JP2019197252A - Vacancy rate estimation device, method and computer program - Google Patents

Abstract

To accurately estimate a vacancy rate on a building unit.SOLUTION: A regression analysis function 40 refers to a rental house recruitment database 28, analyzes a recruitment probability on a building unit of a rental house and a market stay period, and stores regression analysis result 32 in an HDD 22. A recruitment probability/stay period calculation function 42 calculates a recruitment probability P(X) on a building unit of an evaluation object building and a market stay period T(X), by applying object characteristic data (X) related to the evaluation object building to the regression analysis result 32. A vacancy rate calculation function 44 calculates a vacancy rate of the evaluation object building by multiplying the recruitment probability P(X) of the evaluation object building by the market stay period T(X) of the evaluation object building, in accordance with the calculation result of the recruitment probability/stay period calculation function 42.SELECTED DRAWING: Figure 1

Description

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

  The vacancy rate of rental property is the ratio of the period when there is no resident in a certain period, that is, the period when the landlord cannot obtain rental income. The vacancy rate plays a decisive role in calculating or evaluating 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 earnings evaluation.

  In Patent Literature 1, the number of monthly recruitment units and the number of monthly recruitment units are extracted from the rental property solicitation data, and the monthly number of units recruited is divided by the total number of units estimated from the monthly number of recruitment units and averaged monthly. Thus, a technique for estimating the vacancy rate is described.

Japanese Patent No. 6270589

  When evaluating the profits of rental properties, it is only necessary to be able to evaluate the vacancy rate for each building. The vacancy rate obtained by the technology described in Patent Document 1 is an index for each region in units of prefectures and municipalities, and is not a vacancy rate in units of buildings. Therefore, the method described in Patent Document 1 is insufficient for the profit evaluation of each building.

  In addition, for example, recruitment increases momentarily at the time of moving in March, September, etc., but the vacancy rate estimated by the method described in Patent Document 1 at this moving time and other normal periods is However, even if the averaging process is performed, the fluctuation is severe, and therefore, it is difficult to use for profit evaluation.

  An object of this invention is to show the vacancy rate estimation apparatus and method and computer program which can estimate the vacancy rate in a building unit accurately.

  The vacancy rate estimation apparatus according to the present invention includes an input means for inputting property characteristic data relating to an evaluation target ridge, and an analysis means for analyzing a rental house recruitment probability and a market residence period with reference to a rental housing recruitment database And, by applying the property characteristic data of the evaluation target building to the analysis result of the analysis means, the recruitment probability / residence period calculating means for calculating the recruitment probability and market residence period of the building of the evaluation target building, Vacancy rate calculating means for calculating the vacancy rate of the evaluation target building by multiplying the recruitment probability of the evaluation target building by the market residence time of the evaluation target building.

  The vacancy rate estimation apparatus according to the present invention refers to an input means for inputting property characteristic data and location information relating to an evaluation target building, and a rental housing recruitment database, and refers to a rental housing in an area including the location of the evaluation target building. The analysis means for analyzing the recruitment probability and market residence period for each building, and applying the property characteristic data of the evaluation target building to the analysis result of the analysis means, the recruitment probability and market of the evaluation target building Vacancy rate for calculating the vacancy rate of the evaluation target building by multiplying the recruitment probability / retention period calculating means for calculating the residence period and the recruitment probability of the evaluation target building by the market residence period of the evaluation target building And a calculating means.

  The vacancy rate estimation method according to the present invention includes an input step of inputting property characteristic data relating to a building to be evaluated, and a computer, with reference to a rental housing recruitment database, the recruitment probability and market residence period of each rental housing And applying the property characteristics data of the evaluation target building to the analysis result of the analysis step to calculate the recruitment probability and market residence period for each building of the evaluation target building A residence period calculation step, and a vacancy rate calculation step of calculating the vacancy rate of the evaluation target building by multiplying the recruitment probability of the evaluation target building by the market residence period of the evaluation target building. And

  The vacancy rate estimation method according to the present invention includes an input step of inputting property characteristic data and location information relating to an evaluation target building to a computer, and the computer refers to a rental housing recruitment database to determine the location of the evaluation target building. An analysis step that analyzes the recruitment probability and market residence period of rental housing units in the included area, and the computer applies the property characteristic data of the evaluation target building to the analysis result of the analysis step. The recruitment probability and residence period calculation step for calculating the recruitment probability and market residence period of the target building, and the computer multiplies the recruitment probability of the evaluation subject building by the market residence period of the assessment subject building. A vacancy rate calculating step of calculating a vacancy rate of the evaluation target building.

  The computer program according to the present invention includes an analysis function for analyzing a recruitment probability and a market residence period of a rental house by referring to a rental house recruitment database, and a property related to an evaluation target building based on an analysis result of the analysis function. By applying the characteristic data, the recruitment probability / dwelling period calculation function that calculates the recruitment probability and market residence period for each building of the evaluation target building, and the market of the evaluation target building to the recruitment probability of the evaluation building It is a computer program for realizing a vacancy rate calculation function for calculating a vacancy rate of the evaluation target building by multiplying a residence period.

  The computer program according to the present invention refers to a rental housing recruitment database on a computer, an analysis function for analyzing the recruitment probability and market residence period of a rental housing unit in an area including the location of the evaluation target building, By applying the property characteristics data of the evaluation target building to the analysis result of the analysis function, the application probability / residence period calculation function for calculating the recruitment probability and market residence period of each evaluation target building, and the evaluation target It is a computer program for realizing a vacancy rate calculation function for calculating the vacancy rate of the evaluation target building by multiplying the recruitment probability of the building by the market residence time of the evaluation target building.

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

It is a schematic block diagram of Example 1 of the present invention. It is explanatory drawing of the cancellation | release offer and the vacancy period accompanying this. It is a table | surface of the explanatory variable 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 the present invention.

  Hereinafter, embodiments 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. The present 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 conjunction with each other, but of course can also be realized as a dedicated device.

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

  The CPU 12 reads the vacancy estimation program 30 stored in the HDD 22 into the RAM 16 and executes it, thereby realizing the regression analysis function 40, the recruitment probability / dwelling period calculation function 42, and the vacancy rate calculation function 44. The HDD 22 also stores the regression analysis result 32 (coefficient value of the regression model formula) of the regression analysis function 40.

  FIG. 2 shows an example of the vacancy occurrence situation for three rooms A, B, and C in a certain building α. In the example shown in FIG. 2, cancellations are invited in the rooms A and C, and a vacancy period occurs. Although illustrated in FIG. 2 on a monthly basis, this period is for illustrative purposes.

  In the example shown in FIG. 2, cancellation recruitment occurs in the room A and the room C of the building α during the unit period (one month) of the vacancy rate estimation. The period from the cancellation offer to the contract occupancy (market residence period or residence period) is a period equivalent to a vacant room 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 solicitation is published becomes a vacancy period, a period obtained by adding the certain period to the solicitation publication period is defined as a market residence period T. The market residence period T depends on the property characteristic data X of the building α. The property characteristic data X includes, for example, elements such as rent, distance from the nearest station, exclusive area, distance to the nearest station, number of rooms, and frame structure. For example, the number of rooms can be estimated from the total number of floors of the building. On the other hand, the probability that the occupant α becomes a vacant room (rooms A and C in the example shown in FIG. 2) and recruits borrowers, that is, the recruitment probability also depends on the property characteristic X.

Based on such considerations, the vacancy rate estimation device 10 estimates the recruitment probability and the market retention period of the recruitment for each building by regression analysis, and estimates the vacancy rate from the obtained recruitment probability and the market retention period. To do. That is, when the market residence period (days) per 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 relevant building The vacancy rate Vr per month of
Vr = P (X) × T (X) / 30
And

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

  The regression analysis function 40 refers to the real estate rental recruitment DB 28 and determines the regression coefficient of the regression model related to the recruitment probability and market residence period for each building. For example, the regression analysis function 40 executes the regression analysis for each 21 area that divides the whole country and for each apartment and apartment. Of course, the regression analysis function 40 may execute the regression analysis on the area where the evaluation target building is located by separating the house corresponding to the apartment or the apartment. The regression analysis function 40 stores the analysis results (coefficient values of the regression model formula) 32 for each area and each apartment / apartment 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 as: Details of the explanatory variables X, LD, and TD are shown in FIG. The explanatory variable LD is introduced in order to eliminate individuality by location (for example, the difference in policy between Shibuya Ward and Chuo Ward in Tokyo). The explanatory variable TD is introduced to eliminate individuality by time (for example, the difference in prices between 2013 and 2017). The objective variable (explained variable) is the recruitment probability P (X) (monthly) for each building for the recruitment probability (formula (1)), and for the market residence period (formula (2)) , Is the natural logarithm of the number of recruitment days (the number of days from when the offer is published until the contract is concluded).

  The above formulas (1) and (2) are examples, and various modifications are possible. For example, the expression of variables may be changed to adopt a power form instead of logarithmic form, and the regression analysis model formula may be different for each area in consideration of the degree of 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 of the variables may be changed to other variables. For example, an explanatory variable (explanatory variable ED indicating a railway line) that excludes individuality due to a difference in the railway line of the nearest station may be added to the formulas (1) and (2) in the same municipality. 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, but the Yurakucho Line requires a transfer).

  The vacancy rate estimation program 30 that operates on the CPU 12 has an input screen of property information data (data corresponding to X, LD, and TD) of a building (evaluation target building) whose vacancy rate is to be evaluated on the screen of the monitor 20. Is displayed. The operator uses the keyboard 18 to enter the property information data of the evaluation target building on this input screen. The vacancy rate estimation program 30 on the CPU 12 inputs the input data to the recruitment probability / dwell period calculation function 42. The recruitment probability / dwelling period calculation function 42 reads out 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 vacates the evaluation target building for each month. The rate P (X) and the market residence period (the number of recruitment days from the start of recruitment to 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 for one month of the evaluation target building 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) × 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 that 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 evaluation target building can be excluded, and an objective value can be obtained as the vacancy rate.

  The unit period in which the vacancy is estimated from the rental housing recruitment database is set to one month. This is because, generally, the movement of the vacancy rate in the rental market is grasped in units of one month. It may be 4 months or 6 months.

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

  Functions similar to those of the first embodiment can be configured by a server / client model. FIG. 4 shows a schematic block diagram of the configuration. The client 110 is left with a recruitment probability / dwelling period calculation function 142 corresponding to the recruitment probability / dwelling period calculation function 42 and a vacancy rate calculation function 144 corresponding to the vacancy rate calculation function 44, and the server 150 has a regression analysis function. The 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 accesses the rental housing recruitment database 128 periodically or intermittently, and performs logistic regression analysis of the recruitment probability for each building and market retention under the same regression model formula as in the first embodiment. A 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 through the communication device 152, and, similar to the regression analysis function 40, the recruitment probability and market for each building by 20 areas that divide the country, and by apartment / condominium. Perform a regression analysis of the dwell period. The regression analysis function 140 stores in the hard disk 154 analysis results (coefficient values of regression model formulas) 156 for each area and apartment / apartment.

  In the client 110, the vacancy rate estimation program (client part) operating on the CPU 112 is displayed on the monitor 120 screen on the property information data (X, LD, TD) of the building (evaluation target building) whose vacancy rate is to be evaluated. Display the corresponding data input screen. The operator inputs the property information data of the evaluation target building using the keyboard 118 on this input screen. The vacancy rate estimation program on the CPU 112 inputs the input data to the recruitment probability / staying 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 / staying period calculation function 142.

  Similar to the recruitment probability / dwelling period calculation function 42, the recruitment probability / staying 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 vacancy rate P (X) and the market residence period T (X) for each are calculated and supplied to the vacancy rate calculation function 144.

As with the vacancy rate calculation function 44, the vacancy rate calculation function 144 is based on the vacancy rate P (X) and the market residence period T (X) from the recruitment probability / residence period calculation function 142, and one month of the evaluation target building. The vacancy rate Vr around
Vr = P (X) × T (X) / 30
To calculate.

  The regression analysis may be performed on an area including the location of the evaluation target building when necessary. FIG. 5 shows a schematic block diagram of a configuration in which the embodiment shown in FIG. Constituent elements similar to those shown in FIG.

  The vacancy rate estimation program 230 operating on the CPU 212 of the vacancy rate estimation apparatus 210 shown in FIG. 5 is displayed on the screen of the monitor 20 with the location of the building (evaluation target building) for which the vacancy rate is to be evaluated and property information data ( (Data corresponding to X, LD, TD) is displayed. The operator uses the keyboard 18 to input the location of the evaluation target building 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) to the regression analysis function 240, and inputs the input property information data to the recruitment probability / staying period calculation function 42.

  The regression analysis function 240 accesses the rental housing recruitment database 28 via the communication device 24, and performs logistic regression analysis of the recruitment probability for each building and multiple regression analysis of the residence period for each apartment and condominium in the area including the location of the evaluation target building. Execute. 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 / staying period calculation function 42, the recruitment probability / staying 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 Monthly vacancy rate P (X) and market residence period T (X) are calculated. The vacancy rate calculating function 244 follows the same calculation formula as the vacancy rate calculating function 44, and the vacancy rate is calculated from the vacancy rate P (X) from the recruitment probability / staying period calculating function 242 and the market staying period T (X). Vr is calculated.

  In this embodiment, since the calculation is executed only for the necessary area, a desired result can be obtained in a relatively short time even with a small processing capacity.

  In each of the embodiments described above, 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, and 240 can be replaced with supervised machine learning. That is, the regression analysis functions 40, 140, and 240 can be replaced with an analysis function that determines the quantitative relationship between the property property, the recruitment period, and the residence period by supervised machine learning.

  Although the invention has been described with reference to specific illustrative embodiments, various modifications and alterations may be made to the above-described embodiments without departing from the scope of the invention as defined in the claims. This is obvious to an engineer 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 / staying 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 / staying 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 / staying period calculation function 244: Vacancy rate calculation function

Claims (18)

An input means for inputting property characteristic data (X) related to the evaluation target building;
Analyzing means (40, 140) for analyzing the recruitment probability and market residence period of each rented house by referring to the rental house recruitment database (28, 128);
By applying the property characteristic data (X) of the evaluation target building to the analysis results (32, 156) of the analysis means, the recruitment probability P (X) and the market residence period T ( X) recruitment probability / dwelling period calculation means (42, 142);
Vacancy rate calculating means (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 vacancy rate estimation device characterized by comprising: The analysis means (140) is arranged in the server (150),
2. The vacancy according to claim 1, wherein the recruitment probability / staying period calculation means (142) and the vacancy rate calculation means (144) are arranged in a client (110) connectable to the server. Rate estimation device. The analysis means (40, 140) performs a regression analysis of the recruitment probability and the market residence period for each rented house in each of a plurality of areas,
The solicitation probability / staying period calculation means (42, 142) indicates the property of the evaluation target building in 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 about the building to be evaluated;
An analysis means (240) for referring to the rental housing recruitment database (28), and analyzing the recruitment probability and market residence period of each rental housing in the area including the location of the evaluation target building;
By applying the property characteristic data (X) of the evaluation target building to the analysis result of the analysis means, the recruitment probability P (X) and market residence period T (X) of the evaluation target building are calculated. Recruitment probability / dwelling period calculation means (242),
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 comprising:   The analysis means is a regression analysis means (40, 140) that performs a regression analysis of the recruitment probability and market residence period of each rental house by referring to the rental house 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, wherein   6. The vacancy rate estimation apparatus according to claim 5, wherein the predetermined regression model formula includes an explanatory variable (LD) relating to location and an explanatory variable (TD) relating to time. An input step for inputting property characteristic data (X) related to the evaluation target building;
An analysis step (40, 140) of analyzing a rental unit recruitment probability and a market residence period of the rental housing by referring to the rental housing recruitment database (28, 128) using a computer;
By applying the property characteristic data (X) of the evaluation target building to the analysis result (32, 156) of the analysis step, the recruitment probability P (X) and market residence period T of the evaluation target building Recruitment probability / dwelling period calculation step (42, 142) for calculating (X);
Vacancy rate calculation step (44, 144) of 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 method characterized by comprising: The server (150) executes the analysis step (140),
The vacancy rate according to claim 7, wherein the client (142) connectable to the server executes the recruitment probability / dwelling period calculation step (142) and the vacancy rate calculation step (144). Estimation method. The analysis step (40, 140) performs regression analysis on the recruitment probability and the market residence period for each rented housing unit for each of a plurality of areas.
The recruitment probability / staying 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 of inputting property characteristic data (X) and location information about the building to be evaluated to 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 the rental housing in the area including the location of the evaluation target building;
The computer applies the property characteristic data (X) of the evaluation target building to the analysis result of the analysis step, so that the recruitment probability P (X) and the market residence period T (X ) To calculate the recruitment probability / staying period calculating step (242),
Vacancy rate calculating 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 comprising:   The analysis step is a step (40, 140, 240) of regression analysis of the recruitment probability and the market residence period of each rented house by referring to the rental house 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, wherein:   The vacancy rate estimation method according to claim 11, wherein the predetermined regression model formula includes an explanatory variable (LD) related to location and an explanatory variable (TD) related to time. On the computer,
An analysis function (40, 140) for analyzing the recruitment probability and market residence period of each rented house by referring to the rental house recruitment database (28, 128);
By applying the property characteristic data (X) relating to the evaluation target building to the analysis result (32, 156) of the analysis function, the recruitment probability P (X) and market residence period T (X) of the evaluation target building Recruitment probability and residence period calculation function (42, 142) for calculating
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 )
Computer program for realizing. A program for causing the server (150) to realize the analysis function;
14. The computer program according to claim 13, comprising a program that allows the client connectable to the server to realize the recruitment probability / staying period calculation function (142) and the vacancy rate calculation function (144). . The analysis function (40, 140) performs a regression analysis of the recruitment probability and the market residence period of each rented housing unit for each of a plurality of areas,
The offer probability / dwelling period calculation function (42, 142) indicates that the property of the evaluation target building is included in the analysis result (32) of the analysis means for the area where the evaluation target building is located among the plurality of areas. 15. The computer program according to claim 13, wherein the characteristic data (X) is applied. On the computer,
An analysis function (240) for referring to the rental housing recruitment database (28) to analyze the recruitment probability and market residence period of the rental housing in the area including the location of the evaluation target building;
By applying the property characteristic data (X) of the evaluation target building to the analysis result of the analysis function, the recruitment probability P (X) and market residence period T (X) of the evaluation target building are calculated. Recruitment probability / dwelling period calculation function (242),
Vacancy rate calculation function (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
Computer program for realizing.   The analysis function is a regression analysis function (40, 140) that performs regression analysis on the recruitment probability and market residence period of each rented house 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, wherein   The computer program according to claim 17, wherein the predetermined regression model formula includes an explanatory variable (LD) relating to location and an explanatory variable (TD) relating to time.

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