TWI647650B - Real estate value evaluation system and its operation method - Google Patents

Abstract

The real estate evaluation system is used to provide at least one recommended case to evaluate a warranty. The real estate evaluation system includes a data comparison unit, a difference scoring unit, a weight setting unit, and a total score ranking unit. The data comparison unit compares the collateral and the plurality of real estate data stored in a database to extract multiple cases with similar conditions to the collateral. The difference scoring unit calculates the Z score of each basic data of the collateral and these cases, and subtracts the Z scores of the basic data of the collateral and the case as the similarity score. The weight setting unit is used to set the weight of each basic data, and calculate the total similarity score of each case according to the weight of each basic data and the similarity score. The total score sorting unit sorts these cases according to the similarity total score, and extracts a recommended case from the sorting.

Description

   Real estate value evaluation system and its operating method   

The present invention relates to an evaluation system, and particularly relates to an evaluation system of real estate value and its operation method.

Traditionally, when the holder of the premises uses the premises to guarantee the loan to the bank, the bank will first evaluate the value of the collateral to determine the loan percentage. However, it is very tedious and complicated to carry out general property valuation procedures. Generally speaking, when evaluating the price of premises, the cost price of the building can be estimated by the cost method and the individual price of the base can be obtained by the market price comparison method, and then the total price of the premises can be obtained by adding up the total . However, most of the above valuations rely on manpower for valuation on a case-by-case basis. When there are a large number of real estate objects that need to be valued, the personnel, time, and administrative resources required are huge, and it is bound to be impossible to use a traditional valuation system and method for a large number of cases. Valuation.

Therefore, there is an urgent need to provide a real estate valuation system and method that can assist or replace the traditional real estate valuation by manpower for individual valuation.

One aspect of this disclosure is to provide a real estate evaluation system for providing at least one recommended case to evaluate a collateral, wherein the real estate evaluation system includes at least a data comparison unit, a difference scoring unit, A weight setting unit and a total score sorting unit. The data comparison unit compares the collateral and the plurality of real estate data stored in a database to extract a plurality of cases with similar conditions to the collateral. The difference scoring unit calculates the Z score of the plural basic information of the collateral and each of the cases, and the Z score of the basic information of the collateral and the basic of each case respectively The Z score of the data is subtracted as the similarity score of each of the basic data of each of the pen cases. The weight setting unit is used to set the weight of each of the basic data and calculate the total similarity score of each of the cases according to the weight of each of the basic data and the similarity score. The total score sorting unit sorts the cases according to the similarity total scores, and extracts one of them as the at least one recommended case according to the sorting.

In one embodiment, the basic information is a real estate address, a real estate house age, a real estate area, a real estate floor, and a real estate building unit price.

In one embodiment, a price screening unit is further included to exclude the highest unit price and the lowest unit price of the real estate Jianping in the ranking.

In an embodiment, the lowest similarity score in the ranking is the at least one recommended case.

In one embodiment, a similar condition is real estate within a specific range of the address where the collateral is located.

In one embodiment, when the collateral is not a special real estate, the data exclusion unit excludes special real estate cases from these cases, and the special real estate is a luxury house, military house or national residence.

Another aspect of this disclosure is to provide a real estate evaluation method that is used to provide at least one recommended case to evaluate a collateral, where the real estate evaluation method includes at least, first compare the collateral and a database Plural real estate data stored in it to extract plural cases with similar conditions to the collateral; then, calculate the Z score of the plural basic data of the collateral and each of these cases separately The Z score of the basic information of the collateral and the Z score of the basic information of each of the cases are subtracted as the similarity score of each of the basic information of each of the cases; second, set each The weight of the basic data, and calculating the total similarity score of each of the cases according to the weight of each basic data and the similarity score; and sorting the cases according to the total similarity score, And extracting one of them as the at least one recommended case according to the ranking.

In summary, this disclosure can search for a recommended case with the closest similarity to this collateral according to the characteristics of the collateral, so as to recommend the case to evaluate the collateral, so there is no need to manually evaluate the cases one by one. Can save a lot of labor costs.

100‧‧‧Real estate evaluation system

101‧‧‧Data comparison unit

102‧‧‧Difference score unit

103‧‧‧ weight setting unit

104‧‧‧Score unit

105‧‧‧ price screening unit

1011‧‧‧Database

1012‧‧‧User input interface

601-605‧‧‧Step

FIG. 1 is a schematic diagram of a real estate evaluation system according to an embodiment of the present disclosure.

Figure 2 is a flow chart of a method for operating the real estate evaluation system of this case according to an embodiment.

The following is a detailed description of the embodiments in conjunction with the accompanying drawings to better understand the aspect of the present invention, but the provided embodiments are not intended to limit the scope covered by the disclosure, and the description of structural operations is not used to Restricting the order of execution, any structure that is recombined by components to produce devices with equal effects are all covered by this disclosure.

In addition, the words "including", "including", "having", "containing", etc. used in the present invention are all open terms, which means "including but not limited to". In addition, "and / or" used in the present invention includes any one or more of the listed items and all combinations thereof.

Please refer to FIG. 1, which is a schematic diagram of a real estate evaluation system 100 according to an embodiment of the present disclosure. As shown in FIG. 1, the real estate evaluation system 100 includes a data comparison unit 101, a difference scoring unit 102, a weight setting unit 103, a total score ranking unit 104, and a price screening unit 105. In one embodiment of the present invention, each of the above units may be configured as a hardware device, software program, firmware, or a combination thereof. When any unit is configured as a hardware device, it may be implemented by a processor, for example. When any unit is implemented by software, it can be loaded on a computer-readable storage medium and executed by the processor. In addition to the individual type configuration, each unit can also be combined type configuration.

The data comparison unit 101 is used to compare the collateral with the real estate data stored in a database to search for at least one case with similar conditions to the collateral, and use this case to evaluate the collateral. In one embodiment, the data comparison unit 101 is more coupled to a database 1011. The database 1011 stores a plurality of basic real estate data, such as a cloud database, which can be a bank-built database or a government. A public database built by the unit, such as the real-price login information provided by the government unit. In addition, the data comparison unit 101 further provides a user input interface 1012 for the user to input the basic data of the collateral to be evaluated into the data comparison unit 1011, where the basic data may include, but is not limited to, real estate location, real estate type State, the total number of real estate buildings, the floor where the real estate is located, the number of real estate pings, the age of the real estate, whether the real estate has parking spaces or whether the real estate has building management, etc. In one embodiment, the user inputs the location of the real estate as "No. 126, Section 1, Nanya West Road, Banqiao District", the real estate type as "apartment", the total floor of the real estate as "4th floor", and the floor of the real estate as "2 The number of "buildings", real estate pings is "21.95 pings" and the age of real estate is "44 years". Accordingly, the data comparison unit 101 can compare the basic data of the collateral entered by the user with the basic data of the real estate stored in the database 1011 to search for real estate with similar conditions and evaluate the collateral accordingly, such as Table 1 shows a list of cases that may be recommended according to this implementation.

It is worth noting that the basic data used for comparison in this case is not limited to all the basic data listed above. In one embodiment, the basic data such as real estate location, real estate type, real estate age, etc. can be used to compare the database 1011. Determine whether there is similar real estate in the database. In use, the user can set a comparison value of the margin. The cases within this margin are all similar real estate and will be compared. For example, in this embodiment, the threshold is set to 5 years. Therefore, the case of 5 years up and down from the real estate of the collateral will be compared. After calculation by the data comparison unit 101, it will be displayed on the user display As shown in the case shown in Table 1, the serial number 1 is the collateral, and the serial numbers 2-6 are the comparison cases. In addition, because the state house, luxury house and military house are in a specific category, and their prices are special, if the collateral is not a state house and luxury house, the system will exclude the searched cases of state house, luxury house and military house.

The difference scoring unit 102 is coupled to the data comparing unit 101, and is used to calculate the similarity scores between real estate and collateral in each case. In one embodiment, the z-score (Z-SCORE) of each basic data of each case is calculated to represent the difference score of each basic data of each case. Then compare the difference score with the difference score of the basic information corresponding to the collateral to calculate the similarity score. The z-score is based on the standard deviation. The basic data of each case is within several standard deviations of the average of the basic data of all cases. The calculation formula is as follows: Z = (X-M) / S, where X represents the basic data of each case, M represents the average of this basic data in all cases, and S represents the standard deviation of the basic data. Therefore, when the Z score is smaller, the value representing this basic data is closer to the average. In an embodiment, similarity values are calculated based on different basic data, such as real estate age, real estate area, and real estate floors, to avoid different types of basic data having different similarities due to different value ranges. Table 2 shows the z score calculated based on the basic real estate information in Table 1.

Table II:     

Among them, the average age of the five cases of serial number 2 to serial number 6 is 41.2 years, and the standard deviation is 1.3 years, then the calculated age z score of the collateral is 2.15, and the serial age of serial number 2 to serial 6 The z score is 1.38, -0.15, 0.61, -0.92, and -0.92 in this order. Taking the basic data of the area of the real estate construction ping in Table 1 as an example, the average area of the 5 cases of serial number 2 to serial number 6 is 20.29 pings and the standard deviation is 3.88 pings. According to this, the z scores of the pings are calculated respectively, as shown in Table 2 As shown, the z-score of the collateral is 0.53, and the z-score of the case of serial number 2 to serial number 6 is -0.16, 0.45, -1.16, 1.51, and -0.58 in this order. After calculating the z score, the difference scoring unit 102 can judge the similarity score between each case and the collateral according to the difference between the z score of each case and the z score of the collateral. In one embodiment, the difference scoring unit 102 subtracts the z-score of the collateral and the z-score of each case to obtain a similarity score, according to which the smaller the similarity score represents the similarity between the corresponding case and the collateral The higher. Among them, the similarity scores of the five cases of serial number 2 to serial number 6 in real estate age and collateral are shown in Table 2, which are 0.77, 2.30, 1.53, 3.07 and 3.07 in order. On the other hand, the similarity scores between the real estate building area and the collateral for the five cases No. 2 to No. 6 are shown in Table 2, which are 0.69, 0.08, 1.69, 1.09 and 1.11 in this order.

The weight setting unit 103 is coupled to the difference scoring unit 102. After the similarity score is calculated by the difference scoring unit 102, due to various basic data of real estate, such as real estate location, real estate area, real estate age, and collateral may have different evaluations, for example, when each comparison case In the case of similar real estate housing age, the case with the most similar area to the collateral is the most suitable object for comparison. Therefore, in order to highlight the importance of real estate area in this situation, the weight setting unit 103 in this case will give different weights according to the different basic data to reflect the importance of different basic data in the evaluation of collateral, and Based on this weight, the total similarity score of each case is calculated. In an embodiment, the weight setting unit 103 in this case sets the weight of the real estate age similarity to 2 and the real estate area similarity to 4 to highlight the importance of real estate area. Table 3 shows the total similarity score calculated according to this weight setting.

In one embodiment, the weight setting unit 103 calculates the total similarity score of each case according to the weight, for example, the total similarity score of the case of serial number 2 is 0.77 * 2 + 0.69 * 4 = 4.3, and the similarity of other cases The total score can be deduced by analogy.

The total score sorting unit 104 is coupled to the weight setting unit 103, and is used to sort the total similarity score calculated by the weight setting unit 103. In one embodiment, the total similarity score is ranked from small to large. In other embodiments, the total similarity score may also be arranged from large to small. For each basic data in each case, the smaller the similarity score between the corresponding basic data and the guarantee, the higher the similarity between the basic data and the collateral, so the total similarity score calculated is also The smaller. After the total score sorting unit 104 ranks the similarity total scores from small to large, in one embodiment, the total score sorting unit 104 will retrieve the top three of the similarity total scores, as shown in the three cases in Table 3. No. 1-30, Lane 145, Section 2, Nanya West Road, No. 1-30, Lane 12, Guanghua Street, and No. 1-30, Lane 13, Lane 71, Dazhi Street, are candidates for value comparison with collateral.

The price screening unit 105 is coupled to the total score sorting unit 104 for sorting the candidate cases extracted by the total score sorting unit 104 according to the unit price of Jianping, as shown in Table 4.

Which is arranged according to the unit price of Jianping from low to high or from high to low. With this arrangement, the highest and lowest unit price of Jianping is excluded to exclude extreme values. The case left at this time is the recommended case. Because, in terms of various transactions, in extreme cases, such as when the seller is in urgent need of funds, in order to get out of the real estate to obtain the necessary funds, the unit price is often lowered than the market price to smooth out. Conversely, for buyers who have special needs for specific real estate, even if the real estate bid is much higher than the average market price, they will still buy it. Therefore, in these two extreme cases, the unit price of Jianping will be very different from the market. Therefore, in order to exclude the above situation, the price screening unit 105 in this case will exclude the highest and lowest unit price of Jianping to exclude extreme values. Accordingly, the remaining cases are recommended cases, and the collateral can be evaluated based on the recommended cases.

Refer to FIG. 2 for a flowchart of a method for operating the real estate evaluation system of this case according to an embodiment. Please refer to FIG. 2 and FIG. 1 at the same time. Method 600. First, in step 601, the collateral is compared with the real estate data stored in a database to search for at least one case that has similar conditions as the collateral. In one embodiment, the data comparison unit 101 compares the collateral with the real estate data stored in the database 1012 to search for at least one case that has similar conditions as the collateral, so as to use this case to verify the guarantee Product evaluation.

In step 602, the similarity scores between real estate and collateral in each case are calculated separately. In one embodiment, the difference score unit 102 calculates the z-score (Z-SCORE) of each basic data of each case and collateral to represent the difference score of each basic data. Then, the difference score and the difference score of the basic information corresponding to the collateral are subtracted to calculate the similarity score.

After the difference scoring unit 102 calculates the similarity score, in step 603, different weights are given according to the difference of the basic data, so as to reflect the importance of the different basic data in the evaluation of the collateral, and calculate each according to the weight The total similarity score of the case. In one embodiment, the weight setting unit 103 sets different weights on the basic data of the real estate based on different evaluations compared with the collateral, and sets the weights accordingly to calculate the total similarity score of each case.

Next, in step 604, the total similarity scores are sorted, and at least one with the lowest score is extracted therefrom. In an embodiment, the total score sorting unit 104 sorts the total similarity score calculated by the weight setting unit 103, wherein the sorting method can be arranged from small to large, or from large to small. As the total similarity score is smaller, the similarity between this case and the collateral is higher, so when the total score sorting unit 104 sorts the total similarity score from small to large, the total score sorting unit 104 will retrieve the total similarity score The top three are considered as candidate cases.

Finally, in step 605, the cases are sorted according to the unit price of Jianping, and the ranking is used to exclude the highest and lowest unit price of Jianping to exclude extreme values. In one embodiment, the price screening unit 105 sorts the candidate cases extracted by the total score sorting unit 104 according to the unit price of Jianping, and excludes the highest and lowest unit price of Jianping to exclude the extreme value of unit price of Jianping. Accordingly, the remaining cases are recommended cases, and the collateral can be evaluated based on the recommended cases.

In summary, the real estate evaluation system disclosed in this disclosure can search for the most similar cases to collateral from a plurality of cases, and use this to evaluate collateral, so there is no need to rely on manpower to evaluate based on the case, and can save a lot Personnel, time and administrative resources.

Although this disclosure has been disclosed as above by way of implementation, it is not intended to limit this disclosure. Anyone who is familiar with this skill can make various changes and retouching without departing from the spirit and scope of this disclosure. The scope of protection of the disclosure shall be deemed as defined by the scope of the attached patent application.

Claims (10)

A real estate evaluation system is used to provide at least one recommended case to evaluate a collateral, wherein the real estate evaluation system includes at least: a data comparison unit, which compares the collateral with a plurality of data stored in a database Real estate data to extract multiple cases with similar conditions to the collateral; a differential scoring unit to calculate the Z score of the plural basic data of the collateral and each of these cases separately, and to separate the collateral The Z score of the basic data and the Z score of the basic data of each case are subtracted as the similarity score of each basic data of each case; a weight setting unit is used to Set the weight of each of the basic data, and calculate the total similarity score of each of the cases based on the weight of each basic data and the similarity score; and a total score sorting unit, based on the total similarity Sort the cases, and extract one of them as the at least one recommended case according to the sort. The real estate evaluation system as described in item 1 of the scope of patent application, in which the basic information is a real estate address, a real estate house age, a real estate area, a real estate floor, and a real estate building unit price. The real estate evaluation system as described in item 2 of the scope of the patent application further includes a price screening unit to exclude the highest unit price and the lowest unit price of the real estate Jianping in the ranking. The real estate evaluation system as described in item 3 of the patent application scope, wherein the lowest total similarity score in the ranking is the at least one recommended case. The real estate evaluation system as described in item 1 of the scope of patent application, wherein the similar condition is real estate within a specific range of the address where the collateral is located. The real estate evaluation system as described in item 1 of the patent application scope, where when the collateral is not a special real estate, the data comparison unit excludes special real estate cases from these cases. The real estate evaluation system as described in item 6 of the patent application scope, in which the special real estate is a mansion, military residence or national residence. A real estate evaluation method is used to provide at least one recommended case to evaluate a collateral, wherein the real estate evaluation method at least includes: comparing the collateral and a plurality of real estate data stored in a database to extract Plural cases with similar conditions to the collateral; calculate the Z score of the plural basic information of the collateral and each of the cases, and the Z score of the basic information of the collateral and each of the The Z score of the basic data of these cases is subtracted as the similarity score of each basic data of each of these cases; the weight of each basic data is set, and the weight of each basic data is set The weight and the similarity score calculate the total similarity score of each of the cases; and sort the cases according to the total similarity scores, and extract one of them as the at least one recommended case according to the order. The real estate evaluation method as described in item 8 of the patent application scope, wherein the basic information is a real estate address, a real estate house age, a real estate area, a real estate floor, and a real estate building unit price. The real estate evaluation method as described in item 9 of the patent application scope also includes excluding the highest unit price and the lowest unit price of the real estate Jianping in the ranking.