SYSTEMS AND METHODS FOR PREDICTING THE VALUE OF PERSONAL PROPERTY

Accurate, appropriate valuation of the contents of a residence is facilitated based on characteristics of the household and the residence. These factors are used to estimate the proper value of the contents and may be based, at least in part and in various embodiments, on information collected in the course of the insurance underwriting process and from public and non-public consumer spending data.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

FIG. 1illustrates the operation of a representative embodiment of the present invention.

Although the embodiment involves an insurance application, this is solely for purposes of illustration, and it should be understood that the principles of the invention may be applied outside the insurance context.

Policyholder data is stored in a database102, e.g., in the form of a database record associated with each policyholder. The policyholder data may be collected during underwriting or otherwise obtained, and may comprise, without limitation, information including categories of personal property covered by the policyholder's insurance policy, the policyholder's geographic location, and data indicative of when the policyholder became an independent adult consumer.

Claim data is stored in a database103, e.g., in the form of a database record associated with each policyholder. Claim data includes characteristics and values associated with items of personal property that were the subject of actual claims against insurance policies. This data represents the value of personal property by product category actually present in households based on insurance claim data. As explained in greater detail below, this data may be used to calculate a depletion factor.

A bulk source of consumer spending data is also illustrated as stored in a database104, but in fact the database is typically associated with government, academic or other professional sources specializing in this type of data and making it available, freely or by subscription, over the Internet (where it may be accessed by a computer in step106). The bulk source of consumer spending data104accessed in step106may be public and/or non-public, and in some embodiments of the present invention, the bulk source of consumer spending data104may consist of or include the Consumer Expenditure Survey (“CEX”) conducted by the U.S. Bureau of Labor Statistics.

In step106the computer accesses the bulk source of consumer spending data, and in step108computationally segregates the data to extract only those categories of expenditures that are covered under the policyholder's homeowner's insurance, such as clothing, food, electronics and jewelry (and excluding other categories such as travel, movies, gasoline and cable TV). In this way, the bulk spending data is processed to include as many relevant categories of goods as possible, and to exclude as many irrelevant categories as possible. Since the ultimate objective is to provide an estimate, great precision is not necessary.

The consumer spending data may be further parsed based on characteristics common to policyholders and relevant to the value of contents, e.g., geographic location and demographic variables (such as income levels, marital status, age, gender, and size of household). If the bulk consumer spending data is or may be segregated according to such variables—e.g., in tiers each corresponding to a range, such as income levels), then the data may be further tailored to each policyholder record in the database102to the extent the records contain values for these variables.

In these ways, the bulk consumer spending data is filtered based on relevant characteristics of the policy and of the individual policyholder. The computer then compiles an aggregate lifetime spending amount110for each policyholder based on the segregated spending data and the amount of time that has elapsed since the policyholder became an adult consumer. Finally, the computer may computationally adjust the coverage amount by applying at least one depletion factor (step112) to at least one category of insured personal property indicative of an average property retention duration. This depletion factor may be derived using historical property claim data relevant to the geographic and demographic variables.

In some embodiments, the coverage amount is further adjusted (step113) by applying thereto at least one filtering factor which either increases or decreases a category of spending based demographic variables. For example, if the household contains no male children, all spending data related to male children is eliminated. Similarly, if there are several male children present, the factor will more heavily weight the spending data related to male children.

The coverage amount may optionally be further adjusted (step114) by applying thereto at least one depreciation factor indicative of an average decrease in value of personal property over time. Typically the depreciation factor is applied to the cost of the insured personal property after the depletion factor, yielding the ITV amount116. Of course, if the depreciation and depletion factors are static coefficients, or even if they vary over time and are applied as time series, their order of application should not matter. But in embodiments where the value of one or both factors depends on the compiled coverage amount to which it is applied, the order can be important. Furthermore, depending on the nature of the policy, either or both factors may be omitted. For example, depreciation may not be relevant in the context of a full replacement-cost policy.

The policyholder's premium levels118may be computationally calculated based on the ITV amount116. Alternatively or in addition, a risk score120for the policyholder may be computationally calculated, and this score may be based on a plurality of geographic and demographic risk-exposure variables whose values are contained in databases102and103. Furthermore, claim reserve requirements122for an insurance company may be computed, for a particular policy, based on the ITV replacement cost value (“RCV”) or the ITV actual cost value (“ACV” or actual cash value) for a given policy multiplied by a percentage factor representing an estimate by claim adjusters of the portion of the total value of personal or business property that will be the subject of a property claim.

FIG. 2illustrates a representative system200for implementing the techniques described above. The system is typically implemented in a central computing device, described in greater detail below, that has a central processor, memory, mass storage, input/output facilities, a display, etc., all of which are conventional and not shown inFIG. 2. A coverage determination module202communicates with a segregation module204, which accesses a bulk source206of public and/or non-public consumer spending data by means of a conventional communication module216, which is typically configured for communication over local and wide-area networks; for example, source206may be accessed via the Internet. The segregation module204segregates the spending data for each policyholder based on the variables discussed above. The coverage determination module202accesses data from the segregation module204and compiles a coverage amount for each policyholder based on the segregated spending data and data specific to each policyholder.

Policyholder data is stored in a policyholder database212, which contains information collected during underwriting or otherwise obtained regarding each policyholder. Additionally, the coverage determination module202communicates with a depletion module208and a depreciation module210, which apply depletion and depreciation factors, respectively, to coverage amounts computed by the coverage determination module202. The depreciation factors applied by the depreciation module210are indicative of the decrease in value of the insured personal property over time.

The modules208,210may draw upon a depletion and depreciation database214for depletion factors and/or depreciation factors or variable data useful in the computation thereof. The depletion and depreciation database214may, for example, contain depletion factors to be applied to categories of personal property, which may in turn depend on the category of personal property, the amount of time since the policyholder became an adult consumer, and/or demographic information about the policyholder's household. The depletion and depreciation database214may also contain depreciation factors to be applied to categories of personal property, which may depend on the category of personal property, the amount of time since the policyholder became an adult consumer, and demographic information about the policyholder's household.

For instance, some categories of insured personal property, such as children's clothing and toys, may be depleted from the insured personal property as children age, as the policyholders donate items to charity or pass them on to others. Other categories of insured personal property, such as food, may be depleted relatively quickly from the policyholder's ownership. Still other categories of insured personal property, such as clothing, jewelry, or furniture, may have much longer ownership timeframes. The application of depletion factors to the aggregate lifetime spending yields the RCV of the policyholder's personal property, and the application of depreciation factors to the RCV yields the ACV of the policyholder's personal property.

In addition, the depletion factor may be computed based also on the contents of a claim database215, which contains records specifying historical personal property claims across a plurality of insurance carriers. The records comprise data relating to claims paid to policyholder claimants for each insurance carrier, and the data may include or consist of (i) at least one category of claimed personal property, (ii) an adjusted replacement cost value (RCV) for each category of claimed personal property, (iii) quantities and ages of items of claimed personal property, (iv) depreciation applied by the carrier to items of the claimed personal property, (v) brands and vendors for items of the claimed personal property, and (vi) policy limits applied by the carrier for each claimed category of personal property. This data is helpful to computation of a depletion factor because it reflects actual RCV data compiled in the course of claims payment. Accordingly, in some embodiments, the depletion module210accesses this data and computes the depletion factor based at least in part thereon. For statistical accuracy, large numbers (e.g., more than 200) of insurance carriers and larger numbers of actual claims paid (e.g., more than 10,000) across statistically varied geographies are desirable.

In alternative embodiments, the depletion and/or depreciation factors are computed more generically, e.g., based on broad statistical modeling or publicly available data, which is desirably, although not necessarily, differentiated among policyholders to reflect differing demographic characteristics. The objective, as explained above, is to model current property holdings based on historical spending estimates.

In some embodiments, a policy-generation module220assembles an insurance policy for a policy applicant based on the computed RCV of the applicant's personal property, the information supplied by the applicant in his or her policy application, and the criteria conventionally employed by the insurance carrier in writing homeowners' policies. The policy may be furnished to the applicant in paper and/or electronic form.

The various modules described above may be implemented by computer-executable instructions, such as program modules, executed by a conventional computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that performs particular tasks or implement particular abstract data types. Those skilled in the art will appreciate that the invention may be practiced with various computer system configurations, including hand-held wireless devices such as mobile phones or PDAs, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer-storage media including memory storage devices.

The central computing device200may comprise or consist of a general-purpose computing device in the form of a computer including a processing unit, a system memory, and a system bus that couples various system components including the system memory to the processing unit. Computers typically include a variety of computer-readable media that can form part of the system memory and be read by the processing unit. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. The system memory may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) and random access memory (RAM). A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements, such as during start-up, is typically stored in ROM. RAM typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit. The data or program modules may include an operating system, application programs, other program modules, and program data. The operating system may be or include a variety of operating systems such as Microsoft WINDOWS operating system, the Unix operating system, the Linux operating system, the Xenix operating system, the IBM AIX operating system, the Hewlett Packard UX operating system, the Novell NETWARE operating system, the Sun Microsystems SOLARIS operating system, the OS/2 operating system, the BeOS operating system, the MACINTOSH operating system, the APACHE operating system, an OPENSTEP operating system or another operating system of platform.

Any suitable programming language may be used to implement without undue experimentation the data-gathering and analytical functions described above. Illustratively, the programming language used may include assembly language, Ada, APL, Basic, C, C++, C*, COBOL, dBase, Forth, FORTRAN, Java, Modula-2, Pascal, Prolog, Python, REXX, and/or JavaScript for example. Further, it is not necessary that a single type of instruction or programming language be utilized in conjunction with the operation of the system and method of the invention. Rather, any number of different programming languages may be utilized as is necessary or desirable.

The computing environment may also include other removable/nonremovable, volatile/nonvolatile computer storage media. For example, a hard disk drive may read or write to nonremovable, nonvolatile magnetic media. A magnetic disk drive may read from or writes to a removable, nonvolatile magnetic disk, and an optical disk drive may read from or write to a removable, nonvolatile optical disk such as a CD-ROM or other optical media. Other removable/nonremovable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The storage media are typically connected to the system bus through a removable or non-removable memory interface.

The processing unit that executes commands and instructions may be a general purpose computer, but may utilize any of a wide variety of other technologies including a special purpose computer, a microcomputer, mini-computer, mainframe computer, programmed micro-processor, micro-controller, peripheral integrated circuit element, a CSIC (Customer Specific Integrated Circuit), ASIC (Application Specific Integrated Circuit), a logic circuit, a digital signal processor, a programmable logic device such as an FPGA (Field Programmable Gate Array), PLD (Programmable Logic Device), PLA (Programmable Logic Array), RFID processor, smart chip, or any other device or arrangement of devices that is capable of implementing the steps of the processes of the invention.

The network over which communication takes place may include a wired or wireless local area network (LAN) and a wide area network (WAN), wireless personal area network (PAN) and/or other types of networks. When used in a LAN networking environment, computers may be connected to the LAN through a network interface or adapter. When used in a WAN networking environment, computers typically include a modem or other communication mechanism. Modems may be internal or external, and may be connected to the system bus via the user-input interface, or other appropriate mechanism. Computers may be connected over the Internet, an Intranet, Extranet, Ethernet, or any other system that provides communications. Some suitable communications protocols may include TCP/IP, UDP, or OSI for example. For wireless communications, communications protocols may include Bluetooth, Zigbee, IrDa or other suitable protocol. Furthermore, components of the system may communicate through a combination of wired or wireless paths.

While particular embodiments of the invention have been illustrated and described in detail herein, it should be understood that various changes and modifications might be made to the invention without departing from the scope and intent of the invention. For example, embodiments of the invention may be deployed more generically as a workflow system300for assembling a predicted inventory of all personal property present in a home or business, as shown inFIG. 3. In this case, a bulk source of consumer or business spending data is again used, and the coverage determination module202is replaced with a module225for determining aggregate lifetime spend, which performs functions similar to that of module202. In particular, the module225accesses data from the segregation module204and compiles an aggregate lifetime spending amount for at least one category of personal property based on the segregated spending data and an amount of time since an inception date—i.e., when a homeowner became an adult consumer or when a business began operations. The depletion module208applies to the aggregate lifetime spending amount at least one depletion factor indicative of an average property retention duration for the at least one category of personal property.