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Article Excerpt
The appraisal of the "market value" of homes serving as the collateral for mortgages is a fundamental part of the underwriting process. If a loan should default, however, it is not the retail market value that the lender obtains, but rather the "recovery value." In this research, we show how recovery values differ from market values at origination and explore the reasons for the differences Using a large sample of chattel mortgages on manufactured homes, we explore the relationship among the selling prices, the book values, and the fitted values from simple hedonic models with spatial autocorrelation. We then address the differences between selling prices at origination and recoveries from repossessed homes. We find that the spread between them varies systematically with home characteristics and especially with "atypicality," that is, with measures of how unusual a home is. Selling prices both at origination and recovery affect borrower defaults.
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For decades, property appraisal has been a mainstay of mortgage underwriting and is an essential element of the verification process for lenders. The appraiser provides a professional estimate of the value of the property that can be used to verify that the selling price is representative of current market conditions. The lender uses the appraisal to assess whether the loan will perform, that is, repay in full, and whether the loan will be profitable for the lender. But is the simple point estimate sufficient for assessing the risks associated with the collateral? Can lenders improve loan decisions with information on the expected second moment or with estimates of the likely recovery values from a default?
In this research, we explore these issues with a large data set of chattel mortgages on manufactured homes. Manufactured homes, although little researched, are an increasingly important segment of the housing market. The Manufactured Housing Institute reports that 8% of the U.S. population lives in manufactured homes. In 2001, manufactured homes made up about 15% of residential starts. Chattel mortgages, unlike conventional single-family mortgages, are secured only by the structure or "home" and do not include a lien on the underlying land. In most cases, the borrower does not own the land. Instead, the land is either rented from a third-party owner or from the owner of a manufactured housing "park," which, like a single-family subdivision, often groups similar homes in one location. (1) In parks, the land leases are usually month-to-month.
The foreclosure or repossession process can be much quicker and simpler for manufactured homes because the collateral is mobile, albeit at a nontrivial cost. (2) At most times, dealers provide a ready market for repossessed units, although occasionally they are overstocked with inventory and unwilling to provide the usual level of liquidity to the market.
There are many reasons to question the optimality of existing appraisal procedures for residential mortgages in general and manufactured homes specifically. First, there may be agency issues in the appraisal process. Appraisers, like all parties to real estate transactions, are often subjected to pressures or incentives to shade their estimates so that the transaction will be approved. Loan volume drives the compensation of many participants in the process and inevitably colors all of the participants at least indirectly.
There has been a fair amount of recent experimental research investigating appraisers' incentives and biases. For example, it has been shown that appraisers may be influenced by valuations of others (Diaz 1997) and by their own previous appraisals (Diaz and Wolverton 1998). A number of studies find that clients may influence the valuations. (3)
Even more basic to the appraisal process is whether appraisals are unbiased estimates of market values. It is well known that appraisals lag behind market movements (Quan and Quigley 1989). Appraisals can also be unbiased but inaccurate. (4)
A third issue is whether there is a link between appraisal quality and probability of default. Lenders use appraisals to help evaluate the probability of default. Loan-to-value ratios (LTVs) are widely used as a causal variable in statistical modeling of default probabilities. Noordewier. Harrison and Ramagopal (2001), using a sample of 1.428 residential loans, find evidence that properties that are valued above the sales price of "similar and proximate" properties are more prone to default. However, they conclude that the simple variance of property values is not related to default. Lacour-Little and Malpezzi (2003) find that decreasing appraisal quality is associated with an increase in the probability of default. In addition, they find that over-appraisal is significantly related to default, while under-appraisal is not. We have been unable to find any studies of the relationship of point estimates and dispersion estimates of property value and collateral value, which is ultimately what determines recovery risks.
But are current LTVs, whether based on purchase prices or appraisals, the appropriate driver for default equations? At the time of the default decision, the borrower will typically be distressed and the relevant value may not be a retail value. A forced or distressed sale will occur at a reduced price, which may be more relevant for assessing default probabilities at origination. Liquid properties where an owner can obtain a quick sale at only a small discount from market prices may have lower default probabilities because borrowers will be able to avoid default by selling. If, on the other hand, a property is unusual and difficult to sell, default may be the best alternative. Therefore, understanding the marketability and the liquidity of properties may help evaluate loan performance.
It is certainly the case that appraisals are an inappropriate metric for understanding the expected profitability of loans (Guttentag 1992, Capozza and Thomson 2005). Assessing profitability requires that lenders estimate all the future cash flows on the loan including the recoveries from defaulted loans. For this task, estimates of the recovery values will be more useful than estimates of the market value at origination. Therefore, while appraised values, that is, estimates of the market value at origination, are the most commonly used in underwriting, it may be that lenders should estimate recovery or wholesale values. If recovery values are a better predictor of defaults than appraised values, then appraised values are superfluous. If, on the other hand, appraised values are a better predictor of defaults, then estimates of both market values at origination and recovery values in default will be needed.
From the borrower perspective, both the appraisal (retail values) and the distressed or recovery values should be relevant. If the borrower is subject to financial stress and cannot make payments, the first choice is to sell the property to a retail buyer and prepay the loan, thereby salvaging any equity. When trying to sell to a retail buyer, the liquidity of the property will matter. Illiquid properties will be less likely to sell, reducing the chance that the borrower can repay the loan. The difference between the retail value and the recovery value is a measure of the liquidity of the property similar to the bid-ask spread in other financial markets. If the property does not sell to a retail buyer, the borrower may be forced into foreclosure. Therefore, there are many moving parts in the borrower's decision. Both the appraised value and the estimated recovery value play a role and should be included in the lender's assessment of risks and expected losses.
As part of our analysis, we explore two measures of liquidity and heteroskedasticity in some detail. The first, atypicality, (Haurin 1988) is a measure of how unusual a unit is relative to an average unit. The second, sparsity, measures the number of units that have sold recently within a 15-mile radius. Of the two, atypicality is a more important factor in hetroskedasticity.
In the next section, we describe the data and follow with a discussion of the methodology. The third section presents the results of our empirical analysis, which is divided into eight subsections. We first estimate hedonic models of selling prices that exploit spatial autocorrelation and include our measures of atypicality and sparsity. The next subsection compares the 1-year forecast performance of the various models and shows that the best models have mean squared errors (MSEs) of 6-7%. We then compare the results from using the selling price, the appraised value and the various hedonic models for predicting recovery values for repossessions and show that the simplest hedonic models are the best predictors of recoveries. These results suggest that valuation for recovery is quite different from the valuation of selling prices at origination.
The fourth subsection analyzes the characteristics that are important for the retail sales...
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