Wholesale price data used to gauge market

Wholesale mortgage prices are those quoted by wholesale lenders to their clients — mortgage brokers and smaller lenders.

(I will use the word "client" to cover both.) Clients mark up these prices to offer retail prices to borrowers. Wholesale price data have never been available to the general public.

Each wholesale lender views its prices as proprietary information available only to those clients who have been approved to receive it.

The Internet is now the principal method used to distribute wholesale prices, but usually a password is needed to gain access.

The few lenders who don't require passwords probably assume that any borrowers who stumbled on their site would find the data so overwhelming that they'd quickly leave.

Wholesale price data are complicated. It takes time for clients to master all the pricing adjustments contained on any lender's price sheet. To make it worse, no two lenders format their prices in the same way. This is why pricing mistakes are common.

It is too time-consuming for a client receiving prices from multiple wholesale lenders to compare prices for more than a few unless they have a technology that converts all the disparate formats into one format. With a single format, it is possible to find the best wholesale price in any market niche among any number of lenders. Not many clients have this capacity.

One that does is Amerisave, which gave me access to its database of wholesale prices. At any one time the database covers 12 lenders. Over time it changes as some lenders are replaced by others with better pricing.

I used this data to compile daily series on wholesale interest rates covering 14 types of mortgages. It now appears on my Web site. A snapshot covering five products is at the top of my home page.

The data are updated automatically every 30 minutes — not that they change that frequently, but they are reset every morning and sometimes during the day. I am indebted to Daryl Tubbs, who figured out how to do this.

The interest rate shown is for zero points.

Lenders don't actually offer loans at the rates shown; they offer them in even increments of 0.125 percent, e.g., 6 percent, 6.125 percent, 6.25 percent, etc. As the rates go up, the points charge goes down. Points are an upfront charge expressed as a percent of the loan balance.

I interpolate between the rate with the smallest positive points and the rate with the smallest negative points to reach the rate at zero points.

The error from interpolation is small, and having one price makes it easy to compare price differences among programs and at different dates.

So what are these data good for? One purpose to provide an accurate measure of day-to-day changes in the market as a whole.

Wholesale price data are a much better gauge of market conditions than retail price data because they contain much less statistical "noise." Wholesale data do not include retail markups, which vary.

Furthermore, the wholesale data pertain to transactions where the borrower's credit score, loan size, down payment and other factors that affect price are specified. In the retail series, these factors are not reported, and any changes in them will affect the price.

Borrowers can use the wholesale data to protect themselves against one of the most pervasive frauds in this market: price escalation between the day they are quoted a price and the day the price is locked.

If the market price goes down, the borrower's price will stay the same, and if the market price goes up, the borrower's price will go up even more.

Loan providers explain a price increase as stemming from "changes in the market," but the market price on the lock day is what they say it is, and borrowers have had no good way to check it. Now they do.

While the wholesale data are an excellent measure of what has happened, they have no predictive power, so don't try to spot "trends." Even if the price rises 10 days in a row, view the probability that it will rise in day 11 as 50 percent.

Next week I will discuss some other uses of the new data.

Jack Guttentag is professor emeritus of finance at the Wharton School of the University of Pennsylvania. Contact him at jguttentag@mtgprofessor.com.