Patent Application
20150178827
The present disclosure relates to data processing methods for generating and applying computer models for estimating the risk that a certain type of event will occur.
In the United States, real estate taxes are assessed by various taxing authorities. The taxes are generally based on the values of properties, including land. The taxing jurisdictions include counties, cities, towns, boroughs, and schools. Different locations have different types of property taxes. For example, a property in Illinois may only have county taxes assessed, but a property in Texas could have county, city and school taxes assessed.
Mortgage lenders and servicers need to track and pay taxes on escrowed and non-escrowed loans. Servicers disburse taxes to the taxing authorities on escrowed loans from borrowers' escrow accounts. For non-escrowed loans, servicers monitor delinquent taxes and request proof of payment from the borrowers. If a response is not received, servicers commonly advance funds to make the payment. This protects the lender's interest in the property and avoids a tax lien being placed on the property. When a tax lien is placed on a property it often extinguishes the mortgage lien. When incorrect or late tax payments are made by servicers, it incurs penalty and late fees that are not reimbursed by the lenders.
No more than about 2-3% of the properties in a mortgage portfolio are typically tax delinquent at a time. The task of identifying which of the many thousands of properties in a mortgage portfolio are tax delinquent, or at risk of soon becoming tax delinquent, is very labor intensive and time consuming. The failure to promptly identify such properties can be very costly to lenders and services.
A computer model of tax delinquency risk is generated by analyzing historical data, including mortgage loan data, associated with real estate properties that have become property tax delinquent. The model is used to generate property-specific scores representing the likelihood that the corresponding properties will become tax delinquent (absent lender or servicer intervention) within a selected time period, such as six months. The scores may, for example, be used by a mortgage lender or servicer to identify loans/properties for which to take preemptive action to avoid tax delinquency.
Neither this summary nor the following detailed description purports to define the invention. The invention is defined by the claims.
Specific, non-limiting embodiments will now be described with reference to the drawings. Nothing in this description is intended to imply that any particular feature, component or step is essential. The inventive subject matter is defined by the claims.
As discussed below, the tax delinquency predictor 10 generates the scores using data regarding corresponding mortgage loans, such as mortgage payment history data. Mortgage payment history tends to be very useful for predicting future property tax delinquency because mortgage payments are ordinarily due more frequently than property tax payments. (Typically, mortgage payments are due monthly, while property taxes are due quarterly, bi-annually or annually.) Because of this difference in payment frequency, borrowers usually become delinquent on their mortgage payments before becoming delinquent on property tax payments. The tax delinquency predictor 10 preferably takes advantage of this characteristic by relying relatively heavily on the borrower's recent mortgage payment history.
The scores generated by the tax delinquency predictor for a given borrower may also take into account such factors as (1) the amount of time until the borrower's next property tax payment is due, (2) the amount of this payment, (3) the amount or percentage of change between this payment and the preceding property tax payment, and (4) whether this borrower has previously been property-tax-delinquent on the subject property or another property. Where property tax amounts are considered, these amounts may be looked up from property tax records, or may be estimated based on an AVM-based or HPI (Housing Price Index) based valuations and associated tax rates and rules of the relevant jurisdictions.
The scores generated by the system can be used in various ways. For example, a lender or mortgage servicer can use the scores to identify high-risk borrowers and properties for which to take preemptive actions. Such preemptive actions may include, for example, contacting the borrower to negotiate payment, or making a property tax payment on behalf of the borrower to prevent the property from becoming delinquent. For instance, a lender may obtain tax delinquency risk scores for all properties/mortgages in its portfolio, and may use these scores to rank the mortgages in terms of tax delinquency risk. The lender may then use the list to select properties/borrowers for which to take preemptive action. The system is particularly useful for non-escrowed mortgages, as lenders ordinarily have little or no advance warning of imminent tax delinquency for such accounts. As discussed below with reference to
As shown in
The second data repository 16 contains property tax data for specific real estate properties, which may be identified by parcel number and/or property address. The tax data stored for a given property may include, for example, tax amounts due, associated due dates, payment/delinquency history, and current delinquency status. In one embodiment, this data repository 16 is generated based on tax data collected from the public assessor offices in various jurisdictions throughout the United States.
As shown in
As shown by the data repository 18 in
Other types of property-related attributes may also be considered, including attributes that are not tied to a particular mortgage. Examples include multiple-property ownership by the borrower, non-occupancy of the property by the borrower, whether a construction permit or construction loan was recently issued for the property, and whether average housing prices have recently dropped in the neighborhood or region of the property, and whether the borrower/owner has previously failed to make a property tax payment on this or another property. Thus, the system may use data sources other than those shown in
As shown in
where (1) Y=1 represents the tax delinquency case within 6 months, (2) X represents the property-related attributes such as mortgage delinquency, FICO score at loan origination, loan-to-value ratio at origination, etc., and (3) the βs are the coefficients or weights applied to specific property-related attributes. In one embodiment, the final tax delinquency risk score is constructed as:
Score=f(Xβ),
where f(•) is a function to scale the score so that every 50 points doubles the odds that a property will be tax delinquent within 6 months of the current date. Typical scores fall in the range of 400 to 800. The time period of six months allows lenders or services sufficient time to take preemptive actions. Other time periods can alternatively be used, such as time periods falling in the range of 4 to 8 months, 3 to 9 months or 1 to 12 months.
The system shown in
Although not shown in
In block 20, the process identifies real estate properties that have experienced tax delinquency events using data retrieved from the property tax data repository 16. In some embodiments, properties that were the subject of an escrowed mortgage loan (as may be determined from the associated loan-level data) may be excluded or filtered from this list. In some embodiments, the process may also identify properties that have not entered into tax delinquency; consideration of such properties is useful for, e.g., identifying loan attributes or other property-related attributes that are negatively correlated with tax delinquency risk.
In block 22, the process retrieves property-related attributes for the properties identified in block 20. In some embodiments, the property-related attributes for properties that became tax delinquent consist of attributes of the mortgages that were in place on the properties at the time of, or shortly before, the associated tax delinquency events. In other embodiments, the process may also retrieve and use other types of property-related attributes, as described above.
In block 24, the process applies logistic regression to identify the attributes that represent the primary drivers of tax delinquency. In some embodiments, this may involve searching for attribute combinations that are correlated with tax delinquency. For example, the process may determine that the combination of (1) a loan-to-value ratio above a certain threshold, and (2) non-occupancy by the owner/borrower, has a strong correlation with tax delinquency. Preferably, the property-related attributes of both tax delinquent and non-tax delinquent properties are analyzed in block 24.
In block 26, the process generates and stores the explanatory variable definitions and associated coefficients for the identified drivers. One example of a set of explanatory variables and associated coefficients is shown in Table 1 below. A numerical example that uses these variables and coefficients is provided below. Negative coefficients in this example represent negative correlations between the associated attribute and tax delinquency risk.
Although the property-related attributes in this example consist of loan-related attributes, non-loan-related attributes may also be considered, as explained above. The following are examples of other (non-loan-level) explanatory variables that may be used: (1) number of months until next property tax payment is due, (2) percentage increase in next property tax payment amount relative to last property tax payment amount, (3) percentage increase in value of property over last year, (4) whether the borrower has previously been tax delinquent on this property, (5) whether the borrower has previously been tax delinquent on other properties.
For example, suppose the explanatory variables for a particular property are as shown in Table 2.
Using the model coefficients of Table 1, the property's tax delinquency risk score may be generated as follows:
A one-to-one functional relationship exists between log odds and the probability of tax delinquency. In this particular example,
Finally the tax scorecard model will output a tax delinquency score based on the log odds or the tax delinquency risk:
Score=732.19281+72.13475*Xβ=500
A score of 400 corresponds to an odds of 1:100. Scores are scaled in this example such that the odds of tax delinquency double for every 50 point increment in the score. Therefore, the odds (i.e., Pr(Y=1)/Pr(Y=0)) for this sample property to be tax delinquent in the next payment is about 1:25.
In some embodiments, the scores may be generated or adjusted to reflect the different tax delinquency rules of different states or jurisdictions. For example, some states have different rules governing (1) whether a property tax lien trumps a mortgage lien, (2) when foreclosure proceedings can be initiated, and (3) what penalties are assessed for tax delinquency. These rules may impact both the likelihood of tax delinquency and the borrower's consequences for tax delinquency, and may therefore be considered in some embodiments.
In block 40 of
In block 44, the estimated shortfall amounts are summed or otherwise combined to generate the estimated shortfall amount for the entire portfolio or region.
All of the processes and process steps described above (including those of
Thus, all of the methods and tasks described herein may be performed and fully automated by a programmed or specially configured computer system. The computer system may, in some cases, include multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, etc.) that communicate and interoperate over a network to perform the described functions. Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other computer-readable storage medium.
The foregoing description is intended to illustrate, and not limit, the inventive subject matter. The scope of protection is defined by the claims. In the following claims, any reference characters are provided for convenience of description only, and not to imply that the associated steps must be performed in a particular order.
The present disclosure claims the benefit of U.S. Provisional Appl. No. 61/918,413, filed Dec. 19, 2013, the disclosure of which is hereby incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 61918413 | Dec 2013 | US |
