A real estate valuation system based on an estimation algorithm model

Abstract

The application discloses a real estate valuation system based on an evaluation algorithm model, which comprises a center connection module, a classification specification module, a data integration module, a multi-factor verification module and an evaluation module. By integrating more evaluation factor data, more suitable real estate valuation samples can be matched, so that an evaluation benchmark is obtained. By gradually adding factor data and through more evaluation methods, evaluation data that conforms to the real value can be obtained. In addition, while completing the evaluation of a house, the real estate valuation value can be dynamically updated through the established data association and displayed through a map marking mode. Through an interactive mode, one or more real estate data can be associated conveniently and automatically, effective evaluation correction can be carried out according to actual real estate, and visual display can be obtained on a map coordinate, so that the system is more convenient and convenient to use.

Description

Technical Field

[0001] This invention relates to the field of real estate valuation technology, and in particular to a real estate valuation system based on a valuation algorithm model. Background Technology

[0002] Traditional property valuation focuses on individual property valuation, which involves manually importing property data from external systems, then selecting samples from the property data set according to preset rules based on the user's input of the property to be valued, and finally valuing the property based on the selected samples. With the rapid development of technology, more automated property valuation methods have emerged.

[0003] A search revealed that Chinese patent CN109523306A discloses a multi-factor real estate valuation system and its valuation method, which argues that existing valuation schemes do not adequately consider more valuation factors.

[0004] Chinese patent CN108510314B discloses a method and system for processing real estate valuation data based on GIS technology, which proposes a real estate valuation based on GIS technology that takes geographical factors into account.

[0005] However, property valuation is often quite volatile, and each property has a different valuation value based on its basic information. In addition, during the valuation process, it is necessary to avoid anomalies in data from one point in time that could lead to unreasonable property valuations and prevent the spread of valuation anomalies. Therefore, there is an urgent need for a property valuation system based on a valuation algorithm model. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by proposing a real estate valuation system based on a valuation algorithm model.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A real estate valuation system based on a valuation algorithm model, the system comprising:

[0009] The central connection module is used to acquire real-time data from the valuation terminal, historical data stored on the cloud server, data sources from network locations, and to connect to the GIS map engine to obtain corresponding map data.

[0010] The classification standardization module is used to standardize the acquired data, filter the data of properties to be evaluated and the data of required case properties, and perform secondary classification of the data to obtain several secondary data classifications and related classification parameters.

[0011] The data integration module is used to integrate the data that has been classified into secondary categories, and to associate the data of different property categories. The associated data is used as integrated data. At the same time, it generates the comparative project analysis results for the current time point.

[0012] The multi-factor verification module is used to calculate the valuation of the property to be evaluated by using different secondary classification data as conditional factors based on the integrated data, and to dynamically verify the valuation by adding different conditional factors at each level.

[0013] The valuation module is used to establish the property valuation process and determine the required valuation data categories. It assigns weights to the data categories and sets the call priorities. It obtains the property value valuation through analysis and calculation, stores the calculation process and data of the valuation results, and finally performs data visualization processing on the data used in each step of the process and transmits it to the corresponding devices.

[0014] Furthermore, the data integration module includes:

[0015] The association rules module is used to associate data from different categories and establish association rules for valuation.

[0016] The price comparison and association module is used to further associate and compare the data to be compared based on the association rules established by the association rules module, and to determine the weight of each category of data.

[0017] Furthermore, the multi-factor validation module includes:

[0018] The prediction and estimation module is used to perform benchmark price assessment on the property to be assessed and predict the benchmark price range of the property to be assessed.

[0019] The map analysis module is used to connect to the GIS map engine to obtain the geographical location information of stored properties and properties to be evaluated, generate geographical coordinates, and analyze the integrated property data based on the geographical coordinate data.

[0020] The associated map module is used to associate each stored property data information with specific coordinates on the map. At the same time, based on a specified classification dataset, it establishes associated paths for one or more properties on the map.

[0021] Furthermore, the valuation module includes:

[0022] The benchmark price generation module is used to match the property to be evaluated with similar case property data, correct the data, and then obtain the benchmark price of the property to be evaluated.

[0023] The price alert module is used to verify the generated benchmark price and manually set the alert threshold. When the benchmark price exceeds the threshold, an alert is issued and the decision is made manually.

[0024] The visual output module is used to integrate the valuation result data and the data classification used in the valuation process, and transform them into visual data. The data is then output to the corresponding devices through the GIS map engine to achieve data visualization.

[0025] The data interaction module is used to generate a human-computer interaction interface, directly obtain the valuation data and basic information of real estate information under the corresponding geographical coordinates through map information, and display them visually.

[0026] Furthermore, the central connection module, classification standardization module, and data integration module are used to acquire and integrate basic data and case data, generate several secondary classification datasets, and organize and filter the secondary classification datasets to obtain key attribute data.

[0027] The categorized data is then categorized and compared with the data on the properties to be evaluated.

[0028] The comparison results and the information data of the properties to be evaluated are integrated.

[0029] Furthermore, the multi-factor verification module, data integration module, and valuation module are interconnected to provide early warnings for valuation prices and perform cascaded automatic verification based on different stored valuation algorithm models.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] By integrating more valuation factor data, a more suitable property valuation sample can be matched, thereby obtaining a valuation benchmark. By adding factor data step by step and using more valuation methods, valuation data that closely matches the true value can be obtained. In addition, while completing the valuation of a house, the property valuation value can be dynamically updated through the established data association and displayed through map marking. Through interactive methods, one or more property data can be easily linked manually or automatically, and effective valuation corrections can be made according to the actual property. The data can also be displayed on a map coordinate for a visual presentation, making it more convenient and user-friendly. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0033] Figure 1 This is a schematic diagram illustrating the implementation logic of the real estate valuation system based on a valuation algorithm model in an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0035] Example 1

[0036] Reference Figure 1 A real estate valuation system based on a valuation algorithm model, which includes a valuation terminal;

[0037] Valuation terminals include user terminals and feedback terminals;

[0038] It should be noted that, in specific implementation, the feedback terminal is a device with sufficient computing power and storage capacity, including any server device or any PC that meets the usage conditions;

[0039] User terminals and feedback terminals are interconnected via wired or wireless means, and are connected to a real estate valuation system based on valuation algorithm models through network communication to achieve data transmission and real-time data feedback.

[0040] The user terminal meets the following conditions:

[0041] It can connect to real estate valuation systems; take or upload photos and videos; provide feedback on geographical location information; accept and send interactive commands; and send and receive data.

[0042] User terminals include smartphones, tablets, laptops, or any other device that meets the above conditions.

[0043] The dynamic valuation system includes:

[0044] The central connection module is used to acquire real-time data from the valuation terminal, historical data stored on the cloud server, data sources from network locations, and to connect to the GIS map engine to obtain corresponding map data.

[0045] The classification standardization module is used to standardize the acquired data, filter the data of properties to be evaluated and the data of required case properties, and perform secondary classification of the data to obtain several secondary data classifications and related classification parameters.

[0046] The data integration module is used to integrate the data that has been classified into secondary categories, and to associate the data of different property categories. The associated data is used as integrated data. At the same time, it generates the comparative project analysis results for the current time point.

[0047] The multi-factor verification module is used to calculate the valuation of the property to be evaluated by using different secondary classification data as conditional factors based on the integrated data, and to dynamically verify the valuation by adding different conditional factors at each level.

[0048] The valuation module is used to establish the real estate valuation process and determine the required valuation data categories. It assigns weights to the data categories and sets the call priority. It obtains the real estate value valuation through analysis and calculation, stores the calculation process and data of the valuation results, and finally performs data visualization processing on the data used in each step of the process and transmits it to the corresponding devices.

[0049] In this embodiment, the central connection module is connected to the data integration module through the classification standardization module, and the data integration module is connected to the valuation module through the multi-factor verification module. The data integration module, the multi-factor verification module, and the valuation module are all connected to the central connection module.

[0050] In this embodiment, the valuation algorithm used includes:

[0051] Market comparison approach - employing an adaptive algorithm model;

[0052] Correction method for price comparison relationship in residential communities;

[0053] Standard room price comparison algorithm - based on PPMCC price comparison model;

[0054] Case study price comparison algorithm - price comparison model based on PPMCC;

[0055] GIS Spatial Computation - GIS-based Random Forest Model;

[0056] Exponential trend method.

[0057] In a preferred embodiment of this application, the data integration module includes:

[0058] The association rules module is used to associate data from different categories and establish association rules for valuation.

[0059] The price comparison and association module is used to further associate and compare the data to be compared based on the association rules established by the association rules module, and to determine the weight of each category of data.

[0060] In a preferred embodiment of this application, the multi-factor verification module includes:

[0061] The prediction and estimation module is used to perform benchmark price assessment on the property to be assessed and predict the benchmark price range of the property to be assessed.

[0062] The map analysis module is used to connect to the GIS map engine to obtain the geographical location information of stored properties and properties to be evaluated, generate geographical coordinates, and analyze the integrated property data based on the geographical coordinate data.

[0063] The associated map module is used to associate each stored property data information with specific coordinates on the map. At the same time, based on a specified classification dataset, it establishes an association path for one or more properties on the map. The association path is displayed as a line segment connection on the map, with each related property as a connecting line segment node. When the association is manually filtered, the connected properties and line segments are marked as additional markers, and labels are manually added, such as: the property has non-property objective problems, and the valuation has decreased by XX%.

[0064] This property has advantages in terms of owner, property management, community, and neighbors, leading to a XX% increase in valuation compared to similar properties.

[0065] In a preferred embodiment of this application, the valuation module includes:

[0066] The benchmark price generation module is used to match the property to be evaluated with similar case property data, correct the data, and then obtain the benchmark price of the property to be evaluated.

[0067] The price alert module is used to verify the generated benchmark price and manually set the alert threshold. When the benchmark price exceeds the threshold, an alert is issued and the decision is made manually.

[0068] The visual output module is used to integrate the valuation result data and the data classification used in the valuation process, and transform them into visual data. The data is then output to the corresponding devices through the GIS map engine to achieve data visualization.

[0069] The data interaction module is used to generate a human-computer interaction interface, directly obtain the valuation data and basic information of real estate information under the corresponding geographical coordinates through map information, and display them visually.

[0070] Example 2

[0071] Based on Example 1, the central connection module, classification standardization module, and data integration module are used to acquire and integrate basic data and case data, generate several secondary classification datasets, and organize and filter the secondary classification datasets to obtain key attribute data (including apartment structure, room type, property type, area, floor, year of construction, and orientation).

[0072] The specific process is as follows:

[0073] The central connection module obtains valuation instructions and property information to be valued, connects to the cloud server and GIS map engine, and retrieves data from multiple sources via the network based on the input property information to be queried. The data sources include data from the Land and Resources Bureau website, the Housing and Construction Commission website, appraisal agency data, financial institution data, real estate platform data, manual on-site inspection data, and any other compatible external platform data. The data is classified into dynamic data and static data for storage and updating. Dynamic data includes land transaction data, new home transaction data, second-hand home transaction data, rental transaction data, and judicial auction transaction data, which will change continuously within 11-3 days. Static data consists of residential and building data in different regions.

[0074] The classification and standardization module classifies the data in a standardized manner and retrieves relevant transaction information data for sold properties;

[0075] The categorized data is then categorized and compared with the data on the properties to be evaluated.

[0076] The comparison results and the information data of the properties to be evaluated are integrated.

[0077] In a preferred embodiment of this application, the multi-factor verification module, the data integration module, and the valuation module are interconnected to achieve early warning and automatic verification of the valuation price;

[0078] The specific steps are as follows:

[0079] The valuation module obtains the benchmark price of the property to be valued through the market comparison method and assesses whether the benchmark price is within the warning threshold. If it is within the threshold, it is determined manually.

[0080] If it is not within the threshold, data association rules are added through the data integration module to compare prices and reassess whether the final benchmark price is within the warning threshold. If it is within the threshold, it is determined manually.

[0081] If the price is not within the threshold, the geographic location distribution data and geographic location association data are added through the multi-factor verification module. Based on the geographic location data and price index trend, further evaluation is conducted to obtain the final benchmark price, which is then determined after manual correction.

[0082] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A real estate valuation system based on a valuation algorithm model, characterized in that, The system includes: The central connection module is used to acquire real-time data from the valuation terminal, historical data stored on the cloud server, data sources from network locations, and to connect to the GIS map engine to obtain corresponding map data. The classification standardization module is used to standardize the acquired data, filter the data of properties to be evaluated and the data of required case properties, and perform secondary classification of the data to obtain several secondary data classifications and related classification parameters. The data integration module is used to integrate the data that has been classified into secondary categories, and to associate the data of different property categories. The associated data is used as integrated data. At the same time, it generates the comparative project analysis results for the current time point. The multi-factor verification module is used to calculate the valuation of the property to be evaluated by using different secondary classification data as conditional factors based on the integrated data, and to dynamically verify the valuation by adding different conditional factors at each level. The valuation module is used to establish the property valuation process and determine the required valuation data categories. It assigns weights to the data categories and sets the call priorities. It obtains the property value valuation through analysis and calculation, stores the calculation process and data of the valuation results, and finally performs data visualization processing on the data used in each step of the process and transmits it to the corresponding devices.

2. The real estate valuation system based on the valuation algorithm model according to claim 1, characterized in that, The data integration module includes: The association rules module is used to associate data from different categories and establish association rules for valuation. The price comparison and association module is used to further associate and compare the data to be compared based on the association rules established by the association rules module, and to determine the weight of each category of data.

3. The real estate valuation system based on the valuation algorithm model according to claim 2, characterized in that, The multi-factor validation module includes: The prediction and estimation module is used to perform benchmark price assessment on the property to be assessed and predict the benchmark price range of the property to be assessed. The map analysis module is used to connect to the GIS map engine to obtain the geographical location information of stored properties and properties to be evaluated, generate geographical coordinates, and analyze the integrated property data based on the geographical coordinate data. The associated map module is used to associate each stored property data information with specific coordinates on the map. At the same time, based on a specified classification dataset, it establishes associated paths for one or more properties on the map.

4. The real estate valuation system based on the valuation algorithm model according to claim 3, characterized in that, The valuation module includes: The benchmark price generation module is used to match the property to be evaluated with similar case property data, correct the data, and then obtain the benchmark price of the property to be evaluated. The price alert module is used to verify the generated benchmark price and manually set the alert threshold. When the benchmark price exceeds the threshold, an alert is issued and the judgment is made manually. The visual output module is used to integrate the valuation result data and the data classification used in the valuation process, and transform them into visual data. The data is then output to the corresponding devices through the GIS map engine to achieve data visualization. The data interaction module is used to generate a human-computer interaction interface, directly obtain the valuation data and basic information of real estate information under the corresponding geographical coordinates through map information, and display them visually.

5. The real estate valuation system based on the valuation algorithm model according to claim 4, characterized in that, The central connection module, classification standardization module, and data integration module are used to acquire and integrate basic data and case data, generate several secondary classification datasets, and organize and filter the secondary classification datasets to obtain key attribute data.

6. The real estate valuation system based on the valuation algorithm model according to claim 5, characterized in that, The multi-factor verification module, data integration module, and valuation module are interconnected to provide early warnings for valuation prices and perform cascaded automatic verification based on different stored valuation algorithm models.

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