Real estate asset identifier

Abstract

Methods and systems to enable the generation of a unique identifier for a real estate asset are described. The unique identifier (ID) may provide a unique and normalized identifier for a real estate asset. The methods and systems may enable the identifier to be interpreted by query engines to efficiently search for specific real estate assets or analyze changes in a real estate asset over time by comparing changes in the identifier. The systems and methods may be embodied as a system, method, or computer program product embodied in any tangible medium of expression having computer useable program code embodied in the medium.

Description

[0001] REAL ESTATE ASSET IDENTIFIER

[0002] CROSS-REFERENCE TO RELATED APPLICATION

[0003] This application claims the benefit of U.S. Provisional Application No. 63 / 530,672, filed August 3, 2023, the entire contents of which is incorporated herein by reference.

[0004] FIELD OF THE INVENTION

[0005] This disclosure generally relates to methods and systems for identifying an asset, and more specifically, methods and systems for identifying real estate assets.

[0006] BACKGROUND

[0007] Real estate and property assets are important for the world economy, with real estate value accounting for trillions of dollars across various countries. Data regarding real estate assets is necessary for decisions in real estate transactions, management, and development. Real estate data is conventionally stored in online databases, websites, government records, cloud-based platforms, and Application Programming Interfaces.

[0008] It is typically necessary' to obtain real estate data from multiple data sources. Each data source may identify a property using a simple identifier or by description; however, data sources do not use a standard or unique identifier for each real estate asset, causing confusion, inefficient searching, and a lack of consistency.

[0009] SUMMARY

[0010] The presently disclosed methods and systems of the present disclosure enable the generation of a unique identifier for a real estate asset. The unique identifier (ID) of the present disclosure provides a unique and normalized identifier for a real estate asset.

[0011] The presently disclosed methods and systems of the present disclosure enable the identifier of the present disclosure to be interpreted by query7engines to efficiently search for specific real estate assets or analyze changes in a real estate asset over time by comparing changes in the identifier.

[0012] The presently disclosed systems and methods may be embodied as a system, method, or computer program product embodied in any tangible medium of expression having computer useable program code embodied in the medium. DESCRIPTION OF THE DRAWINGS

[0013] It is to be understood that both the foregoing summary’ and the following drawings and detailed description may be exemplary and may not be restrictive of the aspects of the present disclosure as claimed. Certain details may’ be set forth in order to provide a better understanding of various features, aspects, and advantages of the invention. However, one skilled in the art will understand that these features, aspects, and advantages may be practiced without these details. In other instances, well-known structures, methods, and / or processes associated with methods of practicing the various features, aspects, and advantages may not be shown or described in detail to avoid unnecessarily obscuring descriptions of other details of the invention.

[0014] The present disclosure may’ be better understood by reference to the accompanying drawing sheets, in which:

[0015] FIG. 1 includes a block diagram of a method of the present disclosure according to certain aspects.

[0016] FIG. 2 includes a block diagram of a system of the present disclosure.

[0017] FIG. 3 includes a block diagram of a data source module of the present disclosure.

[0018] FIG. 4 includes a block diagram of an address normalization module of the present disclosure.

[0019] FIG. 5 includes a block diagram of a data processing module of the present disclosure.

[0020] FIG. 6 includes a block diagram of a system of the present disclosure.

[0021] DETAILED DESCRIPTION

[0022] This disclosure generally describes methods, systems, and computer program products for a unique and normalized identifier for real estate assets to provide a complete and holistic view- of real estate asset data or information.

[0023] The present disclosure provides a unique identifier (ID) for an asset, including, but not limited to, real estate and property. The ID may include a string of alphanumeric characters. The ID may include at least 5 characters, including but not limited to. 5, 10, 15, 20, and at least 25 characters. The ID may' include 5 characters to 10 characters, 10 characters to 15 characters, 15 characters to 20 characters, and 20 characters to 25 characters. The ID may have a maximum of 25, 20, 15, 10, and 5 characters. The ID may have a minimum of 5. 10, 15, 20, and 25 characters. According to certain aspects, the ID may include 15 characters. The ID may include an asset class identifier, wherein the asset class identifier may include one character to define an asset class. An asset class may include, but is not limited to, property, deal, bond, and loan denoted by a numeric odd digit. As a non-limiting example, property may be denoted by 9, loan may be denoted by 7, deal may be denoted by 5, and bond may be denoted by 3. While property7, deal, bond, and loan asset class identifiers denoted by a numeric odd digit is described, additional asset class identifiers and alphanumeric characters are possible and within the scope of the present disclosure.

[0024] The ID may include label attributes, wherein the label attributes may include up to four characters. Label attributes may include up to four characters to denote a property -t pe code, including, but not limited to. multi-family (MF), co-op housing (CH), retail (RT), condo (CN), data center (DC), education (ED), government (GV), healthcare (HC), industrial (IN), lodging (LO), mobile home (MH), mixed use (MU), office (OF), other (OT), selfstorage (SS), warehouse (WH), and the like.

[0025] The ID may include a segment of 9 characters, wherein the segment may include a combination of 9 alphanumeric characters selected from upper-case consonants and integers 0 through 9. While upper-case consonants are described, lower-case consonants and other characters such as vowels are possible and within the scope of the present disclosure.

[0026] The ID may include a character in the last position (i.e., the 15thcharacter in an ID having 15 alphanumeric characters). The character may include an integer from 0 through 9 calculated by the Luhn algorithm or a similar algorithm known in the art.

[0027] The order of the components of the ID may include the order: Asset Class Identifier - Label Attributes - String of Characters - Last Position Character. While the previous order has been described, other orders of the components are possible and within the scope of the present disclosure.

[0028] The present disclosure provides a computer implemented method 10 (FIG. 1) for generating a unique identifier of the present disclosure for a real estate asset. The method may include aggregating data related to physical properties based on the geolocation of the property. The method 10 may define at least two categories of variables, wherein attributes of a physical property may be selected from at least one data source 20. Categories of variables may include, but are not limited to, sales transaction data, lease data, ownership data, mortgage data, financial data, property data, listing data, availability7data, tax assessment data, and demographic data. Each category7of variables may include subsets of attributes relating to each category. As used herein, “attributes” may be used interchangeable with “data”. Attributes may relate to more than one category of variables. As used herein, the term “category’ of variables” is used interchangeably with “category”.

[0029] Sales transaction data may include, but is not limited to, total sale price, price per square foot, median sale price, date of a sale transaction, amount of time from a first listing to a sale, change in price from one sale to another sale, return on investment ((Net Profit / Total Investment) * 100), loan to value (LTV) ratio (mortgage amount / appraised property value), equity to value ratio (total property equity / total property value), seller name, buyer name, seller address, buyer address, and the like.

[0030] Lease data may include, but is not limited to, number of tenants, average length of tenancy, tenant turnover rate ((# of tenants moved out / total # of tenants) * 100), lease price, change in lease price over time, start date and end date of a lease, categorization of a commercial lease or a residential lease, length of a tenancy, rent price, average rent price, size of the property7, and the like.

[0031] Ownership data may include, but is not limited to, owner name(s), location of recorded deed, owner history’, owner address, owner contact information, owner company information, and the like.

[0032] Mortgage data may include, but is not limited to, loan price, interest rate, lender information, borrower information, interest rate, origination date, recording data, mortgage payment history, mortgage history, and the like.

[0033] Financial data may include, but is not limited to, net operating income, revenue, net cashflow, gross potential rent, outstanding expenses, base rent, vacancy-loss collection, and the like.

[0034] Property' data may include, but is not limited to, parcel identification number, fips (federal information processing standard) code, property address, municipality, owner name, school district, tax code, class (i.e., residential or commercial), farmstead information, homestead information, use code (i.e., single family, multi-family, etc.), neighborhood code, sale date, sale price, lot area, number of stories, year built, square footage, owner history, sale history, property size, latitude, longitude, and the like. The listing data category’ may include, but is not limited to, any listing data available on the internet, listing descriptions, and the like.

[0035] The availability' data category' may include whether an asset is available for rent or purchase. The demographics category may include any demographic information accessible from a data source, including, but not limited to, age, race, gender, income, crime risk, school quality, population growth, warehouse data, hotel data, and the like. Tax assessment data may include, but is not limited to, land value, building value, full base year market value, county’ assessed value, taxable market value, millage rate, tax history, property data, listings, availabilities, demographics, and the like.

[0036] The method 10 may aggregate attributes of a physical property in the at least two categories of variables by analyzing n data sources 30, wherein n is an integer of a number of data sources to be analyzed, n data sources may include, but is not limited to. at least 1, 2, 3, 4, 5, 6. 7. 8, 9, 10, 15, 20, 25, and at least 30 data sources. A maximum n data sources may include, 50, 45. 40, 35, 30, 25, 20, 15, 10, 5, 4, 3, 2, and 1 data sources. A minimum n data sources may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, and 50 data sources. As used herein, a “data source” may refer to anything which produces digital or physical information, including online databases, websites, paper records, historical information that is not digitized, or any location or system that stores and manages data, including, but not limited to, databases, spreadsheets, cloud-based platforms, and / or an Application Programming Interface (API). The analysis of n data sources 30 may be performed over a defined time interval, such as from one specified date to another. The method 10 may define a given time frame to analyze n data sources. The methods of the present disclosure may analyze at least one data source to provide accurate and current information available for an asset.

[0037] After aggregating data in the at least two categories of variables, the method 10 may perform pre-normalization of the data in the at least two categories of variables 40. Prenormalization may include rules to transform attributes in a dataset to a standardized form, wherein pre-normalization may improve a probabilistic match score of an asset after normalization is completed. Pre-normalization may eliminate text parsing issues and distortions caused by disparate data sources.

[0038] After pre-normalization, the method 10 may perform a normalization of the property address 50. Address normalization or standardization may be performed by any method of checking and correcting address attributes to a standard format. Address normalization may include, but is not limited to. checking for location errors, spelling errors, formatting, abbreviation errors, and correcting an address to a normalized or standard format to be used in the ID of the present disclosure. Address normalization may include obtaining a text-based description of an asset, including, but not limited to, obtaining an address or name of a location and determining the geographic coordinates to determine a standardized or normalized address. Geographic coordinates may include, but are not limited to, latitude and longitude. After address normalization, the method 10 may perform post-normalization of the normalized address 60. Post-normalization may transform an address and its longitude and latitude to a standardized format. The transformed address may be stored in a database and correlated to an assigned ID of the present disclosure.

[0039] The method 10 may use the aggregated attributes in the at least two categories of variables and the normalized address to generate an identifier of the present disclosure as described herein 70. The method may utilize hashing, wherein hashing includes a process of transforming a key or a string of characters into another value, wherein the value may be a shorter, fixed-length value or key representing the original string of characters. The method 10 may utilize label encoding to normalize labels, wherein non-numerical labels may be transformed to numerical labels. Accordingly, label encoding may be used in combination with machine learning and data analysis to convert categorical variables into numerical format. Accordingly, hashing and label encoding may be used to generate the ID of the present disclosure 70.

[0040] After an ID is generated, the method 10 may perform data processing to generate a dataset 80. The dataset may include attributes and / or previously aggregated attributes. The dataset may include imputed, derived, predicted, or reported values. The data processing may optionally include enriching the previously aggregated attributes with additional attributes obtained from the at least one data source. Additional attributes my include any of the attributes described above, such as physical property data, ownership data, and transaction data.

[0041] The ID of the present disclosure may include core attributes, wherein core attributes are the attributes necessary for the generation of the ID of the present disclosure. Data processing may include generating the dataset, wherein the dataset may include any attribute, including the core attributes, obtained from at least one data source according to the methods of the present disclosure. Thus, data processing may include linking an ID of the present disclosure to its corresponding dataset, wherein a user may utilize the ID to access the dataset to view any attribute, including core attributes, for a real estate asset.

[0042] Data processing may include normalization of the data / attributes within the dataset, applying business rules, joining variables, rearranging variables, and creating calculated or derived variables. A business rule may include, but is not limited to, a valid value combination rule w herein the rule may validate a combination of data values, a computational rule to mathematically validate multiple numeri columns that have a mathematical relationship, an ordered values rule to validate time and duration relationships, and other conditional rules which may include one or more conditions. The method 10 may apply any business rule or any data rule generally known in the art.

[0043] Data processing may include reconciling the data to validate or verify the data. Reconciliation may include comparing the data from two or more data sources to ensure accuracy and completeness.

[0044] Data processing may include, but is not limited to, accessing an interactive analytics service or a query engine such as Amazon Athena, accessing an open search and analytics engine for textual, numerical, geospatial, structure, and unstructured data such as Elasticsearch, accessing a metadata management and data governance tool such as Apache Atlas, utilizing an API, and / or using a tool for data categorization and governance. Data processing may include utilizing any system, software, method, or tool known in the art for data modeling, processing, and reconciliation.

[0045] The methods of the present disclosure may include storing data or the dataset in a centralized data store or database within a cloud environment. The data store may store and maintain data in both structured and unstructured formats. The methods may include an API Gateway, wherein the API Gateway may manage traffic from the data store, services, and the client. The API Gateway may perform functions including, but not limited to, providing secure access to resources, auditing, usage policies, and other capabilities such as logging.

[0046] The methods may include a data processing and analytics framework that may distribute data processing tasks over multiple computers or systems while handling at least one dataset. The methods may include storing data within more than one memory to improve response times and eliminate disk I / O latency.

[0047] The methods may include at least one computer sendee or cloud service offered by a third-party provider over the internet. A cloud service may include a cloud service generally known in the art having at least one feature including, but not limited to, CPU usage, data storage, bandwidth, and the like. A cloud service may replace physical computer hardware.

[0048] The methods of the present disclosure may regenerate the ID for any real estate asset with a change in an attribute or data. As a non-limiting example, the postal code of an asset address may change, leading to the regeneration of the ID. The method may continuously access the at least one data source to assess or track changes in an attribute and regenerate the ID according to the methods of the present disclosure.

[0049] If a regenerated ID is created, the regenerated ID may be correlated to the previous ID to maintain a lineage of changes for the asset. Accordingly, the method enables a user to interpret asset attribute changes by comparing and analyzing changes in the ID over time. The ID. methods, and systems of the present disclosure provide a complete and holistic view of real estate asset information. Conventional methods known in the art fail to encompass all real estate asset classes. Conventional identifiers are not unique for each asset and change over time cannot be compared and analyzed. As a non-limiting example, there is not a universal standard for commercial property7identification, as users and jurisdictions use different systems or workflows for identifying properties, causing confusion and errors when trying to match different data sources for an asset or compare asset changes over time. Conventional properties may vary widely in terms of physical attributes such as property type and geolocations. Accordingly, conventional identifiers and methods cannot define a single set of attributes that may be used to assign a unique identifier to each property. Asset geolocation normalization may aid in maintaining consistency in unique identifier generation, but updates on core attributes or data causes identifier generation conflicts and inconsistencies. Different industries and users may have different needs for identifying an asset depending on factors including, but not limited to, size, location, and use. Data sources such as government records (i.e. , county records) and third-party data providers do not use common or uniform identifiers. The present disclosure provides improvements and consistent application for loan origination, assessors, investors, banks, tenants, owners, publications, or any other user. Furthermore, the ID, methods, and systems of the present disclosure provide a unique and consistent ID for an asset to cover global financial instruments.

[0050] The present disclosure provides a unique and normalized identifier for assets. The ID of the present disclosure may be consistent from different data sources. The ID of the present disclosure may be verbally transferable, wherein the characters of the ID have a specific meaning instead of conventional identifiers known in the art, wherein the characters may be random or indecipherable.

[0051] The ID of the present disclosure may enable a user to identify the type of real estate asset, such as residential, commercial, or industrial real estate asset. Accordingly, the ID may demonstrate asset type changes over time, wherein a user may compare changes in the ID to determine changes in the type of asset. Thus, the methods of the present disclosure enables a user to interpret asset attribute changes even though an asset type may change.

[0052] As a non-limiting example, an industry professional may desire correlating various datasets, including but not limited to, loan, financial, and tenant and owner information of an asset to generate insights. The ID, methods, and systems of the present disclosure provide a precise and concise identifier that may be efficiently interpreted by query engines at scale. As a non-limiting example, all data sources do not include data at the property' level for complete information about finance, loans, ownership, etc. As a non-limiting example, a single loan may include multiple properties and a property may have multiple loans associated with it, causing difficulties identifying the property' associated with loan information procured from vendor data in a data source. The ID and methods of the present disclosure provides a unique property identifier, which enables the transformation of the vendor data by incorporating the data at the property level.

[0053] The ID of the present disclosure may be used to efficiently be interpreted by query engines to analyze an asset. The generated ID may be used during the data processing methods of the present disclosure to further analyze assets.

[0054] The ID of the present disclosure is applicable to real estate assets world-wide, including, but not limited to, real estate assets in North America, South America, Europe, Asia, the Middle East, and Australia.

[0055] The present disclosure provides methods of using the ID of the present disclosure bygenerating a list of IDs of real estate addresses provided by a user. The real estate addresses may be obtained on a periodic basis such as daily, weekly, or monthly. The present disclosure provides methods of using the IDs of the present disclosure to efficiently and accurately analyze real estate asset data, wherein conventional methods known in the art are unable to reconcile real estate addresses and perform accurate and meaningful analysis of the real estate asset data.

[0056] A user may provide a real estate address from at least one data source to generate an ID of the present disclosure according to the methods of the present disclosure. Once a unique ID is generated that corresponds to the real estate address input by a user, the user may perform analytics according to the present disclosure.

[0057] The present disclosure provides methods of using the ID, wherein the method includes: receiving at least one address of at least one real estate asset from a user or from at least one data source, generating an ID for each real estate asset, and accessing the ID in a client workflow system, wherein the client workflow system may search and / or analyze the ID to access underlying data or analysis corresponding to the real estate asset.

[0058] The present disclosure provides methods of using the ID for debt or equity management, such as in commercial real estate. The ID of the present disclosure may enable a user to connect available data on property details, loan characteristics, tenant attributes, and other attributes discussed herein, which may inform a user of the potential financial performance of an asset in the future. The methods of the present disclosure may utilize machine learning techniques to determine comparable properties within a database of IDs to provide reliable financial performance projections based on disparate data sources. Accordingly, the ID may be used for debt or equity management to select similar real estate assets that correspond to certain criteria, such as real estate assets that are projected to perform at a predetermined level or real estate assets that would be distressed. Thus, the present disclosure may provide financial projections for an ID of the present disclosure to assist a user in debt or equity management, wherein the methods of the present disclosure enable a user to project potential return in the market for an ID and benchmark the potential return against historical returns for the same ID in the past or other related IDs generated by the present disclosure.

[0059] The present disclosure provides methods of using the ID for acquisition and disposition of real estate assets. The methods of the present disclosure enable a user to identify assets using an ID of the present disclosure, wherein a user may access a dataset of the present disclosure or a database of IDs. The methods of the present disclosure enable performing derivative risk and / or return analysis using data obtained by analyzing IDs of the present disclosure. Accordingly, a user may use an ID of the present disclosure to identify real estate assets that fit within pre-determined investment criteria selected by a user, thus improving efficiency and decision making. Data linking and machine learning techniques may be utilized to match IDs of real estate assets to provide improved comparable analysis, allowing a user to identify real estate assets with a greater than average return of investment.

[0060] The present disclosure provides methods of using the ID to determine a valuation of a real estate asset. The methods may include generating an ID of the present disclosure according to the methods described herein. The methods may include analyzing the data, the generated ID, and / or previously generated IDs for the same real estate asset to determine macro or micro economic changes. The methods may access a database having stored therein IDs of the present disclosure, including historical IDs for the same real estate asset. Economic data may be combined with financial data, assessment data, property data, and property' size data such as price per square foot from a generated ID, a previously generated ID, and / or from at least one data source. The method may utilize the information to perform a derivative analysis on the valuation of a real estate asset. Thus, the methods of the present disclosure enable a user to obtain a comprehensive view of a real estate asset from multiple disparate data sources by utilizing IDs of the present disclosure, thereby providing efficient valuations and assessment values for a real estate asset. The present disclosure provides methods of using the ID to provide brokerage services to facilitate the sale, purchase, or lease of a real estate asset. A user providing brokerage services may have differing needs depending on location, transaction size, property performance, ownership, or other factors. Linking and connecting data sources via an ID of the present disclosure to identify real estate assets provides a more efficient method than conventional methods known in the art, which may be time-consuming. The methods of the present disclosure may provide a user with a search interface that combines user selected or all available real estate asset data into one unique identifier by generating an ID of the present disclosure. The ID of the present disclosure may then provide asset metrics to enable a user to efficiently provide brokerage services. The methods of the present disclosure enable a user to view and analyze comparisons between various real estate assets or comparisons of the same real estate asset at different time points. The methods enable a user to view and analyze financial data to provide a more accurate service.

[0061] The present disclosure provides methods of using the ID for education. The ID of the present disclosure may be used to analyze a real estate industry, such as the commercial real estate industry, the residential real estate industry, and the industrial real estate industry. The ID of the present disclosure may provide a user, such as a student, with real estate asset data as described above in order to provide information and analysis of the real estate asset. The methods of the present disclosure provide a unique and interpretable ID for each real property asset. The ID may be used in analytical models and artificial intelligence models in the fields of property risk scores, property sizing, property financial performance, property tenants forecast, or other research activities. While a student user has been described, other users are possible and within the scope of the present disclosure. Conventional methods of conducting research on real estate assets rely upon disparate data sources, resulting in research challenges such as incomplete or insufficient research data and analysis. The methods and ID of the present disclosure enable a user to utilize an ID to analyze user selected real estate asset data to effectively and efficiently research and analyze specific fields of research.

[0062] FIG. 6 depicts a block diagram that schematically illustrates a system 100 according to the present disclosure. The system 100 may comprise a processor 110 that interfaces with memory 120 (which may be separate from or included as part of processor 110). The memory 120 may also employ cloud-based memory. In one aspect, the system may connect to a base station that includes memory and processing capabilities. The system may include an I / O device 140. The processor 110 may interface with a user interface 130. Memory' 120 has stored therein a number of routines that are executable by processor 110. The system of the present disclosure may include a category of variable definition module 200, a data source module 300, an address normalization module 400, an identifier generation module 500, and a data processing module 600 (FIG. 2). The processor 110 in communication with the memory' 120, may be configured to execute the category of variable definition module 200. The category of variable definition module 200 may comprise program instructions executable by processor 110 to define at least two categories of variables according to the methods of the present disclosure.

[0063] The processor 110, in communication with the memory 120, may be configured to execute the data source module 300 (FIG. 3). The data source module 300 may include program instructions executable by processor 110 to analyze n data sources 310 according to the methods of the present disclosure, wherein n is an integer of a number of data sources to be analyzed.

[0064] The processor 110, in communication with the memory 120, may be configured to execute the address normalization module 400 (FIG. 4). The address normalization module 400 may include program instructions executable by processor 110 to analyze core attributes 410 of the data of the categories of variables obtained from the data source module 300 to perform address normalization 420 according to the methods of the present disclosure. The normalization of the address may include program instructions to perform location determination 430. wherein performing location determination 430 may include program instructions to determine the location of the asset according to the methods of the present disclosure. Program instructions to determine the location of the asset may include, but is not limited to, using a location service provider. The address normalization module 400 may include program instructions to perform attributes normalization 440. wherein the core attributes 410 may be normalized to standardize the core attributes 410 in standard or uniform formats. The address normalization module may perform pre-normalization and postnormalization according to the methods of the present disclosure.

[0065] The processor 110, in communication with the memory 120, may be configured to execute the identifier generation module 500, wherein the identifier generation module 500 may include program instructions executable by' processor 110 to generate an identifier according to the methods of the present disclosure.

[0066] The processor 110, in communication with the memory 120, may be configured to execute the data processing module 600 (FIG. 5). wherein the data processing module 600 may include program instructions executable by processor 110 to perform data normalization 610, apply business rules 620, join and rearrange variables 630, and create derived or calculate variables 640 according to the methods of the present disclosure. The data processing module 600 may include program instructions executable by processor 110 to generate a dataset 650 according to the methods of the present disclosure.

[0067] Processor 110 may be one or more microprocessors, microcontroller, an application specific integrated circuit (ASIC), a circuit containing one or more processing components, a group of distributed processing components, circuitry for supporting a microprocessor, or other suitable processing device that interfaces with memory 120. Processor 110 is also configured to execute computer code stored in memory 120 to complete and facilitate the activities described herein.

[0068] I / O device 140 (including, but not limited to, keyboards, displays, pointing devices, DASD, tape, CDs, DVDs, thumb drives and other memory media, etc.) may be coupled to the system either directly or through intervening I / O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems, and Ethernet cards may be just a few of the available types of network adapters.

[0069] The system 100 may include program instructions to store data in a centralized data store within a cloud environment. The data store may store and maintain data in both structured and unstructured formats.

[0070] The system 100 may include program instructions for an API Gateway, wherein the API Gateway may manage traffic from the data store, services, and the client. The API Gateway may perform functions including, but not limited to, providing secure access to resources, auditing, usage policies, and other capabilities such as logging.

[0071] The system 100 may include program instructions for a data processing and analytics framework that may distribute data processing tasks over multiple computers or systems while handling at least one dataset.

[0072] The system 100 may include program instructions to store data within more than one memory to improve response times and eliminate disk I / O latency.

[0073] The system 100 may include at least one computer service or cloud service offered by a third-partj7provider over the internet. A cloud service may include a cloud service generally known in the art having at least one feature including, but not limited to, CPU usage, data storage, bandwidth, and the like. A cloud service may replace physical computer hardware. As will be appreciated by one skilled in the art, the present disclosure may be embodied as a system, method, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “system.” Furthermore, the presently disclosed invention may take the form of a computer program product embodied in any tangible medium of expression having computer useable program code embodied in the medium.

[0074] Any combination of one or more computer useable or computer readable medium(s) may be utilized. The computer-useable or computer-readable medium may be, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Computer-readable medium may also be an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory' (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CDROM). an optical storage device, a transmission media such as those supporting the Internet or an intranet, a magnetic storage device, a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. Note that the computer-useable or computer-readable medium may be paper or another suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary’, and then stored in a computer memory. In the context of this document, a computer-useable or computer-readable medium may be any medium that may contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-useable program code maybe transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable. RF. etc.

[0075] Computer program code for carrying out operations of the presently disclosed invention may be written in any combination of one or more programming languages. The programming language may be, but is not limited to, object-oriented programming languages (Java, Smalltalk, C++, etc.) or conventional procedural programming languages (“C” programming language, etc.). The program code may execute entirely on a user’s computer, partly on the user’s computer, as a stand-alone software package, partly on a user’s computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer, which may include through the Internet using an Internet Services Provider. In some aspects, electronic circuitry including, as a nonlimiting example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0076] The systems and methods of the present disclosure may process data on any commercially available computer. In other aspects, a computer operating system may include, but is not limited to, Linux, Windows, UNIX, Android, MAC OS, and the like. In one aspect of the present disclosure, the forgoing processing devices or any other electronic, computation platform of a type designed for electronic processing of digital data as herein disclosed may be used.

[0077] Aspects of the present disclosure are described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to aspects of the present disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combination of blocks in the flowchart illustrations and / or block diagrams, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, which the instructions execute via the processor of the computer or other programmable data processing apparatus allowing for the implementation of the steps specified in the flowchart and / or block diagram blocks or blocks.

[0078] Various aspects of the present disclosure may be implemented in a data processing system suitable for storing and / or executing program code that includes at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements include, for instance, local memory employed during actual execution of the program code, bulk storage, and cache memory which provide temporary' storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. Computer readable program instructions described herein may be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, as a non-limiting example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0079] A code segment or machine-executable instructions may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data. etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, among others.

[0080] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. As such, terms, such as those defined by commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in a context of a relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0081] As used herein, the term '‘user” refers to any person, entity, corporation, individual, institution, and the like capable of utilizing the methods and systems of the present disclosure.

[0082] As used herein, the term “real estate" or “real estate asset” refers to any piece of land, including any artificial or natural property permanently attached to it. above or beneath, including, but not limited to, a structure, a tree, or minerals. As used herein, “real estate” includes, but is not limited to, residential real estate, commercial real estate, industrial real estate, and the like. As used herein, the term “asset” and “real estate asset” may be used interchangeably. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items. Likewise, as used in the following detailed description, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean nay of the natural inclusive permutations. Thus, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances.

[0083] The terminology used herein is for the purpose of describing particular examples only and is not intended to be limiting. As used herein, the singular forms “a”, “an”, and “the” may be intended to include the plural forms as well, unless the context clearly dictates otherwise. As a non-limiting example, “an” asset may comprise one or more assets, and the like.

[0084] The terms “comprises”, “comprising”, “including”, “having”, and “characterized by”, may be inclusive and therefore specify the presence of stated features, elements, compositions, steps, integers, operations, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Although these open-ended terms may be to be understood as a non-restrictive term used to describe and claim various aspects set forth herein, in certain aspects, the term may alternatively be understood to instead be a more limiting and restrictive term, such as “consisting oT’ or “consisting essentially of.” Thus, for any given aspects reciting compositions, materials, components, elements, features, integers, operations, and / or process steps, described herein also specifically includes aspects consisting of, or consisting essentially of, such recited compositions, materials, components, elements, features, integers, operations, and / or process steps. In the case of “consisting of’, the alternative aspect excludes any additional compositions, materials, components, elements, features, integers, operations, and / or process steps, while in the case of “consisting essentially of’, any additional compositions, materials, components, elements, features, integers, operations, and / or process steps that materially affect the basic and novel characteristics may be excluded from such an aspect, but any compositions, materials, components, elements, features, integers, operations, and / or process steps that do not materially affect the basic and novel characteristics may be included in the aspect.

[0085] Any method steps, processes, and operations described herein may not be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also understood that additional or alternative steps may be employed, unless otherwise indicated. In addition, features described with respect to certain example aspects may be combined in or with various other example aspects in any permutational or combinatory manner. Different aspects or elements of example aspects, as disclosed herein, may be combined in a similar manner. The term “combination”, “combinatory,” or “combinations thereof’ as used herein refers to all permutations and combinations of the listed items preceding the term. For example, “A, B. C, or combinations thereof is intended to include at least one of: A. B, C. AB, AC. BC, or ABC. and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example, expressly included may be combinations that contain repeats of one or more item or term, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context.

[0086] Aspects of the present disclosure may be described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to aspects of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, may be implemented by computer readable program instructions. The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality' is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0087] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various aspects of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. As a non-limiting example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality’ involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flow chart illustration, may be implemented by special purpose hardw are-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0088] Words such as "then." "next.” etc. are not intended to limit the order of the steps; these words may be simply used to guide the reader through the description of the methods. Although process flow’ diagrams may describe the operations as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.

[0089] In the description, certain details are set forth in order to provide a better understanding of various aspects of the systems and methods disclosed herein. However, one skilled in the art will understand that these aspects may be practiced without these details and / or in the absence of any details not described herein. In other instances, w ell-known structures, methods, and / or techniques associated with methods of practicing the various aspects may not be shown or described in detail to avoid unnecessarily obscuring descriptions of other details of the various aspects.

[0090] While specific aspects of the disclosure have been provided hereinabove, the disclosure may, however, be embodied in many different forms and should not be construed as necessarily being limited to only the aspects disclosed herein. Rather, these aspects may be provided so that this disclosure is thorough and complete, and fully conveys various concepts of this disclosure to skilled artisans.

[0091] Furthermore, when this disclosure states that something is “based on” something else, then such statement refers to a basis which may be based on one or more other things as well. In other words, unless expressly indicated otherwise, as used herein “based on” inclusively means “based at least in part on” or “based at least partially on.”

[0092] All numerical quantities stated herein may be approximate, unless stated otherwise. Accordingly, the term “about” may be inferred when not expressly stated. The numerical quantities disclosed herein may be to be understood as not being strictly limited to the exact numerical values recited. Instead, unless stated otherwise, each numerical value stated herein is intended to mean both the recited value and a functionally equivalent range surrounding that value. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical value should at least be construed in light of the number of reported significant digits and by applying ordinary rounding processes. Typical exemplary degrees of error may be within 20%, 10%, or 5% of a given value or range of values. Alternatively, the term “about” refers to values within an order of magnitude, potentially within 5-fold or 2-fold of a given value. Notwithstanding the approximations of numerical quantities stated herein, the numerical quantities described in specific examples of actual measured values may be reported as precisely as possible. Any numerical values, however, inherently contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.

[0093] All numerical ranges stated herein include all sub-ranges subsumed therein. As a non- limiting example, a range of “1 to 10” or “1-10” is intended to include all sub-ranges between and including the recited minimum value of 1 and the recited maximum value of 10 because the disclosed numerical ranges may be continuous and include every value between the minimum and maximum values. Any maximum numerical limitation recited herein is intended to include all lower numerical limitations. Any minimum numerical limitation recited herein is intended to include all higher numerical limitations.

[0094] Features or functionality described with respect to certain example aspects may be combined and sub-combined in and / or with various other example aspects. Also, different aspects and / or elements of example aspects, as disclosed herein, may be combined and subcombined in a similar manner as well. Further, some example aspects, whether individually and / or collectively, may be components of a larger system, wherein other procedures maytake precedence over and / or otherwise modify their application. Additionally, a number of steps may be required before, after, and / or concurrently with example aspects, as disclosed herein. Note that any and / or all methods and / or processes, at least as disclosed herein, may be at least partially performed via at least one entity- or actor in any manner.

[0095] While particular aspects have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications may be made without departing from the spirit and scope of the invention. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, numerous equivalents to the specific apparatuses and methods described herein, including alternatives, variants, additions, deletions, modifications, and substitutions. This application including the appended claims is therefore intended to cover all such changes and modifications that may be within the scope of this application.

[0096] Aspect 1 : A method for generating a unique identifier for a real estate asset, the method comprising: defining at least two categories of variables; selecting at least one attribute of the real estate asset from at least one data source for each of the at least two categories of variables; analyzing the at least one data source to aggregate the at least one attribute of the asset in each of the at least two categories of variables; performing prenormalization of the at least one attribute; performing a normalization of an address of the real estate asset to generate a normalized real estate asset address; performing postnormalization of the normalized real estate asset address to generate a transformed address; generating the unique identifier for the real estate asset from the aggregated attribute data in each of the at least two categories of variables and the transformed address; and performing data processing of the identifier to generate a dataset.

[0097] Aspect 2: The method according to aspect 1, wherein the at least two categories of variables is selected from a group consisting of sales transaction data, lease data, ownership data, mortgage data, financial data, property data, listing data, availability data, tax assessment data, and demographic data.

[0098] Aspect 3 : The method according to any of the foregoing aspects, wherein the at least one attribute relates to more than one category of variable.

[0099] Aspect 4: The method according to any of the foregoing aspects, wherein the method selects at least one attribute of the real estate asset from n data sources to be analyzed.

[0100] Aspect 5 : The method according to any of the foregoing aspects, wherein the at least one data source comprises digital or physical information.

[0101] Aspect 6: The method according to any of the foregoing aspects, wherein analyzing at least one data source comprises analyzing the at least one data source over a defined time interval.

[0102] Aspect 7 : The method according to any of the foregoing aspects, wherein performing pre-normalization of the at least one attribute eliminates text parsing and distortions.

[0103] Aspect 8: The method according to any of the foregoing aspects, wherein performing pre-normalization of the at least one attribute comprises transforming the at least one attribute to a standardized form.

[0104] Aspect 9: The method according to any of the foregoing aspects, wherein performing a normalization of the address of the real estate asset comprises correcting at least one address attribute to a standardized format. Aspect 10: The method according to any of the foregoing aspects, wherein performing a normalization of the address of the real estate asset comprises obtaining a text-based description of the real estate address, comprising: obtaining an address or name of a location, determining geographic coordinates of the real estate asset, and determining a normalized address.

[0105] Aspect 11 : The method according to any of the foregoing aspects, wherein performing post-normalization of the normalized real estate asset address comprises transforming the address of the real estate asset and the geographic coordinates to a standardized format.

[0106] Aspect 12: The method according to any of the foregoing aspects, wherein the identifier comprises: a string of alphanumeric characters, wherein the string comprises 5 to 25 characters, and wherein the string comprises; an asset class identifier, wherein the asset class identifier comprises at least one character to define an asset; at least one label attribute, wherein the at least one label attribute comprises up to four characters; a segment of at least 9 characters, wherein the segment comprises characters selected from upper-case consonants and integers 0 through 9; and a character in the last position, wherein the character includes an integer selected from 0 through 9.

[0107] Aspect 13: The method according to any of the foregoing aspects, wherein the string is 15 alphanumeric characters.

[0108] Aspect 14: The method according to any of the foregoing aspects, wherein the asset class identifier is selected from a group consisting of property, deal, bond, and loan, and wherein the asset class identifier is an odd digit.

[0109] Aspect 15: The method according to any of the foregoing aspects, wherein the at least one label attribute is selected from a group consisting of multi-family (MF), co-op housing (CH), retail (RT), condo (CN), data center (DC), education (ED), government (GV), healthcare (HC), industrial (IN), lodging (LO), mobile home (MH), mixed use (MU), office (OF), other (OT), self-storage (SS), and warehouse (WH).

[0110] Aspect 16: The method according to any of the foregoing aspects, wherein the segment comprises 9 characters.

[0111] Aspect 17: The method according to any of the foregoing aspects, wherein the character in the last position is calculated by a Luhn algorithm.

[0112] Aspect 18: The method according to any of the foregoing aspects, wherein generating an identifier comprises hashing. Aspect 19: The method according to any of the foregoing aspects, wherein generating an identifier comprises label encoding to normalize labels, wherein non-numerical labels may be transformed to numerical labels.

[0113] Aspect 20: The method according to any of the foregoing aspects, wherein generating an identifier comprises label encoding, machine learning, and data analysis to convert categorical variables to numerical format.

[0114] Aspect 21: The method according to any of the foregoing aspects, where performing data processing comprises enriching the aggregated at least one attribute with at least one additional attribute obtained from the at least one data source.

[0115] Aspect 22: The method according to any of the foregoing aspects, wherein performing data processing comprises applying business rules.

[0116] Aspect 23: The method according to any of the foregoing aspects, wherein performing data processing comprises reconciling data to validate or verify the data, wherein reconciling the data comprises comparing the data from two or more data sources to ensure accuracy and completeness.

[0117] Aspect 24: The method according to any of the foregoing aspects, further comprising storing data in a centralized data store within a cloud environment.

[0118] Aspect 25: The method according to any of the foregoing aspects, further comprising storing a first identifier in a database, generating a second identifier if there is a change in at least one attribute, storing the second identifier in the database, and comparing the first identifier and the second identifier to interpret at least one attribute change for the real estate asset.

[0119] Aspect 26: An identifier for a real estate asset, the identifier comprising: a string of alphanumeric characters, wherein the string comprises 5 to 25 characters, and wherein the string comprises: an asset class identifier, wherein the asset class identifier comprises at least one character to define an asset; at least one label attribute, wherein the at least one label attribute comprises up to four characters; a segment of at least 9 characters, wherein the segment comprises characters selected from upper-case consonants and integers 0 through 9; and a character in the last position, wherein the character includes an integer selected from 0 through 9.

[0120] Aspect 27: The identifier of aspect 26, wherein the string is 15 alphanumeric characters. Aspect 28: The identifier according to any of the foregoing aspects, wherein the asset class identifier is selected from a group consisting of property, deal, bond, and loan, and wherein the asset class identifier is an odd digit.

[0121] Aspect 29: The identifier according to any of the foregoing aspects, wherein the at least one label attribute is selected from a group consisting of multi-family (MF), co-op housing (CH), retail (RT), condo (CN), data center (DC), education (ED), government (GV), healthcare (HC). industrial (IN), lodging (LO), mobile home (MH), mixed use (MU), office (OF), other (OT), self-storage (SS), and warehouse (WH).

[0122] Aspect 30: The identifier according to any of the foregoing aspects, wherein the segment comprises 9 characters.

[0123] Aspect 31 : The identifier according to any of the foregoing aspects, wherein the character in the last position is calculated by a Luhn algorithm.

[0124] Aspect 32: A system for generating a unique identifier for a real estate asset, comprising: a processor; and a memory7storing computer-readable instructions that, when executed by the processor, cause the processor to trigger execution of: a category of variable definition module, wherein the category of variable definition module comprises program instructions to define at least two categories of variables a data source module, select at least one attribute of the real estate asset from at least one data source for the at least two categories of variables, and analyze at least one data source to aggregate at least one attribute of the asset in the at least two categories of variables; an address normalization module, wherein the address normalization module comprises program instructions to perform prenormalization of the at least one attribute, perform a normalization of the real estate asset address to generate a normalized real estate asset address, and perform post-normalization of the normalized real estate asset address to generate a transformed address; an identifier generation module, wherein the identifier generation module comprises program instructions to generate the identifier for the real estate asset from the at least one attribute aggregated in the at least two categories of variables and the transformed address; and a data processing module, wherein the data processing module comprises program instructions to perform data processing of the identifier to generate a dataset.

[0125] Aspect 33: A computer program product for generating a unique identifier for a real estate asset, comprising at least one non-transitory computer readable medium including program instructions that, when executed by at least one processor, cause at least one processor to: define at least two categories of variables; select at least one attribute of the real estate asset from at least one data source for the at least two categories of variables; analyze at least one data source to aggregate at least one attribute of the asset in the at least two categories of variables; perform pre-normalization of the at least one attribute; perform a normalization of the real estate asset address to generate a normalized real estate asset address; perform post-normalization of the normalized real estate asset address to generate a transformed address; generate the identifier for the real estate asset from the at least one attribute aggregated in the at least two categories of variables and the transformed address; and perform data processing of the identifier to generate a dataset.

[0126] Aspect 34: A method of using the ID for debt or equity management.

[0127] Aspect 35: A method of using the ID for acquisition and disposition of a real estate asset.

[0128] Aspect 36: A method of using the ID to determine a valuation of a real estate asset.

[0129] Aspect 37: A method of using the ID to provide brokerage services to facilitate the sale, purchase, or lease of a real estate asset.

[0130] Aspect 38: A method of using the ID for education, wherein the ID may be used in analytical and artificial intelligence models to perform research.

Claims

CLAIMSWhat is claimed is:

1. A method for generating a unique identifier for a real estate asset, the method comprising: defining at least two categories of variables; selecting at least one attribute of the real estate asset from at least one data source for each of the at least two categories of variables; analyzing the at least one data source to aggregate the at least one attribute of the real estate asset in each of the at least two categories of variables; performing pre-normalization of the at least one attribute; performing a normalization of an address of the real estate asset to generate a normalized real estate asset address; performing post-normalization of the normalized real estate asset address to generate a transformed address; generating the unique identifier for the real estate asset from the aggregated attribute data in each of the at least two categories of variables and the transformed address; and performing data processing of the identifier to generate a dataset.

2. The method of claim 1 , wherein the at least two categories of variables is selected from a group consisting of sales transaction data, lease data, ownership data, mortgage data, financial data, property’ data, listing data, availability data, tax assessment data, and demographic data.

3. The method of claim 2, wherein the at least one attribute relates to more than one category of variable.

4. The method of claim 1. wherein the method selects at least one attribute of the real estate asset from n data sources to be analyzed.

5. The method of claim 1, wherein the at least one data source comprises digital or physical information.

6. The method of claim 1, wherein analyzing at least one data source comprises analyzing the at least one data source over a defined time interval.

7. The method of claim 1, wherein performing pre-normalization of the at least one attribute reduces text parsing and distortions.

8. The method of claim 1, wherein performing pre-normalization of the at least one attribute comprises transforming the at least one attribute to a standardized format.

9. The method of claim 1, wherein performing a normalization of the address of the real estate asset comprises correcting at least one address attribute to a standardized format.

10. The method of claim 1, wherein performing a normalization of the address of the real estate asset comprises: obtaining a text-based description of the address of the real estate asset including an address or name of a location. determining geographic coordinates of the real estate asset, and determining a normalized address.

11. The method of claim 1 , wherein performing post-normalization of the normalized real estate asset address comprises transforming the address of the real estate asset and the geographic coordinates to a standardized format.

12. The method of claim 1, wherein the identifier comprises: a string of 5 to 25 alphanumeric characters, and wherein the string comprises: an asset class identifier, wherein the asset class identifier comprises at least one character to define an asset; at least one label attribute, wherein the at least one label attribute comprises up to four characters; a segment of at least 9 characters, wherein the segment comprises characters selected from upper-case consonants and integers 0 through 9; and a character in the last position, wherein the character includes an integer selected from 0 through 9.

13. The method of claim 12, wherein the string is 15 alphanumeric characters.

14. The method of claim 13, wherein the asset class identifier is selected from a group consisting of property, deal, bond, and loan, and wherein the asset class identifier is an odd digit.

15. The method of claim 13, wherein the at least one label attribute is selected from a group consisting of multi-family (MF), co-op housing (CH), retail (RT), condo (CN). data center (DC), education (ED), government (GV), healthcare (HC), industrial (IN), lodging (LO), mobile home (MH), mixed use (MU), office (OF), other (OT), self-storage (SS), and warehouse (WH).

16. The method of claim 13, wherein the segment comprises 9 characters.

17. The method of claim 13, wherein the character in the last position is a Luhn algorithm check digit.

18. The method of claim 1, wherein generating the identifier comprises hashing.

19. The method of claim 1. wherein generating the identifier comprises label encoding to normalize labels, wherein non-numerical labels are transformed to numerical labels.

20. The method of claim 1, wherein generating the identifier comprises label encoding, machine learning, and data analysis to convert categorical variables to numerical format.

21. The method of claim 1, where performing data processing comprises enriching the at least one aggregated attribute with at least one additional attribute obtained from the at least one data source.

22. The method of claim 1. wherein performing data processing comprises applying business rules.

23. The method of claim 1, wherein performing data processing comprises reconciling data to validate or verify the data, wherein reconciling the data comprises comparing the data from two or more data sources.

24. The method of claim 1. further comprising storing data in a centralized data store within a cloud environment.

25. The method of claim 1, further comprising storing a first identifier in a database, generating a second identifier when there is a change in at least one attribute, storing the second identifier in the database, and comparing the first identifier and the second identifier to interpret at least one attribute change for the real estate asset.

26. An identifier for a real estate asset, the identifier comprising: a string of 5 to 25 alphanumeric characters, and wherein the string comprises: an asset class identifier, wherein the asset class identifier comprises at least one character to define an asset; at least one label attribute, wherein the at least one label attribute comprises up to four characters; a segment of at least 9 characters, wherein the segment comprises characters selected from upper-case consonants and integers 0 through 9; and a character in the last position, wherein the character includes an integer selected from 0 through 9.

27. The identifier of claim 26, wherein the string is 15 alphanumeric characters.

28. The identifier of claim 26, wherein the asset class identifier is selected from a group consisting of property, deal, bond, and loan, and wherein the asset class identifier is an odd digit.

29. The identifier of claim 26, wherein the at least one label attribute is selected from a group consisting of multi-family (MF), co-op housing (CH), retail (RT), condo (CN), data center (DC), education (ED), government (GV), healthcare (HC), industrial (IN), lodging (LO), mobile home (MH), mixed use (MU), office (OF), other (OT). self-storage (SS), and warehouse (WH).

30. The identifier of claim 26, wherein the segment comprises 9 characters.

31. The identifier of claim 26, wherein the character in the last position is a Luhn algorithm check digit.

32. A system for generating a unique identifier for a real estate asset, comprising: a processor; and a memory storing computer-readable instructions that, when executed by the processor, cause the processor to trigger execution of: a category of variable definition module, wherein the category of variable definition module comprises program instructions to define at least two categories of variables a data source module, select at least one attribute of the real estate asset from at least one data source for the at least two categories of variables, and analyze at least one data source to aggregate at least one attribute of the asset in the at least two categories of variables; an address normalization module, wherein the address normalization module comprises program instructions to perform pre-normalization of the at least one attribute, perform a normalization of the real estate asset address to generate a normalized real estate asset address, and perform post-normalization of the normalized real estate asset address to generate a transformed address; an identifier generation module, wherein the identifier generation module comprises program instructions to generate the identifier for the real estate asset from the at least one attribute aggregated in the at least two categories of variables and the transformed address; and a data processing module, wherein the data processing module comprises program instructions to perform data processing of the identifier to generate a dataset.

33. A computer program product for generating a unique identifier for a real estate asset, comprising at least one non-transitory computer readable medium including program instructions that, when executed by at least one processor, cause at least one processor to: define at least two categories of variables; select at least one attribute of the real estate assetfrom at least one data source for the at least two categories of variables; analyze at least one data source to aggregate at least one attribute of the asset in the at least two categories of variables; perform pre-normalization of the at least one attribute; perform a normalization of the real estate asset address to generate a normalized real estate asset address; perform post- normalization of the normalized real estate asset address to generate a transformed address; generate the identifier for the real estate asset from the at least one attribute aggregated in the at least two categories of variables and the transformed address; and perform data processing of the identifier to generate a dataset.

34. A method of using the ID of claim 26 for debt or equity management.

35. A method of using the ID of claim 26 for acquisition and disposition of a real estate asset.

36. A method of using the ID of claim 26 to determine a valuation of a real estate asset.

37. A method of using the ID of claim 26 to provide brokerage services to facilitate a sale, purchase, or lease of a real estate asset.

38. A method of using the ID of claim 26 for education, wherein the ID may be used in analytical and artificial intelligence models to perform research.

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