Patent Application
20180341958
This application is cross-related to the following U.S. Patent Applications: (Attorney Docket No. 126129.1003) U.S. patent application Ser. No. ______, entitled “System and Method for Generating Specialty Property Demand Index,” filed May ______, 2018; (Attorney Docket No. 126129.1303) U.S. patent application Ser. No. ______, entitled “System and Method for Generating Cost Estimates for Specialty Property,” filed May ______, 2018; (Attorney Docket No. 126129.1603) U.S. patent application Ser. No. ______, entitled “System and Method for Generating Same Property Cost Growth Estimate in Changing Inventory of Specialty Property,” filed May ______, 2018; (Attorney Docket No. 126129.1703) U.S. patent application Ser. No. ______, entitled “System and Method for Generating Variable Importance Factors in Specialty Property Data,” filed May ______, 2018; (Attorney Docket No. 126129.1803) U.S. patent application Ser. No. _______, entitled “System and Method for Generating Indexed Specialty Property Data Influenced by Geographic, Econometric, and Demographic Data,” filed May ______, 2018; (Attorney Docket No. 126129.1903) U.S. patent application Ser. No. ______, entitled “System and Method for Identifying Outlier Data in Indexed Specialty Property Data,” filed May ______, 2018; (Attorney Docket No. 126129.2003) U.S. patent application Ser. No. ______, entitled “System and Method for Generating Indexed Specialty Property Data From Transactional Move-In Data,” filed May ______, 2018. Each of these are incorporated by reference in their entireties herein for all purposes.
Specialty property, such as senior living and assisted care facilities, are growing in demand in the United States and other countries due to a rapidly aging population. As modern medical breakthroughs allow for longer and more actives lives, the demand for senior living facilities continues to rise. Predicting the cost and demand for specialty property can be a difficult task with disparate information available across disparate social, geographic, econometric and demographic strata.
Further, various conventional methods for predicting cost and demand in more conventional properties does not take into account a number of factors unique to specialty property demand and cost, such as the services that may typically be purchased at the initial transaction. Further yet, additional externalities such as walkability and service provider availability may further cloud the ability to predict cost and demand data for specialty properties across ever-changing geographic, econometric and demographic populations.
The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations, and are not intended to limit the scope of the present disclosure.
Corresponding reference numerals indicate corresponding parts throughout the several views of the drawings.
The subject matter of embodiments disclosed herein is described here with specificity to meet statutory requirements, but this description is not necessarily intended to limit the scope of the claims. The claimed subject matter may be embodied in other ways, may include different elements or steps, and may be used in conjunction with other existing or future technologies. This description should not be interpreted as implying any particular order or arrangement among or between various steps or elements except when the order of individual steps or arrangement of elements is explicitly described. Embodiments will be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, exemplary embodiments by which the systems and methods described herein may be practiced. The systems and methods may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy the statutory requirements and convey the scope of the subject matter to those skilled in the art.
Among other things, the present subject matter may be embodied in whole or in part as a system, as one or more methods, or as one or more devices. Embodiments may take the form of a hardware-implemented embodiment, a software implemented embodiment, or an embodiment combining software and hardware aspects. For example, in some embodiments, one or more of the operations, functions, processes, or methods described herein may be implemented by one or more suitable processing elements (such as a processor, microprocessor, CPU, controller, or the like) that is part of a client device, server, network element, or other form of computing device/platform and that is programmed with a set of executable instructions (e.g., software instructions), where the instructions may be stored in a suitable data storage element. In some embodiments, one or more of the operations, functions, processes, or methods described herein may be implemented by a specialized form of hardware, such as a programmable gate array, application specific integrated circuit (ASIC), or the like. The following detailed description is, therefore, not to be taken in a limiting sense.
Prior to discussing specific details of the embodiments described herein, a brief overview of the subject matter is presented. Generally, one or more embodiments are directed to systems, apparatuses, and methods for enabling a user to collect, assemble, manipulate, and utilize data regarding cost in one or more specific markets about specialty properties, such as assisted living, long-term care facilities, and the like. Several factors will affect a specific market and the ebb and flow of regional costs, regional cost, regional demographics, and regional econometrics. Further, intra-regional and extra-regional data may also reflect the behavior of individuals in a market based on additional factors. Collecting this data and assigning relative values to the data based on follow-on activities, such as actual inquiries into property, lead generation for specific properties and move-in data for specific properties leads to an ever-changing cost index that is continuously updated through a machine-learning algorithm by which cost index data may be gleaned at any given moment in time for any specific region. These and other aspects of the specific embodiments are discussed below with respect to
The environment 100 may further include several additional computing entities for data collection, provision, and consumption. These entities include internal data collectors 110, such as employee computing devices and contractor computing devices. Internal data collectors 110 may typically be associated with a company or business entity that administers the server computer 105. As such, internal data collectors 110 may also be located behind the firewall 108 with direct access to the server computer (without using any external network 115). Internal data collectors may collect and assimilate data from various sources of data regarding specialty properties. Such data collected may include data from potential resident inquiries, leads data from advisors working with/for the business entity, and move-in data from property owners and operators. Many other examples of collected data exist but are discussed further below with respect to additional embodiments. The aspects of the specific data collected by internal data collectors 110 is described below with respect to
The environment 100 may further include external data collectors 117, such as partners, operators and property owners. Internal data collectors 110 may typically be third party businesses that have a business relationship with the company or business entity that administers the server computer 105. External data collectors 110 may typically be located outside of the firewall 108 without direct access to the server computer such that credentials are used through the external network 115. Such data collected may include data from potential resident inquiries, leads data from advisors working with/for the business entity, and move-in data from property owners and operators. Many other examples of collected data exist but are discussed further below with respect to additional embodiments. The aspects of the specific data collected by external data collectors 117 is also described below with respect to
The environment 100 may further include third-party data providers 119, that includes private entities such as the American Community Survey (ACS) as well as public entities such as the US Department of Housing and Urban Development (HUD). These third-party data providers may provide geographic, econometric, and demographic data to further lend insights into the collected data about potential resident inquiries, leads, and move-in data. Many other examples of third-party data exist but are discussed further below with respect to additional embodiments.
The environment 100 may further include primary data consumers 112, such as existing and potential residents as well as service providers. The environment 100 may further include, and third-party data consumers 114, such as Real-Estate Investment Trusts (REITs), financiers, third-party operators, and third-party property owners. These primary data consumers 112 and third-party data consumers 114 may use the assimilated data in the database collected from data collectors and third parties to glean information about one or more specialty property markets. Such data consumed may include the very data from potential resident inquiries, leads data and move-in data. Many other examples of consumed data exist but are discussed further below with respect to additional embodiments as well as discussed in related patent applications.
Collectively, the data collected and consumed may be stored in the database 106 and manipulated in various ways described below by the server computer 105. Prior to discussing aspects of the operation and data collection and consumption as well as eth cultivation of the database, a brief description of any one of the computing devices discussed above is provided with respect to
As an example,
It should be understood that the present disclosure as described above can be implemented in the form of control logic using computer software in a modular or integrated manner. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will know and appreciate other ways and/or methods to implement the present disclosure using hardware and a combination of hardware and software.
Any of the software components, processes or functions described in this application may be implemented as software code to be executed by a processor using any suitable computer language such as, for example, R, Java, JavaScript, C++ or Perl using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions, or commands on a computer readable medium, such as a random access memory (RAM), a read only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a CD-ROM. Any such computer readable medium may reside on or within a single computational apparatus, and may be present on or within different computational apparatuses within a system or network.
The machine-learning module 350 may include lists of data delineated by various identifications that are indicative of the type and nature of the information stored in the ordered lists. At the outset, these lists, in this embodiment, include a first list of lead data called DIM_LEAD 325. A lead includes data about an individual who is interested in acquiring rights and services at a specialty property and each record in DIM_LEAD 325 may be identified by a LEAD_ID. In this embodiment, the rights and services may include rents and personal care services at a senior living facility. In other embodiments, the specialty property is not necessarily a senior care facility or senior housing. The LEAD_ID may also include specific geographic data about a preferred location of a specialty property. The data that populates this list may be received at the machine-learning module 350 via a data collection module 321 that facilitates communications from various data collectors and third-party data providers as discussed with respect to
Another list of data includes data about various properties in the pool of available or used specialty properties and this list is called DIM_PROPERY 326. The records in this list may include data about services provided at each property as well as cost data, availability, and specific location. DIM_PROPERY records may also include a history of property attributes over time for each PROPERTY_ID, so that leads can be matched to the property with each respective leads attributes. Records in DIM_PROPERY 326 are identified by a unique identifier called PROPERTY_ID. The data that populates this list may be received at the machine-learning module 350 via a data collection module 321 that facilitates communications from various data collectors and third-party data providers as discussed with respect to
Another list of data includes data about various geographic locations in the pool of available or used specialty properties and this list is called DIM_GEOGRAPHY 327. The records in DIM_GEOGRAPHY 327 may include data about the geographic locations of all properties such as ZIP code, county, city, metropolitan area, state, and region. The records here may also include data about weather associated with various geographic location along with time and season factors. For example, one could collect data about time-stamped weather event to examine the impact of weather on the cost index. Records in this list are identified by a unique identifier called GEOGRAPHY_ID. The data that populates this list may be received at the machine-learning module 350 via a data collection module 321 that facilitates communications from various data collectors and third-party data providers as discussed with respect to
All data from these various lists of data may be updated from time-to-time as various events occur or new data is collected or provided by various data collectors and third-party data providers via data collection module 321. As events takes place, a new conglomerate list, FACT_LEAD_ACTIVITY 330, may be initiated and populated with various events that occur along with associated relevant data from the lists. Records in FACT_LEAD_ACTIVITY 330 include data with regard to lead events and move-in events. A lead event is defined as the event in which an advisor refers a specific property to a potential user of services. A move-in event is defined as an event in which a user of services moves into a recommended property from a lead. As such, the records will also include specific data about the dates of the activity underlying the event as well as specific data about the recommended property (e.g., cost, location, region, demographics of the area) and the user (or potential user) of services (e.g., demographics, budget, services desired).
As mentioned, all data from these various lists of data may be updated from time-to-time as various events occur or new data is collected or provided by various data collectors and third-party data providers via data collection module 321. When an action takes place, such as a referral of a property to a lead or a lead moving in to a referred property, an activity record may be created in the list FACT_LEAD_ACTIVITY 330. This information may include data drawn from the initial three lists discussed above when a specific action takes place. Thus, each record will include a LEAD_ID, a PROPERTY_ID, and a GEOGRAPHY_ID that may be indexed with additional data such as activity type (e.g., referral or move-in) and activity date. For example, a new inquiry may be made, a new lead may be generated, a new property may become part of the property pool, geographic data may be updated as ZIP codes or city/county lines shift, and the like. Further, collected data could be used to update or populate DIM_PROPERY 326, DIM_LEAD 325, DIM_GEOGRAPHY 327 and FACT_LEAD_ACTIVITY 330 in that collected data about economics, demography, and geography (including weather) may be assimilated in any of the lists discussed above.
All data in FACT_LEAD_ACTIVITY 330 may be used by an analytics module 320 to generate several manners of data for use in the system. An operator may enter various analytical constraints and parameters using the operator input 322. The analytics module 320 may be manipulated such operator input to yield a desired analysis of the records stored in FACT_LEAD_ACTIVITY 330. Generally speaking, the data that may be assembled from the FACT_LEAD_ACTIVITY list 330 includes indexed referrals data 334 and indexed move-ins data 336. Such assembled data may be used to generate various cost and demand indexes and probabilities for a specialty property market across the several geographic, economic, and demographic categories. This useful indexed data across the operator desired constraints and parameters may then be communicated to other computing devices via communications module 340.
a. from DIM_PROPERTY: Property location, including ZIP code, city, county, state;
b. from FACT_LEAD_ACTIVITY:
i. First-month rent and care charges;
ii. Type of move-in (e.g., assisted living vs. independent living for senior living communities);
iii. Calendar date of move;
c. From third-party private and public data:
i. Mapping of property locations to Core-Based Statistical Area, United States Census Division, and United States Census Region Demographic variables (e.g., local median household income, local median housing rents, local median home sale price, proportion of population by age group) applicable to each property and each geographic unit in step 1a;
ii. Mapping of property locations to Core-Based Statistical Area, United States Census Division, and United States Census Region Demographic variables (e.g., local median household income, local median housing rents, local median home sale price, proportion of population by age group) applicable to each property and each geographic unit in step 1a.
Using the United States National Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) retrieved at step 352, the algorithm then adjusts the first-month rent and care charges assembled from step 350 for inflation relative to the most recent completed calendar year at step 354. In one embodiment, the adjustment may be accomplished using by using the retrieved monthly CPIAUCSL indices as a numerator in a division with each month's index value by the average monthly index value for the most recent calendar year. Then, the algorithm may divide nominal move-in charges by the adjustment factor calculated in step to arrive at inflation-adjusted data.
At step 356, the algorithm may then detect outlier move-in charges using the following method: i) separately count the number of move-ins by property as delineated by core-based statistical area, e.g., state, census division, and census region, ii) compute the average inflation-adjusted move-in charges by property, calendar year, and type of move-in and iii) hierarchically average and find the standard deviation of the inflation-adjusted move-in charges starting from the property-level averages and ending with region-level averages, iv) for each move-in, define the most granular level of same-year, same-move-in-type aggregation at which there exist at least ten move-ins including said move-in, and v) for each move-in, define it as an outlier if its log-transformed, inflation-adjusted move-in charges lie outside of three standard deviations of the log-transformed average inflation-adjusted move-in charges at the level of aggregation defined.
Once outlier data is identified, the algorithm may use an initial sets of data for training, test, and validation purposes. Such a step excludes the outliers detected in step 356. The algorithm trains and optimizes a boosted generalized additive model for location, scale, and shape from a Gaussian family, with predictive features generated from the information described in step 350, and with log-transformed, inflation-adjusted move-in charges as the outcome variable. At step 362, the algorithm uses the model trained in step 360 to compute the predicted mean and standard deviation of the distribution of log-transformed, inflation-adjusted move-in charges for each ZIP code and move-in type in the dataset.
At step 364, the algorithm, uses relevant census data to estimate the count of eligible specialty property consumers (e.g., adults age 65 or older for senior living communities) in each ZIP code in the dataset. At each level of geographic granularity, the algorithm further estimates for each ZIP code in each location the share of eligible specialty property consumers who live in that ZIP code.
At step 366, the algorithm uses the consumer population shares computed in steps 364 and the predictions calculated in step 362 to compute the weighted average of the predicted mean and standard deviation across all ZIP codes in each location and for each move-in type and level of geographic granularity.
The weighted-average mean and standard deviations for a given location and move-in type can be used to generate, at step 368, any summary statistics of interest (e.g., average, median, interquartile range, 35th percentile) pertaining to the log-transformed, inflation-adjusted move-in charges for that local and move-in type. Summary statistics pertaining to log-transformed move-in charges can be easily back-transformed to the inflation-adjusted dollar scale, usually by the simple exponentiation of the results.
Regardless of the entity conducting the data collection, the event of the inquiry is converted into an indexed record at step 446 that includes various attributes about the inquiry, such as the inquirer's desired budget, desired service level or care needs, desired location, age, time-horizon and the like. Based on the provided data, the advisor may recommend a series of potential properties to the lead at step 447. Some of this initially collected data, such as budget data, may be sent to a machine-learning algorithm 150 at the time the data is collected. This data may be used to populate and/or update DIM_LEAD 325 as discussed above with respect to
As various properties are recommended at step 448, each recommendation generates a “Lead Referral” (which is a tracked activity in FACT_LEAD_ACTIVITY 330) that includes sending lead data to the machine-learning algorithm 150. Further yet, as various leads actually move in to a recommended property at step 450, each move-in generates a “Move-In” event (which is also a tracked activity FACT_LEAD_ACTIVITY 330) that includes sending move-in data to the machine-learning algorithm 150. With all this indexed data being input to the machine-learning algorithm 150, analytics can be used to determine future cost for various property types in the form of projected cost growth probability at step 462. Put another way, a specialty property cost index may be generated based on all past and current data collected through the method of
The example given above with respect to
Another example includes a health care REIT aiming to compare its senior living portfolio to a broader market. Thus, the methods and algorithms disclosed herein provide a means for looking at different cost growth rates across markets as well as market share growth by location. The health care REIT may check comparison stability across various cost stages in various markets.
Yet another example includes a health care REIT wishing to understand which markets may be performing best among consumers with large budgets. Thus, the method and algorithms described above may be used to compare forecasts on a budget category basis. Such a comparison may reveal a specific consumer budget category that exhibits superior cost growth.
All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and/or were set forth in its entirety herein.
The use of the terms “a” and “an” and “the” and similar referents in the specification and in the following claims are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms “having,” “including,” “containing” and similar referents in the specification and in the following claims are to be construed as open-ended terms (e.g., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely indented to serve as a shorthand method of referring individually to each separate value inclusively falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments and does not pose a limitation to the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to each embodiment of the present disclosure.
Different arrangements of the components depicted in the drawings or described above, as well as components and steps not shown or described are possible.
Similarly, some features and sub-combinations are useful and may be employed without reference to other features and sub-combinations. Embodiments have been described for illustrative and not restrictive purposes, and alternative embodiments will become apparent to readers of this patent. Accordingly, the present subject matter is not limited to the embodiments described above or depicted in the drawings, and various embodiments and modifications can be made without departing from the scope of the claims below.
This application claims the benefit of U.S. Provisional Application No. 62/510,993 entitled “System and Method for Generating Specialty Property Cost Index,” filed May 25, 2017, which is incorporated by reference in its entirety herein for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 62510993 | May 2017 | US |
