Systems and methods for assessing property condition

A technique for assessing property condition is provided that determines property condition for an individual property of interest using primary property records specific to the property of interest (e.g., construction records) and secondary property information (weather in the surrounding geographic area, neighborhood characteristics). In certain embodiments, where a primary property record is missing or incomplete, the secondary property information may be used to extrapolate the missing information.

BACKGROUND

The present disclosure relates generally to systems and methods for assessing property condition. More specifically, the present disclosure relates to techniques that facilitate rapid evaluation of a property condition, even in the absence of a physical review of the property. The present disclosure also relates to user interfaces that permit a user to review property condition for a property of interest.

SUMMARY

In one embodiment, a system may include a communication component configured to receive a user input identifying an individual property and a memory storing a file related to the individual property, the file comprising a plurality of property information fields. The system may also include a processor configured receive the user input identifying the individual property; access primary property records associated with the individual property, wherein the primary property records comprise one or more of construction permit information associated with the individual property and an age of the individual property; access secondary property information comprising neighborhood property records for additional properties within a predefined geographic area that includes the individual property and historical weather information relating to the predefined geographic area; populate the plurality of property information fields of the file using the primary property records to generate filled property information fields and missing property information fields; populate the missing property information fields with the secondary property information to generate supplemental filled property information fields; determine a condition of the individual property by accessing the file and using the filled property information fields and the supplemental filled property information fields as variable inputs to a property condition model; generate a property condition output; and provide the property condition output to a user.

Provided herein is also a method that that includes the steps of receiving user input identifying an individual property; creating and storing a file associated with the individual property in response to receiving the user input; accessing primary property records associated with the individual property, wherein the primary property records comprise one or more of construction permit information associated with the individual property and an age of the individual property; accessing secondary property information comprising neighborhood property records for additional properties within a predefined geographic area that includes the individual property and historical weather information relating to the predefined geographic area; populating property information fields of the file using the primary property records and the secondary property information; determining that a minimum set of property information fields is populated in the file; determining a condition of the individual property by accessing the file and using the populated property information fields as variable inputs to a property condition model when the minimum set is populated; generating a property condition output using the property condition model with the variable inputs; and providing the property condition output to a user.

Provided herein is also a method that includes the steps of receiving a request from a user device; determining a location of the user device; accessing primary property records associated with a plurality of individual properties within a predetermined distance of the user device, wherein the primary property records comprise one or more of construction permit information associated with the plurality of individual properties and an age of the plurality of individual properties; accessing weather information for the predetermined geographic area including the plurality of individual properties; estimating a condition of each of the plurality of individual properties based on the primary property records and the weather information; generating a property condition output for each of the plurality of individual properties based on the estimating; and providing the property condition outputs to a user.

DETAILED DESCRIPTION

Home buyers have increasingly relied on internet resources to find and evaluate properties of interest when buying homes. For example, a home buyer may search a database of properties for sale or may rely on internet tools that estimate home value. However, these internet tools do not provide information about the condition of an individual property. Indeed, such determinations are typically made in person and/or after an inspection has been performed. This bottleneck in the home evaluation process may lead home buyers to waste time considering properties that are in subpar condition. Further, ordering inspections is expensive, and these costs may be borne by the home buyer. It would be beneficial to provide a tool for evaluating individual properties that provides a rapid on-demand, e.g., within seconds or minutes from the time of query, estimate of property condition and without requiring an in-person inspection to be performed. In addition, such information may be beneficial for other institutions that rely on home condition. For example, a home buyer or institution may use such a tool to assess potential insurance costs for a property.

To that end, the present techniques provide a tool for evaluating property condition that does not require an in-person inspection and that produces an on-demand condition estimate. The techniques identify properties that are likely to require repairs within a particular time frame. In one embodiment of the disclosed techniques, a user interface is provided that allows users to easily assess the condition of a property, even if the condition assessment is contingent upon multiple complex variables. Provided herein is a novel property condition index, score, or other indicator.

The present techniques provide more rapid and comprehensive home condition analysis relative to other proxy measures for determining home condition, such as a credit score for the property owner or information about notices of potential claims (NPCs) for the property holder. Further, for an on-demand user evaluating neighborhood properties, credit score information of the home owner may be confidential or unavailable for consideration.

By way of introduction,FIG. 1is an example of a system10for property condition assessment that provides an on-demand assessment of one or more individual properties12. The individual properties12may be residential properties (e.g., standalone single family homes, condominium units, townhomes, multifamily dwellings, etc.) and/or may be commercial properties. Included in the system is a user device14from which the initial query regarding property condition is generated. The user device may be a general-purpose personal computer, a laptop computer, a tablet computer, a mobile computer, a mobile device (e.g., cell phone), etc. The system also includes an assessment manager20in communication with the user device14and configured to receive the initial query via a communication component22.

The assessment manager20is also in communication with various sources of primary property records26and secondary property information28. Primary property records26may include recorded information that is specific to the individual property12of interest to the user. For example, such information may be accessed from publically available city records and/or permitting databases30, title databases32, real estate databases34, and/or insurance records36. In one embodiment, the permitting database30may have a record for a construction permit for a new roof for the individual property12. Based on the difference between the permit date and the time of the query from the user device14, the age of the roof may be determined. The permitting database30may also have a record of construction for the individual property12from which the age of the individual property12may be determined as well as the construction materials used. Property age information may also be pulled from a tax assessment database.

In certain embodiment, assessment manager20may also access secondary property information28, i.e., information that is not specific to the individual property12, but instead may provide data about the geographic area that in turn may be used as part of the property condition assessment for the individual property12. Such secondary property information28may be records of nearby properties40. For example, such records may be specific to one or more nearby properties41located within a predetermined geographic distance d (e.g., 1 mile, 5 miles, 10 miles) from the individual property12. In one example, if there is no house age information available in the primary property records26, the assessment manager20may access house ages from the nearby properties41and estimate the house age of the individual property12using rules-based algorithms. For example, if all of the nearby properties41have house ages within a narrow band (e.g., 1-5 years), the assessment manager20may extrapolate that the individual property12has a similar house age.

Other secondary property information may include weather information from a weather database42. The weather information may include number of extreme weather events (flooding, hurricanes) within a predetermined distance (e.g., 50 miles or less) of the individual property12, yearly snow accumulation, yearly rainfall accumulation, wind speed max over a one year period, etc. Additional information may include satellite images of the individual property12and nearby properties41as well as other information from commercial databases46or other types of databases, such as housing survey data or information related to crime in the geographic area in which the individual property12is located.

In another embodiment, secondary property information28may include information related to home renovation activity based on the records for nearby properties40, which may be assessed from the permitting databases30. Because renovation is influenced by neighborhood effects, an individual property12surrounded by nearby properties41with higher-than-average renovation activity may be in better condition. High-than-average activity may be relative to a local, state, or national baseline activity.

The assessment manager20accesses the primary property records26and secondary property information28upon receipt of a user query to obtain information related to the individual property and in turn make an assessment of the property condition.FIG. 2is a block diagram of example components within an exemplary assessment manager20in accordance with present embodiments. However, it should be understood that certain components of the assessment manager20may also be present in the user device14. Accordingly, the following section may also relate to hardware components of the user device14. The assessment manager20may include any suitable computer device, such as a general-purpose personal computer, a laptop computer, a tablet computer, a mobile computer, and the like that is configured in accordance with present embodiments. The assessment manager20may include various types of components that may assist the assessment manager20in performing various types of computer tasks and operations. For example, the assessment manager20may include a communication component22, a processor50, a memory56, input/output (I/O) ports58, a display60, and the like. The communication component22may be a wireless or wired communication component that may facilitate communication between the assessment manager20and various other computing systems via a network, the Internet, or the like.

The processor50may be any type of computer processor or microprocessor capable of executing computer-executable code. The processor50may also include multiple processors that may perform the operations provided herein.

The memory56may be any suitable article of manufacture that can serve as media to store processor-executable code, data, or the like. These articles of manufacture may represent computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor50to perform the presently disclosed techniques. The memory56may represent non-transitory computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor50to perform various techniques described herein. It should be noted that non-transitory merely indicates that the media is tangible and not a signal.

The I/O ports58may be interfaces that may couple to other peripheral components such as input devices (e.g., keyboard, mouse), sensors, input/output (I/O) modules, and the like. The display60may operate to depict visualizations associated with software or executable code being processed by the processor50. In one embodiment, the display60may be a touch display capable of receiving inputs from a user of the assessment manager20. The display60may be any suitable type of display, such as a liquid crystal display (LCD), plasma display, or an organic light emitting diode (OLED) display, for example. Additionally, in one embodiment, the display60may be provided in conjunction with a touch-sensitive mechanism (e.g., a touch screen) that may function as part of a control interface for the assessment manager20.

It should be noted that the components described above with regard to the assessment manager20are exemplary components and the assessment manager20may include additional or fewer components as shown.

In certain embodiments, the memory56stores a property file64that is generated in response to a user initiating a property condition query. The property file includes multiple property information fields66that are populated with information about the property and other variables that may influence property condition, such as extreme weather exposure, etc, as disclosed herein. The property information field designations may be stored in the memory56and accessed as part of generating the property file64. The property information field designations may be universal, e.g., the same across property files64generated for different properties, while the populated information in each field varies as appropriate for respective properties. In one example, the property information field66is a roof material field or a roof age field. In another example, if the property information field66cannot be filled based on one or more of the primary property records26and/or the secondary property information28, the property information field66may be considered a missing field. It should be understood that the property file64may include any suitable number of property information fields66(e.g.,66a,66b,66c, and so on), each representing a different property variable. Examples of such variables may include the following, which are provided as non-limiting examples.1. Year of construction (structure)2. Year of construction (roof)3. Roof type4. Wall type5. Weather conditions of location6. State in which property is located7. High vacancy block group indicator (>=0.16)8. # of shopping centers, 13 km9. Tenure10. # of severe weather days in zip code since 200011. Loss count12. High water-loss frequency zip code clusters13. Age of home specific to high water-loss frequency zip code clusters14. High condition hazard rate states (HCHR) (5 groups: from very low to very high)15. # of severe weather days in zip code since 2000 for HCHR states

In certain embodiments, certain property information fields66may represent a minimum set that, when populated, permit the assessment manger20to proceed with property condition assessment. In such an embodiment, when a property information field66within the minimum set is not populated, an error message may be displayed. In one embodiment, the user may be invited to estimate or provide the missing information. In one example, the assessment manager20may be programmed to only proceed with the property condition assessment as provided herein when information such as property age, roof age, and heating type is available, either from primary property records (e.g., primary property records26, seeFIG. 1) or as estimated or extrapolated from secondary property information (e.g., secondary property information30, seeFIG. 1). Other property information fields66, such as weather, may be assumed to always be available and, accordingly, may not be part of the minimum set. In another embodiment, the property information field66may be populated in a hierarchical manner and from the primary property records26before the secondary property information28. That is, if the primary property records26do not have available data to populate the relevant property information field66, then secondary property information28is used. However, certain property information fields66, such as weather, may be filled only from secondary property information28.

While the property information may be stored as a file, e.g., a table, with populated fields, other data structures are also contemplated. For example, the property information may be stored in a database including information relating to multiple properties.

FIG. 3is a flow diagram of a method100of property condition assessment as provided herein. To initiate the method100, a user may enter a property address into a displayed field of a web-based interface or an application downloaded on the user device (e.g., user device14, seeFIG. 1) to initiate a query of property condition (step102). The user may be an interested home buyer who has received listings from a real estate agent and the method100may be employed to rank or eliminate various properties from consideration. In another embodiment, the user may enter a street address or neighborhood into the query field to perform the method100on a group of properties.

The query information, at least including the home address or other information identifying the individual property, is received by the assessment manager, e.g. assessment manager20, and the assessment manager accesses available primary property records for the individual property (step104) and accesses secondary property information (step106). The available data includes property-specific data, such as date of construction, size of dwelling, estimated property value, etc., as well as data representative of similar properties. The available data from the primary property records and the secondary property information is used to determine variable inputs to a property condition model for the individual property (step108), which in turn generates a property condition output (step110) that is provided to a user (step112).

The techniques disclosed herein model a likelihood that the individual property of interest is in need of repair and/or is in good (e.g. above average) or poor (e.g., below average) condition. The techniques assess the likelihood of a property to have one or more hazard findings. For example, a property with condition hazards that contains substantial deterioration that impacts structural integrity of the property, active (known or unknown) knob and tube (pre-1960s), and missing shingle/tiles (10% of roof or greater) would be categorized as a property found to have a condition hazard. Predicting the number of condition hazards does not necessarily mean that properties with higher numbers of condition hazards are a greater risk than those with fewer number of condition hazards. For example, placing emphasis on properties likely to contain more than one hazard may give the impression that finding more is better. However, it could be that the one hazard that is found is a major hazard. However, other embodiments may be directed to assessing more than one hazard.

In one implementation, a logistic regression is used to provide a binary condition hazard finding=yes/no or 1/0. Techniques for such variables include, but are not limited to, logistic, probit, and complementary log-log models. Logistic regression is used (e.g., a generalized linear model with a logit link function to constrain predictions between 0 and 1) to predict the probability that a property has a condition hazard to guide selecting properties to inspect. The regression equation is the natural log of the probability of finding a condition hazard divided by the probability of not finding a condition hazard following a home inspection, and takes the following form:

ln⁢(p1-p)=α+β1⁢x1+β2⁢x2+βnx⁢n
where:p is the probability that the event occurs [or p(y=1)],(1−p) is the probability that the event does not occur [or p(y=0)]p/(1−p) is the odds,ln[p/(1−p)] is the log odds (or logit),α is the intercept,xi . . . nare the predictor variables, andβi . . . nare the estimated coefficients for variables xi . . . n.

For the model, the variables (x1. . . xn; both quantitative—numeric/continuous/ordered categorical—and qualitative—dummy/nominal/unordered categorical) correspond to the accessed information from primary property records and secondary property information (seeFIG. 1). The βs are estimated using maximum likelihood. In order to estimate the predicted probability (Ŷ) of a condition hazard finding for a property, take the inverse of the logit function, which is:

With regard to the individual variables and their relationship to hazard conditions, older homes are likely to be in poorer conditions than newer homes because of the possible cumulative toll of damage or disrepairs, which are not as likely to occur among newer homes. Likewise, the age of roof is important because of the exposure to weather elements, such as rain, hail, and snow. There is also variation in quality of roof materials. For instance, aluminum roofs may last longer than roofs made of wood. The condition of the home also may depend on how well the home is maintained and many of the condition hazard guidelines are related to maintenance. Homes with existing data were analyzed to test the model.

Table 1 shows example variable thresholds from a potential applied model. For example, houses that are less than 50 years old are not likely to have a condition hazard while houses that are older than 60 years are. The coefficients for the variables may be determined from the training set as provided herein.

TABLE 1Example of Mean of the Distribution for ContinuousVariables for Entire Sample and by Hazard FindingsVariablesAllCH = NoCH = YesYear of construction (structure)50.1748.8160.12Year of construction (roof)16.2915.6820.68

Logit of outcome variable is a linear combination of the independent (predictor) variables. Several non-linear methods were tested and the logit performed among the strongest model forms (excluding ensemble models). A linear relationship between continuous predictors and the logit transform of the outcome variable is observed. Relationships may be linearly or non-linearly related to the logit of the outcome variable. To account for measurement error in the property information, factor analysis may be used in the model. Other assumptions include the absence of multivariate outliers and independence of observations. That is, the outcome of an event (i.e. condition hazard findings) on a property does not depend on another property's finding. An additional assumption was the absence of multicollinearity. Multicollinearity is often tested using the variance inflation factor (VIF).

Results from a potential logistic regression model is shown on Table 2. As homes get older, the likelihood of finding a condition hazard increases; and the results are similar for roof year of construction. The variables provided are by way of example, and the regression model may include additional variables as provided herein (e.g., two or more variables, five or more variables, ten or more variables, twenty or more variables).

For each variable, scores may be assessed. In one example, variables may be associated with a positive or negative score, as shown in Table 3

The method may sum the variable scores to create an index, whereby higher index scores are associated with better maintenance than lower scores. The method may also assess an average score of nearby properties41to determine a neighborhood score.

Table 4 shows a specific embodiment showing examples of variables that may be used to assess property condition. In certain embodiments, all or a subset of the disclosed variables may be used to assess primary property and/or secondary property condition.

TABLE 4Property Assessment VariablesVariable DescriptionYear unit bought/obtained/receivedAge of HomeChange in square footage of unitSmoke detector batteries replaced in last 6 monthsRating of unit as a place to liveUnit has carbon monoxide detectorUnit has a fire extinguisherHouseholder's healthMold present in homeFuses blown or circuit breakers trippedAny inside water leaks in last 12 monthsAny outside water leaks in last 12 monthsAny toilet breakdowns in last 3 monthsSewage system conditionHoles/cracks or crumbling in foundationRoof missing shingles/other roofing materialsRoof has holesRoof's surface sags or is unevenOutside walls missing siding/bricks/etcOutside walls slope/lean/slant/buckleWindows brokenWindows boarded upElectrical wiring concealed by walls/wiringEvery room has working electrical plugOpen cracks wider than dimeHoles in floorArea of peeling paint larger than 8 × 11

In one embodiment, the property condition model may be fit to ordered inspection data. That is, when the model recommends ordering an inspection, the inspection data may then be fed into the model to determine whether the inspection order found a maintenance issue, i.e., a condition concern.

In one embodiment, the techniques use acquired image data (e.g., aerial image data, satellite image data) from available databases to estimate certain condition parameters of the property. Using image analysis techniques, for example, a roof age and/or roof material may be estimated. In another embodiment, the total roof surface area may be estimated from the image data, which in turn may be used as a variable in assessing property condition.FIG. 4is an example of image data120of the individual property12of interest. In one embodiment, the techniques access available image data124of nearby properties. A closest match, shown inFIG. 4as corresponding to the image data124cassociated with nearby property3, triggers the assessment manager to use the roof type and/or age, if available, from the secondary property information, to estimate the roof type and/or age for the individual property12. The image matching may be performed according to suitable techniques, such as pattern matching, feature recognition, etc.

FIG. 5is a schematic diagram of location-based property condition information that is pushed to the user device14located within a predetermined geographic area. For example, for a user that is driving or walking in a particular area, the assessment manager20may identify the user device14, determine its location (e.g., via GPS information), acquire property addresses of individual properties12within the particular area, and automatically push the property condition output130to a display134of the user device14. In one embodiment, the assessment manager20may provide the property condition output130of each individual property12as part of a ranked list. In another embodiment, the user device14may, upon receiving the property condition outputs130, initiate display of a navigation route to an individual property, e.g., individual property12d, indicated as the top-ranked or lowest condition hazard risk property. In another example, the property condition output130may be filtered, so that no properties having greater than a certain percentage of condition hazard are provided to the user device14.

As provided herein, the disclosed techniques provide a condition hazard output, which may be a likelihood of repair within a time frame or a percentage change of a necessary repair being identified during an inspection of the property. The techniques may specify which types of repairs qualify as condition hazards based on their estimated cost to complete and category. For example, a condition hazard may be identified as a faulty roof or a roof that is likely to need replacement within a predetermined time period (e.g., 12 months-36 months from the date of the query). As discussed herein, the techniques may be trained on existing homes and acquired data to determine that, for example, for properties with roofs that are 7 years old or older, there is a 40% chance of a roof replacement within the predetermined time period. A newer roof (e.g., 5 years old or newer) may be associated with a lower percentage chance, e.g. 10% or less of repair. Similarly, for houses with a particular heating type, the likelihood of repair may be a different estimate based on the heating type itself, the age of the house, etc. The techniques may consider a single identified likely repair as triggering an output of a likely condition hazard. Further, not all repair types may be part of the property condition assessment. However, a user may find it beneficial to know that, in general, a particular property has a high likelihood of needing a repair within a particular time frame. The property condition output130may also include parameters or definitions indicating the predetermined time frame and the criteria for assessing what types of likely repairs rise to the level of a condition hazard (e.g., $5000 or greater).

FIGS. 6 and 7are examples of property condition outputs130that may be provided to a user. In one embodiment, the information includes a binary indication of condition hazard likelihood. For example, as shown inFIG. 6, the property condition output130indicates a YES for likelihood that a property inspection for a particular individual property would find a repair, and further provides a percentage likelihood. Based on this information, the user may decide to eliminate this property from consideration or may decide to order a home inspection. In another example, as shown inFIG. 7, the property condition output130indicates a NO for likelihood that a property inspection for a particular individual property would find a repair, and further provides a relatively lower percentage likelihood. Based on this information, the user may decide to consider this property or may decide to forego (i.e., not order) a home inspection. The separation between a YES and NO determination may be at a predetermined percentage likelihood (e.g., equal to or greater than 50% likelihood is YES and less than 50% is NO). In one example, on-demand property condition information that does not require an in-person inspection, as provided herein, may also be beneficial to users interested in foreclosed or auctioned properties that are considered “as-is.” Further, the on-demand property information may reduce costs associated with ordering home inspections.

The disclosed embodiments provide the technical effect of addressing the internet-specific problem of providing on-demand estimates (e.g., within five minutes or less of making the initial query) of property condition without requiring in-person inspection of the property. That is, the disclosed embodiments eliminate the bottleneck of in-person property inspections. The disclosed embodiments assemble data from a wide variety of information sources to make estimates about property condition using primary and secondary data. The present techniques provide improved property condition estimates relative to using single variables (e.g., age of home, most-recent claim, market value).