JP6852015B2 - Vacancy rate estimation device and method and computer program - Google Patents

Description

The present invention relates to a vacancy rate estimation device and method for estimating the vacancy rate of rental real estate, and a computer program.

The vacancy rate of rental real estate is the ratio of the period when there are no tenants in a certain period, that is, the period when the landlord cannot earn rental income. The vacancy rate plays a decisive role in the calculation or evaluation of the rate of return (see, for example, Patent Document 1). Therefore, being able to estimate the vacancy rate with high accuracy is directly linked to the accuracy of real estate profit evaluation.

In Patent Document 1, the number of units offered monthly and the number of buildings offered monthly are extracted from the rental real estate solicitation data, and the number of units offered monthly is divided by the total number of units offered monthly and averaged monthly. Therefore, the technology for estimating the vacancy rate is described.

Japanese Patent No. 6270589

When evaluating the profit of rental real estate on a building-by-building basis, it is sufficient if the vacancy rate can be evaluated on a building-by-building basis. The vacancy rate obtained by the technique described in Patent Document 1 is an index for each region of prefectures and municipalities, not the vacancy rate for each building. Therefore, the method described in Patent Document 1 is insufficient for the profit evaluation of each building.

In addition, for example, recruitment increases momentarily during the moving period such as March and September, but the vacancy rate estimated by the method described in Patent Document 1 is calculated between this moving period and other normal periods. Even if the averaging process is performed, it fluctuates sharply, so it is difficult to use for profit evaluation.

An object of the present invention is to present a vacancy rate estimation device and method and a computer program capable of accurately estimating the vacancy rate for each building.

The vacancy rate estimation device according to the present invention is an input means for inputting property characteristic data related to the building to be evaluated, and an analysis means for analyzing the solicitation probability and market residence period of each rental housing building by referring to the rental housing solicitation database. By applying the property characteristic data of the evaluation target building to the analysis result of the analysis means, the solicitation probability / retention period calculation means for calculating the solicitation probability and market residence period for each building of the evaluation target building, and It is characterized by having a vacancy rate calculation means for calculating the vacancy rate of the evaluation target building by multiplying the solicitation probability of the evaluation target building by the market residence period of the evaluation target building.

The vacancy rate estimation device according to the present invention refers to an input means for inputting property characteristic data and location information regarding the building to be evaluated and a rental housing recruitment database, and refers to the rental housing in the area including the location of the building to be evaluated. By applying the property characteristic data of the evaluation target building to the analysis means for analyzing the recruitment probability and market residence period for each building, and the analysis result of the analysis means, the recruitment probability and market for each building of the evaluation target building. Vacancy rate for calculating the vacancy rate of the evaluation target building by multiplying the recruitment probability of the evaluation target building by the solicitation probability / residence period calculation means for calculating the residence period and the market residence period of the evaluation target building. It is characterized by having a calculation means.

Vacancy rate estimation method according to the present invention includes an input step of inputting a property characteristic data related to the evaluation objects building to a computer, the computer is, recruitment probabilistic and market retention wing unit rental housing with reference to rental housing recruitment database The analysis step that analyzes the period and the computer applies the property characteristic data of the evaluation target building to the analysis result of the analysis step to calculate the solicitation probability and market retention period for each building of the evaluation target building. The solicitation probability / residence period calculation step to be performed and the computer calculates the vacancy rate of the evaluation target building by multiplying the recruitment probability of the evaluation target building by the market residence period T (X) of the evaluation target building. It is characterized by having a vacancy rate calculation step.

In the vacancy rate estimation method according to the present invention, an input step for inputting property characteristic data and location information regarding the building to be evaluated and the computer refer to the rental housing recruitment database to determine the location of the building to be evaluated. The evaluation is performed by analyzing the solicitation probability and market residence period of rental housing in the area including the building, and the computer applying the property characteristic data of the building to be evaluated to the analysis result of the analysis step. The solicitation probability / retention period calculation step for calculating the solicitation probability and market retention period for each building of the target building, and the computer multiplying the solicitation probability of the evaluation target building by the market retention period of the evaluation target building. It is characterized by having a vacancy rate calculation step for calculating the vacancy rate of the building to be evaluated.

The computer program according to the present invention has an analysis function that analyzes the solicitation probability and market residence period of each rental housing building by referring to the rental housing solicitation database, and the property related to the building to be evaluated based on the analysis result of the analysis function. By applying the characteristic data, the solicitation probability / residence period calculation function that calculates the solicitation probability and market retention period for each building of the evaluation target building, and the solicitation probability of the evaluation target building to the market of the evaluation target building. This is a computer program for realizing a vacancy rate calculation function that calculates the vacancy rate of the building to be evaluated by multiplying the residence period.

A computer program according to the present invention, the computer, and rental housing recruitment refers to database analysis function for analyzing the recruitment probabilistic and market dwell period wing unit rental housing in the area including the location of the evaluation object building, the by applying object matter characteristic data of the evaluation object building in the analysis result of the analysis function, and recruitment probabilistic-dwell period calculation function for calculating the recruitment probabilistic and market dwell period of the evaluated building wing unit, the evaluation This is a computer program for realizing a vacancy rate calculation function that calculates the vacancy rate of the evaluation target building by multiplying the solicitation probability of the building by the market residence period of the evaluation target building.

According to the present invention, the vacancy rate for each building is calculated by multiplying the quantitatively estimated recruitment probability for each building and the market residence period (period of vacancy), as compared with the conventional method. The vacancy rate can be estimated accurately.

It is a schematic block diagram of Example 1 of this invention. It is an explanatory diagram of the cancellation request and the vacancy period accompanying this. It is a table of explanatory variables of regression analysis. It is a schematic block diagram of Example 2 of this invention. It is a schematic block diagram of Example 3 of this invention.

Hereinafter, examples of the present invention will be described in detail with reference to the drawings.

FIG. 1 shows a schematic block diagram of an embodiment of the present invention. This embodiment is realized by a vacancy rate estimation program that operates on a single computer device or a plurality of computer devices that operate in cooperation with each other, but of course, it can also be realized as a dedicated device.

The vacancy rate estimation device 10 shown in FIG. 1 includes a computer, and a CPU 12, a ROM 14, a RAM 16, a keyboard 18, a monitor 20, an HDD (or SSD) 22, and a communication device 24 are connected to the bus 26. The communication device 24 can connect to the rental housing recruitment database 28 via the network. Although the details will be described later, the CPU 12 can access the rental housing recruitment database 28 via the communication device 24 for the purpose of estimating the vacancy rate.

The CPU 12 realizes the regression analysis function 40, the recruitment probability / residence period calculation function 42, and the vacancy rate calculation function 44 by reading the vacancy estimation program 30 stored in the HDD 22 into the RAM 16 and executing the vacancy estimation program 30. The regression analysis result 32 (coefficient value of the regression model formula) of the regression analysis function 40 is also stored in the HDD 22.

FIG. 2 shows an example of vacancy occurrence status for three rooms A, B, and C in a certain building α. In the example shown in FIG. 2, there is a cancellation request in rooms A and C, and a vacancy period occurs. In FIG. 2, the example is given in units of one month, but this period is for exemplification purposes.

In the example shown in FIG. 2, cancellation requests are made in rooms A and C of building α during the unit period (1 month) for estimating the vacancy rate. The period from the cancellation offer to the contract move-in (market residence period or residence period) is the period equivalent to the vacancy where rental income cannot be obtained. For a recruitment database in which a period obtained by adding a certain period to the period in which the offer is open is the vacancy period, the period obtained by adding the fixed period to the open offer period is defined as the market retention period T. The market residence period T depends on the property characteristic data X of the building α. Property characteristic data X includes, for example, elements such as rent, distance from the nearest station, occupied area, distance to the nearest station, number of rooms, and skeleton structure. The number of rooms can be estimated, for example, from the total number of floors of the building. On the other hand, the probability of becoming an empty room (rooms A and C in the example shown in FIG. 2) in the building α and recruiting borrowers, that is, the recruitment probability also depends on the property characteristic X.

Based on this consideration, the vacancy rate estimation device 10 estimates the recruitment probability and the market retention period of recruitment on a building-by-building basis by regression analysis, and estimates the vacancy rate from the obtained recruitment probability and market retention period. To do. That is, when the market residence period (days) for each room estimated by multiple regression analysis is T (X) and the recruitment probability for each building estimated by logistic regression analysis is P (X), the building is concerned. Vr. Vr. Vacancy rate per month
Vr = P (X) x T (X) / 30
And.

The real estate rental solicitation database (DB) 28 is a database published on a real estate information site or generated from this public database, and generally has the following information as property characteristic data of individual real estate for regression analysis. including. That is, rent unit price (unit price per square meter including management fee and common service fee), new construction information (1 for new land, 0 for non-new construction), age, occupied area, total number of floors, location floor, station time (to the nearest station) Time required), bus usage information (1 when using the bus, 0 otherwise), time to the city center (time from the nearest station to the station in the central business district), building structure information (RC, SRC, steel structure) Or other than these), apartment / condominium classification information (apartment or condominium), location information, etc. are included. Whether or not it is a new construction may be determined from the age of the building.

The regression analysis function 40 refers to the real estate rental solicitation DB 28 and determines the regression coefficient of the regression model regarding the solicitation probability for each building and the market residence period. The regression analysis function 40 executes regression analysis, for example, by 21 areas that divide the whole country and by apartments and condominiums. Of course, the regression analysis function 40 may execute the regression analysis for the area where the evaluation target building is located by separating the corresponding houses, whether it is an apartment or a condominium. The regression analysis function 40 stores the analysis result (coefficient value of the regression model formula) 32 for each area and each apartment / condominium in the hard disk 22.

で一般的に表現され得る。説明変数Ｘ，ＬＤ，ＴＤの詳細を図３に示す。説明変数ＬＤは、立地による個別性（例えば、東京都で言えば、渋谷区と中央区の政策の違い）を排除するために導入される。説明変数ＴＤは、時間による個別性（例えば、２０１３年と２０１７年の物価の違い）を排除するために導入される。目的変数（被説明変数）は、募集確率（式（１））に対しては棟ごとの募集確率Ｐ（Ｘ）（月単位）であり、市場滞留期間（式（２））に対しては、募集日数（募集公開から成約までの日数）の自然対数である。 The regression model of the regression analysis function 40 is

Can be generally expressed in. Details of the explanatory variables X, LD, and TD are shown in FIG. The explanatory variable LD is introduced to eliminate individuality due to location (for example, in Tokyo, the difference in policy between Shibuya Ward and Chuo Ward). The explanatory variable TD is introduced to eliminate time-dependent individuality (eg, price differences between 2013 and 2017). The objective variable (explained variable) is the solicitation probability P (X) (monthly) for each building for the solicitation probability (formula (1)), and the market retention period (formula (2)) , The natural logarithm of the number of recruitment days (the number of days from the publication of recruitment to the conclusion of a contract).

The above equations (1) and (2) are examples, and various modifications can be made. For example, the expression of the variable may be changed to adopt the exponentiation form instead of the logarithmic form, or the regression analysis model formula may be different for each area in consideration of the magnitude of the influence on the target variable. is there.

Further, the explanatory variable group shown in FIG. 3 is an example, and another explanatory variable may be added or some variables may be changed to other variables. For example, an explanatory variable (explanatory variable ED indicating the railway line) that excludes individuality due to the difference in the railway line of the nearest station may be added to the equations (1) and (2) even within the same city, ward, town, and village. The explanatory variable ED reflects, for example, the ease of access to Tokyo Station in Chuo-ku, Tokyo (the Keiyo Line does not require a transfer to Tokyo Station, while the Yurakucho Line requires a transfer).

The vacancy rate estimation program 30 running on the CPU 12 is an input screen for property information data (data corresponding to X, LD, TD) of the building (evaluation target building) for which the vacancy rate is to be evaluated on the screen of the monitor 20. Is displayed. The operator uses the keyboard 18 to input the property information data of the building to be evaluated on this input screen. The vacancy rate estimation program 30 on the CPU 12 inputs the input data to the solicitation probability / residence period calculation function 42. The recruitment probability / residence period calculation function 42 reads the analysis result of the area including the location of the evaluation target building from the regression analysis result 32, applies the property information data of the evaluation target building, and applies the property information data of the evaluation target building to the monthly vacancy of the evaluation target building. The rate P (X) and the market residence period (the number of recruitment days from the start of recruitment to the conclusion of the contract) T (X) are calculated and supplied to the vacancy rate calculation function 44.

The vacancy rate calculation function 44 calculates the vacancy rate Vr per month of the building to be evaluated with respect to the vacancy rate P (X) and the market residence period T (X) from the recruitment probability / residence period calculation function 42.
Vr = P (X) x T (X) / 30
To calculate.

In this embodiment, the recruitment probability and market residence period for each building are estimated by regression analysis, and the vacancy rate around the building is calculated based on these, so the vacancy rate can be estimated according to the actual situation. That is, not only the individuality of the property used for estimation but also the individuality of the building to be evaluated can be excluded, and an objective value can be obtained as the vacancy rate.

The unit period for expecting vacancy from the rental housing recruitment database is set to one month, because in general, it is one month to grasp the movement of the vacancy rate in the rental market, and other periods, for example, It may be a quarter or six months.

When there is a sufficient amount of data that can be referred to by the rental housing recruitment database 28, the relationship between the recruitment probability and the residence period and the property characteristic data can be quantitatively determined by so-called supervised machine learning. That is, the multiple regression analysis and the logistic regression in the regression analysis function 40 can be replaced with the analysis by supervised machine learning.

The same functions as in the first embodiment can be configured in the server / client model. FIG. 4 shows a schematic block diagram thereof. The recruitment probability / residence period calculation function 142 corresponding to the recruitment probability / residence period calculation function 42 and the vacancy rate calculation function 144 corresponding to the vacancy rate calculation function 44 are left in the client 110, and the regression analysis function is left in the server 150. A regression analysis function 140 corresponding to 40 is arranged. The rental housing recruitment database 128 has the same configuration as the rental housing recruitment database 28.

The regression analysis function 140 of the server 150 periodically or intermittently accesses the rental housing recruitment database 128, and under the same regression model formula as in the first embodiment, logistic regression analysis of the recruitment probability for each building and market retention. The multiple regression analysis of the period is executed, and the regression analysis result 156 is stored in the HDD 154 attached to the server 150. That is, the regression analysis function 140 accesses the rental housing recruitment database 128 by the communication device 152, and like the regression analysis function 40, the recruitment probability and market for each building are classified by 20 areas that divide the whole country and by apartments and condominiums. Perform a regression analysis of the retention period. The regression analysis function 140 stores the analysis results (coefficient values of the regression model formula) 156 for each area and each apartment / condominium on the hard disk 154.

In the client 110, the vacancy rate estimation program (client part) running on the CPU 112 is displayed on the screen of the monitor 120 in the property information data (X, LD, TD) of the building (evaluation target building) for which the vacancy rate is to be evaluated. Display the input screen for (corresponding data). The operator uses the keyboard 118 to input the property information data of the building to be evaluated on this input screen. The vacancy rate estimation program on the CPU 112 inputs the input data to the solicitation probability / residence period calculation function 142. The vacancy rate estimation program on the CPU 112 also reads the regression analysis result of the area including the location of the evaluation target building from the regression analysis result 156 of the server 150 and inputs it to the recruitment probability / residence period calculation function 142.

Similar to the recruitment probability / residence period calculation function 42, the recruitment probability / residence period calculation function 142 applies the property information data of the evaluation target building to the analysis result of the area including the location of the evaluation target building, and the month of the evaluation target building. The vacancy rate P (X) and the market residence period T (X) for each are calculated and supplied to the vacancy rate calculation function 144.

Similar to the vacancy rate calculation function 44, the vacancy rate calculation function 144 is one month of the evaluation target building from the vacancy rate P (X) from the recruitment probability / residence period calculation function 142 and the market residence period T (X). Vacancy rate Vr around,
Vr = P (X) x T (X) / 30
To calculate.

Regression analysis may be performed on the area containing the location of the building to be evaluated as needed. FIG. 5 shows a schematic block diagram of a configuration in which the embodiment shown in FIG. 1 is modified in this way. The same components as those shown in FIG. 1 are designated by the same reference numerals.

The vacancy rate estimation program 230, which operates on the CPU 212 of the vacancy rate estimation device 210 shown in FIG. 5, has the location and property information data (evaluation target building) of the building (evaluation target building) for which the vacancy rate is to be evaluated on the screen of the monitor 20. Display the input screen of (data corresponding to X, LD, TD). The operator uses the keyboard 18 to input the location of the building to be evaluated and the property information data on this input screen. The vacancy rate estimation program 230 on the CPU 212 inputs the input location (and, if necessary, by apartment / condominium) into the regression analysis function 240, and inputs the input property information data into the solicitation probability / residence period calculation function 42.

The regression analysis function 240 accesses the rental housing recruitment database 28 by the communication device 24, and performs logistic regression analysis of recruitment probability and multiple regression analysis of residence period for each apartment / condominium in the area including the location of the building to be evaluated. Run. The regression analysis function 240 supplies the analysis result (coefficient value of the regression model formula) to the recruitment probability / residence period calculation function 242.

Similar to the recruitment probability / residence period calculation function 42, the recruitment probability / residence period calculation function 242 applies the property information data of the evaluation target building to the analysis result of the area including the location of the evaluation target building, and applies the property information data of the evaluation target building to the evaluation target building. The monthly vacancy rate P (X) and the market residence period T (X) are calculated. The vacancy rate calculation function 244 follows the same calculation formula as the vacancy rate calculation function 44, and the vacancy rate P (X) from the recruitment probability / residence period calculation function 242 and the vacancy rate from the market residence period T (X). Calculate Vr.

In this embodiment, the calculation is executed only for the required area, so that the desired result can be obtained in a relatively short time even with a small processing power.

In each of the above-described embodiments, when there is a large amount of public data that can be referred to, the multiple regression analysis and logistic regression in the regression analysis functions 40, 140, 240 can be replaced with supervised machine learning. That is, the regression analysis functions 40, 140, 240 can be replaced with an analysis function that determines the quantitative relationship between the property characteristics and the recruitment period and the residence period by supervised machine learning.

Although the present invention has been described with reference to specific explanatory embodiments, various modifications and modifications may be made to the above embodiments without departing from the technical scope of the invention as defined in the claims. This is self-evident to engineers in the field to which the present invention belongs, and such changes and modifications are also included in the technical scope of the present invention.

10: Vacancy rate estimation device 12: CPU
14: ROM
16: RAM
18: Keyboard 20: Monitor 22: HDD (or SSD)
24: Communication device 26: Bus 28: Rental housing recruitment database (DB)
30: Vacancy rate estimation program 32: Regression analysis result 40: Regression analysis function 42: Recruitment probability / residence period calculation function 44: Vacancy rate calculation function 110: Client 112: CPU
118: Keyboard 120: Monitor 124: Communication device 128: Rental housing recruitment database (DB)
140: Regression analysis function 142: Recruitment probability / residence period calculation function 144: Vacancy rate calculation function 150: Server 152: Communication device 154: HDD
156: Regression analysis result 210: Vacancy rate estimation device 212: CPU
230: Vacancy rate estimation program 240: Regression analysis function 242: Recruitment probability / residence period calculation function 244: Vacancy rate calculation function

Claims (18)

Input means for inputting property characteristic data (X) related to the building to be evaluated,
Analytical means (40, 140) that analyze the solicitation probability and market residence period of each building of rental housing by referring to the rental housing recruitment database (28, 128),
By applying the property characteristic data (X) of the evaluation target building to the analysis result (32, 156) of the analysis means, the solicitation probability P (X) and the market residence period T (of the building unit of the evaluation target building) Recruitment probability / residence period calculation means (42, 142) for calculating X) and
Vacancy rate calculation means (44, 144) for calculating the vacancy rate of the evaluation target building by multiplying the recruitment probability P (X) of the evaluation target building by the market residence period T (X) of the evaluation target building. )
A vacancy rate estimation device characterized by having and. The analysis means (140) is arranged on the server (150).
The vacancy according to claim 1, wherein the recruitment probability / residence period calculation means (142) and the vacancy rate calculation means (144) are arranged on a client (110) that can connect to the server. Rate estimation device. The analysis means (40, 140) regression-analyzes the solicitation probability and the market residence period of each rental housing building for each of a plurality of areas.
The solicitation probability / residence period calculation means (42, 142) is based on the analysis result (32) of the analysis means for the area where the evaluation target building is located among the plurality of areas. The vacancy rate estimation device according to claim 1 or 2, wherein the characteristic data (X) is applied. Input means for inputting property characteristic data (X) and location information related to the building to be evaluated,
With reference to the rental housing recruitment database (28), an analysis means (240) for analyzing the recruitment probability and market residence period of each rental housing in the area including the location of the building to be evaluated, and
By applying the property characteristic data (X) of the evaluation target building to the analysis result of the analysis means, the solicitation probability P (X) and the market residence period T (X) of each building of the evaluation target building are calculated. Recruitment probability / retention period calculation means (242) and
Vacancy rate calculation means (244) for calculating the vacancy rate of the evaluation target building by multiplying the recruitment probability P (X) of the evaluation target building by the market residence period T (X) of the evaluation target building.
A vacancy rate estimation device characterized by having and. The analysis means is a regression analysis means (40, 140) that performs regression analysis on the solicitation probability and market residence period of each rental housing building by referring to the rental housing recruitment database (28, 128) based on a predetermined regression model formula. , 240) The vacancy rate estimation device according to any one of claims 1 to 4. The vacancy rate estimation device according to claim 5, wherein the predetermined regression model formula includes an explanatory variable (LD) for location and an explanatory variable (TD) for time. An input step for inputting property characteristic data (X) related to the building to be evaluated into a computer,
The computer, the analysis step of analyzing the recruitment probabilistic and market dwell period wing unit rental housing with reference to rental housing recruitment database (28, 128) (40, 140),
By applying the property characteristic data (X) of the evaluation target building to the analysis result (32, 156) of the analysis step , the computer applies the recruitment probability P (X) and the market for each building of the evaluation target building. Recruitment probability / retention period calculation steps (42, 142) for calculating the retention period T (X), and
The vacancy rate calculation step in which the computer calculates the vacancy rate of the evaluation target building by multiplying the recruitment probability P (X) of the evaluation target building by the market residence period T (X) of the evaluation target building. (44, 144)
A vacancy rate estimation method characterized by having and. The server (150) performs the analysis step (140) and
The vacancy rate according to claim 7, wherein the client (142) that can connect to the server executes the recruitment probability / residence period calculation step (142) and the vacancy rate calculation step (144). Estimating method. In the analysis step (40,140), the solicitation probability and the market residence period of each rental housing building are regression-analyzed for each of the plurality of areas.
The recruitment probability / residence period calculation step (42, 142) is based on the analysis result (32) of the analysis step for the area where the evaluation target building is located among the plurality of areas. The vacancy rate estimation method according to claim 7 or 8, wherein the characteristic data (X) is applied. An input step for inputting property characteristic data (X) and location information related to the building to be evaluated into a computer,
An analysis step (240) in which the computer refers to the rental housing recruitment database (28) and analyzes the recruitment probability and market residence period of each rental housing in the area including the location of the building to be evaluated.
By applying the property characteristic data (X) of the evaluation target building to the analysis result of the analysis step, the computer applies the solicitation probability P (X) and the market residence period T (X) for each building of the evaluation target building. ), And the recruitment probability / residence period calculation step (242),
The vacancy rate calculation step in which the computer calculates the vacancy rate of the evaluation target building by multiplying the recruitment probability P (X) of the evaluation target building by the market residence period T (X) of the evaluation target building. (244)
A vacancy rate estimation method characterized by having and. The analysis step is a step (40, 140, 240) of regression analysis of the solicitation probability and market residence period of each rental housing building with reference to the rental housing recruitment database (28, 128) based on a predetermined regression model formula. ), The vacancy rate estimation method according to any one of claims 7 to 10. The vacancy rate estimation method according to claim 11, wherein the predetermined regression model formula includes an explanatory variable (LD) for location and an explanatory variable (TD) for time. On the computer
An analysis function (40, 140) that analyzes the solicitation probability and market residence period for each building of rental housing by referring to the rental housing recruitment database (28, 128),
By applying the property characteristic data (X) related to the evaluation target building to the analysis result (32, 156) of the analysis function, the recruitment probability P (X) and the market residence period T (X) for each building of the evaluation target building. Recruitment probability / residence period calculation function (42, 142) to calculate
Vacancy rate calculation function (44, 144) that calculates the vacancy rate of the evaluation target building by multiplying the recruitment probability P (X) of the evaluation target building by the market residence period T (X) of the evaluation target building. )
A computer program to realize and. A program that enables the server (150) to realize the analysis function,
The computer program according to claim 13, further comprising a program for realizing the recruitment probability / residence period calculation function (142) and the vacancy rate calculation function (144) on a client connectable to the server. .. The analysis function (40, 140) regresses the solicitation probability and market residence period of each rental housing building for each of a plurality of areas.
The solicitation probability / residence period calculation function (42, 142) is based on the analysis result (32) of the analysis means for the area where the evaluation target building is located among the plurality of areas. The computer program according to claim 13 or 14, wherein the characteristic data (X) is applied. On the computer
With reference to the rental housing recruiting database (28), the analysis function to analyze the recruitment probability and market the residence period of building units of rental housing in the area, including the location of the evaluation target building and (240),
By applying the evaluation building things matter characteristic data (X) to the analysis result of the analysis function calculates the recruitment probability P of the evaluated building wing units (X) and Market dwell period T (X) Recruitment probability / retention period calculation function (242) and
Vacancy rate calculation function (244) that calculates the vacancy rate of the evaluation target building by multiplying the recruitment probability P (X) of the evaluation target building by the market residence period T (X) of the evaluation target building.
A computer program to realize and. The analysis function is a regression analysis function (40, 140) that performs regression analysis on the solicitation probability and market residence period of each rental housing building by referring to the rental housing recruitment database (28, 128) based on a predetermined regression model formula. , 240) The computer program according to any one of claims 13 to 16. The computer program according to claim 17, wherein the predetermined regression model formula includes an explanatory variable (LD) for location and an explanatory variable (TD) for time.

Images