Index value calculation apparatus, control method of index value calculation apparatus, and program

Abstract

To calculate an index value capable of predicting a sudden fall of a real estate price.SOLUTION: An index value calculation device that calculates an index value with which a sudden drop in real estate price can be predicted by changing the value so as to exceed a predetermined value before the real estate price suddenly drops, the index value calculation device comprising: A score storage section that stores a score associated with each small range, an aggregated data acquisition section that acquires aggregated data for each first predetermined period of each economic index, a score calculation section that obtains a score for each first predetermined period for each economic index, and an index value calculation section that calculates an index value based on a total value of scores for each first predetermined period, the reference value of each economic index is determined so that a second predetermined period is set between a first timing when the real estate price has fallen suddenly in the past and a second timing when the index value has exceeded a predetermined value before the first timing.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to an index value calculation device, a control method for an index value calculation device, and a program. [Background technology]

[0002] The prices of real estate such as land, buildings, and office buildings change in response to the economic situation at the time, just like financial assets such as stocks and bonds. However, because they are heavily influenced by the social environment, which takes time to change, such as the regional characteristics of the target area, the state of the transportation network, and demographics, the fluctuations are more moderate than those of stocks and bonds.

[0003] Furthermore, various techniques for predicting real estate prices have been developed, and for example, the technique disclosed in Patent Document 1 is known. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-066712 Summary of the Invention [Problem to be solved by the invention]

[0005] However, real estate prices can suddenly crash. There are various reasons why real estate prices crash, but it is difficult to predict a crash in advance.

[0006] On the other hand, a crash in real estate prices has a major impact on subsequent socio-economic activities, so technology to predict crashes in real estate prices is desired.

[0007] The present invention has been made in consideration of such problems, and aims to provide an index value calculation device, a control method for an index value calculation device, and a program for calculating an index value that can predict a crash in real estate prices by changing the value so that it exceeds a predetermined value before the crash in real estate prices. [Means for solving the problem]

[0008] An index value calculation device according to one embodiment of the present invention is an index value calculation device that calculates an index value that can predict a crash in real estate prices by changing its value to exceed a predetermined value before the crash, and includes a reference value storage unit that stores, for each of a plurality of types of economic indexes related to the real estate prices, reference values ​​that divide the fluctuation range of the value of aggregated data for each first predetermined period of each economic index into a predetermined number of small ranges, a score storage unit that stores scores that represent the degree of deterioration of economic conditions and are associated with each small range of the economic indexes, and a score storage unit that stores the first predetermined value of each of the plurality of types of economic indexes. The system comprises an aggregated data acquisition unit that acquires aggregated data for each fixed period; a score calculation unit that calculates a score for each first fixed period based on the results of comparing the aggregated data for each first fixed period with the reference value for each economic indicator; and an index value calculation unit that calculates the index value for each first fixed period based on the total value of the scores for each of the multiple types of economic indicators for each first fixed period, wherein the reference value for each economic indicator is determined so that the second fixed period is the period between a first timing when real estate prices crashed in the past and a second timing when the index value exceeded the specified value before the first timing.

[0009] In addition, the problems and solutions disclosed in this application will be made clear by the description in the section on the preferred embodiment of the invention and the drawings. [Effects of the Invention]

[0010] It will be possible to calculate index values ​​that can predict a collapse in real estate prices. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 2 is a diagram illustrating a hardware configuration of an index value calculation device. [Figure 2] FIG. 2 is a diagram illustrating a storage device of the index value calculation device. [Figure 3] FIG. 10 is a diagram illustrating an economic indicator management table. [Figure 4]FIG. 10 is a diagram illustrating a reference value score management table. [Figure 5] FIG. 10 is a diagram illustrating a calculation process of aggregated data and a reference value. [Figure 6] FIG. 10 is a diagram illustrating a score calculation process. [Figure 7] FIG. 10 is a diagram for explaining the index value N-EWS. [Figure 8] FIG. 10 is a diagram for explaining a cool map. [Figure 9] This is a diagram showing an example of the ripple effect that leads to a decline in real estate prices. [Figure 10] FIG. 2 is a diagram illustrating a functional configuration of an index value calculation device. [Figure 11] FIG. 10 is a flowchart showing the flow of processing by the index value calculation device. DETAILED DESCRIPTION OF THE INVENTION

[0012] At least the following matters will become apparent from the description of this specification and the accompanying drawings. Hereinafter, the present invention will be described in accordance with one embodiment thereof with reference to the accompanying drawings. ==Index value calculation device== 1 shows the hardware configuration of an index value calculation device 200 according to one embodiment of the present invention. The index value calculation device 200 is an information processing device such as a personal computer, server, smartphone, or tablet that calculates an index value (hereinafter also referred to as N-EWS) that can predict a crash in real estate prices. A crash is one type of decline, and refers to a state in which prices fall more significantly over a shorter period of time. In this embodiment, a price crash of more than 5% over a one-year period will be described as an example of a crash.

[0013] The index value N-EWS fluctuates between 0 and 100, with the value increasing as the economic situation worsens, and changes so that the value exceeds (goes above) 50 (predetermined value) approximately one year (second predetermined period) before real estate prices crash.

[0014] As an example, the index value N-EWS uses the office real estate price index (commercial real estate) in Tokyo, one of the real estate price indices published by the Ministry of Land, Infrastructure, Transport and Tourism, as a benchmark for real estate prices (a specified real estate price trend index).

[0015] Therefore, the index value N-EWS is an index value that can predict a crash in real estate prices by changing its value so that it exceeds 50 (a predetermined value) before the office real estate price index (commercial real estate) in Tokyo crashes.

[0016] The index value calculation device 200 calculates the above-mentioned index value N-EWS by using aggregated data of multiple types (12 types in this embodiment) of economic indicators correlated with real estate prices, which are aggregated every first predetermined period (every three months in this embodiment).

[0017] Details of the calculation procedure and accuracy of this index value N-EWS will be discussed later, but as shown in Figure 7, for example, when the value of N-EWS (the bar graph in the lower part of Figure 7) exceeds 50 (2001, 2007, 2020), as shown in the bar graph in the upper part of Figure 7, it can be seen that within about a year thereafter, the year-on-year decline in real estate prices exceeds 5%, resulting in a crash.

[0018] In this way, the index value calculation device 200 according to this embodiment can calculate an index value N-EWS that can accurately predict a crash in real estate prices. <Hardware configuration> Returning to FIG. 1, the index value calculation device 200 includes a CPU (Central Processing Unit) 210, a storage device 220, a communication device 230, an input device 250, an output device 260, and a recording medium reader 270.

[0019] The storage device 220 is configured from a volatile temporary recording medium such as RAM (Random Access Memory) or a non-volatile non-temporary recording medium such as ROM (Read Only Memory), a hard disk, SSD (Solid State Drive), or flash memory, and stores various programs and data to be executed or processed by the CPU 210. The various functions of the index value calculation device 200 are realized by the CPU 210 executing or processing these programs and data.

[0020] As shown in FIG. 2, the storage device 220 stores an index value calculation device control program 700, an economic index management table 600, a reference value score management table 610, and an index value management table 620.

[0021] The recording medium reader 270 reads the index value calculation device control program 700 and data recorded on a portable recording medium 800 such as a CD, DVD, or SD card, and stores them in the storage device 220 .

[0022] The communication device 230 is communicably connected to other information processing devices (not shown) via the network 500, and exchanges the index value calculation device control program 700 and various data. For example, the index value calculation device control program 700 and various data described above can be stored in another computer, and the index value calculation device 200 can download the index value calculation device control program 700 and data from this computer.

[0023] The input device 250 is a device such as various buttons, switches, a mouse, or a keyboard that accepts input of commands and data by an operator of the index value calculation device 200 .

[0024] The output device 260 is, for example, a display device such as a display, a speaker, or the like.

[0025] The network 500 is any of various information and communication networks such as the Internet, a LAN (Local Area Network), and a telephone network.

[0026] The index value calculation device 200 may be configured by one information processing device or by multiple information processing devices. The index value calculation device 200 may also be a virtual machine or a cloud computer. <Economic indicator management table> 3 shows an example of the economic indicator management table 600 stored in the storage device 220. The economic indicator management table 600 is a table that stores aggregated data for each first predetermined period (e.g., every three months) of multiple types (e.g., 12 types) of economic indicators used to calculate the index value N-EWS.

[0027] These 12 economic indicators were selected from among various economic indicators used both domestically and internationally as being suitable for calculating the index value N-EWS.

[0028] Specifically, as shown in Figure 3, 180 types of economic indicators highly correlated with real estate prices were first extracted, and then correlation coefficients and volatility with real estate prices were calculated, and based on these calculation results, the list was narrowed down to 57 types of economic indicators.From these, the predictive performance of real estate price crashes and diversity of perspectives were examined, and the 12 types of economic indicators shown in Figure 3 were selected.

[0029] As shown in Figure 3, these 12 economic indicators are: MSCI World Real Estate Index 600a, Conference Board US Leading Economic Index 600b, Federal Reserve Senior Lending Officer Survey 600c, Tokyo Stock Price Index 600d, Tokyo Stock Exchange REIT Index 600e, Consumer Confidence Index 600f, Business Sentiment Index (CI) 600g, Lending Attitude of Financial Institutions (Small and Medium-sized Enterprises, All Industries) 600h, Lending Attitude of Financial Institutions (Large Enterprises, Real Estate Industry) 600i, Bank of Japan Tankan Weighted Average DI 600j, Employment Judgment 600k, and Job Openings to Applicants Ratio 600l.

[0030] By using these economic indicators (600a-600l), it is possible to grasp changes in the economic situation from a variety of perspectives both domestically and internationally, making it possible to more accurately predict collapses in real estate prices.

[0031] For example, Figure 9 shows a schematic diagram illustrating the ripple effect of a change in economic policy in the United States (interest rate hike), which affects the economic conditions in Japan and the United States and further leads to a decline in office real estate prices in Tokyo.The 12 economic indicators mentioned above (600a to 600l) make up this ripple effect (the letters a, b, ..., l in Figure 9 correspond to the symbols of each economic indicator in Figure 3).

[0032] In order to more accurately predict a crash in real estate prices, the index value calculation device 200 uses the published data d of these economic indexes. t As shown in S1000 and S1010 of Figure 5, for at least some economic indicators, the published data d t After performing the required preprocessing, the aggregated data D t is generated and stored in the economic indicator management table 600.

[0033] The pre-processing may involve calculating the year-on-year change or the difference from the same period of the previous year. When calculating the year-on-year change, the published data d t and the published data d t-i The percentage change from (d t / d t-i ) to calculate the year-on-year change, use published data d t and the published data d t-i The change from (d t -d t-i )

[0034] Then, the index value calculation device 200 calculates the published data d after the preprocessing. t The representative value for each first predetermined period (3 months) is calculated as data D. tAggregate as such. Representative values include the average value, maximum value, minimum value, median value, etc. In this embodiment, for the published data d t the average value for the first predetermined period is aggregated data D t .

[0035] Note that for each economic indicator, what kind of preprocessing is performed is described in the "Preprocessing Method" column of the reference value score management table 610 in FIG. 4. Note that the economic indicators described as "original series" are the published data d t used to aggregate the aggregated data D t .

[0036] Thus, by performing preprocessing on at least some of the economic indicators, it becomes possible to more accurately predict a sharp drop in real estate prices. <Reference Value Score Management Table> Next, the reference value score management table 610 is shown in FIG. 4. The reference value score management table 610 is a table that stores, for the above-mentioned multiple types (12 types) of economic indicators, reference values that divide the range of variation of the values of the aggregated data D t of each economic indicator into a predetermined number (three in this embodiment) of small ranges, and scores representing the degree of deterioration of the economic situation, which are associated with each of these small ranges of the economic indicators.

[0037] The reference values are described in the "Judgment Reference Value" column of FIG. 4, and the scores are described in the "Score" column. As described above, in this embodiment, since there are three small ranges, there are two reference values and three scores corresponding to each small range.

[0038] The aggregated data D t will be described while referring to FIGS. 4 and 6 for the state where the range of variation is divided into three small ranges and scores are associated with each small range.

[0039] First, assuming that the two reference values are P1 and P2 (where P1 < P2), the three small ranges are as follows. (1) The first small range where the aggregated data D t is P2 or more (2) Aggregated Data D t The second small range is greater than or equal to P1 and less than P2 (3) Aggregated Data D t The third small range is less than P1 This aggregated data D t The economic situation indicated by t is good when it is within the first small range, and D t When is within the second subrange, it is slightly worse, and D t is worse when it is within the third subrange.

[0040] FIG. 6 shows that the first small range is associated with a score of 0, the second small range is associated with a score of 2, and the third small range is associated with a score of 4.

[0041] To explain this situation using the MSCI World Estimate Index in the reference value score management table 610 in FIG. 4 as an example, P1=Ma-1.5×σa and P2=Ma-(1.5 / 2)×σa.

[0042] However, Ma is the aggregate data of the MSCI World Estimate Index. t is the backward moving average value of the past three years (third specified period), and σa is the aggregate data D t is the standard deviation over the past three years (third specified period).

[0043] In this way, the reference values ​​P1 and P2 are calculated based on the aggregate data D t It is determined using the backward moving average value M within the third predetermined period and the amount of deviation from the backward moving average value M in the direction of the economic indicator worsening (for example, "-(1.5 / 2) × σa" or "-1.5 × σa").

[0044] And the amount of deviation is the aggregated data D t The coefficient γ is determined by multiplying the standard deviation σ in the third predetermined period by a predetermined coefficient γ (for example, “1.5 / 2” or “1.5”).

[0045] Furthermore, this predetermined coefficient γ is determined for each economic indicator, and as shown in Figure 4, for example, it is "1.0 / 2" and "1.0" for the Tokyo Stock Exchange REIT Index, and "1.25 / 2" and "1.25" for the Business Activity Index CI Index.

[0046] These predetermined coefficients γ are used for each aggregate data D t For example, the larger the standard deviation σ of an economic indicator, the larger the value of the aggregated data D t Even if the economic indicators have large variations in score, the fluctuations in the scores for each first predetermined period can be suppressed, so the temporary aggregate data D t This prevents misjudgment of economic status due to fluctuations in the income level, and makes it possible to calculate a score that more accurately reflects actual economic status.

[0047] In addition, the aggregated data D t The process of determining the backward moving average M, the standard deviation σ, and the reference values ​​P1 and P2 based on the above is shown in S1020 to S1040 of FIG. <Index value management table> Next, the index value management table 620 (not shown) will be described. The index value management table 620 is a table that stores the index value N-EWS value for each first predetermined period (every three months) calculated by the index value calculation device 200. The index value management table 620 may further store the score of each economic index calculated by the index value calculation device 200 for each first predetermined period.

[0048] The index value calculation device 200 calculates the index value N-EWS every first predetermined period (three months) as described below, and stores it in the index value management table 620.

[0049] First, the index value calculation device 200 refers to the economic index management table 600 and calculates the aggregate data D for each first predetermined period (3 months) of multiple types (12 types) of economic indexes. t Get.

[0050] Then, the index value calculation device 200 calculates the aggregate data D for each of the first predetermined periods for each of these economic indexes. t are compared with the reference values ​​P1 and P2 stored in the reference value score management table 610, and the score for each first predetermined period is calculated based on the comparison result.

[0051] The index value calculation device 200 then sums up the scores of these economic indicators for each first predetermined period. As described above, the score of each economic indicator is 0, 2, or 4 points. There are 12 types of economic indicators. Therefore, the total score for each first predetermined period will range from a minimum of 0 points (0 points x 12) to a maximum of 48 points (4 points x 12).

[0052] The index value calculation device 200 calculates the index value N-EWS by normalizing the total score for each first predetermined period so that the minimum value is 0 points and the maximum value is 100 points, and records this in the index value management table 620.

[0053] The index value N-EWS thus determined is shown in a graph in Figure 7. In Figure 7, the index value N-EWS is represented by the bar graph in the lower part.

[0054] As shown in Figure 7, for example, the N-EWS exceeded 50 at the time of the third quarter of 2001 (the second timing), but approximately nine months later, at the time of the second quarter of 2002 (the first timing), real estate prices fell (crash) by 8.3% compared to the previous year.

[0055] For example, the N-EWS exceeded 50 at the time of the third quarter of 2007 (second timing), but approximately nine months later, at the time of the second quarter of 2008 (first timing), real estate prices fell (crash) by 8.6% compared to the previous year.

[0056] Furthermore, for example, the N-EWS exceeded 50 at the time of the second quarter of 2020 (second timing), but approximately nine months later, at the time of the first quarter of 2021 (first timing), real estate prices fell (crash) by 5.3% compared to the previous year.

[0057] In this way, the index value N-EWS makes it possible to predict a crash in real estate prices in advance.

[0058] In this embodiment, if the reference values ​​P1 and P2 of each of the above-mentioned economic indicators are set to smaller values ​​(the predetermined coefficient γ is set to a larger value), each subrange shifts in the direction of worsening economic conditions, and the scores of each economic indicator become smaller, so the N-EWS value becomes smaller and less likely to exceed 50. In this case, compared to when the reference values ​​P1 and P2 are not changed, the N-EWS value will exceed 50 when the economic conditions deteriorate further, and the period from that point (second point) to the point (first point) when real estate prices crash will be shorter.

[0059] On the other hand, if the reference values ​​P1 and P2 for each economic indicator are set to larger values ​​(the predetermined coefficient γ is set to a smaller value), each subrange will shift in the direction of favorable economic conditions, and the scores of each economic indicator will become larger, making the N-EWS value larger and more likely to exceed 50. In this case, compared to when the reference values ​​P1 and P2 are not changed, the N-EWS value will exceed 50 at a time when the economic situation has not yet deteriorated significantly, and the period from that point (timing 2) to the point when real estate prices crash (timing 1) will be longer.

[0060] Therefore, by adjusting the reference values ​​P1 and P2 (adjusting the predetermined coefficient γ), it is possible to adjust the period from when N-EWS exceeds 50 (second timing) to when real estate prices plummet (first timing). In this embodiment, this period is adjusted to be approximately one year.

[0061] Therefore, the index value calculation device 200 may have a function of adjusting a predetermined coefficient γ of each of the above economic indexes when, for example, the length of the period from the second timing to the first timing is given as a target value.

[0062] In this case, the index value calculation device 200 generates multiple γ values ​​for each economic index by, for example, applying multiple small perturbations (errors) of different magnitudes to the initial value or current value of a predetermined coefficient γ for each economic index, and searches for a combination of γ values ​​for each economic index that brings the length of the period from the second timing to the first timing closer to the target value described above while changing the combination of γ values ​​for each economic index in various ways.

[0063] According to this embodiment, it is possible to automate the determination of the optimal reference values ​​P1 and P2. Furthermore, even if the correlation between real estate prices and each economic indicator changes over time, it is possible to maintain the period from the second timing when the index value N-EWS exceeds 50 to the first timing when real estate prices crash.

[0064] Furthermore, the index value calculation device 200 may output a cool map as shown in Fig. 8. The cool map displays information indicating the scores (4 points, 2 points, 0 points) for each first predetermined period (3 months) obtained for each economic indicator in a matrix format so that scores for the same period and scores for the same economic indicator are aligned vertically and horizontally.

[0065] In the example shown in Figure 8, the information representing each score is displayed in different colors depending on the score value. Specifically, a score of 4 is displayed in black, a score of 2 is displayed in dark gray, and a score of 0 is displayed in light gray.

[0066] In this manner, the scores for each economic indicator can be visualized, and the possibility of a crash in real estate prices can be analyzed for each economic indicator. <Functional configuration of the index value calculation device> Next, the functional configuration of the index value calculation device 200 will be described with reference to the functional configuration diagram shown in FIG.

[0067] As described above, the index value calculation device 200 realizes various functions as the index value calculation device 200 by the CPU 210 executing or processing the index value calculation device control program 700 and various data stored in the storage device 220.

[0068] Specifically, the index value calculation device 200 realizes the functions of a reference value storage unit 201, a score storage unit 202, a summary data acquisition unit 203, a score calculation unit 204, an index value calculation unit 205, and a score display unit 206.

[0069] The reference value storage unit 201 stores the aggregate data D of each economic index for a plurality of types of economic indexes related to real estate prices. t In this embodiment, the reference value storage unit 201 is embodied as a reference value score management table 610, and the aggregated data D of each economic indicator is stored for 12 types of economic indicators. t Two reference values ​​P1 and P2 that divide the fluctuation range of the index into three subranges are stored for each economic indicator.

[0070] The reference value of each economic indicator is the aggregate data D t and the amount of deviation from the backward moving average in the direction in which the economic indicator deteriorates.

[0071] In this manner, it is possible to determine the degree of deterioration of the economic situation based on the magnitude of deviation from the trend (backward moving average value) of the change in the economic indicator.

[0072] The amount of deviation is calculated based on the aggregate data D of the relevant economic indicator. t The coefficient γ is determined by multiplying the standard deviation σ in the third predetermined period by a predetermined coefficient γ, and the larger the standard deviation σ of the economic index, the larger the predetermined coefficient γ is set to be.

[0073] In this manner, the aggregate data D for each first predetermined period (3 months) tEven if the economic indicator has a large variance, the fluctuation of the score for each first predetermined period can be suppressed, so it is possible to calculate a score that more accurately reflects the degree of deterioration of the actual economic situation.

[0074] The reference values ​​for each of these economic indicators are set so that the period between the first timing when real estate prices crashed in the past and the most recent second timing when the index value N-EWS exceeded the specified value (50) before the first timing is a second specified period (approximately one year).

[0075] In this manner, when the index value N-EWS exceeds a predetermined value, it becomes possible to predict how long it will take for real estate prices to plummet.

[0076] The first timing is the timing when a predetermined real estate price trend index (in this embodiment, the office real estate price index (commercial real estate) in Tokyo, among the real estate price indices published by the Ministry of Land, Infrastructure, Transport and Tourism) that represents the trend in real estate prices has decreased by more than a predetermined percentage (for example, more than 5%) compared to the previous year.

[0077] This method makes it possible to more accurately predict a crash in office real estate prices in Tokyo. Furthermore, because fluctuations in real estate prices in Tokyo have a significant impact on real estate prices in other regions and sectors other than offices, it is also possible to predict a crash in real estate prices throughout Japan. Of course, the first timing may be a time when real estate prices in other regions or sectors other than offices crash, whether domestic or overseas.

[0078] Furthermore, the multiple types of economic indicators used in this embodiment include economic indicators that represent the state of the overseas economy and economic indicators that represent the state of the Japanese economy. This makes it possible to grasp the state of the economy broadly from various perspectives, thereby improving the accuracy of predicting a crash in real estate prices.

[0079] These economic indicators are selected from among those with a correlation coefficient of a certain value (e.g., 0.7) or more with the above-mentioned real estate price trend index. This approach makes it possible to more accurately predict a crash in real estate prices.

[0080] The score storage unit 202 stores a score that indicates the degree of deterioration of the economic situation and is associated with each small range of the economic indicator. In this embodiment, the score storage unit 202 is embodied as a reference value score management table 610, and scores of 0, 2, and 4 are associated with the three small ranges according to the degree of deterioration of the economic situation. Note that although the reference value score management table 610 stores a score for each economic indicator, if the scores for each economic indicator are the same, they may be stored together. Furthermore, the score for each economic indicator may be a score other than 0, 2, or 4, and further, the score may be different for each economic indicator.

[0081] The aggregate data acquisition unit 203 acquires aggregate data D of a plurality of types of economic indicators for each first predetermined period. t In this embodiment, the aggregate data acquisition unit 203 refers to the economic indicator management table 600 and acquires aggregate data D for each first predetermined period (3 months) of multiple types (12 types) of economic indicators. t Get.

[0082] In addition, these aggregated data D t The aggregate data acquisition unit 203 acquires the published data d of each economic indicator. t After obtaining these, the representative values ​​(average values) for each first predetermined period (3 months) are calculated as the aggregate data D t are stored in the economic indicator management table 600 as

[0083] At this time, the aggregate data acquisition unit 203 also acquires the published data d of at least some of the economic indicators. t are the published data for the fourth specified period (one year ago) d t Preprocessing may be performed to replace the value with the rate or amount of change from the original value.

[0084] In this manner, the direction of change in the economic situation can be known, making it possible to detect the timing of the deterioration of the economic situation.

[0085] The score calculation unit 204 calculates the aggregate data D for each first predetermined period (3 months) for each economic indicator. t Based on the result of the comparison between the data and the reference value, a score for each first predetermined period is calculated. t If P2 or more, 0 points, summary data D t If P1 or more and less than P2, 2 points, and summary data D t If it is less than P1, the score is 4 points.

[0086] The index value calculation unit 205 calculates the index value N-EWS for each first predetermined period based on the total score of multiple types of economic indicators for each first predetermined period. In this embodiment, the index value calculation unit 205 calculates the total score (0 to 48 points) of each economic indicator for each first predetermined period, and normalizes this total so that it falls within the range of 0 to 100 points, thereby calculating the index value N-EWS for each first predetermined period.

[0087] As mentioned above, the reference values ​​for each economic indicator are set so that the second specified period (approximately one year) is the period between the first timing when real estate prices crashed in the past and the second timing when the index value N-EWS exceeded the specified value (50) immediately before the first timing. Therefore, if the index value N-EWS calculated as described above exceeds the specified value (50), there is a high possibility that real estate prices will crash within the second specified period (approximately one year) thereafter.

[0088] In this way, the index value calculation device 200 according to this embodiment makes it possible to predict a sudden drop in real estate prices.

[0089] The score display unit 206 displays information representing the scores for each first predetermined period calculated for each economic indicator in a matrix format so that scores for the same period and scores for the same economic indicator are aligned vertically and horizontally. In this case, the score display unit 206 may display the information representing the scores in different colors depending on the score value.

[0090] In this manner, the scores for each economic indicator can be visualized, and the possibility of a crash in real estate prices can be analyzed for each economic indicator. ==Processing flow== Next, the flow of processing by the index value calculation device 200 according to this embodiment will be described with reference to the flowchart shown in FIG.

[0091] First, the index value calculation device 200 is premised on the fact that it is a system that collects aggregate data D of multiple types (12 types) of economic indexes. t are stored in the economic indicator management table 600, and the aggregate data D t The reference value score management table 610 stores reference values ​​that divide the range of fluctuation of the value into a predetermined number of small ranges (three in this embodiment), and scores that indicate the degree of deterioration of the economic situation and are associated with each small range of these economic indicators.

[0092] The index value calculation device 200 then calculates the aggregate data D of these multiple types of economic indexes for each first predetermined period (for example, every three months). t (S2000).

[0093] Thereafter, the index value calculation device 200 calculates the aggregate data D for each first predetermined period for each economic index. t Based on the result of the comparison with the reference value, a score for each first predetermined period is calculated (S2010).

[0094] The index value calculation device 200 then calculates the index value N-EWS for each first predetermined period based on the total score of multiple types of economic indicators for each first predetermined period (S2020). Specifically, the index value calculation device 200 calculates the index value N-EWS by normalizing the total score (0 to 48) for each first predetermined period to fall within the range of 0 to 100. The index value calculation device 200 also stores the index value N-EWS calculated for each first predetermined period and the score of each economic indicator in the index value management table 620.

[0095] Then, the index calculation device 200 displays the index N-EWS on the output device 260 or the like (S2030).

[0096] At this time, the index value calculation device 200 may display the index value N-EWS in a graph format as shown in Fig. 7, for example. Alternatively, the index value calculation device 200 may display a cool map as shown in Fig. 8. In this manner, the score for each economic indicator can be visualized, and the possibility of a crash in real estate prices can be analyzed for each economic indicator.

[0097] The index value calculation device 200, the control method for the index value calculation device 200, and the program according to this embodiment have been described above. According to this embodiment, it is possible to calculate an index value N-EWS that can predict a crash in real estate prices by changing the value so that it exceeds a predetermined value before the real estate prices crash. As shown in Figure 7, this index value N-EWS was able to accurately predict the three crashes in real estate prices that occurred between 2000 and 2024.

[0098] In addition, the index value N-EWS is created using multiple types of publicly available economic indicators, making it objective, quantitative, and highly reliable.

[0099] Furthermore, the calculation procedure for the index value N-EWS is simple and easy to understand, making it easy to replace, add, or delete the underlying economic indicators, or to change the preprocessing, making it highly versatile.

[0100] By using the index value N-EWS, which has many such excellent features, it is possible to accurately predict a crash in real estate prices.

[0101] The above-described embodiment is intended to facilitate understanding of the present invention, and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present invention.

[0102] For example, in this embodiment, the index value N-EWS fluctuates between 0 and 100 so that the worse the economic situation, the larger the value, and changes so that the value exceeds 50 (predetermined value) (goes above) approximately one year (second specified period) before real estate prices plummet; however, it may also be configured so that the value fluctuates between 0 and 100 so that the worse the economic situation, the smaller the value, and changes so that the value exceeds 50 (predetermined value) (goes below) approximately one year (second specified period) before real estate prices plummet.

[0103] In this embodiment, the index value N-EWS is calculated using 12 types of economic indicators, but the number of economic indicators used to calculate the index value N-EWS is not limited to 12, and may be two or more. The economic indicators also include data on real estate, etc. For example, the aggregate data D of each economic indicator t The number of subranges dividing the range of variation of the value of is not limited to three, but may be two or more. [Explanation of symbols]

[0104] 200 Index value calculation device 201 Reference value memory unit 202 Score memory section 203 Aggregate Data Acquisition Department 204 Score Calculation Unit 205 Index value calculation unit 206 Score display 210 CPU 220 Storage device 230 Communication Equipment 250 Input Device 260 Output Device 270 Recording medium reader 500 Network 600 Economic Indicator Management Table 610 Standard score management table 620 Index Value Management Table 700 Index value calculation device control program 800 Recording Media

Claims

1. An index value calculation device that calculates an index value that can predict a crash in real estate prices by changing the value so that it exceeds a predetermined value before the crash in real estate prices, a reference value storage unit configured to store, for each of a plurality of types of economic indicators related to the real estate prices, reference values ​​for dividing the fluctuation range of the value of the aggregated data of each economic indicator for each first predetermined period into a predetermined number of small ranges; a score storage unit that stores scores that correspond to each small range of the economic index and indicate the degree of deterioration of the economic situation; an aggregate data acquisition unit that acquires aggregate data of the plurality of types of economic indicators for each first predetermined period; a score calculation unit that calculates a score for each of the first predetermined periods based on a result of comparing the aggregated data for each of the first predetermined periods with the reference value for each of the economic indicators; an index value calculation unit that calculates the index value for each first predetermined period based on a total value of the scores of the plurality of types of economic indexes for each first predetermined period; Equipped with The reference value of each of the economic indicators is determined so that a second predetermined period is the period between a first timing when real estate prices have crashed in the past and a second timing when the index value has exceeded the predetermined value before the first timing. Index value calculation device.

2. 2. The index value calculation device according to claim 1, the reference value of the economic indicator is determined using a backward moving average value of the aggregated data of the economic indicator within a third predetermined period and an amount of deviation of the economic indicator from the backward moving average value in a direction in which the economic indicator deteriorates; Index value calculation device.

3. 3. The index value calculation device according to claim 2, the amount of deviation is determined by multiplying the standard deviation of the aggregated data of the economic indicator for the third predetermined period by a predetermined coefficient; The predetermined coefficient is set to a larger value as the standard deviation of the economic indicator increases. Index value calculation device.

4. 2. The index value calculation device according to claim 1, The aggregated data for each first predetermined period of at least some of the economic indicators is obtained by replacing published data of the economic indicators with a rate of change from published data four predetermined periods ago, and aggregating the data for each first predetermined period. Index value calculation device.

5. 2. The index value calculation device according to claim 1, The aggregated data for each first predetermined period of at least some of the economic indicators is obtained by replacing published data of the economic indicators with the amount of change from published data four predetermined periods ago, and aggregating the data for each first predetermined period. Index value calculation device.

6. 2. The index value calculation device according to claim 1, The plurality of types of economic indicators include economic indicators representing overseas economic conditions and economic indicators representing Japan's domestic economic conditions. Index value calculation device.

7. 2. The index value calculation device according to claim 1, The plurality of types of economic indicators are selected from economic indicators having a correlation coefficient of a certain value or more with a predetermined real estate price trend index that indicates the trend of real estate prices. Index value calculation device.

8. 2. The index value calculation device according to claim 1, The first timing is a timing when a predetermined real estate price trend index representing the trend of real estate prices has decreased by a predetermined percentage or more compared to the previous year. Index value calculation device.

9. 2. The index value calculation device according to claim 1, a score display unit that displays information representing the scores for each first predetermined period calculated for each economic indicator in a matrix format such that scores for the same period and scores for the same economic indicator are aligned vertically and horizontally; The index value calculation device further comprises:

10. 10. The index value calculation device according to claim 9, the score display unit displays the information representing the score in different colors according to the value of the score. Index value calculation device.

11. A control method for an index value calculation device that calculates an index value that can predict a crash in real estate prices by changing the value so that it exceeds a predetermined value before the crash in real estate prices, The index value calculation device a step of storing, for each of a plurality of types of economic indicators related to the real estate prices, a reference value for dividing the fluctuation range of the value of the aggregated data of each economic indicator for each first predetermined period into a predetermined number of small ranges; a step of storing a score corresponding to each small range of the economic indicator and representing the degree of deterioration of the economic situation; acquiring aggregate data of the plurality of types of economic indicators for each first predetermined period; calculating a score for each of the first predetermined periods based on a result of comparing the aggregated data for each of the first predetermined periods with the reference value for each of the economic indicators; calculating the index value for each first predetermined period based on the total score of the plurality of types of economic indexes for each first predetermined period; Execute The reference value of each of the economic indicators is determined so that a second predetermined period is the period between a first timing when real estate prices have crashed in the past and a second timing when the index value has exceeded the predetermined value before the first timing. A control method for an index value calculation device.

12. A program for causing a computer to calculate an index value that can predict a crash in real estate prices by changing the value so that the value exceeds a predetermined value before the crash, The computer, a function of storing, for each of a plurality of types of economic indicators related to the real estate prices, a reference value for dividing the fluctuation range of the value of the aggregated data of each economic indicator for each first predetermined period into a predetermined number of small ranges; a function of storing a score that indicates the degree of deterioration of the economic situation and is associated with each small range of the economic indicator; a function of acquiring aggregate data of the plurality of types of economic indicators for each first predetermined period; a function of calculating a score for each of the first predetermined periods based on a result of comparing the aggregated data for each of the first predetermined periods with the reference value for each of the economic indicators; a function of calculating the index value for each first predetermined period based on the total score of the plurality of types of economic indexes for each first predetermined period; A program for realizing the above, The reference value of each of the economic indicators is determined so that a second predetermined period is the period between a first timing when real estate prices have crashed in the past and a second timing when the index value has exceeded the predetermined value before the first timing. program.

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