CN115689290B - Real estate market plot development vacant monitoring early warning analysis method - Google Patents
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Abstract
The invention provides a method for monitoring, early warning and analyzing the development of land parcels in real estate market, which comprises the following steps: s1, acquiring historical real estate development data, calling planning geographic information in a database to match with the space data of the land according to the space data of the land at the corresponding position, carrying out normalization processing on the matched space data, S2, forming classification attribute information of the real estate space data after the normalization processing, carrying out regression fitting data calculation on the real estate space data acquired in real time, and obtaining the use balance degree of the real estate space data; and S3, comparing and analyzing the superimposed real estate development data according to the calculated balance degree by the regression fitting method, and analyzing the data of the empty situation by the set threshold judgment condition so as to upload early warning data.
Description
Technical Field
The invention relates to the field of spatial data analysis, in particular to a real estate market plot development vacant monitoring early warning analysis method.
Background
After the real estate forms a certain scale, the regulation and control monitoring early warning work needs to be realized in a real-time manner, an early warning index system is continuously perfected, the real estate empty state is scientifically and reasonably judged, engineering resource waste is avoided, and after the real estate is planned to be a real estate project, the land attachment formed by occupied land is the real estate, if a large number of empty phenomena in the development process occur, huge engineering waste is undoubtedly caused, the existing real estate empty problem analysis cannot be analyzed and judged from all directions and multiple angles, so that output early warning information is inaccurate, and the corresponding technical problems are needed to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly creatively provides a real estate market land development vacant monitoring early warning analysis method.
In order to achieve the above purpose, the invention provides a real estate market plot development vacant monitoring early warning analysis method, comprising the following steps:
s1, acquiring historical real estate development data, calling planning geographic information in a database to match with the space data of the land according to the space data of the land at the corresponding position, carrying out normalization processing on the matched space data,
s2, after normalization processing, classification attribute information of real estate space data is formed, regression fitting data calculation is carried out on real estate space data acquired in real time, and the usage balance of the real estate space data is obtained;
and S3, comparing and analyzing the superimposed real estate development data according to the calculated balance degree by the regression fitting method, and analyzing the data of the empty situation by the set threshold judgment condition so as to upload early warning data.
According to the above technical solution, the step S1 preferably includes:
s1-1, performing data processing according to planning data and current state data of the land parcel, performing historical data replacement according to updated content of the historical data, storing normalization processing results into a database under a specified path, periodically collecting the planning data and the current state data of the land parcel, and performing historical data replacement on corresponding spatial data;
s1-2, set X ij Is the normalized value of the jth real estate space data of the ith year; v (V) ij Is the actual value of the jth real estate space data of the ith year, and m is the total year of investigation. Then:
the normalization method is to compare the difference between the actual value and the minimum value of a certain real estate space data with the difference between the maximum value and the minimum value to form a value between 0 and 1.
According to the above technical solution, preferably, the S1 further includes:
s1-3, after numerical normalization, using X ij Calculating the specific gravity of the jth real estate space data accounting for the real estate space data in the ith year:
calculating entropy value of the j-th real estate space data:
The calculated information entropy redundancy calculation result is d j ＝1-e j ，
Thereby calculating the weight of each real estate space data:
and (3) obtaining normalized expression of real estate space data through the calculation, and carrying out weight calculation on the real estate space data in each item of planning data after normalization.
According to the above technical solution, the S2 preferably includes:
s2-1, obtaining a change result of land utilization data based on land utilization data of a real estate area to be developed, and performing overlapping calculation of the change result of the real estate space data according to a space data evolution result and space-time evolution of a real estate space data weight value, wherein the overlapping calculation is regression fit calculation of pre-stored real estate space data of trial construction and real estate space data, so that a regression curve is obtained, and a prediction progress of real estate development is obtained;
mathematical model expressed as l=β 0 +β 1 ·W j ·r i +β p ε i, in the formula β0 ，β 1 …β p Is p evaluation parameters to be estimated, and carries out fitting operation on real estate space data through the evaluation parameters, epsilon i Is a random variable subject to the same normal distribution, L is a random variable; r is (r) i Referred to as regression coefficients, characterize the extent to which an independent variable affects a dependent variable.
According to the above technical solution, preferably, the S2 further includes:
s2-2, generating an initial demarcation of land empty conditions of a real estate space data change area according to the real estate space data change result and by fitting regression curve evolution results;
s2-3, calculating the number of layers of attachments on the land through planning information, thereby calculating the construction area of the real estate, obtaining the use state of the real estate according to the construction area and the registered information of the real estate, measuring the balance degree M of the vacant scale according to the balance degree of the land supply generated by calculating the balance level of the space data of the real estate,
wherein M is the real estate construction scale balance, the balance is measured by the supply area, the numerator represents the M-th registration state real estate space data use total amount of the region, and the denominator represents the total amount of all real estate space data supply scale of the region.
According to the above technical solution, the step S3 preferably includes:
s3-2, calculating land supply scale balance, carrying out standardized processing on real estate space data by adopting a range method, and obtaining construction land use intensity (LD) and land approval supply capacity (LS) by weighting calculation; the specific calculation formula is as follows:
wherein LB is land use intensity, PE is a human mouth capacity index, EC is an economic density index, EV is an environmental pressure index, PA is a human cultivated land, and RS is a resource guarantee index.
According to the above technical solution, preferably, the S3 further includes:
and S3-3, establishing an equilibrium degree model and an unbalance degree model according to the standardized data. The coordination of the construction land use strength and the land approval supply capacity is measured by the real estate development balance, and the strength and weakness of the construction land use strength and the land approval supply capacity are measured in the real estate development unbalance area, and the concrete calculation mode is as follows:
wherein: DS is an real estate space data using balance index, lambda and eta are weights, and an intermediate value is taken in an experiment; λ, η=0.5, k is used as the adjustment coefficient in order to make the calculation result more hierarchical.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
and (3) carrying out first-round screening on the primary selection indexes by an expert scoring method, and finally carrying out quantitative analysis on the regulation and control monitoring early warning index system by regression fitting and correlation analysis to further screen, thereby finally obtaining the regulation and control monitoring early warning index system.
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.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a schematic diagram of an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
As shown in fig. 1 and 2, the invention discloses a real estate market plot development vacant monitoring early warning analysis method, which comprises the following steps:
s1, acquiring historical real estate development data, calling planning geographic information in a database to match with the space data of the land according to the space data of the land at the corresponding position, carrying out normalization processing on the matched space data,
calling corresponding planning land data according to the land space data of the corresponding position in the information, carrying out information matching to obtain corresponding real estate construction data, thereby identifying the space data of the original land,
s2, after normalization processing, classification attribute information (occupied land for construction and number of layers of attachments on the ground, and empty data of buildings for approval) of the real estate space data is formed, regression fitting data calculation is carried out on the real estate space data acquired in real time, and the use balance degree of the real estate space data is obtained;
and S3, comparing and analyzing the superimposed real estate development data according to the calculated balance degree by the regression fitting method, and analyzing the data of the empty situation by the set threshold judgment condition so as to upload early warning data.
According to the above technical solution, the step S1 preferably includes:
s1-1, performing data processing according to planning data and current state data of the land parcel, performing historical data replacement according to updated content of the historical data, storing normalization processing results into a database under a specified path, periodically collecting the planning data and the current state data of the land parcel, and performing historical data replacement on corresponding spatial data;
s1-2, set X ij Is the normalized value of the jth real estate space data of the ith year; v (V) ij Is the actual value of the jth real estate space data of the ith year, and m is the total year of investigation. Then:
the normalization method is to compare the difference value between the actual value and the minimum value of a certain real estate space data with the difference value between the maximum value and the minimum value to form a numerical value between 0 and 1;
s1-3, after numerical normalization, using X ij Calculating the specific gravity of the jth real estate space data accounting for the real estate space data in the ith year:
calculating entropy value of the j-th real estate space data:
The calculated information entropy redundancy calculation result is d j ＝1-e j ，
Thereby calculating the weight of each real estate space data:
and (3) obtaining normalized expression of real estate space data through the calculation, and carrying out weight calculation on the real estate space data in each item of planning data after normalization.
According to the above technical solution, the S2 preferably includes:
s2-1, obtaining a change result of land utilization data based on land utilization data of a real estate area to be developed, and performing overlapping calculation of the change result of the real estate space data according to a space data evolution result and space-time evolution of a real estate space data weight value, wherein the overlapping calculation is regression fit calculation of pre-stored real estate space data of trial construction and real estate space data, so that a regression curve is obtained, and a prediction progress of real estate development is obtained;
mathematical model expressed as l=β 0 +β 1 ·W j ·r i +β p ε i, in the formula β0 ，β 1 …β p Is p evaluation parameters to be estimated, the real estate space data is subjected to fitting operation through the evaluation parameters,ε i is a random variable subject to the same normal distribution, L is a random variable; r is (r) i Referred to as regression coefficients, characterize the extent to which an independent variable affects a dependent variable.
S2-2, generating an initial demarcation of land empty conditions of a real estate space data change area according to the real estate space data change result and by fitting regression curve evolution results;
calculating the actual usage amount of the construction land in the real estate space data according to the occupation condition of the construction land,
s2-3, calculating the number of layers of attachments on the land through planning information, thereby calculating the construction area of the real estate, obtaining the use state of the real estate according to the construction area and the registered information of the real estate, measuring the balance degree M of the vacant scale according to the balance degree of the land supply generated by calculating the balance level of the space data of the real estate,
wherein M is the real estate construction scale balance, the balance is measured by the supply area, the numerator represents the M-th registration state real estate space data use total amount of the region, and the denominator represents the total amount of all real estate space data supply scale of the region.
After the balance degree of the idle scale is calculated, balance degree data is obtained for real estate space data of the registration state, so that the subsequent analysis of the idle state of the real estate is facilitated.
According to the above technical solution, the step S3 preferably includes:
s3-1, if S u The area of use for the u-th housing of a certain year, i.e., the area registered under the user's user name from the beginning of loading real estate space data to the end of registering; regression fitting of total registered area in spatial data of a property of a certain plotThe following linear regression model is established
N＝α 0 road+α 1 ·layer+α 2 ·location+α 3 ·S
Wherein road is the traffic mileage, and layer is the layer number of the building in real estate; location is the number of real estate plots; s is real estate development area, estimated value alpha by parameter 0 、α 1 、α 2 、α 3 And performing linear regression operation to respectively represent the influence of traffic mileage, building layer number, real estate position and real estate area on real estate empty.
According to the expert scoring method, an expert group is established, the number of people is about 20-30, the expert group members are distributed in the fields of education, administration, real estate industry, financial institutions, ordinary residents and the like, each expert group member scores the real estate regulation and control intensity in the current year, the score is fully divided into 5 scores, and the higher the score is, the stronger the real estate regulation and control intensity in the current year is represented. And finally, summarizing and averaging all expert group scores, wherein the obtained result is used for measuring the strength of the real estate regulation policy in the current year.
S3-2, calculating land supply scale balance, carrying out standardized processing on real estate space data by adopting a range method, and obtaining construction land use intensity (LD) and land approval supply capacity (LS) by weighting calculation; the specific calculation formula is as follows:
wherein ,
index for calculating land balance and description thereof
And S3-3, establishing an equilibrium degree model and an unbalance degree model according to the standardized data. The coordination of the construction land use strength and the land approval supply capacity is measured by the real estate development balance, and the strength and weakness of the construction land use strength and the land approval supply capacity are measured in the real estate development unbalance area, and the concrete calculation mode is as follows:
wherein: DS is an real estate space data using balance index, lambda and eta are weights, and an intermediate value is taken in an experiment; λ, η=0.5, k is used as the adjustment coefficient in order to make the calculation result more hierarchical.
1. Connotation of real estate risks
Real estate risks refer to the opportunity or likelihood of experiencing economic losses in real estate development and operating activities that, under the influence of a number of factors affecting development and operating profits, cannot reclaim the invested capital or reach the expected profits. In real estate risks, an event is all the development and management activities involving real estate, including the whole process from early engineering investigation, building design stand, land removal to mid-term building construction, house selling and use, and post-property management. Accordingly, the subjects of the event are also multiparty, and these subjects are distributed throughout the links of the real estate development and management chain, including the commercial banks participating in loans, real estate investors, real estate developers, etc. The investment recovery period of real estate investment is long, the investment occupation is large, the rendering capability is poor, and the influencing factors are numerous, so that the real estate investment risk is complex and various, and once the risk occurs, the loss is very tragic.
3. Risk-prevention index system construction
Construction of risk-prevention layer index system
Scale ratio of various land areas. Because land itself has multiple uses, and the land supply structure can only be at a healthier level when the scale ratio of land of each type is maintained at a relatively balanced level, if an imbalance in the land supply structure occurs, such as in a city, the supply ratio of residential land, commercial land and newly added construction land is significantly lower, precious land resources cannot be allocated to those areas where most urgent uses and productivity are highest, resulting in empty waste after construction of real estate. The empty scale balance M is measured by calculating the land supply balance produced by the real estate space data balance level.
And (5) the empty rate. The empty rate refers to the ratio of the empty room area to the total room area at a certain moment. According to international passing practice, the commodity room space ratio is a reasonable area between 5% -10%, and the commodity room supply and demand are balanced, so that the healthy development of national economy is facilitated; the empty rate is 10% -20% which is an empty dangerous area, and certain measures are taken to increase the sales force of the commodity houses so as to ensure the normal development of the real estate market and the normal operation of national economy; the empty rate of more than 20% is a serious backlog area of the commodity house. The empty rate is an important index reflecting the market absorbing capacity in a certain period, the type of empty house and the ratio of the empty house to the market, reflects the effective demand of the market, guides the market supply, and influences the real estate investment scale and market trend to a certain extent. Objectively, the index should have a reasonable scale interval. Too low proportion, insufficient description demand and colder market; too high a ratio indicates a strong demand and a hot market.
And establishing an equilibrium degree model and an unbalance degree model according to the standardized data. The coordination of the construction land use strength and the land approval supply capacity is measured by the real estate development balance, and the strength and weakness of the construction land use strength and the land approval supply capacity are measured in the real estate development unbalance area, and the concrete calculation mode is as follows:
wherein: DS is an real estate space data using balance index, lambda and eta are weights, and an intermediate value is taken in an experiment; λ, η=0.5, k is used as the adjustment coefficient in order to make the calculation result more hierarchical.
The average value mu of the selected real estate space data early warning indexes and the standard deviation rho of all the early warning indexes are obtained by using a U-rho method, and mu-2rho, mu+rho and mu+2rho are used as four nodes, and the four nodes are divided into five states of serious real estate idling, moderate real estate idling, saturation, moderate real estate use and vigorous real estate demand. In general, through the investigation of the previous study, the experience value of the police in the real estate empty early warning mechanism is obtained, so that the early warning is divided more accurately. The real estate obtained by the mu-rho method is usually compared with all critical values of serious real estate idling, moderate real estate idling, saturation, moderate real estate use and vigorous real estate requirement, and the warning value of a more reliable real estate development early warning system is obtained through adjustment after comparison.
Because the risk conduction mechanism exists, serious results of land vacancy can be generated by a little carelessness, so that the work of preventing the land vacancy risk is very important, and the measurement of the risk degree is also necessary and important enough, so that the relationship between the number of houses produced by the land and the vacancy is used as the main content of the construction of a monitoring and early warning index system.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (5)
1. The empty monitoring, early warning and analyzing method for land development of the real estate market is characterized by comprising the following steps:
s1, acquiring historical real estate development data, calling planning geographic information in a database to match with the space data of the land according to the space data of the land at the corresponding position, carrying out normalization processing on the matched space data,
s2, after normalization processing, classification attribute information of real estate space data is formed, regression fitting data calculation is carried out on real estate space data acquired in real time, and the usage balance of the real estate space data is obtained;
s2-1, obtaining a change result of land utilization data based on land utilization data of a real estate area to be developed, and performing overlapping calculation of the change result of the real estate space data according to a space data evolution result and space-time evolution of a real estate space data weight value, wherein the overlapping calculation is regression fit calculation of pre-stored real estate space data of trial construction and real estate space data, so that a regression curve is obtained, and a prediction progress of real estate development is obtained;
mathematical model expressed as l=β 0 +β 1 ·W j ·r i +β p ε i, in the formula β0 ，β 1 …β p Is p evaluation parameters to be estimated, and carries out fitting operation on real estate space data through the evaluation parameters, epsilon i Is a random variable subject to the same normal distribution, L is a random variable; r is (r) i Called regression coefficients, characterizing the degree of influence of an independent variable on a dependent variable, W j Weights for each item of real estate space data;
s2-2, generating an initial demarcation of the land empty condition of the real estate space data change area by carrying out regression fitting calculation on the real estate space data according to the regression fitting curve evolution result;
s2-3, calculating the number of layers of attachments on the land through planning information, thereby calculating the construction area of the real estate, obtaining the use state of the real estate according to the construction area and the registered information of the real estate, measuring the balance degree M of the vacant scale according to the balance degree of the land supply generated by calculating the balance level of the space data of the real estate,
wherein M is the real estate construction scale balance degree, the balance degree is measured by the supply area, the numerator represents the M-th registration state real estate space data use total amount of the region, and the denominator represents the total amount of all real estate space data supply scale of the region;
s3, according to the calculated balance degree of the regression fitting method, comparing and analyzing the superimposed real estate development data, and carrying out data analysis on the empty situation through the set threshold judgment condition so as to upload early warning data;
s3-1, if S u The area of use for the u-th housing of a certain year, i.e., the area registered under the user's user name from the beginning of loading real estate space data to the end of registering; regression fitting method for total area registered in space data of real estate in a certain land area is used for establishing the following linear regression model
N＝α 0 road+α 1 ·layer+α 2 ·location+α 3 ·S
Wherein road is the traffic mileage, and layer is the layer number of the building in real estate; location is the number of real estate plots; s is real estate development area, estimated value alpha by parameter 0 、α 1 、α 2 、α 3 Performing linear regression operation to respectively represent the influence of traffic mileage, building layer number, real estate position and real estate area on real estate empty;
according to the expert scoring method, an expert group is built, the number of people is 20-30, the expert group members are distributed in the fields of education, administration, real estate industry, financial institutions and common residents, each expert group member scores the real estate regulation and control intensity in the current year, the score is fully divided into 5 scores, and the higher the score is, the stronger the real estate regulation and control intensity in the current year is represented; finally, summarizing and averaging all expert group scores, and measuring the strength of the current annual regional property regulation policy by the obtained result;
s3-2, calculating land supply scale balance, carrying out standardized processing on real estate space data by adopting a range method, and obtaining construction land use intensity (LD) and land approval supply capacity (LS) by weighting calculation; the specific calculation formula is as follows:
wherein ,
s3-3, establishing an equilibrium degree model and an unbalance degree model according to the standardized data; the coordination of the construction land use strength and the land approval supply capacity is measured by the real estate development balance, and the strength and weakness of the construction land use strength and the land approval supply capacity are measured in the real estate development unbalance area, and the concrete calculation mode is as follows:
wherein: DS is an real estate space data using balance index, lambda and eta are weights, and an intermediate value is taken in an experiment; λ, η=0.5, in order to make the calculation result more hierarchical, k is adopted as the adjustment coefficient;
the average value mu of the selected real estate space data early warning indexes and the standard deviation rho of all the early warning indexes are obtained by using a U-rho method, and mu-2rho, mu+rho and mu+2rho are used as four nodes, and the four nodes are divided into five states of serious real estate idling, moderate real estate idling, saturation, moderate real estate use and vigorous real estate demand.
2. The real estate market parcel development vacancy monitoring pre-warning analysis method of claim 1 wherein the S1 includes:
s1-1, performing data processing according to the planning data and the current state data of the land parcel, performing historical data replacement according to the updated content of the historical data, storing the normalization processing result into a database under a specified path, periodically collecting the planning data and the current state data of the land parcel, and performing historical data replacement on the corresponding spatial data.
3. The real estate market parcel development vacancy monitoring pre-warning analysis method of claim 2 wherein the S1 further comprises:
s1-2, set X ij Is the normalized value of the jth real estate space data of the ith year; v (V) ij Is the actual value of the jth real estate space data of the ith year, and m is the total year of investigation; then:
the normalization method is to compare the difference between the actual value and the minimum value of a certain real estate space data with the difference between the maximum value and the minimum value to form a value between [0,1 ].
4. The real estate market parcel development vacancy monitoring pre-warning analysis method of claim 1 wherein the S1 further comprises:
s1-3, after numerical normalization, using X ij Calculating the specific gravity of the jth real estate space data accounting for the real estate space data in the ith year:
calculating entropy value of the j-th real estate space data:
The calculated information entropy redundancy calculation result is d j ＝1-e j 。
5. The real estate market parcel development vacancy monitoring pre-warning analysis method of claim 4 wherein the S1 further comprises: thereby calculating the weight of each real estate space data:
and (3) obtaining normalized expression of real estate space data through the calculation, and carrying out weight calculation on the real estate space data in each item of planning data after normalization.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109784667A (en) * | 2018-12-21 | 2019-05-21 | 中国科学院遥感与数字地球研究所 | A kind of vacant monitoring method in house based on noctilucence remotely-sensed data |
| CN110059114A (en) * | 2019-03-16 | 2019-07-26 | 平安城市建设科技(深圳)有限公司 | Housing vacancy rate acquisition methods, device, terminal and computer readable storage medium |
Family Cites Families (6)
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| US20020082903A1 (en) * | 2000-06-22 | 2002-06-27 | Seiichiro Yasuzawa | Real estate net-market system |
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| CN112907063B (en) * | 2021-02-10 | 2023-02-28 | 国网河北省电力有限公司信息通信分公司 | Population mobility rate and house vacancy rate determining method and terminal equipment |
| CN112966030B (en) * | 2021-03-29 | 2022-02-01 | 重庆市规划和自然资源信息中心 | Full life cycle data fusion management method for planning construction |
| CN113094448B (en) * | 2021-04-06 | 2023-10-27 | 国网北京市电力公司 | Analysis method and analysis device for residence empty state and electronic equipment |
| CN114201922A (en) * | 2021-12-22 | 2022-03-18 | 云南大学 | Dynamic landslide sensitivity prediction method and system based on InSAR technology |
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Patent Citations (2)
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| CN109784667A (en) * | 2018-12-21 | 2019-05-21 | 中国科学院遥感与数字地球研究所 | A kind of vacant monitoring method in house based on noctilucence remotely-sensed data |
| CN110059114A (en) * | 2019-03-16 | 2019-07-26 | 平安城市建设科技(深圳)有限公司 | Housing vacancy rate acquisition methods, device, terminal and computer readable storage medium |
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