TW201835845A - Method for estimating price of transaction object for appraisal of real estates facilitating the public to automatically estimate a price of a transaction object of interest for appraisal of real estates in real time - Google Patents

Abstract

This invention relates to a method for estimating a price of a transaction object for appraisal of real estates. The method includes the following steps: providing a real-time online platform by an evaluation price system of a user terminal and exchanging information with the user terminal through a network. When a user logs into the real-time online platform and inputs multiple pieces of query condition information, data processing is carried out according to contents of the appraisal of real estates and the multiple pieces of query condition information in order to achieve a purpose of facilitating the public to automatically estimate the price of the transaction object of interest for appraisal of real estates in real time.

Description

Method for estimating the price of a transaction target for real estate valuation purposes

The present invention relates to a method for estimating the price of a transaction target, and more particularly to a method for estimating the price of a transaction target for real estate valuation purposes.

The traditional real estate valuation method includes comparison method, cost method and income method. The comparison method is based on comparing the target price, mainly by comparing and analyzing to calculate the target price. The income method is mainly based on direct capitalization method. The current cash flow analysis method, and the price obtained according to the income method is the income price. The cost method mainly obtains the reconstruction cost or replacement cost of the survey target at the price date, deducting the accumulated depreciation amount or other deductibles. In order to estimate the price of the target, the price obtained is the cost price.

In the prior art, whether it is a comparison method, a cost method or a revenue method, there are calculation formulas, analysis equations, and feature function models. The accuracy is determined according to the number of data and the complexity of the method, but the cost method is more cost-conscious and easy to ignore. The market factor, the income method is not easy to estimate the correct impact by cash flow, it is more difficult to find the correct discount rate. Although the comparison method considers the market factor, it relies on the valuer's judgment and adjustment, so it is needed for the mass users. It is not the analysis result of the whole market, but a more convenient, immediate and more flexible valuation method, and it is an adaptive evaluation of the real estate target that the user is interested in. It has a reference value for the user, so the existing Technology does have to be asked for further improvements.

In view of the above-mentioned deficiencies of the prior art, the main object of the present invention is to provide a method for estimating the price of a transaction target for real estate valuation purposes, which utilizes cloud and network technologies to facilitate the public, and provides an instant data calculation program to facilitate public interest. The price of the real estate transaction target is automatically estimated.

The main technical means adopted for the above purposes is to estimate the price of the transaction target for the aforementioned real estate valuation purposes by linking the evaluation price system to the user end via the network to exchange information, and the evaluation price system performs the following steps. : Real estate transaction target recognition; data collection, screening; real estate transaction target model estimation; real estate transaction target model evaluation; provide the most likely transaction price recommendations and surrounding market data for real estate transactions.

In the foregoing steps, the data inventory of the evaluation price system has the real estate transaction content, and according to the user's query condition information and the real estate transaction content to generate the real estate transaction target knowledge, the data collection and screening are performed, and the real estate transaction target is estimated and evaluated. The most probable transaction price suggestion and the surrounding market data of the real estate transaction target can be provided quickly and accurately, so the invention can achieve the purpose of facilitating the immediate automatic estimation of the price of the real estate transaction target of interest.

Referring to FIG. 1 , the system architecture of the preferred embodiment of the present invention includes an evaluation price system 10 and a real-time online platform 20 . The evaluation price system 10 is connected to a network. The 20 is provided by the evaluation price system 10 and is used by the user to log in through the network. In the preferred embodiment, the instant online platform 20 can be a web page platform.

The evaluation price system 10 provides a built-in database (not shown) that stores most of the real estate transaction content (eg, type, type, etc. of the building), when the user holds a mobile device Or a computer device logs in to the instant online platform 20 through the network on the user end. As shown in FIG. 2 and FIG. 3, the user inputs most of the query condition information on the instant online platform 20 (eg, location, real estate type, Real estate internal configuration, parking space, etc.), after the user confirms the input, the evaluation price system 10 receives the majority of the query condition information input by the user, and according to the real estate transaction content and the query conditions in the database The information is compared or compared to generate a recognition of the real estate transaction target, and data collection, screening and production of a result, the evaluation price system 10 based on the result, the real estate transaction target model estimation, evaluation, to provide the real estate transaction target is most likely Transaction price advice and surrounding market data (for example: house unit price, total price, neighboring house transaction price, average unit price, single Range, median ... etc), as shown in Figure 3, the proposed transaction is the subject of conjecture estate prices show the real-time online platform to 20.

By logging in to the instant online platform 20 provided by the evaluation price system 10, the user can quickly and accurately query the recommended estimated price of the real estate transaction target, so as to facilitate the public to automatically push the price of the real estate transaction target that is of interest. The purpose of the assessment.

In the preferred embodiment of the present invention, the database includes a real-time login database, a neighborhood population structure database, and a public facility space database, wherein the real-price login database stores the real-time registration coordinates information. To identify the location of the neighborhood of the transaction case, to generate new variables related to the neighborhood; the neighborhood population structure database stores the neighborhood population structure information provided by the open platforms of the municipal governments, and converts the data into 0 to 14 years old. 7 different age groups, 15 to 24 years old, 25 to 34 years old, 35 to 44 years old, 45 to 54 years old, 55 to 64 years old and 65 years old or older; the public facilities space database stores all real price registrations. The distance between the neighborhood in which the transaction case is located and the nearest public facility.

In the preferred embodiment of the present invention, the condition information for the data screening includes a data period, a transaction target, a main purpose, a residential type, an abnormal transaction item, a remark/addition, and an amendment. An extreme value, where the extreme value is calculated as "house age", "total floor", "building area" and "total price". Taking the housing age as an example, the upper limit of the housing age is the calculated average plus 1.96 times the standard deviation. The value obtained is 39.115 years, which is the upper limit of the housing age; the lower limit of the housing age is the data with more than 0.

In the preferred embodiment of the present invention, the foregoing method for grouping the real estate transaction subject according to the screening result includes the following steps: 1. Using a stepwise regression analysis method to analyze the relationship between the population structure (quantity) and the house price to find out The stratification of the demographic structure associated with the region of interest, the formula for this stepwise regression analysis is: ;among them, ; ; ; ; .

2. The positive or negative impact of each age structure on the transaction price is determined by the average number as the benchmark. As shown in Figure 4, the concept of the category indicator is assumed to have five demographic structures (65). More than 32 years old, 25 to 34 years old, 0 to 14 years old, 15 to 24 years old, 55 to 64 years old, at most 32 groups. 3. Confirm the number of data for each administrative district/product (for example: building type) and each group, and each group should be built above a certain number of pens.

In the preferred embodiment of the present invention, the foregoing method for establishing the feature price estimation model includes the following steps: 1. Perform another stepwise regression analysis method to test the individual characteristic variables of each group, and the other stepwise regression analysis method The formula is: ;among them, ; ; ; ; .

By the aforementioned population structure grouping, the valuation models of each group are separately constructed. 2. Establish a characteristic price estimation model for each product market in most cities (for example, Taipei City, New Taipei City, Taichung City, and Tainan City), as shown in Figure 5, which includes the individual characteristics of the house and the transaction price. (total price), object type (building / Huaxia, apartment), etc.

In the preferred embodiment of the present invention, after the feature price estimation model is established, the model is estimated by the feature price to generate a recommended price estimation method for the real estate transaction target, and the step is to establish the price of the real estate transaction target first. The permissible error criteria for the estimation are based on the characteristic price estimation model of the real estate transaction target price and the neighboring area case of the real estate transaction target, and the valuation target object is within a certain range (500/750/1000 meters) and within 1 year. Neighboring transactions in Newcast House (within 2 years) or Nagoya (within 3 years) ages ±10 years (or ±3 years, ±5 years, ±7 years) to estimate the median per ping The unit price is used as the recommended price for the real estate transaction target, and further estimates the allowable error standard for the median unit price per ping; for the allowable error standard, it can be set differently. Under the condition of hit rate (5%, 10%, 15% and 20%), the absolute value of the difference between the house price and the median was calculated, and the average, median and standard deviation were used as the judgment conditions.

According to the above preferred embodiment, a method for estimating the price of a transaction target for real estate valuation use may be further provided, wherein the evaluation price system 10 is connected to the user end through the network to exchange information, as shown in FIG. The evaluation price system 10 performs the following steps: providing a database, comparing or comparing the real estate transaction content in the database with the query condition information to generate a real estate transaction target (S21), in the preferred embodiment The database may include a real-time login database, an neighborhood population structure database, and a public facility spatial database; data collection and screening (S22) and a result; and estimation of the real estate transaction target model according to the result (S23), and a model evaluation of the real estate transaction target (S24). In the preferred embodiment, the method of estimating and evaluating the real estate transaction subject is performed by a stepwise regression analysis method for demographic structure (quantity) and housing Analysis of the relationship between prices to identify stratification of the demographic structure associated with areas of interest, and then In the positive or negative impact of the transaction price, the average is used as the benchmark to determine the category, to confirm the number of data for each group, and each group is higher than a certain number of times to build; provide real estate The most likely transaction price suggestion and the surrounding market data (S25) of the transaction target, in the preferred embodiment, further obtaining a characteristic price estimation (such as the median unit price per ping), to calculate and estimate each ping The allowable error criterion for the unit price of a single digit yields a suggested price for a real estate transaction.

In the preferred embodiment, when the above steps are performed to the step of "generating the real estate transaction subject recognition (S21)", as shown in FIG. 7, the method further includes the following steps: real estate transaction target attribute data analysis (S26); wherein, the real estate The property data analysis of the transaction target refers to the total land transfer area (m2) of the real estate transaction case, the total construction transfer area (m2), the age of the house, the age of the house (year), the total floor, whether it is on the first floor, Whether it is in the top floor, the current situation of the building structure - the number of houses, the current situation of the building structure - the number of halls, the current situation of the building - the number of the building, the current state of the building - the number of compartments, whether there is a parking space, whether it is close to the roadside, whether it is next to the street, Property information such as alleys and buildings above reinforced concrete. And the main purpose of the case is to live in the household; the secondary market cutting of the real estate transaction target (S27); the sub-field cutting of the target refers to the product-suite (1 room, 1 living room, 1 bathroom) and apartment (5th floor with the following) There is no elevator), Huaxia (10 floors with the following elevators), residential buildings (11 floors with elevators above), and Tianzhu to distinguish the sub-market; among them, Taipei City and Xinbei City are divided into apartments, Huaxia/residential The building market and the overall market; Taichung City, Tainan City and Kaohsiung City are divided into the market of Tianzhu, Huaxia/Residential Building and the overall market.

For example, a test case is located at 31-60 Jiang Street, Wanhua District, Taipei City. Analysis of the attributes of the real estate transaction target: firstly, the attribute data is constructed, and the total land transfer area is 68 square meters, the total construction area is 98.2 square meters, the house age is 23.24 years, 2 rooms, 1 hall, 2 bathrooms. 1, 1 compartment, no parking space, non-first floor, non-top floor, next to the street, is a building above reinforced concrete, using the partition for living. Target sub-market cutting: The product belongs to the sub-market of the apartment in Taipei City (5th floor with no elevator below).

In the preferred embodiment, when the step is performed to the step of performing data collection and screening (S22), as shown in FIG. 8, the method further includes the following steps: obtaining regional spatial attribute data (S281); In the embodiment, the regional neighborhood population data (S2811), the regional public facility data (S2812), and the other regional spatial attribute data (S2813) may be obtained. In the preferred embodiment, the regional neighborhood population data refers to each The population structure of neighbors in the county and city is 0-14, 15-24, 25-34, 35-44, 45-54, 55-64 and 65. The data is from the municipal governments. Information open platform query; regional public facility data refers to the location of each real estate transaction case (XY coordinates) and the proximity of public facilities, where the public facilities include the straight-line distance between the transaction case and the nearest MRT station, the transaction case and the nearest high-speed railway station line Distance, straight-line distance between the transaction case and the nearest light rail station, straight-line distance between the transaction case and the nearest air station, straight-line distance between the transaction case and the nearest national elementary school, transaction A straight line from the nearest university, dealing cases with a straight line from the nearest neighborhood park (area of 2 hectares), dealing cases with a straight line from the nearest airport, the nearest post office dealing cases linear distance, distance units are meters. According to different product categories in different counties and cities, different public facilities are included as reference variables; other regional spatial attribute data refers to other variables that can represent spatial attributes, such as land price (land transaction price, announced land present value, and announced land price) And so on; screening historical transaction data (S282); in the preferred embodiment, the actual price registration data (S2821), screening Jianye company performance guarantee data (S2822), screening other historical transaction data sources (S2823) may be further screened. In the preferred embodiment, the actual price registration data refers to the policy of mandatory registration of real estate transaction cases from a certain day (such as August 1, 101), which is provided by the Ministry of the Interior; Assurance data refers to the real estate transaction database that has been established since a certain year (such as 97 years). The information is provided by a Jianjing company (such as Qiaoyu Construction Manager Co., Ltd.); other historical transaction sources refer to In the future, it can also be included in other housing companies (such as Xinyi, Yongqing, Taiwan Housing, etc.), the case of the intermediary has been sold, the real estate transaction price provided Material.

For example, the test case is located at 31-60 Jiang Street, Wanhua District, Taipei City. Regional neighborhood population data: According to the December of the Republic of China, the test case is located in Xinqiuli. The demographic data of Xinqili is the population of all ages, 726 people aged 15-14, 15-24 years old. 631 people, 941 people aged 25-34, 1,097 people aged 35-44, 991 people aged 45-54, 1,097 people aged 55-64, 1,253 people over 65 years old; information on regional public facilities: the distance between the transaction and various public facilities , 200 meters from the recent MRT station, 500 meters from the high-speed railway station, etc.; other regional spatial attribute data: land transaction price 400,000 yuan, announced land present value 160,000 yuan, announced land price 120,000 yuan; real-time login information: Collecting the latest real-price registration from 101 to the announcement, the transaction product is not the apartment (5th floor with no elevator below) secondary market, and belongs to the real estate transaction of the Taipei apartment market; Jianjing company performance guarantee information: collected from From the year of 1997 to the publication of the Qiaojing Jianjing Performance Guarantee Data, it was established for the real estate transaction and apartment information of Taipei City. The above values are all numerical values as exemplified.

In the preferred embodiment, a first mode I and a second mode II are further provided. With respect to the first mode I, when the step is performed to the “real estate transaction target mode estimation (S23)” step, As shown in FIG. 9, the method further includes the following steps:: setting up a regional spatial sub-market clustering indicator (S291); in the preferred embodiment, the regional spatial sub-market clustering indexing refers to the neighboring population data of the regional spatial attribute data. According to the demographic structure of the population structure 0-14 years old, 15-24 years old, 25-34 years old, 35-44 years old, 45-54 years old, 55-64 years old and 65 years old and above, the stepwise regression method is carried out to confirm The age structure of the population structure to be included; the confirmation and rule setting of the regional spatial submarket grouping (S292); in the preferred embodiment, the regional spatial submarket clustering confirmation and rules refer to the use of category indicators for sub-market segmentation. ; Category indicator construction refers to the positive or negative impact of each age structure on the transaction price of real estate, based on the average number, the category determination. According to the degree of influence on the transaction price (1~K points), distinguish each sub-market (1~K group), in addition to each sub-group sub-market, and build an overall sub-market (including all product categories: building, China Xiamen, apartment, and through-the-sky); the establishment of the characteristic price model of each sub-group of the regional spatial sub-market, and the price estimation of the real-estate transaction (S293).

For example, the test case is located at 31-60 Jiang Street, Wanhua District, Taipei City. Regional Space Sub-Market Clustering Indicators: Each neighborhood in Taipei is based on its demographic structure: 0-14 years old, 15-24 years old, 25-34 years old, 35-44 years old, 45-54 years old, 55-64 years old and over 65 years old. The data and the transaction case price are subjected to a stepwise regression method to confirm the age structure of the population structure to be included. Regional Space Sub-Market Grouping Confirmation and Rules: The overall market and apartment market are separately constructed for the regional space sub-test site rules. Only the apartment market description is used, and the stepwise regression method is used to confirm that the Taipei apartment market is included in the population structure of 0-14 years old. There is a positive impact on the transaction price. 24-35 years old have a negative impact on the transaction price. 44-55 years old have a positive impact on the transaction price, which can be divided into 0~3 points and 4 points. Judging the sub-group of real estate transactions: The test case is located at No. 31~60, Jiang Street, Wanhua District, Taipei City. It belongs to Xinqili and belongs to Group 1. The establishment of the characteristic price model of each sub-group in the regional spatial sub-market: using the transaction case of group 1 to establish the characteristic price model, including the total area of the variable construction (square meter), the age of the house (year), the total floor, the building The current situation pattern - the number of rooms, whether it is close to the street and other five variables. Enter the characteristics of the test case and get the transaction price estimate, which is 9,000,000 yuan.

When the foregoing steps are performed to the step of "confirmation and rule setting of the regional space submarket grouping (S292)", the method further includes the following steps: determining the grouping of the real estate transaction subject (S294); in the preferred embodiment, determining the grouping of the real estate transaction subject Refers to the neighborhood in which the test case belongs, and confirms the subgroup to which it belongs according to the regional sub-market grouping rules; Continues to implement the above-mentioned "Regional Space Submarket Segmentation of the Characteristic Price Model and the Price Estimation of the Real Estate Transaction Target (S293) In the preferred embodiment, the characteristic price model of each sub-group of the regional spatial sub-market is determined according to the degree of influence of the transaction price (1~K points), and each sub-market (1~K group) is distinguished and separately constructed. Set the characteristic price pattern of each sub-market, the attribute variables of the characteristic price model have the total area of land transfer (square meters), the total area of building transfer (square meters), the age of the house (year), the total floor, whether it is in one Building, whether it is in the top floor, the current situation of the building structure - the number of houses, the current situation of the building - the number of halls, the current situation of the building - the number of the building, the building is now Pattern - the number of compartments, whether there are parking spaces, whether it is close to the roadside, whether it is close to the roadside, located in alleys, above the reinforced concrete buildings and other property. Step-by-step regression of all variables to screen out appropriate variables for each market. The aforementioned symbol K represents that the number of cluster scores is equal to the number of subgroups.

In the preferred embodiment, with respect to the second mode II, when the above steps are performed to the "real estate transaction target mode estimation (S23)" step, as shown in FIG. 9, the method further includes the following steps: Selecting a historical transaction case with appropriate comparison (S295); in the preferred embodiment, the similar case criterion refers to the neighborhood within 750 meters, the age of the house ± 5 years, the transaction within one year, the same product type and rejection The actual transaction case is accompanied by a special transaction case with annotations; the historical transaction case is continuously collected (S296); in the preferred embodiment, the historical transaction case refers to a historical transaction case that meets the similar case criteria; the confirmation of the number of historical transaction cases (S297) When the number of historical transaction cases is greater than "0" and less than or equal to "14", then the above-mentioned "establishment of the characteristic price model of each sub-group of regional spatial sub-markets and price estimation of real estate transactions (S293)" will be carried out. Steps; in the preferred embodiment, the confirmation of the number of historical transaction cases refers to the number of historical transaction cases that meet the similar case criteria; The number of trading cases is 0>N≥14, which is an area with insufficient transaction data. When the estimated price is displayed, the user is informed in advance that there are few trading cases in the area. Therefore, the valuation model is built and directly simulated. For areas with similar regional attributes, the valuation model is estimated, that is, the model I is used to estimate the price; when the number of historical transactions is greater than or equal to “30”, the price of the real estate transaction is estimated by comparing the case-based characteristic price model (S298). In the preferred embodiment, the comparative case construction feature price mode means that when the number of transaction cases is N ≥ 30, the characteristic price model is constructed by using the appropriate comparative historical transaction cases selected, and each product is The characteristic price model of the category includes the variables of Huaxia/Building Market: total construction area (m2), age (year), construction status pattern - number of rooms, total floor square; apartment market: total construction transfer Area (mectare meter), age (year), current situation of building structure - number of houses; translucent market: total land transfer area (m2), construction Total area of transfer (m2), age of house (year), current situation of building structure - number of houses; and judged whether the mode is built or not based on the square of the explanatory power of the characteristic price model (R2). %, the model is built, but if the square of the explanatory power (R2) is less than 70%, the price is estimated by the number of transactions: 15≤N<30; when the number of historical transactions is less than "30", and When the value is greater than or equal to "15", the price of the real estate transaction is estimated based on the median of the comparison case (S299); in the preferred embodiment, the median of the comparison case is the median number of transactions Price; the aforementioned symbol N represents the number of transactions; R2 represents the explanatory power of the characteristic price model; if the number of transactions is 15 ≤ N < 30, the screening criteria are again limited to filter the surrounding area within 750 meters, and the age of the house ±3 years, within one year of trading, the same product type and the elimination of special transaction cases, re-selecting appropriate comparative historical transaction cases, estimating the price of the fixed transaction subject according to the screening principle, and presenting the median price of the transaction case Now the result.

For example, the test case is located at 31-60 Jiang Street, Wanhua District, Taipei City. The four cases of the second mode II are illustrated. Similar case criteria: Select a transaction case in which the test case meets the similar case criteria (in the vicinity of 750 meters, the age of the house is ±5 years, the transaction within one year, the type of apartment and the special transaction case for the actual price), there are 14 transactions. case. Historical trading cases: 14 trading cases are No. 75 Jiang Street, Wanding District, No. 20 Kangding Road, No. 15 Chengdu Road, etc. Confirmation of the number of historical trading cases: If there are 14 comparative trading cases, because the number of trading cases is too small, the transaction price is estimated by Mode I, and the transaction price is estimated to be 8,000,000 yuan. Among them, mode I, please refer to the above description. If there are 15 comparative trading cases, re-screening, the screening conditions are within 750 meters of the surrounding area, the age of the house is ±3 years, the transaction within one year, the type of apartment and the special transaction of the actual price are excluded, and 10 trading cases are obtained. Using these 10 transactions, the estimated price is 8,000,000 yuan. Compare case construction characteristic price mode: If there are 30 comparative transaction cases, then use the 30 transactions to establish the characteristic price model, including the variable total construction area (m2), house age (year), building Current situation - the number of rooms, after the characteristic price model, the explanatory power is 60%, then re-screening, the screening conditions are within 750 meters of the surrounding area, the age of the house ± 3 years, the transaction within one year, the type of apartment and the elimination The actual price is registered in a special transaction, and 10 transactions cases are obtained. Using these 10 transactions, the price is estimated to be 8,000,000 yuan. If there are 30 comparative trading cases, then use the 30 transactions to establish a characteristic price model, including the variable total construction area (m2), the age of the house (year), the current status of the building - the number of houses, and the characteristics After the price model, the explanatory power reached 75%, and the characteristics of the test case were put into the model, and the estimated price was 8,000,000 yuan.

In the preferred embodiment, when the above steps are performed to the step of "evaluating the real estate transaction target mode (S24)", as shown in FIG. 10, the method further includes the following steps: the real estate transaction target estimation price allowable error criterion is established ( S301); the model of the real estate transaction should be evaluated (S302); in the preferred embodiment, the model evaluation of the real estate transaction target is to use the real estate transaction target to estimate the allowable error criterion to determine the first mode I or The second mode II, when the criterion is met, estimates the price of the real estate transaction subject according to the first mode I, and when the criterion is not met, estimates the price of the real estate transaction target by the second mode II; When the criterion is met, the price of the real estate transaction target is estimated according to the first mode I (S303), and when the criterion is not met, the price of the real estate transaction target is estimated according to the second mode II (S304).

Further, in the preferred embodiment, the predictive price allowable error criterion is established with respect to the foregoing real estate transaction target, wherein the foregoing allowable error criterion refers to the condition that the valuation model estimates the price of the subject matter of the valuation ( Using the first mode I, hereinafter referred to as "estimating", the allowable difference between the estimated median transaction price (using the second mode II, hereinafter referred to as "the middle") of the adjacent transaction object of the valuation target is a criterion. Under the condition of different allowable hit error rates (5%, 10%, 15% and 20%), the absolute value of the difference between the estimated and the Ŷ is calculated and judged by the mean, median and standard deviation. Condition, the mode criterion uses the median as the judgment condition; the aforementioned allowable hit error rate refers to the difference between the estimated target price and the real transaction registration price, and the percentage of the real transaction registration price, which can be set to 5 %, 10%, 15%, and 20%; and hit means that the price of the estimated subject matter is within the allowable hit error rate; the above-mentioned compliance criterion means that the absolute difference between the two cases in the test case falls here. The interval is the compliance criterion, and the first model I will be used to estimate the price of the real estate transaction target; the aforementioned non-conformity criterion means that when the absolute difference between the two cases of the test case cannot fall within the interval, the non-compliance criterion is The price of the real estate transaction target will be estimated in this second mode II.

For example, the test case is located at 31-60 Jiang Street, Wanhua District, Taipei City. The model of the real estate transaction should be evaluated by the model: the model of the test case I estimates that the price of the real estate transaction is 9,000,000 yuan, and the model II estimates the price of the real estate transaction as 8,000,000 yuan. Permissible hit error: The transaction price of the historical transaction case is priced at 7,500,000 yuan and the price of the model I is estimated to be 8,000,000 yuan, and the difference between the two is 1,000,000 yuan. Permissible hit error rate: Under the condition of different hit ratios (5%, 10%, 15% and 20%), calculate the absolute value of the median difference between the estimated and the Ŷ, which are 21,324, 34,606, 42,941 and 48,480 respectively. yuan. Hit: Lists the hit rate for each transaction case, calculated as (8,500,000-7,500,000)/7,500,000=13.3%, and so on. Estimation of the allowable error criteria for the real estate transaction target: Calculate the absolute value of the median difference between the estimated and the Ŷ under different conditions (5%, 10%, 15% and 20%), respectively For 21,324, 34,606, 42,941 and 48,480 yuan, if 5% is the principle of judgment. Compliance criteria: When the test case will calculate the absolute difference between the median estimate and the median value of 20,000 yuan, it falls within the standard range, indicating that the price of the real estate transaction target will be estimated at 9,000,000 yuan. Non-compliance with the criteria: When the test case calculates the median difference between the estimated and the median, the absolute value is 30,000 yuan, which cannot fall within the standard interval, indicating that the price of the real estate transaction target will be estimated at 8,000,000 yuan.

In the preferred embodiment, when the above steps are performed to the step of "providing the most likely transaction price recommendation and the surrounding market data (S25) of the real estate transaction target", as shown in FIG. 11, the method further includes the following steps: Estimation (such as unit price/total price, estimated unit price/total price confidence interval range) (S31); in the preferred embodiment, the price estimation of the real estate transaction target (unit price/total price): using the first mode I and After the second model II estimates the price of the real estate transaction target, using the concept of point estimation, the building/Huaxia/apartment market presents the valuation result in a way of estimating the unit price, while the transcendental market is used to estimate the total price. The way the valuation results are presented. The price estimation of the real estate transaction target (estimated unit price/total price confidence interval range): After using the first mode I and the second mode II to estimate the price of the real estate transaction target, the concept of the interval estimation is used to construct the building/Huaxia/ The apartment market presents the valuation results in an estimated unit price range, while the transcendental market presents the valuation results in the form of an estimated total price range.

In addition, after performing the above-mentioned "data collection and screening (S22)" step, the steps of "providing the most possible transaction price recommendation and surrounding market data (S25) for the real estate transaction target" may be successively executed; in the preferred embodiment The above steps of “collecting and screening data (S22)” are to collect the existing real-time registration data, and to organize the surrounding trading market data according to each test case and surrounding transaction case; further, as shown in FIG. : Surrounding historical transaction case criteria (S32); in the preferred embodiment, the historical transaction case criterion refers to the test case adjacent to the surrounding area of 750 meters, the age of the house ± 5 years, the transaction within one year, the same product type State and exclude the actual price registration special transaction; select historical transaction case (S33); surrounding historical transaction case narrative statistics, and historical transaction case original data (S34); in the preferred embodiment, the surrounding historical transaction case narrative Statistics refers to the median transaction price, average transaction price, standard deviation, etc. of historical transactions in the surrounding area. Nett case raw data means the historical trading cases around the login details of column presents.

For example, the test case is located at 31-60 Jiang Street, Wanhua District, Taipei City. Real estate transaction target price estimation (unit price / total price): The test case uses mode I or mode II to estimate 9,000,000 yuan or 8,000,000 yuan. The product type is the apartment market, so it is presented in unit price, which should be 300,000/ping. . The price estimation of the real estate transaction target (estimated unit price/total price confidence interval range): The estimated unit price range is 25~350,000 yuan/ping. The historical transaction case criteria: a special transaction is registered within 750 meters of the surrounding area, the age of the house is ±5 years, the transaction within one year, the same product type and the actual price are excluded, and 15 historical transaction cases are selected by criteria. Narrative statistics of historical transactions in the surrounding area: the median price is 220,000/ping, the average price is 260,000/ping, and the standard deviation is 200,000/ping. Historical transaction case source data: List of historical transaction cases original data No. 75 Jiang Street, Wanhua District, No. 20 Kangding Road, No. 15 Chengdu Road, etc.

In summary, the evaluation price system 10 of the present invention performs data collection and screening according to the user's query condition information and the real estate transaction content to generate the real estate transaction target knowledge, and estimates and evaluates the real estate transaction target, which can be fast and accurate. Providing the most likely transaction price advice and surrounding market data for the real estate transaction target, the present invention can indeed achieve the purpose of facilitating the immediate automatic estimation of the price of the real estate transaction target of interest.

10‧‧‧Evaluation price system

20‧‧‧Online online platform

1 is a block diagram of a system architecture in accordance with a preferred embodiment of the present invention. 2 is a schematic diagram of an application state of a preferred embodiment of the present invention. 3 is a schematic diagram of another application state of a preferred embodiment of the present invention. 4 is a schematic diagram of a category indicator group in accordance with a preferred embodiment of the present invention. Figure 5 is a schematic diagram of a data variable table in accordance with a preferred embodiment of the present invention. 6 is a flow chart of a method for estimating a price of a transaction target according to a preferred embodiment of the present invention. FIG. 7 is another flow chart of a method for estimating the price of a transaction target according to a preferred embodiment of the present invention. FIG. 8 is a flow chart of data collection and screening of a method for estimating a transaction target price according to a preferred embodiment of the present invention. 9 is a flow chart showing mode estimation of a method for estimating a price of a transaction target according to a preferred embodiment of the present invention. Figure 10 is another flow chart of mode estimation of a method for estimating the price of a transaction target in accordance with a preferred embodiment of the present invention. 11 is a flow chart showing the provision of transaction price recommendations and surrounding market data for a method for estimating the price of a transaction target according to a preferred embodiment of the present invention.

Claims (10)

A method for estimating the price of a transaction target for real estate valuation purposes is to exchange information with the user end through an evaluation price system, and the evaluation price system performs the following steps: recognition of the real estate transaction target (S21); data collection, Screening (S22); Estimation of the model of real estate transactions (S23); Evaluation of the model of real estate transactions (S24); Provision of the most likely transaction price recommendations for real estate transactions and surrounding market data (S25). The method for estimating the price of the transaction target for the real estate valuation use as claimed in claim 1, when the above step is performed to the step of "cognition of the real estate transaction target", the method further comprises the following steps: analysis of the attribute data of the real estate transaction target; Market cutting. For the method for estimating the price of the transaction target for the real estate valuation use described in claim 1, when the above steps are performed to the step of “data collection and screening”, the method further includes the following steps: obtaining regional spatial attribute data, obtaining regional neighborhood population data, Obtain regional public facilities materials, obtain spatial attribute data of other regions in the region; screen historical transaction data, screen actual price registration data, screen Jianye company performance guarantee data, and screen other historical transaction data sources. The method for estimating the price of the transaction target for the real estate valuation use described in claim 1 further provides a first mode, and when the step is performed to the “real estate transaction target mode estimation” step, the method further comprises the following steps: The establishment of clustering indicators; the identification and rule setting of regional spatial submarket clustering; the establishment of the characteristic price model of each subgroup of regional spatial submarkets, and the estimation of the price of real estate transactions. The method for estimating the price of the transaction target for the real estate valuation use described in claim 4, when the above steps are performed to the step of "confirmation and rule setting of the regional space submarket grouping", the method further comprises the following steps: determining the grouping of the real estate transaction target; The above-mentioned steps of “establishing the characteristic price model of each sub-group of the regional space sub-market and estimating the price of the real-estate transaction” will be carried out. The method for estimating the price of the transaction target for the real estate valuation use described in claim 1 further provides a second mode, wherein when the step is performed to the “real estate transaction target mode estimation” step, the method further comprises the following steps: Select historical transaction cases for appropriate comparison; continue to collect historical transaction cases; confirmation of the number of historical transaction cases. The method for estimating the price of a transaction target for the real estate valuation use as claimed in claim 6, wherein when the number of historical transaction cases is greater than "0" and less than or equal to "14", the foregoing "characteristics of each sub-group of regional spatial sub-markets are successively performed. The step of establishing the price model and the price estimation of the real estate transaction target; when the number of historical transaction cases is greater than or equal to "30", the price of the real estate transaction is estimated by comparing the case-based characteristic price model; If it is less than "30" and greater than or equal to "15", the price of the real estate transaction is estimated based on the median of the comparison case. The method for estimating the price of the transaction target for the real estate valuation use as claimed in claim 1, when the above step is performed to the step of "assessment of the real estate transaction target mode", the method further comprises the following steps: the real estate transaction target estimation price allowable error criterion is established The real estate transaction target shall be evaluated by the mode; based on the model evaluation result, the price of the real estate transaction target shall be estimated. The method for estimating the price of the transaction target of the real estate valuation use as claimed in claim 1, when the above steps are performed to the step of providing the most likely transaction price recommendation and the surrounding market information of the real estate transaction target, the method further includes the following steps: the real estate transaction target Price estimation. For the method of estimating the price of the transaction target for the real estate valuation use as described in claim 1, when the above steps are carried out to the “data collection and screening” step, the above “providing the most likely transaction price proposal and the surrounding market information for the real estate transaction target” is continued. The method further includes the following steps: establishing a historical transaction case criterion; selecting a historical transaction case; descriptive statistics of the historical transaction case, and original data of the historical transaction case.