System for estimating floor area ratio and business sales revenue on basis of ai

Abstract

The present invention relates to a system for estimating a floor area ratio and a business sales revenue on the basis of AI. According to the present invention, a system for estimating a floor area ratio and a business sales revenue on the basis of AI comprises: a land information input unit for receiving, from a user terminal, a location and a development purpose of a target lot subject to development; a floor area ratio prediction unit for predicting a floor area ratio of the target lot on the basis of the location and land information of the target lot; a presale price prediction unit for predicting a presale price per pyeong (3.3058 m²) of the target lot by applying, to an artificial intelligence-based prediction model, siting environment data of the target lot and actual transaction price data of surrounding buildings selected in consideration of the location and the development purpose of the target lot; a sales revenue estimation unit for calculating an expected sales revenue of the target lot by using the predicted floor area ratio and the presale price per pyeong; and an analysis result output unit for outputting an analysis result including the expected sales revenue for the target lot.

Description

AI-based floor area ratio and business revenue estimation system

[0001] The present invention relates to an AI-based floor area ratio and business revenue estimation system, and more specifically, to an AI-based floor area ratio and business revenue estimation system capable of automatically estimating and providing the floor area ratio and expected revenue for a said plot by analyzing a target plot input through a user terminal based on artificial intelligence.

[0002] Real estate involves a very large scale of capital and is entangled with various regulations related to development. Consequently, real estate developers and investors must consider a vast number of factors when proceeding with a project, and there is a problem in that losses can be substantial if elements not considered at the initial stage are discovered belatedly.

[0003] Therefore, real estate development projects require a more thorough review considering regulatory conditions compared to other businesses.

[0004] Traditionally, real estate developers and investors have individually collected information on properties and directly reviewed their marketability and investment potential through their own experience and know-how, and they also frequently commissioned comprehensive real estate analysis reports from external firms.

[0005] However, in the case of direct review methods, the content of the analysis varies depending on the level of experience and expertise, while outsourcing poses a problem due to the significant cost burden. Therefore, there is a need for the development of technology that utilizes big data and artificial intelligence to enable rapid and professional initial business feasibility reviews for business promotion decisions, and to generate business feasibility analysis reports more objectively and quickly.

[0006] The technology forming the background of the present invention is disclosed in Korean Published Patent No. 10-2023-0045251 (published April 4, 2023).

[0007] The present invention aims to provide an AI-based floor area ratio and business revenue estimation system capable of analyzing a target plot input by a user based on artificial intelligence to automatically estimate and provide the floor area ratio and expected revenue for the project.

[0008] The present invention relates to an AI-based floor area ratio and business revenue estimation system, comprising: a land information input unit that receives the location and development use of a target plot to be developed from a user terminal; a floor area ratio prediction unit that predicts the floor area ratio of the target plot based on the land information and location of the target plot; a sale price prediction unit that predicts the sale price per pyeong of the target plot by applying actual transaction price data of surrounding buildings selected considering the location and development use of the target plot and location environment data of the target plot to an AI-based prediction model; a sales revenue estimation unit that calculates the expected sales revenue when the business is promoted for the target plot using the predicted floor area ratio and the sale price per pyeong; and an analysis result output unit that outputs an analysis result including the expected sales revenue for the target plot.

[0009] In addition, the land information input unit can receive a selection of an area corresponding to the target parcel on a cadastral map provided to the user terminal, recognize the location of the target parcel, and acquire land information corresponding to the recognized location from the land information DB.

[0010] In addition, the floor area ratio prediction unit can select a floor area ratio that matches the zone and development use to which the target plot belongs from a legal floor area ratio DB that defines floor area ratios based on zone units and land use for each region, and estimate the selected floor area ratio as the floor area ratio of the target plot.

[0011] In addition, the above-mentioned sales price prediction unit can predict the above-mentioned sales amount by using actual transaction price information within two years of surrounding buildings of the same use located within a 1km radius from the above-mentioned target plot.

[0012] In addition, the above location environment data may include at least one evaluation score among the traffic environment, residential convenience facility environment, workplace environment, and educational environment for each distance radius based on the location of the target plot.

[0013] In addition, the actual transaction price data of the surrounding buildings may include a sales price level calculated by the ratio of the average actual transaction price per pyeong over the past two years for surrounding buildings of the same use located within a 1km radius of the target plot to the maximum actual transaction price per pyeong.

[0014] In addition, the above-mentioned sale price prediction unit can predict the sale price per pyeong of the above-mentioned target plot by additionally applying the evaluation scores for each additional point factor and deduction factor evaluated for the above-mentioned target plot to the above-mentioned prediction model.

[0015] In addition, the sales revenue estimation unit can calculate the gross floor area by multiplying the predicted floor area ratio by the land area of ​​the target plot, and then calculate the expected sales revenue of the target plot by multiplying the gross floor area by the predicted sale price per pyeong.

[0016] According to the present invention, a target plot input through a user terminal can be analyzed based on artificial intelligence to automatically estimate and provide the floor area ratio and expected sales revenue for the said plot when the project is promoted.

[0017] Figure 1 is a diagram showing the configuration of an AI-based floor area ratio and business revenue estimation system.

[0018] Figure 2 is a diagram that explains in detail the configuration of the floor area ratio and business sales revenue estimation system shown in Figure 1.

[0019] Figures 3 and 4 are drawings showing an example of receiving input for the location and development use of a target plot.

[0020] Figure 5 is a diagram conceptually showing the floor area ratio, which is defined differently depending on the region and use.

[0021] Figure 6 is a diagram exemplifying the results of the location analysis for a target plot.

[0022] Then, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the invention. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification have been given similar reference numerals.

[0023] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "electrically connected" with other components interposed between them. Furthermore, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0024] Figure 1 is a diagram showing the configuration of an AI-based floor area ratio and business revenue estimation system.

[0025] As shown in FIG. 1, the AI-based volume ratio and business revenue estimation system (100) according to an embodiment of the present invention can be connected to at least one user terminal (200) via a wired, wireless, or wired-wireless combined network to transmit and receive information to and from each other. The wireless network may include at least one of RF, WLAN, Wi-Fi, and Bluetooth methods, and various known wireless network methods may be used.

[0026] The floor area ratio and business revenue estimation system (100) may be implemented as an online / offline platform, such as a web server or app server, that provides a floor area ratio and business revenue estimation service for a target plot to a network-connected user terminal (200), or may be implemented in the form of an application, such as an application, on the user terminal, etc.

[0027] The floor area ratio and business revenue estimation system (100) can provide a service platform implemented as an application or web to a network-connected user terminal (200). The service platform may be an application running in an application or web environment.

[0028] In this way, the floor area ratio and business revenue estimation system (100) of the present invention may be implemented as an application program executed on a platform server or user terminal (200) that provides a floor area ratio and business revenue estimation service for a target plot, and the user terminal (200) may be connected to the system (100) via a network while the relevant application program is running to receive the relevant service.

[0029] The user terminal (200) may include a device capable of exchanging information by connecting to a wired or wireless network, such as a PC, desktop, smartphone, tablet, or notebook.

[0030] The system of the present invention, as described above, can rapidly analyze and present the business feasibility of a target plot selected by a user based on AI. More specifically, it analyzes the business feasibility of a target plot input by the user based on AI to automatically estimate the floor area ratio and expected revenue upon project implementation, and can intuitively provide a report on the analysis results.

[0031] Figure 2 is a diagram that explains in detail the configuration of the floor area ratio and business sales revenue estimation system shown in Figure 1.

[0032] As shown in FIG. 2, a floor area ratio and business sales revenue estimation system (100) according to an embodiment of the present invention includes a land information input unit (110), a floor area ratio prediction unit (120), a sale price prediction unit (130), a sales revenue estimation unit (140), and an analysis result output unit (150). Here, the operation of each unit (110 to 150) and the data flow between each unit can be controlled by a control unit (not shown) corresponding to a processor within the system.

[0033] The system for estimating the volume ratio and business sales revenue (100) may be physically configured and implemented as a computer device including a processor, memory, user interface input / output device and storage device, network input / output unit, etc., or may be implemented as an application program running on a computer device or user terminal.

[0034] In addition, the floor area ratio and business revenue estimation system (100) may be linked with a legal floor area ratio DB (10), a land information DB (20), and a building information DB (30) built on a network-connected external server, and may be implemented including the legal floor area ratio DB (10), the land information DB (20), and the building information DB (30).

[0035] The land information input unit (110) can receive the location and development use of the target plot to be developed from the user terminal (200). Here, the development use may be one selected from row / multi-family housing, apartment / multi-unit housing, neighborhood living facilities, officetels, mixed-use, business / industrial housing.

[0036] The land information input unit (110) can recognize the location of a target parcel by receiving a designated area corresponding to the target parcel on a cadastral map provided to the user terminal (200) or by receiving the address of the target parcel as text, and can retrieve land information corresponding to the location of the recognized target parcel from the land information DB (20).

[0037] Figures 3 and 4 are drawings showing an example of receiving input for the location and development use of a target parcel. The right side of Figure 3 shows the area of ​​the target parcel to be developed selected by the user on a cadastral map, while the left side shows the display of land information corresponding to the selected parcel. Of course, the selection of the target parcel can be performed not only by the area designation method but also by the text input method of the desired address.

[0038] That is, the land information input unit (110) can receive a target parcel by clicking or selecting an area corresponding to the target parcel through a user terminal (as shown in FIG. 3) or by directly inputting the address information. In addition, the land information input unit (110) can receive a development use for the selected parcel from the user terminal (200) as shown in FIG. 4.

[0039] Next, the floor area ratio prediction unit (120) can predict the floor area ratio of the target plot based on the land information and location of the target plot. To do this, the floor area ratio prediction unit (120) can utilize information from the legal floor area ratio DB (10).

[0040] Specifically, the floor area ratio prediction unit (120) can select a floor area ratio that matches the zone and development use to which the target plot belongs from a legal floor area ratio DB (10) that defines the floor area ratio based on zone units and land use for each region, and can estimate the selected floor area ratio as the floor area ratio of the target plot.

[0041] FIG. 5 is a diagram conceptually showing the floor area ratio that is defined differently depending on the region and use. As such, the embodiment of the present invention can verify the floor area ratio value corresponding to the location and use of the target project site by considering the cluster profile. In addition, the legal floor area ratio DB (10) can provide floor area ratio matching data based on clustering results obtained through statistical analysis of floor area ratio data of previously constructed buildings, rather than a simple DB concept. Here, since regulations related to floor area ratio are elements that can be changed by policy, real-time legal data can be utilized for floor area ratio analysis.

[0042] Next, the sale price prediction unit (130) can predict the sale price per pyeong of the target plot by applying the actual transaction price data of surrounding buildings selected considering the location and development use of the target plot and the location environment data of the target plot to an artificial intelligence-based prediction model.

[0043] At this time, the price prediction unit (130) can predict the price per pyeong of the target plot by focusing on actual transaction price data within 2 years of surrounding buildings of the same use located within a 1km radius of the target plot or by applying high weights to these data, and based on this, the reliability of the prediction model can be increased by considering proximity and recent sales prices.

[0044] According to this, the sale price can be efficiently predicted based on the actual transaction price of the same product (apartment, commercial building, officetel, complex, etc.) within a radius of 1 km of the address entered from the user terminal (200).

[0045] Here, the prediction model can be implemented by including various known machine learning algorithms, such as linear regression analysis algorithms and deep neural network (DNN) algorithms. In addition, the prediction model can be pre-trained based on historical big data regarding building usage by plot in each city, actual transaction prices per pyeong, surrounding location environment information, and actual sales revenue achieved during business operations, and can be continuously updated.

[0046] The data used for predicting the sale price can utilize the stored data of the building information DB (30). The building information DB (30) may include big data regarding the actual transaction price of buildings located at each plot, surrounding location environment, etc., based on map information.

[0047] Here, the location environment data used for the sale price prediction may include at least one evaluation score among the traffic environment, residential convenience facility environment, workplace environment, and educational environment for each distance radius based on the location of the target plot.

[0048] Figure 6 is a diagram illustrating an exemplary summary table of location analysis for a target plot. Figure 6 shows the results of counting transportation (subway, bus stop), residential amenities (mart / department store, park, government office, hospital), education (school), workplace, etc. by radius, and the data scored according to the setting rules can be used as input values ​​for a prediction model.

[0049] In addition, the actual transaction price data of surrounding buildings used for predicting the presale price may include a sales price level value calculated by the ratio obtained by dividing the average actual transaction price per pyeong over the past two years for surrounding buildings of the same use located within a 1km radius of the target plot by the maximum actual transaction price per pyeong. In other words, by dividing the average actual transaction price per pyeong of nearby buildings of the same use as the target plot by its maximum value, one can determine the sales price level per pyeong of the adjacent area within a 1km radius of the target plot. This sales price level can be utilized as an important parameter for predicting the presale price of the target plot.

[0050] Meanwhile, the presale price prediction unit (130) can predict the presale price per pyeong of the target plot by applying the actual transaction price data of surrounding buildings and location environment data, as well as additional evaluation scores for the bonus and penalty factors calculated for the target plot, to the prediction model. Items acting as bonus and penalty factors can be set in advance.

[0051] For example, factors for adding points may include the size of the complex, adjacent roads, nearby landmarks, large parks, and information on the contractor's construction capabilities, while factors for deducting points may include the age of existing buildings on the target plot.

[0052] Next, the sales estimation unit (140) can calculate the expected sales of the target plot using the predicted floor area ratio and the sale price per pyeong.

[0053] More specifically, the sales revenue estimation unit (140) calculates the total floor area of ​​the target plot by multiplying the previously predicted floor area ratio by the land area of ​​the target plot. At this time, the land area value of the target plot is a value that can be easily obtained from the land information DB (20). Afterwards, the expected sales revenue at the time of business promotion of the target plot can be calculated by multiplying the calculated total floor area by the previously predicted sale price per pyeong.

[0054] The analysis result output unit (150) can output analysis results including expected sales for the target plot. The analysis results may be provided in the form of a report summarizing not only the expected sales of the target plot, but also land information of the target plot, location environment information, and recent actual transaction price information of nearby buildings in the form of graphs, tables, text, etc.

[0055] According to the present invention, it is possible to analyze and provide the expected sale price, business revenue, and business cost components for a desired plot according to type, such as small-scale construction, redevelopment, mixed-use development, or commercial development.

[0056] The present invention has been described with reference to embodiments illustrated in the drawings, but this is merely illustrative, and those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible therefrom. Accordingly, the true technical scope of protection of the present invention should be determined by the technical spirit of the appended claims.

Claims

1. In an AI-based floor area ratio and business revenue estimation system, A land information input unit that receives the location and development use of a target plot to be developed from a user terminal; A floor area ratio prediction unit that predicts the floor area ratio of a target plot based on the land information and location of the target plot; A presale price prediction unit that predicts the presale price per pyeong of the target plot by applying actual transaction price data of surrounding buildings selected considering the location and development use of the target plot and location environment data of the target plot to an artificial intelligence-based prediction model; A sales revenue estimation unit that calculates the expected sales revenue when the project of the target plot is promoted using the predicted floor area ratio and the sale price per pyeong; and A system for estimating floor area ratio and business revenue, comprising an analysis result output unit that outputs analysis results including expected revenue for the above-mentioned target plot.

2. In Claim 1, The above land information input section is, A system for estimating floor area ratio and business revenue, which receives a selection of an area corresponding to the target parcel on a cadastral map provided to the user terminal, recognizes the location of the target parcel, and acquires land information corresponding to the recognized location from a land information DB.

3. In Claim 1, The above-mentioned volume ratio prediction unit is, A system for estimating floor area ratio and business revenue that selects a floor area ratio matching the zone and development use to which a target plot belongs from a legal floor area ratio database that defines floor area ratios based on zone units and land use for each region, and estimates the selected floor area ratio as the floor area ratio of the target plot.

4. In Claim 1, The above-mentioned presale price prediction unit is, A floor area ratio and business revenue estimation system that predicts the above revenue using actual transaction price information within 2 years of surrounding buildings of the same use located within a 1km radius from the above target plot.

5. In Claim 1, The above location environment data is, A system for estimating floor area ratio and business revenue, comprising at least one evaluation score among traffic environment, residential convenience facility environment, workplace environment, and educational environment for each distance radius based on the location of the above-mentioned target plot.

6. In Claim 1, The actual transaction price data for the surrounding buildings mentioned above is, A system for estimating floor area ratio and business revenue, including a sales price level calculated by the ratio obtained by dividing the average actual transaction price per pyeong of surrounding buildings of the same use located within a 1km radius of the above target plot over the past two years by the maximum actual transaction price per pyeong.

7. In Claim 1, The above-mentioned presale price prediction unit is, A system for estimating floor area ratio and business revenue that predicts the sale price per pyeong of the target plot by additionally applying evaluation scores for each bonus and penalty factor evaluated for the target plot to the prediction model.

8. In Claim 1, The above sales revenue estimation unit, A system for estimating floor area ratio and business revenue, which calculates the gross floor area by multiplying the predicted floor area ratio by the land area of ​​the target plot, and then calculates the expected revenue of the target plot by multiplying the gross floor area by the predicted sale price per pyeong.

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