KR20220126488A - A modeling server of proper valuation based on real-estate database and modeling method - Google Patents

Abstract

A real-estate database-based reasonable price modeling server according to the present invention comprises: a reasonable range modeling module that receives a real estate address and an asking price, extracts a plurality of measurement variables for measuring purchase reasonability based on the received address, assigns a weight to each selected measurement variable, and calculates a reasonable range of transaction price of the real estate calculated according to the weight; a purchase reasonability calculation module that generates a purchase reasonability result classified as reasonable or unreasonable by applying the generated purchase reasonable range to the asking price; a register copy-based rights analysis module that, when a user requests reading and right analysis of a real estate register copy, analyzes prior order rights based on the real estate register copy and indicates a transaction warning regardless of reasonability; an unreasonableness additional explanation module that, when an unreasonableness is found as a result of calculating reasonability, indicates a reason of the result for each keyword; and a result value change notification module that provides a notification when the evaluation result value changes due to changes in the reasonable and unreasonable elements of an object of interest in the future. The modeling server makes a real estate transaction market sound and transparent and reduces transaction costs.

Description

{A modeling server of proper valuation based on real-estate database and modeling method}

The present invention relates to a real estate data-based fair price modeling server and modeling method.

Despite the fact that an environment has been created in which information asymmetry between the parties to a transaction can be dramatically resolved through numerous public data that can confirm real estate objects on the Internet, interest or interest in evaluating the objective value of the real estate to be traded Due to lack of knowledge, lack of time for individual analysis, and difficulty in real-time information inquiry when receiving an introduction to nearby objects, information asymmetry with counterparties represented by real estate agents, etc. still exists.

In particular, due to insufficient analysis of basic rights on real estate registers or lack of analysis of market prices, actual transaction prices, and locations, the majority of people do not even know whether an appropriate contract has been made or whether there are any defects in rights even after the contract is made. live this Therefore, before signing a real estate contract, quickly and intuitively analyze the real estate subject to be contracted to determine whether the asking price is appropriate, where it is not appropriate, and what level is the appropriate section for negotiation. It is necessary to objectively resolve information asymmetry.

In order to solve the above problems, the present invention is to provide a real estate data-based reasonable price modeling server and modeling method.

In order to solve the above problem, the present invention sets the appropriate real estate value section based on the public real estate information on the relevant lot when entering the contracted land by lot and entering the asking price, and comparing it with the asking price to see if the asking price is appropriate The purpose of this is to resolve information asymmetry between the parties to a real estate contract by notifying the user of whether or not it is inappropriate, in which part it is inappropriate, and notifying the user when the evaluation is changed due to a change in an additional inappropriate or appropriate element.

Real estate information, such as market price, actual transaction price, building ledger, registered copy, and location, is transparently disclosed, so the conditions for resolving information asymmetry are in place. There is also a limit to real estate, and it is not easy to check many objects in one place, even for a person who is knowledgeable in real estate. At the same time, it allows the contractor to figure out whether he is making an appropriate contract, and at the same time, he can have power during negotiation, which reduces transaction costs, and ultimately aims to improve soundness and transparency in the real estate transaction market.

Therefore, it aims to make the real estate transaction market more sound and transparent, and furthermore, to reduce transaction costs by not going through a broker for goods that do not require much judgment.

The real estate data-based fair price modeling server receives a real estate address and a quote price, extracts a plurality of measurement variables for purchase adequacy measurement based on the input address, assigns a weight to each selected measurement variable, and according to the weight an appropriate section modeling module for calculating an appropriate section of the calculated real estate transaction price; an adequacy calculation module for generating a buying adequacy result classified as appropriate or unsuitable by applying the generated appropriate section to the asking price; When a user requests to view a certified copy of the real estate register and analyze the rights, the senior rights are analyzed based on this, and the right analysis module based on the registered copy that displays a transaction warning regardless of whether or not it is appropriate. Inappropriate additional explanation module indicating; and

and a result value change notification module that provides a notification when the evaluation result value changes due to changes in appropriate and inappropriate factors of the object of interest later.

The plurality of measurement variables include a first measurement variable including market price information, a second measurement variable including actual transaction information, a third measurement variable including building characteristic information, a fourth measurement variable including location characteristic information, and revenue information. It may include at least two or more of the fifth measurement variable including the, and the sixth measurement variable including the register certified copy information.

The appropriate interval modeling module,

an input unit for inputting real estate addresses and quotations; an extraction unit for extracting a plurality of measurement variables for measuring purchase adequacy based on the input address; a weight setting unit for assigning a weight to each selected measurement variable; a section setting unit for calculating an appropriate section of the corresponding real estate transaction price calculated according to the weight; and an output unit for outputting an appropriate number of sections generated by the section setting unit.

In the section setting unit, the weight of each selected measurement variable may be flexibly changed based on predetermined statistical information and according to the contract type, contract period, and preferred conditions.

The weight setting unit may include at least one of a basic weight preset, a first special preset based on a real estate address input through the input unit, a second special preset based on a real estate sale received through the input unit, and a user input preset received through the input unit. It may be characterized in that the weight is set based on one.

The result value change notification module scrapes the measurement variable at a preset time interval to recheck whether it is appropriate and inappropriate through the adequacy calculation module, and notifies the user of whether the result value has changed when the appropriateness changes to the opposite state can do.

The result value change notification module reads the measured variable trading price and detailed measured variable information at the time when the user judged whether it is appropriate, and the measured variable trading price and detailed measured variable values at the time when a preset time interval has elapsed In contrast, it is possible to individually determine whether the fair value of the measurement variable and the fair value of the detailed measurement variable are maintained/decreased/increased, and the change factor analysis can be delivered to the user.

The inappropriate additional explanation module transmits detailed data on the measurement variable that became the determining factor of the inappropriate decision to the user terminal. can be determined based on whether

According to another embodiment, a method for modeling a fair price based on real estate data includes: receiving an input of a real estate address and a quote; extracting a plurality of measurement variables for purchase adequacy measurement based on the input address; assigning a weight to each selected measurement variable; calculating an appropriate section of the real estate transaction price calculated according to the weight; outputting the appropriate purchase section; includes, wherein the plurality of measurement variables include a first measurement variable including market price information, a second measurement variable including actual transaction information, a third measurement variable including building characteristic information, It may include two or more of a fourth measurement variable including location characteristic information, a fifth measurement variable including revenue information, and a sixth measurement variable including information on a certified copy of the register.

A computer program according to another embodiment is configured to perform the above-described method, and may be recorded in a computer-readable storage medium.

The present invention has the effect of minimizing the asymmetry of information between transaction subjects.

Despite the fact that a variety of real estate information has been disclosed and legal devices are gradually developing, depending on the type of real estate contract (sale, jeonse, monthly rent), the entity (hereinafter referred to as "B") and When the subject (seller, lessor, real estate agent, etc. - hereinafter "A") makes real estate transactions, information asymmetry inevitably exists.

The reason is mainly due to the lack of real estate knowledge of "B", lack of time, and lack of judgment of discrimination on the conditions presented by "A". “If you present an item other than the one you originally wanted to contract with, the meaning of the preliminary investigation will be significantly weakened.

In such an environment, it is necessary to intuitively determine the appropriateness of a newly presented object in real time, and the present invention can solve such difficulties.

In addition, it is true that people who do not have real estate knowledge or come from other regions (eg, college students, local workers, etc.) invest time and effort when looking for a house, but it is true that it is vague, Since the current situation is unfamiliar from how to do it, transactions are carried out by checking the properties on the Internet and visiting the real estate agent's site.

In this process, there is a feeling of regret depending on the transaction result immediately after the transaction with "B" is closed, and there are cases where the person resides for the duration of the contract without checking whether the transaction was reasonable compared to the market price and there was no defect in rights.

Therefore, the present invention, before signing a real estate contract, quickly and intuitively analyzes the contracted real estate object to determine whether the asking price is appropriate, if not, in which part it is not appropriate, and at what level the appropriate section for negotiation is This has the effect of resolving the information asymmetry between the contract applicant and the real estate agent at the time of the contract, and enhancing the soundness of real estate transactions by compensating for the disadvantageous aspect from the contract applicant side by enabling the transaction and contract negotiation based on objective information.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram illustrating a network connection relationship between a real estate data-based appropriate modeling server and other components according to the present invention.
2 is a diagram illustrating the configuration of a real estate data-based appropriate modeling server according to the present invention.
3 is a diagram illustrating the configuration of an appropriate section modeling module according to the present invention.
4 is a diagram illustrating an input example of an input unit according to the present invention.
5 is a diagram illustrating an example of providing a preset by a weight input unit according to the present invention.
6 is a view showing an example of providing additional explanation of the inappropriate additional explanation module according to the present invention.
7 is a diagram illustrating an example of providing a result value change notification of the result value change notification module according to the present invention.
8 is a flowchart illustrating an appropriate modeling method based on real estate data according to the present invention.
9 is a diagram illustrating an example of collecting information related to a first measurement variable through a KB price.
10 is a diagram illustrating an example of collection of information related to a second measurement variable including actual transaction information related to a target sale.
11 is a diagram illustrating collection of third measurement variable information including building-related information.
12 is a view showing the collection of the fifth measurement variable information related to the monthly rental rate through the Korea Real Estate Agency.

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Advantages and features of the present disclosure and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the technical spirit of the present disclosure is not limited to the following embodiments, but may be implemented in various different forms, and only the following embodiments complete the technical spirit of the present disclosure, and in the technical field to which the present disclosure belongs It is provided to fully inform those of ordinary skill in the scope of the present disclosure, and the technical spirit of the present disclosure is only defined by the scope of the claims.

In adding reference numerals to the components of each drawing, it should be noted that the same components are given the same reference numerals as much as possible even though they are indicated on different drawings. In addition, in describing the present disclosure, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the present disclosure, the detailed description thereof will be omitted.

Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used with the meaning commonly understood by those of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly defined in particular. The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present disclosure. In this specification, the singular also includes the plural, unless specifically stated otherwise in the phrase.

In addition, in describing the components of the present disclosure, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only for distinguishing the elements from other elements, and the essence, order, or order of the elements are not limited by the terms. When it is described that a component is “connected”, “coupled” or “connected” to another component, the component may be directly connected or connected to the other component, but another component is between each component. It should be understood that elements may be “connected,” “coupled,” or “connected.”

As used herein, “comprises” and/or “comprising” refers to a referenced component, step, operation and/or element of one or more other components, steps, operations and/or elements. The presence or addition is not excluded.

A component included in one embodiment and a component having a common function may be described using the same name in another embodiment. Unless otherwise stated, the descriptions described in one embodiment may be applied to other embodiments, and specific descriptions will be omitted within the overlapping range or within the range that can be clearly understood by those skilled in the art. can

Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

Hereinafter, the present invention will be described in detail with reference to preferred embodiments of the present invention and the accompanying drawings.

1 is an exemplary diagram of a network environment according to an embodiment of the present invention. As shown in FIG. 1 , the real estate data-based reasonable price modeling server 100 is connected to the database 200 , the external public data 300 , and the user terminal 400 through a network.

In the embodiment of the present invention, the user terminal 400 is described as an example for convenience of description, but is not necessarily limited thereto, and a plurality of user terminals may be used, of course.

Real estate data-based fair price modeling server 100 is based on the real estate address and quote information input through the user terminal 400, the public data of a plurality of measurement variables for determining purchase adequacy through the external public data 300 may be scraped, and information may be transmitted to output the suitability result to the user terminal 400 based on the corresponding information.

2, the real estate data-based fair price modeling server 100 includes an appropriate section modeling module 110, an adequacy calculation module 120, a registered copy-based right analysis module 130, an inappropriate additional explanation module 140 and It includes a result value change notification module 150 .

The appropriate section modeling module 110 receives a real estate address and a quote price, extracts a plurality of measurement variables for measuring purchase adequacy based on the input address, assigns a weight to each selected measurement variable, and calculates according to the weight Calculate the appropriate range of the corresponding real estate transaction price.

The adequacy calculation module 120 applies the generated appropriate buying interval to the asking price to generate a buying adequacy result classified into appropriate and inappropriate.

When a user requests to view and analyze a copy of a real estate register, the right analysis module 130 based on a registered copy analyzes the priority right based on this, and displays a transaction warning regardless of whether or not it is appropriate.

The inappropriate supplementary explanation module 140 indicates the reason for each keyword when inappropriateness is found as a result of the adequacy calculation.

The result value change notification module 150 provides a notification when the evaluation result value changes due to changes in appropriate and inappropriate factors of the object of interest later.

In the present invention, the plurality of measurement variables include a first measurement variable including market price information, a second measurement variable including actual transaction information, a third measurement variable including building characteristic information, and a fourth measurement variable including location characteristic information. , may include a fifth measurement variable including revenue information, and a sixth measurement variable including information on a certified copy of the register. The measurement variable may include various variable information for estimating the appropriate market price of real estate, and is not limited to the above example.

The weight of the measurement variable may be flexibly changed based on predetermined statistical information and according to the contract type, contract period, and preferred conditions.

The first measurement variable may be obtained by scraping external quote data accessible through a network. For example, reliable KB price information related to real estate sales, jeonse, and monthly rent can be acquired over a certain period of time. By processing KB price information, it is possible to derive the appropriate cost for sale, jeonse, and monthly rent.

The appropriate section modeling module 110 may derive the appropriate section by reflecting the current price of the external price data and the rate of change according to the rising and falling rates as the CAGR for the first measurement variable.

Item shame Contents Sales price 12 months ago 140,000,000 KB price Sales price 6 months ago 140,000,000 KB price 3 months ago sales price 145,000,000 KB price current price 145,000,000 KB price Annual growth rate (CAGR) 101.18% arithmetic Deduction of fair price 146,706,039 101.18% reflected 1st measurement variable weight 25% Weight can be changed according to user settings 1st measurement variable change price 36,676,510 Fair Value * Weight

Referring to Table 1, it is possible to determine the average annual growth rate by checking the fluctuation range of the sale price for the target real estate from the KB market price, and to calculate the fair price reflecting the future value rather than the simple market price by reflecting the growth rate in the relevant market price. The fair price may be provided to the user as it is, but for a comprehensive judgment with other measured variables, the first measured variable weight, for example, by multiplying by 25%, the first measured variable change price value may be obtained. It is possible to calculate the final appropriate market price by summing it with the changed price obtained from the remaining measurement variables.

Item shame Contents 12 months ago 140,000,000 KB price 6 months ago 125,000,000 KB price 3 months ago 125,000,000 KB price current price 140,000,000 KB price Annual growth rate (CAGR) 103.85% arithmetic Deduction of fair price 137,750,000 103.85% reflected 1st measurement variable weight 25% Weight can be changed according to user settings Price change value of the first measurement variable 34,437,500 Fair Value * Weight

Item shame Contents deposit 5,000,000 KB price monthly 470,000 KB price Weight of the first measurement variable 25% Weight can be changed according to user settings Price change value of the first measurement variable 117,500 monthly rent* weight

Similarly, it is possible to secure the change price value of the first measured variable for jeonse and monthly rent, etc. The second measured variable may be obtained by scraping external actual transaction data accessible through a network. For example, reliable real transaction price information from the Ministry of Land, Infrastructure and Transport related to real estate sales, jeonse, and monthly rent can be acquired over a certain period of time. By processing this actual transaction information, it is possible to derive the appropriate cost for sale, jeonse, and monthly rent.

Item shame Contents 12 months ago
Actual transaction average 130,000,000 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Average of 6 months ago sales and sales 133,750,000 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Average of 3 months ago 135,090,909 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Annual growth rate (CAGR) 98.10 arithmetic Deduction of fair price 127,526,948 98.10% reflected Second measurement variable weight 65% Weight can be changed according to user settings 2nd measurement variable change price 82,892,516 Fair Value * Weight

Referring to Table 4, it is possible to determine the average annual growth rate by checking the range of fluctuations in the average real transaction transaction for the target real estate from the actual transaction price information system of the Ministry of Land, Infrastructure and Transport, and to calculate the fair price reflecting the future value rather than the simple actual transaction price by reflecting the growth rate in the average actual transaction price. can The fair price may be provided to the user as it is, but for a comprehensive judgment with other measurement variables, the second measurement variable weight, for example, 65%, may be multiplied to secure the second measurement variable change price value. It is possible to calculate the final appropriate market price by summing it with the changed price obtained from the remaining measurement variables.

Item shame Contents 12 months ago
Actual transaction average 126,356,000 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Average of 6 months ago jeonse room transaction 127,655,909 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Average of charter transaction 3 months ago 126,126,304 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Annual growth rate (CAGR) 100.09% arithmetic Deduction of fair price 126,471,005 100.09% reflected Second measurement variable weight 65% Weight can be changed according to user settings 2nd measurement variable change price 82,206,153 Fair Value * Weight

Item shame Contents 12 months in advance
Actual transaction average 496,000 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Average monthly rental transaction 6 months ago 490,000 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Average monthly rental transaction 3 months ago 479,200 Ministry of Land, Infrastructure and Transport Actual Transaction Price Information System Annual growth rate (CAGR) 101.74% arithmetic Deduction of fair price 500,000 101.74% reflected Second measurement variable weight 60% Weight can be changed according to user settings Price change value of the second measurement variable 300,000 Fair Value * Weight

Similarly, it is possible to secure the price value of the second measured variable change for jeonse and monthly rent, etc. The third measured variable can be obtained by scraping external real transaction data that can be accessed through a network. The third measurement variable includes building characteristic information and may be scraped through a building ledger, a real estate transaction website, and the like. The third measurement variable may specifically include at least one of a complex size, a year, a parking lot size, a building-to-land ratio, and a floor area ratio.

For example, the price change value of the third measurement variable may be determined by adding up values obtained by assigning weights to the complex size, year, parking lot size, building-to-land ratio, and floor area ratio.

3rd here The estimation value corresponds to the fair value of the third measurement variable, cp is the current target real estate price, p_t1 is the multiplier value according to the complex size, wt_t1 is the complex size weight value, p_t2 is the multiplier value according to the year, wt_t2 is the year weight value, p_t3 is the scale value according to the parking lot size, wt_t3 is the parking lot size weight value, p_t4 is the scale value according to the building-to-coverage ratio, wt_t4 is the building-to-coverage ratio weight value, p_t5 is the scale value according to the floor area ratio, and wt_t5 is the floor area ratio weight value.

Complex size (large complex, single apartment) wt_t1 : 20% Judgment criteria by equal number magnification (p_t1) Rating
more than 10 100% 5 to less than 10 95% 3 to less than 5 90% 2 or more 85% stand-alone building 80%

year wt_t2 : 30% Compared to the base year magnification (p_t2) Rating new construction 100% within 3 years 95% within 5 years 90% within 10 years 85% within 20 years 80% within 30 years 75% over 30 years 70%

parking lot wt_t3 : 30% Number of parking spaces per household magnification (p_t3) Rating
1.4 or more 100% 1.3 or more to less than 1.4 95% More than 1.2 to less than 1.3 90% 1 or more to less than 1.2 85% 0.7 or more to less than 1 80% Less than 0.7 75%

building-to-cover ratio wt_t4: 10% Distance (m) magnification (p_t4) Rating less than 50% 100% More than 50 to less than 60% 95% More than 60 to less than 70% 90%

floor area ratio wt_t5 : 10% ratio(%) magnification (p_t5) Rating More than 50 to less than 100% 100% More than 100 to less than 150% 95% More than 150 to less than 250% 90% More than 250 to less than 300% 85% more than 300% 80%

Referring to Tables 7 to 11, weight values of wt_t1 to wt_t5 are given to all variables belonging to the third measurement variable, and wt_t1+wt_t2+wt_t3+wt_t4+wt_t5 = 100%. The weight for each variable may be determined depending on whether the price has a large influence, and may be determined differently according to the user's intention. p_t1 to p_pt5 may be determined depending on whether each detailed variable has a positive effect on the market price. For example, considering the effect of the size of the complex on the market price, if there are 10 or more, the magnification (p_t1) may be given as 100%, and in the case of a detached house, it may be assigned as 80%. For example, considering the effect of the age on the market price, in the case of new construction, the magnification (p_t2) may be given as 100%, and if it exceeds 30 years, it may be given as 70%. For example, the multiplier (p_t3) may be differentiated from 100% to 75% in consideration of the effect of the number of parking spaces per household in the parking lot on the market price. For example, in consideration of the effect of the building-to-land ratio on the market price, the magnification p_t4 may be differentiated from 100% to 80%. For example, in consideration of the effect of the floor area ratio on the market price, the magnification p_t5 may be differentiated from 100% to 80%. Setting the magnification of the detailed variables of the third measurement variable to 100% or less is generally considered that the user can use the server and program for the purpose of lowering the price in a transaction with a real estate agent. Factors were exemplarily set. Of course, this is only an example, and when a measurement variable reflecting a price increase factor is added, the magnification of each item may exceed 100%. (However, as the detailed variable of the measurement variable increases, the total weight of each variable must be readjusted to be 100%. For example, if there are 6 detailed variables, wt_t1+wt_t2+wt_t3+wt_t4+wt_t5+wt_t6 = 100% must be satisfied.

Here, the ^3rd estimated modified value is the price change value of the third measurement variable, 3rd The estimation value corresponds to the fair value of the third measured variable, and wt_3rdestimationvalue corresponds to the weight of the third measured variable. For example, wt_3rdestimationvalue may be 5%.

The fourth measurement variable may be obtained by scraping external public data accessible through a network. For example, in order to reflect the location characteristics of the target real estate, based on the location, the proximity of public transportation such as subways and buses, the level of commercial areas such as large marts, school district level, and the proximity of harmful facilities such as entertainment districts are comprehensively judged based on the location. can be reflected in the price.

For example, the price change value of the fourth measurement variable may be determined by adding up values obtained by assigning weights to the complex size, year, parking lot size, building-to-land ratio, and floor area ratio.

Here, the 4th estimation value corresponds to the fair value of the fourth measurement variable, cp is the current target real estate price, ^p_t6 is the multiplier value according to the station area or not, wt_t6 is the weighted value of the station area, p_t7 is the multiplier value according to the commercial area, and wt_t7 is the commercial area A weight value, p_t8 is a scale value according to school district, wt_t8 is a school district weight value, p_t9 is a scale value according to hazardous facilities, and wt_t9 is a weight value of hazardous facilities.

Is it near the station? wt_t6 : 40% Distance (m) magnification (p_t6) Rating
0-100 100% 100-300 95% 300-500 90% 500~ 85%

Mart wt_t7 : 30% Distance (m) magnification (p_t7) Rating 0-100 100% 100-300 95% 300-500 90% 500~ 85%

school district wt_t8 : 20% Distance (m) magnification (p_t8) Rating
0-100 100% 100-300 95% 300-500 90% 500~ 85%

Harmful facilities such as entertainment districts wt_t9: 10% Distance (m) magnification (p_t9) Rating 300~ 100% 200-300 95% 100-200 90% 0~200 85%

Referring to Tables 12 to 15, weight values of wt_t6 to wt_t9 are given to all variables belonging to the fourth measurement variable, and wt_t6+wt_t7+wt_t8+wt_t9 = 100%. The weight for each variable may be determined depending on whether the price has a large influence, and may be determined differently according to the user's intention. p_t6 to p_pt9 may be determined depending on whether each detailed variable has a positive effect on the market price. Setting the weight of the detailed variables of the fourth measurement variable to 100% or less is generally, considering that the user can use the server and program for the purpose of lowering the price in a transaction with a real estate agent, the price decrease Factors were exemplarily set. Of course, this is only an example, and when a measurement variable reflecting a price increase factor is added, the magnification of each item may exceed 100%. However, when adding measurement variables, the total weight of each variable should be readjusted to be 100%.

Here, the 4th estimated modified value is the price change value of the fourth measurement variable, and the 4th The estimation value corresponds to the appropriate value of the fourth measurement variable, and wt_4thestimationvalue corresponds to the weight of the fourth measurement variable. For example, wt_4thestimationvalue may be 5%.

The calculation method of the price change value of this fourth measurement variable can be applied to all sales, jeonse, and monthly rent.

The fifth measurement variable may be obtained by scraping external public data accessible through a network. For example, the fifth measurement variable can derive the rental yield of the corresponding real estate through the regional average rental rate of return information based on the reliable statistical information of the local rate of return at the base point of the Korea Real Estate Agency.

Item shame Contents Regional average rental yield 4.33% Korea Real Estate Agency deposit 5,000,000 KB price monthly 470,000 KB price 5th measurement variable weight 5% Weight can be changed according to user settings Fifth Measured Variable Fair Monthly Rent 436,000 5th measurement variable change price 130,800 Fair Value * Weight

The sixth measurement variable may be obtained by scraping external public data accessible through a network. For example, the sixth measurement variable is extracted from the measurement variable extraction unit 112 and then transferred to the registered copy-based rights analysis module (130 in FIG. 1) to analyze the contents of the real estate registration to determine whether the real estate is in trust, whether it is an auction, or foreclosure. It is possible to determine whether or not there is a root mortgage, and a warning to the user as a pop-up is inappropriate.

type Contents magnification Gapgu trust Absence of documents such as trust register -100% Whether to apply for auction Y -100% foreclosure or injunction Y -80% Eul-gu mortgage right Loan amount> Deposit -100% jeonseok Y -100%

Referring to FIG. 3 , the appropriate section modeling module 110 includes an input unit 111 for inputting a real estate address and a quote price, and a measurement variable extraction unit for extracting a plurality of measurement variables for measuring purchase adequacy based on the input address ( 112), a weight setting unit 113 that assigns a weight to each selected measurement variable, an interval setting unit 114 that calculates an appropriate interval of the corresponding real estate transaction price calculated according to the weight, and the number of purchases generated by the interval setting unit and an output unit 115 for outputting a section.

Referring to FIG. 4 , the input unit 111 may receive a real estate address and a quote price through the user terminal 400 .

The measurement variable extraction unit 112 may extract a plurality of measurement variables for measuring purchase adequacy based on the input address. As described above, the plurality of measurement variables include a first measurement variable including market price information, a second measurement variable including actual transaction information, a third measurement variable including building characteristic information, and a fourth measurement including location characteristic information. It may include a variable, a fifth measurement variable including revenue information, and a sixth measurement variable including information on a certified copy of the register.

The weight setting unit 113 may set the weight of each measurement variable for each measurement variable in order to derive an appropriate conversion value by multiplying and summing the appropriate values of the measurement variables for each item with respect to the first to sixth measurement variables.

For example, in the case of trading, the weight setting unit 113 has a weight of a first measurement variable including market price information of 25%, a weight of a second measurement variable including actual transaction information of 65%, and a weight of the second measurement variable including information on building characteristics. A weight of a measurement variable of 100% may be set as the 3 measurement variable is 5%, and the fourth measurement variable including location characteristic information is 5%.

For example, in the case of jeonse, the weight setting unit 113 sets the weight of the first measurement variable including the market price information by 25%, the weight of the second measurement variable including the actual transaction information by 65%, and the second measurement variable including the building characteristic information. A weight of a measurement variable of 100% may be set as the 3 measurement variable is 5%, and the fourth measurement variable including location characteristic information is 5%.

For example, in the case of monthly rent, the weight setting unit 113 sets the weight of the first measurement variable including the market price information by 20%, the weight of the second measurement variable including the actual transaction information by 65%, and the second measurement variable including the building characteristic information by 65%. A weight of 100% of the measurement variable can be set as the 3 measurement variable is 5%, the fourth measurement variable including location characteristic information is 5%, and the fifth measurement variable including the monthly rental income information is 5%.

The sixth measurement variable including the registered copy information is given a weight of -100% in order not to recognize the fair value if there is register information with a serious risk in the transaction in advance. It is possible to provide a notification for this low sale and at the same time invalidate the derived fair value information.

Furthermore, the weight setting unit 113 may also set weights for detailed variables for deriving appropriate values for each measurement variable.

The weight setting unit 113 includes detailed variables of the complex size, year, parking lot, building-to-land ratio, and floor area ratio as described above in Tables 7 to 11 for the third measurement variable, and weights (wt_1, wt_2, wt_3) for each detailed variable. , wt_4, wt_5) may be determined, and the sum of each weight may be 100%. As for the fourth measurement variable, as described above with respect to Tables 12 to 15, detailed variables of station areas, marts, school districts, and hazardous facilities exist, and weights (wt_6, wt_7, wt_8, wt_9) for each detailed variable can be determined. , the sum of each weight may be 100%.

The interval setting unit 114 derives the final fair value by summing all the measured variable price change values obtained by multiplying the measured variable weights by the measured variable appropriate price obtained for each measurement variable, and may derive the appropriate interval based on the final appropriate price.

Referring to FIG. 5 , the interval setting unit 114 may have a preset weight preset (bp) in general transaction requirements, but is not limited thereto. For example, a weight preset (preset) mainly used for a specific location and a specific target property is stored in the database (200 in FIG. 1), and when the user wants to use the local property, the default weight preset (bp) It is possible to select whether to use it, whether to use a weight preset (sp-1) mainly utilized in the corresponding area, or a weight preset (sp-2) mainly used in the corresponding property. The user may set the weight for each measurement variable and detailed variable in the preset as desired, and the corresponding information may be input through the input unit 111 . Individual presets set by the user are stored in the database 200, and the presets that the user mainly used for the region and specific property are stored in the database as a specific preset according to the corresponding condition, so that the user with the same/similar condition wants to set the weight. When doing so, the specific preset may be provided as an option.

The appropriate interval may be set in an area of 95% to 105% based on the final fair value.

The adequacy calculation module (120 in FIG. 2 ) outputs the appropriate section information determined in the appropriate section 114 through the output unit 115, for example, the appropriate section price based on the final fair price for a specific real estate sale from 1.2 billion to 13 billion won. When set to billions, if the asking price of real estate is 1.4 billion won, it is judged to be inappropriate because it exceeds the range, and if the real estate asking price is 1.25 billion won, it can be judged as appropriate.

Referring to FIG. 6 , the inappropriate supplementary explanation module 140 may transmit detailed data on the measurement variable that is an important factor to the inappropriate factor to the user terminal ( 400 in FIG. 1 ) when it is determined to be inappropriate. For example, if the fair value of a specific measured variable differs from other measured variables by a preset standard (for example, 5% or more), it is determined that the impact on the measured variable is high and the relevant details are notified to the user can do. For example, the user prioritizes the parking lot and sets the weight for the third measurement variable high, and among them, the detailed weight for the parking lot is set higher. It can be set lower. In this case, it is possible to notify the user that the contents due to the detailed variable of the parking lot requirement among the third measurement variables for inappropriateness were inappropriate.

As another example, the user prioritized the presence of hazardous facilities for educational purposes and set a high weight for the fourth measurement variable, and among them, the detailed weight for hazardous facilities was set higher. In this case, the fair value of the fourth measurement variable may be set low. In this case, the user may be notified that the contents due to the detailed parameters of hazardous facility requirements among the fourth measurement variables were inappropriate for inappropriateness.

Regarding the results of such titration and inadequacy, corresponding information may be managed through a database (200 in FIG. 1 ). The measured variable extractor 112 may be controlled to scrape the measured variable at a preset time interval, and to continuously recheck whether it is appropriate or inappropriate through the adequacy calculation module 120 . The preset time interval may be, for example, any one of 3 months, 6 months, 9 months, and 12 months, but is not limited thereto. The user may receive reconfirmation related to the appropriateness at a desired time interval through the input unit 111 .

The result value change notification module 150 can deliver to the user that the result value has changed to the user terminal ( 400 in FIG. 1 ) when the appropriateness and inappropriateness change to the opposite state due to a change in the relevant important variable. have.

Referring to FIG. 7 , the result value change notification module 150 reads the measurement variable trading price and detailed measurement variable information at the time when the user judged whether it is appropriate from the database (200 in FIG. 1 ), and measures the measurement at the current time. It is possible to determine whether to maintain/fall/rise in comparison with the variable trading price and detailed measured variable values, and to analyze the contents of the change factors and provide them to the user terminal (400 in FIG. 1).

Referring to FIG. 8 , the method of modeling a fair price based on real estate data includes the input step (S110) of the quotation (sales, jeonse, monthly rent) according to the location of the contract target and the contract conditions, importing external data based on the input location of the object, and Extraction of measurement variables for purchase adequacy measurement (sales, jeonse: 4, monthly rental: 5) step (S120), weighting for each selected measurement variable, calculation of the fair price of the property and comparison with the asking price Calculation step (S130), step of judging appropriate/unsuitable after comparing whether the asking price is set in the appropriate transaction section (S140), external information value (market price, actual transaction price, complex characteristics, location characteristics) A step of displaying where the actual transaction is located on the graph (S150), and additionally, if desired, reading a registered copy and supporting basic right analysis When a senior right exists, a step of notifying the reconsideration of the purchase through a warning window despite the transaction conditions (S160), And if it falls under the exception, it includes a step (S170) of notifying additionally in addition to adequacy.

In the case of the additional notification step (S170), 1. Exceeding the upper limit of the jeonse rate, 2. If it is judged to be inappropriate, the result of the keyword preset by the user 3. If the inappropriate factor is resolved later and it is converted to appropriate, the fact is notified. If an item is later converted into an inappropriate element, the relevant matter may be notified.

Referring to FIG. 9 , information related to the first measurement variable may be collected through the KB price.

Referring to FIG. 10 , information related to a second measurement variable including actual transaction information related to a target product may be collected.

Referring to FIG. 11 , third measurement variable information including building-related information may be collected.

Referring to FIG. 12 , information on the fifth measurement variable related to the monthly rental rate may be collected through the Korea Real Estate Agency.

The steps of the control method or algorithm described in relation to the embodiment of the present invention may be implemented directly by hardware, as a software module executed by hardware, or by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The apparatus for determining fake information through self-supervised learning according to the present invention includes a control unit, wherein the control unit prepares unlabeled original data, generates a problem for supervised learning from the original data itself, and artificial intelligence Learning artificial intelligence by having a user solve a problem, changing the problem until the artificial intelligence derives its own characteristics, and repeatedly learning artificial intelligence, and whether the data is abnormal data based on the artificial intelligence It is possible to perform a step of discriminating fake information based on the

Exemplary embodiments have been disclosed in the drawings and specification as described above. Although the embodiments have been described using specific terms in the present specification, these are used only for the purpose of explaining the technical spirit of the present disclosure, and are not used to limit the meaning or the scope of the present disclosure described in the claims. Therefore, it will be understood by those skilled in the art that various modifications and equivalent other embodiments are possible therefrom. Accordingly, the true technical protection scope of the present disclosure should be defined by the technical spirit of the appended claims.

Claims (9)

In the real estate data-based fair price modeling server,
Receive real estate address and quote price, extract a plurality of measurement variables for purchase adequacy measurement based on the input address, assign weights to each selected measurement variable, and calculate the appropriate range of the real estate transaction price calculated according to the weight. an appropriate section modeling module to calculate;
an adequacy calculation module for generating a buying adequacy result classified as appropriate or unsuitable by applying the generated appropriate section to the asking price;
When a user requests to view a certified copy of the real estate register and analyze the rights, a right analysis module based on the registered copy that analyzes the priority right based on this and displays a transaction warning regardless of whether it is appropriate;
Inappropriateness supplementary explanation module indicating the reason for each keyword when inappropriateness is found as a result of adequacy calculation; and
Including; a result value change notification module that provides a notification when the evaluation result value changes due to changes in appropriate and inappropriate factors of the object of interest later;
The plurality of measurement variables include a first measurement variable including market price information, a second measurement variable including actual transaction information, a third measurement variable including building characteristic information, a fourth measurement variable including location characteristic information, and revenue information. A real estate data-based fair price modeling server including at least two or more of a fifth measurement variable including a, and a sixth measurement variable including registered copy information. The method of claim 1,
The appropriate interval modeling module,
an input unit for inputting real estate addresses and quotations;
an extraction unit for extracting a plurality of measurement variables for measuring purchase adequacy based on the input address;
a weight setting unit for assigning a weight to each selected measurement variable;
a section setting unit for calculating an appropriate section of the corresponding real estate transaction price calculated according to the weight; and
A real estate data-based fair price modeling server including an output unit for outputting an appropriate purchase interval generated by the interval setting unit. 3. The method of claim 2,
In the weight setting unit,
A real estate data-based fair price modeling server, characterized in that the weight of each selected measurement variable is flexibly changed based on predetermined statistical information and according to the contract type, contract period, and preferred conditions. 3. The method of claim 2,
The weight setting unit may include at least one of a basic weight preset, a first special preset based on a real estate address input through the input unit, a second special preset based on a real estate sale received through the input unit, and a user input preset received through the input unit. Real estate data-based fair price modeling server, characterized in that the weight is set based on one. 5. The method of claim 4,
The result value change notification module,
By scraping the measured variable at a preset time interval, reconfirming whether it is appropriate or inappropriate through the adequacy calculation module,
A real estate data-based fair price modeling server that notifies the user of whether the result value has changed when the appropriateness changes to the opposite state. 7. The method of claim 6,
The result value change notification module,
It reads the measured variable trade fair price and detailed measured variable information at the time when the user judged whether it is appropriate, and compares the measured variable trade fair price and detailed measured variable value at the time when the preset time interval has elapsed, A real estate data-based fair price modeling server that individually determines whether the fair price of detailed measurement variables is maintained/decreased/increased, and delivers change factor analysis to users. The method of claim 1,
The inappropriate additional explanation module is,
By delivering detailed data on the measurement variable that became the determinant of inappropriate judgment to the user terminal,
The determining factor is a real estate data-based fair price modeling server that is determined based on whether the fair value for a specific measured variable differs by more than a preset standard compared to other measured variables. A fair price modeling method based on real estate data,
receiving a real estate address and asking price;
extracting a plurality of measurement variables for purchase adequacy measurement based on the input address;
assigning a weight to each selected measurement variable;
calculating an appropriate section of the corresponding real estate transaction price calculated according to the weight;
Including; outputting the appropriate number of sections;
The plurality of measurement variables include a first measurement variable including market price information, a second measurement variable including actual transaction information, a third measurement variable including building characteristic information, a fourth measurement variable including location characteristic information, and revenue information. A real estate data-based fair price modeling method including two or more of a fifth measurement variable including, and a sixth measurement variable including information on a certified copy of the register. A computer program configured to perform the method according to claim 8, recorded on a computer-readable storage medium.

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