Patent Publication Number: US-10776888-B1

Title: Systems and computer-implemented methods to compare and identify duplicative presentations of comparable data files including imagery and data representations of rental property listings

Description:
FIELD 
     The present application relates generally to systems, methods, software and computing devices for property listings. More specifically, to systems, method, software and computing devices for identifying and managing duplicate property listings. 
     BACKGROUND 
     Properties, such as vacation rental property, rental homes, rental condos and the like, may often have duplicate property listings that are listed on different property listing resources, such as different databases, different web sites, different rental agencies, or different property management systems, for example. In some examples, a property owner and/or an agent acting on behalf of an owner may list the same property on different property listing resources, resulting in multiple property listings that are associated with the same property. In some examples, the different property listing resources may be geographically diverse (e.g., in different countries). In other examples, the different property listing resources may be operated by a common corporate entity under different brand names. In yet other examples, the different property listing resources may be operated by different corporate entities. However, there may be duplicate property listings that exist among different property listing resources within the same corporate entity and/or among different property listing resources of different corporate entities. The duplication may be due to owners and/or agents posting their property listings among a variety of property listing resources, some within the same company but spread across different brands operated by the company and/or spread across one or more brands of different companies. 
     In some examples, one company having X property listings may acquire or may be acquired by another company having Y property listings. Due to a real possibility of duplication in property listings between the two companies, it may be unknown or not readily discernible how many of their respective property listings are duplicates and how many are actually property listings that are unique to each company. Accordingly, an accurate monetary valuation of the unique property listings of the company to be acquired may be difficult unless duplicate property listings are factored out of the valuation. 
     Duplicate property listings may require more data storage space and may make searches for property listings more compute intensive, time consuming, and less accurate due to duplication of property listings that may be output as search results. In some examples, an owner or agent who has listed a property may be removed by a property listing service or website due to failure to pay fees (e.g., listing fees) or due to posting fraudulent property listings. An example of a fraudulent listing may include a scenario where photos and/or other information for another property listing are used to represent the property listing that was posted. For example, if the fraudulent owner/agent has a dive property to list, they may use photos and/or other information taken from a nicer listing to represent the dive listing. It may be difficult to detect the use of misrepresented information associated with a property listing. Further, if the fraud is detected and the offending entity is barred from posting new listings, that entity may nevertheless use a new user name, new password, new email address or other form of access credentials to establish a new account from which fraudulent listing may again be posted using fake information, such as photos and the like. 
     Thus, there is a need for systems, methods and software to detect and identify duplicate property listings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments or examples (“examples”) of the present application are disclosed in the following detailed description and the accompanying drawings. The drawings are not necessarily to scale: 
         FIG. 1A  depicts examples of data representing property listings; 
         FIG. 1B  depicts an example of a system for identifying duplicate property listings; 
         FIG. 2  depicts example of a list that may constitute a single file or multiple files; 
         FIG. 3  depicts an example of two files generated from two attribute search keys; 
         FIG. 4  depicts an example of determination of a sub-set of a list to be processed by multiple computing devices; 
         FIG. 5  depicts examples of reports; 
         FIG. 6  depicts an example of sorted weighted scores; 
         FIG. 7  depicts an example of arbitrary file input; 
         FIG. 8  depicts one example of vetting data representing a newly received property listing; 
         FIG. 9  depicts another example of vetting data representing a newly received property listing; and 
         FIG. 10  depicts one example of a computer system. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments or examples may be implemented in numerous ways, including as a system, a process, a method, an apparatus, a user interface, or a series of program instructions on a non-transitory computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in an arbitrary order, unless otherwise provided in the claims. 
     A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples, but is not limited to any particular example. The scope is limited only by the claims and numerous alternatives, modifications, and equivalents are encompassed. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description. 
       FIG. 1A  depicts examples of data representing property listings. In  FIG. 1A  a data warehouse  130  may include data representing property listings  110  that are stored in data warehouse  130 . Although one such property listing is depicted in example  100 , the data warehouse  130  may include several property listings  110 , such as several hundred thousand to several million or more, for example. The data representing each property listing may include data for one or more images  111  (e.g., photos of vacation rental units, real property, etc.), an image identifier  113  associated with each image  111 , a listing identifier  114  associated with each image  111  and listing attributes associated with each property listing (e.g., zip code, address, number of bed rooms, number of bath rooms, number of sleeps, a geolocation, etc.). For each property listing  110 , the data representing each image  111  may be received by a thumb printing function (e.g., a thumb printing algorithm) configured to generate a summary data value  112  for each image. For example, summary data value  112  may constitute a summary data value  112  of digital data in image  111 . The thumb printing function generates summary data values  112  (SV&#39;s) for each image  111  that is configured to uniquely distinguish images  111  associated with property (e.g., rental property, property listings, vacation rental listings, real property) from other images  111  associated with property. Summary data values  112  may also be referred to herein as thumbprint values  112 . The thumbprint value  112  may be associated with its respective image  111  via the image identifier  113  for the image  111 . The thumb printing function may generate thumbprint values  112  for each image  111  in every property listing  110  in data warehouse  130 . Each thumbprint value  112  may uniquely identify each image  111  the thumbprint value  112  is associated with and the thumbprint value  112  may be used to compare an image  111  in a property listing  110  with another image  111  in another property listing  110  to determine if the images  111  are duplicate images  111  based on a comparison between their thumbprint values  112  that indicates the thumbprint values  112  match each other. A match need not be an exact match and images  111  that may be substantially similar to each other may have thumbprint values  112  that match each other. 
     Potentially duplicate property listings  110  in data warehouse  130  may be identified by first determining if one or more thumbprint values  112  in one property listing  110  matches one or more thumbprint values  112  in a different property listing  110 . Matching thumbprint values  112  may be just one indication of potentially duplicate property listings  110  and other data included in the data representing each property listing  110  may be used in addition to thumbprint values  112  to increase confidence in determining whether or not data warehouse  130  includes duplicate property listings  110 . 
     In example  100   a  a search key  151  (e.g., using zip code as the search parameter) may be used to search for property listings  110  in data warehouse  130  that may include data that matches the search key  151 . For example, listing attributes  115  in each listing  110  may include data representing zip codes and a zip code as the search key  151  may match listing attributes  115  in a listing  110 ′ and in another listing  110 ″, for example. The two listings having listing attributes  115  that matched the search key  151  may be further inspected to determine if they are potentially duplicate listings  110  by comparing thumbprint values  112 . Accordingly, each thumbprint value  112  in listing  110 ′ may be compared with each thumbprint value  112  in listing  110 ″ to determine if one or more of the thumbprint values  112  being compared match each other. 
     In example  100   b , each image identifier  113  associated with each image  111  in listing  110 ′ may be used to select the thumbprint value  112  associated with each image  111  in listing  110 ′ and the selected summary data value  112  may be received by comparator configured to compare summary data values (SV&#39;s). Similarly, each image identifier  113  associated with each image  111  in listing  110 ″ may be used to select the selected summary data value  112  associated with each image  111  in listing  110 ″ and the selected summary data value  112  may be received by the comparator. The comparator may compare each pair of summary data values  112  (SV&#39;s) it receives and determine if the received summary data values  112  match each other. If a match is determined the comparator may output data representing a match. An image match counter may receive the data representing the match and may increment an image count  137  for each match detected by the comparator. For example, if listing  110 ′ includes image data for 10 images  111  and listing  110 ″ includes image data for 15 images  111 , if comparison of the summary data values  112  for those images indicates that between listings  110 ′ and  110 ″ there are potentially 7 images  111  that had summary data values  112  that matched, then the image count  137  may be incremented seven times and have a value of 7. If none of the summary data values  112  of the images  111  being compared match, then the image count  137  may be zero (0), for example. 
     Further to example  100   b , secondary data may be used to determine a correlation value that two listings ( 110 ′ and  100 ″) are potentially duplicate listings  110 . In example  100   b , the listing attributes  115  of listing  110 ′ may be compared with the listing attributes  115  of listing  110 ″ by another comparator configured to detect matches between data representing attribute listings  115  (e.g., scalar data, numeric data, alpha-numeric data). Each listing attribute  115  in listing  110 ′ that matches a listing attribute  115  in listing  110 ″ may generate data representing a match and an attribute match counter may be incremented with each detected match and may generate and attribute count  139 . If none of the listing attributes  115  being compared match, then the attribute count  139  may be zero (0). 
     As one example, the listing attributes  139  for a zip code have already been determined to match based on the selection key  151 ; therefore, attribute count  139  may be incremented to a value of 1. Further to the example, listing  110 ′ may include listing attributes  115  for; number of bed rooms=4; number of bathrooms=2; and number of sleeps=5. listing  110 ″ may include listing attributes  115  for; number of bedrooms=4; number of bathrooms=3; and number of sleeps=5. The listing attributes  115  for the number of bed rooms and number of sleeps match, but the number of bath rooms do not match. Accordingly, the attribute count  139  may be incremented by 2 to a value of 3 to indicate that two other listing attributes  115  matched between listings  110 ′ and  110 ″ (e.g., zip code, number of bed rooms and number of sleeps matched). 
     In example  100   b , the image count  137  and the attribute count  139  may optionally be inputs to a confidence threshold determinator configured to determine if the image count  137  meets or exceeds an image threshold count and if the attribute count  139  meets or exceeds an attribute threshold count. For example, the image threshold count may be set to a value of 2 such that at least two images  111  (e.g., based on comparing their respective summary data values  112 ) in listing  110 ′ and  110 ″ matched each other. Further to the example, the attribute threshold count may be set to a value of 3 such that at least three listing attributes  115  in listing  110 ′ and  110 ″ must match. Accordingly, if image count  137  is ≥2 and the attribute count  139  is ≥3, then confidence threshold determinator may output a signal or data (e.g., a Correlation Value) that indicates that the two listings ( 110 ′,  110 ″) may be potentially duplicate listings. For example, the Correlation Value may be 0 or 1, with 0 being low or no correlation indicating that the two listings ( 110 ′,  110 ″) being duplicate listings, or with 1 being a high correlation that the two listings ( 110 ′,  110 ″) are duplicate listings. A corrective action may be taken in response to the signal or data, such as computer resource  120  accessing data warehouse  130  to modify, delete, consolidate, or replace data in one or more duplicate property listings  110 . For example, if two duplicate listings  110  have the same zip code in their respective listing attributes  115 , then a corrective action may include replacing the data representing the zip code in one of the duplicate listings  110  and replacing that data with a pointer, link, hyperlink or the like, to the identical item of data in the other duplicate listing  110 . As another example, data in duplicate listings  110  may be combined into a single listing  110  and the data in the single listing  110  may constitute a superset of the data from the duplicate listings. Other corrective actions may be taken and the present application is not limited to the examples described or depicted herein. 
     In example  100   c  a score may be computed for each potentially duplicate listing  110  in data warehouse  130 . Higher scores may be indicative of a higher correlation value that two listings  110  that were compared to each other are duplicate listings  110 . Therefore, a correlation value of 1 may indicate a high confidence that two listing  110  are duplicated; whereas, a correlation value of 0 may indicate no confidence in two listing  110  being duplicated listings. 
     In example  100   c , the score computed for each listing  110  may be a weighted score. For example, the score may constitute the sum of an image count  137  for a listing multiplied by an image weighting factor and the attribute count  139  for a listing multiplied by an attribute weighting factor. The score may be used in place of or in conjunction with the Correlation Value output of the correlation threshold determinator of example  100   b.    
     Computer resource  120  and/or computer server resources  140  may reduce data storage space in the data warehouse  130  that is allocated to duplicate listings  110 . Duplicate listings  110  may be consolidated into a single listing  110 , may be deleted or otherwise removed from the data warehouse  130 , or may be included in a data representing a report, for example. Data storage space in data warehouse  130  may be increased (e.g., freed up) by identifying and removing or consolidating duplicate listings  110 . Searches of listings  110  in data warehouse  130  may be optimized (e.g., reduced data search latency times) by identifying and removing duplicate listings  110  or consolidating duplicate listings  110 . For example, data included in duplicate listings may be different. One listing may have 10 images and another listing may have 15 images. Similarly, listing attributes may differ among duplicate listings (e.g., one listing includes the number of sleeps and the other listing does not). Consolidation may constitute creating (e.g., using a computer resource) a new property listing  110  that includes all of the data in the duplicate listings  110  and deleting or otherwise removing the duplicate listings  110 . On the other hand, consolidation may constitute determining which data is common to the duplicate listings, which data is unique to each of the duplicate listings, and combining the common and unique data into one of the duplicate listings  110  and deleting or otherwise removing the other duplicate listing  110 . 
     In  FIG. 1A , a hashing function (e.g., a hash algorithm) may be one example of a thumb printing function that generates summary data values  112 . For example summary data values  112  may be hash values generated by a hashing function. In some examples, a perceptual hashing function (e.g., p− hash) may be one example of a thumb printing function. Hashing functions and hash values are non-limiting examples of types of thumb printing functions and thumbprint values and the present application is not limited to using hashing functions and/or hash values as summarization values of image data. 
       FIG. 1B  depicts an example of a system for identifying duplicate property listings. In  FIG. 1B , system  199  may include data warehouse  130  that includes stored data representing property listings  110 . Each property listing  110  may include several items of data associated with a property listing (e.g., a vacation rental unit, a rental unit, rental property, real property, etc.). Non-limiting examples of data that may be included in the data representing property listings  110  include but are not limited to data representing one or more images  111  (e.g., digital data from a captured image), a summary data value  112  of image  111  (e.g., generated by a thumb printing function applied to each image  111  to generate the summary data value  112 ), an image ID  113  for each image  111 , a listing ID  114  that associates each image  111  with the property listing  110  the image  111  belongs to, and listing attributes  115  associated with the property listing  110 . The image ID  113  and/or listing ID  114  may constitute unique numeric or alpha-numeric data assigned (e.g., by a computing resource) to the ID&#39;s ( 113 ,  114 ), for example. Listing attributes  115  may differ among the property listing  110  stored in data warehouse  130 . The image ID  113  and listing ID  114  may be unique to each property listing  110  and may be used as a search key to access data representing the property listing  110  and its associated data  111 - 115 . Property listing  110  and its associated data  111 - 115  may be represented by a variety of data structures including but not limited to a data base, one or more files, linked lists, pointers to data, a look-up table, a thumbprint table, a hash table, or other data structures. 
     Images  111  may include metadata associated with the image  111  at the time image was captured and/or included in the image  111  after the image  111  was captured. The metadata may include data representing temporal information (e.g., date and/or time the image was captured), geolocation data (e.g., a longitude and latitude where the image was captured), scalar data such as notes, comments, captions or other information that may be included in metadata (e.g., by the device that captured the image  111  and/or software that edited/processed the image  111 ). 
     Listing attributes  115  may constitute data (e.g., in numeric form, alpha-numeric form, scalar data, and/or other forms of data) that may be associated with a property listing  110 , including but not limited to an address, a zip code, a postal code, a state, a country, a town, a geographic descriptor, number of bed rooms, number of bath rooms, number of people the property listing will sleep (number of sleeps), amenities for the property listing (e.g., pool, Jacuzzi, sauna, scenic views, fireplace, etc.), kitchen information, parking information, a brand (e.g., a vacation rental company), property listing rules (e.g., non-smoking, no pets), a property description (e.g., Condo, townhouse, detached home, duplex, number of stories, etc.), and metadata, for example. 
     Data warehouse  130  may constitute a data storage resource (e.g., RAID, NAS, hard disc drive(s), solid state drive(s), a memory, RAM, Flash memory, etc.) that may be in data communication  101  (e.g., networked via wired and/or wireless communications links) with other devices, such as a computer resource  120 , computing device  150 , one or more computer server resources S 1 -Sn, denoted generally as computer server resources  140 . Computer server resources  140  may include one or more compute engines, computer servers, virtual machines, or some combination of the foregoing. Data communication  101  may constitute wired and/or wireless communications via one or more communication mediums. Computer server resources  140  may be networked with each other and may be networked with one or more external resources (e.g.,  120 ,  130 ,  150 ). Processing of data may be accomplished by computer resource  120 , by computer server resources  140  or by computer resource  120  and computer server resources  140 . When several computing resources are used (e.g., to parallelize processing tasks), each computing resources may be assigned to operate on a portion of a larger processing task. 
     The data representing the property listings  110  may derive from several sources, such as owners of property listings, agents acting on behalf of one or more owners of property listings, a vacation rental business, just to name a few. Sources that input at least a portion of the data representing the property listings  110  (e.g., using a menu and/or a GUI on a web site, smartphone/pad application, a laptop, or PC) may make “unique” entries for property listings  110  that are not duplicated within data warehouse  130 , may make “duplicate” entries of the same physical property listing  110  for which there may be two or more instances of the “duplicate” entries stored in data warehouse  130 , and may make entries that may constitute a “suspect” property listing  110  stored in data warehouse  130 . “Suspect” property listings  110  will be described in greater detail below. 
     Duplicate entries of property listings  110  in data warehouse  130  may occur for a variety of reasons. First, an owner may inadvertently post duplicate property listings for the same physical property, or may list a property listing and an agent list may list the same property listing. Second, an owner may choose to enlist several agents to list one or more properties of the owner and each agent may list the same property. 
     Due to differences and/or inaccuracies in the data that may be entered for a property listing  110 , the above described listing attributes  115  may not be identical in duplicated property listings. For example, for the same physical property, one property listing  110  may include a zip code and another duplicate property listing  110  for the same physical property may not include the zip code. Similarly, other data in the listing attributes  115  may be textually different, omitted, may include typographical errors, and the like. As one example, address data for one entry may use “Street” or “Avenue” and a duplicate listing may use the abbreviation “St” for street or “Ave” for avenue. As another example, two property listings  110  for the same physical property may use different abbreviations for apartment numbers, such as one listing using “Apt. 202” and the other listing using “Unit 202” or “#202”. 
     Accordingly, due to possible errors and/or inconsistencies in listing attributes  115  in potentially duplicate listings  110 , other data in the data representing the property listings  110  may be useful in identifying with a higher degree of certainty, whether or not data warehouse  130  includes two or more property listings  110  that are duplicates of each other, even though there may be differences in one or more of the listing attributes  115  for those duplicate listings. 
     Property owners and/or their agents may include one or more images  111  (e.g., scanned images, images from digital capture devices, images from smartphones, tablets, pads, etc.) of their property listings  110  along with other data, such as the listing attributes  115 , for example. Those images  111  may be uploaded or otherwise received by data warehouse  130  and stored for access by a computing device. Each image  111  may have an associated thumbprint value  112  that may be generated by a thumb printing algorithm that receives data representing the image  111  as an input, processes the image data according to the thumb printing algorithm, and generates as an output the thumbprint value  112  for the image  111 . Images  111  may include interior and/or exterior images of the property listing, for example. 
     Data warehouse  130  may include a large number of property listings  110 . A search to compare each property listing  110  with every other property listing  110  may not be practicable due to the computer resources and/or compute time necessary to do an exhaustive search. The number of property listings  110  to be searched may be reduced to a portion of the property listings  110  in data warehouse  130  by searching data warehouse  130  for property listings  110  that include data (e.g.,  111 - 115 ) that match one or more search parameters. Results from the search may be further searched to determine if there is a similarity between images  111  in one or more property listings  110  by comparing thumbprint values  112  for images  111  in a property listing  110  with the thumbprint values  112  for images  111  in another property listing  110 . Property listings  110  having one or more matching images  111  (e.g., as determined by thumbprint values  112 ) and having one or more listing attributes  115  that match may be potentially duplicated property listings  110 . Removing, consolidating, or merging data in duplicated property listings  110  may increase data storage space in data warehouse  130 , may reduce the number of property listings  110  that need to be searched in future searches, or may reduce data latency in queries made to data warehouse  130  (e.g., by computer resource  120 ). 
     In  FIG. 1B , computer resource  120  may receive data representing attribute selection criteria  151  (e.g., data  151  received  152  from computing device  150 ). Attribute selection criteria  151  may include one or more attribute search keys (denoted as a 1 -aN) that may describe data representing one or more listing attributes  115  to be searched for in property listings  110  in data warehouse  130  using one or more of the attribute search keys a 1 -aN as search keys. 
     Data warehouse  130  may be searched  121  by the computer resource  120  with the computer resource  120  using the attribute search key(s) a 1 -aN to identify property listings  110  that include listing attributes  115  that match the attribute search key(s) a 1 -aN. Property listings  110  that match the attribute search key(s) a 1 -aN may be received  123  by the computing resource  120 . As one example, the attribute search key a 1  may be a single search key for a zip code (e.g., for Zip Code 95160). Each property listing  110  in data warehouse  130  that includes among its listing attributes  115  the zip code 95160 may be returned as a matching property listing  110  to computer resource  120 . 
     To further refine the search for duplicate listings  110 , computer resource  120  may generate data representing a list  125  of property listings  110  that matched the attribute search key. The list  125  may include image identifiers  113  for each image  111  in the property listings  110  having listing attributes  115  (e.g., for Zip Code 95160) that matched the attribute search key a 1 . The image identifiers  113  are denoted as ID- 1 -ID-N. Each image identifier  113  in the list  125  is associated with the thumbprint value  112  of its associated image  111 . For example, if a property listing  110  includes N images  111 , then for each image  111  the property listing  110  will include N thumbprint values  112 , N image ID&#39;s  113 , and N listing ID&#39;s  114 . List  125  may constitute a single file or multiple files (e.g., two files), for example. In a single file, data associated with each image  111  (e.g.,  113 ) may be compared with the data associated with others images  111  in the file, for example. On the other hand, if list  125  constitutes two files, then data associated with each image  111  (e.g.,  113 ) in the first file may be compared with data associated with each the image  111  in the second file, for example. As one example, if the first file has 125 entries for 125 property listings  110  to be compared and the second file has 187 entries for 187 property listings  110  to be compared, then each entry in the second file is compared with all 125 entries in the first file. The number of entries in the first and second files may not be equal, so the number of cross-comparisons between the two files will be determined by the file having the highest number of entries. Each entry may include the listing attributes  115  associated with the property listing  110 . 
     Optionally, list  125  may include data representing a thumb printing algorithm TA  126 . Thumb printing algorithm TA  126  may be used to generate thumbprint values  112  for each image  111  to be compared, and the thumbprint values  112  generated, may be used instead of or in addition to the thumbprint values  112  that are included with each property listing  110 . Thumb printing algorithm TA  126  may be different than the thumb printing algorithm used to generate the thumbprint values  112  that are included with each property listing  110 . Thumb printing algorithm TA  126  may be applied to images  111  on a per image basis. Different thumb printing algorithms TA  126  may be applied on a per image basis to different images  111  to be compared. Thumbprint values  112  being compared need not be generated by the same thumb printing algorithm. For example, an image  111  having an associated  112  may be compared with a thumbprint value generated by TA  126 . 
     The data representing the list  125  may be communicated  124  to the plurality of computer server resources  140 . Each computer server resource (e.g., S 1 , S 2 , S 3 , . . . , Sn) may process the data representing the list  125  to generate a sub-set La-Ln of the data representing the list  125 . Each computer server resource may select from its respective sub-set, a first set of image identifier  113 ′ for a first property listing  110  and a second set of image identifiers  113 ″ for a second property listing  110 . Each computer server resource may search  147  the data warehouse  130  using the first set of image identifiers  113 ′ and the second set of image identifiers  113 ″ as search keys to retrieve a first set of thumbprint values  112 ′ associated with images  111  identified by the first set of image identifiers  113 ′ (e.g., from property listing  110 ′) and a second set of thumbprint values  112 ″ associated with images identified by the second set of image identifiers  113 ″ (e.g., from property listing  110 ″). Image identifiers  113  within property listings  110  and among different property listings  11 O are unique (e.g., a unique numeric identifier), such that the first and second set of image identifiers ( 113 ′,  113 ″) being searched for by computer server resource  51  are different than the first and second set of image identifiers ( 113 ′,  113 ″) being searched for by computer server resource  53 , for example. 
     The search  147  by each of the computer server resources  140  may return thumbprint values  112  associated with the images  111  associated with the first and second set of image identifiers ( 113 ′,  113 ″). For example, the first set of image identifiers  113 ′ may return the first set of thumbprint values  112 ′ and the second set of image identifier  113 ″ may return the second set of thumbprint value  112 ″. Each computer server resource may compare its respective first and second sets of thumbprint values ( 112 ′,  112 ″) with each other to determine if the first and second sets of thumbprint values ( 112 ′,  112 ″) match each other. For example, if a perceptual thumb printing function (e.g., a p-hash function) is used to generate the thumbprint values  112 , then a comparison algorithm for perceptual thumbprint values may be used to compare the first and second sets of thumbprint values ( 112 ′,  112 ″) with each other. A match between thumbprint values  112  in the first and second sets of thumbprint values ( 112 ′,  112 ″) need not be an exact match. As one example, the first and second sets of thumbprint values may be approximately equivalent to each other or may be substantially similar to each other. For example, two images  111  of the same physical property listing  110  may differ in how the images were cropped, image file format (e.g., JPEG, GIF, PNG, BMP, BPG, TIFF, Raw, etc.) image resolution, image data size (e.g., in MB), differences in image metadata, image color space, image form factor (e.g., portrait or landscape), image scaling, image borders, differences in lighting on the property listing  110  at the time the image  111  was captured, differences in surrounding imagery, such as weather, flora and fauna, just to name a few. Each computer server resource may increment the image count  137  for each match between thumbprint values ( 112 ′,  112 ″) that is determined. 
     Subsequent to matches between thumbprint values  112  in the first and second sets of thumbprint values ( 112 ′,  112 ″) being determined, each computer server resource may communicate  149  data representing match results  148  (e.g., results  141 - 145  from servers S 1 -Sn) to the computer resource  120 . The data representing the match results  141 - 145  may include the image identifiers ( 113 ′,  113 ″) for the respective matching thumbprint values ( 112 ′,  112 ″). Optionally, the data representing the match results  141 - 145  may include data representing an algorithm that was used by the computer server resources (e.g., S 1 , S 2 , S 3 , . . . , Sn) to compare the first and second thumbprint values ( 112 ′,  112 ″) to each other. For example, comparing the first and second thumbprint values ( 112 ′,  112 ″) to each other may be computed using different comparison algorithms which may produce different comparison results. Including the data representing the algorithm used for the comparison in the match results  141 - 145  may be used to log which algorithm was used for future use and/or analysis of match results  141 - 145  by computer resource  120 . For example, the comparison of the first and second thumbprint values ( 112 ′,  112 ″) may be an iterative process. A different comparison algorithm may be used for different iterations of the comparison. Computer resource  120  may analyze the match results  141 - 145  to determine which comparison algorithm produces more matches, fewer matches, or the same number of matches, for example. Comparison algorithms may be selected based on one or more factors, such as which algorithm produces the fewest false positive identifications of duplicate property listings  110 , the algorithm that produces the fastest run time, or the algorithm that requires less memory space to run, for example. 
     Optionally, the data representing the match results  141 - 145  may include data representing a correlation value computed by the algorithm that was used by the computer server resources (e.g., S 1 , S 2 , S 3 , . . . , Sn) to compare the thumbprint values  112  to each other. The correlation value may be a number between 0 and 1. A correlation value of 0 may constitute an indication of no similarity between the images  111  associated with the thumbprint values  112  that were compared. A correlation value of 1 may constitute an indication of similarity between the images  111  associated with the thumbprint values  112  that were compared. The correlation value may be generated for each image  111  having its associated thumbprint value  112  was compared with the thumbprint value  112  of another image  111 . 
     Computer resource  120  may compare the listing attributes  115  in the first property listing  110 ′ with the listing attributes  115  in the second property listing  110 ″ to determine how many of the listing attributes  115  being compared match each other. Each listing attribute  115  match that is determined may increment an attribute count. 
     Computer resource  120  may parse or otherwise inspect the data representing the match results  141 - 145  to generate data representing a report  160 . Report  160  may include for each matching result ( 141 - 145 ), an entry that includes the listing identifier  114 , the image identifier  113  for each image  111 , the image count  137  of images  111  that matched, and the attribute count  139  of listing attributes  115  that matched. For example, a zip code listing attribute  115  in the first property listing  110  matching the zip code listing attribute  115  in the second property listing  110  may increment the attribute count by 1. Report  160  may include data represented as comma-separated values or column-separated values (e.g., a CSV format using a character, such as a “,” or “;” to delineate data fields). 
     Computer resource  120  may compute using the data representing the report  160 , a weighted score for each entry in report  160 . The weighted score may constitute a sum of the image count  137  multiplied by a first weighting factor and the attribute count  139  multiplied by a second weighting factor. The weighting factors for the image count  137  and attribute count  139  may not be the same. For example, if greater confidence in matching results is derived from images  111  that match between compared property listings  110  (e.g., based on matches between thumbprint values  112 ), then the image weighting factor may be set higher 60% (e.g., 0.6) and the attribute weighting factor may be set lower at 40% (e.g., 0.4). Alternatively, if more weight is placed on listing attributes  115  that match, then the attribute weighting factor may be set higher at 70% (e.g., 0.7) and the image weighting factor may be set lower at 30% (e.g., 0.3), for example. 
     Computer resource  120  may analyze the data representing the report  160  to identify potentially matching property listings  110  based on the weighted score of each entry in report  160 . As one example, computer resource  120  may generate, based on the analysis of report  160 , another report  170  of potentially duplicated property listings  110  where a correlation value of duplicate listings  110  may be indicated by a ranking of the weighted scores from highest to lowest, with property listings  110  having higher weighted scores being more likely to be duplicate listings than property listings  110  having lower weighted scores, for example. Computer resource  120  may sort weighted scores in a sort order from highest weighted scores to lowest weighted scores, or vice-versa, for example. 
       FIG. 2  depicts example of a list  125  that may constitute a single file or multiple files. In example  200 , a single file  225  may constitute the list  125 . File  225  may include entries (e.g., ID- 1 -ID-N) for each listing  123  that matched the list search  121  of data warehouse  130  as described above in reference to  FIG. 1B . Each entry in file  225  is compared  203  with all other entries in file  225 . For example, comparison may begin with the first entry ID- 1  being compared with all other entries (e.g., in comparison order denoted by arrow  201 ), followed by entry ID- 2  being compared with all other entries that have not already been compared with  10 - 2 . For example, since 10-1 was already compared with  10 - 2 , a redundant comparison of  10 - 2  with  10 - 1  may be skipped. 
     In example  250 , two files,  255  and  265  may constitute the list  125 . Entries in each file may not be equal to each other. For example, file  265  may have more entries than file  255 . Entries within the same file  255  or  265  are not compared with each other. Entries between the two files  255  and  266  are compared with each other. For example, entry ID- 1  in file  255  may be compared against each entry ID 1 -ID-N in file  265 , followed by entry ID- 2  in file  255  being compared with each ID 1 -ID-N in file  265  and so forth with each entry in file  255 . In example  250 , if file  265  has more entries than file  255 , then each entry in the larger file  265  may be compared with each entry in the smaller file  255 , for example. Entries in files  255  and/or  265  may be compared in the order denoted by dashed lines  251  and  261 , for example. 
     Referring back to example  200 , attribute selection criteria  151  received  152  by computer resource  120  may include one or more attribute search keys (one is shown), such as attribute search key a 1  for property listings  110  having listing attributes  115  that match zip code 95160, for example. Data warehouse  130  may have several property listings  110  having listing attributes  115  that match (e.g.,  115 =95160) the attribute search key a 1 =95160. Data warehouse  130  may have several property listings  110  having listing attributes  115  that do not match (e.g.,  115 ≠95160) the attribute search key a 1 =95160. Other property listings  110  may not have listing attributes  115  for a zip code and those listings  110  would not be included in the search results. Search  121  by computer resource  120  may return results  123  for all property listings  110  in data warehouse  130  having listing attributes  115  that match (e.g.,  115 =95160) the attribute search key a 1 =95160 as denoted by dashed lines  207 . File  225  may have each entry populated with the data for the attribute search key a 1  and listing attributes  115  as denoted by dashed lines  205  and  207 . 
       FIG. 3  depicts an example of two files generated from two attribute search keys. In  FIG. 3 , attribute selection criteria  151  received  152  by computer resource  120  may include one or more attribute search keys, such as attribute search keys such as a 1 =Brand-A and a 2 =Brand-B. In some examples, brands A and B may be two different brand names owned by a common corporate entity, such as a vacation rental enterprise. In other examples, Brands A and B may be two different brand names owned by two separate corporate entities. In yet other examples, brand A may be a brand associated with corporate entity and brand B may be a brand associated with a target corporate entity that Brand A has already acquired or may acquire as part of a merger and/or acquisition. 
     In the example where brands A and B may be two different brand names owned by a common corporate entity, the corporate entity may desire to know how many property listings  110  are duplicated between the two brands A and B. For example if Brand-A has 10,000 property listings  110 , Brand-B has 15,000 property listings  110 , and 1,000 of the property listings  110  constitute duplicate listings, then the corporate entity actually has 24,000 unique property listings (A+B−Duplicates=10,000+15,000−1,000) and not 25,000 (A+B=10,000+25,000). 
     In the example where brands A and B may be two different brand names owned by two separate corporate entities, another corporate entity may be consider acquiring brands A and B and therefore may wish to know how many of the combined property listings of brands A and B are unique listings and how many are duplicate listings. The number of unique listings may factor into a financial valuation of the acquisition as part of a due diligence exercise. 
     In the example where Brand A is considering acquiring Brand B, the same due diligence exercise may apply and overlap of property listing inventory between brands A and B may be determined to arrive at the number of unique listings  110  that are listed with Brand B by factoring out duplicate listings that are listed with both Brand A and Brand B. For example, if Brand A has 9,000 listings, Brand B has 4,000 listings, and there are 500 duplicate listings, then the actual inventory of unique listings that Brand A may acquire is 4,000-500=3,500 unique listings. 
     Returning to  FIG. 3 , attribute search keys a 1 =Brand-A and a 2 =Brand-B may be used by computer resource  120  to search  121  data warehouse  130  for all property listings having attribute data  115  that match Brand-A and populate file  255  with data from matching listings. Similarly, computer resource  120  may search  121  data warehouse  130  for all property listings having attribute data  115  that match Brand-b and populate file  265  with data from matching listings. Data warehouse  130  may have listings  110  that don&#39;t match Brand-A or Brand-B, and may have other listings  110  that lack a listing attribute  115  for a brand. Those listings will not match the search keys (a 1 , a 2 ) and will not be returned  123  as results from search  121  by computer resource  120 . 
       FIG. 4  depicts an example of determination of a sub-set of a list to be processed by multiple computing devices. In example  400  a sub-set determinater  450  may receive as input data a row number  453  from a list (e.g., list  125 ) and a server ID number  451 . A modulus calculator  452  may compute a modulus of the row number  453  divided by the server ID number  451  and output data representing a row  457  in the list to be processed by a server. Each computer server resource (e.g., S 1 -S 12 ) may communicate its server ID number  451  to the sub-set determinator  450  and increment the row number  453  from lowest row number to highest row number. Each computed modulus that equals the server ID number  451  is the row in the list to be processed by that server resource. Sub-set determinator  450  may be implemented internal to each server resource or external to each server resource (e.g., as hardware, software or both). 
     In  FIG. 4  each computer server resource S 1 -S 12  (e.g., a virtual machine and/or server) may be assigned a server ID number 1-12, for example. Files  455  and  465  may be assigned row numbers for each file entry such that file  455  may have rows 1-12 and file  465  may have rows 1-20 (e.g., files  455  and  465  may have different numbers of entries). Each computer serve resource S 1 -S 12  may compute the row in the list  125  by determining the row number in the list (e.g., a row number in file  455  and/or  465 ), dividing the row number by its assigned server number and taking the modulus (MOD) of the quotient. If the MOD is equal to the server ID number, then that server processes that row in the single file or that row in both files ( 455 ,  465 ). In a single file  425 , each row entry is compared with all other row entries. In two files ( 455 ,  465 ), row entries in one file (e.g.,  465 ) are compared with all of the row entries in the other file (e.g.,  455 ). 
       FIG. 5  depicts examples of reports. In example  500 , match results  148  communicated  149  to computer resource  120  by the plurality of server resources  140  (e.g., match results  141 - 145  from servers  51 -Sn) may include entries for each property listing  110 ′ and  110 ″ that were compared to each other. Each entry (e.g., along the row direction) may include the image identifiers  113 ′ and  113 ″, the listing attributes  115 ′ and  115 ″ and an image count  137 ′ and  137 ″ for listings  110 ′ and  110 ″ respectively. Each image identifier  113 ′ and  113 ″ are associated with the images  111 ′ and  111 ″ that were compared via their respective thumbprint values  112 ′ and  112 ″ to determine if the thumbprint values  112 ′ and  112 ″ matched each other. Each match incremented an image match counter that outputs data representing the image counts  137 ′ and  137 ″. Match results  148  may include more or less data than depicted in example  500 . For example, match results  148  may include other data, such as the attribute count  139 , the algorithm used to compare thumbprint values  112 , the thumb printing function used to generate thumbprint values  112 , just to name a few. 
     In example  520 , the computer resource  120  and/or the plurality of server resources may compute an attribute count  514 . Listing attributes  115 ′ and  115 ″ for each listing being compared (e.g., property listing  110 ′ and  110 ″) may be inputs to a listing attribute comparator  510  configured to compare data representing listing attribute  115 ′ with data representing listing attribute  115 ″ and to determine whether the listing attributes  115 ′ and  115 ″ match each other. If the listing attributes  115 ′ and  115 ″ are determined to match each other, then listing attribute comparator  510  outputs data representing a match  513 . An attribute match counter  512  may increment data representing an attribute count  514  for each match determined by the listing attribute comparator  510 . 
     For example, if listing attributes  115 ′ and  115 ″ are Zip Codes 95160 and 95160-2744 respectively, the listing attribute comparator  510  may determine that the Zip Codes match based on the first five digits matching and may be configured (e.g., via an algorithm) to ignore all characters after the hyphen sign “-”, such that ZIP+4 Zip Codes may be compared to 5-Digit Zip Codes. Accordingly, the attribute count  514  would be incremented. 
     As another example, if listing attributes  115 ′ and  115 ″ are for Street Address 777 Birch Street and 777 Birch St, respectively, then a match may be determined by the listing attribute comparator  510  because the comparator  510  may be configured to recognize abbreviations in addresses, apartment names and numbers, states, countries, etc. Accordingly, the attribute count  514  would be incremented. 
     As yet another example, if listing attributes  115 ′ and  115 ″ are for geolocations of property listing  110 ′ and  110 ″ and listing attribute  115 ′ has data representing a geolocation of {“latitude”: 48.858844300000001, “longitude”:  2  0.2943506} and listing attribute  115 ″ has data representing a geolocation of {“latitude”: 49.858877300029004, “longitude”: 2.3943996}, then the listing attribute comparator  510  would not determine a match and the attribute count  514  would not be incremented. In some examples, a radius or distance from a longitude and latitude may be used to determine if listing attributes  115 ′ and  115 ″ for a geolocation match. For example, if listing attribute  115 ′ includes data representing a first geolocation in longitude and latitude and listing attribute  115 ′ includes data representing a second geolocation in longitude and latitude, then listing attributes  115 ′ and  115 ″ may be determined to match based on the second geolocation being within a predetermined radius of the first geolocation (e.g., within radius of 30 meters or less). 
     In example  540  a report  160  may be formatted as a number of entries (e.g., in table, file, array or other data structure). For example, report  160  may be formatted as a comma-separated values or column-separated values (CSV list) with the character semi-colon “;” character as a data field separator. List  160  may have N entries for listings  110  that were compared and determined to have one or more images  111  that matched. Each entry may include the listing ID&#39;s  114 , the image ID&#39;s  113 , image count  137 , and attribute count  139 . Optionally, each entry may include an image weighting factor  542  and an attribute weighting factor  544 . Weighting factors ( 542 ,  544 ) may be the same or may be different among the entries in report  160 . 
     In example  560 , data representing the report  160  may be analyzed by computer resource  120  to identify potentially matching property listings  110  based on the weighted score of each entry in report  160 . As one example, a score determinator  570  may receive as inputs, data representing the image count  137 , the image weighting factor  542 , the attribute count  514 , and the image weighting factor  542 . Score determinator  570  may compute the weighted score for an entry as: Weighted Score=Image Count×Image Weighting Factor+Attribute Count×Attribute Weighting Factor, for example. 
       FIG. 6  depicts an example  600  of sorted weighted scores. In  FIG. 6 , computer resource  120  may sort data representing the weighted scores  575  into a sort order from lowest weighted score (e.g., a low correlation value of 0 that listings  110  are duplicates) to highest weighted score (e.g., a high correlation value of 1 that property listings  110  are duplicated). The computer resource may analyze data associated with each weighted score to determine if the score is indicative of property listings  110  with higher weighted scores being duplicate property listings  110 . As one example, for correlation values in a range from 0 to 1, a value of 0.7 or higher may be indicative of duplicate property listings  110 . In other examples, a value of 0.8 or higher for the correlation value may be indicative of duplicate property listings  110 . Actual values for the correlation value may be application dependent and are not limited to the examples described and/or depicted herein. A predetermined value for the correlation value may be set (e.g., &gt;0.5 or ≥0.66) and a correlation value that meets and/or exceeds the predetermined value is indicative of duplicate listings  110 , for example. A control signal may be generated in response to the correlation value having the predetermined value and/or being within a predetermined range. The control signal (e.g., a digital logic signal or data) may be generated by computer resource  120 , computing device  150 , or computer server resources  140 , for example. 
     Computer resource may extract  611  and  613  data, such as image identifiers from the entries in report  170  and re-run the thumbprint value comparison process using different thumb printing functions TA  625  (e.g., different thumb printing functions) to yield different thumbprint values for comparison in a compute engine  620  (e.g., the plurality of server resources). The process may be iterative with different thumb printing functions TA  625  being received  624  by compute engine  620 . The resulting weighted scores from re-running the thumbprint value comparison process may be compared to the weighted scores in report  170  to determine if confidence in listings  110  being duplicate listings or not being duplicate listings may be justified. For example, if re-running the thumbprint value comparison process results in high weighted scores for entries  1  and  2 , then confidence in those listings being duplicate listings may be justified. 
       FIG. 7  depicts an example of arbitrary file input. In example  700 , computer resource  120  may receive  752  data representing arbitrary file input  751  that includes data representing specific data in specific listings  710  to be searched for  721  in data warehouse  130 . Matching results from search  721  may be received  723  by computer resource  120  and may be processed by computer resource  120  and/or the plurality of server resources  140  as was described above in reference to  FIGS. 1A-6 , for example. Data  710  may include  755  data representing specific property listings  110  and/or other data included in the specific property listing  110 , such as data representing images  111 , thumbprint values  112 , image identifiers  113 , listing identifiers  114  and listing attributes  115 , for example. 
     Data  710  may include  757  or have pointer, links, or the like to other data including but not limited to one or more thumb printing algorithms (TA 1 , TA 2 ) to be applied to images  111  to generate thumbprint values  112 , an algorithm CMP for comparing thumbprint values  112  to each other, image weighting factors IWF, and attribute weighting factors AWF, for example. Thumb printing algorithms (TA 1 , TA 2 ) may constitute hashing algorithms (e.g., a p-hash algorithm) and generated thumbprint values  112  may constitute hash values, for example. 
     As one example, in reference to  FIG. 6 , if entries in report  170  require further processing to double check or refine their weighted scores, compute engine  150  may generate data representing the arbitrary file input  751  and data in file  751  may select specific listings  110  (denoted in dashed line in data warehouse  130 ) and their associated data for further processing, such as re-thumb printing the images  111  using TA 1  and/or TA 2  to generate new thumbprint values  112  for each image  111 . File  751  may different weighting factors IWF and/or AWF to be used in a re-scoring of the results for the selected listings  110 . The re-scoring may be used to verify confidence or lack of confidence in the previous weighted scores, that is, if the weighted scores were high (e.g., in a range from about 0.8 to about 1) and the re-scoring results in weighted scores within that range, then confidence in the listings being duplicates may be justified, for example. On the other hand, if the re-scoring results in weighted scores outside that range (e.g., below about 0.6), then confidence in the listings being duplicates may not be justified, for example. 
       FIG. 8  depicts one example  800  of vetting data representing a newly received property listing. In the example  800  of  FIG. 8  an owner  813  and/or an agent  811  for an owner may list a rental property  801  with a vacation rental management entity  850 . A computing device  820  may be used to enter information  804  related to the rental property  801  using an application APP presented via a GUI on a display  821  of computing device  820 . Images (I 1 , I 2 ) of the rental property  801  may be captured (e.g., by device  820 ) and may be included for use in promoting rental of the rental property  801  by the vacation rental management entity  850  (e.g., on a website run by entity  850 ). There may be more or fewer images than depicted as denoted by  805 . 
     The APP on computing device  820  may package the data representing images ( 11 ,  12 ) and the data  804  describing the rental property  801  as data  830  that is received  831  by computing resource  120 . Prior to creating an instance of a new property listing  110  in data warehouse  130 , computing resource  120  may generate  832  data representing a temporary property listing  810 . The temporary property listing  810  may include data similar to property listings  110 , such as data for images  811 , thumbprint values  812  of the images  811 , image identifiers  813  for each image  811 , listing identifiers  814 , and listing attributes  815 . Listing attributes  815  may be generated by computing resource  120  parsing data  804  to extract information associated with rental property  801 . Computer resource  120  may apply a thumb printing function TF on image data  811  to generate thumbprint values  812 . The thumb printing function TF may be the same or may be different than a thumb printing function used to generate thumbprint values  112  in property listings  110  in data warehouse  130 . 
     Computer resource  120  may receive  152  or otherwise extract from  815  an attribute search key to search  121  data warehouse  130  for listings  110  having images  111  having thumbprint values  112  that may match thumbprint values  812 , as has been described above in reference to  FIGS. 1A-7 . If matching images are determined, based on comparing thumbprint values  812  with thumbprint values  112 , then temporary property listing  810  may be a duplicate listing and the data representing the temporary property listing  810  may not be added to the data warehouse  130  as a new listing, such that the data representing the temporary property listing  810  may be barred from storage in the data warehouse  130 . On the other hand, if matching images are not determined, then the data representing the temporary property listing  810  may be added as a new “unique” property listing  110  (denoted in dashed line) in data warehouse  130 . 
     In example  800 , vacation rental management entity  850  may operate several brand names A, B, X, or other brands as denoted by  855 . Owner  813  and/or agent  811  may have previously listed rental property  801  with one or more of those brands and may be attempting to list the same property with a different brand operated by vacation rental management entity  850 . The vetting process of checking listing attribute data and image data with existing listing may be used prevent duplication of listings, to reduce data storage capacity requirements for data warehouse  130  by eliminating or reducing wasted data storage space that would otherwise be caused by duplicated listings. Searches of data warehouse  130  by computer resource  130  and/or computer server resources  140  may be faster due to fewer listing to search when the population of listings  110  does not include duplicated listings. 
       FIG. 9  depicts another example  900  of vetting data representing a newly received property listing. In  FIG. 9 , a user  923  may attempt to post images (I 1 , I 2 ) of another rental property  901  with a listing for property  903 , thereby misrepresenting the appearance (e.g., actual image of the property  13 ) of the actual rental listing  903 . In example  900 , information  905  associated with the misrepresented images (I 1 , I 2 ) is different than the information  904  associated with the actual rental listing  903 . For example, ZIP Code, Address, number of bed rooms, number of bath rooms, number of sleeps and geolocation data may be different between rental listings  901  and  903 . 
     Information  930  that includes the listing attributes  904  and the misrepresented images (I 1 , I 2 ) may be received  931  by computer resource  120  and be processed in the manner described above for  FIG. 8 . In  FIG. 9 , a comparison of the thumbprint values  912  with thumbprint values  112  from data warehouse  130  may indicate a match due to misrepresented images (I 1 , I 2 ) having been copied from listings  110  that are already in data warehouse  130 . However, listing attributes  115  associated with the images  111  for thumbprint values  112  may not match the listing attributes  915  for rental  903  due to the differences noted above. Accordingly, the image count  137  for thumbprint values  112  and  912  may be 2 for both of the images (I 1 , I 2 ), but the attribute count  139  may be 0 due to differences between the source data  905  and  904 . 
     Computer resource  120  may detect and report that the attempted listing of rental  903  is not legitimate and may flag its data as “suspect” and prevent a legitimate listing  110  from being stored in data warehouse  130 . Furthermore, computer resource  130  may flag the data for rental  903  as potentially fraudulent and generate data representing the fraudulent information. 
     As one example, user  923  may have failed to pay listing fees or failed to comply with some other contractual obligation to vacation rental management entity  850 . Consequently, access credentials, membership or the like of user  923  may have been revoked. However, the user  923  may use another user name, email address and password to establish a new account with vacation rental management entity  850 . User  923  may attempt to relist rental unit  903  with vacation rental management entity  850  using the copied images (I 1 , I 2 ); however, the root information related to rental  903  may still be the same and the inability to change the physical address and other attributes may allow for the detection of the fraud/suspect listing using computer resource  120 , computer server resources  140  and data warehouse  130 . 
       FIG. 10  depicts one example of a computer system.  FIG. 10  illustrates an exemplary computer system  1000  suitable for use in one or more systems, devices, compute engines, apparatus, traveler devices, owner devices, wireless devices, wireless systems, backend systems, front end systems, networked systems, data stores, data warehouses, vacation rental management systems, platforms, data storage devices, data storage systems, external resources, host devices or others described in reference to  FIGS. 1A-9 . In some examples, computer system  1000  may be used to implement computer programs, algorithms, an application (APP), an application programming interface (API), configurations, methods, processes, or other software to perform the above-described techniques. Computer system  1000  may include circuitry, hardware, and other electronic systems to perform the above-described techniques. Computer system  1000  may include a bus  1002  or other communication mechanism for communicating information, which interconnects subsystems and devices, such as one or more processors  1004  (e.g., μC, μP, DSP, ASIC, FPGA, Baseband, etc.), system memory  1006  (e.g., RAM, SRAM, DRAM, Flash), storage device  1008  (e.g., Flash, ROM), disk drive  1010  (e.g., magnetic, optical, solid state), communication interface  1012  (e.g., modem, Ethernet, WiFi, Cellular), display  1014  (e.g., CRT, LCD, LED, OLEO, touch screen), input device  1016  (e.g., keyboard, stylus, touch screen, mouse, track pad), and cursor control  1018  (e.g., mouse, trackball, stylus). Some of the elements depicted in computer system  1000  may be optional, such as elements  1014 - 1018 , and one or more clocks  1040  which may provide temporal data, for example, one or more sensors  1030  which may provide location data, rate of motion data and other data associated with movement, and computer system  1000  need not include all of the elements depicted. Display  1014  may present a user interface (UI), such as a graphical user interface (GUI)  1014   a . Memory  1006  may include computer executable programs and/or data embodied in a non-transitory computer readable medium, such as an operating system (OS)  1006   a , an application (APP)  1006   b , and executable code (Ex-Code)  1006   c , for example. APP  1006   b  may be an application that may be downloaded or otherwise installed from a location such as an application store (e.g., Google Play® or the App Store®), for example. 
     According to some examples, computer system  1000  performs specific operations by one or more processors  1004  executing one or more sequences of one or more instructions stored in system memory  1006 . Such instructions may be read into system memory  1006  from another non-transitory computer readable medium, such as storage device  1008  or disk drive  1010  (e.g., a HOD or SSD). In some examples, circuitry may be used in place of or in combination with software instructions for implementation. The term “non-transitory computer readable medium” refers to any tangible medium that participates in providing instructions and/or data to processor(s)  1004  for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical, magnetic, or solid state disks, such as disk drive  1010 . Volatile media includes dynamic memory, such as system memory  1006 . Common forms of non-transitory computer readable media includes, for example, floppy disk, flexible disk, hard disk, SSD, magnetic tape, any other magnetic medium, CD-ROM, DVD-ROM, Blu-Ray ROM, USB thumb drive, SD Card, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer may read. 
     Sensor(s)  1030  may include but are not limited to one or more inertial sensors (e.g., an accelerometer, a multi-axis accelerometer, a gyroscope, a magnetometer, etc.), an altimeter, and a barometer, for example. One or more sensors in sensor(s)  1030  may be used to determine location data for a device that includes computer system  1000  and/or is in communication with computer system  1000  (e.g., a client device, a smartphone, a tablet, a pad, a laptop, PC, a wireless device, a portal computing device, a computing device, a networked computing device, a platform, a backend service, etc.). One or more of the memory  1006 , storage device  1008 , or disk drive  1010  may be accessed as a data store for location data from sensor(s)  1030  or other systems in communication (e.g., via communications interface  1012 ) the computer system  1000 . Location data may be communicated to/from the computer system  1000  via one or more of the wireless transceivers  1013 . 
     For example, radio frequency signal sources including but not limited to GPS satellite signals (e.g., signals from one or more GPS satellites), terrestrial location transmitters (e.g., one or more cellular towers), WiFi signals, WiMAX signals, WiFi routers, WiFi access points, Bluetooth signals (e.g., Bluetooth beacons), near field communication signals, iBeacons, data from external resource, and platform. Other signal and/or data sources for location data may include but are not limited to audio signals (e.g., ultrasonic signals) and signals and/or data generated by location tracking software (e.g., internal to and/or external to computer system  1000 ), for example. In some examples, location data and/or signals may be communicated wireless communications link and/or a wired communications link. Location data accessed by computer system  1000  may include but is not limited to a location history data, for example. The location data may be updated, revised or otherwise change on a dynamic basis as a guest device moves around in area. 
     Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media may include coaxial cables, copper wire, and fiber optics, including wires that comprise bus  1002  for transmitting a computer data signal or other signals (e.g., from hardware or circuitry). In some examples, execution of the sequences of instructions may be performed by a single computer system  1000 . According to some examples, two or more computer systems  1000  coupled by communication link  1020  (e.g., LAN, Ethernet, PSTN, USB, or wireless network) may perform the sequence of instructions in coordination with one another. Computer system  1000  may transmit and receive messages, data, and instructions, including programs, (i.e., application code), through communication link  1020  and communication interface  1012 . Received program code may be executed by processor  1004  as it is received, and/or stored in disk drive  1010 , or other non-volatile storage for later execution. Computer system  1000  may optionally include a wireless transceiver  1013  coupled with the communication interface  1012  and coupled  1015  with an antenna  1017  for receiving and generating RF signals (e.g.,  101 ), such as from a WiFi network, WiMAX network, BT radio, Cellular network, networked computing resources, external wireless resources, client devices, owner devices, near field communication (NFC), satellite network, data storage network, or other wireless network and/or wireless devices, for example. Communications interface  1012  may be coupled  1022  with data storage external to computer system  1000 . Communications interface  1012  may be coupled with external resources and/or systems, such as those depicted in  FIGS. 1A-9 , for example. Computer system  1000  may be used to implement a networked computing device (e.g.,  120 ,  140 ,  130 ), for example. 
     In some examples, the described techniques may be implemented as a computer program or application (“application”) or as a plug-in, module, or sub-component of another application. The described techniques may be implemented as software, hardware, firmware, circuitry, or a combination thereof. If implemented as software, the described techniques may be implemented using various types of programming, development, scripting, or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques, including ASP, ASP.net, .Net framework, Ruby, Ruby on Rails, C, Objective C, C++, C #, Adobe® Integrated Runtime™ (Adobe® AIR™), ActionScript™, Flex™, Lingo™, Java™ Javascript™, Ajax, Perl, COBOL, Fortran, ADA, XML, MXML, HTML, DHTML, XHTML, HTTP, XMPP, PHP, and others. Design, publishing, and other types of applications such as Dreamweaver®, Shockwave®, Flash®, Drupal and Fireworks® may also be used to implement the described techniques. Database management systems (i.e., “DBMS”), search facilities and platforms, web crawlers (i.e., computer programs that automatically or semi-automatically visit, index, archive or copy content from, various websites (hereafter referred to as “crawlers”)), and other features may be implemented using various types of proprietary or open source technologies, including MySQL, Oracle (from Oracle of Redwood Shores, Calif.), Solr and Nutch from The Apache Software Foundation of Forest Hill, Md., among others and without limitation. The described techniques may be varied and are not limited to the examples or descriptions provided. 
     The structures and/or functions of any of the above-described features may be implemented in software, hardware, firmware, circuitry, or any combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated or combined with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, at least some of the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. These can be varied and are not limited to the examples or descriptions provided. 
     As hardware and/or firmware, the above-described structures and techniques can be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), multi-chip modules, or any other type of integrated circuit. 
     According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof (i.e., a module can be implemented as a circuit). In some embodiments, algorithms and/or the memory in which the algorithms are stored are “components” of a circuit. Thus, the term “circuit” can also refer, for example, to a system of components, including algorithms. These can be varied and are not limited to the examples or descriptions provided. 
     Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described conceptual techniques are not limited to the details provided. There are many alternative ways of implementing the above-described conceptual techniques. The disclosed examples are illustrative and not restrictive.