CN114611916A - Method, system and storage medium for evaluating comprehensive value of real estate - Google Patents
Method, system and storage medium for evaluating comprehensive value of real estate Download PDFInfo
- Publication number
- CN114611916A CN114611916A CN202210226505.4A CN202210226505A CN114611916A CN 114611916 A CN114611916 A CN 114611916A CN 202210226505 A CN202210226505 A CN 202210226505A CN 114611916 A CN114611916 A CN 114611916A
- Authority
- CN
- China
- Prior art keywords
- factor
- property
- factors
- value
- influence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000011156 evaluation Methods 0.000 claims abstract description 50
- 230000003993 interaction Effects 0.000 claims abstract description 41
- 239000002131 composite material Substances 0.000 claims description 15
- 230000015654 memory Effects 0.000 claims description 15
- 230000002452 interceptive effect Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 230000009467 reduction Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims 1
- 238000010219 correlation analysis Methods 0.000 abstract description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000004445 quantitative analysis Methods 0.000 description 3
- 238000012216 screening Methods 0.000 description 3
- 238000000540 analysis of variance Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000000556 factor analysis Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/16—Real estate
- G06Q50/165—Land development
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Operations Research (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Quality & Reliability (AREA)
- Bioinformatics & Computational Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Primary Health Care (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a method, a system and a storage medium for evaluating the comprehensive value of a real estate, comprising the following steps: acquiring the house property information to be evaluated; selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result; and evaluating the comprehensive value of the property according to the property information, the single-factor weight and the multi-factor interaction weight. The method is easy to carry out correlation analysis in a multi-factor interaction state, is convenient for comprehensively evaluating single and multi-factor influences, and the implanted multi-factor comprehensive evaluation module can quantitatively feed back the correlation of the multi-factor interaction influences and greatly improve the accuracy of the weight of the influencing factors. The comparison of the transverse value and the longitudinal value can be prompted in real time according to the timing setting, and the real-time or timing house property value evaluation can be automatically carried out according to the setting of the model.
Description
Technical Field
The invention relates to the technical field of house property value evaluation, in particular to a house property comprehensive value evaluation method, a house property comprehensive value evaluation system and a storage medium.
Background
Aiming at the problems of numerous house sources, frequent price fluctuation, huge house value screening, evaluation and benchmarking requirements, strong subjectivity in value judgment and easy deviation in cities, the existing house data screening mode and the house value evaluation method in patent documents are deeply analyzed. Most of the mainstream house property intermediary companies and websites in the market give qualitative recommendations according to passively input screening conditions, and visual quantitative analysis and active value fluctuation reminding are lacked.
Disclosure of Invention
The invention aims to overcome the technical defects and provide a house property comprehensive value evaluation method which can visually perform quantitative analysis evaluation and active value fluctuation reminding on house property values.
In order to achieve the above technical object, in a first aspect, the technical solution of the present invention provides a method for evaluating a comprehensive property value, including the steps of:
acquiring the house property information to be evaluated;
selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors;
calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result;
and evaluating the comprehensive value of the property according to the property information, the single-factor weight and the multi-factor interaction weight.
Compared with the prior art, the invention has the beneficial effects that:
the house property comprehensive value evaluation method provided by the invention is easier to perform correlation analysis in a multi-factor interaction state, is convenient for comprehensively evaluating single and multi-factor influences, and the implanted multi-factor comprehensive evaluation module can quantitatively feed back the correlation of the multi-factor interaction influences and greatly improve the accuracy of the weight of the influencing factors. The comparison of the transverse value and the longitudinal value can be prompted in real time according to the timing setting, the model can automatically evaluate the value of real-time or timed properties according to the setting, and the transverse data comparison among different properties and the longitudinal data comparison of specific properties are carried out; according to history and horizontal data, the comprehensive value change of the house property after the factors such as future school districts, businesses, public transportation and the like are changed is quantitatively predicted, and the quantitative analysis and evaluation and the value fluctuation active reminding of the house property value can be intuitively carried out.
According to some embodiments of the invention, the acquiring the property information to be evaluated comprises the steps of:
and collecting the property information by using a python network data traversal acquisition algorithm, and performing data reduction processing on the property information to remove redundant data.
According to some embodiments of the invention, the influencing factors comprise: school district factors, medical factors, traffic factors, business factors, greening factors, volume rate factors and house type factors;
the property information includes: house location information, historical trading unit price, house area, house type, and house age information.
According to some embodiments of the invention, the method for evaluating a property composite value further comprises the steps of:
and periodically evaluating the comprehensive value of the property, and sending out reminding information when the comprehensive value of the property exceeds a preset interval.
According to some embodiments of the present invention, the calculating the single-factor weight and the multi-factor interaction weight of the influencing factor according to the result of the ranking comprises:
calculating to obtain the square sum and the degree of freedom of the influence factors, and calculating to obtain the error square sum and the error degree of freedom;
calculating according to the square sum and the freedom of the influence factors to obtain the mean square of the influence factors, and calculating according to the square sum and the freedom of errors to obtain the mean square of errors;
and calculating the single factor weight of the influence factor according to the mean square of the influence factor and the mean square error.
According to some embodiments of the present invention, the calculating the single-factor weight and the multi-factor interaction weight of the influencing factor according to the result of the ranking comprises:
calculating to obtain the interactive square sum of a plurality of influence factors;
calculating according to the interactive square sum and the degree of freedom of the influence factors to obtain an interactive mean square;
and calculating the multi-factor interaction weight of the plurality of influence factors according to the interactive mean square and the error mean square.
According to some embodiments of the invention, after said assessing the composite value of said property, the method comprises the steps of:
and verifying the evaluation result by using a fuzzy comprehensive evaluation method.
According to some embodiments of the invention, the verifying the evaluation result by using a fuzzy comprehensive evaluation method comprises the following steps:
quantifying and assigning qualitative indexes by using a semantic difference membership assignment method;
carrying out dimensionless treatment on the qualitative indexes by adopting a membership assignment method in fuzzy mathematics;
and establishing a fuzzy matrix, and carrying out layer-by-layer comprehensive evaluation on the influence factors according to the sequence.
In a second aspect, the present invention provides a system for evaluating a comprehensive value of a real estate, including: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for evaluating a property composite value according to any one of the first aspect when executing the computer program.
In a third aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the method for evaluating a property composite value according to any one of the first aspect.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which the abstract is to be fully consistent with one of the figures of the specification:
FIG. 1 is a flow chart of a method for evaluating a property composite value according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for evaluating a property composite value according to another embodiment of the present invention;
fig. 3 is a flowchart of a method for evaluating a property composite value according to another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It is noted that while a division of functional blocks is depicted in the system diagram, and logical order is depicted in the flowchart, in some cases the steps depicted and described may be performed in a different order than the division of blocks in the system or the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The invention provides a house property comprehensive value evaluation method, which is easier to perform correlation analysis in a multi-factor interaction state and convenient to comprehensively evaluate single and multi-factor influence.
The embodiments of the present invention will be further explained with reference to the drawings.
Referring to fig. 1, fig. 1 is a flowchart of a method for evaluating a property composite value according to an embodiment of the present invention; the property integrated value evaluation method includes, but is not limited to, steps S110 to S140.
Step S110, acquiring the house property information to be evaluated; step S120, selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; step S130, calculating to obtain single-factor weight and multi-factor interaction weight of the influence factors according to the grading result; and step S140, evaluating the comprehensive value of the property according to the property information, the single-factor weight and the multi-factor interaction weight.
In one embodiment, the method for evaluating the comprehensive value of the property comprises the following steps: acquiring the house property information to be evaluated; selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result; and evaluating the comprehensive value of the property according to the property information, the single-factor weight and the multi-factor interaction weight.
The method for evaluating the comprehensive value of the real estate comprises the following steps:
step S1: collecting n samples of a target area from a credible way, summarizing a plurality of possible influence factors A, B, C and D … for each sample, and grading each influence factor, wherein the academic area of the A factor can be divided into 3 levels of no academic area, general academic area and high-quality academic area; subway trips can be divided into 3 levels of no subway, single subway and double subway aiming at the B factor; the matching can be divided into 2 levels for a large commercial district, and the number of the levels of the influencing factors is not necessarily the same. Because the house property information is convenient to obtain and high in authenticity, and the analysis precision is improved by processing by using a mathematical modeling method.
A 3-level partitioning is already sufficient to rank most of the influencing factors and can guarantee prediction evaluation accuracy. But if more levels need to be partitioned, the model can be expanded to m levels.
Step S2: after finishing sample data sorting, applying m of N sampleskThe factor design method obtains an analysis of variance table, which is shown as follows:
TABLE 1 mkFactor design analysis of variance table
Step S3: selecting a quantile point alpha which is generally 0.01 or 0.05, and searching data F of F (factor freedom degree and error freedom degree) in an F distribution table of each influence factor (interactive influence factor)K. For example, in the present embodiment, α =0.01 and the error degree of freedom is mk(n-1) with a single factor C with m-1, F should be looked for0.01(m-1，mk(n-1)); the degree of freedom is (m-1) under the interaction of three-factor ABD3If F should be found0.01((m-1)3，mk(n-1)). Comparing the F value of each factor in the fifth column in the table 1 with the corresponding F distribution table result, and for single factor analysis, if the single factor F value is larger than the value in the factor table, indicating that the factor has obvious influence on the house property price, otherwise, no obvious influence is caused; for multi-factor interaction, if the F value is larger than the corresponding value of the factor table, the interaction obviously influences the house property price, otherwise, no obvious influence is shown.
Step S4: a method for analyzing data as claimed in claim S3, wherein the weighting of the influence of each factor on the property price in the current sample space is quantified. The larger the value of F, the higher the weight of the factor in the price, and the lower the factor in the price.
The fuzzy comprehensive evaluation method verifies the evaluation result and comprises the following steps:
step B1: and (3) carrying out assignment division on the index weight levels on the F values, wherein all the F values can be divided into 9 levels, and the division of the 9 levels is shown as the following table:
table 2 judgement matrix follows the following evaluation rule of index weight class
| Weight criteria | Of utmost importance | Is very important | Of importance | Of slight importance | Of equal importance | Of less importance | Is not important | Of little importance | Of utmost importance |
| Fij | 9 | 7 | 5 | 3 | 1 | 1/3 | 1/5 | 1/7 | 1/9 |
| Fji | 1/9 | 1/7 | 1/5 | 1/3 | 1 | 3 | 5 | 7 | 9 |
Note that: the median values of adjacent evaluations may be 8, 6, 4, 2, 1/2, 1/4, 1/6, 1/8
Step B2: taking the case of selecting and calculating two factors for interaction as an example, the factors are divided into three levels shown in the following table:
TABLE 3 target course validity evaluation index system
As shown in the table above, a weight subset is first established, and a first-level evaluation index weight is set as an independent factor level weight W1Then W is1={WA,WB,WC,WDTherein of
. Each secondary index is two-factor interaction level weight W2Then there is W2A={ WAB, WAC, WAD}, W2B={ WBA, WBC, WBD}, W2C={ WCA, WCB, WCD}, W2D={ WDA, WDB, WDC}。
Step B3: starting to determine the weights according to the above data, the steps are as follows:
respectively calculating judgment matrixes FijProduct of each row element:
；
calculating the square root and carrying out normalization treatment:
；
and then carrying out consistency check: first calculating the matrix FijMaximum feature root of
Then, normalized feature vectors W (W1, W2, W3, …, Wn), i.e., FW =, are obtained
W, the component of W at this time (W1, W2, W3, …, Wn), i.e., the weight of the respective n factors, i.e., the relative weight.
And (4) judging the standard: CI = (λ)max-n)/n-1, when tested, wherein
Random consistency ratios are used, i.e.
| n | 1 | 2 | 3 | 4 | 5 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 |
CR = CI/RI, where the decision matrix is considered to have satisfactory consistency when CR ≦ 0.1.
Step B4: evaluating by using a fuzzy comprehensive evaluation method:
b4.1: firstly, qualitative indexes are quantified and assigned into 4 grades (very effective, generally effective and less effective) by using a semantic difference membership assignment method, and corresponding assignments are given according to the tendency degree of contents, and the corresponding index scores can be 90, 80, 70 and 50.
B4.2: and carrying out dimensionless treatment on each index by adopting a membership assignment method in fuzzy mathematics. In the formula, the maximum value, the minimum value and the average value in a grading system suitable for the quantitative index are taken as standards, non-dimensionalization processing is carried out, and the original value of the quantitative index is converted into the average value of the index.
B4.3: and establishing a fuzzy matrix. And R is a fuzzy matrix formed by fuzzy mapping and represents the comprehensive evaluation of the grade of the weight. Judging each factor to give U
A fuzzy mapping of V, as follows:
for each U, the relationship R can be represented by a fuzzy matrix:
wherein r isjkPresentation criterion UijFor K-th comment VkDegree of membership rjkCan be determined empirically for the criterion UijThere are s1 th VmGrade comment, fuzzy comprehensive evaluation for each factor Ui(i =1,2 …, n) by single factor evaluation
A blur matrix R is constructed.
When in use
Then, normalization processing can be performed to order
B4.4: performing multi-stage fuzzy comprehensive evaluation, namely performing layer-by-layer comprehensive evaluation on all factors from the bottommost layer, wherein the final comprehensive evaluation result is as follows:
referring to fig. 2, fig. 2 is a flowchart of a method for evaluating a property composite value according to another embodiment of the present invention; the property composite value evaluation method includes, but is not limited to, step S210.
And step S210, collecting the property information by using a python network data traversal acquisition algorithm, and performing data reduction processing on the property information to remove redundant data.
In one embodiment, the method for evaluating the comprehensive value of the property comprises the following steps: acquiring the house information to be evaluated; selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result; and evaluating the comprehensive value of the property according to the property information, the single-factor weight and the multi-factor interaction weight. Acquiring the property information to be evaluated, comprising the following steps: and (3) collecting the property information by using a python network data traversal acquisition algorithm, and performing data reduction processing on the property information to remove redundant data.
In one embodiment, the method for evaluating the comprehensive value of the property comprises the following steps: acquiring the house property information to be evaluated; selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result; and evaluating the comprehensive value of the property according to the property information, the single-factor weight and the multi-factor interaction weight. The influencing factors include: school district factors, medical factors, traffic factors, business factors, greening factors, volume rate factors and house type factors; the property information includes: house location information, historical trading unit price, house area, house type, and house age information.
Referring to fig. 3, fig. 3 is a flowchart of a method for evaluating a property composite value according to another embodiment of the present invention; the property composite value evaluation method includes, but is not limited to, step S310.
And S310, periodically evaluating the comprehensive value of the house property, and sending out reminding information when the comprehensive value of the house property exceeds a preset interval.
In one embodiment, the method for evaluating the comprehensive value of the property comprises the following steps: acquiring the house property information to be evaluated; selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result; and evaluating the comprehensive value of the house property according to the house property information, the single-factor weight and the multi-factor interaction weight. The comprehensive value of the house property is periodically evaluated, when the comprehensive value of the house property exceeds a preset interval, a reminding message is sent, the comprehensive value of the house property can be evaluated every week, every month, every quarter or every year, and the embodiment does not limit the comprehensive value of the house property.
In one embodiment, the method for evaluating the comprehensive value of the property comprises the following steps: acquiring the house property information to be evaluated; selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result; and evaluating the comprehensive value of the house property according to the house property information, the single-factor weight and the multi-factor interaction weight. Calculating the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result, and comprising the following steps of: calculating to obtain the square sum and the degree of freedom of the influence factors, and calculating to obtain the error square sum and the error degree of freedom; calculating according to the square sum of the influencing factors and the degree of freedom to obtain the mean square of the influencing factors, and calculating according to the square sum of errors and the degree of freedom of errors to obtain the mean square of errors; and calculating to obtain the single-factor weight of the influence factors according to the mean square and the error mean square of the influence factors.
In one embodiment, the method for evaluating the comprehensive value of the property comprises the following steps: acquiring the house property information to be evaluated; selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result; and evaluating the comprehensive value of the property according to the property information, the single-factor weight and the multi-factor interaction weight.
Calculating the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result, and comprising the following steps of: calculating to obtain the square sum and the degree of freedom of the influence factors, and calculating to obtain the error square sum and the error degree of freedom; calculating to obtain the mean square of the influence factors according to the square sum of the influence factors and the degree of freedom, and calculating to obtain the mean square of the error according to the square sum of the error and the degree of freedom of the error; and calculating to obtain the single-factor weight of the influence factors according to the mean square and the error mean square of the influence factors. Calculating to obtain the interactive square sum of a plurality of influence factors; calculating to obtain an interactive mean square according to the interactive square sum and the degree of freedom of the influence factors; and calculating the multi-factor interaction weight of a plurality of influence factors according to the interaction mean square and the error mean square.
In one embodiment, the method for evaluating the comprehensive value of the property comprises the following steps: acquiring the house property information to be evaluated; selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors; calculating to obtain the single-factor weight and the multi-factor interaction weight of the influence factors according to the grading result; and evaluating the comprehensive value of the property according to the property information, the single-factor weight and the multi-factor interaction weight.
Verifying the evaluation result by using a fuzzy comprehensive evaluation method: quantifying and assigning qualitative indexes by using a semantic difference membership assignment method; carrying out dimensionless processing on qualitative indexes by adopting a membership assignment method in fuzzy mathematics; and establishing a fuzzy matrix, and carrying out layer-by-layer comprehensive evaluation on the influence factors according to the sequence.
The invention also provides a system for evaluating the comprehensive value of the real estate, which comprises the following components: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method for evaluating the comprehensive value of the real estate.
The processor and memory may be connected by a bus or other means.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
It should be noted that the system for evaluating a comprehensive value of a property in this embodiment may include a service processing module, an edge database, a server version information register, and a data synchronization module, and when the processor executes a computer program, the method for evaluating a comprehensive value of a property applied to the system for evaluating a comprehensive value of a property is implemented.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, which are executed by a processor or a controller, for example, by a processor in the terminal embodiment, and can make the processor execute the house property comprehensive value evaluation method in the above embodiment.
It will be understood by those of ordinary skill in the art that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are to be included within the scope of the present invention defined by the appended claims.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention. Any other corresponding changes and modifications made according to the technical idea of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. A house property comprehensive value evaluation method is characterized by comprising the following steps:
acquiring the house property information to be evaluated;
selecting a preset number of influence factors, and grading according to the types and the influence levels of the influence factors, wherein the influence factors comprise: school district factors, traffic factors, business factors;
calculating to obtain the multi-factor interaction weight of the influence factors according to the grading result;
evaluating the comprehensive value of the property according to the property information and the multi-factor interaction weight;
classifying according to the types and the influence levels of the influence factors, specifically comprising the following steps:
dividing the factors of the school districts into a non-school district, a general school district and a high-quality school district; dividing the traffic factors into no subway, single subway and double subway; dividing the business factors into a large business area matching and a non-large business area matching;
calculating the multi-factor interaction weight of the influence factors according to the grading result, wherein the calculation comprises the following steps:
calculating to obtain the square sum and the degree of freedom of each influence factor, and calculating to obtain the error square sum and the error degree of freedom;
calculating according to the square sum and the freedom of the influence factors to obtain the mean square of the influence factors, and calculating according to the square sum and the freedom of errors to obtain the mean square of errors;
calculating to obtain the single factor weight of the influence factor according to the mean square of the influence factor and the mean square error;
calculating to obtain the interactive square sum of a plurality of influence factors;
calculating according to the interactive square sum and the degree of freedom of the influence factors to obtain an interactive mean square;
and calculating the multi-factor interaction weight of the plurality of influence factors according to the interactive mean square and the error mean square.
2. The method for evaluating the comprehensive value of property according to claim 1, wherein the step of obtaining the property information to be evaluated comprises the steps of:
and collecting the property information by using a python network data traversal collection algorithm, and performing data reduction processing on the property information to remove redundant data.
3. The method as claimed in claim 1, wherein the influencing factors further comprise: medical, greening, volume rate and house type factors;
the property information includes: house location information, historical trading unit price, house area, house type, and house age information.
4. The method for evaluating the integrated value of property according to claim 1, further comprising the steps of:
and periodically evaluating the comprehensive value of the property, and sending out reminding information when the comprehensive value of the property exceeds a preset interval.
5. The method for evaluating the integrated value of a property according to claim 1, comprising the steps of, after said evaluation of the integrated value of the property:
and verifying the evaluation result by using a fuzzy comprehensive evaluation method.
6. The method for evaluating a comprehensive value of a real estate according to claim 5 wherein the verifying the evaluation result using a fuzzy comprehensive evaluation method comprises the steps of:
quantifying and assigning qualitative indexes by using a semantic difference membership assignment method;
carrying out dimensionless treatment on the qualitative indexes by adopting a membership assignment method in fuzzy mathematics;
and establishing a fuzzy matrix, and carrying out layer-by-layer comprehensive evaluation on the influence factors according to the sequence.
7. A system for evaluating a property composite value, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of assessing a property composite value as claimed in any one of claims 1 to 6 when executing the computer program.
8. A computer-readable storage medium storing computer-executable instructions for causing a computer to execute the house property composite value evaluation method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210226505.4A CN114611916A (en) | 2022-03-09 | 2022-03-09 | Method, system and storage medium for evaluating comprehensive value of real estate |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210226505.4A CN114611916A (en) | 2022-03-09 | 2022-03-09 | Method, system and storage medium for evaluating comprehensive value of real estate |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114611916A true CN114611916A (en) | 2022-06-10 |
Family
ID=81860749
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210226505.4A Withdrawn CN114611916A (en) | 2022-03-09 | 2022-03-09 | Method, system and storage medium for evaluating comprehensive value of real estate |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114611916A (en) |
-
2022
- 2022-03-09 CN CN202210226505.4A patent/CN114611916A/en not_active Withdrawn
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2014055238A1 (en) | System and method for building and validating a credit scoring function | |
| CN107507016A (en) | A kind of information push method and system | |
| US20140257924A1 (en) | Automated rental amount modeling and prediction | |
| CN106295351B (en) | A kind of Risk Identification Method and device | |
| CN111177473A (en) | Personnel relationship analysis method and device and readable storage medium | |
| CN114971891A (en) | Risk prediction method and device, processor and electronic equipment | |
| CN107256461B (en) | Charging facility construction address evaluation method and system | |
| CN111798246A (en) | Financial risk grade assessment method and device | |
| CN116468273A (en) | Customer risk identification method and device | |
| CN112734352A (en) | Document auditing method and device based on data dimensionality | |
| CN117934154A (en) | Transaction risk prediction method, model training method, device, equipment, medium and program product | |
| CN113205442A (en) | E-government data feedback management method and device based on block chain | |
| US20180089774A1 (en) | Method for automatic property valuation | |
| CN112926794A (en) | Bank loan information prediction method and device | |
| CN112948469A (en) | Data mining method and device, computer equipment and storage medium | |
| CN115034788A (en) | Transaction risk assessment method and device, electronic equipment and storage medium | |
| CN115660774B (en) | Block chain-based material supply chain system credit evaluation method | |
| CN114611916A (en) | Method, system and storage medium for evaluating comprehensive value of real estate | |
| EP2541409A1 (en) | Parallelization of large scale data clustering analytics | |
| CN116308615A (en) | Product recommendation method and device, electronic equipment and storage medium | |
| US8396875B2 (en) | Online stratified sampling for classifier evaluation | |
| CN111144429A (en) | Object classification method and system, computer system and computer readable medium | |
| Croicu et al. | A fast spatial multiple imputation procedure for imprecise armed conflict events | |
| CN112488743A (en) | Satisfaction degree prediction method, network equipment and storage medium | |
| CN118644294A (en) | Business circle evaluation determination method, system, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220610 |
|
| WW01 | Invention patent application withdrawn after publication |