KR101930948B1 - Method for estimation price of house, and system thereof - Google Patents

Abstract

The present invention relates to a home price estimation method and system, and more particularly, to a home price estimation method and system, in which users searching for price information on a conventional home search for apartments and desired equilibrium, And a housing price estimation method and system for enabling personalized personal information to be transmitted through correction of big data according to the user's individual characteristics by inputting only the number of apartments through individual price estimation.
In this way, it is possible to create a healthy housing transaction market based on data and actual cases by further mobilizing the subject-oriented housing transaction market more objectively through housing price estimation.
In addition, the present invention can analyze and measure the risk due to the price change of the collateralized asset in real time according to the house price estimation in the financial institution and utilize it to maintain the bank soundness. In accordance with the development of pin tech and crowdfunding technology, As a result of the dis-intermediation of financial products and the dis-institutionalization of transactions that have been made only by institutions, they provide important effects for the development of financial products using collateral.

Description

[0001] METHOD AND SYSTEM FOR ESTIMATING HOUSING PRICE [0002]

The present invention relates to a home price estimation method and system, and more particularly, to a home price estimation method and system, in which users searching for price information on a conventional home search for apartments and desired equilibrium, And a housing price estimation method and system for enabling personalized personal information to be transmitted through correction of big data according to the user's individual characteristics by inputting only the number of apartments through individual price estimation.

The field of real estate valuation plays a very important role from the people who consume real estate to investors to banks. The real estate market is a very big part of the modern financial market, and it is an area where many people participate directly and indirectly.

In particular, the housing market is an important axis inevitably involving all citizens inevitably in any form. In the reality that the substantial amount of Korean household assets is concentrated on real estate, the right information about property value, especially price, It will be very big.

Meanwhile, FIG. 1 is a chart that classifies approaches to existing real estate prices.

The prices according to the existing approach are largely 1. quotation, 2. disclosure price, 3. real transaction, 4. quotation, 5. appraisal, and 6. successful bidder.

division Subject Characteristic Limit Published price government
(Published by Korea Appraisers) It is individual information about each apartment lake and it is calculated for tax purposes. It can be adjusted through appeal (if it is aimed for long term, Individual level information but not realistic value A real deal government
(Ministry of Land, Transport and Maritime Affairs published according to certain requirements) Actual facts are reported, but only limited information (low floor - middle floor - high floor level information) However, the amount announced after the balance transfer (not including the contract date) but after the transfer of ownership (time difference occurs) Arc proprietor In regions where investment demand is high, such as those with high per square meter prices, prices tend to be higher than prices. There is no clear grounds, and it varies inconsistently depending on government policies and market conditions. Collect quotations to create ticker information at the broker quote Real estate agent It collects the information produced by the relevant agency and provides price information at an appropriate level. connoisseur Appraiser This is the most reliable price information that reflects characteristics of individual real estate properties and market conditions at the time of appraisal evaluation. Based on this, the collateral value of financial institutions is evaluated (used for calculating loan amount) You can estimate the exact value of an individual item, but there is a cost of appraisal for each case Winning bidder Auction result It is common for the price to be lower than the general transaction price at the price auctioned to the public through a court auction or an auction In the worst case, the amount that the creditor can recover can not sufficiently reflect the value of the property.

Since the typical housing type in Korea is apartment, it is good to quantify the house price. In the current real estate market, price information based on the actual transaction amount is needed because information on the market price of individual real estate mainly reflects the price of the landlord collected from the real estate brokerage, so the price is based on the call price. Although the actual transaction price database (DB) of the Ministry of Land, Transport and Maritime uses actual transactions, it is difficult to estimate the exact price if there is not a direct comparison case.

[Related Technical Literature]

1. Survey system, survey method, and recording medium on which the program is recorded for recording the property price ratification table (Surveys system and method for making property price ratification table and recording medium having a program to execute the method thereof ) (Patent Application No. 10-2012-0051903)

2. The automatic price verification system of real estate using internet (Patent Application No. 10-2000-0007148)

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide a method and apparatus for searching for price information on a house by searching individual apartments and desired equilibrium, The present invention provides a method and system for estimating a house price to be able to transmit individual personalized information by calibrating a big data analysis according to a user's individual characteristics.

Further, according to the present invention, when more variables are corrected over time, it becomes possible to analyze the parameters including the market reaction, and in addition to the results of the analysis, Estimation method and system.

In addition, the present invention provides information on objective prices in a liquidity market where prices fluctuate for a short period of time, thereby reducing the price volatility due to rapid changes in investment sentiment and stabilizing the property market Estimation method and system.

The present invention also provides a housing price estimation method for estimating price information corresponding to the real estate information through statistical prediction in a case where the real estate market is stagnated and actual transaction cases become insufficient, And a system.

In addition, the present invention finds variables having statistical significance that affect the price of a real estate by using time-series information of a database (DB) and an external variable in which a house price is estimated, and analyzes scenarios (combinations of variables) To estimate the future price, and to provide a method and system for estimating the house price to make reference to the investment decision of the real estate and to utilize it for the investment analysis.

However, the objects of the present invention are not limited to the above-mentioned objects, and other objects not mentioned can be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, a home price estimation system according to an embodiment of the present invention is a system for estimating a home price by a home price estimation server (80) through a network (20) A user terminal 10 for transmitting data to the user terminal 10; From the Korean Appraisers' Individual Disclosure Price Server 30 of the apartment or house that has been the target of the home price estimation request through the network 20 and in the same manner as the public real estate market information server 40, Quot; statistical information " of the target apartment or the target house from the house price survey statistics server 50 and receives the 'statistical estimate information' according to the price change of the sample house representing the entire house measured by the commercial bank And receives the contract price information from the auction winning price information server 60. The real maritime coupon transaction receives the "actual price information" from the information server 70 and stores it in the database 83. At least one of the received price information A home price estimation server 80 that performs price estimation that reflects the individual characteristics of the apartment, the market change, and the factors according to the time flow; , And the house price survey statistics server 50 stores the "survey statistical information" prepared for the market standard price surveyed by the real estate agent for the sample house selected to represent the entire house for the analysis of the commercial bank house price survey, Quot; statistical information " provided by the commercial real estate market information server 40 corresponds to " reporting statistics " based on the reported transaction price for the contracted transaction house, and the housing price estimation server 80 , House price survey statistics, auction bidding price update information, and update information (restricted information of the traded objects) of real transactions by Ministry of Land, Transport and Maritime Affairs are classified into apartment complexes The home price estimation server 80 calculates the value of the individual mortgage value corresponding to the home price estimation The user searching for the beam can search the apartment and equilibrium of his / her apartment and control the transmission / reception unit 81 so that it is not necessary to selectively search for the information corresponding to the apartment, An apartment dynamic lake receiving module (82a) for receiving a home price estimation request including address information of a house; In order to correct the individual announcement price (price per apartment lane) of the Korean appraisal agency, it is necessary to check the data of the real estate market price, the house price survey statistics, the auction price update information, the update data (restricted information of the traded objects) 83), and performs a predetermined operation with the Korean announcement price (apartment price per apartment) after extracting the floor-level difference in the same apartment complex for the at least one information among the floor, ; In order to correct the individual announcement price (price per apartment lane) of the Korean appraisal agency, it is necessary to check the data of the real estate market price, the house price survey statistics, the auction price update information, the update data (restricted information of the traded objects) 82) for extracting a view price related price difference in the same apartment complex for at least one of the information items (83, 83), and performing a preset operation with the Korean announcement price (apartment price per apartment); In order to compensate for the more variables over time, the price of the individual appraisal price (apartment price per apartment) in the central area of the Korean appraisal system, (The limited information of the traded objects) in the same apartment complex for at least one of the stored databases 83, and then performs preliminary computation with respect to the individual disclosure price A seasonal factor analysis module 82d; In order to compensate for the more variables over time, the price of the individual appraisal price (apartment price per apartment) in the central area of the Korean appraisal system, (The limited information of the traded objects) is extracted in the same apartment complex for at least one of the stored databases 83, and the price of each local announcement price A local presence cause application module 82e for performing a preset operation; A month-end effect applying module 82f and a moving-effect applying module 82g for analyzing the market response according to the local month-end effect, the moving effect, and the policy variable for all apartment complexes and houses stored in the database 83, A policy variable applying module 82h; The month-end effect applying module 82f, the moving iron effect applying module 82g and the policy variable applying module 82h are provided for each of the apartments and houses stored in the database 83, The predetermined deviation is set to a predetermined weight in the Korean announcement price (apartment price per apartment), and the predetermined operation performed by the layer-based analysis module 82b is the same apartment The price difference related to the viewing right extracted by the viewing right analysis module 82c is determined by the presence or absence of other apartments in the front and the rear of the apartment, And the presence or absence of a steel, a sea, and an acid in the rear, price deviation information according to the first floor or the top floor, and the analysis module 82b and the floor- The network analysis module 82c is adapted to analyze the individual characteristics of the user apartment (price difference according to the number of floors, price fluctuation according to the first floor, premium according to the Han river view) through the big data analysis, It is preferable that the analysis module 82b extracts and corrects the price deviation according to the apartment lake as well as the apartment floor.
At this time, the house price estimation server 80 includes a floor-based analysis module 82b, a viewpoint analysis module 82c, a seasonal factor analysis module 82d, a regional hallmark cause application module 82e, a month-end effect application module 82f ), The moving-rail effect applying module 82g, and the policy variable applying module 82h to the individual announcement price (price per apartment lane) of the Korean appraisal agent sequentially, and finally, And the value estimation is completed and the apartment dynamic lake receiving module 82a, the layer-based analysis module 82b, the viewpoint analysis module 82c, the seasonal factor analysis module 82d, the regional fuzzy cause application module 82e, The individual price estimation through statistical analysis for each of the application module 82f, the moving effect application module 82g and the policy variable application module 82h is implemented in the system using the hierachical Bayesian modeling, Market changes and It is possible to make accurate price estimates based on time-series factors at individual level, and analyze hierachical Bayesian modeling using past transaction information and similar housing information. A hierachical Bayesian modeling module 82i that applies the statistical error at 5% line; .

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The method and system for estimating the house price according to the embodiment of the present invention provides an effect of forming a healthy housing transaction market based on data and actual cases by further objectifying and mobilizing the housing transaction market focused on the subjective price through the housing price estimation .

In addition, the method and system for estimating the house price according to another embodiment of the present invention can analyze and measure the risk due to the price change of the collateral asset in real time according to the house price estimation in the financial institution, With the development of fin tech and crowdfunding technology, as financials became non-monopoly and online, financial products were dis-intermediated, and transactions that were only made by institutions were dis-institutionalized, It provides effects that are important for development.

FIG. 1 is a diagram illustrating a home price estimation system according to an embodiment of the present invention.
2 is a conceptual diagram illustrating a method of estimating a house price using the housing price estimation system of FIG.
3 is a block diagram illustrating the components of the home price estimation server 80 of the housing price estimation system of FIG.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a detailed description of preferred embodiments of the present invention will be given with reference to the accompanying drawings. In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

In the present specification, when any one element 'transmits' data or signals to another element, the element can transmit the data or signal directly to the other element, and through at least one other element Data or signal can be transmitted to another component.

FIG. 1 is a diagram illustrating a home price estimation system according to an embodiment of the present invention. 1, the home price estimation system includes a user terminal 10, a network 20, a Korean Appraisal Individual Disclosure Price Server 30, a commercial real estate market information server 40, a housing price survey statistics server 50, An auction winning bid price information server 60, a real land transaction bucket, an information server 70 and a home price estimation server 80.

The user terminal 10 transmits a home price estimation request including the address information of the apartment of the apartment or the house to the home price estimation server 80 via the network 20. [

 The home price estimation server 80 receives the 'disclosure price information' of the apartment or the house that is the subject of the home price estimation request through the network 20 from the Korean Delphi source individual disclosure price server 30. In the same manner, the home price estimation server 80 receives the 'quotation information' of the target apartment or the target house from the commercial real estate market information server 40, Statistical estimate information "according to the price change of the representative house representing the entire house, receives the contract price information from the auction price information server 60, and transmits the" actual price information "from the information server 70 to the real- And stores it in the database 83.

The network 20 is a communication network that is a high speed period network of a large communication network capable of a large capacity, long distance voice and data service, and may be a next generation wired and wireless network for providing Internet or high-speed multimedia service. When the network 20 is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an embodiment of the asynchronous mobile communication network, a WCDMA (Wideband Code Division Multiple Access) communication network is exemplified. In this case, although not shown in the drawings, the network 20 may include a Radio Network Controller (RNC). Meanwhile, although the WCDMA network is exemplified, it may be a next generation communication network such as a 3G LTE network or a 4G network, or an IP network based on other IP. The network 20 includes a user terminal 10, an individual disclosure price server 30, a commercial real estate market information server 40, a housing price survey statistics server 50, an auction sale price information server 60, Transmits the signals and data of the information server 70, the home price estimation server 80, and other systems to each other.

 Here, the house price survey statistics server 50 collects " survey statistical information " for the standard price surveyed by the real estate agent for the sample houses selected to represent the entire house for the bank house price survey statistics Quot; statistical information statistics provided by the commercial real estate market information server 40 correspond to " reporting statistics " based on the reported transaction prices for the contracted house. The housing price survey statistics provided by the housing price survey statistics server 50 are intended to estimate the price fluctuation of the entire house by measuring the price fluctuation of the representative house representing the entire house, By measuring price fluctuations, they can be seen as different statistics. The housing price survey statistics are different according to the purpose of the survey. Since each method has different strengths and weaknesses, it is necessary to clearly understand the difference in the method of making the survey according to the purpose of the survey and to increase the utilization rate for each purpose.

2 is a conceptual diagram illustrating a method of estimating a house price using the housing price estimation system of FIG. Referring to FIG. 2, the home price estimation server 80 updates the current price information (price per apartment lake) of the Korean appraisal office by comparing the current market price information, the housing price survey statistics, the auction price update information, The information (limited information of the traded objects) is adjusted for the individual characteristics of the apartment including the apartment complex, the apartment building, the floor, and the factors related to the market change and time. do.

3 is a block diagram illustrating the components of the home price estimation server 80 of the housing price estimation system of FIG. 3, the home price estimation server 80 includes a transmission / reception unit 81, a control unit 82, and a database 83. Meanwhile, the control unit 82 includes an apartment house receiving module 82a, a layer-based analysis module 82b, a viewpoint analysis module 82c, a seasonal factor analysis module 82d, a regional hallmark cause application module 82e, An effect applying module 82f, a moving iron effect applying module 82g, a policy variable applying module 82h and a hierarchical bayesian modeling module 82i.

The apartment dynamic lake receiving module 82a controls the transmission / reception unit 81 using the service so that users searching for information about the house need not search for their apartments and equilibrium and select the information corresponding to them, And receives a home price estimation request including the address information of the apartment of the apartment or the house from the user terminal 10 via the network 20.

The analysis module 82b according to the floor is used for correcting the individual announcement price (price per apartment lane) of the Korean appraisal office by using the information of the real estate market price, the house price survey statistics, the auction sale price update information, The price of the floor is extracted from the same apartment complex with respect to at least one of the stored databases 83, and then a predetermined calculation is performed with respect to the Korean announcement price (apartment price per apartment). In the present invention, the predetermined calculation may be an average value of the floor-level deviation and the individual announcement price (price per apartment lake) in the same apartment complex.

In order to compensate for the individual announcement price (price per apartment lane) of the Korean appraisal agency, the view-point analysis module 82c updates the information on the real estate market price, the house price survey statistics, the auction sale price update information, Information related to at least one of the stored databases 83 is extracted from the same apartment complex, and the predetermined arithmetic operation is performed with respect to the individual disclosure price (apartment price per apartment) of the Korean appraisal agency. In the present invention, the viewpoint-related deviation may be the presence or absence of other apartments in the front and rear, presence or absence of the river, sea, mountains in the front and rear, and the price deviation information according to the first floor or the top floor.

The layer-based analysis module 82b and the view-point analysis module 82c are corrected through the big data analysis on the individual characteristics (price difference according to the number of floors, price fluctuation according to the first floor, premium according to the Han river view) The individual analysis module 82b can extract and correct price deviations according to the apartment floors as well as apartment floors.

The seasonal factor analysis module 82d analyzes the price of each apartment (the price per apartment lake) of the Korean appraisers to compensate for more variables over time, , The Ministry of Land Transport and Maritime Affairs extracts seasonal price deviations in the same apartment complex for at least one of the stored databases 83 for updated information (limited information on the traded objects) ). ≪ / RTI >

The local reason factor application module 82e updates the market price information of the real estate market, the housing price survey statistics, the auction winning bid price update Information and information on at least one of the database 83 stored for update information (limited information on the traded objects) in the same apartment complex is extracted from the database 83, And performs a preset operation with the price (price per apartment lump).

Each of the month-end effect application module 82f, the movement effect application module 82g and the policy variable application module 82h can be classified into the following modules according to the variables of the local month effect, moving effect, and policy on all the apartment complexes and houses stored in the database 83 And analyzing the market response, respectively.

In this case, each of the month-end effect applying module 82f, the moving iron effect applying module 82g, and the policy variable applying module 82h stores the month-end price fluctuation price deviation for all the apartments and houses stored in the database 83, , And the price deviation due to the application of the policy variable is set to a predetermined weight in each of the Korean announcement prices (price per apartment lump).

The control unit 82 includes an analysis module 82b, a viewpoint analysis module 82c, a seasonal factor analysis module 82d, a regional hallmark cause application module 82e, a month-end effect application module 82f, The price deviation information by module 82g and policy variable application module 82h are sequentially applied to the individual announcement prices of Korean appraisers (price per apartment), and finally the individual collateral value estimation Can be completed.

The hierarchical Bayesian modeling module 82i includes an apartment dynamic lake receiving module 82a, a layered analysis module 82b, a viewpoint analysis module 82c, a seasonal factor analysis module 82d, The individual price estimation is performed by hierachical Bayesian modeling by statistical analysis on the cause application module 82e, the month-end effect application module 82f, the movement effect application module 82g, and the policy variable application module 82h. , It is possible to make the accurate price estimation based on the market change and the time-dependent factors at the individual level, and by using the past transaction information and the information of the similar house By applying hierachical Bayesian modeling analysis, the statistical error can be caught at 5%.

The present invention can also be embodied as computer-readable codes on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored.

Examples of the computer-readable recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device and the like, and also implemented in the form of a carrier wave (for example, transmission over the Internet) .

The computer readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner. And functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers skilled in the art to which the present invention pertains.

As described above, preferred embodiments of the present invention have been disclosed in the present specification and drawings, and although specific terms have been used, they have been used only in a general sense to easily describe the technical contents of the present invention and to facilitate understanding of the invention , And are not intended to limit the scope of the present invention. It is to be understood by those skilled in the art that other modifications based on the technical idea of the present invention are possible in addition to the embodiments disclosed herein.

10: User terminal
20: Network
30: Korean Appraisal Individual Disclosure Price Server
40: Real estate market information server
50: Home Price Survey Statistics Server
60: auction winning bidder information server
70: Ministry of Land, Transport and Maritime Affairs
80: Home price estimation server
81: Transmitting /
82:
82a: Apartment Dong lake receiving module
82b: layer-by-layer analysis module
82c: Viewing rights analysis module
82d: Seasonal factor analysis module
82e: Local Reason Cause Application Module
82f: Monthly effect application module
82g: Moving effect application module
82h: Policy variable application module
82i: Hierachical Bayesian modeling module
83: Database

Claims (7)

A user terminal 10 for transmitting a home price estimation request including address information of an apartment house or a house to a home price estimation server 80 through a network 20; And
Quot; published price information " of the apartment or house that is the subject of the housing price estimation request through the network 20 from the Korean Appraisers' individual disclosure price server 30, and in the same way, from the current real estate market information server 40 Receives the 'market information' of the target apartment or the target house, receives the 'statistical estimate information' according to the price change of the sample house representing the entire house measured by the commercial bank from the housing price survey statistics server 50, The real marriage department real transaction receives the "actual transaction price information" from the information server 70 and stores it in the database 83. At least one or more of the received price information A home price estimation server 80 that performs price estimation that reflects individual characteristics of the apartment, factors of the market change and the flow of time; / RTI >
The house price survey statistics server 50 collects "survey statistical information" about the standard price of the market surveyed by the real estate agency for the sample house selected to represent the entire house for the survey of the commercial bank house price survey, Quot; statistical information statistics provided by the commercial real estate market information server 40 correspond to " reporting statistics " based on the declared transaction price for the contracted house,
The home price estimation server 80 has a function of calculating the price of the real estate market information, the home price survey statistics, the auction sale price update information, the update information (the transaction object , Which is estimated by the apartment complex, is adjusted to the apartment complex,
The home price estimation server 80,
The users searching for information about the house search for their apartments and equilibrium and control the transmission / reception unit 81 so as not to select the information corresponding to them, An apartment east lake receiving module (82a) for receiving a home price estimation request including address information of the east lake or the house of the apartment;
In order to correct the individual announcement price (price per apartment lane) of the Korean appraisal agency, it is necessary to check the data of the real estate market price, the house price survey statistics, the auction price update information, the update data (restricted information of the traded objects) 83), and performs a predetermined operation with the Korean announcement price (apartment price per apartment) after extracting the floor-level difference in the same apartment complex for the at least one information among the floor, ;
In order to correct the individual announcement price (price per apartment lane) of the Korean appraisal agency, it is necessary to check the data of the real estate market price, the house price survey statistics, the auction price update information, the update data (restricted information of the traded objects) 82) for extracting a view price related price difference in the same apartment complex for at least one of the information items (83, 83), and performing a preset operation with the Korean announcement price (apartment price per apartment);
In order to compensate for the more variables over time, the price of the individual appraisal price (apartment price per apartment) in the central area of the Korean appraisal system, (The limited information of the traded objects) in the same apartment complex for at least one of the stored databases 83, and then performs preliminary computation with respect to the individual disclosure price A seasonal factor analysis module 82d;
In order to compensate for the more variables over time, the price of the individual appraisal price (apartment price per apartment) in the central area of the Korean appraisal system, (The limited information of the traded objects) is extracted in the same apartment complex for at least one of the stored databases 83, and the price of each local announcement price A local presence cause application module 82e for performing a preset operation; And
A month-end effect applying module 82f, a moving-effect applying module 82g, and a moving-effect applying module 82b for analyzing market responses according to the local month-end effect, the moving effect, and the policy variable for all apartment complexes and houses stored in the database 83, A policy variable application module 82h; / RTI >
The month-end effect applying module 82f, the moving iron effect applying module 82g and the policy variable applying module 82h are provided for each of the apartments and houses stored in the database 83 at the end-of-month price fluctuation price deviation, The price deviation according to the application is applied to the individual announcement price (apartment price per apartment) according to the preset weight, respectively,
The predetermined operation performed by the floor-based analysis module 82b is to obtain an average value of the floor-level deviation and the individual announcement price (price per apartment lane) of the Korean appraisal center in the same apartment complex,
The price difference related to the viewpoint extracted by the viewpoint analysis module 82c is information on the presence or absence of other apartments in the front and the rear, existence of the river, sea, mountains in the front and rear, price deviation information according to the first floor or the top floor,
The layer-based analysis module 82b and the view-point analysis module 82c are corrected through the big data analysis on the individual characteristics (price difference according to the number of floors, price fluctuation according to the first floor, premium according to the Han river view) And the floor-specific analysis module 82b extracts and corrects the price deviation according to the apartment floor as well as the apartment floor.
The method according to claim 1, wherein the housing price estimation server (80)
A seasonal factor analysis module 82d, a regional hallmark cause application module 82e, a month-end effect application module 82f, a moving rail effect application module 82g, a policy And the price variance information by the variable application module 82h is sequentially applied to the individual announcement price (apartment price per apartment) of the Korean appraisal agency to finally complete the individual collateral value estimation corresponding to the house price estimation,
An apartment dynamic lake receiving module 82a, a floor-based analysis module 82b, a viewpoint analysis module 82c, a seasonal factor analysis module 82d, a local public factor application module 82e, a month-end effect application module 82f, The individual price estimation by statistical analysis for each of the application module 82g and the policy variable application module 82h is implemented in the system using the hierachical Bayesian modeling, , Which is a statistical model for estimating the price of a real estate, by applying the hierachical Bayesian modeling analysis using the past transaction information and the information of the traded house. A hierachical Bayesian modeling module 82i that allows the error to be taken at 5%; Further comprising a home price estimation system.
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