CN116402398A - House leasing potential assessment method, device, computer equipment and storage medium - Google Patents

House leasing potential assessment method, device, computer equipment and storage medium Download PDF

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CN116402398A
CN116402398A CN202310396165.4A CN202310396165A CN116402398A CN 116402398 A CN116402398 A CN 116402398A CN 202310396165 A CN202310396165 A CN 202310396165A CN 116402398 A CN116402398 A CN 116402398A
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林英志
刘娟
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Beijing Ziroom Information Technology Co Ltd
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Abstract

The invention relates to the technical field of house renting, and discloses a house renting potential evaluation method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring house lease information of a target area, wherein the house lease information comprises a plurality of index sets for representing supply and demand, lease liveness and lease adaptation degree; discretizing the house leasing information to obtain scores of all index sets in the house leasing information; the method and the system can eliminate the possible complex nonlinear relation between the original value of the index and the house renting potential and the influence of singular values, and can discover the advantages and short plates of the house renting potential of the target area through the relation between the house renting potential data of the target area and the index scores of the index sets, thereby providing prepositive research information and comprehensive reference for the development of long-rented apartments in cities.

Description

House leasing potential assessment method, device, computer equipment and storage medium
Technical Field
The invention relates to the technical field of house renting, in particular to a house renting potential evaluation method, a house renting potential evaluation device, computer equipment and a storage medium.
Background
With the increase of the life rhythm of people, the urban population mobility is increased, so that a large number of people needing to rent rooms are generated, the renting market is fast prosperous, and long rented apartments become non-negligible components of the renting market. The long-leased apartment, also called as white-collar apartment and single-body-leased apartment, is an emerging industry in real estate market, leases home of owners, carries out decoration modification, matches furniture home appliances and leases to people in need of the single-body form.
However, to contend for market share, some long rented apartments "high in low out", "long pay-for-short pay" under capital assistance, after the capital chain breaks, carry money to run, or to compensate for the deficit, maintain the operation of the enterprise, some operators force or induce the tenant to use "rent credits" in large quantities, allowing the financial institution to pay all rents at one time. Namely, the tenant is loaned on the back, and a monthly payment mode is adopted for the homeowner, so that the interests of owners and tenants are greatly damaged. The reason for this is that the development of long-leased apartments is due to the fact that the quality and the quantity of the source of the rented market are not matched, and the long-leased apartments only have development opportunities when the original rented market has obvious supply and demand dislocation.
Therefore, it is necessary to analyze the rented markets of different cities and evaluate the development potential of the long-leased apartments, namely the potential of supply and demand, so as to provide decision reference information of 'prepositivity (pre-judgment is given before the industry of the long-leased apartments in cities)' for industry development.
Disclosure of Invention
In view of the above, the present invention provides a house renting potential evaluation method, apparatus, computer device and storage medium, so as to solve the problem that the development of long rented apartments lacks prepositive decision reference information.
In a first aspect, the present invention provides a house lease potential evaluation method, comprising: acquiring house lease information of a target area, wherein the house lease information comprises a plurality of index sets for representing supply and demand, lease liveness and lease adaptation degree; discretizing the house leasing information to obtain scores of all index sets in the house leasing information; and comprehensively integrating the scores of the index sets to obtain house leasing potential data of the target area. Through the process, the influence of a possibly existing complex nonlinear relation between the original value of the index and the house renting potential and the singular value can be eliminated, meanwhile, the advantages and the short plates of the house renting potential of the target area can be discovered through the relation between the house renting potential data of the target area and the scores of the indexes in the index set, and the prepositive research information and the comprehensive reference are provided for the development of long rented apartments in cities.
In a second aspect, the present invention provides a house renting potential assessment device according to an embodiment of the present invention, where the assessment device mainly includes: the system comprises an information acquisition module, a discrete processing module and a comprehensive calculation module, wherein the information acquisition module is used for acquiring house lease information of a target area, and the house lease information comprises a plurality of index sets used for representing supply and demand, lease activity and lease adaptation; the discrete processing module is used for carrying out discretization processing on the house leasing information to obtain scores of all index sets in the house leasing information; and the comprehensive calculation module is used for comprehensively calculating the scores of the index sets to obtain the house renting potential of the target area. Through the process, the influence of a possibly existing complex nonlinear relation between the original value of the index and the house renting potential and the singular value can be eliminated, meanwhile, the advantages and the short plates of the house renting potential of the target area can be discovered through the relation between the house renting potential data of the target area and the scores of the indexes in the index set, and the prepositive research information and the comprehensive reference are provided for the development of long rented apartments in cities.
In a third aspect, the present invention provides a computer device comprising: the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so that the house lease potential evaluation method of the first aspect or any corresponding implementation mode is executed.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the house rental potential assessment method of the first aspect or any one of its corresponding embodiments.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic illustration of an application environment of an embodiment of the present invention;
FIG. 2 is a flow chart of a house lease potential evaluation method according to an embodiment of the present invention;
FIG. 3 is a flow chart of another house lease potential evaluation method of an embodiment of the present invention;
FIG. 4 is a block diagram of a house lease potential evaluation apparatus of an embodiment of the present invention;
fig. 5 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of an application environment provided in an embodiment of the present application, where the schematic diagram includes a client 10 and a server 20, and after the server 10 receives house lease information of a target area uploaded by the client 20, discretization processing may be performed on the house lease information, so as to finally obtain house lease potential data of the target area.
Specifically, in the embodiment of the present application, the client 10 for transmitting house lease information in the target area shown in fig. 1 may be a smart phone, a desktop computer, a tablet computer, a notebook computer, a digital assistant, an intelligent wearable device, or other type of entity device of the user; wherein, intelligent wearable equipment can include intelligent bracelet, intelligent wrist-watch, intelligent glasses, intelligent helmet etc.. Of course, the client 10 is not limited to the electronic device with a certain entity, but may be software running in the electronic device, for example, the client 10 may be a web page or an application provided to a user by a service provider.
Alternatively, the client 10 may include a display, a memory device, and a processor connected by a data bus. The display screen is used for house lease information, and can be a touch screen of a mobile phone or a tablet computer. The storage device is used for storing house lease information or other data materials, and the storage device can be a memory of the client 10, or can be a storage device such as a smart media card (smart media card), a secure digital card (secure digital card), a flash memory card (flash card), and the like. The processor may be a single core or multi-core processor.
In the embodiment of the present application, the server 20 shown in fig. 1 may be used to receive house lease information of the target area, or other computer terminals having the same functions as the server, or similar computing devices. Further, the server 20 may be replaced with a server system, an operation platform, or a server cluster including a plurality of servers.
In accordance with an embodiment of the present invention, there is provided a house rental potential assessment method embodiment, it being noted that the steps shown in the flowchart of the drawings can be performed in a computer system, such as a set of computer-executable instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in an order other than that shown or described herein.
In this embodiment, a house lease potential evaluation method is provided, which may be used for the above-mentioned clients, such as smartphones and tablet computers, and fig. 2 is a flowchart of a house lease potential evaluation method according to an embodiment of the present invention, and as shown in fig. 2, the flowchart includes the following steps:
step S201, obtaining house lease information of a target area.
It should be noted that, because the market and region of house renting are wide, there is a large difference in house renting information between different regions, so the present embodiment divides the house renting market or region into a plurality of target regions according to cities. The house lease information in this embodiment includes a plurality of index sets for characterizing supply and demand, lease activity, and lease adaptation.
Optionally, the plurality of index sets for characterizing supply and demand, rental activity, and rental adaptation are primary indexes, including: a supply scale index set, a demand scale index set, a rental activity index set, and a rental fit index set. The method comprises the steps that content represented by a supply scale index set is the supply scale of a common rental house source in a target area, content represented by a demand scale index set is the growth and stability of the rental demand scale in the target area, content represented by a rental activity index set is the activity of a rental market in the target area, such as transaction behavior, and content represented by a rental adaptation index set is the adaptation of the rental in the target area, such as product income.
The supply scale index set described above may include: and the total amount index of the rental house sources, the duty ratio index of the rental house sources and other secondary indexes in the first history period. The total rental source index in the first history period can be the total rental source of the last 1 year.
The set of demand scale indicators may include: target area resident population index, rental room population proportion index, floating population ratio index, and average human GDP speed increasing index in the second history period. Wherein the average-person GDP speed-up index in the second history period may be average-person GDP speed-up index of approximately 3 years (d 4 )。
The lease activity index set may include: spectrum renting and delivering period index, and ratio index of floating population to rented room number.
The lease adaptation index set may include: the method comprises the steps of fine-packing rent level index, non-fine-packing house occupation ratio index, new house occupation ratio index in a third history period, rent income ratio index, decoration rent ratio index, and simple-packing price difference index. The new house duty index in the third history period may be the new house duty of 15 years.
Index information of index sets for characterizing supply and demand, rental activity, and rental adaptation is shown in table 1.
Table 1 index information of index set for characterizing supply and demand, rental activity, and rental adaptation
Figure BDA0004177644000000051
Step S202, discretizing the house leasing information to obtain the scores of all index sets in the house leasing information.
In this embodiment, discretization is performed on the indexes of each index set in the house lease information, such as the supply scale index set, the demand scale index set, the lease activity index set and each index in the lease adaptation index set, so as to eliminate the nonlinear relationship between the original value of each index and the house lease potential data and the influence of the singular value, thereby obtaining the section and the corresponding score of each index, and then weighting calculation is performed on the scores of each index in the index sets, so as to obtain the score of each index set. The weighted value of each index in the index set can be obtained according to a correlation coefficient method, or can be obtained according to a hierarchical analysis method, an entropy value method, an information quantity weight, or a principal component analysis method.
Alternatively, the score range of each index in the index set may be 0-5, and the index range of each index is related to the index score. For example, when the interval to which the total amount of the rental room source of the target area is discretized in the last 1 year is [0, 10), the score is 0; when the interval to which the total amount of the common rental house sources in the target area is discretized in the last 1 year is [10, 20 ], the score is 1; when the interval to which the total amount of the common rental house sources in the target area is discretized in the last 1 year is [20, 30 ], the score is 2; when the interval to which the total amount of the common rental house source in the target area is discretized in the last 1 year is [30, 40 ], the score is 3; when the interval to which the total amount of the common rental house sources in the target area is discretized in the last 1 year is [40, 50 ], the score is 4; when the interval to which the total amount of the common rental room source of the target area is discretized in the last 1 year is [50, + -infinity ], the score is 5.
And step S203, comprehensively integrating the scores of the index sets to obtain house leasing potential data of the target area.
In this embodiment, after discretizing the indexes of each index set in the house renting information to obtain the scores of each index set, the scores of each index set are weighted, so as to obtain the house renting potential data of the target area. The weighted value of each index set can be obtained according to a correlation coefficient method, or can be obtained according to a hierarchical analysis method, an entropy value method, an information quantity weight, or a principal component analysis method.
According to the house renting potential evaluation method provided by the embodiment, the indexes of each index set in the house renting information are discretized, so that the influence of a complex nonlinear relation and singular values possibly existing between the original values of the indexes and the house renting potential is eliminated; and meanwhile, the scores of the index sets are comprehensively calculated to obtain the house renting potential data of the target area, so that the advantages and the short plates of the house renting potential of the target area are discovered according to the relation between the house renting potential data of the target area and the scores of the indexes in the index sets, and the prepositive research information and the comprehensive reference are provided for the development of long-rented apartments in cities.
In this embodiment, a house lease potential evaluation method is provided, which may be used in the above mobile terminal, such as a smart phone, a tablet computer, etc., and fig. 3 is a flowchart of a house lease potential evaluation method according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
in step S301, house lease information of the target area is obtained, where the house lease information includes a plurality of index sets for characterizing supply and demand, lease activity, and lease adaptation.
Wherein, a plurality of index sets for representing supply and demand, lease liveness, and lease adaptation degree include: a supply scale index set, a demand scale index set, a rental activity index set, and a rental fit index set.
Please refer to step S201 in the embodiment shown in fig. 2 in detail, which is not described herein.
Step S302, discretizing the indexes of each index set in the house leasing information to obtain the scores of each index set in the house leasing information.
Specifically, the step S302 includes:
in step S3021, discretization is performed on the indices in each index set.
In an alternative embodiment, the server performs discretization processing on the total rental-house source index and the rental-house source duty index in the first history period in the supply scale index set respectively; discretizing target area resident population indexes, rental room population proportion indexes, floating population proportion indexes, average person GDP speed increasing indexes in a second historical period, second-hand room sales period indexes and room price income ratio indexes in a demand scale index set respectively, wherein the specific discretization method can refer to lines 3-8 of a table 2; discretizing the spectrum rental period index and the ratio index of the floating population to the rented room number in the rental activity index set respectively, wherein the specific discretization method can refer to lines 9-10 of the table 2; discretizing the fine-packaging rent level index, the non-fine-packaging house occupation ratio index, the newly-built house occupation ratio index, the rent income ratio index, the decoration rent ratio index and the simplified-packaging price difference index in the lease adaptation index set respectively.
Step S3022, obtaining weights of the indicators in the indicator set.
In an alternative implementation manner, the server respectively acquires the weight of the total amount index of the rental resources in the first history period in the supply scale index set and the weight of the discretization processing index of the rental resource duty ratio; respectively acquiring the weight of the target area resident population index, the weight of the lease population proportion index, the weight of the floating population proportion index, the weight of the average human GDP speed increasing index in the second history period, the weight of the second-hand house sales period index and the weight of the room price income ratio index in the requirement scale index set; respectively obtaining the weight of a spectrum renting period index in the renting activity index set and the weight of a ratio index of the floating population to the renting room number; the weight index of the lease adaptation degree index set fine-packing lease level, the weight of the non-fine packing house occupation ratio index, the weight of the newly built house occupation ratio index in the third history period, the weight of the lease income ratio index, the weight of the decoration lease ratio index and the weight of the streamline packing price difference index are respectively obtained.
In step S3023, the score of each index set is calculated from the index in each index set after the discretization process and the weight of the index in each index set.
Optionally, the weights of the indexes in the supply scale index set are: the correlation coefficient between the occupancy and the score of each index is divided by the sum of the correlation coefficients of each index in the feed scale index set.
Wherein, based on each index score in the supply scale, the expression for calculating the supply scale index set score may be:
Figure BDA0004177644000000071
wherein S is 1 Scoring the total amount index of the house source of the last 1 year; s is S 2 Scoring a rental house source duty cycle index;
Figure BDA0004177644000000072
and->
Figure BDA0004177644000000073
For weight, propose to take +.>
Figure BDA0004177644000000074
And->
Figure BDA0004177644000000075
The weight values suggested in this example are from 10 main cities in 2021 China (Beijing, shanghai, guangzhou, shenzhen, hangzhou, nanjing, tianjin, chengdu, wuhan, suzhou)) The method for calculating the correlation coefficient of the long-leased apartment management result index ' check-in rate ' and the index score ' in the supply scale index set is characterized by comprising the following steps:
Figure BDA0004177644000000081
wherein R is k An index value of "check-in rate" for the kth city (which may be directly obtained from daily operation data or annual report data of a long-rental apartment facility);
Figure BDA0004177644000000082
the average value of index values of the 'check-in rate' of all cities; s is S i,k A total rental source indicator score (i=1) or a rental source duty indicator score (i=2) for the nearest 1 year of the kth city; />
Figure BDA0004177644000000083
The average value of the index scores of the total amount of the rental house sources in the last 1 year of all cities (i=1) or the average value of the index scores of the ratio of the rental house sources (i=2); / >
Figure BDA00041776440000000810
Correlation coefficients of occupancy and total rental source index score (i=1) or rental source duty index score (i=2) of the last 1 year; />
Figure BDA00041776440000000811
Correlation coefficients of occupancy and total rental source index score (j=1) or rental source duty index score (j=2) of the last 1 year old; />
Figure BDA00041776440000000812
The weight corresponding to the total rental house source index score (i=1) or the rental house source duty index score (i=2) in the last 1 year.
Optionally, the weights of the indexes in the requirement scale index set are as follows: the correlation coefficient between the occupancy and the score of each index is divided by the sum of the correlation coefficients of each index in the demand scale index set.
Wherein, based on each index score in the demand scale index set, the expression for calculating the score of the demand scale index set may be:
Figure BDA0004177644000000084
wherein D is 1 Scoring for urban resident population indicators; d (D) 2 Scoring a ratio index of a rented room population; d (D) 3 Scoring a ratio indicator for a floating population; d (D) 4 The GDP speed-up index score is about 3 years of people average; d (D) 5 Scoring a second house sales cycle index; d (D) 6 Scoring a price revenue ratio indicator;
Figure BDA00041776440000000813
for weight, propose to take +.>
Figure BDA0004177644000000085
Figure BDA0004177644000000086
And->
Figure BDA0004177644000000087
The weight values suggested in this embodiment are derived from and normalized by the correlation coefficients of the long-rental apartment operation result index "occupancy" and the index scores in the "demand scale index set" of 10 main cities (Beijing, shanghai, guangzhou, shenzhen, hangzhou, nanjing, tianjin, chengdu, wuhan, suzhou) in 2021, and the calculation method is as follows:
Figure BDA0004177644000000088
Figure BDA0004177644000000089
Wherein R is k "check-in" index value for kth city(can be obtained directly from daily operation data or annual report data of the long rented apartment facility);
Figure BDA0004177644000000091
the average value of index values of the 'check-in rate' of all cities; d (D) i,k A resident index score (i=1), a lease population ratio index score (i=2), a mobile population ratio index score (i=3), a recent 3 year average GDP speed increase index score (i=4), a second-hand house sales cycle index score (i=5) or a house price income ratio index score (i=6) for the kth urban area; />
Figure BDA0004177644000000092
Average resident population index score (i=1), rental population proportion index score (i=2), floating population proportion index score (i=3), GDP speed-up index score (i=4), second-hand house sales period index score (i=5) or house price income proportion index score (i=6) for all urban areas; />
Figure BDA00041776440000000910
Correlation coefficients for occupancy and urban resident population index score (i=1), lease population ratio index score (i=2), mobile population ratio index score (i=3), recent 3 year average GDP speed increase index score (i=4), second-hand house sales cycle index score (i=5) or rate of income ratio index score (i=6); / >
Figure BDA00041776440000000911
Correlation coefficients for occupancy and urban resident population index score (j=1), lease population ratio index score (j=2), mobile population ratio index score (j=3), recent 3 year average GDP speed increase index score (j=4), second-hand house sales cycle index score (j=5) or rate revenue ratio index score (j=6); />
Figure BDA00041776440000000912
Index score (i=1) for urban resident population, index score (i=2) for rental room population proportion, and floating populationThe weight corresponding to the duty index score (i=3), the recent 3 year average GDP speed increase index score (i=4), the second-hand house sales cycle index score (i=5), or the price income ratio index score (i=6).
Optionally, the weights of the indexes in the leasing activity index set are as follows: the correlation coefficient of the occupancy and the score of each index is divided by the sum of the correlation coefficients of each index in the rental activity index set.
Wherein, based on each index score in the rental activity index set, the expression for calculating the rental activity index set score may be:
Figure BDA0004177644000000093
wherein T is 1 Scoring the index of the period of the taxi-taking period; t (T) 2 Scoring a ratio indicator of the number of floating population to the number of rented rooms;
Figure BDA0004177644000000094
and->
Figure BDA0004177644000000095
For weight, propose to take +.>
Figure BDA0004177644000000096
And->
Figure BDA0004177644000000097
The weight values suggested in this embodiment are derived from and normalized by the correlation coefficients of the long-rental apartment operation result index "occupancy" and the index scores in the rental activity index set "of 10 main cities (Beijing, shanghai, guangzhou, shenzhen, hangzhou, nanjing, tianjin, chengdu, wuhan, suzhou) in 2021, and the calculation method is as follows:
Figure BDA0004177644000000098
Figure BDA0004177644000000099
Wherein R is k An index value of "check-in rate" for the kth city (which may be directly obtained from daily operation data or annual report data of a long-rental apartment facility);
Figure BDA0004177644000000101
the average value of index values of the 'check-in rate' of all cities; t (T) i,k A taxi-period index score (i=1) or a ratio index score (i=2) of the number of floating population to the number of rented rooms for the kth city; />
Figure BDA0004177644000000102
Average value of index score of period of taxi-through for all cities (i=1) or average value of index ratio of floating population to number of taxi-through rooms (i=2); />
Figure BDA00041776440000001010
Correlation coefficients for occupancy and regular lease lead period index score (i=1) or mobile population and lease room number ratio index score (i=2); />
Figure BDA00041776440000001011
Correlation coefficients for occupancy and lease lead time index score (j=1) or floating population and lease number ratio index score (j=2); />
Figure BDA00041776440000001012
The weight corresponding to the lesson achievement cycle index score (i=1) or the ratio index score (i=2) of the number of free population to the number of lessons.
Optionally, the weight of each index in the leasing fitness index set is: the correlation coefficient of the occupancy rate and the index score is divided by the sum of the correlation coefficients of the indexes in the leasing fitness index set.
Wherein, based on each index score in the rental-fit index set, the expression for calculating the rental-fit index set score may be:
Figure BDA0004177644000000103
Wherein P is 1 Scoring a level index of the rent for the smart package; p (P) 2 Scoring a non-hardcover room occupancy index; p (P) 3 The score of the occupancy index of the newly built house is about 15 years; p (P) 4 A lease income ratio index score; p (P) 5 Scoring a rent ratio index for decoration; p (P) 6 Price difference index scores for the thin-pack;
Figure BDA00041776440000001013
for weight, propose to take +.>
Figure BDA0004177644000000104
Figure BDA0004177644000000105
And->
Figure BDA0004177644000000106
The weight values suggested in this embodiment are derived from and normalized by the correlation coefficients of the long-rental apartment operation result index "occupancy" and the index scores in the rental suitability index set "of 10 main cities (Beijing, shanghai, guangzhou, shenzhen, hangzhou, nanjing, tianjin, chengdu, wuhan, suzhou) in 2021, and the calculation method is as follows:
Figure BDA0004177644000000107
wherein R is k An index value of "check-in rate" for the kth city (which may be directly obtained from daily operation data or annual report data of a long-rental apartment facility);
Figure BDA0004177644000000108
the average value of index values of the 'check-in rate' of all cities; p (P) i,k A level index score (i=1), a non-smart package room occupancy index score (i=2) for the kth city smart package rent,A new house occupancy index score (i=3), a rent income ratio index score (i=4), a decoration rent ratio index score (i=5) or a reduced price difference index score (i=6) in the last 15 years; / >
Figure BDA0004177644000000109
For all cities, the average value of the fine-packing rent level index score (i=1), the average value of the non-fine packing house occupation index score (i=2), the average value of the new house occupation index score (i=3) in the last 15 years, the average value of the rent income ratio index score (i=4), the average value of the decoration rent ratio index score (i=5) or the average value of the fine-packing price difference index score (i=6); />
Figure BDA0004177644000000112
Correlation coefficients for a check-in rate and a level of fine-load rental metrics score (i=1), a non-fine-load house occupancy metrics score (i=2), a new house occupancy metrics score of 15 years old (i=3), a rental income ratio metrics score (i=4), a fine-load rental metrics score (i=5), or a fine-load price difference metrics score (i=6); />
Figure BDA0004177644000000113
Correlation coefficients for a check-in rate and a level index score of a fine-packing rental lot (j=1), a duty index score of a non-fine-packing house (j=2), a duty index score of a new house built in the last 15 years (j=3), a income ratio index score of a rental lot (j=4), a rate index score of a fine-packing rental lot (j=5), or a price difference index score of a fine-packing price (j=6); />
Figure BDA0004177644000000114
Weights corresponding to the fine-packing rental level index score (i=1), the non-fine packing house occupation index score (i=2), the recent 15 years new house occupation index score (i=3), the rental income ratio index score (i=4), the decoration rental ratio index score (i=5) or the fine packing price difference index score (i=6).
The section to which the index of each index set belongs and the corresponding score are shown in table 2.
Table 2 belonging intervals and corresponding scores of the indices of the index sets
Figure BDA0004177644000000111
Note that: taking the index (s 1) of the total amount of the rental house resources in the last 1 year as an example, if s1 is E [40,50 ], the index corresponding score is 4; qr (v) represents the r×100% quantile of v index data, where v may be taken as: GDP acceleration index (d) of people average in recent 3 years 4 ) Index of sales cycle of second-hand house (d) 5 ) House price income ratio index (d) 6 ) Cycle index of taxi-taking-in (t) 1 ) Level index of the level of the rent of the smart package (p) 1 ) Income ratio index (p) 4 ) Decoration rent ratio index (p) 5 )。
And step S303, comprehensively integrating the scores of the index sets to obtain house leasing potential data of the target area.
Specifically, the step S303 includes:
step S3031, obtains the weight of the supply scale index set, the weight of the demand scale index set, the weight of the rental activity index set, and the weight of the rental adaptation index set.
Step S3032, calculating house leasing potential of the target area according to the weight and the score of the supply scale index set, the weight and the score of the demand scale index set, the weight and the score of the leasing liveness index set and the weight and the score of the leasing adaptation index set.
The weight of each index set in the house lease information is as follows: the correlation coefficient of the occupancy rate and the score of each index set is divided by the sum of the correlation coefficients of each index in the leasing fitness index set.
In an alternative embodiment, the house lease potential data (Z) of the target area is calculated by weighting a supply scale index set score (S), a demand scale index set score (D), a lease liveness index set score (T) and a lease suitability index set score (P):
Z＝w S S+w D D+w T T+w P P
wherein S is a supply scale index set score; d is a demand scale index set score;t is a lease liveness index set score; p is the lease adaptation index set score; w (w) S 、w D 、w T 、w P For the weight coefficient, it is suggested to take w respectively S ＝0.45、w D ＝0.15、w T =0.05 and w P =0.35. The weight values suggested in this embodiment are derived from the correlation coefficients of the long-rental apartment operation result indexes of 10 main cities (Beijing, shanghai, guangzhou, shenzhen, hangzhou, nanjing, tianjin, chengdu, wuhan and Suzhou) in 2021 in China, namely "check-in rate" and "supply scale index set score", "demand scale index set score", "rental activity index set score" and "rental adaptation index set score", respectively, and the calculation method is as follows:
Figure BDA0004177644000000121
Figure BDA0004177644000000122
Figure BDA0004177644000000123
Figure BDA0004177644000000131
Figure BDA0004177644000000132
Wherein R is k An index value of "check-in rate" for the kth city (which may be directly obtained from daily operation data or annual report data of a long-rental apartment facility);
Figure BDA0004177644000000133
the average value of index values of the 'check-in rate' of all cities; s is S k Supply Scale index set for kth CityScoring; d (D) k Scoring a set of demand scale indicators for a kth city; t (T) k A lease activity index set score for a kth city; p (P) k A lease adaptation index set score for the kth city; />
Figure BDA0004177644000000134
Score means for the supply scale index set for all cities; />
Figure BDA0004177644000000135
The method comprises the steps of scoring an average value of a demand scale index set of all cities; />
Figure BDA0004177644000000136
The average value of the leasing activity index sets of all cities is scored; />
Figure BDA0004177644000000137
The average value of the leasing fitness index sets of all cities is scored; i, j can be taken from { S, D, T, P }; r is (r) S 、r D 、r T And r P Correlation coefficients of the check-in rate and the supply scale index set score, the demand scale index set score, the lease liveness index set score and the lease adaptation index set score are respectively obtained; w (w) i Weights corresponding to the supply scale index set score (i=s), the demand scale index set score (i=d), the rental activity index set score (i=t), and the rental suitability index set score (i=p).
According to the house renting potential evaluation method provided by the embodiment, the indexes of each index set in the house renting information are discretized, so that the influence of a complex nonlinear relation and singular values possibly existing between the original values of the indexes and the house renting potential is eliminated; and meanwhile, the scores of the index sets are comprehensively calculated to obtain the house renting potential data of the target area, so that the advantages and the short plates of the house renting potential of the target area are discovered according to the relation between the house renting potential data of the target area and the scores of the indexes in the index sets, and the prepositive research information and the comprehensive reference are provided for the development of long-rented apartments in cities.
The embodiment also provides a house renting potential evaluation device, which is used for realizing the above embodiment and the preferred implementation manner, and the description is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a house lease potential evaluation apparatus, as shown in fig. 4, including:
the information obtaining module 401 is configured to obtain house lease information of a target area, where the house lease information includes a plurality of index sets for characterizing supply and demand, lease activity, and lease adaptation.
Wherein, a plurality of index sets for representing supply and demand, lease liveness, and lease adaptation degree include: a supply scale index set, a demand scale index set, a rental activity index set, and a rental fit index set.
And the discrete processing module 402 is configured to perform discretization processing on the house leasing information to obtain scores of all index sets in the house leasing information.
And the comprehensive calculation module 403 is configured to comprehensively calculate the scores of the index sets, and obtain the house renting potential of the target area.
In some alternative embodiments, the discrete processing module 402 includes:
the index discrete processing unit is used for performing discretization processing on the total rental room source index and the rental room source duty ratio index in the first history period respectively;
the system is also used for discretizing the target area resident population index, the rental room population proportion index, the floating population ratio index, the average human GDP speed increasing index in the second history period, the second-hand room sales period index and the room price income ratio index respectively;
the system is also used for discretizing the spectrum renting cycle index and the ratio index of the floating population to the rented rooms respectively;
and the system is also used for discretizing the level index of the fine-packaging rent, the duty index of the non-fine-packaging room, the duty index of the newly-built room in the third historical period, the income ratio index of the rent, the ratio index of the decoration rent and the price difference index of the simplified package respectively.
The index weight acquisition unit is used for respectively acquiring the weight of the total rental house source index and the weight of the rental house source duty ratio index in the first history period;
the system is also used for respectively acquiring the weight of the target area resident population index, the weight of the lease population proportion index, the weight of the floating population proportion index, the weight of the average human GDP speed increasing index in the second history period, the weight of the second-hand house sales period index and the weight of the room price income ratio index;
The method is also used for acquiring the weight of the index of the lease period of the spectrum and the weight of the index of the ratio of the floating population to the number of leased rooms;
the method is also used for obtaining the weight of the level index of the fine-packing rents, the weight of the duty index of the non-fine-packing rooms, the weight of the duty index of the newly-built houses in the third historical period, the weight of the income ratio index of the rents, the weight of the decoration renting ratio and the weight of the price index of the simplified packing.
Wherein the weights of the indexes in the supply scale index set are as follows: the correlation coefficient between the occupancy and the score of each index is divided by the sum of the correlation coefficients of each index in the feed scale index set. The weights of the indexes in the demand scale index set are as follows: the correlation coefficient between the occupancy and the score of each index is divided by the sum of the correlation coefficients of each index in the demand scale index set. The weight of each index in the leasing activity index set is as follows: the correlation coefficient of the occupancy and the score of each index is divided by the sum of the correlation coefficients of each index in the rental activity index set. The weight of each index in the leasing adaptation index set is as follows: the correlation coefficient of the occupancy rate and the index score is divided by the sum of the correlation coefficients of the indexes in the leasing fitness index set.
The index score calculating unit is used for calculating the score of the supply scale index set according to the total rental room source index and the rental room source duty ratio index in the first history period after discretization processing, as well as the weight of the total rental room source index and the weight of the rental room source duty ratio index in the first history period;
The method is also used for calculating the score of the demand scale index set according to the discretized target area resident population index, the rental room population proportion index, the floating population proportion index, the average person GDP speed-up index in the second historical period, the second-hand room sales period index and the room price income ratio index, as well as the weight of the target area resident population index, the weight of the rental room population proportion index, the weight of the floating population proportion index, the average person GDP speed-up index weight in the second historical period, the weight of the second-hand room sales period index and the weight of the room price income ratio index;
the method is also used for calculating the score of the lease activity index set according to the discretized spectrum lease period index, the ratio index of the floating population to the number of leased rooms, the weight of the spectrum lease period index, and the weight of the ratio index of the floating population to the number of leased rooms;
and calculating the score of the lease adaptation degree index set according to the discretized fine-packaging lease level index, the non-fine-packaging room occupation rate index, the newly-built house occupation rate index in the third history period, the lease income ratio index, the decoration lease rate index and the reduced price index, as well as the weight of the fine-packaging lease level index, the non-fine-packaging room occupation rate index, the weight of the newly-built house occupation rate index in the third history period, the lease income ratio index, the decoration lease rate index and the reduced price index.
In some alternative embodiments, the comprehensive summary module 403 includes:
an index set acquisition unit configured to acquire a weight of a supply scale index set, a weight of a demand scale index set, a weight of a rental activity index set, and a weight of a rental adaptation index set;
the index set score calculating unit is used for calculating house leasing potential of the target area according to the weight and score of the supply scale index set, the weight and score of the demand scale index set, the weight and score of the leasing activity index set and the weight and score of the leasing adaptation index set.
The weight of each index set in the house lease information is as follows: the correlation coefficient of the check-in rate and the score of each index set is divided by the sum of the correlation coefficients of each index set in the house lease information.
The house lease potential evaluation apparatus in this embodiment is presented in the form of a functional unit, which means an ASIC circuit, a processor and a memory executing one or more software or a fixed program, and/or other means that can provide the above-mentioned functions.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The embodiment of the invention also provides computer equipment, which is provided with the house lease potential evaluation device shown in the figure 4.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 5, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 5.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created from the use of the computer device of the presentation of a sort of applet landing page, and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.

Claims (10)

1. A house renting potential assessment method, the method comprising:
acquiring house lease information of a target area, wherein the house lease information comprises a plurality of index sets used for representing supply and demand, lease liveness and lease adaptation degree;
discretizing the house leasing information to obtain scores of all index sets in the house leasing information;
and comprehensively integrating the scores of the index sets to obtain house lease potential data of the target area.
2. The method of claim 1, wherein the plurality of sets of metrics for characterizing supply and demand, rental activity, and rental fit comprise: a supply scale index set, a demand scale index set, a rental activity index set, and a rental fit index set;
the supply scale index set includes: the total amount index of the rental house sources and the ratio index of the rental house sources in the first history period;
The set of demand scale indicators includes: target area resident population index, rental room population proportion index, floating population ratio index, average person GDP speed increasing index in the second history period, second-hand room sales period index and room price income ratio index;
the rental activity index set includes: a spectrum renting and delivering period index, and a ratio index of the number of floating population and rented rooms;
the lease adaptation index set includes: the method comprises the steps of fine-packaging rent level index, non-fine-packaging house occupation ratio index, newly-built house occupation ratio index in a third historical period, rent income ratio index, decoration rent ratio index and thin-packaging price difference index.
3. The method of claim 2, wherein when the house-rental information is a supply-scale index set, the step of discretizing the house-rental information to obtain scores for each index set in the house-rental information comprises:
discretizing the total rental room source index and the rental room source duty ratio index in the first history period respectively;
respectively obtaining the weight of the total amount index of the common rental house source and the weight of the ratio index of the rental house source in the first history period;
Calculating the score of the supply scale index set according to the total rental room source index and the rental room source duty ratio index in the first history period after discretization processing, as well as the weight of the total rental room source index and the weight of the rental room source duty ratio index in the first history period;
the weights of the indexes in the supply scale index set are as follows: the correlation coefficient between the occupancy and the score of each index is divided by the sum of the correlation coefficients of each index in the supply scale index set.
4. The method of claim 2, wherein when the house-rental information is a demand-scale index set, the step of discretizing the house-rental information to obtain scores of the index sets in the house-rental information comprises:
discretizing the target area resident population index, the rental room population proportion index, the floating population proportion index, the average human GDP speed increasing index in the second history period, the second-hand room sales period index and the room price income ratio index respectively;
respectively acquiring the weight of the target area resident population index, the weight of the renting population proportion index, the weight of the floating population proportion index, the weight of the average person GDP speed increasing index in the second history period, the weight of the second-hand house sales period index and the weight of the house price income ratio index;
Discretizing according to the target area resident population index, the lease population proportion index, the floating population proportion index, the average human GDP speed-increasing index in a second history period, the second-hand room sales period index and the room price income ratio index after discretization, and calculating the score of the demand scale index set according to the weight of the target area resident population index, the lease population proportion index, the floating population proportion index, the average human GDP speed-increasing index in the second history period and the room price income ratio index;
the weights of the indexes in the demand scale index set are as follows: the correlation coefficient of the occupancy and the index score is divided by the sum of the correlation coefficients of the indexes in the requirement scale index set.
5. The method of claim 2, wherein when the house lease information is a lease activity index set, the step of discretizing the house lease information to obtain a score of each index set in the house lease information comprises:
discretizing the spectrum lease period index and the ratio index of the floating population to the lease room number respectively;
Acquiring the weight of the index of the lease period of the spectrum and the weight of the ratio index of the floating population to the number of leased rooms;
calculating the lease activity index set score according to the discretized spectrum lease cycle index, the ratio index of the floating population to the number of leased rooms, the weight of the spectrum lease cycle index and the weight of the ratio index of the floating population to the number of leased rooms;
the weight of each index in the leasing activity index set is as follows: and dividing the correlation coefficient of the occupancy rate and each index score by the sum of the correlation coefficients of each index in the leasing activity index set.
6. The method of claim 2, wherein when the house lease information is a lease adaptation index set, the step of discretizing the house lease information to obtain scores of the index sets in the house lease information comprises:
discretizing the fine-packaging rent level index, the non-fine-packaging house occupation rate index, the new house occupation rate index in the third historical period, the rent income ratio index, the decoration rent ratio index and the simplified-packaging price difference index respectively;
acquiring the weight of the level index of the fine-packing rent, the weight of the duty index of the non-fine-packing house, the weight of the duty index of the newly-built house in the third historical period, the weight of the income ratio index of the rent, the weight of the ratio index of the decoration rent and the weight of the price index of the simplified package;
Calculating a score of a lease adaptation index set according to the discretized fine-packaging lease level index, the non-fine-packaging house occupation rate index, the newly-built house occupation rate index in a third history period, the lease income ratio index, the decoration lease rate index and the reduced price index, as well as the weight of the fine-packaging lease level index, the non-fine-packaging house occupation rate index, the newly-built house occupation rate index in the third history period, the lease income ratio index, the decoration lease rate index and the reduced price index;
the weight of each index in the leasing adaptation index set is as follows: and dividing the correlation coefficient of the occupancy rate and the index score by the sum of the correlation coefficients of the indexes in the leasing fitness index set.
7. The method of any one of claims 2 or 4 to 6, wherein the step of comprehensively pooling the scores of the index sets to obtain the house renting potential of the target area comprises:
acquiring the weight of a supply scale index set, the weight of a demand scale index set, the weight of a lease activity index set and the weight of a lease adaptation index set;
Calculating house lease potential of the target area according to the weight and score of the supply scale index set, the weight and score of the demand scale index set, the weight and score of the lease liveness index set and the weight and score of the lease adaptation index set;
the weight of each index set in the house lease information is as follows: and dividing the correlation coefficient of the check-in rate and the score of each index set by the sum of the correlation coefficients of each index set in the house lease information.
8. A house lease potential evaluation apparatus, characterized in that said apparatus comprises:
the information acquisition module is used for acquiring house leasing information of the target area, wherein the house leasing information comprises a plurality of index sets used for representing supply and demand, leasing activity and leasing adaptation;
the discrete processing module is used for carrying out discretization processing on the house leasing information to obtain scores of all index sets in the house leasing information;
and the comprehensive calculation module is used for comprehensively calculating the scores of the index sets to obtain the house renting potential of the target area.
9. A computer device, comprising:
a memory and a processor in communication with each other, the memory having stored therein computer instructions which, upon execution, cause the processor to perform the method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
CN202310396165.4A 2023-04-13 2023-04-13 House leasing potential assessment method, device, computer equipment and storage medium Pending CN116402398A (en)

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