Driving Better Decision Making in the Foreclosure Process

Rising foreclosure rates and REO inventories continue to strain servicing organizations across the mortgage industry. Understandably, servicers are extremely focused on enhancing the effectiveness of their foreclosure and REO processes to avoid the associated risks and keep losses to a minimum. The ability to quickly and accurately make decisions is at the core of improving those processes, especially in such a volatile market.

To fuel effective decision making, most servicers are evaluating their current processes and looking for ways to improve them. In most cases, access to deeper, more accurate data is an important part of the answer. Standard Input for Loss Mit Decision Making

When it comes to foreclosure and REO decision making — such as deciding whether or not to proceed with a short sale or where to price REO properties — most servicers have developed models that worked fairly well during more stable times. These models typically factor in a servicer’s current dataset as well as valuation information provided by brokers.

Relying on existing internal datasets, it is certainly an appropriate and logical place to start for servicers. However, these datasets are usually limited in some ways — and in other ways they are too general (since information is often derived from nationwide data). Especially in today’s challenging marketplace, servicers must work harder to draw the most accurate conclusions. To do so, they need access to higher concentrations of local information very specific to the areas surrounding subject properties.

As servicers move through the foreclosure process, they often obtain at least one Broker Price Opinion to help verify a subject property’s value. However, while brokers can provide valuable information about a subject property and sales comparables, they are usually unable to factor in important neighborhood trend information, such as current and projected delinquency rates. Yet, this type of information can have a tremendous impact on a servicer’s decision to proceed with a short sale as well as how it prices an REO property.

While servicer datasets and BPOs provide a good foundation for decision making in the foreclosure and REO processes, they do not offer the level of detail servicers ultimately need to make the most effective decisions in today’s environment. Fortunately, servicers can strengthen the accuracy of their decision-making models by incorporating a more granular level of data.

By deploying highly localized information, servicers are better equipped to analyze market conditions at the neighborhood level instead of relying on broader, more generalized state or city averages. This enables servicers to better understand current and future neighborhood factors that are likely to affect pricing, delinquency trends and REO inventories in the area where subject properties are located. Ultimately, this can have a tremendous impact on their ability to make better loss mitigation decisions. To Short Sale or Not to Short Sale Most servicers must use short sales as part of their loss mitigation strategy. However, short sales require servicers to make very quick decisions. With today’s high delinquency volumes, many servicers set a blanket discount rate based on a combination of state or city averages and the property value information they receive from brokers. However, by taking this approach, servicers risk overestimating or underestimating property values because circumstances can vary so greatly from one neighborhood to the next or even from one property type to another.

When a servicer is considering a short sale, the property values in the subject property’s neighborhood could be on the upswing, allowing servicers to minimize discounting. Alternatively, property values in a neighborhood could be decreasing — even while other nearby neighborhoods are stable. In this case, a short sale offer could be a positive move for the servicer. Of course, when borrowers are qualified for a loan modification under federal assistance programs, short sales can be avoided.

However, since loan modifications are not appropriate for all borrowers, the key to making effective asset disposition decisions is to have access to neighborhood-specific information as early on in the process as possible.

When it comes to deciding on a short sale, servicers should append their current datasets with market supply and predictive analytics for the neighborhood surrounding the subject property. It should contain details about the number of REO properties already on the market, foreclosures in progress and 0-120 day delinquencies. Servicers should also attempt to access a model with a one-year market forecast, as well as a market supply indicator.

It can also be very helpful to servicers to access details on mortgage loan performance by vintage, percentage of exotic loans for all vintages vs. delinquencies, and detailed foreclosure and REO costs and timelines for a specified neighborhood. When using this detailed level of information in conjunction with existing datasets and BPOs, servicers are sufficiently prepared to make quick, accurate judgments regarding short sales. Making Sure the REO Price Is Right When foreclosures occur and properties move into the REO disposition phase, servicers must also make wise pricing decisions since so much is at stake. Just as in the short sale decision, pricing a property too low means losses are greater than necessary. If the price is set too high, a property might sit on the market for months as the servicer incurs additional losses.

Certainly, servicers are working as hard as they can to minimize their foreclosure and REO related losses. To do so, many order more than one BPO or even full appraisals when the loss potential reaches certain thresholds. Still, even this extra due diligence is not enough to ensure the best decisions.

Detailed neighborhood data is critical to further refine valuation data received through BPOs and other avenues. This data should include independent sales comparables and active property listings, as well as neighborhood REO and delinquency trend information. It provides an important quality control function for the BPO process, while factoring in how REO and delinquency projections are likely to impact the servicer’s ability to quickly dispose of a property at a given price point.

With the need to manage record delinquencies and REO dispositions, servicers know they need access to the information that will enable then to make fast, accurate decisions. Whether looking to enhance existing datasets, implement additional screening and quality control into the valuation process or both, neighborhood-level data provides servicers with the resource they need to improve decision making and minimize losses.

Jon Davis is president of Valuation Solutions at Lender Processing Services, which provides processing services, settlement services and default solutions to mortgage lenders. He specializes in the valuation of complex residential properties, HUD foreclosures, and residential income properties.