The effect of mortgage securitization on assetliquidation decisions

Anurag Mehrotra 1 Adam Nowak 2 Patrick Smith 1

1San Diego State

2West Virginia University

June 16, 2021

Motivation

I I moved to Atlanta in August 2011

I All-cash offer of $34K on REO condo listed for $40K

I Seller countered by reducing price by $100 but added a “Weband Technology Fee” of $199

I I countered with offer of $33,801 plus the $199 fee

I Seller accepted my offer

Motivation

Securitization in U.S.

Securitization in U.S.

Delinquency Rates

Background

I Over 70% of mortgages are securitized

I Securitization affects modification rates- Piskorski et al. (2010); Agarwal et al. (2011); Kruger (2018)

I However, 10% of loans are modified and 50% redefault- Adelino et al. (2013); Maturana (2017)

I Yet, no study has examined the effect of securitization onliquidation decisions

Motivation

We study the effect of mortgage securitization on servicers’asset liquidation decisions

(1) Liquidation channel:I Short sale: no foreclosure, lender does not take ownership of

the house, borrower remains in house until sold

I Real estate owned (REO): foreclosure, lender takes ownershipof the house, borrower is removed

(2) Liquidation price

Why do liquidation decisions matter?

Short Sales benefit lenders, investors, and neighborhoodsI Sell for ≈9% premium to REOs

I Clauretie and Daneshvary (2009); Zhang (2019)

I Smaller, negative price spillovers than REOsI Zhang (2019)

Benefits are less clear for borrowers

I Short Sales: lesser impact on credit score

I REOs: live in the house rent free until foreclosure

What about servicer incentives?

...increased operating expenses in times like this tend to befully offset by increases in ancillary income in our servicingoperation, greater fee income from items like late charges,and importantly from in-sourced vendor functions

- David Sambol, President Countrywide Financial, Q3 2007Earnings Call

Countrywide ordered property inspections, lawn mow-ing, and other services meant to protect the lender’sinterest in the property... But rather than simply hirethird-party vendors to perform the services, Countrywidecreated subsidiaries to hire the vendors. The subsidiariesmarked up the price of the services charged by the vendorsoften by 100% or more.

- FTC, Press Release, June 7, 2010

Results

What are the effects of securitization on the Short Sale / REOdecision and nearby defaults?

I Conditional on default, securitization is a strong predictor ofShort Sale / REO

I The initial default sold as an REO increases probability ofnearby (0.1mi) default by 0.03pp on top of a base probabilityof 0.04pp.

What are the PLS / GSE incentive effects?

I GSE REOs sell for a 6.7% premium relative to PLS REOs

I After adjusting for quality, premium is reduced to ≈3.8%

Data

I Merge data from three sources

1. MLS: short sales and agent remarksI Property needs TLC

2. ZTRAX: seller names and default recordsI AMERIQUEST MTG SECS INC 2004

3. HMDA: securitization for ambiguous seller namesI BANK OF AMERICA

I Nearly 1.75M transactions from 2007 - 2016 from judicial andnon-judicial states

1. Chicago, Miami2. Los Angeles, Phoenix

Distressed Transactions: Miami, FL

0

5000

10000

15000

Year

Tran

sact

ion

Cou

nt

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

SS ListSS SoldREO ListREO Sold

Question 1: Did securitization affect the method ofliquidation?

Linear Probability Model

Listing Sale

prev gse −0.190∗∗∗ −0.308∗∗∗

(0.003) (0.004)prev pls −0.182∗∗∗ −0.224∗∗∗

(0.003) (0.005)prev other 0.011∗∗∗ 0.012∗∗∗

(0.002) (0.003)prev unknown −0.063∗∗∗ −0.033∗∗∗

(0.003) (0.003)

Observations 557,318 277,600Set Short Sale or REO Short Sale ListingsAdjusted R2 0.165 0.149∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Question 2: Did servicers’ liquidation decisionsaffect nearby default behavior?

IV Spillover Effect on Nearby Defaults

First Stage ITT Second Stage Clusters N Base Rate(1) (2) (3) (4) (5) (6)

Combined 0.243∗∗∗ 0.007∗∗∗ 0.030∗∗∗ 14,933 313,683 0.040(0.008) (0.001) (0.004)

Subject Controls X XNeighbor Controls X XDep Variable: REO Default Default

Key Regressor: prev pls prev pls R̂EO∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Construct 0.1-mile radius clusters, j(i), around Short Sale or REO i

[SS/REO] : REOit = x ′itβF + prev plsitφ

F + vFit

[Neighbors] : Defaultj(i)t = x ′j(i)tβS + r̂eo itφ

S + vSj(i)t

Question 3: Why did securitization affect nearbydefaults?

Why?

I Short sales appear to be a win-win for the homeowner andlender/investor... but not necessarily the servicer

I PLS servicers are incentivized to pursue REOs but notincentivized to maximize sale price

I GSEs take ownership of the default management process afterforeclosure and bear the credit risk

HedonicBaseline Text Controls

(1) (2)

SS −0.194∗∗∗ −0.130∗∗∗

(0.002) (0.002)SSxGSE 0.009∗∗∗ 0.005∗∗∗

(0.002) (0.002)SSxPLS −0.020∗∗∗ −0.009∗∗∗

(0.003) (0.003)REO −0.270∗∗∗ −0.156∗∗∗

(0.003) (0.002)REOxGSE 0.046∗∗∗ 0.022∗∗∗

(0.002) (0.002)REOxPLS −0.021∗∗∗ −0.016∗∗∗

(0.002) (0.002)

Observations 1,749,883 1,749,883Adjusted R2 0.926 0.942Fixed Effect Tract×Q Tract×QTokens X∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Computer-Assisted Text Cleaning

King et al. (2017) demonstrate that neither human-only normachine-learning dictionary-creation methods are ideal and suggestan iterative procedure combining both approaches

Algorithm

1. Find strong predictors of d

2. Identify redundant or vestigial tokens that are strongpredictors

3. Remove all sentences with redundant or vestigial tokens

4. Repeat

Agent Remarks

Full Remark: Banked owned! This property is part of FreddieMac first look initiative. This spacious home is located in a gatedcommunity. Home has vaulted ceilings and an upstairs loft thatcan be used as a fourth bedroom. Handyman special with possibleroof leak. Sold as-is. Buyer responsible to verify all information.

Cleaned Remark: This spacious home is located in a gatedcommunity. Home has vaulted ceilings and an upstairs loft thatcan be used as a fourth bedroom. Handyman special with possibleroof leak. Sold as-is.

HedonicBaseline Text Controls

(1) (2)

SS −0.194∗∗∗ −0.130∗∗∗

(0.002) (0.002)SSxGSE 0.009∗∗∗ 0.005∗∗∗

(0.002) (0.002)SSxPLS −0.020∗∗∗ −0.009∗∗∗

(0.003) (0.003)REO −0.270∗∗∗ −0.156∗∗∗

(0.003) (0.002)REOxGSE 0.046∗∗∗ 0.022∗∗∗

(0.002) (0.002)REOxPLS −0.021∗∗∗ −0.016∗∗∗

(0.002) (0.002)

Observations 1,749,883 1,749,883Adjusted R2 0.926 0.942Fixed Effect Tract×Q Tract×QTokens X∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

HedonicBaseline Text Controls

(1) (2)

SS −0.194∗∗∗ −0.130∗∗∗

(0.002) (0.002)SSxGSE 0.009∗∗∗ 0.005∗∗∗

(0.002) (0.002)SSxPLS −0.020∗∗∗ −0.009∗∗∗

(0.003) (0.003)REO −0.270∗∗∗ −0.156∗∗∗

(0.003) (0.002)REOxGSE 0.046∗∗∗ 0.022∗∗∗

(0.002) (0.002)REOxPLS −0.021∗∗∗ −0.016∗∗∗

(0.002) (0.002)

Observations 1,749,883 1,749,883Adjusted R2 0.926 0.942Fixed Effect Tract×Q Tract×QTokens X∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Distressed Coefficient Adjustments: Miami

0.000 0.001 0.002 0.003 0.004 0.005 0.006

0.00

00.

001

0.00

20.

003

0.00

40.

005

τ̂REO

Adjustment

τ̂ Sho

rtS

ale

Adj

ustm

ent

home

kitchen

pool

floor

granit

view

built

fresh

crown

opportun

marbl

acr

asisold

impact

tlc

dock

repair

Negative Implicit Price Positive Implicit Price

Conclusion

I Securitization and the incentives of mortgage servicers affecthow properties are liquidated

I Liquidation decisions affect nearby property owners

I GSE policies mitigate principal-agent problem at REO stage

Conclusion

Bibliography

Adelino, M., Gerardi, K., and Willen, P. S. (2013). Why don’tlenders renegotiate more home mortgages? Redefaults, self-curesand securitization. Journal of Monetary Economics,60(7):835–853.

Agarwal, S., Amromin, G., Ben-David, I., Chomsisengphet, S., andEvanoff, D. D. (2011). The role of securitization in mortgagerenegotiation. Journal of Financial Economics, 102(3):559–578.

Clauretie, T. M. and Daneshvary, N. (2009). Estimating the houseforeclosure discount corrected for spatial price interdependenceand endogeneity of marketing time. Real Estate Economics,37(1):43–67.

King, G., Lam, P., and Roberts, M. E. (2017). Computer-assistedkeyword and document set discovery from unstructured text.American Journal of Political Science, 61(4):971–988.

Kruger, S. (2018). The effect of mortgage securitization onforeclosure and modification. Journal of Financial Economics,129(3):586–607.

Maturana, G. (2017). When are modifications of securitized loansbeneficial to investors? The Review of Financial Studies,30(11):3824–3857.

Piskorski, T., Seru, A., and Vig, V. (2010). Securitization anddistressed loan renegotiation: Evidence from the subprimemortgage crisis. Journal of Financial Economics, 97(3):369–397.

Zhang, C. (2019). A shortage of short sales: Explaining theunderutilization of a foreclosure alternative. Federal ReserveBoard of Philadelphia Working Paper 19-13.