Device and method for estimating rent changes in subway-accessible areas

Abstract

An apparatus for performing location analysis based on a station area is disclosed. The apparatus includes a rent information collection unit configured to collect rent information for lease contracts in which actual transactions have taken place, a converted rent calculation unit configured to calculate a converted rent for each lease contract based on the collected rent information, and a location analysis unit configured to estimate the rate of change in rent of a target station area based on location information of the target station area, location information of a reference station area adjacent to the target station area, and the rate of change in rent of the reference station area, for a target station area for which actual transaction price data is unavailable.

Description

Technology Field

[0001] Embodiments of the present invention relate to an apparatus for performing station area-based location analysis and a method of operating the same. Background Technology

[0002] Generally, in the real estate rental market, the monthly rent of a property is determined through negotiation between the parties, and market prices can be identified through actual transaction data. However, for properties where actual transaction data is unavailable, there is a problem in objectively calculating an appropriate monthly rent. In particular, in areas with excellent transportation accessibility, such as those near subway stations, rents vary significantly depending on location conditions; therefore, reliable price prediction is difficult using methods that rely on experience or intuition.

[0003] Furthermore, most currently available rental information services provide data only on an individual property basis and have limitations in offering map-based information that allows for a visual and intuitive understanding of rental rates at the regional level. In particular, despite the increasing demand for technology that comprehensively represents rental rates by analyzing spatial data based on specific conditions such as proximity to subway stations, the technology adequately responding to this demand remains insufficient.

[0004] Therefore, technology is required to estimate the rate of change in rent based on various real estate-related information, even for properties for which actual transaction data is unavailable. The problem to be solved

[0005] The objective of the present invention, which aims to solve the above-mentioned problems, is to provide a device capable of displaying a rent map that visually represents the monthly rent of a station area and estimating the rate of change in rent for each station area, and a method of operating the same. means of solving the problem

[0006] An apparatus for performing location analysis based on a station area according to embodiments of the present invention comprises: a rent information collection unit configured to collect rent information for lease contracts in which actual transactions have taken place; a converted rent calculation unit configured to calculate a converted rent for each lease contract based on the collected rent information; and a location analysis unit configured to estimate the rate of change in rent of a target station area based on location information of the target station area, location information of a reference station area adjacent to the target station area, and the rate of change in rent of the reference station area, for a target station area for which actual transaction price data is unavailable. Effects of the invention

[0007] According to embodiments of the present invention, it is possible to display a rent map that visually represents the monthly rent of a station area, and to estimate the rate of change in rent for each station area. Brief explanation of the drawing

[0008] FIG. 1 shows an apparatus for performing location analysis according to embodiments of the present invention. FIG. 2 shows rental fee information collected according to embodiments of the present invention. FIG. 3 illustrates the process of calculating the converted rent according to embodiments of the present invention. FIGS. 4 and FIGS. 5 are drawings for illustrating a rental map according to embodiments of the present invention. FIG. 6 is a drawing for explaining a location analysis unit according to embodiments of the present invention. FIG. 7 is a diagram illustrating the hardware configuration of an operating server according to embodiments of the present invention. Specific details for implementing the invention

[0009] The present invention is susceptible to various modifications and may have various embodiments; specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0010] Terms such as first, second, A, B, etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0011] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0012] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0013] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0014] The server referred to in the present invention may be constructed as a server performing at least one of the roles of a web server, a database server, and a mobile server; for example, it may display processed results on a webpage via an online network or receive necessary input data through a webpage. Here, a webpage should be understood as a page that includes text, images, sound, and video, as well as a page where software for performing specific tasks, such as a web application, is loaded. Furthermore, the server may perform at least one of the functions of a web application server, a web server, a mobile server, and a database server on a single physical server, or it may be composed of and operated by multiple physically separated servers. However, it is not limited thereto, and the type of server can be varied to a level obvious to a person skilled in the art.

[0016] Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings.

[0017] FIG. 1 illustrates an apparatus for performing location analysis according to embodiments of the present invention. Referring to FIG. 1, the apparatus (100) for performing location analysis can generate a rent map showing the average rent of each region. At this time, the average rent is based on the rent of residential buildings (e.g., officetels or apartments, etc.) within the region, and may be the average rent of residential buildings, but is not limited thereto.

[0018] An apparatus (100) for performing location analysis according to embodiments of the present invention can divide a predetermined area (e.g., Seoul) into station areas adjacent to at least one subway station, calculate the average rent of each station area, and generate a rent map indicating the station area and the average rent. In particular, the apparatus (100) for performing location analysis can not only generate a rent map based on rent information of lease contracts actually traded for buildings within each station area (i.e., actual transaction price information), but also has the effect of estimating the appropriate average rent of a station area based on actual transaction price information of buildings within surrounding station areas, even for station areas where the latest actual transaction price information is unavailable.

[0019] Meanwhile, in the present specification, the term "station area" refers to an area within a predetermined distance from at least one subway station, and, for example, may refer to an area located within 500m from at least one subway station. In addition, such station areas may be designated and partitioned in advance, and a device (100) for performing location analysis may identify each station area by utilizing such station area partitioning information.

[0020] The device (100) for performing location analysis is a device having computational processing capabilities, and may be, for example, a computing device including a processor and memory (e.g., a server), but is not limited thereto.

[0021] A device (100) for performing location analysis includes a rent information collection unit (110), a converted rent calculation unit (120), a rent map generation unit (130), and a location analysis unit (140).

[0022] The rent information collection unit (110) collects rent information from lease contract information of buildings (officetels or apartments) located in a station area within a designated region. According to embodiments, the rent information collection unit (110) may collect actual transaction price data from the Ministry of Land, Infrastructure and Transport of the Republic of Korea. The actual transaction price data includes information regarding the address, landlord, tenant, lease contract period, rent, etc., of the buildings where actual lease contracts have been concluded.

[0023] According to embodiments, the rent information collection unit (110) may collect rent information including information on the transaction date, address, detailed address, building type, exclusive area, deposit, and monthly rent as illustrated in FIG. 2. Here, building type refers to information indicating whether the building is an officetel or an apartment.

[0024] For example, the rent information collection unit (110) can collect more than 400,000 actual transaction price data around 642 stations in the metropolitan area.

[0025] According to the embodiments, the rent information collection unit (110) can collect actual transaction price data for a predetermined period (e.g., 2 years).

[0026] The converted rent calculation unit (120) can calculate the converted rent for each lease contract using rent information collected from the rent information collection unit (110).

[0027] Lease contracts are broadly classified into monthly rent contracts, where only monthly rent is paid, and monthly rent contracts with a security deposit, where monthly rent is paid along with a specified deposit. In the case of monthly rent contracts with a security deposit, the security deposit may vary for each lease contract, and accordingly, the monthly rent paid together may also vary. Therefore, it is required to calculate a monthly rent based on a specified common standard, that is, a converted rent.

[0028] The converted rent calculation unit (120) calculates the converted rent by processing the actual transaction deposit and actual transaction rent of the lease contracts in the actual transaction price data according to a predetermined method, allocating a fixed deposit (i.e., converted deposit) according to the area and type of each building, and converting the remaining deposit difference into a monthly rent according to a predetermined conversion rate and adding it to the actual rent. Through this, various lease contracts can be compared under the same conditions.

[0029] For example, since it is difficult to compare a lease agreement with a security deposit of 30 million won and a monthly rent of 1 million won with a lease agreement with a security deposit of 60 million won and a monthly rent of 800,000 won, it means that the security deposits for the two lease agreements are unified (e.g., 20 million won, etc.), and the remaining difference in security deposits is converted into a monthly rent through a predetermined conversion rate and added to the existing monthly rent.

[0030] According to embodiments, the converted rent calculation unit (120) calculates the full rent by applying a predetermined conversion rate to each actual transaction deposit and summing the accompanying actual transaction rents. Additionally, the converted rent calculation unit (120) can calculate the converted rent by subtracting the monthly rent, calculated by applying the conversion rate to the converted deposit corresponding to the building subject to the lease agreement, from the calculated full rent. This converted rent is used to generate a rent map.

[0031] For example, the converted rent calculation unit (120) can calculate the full rent according to the following mathematical formula 1 and calculate the converted rent according to the following mathematical formula 2.

[0032]

[0033]

[0034] Here, R f is the full rent, and R ct is the actual transaction rent, and D ctis the actual transaction deposit, t is a predefined deposit-to-rent conversion rate, and R con is converted rent, D con is the converted deposit. In this case, the conversion rate t and the converted deposit D con It can be defined in advance according to the building type (whether it is an officetel or an apartment) and the exclusive area of ​​each building.

[0035] Finally, the converted deposit and converted rent according to the embodiments of the present invention can be determined as shown in FIG. 3. As shown in FIG. 3, the converted deposit and converted rent may vary depending on the building type (apartment / officetel) and the building area. Referring to FIG. 3, the device (100) for performing location analysis according to the embodiments of the present invention calculates the converted rent by unifying the deposit (converted deposit) when calculating the rent of a building in each station area, and converting and summing the remaining difference into monthly rent. Through this, there is an effect of being able to compare rents based on a more common standard.

[0036] For the following, unless otherwise stated, it is assumed that the rents shown on the rent map are converted rents.

[0037] The rent map generation unit (130) can generate a rent map showing converted rents by exclusive area for each station area by using the converted rents of each actual transaction price data. For example, the rent map generation unit (130) generates a rent map showing converted rents for officetels and apartments of various exclusive areas.

[0038] The rent map generation unit (130) can generate rent map data that displays a station area map and converted rents for each station area. For example, the rent map generation unit (130) can generate rent map data that displays rents for officetels and apartments of various exclusive areas for each station area.

[0039] According to embodiments, the rent map generation unit (130) can generate rent map data based on the converted rent by exclusive area of ​​each building type (officetel / apartment) for each station area during a predetermined period. This will be described later.

[0040] The location analysis unit (140) can estimate the rate of increase in rent for each station area based on location information for each station area. The location of a station area represents the real estate value of the station area, and the better the location, the higher the likelihood that the rent will be.

[0041] The location analysis unit (140) according to the embodiments of the present invention has the effect of analyzing the location of each station area and estimating the rate of increase in rent of the station area based on the analysis results. The location analysis unit (140) can estimate the rate of increase in rent of the station area from information on factors reflecting the location of each station area.

[0042] FIGS. 4 and 5 are drawings for illustrating a rent map according to embodiments of the present invention. The rent map of FIGS. 4 and 5 may be shown based on rent map data generated by a rent map generation unit (130). For example, a certain electronic device may receive rent map data and display a rent map based on the rent map data through a display of the electronic device.

[0043] Referring to Figure 4, the rent map shows a station area map (MAP) and average rent information (RMI).

[0044] A station area map (MAP) indicates the geographical location of a station area, and for example, as shown in Fig. 4, a station area map (MAP) indicating the locations of Sinsa Station, Nonhyeon Station, Hakdong Station, etc. in the Gangnam area of ​​Seoul is displayed on the rent map.

[0045] The average rent information (RMI) represents information on the average of the converted rent by exclusive area of ​​each station area. For example, as shown in Fig. 4, the average rent information (RMI) for the average rents of each station area (e.g., Sinsa Station, Nonhyeon Station, Hakdong Station, etc.) is displayed on the rent map.

[0046] Referring to Fig. 5, the average rent information displayed on the rent map is explained in detail. Referring to Fig. 5, information on the average rent by exclusive area and period for each station area can be displayed as a table. At this time, the information on the average rent can be displayed separately by building type. At this time, for the user's visual identification, an identifier 'A' can be displayed for apartments, and an identifier 'O' can be displayed for officetels.

[0047] According to embodiments of the present invention, the rent map generation unit (130) can determine the average rent by exclusive area and by period for each of the station areas based on the converted rent calculated by exclusive area for each of the officetels and apartments within the station area, and generate average rent information (RMI). For example, the rent map generation unit (130) can generate average rent information (RMI) including the average rent for each exclusive area of ​​the 'apartment' at 'Seonjeongneung Station' during the years 2023 to 2024. At this time, information regarding the average rent may be stored in a structure that includes attribute fields of station area name, building type, period, exclusive area, and rent within a predetermined database.

[0048] According to embodiments, the rent map generating unit (130) may generate a rent map such that the average rent for each exclusive area is displayed in a different color for the user's visual identification. For example, an exclusive area of ​​20m² 3 Average rents of less than 20m² are indicated in red, and exclusive area 20m² 3 more than 50m3 Average rents of less than 50m² are displayed in green, and exclusive area 50m² 3 Over 84m 3 Average rents for less than 84m² are indicated in blue, and the exclusive area is 84m². 3 More than 135m 3 Average rents below a certain level may be displayed in black.

[0049] Accordingly, the user can use the rent map generated by the rent map generation unit (130) to check the average rent by building type and exclusive area in each station area during a specified period.

[0050] FIG. 6 is a diagram illustrating the operation of a location analysis unit according to embodiments of the present invention.

[0051] Referring to FIG. 6, the location analysis unit (140) can analyze the intrinsic and extrinsic factors of each station area and estimate the rent increase rate of the station area based on the analysis results. According to the embodiments, the location analysis unit (140) may use the rent increase rate of a reference station area that has similarity to the target station area to estimate the rent increase rate of the target station area. At this time, to estimate the rent increase rate more accurately, the location of the target station area, the location of the reference station area, and the rent increase rate of the reference station area may be used. Meanwhile, the location analysis unit (140) may calculate and utilize the rent increase rate of the reference station area by utilizing the converted rent calculated by the converted rent calculation unit (120).

[0052] Intrinsic factors refer to elements inherent to the station area itself that have accumulated over a long period. Consequently, as these factors do not change significantly over time, they can serve as a basis for supporting the downward rigidity of rental prices. For example, intrinsic factors may include the quality of the school district, population size, and topography (such as the proportion of flat land). When intrinsic factors are similar, the trends in the rise or fall of rental prices are likely to be similar.

[0053] Extrinsic factors are those located outside the station area and may include external policies, economic conditions, or favorable developments. In many cases, extrinsic factors only affect the rise or fall of rents within the station area where they are present.

[0054] Considering the characteristics of the extrinsic and intrinsic factors mentioned above, the location analysis unit (140) according to the embodiments of the present invention has the effect of estimating the rate of increase (or rate of decrease) of rent in the target station area. In particular, the location analysis unit (140) has the effect of estimating the rate of change in rent in the target station area by using the rate of increase (or rate of decrease) of rent in the reference station area adjacent to the target station area, even for station areas (target station areas) where it is difficult to calculate a reliable rate of increase (or rate of decrease) of rent because actual transactions have not yet been sufficiently carried out. Here, the reference station area refers to a station area located within a predetermined distance (e.g., 500m) of the target station area, but is not limited thereto.

[0055] Estimating the rise or fall of rent requires considering various variables, and there is a problem in that accurate estimation is difficult. In particular, accurately estimating the rent (or rate of change) for a specific type of area is a very difficult problem. On the other hand, compared to estimating the rent for a specific type of area, estimating the overall rent (or rate of change) of the station area may be within a relatively feasible range. Accordingly, the embodiments of the present invention have the effect of estimating the rate of change in rent for a desired station area through data on the actual rate of change in rent of surrounding station areas.

[0056] As illustrated in FIG. 6, the location analysis unit (140) uses the rate of change in rent of another reference station area (station area B) with actual transaction price information to calculate the rate of change in rent of the target station area (station area A). Furthermore, the location analysis unit (140) uses the intrinsic factors of the target station area and the reference station area, and the extrinsic factors of the target station area.

[0057] Specifically, the location analysis unit (140) calculates the rate of change in rent of the target station area (station area A) based on the rate of change in rent of the reference station area (station area B), the intrinsic factors of the target station area and the reference station area, and the extrinsic factors of the target station area. In the case of intrinsic factors, they are factors inherent in the station area and play a role in supporting rent, so the relevance of intrinsic factors can be reflected as a weight in the rent. On the other hand, in the case of extrinsic factors, since they are factors that are specifically reflected only for the station area, the extrinsic factors of the reference station area are not considered when estimating the rate of change in rent of the target station area.

[0058] According to embodiments of the present invention, the location analysis unit (140) considers the school district level as an example of an intrinsic factor of each station area. According to embodiments, the location analysis unit (140) estimates the rate of change in rent of the target station area according to the following mathematical formula 3.

[0059]

[0060] Here, G is the rate of change in rent of the target station area, and G ref,i is the rate of change in rent of the i-th reference station area, and W reg,i is a regional weight representing the relationship between the i-th reference station area and the target station area, and W sch,iis a school district weight representing the relationship between the school district level of the i-th reference station area and the school district level of the target station area, M is the total number of reference station areas, and K is a positive constant used to adjust the value of G, which is calculated as a form of an average. E is a value reflecting the growth rate due to extrinsic factors and is referred to as the external growth rate. Meanwhile, in some cases, if extrinsic factors are not considered, E=0.

[0061] For example, if K is M, G is the simple arithmetic mean, and K is If so, G is calculated as a weighted average.

[0062] Local weight W reg,i is a value representing the relationship between the topographical conditions of the target station area and the topographical conditions of the reference station area, and can be determined in advance. Alternatively, the regional weight is W reg,i It can be defined according to the following mathematical formula 4.

[0063]

[0064] Here, a and b are positive constants, p is the daily average number of passengers boarding and alighting at the subway station corresponding to the target station area, and p i is the daily average number of passengers boarding and alighting at the subway station corresponding to the i-th reference station area. Referring to Equation 4, the higher the ratio of the number of passengers boarding and alighting at the target station area to the number of passengers boarding and alighting at the reference station area, the higher the value of the target station area as a station area, so W reg,i The value exceeds 1, and this value is proportional to that ratio.

[0065] In addition, school district weighting W sch,i It can be defined according to the following mathematical formula 5.

[0066]

[0067] Here, SCH is the school district index of the target station area, and SCH iis the school district index of the i-th reference station area, normalized to values ​​between 0 and 1, x, y, and z are positive constants, and w is the representative value (mean, median) of the school district index of the reference station area. Referring to Equation 5, the school district weight W sch,i It is designed so that the value changes significantly when the school district index of the target station area and the reference station area is in the middle range. This is because, by nature, the level of school districts differs significantly in the middle range, and market consumers are also sensitive to this.

[0068] Meanwhile, the school district index SCH, SCH i This represents the school district of each station area as a value between 0 and 1. This may be a predetermined value for each station area.

[0070] FIG. 7 is a diagram illustrating the hardware configuration of an operating server according to embodiments of the present invention. The electronic device (300) of FIG. 7 represents a device (100) for performing location analysis as described with reference to FIG. 1 to 6.

[0071] Referring to FIG. 7, the electronic device (300) may include at least one processor (310) and a memory (320) that stores instructions that instruct the at least one processor (310) to perform at least one operation.

[0072] The above at least one operation is interpreted to include at least one of the operations of the aforementioned electronic device (300) or the operations of the functional part, and a specific description is omitted to prevent redundant explanation.

[0073] Here, at least one processor (310) may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed.

[0074] The memory (320) may be composed of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory (320) may be composed of at least one of a read-only memory (ROM) and a random access memory (RAM).

[0075] Additionally, the electronic device (300) may include a transceiver (330) that performs communication via a wireless network. Additionally, the electronic device (300) may further include an input interface device (340), an output interface device (350), a storage device (360, which may be referred to interchangeably with internal storage), etc. Each component included in the electronic device (300) may be connected by a bus (370) to communicate with one another.

[0076] The methods according to the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the computer-readable medium may be those specifically designed and configured for the present invention, or they may be those known and available to those skilled in the art of computer software.

[0077] Examples of computer-readable media may include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions may include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The aforementioned hardware devices may be configured to operate as at least one software module to perform the operation of the present invention, and vice versa.

[0078] In addition, the above-described method or device may be implemented by combining all or part of its configuration or function, or by implementing it separately.

[0079] Although the present invention has been described above with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as described in the following claims.

Claims

Claim 1 An apparatus for performing location analysis based on a station area comprises: a rent information collection unit configured to collect rent information for lease contracts in which actual transactions have taken place; a converted rent calculation unit configured to calculate a converted rent for each lease contract based on the collected rent information; and a location analysis unit configured to estimate the rent change rate of the target station area based on location information of the target station area, location information of a reference station area adjacent to the target station area, and the rent change rate of the reference station area, for a target station area for which actual transaction price data is unavailable, wherein the location analysis unit estimates the rent change rate of the target station area according to the following Equation 1 based on the rent change rate of the reference station area, a regional weight representing the relationship between the reference station area and the target station area, and a school district weight representing the relationship between the school district level of the reference station area and the school district level of the target station area, [Equation 1] (In the above mathematical formula 1, G is the rate of change in rent of the target station area, and G ref,i is the rate of change in rent of the i-th reference station area, and W reg,i is a regional weight representing the relationship between the i-th reference station area and the target station area, and W sch,i is a school district weight representing the relationship between the school district level of the i-th reference station area and the school district level of the target station area, M is the total number of reference station areas, K is a positive constant used to adjust the value of G calculated as a form of average, E is a value reflecting the growth rate due to extrinsic factors, referred to as the external growth rate, and E=0 when extrinsic factors are not considered) The above regional weight is defined according to the following mathematical formula 2, [Mathematical Formula 2] (In the above mathematical formula 2, a and b are positive constants, p is the daily average number of passengers boarding and alighting at the subway station corresponding to the target station area, and p i (is the daily average number of passengers boarding and alighting at the subway station corresponding to the i-th reference station area) A device for performing station area-based location analysis. Claim 2 delete Claim 3 delete

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