KR101794031B1 - Risk assessment system and method of property deposit for lease - Google Patents

Abstract

A risk assessment system and method for real estate lease deposits are disclosed. Real estate database; A contract lease deposit input module for receiving a lease deposit in contract; In order to estimate the lease deposits of the real estate, we select the explanatory variable to estimate the lease deposit of the real estate, and then generate the appropriate lease deposit estimation model based on the hedonic price model using the selected explanatory variables. An appropriate lease deposit estimation module for estimating an appropriate lease deposit by an appropriate lease deposit estimation model; The optimal selling price estimation module which selects the explanatory variables for estimating the optimal selling price of real estate, generates the optimal selling price estimation model based on the hedonic price model using the selected explanatory variables, and estimates the optimal selling price by the generated optimal selling price estimation model ; A winning bidder rate estimation module for selecting an explanatory variable for estimating a winning bid price of a real estate, generating a winning bid rate estimation model based on a hedonic price model using selected explanatory variables, and estimating a winning bid rate by a generated winning bid rate model; A winning bid amount calculation module for calculating a winning bid amount by multiplying an appropriate selling price and a winning bid price; A residual dividend calculation module for calculating a remaining dividend from the winning bid amount; The lease deposit risk diagnostic module is configured to diagnose and output the risk of the contract lease deposit against the appropriate lease deposit and the remaining dividend, respectively.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a system and method for risk deposit of real estate lease deposits,

More particularly, the present invention relates to a risk assessment system and method for estimating the fair value of a real estate lease deposit and determining the probability of recovery.

The tenant has difficulty in finding out whether the contract lease deposit is at an appropriate level while contracting the lease. In order to judge the appropriateness of the lease deposit, it is necessary to inquire from the real estate brokerage companies near the real estate through field survey. In addition, it is inevitable for the user to judge for themselves by examining the rent deposits of nearby shopping malls and houses by surveying various websites.

Such a method is very troublesome and difficult, and there is also a problem that the reliability of the judgment of the adequacy is not ensured.

This adequacy has a great influence on the possibility of the recovery of the security deposit after the expiration of the lease contract.

In particular, if there is a superficial right or mortgage on the real estate, or if there is another lease contract that has a fixed date, it should individually judge the possibility of recovering the lease deposit. If you do not know about the right relation of real estate, you can not easily grasp the possibility of recovering it, and you can not grasp the appropriateness of the lease deposit accordingly.

The tenant is always in an unstable position while contracting, and even if it can not be recovered after the expiration of the lease contract, the tenant must suffer a great economic damage.

Therefore, it is necessary for tenants who are not well informed about the real estate market or who can not grasp rights relations to judge the appropriate amount of safe and accurate lease deposit.

It is an object of the present invention to provide a risk diagnosis system of real estate lease deposits.

Another object of the present invention is to provide a method for diagnosing the risk of real estate lease deposits.

The risk diagnosis system for real estate lease deposits for the purpose of the present invention described above includes a real estate database in which data including rent report data, actual transaction data, court auction article data, building data book, and housing disclosure price data are stored in advance; A contract lease deposit input module for receiving a contract lease deposit from a user; A range of the area where the real estate belongs is set, and explanatory variables for estimating the lease deposit of the real estate are selected in consideration of the predetermined area range, and a suitable rental deposit based on the hedonic pricing model An appropriate lease deposit estimation module for generating an estimation model and estimating an appropriate lease deposit based on the generated lease deposit estimation model; The explanatory variables for estimating the optimal selling price of the real estate are selected in consideration of the set range of the real estate, and the optimal selling price estimation model based on the hedonic price model is generated using the selected explanatory variables. Module for estimating the selling price; The selected parameters for estimating the winning bid price of the real estate are selected in consideration of the set range of the real estate, a winning bid rate estimation model based on the hedonic price model is created using the selected explanatory variables, and the winning bid rate is estimated by the generated winning bid rate estimation model A winning bid rate estimation module; A winning bid amount calculation module for calculating a winning bid amount by multiplying an appropriate selling price estimated by the appropriate selling price estimation module and a winning bid rate estimated by the winning bid rate estimation module; A residual dividend calculation module for calculating a remaining dividend by subtracting auction cost, a top priority liquidation small lease deposit, a national tax / local tax, a seniority security right, or another lease deposit received on a fixed date from the winning bid calculated in the winning bid amount calculation module; The risk of the contract lease deposit is diagnosed by comparing the contract lease deposit inputted by the contract lease deposit input module with the appropriate lease deposit estimated by the appropriate lease deposit estimation module and the residual dividend calculated by the residual dividend calculation module And outputting a lease deposit risk diagnostic module.

The system may further include a lease deposit upper limit value calculating module for calculating a lease deposit upper limit value according to a predetermined confidence interval with respect to the appropriate lease deposit estimated by the appropriate lease deposit estimating module.

Further, the lease deposit risk diagnostic module further diagnoses the risk of the contract lease deposit by comparing the contract lease deposit inputted by the contract lease deposit input module with the lease deposit upper limit estimated by the lease deposit upper limit value calculation module Lt; / RTI >

Also, the appropriate lease deposit estimation module may calculate the lease deposit estimation model by comparing the contract lease deposit received from the contract lease deposit input module with the appropriate lease deposit estimated by the generated lease deposit estimation model, And determine the appropriate lease deposit estimation model.

Meanwhile, the appropriate selling price estimation module may be configured to determine the appropriate selling price estimation model by verifying the performance of the appropriate selling price estimation model by comparing the estimated optimal selling price and actual transaction stored in advance in the real estate database with each other have.

Further, the winning bidder rate estimation module may be configured to additionally select, as the explanatory variables, the number of the land register, the land registry non-register, the lien, the legal right, the notice, and the number of tenants as additional risk factors that may cause the change in the winning bid rate.

According to another aspect of the present invention, there is provided a method for diagnosing a property lease deposit, the method comprising: receiving a contract lease deposit from a user; In order to estimate the lease deposit of the real estate in consideration of the area of the real estate, the estimation module of the appropriate lease deposit estimation module is selected and the hedonic pricing model (hedonic pricing model, and estimating an appropriate lease deposit based on the generated appropriate lease deposit estimation model; The appropriate trading price estimation module selects the explanatory variables for estimating the appropriate selling price of the real estate in consideration of the set area range, generates the appropriate selling price estimation model based on the hedonic price model using the selected explanatory variables, Estimating an optimal selling price by an estimation model; Calculating a ceiling deposit upper limit value according to a predetermined confidence interval with respect to an appropriate lease deposit estimated by the appropriate lease deposit estimating module; The winning bidder rate estimation module selects the explanatory variables for estimating the winning bidder ratio of the real estate in consideration of the set range of the real estate, generates a winning bid rate model based on the hedonic price model using the selected explanatory variables, Estimating a winning bid price; Calculating a winning bid amount by multiplying an appropriate selling price estimated by the appropriate selling price estimation module and a winning bid price estimated by the winning bid rate calculating module; Calculating a remaining dividend by subtracting auction cost, a highest priority liquidation small lease deposit, a national tax / local tax, a seniority guarantee money or another lease deposit received on a fixed date from the winning money calculated in the award calculation module; The lease deposit risk diagnosis module compares the contract lease deposit inputted by the contract lease deposit input module with the appropriate lease deposit estimated by the appropriate lease deposit estimation module, the lease deposit upper limit estimated from the lease deposit upper limit value calculation module, and the residual dividend calculation And diagnosing and outputting the risk of the contract lease deposit with respect to each of the remaining dividend calculated in the module.

At this time, the appropriate lease deposit estimation module sets an area range to which the real estate belongs, selects the explanatory variables for estimating the lease deposit of the real estate in consideration of the set range of the real estate, and uses the selected hedonic price model The step of estimating the appropriate lease deposit based on the generated lease deposit estimation model may include the step of calculating a lease deposit amount inputted from the contract lease deposit input module and the generated lease deposit estimation model To determine the appropriate lease deposit estimation model by verifying the performance of the appropriate lease deposit estimation model with respect to the estimated appropriate lease deposit.

Then, the appropriate trading price estimation module selects an explanatory variable for estimating an appropriate selling price of the real estate in consideration of the set area range, generates an appropriate selling price estimation model based on the hedonic price model using the selected explanatory variables, The step of estimating the optimal selling price by the optimal selling price estimation model comprises the steps of: comparing the estimated optimal selling price and actual transaction stored in advance in the real estate database with each other to compare the reported data with each other; . ≪ / RTI >

The winning bidder rate estimation module selects the explanatory variables for estimating the winning bidder ratio of the real estate considering the set range of the real estate, generates a winning bid rate model based on the hedonic price model using the selected explanatory variables, , The step of estimating the winning bid rate may be configured to additionally select the number of the landmark additional registration, the land registry non-register, the lien, the legal right, the notice, and the number of the tenant as the above explanatory variables, have.

According to the system and method for risk assessment of real estate lease deposits described above, the appropriate lease deposit for individual real estate is automatically calculated without the assistance of an appraiser or a lawyer, and the interest in the real estate is automatically recognized, It is possible to know the risk level of the lease deposit conveniently and accurately.

Accordingly, it is possible to prevent a lease contract which is extremely unfavorable to the tenant from being performed and to prevent the infringement of the tenant's right in advance.

1 is a block diagram of a risk diagnosis system for real estate lease deposits according to an embodiment of the present invention.
2 is a flowchart of a method for diagnosing a risk of a real estate lease deposit according to an embodiment of the present invention.

While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail to the concrete inventive concept. It should be understood, however, that the invention is not intended to be limited to the particular embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. Like reference numerals are used for like elements in describing each drawing.

The terms first, second, A, B, etc. may be used to describe various elements, but the elements should not be limited by the terms. The terms are used only 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 referred to as a second component, and similarly, the second component may also be referred to as a first component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Do not.

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

1 is a block diagram of a risk diagnosis system for real estate lease deposits according to an embodiment of the present invention.

Referring to FIG. 1, a risk diagnosis system (hereinafter, referred to as a 'risk diagnosis system') 100 according to an embodiment of the present invention includes a real estate database 110, a proper rental deposit estimation module 120, The lease deposit upper limit value calculating module 130, the appropriate selling price estimating module 140, the winning bid rate calculating module 150, the winning bid calculating module 160, the remaining dividend calculating module 170 and the lease deposit risk evaluating module 180 . ≪ / RTI >

The risk diagnosis system 100 is configured to calculate and provide an appropriate lease deposit at the time of lease contract and to diagnose the possibility of recovering the lease deposit at the expiration of the lease period by grasping the right relation of the real estate.

Hereinafter, the detailed configuration will be described.

The real estate database 110 may be configured to store data including rent report data, actual transaction data, court auction article data, building data book, and housing disclosure price data in advance. Such data can be configured to be searchable according to regional ranges such as city, county, county, and county.

The contract lease deposit input module 120 may be configured to receive the contract lease deposit from the user. The contractual lease deposit may be the amount after the contract or the amount before the contract. Here, the risk diagnosis system 100 can diagnose the adequacy or risk of a lease contract already made after the lease contract, and it is useful for completing or canceling the lease contract before the contract.

The contract lease deposit input module 120 may be configured to receive a lot number of the real estate and various various matters about the real estate. For example, it could be a residential area, the number of floors, existing tenants, payment of monthly rent, reconstruction, redevelopment, and real estate registration.

The appropriate lease deposit estimation module 130 may be configured to set an area range to which the real estate belongs and to select explanatory variables for estimating the lease deposit of the real estate in consideration of the set area range. Here, the regional scope may be an administrative unit such as a nation, city, province, county, district, etc., which is set up to estimate lease deposits on a regional basis. On the other hand, the explanatory variables can be the housing area, the number of floors, the existing tenants, and the payment of monthly rent.

The appropriate lease deposit estimating module 130 may be configured to generate an appropriate lease deposit estimation model based on the hedonic pricing model using the selected explanatory variables.

The hedonic price model assumes that the value of a good is determined by the characteristics of the good. In other words, it is assumed that the price of goods is determined by the price and quantity of the characteristics contained in the goods.

For example, the pricing of real estate by the hedonic price model can be configured to generate a linear or nonlinear function model using the explanatory variables such as its structural characteristics, neighborhood characteristics, and municipal characteristics to determine prices . (1).

Here, S is a structural characteristic, N is a neighborhood characteristic, and L is an input characteristic.

Examples of structural characteristics include land area, building structure, and the number of years since the new construction, and nearby characteristics may include income levels of residents, school districts, and crime rates. And, as for the characteristic point, it can be the distance to the subway station, the distance to the convenience facility, and so on.

Generally, in the real estate price determination, the hedonic price model can be created assuming a linear form and can be modeled as Equation (2).

Here, X represents various explanatory variables affecting real estate prices such as land area, building structure, income level of residents.

For example, here, the real estate whose land area is 150 m ² and whose elapsed years since the construction are eight years can be calculated as shown in Equation (3).

The appropriate lease deposit estimation module 130 may be configured to generate the hedonic price model as described above to estimate the appropriate lease deposit. At this time, the appropriate lease deposit estimation module 130 should generate an optimal lease deposit estimation model, which can be verified against the actual lease deposit to secure the reliability of the model. Therefore, it is required to verify the accumulated actual lease deposit data and construct a proper lease deposit estimation model.

The appropriate lease deposit estimation module 130 compares the actual contract lease deposit inputted from the contract lease deposit input module 120 with the appropriate lease deposit estimated by the appropriate lease deposit estimation model and provides the result to the user .

The lease deposit upper limit value calculation module 140 may be configured to calculate a lease deposit upper limit value according to a predetermined confidence interval with respect to the appropriate lease deposit estimated by the appropriate lease deposit estimation module 130. [ Here, the lease deposit upper limit value calculation module 140 may calculate the 95% confidence interval of the lease deposit and calculate the upper limit value of the confidence interval as the lease deposit upper limit value.

For example, if the hedonic price model is based on the maximum likelihood estimate, the standard margin of error σ of the predicted lease term can be calculated, and the estimated lease term + 2σ can be calculated as the upper limit of the lease deposit. If the hedonic price model is based on Bayesian inference, the estimated lease deposit corresponding to the 95th percentile of the simulation results can be calculated as the upper limit.

The appropriate selling price estimation module 150 can be configured to estimate the appropriate selling price of the real estate in consideration of the area range to which the real estate belongs.

The appropriate trading price estimation module 150 may be configured to select the explanatory variables for estimating the optimal selling price of the real estate and to generate the optimal selling price estimation model based on the hedonic price model using the selected explanatory variables have.

Here, the explanatory variables can be housing area, number of floors, building year, reconstruction or redevelopment. The appropriate selling price estimation module 150 verifies the performance of the optimal selling price model by comparing the appropriate selling price estimated through the optimal selling price estimation model with the actual selling price stored in the real estate database 110, . ≪ / RTI >

The winning bidder rate estimation module 160 may be configured to estimate a winning bid rate at auction of real estate. The winning bid price estimation module 160 may also be configured to estimate the winning bid price by a hedonic price model.

The bid price estimation module 160 may be configured to select the explanatory variables for estimating the winning bid rate of the real estate in consideration of the real estate range of the real estate and to generate the bid price coefficient estimation model based on the hedonic price model using the selected explanatory variables .

Here, the explanatory variables may include local variables, real estate types, and the time of a successful bid. And the winning bid rate is the winning bid divided by the valuation amount.

The winning bidder rate estimation module 160 may be configured to further include an additional risk factor that may cause a change in the winning bid price in the winning bid rate estimation model. Additional risk factors include land registration, unregistered land registry, lien, statutory superficies, notice registration, and the number of tenants.

The winning bidder rate estimation module 160 may be configured to estimate a winning bid rate through the winning bid rate estimation model and to verify the performance of the winning bid rate estimation model by comparing the estimated winning bid rate and the actual winning bid rate.

The winning bid amount calculating module 170 may be configured to calculate the winning bid amount by multiplying the appropriate selling price estimated by the appropriate trading price estimation module 150 by the winning bid rate estimated by the winning bid rate determining module 160. [

The remaining dividend calculation module 180 subtracts the remaining dividend from the winning bid amount calculated in the winning bid amount calculation module 170 by subtracting the auction cost, the highest priority small amount lease deposit, the national tax / local tax, the seniority guarantee right, . ≪ / RTI > The possibility of recovering the tenancy deposit can be judged by the residual dividend remaining after deducting the senior bond first.

The lease deposit risk risk assessment module 190 compares the contract lease deposit inputted by the contract lease deposit input module 120 with the lease deposit estimated from the appropriate lease deposit estimation module 130 and the lease deposit amount calculated by the residual dividend calculation module 180 And to diagnose and output the risk of the contract lease deposit against each of the remaining dividend.

If the contract lease deposit is larger than the appropriate lease deposit, it can be dangerous depending on the lease amount. Even if the lease deposit is larger than the remaining dividend, it can be considered that there is a risk of recovery of the lease deposit.

Meanwhile, the lease deposit risk diagnostic module 190 further compares the contract lease deposit inputted by the contract lease deposit input module 120 with the lease deposit upper limit estimated by the lease deposit upper limit value calculation module 140, And outputs the diagnosis result.

If the contract lease deposit is larger than the upper limit of the lease deposit, it can be regarded as an excessive amount exceeding the estimated confidence interval of the appropriate lease deposit.

The risk diagnosis of the lease deposit risk diagnostic module 190 and the risk level thereof can be variously set.

2 is a flowchart of a method for diagnosing a risk of a real estate lease deposit according to an embodiment of the present invention.

Referring to FIG. 2, the contract lease deposit input module 120 receives the contract lease deposit from the user (S101).

Next, the appropriate lease deposit estimation module 130 sets an area range to which the real estate belongs, selects a descriptive parameter for estimating the lease deposit of the real estate in consideration of the set area range, and uses the selected descriptive variable to calculate the hedonic price A suitable lease deposit estimation model based on a hedonic pricing model is generated and an appropriate lease deposit is estimated by the generated optimal lease deposit estimation model (S102).

Here, the performance of the appropriate lease deposit estimation model is verified by comparing the contract lease deposit inputted from the contract lease deposit input module 120 and the appropriate lease deposit estimated by the generated lease deposit estimation model, and the appropriate lease deposit estimation model . ≪ / RTI >

Next, the appropriate trading price estimation module 150 selects an explanatory variable for estimating the appropriate selling price of the real estate in consideration of the set area of the real estate, and generates a proper selling price estimation model based on the hedonic price model using the selected explanatory variables The optimal selling price is estimated by the generated optimal selling price estimation model (S103).

At this time, the estimated optimal selling price and actual transaction stored in the real estate database 110 may be configured to determine the optimal selling price estimation model by verifying the performance of the optimal selling price estimation model by comparing the reported data with each other.

Next, the lease deposit upper limit value calculation module 140 calculates a lease deposit upper limit value according to a predetermined confidence interval with respect to the appropriate lease deposit estimated by the appropriate lease deposit estimation module 130 (S104).

Next, considering the area range set by the winning bidder rate estimation module 160, a description parameter for estimating a winning bid price of a real estate is selected and a winning bid rate estimation model 160 based on the hedonic price model is created using the selected explanatory variables The winning bid rate is estimated by the generated winning bid rate estimation model (S105).

In this case, the additional risk factors that may cause the change in the winning bid rate may be additionally selected as the explanatory variables such as the land registration, the land registry non-registration, the lien, the legal right, the notice registration, and the number of tenants.

Next, the winning amount calculation module 170 calculates the winning amount by multiplying the appropriate selling price estimated by the appropriate selling price estimation module 150 and the winning bid price estimated by the winning bid price estimation module (S106).

Next, the remaining dividend calculation module 180 subtracts the auction cost, the highest priority liquidated lease deposit, the national tax / local tax, the seniority guarantee money or the other lease deposit received on the fixed date from the winning bid amount calculated in the winning bid amount calculation module 170 The remaining dividend is calculated (S107).

Next, the lease deposit risk diagnostic module 190 transmits the contract lease deposit inputted by the contract lease deposit input module 120 to the appropriate lease deposit, lease deposit upper limit value calculation module 140 estimated from the appropriate lease deposit estimation module 130 ) And the residual dividend calculated in the residual dividend calculation module 180, and outputs the diagnosed risk (S108).

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the following claims. There will be.

110: Real Estate Database
120: Contract lease deposit input module
130: Appropriate Lease Deposit Estimation Module
140: Lease deposit upper limit calculation module
150: Appropriate trading price estimation module
160: Bid rate estimation module
170: Successful bid amount calculation module
180: Remaining Dividend Calculation Module
190: Tenancy Deposit Risk Module

Claims (10)

A real estate database in which data including rent report data, actual transaction data, court auction article data, building book data, and housing disclosure price data are stored in advance;
A contract lease deposit input module for receiving a contract lease deposit from a user;
A range of the area where the real estate belongs is set, and explanatory variables for estimating the lease deposit of the real estate are selected in consideration of the predetermined area range, and a suitable rental deposit based on the hedonic pricing model An appropriate lease deposit estimation module for generating an estimation model and estimating an appropriate lease deposit based on the generated lease deposit estimation model;
The explanatory variables for estimating the optimal selling price of the real estate are selected in consideration of the set range of the real estate, and the optimal selling price estimation model based on the hedonic price model is generated using the selected explanatory variables. Module for estimating the selling price;
The selected parameters for estimating the winning bid price of the real estate are selected in consideration of the set range of the real estate, a winning bid rate estimation model based on the hedonic price model is created using the selected explanatory variables, and the winning bid rate is estimated by the generated winning bid rate estimation model A winning bid rate estimation module;
A winning bid amount calculation module for calculating a winning bid amount by multiplying an appropriate selling price estimated by the appropriate selling price estimation module and a winning bid rate estimated by the winning bid rate estimation module;
A residual dividend calculation module for calculating a remaining dividend by subtracting auction cost, a highest priority liquidation small lease deposit, a national tax / local tax, a senior security deposit, or another lease deposit received on a fixed date from the winning amount calculated in the winning amount calculation module;
The risk of the contract lease deposit is diagnosed by comparing the contract lease deposit inputted by the contract lease deposit input module with the appropriate lease deposit estimated by the appropriate lease deposit estimation module and the residual dividend calculated by the residual dividend calculation module Output lease deposit risk analysis module,
The optimal selling price estimation module,
And estimates an appropriate selling price according to the following equation,
[Mathematical Expression]

Where S is the structural property, N is the neighborhood property, and L is the municipal property, and the structural property includes the land area, the building structure and the elapsed years since the new construction, and the neighborhood property is the income level, Crime rate, the municipal characteristic being configured to include a distance to a subway station and a distance to a convenience facility,
Specifically, it is configured to estimate an appropriate selling price according to the following equation,
[Mathematical Expression]

Here,

Is a predetermined reference selling price,

Is a predetermined coefficient,

Is a value selected from among the structural characteristic, the neighborhood characteristic and the intelligent characteristic,

Is composed of any one of the structural characteristic, the nearby characteristic, and the characteristic characteristic,
Wherein the estimated selling price and the actual transaction stored in advance in the real estate database are configured to compare the reported data with each other to determine the appropriate selling price estimation model by verifying the performance of the appropriate selling price estimation model. system. The method according to claim 1,
And a lease deposit upper limit value calculating module for calculating a lease deposit upper limit value according to a predetermined confidence interval with respect to the appropriate lease deposit estimated by the appropriate lease deposit estimating module. 3. The risk management system according to claim 2,
Wherein the risk management module is configured to further diagnose the risk of the contract lease deposit by comparing the contract lease deposit inputted by the contract lease deposit input module with the lease deposit upper limit estimated by the lease deposit upper limit value calculation module, Risk Assessment System. The system of claim 1, wherein the appropriate lease deposit estimation module comprises:
The performance of the appropriate lease deposit estimation model is verified by comparing the contract lease deposit inputted from the contract lease deposit input module with the appropriate lease deposit estimated by the generated lease deposit estimation model to determine the appropriate lease deposit estimation model Wherein the system is configured to determine the real estate lease deposit risk. delete The method according to claim 1,
The landlord not registering, the landlord non-registering, the lien, the legal ground, the notice, and the number of the tenant as the above explanatory variables as the additional risk factors that may cause the fluctuation of the winning bid rate. system. Wherein the contract lease deposit input module receives a contract lease deposit from a user;
In order to estimate the lease deposit of the real estate in consideration of the area of the real estate, the estimation module of the appropriate lease deposit estimation module is selected and the hedonic pricing model (hedonic pricing model, and estimating an appropriate lease deposit by the generated appropriate lease deposit estimation model;
The appropriate trading price estimation module selects the explanatory variables for estimating the appropriate selling price of the real estate in consideration of the set area range, generates the appropriate selling price estimation model based on the hedonic price model using the selected explanatory variables, Estimating an optimal selling price by an estimation model;
Calculating a ceiling deposit upper limit value according to a predetermined confidence interval with respect to an appropriate lease deposit estimated by the appropriate lease deposit estimating module;
The winning bidder rate estimation module selects the explanatory variables for estimating the winning bidder ratio of the real estate in consideration of the set range of the real estate, generates a winning bid rate model based on the hedonic price model using the selected explanatory variables, Estimating a winning bid price;
Calculating a winning bid amount by multiplying an appropriate selling price estimated by the appropriate selling price estimation module and a winning bid price estimated by the winning bid rate calculating module;
Calculating a remaining dividend by subtracting auction cost, a highest priority liquidation small lease deposit, a national tax / local tax, a seniority guarantee money or another lease deposit received on a fixed date from the winning money calculated in the award calculation module;
The lease deposit risk diagnosis module compares the contract lease deposit inputted by the contract lease deposit input module with the appropriate lease deposit estimated by the appropriate lease deposit estimation module, the lease deposit upper limit estimated from the lease deposit upper limit value calculation module, and the residual dividend calculation And diagnosing and outputting the risk of the contract lease deposit with respect to each of the remaining dividends calculated in the module,
The appropriate trading price estimation module selects an explanatory variable for estimating an appropriate selling price of the real estate in consideration of the set area range, generates an appropriate selling price estimation model based on the hedonic price model using the selected explanatory variables, The step of estimating the optimal selling price by the selling price estimation model,
And estimates an appropriate selling price according to the following equation,
[Mathematical Expression]

Where S is the structural property, N is the neighborhood property, and L is the municipal property, and the structural property includes the land area, the building structure and the elapsed years since the new construction, and the neighborhood property is the income level, Crime rate, the municipal characteristic being configured to include a distance to a subway station and a distance to a convenience facility,
Specifically, it is configured to estimate an appropriate selling price according to the following equation,
[Mathematical Expression]

Here,

Is a predetermined reference selling price,

Is a predetermined coefficient,

Is a value selected from among the structural characteristic, the neighborhood characteristic and the intelligent characteristic,

Is composed of any one of the structural characteristic, the nearby characteristic, and the characteristic characteristic,
Wherein the estimated selling price and the real transaction stored in advance in the real estate database are configured to compare the reported data with each other to determine the appropriate selling price estimation model by verifying the performance of the appropriate selling price estimation model . 8. The method according to claim 7, wherein the appropriate lease deposit estimation module sets an area range to which the real estate belongs, selects an explanatory variable for estimating the lease deposit of the real estate in consideration of the set area range, The step of generating an appropriate lease deposit estimation model based on the hedonic price model and estimating the appropriate lease deposit based on the generated appropriate lease deposit estimation model,
The performance of the appropriate lease deposit estimation model is verified by comparing the contract lease deposit inputted from the contract lease deposit input module with the appropriate lease deposit estimated by the generated lease deposit estimation model to determine the appropriate lease deposit estimation model And determining the risk of the property lease deposit. delete 8. The method according to claim 7, wherein the bidder rate estimation module selects an explanatory variable for estimating the bid price of the real estate in consideration of the set area range, generates a bid price model based on the hedonic price model using the selected explanatory variables, The step of estimating the winning bid rate by the generated winning bid probability model,
The landlord not registering, the landlord non-registering, the lien, the legal ground, the notice, and the number of the tenant as the above explanatory variables as the additional risk factors that may cause the fluctuation of the winning bid rate. Way.

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