TWI759702B - Real estate appraisal system - Google Patents

Real estate appraisal system Download PDF

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TWI759702B
TWI759702B TW109109298A TW109109298A TWI759702B TW I759702 B TWI759702 B TW I759702B TW 109109298 A TW109109298 A TW 109109298A TW 109109298 A TW109109298 A TW 109109298A TW I759702 B TWI759702 B TW I759702B
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real estate
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TW202137125A (en
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杜宗燁
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兆豐國際商業銀行股份有限公司
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Abstract

The present disclosure provides a real estate appraisal system including a system host and an application host coupled to the system host. The system host stores an appraisal model, wherein the appraisal model includes an ensemble learning model. The ensemble learning model includes a first model and a second model and the ensemble learning model receives an input data and generates an output data. The system host receives multiple refresh data in an time interval and input the refresh data into the appraisal model to perform a validity verification. When an output result of the refresh data does not pass the validity verification, the system host does not adjust multiple parameters of the appraisal model and remodel the appraisal model according to the refresh data. When the remodeled appraisal model passes the validity verification, the system host deploys the appraisal model to the application host to perform an appraisal operation.

Description

不動產鑑價系統real estate appraisal system

本揭露是有關於一種不動產鑑價系統，且特別是有關於一種自動更新鑑價模型的不動產鑑價系統。The present disclosure relates to a real estate appraisal system, and in particular, to a real estate appraisal system that automatically updates the appraisal model.

對一般民眾而言購屋所費不貲，因此成交前常會挑選幾個標的物件做條件、價格確認與比較。例如，民眾可透過實價登錄網站、房仲業者、物件屋主報價等多種管道確認價格合理性。然而此類工作耗時費力且詢價對象亦可能有隱惡揚善或高報價格之情況。為解決上述購屋問題，線上不動產鑑價系統開始被推出。然而，現有的線上不動產鑑價系統大多因為參考數據不足導致鑑價結果與實際價格有較大落差，因此如何能提供一個高精準度的不動產鑑價系統是本領域技術人員應致力的目標。For the general public, it is expensive to buy a house. Therefore, several target items are often selected for condition, price confirmation and comparison before the transaction. For example, the public can confirm the reasonableness of the price through various channels such as real-price login websites, real estate brokers, and property owners' quotations. However, this kind of work is time-consuming and labor-intensive, and the inquiries may also have hidden evils or high prices. In order to solve the above-mentioned problem of buying a house, an online real estate appraisal system has been launched. However, most of the existing online real estate appraisal systems have a large gap between the appraisal result and the actual price due to insufficient reference data. Therefore, how to provide a high-precision real estate appraisal system is the goal of those skilled in the art.

有鑑於此，本揭露提供一種不動產鑑價系統，能自動更新鑑價模型以提供更精確的鑑價結果。In view of this, the present disclosure provides a real estate appraisal system that can automatically update the appraisal model to provide more accurate appraisal results.

本揭露提出一種不動產鑑價系統，包括系統主機及應用程式主機耦接到系統主機。系統主機儲存鑑價模型，其中鑑價模型包括集成學習模型，集成學習模型包括第一模型及第二模型且集成學習模型接收輸入資料並產生輸出資料。系統主機在時間間隔接收多個更新資料並將更新資料輸入鑑價模型以進行效度驗證。當更新資料的輸出結果不通過效度驗證時，系統主機不調整鑑價模型的多個參數並根據更新資料重新建模鑑價模型。當重新建模的鑑價模型通過效度驗證時，系統主機將鑑價模型佈署到應用程式主機以提供鑑價操作。The present disclosure provides a real estate appraisal system, including a system host and an application host coupled to the system host. The system host stores the appraisal model, wherein the appraisal model includes an integrated learning model, the integrated learning model includes a first model and a second model, and the integrated learning model receives input data and generates output data. The system host receives multiple updates at time intervals and inputs the updates into the appraisal model for validity verification. When the output result of the updated data fails the validity verification, the system host does not adjust multiple parameters of the appraisal model and re-models the appraisal model according to the updated data. When the remodeled appraisal model passes the validity verification, the system host deploys the appraisal model to the application host to provide appraisal operations.

基於上述，本揭露的不動產鑑價系統利用集成學習模型作為鑑價模型。系統主機例如每月接收更新資料並將更新資料輸入鑑價模型以進行效度驗證。若效度驗證沒有通過時系統主機暫時不調整鑑價模型的多個參數而先根據更新資料重新建模鑑價模型。若重新建模的鑑價模型通過效度驗證時系統主機就能將鑑價模型佈署到應用程式主機以提供鑑價操作。Based on the above, the real estate appraisal system of the present disclosure utilizes the ensemble learning model as the appraisal model. The system host receives updates, such as monthly, and enters the updates into the appraisal model for validity verification. If the validity verification fails, the system host does not temporarily adjust multiple parameters of the appraisal model, but first re-models the appraisal model according to the updated data. If the remodeled appraisal model passes the validity verification, the system host can deploy the appraisal model to the application host to provide appraisal operations.

圖1為根據本揭露一實施例的不動產鑑價系統的方塊圖。FIG. 1 is a block diagram of a real estate appraisal system according to an embodiment of the present disclosure.

請參照圖1，本揭露一實施例的不動產鑑價系統100包括系統主機110及應用程式主機120。應用程式主機120透過有線或無線網路耦接到系統主機110。Referring to FIG. 1 , a real estate appraisal system 100 according to an embodiment of the present disclosure includes a system host 110 and an application host 120 . The application host 120 is coupled to the system host 110 through a wired or wireless network.

在一實施例中，系統主機110儲存鑑價模型，其中鑑價模型包括集成學習模型。集成學習模型包括第一模型及第二模型。第一模型例如是隨機森林模型且第二模型例如是極限梯度提升(eXtreme Gradient Boosting，XgBoost)模型。集成學習模型接收輸入資料並產生輸出資料。集成學習模型中的多個模型輸出可組成線性回歸(Linear Regression)的堆疊(stacking)模型。輸入資料可包括授信資料、實價登錄資料、興趣點資料(Point of Interest，POI)及經濟指標資料等與不動產相關的資料且輸出資料例如是不動產鑑價結果。系統主機110在時間間隔(例如，每一個月)接收多個更新資料並將更新資料輸入鑑價模型以進行效度驗證。當更新資料的輸出結果不通過效度驗證時，系統主機110不調整鑑價模型的多個參數並根據更新資料重新建模鑑價模型。當重新建模的鑑價模型通過效度驗證時，系統主機110將鑑價模型佈署到應用程式主機120以提供鑑價操作，並更新此鑑價模型應用程式介面(Application Program Interface，API)的模型檔以供前台呼叫最新鑑價結果。當更新資料的輸出結果不通過重新建模的鑑價模型的效度驗證時，系統主機110更新對應鑑價模型的多個參數並將這些參數套用到鑑價模型。In one embodiment, the system host 110 stores an appraisal model, wherein the appraisal model includes an ensemble learning model. The integrated learning model includes a first model and a second model. The first model is, for example, a random forest model and the second model is, for example, an eXtreme Gradient Boosting (XgBoost) model. An ensemble learning model receives input data and produces output data. Multiple model outputs in the ensemble learning model can form a stacking model of Linear Regression. The input data may include credit information, actual price registration data, point of interest (POI) data, and economic indicator data and other real estate-related data, and the output data are, for example, real estate appraisal results. The system host 110 receives multiple updates at time intervals (eg, every month) and enters the updates into the appraisal model for validity verification. When the output result of the updated data fails the validity verification, the system host 110 does not adjust a plurality of parameters of the appraisal model and re-models the appraisal model according to the updated data. When the remodeled appraisal model passes the validity verification, the system host 110 deploys the appraisal model to the application host 120 to provide appraisal operations, and updates the appraisal model Application Program Interface (API) The model file for the front desk to call the latest appraisal results. When the output result of the updated data does not pass the validity verification of the remodeled appraisal model, the system host 110 updates a plurality of parameters corresponding to the appraisal model and applies these parameters to the appraisal model.

在一實施例中，系統主機110可根據網格搜尋演算法或基因演算法更新對應鑑價模型的多個參數。以網格搜尋(Grid Search)演算法為例，網格搜尋演算法可在所有候選的參數組合中透過迴圈方式嘗試每一種可能性，並以表現最好的參數組合作為最終結果。在一實施例中，隨機森林模型及極限梯度提升模型都可利用網格搜尋演算法自動尋找鑑價模型的最佳參數組合。表一為網格搜尋的模型、參數及參數說明。In one embodiment, the system host 110 may update a plurality of parameters corresponding to the appraisal model according to a grid search algorithm or a genetic algorithm. Taking the grid search algorithm as an example, the grid search algorithm can try every possibility in a round-robin manner among all the candidate parameter combinations, and take the best performance parameter combination as the final result. In one embodiment, both the random forest model and the extreme gradient boosting model can use a grid search algorithm to automatically find the best parameter combination of the appraisal model. Table 1 shows the model, parameters and parameter descriptions of grid search.

表一 模型 參數 參數說明 隨機森林模型 n_estimators 隨機森林中樹的數量 max_depth 隨機森林中樹的最大深度 min_samples_split 分支所需樣本數下限 min_samples_leaf 葉節點的樣本數下限 極限梯度提升模型 eta 學習速率 max_depth 樹的最大深度 subsample 樣本抽樣比例 colsample_bytree 變數抽樣比例 gamma 損失函數值下限 min_child_weight 子節點樣本權重和 Table I Model parameter Parameter Description Random Forest Model n_estimators Number of trees in random forest max_depth Maximum depth of tree in random forest min_samples_split Minimum number of samples required for branching min_samples_leaf Minimum sample size for leaf nodes Extreme Gradient Boosting Model eta learning rate max_depth maximum depth of tree subsample Sample sampling ratio colsample_bytree Variable sampling ratio gamma Lower bound of loss function value min_child_weight Child node sample weights and

另一方面，系統主機110也可根據基因演算法更新對應鑑價模型的多個參數。利用基因演算法在決定最適參數的效率遠大於網格搜尋。On the other hand, the system host 110 can also update a plurality of parameters corresponding to the appraisal model according to the genetic algorithm. Using genetic algorithms to determine the optimal parameters is much more efficient than grid search.

在一實施例中，更新資料包括多個不動產資料，當這些不動產資料輸入鑑價模型產生的輸出結果與些不動產資料對應的多個預定鑑價結果的差異小於等於門檻值，則輸出結果通過效度驗證。當輸出結果與預定鑑價結果的差異大於門檻值，則輸出結果不通過效度驗證。In one embodiment, the update data includes a plurality of real estate data, and when the difference between the output results generated by the real estate data input into the appraisal model and the plurality of predetermined appraisal results corresponding to the real estate data is less than or equal to a threshold value, the output results pass the validation. degree verification. When the difference between the output result and the predetermined appraisal result is greater than the threshold value, the output result does not pass the validity verification.

舉例來說，系統主機110可計算對應輸出結果的平均絕對誤差百分比(Mean Absolute Percentage Error，MAPE)及命中率。當平均絕對誤差百分比小於等於預定百分比(例如，15%)且誤差在第一百分比(例如，10%)內的命中率大於等於第二預定百分比(例如，50%)且誤差在第二百分比(例如，20%)內的命中率大於等於第三預定百分比(例如，80%)，則輸出結果通過效度驗證。透過平均絕對誤差百分比及命中率的雙重驗證，可大幅增加效度驗證的準確性。For example, the system host 110 may calculate the Mean Absolute Percentage Error (MAPE) and the hit rate of the corresponding output result. When the mean absolute error percentage is less than or equal to a predetermined percentage (eg, 15%) and the error is within a first percentage (eg, 10%), the hit rate is greater than or equal to a second predetermined percentage (eg, 50%) and the error is within a second If the hit rate within the percentage (for example, 20%) is greater than or equal to the third predetermined percentage (for example, 80%), the output result passes the validity verification. Through the double verification of mean absolute error percentage and hit rate, the accuracy of validity verification can be greatly increased.

平均絕對誤差百分比是用來衡量實際值與預測值之間差距與實際值的比值，誤差取絕對值，高估或低估幅度相等，誤差也不會彼此抵銷。平均絕對誤差百分比的計算公式為

Figure 02_image001
，其中y為鑑價結果實際值且ŷ為鑑價結果預測值且n為樣本數。命中率是指在特定誤差範圍內，預測值落於該區間的機率，命中率越高，表示預測值接近實際值的機率越高。命中率的計算公式為
Figure 02_image003
，
Figure 02_image005
，其中y為鑑價結果實際值且ŷ為鑑價結果預測值且
Figure 02_image007
為信心水準且N為測試樣本數且n為命中區間樣本數。The mean absolute error percentage is used to measure the ratio of the difference between the actual value and the predicted value to the actual value. The formula for calculating the mean absolute error percentage is
Figure 02_image001
, where y is the actual value of the appraisal result and ŷ is the predicted value of the appraisal result and n is the number of samples. The hit rate refers to the probability that the predicted value falls within this range within a specific error range. The higher the hit rate, the higher the probability that the predicted value is close to the actual value. The formula for calculating the hit rate is
Figure 02_image003
,
Figure 02_image005
, where y is the actual value of the appraisal result and ŷ is the predicted value of the appraisal result and
Figure 02_image007
is the confidence level and N is the number of test samples and n is the number of hit interval samples.

綜上所述，本揭露的不動產鑑價系統利用集成學習模型作為鑑價模型。系統主機例如每月接收更新資料並將更新資料輸入鑑價模型以進行效度驗證。若效度驗證沒有通過時系統主機暫時不調整鑑價模型的多個參數而先根據更新資料重新建模鑑價模型。若重新建模的鑑價模型通過效度驗證時系統主機就能將鑑價模型佈署到應用程式主機以提供鑑價操作。若重新建模的鑑價模型仍無法通過效度驗證則根據網格搜尋演算法或基因演算法更新鑑價模型的參數以提高其通過效度驗證的機會。To sum up, the real estate appraisal system of the present disclosure utilizes the ensemble learning model as the appraisal model. The system host receives updates, such as monthly, and enters the updates into the appraisal model for validity verification. If the validity verification fails, the system host does not temporarily adjust multiple parameters of the appraisal model, but first re-models the appraisal model according to the updated data. If the remodeled appraisal model passes the validity verification, the system host can deploy the appraisal model to the application host to provide appraisal operations. If the re-modeled appraisal model still fails to pass the validity verification, the parameters of the appraisal model are updated according to the grid search algorithm or the genetic algorithm to improve the chance of passing the validity verification.

雖然本揭露已以實施例揭露如上，然其並非用以限定本揭露，任何所屬技術領域中具有通常知識者，在不脫離本揭露的精神和範圍內，當可作些許的更動與潤飾，故本揭露的保護範圍當視後附的申請專利範圍所界定者為準。Although the present disclosure has been disclosed above with examples, it is not intended to limit the present disclosure. Anyone with ordinary knowledge in the technical field may make some changes and modifications without departing from the spirit and scope of the present disclosure. The scope of protection of the present disclosure shall be determined by the scope of the appended patent application.

100:不動產鑑價系統 110:系統主機 120:應用程式主機100: Real estate appraisal system 110: System host 120: Application Host

圖1為根據本揭露一實施例的不動產鑑價系統的方塊圖。FIG. 1 is a block diagram of a real estate appraisal system according to an embodiment of the present disclosure.

100:不動產鑑價系統100: Real estate appraisal system

110:系統主機110: System host

120:應用程式主機120: Application Host

Claims (9)

一種不動產鑑價系統，包括：一系統主機；以及一應用程式主機，耦接到該系統主機，其中該系統主機儲存一鑑價模型，其中該鑑價模型包括一集成學習模型，該集成學習模型包括一第一模型及一第二模型且該集成學習模型接收一輸入資料並產生一輸出資料；該系統主機在一時間間隔接收多個更新資料並將該些更新資料輸入該鑑價模型以進行一效度驗證；當該些更新資料的一輸出結果不通過該效度驗證時，該系統主機不調整該鑑價模型的多個參數並根據該些更新資料重新建模該鑑價模型；當重新建模的該鑑價模型通過該效度驗證時，該系統主機將該鑑價模型佈署到該應用程式主機以提供一鑑價操作；以及當該些更新資料的該輸出結果不通過重新建模的該鑑價模型的該效度驗證時，該系統主機更新對應該鑑價模型的該些參數並將該些參數套用到該鑑價模型。 A real estate appraisal system, comprising: a system host; and an application host coupled to the system host, wherein the system host stores an appraisal model, wherein the appraisal model includes an integrated learning model, the integrated learning model A first model and a second model are included and the integrated learning model receives an input data and generates an output data; the system host receives a plurality of update data at a time interval and inputs the updated data into the appraisal model for performing a validity verification; when an output result of the updated data fails the validity verification, the system host does not adjust a plurality of parameters of the appraisal model and re-models the appraisal model according to the updated data; when When the remodeled appraisal model passes the validity verification, the system host deploys the appraisal model to the application host to provide a appraisal operation; and when the output of the updated data does not pass the reappraisal When the validity of the modeled appraisal model is verified, the system host updates the parameters corresponding to the appraisal model and applies the parameters to the appraisal model. 如請求項1所述的不動產鑑價系統，其中該更新資料包括多個不動產資料，當該些不動產資料輸入該鑑價模型產生的該輸出結果與該些不動產資料對應的多個預定鑑價結果的差異小於等於一門檻值，則該輸出結果通過該效度驗證，當該輸出結果與 該些預定鑑價結果的差異大於該門檻值，則該輸出結果不通過該效度驗證。 The real estate appraisal system according to claim 1, wherein the update data includes a plurality of real estate data, and when the real estate data is input into the appraisal model, the output result generated by the appraisal model corresponds to a plurality of predetermined appraisal results corresponding to the real estate data The difference is less than or equal to a threshold value, then the output result passes the validity verification, when the output result is different from The difference between the predetermined evaluation results is greater than the threshold value, and the output result does not pass the validity verification. 如請求項2所述的不動產鑑價系統，其中該系統主機計算對應該輸出結果的一平均絕對誤差百分比及一命中率，當該平均絕對誤差百分比小於等於一預定百分比且誤差在第一百分比內的命中率大於等於第二預定百分比且誤差在第二百分比內的命中率大於等於第三預定百分比，則該輸出結果通過該效度驗證。 The real estate appraisal system according to claim 2, wherein the system host calculates a mean absolute error percentage and a hit rate corresponding to the output result, when the mean absolute error percentage is less than or equal to a predetermined percentage and the error is within the first percentage If the hit rate within the ratio is greater than or equal to the second predetermined percentage and the hit rate within the second percentage is greater than or equal to the third predetermined percentage, the output result passes the validity verification. 如請求項3所述的不動產鑑價系統，其中該平均絕對誤差百分比為
Figure 109109298-A0305-02-0010-1
，其中y為鑑價結果實際值且
Figure 109109298-A0305-02-0010-3
為鑑價結果 預測值且n為樣本數，該命中率為
Figure 109109298-A0305-02-0010-2
，y i -y i(α)
Figure 109109298-A0305-02-0010-4
y i +y i(α)，其中y為鑑價結果實際值且
Figure 109109298-A0305-02-0010-6
為鑑價結果預測值且α為信心水準且N為測試樣本數且n為命中區間樣本數。 The real estate appraisal system of claim 3, wherein the mean absolute percentage error is
Figure 109109298-A0305-02-0010-1
, where y is the actual value of the appraisal result and
Figure 109109298-A0305-02-0010-3
is the predicted value of the appraisal result and n is the number of samples, the hit rate is
Figure 109109298-A0305-02-0010-2
, y i - y i (α)
Figure 109109298-A0305-02-0010-4
y i + y i (α) , where y is the actual value of the appraisal result and
Figure 109109298-A0305-02-0010-6
is the predicted value of the appraisal result and α is the confidence level and N is the number of test samples and n is the number of hit interval samples. 如請求項1所述的不動產鑑價系統，其中該系統主機根據一網格搜尋演算法或一基因演算法更新對應該鑑價模型的該些參數。 The real estate appraisal system of claim 1, wherein the system host updates the parameters corresponding to the appraisal model according to a grid search algorithm or a genetic algorithm. 如請求項1所述的不動產鑑價系統，其中該第一模型為一隨機森林模型。 The real estate appraisal system of claim 1, wherein the first model is a random forest model. 如請求項1所述的不動產鑑價系統，其中該第二模型為一極限梯度提升(eXtreme Gradient Boosting，XgBoost)模型。 The real estate appraisal system of claim 1, wherein the second model is an extreme gradient boosting (eXtreme Gradient Boosting, XgBoost) model. 如請求項1所述的不動產鑑價系統，其中該輸入資料包括一授信資料、一實價登錄資料、一興趣點資料及一經濟指標資料。 The real estate appraisal system according to claim 1, wherein the input data includes a credit information, a real-price registration data, a point of interest data and an economic indicator data. 如請求項1所述的不動產鑑價系統，其中該輸出資料包括一不動產鑑價結果。 The real estate appraisal system of claim 1, wherein the output data includes a real estate appraisal result.
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TWM579794U (en) * 2019-02-26 2019-06-21 第一商業銀行股份有限公司 Building appraisal system
TW201935371A (en) * 2018-02-01 2019-09-01 安富財經科技股份有限公司 Automatic valuation system for real estate capable of effectively responding to fluctuations in house prices and providing objective valuation results immediately
TWM596940U (en) * 2020-03-20 2020-06-11 兆豐國際商業銀行股份有限公司 Real estate appraisal system

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