June 17: Jobs & a business opportunity vs. Discover Financial closing mortgage biz; primer on hedge costs; is there a lack of appraisers?

Rob Chrisman

Rob Chrisman began his career in mortgage banking – primarily capital markets – 31 years ago in 1985 with First California Mortgage, assisting in Secondary Marketing until 1988, when he joined Tuttle & Co., a leading mortgage pipeline risk management firm. He was an account manager and partner at Tuttle & Co. until 1996, when he moved to Scotland with his family for 9 months. Read more...

If you don’t like numbers skip this paragraph. The U.S. Census Bureau has published highlights of the Annual 2014 Characteristics of New Housing. There were 620,000 single-family homes completed in 2014 and of these, 565,000 had air conditioning, 64,000 had two bedrooms or less whereas 282,000 had four bedrooms or more, 263,000 were one-story homes and 221,000 homes had three or more bathrooms. In 2014, 264,000 multifamily units were completed, with 127,000 in buildings with 50 units or more, 123,000 had two or more bathrooms and 23,000 were age-restricted. Of the 437,000 single family homes sold in 2014, 314,000 were in a homeowner’s association, 311,000 were paid using conventional financing whereas 37,000 were paid for in cash, 137,000 had both a patio and porch and 244,000 had two stories. The average sales price of new single-family homes sold was $345,800, the average price per square foot for new single-family homes sold was $97.09 and the median size of a new single-family home sold was 2,506 square feet.

 

Lenders are gradually turning into compliance companies that happen to do mortgages, leading some to ask, “Compliance – sword or shield?” “At Norcom Mortgage we see compliance as a tactical advantage, not just a defensive necessity. As a company with over 25 years of experience in the mortgage industry, and licensed in 24 states, we believe in investing in our compliance team and thus protecting the customer’s best interest, the company, and the individual loan officer. What’s the sword? Right now, TRID. TRID training and implementation is just one example, but right now it’s the one that matters. Jeremy Potter, chief counsel, said, ‘Our compliance group has worked closely with the MBA, our vendors, and our industry partners to make sure our company is supported from all angles. TRID has tentacles in all areas of our business and our industry and we are following each one to have the most prepared staff in our market on August 1st.’ If you are the type of salesperson that wants work in a company that focuses on the value of compliance and understands how to make the most of it, contact James Morin. Norcom Mortgage, one of the fastest growing independent mortgage companies in the Northeast, is a direct Fannie Mae, Freddie Mac Seller Servicer and a Ginnie Mae Issuer.

 

“Here’s an excellent opportunity for a Mortgage Broker looking to become a Lender. A Mortgage Lender is looking for a purchaser to buy the company which includes a Title 2 (DE Forward, DE Reverse, and LI Forward) non-supervised status.  Interested parties should email me. An NDA will be required to learn anything more than noted above and initiate discussions; all inquiries are entirely confidential. Written bids will be accepted through June 26th.”

 

On the flip side, and it isn’t the first and won’t be the last, after three years of giving it a go Discover Financial Services is shutting down its struggling home-lending business and laying off 460 employees, many of them in Irvine, California. I am sure compliance costs factored into things… let me know when those borrowers become better off! [Readers can post resumes at www.LenderNews.com.]

 

(For the next several days I am finishing up some bicycling and traveling in Croatia. During this time “guest writers” are doing the bulk of the 6-day a week commentary. I appreciate what they’ve done, and you can write to them directly.)

 

Phil Rasori, Chief Operating Officer of Mortgage Capital Trading (MCT), pens…

 

A Primer (from MCT) on Loan Level Hedge Cost

 

Over the last 18 months we have seen a sharp increase in the number of hedging clients who are requesting the calculation of loan level hedge cost estimates.  While there are some required assumptions and a few limitations to any such analysis, we will briefly outline an intuitive and mathematically sound method for estimating loan level hedge cost.  We will then thoroughly review the necessary assumptions.

 

In explaining this methodology, we will use the case of a locked open pipeline hedged with TBAs and accordingly all hedge costs in our example are the result of TBA pair offs. Hedge cost resulting from forward contract pair offs can be stratified using the same method. We define the hedge period as the window of time between the point the lender confirms the interest rate lock commitment to the borrower (pipeline entry) and the loan is funded and immediately committed to the agency or aggregator (pipeline exit). For purposes of this example we use the term hedge cost, although in a rising rate environment hedge gains would be distributed in the same manner.

 

The first step in the calculation will be to distribute the hedge costs across all loans in the population, on a loan amount weighted basis. The primary factor in determining the hedge cost associated with a particular loan is the market movement that it has experienced while in the hedge. Thus, the next step will be to calculate the weighted average market movement of all loans in the analysis by taking the difference in market levels between the time at which the loan was initially locked and the time it was committed out of the pipeline. In order to correctly include the loan’s duration in the analysis, the price movement of the appropriate security type and coupon must be used. This is needed because when hedge costs are compared across a population of loans, assets with higher durations carry more weight due to the fact that they will move faster given a change in interest rates compared to those with lower durations.

 

The next step of the analysis will be to calculate the deviation from mean market movement for each loan and then apply this figure to the corresponding loan amount weighted hedge cost.  This will give us a loan level hedge cost that has been adjusted for the given loan’s market experience versus the overall loan population in the analysis.

 

The second and final adjustment will be made for time in the hedge. As with the market movement adjustment we first calculate the weighted average time that the population of loans spent in the hedged pipeline. We then calculate the deviation from the mean lock period for each loan. Finally, we multiply this figure by the weighted average TBA roll cost for the securities that were paired off during the analysis period.

 

We now have arrived at a loan level hedge cost estimate that accounts for duration weighted market movement and the time decay that occurred on the hedge while the loan was in the hedged pipeline. The results are intuitive in that the sum of these loan level hedge costs will always exactly tie back to the actual hedge costs that were incurred during the given period.

A significant benefit to this calculation is that hedge cost by channel, branch or even originator can be estimated by using an adaptation of the model. If we distribute the hedge cost incurred over the period of analysis across fallout as well as funded production, the hedge cost generated from a specific segment of production can be estimated. With the hedge cost of fallout included, we can get closer to arriving at the actual hedge performance of any segment of production (i.e., Product, Channel, Branch or Originator).

 

As discussed in the introduction, this analysis has one limitation that can potentially compromise the accuracy of the result. Timing discrepancies between hedge and asset execution will cause gains or losses to be pent up in the open pipeline and thereby introduce noise into the historical population. As a result, the primary limitation of this analysis is a function of the look back period. Hedge cost values calculated from two different time periods cannot be compared and thus a specific time horizon for the analysis must be chosen. The loan level value calculated above is subject to profitability timing with the analysis period. A hedge cost analysis that examines a one month window of production will be extremely susceptible to timing issues.  A three month period is better and twelve month look back period would be optimal. The problem of course is that a year is long time to wait for an estimate of hedge cost on a loan that funded today. Thus, accuracy is obtained at the expense of the utility of the result.

 

The primary assumption in the analysis is relatively intuitive in that all loans are considered to be equally lucky or unlucky in the market as well as with regards to pull through model errors.  In other words, we assume the hedge position was initiated with the same priority for all loans and pull through assumptions were equally as accurate or inaccurate for all loans. The error equivalence can be relieved by adding pull through back testing to the examination; however this substantially complicates the analysis. Additionally, it is important to note that a discrepancy or error between forecasted and actual pull through error does not necessarily equate to a higher hedge cost for a given loan during a certain period (i.e. forecasting too high of a pull through rate during a period in which rates rose).

 

While there are various methods that can be employed to control for timing as well as relieving other assumptions, accuracy is inevitably traded for practicality and usefulness of the result.  Bottom line, we at MCT have found this to be the most reliable method for calculating interpretable and intuitive loan level hedge cost figures. As illustrated in the assumptions above there are far too many caveats that accompany the analysis for any decisions to be made solely from the results. Most importantly, we implore any lender not to use the results of this analysis for compensation purposes. With that said, stratifying hedge costs across a given population of production in this manner will give solid directional guidance to hedge performance.

 

Sometimes even people in the lending industry wonder how a property is appraised, and certainly might use something they can pass on to borrowers. Although an appraisal is the opinion of the appraiser, there are certain standards they have to abide by in order to be consistent, dependable and uniform. The Uniform Appraisal Dataset (UAD) is used to ensure accuracy and quality of property appraisals and was developed to create a set of standards used to bring conformity to the appraisal process. The UAD requires specific standardized appraisal forms, ratings, methods and entry formats used in the report. The appraiser will gather information on comparable sales and evaluate the differences and similarities between them and the subject property. The rating of the property is based on a Condition (C) and Quality (Q) scale. For example, C1 stands for recently constructed; not previously occupied; new components, fixtures and mechanical systems and Q1 means architecturally designed/custom built; extremely high quality finishes/fixtures.

 

On the opposite end of the scale, C6 stands for substantial damage; lack of maintenance; major repairs needed and Q6 indicates basic quality/cost; may not be suitable for full-time occupancy; possible safety issues. The comparables used should also be in the same Condition and Quality category as the subject property and should be recent closed sales, located within a mile of the subject property. Adjustments are also considered when evaluating the value of a property which include, age, lot size, livable square footage, number of rooms, amenities such as a pool or garage, upgraded kitchen/bathroom, fixtures and finishes. The contract price is used as the baseline for comparison and if the comparables’ values are higher than the subject property’s purchase price, the appraise value could increase and if the comparables’ values are lower, the appraised value could drop. It’s important that sellers and buyers understand the appraisal process and what options they have should an issue arise.

 

Along those lines, thank you to Joan Trice, founder & CEO of Alterra Group, LLC who passed along some current information on appraiser supply.

 

And Mark Lyons, SVP with the William Fall Group/Valuation Partners, writes, “We are still seeing a lot of client inquiries regarding the reported results from Fannie Mae’s Collateral Underwriter scoring. In particular, lenders are asking for deeper substance when CU indicates an appraisal report has a higher risk – usually a 4 or 5 rating. In response to this, we have developed a solution called Market $en$e that determines if the ratings are valid concerns and if they can be re-addressed by the reviewer as a False Positive or the appraiser needs to correct, or re-address with additional commentary in the report. Bottom line, I believe the valuation industry is reacting well to the increased oversight by Fannie.”

 

Housing starts dropped 11.1% in May to 1.036 million although April was revised higher from 1.135 to 1.165 million. Building permits rose 12% however to 1.275 million – the highest in 8 years. Housing starts have been very volatile, so it makes more sense to look at the trend, which is generally up.

 

For interest rates we closed out the 10-year at 2.32% on Tuesday. The scheduled, potentially market-moving news for today consists of the FOMC rate decision at 2PM EDT, 8PM Croatia time. At this point it would be a real shock if the Fed did anything to short-term rates.

 

 

Thanks to Len Tichy who passed this one along.

Supposedly a teacher once asked a class to write a short story that included royalty, religion, romance, and mystery.

The winner, and the shortest story, was something like this:

“Oh my God!” cried the Queen. “I’m pregnant! I wonder who the father is?”

 

 

Rob

 

(Copyright 2015 Chrisman LLC. All rights reserved. Occasional paid job listings do appear. This report or any portion hereof may not be reprinted, sold or redistributed without the written consent of Rob Chrisman.)