a computer-readable (e.g., non-transitory) recording medium. Also, in the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0020] In the following description, an identification number is used as identification information for various objects, but other types of identification information (e.g., an identifier including letters or symbols) may be adopted.

[0021] In the following description, when describing elements of the same type without distinction, reference signs (or common signs among the reference signs) are used, and when describing elements of the same type by distinction, the identification numbers (or reference signs) of the elements may be used.

[0022] In the following description, the control lines and information lines indicate those considered necessary for the description, and not necessarily all the control lines and information lines on the product. All components may be interconnected with each other.

[0023] <0 System Overview> The system according to the present disclosure is a system that calculates a floor and location specific index of a used apartment building using the price list at the time of new construction of a used apartment building that a user wants to obtain a floor and location specific index, and the prices of two or more individual apartments in this used apartment building.

[0024] In this specification, a used apartment building literally refers to an apartment building that is not newly built. Apartment buildings can be divided into condominiums and row houses, but the system of this embodiment can be applied to either condominiums or row houses. Further, the system of this embodiment is basically used in the sale and purchase of condominiums, but can also be applied to rental housing. There is no particular limitation on the name of the apartment building, and any existing apartment building such as a mansion, an apartment, or a housing complex may be used. Since it is presumed that the apartment building generally used in this embodiment is a mansion, in the following description, the term "mansion" is used as a representative of the apartment building.

[0025] An apartment building is a collection of multiple households. In this specification, basically, when indicating an individual household, it is referred to as a "household", and when indicating the entire apartment building, it is referred to as an "apartment building".

[0026] The "floor and location specific index" in this specification is such that the unit price per square meter of the proprietary area of a medium household in the target mansion is set to 100, and the unit price per square meter of the proprietary area when the floor and direction (east, south, west, north) change for this household 2 is shown. This medium household theoretically corresponds to a household with the same unit price as the average unit price of the mansion, but whether it will exactly be an index of 100 may vary from mansion to mansion. 2 is shown. This medium household theoretically corresponds to a household with the same unit price as the average unit price of the mansion, but whether it will exactly be an index of 100 may vary from mansion to mansion.

[0027] An example of the floor- and position-specific index is shown in Fig. 7. In the example shown in Fig. 7, it includes the corner index, which is the variation ratio when the dwelling unit is a corner room, and the roof balcony index, which is the variation ratio when the dwelling unit has a roof balcony. However, at a minimum, it is sufficient to obtain the index for the floor and the azimuth. In the floor- and position-specific index shown in Fig. 7, the median dwelling unit is the south-facing dwelling unit on the second floor. However, as described above, the floor- and position-specific index of this dwelling unit does not exactly become 100.

[0028] Similar to the floor- and position-specific index in this specification, there are the "floor-specific utility ratio" and the "position-specific utility ratio" described in the "New Revised Real Estate Appraisal Standards" supervised by the All-Japan Real Estate Appraisers Association Federation, a public interest incorporated association. However, there are no specific criteria or laws described in the real estate appraisal standards regarding the floor-specific utility ratio and the like. In this specification, the floor- and position-specific index is calculated by the method described above.

[0029] In this specification, for a used apartment building for which the floor- and position-specific index is to be obtained, obtaining the floor- and position-specific index at the time of new construction (hereinafter referred to as the first floor- and position-specific index) is based on the premise that the new construction price list can be obtained in the system of this embodiment. Therefore, an index without deficiency can be obtained for the entire used apartment building (more precisely, for the dwelling units whose prices are described in the new construction price list). However, regarding the current floor- and position-specific index of the used apartment building (hereinafter referred to as the second floor- and position-specific index), in the system of this embodiment, since it is based on the premise that the prices of two or more dwelling units can be obtained, it may only be calculated for the two or more dwelling units that can be obtained. Furthermore, regarding the floor- and position-specific index of the used apartment building calculated based on the first and second floor- and position-specific indexes (hereinafter referred to as the third floor- and position-specific index), it may also be calculated as a correction value for the first floor- and position-specific index. In this specification, including the above-described aspects, all are referred to as "calculation of the floor- and position-specific index".

[0030] <One Embodiment> <1 Configuration Diagram of the Whole System> FIG. 1 is a diagram showing the overall configuration of a hierarchical position-specific index calculation system 1 (hereinafter simply referred to as system 1) according to the present embodiment. As shown in FIG. 1, the system 1 includes a plurality of terminal devices (in FIG. 1, terminal devices 10a and 10b are shown. Hereinafter, they may be collectively referred to as "terminal devices 10"), a server 20, and an external data server 30. The terminal devices 10 and the server 20 are communicably connected to each other via a network 80. The network 80 is constituted by a wired or wireless network. In the present embodiment, the server 20 is a server having a function as a web server (including a cloud server), and exchanges information with the terminal devices 10 via web pages. Further, a web page browser for browsing web pages is installed in the terminal devices 10, but a dedicated application for providing the services of the server 20 may be installed and configured to be browsable by the dedicated application.

[0031] Since the hardware configuration of the terminal device 10a is common to the hardware configuration of the terminal device 10b, the description of the hardware configuration of the terminal device 10 will be omitted by describing the hardware configuration of the terminal device 10a.

[0032] The terminal device 10 is a device operated by a user who acquires a hierarchical position-specific index. The terminal device 10 is realized by a stationary PC (Personal Computer), a laptop PC, or the like. In addition, the terminal device 10 may be, for example, a tablet corresponding to a mobile communication system, or a mobile terminal such as a smartphone.

[0033] The terminal device 10 is communicably connected to the server 20 via the network 80. The terminal device 10 is connected to the network 80 by communicating with communication devices such as a radio base station 81 compatible with communication standards such as 4G, 5G, and LTE (Long Term Evolution), and a wireless LAN router 82 compatible with wireless LAN (Local Area Network) standards such as IEEE (Institute of Electrical and Electronics Engineers) 802.11. As shown in FIG. 1, the terminal device 10 includes a communication IF (Interface) 12, an input device 13, an output device 14, a memory 15, a storage unit 16, and a processor 19.

[0034] The communication IF 12 is an interface for inputting and outputting signals so that the terminal device 10 can communicate with external devices. The input device 13 is an input device for receiving input operations from the user (for example, a keyboard, a touch panel, a touch pad, a pointing device such as a mouse, etc.). The output device 14 is an output device (display, speaker, etc.) for presenting information to the user. The memory 15 is for temporarily storing programs and data processed by programs and the like, and is a volatile memory such as DRAM (Dynamic Random Access Memory), for example. The storage unit 16 is a storage device for storing data, such as a flash memory or an HDD (Hard Disc Drive), for example. The processor 19 is hardware for executing an instruction set described in a program, and is composed of an arithmetic unit, registers, peripheral circuits, and the like.

[0035] The server 20 is managed by the administrator of the system 1 of the present embodiment, and the stored content can be appropriately modified / added / deleted by the user of the terminal device 10.

[0036] The server 20 is a computer connected to the network 80. The server 20 includes a communication IF 22, an input / output IF 23, a memory 25, a storage 26, and a processor 29.

[0037] The communication IF 22 is an interface for inputting and outputting signals for the server 20 to communicate with an external device. The input / output IF 23 functions as an interface with an input device for receiving input operations from a user and an output device for presenting information to the user. The memory 25 is for temporarily storing programs and data processed by programs, etc., and is a volatile memory such as a DRAM (Dynamic Random Access Memory). The storage 26 is a storage device for storing data, such as a flash memory or an HDD (Hard Disc Drive). The processor 29 is hardware for executing an instruction set described in a program and is composed of an arithmetic unit, registers, peripheral circuits, etc.

[0038] The external data server 30 is a server in which new construction price list data of various apartment houses and price data of used apartment houses are stored. Such an external data server 30 is, for example, operated by a used real estate broker. It may also be operated by a new construction apartment house development and sales company called a so-called developer. The external data server 30 provides the requested data to the server 20, etc. in response to a data acquisition request from the server 20, the terminal device 10, etc. Regarding the new construction price list data of various apartment houses and the price data of used apartment houses, the server 20 may collect them independently and store them in the memory 25 and the storage 26.

[0039] <1.1 Functional Configuration of Terminal Device 10> FIG. 2 is a block diagram showing an example of the functional configuration of the terminal device 10 shown in FIG. 1. The terminal device 10 shown in FIG. 2 is realized by, for example, a PC, a mobile terminal, or a wearable terminal. As shown in FIG. 2, the terminal device 10 includes a communication unit 120, an input device 13, an output device 14, an audio processing unit 17, a microphone 171, a speaker 172, a storage unit 180, and a control unit 190. Each block included in the terminal device 10 is electrically connected by, for example, a bus or the like.

[0040] The communication unit 120 performs processes such as modulation / demodulation processing for the terminal device 10 to communicate with other devices. The communication unit 120 performs transmission processing on the signal generated by the control unit 190 and transmits it to the outside (for example, the server 20). The communication unit 120 performs reception processing on the signal received from the outside and outputs it to the control unit 190.

[0041] The input device 13 is a device for a user operating the terminal device 10 to input instructions or information. The input device 13 may be realized by, for example, a keyboard, a mouse, a reader, etc. When the terminal device 10 is a mobile terminal or the like, it is realized by a touch-sensitive device 131 or the like where an instruction is input by touching the operation surface. The input device 13 converts the instruction input from the user into an electrical signal and outputs the electrical signal to the control unit 190. Note that the input device 13 may include, for example, a reception port for receiving an electrical signal input from an external input device.

[0042] The output device 14 is a device for presenting information to the user operating the terminal device 10. The output device 14 is realized by, for example, a display 141 or the like. The display 141 displays data according to the control of the control unit 190. The display 141 is realized by, for example, an LCD (Liquid Crystal Display), an organic EL (Electro-Luminescence) display, or the like.

[0043] The audio processing unit 17 performs, for example, digital-to-analog conversion processing of audio signals. The audio processing unit 17 converts the signal given from the microphone 171 into a digital signal and gives the converted signal to the control unit 190. Also, the audio processing unit 17 gives the audio signal to the speaker 172. The audio processing unit 17 is realized by, for example, a processor for audio processing. The microphone 171 receives an audio input and gives an audio signal corresponding to the audio input to the audio processing unit 17. The speaker 172 converts the audio signal given from the audio processing unit 17 into audio and outputs the audio to the outside of the terminal device 10.

[0044] The storage unit 180 is realized by, for example, the memory 15, the storage unit 16, etc., and stores data and programs used by the terminal device 10.

[0045] The control unit 190 is realized by the processor 19 reading the application program 181 stored in the storage unit 180 and executing the instructions included in the application program 181. The control unit 190 controls the operation of the terminal device 10. By operating according to the application program 181 stored in the storage unit 180, the control unit 190 functions as an operation reception unit 191, a transmission / reception unit 192, a data processing unit 193, and a presentation control unit 194.

[0046] The operation reception unit 191 performs processing for receiving instructions or information input from the input device 13. Specifically, for example, the operation reception unit 191 receives information based on instructions input from a keyboard, a mouse, etc.

[0047] Also, the operation reception unit 191 receives voice instructions input from the microphone 171. Specifically, for example, the operation reception unit 191 receives a voice signal input from the microphone 171 and converted into a digital signal by the voice processing unit 17. The operation reception unit 191 obtains an instruction from the user, for example, by analyzing the received voice signal and extracting a predetermined noun.

[0048] The transmission / reception unit 192 performs processing for the terminal device 10 to transmit and receive data to and from an external device such as the server 20 according to a communication protocol. Specifically, for example, the transmission / reception unit 192 transmits the business content input by the user to the server 20. Also, the transmission / reception unit 192 receives information about the user from the server 20.

[0049] The data processing unit 193 performs processing of performing an operation on the data received by the terminal device 10 according to the application program 181 and outputting the operation result to the memory 15 or the like.

[0050] The presentation control unit 194 controls the output device 14 in order to present the information provided from the server 20 to the user. Specifically, for example, the presentation control unit 194 causes the information transmitted from the server 20 to be displayed on the display 141. Also, the presentation control unit 194 causes the information transmitted from the server 20 to be output from the speaker 172.

[0051] <1.2 Functional Configuration of Server 20> FIG. 3 is a diagram showing an example of the functional configuration of the server 20. As shown in FIG. 3, the server 20 functions as a communication unit 201, a storage unit 202, and a control unit 203.

[0052] The communication unit 201 performs processes for the server 20 to communicate with external devices.

[0053] The storage unit 202 has, for example, a price list DB (DataBase) 2022, price list data 2023, hierarchical position-specific index data 2024, teacher data 2025, a learning model 2026, and the like.

[0054] The price list DB 2022 is a database for managing at least the new construction price list of the used condominiums subject to the calculation of the hierarchical position-specific index, and the prices of two or more units for this used condominium. The new construction price list and the like managed by the price list DB 2022 may be obtained from the above-described external data server 30, or may be collected independently by the server 20. Details will be described later.

[0055] The price list data 2023 is data on the new construction price list of used condominiums that are at least subject to the calculation of the index by floor and location, and data on the prices of two or more households for these used condominiums. As described in the explanation of the price list DB 2022, the price list data 2023 may be obtained from the above-mentioned external data server 30, or may be collected independently by the server 20. Also, since it is difficult for the server 20 to know in advance from the user of the terminal device 10 what kind of used condominiums in what area, etc. are specified (although it may be presented to the user of the terminal device 10 in advance as a condition for calculating the index by floor and location), it is preferable that a large number of price list data 2023 are accumulated in the server 20. However, the scale of the server 20 also varies depending on the data scale, and as the data scale increases, the operating costs, etc. also increase. Therefore, the operator of the server 20 may appropriately determine what kind of price list data 2023 to accumulate in the server 20.

[0056] In particular, as will be described later, assuming that price data for two or more households cannot be obtained for the used condominiums that are the subject of the calculation of the index by floor and location, it is preferable that a plurality of price list data 2023 are stored in the storage unit 202 for each region.

[0057] Here, the price described in the new construction price list is the public sales price, but it may also be the actual sales price at which the developer actually sold, or both the price described in the new construction price list and the actual sales price may be included in the price list data 2023. Similarly, for the prices of two or more households for used condominiums, the concluded price is desirable, but the sales price, etc. may be appropriately corrected and utilized.

[0058] Also, there is no particular limitation on the data format of the price list data 2023. It may be a so-called spreadsheet or a relational database. However, it is preferable that the data format is such that the room number, floor number, exclusive area of the dwelling unit, and the nature of the dwelling unit that affects the price (direction, price, layout, whether it is a corner room, whether there is a roof balcony) are associated.

[0059] The hierarchical and location-specific index data 2024 is data indicating the hierarchical and location-specific indices of individual second-hand apartment buildings. Details will be described later.

[0060] The teacher data 2025 is data regarding the prices of two or more per-household for second-hand apartment buildings clustered by a cluster formation module 2038 described later. The teacher data 2025 is generated by the cluster formation module 2038.

[0061] The learning model 2026 is obtained by causing a machine learning model to perform machine learning according to a model learning program (not shown) based on the above-described teacher data 2025. When a new construction price list of a second-hand apartment building for which the hierarchical and location-specific index is to be calculated is input to this learning model 2026, prices at which any of the households in this second-hand apartment building are sold at present are output in two or more.

[0062] The learning model 2026 according to the present embodiment is, for example, a parameterized composite function in which a plurality of functions are combined. The parameterized composite function is defined by a combination of a plurality of adjustable functions and parameters. The prediction model according to the present embodiment may be any parameterized composite function that satisfies the above requirements, but is assumed to be a multi-layer network model (hereinafter referred to as a multi-layered network). A prediction model using a multi-layered network has an input layer, an output layer, and at least one intermediate layer or hidden layer provided between the input layer and the output layer. The prediction model is assumed to be used as a program module that is part of artificial intelligence software.

[0063] As the multi-layered network according to the present embodiment, for example, a deep neural network (DNN), which is a multi-layer neural network targeted for deep learning, can be used. As the DNN, for example, a convolutional neural network (CNN) targeted for images may be used.

[0064] Furthermore, the above is merely an example of a prediction model, and the prediction model may have other configurations.

[0065] The control unit 203 is realized by the processor 29 reading the application program 2021 stored in the storage unit 202 and executing instructions included in the application program 2021. The control unit 203 operates in accordance with the application program 2021 to perform functions shown as a reception control module 2031, a transmission control module 2032, a price list acquisition module 2033, a price list search module 2034, a time correction calculation module 2035, a hierarchical and positional index calculation module 2036, a correlation coefficient calculation module 2037, a cluster formation module 2038, and a used price calculation module 2039.

[0066] The reception control module 2031 controls the process in which the server 20 receives a signal from an external device in accordance with a communication protocol.

[0067] The transmission control module 2032 controls the process in which the server 20 transmits signals to external devices in accordance with a communication protocol.

[0068] The price list acquisition module 2033 acquires the price list data 2023 from, for example, the external data server 30. The timing and interval at which the price list acquisition module 2033 acquires the price list data 2023 are arbitrary, and the price list data 2023 may be acquired periodically, or the price list data 2023 may be acquired in response to (triggered by) an instruction to calculate an index by hierarchical position from the user of the terminal device 10. In addition, the price list data 2023 acquired by the price list acquisition module 2033 is mainly assumed to be the external data server 30, but may be acquired from a server other than the external data server 30.

[0069] When the price list search module 2034 searches for the new construction price list of used condominiums and the prices of two or more households in used condominiums, which are the basis for calculating the hierarchical location-specific index, from the price list DB 2022 for the hierarchical location-specific index calculation module 2036 to calculate the hierarchical location-specific index according to an instruction from the user of the terminal device 10, it searches for the price list data 2023 through the price list DB 2022. The search result by the price list search module 2034 is passed to the time-point correction calculation module 2035.

[0070] The time-point correction calculation module 2035 performs time-point correction processing on the prices of two or more households in used condominiums among the price list data 2023 searched by the price list search module 2034. That is, the prices of two or more households in used condominiums are sold as used at different times, and moreover, it is expected that the prices will be different from the prices that would be sold (now). Therefore, the prices of two or more households in used condominiums searched as the price list data 2023 are corrected to the prices that would be expected to be this price if sold now. This is called time-point correction processing. Since the time-point correction processing itself is known, the time-point correction calculation module 2035 may perform the time-point correction processing using the existing time-point correction processing procedure. As an example, the time-point correction processing obtains the time-point correction rate by referring to the price fluctuations of each household in the area where the used condominium is located (built), and is performed based on this time-point correction rate. Then, the time-point correction calculation module 2035 sends the prices of two or more households in the used condominium for which the time-point correction processing has been performed and the new construction price list to the hierarchical location-specific index calculation module 2036.

[0071] Here, the time-point correction rate may reflect not only the price fluctuations of the used condominium over time but also the environmental changes over time with respect to the price index at the time of new construction. As an example, at the time of new construction, there were no other condominiums in the neighborhood that obstructed sunlight, but later, high-rise condominiums that affect sunlight were built, or a road was opened in the neighborhood and noise from this road began to have an impact. It may also reflect such surrounding environmental changes.

[0072] Based on the price list data 2023 after the time point correction process, the floor and position specific index calculation module 2036 calculates the floor and position specific index at the time of new construction of the target used mid - rise condominium and the floor and position specific index at the current time respectively. An example of the floor and position specific index at the time of new construction is shown in Figure 7. Here, regarding the floor and position specific index at the current time, since the original price list data 2023 is price list data 2023 of two or more households, there may be cases where indices cannot be calculated for all columns in the floor and position specific index at the time of new construction shown in Figure 7.

[0073] Furthermore, based on the correlation coefficient calculated by the correlation coefficient calculation module 2037 described later, the floor and position specific index calculation module 2036 calculates the floor and position specific index at the current time for all households of the target used mid - rise condominium. In the case where the floor and position specific index at the current time for all households of the target used mid - rise condominium cannot be calculated, it is preferable to calculate the floor and position specific index for as many households as possible.

[0074] In addition, when price list data 2023 of two or more households for the used mid - rise condominium cannot be obtained, by inputting the new construction price list into the learning model 2026, the floor and position specific index at the current time for all households of the target used mid - rise condominium is calculated.

[0075] Based on the floor and position specific index at the time of new construction and the floor and position specific index at the current time of the target used mid - rise condominium calculated by the floor and position specific index calculation module 2036, the correlation coefficient calculation module 2037 calculates the correlation coefficient of these floor and position specific indices. Since the calculation formula of the correlation coefficient itself is known, the explanation here is omitted.

[0076] An example of the procedure for calculating the correlation coefficient of the hierarchical and location-specific indices will be described with reference to FIG. 10. As shown in FIG. 10, the horizontal axis of the graph represents the hierarchical and location-specific index at the time of new construction, and the vertical axis of the graph represents the current hierarchical and location-specific index. The hierarchical and location-specific indices at the time of new construction and the current ones for the corresponding households are plotted. In the system of this embodiment, since two or more current hierarchical and location-specific indices are obtained, the correlation coefficient for the plotted points can be determined. In the example shown in FIG. 10, since the hierarchical and location-specific indices of two households are plotted, the correlation coefficient can be immediately determined from the straight line connecting the two plotted points (in this case, the slope of the straight line becomes the correlation coefficient). However, if the hierarchical and location-specific indices of three or more households are obtained, considering the three plotted points as a scatter diagram and substituting the numerical values into the formula for the correlation coefficient, the correlation coefficient can be determined. It is considered that the more the number of plotted points, the more accurately the correlation coefficient (that is, the hierarchical and location-specific index of the entire used collective housing according to the actual situation) can be calculated.

[0077] When the price list data 2023 of two or more households for the used collective housing cannot be obtained, the cluster formation module 2038 clusters other used collective housing similar to the target used collective housing, and performs a regression analysis on the clustered multiple used collective housing with the number of floors and orientation as explanatory variables and the hierarchical and location-specific index as the explained variable, and generates a regression analysis model for obtaining the hierarchical and location-specific index for the target used collective housing. Alternatively, the multiple price list data 2023 of these clustered used collective housing are collected as teacher data 2025, and machine learning is performed using this teacher data 2025 to generate a learning model 2026. The similarity mentioned here means, for example, that the regionality, traffic information, building scale (number of floors, etc.)·shape, construction year, etc. are common or similar.

[0078] Based on the current hierarchical and location-specific index for all households of the target used collective housing calculated by the hierarchical and location-specific index calculation module 2036, the used price calculation module 2039 calculates the price predicted at the current time for the household specified by the user of the terminal device 10 and presents it to the terminal device 10.

[0079] <2 Data Structure> FIG. 4 is a diagram showing the data structure of the database stored in server 20. Note that FIG. 4 is an example and does not exclude data not described.

[0080] The database shown in FIG. 4 refers to a relational database and is for managing a set of data called a table, which is structurally defined by rows and columns, in association with each other. In a database, a table is called a table, a column of a table is called a column, and a row of a table is called a record. In a relational database, relationships between tables can be set and associated.

[0081] Normally, each table is set with a column that serves as a primary key for uniquely identifying a record, but setting a primary key for a column is not essential. The control unit 203 of server 20 can cause the processor 29 to add, delete, and update records in a specific table stored in the storage unit 202 according to various programs.

[0082] FIG. 4 is a diagram showing the data structure of price list DB2022. As shown in FIG. 4, each record of price list DB2022 includes, for example, an item "price list ID", an item "property name", an item "address", an item "year of construction", an item "number of floors", an item "new construction data", an item "used housing data", an item "floor-by-floor data", and an item "used housing adjustment". Each item of price list DB2022 is input by price list acquisition module 2033 when price list acquisition module 2033 acquires price list data 2023 of used collective housing. The information stored in price list DB2022 can be changed and updated as appropriate.

[0083] The item "Price List ID" is an ID for specifying price list data 2023 of used condominium units (including new construction price lists and prices for two or more households) managed by the system 1 (especially the server 20) of the present embodiment. The item "Property Name" is information regarding the property name of the used condominium related to the price list data 2023 specified by the item "Price List ID". The item "Address" is information regarding the address of the location of the used condominium specified by the item "Property Name". The item "Year of Construction" is information regarding the year of construction of the used condominium specified by the item "Property Name". The item "Number of Floors" is information regarding the number of floors of the used condominium specified by the item "Property Name". The item "New Construction Data" is information regarding the file name of the new construction price list of the used condominium related to the price list data 2023 specified by the item "Price List ID". The item "Used Data" is information regarding the file name of the price data for two or more households of the used condominium related to the price list data 2023 specified by the item "Price List ID". The item "Floor-by-Floor Data" is information regarding the file name of the floor-by-location index of the used condominium related to the price list data 2023 specified by the item "Price List ID". The item "Used Adjustment" is information regarding the time correction rate of the used condominium related to the price list data 2023 specified by the item "Price List ID".

[0084] Figure 5 is a diagram showing the data structure of a new construction price list, which is the price list data 2023 of a certain used condominium (property name: AA Mansion). As shown in Figure 5, the new construction price list has a data structure in which room numbers, floor plans, floor areas, and selling prices are stored in cells representing each household. Cells representing each household are arranged in the row direction (horizontal direction in Figure 5) for those on the same floor and in the column direction (vertical direction in Figure 5) for those that are approximately in the same position and connected in the floor direction (i.e., up and down direction). Note that, in order to avoid complication in the illustration in Figure 5, information regarding the types of floor plans (e.g., corner rooms, with / without roof balconies) is not shown, but such information can also be included in the new construction price list.

[0085] Figure 6 shows the data structure of the price data for two or more households in the used apartment building price list data 2023 shown in Figure 5. Since it is rare to obtain price data for all households when sold as used, the price data is stored in a form linked to the locations corresponding to the households sold as used.

[0086] Figure 7 is a diagram showing the data structure of the floor and location index data 2024 based on the new construction price list of the used apartment building shown in Figure 5. The floor and location index data 2024 shown in Figure 7 has the floor number direction in the vertical direction in the figure, similar to the new construction price list in Figure 5. As previously explained, the floor and location index data 2024 calculates an index that varies based on the floor number, direction (east, south, west, north), whether it is a corner room, and whether it is a roof balcony room, with the average unit price in the new construction price list of the target used apartment building set as 100.

[0087] <3 Operation Example> An example of the operation of the server 20 will be described below.

[0088] Figure 8 is a flowchart showing an example of the main operation of the server 20.

[0089] In step S800, the control unit 203 accepts the input of the property name of the used apartment building to be calculated from the terminal device 10. Specifically, for example, the control unit 203 accepts the operation input of the property name of the used apartment building to be calculated, which is input by the user of the terminal device 10 using the input device 13 or the like, through the reception control module 2031.

[0090] Next, in step S801, the control unit 203 searches the price list DB2022 using the property name accepted in step S800 as a search key, and extracts the price list data 2023, which is the new construction price list corresponding to the property name. Specifically, for example, the control unit 203 searches the price list DB2022 using the property name as a search key through the price list search module 2034, and extracts the price list data 2023, which is the new construction price list corresponding to the property name.

[0091] In step S802, the price list data 2023 retrieved in step S801 is imported into the storage unit 202. Specifically, for example, the control unit 203 imports the price list data 2023 retrieved in step S801 into the storage unit 202 through the price list search module 2034. Note that the operation when the price list data 2023 corresponding to the property name cannot be retrieved in step S801 is optional, and there can be various operations such as asking the terminal device 10 to input another property name again in step S800 or ending the flowchart shown in FIG. 8.

[0092] In step S803, the control unit 203 searches for used price list data 2023 for two or more properties corresponding to the property name accepted in step S800. Specifically, for example, the control unit 203 searches the price list DB 2022 through the price list search module 2034 to search for used price list data 2023 for two or more properties.

[0093] In step S804, the control unit 203 determines whether two or more used price list data 2023 have been retrieved in step S803. Specifically, for example, the control unit 203 determines whether two or more used price list data 2023 can be retrieved from the price list DB 2022 through the price list search module 2034.

[0094] If it is determined that two or more used price list data 2023 have been retrieved (YES in step S804), the program proceeds to step S805. If it is determined that only used price list data 2023 for one or fewer properties have been retrieved, or if the used price list data 2023 corresponding to the property name accepted in step S800 cannot be retrieved (NO in step S804), the program proceeds to FIG. 9.

[0095] In step S805, the control unit 203 performs a time-point correction process on the used price list data 2023 of two or more households retrieved in step S803. Specifically, for example, the control unit 203 performs a time-point correction process on the used price list data 2023 of two or more households retrieved in step S803 by the time-point correction calculation module 2035. Since the details of the time-point correction process have been described above, the description here is omitted.

[0096] In step S806, the control unit 203 calculates the above-mentioned floor-level and position-level index at the time of new construction and the current floor-level and position-level index respectively based on the used price list data 2023 of two or more households that have undergone the time-point correction process in step S805 and the price list data 2023 which is the newly built price list imported in step S802. Specifically, for example, the control unit 203 calculates the above-mentioned floor-level and position-level index at the time of new construction and the current floor-level and position-level index respectively by the floor-level and position-level index calculation module 2036 based on the used price list data 2023 of two or more households that have undergone the time-point correction process in step S805 and the price list data 2023 which is the newly built price list imported in step S802. Since the calculation procedure of the floor-level and position-level index by the floor-level and position-level index calculation module 2036 has been described above, the description here is omitted.

[0097] In step S807, the control unit 203 calculates the correlation coefficient of these floor-level and position-level indexes based on the floor-level and position-level index at the time of new construction and the current floor-level and position-level index calculated in step S806. Specifically, for example, the control unit 203 calculates the correlation coefficient of these floor-level and position-level indexes by the correlation coefficient calculation module 2037 based on the floor-level and position-level index at the time of new construction and the current floor-level and position-level index calculated in step S806. Since the calculation procedure of the correlation coefficient has been described above, the description here is omitted.

[0098] In step S808, the control unit 203 estimates and calculates the floor and position-specific indices for all households of the target used collective housing based on the correlation coefficient calculated in step S807. Specifically, the control unit 203 estimates and calculates the floor and position-specific indices for all households of the target used collective housing based on the correlation coefficient calculated in step S807 by the correlation coefficient calculation module 2037.

[0099] An example of the floor and position-specific indices for all households of the target used collective housing calculated by the correlation coefficient calculation module 2037 in step S808 is shown in FIG. 11. In the example shown in FIG. 11, the floor and position-specific indices for all households of the target used collective housing are estimated and calculated as the used adjustment magnification (0.4 times in the illustrated example) with respect to the floor and position-specific indices at the time of new construction of this used collective housing. That is, by multiplying the floor and position-specific indices at the time of new construction by the used adjustment magnification, the floor and position-specific indices for any household can be obtained. This used adjustment magnification substantially corresponds to the correlation coefficient calculated by the correlation coefficient calculation module 2037 in step S807.

[0100] FIG. 9 is a flowchart showing an example of the operation of the server 20 and is a flowchart corresponding to the branch destination of step S804 in FIG. 8.

[0101] In step S900, the control unit 203 searches the price list DB2022 for price list data 2023, which is a new construction price list similar to the property name for which the input from the user of the terminal device 10 was received in step S800 of FIG. 8. Specifically, for example, the control unit 203 searches the price list DB2022 for price list data 2023, which is a new construction price list similar to the property name for which the input from the user of the terminal device 10 was received in step S800 of FIG. 8, by the cluster formation module 2038. Here, the condition of "similar" is set by the cluster formation module 2038. The criteria for similarity can be set arbitrarily, but as already described, examples of the criteria include that the area is common with the target used collective housing, the year of construction is common or approximate, and the number of floors is common or approximate.

[0102] Note that since the price list data 2023 for which the search is performed in step S900 is the original data for clustering processing and the like described later, it is preferable to search for a plurality of price list data 2023 from the viewpoint of performing accurate processing.

[0103] In step S901, the control unit 203 determines whether price list data 2023, which is a similar newly-built price list based on the search in step S900, can be found. Specifically, for example, the control unit 203 determines whether price list data 2023, which is a similar newly-built price list based on the search in step S900, can be found by the cluster formation module 2038. If it is determined that price list data 2023, which is a similar newly-built price list, can be found (YES in step S901), the program proceeds to step S903. If it is determined that price list data 2023, which is a similar newly-built price list, cannot be found (NO in step S901), in step S902, the cluster formation module 2038 changes the search conditions (that is, the similar criteria) and returns to step S900 to perform the search operation again.

[0104] In step S903, the control unit 203 clusters the target used housing and the used housing similar to the target used housing that could be extracted as a result of the search in step S900. Specifically, for example, the control unit 203 clusters the target used housing and the used housing similar to the target used housing that could be extracted as a result of the search in step S900 by the cluster formation module 2038. Since the specific procedure of clustering is well-known, the description here is omitted.

[0105] In step S904, the control unit 203 generates a regression analysis model for the used apartment houses clustered in step S903, using the number of floors and the orientation as explanatory variables and the index by floor and position as the explained variable. Alternatively, the control unit 203 also performs machine learning processing or the like using a plurality of price list data 2023 of the used apartment houses clustered in step S903 as teacher data 2025 to generate a learning model 2026. Specifically, for example, the control unit 203 generates a regression analysis model for the used apartment houses clustered in step S903, using the number of floors and the orientation as explanatory variables and the index by floor and position as the explained variable, by means of the cluster formation module 2038. Alternatively, the control unit 203 also performs machine learning processing or the like using a plurality of price list data 2023 of the used apartment houses clustered in step S903 as teacher data 2025 to generate a learning model 2026. Since the specific procedure of the machine learning processing is well known, the description here is omitted.

[0106] In step S905, the control unit 203 causes the learning model 2026 created in step S904 to perform an inference operation by inputting the price list data 2023, which is a new construction price list, into the learning model 2026. Specifically, for example, the control unit 203 causes the learning model 2026 created in step S904 to perform an inference operation by inputting the price list data 2023, which is a new construction price list, into the learning model 2026, by means of the cluster formation module 2038.

[0107] Then, in step S906, the control unit 203 obtains the hierarchical position-specific index that is the result of causing the learning model 2026 to perform an inference operation in step S905, and sets this as the current hierarchical position-specific index of the target used mid-rise condominium. Alternatively, in step S906, the control unit 203 also obtains the current hierarchical position-specific index of the target used mid-rise condominium by using the regression analysis model generated in step S904. Specifically, for example, the control unit 203 obtains, by the cluster formation module 2038, the hierarchical position-specific index that is the result of causing the learning model 2026 to perform an inference operation in step S905, and sets this as the current hierarchical position-specific index of the target used mid-rise condominium. Alternatively, in step S906, the control unit 203 also obtains the current hierarchical position-specific index of the target used mid-rise condominium by using the regression analysis model generated in step S904 by the cluster formation module 2038. The current hierarchical position-specific index of the target used mid-rise condominium obtained in step S906 is obtained as the within-cluster used adjustment magnification as exemplified in step S808 of FIG. 8. Note that the used adjustment magnification in FIG. 8 is based on the correlation coefficient, but the within-cluster used adjustment magnification in step S906 of FIG. 9 takes into account the differences between the attributes (region, floor number, etc.) of the target used mid-rise condominium within the cluster and the attributes of used mid-rise condominiums similar to this used mid-rise condominium because the clustering process is being performed. After this, the program returns to step S805 of FIG. 8.

[0108] <5 Effects of One Embodiment> As described in detail above, according to the system 1 of the present embodiment, it is possible to provide a novel calculation technique for the hierarchical position-specific index in the used condominium market.

[0109] Specifically, according to the system 1 of the present embodiment, based on the new construction price list of the used apartment building to be calculated and the prices of two or more individual apartments in this used apartment building, the first and second floor and position indexes are obtained respectively, and from the correlation coefficient of these first and second floor and position indexes, the third floor and position index of any individual apartment in the used apartment building is calculated. Therefore, the third floor and position index can be calculated reasonably (that is, with a certain basis, as much as possible excluding the personal experience of the creator, etc.), and this third floor and position index can be obtained with high accuracy.

[0110] The third floor and position index is preferable because the accuracy of the correlation coefficient result increases as the number of samples of the price of each individual apartment in the used apartment building increases. However, the price of each individual apartment in the used apartment building is based on the actual sales performance of the actual households, and it is not always possible to obtain a large number of samples. Therefore, since the correlation coefficient can be calculated if there are two or more samples, the third floor and position index can be obtained as long as the prices of at least two apartments are obtained.

[0111] In addition, the first floor and position index at the time of new construction can be calculated by excluding personal experience, etc. based on certain criteria. However, as already explained, the current second floor and position index of the used apartment building has so far had to be calculated based on the personal experience of appraisers, etc. According to the system 1 of the present embodiment, based on clear grounds and a certain calculation procedure, the third floor and position index can be calculated by excluding personal experience, etc. This means that a new calculation technique for the floor and position index can be provided.

[0112] In addition, in the system 1 of the present embodiment, when the prices of two or more individual apartments in the used apartment building cannot be obtained, a cluster is formed between the target used apartment building and other used apartment buildings similar to the target used apartment building, and the second floor and position index is calculated within these clusters. Therefore, the opportunity to calculate the third floor and position index can be increased.

[0113] <6 Supplementary Note> Note that the above-described embodiments have been described in detail for the purpose of explaining the present disclosure clearly, and are not necessarily limited to those having all the configurations described. Also, with respect to a part of the configuration of each embodiment, it is possible to add to, delete from, or replace with other configurations.

[0114] In addition, each of the above-described configurations, functions, processing units, processing means, etc. may be realized in hardware by designing part or all of them, for example, by using an integrated circuit. Also, the present invention can be realized by a program code of software that realizes the functions of the embodiments. In this case, a storage medium recording the program code is provided to a computer, and a processor included in the computer reads out the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the program code itself and the storage medium storing it constitute the present invention. As a storage medium for supplying such a program code, for example, a flexible disk, CD-ROM, DVD-ROM, hard disk, SSD, optical disk, magneto-optical disk, CD-R, magnetic tape, non-volatile memory card, ROM, etc. are used.

[0115] Also, the program code for realizing the functions described in this embodiment can be implemented in a wide range of programs or script languages such as assembler, C / C++, perl, Shell, PHP, Java (registered trademark), etc.

[0116] Furthermore, by distributing the program code of software that realizes the functions of the embodiments via a network, it can be stored in a storage means such as a hard disk or memory of a computer or a storage medium such as a CD-RW or CD-R, and a processor included in the computer reads out and executes the program code stored in the storage means or the storage medium.

[0117] The matters described in each of the above embodiments are appended below.

[0118] (Appendix 1) A program (2021) for operating a computer (20) including a processor (29), the program (2021) causes the processor (29) to execute a first step (step S802) of receiving an input of a price list at the time of new construction of an old apartment building for which a hierarchical and location-specific index is to be obtained, a second step (step S806) of calculating a first hierarchical and location-specific index at the time of new construction of the old apartment building from the price list at the time of new construction, a third step (step S803) of receiving inputs of prices for each unit of two or more old apartment buildings, a fourth step (step S806) of calculating two or more second hierarchical and location-specific indices for each unit of the two or more old apartment buildings from the prices for each unit of the two or more old apartment buildings, a fifth step (step S807) of calculating a correlation coefficient between the second hierarchical and location-specific index and the first hierarchical and location-specific index corresponding to the second hierarchical and location-specific index, and a sixth step (step S808) of calculating a third hierarchical and location-specific index for an arbitrary unit of the old apartment building based on the correlation coefficient. (Appendix 2) In the fourth step (step S806), the program corrects the price for each unit of the old apartment building based on the age of the building, and / or in the second step (step S806), the program corrects the first hierarchical and location-specific index at the time of new construction of the old apartment building based on changes in the surrounding environment since the time of new construction. The program (2021) according to Appendix 1. (Appendix 3) If, in the third step (step S803), it is not possible to receive inputs of prices for each unit of two or more old apartment buildings, then in the fourth step (step S806), clusters are formed for other old apartment buildings similar to the old apartment building, and two or more second hierarchical and location-specific indices are calculated from the hierarchical and location-specific indices for each unit of the other multiple old apartment buildings belonging to the same cluster. The program (2021) according to Appendix 1 or 2. (Appendix 4) In the fourth step (step S806), a learning model is generated using the price per household of a plurality of other second-hand apartment buildings belonging to the cluster as teacher data, and by inputting the first floor- and location-specific index into this learning model, two or more second floor- and location-specific indices are calculated. The program (2021) described in Supplementary Note 3. (Supplementary Note 5) The program (2021) further causes the processor (29) to execute a seventh step of calculating the price per household of a specific second-hand apartment building based on the third floor- and location-specific index obtained in the sixth step (step S808). The program (2021) described in any one of Supplementary Notes 1 to 4. (Supplementary Note 6) An information processing apparatus including a processor (29), wherein the processor (29) performs: a first step (step S802) of receiving an input of a price list at the time of new construction of a second-hand apartment building for which a floor- and location-specific index is to be determined; a second step (step S806) of calculating a first floor- and location-specific index at the time of new construction of the second-hand apartment building from the price list at the time of new construction; a third step (step S803) of receiving inputs of the prices per household of two or more second-hand apartment buildings; a fourth step (step S806) of calculating two or more second floor- and location-specific indices for the second-hand apartment buildings per household from the prices per household of two or more second-hand apartment buildings; a fifth step (step S807) of calculating a correlation coefficient between the second floor- and location-specific index and the first floor- and location-specific index corresponding to the second floor- and location-specific index; and a sixth step (step S808) of calculating an arbitrary third floor- and location-specific index for the second-hand apartment building per household based on the correlation coefficient. (Supplementary Note 7) A method executed by a computer (20) including a processor (29), the processor (29) performing: a first step (step S802) of receiving an input of a new construction price list of an old apartment building for which a floor and location specific index is to be determined; a second step (step S806) of calculating a first floor and location specific index at the time of new construction of the old apartment building from the new construction price list; a third step (step S803) of receiving inputs of prices for each household of the old apartment building for two or more households; a fourth step (step S806) of calculating two or more second floor and location specific indexes for each household of the old apartment building from the prices for each household of two or more old apartment buildings; a fifth step (step S807) of calculating a correlation coefficient between the second floor and location specific index and the first floor and location specific index corresponding to the second floor and location specific index; and a sixth step (step S808) of calculating a third floor and location specific index for an arbitrary household of the old apartment building based on the correlation coefficient. (Appendix 8) A system comprising: means (2033) for receiving an input of a new construction price list of an old apartment building for which a floor and location specific index is to be determined; means (2036) for calculating a first floor and location specific index at the time of new construction of the old apartment building from the new construction price list; means (2033) for receiving inputs of prices for each household of the old apartment building for two or more households; means (2036) for calculating two or more second floor and location specific indexes for each household of the old apartment building from the prices for each household of two or more old apartment buildings; means (2037) for calculating a correlation coefficient between the second floor and location specific index and the first floor and location specific index corresponding to the second floor and location specific index; and means (2039) for calculating a third floor and location specific index for an arbitrary household of the old apartment building based on the correlation coefficient.

Explanation of Signs

[0119] 1... System 10... Terminal devices 15, 25... Memory 16... Storage unit 19, 29... Processor 20... Server 26... Storage 29... Processor 180, 202... Storage unit 181, 2021... Application program 190, 203... Control unit 2022: Price list DB 2023: Price list data 2024: Hierarchical and location-specific index data 2025: Teacher data 2026: Learning model 2033: Price list acquisition module 2034: Price list search module 2035: Point-in-time correction calculation module 2036: Hierarchical and location-specific index calculation module 2037: Correlation coefficient calculation module 2038: Cluster formation module 2039: Used price calculation module

Claims

1. A program for operating a computer having a processor, The program causes the processor to: The first step is to input a price list of the used apartment complexes at the time of their new construction for which a floor-by-location index is to be calculated; A second step of calculating a first floor-by-location-by-floor index for the second-hand apartment building at the time of new construction from the price list at the time of new construction; A third step of accepting two or more inputs of prices of individual units of the used apartment complex; a fourth step of calculating two or more second floor-position-specific indices for each unit of the used apartment building from the prices for each unit of the two or more used apartment buildings; a fifth step of calculating a correlation coefficient between the second hierarchical position index and the first hierarchical position index corresponding to the second hierarchical position index; A sixth step of calculating a third floor-by-location-by-floor index for any unit of the used apartment building based on the correlation coefficient; A program to execute.

2. The program is In the fourth step, the price of each unit of the used apartment building is corrected based on the age of the used apartment building; and / or In the second step, the first floor-by-location index at the time of new construction of the used apartment building is corrected based on changes in the surrounding environment of the used apartment building since its new construction. The program according to claim 1.

3. If two or more inputs of the unit prices of the used apartment house cannot be received in the third step, a cluster is formed for other used apartment houses similar to the used apartment house, and two or more second floor-position-specific indices are calculated from the floor-position-specific indices of the other multiple used apartment houses belonging to the same cluster in the fourth step. The program according to claim 1.

4. In the fourth step, a learning model is generated using the prices of the other multiple used apartments belonging to the cluster as training data, and the first level-by-level location index is input to the learning model to calculate two or more of the second level-by-level location indexes. The program according to claim 3.

5. The program further causes the processor to: Executing a seventh step of calculating the price of a specific unit of the used apartment building based on the third floor-position-specific index calculated in the sixth step; The program according to claim 1.

6. An information processing device including a processor, The processor, The first step is to input a price list of the used apartment complexes at the time of their new construction for which a floor-by-location index is to be calculated; A second step of calculating a first floor-by-location-by-floor index for the second-hand apartment building at the time of new construction from the price list at the time of new construction; A third step of accepting two or more inputs of prices of individual units of the used apartment complex; a fourth step of calculating two or more second floor-position-specific indices for each unit of the used apartment building from the prices for each unit of the two or more used apartment buildings; a fifth step of calculating a correlation coefficient between the second hierarchical position index and the first hierarchical position index corresponding to the second hierarchical position index; A sixth step of calculating a third floor-by-location-by-floor index for any unit of the used apartment building based on the correlation coefficient; An information processing device that executes the above.

7. 1. A method implemented by a computer having a processor, comprising: The processor, The first step is to input a price list of the used apartment complexes at the time of their new construction for which a floor-by-location index is to be calculated; A second step of calculating a first floor-by-location-by-floor index for the second-hand apartment building at the time of new construction from the price list at the time of new construction; A third step of accepting two or more inputs of prices of individual units of the used apartment complex; a fourth step of calculating two or more second floor-position-specific indices for each unit of the used apartment building from the prices for each unit of the two or more used apartment buildings; a fifth step of calculating a correlation coefficient between the second hierarchical position index and the first hierarchical position index corresponding to the second hierarchical position index; A sixth step of calculating a third floor-by-location-by-floor index for any unit of the used apartment building based on the correlation coefficient; A method for performing.

8. A means for receiving input of a price list of a used apartment building at the time of new construction for which a floor-by-location index is to be calculated; A means for calculating a first floor-by-location-by-floor index for the second-hand apartment building at the time of new construction from the price list at the time of new construction; A means for receiving two or more inputs of prices of individual units of the used apartment complex; means for calculating two or more second floor-position-specific indices for each unit of the used apartment complex from the prices for each unit of the two or more used apartment complexes; means for calculating a correlation coefficient between the second hierarchical position index and the first hierarchical position index corresponding to the second hierarchical position index; A means for calculating a third floor-by-location-by-floor index for any unit of the used apartment building based on the correlation coefficient; A system comprising:

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