KR101888010B1 - System and method for providing of statistical trends service of real-estate - Google Patents

Abstract

The present invention relates to a system and a method for providing a statistical real estate trend service. Provided is a service server which comprises: an interest statistical processing unit for collecting service using log information of pre-stored real estate sale information or post related information including a predefined keyword/hash tag posted on a social network service, and generating interest statistical information by classifying the service using log information or the post related information into a predefined item; an actual transaction statistical processing unit for dividing real estate actual transaction information into the predefined item, and calculating a score for each divided item to generate actual transaction statistical information; and a trend statistical service unit for providing trend statistical information including the interest statistical information and the actual transaction statistical information when statistical real estate trend information is requested from a user.

Description

TECHNICAL FIELD [0001] The present invention relates to a real estate trend statistics service system and a real estate trend statistical service system,

The present invention relates to a system and method for real estate trend statistics service, and more particularly, to a real estate trend statistic service system and method for providing real statistical information based on interest statistical information and actual transaction data based on customer interest .

Recently, as the use of the Internet has increased so that anyone can easily access and share information in any part of the world, the demand of the general people who want to acquire all the information related to real estate easily, accurately and quickly using the Internet is gradually increasing have.

Also, in order to check the information about real estate, general people visit real estate information by visiting Internet registry site, Ministry of Land and Transportation site, Korea e-government site, numerous real estate portal site, bank real estate site, map information site and so on. In addition, the general public uses the real estate listing information by internet.

However, the conventional real estate information providing system provides real estate sale information but fails to provide trend information such as real estate interest information and real transaction related statistics.

Accordingly, the user can access the real estate interest information or actual real-estate information through industry workers (real estate agents, sales employees, etc.) and on-line (press releases, SNS, advertisement media, etc.), but the objectivity is insufficient, And there is a high probability of making a wrong judgment.

In addition, data that can refer to real estate trend statistics depend on intermediary offices, sales employees, press releases, and company advertisements, so it is important to know which areas are popular, which subway stations are popular, the age of buyers, family members, It is difficult to know whether or not the price range of the market will be affected.

Prior Art 1: Korean Patent No. 10-1691187 (published on December 30, 2016)

It is an object of the present invention to provide a real estate trend statistical service system and method capable of providing real statistical information based on interest statistical information and real estate real estate information based on a real estate interest of a customer.

It is another object of the present invention to provide a method and apparatus for analyzing real estate contents consumption pattern data based on real estate contents consumption pattern data using a web site, an app service, and SNS (Social Network Services / Sites) To provide statistical information about real estate interests.

Another object of the present invention is to provide real estate real-time statistical information for each item such as a popular area, a subway line, a sale price, a dedicated area, a loan type, a buyer's age,

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.

According to an aspect of the present invention, there is provided a service management method for collecting service related log information of pre-stored real estate sale information or post related information including a predefined keyword / hash tag posted on a social network service, The interest statistical processing unit which generates the interest statistical information by classifying the collected service utilization log information or the post related information into the predefined items, and the real estate real-estate information into the predefined items, And a trend statistics service unit for providing trend statistical information including the interest statistical information and actual transaction statistics information when the real-time trend statistical information is requested from the user.

The interest statistic processing unit may collect service use log information including at least one of various sorts of the real estate sale information, the number of squeeze times, the number of sales inquiries, and the customer inquiry, or may collect the service use log information including the keyword / A collection module for collecting post related information including at least one of a post, a number of shares of the post, a number of likes, and a comment number, analyzing the collected service use log information or post related information, A specific area, a loan type, and a buyer's age, calculating interest statistics points for each item, and generating interest statistics for each item based on the interest statistics score.

The analysis module assigns weights to the service use log information or the post related information for each item to calculate the interest statistic score, sorting the calculated interest statistical score in descending order, and generating interest statistical information for each item can do. At this time, the analysis module can map and store real estate sale information associated with each interest statistical information.

The real transaction statistics processing unit may include a collection module for periodically collecting and storing the real estate real transaction information, and analyzing the real estate real transaction information collected by the collection module to calculate at least one of the area, the subway line, An analysis module for classifying the items into one item, calculating the score for each item, and sorting the items in descending order of the score; and an abnormal module for classifying abnormal data And generating a real transaction statistical information.

The predefined abnormal data filtering condition is that when the predetermined abnormal ranking is lowered than the previous month, it is ranked in the previous month but is excluded, Quot; and / or " selling price ".

The trend statistics service unit may provide trend statistical information or cumulative trend statistical information for each period in the form of at least one of list, graph, map based, and subway route based.

The trend statistical information for each period includes the statistical information of interest in which the statistical information is displayed in descending order of the degree of interest for each item in the period and the actual transaction statistics information in which statistical information having a large number of actual transactions are displayed for each item in the period, The statistical service department provides the trend statistics information for each period in the form of a list or an occupancy rate graph, and each of the lists can display a real estate listing information button associated with the list.

The trend statistics service unit can provide cumulative trend statistical information including a graph of past data graphically displayed on the basis of the view month for each item and interest statistical information showing statistical information in descending order of interest for each item have.

According to another aspect of the present invention, there is provided a method for a service server to provide real estate trend statistics service, the method comprising the steps of: storing service history information of pre-stored real estate sale information or postings including predefined keywords / Generating trend statistics information on the basis of the real estate transaction information and generating actual transaction statistics information based on the real estate real transaction information, and when the trend information of the real estate trend is requested from the user, the trend statistical information including the interest statistics information and the actual transaction statistics information And providing the real-time trend statistical service method.

The interest statistic information may include service utilization log information including at least one of various sorts of the real estate sale information, the number of tastings, the number of sales inquiries, and the customer inquiry, or to collect the service use log information including the keyword / Related information including at least one of a post, a number of shares of the post, a number of likes, and a number of comments, and analyzing the collected service use log information or post related information to calculate a region, a subway route, Mapping at least one item of an area, a loan type, and a buyer's age; calculating a interest statistic score by assigning a weight to each of the service use log information or the post related information mapped by each item; Ranking the statistical scores in descending order, and generating interest statistics information for each item It can be generated.

The actual transaction statistics information includes periodically collecting the real-estate transaction information, analyzing the collected real-estate transaction information, and classifying the transaction information into at least one of an area, a subway line, a transaction price, a dedicated area, a loan type, Calculating score for each item and sorting the scores in descending order; detecting and processing abnormal data based on the abnormal data filtering condition predefined in the sorted information for each item; Lt; / RTI >

The predefined abnormal data filtering condition is that when the predetermined abnormal ranking is lowered than the previous month, it is ranked in the previous month but is excluded, Quot; and / or " selling price ".

In the step of providing the trend statistical information, the trend statistical information may include trend statistical information or cumulative trend statistical information for each period, and may be provided in at least one of list, graph, map based, and subway route based.

According to the present invention, by providing the interest statistical information based on the real estate interest of the customer and the real transaction information based on the real estate real transaction information, the user can easily confirm the current real estate trend without distortion, , It is easy to access the goods according to the trend, and it is possible to distinguish between the actual transaction statistics and the interest statistics.

In addition, based on real estate contents consumption pattern data using web site, app service and social network services / sites, real estate interest statistics such as popular area, subway line, sale price, private area, loan type, By providing information, users and markets can quickly understand the real estate trends they want and can contribute to the advancement of real estate transactions.

In addition, based on actual real estate information, it provides real estate real-time statistics by popular category, subway line, sales price, private area, loan type, and buyer age, The age of the buyer, the family members, the occupation, the desired selling price, and the selling price range.

The effects of the present invention are not limited to the above-mentioned effects, and various effects can be included within the scope of what is well known to a person skilled in the art from the following description.

1 is a diagram illustrating a real-time trend statistics service system according to the present invention.
2 is a block diagram schematically illustrating a configuration of a service server according to an embodiment of the present invention.
FIG. 3 is a block diagram showing the configuration of the interest statistics processing unit shown in FIG. 2. FIG.
4 is a block diagram showing a configuration of the real-transaction statistic processing unit shown in Fig.
5 is a flowchart illustrating a method of generating interest statistics information by the service server according to the present invention.
6 is a flowchart illustrating a method for generating real transaction statistics information by the service server according to the present invention.
7 is a diagram illustrating a method of providing a real-time trend statistical service by a service server according to the present invention.
FIG. 8 is an exemplary view illustrating a main screen for real estate trend statistics service according to the present invention.
FIG. 9 is a view illustrating an exemplary monthly trend statistics service screen according to the present invention.
10 is a diagram illustrating an example of a cumulative trend statistical service screen according to the present invention.
11 is a diagram illustrating an example of a map-based trend statistics service screen according to the present invention.

The foregoing and other objects, features, and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: FIG.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a system and method for real estate trend statistics service according to the present invention will be described in detail with reference to the accompanying drawings. The embodiments are provided so that those skilled in the art can easily understand the technical spirit of the present invention, and thus the present invention is not limited thereto. In addition, the matters described in the attached drawings may be different from those actually implemented by the schematic drawings to easily describe the embodiments of the present invention.

In the meantime, each constituent unit described below is only an example for implementing the present invention. Thus, in other implementations of the present invention, other components may be used without departing from the spirit and scope of the present invention. In addition, each component may be implemented solely by hardware or software configuration, but may be implemented by a combination of various hardware and software configurations performing the same function. Also, two or more components may be implemented together by one hardware or software.

Also, the expression " comprising " is intended to merely denote that such elements are present as an expression of " open ", and should not be understood to exclude additional elements.

1 is a diagram illustrating a real-time trend statistics service system according to an embodiment of the present invention.

Referring to FIG. 1, a real estate trend service system includes a plurality of user terminals 100 and a service server 200, which transmit and receive information through a communication network.

The user terminal 100 may be an administrator terminal for inputting real estate sale information or a customer terminal using real estate trend statistics service. At this time, the plurality of user terminals 100 may be implemented as a computing device having a communication function such as a notebook computer or a tablet PC, but may be implemented as a portable communication device such as a smart phone or a tablet PC.

The service server 200 generates trend statistical information including interest statistics information and actual transaction statistics information based on the real estate information collected from the past to the present. In other words, the service server 200 generates interest statistics information for each item such as a popular area, a subway line, a sale price, a dedicated area, a loan type, and a buyer's age based on real estate contents consumption pattern data using a web site, Based on the real estate information, generates real-time statistical information for each item such as popular area, subway line, sale price, private area, loan type, and buyer age. Here, the interest statistical information may be a real estate related rank with high interest by users such as a popular area, a subway line, a sale price, a private area, a loan type, a buyer age, and the like. Actual transaction statistics can be real estate related rankings by region, subway line, sales price, private area, loan type, buyer age, etc.

In addition, when the real estate trend statistical information is requested from the user terminal 100, the service server 200 provides trend statistical information including interest statistical information and actual transaction statistical information. At this time, the service server 200 can provide trend statistical information by list, graph, map-based graph, subway route-based graph, etc. for each period of the day, month, and the like.

Meanwhile, the service server 200 may be implemented as a single computing device or in the form of an aggregate device in which two or more computing devices are interconnected. For example, the villa service server 200 may be implemented as a single server, or two or more servers may be connected.

This service server 200 will be described in detail with reference to FIG.

FIG. 2 is a block diagram schematically showing a configuration of a service server according to an embodiment of the present invention. FIG. 3 is a block diagram showing the configuration of the interest statistics processing unit shown in FIG. 2. FIG. Fig.

2, the service server 200 includes a merchandise information database 210, a interest statistics processing unit 220, a real transaction statistics processing unit 230, and a trend statistics service unit 240.

The property information database 210 stores real estate property information. Here, the real estate listing information may include a name, a location, an area, a price, a sale type, a transaction type, a transaction priority (quick sale, general), a surrounding commercial area, traffic information, Newly constructed apartment, Apartment, Newly built single apartment, Newly built single apartment, Newly built office building, Newly built office building, etc.

The interest statistic processing unit 220 maps the service related log information of the real estate sale information stored in the sale information database 210 or the post related information including the predefined keyword / hash tag posted in the SNS to the selling information, Information. Here, the interest statistical information may be a real estate related rank with high interest by users such as a popular area, a subway line, a sale price, a private area, a loan type, a buyer age, and the like.

Referring to FIG. 3 for the interest statistics processing unit 220, the interest statistics processing unit 220 includes a collection module 222, an analysis module 224, and the like.

Collection module 222 collects posting related information including service use log information of real estate sale information or predefined keyword / hashtags posted on a social network service. Here, the service use log information may include various searched numbers of merchandise information stored in the seller information database 210, number of squeeze outs, number of seller inquiries, customer inquiries, and the like. The information related to a post may include a post including a predefined keyword or a hash tag, the number of shares, the number of likes, the number of comments, and the like of the post. The predefined keywords may include real estate, villa rent, apartment quotes, , Sale, rental, rent, semi-charter, etc. That is, the collecting module 222 collects service use log information such as the number of various searches related to real estate, the number of property inquiries, the number of sales inquiries, and customer inquiry retrieved by a user who has accessed the service server 200 through a web site or an app service Periodically collect and store daily, weekly, monthly, etc.

In addition, the collection module 222 collects the posts including the predefined keywords and hash tags in the posts published in the SNS service such as Facebook, Instagram, Twitter, Cacao Story, Cafe, Blog, etc., Weekly, monthly, and other information related to the article including the number of comments and the number of comments. That is, the collecting module 222 searches the posts by keywords or hash tags previously defined in the SNS service, and obtains information related to the posts, such as the number of shares, likes, comments, ID, Collect. Then, the collection module 222 filters the collected post-related information without collecting posts such as advertisements and publicity information. That is, the collection module 222 filters information corresponding to a predetermined filtering condition such as an advertisement and a PR ID, an advertisement keyword, a combination of several words, and a sentence in the information related to a post.

The analysis module 224 maps the service utilization log information or the bulletin related information collected by the collection module 222 to each item such as a region, a subway line, a sale price, a dedicated area, a loan type, a buyer age, Calculate the interest statistical score, and generate interest statistical information for each item based on the interest statistical score.

That is, the analysis module 224 analyzes the service log information such as the sale information, the sale information, and the like, which is searched by the user, and maps the service log information to each item such as a corresponding area, a subway line, do. For example, if the search module 224 searches for '30 pyeong apartment with a sale price of 9 billion KRW in Seoul Jangsil-dong', the analysis module 224 stores the search information as a local item 'Seoul Jamsil dong', a subway line item Jamsil new item, Item '900 million', and the exclusive area item '30 pyeong'. In addition, the analysis module 224 analyzes the post-related information and maps it to each item such as a corresponding area, a subway line, a sale price, a dedicated area, a loan type, and a buyer age. For example, the analytical module (224) of the publication 'Seoul Jamsil-dong, Seoul, which has a bargain price of 9 billion won, , And a special area item '30 pyeong'.

Then, the analysis module 224 assigns predetermined weights to the service utilization log information or the bulletin related information classified by each item, and calculates the interest statistic score of the item. For example, the analysis module 224 may calculate a first score of a specific region for a specific period by assigning a weight to each of the number of searches, the number of searches, The second score is calculated by weighting each of the number of views, likes, and shares of posts related to the area. The analysis module 224 may then calculate (e.g., add) the first score and the second score to calculate a point of interest statistics score for a particular region. In this way, the analysis module 224 can calculate the interest statistic score for each item over a specific period of time.

Then, the analysis module 224 generates interest statistics information by sorting the items of interest in descending order of interest statistics score. That is, the analysis module 224 generates interest statistics information for each item by sorting the items in the descending order of the interest statistics score for each item such as a popular area, a subway line, a sale price, a dedicated area, a loan type, For example, interest statistics information of popular area items can be generated in the order of Seoul Gangseo-gu, Seoul Gurogu, Seoul Eunpyeong, Incheon, and Incheon in that order. These interest statistical information can be used to provide information on the most frequently mentioned (popular) areas such as subway lines, sale prices, and private areas.

Also, the analysis module 224 is interested to include the listing information associated with the interest statistical information. For example, the interest statistical information of the popular area item is information of interest information of Seoul Gangseo-gu, Seoul Gurogu, Seoul Eunpyeong, Incheon South, Incheon Namdong, Seoul, Gangseo-gu, It can map the property information and the property information of Namdong-gu in Incheon. At this time, the selling information can be mapped to the selling order having high interest.

The actual transaction statistics processing unit 230 divides the real transaction information into fields such as a region, a subway line, a transaction price, a dedicated area, a loan type, a buyer's age, and generates actual transaction statistics information by calculating the score for each item . Actual transaction statistics can be ranked according to each item such as area, subway line, sales price, exclusive area, loan type, and buyer age.

4, the actual transaction statistics processing unit 230 includes a collection module 232, an analysis module 234, and a verification module 236. The real transaction statistics processing unit 230 includes a collection module 232, an analysis module 234,

The collection module 232 periodically collects real estate real-estate information from the real estate information database 210 or an external server such as daily, weekly, or monthly. Real estate real-estate information includes the address of the real estate transaction, the sale price, the specific area, the type of loan, and the tenant / landlord information.

The analysis module 234 divides the real estate real-estate information collected by the collection module 232 into items such as a region, a subway line, a sale price, a dedicated area, a loan type, a buyer's age, And the score is sorted in descending order.

Since the real estate real estate information includes the address, the sale price, the private area, the type of the loan, and the tenant / landlord information, the analysis module 234 can grasp the age of the buyer from the address, the tenant / Accordingly, the analysis module 234 divides each real estate real-estate information into predetermined items such as a region, a subway line, a sale price, a dedicated area, a loan type, a buyer's age, (For example, addition) so as to calculate a score for each item. As described above, the analysis module 233 calculates the number of real estate transactions by region, the number of real estate transactions by subway line, the number of real transactions by transaction price, the number of real transactions by specific area, the number of real transactions by loan type, At this time, the analysis module 234 can calculate the number of real-life transactions per one day, one week, one month, three months, and so on.

The analysis module 233 arranges the number of actual transactions in descending order of each item such as a popular area, a subway line, a sale price, a dedicated area, a loan type, a buyer's age, and the like. For example, it is possible to generate actual transaction statistics of popular area items in the order of Seoul Gangseo-gu, Seoul Guro-Gu, Seoul Eunpyeong, Incheon, and Incheon in that order.

In addition, the analysis module 233 is interested in including commercial information linked to actual transaction statistics. For example, in the case of popular area items, actual transaction statistics are provided for Seoul Gangseo-gu, Seoul Gurogu, Seoul Eunpyeong, Incheon, Sinchon, Incheon, Gangseo-gu, Seoul, It can map the property information and the property information of Namdong-gu in Incheon.

The verification module 236 detects and processes the abnormal data based on the rank of each item analyzed by the analysis module 234 to generate the actual transaction information. For example, the verification module 236 detects unsteady data such as a region that has surpassed a predetermined rank or higher compared to the previous month, a region where a predetermined schedule has fallen, a case where it has been ranked in the previous month but is excluded, Inform the administrator of the data and check whether the data is missing or not. In addition, the verification module 236 detects unsteady data such as a sale price less than a specific number and a bargain price that deviates from a normal distribution according to an internal criterion, and notifies the manager of the detected abnormal data to check whether data is missing or not, .

The trend statistics service unit 240 provides the user with trend statistical information including interest statistical information and actual transaction statistics information when the real-time trend statistical information is requested from the user. At this time, the trend statistics service unit 240 can provide trend statistical information in the form of a list, a graph, a map-based graph, a subway route-based graph, etc. for each period of the day, month,

The service server 200 may further include a controller (not shown) for controlling operations of various components of the service server 200. The control unit may include at least one computing device, which may be a general purpose central processing unit (CPU), programmable device elements (CPLDs, FPGAs), application specific integrated circuits ) Or a microcontroller chip.

In the case of each configuration in the service server 200 described above, the configuration may be implemented in the form of a software module or a hardware module executed by a processor, or a combination of a software module and a hardware module.

As such, any combination of software modules, hardware modules, or software modules and hardware modules executed by a processor may be implemented in a hardware system (e.g., a computer system).

5 is a flowchart illustrating a method of generating interest statistics information by the service server according to the present invention.

Referring to FIG. 5, the service server collects service-related log information of pre-stored real estate sale information or post-related information including a keyword / hash tag predefined in the social network service (S502). At this time, the service server can collect service use log information or information related to the posting at a predetermined period.

When the step S502 is performed, the service server maps the collected service use log information or the post-related information to predefined items such as area, subway line, sales price, exclusive area, loan type, . That is, the service server analyzes service use log information or information related to a post and classifies the information into corresponding items such as a region, a subway line, a sale price, a dedicated area, a loan type, and a buyer age.

Then, the service server calculates a concern statistic score by applying predetermined weights to the service use log information or the bulletin related information classified by each item (S506), arranges the interest statistic score in descending order of interest statistic score for each item, Information is generated (S508). At this time, the service server generates interested statistical information in which the selling information is sorted in the order of the highest interest statistics score for each item such as a popular area, a subway line, a selling price, a private area, a loan type, Interest statistics information allows you to view listings of the most frequently mentioned (popular) areas, subway lines, bargain prices, and private areas by users.

6 is a flowchart illustrating a method for generating real transaction statistics information by the service server according to the present invention.

Referring to FIG. 6, the service server collects real estate real-estate information from pre-stored property information or an external server (S602). At this time, the service server can periodically collect and store real estate real-estate information.

When the step S602 is performed, the service server divides the collected real estate real-estate information into items such as area, subway line, selling price, exclusive area, loan type, and buyer age (S604).

Thereafter, the service server calculates a real transaction statistics score by applying a weight to each real estate transaction information item for each item (S606), and arranges the actual transaction statistics score in ascending order of each item to generate real transaction statistics information (S608) . That is, the service server calculates (for example, adds) a predetermined number of items to each item according to the number of real transactions, and calculates a score for each item. Thus, the service server calculates the number of real estate transactions by region, the number of real estate transactions by subway line, the number of real transactions by transaction price, the number of real transactions by specific area, the number of real transactions by loan type, At this time, the service server can calculate the number of actual real transactions by a period of one day, one week, one month, three months, and the like. Then, the service server generates real transaction statistics by sorting the items in order of the number of real transactions.

FIG. 7 is a diagram illustrating a method of providing a real estate trend statistics service according to the present invention. FIG. 8 is a view illustrating a main screen for a real estate trend statistics service according to the present invention. FIG. 10 is a view illustrating an example of a trend statistics service screen according to the present invention, and FIG. 11 is a diagram illustrating an example of a map based trend statistics service screen according to the present invention.

Referring to FIG. 7, when the user terminal is connected to the service server (S702), the user terminal displays a main screen for the real estate service (S704).

When the user selects the trend statistics menu on the main screen (S706), the user terminal transmits a trend statistical information request signal to the service server (S708). Here, the trend statistical information request signal may include user terminal identification information, standard information of the user terminal (e.g., screen information, OS, etc.).

The service server receiving the trend statistical information request signal transmits trend statistical information including interest statistics or actual transaction statistics to the user terminal (S710). At this time, the service server can generate and provide trend statistical information based on the standard information of the user terminal.

Hereinafter, a method of providing a real-time trend statistics service by a service server will be described with reference to a screen diagram.

First, when the user terminal is connected to the service server, the user terminal displays the main screen as shown in FIG. The main screen 800 includes a menu area in which menus such as an entire house search, a villa registration, a villa tour application, and trend statistics are displayed.

When the user selects the 'Trend Statistics' menu 810 in the menu area, the service server can provide monthly trend statistics information. Then, the user terminal can display the monthly trend statistical information screen 900 as shown in FIG. Monthly trend statistics information shows interest statistical information and actual transaction statistics based on the data of the month, such as popular area, subway line, sale price, exclusive area, loan type, buyer age and so on. Therefore, the monthly trend statistical information screen 900 includes an item area 910, a interest statistical area 920, and a real statistical area 930. In the item area 910, items such as a popular area, a popular subway line, a sale price, a private area, a loan type, a buyer's age and the like are displayed. In the interest statistics area 920, In the actual transaction statistical area 930, real estate information having a large number of actual transactions in the month is displayed. The user can select a desired item in the item area 910 and check interest statistics information and actual transaction statistics information of the item. For example, when the user selects the popular area item in the item area 910, the popular area is displayed in the interest statistics area 920 in order of popularity (high interest) in March 2017, and the actual transaction area 930) will be displayed in order of the number of transactions in March 2017. Further, when the user selects the popular subway line item in the item area 910, the subway line is displayed in the interest statistical area 920 in the order of popular (highly-interested) real estate in March 2017, The subway line is displayed in the area 930 in order of the number of transactions in March 2017. At this time, there is a 'view for sale' button which is connected to a sale, next to each rank of a popular area, a subway station / route, a sale price and a private area, and when this button is pressed, the corresponding property is displayed.

The service server can display the real estate trend statistical information as well as the occupancy ratio graphically. Accordingly, the interest statistics section 920 may display a list of interest statistics and a graph showing the occupancy percentage, and the actual transaction statistics area 920 may display a list of real transactions statistics and an occupancy rate graph.

Also, the service server can provide accumulated cumulative trend statistical information for a certain period of time. Then, the user terminal can display the cumulative trend statistical information screen 1000 as shown in FIG. Cumulative trend statistics information is information that shows the trend change trend for a certain period of time. In the cumulative trend statistical information screen 1000, a ranking graph in which past data is graphically displayed based on the view month and interest statistics having a high interest in the month are displayed. The ranking graph shows the past data as a graph based on the view month, and does not show it if there is no data in the middle. For example, if you were to show trend statistics for December of 2016 for the past six months, show 6 months from July 2016 to December 2016, but if the data is missing from July to October 2016, November 2016, December 2016 data will only appear in the graph.

The user can select a desired item in the item area 1010 and check cumulative trend statistical information of the item. For example, when a user selects a popular area item in the item area 1010, the popular area is displayed in the rank graph of April 2017 and popular (high interest) in April 2017. Further, when the user selects a popular subway line item in the item area 1010, the subway line is displayed in order of rank in April, 2017 and popular (in high interest) in April, 2017. At this time, there is a 'view for sale' button which is connected to a sale, next to each rank of a popular area, a subway station / route, a sale price and a private area, and when this button is pressed, the corresponding property is displayed.

The service server may also provide map-based trend information that displays trend statistics on the map. Then, the user terminal can display the monthly trend statistical information screen 1100 as shown in FIG. Referring to FIG. 11, trend statistics for items such as popular area, subway line, sale price, exclusive area, loan type, and buyer age are displayed in Seoul / Gyeonggi / Incheon. When you select or zoom in a specific area on the map, that information appears. For example, if you select Seoul, the distinction information is displayed on the map, and if you select the phrase again, the distinction information is displayed on the map.

Meanwhile, the real estate trend statistics service method can be written in a program, and the codes and code segments constituting the program can be easily deduced by programmers in the field. In addition, the program relating to the method of real estate trend statistics service can be stored in an information storage medium (Readable Media) that can be read by an electronic device, and can be read and executed by an electronic device.

Thus, those skilled in the art will appreciate that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. It is therefore to be understood that the above-described embodiments are illustrative only and not restrictive of the scope of the invention. It is also to be understood that the flow charts shown in the figures are merely the sequential steps illustrated in order to achieve the most desirable results in practicing the present invention and that other additional steps may be provided or some steps may be deleted .

The technical features and implementations described herein may be implemented in digital electronic circuitry, or may be implemented in computer software, firmware, or hardware, including the structures described herein, and structural equivalents thereof, . Also, implementations that implement the technical features described herein may be implemented as computer program products, that is, modules relating to computer program instructions encoded on a program storage medium of the type for execution by, or for controlling, the operation of the processing system .

In the present specification, the term " system ", "device" includes all apparatuses, apparatuses, and machines for processing data including, for example, a processor, a computer or a multiprocessor or a computer. The processing system may include any code that, in addition to the hardware, forms an execution environment for a computer program upon request, such as, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, can do. A computer program, known as a program, software, software application, script or code, may be written in any form of programming language, including compiled or interpreted language or a priori, procedural language, Routines, or other units suitable for use in a computer environment.

Configurations implementing the technical features of the present invention, which are included in the blocks and flowcharts shown in the drawings attached hereto, refer to the logical boundaries between the configurations. However, according to an embodiment of the software or hardware, the depicted arrangements and their functions may be implemented in the form of a stand alone software module, a monolithic software structure, a code, a service and a combination thereof and may execute stored program code, All such embodiments are to be regarded as being within the scope of the present invention since they can be stored in a medium executable on a computer with a processor and their functions can be implemented.

Accordingly, the appended drawings and the description thereof illustrate the technical features of the present invention, but should not be inferred unless a specific arrangement of software for implementing such technical features is explicitly mentioned. That is, various embodiments described above may exist, and some embodiments may be modified while retaining the same technical features as those of the present invention, and these should also be considered to be within the scope of the present invention.

It should also be understood that although the flowcharts depict the operations in the drawings in a particular order, they are shown for the sake of obtaining the most desirable results, and such operations must necessarily be performed in the specific order or sequential order shown, Should not be construed as being. In certain cases, multitasking and parallel processing may be advantageous. In addition, the separation of the various system components of the above-described embodiments should not be understood as requiring such separation in all embodiments, and the described program components and systems are generally integrated into a single software product, It can be packaged.

As such, the specification is not intended to limit the invention to the precise form disclosed. While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art without departing from the spirit and scope of the present invention as defined by the appended claims. It is possible to apply a deformation. The scope of the present invention is defined by the appended claims rather than the foregoing description, and all changes or modifications derived from the meaning and scope of the claims and equivalents thereof are deemed to be included in the scope of the present invention. .

100: user terminal 200: service server
210: database 220: interest statistics processor
222, 232: collection module 224, 234: analysis module
230: actual transaction statistics processing unit 236: verification module
240: Trend Statistics Service Department

Claims (14)

Related information including service use log information of pre-stored real estate sale information or a pre-defined keyword / hash tag posted on a social network service, and transmits the collected service use log information or post related information to predefined items An interest statistics processor for generating interest statistics information;
A real transaction processor for dividing the real estate transaction information into predefined items and generating the real transaction statistics by calculating the score for each of the separated items; And
A trend statistics service unit for providing trend statistical information including the interest statistical information and actual transaction statistics information when the real estate trend statistical information is requested from the user; Lt; / RTI >
The predefined items include at least one of a popular area, a popular subway line, a sale price, a dedicated area, a loan type, and a buyer's age, and the real estate listing information includes at least one of a listing name, a location, , Transaction priorities, surrounding merchandise and traffic information,
The interest statistics processor generates the interest statistical information for each of the predefined items, and the real transaction statistics processor generates the real transaction statistics for each predefined item,
The trend statistics service unit provides trend statistical information or cumulative trend statistical information for each period in at least one of list, graph, map based, and subway route based,
The trend statistical information for each period includes the statistical information of interest in which the statistical information is displayed in descending order of the degree of interest for each item in the period and the actual statistical information in which statistical information with a large number of actual transactions are displayed for each item in the period,
The trend statistics service unit simultaneously provides the interest statistic information and the actual statistical information of the corresponding period in the form of a list or an occupation ratio graph, and each of the lists is correlated with real estate listing information related to the list, Service server. The method according to claim 1,
The interest statistics processing unit,
Collecting the service use log information including at least one of various sorts of the real estate sale information, the number of the searched real estate, the number of for-sale inquiries, and the customer inquiry, or collecting the post including the keyword / hashtag in the social network service, A collection module for collecting post related information including at least one of numbers, likes, and comments;
Analyzing the collected service utilization log information or information related to the post and mapping it to at least one item of the area, the subway line, the sale price, the exclusive area, the loan type, and the buyer age, calculates the interest statistics score for each item, And an analysis module for generating interest statistics for each item based on the statistical score. 3. The method of claim 2,
The analysis module assigns weights to the service use log information or the post related information for each item to calculate the interest statistic score, sorting the calculated interest statistical score in descending order, and generating interest statistical information for each item And the service server. 3. The method of claim 2,
Wherein the analysis module maps real estate sale information associated with each interest statistical information and stores the information. The method according to claim 1,
The actual transaction statistics processor,
A collection module for periodically collecting and storing the real estate real-estate information;
The real estate real-estate information collected by the collection module is analyzed and classified into at least one of an area, a subway line, a sale price, a dedicated area, a loan type, and a buyer's age. An analysis module arranged in order; And
And a verification module for detecting and processing the abnormal data based on the abnormal data filtering condition predefined in the information sorted by each item in the analysis module to generate actual transaction statistics information. 6. The method of claim 5,
The predefined abnormal data filtering condition is that when the predetermined abnormal ranking is lowered than the previous month, it is ranked in the previous month but is excluded, And a selling price of the service server. delete delete The method according to claim 1,
The trend statistics service section provides cumulative trend statistical information including a graph of past data graphically displayed on the basis of the view month for each item and interest statistics information showing statistical information in descending order of degree of interest for each item Features a service server. In a method for a service server to service real estate trend statistics,
Related information including the service log information of the pre-stored real estate sale information or the post-related information including the predefined keyword / hash tag posted on the social network service is generated as the predefined item to generate the interest statistical information, Generating real transaction statistics by dividing the transaction into the defined items; And
Providing trend statistical information including the interest statistical information and actual transaction information when the real-time trend statistical information is requested from the user; Lt; / RTI >
The predefined items include at least one of a popular area, a popular subway line, a sale price, a dedicated area, a loan type, and a buyer's age, and the real estate listing information includes at least one of a listing name, a location, , Transaction priorities, surrounding merchandise and traffic information,
Wherein the step of providing the trend statistical information comprises:
The trend statistical information including the trend statistical information or the cumulative trend statistical information for each period is provided in at least one of a list, a graph, a map based, and a subway route based,
The trend statistical information for each period includes the statistical information of interest in which the statistical information is displayed in descending order of the degree of interest for each item in the period, and the actual transaction statistics information in which the statistical information is frequently displayed for each item in the corresponding period.
Wherein the providing of the trend statistical information simultaneously provides the interest statistical information and the actual statistical information of the corresponding period in the form of a list or an occupation ratio graph, Wherein the real-time trend statistics service method comprises the steps of: 11. The method of claim 10,
The interest statistic information may include service utilization log information including at least one of various sorts of the real estate sale information, the number of tastings, the number of sales inquiries, and the customer inquiry, or to collect the service use log information including the keyword / Related information including at least one of the number of posts, the number of shares of the post, the number of likes, and the number of comments, and analyzing the collected service use log information or post related information to calculate a region, a subway route, , Loan type, and buyer age, and calculates interest statistics score by assigning weights to service usage log information or post related information mapped by each item, and calculates the interest statistics score Wherein the plurality of real-time trend statistical service methods are arranged in descending order. 11. The method of claim 10,
The real transaction statistics information periodically collects the real estate real transaction information and analyzes the collected real estate real transaction information to divide it into at least one of an area, a subway line, a sale price, a dedicated area, a loan type, and a buyer's age A score is calculated for each item, and the score is sorted in descending order; and abnormal data is detected and processed based on the abnormal data filtering condition predefined in the information sorted by each item How to service real estate trend statistics. 11. The method of claim 10,
The predefined abnormal data filtering condition is that when the predetermined abnormal ranking is lowered than the previous month, it is ranked in the previous month but is excluded, And a selling price of the real estate. delete

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