House price analysis and evaluation method and system based on software system

Abstract

The invention discloses a house price analysis and evaluation method and system based on a software system, and the method comprises the steps: obtaining the house information of a to-be-evaluated house in a set time interval before a starting point and the house transaction data of a region where the to-be-evaluated house is located, and carrying out the comprehensive evaluation of the data, thereby obtaining a similar transaction unit price and a recent transaction unit price; and the similar transaction unit price and the recent transaction unit price are summed according to the weight to obtain the predicted housing price, so that the information of the similar housing price and the recent market trend is integrated, the scientificity and the accuracy of prediction are improved, a more reliable housing price evaluation result is provided for the user, and the problems that the prior art mostly depends on the subjective experience of an evaluator, and the cost is low are solved. And the evaluation process consumes long time while objectivity exists.

Description

Technical Field

[0001] The present invention relates to the technical field of housing price evaluation, and in particular to a housing price analysis and evaluation method and system based on a software system. Background Art

[0002] In the real estate market, the price of second-hand housing has always been a core issue of concern to both buyers and sellers. For buyers, due to the lack of comprehensive and in-depth understanding of the real estate market, it is difficult to accurately judge whether the price of the purchased property is reasonable, and they are prone to the risk of buying at a high price; and sellers often have difficulty in setting a selling price that meets the market value due to insufficient information, which may lead to long-term unsalability of the property or loss of profit by selling at a low price.

[0003] However, the existing housing price evaluation methods mostly rely on the subjective experience of the appraiser. While there is objectivity, the evaluation process is time-consuming and the historical trend of housing prices is ignored when evaluating housing prices.

[0004] At the same time, existing housing price analysis and evaluation methods often provide standardized evaluation results, ignoring users' needs for housing investment value and surrounding supporting facilities, and lack personalized services.

[0005] Therefore, a housing price analysis and evaluation method and system based on software system is introduced. Summary of the invention

[0006] In view of this, the present invention provides a housing price analysis and evaluation method and system based on a software system to solve the problems raised by the above background technology.

[0007] The purpose of the present invention can be achieved by the following technical solution: A housing price analysis and evaluation method based on a software system, comprising:

[0008] House price evaluation: After the user selects a house to be evaluated, the current time point is taken as the starting point to obtain the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located; the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located are comprehensively evaluated to obtain the predicted house price of the house to be evaluated currently selected by the user at the current time point;

[0009] Generate report: Based on the predicted house price of the house to be evaluated currently selected by the user at the current time point, further analyze the house price change trend of the area where the house to be evaluated is located within the set time period, and adjust the predicted house price according to the analysis results. After the adjustment is completed, generate the house price report of the house to be evaluated currently selected by the user;

[0010] User preference: After generating the housing price report of the house to be evaluated currently selected by the user, it is pushed to the user; after receiving the housing price report of the house to be evaluated, the user can selectively trigger the potential evaluation signaling;

[0011] Output result: After the user actively triggers the potential evaluation signaling, the corresponding steps are executed to obtain the potential score of the house currently selected by the user at the current time point.

[0012] In some embodiments, a comprehensive evaluation is performed on the house information of the house to be evaluated within a set time interval before the starting point and the house transaction data of the area in which the house is located, specifically:

[0013] S1: Clean the acquired housing transaction data;

[0014] S2: extracting various groups of features of the house to be evaluated and encoding them according to the preset coding rules; each group of features is the house location, house type, house age and house decoration;

[0015] Extracting the house transaction data of each group of traded houses at the time of the transaction from the house transaction data within a set time interval before the starting point, and encoding each group of features in the house transaction data corresponding to each group of traded houses according to a preset encoding rule;

[0016] After the coding is completed, the similarity of each group of features between each group of traded houses and the houses to be evaluated is calculated respectively, so as to obtain the similarity value between each group of traded houses and the houses to be evaluated;

[0017] According to the preset number N, where N≥5, the similarity values ​​are sorted from small to large, and the first N traded houses are selected as reference similar houses. The transaction time of each reference similar house is obtained, and the time difference between the transaction time and the starting point is calculated to obtain the interval time.

[0018] The similarity value and interval duration of each reference similar house are marked as te1 and te2 respectively; the similarity value te1 and interval duration te2 of each reference similar house are normalized and then inserted into the formula Calculation is performed to obtain the reference index tp of each similar house; wherein a1 and a2 are the influence weight factors corresponding to the similarity value te1 and the interval time te2 respectively;

[0019] Select the reference similar house with the highest reference index tp, and extract the transaction unit price as the similar transaction unit price.

[0020] In some embodiments, the predicted house price of the house to be evaluated currently selected by the user at the current time point is obtained, specifically:

[0021] S3: Arrange the transaction time of each group of traded houses in ascending order according to the time interval between the transaction time and the starting point, extract the first N traded houses in the arrangement as reference recent houses, obtain the transaction price of each group of reference recent houses at the time of transaction and take the average as the recent transaction price;

[0022] S4: Preset weight coefficients corresponding to similar transaction unit prices and recent transaction unit prices respectively; multiply the similar transaction unit prices and recent transaction unit prices by the corresponding preset weight coefficients respectively, and then sum them up to obtain the predicted house price of the house to be evaluated currently selected by the user at the current time point.

[0023] In some embodiments, the trend of house price changes in the area where the house to be evaluated is located within a set time period is analyzed, specifically:

[0024] Get the average transaction price of the area where the house to be evaluated is located at each time point within the set time period; the average transaction price represents the average price per square meter of the houses that have been traded before each time point; for the average transaction price of the area where the house to be evaluated is located at each time point, calculate the difference between the average transaction prices of two adjacent time points, that is, subtract the average transaction price at the previous time point from the average transaction price at the current time point;

[0025] If the difference between the average transaction prices of two adjacent time points is negative, the negative value is marked as the average price decrease value, and the average price decrease rate corresponding to the difference is calculated by (the average transaction price of the previous time point - the average transaction price of the current time point) / the average transaction price of the current time point;

[0026] If the difference between the average transaction prices of two adjacent time points is positive, the positive value is marked as the average price increase value, and the average price increase rate corresponding to the difference is calculated by (the average transaction price at the current time point - the average transaction price at the previous time point) / the average transaction price at the current time point;

[0027] Sum up all the average price decrease rates and average price increase rates respectively to get the total average price decrease value and the total average price increase value, and calculate the ratio between the total average price increase value and the total average price decrease value, that is, calculate through the total average price increase value / the total average price decrease value to get the housing price change index of the area where the house to be evaluated is located within the set time period.

[0028] In some embodiments, the predicted housing price is adjusted according to the analysis results, specifically:

[0029] If the house price change index is greater than 1, it is determined that the area where the house to be evaluated is located has a potential upward trend in house prices within the set time period;

[0030] If the house price change index is less than 1, it is determined that the area where the house to be evaluated is located has a potential downward trend in house prices within the set time period;

[0031] Preset the range of index values ​​corresponding to the house price change index, and set each index value range to correspond to a house price adjustment rate; match the house price change index of the area where the house to be evaluated is located within the set time period with the range of index values ​​of each group, so as to obtain the house price adjustment rate corresponding to the predicted house price of the house to be evaluated;

[0032] The potential upward trend of housing prices and the potential downward trend of housing prices are represented by G1 and G2 respectively, and the formula Adjust the predicted house price of the house to be evaluated; where Tz represents the adjusted predicted house price, Yc represents the predicted house price of the house to be evaluated, and σ represents the house price adjustment rate corresponding to the predicted house price of the house to be evaluated;

[0033] Extract the adjusted predicted house price of the house to be evaluated, and compare it with the current listing price of the house to be evaluated. If the adjusted predicted house price is greater than the current listing price, it means that the current listing price of the house to be evaluated is too low, otherwise it means that the current listing price of the house to be evaluated is too high;

[0034] The house information, listing price, adjusted predicted house price and the comparison result between the adjusted predicted house price and the current listing price of the house to be evaluated are extracted, and the preset data template is output for filling, thereby generating a house price report for the house to be evaluated currently selected by the user.

[0035] In some embodiments, corresponding steps are performed after the potential assessment signaling is triggered, specifically:

[0036] M1: Extract the rental houses in the area where the house to be evaluated is located, analyze the similarity between each rental house and the house to be evaluated, extract the rental house with the lowest similarity value as the return evaluation house, and obtain the annual rental price of the return evaluation house; Calculate and obtain the rental return rate f1 of the house to be evaluated;

[0037] M2: The potential upward trend and the potential downward trend of the house price are respectively corresponding to a house price appreciation rate, which is matched based on the trend determined by the current house to be evaluated to obtain the house price appreciation rate f2 of the house to be evaluated;

[0038] Preset the weight coefficients corresponding to the rental return rate f1 and the house price appreciation rate f2 respectively, multiply the rental return rate f1 and the house price appreciation rate f2 of the house to be evaluated by the corresponding preset weight coefficients respectively, and then sum them to obtain the investment potential index fu of the house to be evaluated;

[0039] M3: Draw a circle with the house to be evaluated as the center and the distance as the radius, and select the supporting facilities within the circle, where the types of supporting facilities include schools, hospitals, subways and businesses; count the number of types of supporting facilities within the circle as the environmental potential index fp of the house to be evaluated;

[0040] M4: The investment potential index fu and the environmental potential index fp of the house to be evaluated are normalized and then inserted into the formula The comprehensive potential index fz of the house to be evaluated is calculated; wherein r1 and r2 are the influencing weight factors of the investment potential index fu and the environmental potential index fp respectively.

[0041] In some embodiments, the potential score of the house to be evaluated currently selected by the user at the current time point is obtained, specifically: the index value ranges corresponding to the preset comprehensive potential index fz are set, each group of index value ranges is set to correspond to a potential score, and the comprehensive potential index fz of the house to be evaluated is matched with the preset index value ranges, so as to obtain the potential score of the house to be evaluated.

[0042] A housing price analysis and evaluation system based on a software system, comprising:

[0043] The data acquisition module is used to obtain the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located, and conduct a comprehensive evaluation on the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located, so as to obtain the predicted house price of the house to be evaluated currently selected by the user at the current time point;

[0044] The report generation module is used to analyze the housing price change trend of the area where the house to be evaluated is located within a set time period, and adjust the predicted housing price according to the analysis results. After the adjustment is completed, a housing price report for the house to be evaluated currently selected by the user is generated;

[0045] The user selection module is used for the user to selectively trigger the potential evaluation signaling after receiving the housing price report of the house to be evaluated;

[0046] The demand output module is used to execute corresponding steps after the user triggers the potential evaluation signaling, so as to obtain the potential score of the house currently selected by the user at the current time point.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention obtains the house information of the house to be evaluated within a set time interval before the starting point and the house transaction data of the area where the house is located, and comprehensively evaluates these data to obtain similar transaction unit prices and recent transaction unit prices, and further sums the similar transaction unit prices and recent transaction unit prices according to weights to obtain the predicted house price, which integrates the information of similar house prices and recent market trends, improves the scientificity and accuracy of the prediction, and provides users with more reliable house price evaluation results, solving the problem that most of the existing technologies rely on the subjective experience of appraisers, and the evaluation process is time-consuming while being objective;

[0049] The present invention analyzes the housing price change trend of the area where the house to be evaluated is located within a set time period during the report generation stage, calculates the change rate of the average transaction price to obtain the housing price change index, judges the trend according to the index and adjusts the predicted housing price, so that the evaluation result can reflect the market dynamics in a timely manner, solving the problem that the housing price evaluation in the prior art lags behind the actual market situation;

[0050] The present invention can selectively trigger potential evaluation signaling after the user receives the housing price report; calculate the comprehensive potential index by executing a series of steps and match it to obtain a potential score; this method incorporates the investment value of the house and the surrounding environment supporting facilities into the evaluation system, provides personalized services for users, and meets the user's needs for housing potential evaluation, while the existing technology often only provides standardized evaluation results and ignores these personalized factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0052] Figure 1 is a flow chart of the present invention;

[0053] Figure 2 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0054] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete, and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0055] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and / or this specification, and will not be interpreted in an idealized or overly formal sense, unless explicitly defined as such herein.

[0056] Example 1

[0057] See also Figure 1 As shown, a housing price analysis and evaluation method based on a software system comprises:

[0058] House price evaluation: After the user selects a house to be evaluated, the current time point is taken as the starting point to obtain the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where it is located; the house information includes the house location, house type, house age, house decoration; the house transaction data includes the house location, house type, house age, house decoration, transaction price and transaction time; the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where it is located are comprehensively evaluated, so as to obtain the predicted house price of the house to be evaluated currently selected by the user at the current time point;

[0059] The time period can be set to the past few months or years, depending on the market activity;

[0060] Specifically:

[0061] S1: Clean the acquired housing transaction data to remove duplicate records and erroneous data;

[0062] S2: extracting various groups of features of the house to be evaluated and encoding them according to the preset coding rules; each group of features is the house location, house type, house age and house decoration;

[0063] For example, for house types, they can be coded as 1, 2, 3, etc. according to different types such as one bedroom and one living room, two bedrooms and one living room, and two bedrooms and two living rooms.

[0064] For the location of the house, it is coded according to the specific floor range; if there is coordinate information, it can be used directly;

[0065] Extracting the house transaction data of each group of traded houses at the time of the transaction from the house transaction data within a set time interval before the starting point, and encoding each group of features in the house transaction data corresponding to each group of traded houses according to a preset encoding rule;

[0066] After the coding is completed, the distance measurement method is used to calculate the similarity of each group of features between each group of traded houses and the houses to be evaluated, so as to obtain the similarity value between each group of traded houses and the houses to be evaluated;

[0067] The distance measurement method is specifically Euclidean distance, and the specific calculation process is:

[0068] Using the formula Calculate and obtain the similarity value between each group of traded houses and the houses to be evaluated, where x represents the number of the houses to be evaluated, y represents the number of each group of traded houses, d represents the calculated similarity value, and i represents the number of each group of features;

[0069] According to a preset number N, where N≥5, the specific value of N is preset by the technical staff; the similarity values ​​are sorted from small to large, and the first N traded houses are selected as reference similar houses. The transaction time of each reference similar house is obtained respectively, and the time difference between the transaction time and the starting point is calculated to obtain the interval time;

[0070] The similarity value and interval duration of each reference similar house are marked as te1 and te2 respectively; the unit of te2 is week; the similarity value te1 and interval duration te2 of each reference similar house are normalized and then inserted into the formula Calculation is performed to obtain the reference index tp of each similar house; wherein a1 and a2 are the influence weight factors corresponding to the similarity value te1 and the interval time te2 respectively;

[0071] Select the reference similar house with the highest reference index tp, and extract the transaction price as the similar transaction price;

[0072] S3: Arrange the transaction time of each group of traded houses in ascending order according to the time interval between the transaction time and the starting point, extract the first N traded houses in the arrangement as reference recent houses, obtain the transaction price of each group of reference recent houses at the time of transaction and take the average as the recent transaction price;

[0073] S4: Preset weight coefficients corresponding to similar transaction unit prices and recent transaction unit prices respectively; determine the weights of similarity and time factors in house price prediction based on experience or data analysis, and the sum of the two sets of weight coefficients is equal to one; multiply the similar transaction unit price and the recent transaction unit price by the corresponding preset weight coefficients respectively, and then sum them to obtain the predicted house price of the house to be evaluated currently selected by the user at the current time point;

[0074] It should be noted that the predicted house price is obtained by summing up the similar transaction unit prices and recent transaction unit prices by weight, which combines information on similar house prices and recent market trends, improves the scientificity and accuracy of the prediction, and provides users with more reliable house price evaluation results.

[0075] Generate report: Based on the predicted house price of the house to be evaluated currently selected by the user at the current time point, further analyze the house price change trend of the area where the house to be evaluated is located within the set time period, and adjust the predicted house price according to the analysis results. After the adjustment is completed, generate the house price report of the house to be evaluated currently selected by the user;

[0076] Specifically:

[0077] Get the average transaction price of the area where the house to be evaluated is located at each time point within the set time period; the average transaction price represents the average price per square meter of the houses that have been traded before each time point; for the average transaction price of the area where the house to be evaluated is located at each time point, calculate the difference between the average transaction prices of two adjacent time points, that is, subtract the average transaction price at the previous time point from the average transaction price at the current time point;

[0078] If the difference between the average transaction prices of two adjacent time points is negative, the negative value is marked as the average price decrease value, and the average price decrease rate corresponding to the difference is calculated by (the average transaction price of the previous time point - the average transaction price of the current time point) / the average transaction price of the current time point;

[0079] If the difference between the average transaction prices of two adjacent time points is positive, the positive value is marked as the average price increase value, and the average price increase rate corresponding to the difference is calculated by (the average transaction price at the current time point - the average transaction price at the previous time point) / the average transaction price at the current time point;

[0080] Sum up all the average price decrease rates and average price increase rates respectively to obtain the total average price decrease value and the total average price increase value, and calculate the ratio between the total average price increase value and the total average price decrease value, that is, calculate by the total average price increase value / the total average price decrease value to obtain the housing price change index of the area where the house to be evaluated is located within the set time period;

[0081] If the house price change index is greater than 1, it is determined that the area where the house to be evaluated is located has a potential upward trend in house prices within the set time period;

[0082] If the house price change index is less than 1, it is determined that the area where the house to be evaluated is located has a potential downward trend in house prices within the set time period;

[0083] Preset the range of index values ​​corresponding to the house price change index, and set each index value range to correspond to a house price adjustment rate; match the house price change index of the area where the house to be evaluated is located within the set time period with the range of index values ​​of each group, so as to obtain the house price adjustment rate corresponding to the predicted house price of the house to be evaluated;

[0084] The potential upward trend of housing prices and the potential downward trend of housing prices are represented by G1 and G2 respectively, and the formula Adjust the predicted house price of the house to be evaluated; where Tz represents the adjusted predicted house price, Yc represents the predicted house price of the house to be evaluated, and σ represents the house price adjustment rate corresponding to the predicted house price of the house to be evaluated;

[0085] Extract the adjusted predicted house price of the house to be evaluated, and compare it with the current listing price of the house to be evaluated. If the adjusted predicted house price is greater than the current listing price, it means that the current listing price of the house to be evaluated is too low, otherwise it means that the current listing price of the house to be evaluated is too high;

[0086] Extract the house information, listing price, adjusted predicted house price and the comparison result between the adjusted predicted house price and the current listing price of the house to be evaluated, and output it to fill in the preset data template, so as to generate the house price report of the house to be evaluated currently selected by the user;

[0087] It should be noted that the house price adjustment rate is obtained by matching the house price change index with the preset index value range, and then the predicted house price is adjusted. This method fully considers the impact of the overall change trend of regional house prices on the price of a single house to be evaluated, making the predicted house price more in line with the actual market situation and improving the accuracy of house price prediction.

[0088] User preference: After generating the housing price report of the house to be evaluated currently selected by the user, it is pushed to the user; after receiving the housing price report of the house to be evaluated, the user can selectively trigger the potential evaluation signaling;

[0089] Output result: After the user actively triggers the potential evaluation signaling, the corresponding steps are executed to obtain the potential score of the house currently selected by the user at the current time point;

[0090] Specifically:

[0091] M1: Extract the rental houses in the area where the house to be evaluated is located from the real estate agency platform, analyze the similarity between each rental house and the house to be evaluated, extract the rental house with the lowest similarity value as the return evaluation house, and obtain the annual rental price of the return evaluation house; Calculate and obtain the rental return rate f1 of the house to be evaluated;

[0092] M2: The potential upward trend and the potential downward trend of the house price are respectively corresponding to a house price appreciation rate, which is matched based on the trend determined by the current house to be evaluated to obtain the house price appreciation rate f2 of the house to be evaluated;

[0093] Preset the weight coefficients corresponding to the rental return rate f1 and the house price appreciation rate f2 respectively, multiply the rental return rate f1 and the house price appreciation rate f2 of the house to be evaluated by the corresponding preset weight coefficients respectively, and then sum them to obtain the investment potential index fu of the house to be evaluated;

[0094] M3: Draw a circle with the house to be evaluated as the center and the distance as the radius, and select the supporting facilities within the circle, where the types of supporting facilities include schools, hospitals, subways and businesses; count the number of types of supporting facilities within the circle as the environmental potential index fp of the house to be evaluated;

[0095] M4: The investment potential index fu and the environmental potential index fp of the house to be evaluated are normalized and then inserted into the formula The comprehensive potential index fz of the house to be evaluated is calculated; wherein r1 and r2 are the influencing weight factors of the investment potential index fu and the environmental potential index fp respectively;

[0096] M5: preset the value ranges of each group of indexes corresponding to the comprehensive potential index fz, set each group of index value ranges to correspond to a potential score, match the comprehensive potential index fz of the house to be evaluated with the preset value ranges of each group of indexes, and thus obtain the potential score of the house to be evaluated; the potential score range is set at 50-100, and the higher the score, the greater the potential of the house;

[0097] It should be noted that after receiving the housing price report, the user can actively trigger the potential assessment signaling. This design reflects the user's autonomy. After having a basic understanding of the housing price, the user can further understand the potential of the house according to their own needs, normalize the investment potential index and the environmental potential index, calculate the comprehensive potential index according to their respective weight factors, and further match them to obtain the potential score. This process organically combines the economic investment value and environmental value of the house to form a comprehensive assessment result. The potential score allows users to intuitively understand the potential level of the house under the combined effect of multiple factors, making it easier for users to compare and choose between different houses, and providing more comprehensive information for investment or purchase decisions.

[0098] Example 2

[0099] See also Figure 2As shown, based on the housing price analysis and evaluation method based on a software system provided in Example 1 of the present application, Example 2 of the present application proposes a housing price analysis and evaluation system based on a software system. Example 2 is merely a preferred method of Example 1, and the implementation of Example 2 will not affect the independent implementation of Example 1.

[0100] Specifically, the housing price analysis and evaluation system based on a software system provided in Embodiment 2 of the present application is different in that it includes:

[0101] The data acquisition module is used to obtain the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located, and conduct a comprehensive evaluation on the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located, so as to obtain the predicted house price of the house to be evaluated currently selected by the user at the current time point;

[0102] The report generation module is used to analyze the housing price change trend of the area where the house to be evaluated is located within a set time period, and adjust the predicted housing price according to the analysis results. After the adjustment is completed, a housing price report for the house to be evaluated currently selected by the user is generated;

[0103] The user selection module is used for the user to selectively trigger the potential evaluation signaling after receiving the housing price report of the house to be evaluated;

[0104] The demand output module is used to execute corresponding steps after the user triggers the potential evaluation signaling, so as to obtain the potential score of the house to be evaluated currently selected by the user at the current time point;

[0105] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A housing price analysis and evaluation method based on a software system, characterized in that: include: House price evaluation: After the user selects a house to be evaluated, the current time point is used as the starting point to obtain the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located; Comprehensively evaluate the housing information of the houses to be evaluated within the set time interval before the starting point and the housing transaction data in the area, so as to obtain the predicted housing price of the house to be evaluated currently selected by the user at the current time point; Generate report: Based on the predicted house price of the house to be evaluated currently selected by the user at the current time point, further analyze the house price change trend of the area where the house to be evaluated is located within the set time period, and adjust the predicted house price according to the analysis results. After the adjustment is completed, generate the house price report of the house to be evaluated currently selected by the user; User preference: After generating the house price report of the house currently selected by the user, push it to the user; After receiving the housing price report of the house to be evaluated, the user can selectively trigger the potential evaluation signaling; Output result: After the user actively triggers the potential evaluation signaling, the corresponding steps are executed to obtain the potential score of the house currently selected by the user at the current time point.

2. A housing price analysis and evaluation method based on a software system according to claim 1, characterized in that: Comprehensively evaluate the housing information of the houses to be evaluated within the set time interval before the starting point and the housing transaction data in the area, specifically: S1: Clean the acquired housing transaction data; S2: extracting various groups of features of the house to be evaluated and encoding them according to the preset coding rules; each group of features is the house location, house type, house age and house decoration; Extracting the house transaction data of each group of traded houses at the time of the transaction from the house transaction data within a set time interval before the starting point, and encoding each group of features in the house transaction data corresponding to each group of traded houses according to a preset encoding rule; After the coding is completed, the similarity of each group of features between each group of traded houses and the houses to be evaluated is calculated respectively, so as to obtain the similarity value between each group of traded houses and the houses to be evaluated; According to the preset number N, where N≥5, the similarity values ​​are sorted from small to large, and the first N traded houses are selected as reference similar houses. The transaction time of each reference similar house is obtained, and the time difference between the transaction time and the starting point is calculated to obtain the interval time. The similarity value and interval duration of each reference similar house are marked as te1 and te2 respectively; The similarity value te1 and interval duration te2 of each reference similar house are normalized and then inserted into the formula Calculation is performed to obtain the reference index tp of each similar house; wherein a1 and a2 are the influence weight factors corresponding to the similarity value te1 and the interval time te2 respectively; Select the reference similar house with the highest reference index tp, and extract the transaction unit price as the similar transaction unit price.

3. A housing price analysis and evaluation method based on a software system according to claim 2, characterized in that: Get the predicted house price of the house currently selected by the user at the current time point, specifically: S3: Arrange the transaction time of each group of traded houses in ascending order according to the time interval between the transaction time and the starting point, extract the first N traded houses in the arrangement as reference recent houses, obtain the transaction price of each group of reference recent houses at the time of transaction and take the average as the recent transaction price; S4: Preset weight coefficients corresponding to similar transaction unit prices and recent transaction unit prices respectively; multiply the similar transaction unit prices and recent transaction unit prices by the corresponding preset weight coefficients respectively, and then sum them up to obtain the predicted house price of the house to be evaluated currently selected by the user at the current time point.

4. A housing price analysis and evaluation method based on a software system according to claim 1, characterized in that: Analyze the housing price trend in the area where the house to be evaluated is located within the set time period, specifically: Get the average transaction price of the area where the house to be evaluated is located at each time point within the set time period; the average transaction price represents the average price per square meter of the houses that have been traded before each time point; for the average transaction price of the area where the house to be evaluated is located at each time point, calculate the difference between the average transaction prices of two adjacent time points, that is, subtract the average transaction price at the previous time point from the average transaction price at the current time point; If the difference between the average transaction prices of two adjacent time points is negative, the negative value is marked as the average price decrease value, and the average price decrease rate corresponding to the difference is calculated by (the average transaction price of the previous time point - the average transaction price of the current time point) / the average transaction price of the current time point; If the difference between the average transaction prices of two adjacent time points is positive, the positive value is marked as the average price increase value, and the average price increase rate corresponding to the difference is calculated by (the average transaction price at the current time point - the average transaction price at the previous time point) / the average transaction price at the current time point; Sum up all the average price decrease rates and average price increase rates respectively to get the total average price decrease value and the total average price increase value, and calculate the ratio between the total average price increase value and the total average price decrease value, that is, calculate through the total average price increase value / the total average price decrease value to get the housing price change index of the area where the house to be evaluated is located within the set time period.

5. A housing price analysis and evaluation method based on a software system according to claim 4, characterized in that: The predicted house prices are adjusted according to the analysis results, specifically: If the house price change index is greater than 1, it is determined that the area where the house to be evaluated is located has a potential upward trend in house prices within the set time period; If the house price change index is less than 1, it is determined that the area where the house to be evaluated is located has a potential downward trend in house prices within the set time period; Preset the range of index values ​​corresponding to the house price change index, and set each index value range to correspond to a house price adjustment rate; match the house price change index of the area where the house to be evaluated is located within the set time period with the range of index values ​​of each group, so as to obtain the house price adjustment rate corresponding to the predicted house price of the house to be evaluated; The potential upward trend of housing prices and the potential downward trend of housing prices are represented by G1 and G2 respectively, and the formula Adjust the predicted house price of the house to be evaluated; where Tz represents the adjusted predicted house price, Yc represents the predicted house price of the house to be evaluated, and σ represents the house price adjustment rate corresponding to the predicted house price of the house to be evaluated; Extract the adjusted predicted house price of the house to be evaluated, and compare it with the current listing price of the house to be evaluated. If the adjusted predicted house price is greater than the current listing price, it means that the current listing price of the house to be evaluated is too low, otherwise it means that the current listing price of the house to be evaluated is too high; The house information, listing price, adjusted predicted house price and the comparison result between the adjusted predicted house price and the current listing price of the house to be evaluated are extracted, and the preset data template is output for filling, thereby generating a house price report for the house to be evaluated currently selected by the user.

6. A housing price analysis and evaluation method based on a software system according to claim 1, characterized in that: After the potential assessment signal is triggered, the corresponding steps are executed, specifically: M1: Extract the rental houses in the area where the house to be evaluated is located, analyze the similarity between each rental house and the house to be evaluated, extract the rental house with the lowest similarity value as the return evaluation house, and obtain the annual rental price of the return evaluation house; Calculate and obtain the rental return rate f1 of the house to be evaluated; M2: The potential upward trend and the potential downward trend of the house price are respectively corresponding to a house price appreciation rate, which is matched based on the trend determined by the current house to be evaluated to obtain the house price appreciation rate f2 of the house to be evaluated; Preset the weight coefficients corresponding to the rental return rate f1 and the house price appreciation rate f2 respectively, multiply the rental return rate f1 and the house price appreciation rate f2 of the house to be evaluated by the corresponding preset weight coefficients respectively, and then sum them to obtain the investment potential index fu of the house to be evaluated; M3: Draw a circle with the house to be evaluated as the center and the distance as the radius, and select the supporting facilities within the circle, where the types of supporting facilities include schools, hospitals, subways and businesses; count the number of types of supporting facilities within the circle as the environmental potential index fp of the house to be evaluated; M4: The investment potential index fu and the environmental potential index fp of the house to be evaluated are normalized and then inserted into the formula The comprehensive potential index fz of the house to be evaluated is calculated; wherein r1 and r2 are the influencing weight factors of the investment potential index fu and the environmental potential index fp respectively.

7. A housing price analysis and evaluation method based on a software system according to claim 1, characterized in that: The potential score of the house to be evaluated currently selected by the user at the current time point is obtained, specifically: the range of index values ​​corresponding to the preset comprehensive potential index fz is set, each group of index value ranges is set to correspond to a potential score, and the comprehensive potential index fz of the house to be evaluated is matched with the preset range of index values ​​of each group, so as to obtain the potential score of the house to be evaluated.

8. A housing price analysis and evaluation system based on a software system, applied to a housing price analysis and evaluation method based on a software system as claimed in any one of claims 1 to 7, characterized in that: include: The data acquisition module is used to obtain the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located, and conduct a comprehensive evaluation on the house information of the house to be evaluated within the set time interval before the starting point and the house transaction data of the area where the house is located, so as to obtain the predicted house price of the house to be evaluated currently selected by the user at the current time point; The report generation module is used to analyze the housing price change trend of the area where the house to be evaluated is located within a set time period, and adjust the predicted housing price according to the analysis results. After the adjustment is completed, a housing price report for the house to be evaluated currently selected by the user is generated; The user selection module is used for the user to selectively trigger the potential evaluation signaling after receiving the housing price report of the house to be evaluated; The demand output module is used to execute corresponding steps after the user triggers the potential evaluation signaling, so as to obtain the potential score of the house to be evaluated currently selected by the user at the current time point.

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