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The Journal of Real Estate Finance and Economics
Published in: The Journal of Real Estate Finance and Economics 2/2024

09-06-2022

Distressed Property Sales: Differences and Similarities Across Types of Distress

Authors: Marcus T. Allen, Justin D. Benefield, Christopher L. Cain, Norman Maynard

Published in: The Journal of Real Estate Finance and Economics | Issue 2/2024

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Abstract

This study analyzes the price and time-on-market effects of short sale and lender-owned properties in the single-family housing market during the recent housing crisis. Short sales increased dramatically during the downturn as an alternative to foreclosures and deed-in-lieu of foreclosure transactions for resolution of defaulted mortgage loans. Using multiple listing service data, this study provides evidence that the price discounts associated with short sales and lender-owned properties differed significantly early in the crisis, but by the end of our sample the discounts were relatively similar in the sample market, which was not as hard-hit by the housing market downturn as markets examined in prior research. Using new time-on-market estimation methodology, the time-on-market results indicate that both short sale properties and foreclosed properties stayed on the market significantly longer than non-distressed properties, which differs from prior findings that foreclosed properties sell faster than non-distressed properties. The study also provides some evidence that different seller types (i.e. short sale-seller versus lender-seller) exhibit differences in their abilities to time their market entry.
previous article The Valuation Effect and Consequences of Clawback Adoption in Real Estate Investment Trusts

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Footnotes
1
Zhu et al. (2015) and Schmeiser and Gross (2016) investigate several alternative “workout” structures offered during the financial crisis through the Home Affordable Refinance Program and to subprime borrowers more broadly, respectively.
 
2
The topic of subprime mortgage default has, in fact, grown so popular that it has spawned two separate literature reviews aimed solely at the growing body of work surrounding that single area of research interest (please see Jones and Sirmans (2015) and Jones and Sirmans (2019)).
 
3
Two of these topic areas are arguably more closely related than the others: strategic default and foreclosure laws. These topics have generated substantial interest and have produced some interesting results. On the topic of strategic default, the interested reader is referred to Seiler et al. (2012), Guiso et al. (2013), Seiler et al. (2013), Seiler and Walden (2014), Bradley et al. (2015), Seiler (2015), Seiler and Walden (2016), Bhutta et al. (2017), Seiler (2017), and Gerardi et al. (2018), among many other possibilities. On the topic of state foreclosure laws, the interested reader is referred to Ghent and Kudlyak (2011), Desai et al. (2013), Curtis (2014), Cordell et al. (2015), Mian et al. (2015), Bao and Ding (2016), Cao and Liu (2016), Dagher and Sun (2016), and Zhao et al. (2019), also among many others.
 
4
Mortgage fraud-for-profit differs from the standard type of mortgage fraud where a buyer/borrower falsifies documentation to gain loan approval. In a mortgage fraud-for-profit scheme, some or all of the principals are actively colluding to gain some or all of the following benefits: free housing, inflated commissions, inflated origination fees, unearned appraisal fees, etc.
 
5
It should be noted that Depken II et al. (2015), using data from the same market as Clauretie and Daneshvary (2011), do not find it necessary to distinguish between sales-in-process-of-foreclosure and short sales. This distinction is not included in other literature on short sales either (e.g. Aroul and Hansz (2014)).
 
6
Due the high granularity of our location controls, some areas either lack a sufficient number of sales to be included in our analysis or lack sufficient variation in distressed status to be included in one or more regressions. This leads to minor variations in the number of observations included in distress-status regressions.
 
7
There is a large literature analyzing brokerage effects of various types. See, for example, Janssen and Jobson (1980); Jud and Winkler (1994); Baryla Jr. and Zumpano (1995); Hughes Jr. (1995); Yang and Yavas (1995); Jud et al. (1996); Dale-Johnson and Hamilton (1998); Munneke and Yavas (2001); Huang and Rutherford (2007); Turnbull and Dombrow (2007); and Salter et al. (2010).
 
8
State license law requires that agent ownership be disclosed in the listing information available on the MLS.
 
9
Since real estate transactions take time, the current mortgage rate does not seem particularly applicable in trying to capture an effect of mortgage rates on transaction outcomes. Six months seems a reasonable choice if the time from starting to shop in earnest until consummated sale is considered. Regardless, other choices of the lag period provided qualitatively similar results.
 
10
Some studies have interpreted this as a sample selection issue and used the model described to calculate an Inverse Mills’ Ratio (IMR) as a corrective measure. However, econometrically this IMR is only valid when the dependent variable in the final models (pricing and TOM) has been censored, such as if short sales or REO sales were excluded from the available dataset. Because of the quality of our MLS data, we do not have this problem. However, the effect of observable characteristics on the probability of housing distress is important to our research question in its own right.
 
11
Technically, the model estimated is a binary logistic regression specifying the probit link function. However, it is common practice in the literature to simply refer to such models as probit models, so that nomenclature is adopted throughout the remainder of the text.
 
12
We also considered multinomial logit as an alternative specification for looking at both short sales and REO sales simultaneously. However, multinomial logit requires sufficient variation in the dependent variable within each included location to conduct inference. Because we use census geocodes, which are a very high resolution location control, a prohibitively large share of the dataset would have to be dropped because their geocode areas do not include enough of both types of distress.
 
13
Of the 28 studies surveyed in Benefield et al. (2014) that utilize hazard modeling, 19 of them assume a Weibull distribution for the error terms, 4 use a Cox proportional hazard model, and 1 uses both methodologies. One additional study uses an empirical technique equivalent to a Weibull hazard model, another study assumes a special case of the Weibull distribution (the exponential distribution) for the error terms, and the remaining 2 studies have no relation to the Weibull distribution or the Cox proportional hazard model.
 
14
Calculation of both ATYP and DOP first requires that hedonically suggested list prices be obtained for each sample property. These suggested list prices are obtained by regressing the natural log of the observed original list price on all observable property characteristics—ln(SF), ln(AGE), BEDS, BATHS, HBATH, PARKING, FP, GOLF, IRRIG, Island, MultiStory, NC, POOL, SpCeil, and WATER—as well as the same controls for quarter and location used throughout the paper.
 
15
Note that the second-stage estimation of time-on-market suffers from many of the same shortcomings as OLS modeling of this transaction metric. While 2SLS has gained some ground among real estate researchers, its use is not as widely accepted as hazard modeling. Of the 52 studies surveyed in Benefield et al. (2014) that contain a time-on-market estimation, 7 utilize both hazard and 2SLS models, 21 utilize a hazard model only, 15 utilize a 2SLS (or similar) model only, and 9 utilize neither a hazard nor 2SLS model.
 
16
Our hedonic pricing and TOM models do suffer from sample selection related to sold vs. unsold status, as is always the case for models that use sale price. Including an IMR derived from a probability of sale model is a common corrective for such selection issues. However, IMRs are not appropriate for nonlinear models such as the Weibull TOM model. In order to ensure that inference for the pricing models matches the TOM models, such IMRs have not been used.
 
17
It is worth noting that the Fresno market peaked a full year prior to the Charleston market. It could be the case that the Fresno market “turned a corner” regarding distressed sales that would similarly be observable in the Charleston data if the Charleston sample continued for one more year. However, that is an empirical question beyond the scope of the current study. The fact remains that, for the sample periods studied, the evolutionary patterns differ between these two markets.
 
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Metadata
Title
Distressed Property Sales: Differences and Similarities Across Types of Distress
Authors
Marcus T. Allen
Justin D. Benefield
Christopher L. Cain
Norman Maynard
Publication date
09-06-2022
Publisher
Springer US
Published in
The Journal of Real Estate Finance and Economics / Issue 2/2024
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-022-09911-2

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