Prediction device and program

Abstract

To provide a prediction device and a program for forecasting a future asset value of a building in an area of interest.SOLUTION: A prediction device 10 includes: a construction prediction part that predicts the time transition of the construction number of buildings constructed in an area; a remaining prediction part that predicts the time transition of the remaining number of the building of the construction number; and a value calculation part that calculates a total asset value of buildings in a specific year in a future for the area according to the remaining number and an asset value per building. A program makes a computer serve as the prediction device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a prediction device and a program. [Background technology]

[0002] Patent Document 1 describes "receiving income information including rental income related to a real estate property, receiving expense information including depreciation related to the real estate property, receiving borrowing information including the borrowing amount related to the real estate property, and predicting each piece of future information based on each piece of received information." Patent Document 2 describes "a system for predicting future trends in target data that fluctuates over time using multiple pieces of economic data that fluctuate over time." [Prior art document] [Patent documents] [Patent Document 1] JP 2022-111994 [Patent Document 2] JP 2020-30839 Summary of the Invention

[0003] In a first aspect of the present invention, a prediction device predicts the future asset value of buildings in a region of interest, and includes a generation prediction unit that predicts the time trend of the number of buildings that will be generated in the region, a remaining prediction unit that predicts the time trend of the number of buildings that will remain after the number of generated buildings, and a value calculation unit that calculates the asset value of the total number of buildings in the region in a specific future year based on the number of remaining buildings and the asset value per building.

[0004] The production prediction unit may predict the time transition of the number of productions using statistical data on past construction starts.The production prediction unit may further predict the time transition of the number of productions using time transition of the population.The production prediction unit may further predict the time transition of the number of productions using a prediction of the economy.The production prediction unit may predict the time transition of the number of productions using a prediction that the economy fluctuates cyclically.

[0005] The remaining number prediction unit may predict the time transition of the remaining number using the relationship between the useful life of the building and the remaining rate.

[0006] The value calculation unit may predict the asset value using a replacement price corresponding to the cost of replacing the building. The value calculation unit may calculate the asset value based on the total number of buildings in the area in a particular year based on the remaining number, the average floor area per building, and the replacement price per floor area.

[0007] The generation prediction unit, remaining prediction unit, and value calculation unit may predict the time transition of the number of generations, the time transition of the number of remaining units, and the asset value for each structural type of building.

[0008] In a second aspect of the present invention, there is provided a program that causes a computer to function as a prediction device that predicts the future asset value of buildings in a region of interest, and that causes the computer to realize a generation prediction function that predicts the time trend of the number of buildings that will be generated in the region, a surviving prediction function that predicts the time trend of the number of remaining buildings that will remain after the number of generated buildings, and a value calculation function that calculates the asset value of the total number of buildings in the region in a specific future year based on the surviving number and the asset value per building.

[0009] The above summary of the invention does not list all of the features of the present invention, and subcombinations of these features may also be inventions. [Brief explanation of the drawings]

[0010] [Figure 1] 1 shows a schematic diagram of the functional blocks of an apparatus 10 for predicting future property values ​​of buildings in an area of ​​interest. [Figure 2] FIG. 2 is an explanatory diagram illustrating meshes stored in a storage unit 130. [Figure 3] 1 shows an example of an information table 132 stored in the storage unit 130. [Figure 4] An example of a flow S10 for predicting the total asset value of a building in a specific future year using the device 10 is shown below. [Figure 5] An example of the number of construction starts for wooden buildings, 200, predicted in step S110 is shown below. [Figure 6]An example of the scenario 204 used in step S120 is shown below. [Figure 7] The coefficient 202 based on the population change over time based on the scenario 204 is shown. [Figure 8] An example of the survival rate of 210 for wooden buildings relative to the number of years since construction is shown below. [Figure 9] 20A and 20B show schematic diagrams of estimated construction costs 222 for calculating replacement cost per floor area. [Figure 10] An example of a display image 12 in which the prediction results are displayed on a display is shown schematically. [Figure 11] FIG. 10 is a conceptual diagram showing another example of the prediction of the number of construction starts in step S110. [Figure 12] 22 illustrates an example computer 2200 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE INVENTION

[0011] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0012] In this embodiment, the future asset value of buildings in a region of interest is predicted. The future asset value is used to predict the extent of housing damage caused by natural disasters such as typhoons. Furthermore, the predicted extent of housing damage can also be used to predict the amount of insurance coverage for disasters.

[0013] FIG. 1 shows a schematic functional block diagram of an apparatus 10 for predicting the future asset value of buildings in a region of interest. The apparatus 10 includes a generation prediction unit 100 for predicting the time course of the number of buildings to be generated in the region, a remaining prediction unit 110 for predicting the time course of the number of remaining buildings of the generated number that will remain, and a value calculation unit 120 for calculating the total asset value of buildings in the region in a specific future year based on the remaining number and the asset value per building in the region. The apparatus 10 further includes a memory unit 130 for storing data used by the apparatus 10. The apparatus 10 may be an information processing apparatus such as a personal computer.

[0014] 2 is an explanatory diagram illustrating meshes stored in the storage unit 130. The storage unit 130 divides an area including a region of interest into meshes, and stores mesh information in which a mesh ID that identifies each mesh is associated with the administrative division to which the mesh belongs.

[0015] In the example of Figure 2, the area of ​​interest is Adachi Ward, Tokyo. Figure 2 shows an enlarged view of the 23 wards of Tokyo on the map of Japan. Furthermore, the 23 wards of Tokyo are divided into meshes, and the ID of a specific mesh included in Adachi Ward is shown as "JP411011." The mesh size may be set arbitrarily depending on the scale of the calculation, such as 1 km square, 250 m square, or 100 m square. Furthermore, while Figure 2 shows an enlarged view of the 23 wards of Tokyo, any administrative division may be divided into meshes and assigned mesh IDs, such as the entirety of Japan, the entirety of Asia, or only specific cities, wards, towns, and villages in Japan.

[0016] 3 shows an example of the information table 132 stored in the storage unit 130. The information table 132 stores mesh IDs, prefectures, municipalities, construction years, scenarios, number of construction starts, number remaining, replacement costs, and total asset values ​​in association with the forecast year and structure type.

[0017] Of the above information in the information table 132, the mesh ID, prefecture, and city / ward / town / village are pre-stored, while the forecast year and scenario are specified by the user. The construction year, scenario, number of construction starts, number remaining, replacement cost, and total asset value are predicted or calculated by the device 10. The construction types include wood, steel, and reinforced concrete, which will be described later.

[0018] 4 shows an example of a flow S10 for predicting the total asset value of a building in a specific future year using the device 10. The flow S10 starts when the device 10 is activated by the user.

[0019] The generation and prediction unit 100 of the device 10 accepts the specification of calculation conditions from the user via an input interface such as a keyboard (S100). One of the calculation conditions is the predicted year, which is the Gregorian calendar year for which prediction is desired. Instead of inputting the Gregorian calendar year, input of the number of years in the future may also be accepted. In the following explanation, an example will be given assuming that the current year is 2024 and 2044 has been input as the predicted year. In addition, in this example, the predicted year is 20 years from the present. Another example of a calculation condition is the specification of a scenario in FIG. 3. This will be explained in step S120.

[0020] The following steps S110 to S140 are executed for each structure type. The following describes the case of the structure type "wooden construction."

[0021] The generation and prediction unit 100 predicts the number of construction starts for buildings of the structure type (S110). In this case, the generation and prediction unit 100 uses statistical data on past construction starts going back a predetermined number of years from the present.

[0022] Fig. 5 shows an example of the number of construction starts of wooden buildings, 200, predicted in step S110. In the example of Fig. 5, statistical data on the number of construction starts from 1974, 50 years back from the present, to the present is used.

[0023] The generation and prediction unit 100 may use, for example, data from the "Statistics on Building Construction Starts" published by the Ministry of Internal Affairs and Communications, which is obtained via the Internet. The "Statistics on Building Construction Starts" publishes the annual number of construction starts by prefecture and by structure type. The black circles in Figure 5 schematically show the number of wooden construction starts in Tokyo from 1974 to 2024 based on the statistics.

[0024] The generating and predicting unit 100 further predicts the number of construction starts from the present to the predicted year. In this case, the generating and predicting unit 100 extrapolates the temporal change in the number of construction starts in the future from the past number of construction starts using a statistical method or the like. When the past number of construction starts by prefecture is used, it is preferable to also predict the temporal change in the number of construction starts in the future by prefecture.

[0025] In some cases, data from the "Building Construction Start Statistics" is not provided annually, but every five or ten years. In such cases, annual data is interpolated using linear interpolation or spline interpolation. Furthermore, time series forecasting or linear regression forecasting may be used to extrapolate the number of construction starts into the future. In the case of time series forecasting, for example, the average of the past 10 years may be used to predict the next year, a forecast that takes into account the trends and differences over the past N years, or a forecast that uses an autoregressive model (AR) may be used. In the case of linear regression forecasting, for example, past trends may be converted into an nth-order polynomial and forecasted.

[0026] In this case, an upper and / or lower limit may be set for the number of future construction starts. For example, the lower limit may be set to 0.25 times the current level, and the upper limit may be set to 4.5 times the current level. In the example shown in Figure 5, the period from the present to the forecast year is divided into 10-year intervals, and a narrow upper and lower limit range is set for the period from the present to 10 years from now, while a wider upper and lower limit range is set from 10 years from now to the forecast year. The upper and lower limits are indicated by dashed lines in the figure. In this way, a wider range may be set in multiple stages as the number of years from the present increases. The example in Figure 5 shows a state in which the upper limit is reached several years before the year 2044. Whether or not to set upper and lower limits, and if so, the range of the upper and lower limits, may be stored in advance in the storage unit 130.

[0027] The generation and prediction unit 100 further adjusts the future number of construction starts based on the change in population over time (S120). In this case, the generation and prediction unit 100 calculates a coefficient based on the change in population over time based on, for example, one of multiple scenarios, and performs the adjustment by multiplying the predicted number of construction starts by the coefficient.

[0028] 6 shows an example of the scenario 204 used in step S120. Five scenarios are set in the scenario 204. These scenarios are stored in advance in the storage unit 130 and are specified by the user in step S100. In each scenario, the highs and lows of "birth rate," "death rate," and "immigration" are set according to the future trends assumed in the "overview," and the population is predicted.

[0029] The time transition of population based on the above scenario may be based on published data. For example, "Japan Version SSP Municipal Population Estimates" (National Institute for Environmental Studies, Japan Version SSP Development Team) has been published. The generation and prediction unit 100 may obtain this data via the Internet, store it in the storage unit 130, and use it.

[0030] FIG. 7 shows a coefficient 202 based on the time trend of population based on a scenario 204. The generation and prediction unit 100 calculates the dependency ratio for each scenario based on the time trend of the population. The dependency ratio is "(population under 15 years old + population 65 years old and over) / working-age population x 100". There is a strong negative correlation between the dependency ratio and the number of housing construction starts. This can also be said to mean that a decrease in the working-age population affects a decrease in the number of housing construction starts.

[0031] The generation prediction unit 100 sets a specific year as a reference coefficient of "1" and displays other years as a ratio to the reference year. In the example of Fig. 7, 2015 is used as the reference year.

[0032] In this embodiment, in response to the prediction of the number of construction starts for each prefecture in step S110, the coefficient 202 is calculated for each prefecture in step S120 using the time transition of population for each prefecture. However, the administrative division for predicting the number of construction starts and the administrative division for predicting the time transition of population and calculating the coefficient may be different. For example, the number of construction starts may be predicted for each prefecture, and the time transition of population and the coefficient may be calculated for Japan as a whole.

[0033] The generation and prediction unit 100 multiplies the future number of construction starts predicted in step S110 by a coefficient corresponding to the scenario specified by the user for each year. For example, when scenario "1" is selected, if the number of construction starts (buildings) for construction year "2025" predicted in step S110 is "20,000" and the coefficient for year "2025" of scenario "1" is "0.7", the adjusted number of construction starts (buildings) is calculated to be "14,000".

[0034] The generation and prediction unit 100 allocates the number of construction starts for each mesh by dividing the number of wooden building construction starts in the prefecture to which the mesh belongs by the number of meshes in the prefecture. In this case, for the period from 1974 to 2024, the statistical data itself may be divided by the number of meshes, and for the period from 2025 to 2044, the number of construction starts predicted in step S110 and adjusted in S120 may be divided by the number of meshes.

[0035] The generation and prediction unit 100 associates the number of construction starts calculated as described above with the construction year and stores them in the information table 132. Figure 3 shows a schematic diagram of the number of construction starts for wooden buildings in Tokyo assigned to the mesh ID "JP411011."

[0036] As a result of the above, the number of construction starts, which is the number of wooden buildings that will be generated in the area of ​​interest, is predicted to change over time from 50 years ago to the forecast year. Note that instead of allocating evenly, it is also possible to allocate them by weighting, based on information such as population density within the prefecture, so that the higher the population density, the higher the number of construction starts.

[0037] The remaining number prediction unit 110 predicts the remaining number of buildings (S130). In this case, the remaining number prediction unit 110 predicts the remaining number for each construction year by multiplying the number of construction starts by a remaining rate corresponding to the number of years from the construction year to the prediction year.

[0038] FIG. 8 shows an example of the survival rate 210 for wooden structures relative to the number of years since construction. The survival rate 210 may be set to roughly follow a Weibull distribution. In this case, for example, the survival rate may be set so that 80% remains until the end of its useful life and then gradually decreases after the end of its useful life. The useful life of wooden structures may be set to 30 years, that of steel structures to 50 years, and that of reinforced concrete to 65 years.

[0039] The remaining rate 210 for each structure type is set in advance and stored in the memory unit 130, and in step S130, the remaining rate prediction unit 110 reads out the remaining rate 210 for the corresponding structure type from the memory unit 130. The remaining rate prediction unit 110 calculates the number of years that have passed from the difference between the predicted year and the construction year, and calculates the number of buildings in the construction year that will remain in the predicted year by multiplying the value of the number of years that have passed in the remaining rate 210 by the number of construction starts in the construction year.

[0040] In the example of Figure 3, since the construction year "2025" is 19 years after the predicted year "2044", the remaining property prediction unit 110 reads the remaining property rate 210 for the number of years elapsed "19". If the remaining property rate is "0.85", the remaining property prediction unit 110 calculates the remaining property number "127.877" by multiplying the number of construction starts for the construction year "2025" (150.444) by the remaining property rate "0.85".

[0041] The remaining building prediction unit 110 associates the calculated remaining number with the construction year and stores it in the information table 132. As described above, the time transition of the remaining number of wooden buildings in the area of ​​interest from 50 years ago to the predicted year is predicted using the relationship between the building's useful life and the remaining building rate.

[0042] The value calculation unit 120 calculates the asset value of the total number of buildings in the predicted year (S140). In this case, the value calculation unit 120 may use a replacement price corresponding to the cost of replacing the buildings. For example, the value calculation unit 120 may use the total number of buildings based on the remaining number calculated in step S130, the average floor area per building, and the replacement price per floor area. Specifically, the value calculation unit 120 may calculate the asset value by multiplying the total number of buildings based on the remaining number, the average floor area per building, and the replacement price per floor area.

[0043] The average floor area per building may be calculated in advance based on information held by the user of the device 10, for example, and stored in the storage unit 130. Alternatively, information published by a public institution may be used.

[0044] Figure 9 shows a schematic diagram of the estimated construction cost 222 for calculating the replacement cost per floor area. The total annual construction floor area of ​​buildings across Japan and the total estimated construction cost are obtained from the data in the "Building Construction Start Statistics" published by the Ministry of Internal Affairs and Communications, as explained in Figure 5. The value calculation unit 120 may obtain this data via the Internet, store it in the storage unit 130, and use it to calculate the replacement cost per floor area.

[0045] In this case, the value calculation unit 120 calculates the replacement price per floor area by dividing the total estimated annual construction cost of the building by the total construction floor area. In the example of FIG. 9, 2 ) By dividing "2,606,417" by the estimated construction cost (in millions of yen) "93,772,044", the replacement cost per floor area (in millions of yen) is calculated as "36".

[0046] In the above example, the average floor area per building (m 2 ) is "20", the value calculation unit 120 calculates the remaining number "127.877" x average floor area per building (m 2The replacement cost (in 10,000 yen) of 89,513 yen is calculated from the replacement cost (in 10,000 yen) per floor area of ​​35 yen. The value calculation unit 120 performs the same calculation for the construction years from 1974 to 2044 and sums them up. This calculates the total asset value, which is the asset value of all buildings in the mesh ID with the structural type "wooden" in the predicted year of 2044. In the example of Figure 3, the total asset value (in 10,000 yen) is 3,689,541 yen.

[0047] The value calculation unit 120 associates the calculated asset value with the structure type and mesh ID and stores it in the information table 132. In the above explanation, the asset value is calculated for each construction year and then totaled. However, if the replacement cost per floor area is the same regardless of construction year, the remaining number for each construction year may be totaled and then multiplied by the average floor area per building and the replacement cost per floor area.

[0048] If the total annual construction floor area of ​​buildings and the total estimated construction costs are available for each prefecture, they may be used. Specifically, the replacement cost per floor area may be calculated for each prefecture, and the asset value of the total buildings in the mesh ID may be calculated using the replacement cost per floor area for the prefecture corresponding to the mesh.

[0049] The device 10 performs steps S110 to S140 for all structure types and calculates the asset value for each (S150). In this case, the number of construction starts 200 in Figure 5 and the remaining rate in Figure 8 are set and calculated for each structure type. If the estimated construction cost 222 in Figure 9 is available for each structure type, the replacement cost per floor area may also be calculated for each structure type.

[0050] The value calculation unit 120 outputs the above prediction results to a display or the like (S160) and ends the operation. The value calculation unit 120 stores the results in the storage unit 130 and ends the operation, and may display the results on a display or the like based on a new instruction from the user.

[0051] 10 is a schematic diagram showing an example of a display image 12 in which the prediction results are displayed on a display. Display image 12 shows an example in which a user specifies a specific mesh using an input interface such as a mouse.

[0052] In order to visually identify the location of the identified mesh, the display image 12 shows a map of Japan and the 23 wards of Tokyo in which the mesh is included, in relation to each other. Furthermore, the location of the mesh within the 23 wards of Tokyo is displayed in black to distinguish it from the others.

[0053] Furthermore, a line is drawn from the grid on the display image 12, and the predicted year (Gregorian calendar year), the grid's location and ID, and the grid's total asset value are displayed as text information outside the grid of Tokyo's 23 wards. The total asset value is the sum of the asset values ​​of each structure type calculated in step S10 above. This allows the user to visually see the total asset value of buildings in the area in the predicted year.

[0054] Instead of or in addition to the display image 12 in Figure 10, each mesh may be color-coded according to the magnitude of its total asset value and displayed on the entire map of Japan. For example, each mesh may be colored in a gradation such that the greater the total asset value, the closer to red it is, and the smaller the total asset value, the closer to blue it is. Also, instead of or in addition to the total asset value, the display image 12 may display the asset value of each structure type.

[0055] Fig. 11 is a conceptual diagram showing another example of the prediction of the number of construction starts in step S 110. The number of construction starts 206 in Fig. 11 predicts the time transition of the number of construction starts using a forecast of the economy.

[0056] The number of construction starts 206 is predicted based on the assumption that the economy fluctuates in cycles. More specifically, it uses the assumption that the economy cycles in a sinusoidal pattern over a period of about 20 years, known as the Kuznets boom or boom cycle.

[0057] The generation and prediction unit 100 obtains the past numbers of construction starts indicated by black circles in the same way as in Figure 5, and predicts the future number of construction starts by fitting a sine wave to the black circles. In the example shown in Figure 11, the number of construction starts is further adjusted using the scenario in Figure 6. Specifically, first, the minimum and maximum amplitude of the sine wave are set based on the past number of construction starts. Furthermore, for the future, the coefficient calculated in Figure 7 is incorporated into the sine wave, for example, by multiplying it by the coefficient, so that the long-term trend corresponds to the temporal transition of the coefficient (in the example of Figure 7, all scenarios show a long-term decline).

[0058] 1 to 11, in addition to scenarios "1" to "5," a scenario "0" may be set in which "no adjustment based on population projections is performed." In this case, the adjustment in step S120 is skipped.

[0059] As described above, according to this embodiment, it is possible to predict the future asset value of buildings in an area of ​​interest.

[0060] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of an apparatus responsible for performing the operations. Particular stages and sections may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable medium, and / or a processor provided with computer-readable instructions stored on a computer-readable medium. Dedicated circuitry may include digital and / or analog hardware circuitry, and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuitry may include reconfigurable hardware circuitry, including logical AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, memory elements such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like.

[0061] A computer-readable medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that the computer-readable medium having instructions stored thereon comprises an article of manufacture containing instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable media may include electronic, magnetic, optical, electromagnetic, and semiconductor storage media. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray (RTM) disc, memory stick, integrated circuit card, and the like.

[0062] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0063] The computer-readable instructions may be provided to a processor or programmable circuitry of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, either locally or over a wide-area network (WAN) such as a local area network (LAN), the Internet, etc., which executes the computer-readable instructions to create means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0064] 12 illustrates an example of a computer 2200 in which aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 2200 may cause the computer 2200 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to embodiments of the present invention, and / or to perform a process or steps of a process according to embodiments of the present invention. Such programs may be executed by the CPU 2212 to cause the computer 2200 to perform specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0065] A computer 2200 according to this embodiment includes a CPU 2212, a RAM 2214, a graphics controller 2216, and a display device 2218, which are interconnected by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via an input / output controller 2220. The computer also includes legacy input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0066] The CPU 2212 operates according to programs stored in the ROM 2230 and RAM 2214, thereby controlling each unit. The graphics controller 2216 acquires image data generated by the CPU 2212 into a frame buffer or the like provided in the RAM 2214 or into the graphics controller 2216 itself, and displays the image data on the display device 2218.

[0067] The communication interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0068] The ROM 2230 stores therein a boot program or the like that is executed by the computer 2200 upon activation, and / or programs that depend on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0069] The programs are provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The programs are read from the computer-readable medium, installed in the hard disk drive 2224, RAM 2214, or ROM 2230, which are also examples of computer-readable media, and executed by the CPU 2212. Information processing described in these programs is read by the computer 2200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be configured by realizing information manipulation or processing in accordance with the use of the computer 2200.

[0070] For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded into the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 2212, the communication interface 2222 reads transmission data stored in a transmission buffer processing area provided in the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0071] The CPU 2212 may also cause all or a necessary portion of a file or database stored on an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. to be read into the RAM 2214, and perform various types of processing on the data on the RAM 2214. The CPU 2212 then writes back the processed data to the external recording medium.

[0072] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. The CPU 2212 may perform various types of processing on data read from the RAM 2214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to the RAM 2214. The CPU 2212 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored on the recording medium, the CPU 2212 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0073] The above-described programs or software modules may be stored in a computer-readable medium on or near the computer 2200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable medium, thereby providing the programs to the computer 2200 via the network.

[0074] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0075] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0076] 10 device, 12 display image, 100 generation prediction unit, 110 remaining prediction unit, 120 value calculation unit, 130 storage unit, 132 information table, 200 number of construction starts, 202 coefficient, 204 scenario, 206 number of construction starts, 210 remaining rate, 222 estimated construction cost

Claims

1. A prediction device for predicting future asset values ​​of buildings in a region of interest, a generation prediction unit that predicts a time transition of the number of buildings to be generated in the area; a remaining prediction unit that predicts a time transition of the remaining number of buildings that remain in the generated number; a value calculation unit that calculates the asset value of the total buildings in the area in a specific future year based on the remaining number and the asset value per building; A prediction device comprising:

2. The prediction device according to claim 1 , wherein the production prediction unit predicts the time transition of the number of productions using statistical data of past construction starts.

3. The prediction device according to claim 2 , wherein the generation prediction unit further predicts the time transition of the number of generations using a time transition of population.

4. The prediction device according to claim 2 , wherein the generation prediction unit further predicts the time transition of the number of generations using a forecast of business conditions.

5. The prediction device according to claim 4 , wherein the production prediction unit predicts the time transition of the number of productions using a prediction that the business climate fluctuates cyclically.

6. The prediction device according to claim 1 , wherein the remaining number prediction unit predicts the time transition of the remaining number using a relationship between the useful life of a building and a remaining rate.

7. The prediction device according to claim 1 , wherein the value calculation unit predicts the asset value using a replacement price corresponding to the cost of replacing a building.

8. The prediction device according to claim 7 , wherein the value calculation unit calculates the asset value based on the total number of buildings in the area in the specific year based on the remaining number, the average floor area per building, and the replacement price per floor area.

9. The prediction device according to claim 1 , wherein the generation prediction unit, the remaining prediction unit, and the value calculation unit predict the time transition of the generation number, the time transition of the remaining number, and the asset value for each structural type of building.

10. A program that causes a computer to function as a forecasting device that forecasts future asset values ​​of buildings in a region of interest, The computer, a generation prediction function that predicts the time transition of the number of buildings generated in the area; A remaining prediction function that predicts the time transition of the remaining number of buildings that remain in the generated number; and A value calculation function that calculates the total asset value of buildings in the area in a specific future year based on the remaining number and the asset value per building. A program to achieve this.

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