Development volume equal value transfer method based on spatial feature value measurement

Abstract

The invention discloses a development volume equal-value transfer method based on spatial feature value measurement, and the method comprises the steps: S1, spatial feature recognition: recognizing various spatial features which affect the development right value, and carrying out the feature recognition of specific function spaces, such as office space features and living space features; s2, market transaction data collection: collecting real estate market transaction data; s3, feature price model construction: constructing feature price models of different functional spaces such as office space and living space based on a multiple linear regression method; s4, development right value evaluation: according to the collected data and the constructed feature price model, evaluating the development right value of the specific space before and after function change; s5, value transfer calculation: calculating the development capacity of value transfer such as the development capacity; according to the method, the value difference before and after the function change is calculated, so that a reasonable basis for transferring the development right and compensating or redeeming the allowance is provided for the government.

Description

Technical Field

[0001] The present invention relates to the field of land economic technology, and in particular to a value transfer method such as development volume based on spatial characteristic value measurement. Background Art

[0002] At present, the main construction mode of Chinese cities is indeed shifting from "incremental expansion" to "existing potential", and this shift reflects the profound changes in the concept and model of urban development in China. In the past, the development of Chinese cities mainly relied on large-scale incremental construction, that is, constantly expanding urban boundaries, increasing urban area and population. However, with the advancement of urbanization and the increasing shortage of resources, this development model has become unsustainable. Therefore, the main construction mode is indeed shifting from "incremental expansion" to "existing potential" in order to meet this challenge and promote the transformation of cities from extensive expansion to intensive development.

[0003] In the existing technology, when evaluating the value of different functional spaces separately, it is not appropriate to use the public land price correction method. The benchmark land price or calibrated land price determined periodically by this method has a certain time lag, and the correction coefficient system itself has a certain "one-size-fits-all" problem, which makes it difficult to flexibly reflect the degree to which the spatial equity price in different urban areas is affected by the spatial location; the core parameter in the income restoration method, the land restoration rate, also has similar lag and flexibility problems. In practical applications, it is usually determined according to the one-year loan interest rate announced by the People's Bank of China and the floating proportion of the functional type; the cost approximation method is generally applicable to the land price evaluation of newly developed land or areas or types where the land market is underdeveloped and there are few transactions, so it is excluded; the basic logic of the market comparison method is more suitable, because local progressive renewal usually occurs in urban built-up areas with relatively mature real estate markets, and it is relatively easy to obtain real estate transaction samples with different functions in different spatial locations, but the price correction methods such as the transaction situation index and regional factor index determined by this method ignore the matching degree between spatial location characteristics and functions. Therefore, a value transfer method such as development volume based on spatial characteristic value measurement is proposed. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcoming in the prior art that economic value is difficult to calculate, and to propose a value transfer method such as development volume based on spatial characteristic value measurement.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The value transfer method of development volume based on spatial characteristic value measurement includes:

[0007] S1: Spatial feature identification: Identify various spatial features that affect the value of development rights, including office space features, residential space features and other functional space features;

[0008] S2: Market transaction data collection: Market transaction data is collected through multiple channels such as real estate transaction platforms, intermediary agencies, and government departments to ensure the comprehensiveness and timeliness of the data. The market transaction data includes the unit area price of spaces with different features such as floors, orientations, and landscape views. The market transaction data is cleaned, including eliminating outliers (such as extremely high or low price transactions) and incomplete records to ensure data quality and standardize different features;

[0009] S3: Construction of hedonic price model: Based on the multiple linear regression method, the hedonic price model of office and residential functional space is constructed, and different characteristics in the market transaction data are introduced one by one and the VIF test is performed;

[0010] S4: Development rights value assessment: Based on the collected data and the constructed characteristic price model, the development rights value of a specific space before and after the functional change is assessed;

[0011] S5: Value transfer calculation: Calculate the value transfer of development volume, that is, the development capacity in which the value of the space after the function change is equal to the value before the function change, or use the difference in the value of different functional spaces under the same development capacity as the basis for the government to compensate or redeem the change of development rights.

[0012] The above technical solution further includes:

[0013] Further, in S1, the space feature recognition includes office space feature recognition, and the office space features include:

[0014] Remaining years of right of use x 0 , remaining years of use right x 0 The larger it is, the longer the period of use and the more stable the return expectations for investors;

[0015] The floor where the space is located x 1 、Total number of floors in the building x 2 , spatial orientation x 3 , reflects the matching degree between office function and three-dimensional space, among which the floor x 1 , high floors have a wide view and good privacy, but there may be problems such as long waiting time for the elevator and increased difficulty in escaping. Low floors are easy to enter and exit, but may be disturbed by noise and traffic; Total number of floors in the building x 2 , high-rise buildings tend to have better city views and higher rent levels; space towards x 3, the orientation directly affects the lighting and ventilation of the office space. South-facing rooms usually have sufficient lighting, are warm in winter and cool in summer, so they are more popular;

[0016] Unit property area for sale x 4 , Standard floor area x 5 、Number of floors occupied by the units for sale x 6 , Can the property rights of the unit be divided when it is sold? 7 This reflects the preferences of owners in the region for office spaces of different sizes and types. For example, office spaces divided into smaller units in large office buildings may be more popular with small and medium-sized enterprises and entrepreneurs, resulting in better market rental and sales performance.

[0017] Floor area ratio x 8 , which represents the overall development intensity and density of the land where the building is located;

[0018] Single parking space service area x 9 and single elevator service area x 10 , which reflects the service level of the building's supporting functions for local spaces;

[0019] Commercial retail area ratio x 11 , the degree of mixing of commercial and office space in the building. The right amount of commercial retail space can provide convenient shopping and leisure services for office workers and increase the value of real estate;

[0020] Is there a sea view? 12 Unique landscape resources such as sea views and mountain views can significantly enhance the added value of office space.

[0021] Further, in S1, the spatial feature recognition includes residential space feature recognition, and the residential space features include:

[0022] Building age x 20 , the said residential space does not need to consider the remaining years of use rights because the property rights are automatically renewed;

[0023] The floor where the space is located x 21 、Total number of floors in the building x 22 , spatial orientation x 23 , which reflects the matching degree between the residential function and the three-dimensional space. Similar to the characteristics of office space, the floor location of the residential space also affects its value and living experience. The lighting, ventilation, noise and other conditions on different floors are different;

[0024] Unit property area for sale x 24 ;

[0025] Building form 25 , the building form x 25Including towers, slabs and tower-slab combinations. The towers, slabs and tower-slab combinations have significant impacts on the layout, lighting and ventilation of the living space. Towers usually have a higher volume ratio but may have insufficient lighting; slabs have better lighting and ventilation effects but a lower volume ratio.

[0026] Apartment structure x 26 , the apartment structure x 26 Including flats and duplexes, the apartment structure is x 26 Directly affects the comfort and practicality of the living space. The spatial layout of the flat-floor apartment is relatively simple and direct, while the duplex apartment increases the sense of hierarchy and privacy of the space through the design of the upper and lower floors;

[0027] Floor area ratio x 27 , which represents the overall development intensity and density of the land where the building is located;

[0028] Number of parking spaces per household x 28 And the number of households served by a single elevator x 29 , which reflects the service level and living convenience of the residential area. The more parking spaces per household and the fewer households served by elevators, the better the living experience.

[0029] Commercial retail supporting facilities x 30 , the level of commercial retail facilities around residential areas directly affects residents’ living convenience and property value;

[0030] Is there a sea view? 31 , unique landscape resources are also an important factor in enhancing the value of living space.

[0031] Furthermore, in S2, the market transaction data is collected through multiple channels to maximize the diversity and comprehensiveness of the data. Data is collected through channels such as real estate transaction platforms (such as Beike.com and Fangtianxia.com), intermediary agencies, and government departments. These platforms usually cover a large number of transaction records, including detailed information such as price, area, floor, orientation, and landscape. Data within a specific time period and the selection of data within the past year are helpful to capture the latest dynamics and trends in the market and make the research results more realistic.

[0032] Furthermore, in S2, data cleaning includes removing outliers and processing incomplete records. According to business rules or statistical methods, outliers that have a significant impact on the analysis results are removed. Outliers (such as extremely high or low price transactions) may be caused by special transaction conditions (such as urgent sales, non-market pricing, etc.). For records that lack key information, such as area, price, etc., it should be decided whether to retain or delete them depending on the situation. If the missing data accounts for a small proportion and does not affect the overall analysis, you can choose to directly delete the records containing missing values, or use the characteristics and rules of the existing data to estimate the missing values ​​through polynomial interpolation, or choose to manually search and complete the missing information.

[0033] Furthermore, in S2, the characteristics of the market transaction data are standardized:

[0034] For numerical features (such as floors), Z-score standardization is directly used to convert them into a distribution with a mean of 0 and a standard deviation of 1;

[0035] For categorical features (such as orientation and landscape), they are converted into numerical values ​​or ratings through mapping. For example, different orientations (east, south, west, north) are mapped into different numerical values ​​(such as 1, 2, 3, 4), or ratings are given based on the quality of the landscape. S3: Construction of characteristic price model: Based on the multiple linear regression method, a characteristic price model for office and residential functional spaces is constructed. Different features in the market transaction data are introduced one by one and a VIF test is performed.

[0036] Furthermore, in S3, the construction of the office functional space characteristic price model and the residential functional space characteristic price model comprises the following specific steps:

[0037] Multiple linear regression model construction: Multiple linear regression model formula:

[0038] Y=β 0 +β 1 X 1 +β 2 X 2 +···+β n X n +ε, where Y is the dependent variable (such as the price of office space) and X 1 , X 2 ,···,X n is the independent variable (such as the remaining years of use rights, the floor where the space is located, etc.), β 0 , β 1 ,···,β n is the regression coefficient, is the dependent variable (e.g., the price of office space), and is the error term;

[0039] Stepwise introduction of variables: adding variables by forward selection, that is, starting with an empty model, adding one at a time to improve the model's goodness of fit (such as R 2 ) until no variable can significantly improve the model, and then remove the insignificant variables by backward elimination, that is, starting from the model containing all candidate variables, remove one variable at a time that makes the model fit least worse, until all remaining variables are significant;

[0040] VIF test: After stepwise regression, check whether there is multicollinearity problem in the model and perform VIF test. The calculation formula of VIF is:

[0041]

[0042] in, is the independent variable X j The value of linear regression R between 2 , the larger the VIF value, the stronger the collinearity between the independent variable and other independent variables;

[0043] Judgment criteria: A VIF value greater than 10 is considered to have serious multicollinearity problems. If the VIF value of an independent variable exceeds this threshold, you may need to consider removing the variable or reconsidering the model settings.

[0044] Repeat steps: According to the results of the VIF test, the variables and VIF test are introduced step by step until all variables in the model pass the VIF test and the goodness of fit and explanatory power of the model reach a satisfactory level;

[0045] Final Model: The final model obtained will contain a set of independent variables that pass statistical tests and collinearity tests and will jointly explain the variation in the dependent variable.

[0046] Furthermore, in the final model, the model is evaluated and the F value is calculated. The F value is a statistic used to test whether all independent variables in the model as a whole have a significant impact on the dependent variable. The calculation formula of the F value is:

[0047]

[0048] Where TSS is the total sum of squares of the model, RSS is the residual sum of squares of the model, p is the number of independent variables (excluding the constant term), n is the sample size, and R 2 is the R-square value;

[0049] Calculate the P value, which is the probability value associated with the F value. The P value represents the probability of observing the current F value or a more extreme value under the null hypothesis (i.e., all independent variable coefficients are 0). The F value and P value are directly calculated by statistical software (such as R, Python's statsmodels library, SPSS, etc.);

[0050] R 2 It is an indicator to measure the goodness of fit of the model, indicating the proportion of the variation explained by the model to the total variation. 2 The calculation formula is:

[0051]

[0052] Among them, y i is the actual observed value, is the model prediction value, is the mean of the actual observed values;

[0053] Adjusted R-squared takes into account the number of independent variables in the model and has an impact on R 2 Adjustments were made to avoid R due to the addition of irrelevant variables 2 The value is artificially increased, and the adjusted R 2 The calculation formula is:

[0054]

[0055] R 2 and textAdjust edR 2 Although it can reflect the goodness of fit of the model, the higher the value, the better. 2 It may mean that the model is overfitting the training data, resulting in poor generalization ability on new data.

[0056] Furthermore, in the construction of the office functional space price model, a total of 9 models were fitted, and 7 variables were included in the final model, namely, the remaining years of use right x 0 、The floor where the space is located x 1 , spatial orientation x 3 , Standard floor area x 5 , Can the property rights of the unit be divided when it is sold? 7 , Floor Area Ratio x 8 , Commercial retail area ratio x 11 , conduct hypothesis tests, calculate F values, ensure that the P values ​​of each fitting model are less than 0.001, indicating that the regression model is statistically significant, and ensure that the R-square and adjusted R-square of each model increase with the increase in the number of variables. 2 All are > 0.5, and the characteristic price model of office functional space is established:

[0057] In the price modeling of residential functional space, multiple models are fitted and multiple variables are selected to be included in the final model, including but not limited to the floor x of the space. 21 、Total number of floors in the building x 22 , Sales unit property area x 24 、House structure x 26 、Number of households served by a single elevator x 29 , whether there is a sea view x 31 , perform hypothesis testing, calculate F value, ensure that the P value of each fitting model is less than 0.001, and the model textAdjusetdR 2 All of them are greater than 0.5, and a residential functional space characteristic price model is established.

[0058] Further, in S5, the office function space characteristic price model and the residential function space characteristic price model are substituted into PD=|PD R -PD S |×(1+C), we get the value calculation model of the development right of updating office space into residential space: PD=(9470.4+1785.5X 21 +490.6X 22 -406.4X 24 +6435.4X 26 +714.7X 29 +24781.2X 31 -1300.9X 0 +2894.8X 1 +3258.3X 3 -1488.4X 5 -9433.5X 7 +1454.3X 8 -8635.3X 11 )×C;

[0059] PD=|PD R -PD S |×(1+C) is the basic formula of the residual method, where PD is the government’s pricing of the urban renewal development rights for local space, PD R is the development right value of the local space after update, PD S It is the value of development rights before local space renewal.

[0060] The present invention has the following beneficial effects:

[0061] 1. In the present invention, by identifying and quantifying in detail various spatial features that affect the value of development rights, such as floor, orientation, landscape view, etc., this method can more accurately reflect the impact of different spatial features on the value of development rights, thereby improving the accuracy and reliability of the evaluation results.

[0062] 2. In the present invention, by constructing a characteristic price model, this method can scientifically evaluate the value of a specific space under different functions, providing a basis for decision-making for governments and enterprises. In the process of changing development rights, reasonable value assessment helps to achieve the optimal allocation of land and space, improve resource utilization efficiency, and provide an effective path to solve the dilemma of the property rights system in changing building functions, that is, taking the ownership of the building location as the basis of rights, allowing changes in the "differentiated land use rights" attached to the ownership.

[0063] 3. In the present invention, during the process of changing development rights, the method provides the government with a reasonable basis for compensation or redemption of the change in development rights by calculating the value difference before and after the function change. It improves accuracy by compressing the time and geographical scope of market comparison, and also realizes the measurement of local space rather than land value, which helps to protect the legitimate rights and interests of both parties to the transaction and promote the realization of fair transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flow chart of the development volume equal value transfer method based on spatial feature value measurement proposed by the present invention;

[0065] Figure 2 The present invention is a flowchart for constructing a characteristic price model for office functional space and a characteristic price model for residential functional space. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figure 1-Figure 2 As shown, the present invention is a method for transferring development volume and other values ​​based on spatial feature value measurement, comprising:

[0068] S1: Spatial feature identification: Identify various spatial features that affect the value of development rights, including office space features, residential space features and other functional space features;

[0069] S2: Market transaction data collection: Market transaction data is collected through multiple channels such as real estate transaction platforms, intermediary agencies, and government departments to ensure the comprehensiveness and timeliness of the data. The market transaction data includes the unit area price of spaces with different features such as floors, orientations, and landscape views. The market transaction data is cleaned, including eliminating outliers (such as extremely high or low price transactions) and incomplete records to ensure data quality and standardize different features;

[0070] S3: Construction of hedonic price model: Based on the multiple linear regression method, the hedonic price model of office and residential functional space is constructed, and different characteristics in the market transaction data are introduced one by one and the VIF test is performed;

[0071] S4: Development rights value assessment: Based on the collected data and the constructed characteristic price model, the development rights value of a specific space before and after the functional change is assessed;

[0072] S5: Value transfer calculation: Calculate the value transfer of development volume, that is, the value of the space after the function change is equal to the value before the function change, or use the difference in value of different functional spaces under the same development capacity as the basis for the government to compensate or redeem the change of development rights.

[0073] In one embodiment, for the above S1, in S1, the space feature recognition includes office space feature recognition, and the office space features include:

[0074] Remaining years of right of use x 0 , remaining years of use right x 0 The larger it is, the longer the period of use and the more stable the return expectations for investors;

[0075] The floor where the space is located x 1 、Total number of floors in the building x 2 , spatial orientation x 3 , reflects the matching degree between office function and three-dimensional space, among which the floor x 1 , high floors have a wide view and good privacy, but there may be problems such as long waiting time for the elevator and increased difficulty in escaping. Low floors are easy to enter and exit, but may be disturbed by noise and traffic; Total number of floors in the building x 2 , high-rise buildings tend to have better city views and higher rent levels; space towards x 3 , the orientation directly affects the lighting and ventilation of the office space. South-facing rooms usually have sufficient lighting, are warm in winter and cool in summer, so they are more popular;

[0076] Unit property area for sale x 4 , Standard floor area x 5 、Number of floors occupied by the units for sale x 6 , Can the property rights of the unit be divided when it is sold?7 This reflects the preferences of owners in the region for office spaces of different sizes and types. For example, office spaces divided into smaller units in large office buildings may be more popular with small and medium-sized enterprises and entrepreneurs, resulting in better market rental and sales performance.

[0077] Floor area ratio x 8 , characterizes the overall development intensity and density of the land where the building is located;

[0078] Single parking space service area x 9 and single elevator service area x 10 , which reflects the service level of the building's supporting functions for local spaces;

[0079] Commercial retail area ratio x 11 , the degree of mixing of commercial and office space in the building. The right amount of commercial retail space can provide convenient shopping and leisure services for office workers and increase the value of real estate;

[0080] Is there a sea view? 12 Unique landscape resources such as sea views and mountain views can significantly enhance the added value of office space.

[0081] In one embodiment, for the above S1, in S1, the spatial feature recognition includes residential space feature recognition, and the residential space features include:

[0082] Building age x 20 , the said residential space does not need to consider the remaining years of use rights because the property rights are automatically renewed;

[0083] The floor where the space is located x 21 、Total number of floors in the building x 22 , spatial orientation x 23 , which reflects the matching degree between the residential function and the three-dimensional space. Similar to the characteristics of office space, the floor location of the residential space also affects its value and living experience. The lighting, ventilation, noise and other conditions on different floors are different;

[0084] Unit property area for sale x 24 ;

[0085] Building form 25 , the building form x 25 Including towers, slabs and tower-slab combinations. The towers, slabs and tower-slab combinations have significant impacts on the layout, lighting and ventilation of the living space. Towers usually have a higher volume ratio but may have insufficient lighting; slabs have better lighting and ventilation effects but a lower volume ratio.

[0086] Apartment structure x 26 , the apartment structure x26 Including flats and duplexes, the apartment structure is x 26 Directly affects the comfort and practicality of the living space. The spatial layout of the flat-floor apartment is relatively simple and direct, while the duplex apartment increases the sense of hierarchy and privacy of the space through the design of the upper and lower floors;

[0087] Floor area ratio x 27 , characterizes the overall development intensity and density of the land where the building is located;

[0088] Number of parking spaces per household x 28 And the number of households served by a single elevator x 29 , which reflects the service level and living convenience of the residential area. The more parking spaces per household and the fewer households served by elevators, the better the living experience.

[0089] Commercial retail supporting facilities x 30 , the level of commercial retail facilities around residential areas directly affects residents’ living convenience and property value;

[0090] Is there a sea view? 31 , unique landscape resources are also an important factor in enhancing the value of living space.

[0091] In one embodiment, for the above S2, in S2, the market transaction data is collected and multi-channel collection is carried out to maximize the diversity and comprehensiveness of the data. Data is collected through channels such as real estate transaction platforms (such as Beike.com and Fangtianxia.com), intermediary agencies and government departments. These platforms usually cover a large number of transaction records, including detailed information such as price, area, floor, orientation, landscape, etc., and a specific time period is carried out. Selecting data within the past year helps to capture the latest dynamics and trends in the market and make the research results more realistic.

[0092] In one embodiment, for the above S2, in S2, data cleaning includes removing outliers and processing incomplete records. According to business rules or statistical methods, outliers that have a significant impact on the analysis results are removed. Outliers (such as extremely high or low price transactions) may be caused by special transaction conditions (such as urgent sales, non-market pricing, etc.). For records that lack key information, such as area, price, etc., it should be decided whether to retain or delete them depending on the situation. If the missing data accounts for a small proportion and does not affect the overall analysis, you can choose to directly delete the records containing missing values, or use the characteristics and rules of the existing data to estimate the missing values ​​through polynomial interpolation, or choose to manually search and complete the missing information.

[0093] In one embodiment, for the above S2, in S2, the features of the market transaction data are standardized:

[0094] For numerical features (such as floors), Z-score standardization is directly used to convert them into a distribution with a mean of 0 and a standard deviation of 1;

[0095] For categorical features (such as orientation and landscape), they are converted into numerical values ​​or ratings through mapping. For example, different orientations (east, south, west, north) are mapped into different numerical values ​​(such as 1, 2, 3, 4), or ratings are given based on the quality of the landscape. S3: Construction of characteristic price model: Based on the multiple linear regression method, a characteristic price model for office and residential functional spaces is constructed. Different features in the market transaction data are introduced one by one and a VIF test is performed.

[0096] In one embodiment, for the above S3, in S3, the construction of the office functional space characteristic price model and the residential functional space characteristic price model comprises the following specific steps:

[0097] Multiple linear regression model construction: Multiple linear regression model formula:

[0098] Y=β 0 +β 1 X 1 +β 2 X 2 +···+β n X n +ε, where Y is the dependent variable (such as the price of office space) and X 1 , X 2 ,···,X n is the independent variable (such as the remaining years of use rights, the floor where the space is located, etc.), β 0 , β 1 ,···,β n is the regression coefficient, is the dependent variable (e.g., the price of office space), and is the error term;

[0099] Stepwise introduction of variables: adding variables by forward selection, that is, starting with an empty model, adding one at a time to improve the model's goodness of fit (such as R 2 ) until no variable can significantly improve the model, and then remove the insignificant variables by backward elimination, that is, starting from the model containing all candidate variables, remove one variable at a time that makes the model fit least worse, until all remaining variables are significant;

[0100] VIF test: After stepwise regression, check whether there is multicollinearity problem in the model and perform VIF test. The calculation formula of VIF is:

[0101]

[0102] in, is the independent variable Xj The value of linear regression R between 2 , the larger the VIF value, the stronger the collinearity between the independent variable and other independent variables;

[0103] Judgment criteria: A VIF value greater than 10 is considered to have serious multicollinearity problems. If the VIF value of an independent variable exceeds this threshold, you may need to consider removing the variable or reconsidering the model settings.

[0104] Repeat steps: According to the results of the VIF test, the variables and VIF test are introduced step by step until all variables in the model pass the VIF test and the goodness of fit and explanatory power of the model reach a satisfactory level;

[0105] Final Model: The final model obtained will contain a set of independent variables that pass statistical tests and collinearity tests and will jointly explain the variation in the dependent variable.

[0106] In one embodiment, for the above-mentioned final model, in the final model, the model is evaluated and the F value is calculated. The F value is a statistic used to test whether all independent variables in the model as a whole have a significant effect on the dependent variable. The calculation formula of the F value is:

[0107]

[0108] Where TSS is the total sum of squares of the model, RSS is the residual sum of squares of the model, p is the number of independent variables (excluding the constant term), n is the sample size, and R 2 is the R-square value;

[0109] Calculate the P value, which is the probability value associated with the F value. The P value represents the probability of observing the current F value or a more extreme value under the null hypothesis (i.e., all independent variable coefficients are 0). The F value and P value are directly calculated by statistical software (such as R, Python's statsmodels library, SPSS, etc.);

[0110] R 2 It is an indicator to measure the goodness of fit of the model, indicating the proportion of the variation explained by the model to the total variation. 2 The calculation formula is:

[0111]

[0112] Among them, y i is the actual observed value, is the model prediction value, is the mean of the actual observed values;

[0113] Adjusted R 2Taking into account the number of independent variables in the model, the R 2 Adjustments were made to avoid R due to the addition of irrelevant variables 2 The value is artificially increased, and the adjusted R 2 The calculation formula is:

[0114]

[0115] R 2 and textAdjust edR 2 Although it can reflect the goodness of fit of the model, the higher the value, the better. 2 It may mean that the model is overfitting the training data, resulting in poor generalization ability on new data.

[0116] In one embodiment, for the construction of the office function space price model, a total of 9 models are fitted, and 7 variables are included in the final model, namely, the remaining use right years x 0 、The floor where the space is located x 1 , empty space towards x 3 , Standard floor area x 5 , Can the property rights of the unit be divided when it is sold? 7 , Floor Area Ratio x 8 , Commercial retail area ratio x 11 The hypothesis test results show that the F values ​​are 129.728, 101.696, 72.751, 56.288, 47.988, 43.078, 37.694, 33.668, and 38.528, respectively. The P values ​​of each fitting model are all less than 0.001, indicating that the regression model is statistically significant. With the increase of the number of variables, the R-square and adjusted R-square of each model increase, while model 9 shows a downward trend. The textAdjusetdR 2 All are > 0.5, and the characteristic price model of office functional space is established:

[0117] P = 50195.3 + 1300.9X 0 -2894.8X 1 -3258.3X 3 +1488.4X 5 +9433.5X 7 -1454.3X 8 +8635.3X 11 ;

[0118] In the price modeling of residential functional space, a total of 6 models were fitted and 6 variables were included in the final model. 21 、Total number of floors in the building x 22, Sales unit property area x 24 、House structure x 26 、Number of households served by a single elevator x 29 , whether there is a sea view x 31 The hypothesis test results showed that the F values ​​were 44.39, 26.23, 19.3, 16.09, 13.95, and 12.23, respectively. The P values ​​of each fitting model were all less than 0.001. The adjusted R values ​​of models 3-6 were 2 All of them are greater than 0.5, and the residential functional space characteristic price model is established:

[0119] P=59665.7+1785.5X 21 +490.6X 22 -406.4X 24 +6435.4X 26 +714.7X 29 +24781.2X 31 .

[0120] In this embodiment, the office functional space model analysis results are:

[0121]

[0122] Residential functional space model analysis results:

[0123]

[0124]

[0125] In one embodiment, for the above S5, in S5, the office functional space characteristic price model and the residential functional space characteristic price model are substituted into PD=|PD R -PD S |×(1+C), we get the value calculation model of the development right of updating office space into residential space: PD=(9470.4+1785.5X 21 +490.6X 22 -406.4X 24 +6435.4X 26 +714.7X 29 +24781.2X 31 -1300.9X 0 +2894.8X 1 +3258.3X 3 -1488.4X 5 -9433.5X 7 +1454.3X 8 -8635.3X 11 )×C;

[0126] PD=|PD R -PD S |×(1+C) is the basic formula of the residual method, where PD is the government’s pricing of the urban renewal development rights for local space, PD R is the development right value of the local space after update, PD S It is the value of development rights before local space renewal.

[0127] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A value transfer method based on the development volume and other values ​​of spatial feature value measurement, characterized in that: include: S1: Spatial feature identification: Identify various spatial features that affect the value of development rights, including the identification of spatial features of different functions; S2: Market transaction data collection: collect real estate market transaction data, clean the market transaction data, and standardize different features; S3: Construction of hedonic price model: Based on the multiple linear regression method, the hedonic price model of office and residential functional space is constructed, and different characteristics in the market transaction data are introduced one by one and the VIF test is performed; S4: Development rights value assessment: Based on the collected data and the constructed characteristic price model, the development rights value of a specific space before and after the functional change is assessed; S5: Value transfer calculation: Calculate the value transfer of development volume, that is, the difference between the value of the space after the function change and the value before the function change.

2. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 1 is characterized in that: In S1, the space feature recognition includes office space feature recognition, and the office space features include: The remaining period of use right is x0. The larger the remaining period of use right is x0, the longer the investor has the longer period of use and the more stable the expected return. The floor where the space is located x1, the total number of floors of the building x2, and the orientation of the space x3 reflect the degree of matching between the office function and the three-dimensional space; The property area of ​​the selling unit x4, the area of ​​the standard floor x5, the number of floors occupied by the selling unit x6, and whether the property right of the selling unit can be divided x7 reflect the preferences of the owners in the area for office spaces of different sizes and types; The plot ratio x8 represents the overall development intensity and density of the plot where the building is located; Single parking space service area x9 and single elevator service area x 10 , which reflects the service level of the building's supporting functions for local spaces; Commercial retail area ratio x 11 , the mix of commercial and office space within the building; Whether there is a specific landscape such as the seaside 12 .

3. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 1, characterized in that: In S1, the spatial feature recognition includes residential space feature recognition, and the residential space features include: Building age x 20 , the said residential space does not need to consider the remaining years of use rights because the property rights are automatically renewed; The floor where the space is located x 21 、Total number of floors in the building x 22 , spatial orientation x 23 , which reflects the matching degree between residential function and three-dimensional space; Unit property area for sale x 24 ; Building form 25 , the building form x 25 Including towers, slabs and tower-slab combinations, the towers, slabs and tower-slab combinations have significant impacts on living space; Apartment structure x 26 , the apartment structure x 26 Including flats and duplexes; Floor area ratio x 27 , which represents the overall development intensity and density of the land where the building is located; Number of parking spaces per household x 28 And the number of households served by a single elevator x 29 , which reflects the service level and living convenience of the residential area; Commercial retail supporting facilities x 30 ,The level of commercial retail supporting facilities around residential areas directly affects the convenience of residents’ lives and the value of their properties; Whether there is a specific landscape such as the seaside 31 .

4. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 1, characterized in that: In S2, the market transaction data is collected through multiple channels to maximize the diversity and comprehensiveness of the data. The data within a specific time period and the data within the past year are selected to help capture the latest dynamics and trends in the market.

5. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 1, characterized in that: In S2, data cleaning includes removing outliers and incomplete records. According to business rules or statistical methods, outliers that have a significant impact on the analysis results are removed. For records that lack key information, it should be decided whether to retain or delete them depending on the situation. If the missing data accounts for a small proportion and does not affect the overall analysis, you can choose to directly delete the records with missing values, or use the characteristics and rules of the existing data to estimate the missing values ​​through polynomial interpolation, or choose to manually search and complete the missing information.

6. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 1, characterized in that: In S2, the characteristics of market transaction data are standardized: For numerical features, Z-score standardization is used directly to convert them into a distribution with a mean of 0 and a standard deviation of 1; For categorical features, they are converted into numerical values ​​or rating scores through mapping.

7. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 1 is characterized in that: In S3, the characteristic price model of office and residential functional space is constructed, and the specific steps are: Multiple linear regression model construction: Multiple linear regression model formula: Y=β0+β1X1+β2X2+···+β n X n +ε, where Y is the dependent variable, X1, X2, ···, X n is the independent variable, β0, β1, ···, β n is the regression coefficient, ε is the dependent variable and is the error term; Stepwise introduction of variables: adding variables by forward selection, that is, starting from an empty model, adding one variable each time that improves the model's goodness of fit the most, until no variable significantly improves the model, and then removing insignificant variables by backward elimination, that is, starting from a model containing all candidate variables, removing one variable each time that deteriorates the model's goodness of fit the least, until all remaining variables are significant; VIF test: After stepwise regression, check whether there is multicollinearity problem in the model and perform VIF test. The calculation formula of VIF is: in, is the independent variable X j The value of linear regression R between 2 , the larger the VIF value, the stronger the collinearity between the independent variable and other independent variables; Judgment criteria: A VIF value greater than 10 is considered to have serious multicollinearity problems; Repeat steps: According to the results of the VIF test, the variables and VIF test are introduced step by step until all variables in the model pass the VIF test and the goodness of fit and explanatory power of the model reach a satisfactory level; Final Model: The final model obtained will contain a set of independent variables that pass statistical tests and collinearity tests.

8. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 7 is characterized in that: In the final model, the model is evaluated and the F value is calculated. The calculation formula of the F value is: Where TSS is the total sum of squares of the model, RSS is the residual sum of squares of the model, p is the number of independent variables, n is the sample size, and R 2 is the R-square value; Calculate the P value, which represents the probability of observing the current F value or a more extreme value under the null hypothesis; R 2 It is an indicator to measure the goodness of fit of the model, indicating the proportion of the variation explained by the model to the total variation. 2 The calculation formula is: Among them, y i is the actual observed value, is the model prediction value, is the mean of the actual observed values; Adjusted R 2 Taking into account the number of independent variables in the model, R 2 Adjustments were made to avoid R due to the addition of irrelevant variables 2 The value is artificially increased, and the adjusted R 2 The calculation formula is: Among them, textAdjust edR 2 is the adjusted R 2 .

9. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 8, characterized in that: In the construction of the office functional space price model, multiple models are fitted, and multiple variables are selected to be included in the final model, including the remaining years of use rights x0, the floor where the space is located x1, the orientation of the empty space x3, the area of ​​the standard floor x5, whether the property rights of the unit can be divided x7, the plot ratio x8, the proportion of commercial retail area x 11 Etc., to establish a characteristic price model for office functional space; In the price modeling of residential functional space, multiple models are fitted and multiple variables are selected to be included in the final model, including the floor x 21 、Total number of floors in the building x 22 , Sales unit property area x 24 、House structure x 26 、Number of households served by a single elevator x 29 , whether there is a sea view x 31 Etc., to establish a residential functional space characteristic price model.

10. The method for transferring development volume and other values ​​based on spatial feature value measurement according to claim 9, characterized in that: In S5, the office function space characteristic price model and the residential function space characteristic price model are substituted into PD=|PD R -PD S |×(1+C), we get the value calculation model of development rights when office space is updated to residential space; PD=|PD R -PD S |×(1+C) is the basic formula of the residual method, where PD is the pricing amount of the urban renewal development rights for the local space, PD R is the development right value of the local space after update, PD S It is the value of development rights before local space renewal.

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