JP2000137746A - Real estate investment index system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a real estate investment index system for presenting an index showing the profit rate of real estate to an organized investor by estimating unknown contracted rent from plural factor data, calculating the real estate profit rate from the estimated contracted charge data and outputting it in a prescribed format. SOLUTION: A real estate investment index system 10 is constituted by providing a real estate contracted rent estimating part 11 for estimating unknown contracted rent, real estate profit rate calculating part 12 for calculating the real estate profit rate based on the estimated contracted rent data, and real estate profit rate output part 13 for outputting the real estate profit rate in the prescribed format. Then, the theoretical value of contracted rent is statistically found and based on this theoretical value, a real estate investment index is prepared. Therefore, the real estate investment index of extremely high reliability different from an index based on the rent data of recruit base can be provided and an attractive investment object is actually guaranteed.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention belongs to the technical field of a real estate investment index system.

[0002]

[Problems to be Solved by the Invention] The liberalization of the financial business, such as the opening of foreign exchange transactions, is gradually progressing, and it is expected that the financial market will be activated in preparation for the financial bang in the 21st century. Institutional investors who manage large amounts of money in the financial markets, such as life insurance, non-life insurance and investment trusts, currently mainly invest in stocks and bonds in the Japanese market,
It has gained a certain amount of investment profit For example, in the stock market, there are indexes such as the Nikkei Stock Average and TOPIX, and the actual situation of daily trading is widely disclosed for each issue. In the bond market, there is also an index called NRI / BPI, and institutional investors can use such information. The fund is managed according to the purpose while taking into account risks and returns while referring to the information. Real estate, on the other hand, is an attractive investment that can expect stable and high investment returns, just like stocks and bonds, but the proportion of institutional investors in its portfolio is not high. This is considered to be one of the reasons that the investment environment for real estate has not been improved, that is, there is almost no information on real estate transactions, and there is no reliable real estate investment index. An object of the present invention is to construct a real estate investment index system that provides an institutional investor with an index indicating the rate of return of real estate,
TOPIX, NRI, BPI
It should be highly reliable, comparable to other financial indicators.

[0003]

According to the invention, the above-mentioned object is achieved by the means described in the claims. That is, the invention of claim 1 performs statistical processing on raw contracted rent data and one or more factor data that is a factor for forming the raw contracted rent, and calculates one or more factor data given. A real estate contracted rent estimator that estimates unknown contracted rents, a real estate return calculating unit that calculates a real estate return based on the estimated contracted rent data, and a real estate return that outputs this real estate return in a predetermined format A real estate investment index system comprising an output unit, and the invention of claim 2 is a real estate investment index system for inputting raw contract rent data and outputting a real estate return, wherein the raw contract rent data and the raw contract rent data are output. Means for regression analysis of one or more factor data that constitute the contract rent, and means for estimating the contract rent of the assumed building based on the result of the regression analysis And a means for obtaining a real estate return based on the estimated contracted rent. The invention of claim 3 provides a method for forming raw contracted rent data and raw contracted rent. One or more factor data is regression-analyzed, and a function to accumulate the contract rent regression formula data for obtaining the theoretical value of the contract rent from one or more given factor data, and when a predetermined index search condition is input A real estate investment index system having a function of calculating a real estate return based on the theoretical value of the contracted rent according to the search condition and returning a response. A regression analysis is performed on the OMR rent data, which indicates the rent market price that constitutes the raw contract rent, and other factor data. Means for determining one or more basic factor data determined to be useful for estimating the contracted rent and regression coefficients thereof and accumulating the contracted rent regression equation data, and the contracted rent regression equation data and the basic factor A real estate investment index system comprising means for calculating the investment yield of the real estate using the data and outputting the result, and wherein the invention of claim 5 provides the raw contract rent data and the raw contract rent. Means for selecting an important explanatory variable Xi for the contracted rent Y and determining its regression coefficient by regression analysis of one or a plurality of factor data to be formed factors, and accumulating contracted rent regression equation data; Assuming a building full of the floor area ratio limit on a predetermined standard ground, calculate the individual yield of the assumed building on the standard ground based on the contract rent regression data. In addition, the real estate investment index system includes means for calculating the real estate investment yield for each category by totalizing and averaging the individual yields for each predetermined category.

[0004]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The NCREIF Index System is a real estate investment index system jointly developed by the United States Real Estate Investment Trustees and Franklassell in the United States. Although the main source of income generated from real estate is tenant rent, the NCREIF index system in the United States, where the spread of real estate transaction information is progressing, allows tenant rent data on an arbitrary contract basis (data indicating the rent paid by the tenant to the owner). ) Are collected from council members and used for indexing. But,
In Japan, where many contract-based tenant rents are kept secret between the parties, a reliable real estate investment index is hardly expected because NCREIF-scale data cannot be grasped. For example, recruitment-based tenant rent data (data indicating the rent presented by the owner to applicants) can be widely collected, but using recruitment-based rents is far from the actual situation, and do not become. Here, when constructing a reliable real estate investment index system, it is necessary to consider what data is used to create the index, what kind of character is to be created, how to create that index, etc. However, if raw contract rent data can only be collected in a fragmentary manner, it is necessary to devise a method of adopting and calculating tenant rent.

FIG. 5 shows a functional block diagram of a real estate investment index system according to the present invention. In FIG.
The real estate investment index system 10 includes a real estate contracted rent estimation unit 11 for estimating unknown contracted rents by statistical analysis, a real estate return rate calculating unit 12 for calculating a real estate return rate based on the estimated contracted rent data, and a real estate return rate. Is output in a predetermined format. Functional blocks 11 to 13
May be provided on a single host computer, for example, or may be provided separately on different personal computers. In the latter case, the recording medium such as an MO or a CD-R or a communication line may be used. The system can be systemized by transferring data to

FIG. 6 is a diagram showing a configuration example of a real estate contract rent estimation unit. In FIG. 6, data relating to tenant rent for each region is stored in a storage device 21 in chronological order, and in each file, a property code, a contract rent, an offer rent, a building scale, Records in which fields such as the number of approach roads, the number of parking lots, the air conditioning system, and the floor wiring system are defined are stored. The rents offered, building size, number of roads, number of parking lots, number of parking lots, air-conditioning system, floor wiring system, etc. are considered to be factors contributing to the contracted rent. If not, the estimated rent will be determined. In addition,
“Raw data” refers to data that indicates actual values that are actually observed, not estimated theoretical data.

In FIG. 6, a statistical processing unit 22 analyzes a factor for forming a contract rent according to a predetermined processing procedure, and estimates an unknown contract rent from factors forming the contract rent, such as an offered rent or the number of elevators. Get the data.
As the statistical method, an optimal method can be selected and applied as appropriate according to the development of various statistical theories. In this example, a rent regression equation for obtaining an estimated contract rent is searched by multiple regression analysis.

First, the statistical processing section 22 selects a specific file and sequentially sets the value of the record in a predetermined register. Set rent data is set in the y register, and factor data such as the air conditioning system is set in the Xi register. A rent regression equation such as y = (A * X1) + (B * X2) +. A and B are explanatory variables X1,
X2 is a regression coefficient, and ε is a constant term. Various rent regression equations can be verified by setting a part or all of the factor data in the Xi register. However, when only a part of the factor data is selected, some Xi registers are used and other Xi register becomes empty. Further, when the factor data to be used can be narrowed down in advance from the past actual values or the like, by setting only the factor data in the Xi register, the load on the computer can be reduced.

Next, the statistical processing unit 22 obtains a coefficient of determination R 2 and evaluates the goodness of the applied rent regression equation. The determination coefficient takes a value of 0 ≦ R2 ≦ 1, and it can be evaluated that the closer to 1, the better the fitting of the rent regression equation is. Therefore, for example, a certain threshold value is set and this threshold value and the determination coefficient By examining the magnitude of, we can evaluate the rent regression equation. Subsequently, the statistical processing unit 22 performs a test by analysis of variance, and tests whether the given rent regression equation is useful for predicting the contracted rent. For example, by setting a rejection area based on the F distribution and verifying whether or not the test statistic F value is included in the rejection area, it is possible to determine whether the given rent regression equation is useful for predicting the contracted rent. Can be tested.

Further, the statistical processing section 22 tests the regression coefficient in the given rent regression equation. For example, by setting the significance level α in advance and comparing the significance probability based on the test statistic t value with the significance level α, it can be determined whether the factor data set in the Xi register is useful for predicting the contract rent. You can test. Finally, the statistical processing unit 2
2 searches for significant explanatory variables by comparing standard regression coefficients. The standard regression coefficient is a regression coefficient obtained after standardizing each factor data, and is a numerical value that does not depend on how to take the unit of the factor data, so the magnitude of the value indicates the strength of the relationship with the contracted rent. . Thus, the statistical processing unit 22
Is stored in the storage device 23.

If two important explanatory variables are selected by regression analysis, the estimated contracted rent regression equation is obtained as Y = (A * X1) + (B * X2). The rent Y is obtained as the sum of a value obtained by multiplying the explanatory variable X1 by the regression coefficient A and a value obtained by multiplying the explanatory variable X2 by the regression coefficient B. The choice of the explanatory variable for obtaining the estimated contracted rent Y is a very important factor, and the choice of the explanatory variable determines the quality of the contracted rent model. Considering that the contracted rent of an office building is formed based on the regional characteristics of the target office building, the market conditions at the time of the contract, etc., continuous data that represents the regional disparity as well as the regional disparity as an explanatory variable It is desirable to adopt rent data that is grasped as That is,
It has the two characteristics of regional disparity of rent and time disparity,
Factor data indicating the rent market should be adopted as an explanatory variable for obtaining the estimated contracted rent Y. In the present specification, such factor data indicating the rent market is collectively called OMR rent data.

FIG. 8 is a diagram showing an example of the configuration of the real estate return rate calculation unit. In FIG. 8, the estimated contracted rent calculation function 30
Obtains the contract rent regression equation from the real estate contract rent estimation unit 11 and obtains standard land data representing a predetermined area from the standard land DB 31 and factor data from the factor data DB 32 to calculate the estimated contract rent. . In order to calculate a standard real estate investment yield as one of the real estate investment indices, the real estate return rate calculation unit 12 assumes a building with a floor area ratio full of limits on a standard ground based on the land price disclosure method, and By calculating the individual yield of the building and averaging the individual yield for each category, it is possible to calculate the standard real estate investment yield for each category. Data such as floor area ratio required for assuming a kind of benchmark building on the standard ground is stored in the standard land DB 31, and the above-mentioned OMR rent data and the like are categorized and stored in the factor data DB 32. .

The income return rate calculation function 35 includes an estimated contracted rent Y calculated by a contracted rent regression equation, and an asset DB
The income return is calculated from the asset price obtained from 33. The income return is the income gain divided by the asset value at the beginning of the period, and the income gain is the sum of the tenant rent after deducting expenses and the operating profit of the deposit. In addition, it is also possible to include the investment profit in the rent in advance and process it. The correction value DB 34 is used in other circumstances, for example, when the income rate of return is corrected when there are many vacancies. The overall rate of return from real estate investment can be expressed as the sum of the rate of return on income based on tenant rent and the rate of return on capital as property prices increase or decrease. The real estate return rate calculation unit 12 creates an overall return rate DB 38 from the income return rate DB 36 and the capital return rate DB 35.

The real estate return rate output unit 13 can list the real estate return rates calculated based on the estimated contracted rents and print the list on a list. Can also. FIG. 1 shows a menu screen for setting search conditions, from which a time and a real estate investment index in which region can be specified. From this menu screen, the total rate of return is used as an evaluation index, each zone and each municipal area as an area specification, the whole year from 1970 to 1997 as a period specification, financial data as other indexes, and a higher ranking as a ranking display. Is specified, and click the [Index] button at the bottom right of the screen.
The screen switches to the search result list display screen. In the search result list display screen of FIG. 2, the Nikkei average, TO
After PIX, NRI and BPI financial indicators are displayed,
The total real estate return by region is displayed in descending order. Although only the data up to 1979 at the right end is displayed on the screen, by scrolling the screen, it is possible to refer to the data of 1997 specified in advance. Here, check the desired data and then click the [Graph] button at the bottom left of the screen.
The screen switches to the graph screen. From this graph, it can be seen at a glance that the real estate return is different from stocks and bonds.

Further, when the user returns to the menu screen by clicking the [Return] button at the lower right of the screen and selects the risk return in the evaluation index column, the screen of the risk return distribution diagram of FIG. 4 is displayed. Referring to the vertical axis in FIG. 4, it can be seen that real estate can be expected to have a high rate of return alongside bonds and stocks, and that in some regions, it is possible to obtain a remarkably high rate of return. Referring to the horizontal axis, it is clear that the real estate return is higher risk than bonds, but not as risky as stocks. However, the risk in modern portfolio theory does not indicate the risk of loss, but the magnitude of future uncertainty.

[0016]

As described above, according to the present invention,
Since the theoretical value of the contracted rent is statistically calculated and the real estate investment index is created based on this theoretical value, unlike the index based on rent data on the basis of recruitment, etc., a very reliable real estate investment index Can be obtained. The use of the real estate investment index system according to the present invention proves that real estate is an attractive investment object as well as stocks and bonds, and is expected to activate the real estate market in the future.

[Brief description of the drawings]

FIG. 1 is a diagram showing a menu screen for setting search conditions.

FIG. 2 is a diagram showing a search result table screen.

FIG. 3 is a diagram showing a graph screen of a search result.

FIG. 4 is a view showing a risk / return distribution diagram screen of search results.

FIG. 5 is a functional block diagram of a real estate investment index system.

FIG. 6 is a diagram illustrating a configuration example of a real estate contracted rent estimation unit.

FIG. 7 is a diagram illustrating a record format of raw data.

FIG. 8 is a diagram illustrating a configuration example of a real estate return rate calculation unit.

[Explanation of symbols]

 REFERENCE SIGNS LIST 10 real estate investment index system 11 real estate contracted rent estimation unit 12 real estate return rate calculation unit 13 real estate return rate output unit 21, 23, 31-34, 36-38 storage device 22 statistical processing unit 30 estimated contracted rent calculation function 35 income return rate Calculation function

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Kazuo Matsumoto, 1-4-5 Marunouchi, Chiyoda-ku, Tokyo Mitsui Trust Bank, Limited (72) Shinsuke Tsuji 1-4-5, Marunouchi, Chiyoda-ku, Tokyo (72) Inventor Osamu Sekiguchi 2-1-212 Hamamatsucho, Minato-ku, Tokyo In-house Ikoma Data Service System (72) Inventor Junichi Shimizu 2-2-2 Hamamatsucho, Minato-ku, Tokyo No. 12 F-term in the Ikoma Data Service System (reference) 5B049 BB47 CC05 EE01 EE03 EE05 FF03 GG02 GG07

Claims (5)

[Claims] Claims 1. A raw contract rent data and one or a plurality of factor data which is a factor forming the raw contract rent are statistically processed, and an unknown contract rent is obtained from one or a plurality of the provided factor data. A real estate contracted rent estimating unit for estimating the real estate return rate, a real estate profit rate calculating unit for calculating the real estate profit rate based on the estimated contracted rent data, and a real estate profit rate output unit for outputting the real estate profit rate in a predetermined format. Equipped real estate investment index system. 2. A real estate investment index system for inputting raw contracted rent data and outputting a real estate return rate, wherein the raw contracted rent data and one or a plurality of factor data which constitute the raw contracted rents And a means for estimating a contract rent of the assumed building based on the result of the regression analysis, and a means for calculating a real estate return based on the estimated contract rent. 3. A regression analysis of raw contracted rent data and one or more factor data which is a factor forming the raw contracted rent, and a theoretical value of the contracted rent from one or more given factor data. A function to accumulate the contracted rent regression equation data to calculate the real estate return rate based on the theoretical value of the contracted rent according to the search condition when a predetermined index search condition is input, and a function to return a response Real estate investment index system equipped with. 4. Regression analysis of raw contracted rent data, OMR rent data indicating a rent market rate which is a factor forming the raw contracted rent, and other factor data, and satisfying predetermined conditions. Means for storing one or more basic factor data determined to be useful for estimating the contracted rent and regression coefficients thereof and accumulating the contracted rent regression formula data, and the contracted rent regression formula data and the basic factor data; And a means for calculating the investment yield of the real estate and outputting the result. 5. An important explanatory variable Xi for the contracted rent Y is selected by performing a regression analysis of the raw contracted rent data and one or a plurality of factor data which is a factor forming the raw contracted rent. A means for determining the regression coefficient and accumulating the contract rent regression equation data, and assuming a building having a floor area ratio full on a predetermined standard ground, and individualizing the assumed building on the standard ground based on the contract rent regression equation data And a means for calculating a real estate investment yield for each category by calculating a yield of each category and averaging the individual yields for each predetermined category.