WO2018151343A1 - Système et procédé pour évaluer automatiquement le prix d'un bien immobilier sur la base d'une technique d'apprentissage d'ensemble - Google Patents

Système et procédé pour évaluer automatiquement le prix d'un bien immobilier sur la base d'une technique d'apprentissage d'ensemble Download PDF

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WO2018151343A1
WO2018151343A1 PCT/KR2017/001642 KR2017001642W WO2018151343A1 WO 2018151343 A1 WO2018151343 A1 WO 2018151343A1 KR 2017001642 W KR2017001642 W KR 2017001642W WO 2018151343 A1 WO2018151343 A1 WO 2018151343A1
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WIPO (PCT)
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price
real estate
estimated
model
module
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PCT/KR2017/001642
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English (en)
Korean (ko)
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맹준영
이창노
최우현
장명수
김경주
김정은
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맹준영
이창노
최우현
장명수
김경주
김정은
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Priority to PCT/KR2017/001642 priority Critical patent/WO2018151343A1/fr
Publication of WO2018151343A1 publication Critical patent/WO2018151343A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/16—Real estate

Definitions

  • the present invention relates to an automatic real estate price evaluation system and method, and more particularly, to a real estate price automatic evaluation system and method based on an ensemble learning technique.
  • real estate prices vary greatly depending on their physical location, which is called spatial dependence.
  • the characteristics of the real estate price formation that is, the factors such as physical location and distance have not been reflected, and the accuracy in estimating the real estate price is inevitably deteriorated.
  • An object of the present invention to provide an automatic real estate price evaluation system based on the ensemble learning technique.
  • Another object of the present invention is to provide an automatic real estate price evaluation method based on an ensemble learning technique.
  • the automatic real estate price evaluation system based on the ensemble learning method according to the above object of the present invention includes a real estate database in which real transaction prices, geographic coordinates, and similar price zone division data are stored in advance; At least one parametric model and at least one non-parametric model are applied to the real estate, geographic coordinates, and pseudo-price parity data of the real estate previously stored in the real estate database. Calculate the weighted price by calculating the price for each model for each model, and assigning the same or different weights to the calculated price for each model, wherein the calculated weighted price is compared to the similar price range partition data of the property.
  • the price difference according to the physical location is determined by the similarity, and if the price difference according to the physical location of the weighted price is more than a predetermined reference value, the weighted price is estimated by interpolating the space in consideration of the physical location.
  • Price estimation model to calculate the price System module An estimated price validation module for validating the estimated price against the estimated price compared with the actual transaction price stored in the real estate database;
  • a population price estimation module that calculates an estimated price of the remaining real estate of the population according to a calculation method of the price estimation model design module, when the validation is validated in the estimated price validation module;
  • Estimated price adequacy review module that examines the adequacy of the estimated price of the remaining real estate of the population against the price range of the estimated price of the real estate estimated by the population price estimation module and the price range of the actual transaction price stored in the real estate database ;
  • the estimated price adequacy module may be configured to include an estimated price providing module that
  • the price estimating model design module may be configured to apply a linear regression model among the parameter models and to apply a random forest model or a boosting model among the non-parametric models.
  • the price estimating model design module applies an arithmetic mean weight to the price of each model or uses an artificial neural network model to calculate an optimal weight for each model according to the reliability of the price of each model. It can be configured to calculate and apply.
  • the price estimating model design module linearly combines the actual transaction price of the real estate in consideration of the corresponding physical location of the real estate in which the transaction has already been stored in the real estate database, and spaces the weighted price using the linear combined actual transaction price.
  • the estimated price validation module calculates a price ratio, a variance coefficient, and an absolute average error rate by comparing the estimated price with an actual transaction price stored in the real estate database, and verifies the estimated price according to the calculation result. Can be configured.
  • the price estimation model design module is at least one or more to the actual price, geographic coordinates and similar price zone partition data pre-stored in the real estate database Calculating a model-specific price for a given real estate of a population by applying a parametric model and at least one non-parametric model; Calculating, by the price estimating model design module, a weighted price by assigning the same or different weights to each of the calculated prices for each model; Determining, by the price estimating model design module, a price difference according to a physical location based on whether the calculated weighted price is similar to the similar price zone segment data of the real estate; Calculating the estimated price by interpolating the weighted price in space in consideration of the physical location when the price difference according to the physical location of the weighted price is greater than or equal to a predetermined reference value as a result of the determination; A step of validating the estimated price by the estimated price validation module comparing the estimated price with an
  • the price estimating model design module applies at least one parameter model and at least one non-parametric model to the actual price, geographic coordinates, and pseudo-price parity data of the real estate previously stored in the real estate database, and then models the predetermined real estate of the population.
  • the calculating of each price may be configured to apply a linear regression model among the parameter models and to apply a random forest model or a boosting model among the non-parametric models.
  • the calculating of the weighted price by assigning the same or different weights to the calculated price for each model by the price estimation model design module may apply an arithmetic mean weight to the price of each model or for each model.
  • an artificial neural network model can be configured to calculate and apply an optimal weight for each model price.
  • the price estimation model design module calculates an estimated price by interpolating the weighted price in space in consideration of the physical location. Krigging for linearly combining the actual transaction price of the real estate in consideration of the corresponding physical location of the real estate that has already occurred transaction stored in the real estate database and interpolating the weighted price in space using the linear combined actual transaction price
  • the technique may be configured to calculate an estimated price.
  • the validating of the estimated price by comparing the estimated price with the actual transaction price stored in the real estate database may include comparing the estimated price with the actual transaction price stored in the real estate database.
  • the price ratio, the variance coefficient, and the absolute mean error rate may be calculated, and the validity of the estimated price may be verified according to the calculation result.
  • the estimations of various models are optimized and reflected, and the accuracy of estimation is improved.
  • the real estate price in consideration of the physical location of the real estate, it is effective to reflect an important factor of the formation of the real estate price and increase the accuracy of estimation.
  • FIG. 1 is a block diagram of an automatic real estate price evaluation system based on an ensemble learning method according to an embodiment of the present invention.
  • Figure 2 is an illustration of an artificial neural network model according to an embodiment of the present invention.
  • FIG. 3 is an exemplary diagram illustrating a result of spatial interpolation according to an embodiment of the present invention.
  • FIG. 4 is an exemplary view showing a validation result of a price estimation model according to an embodiment of the present invention.
  • FIG 5 is an exemplary diagram of an online providing screen of an estimated price according to an embodiment of the present invention.
  • FIG. 6 is a flowchart of a method for automatically evaluating real estate prices based on an ensemble learning method according to an embodiment of the present invention.
  • first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.
  • the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.
  • FIG. 1 is a block diagram of an automatic real estate price evaluation system based on an ensemble learning method according to an embodiment of the present invention.
  • an automatic real estate price evaluation system based on an ensemble learning method according to an embodiment of the present invention (hereinafter, referred to as an “real estate price automatic evaluation system”) 100 is a real estate database 110 and a price estimation model. It may be configured to include the design module 120, the estimated price validation module 130, the population price estimation module 140, the estimated price adequacy review module 150, and the estimated price providing module 160.
  • the real estate database 110 may be configured to store property-specific characteristics, actual transaction prices, geographic coordinates, and similar price range partition data in advance.
  • the similar price zone partition data may be configured to be databased in advance as partition data forming a similar price zone according to a physical location. For example, if the price range is high in the backcountry zone, it means data about the area around the station where the advantage of the backyard zone applies.
  • the price estimation model design module 120 is a component for designing an optimal price estimation model by applying various models for estimating real estate prices.
  • the price estimating model design module 120 first generates a new price estimating model by applying weights to various price estimating models, and performs the interpolation according to the physical location to reflect the change characteristics of real estate prices according to the physical location.
  • the price estimating model design module 120 includes at least one parametric model and at least one non-parametric model in the real price, geographic coordinates, and pseudo-price parcel data of the property previously stored in the real estate database 110.
  • a parametric model may be applied to calculate a price for each model for a given real estate.
  • the linear regression model is applied to the parameter model
  • the random forest model, boosting model, support vector machine, or multivariate adaptive regression splines are applied to the non-parametric model.
  • the non-parametric model is highly realistic because it estimates real estate prices without strict assumptions.
  • the price estimating model design module 120 combines and applies these price estimating models.
  • the price estimating model design module 120 applies arithmetic mean weights that give the same weights to the prices of each model, or according to the reliability of the price of each model.
  • An artificial neural network model may be used to calculate and apply an optimal weight for each model price to calculate a weighted price.
  • an ensemble learning technique in which different weights are applied to the price of each model is applied.
  • the ensemble learning technique can cancel out different tendencies such as overestimation or underestimation.
  • the price estimation model design module 120 cancels the price difference according to the physical location with respect to the weighted price.
  • the weighted price is configured to determine whether the weighted price is similar to the similar price zone segment data of the property. Can be.
  • the price estimation model design module 120 may be configured to determine a price difference according to the physical location of the property through the determination result. As a result, when the price difference according to the physical location of the weighted price is more than a predetermined reference value, the price estimation model design module 120 may be configured to calculate the estimated price by interpolating the weighted price in space in consideration of the physical location. have.
  • the price estimating model design module 120 may be configured to calculate the estimated price by interpolating the weighted price by a kriging technique.
  • the price estimating model design module 120 linearly combines the actual transaction prices of the real estate in consideration of the corresponding physical location of the real estate in which the transaction has already been stored in the real estate database 110 and weights using the linear combined actual transaction prices.
  • the application price may be configured to interpolate in space.
  • the estimated price validation module 130 may be configured to validate the estimated price.
  • the estimated price validation module 130 may be configured to verify the validity of the estimated price by comparing the estimated price with the actual transaction price stored in the real estate database 110. Diagnostic indicators for validating may include a price ratio for determining the accuracy of the estimated price, a coefficient of dispersion for determining the balance, and a mean absolute percentage error. That is, the estimated price validation module 130 calculates a price ratio, a variance coefficient, and an absolute average error rate by comparing the estimated price with the actual transaction price stored in the real estate database 110, and verifies the validity of the estimated price according to the calculation result. It can be configured to.
  • the population price estimation module 140 may be configured to calculate the estimated price of the remaining real estate of the population according to the calculation method of the price estimation model design module 120 when the validation is performed in the estimated price validation module 130. . At this time, the remaining real estate is real estate that is not traded among the population.
  • the estimated price adequacy review module 150 estimates the remaining real estate of the population by comparing the price range of the estimated price of the real estate estimated by the population price estimation module 140 with the price range of the actual transaction price stored in the real estate database 110. It may be configured to review the adequacy of the price.
  • the indicator for examining the adequacy of the estimated price through the combined model may include the average unit price, the minimum unit price and the maximum unit price of the actual transaction price included in the real estate database 110. That is, the estimated price adequacy review module 150 may be configured to review the adequacy of the average unit price, the minimum unit price, and the maximum unit price of the actual transaction price with respect to the estimated price.
  • the estimated price providing module 160 may be configured to provide the estimated price reviewed online online.
  • Figure 2 is an illustration of an artificial neural network model according to an embodiment of the present invention.
  • the artificial neural network model is a method of linearly combining explanatory variables in various ways, and then predicting the dependent variable using the linear combination in the form of a nonlinear function.
  • a single hidden layer and a feedforward neural network model will be described. However, this description is equally extended to more complex neural network models such as multiple hidden layers.
  • Equation 3 Equation 3
  • Equation 3 is re-expressed using an activation function as in Equation 5 below.
  • Is a parameter Is an explanatory variable, Is the residual.
  • the logistic activation function has a value between 0 and 1, so if the dependent variable is continuous You need to convert it so that takes a value between 0 and 1.
  • Neural network models usually represent their structure in the form of a network.
  • a typical linear regression model and a neural network model are shown in FIG. 2.
  • the middle H represents m hidden nodes in the right neural network model.
  • Activation function Identity function not a logistic function
  • the neural network model is simplified to a general linear regression model.
  • the neural network model can be interpreted as a generalization of the linear regression model.
  • the process of finding parameter values in such a neural network model is often performed through a procedure called 'back-propagation', and the present invention may be configured to estimate parameters according to this method.
  • FIG. 3 is an exemplary diagram illustrating a result of spatial interpolation according to an embodiment of the present invention.
  • FIG. 4 is an exemplary view showing a validation result of a price estimation model according to an embodiment of the present invention.
  • FIG 5 is an exemplary diagram of an online providing screen of an estimated price according to an embodiment of the present invention.
  • the estimate may be configured to provide the estimated price such as FIG. 5 online.
  • FIG. 6 is a flowchart of a method for automatically evaluating real estate prices based on an ensemble learning method according to an embodiment of the present invention.
  • the price estimation model design module 120 may include at least one parametric model and at least one of the real price, geographic coordinates, and pseudo-price parity data of the real estate previously stored in the real estate database 110.
  • the price estimation model design module 120 may be configured to apply a linear regression model among the parameter models, and apply a random forest model or a boosting model among the non-parametric models.
  • the price estimation model design module 120 calculates the weighted price by giving the same or different weights to the calculated price for each model, respectively (S102).
  • the price estimation model design module 120 applies an arithmetic mean weight to the price of each model or optimizes the price of each model using an artificial neural network model according to the reliability of the price of each model. It may be configured to calculate and apply a weight.
  • the price estimation model design module 120 determines the price difference according to the physical location based on whether the weighted price calculated above is similar to the similar price zone partition data of the property (S103).
  • the price estimation model design module 120 calculates an estimated price by interpolating the weighted price in space in consideration of the physical location (S104). .
  • the price estimation model design module 120 linearly combines the actual transaction price of the real estate in consideration of the physical location of the real estate that has already occurred transaction stored in the real estate database 110 and apply a weight using the linear combined actual transaction price It can be configured to calculate an estimated price by a kriging technique that interpolates the price in space.
  • the estimated price validation module 130 verifies the validity of the estimated price by comparing the estimated price with the actual transaction price stored in the real estate database 110 (S105).
  • the estimated price validation module 130 calculates a price ratio, a variance coefficient, and an absolute average error rate by comparing the estimated price with the actual transaction price stored in the real estate database 110, and verifies the validity of the estimated price according to the calculation result. It can be configured to.
  • the population price estimation module calculates an estimated price of the remaining real estate of the population according to the calculation method of the price estimation model design module 120 (S106).
  • the estimated price adequacy review module 150 compares the price range for the estimated price of the real estate estimated by the population price estimation module 140 with the price range of the actual transaction price stored in the real estate database 110. Review the appropriateness of the estimated price of the real estate (S107).
  • the estimated price providing module 160 provides the reviewed estimated price online (S108).

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Abstract

La présente invention comprend : une base de données de biens immobiliers dans laquelle un prix de transaction réelle spécifique à un bien immobilier, des coordonnées géographiques et des données de segment de zone de prix similaire sont enregistrées à l'avance ; un module de conception de modèle d'estimation de prix, qui applique un modèle paramétrique et un modèle non paramétrique au prix réel, aux coordonnées géographique et aux données de segment de zone de prix similaire du bien immobilier correspondant de manière à calculer respectivement un prix spécifique au modèle pour un bien immobilier prédéterminé parmi un parc, attribue des poids, qui sont identiques ou différents les uns des autres, à chaque prix spécifique au modèle calculé de manière à calculer un prix appliqué au poids, compare le prix appliqué au poids calculé aux données de segment de zone de prix du bien immobilier correspondant de manière à déterminer la similarité entre eux de telle sorte que le différence de prix est déterminée conformément à l'emplacement physique, et interpole le prix appliqué au poids dans un espace en considération de l'emplacement physique, de manière à calculer un prix estimé, lorsque la différence de prix résultant de la détermination en fonction de l'emplacement physique du prix appliqué au poids est égale ou supérieure à une valeur de référence prédéterminée ; un module de vérification de validation de prix estimé destiné à vérifier la validation du prix estimé en comparant le prix estimé avec le prix de transaction réel ; un module d'estimation de prix de parc destiné à calculer le prix estimé pour les biens immobiliers restants dans le parc conformément au procédé de calcul du module de conception de modèle d'estimation de prix lorsque la validité est vérifiée ; un module de revue de caractère adéquat du prix estimé destiné à revoir le caractère adéquat du prix estimé des biens immobiliers restants dans le parc en comparant la plage de prix du prix estimé du parc de biens immobiliers et la plage de prix du prix de transaction réel ; et un module de fourniture de prix estimé destiné à fournir le prix estimé revu en ligne lorsque la revue du prix estimé le considère approprié.
PCT/KR2017/001642 2017-02-15 2017-02-15 Système et procédé pour évaluer automatiquement le prix d'un bien immobilier sur la base d'une technique d'apprentissage d'ensemble WO2018151343A1 (fr)

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