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© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.i
| The MarketPulse g December 2017 g Volume 6, Issue 12
The MarketPulse
DECEMBER 2017
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.ii
Table of Contents | The MarketPulse g December 2017 g Volume 6, Issue 12
Table of Contents
Peering into 2018: The Outlook for U.S. Housing Markets .............................1
Erosion of housing affordability likely to spread to more markets
Housing Inventory ............................................................................................................ 2
Inventory Constraints Driving Up Home Prices
How Much Is Your Home’s Collateral Value? ....................................................... 3
Traditional Appraisal and Automated Valuation Models Don’t Always See Eye to Eye
Credit Characteristics of Renters ............................................................................. 5
Patterns In Risk Factors
In the News .............................................................................................................................................................. 6
10 Largest CBSA — Loan Performance Insights Report September 2017 .................................7
Home Price Index State-Level Detail — Combined Single Family Including Distressed October 2017 ............................................................................................................................................................7
Home Price Index .................................................................................................................................................. 8
Overview of Loan Performance ..................................................................................................................... 8
CoreLogic HPI® Market Condition Overview............................................................................................ 9October 2017October 2022 Forecast
National Home Equity Distribution .............................................................................................................10
Map of Average Year-Over-Year Equity Gain per Borrower ...........................................................10
Variable Descriptions .......................................................................................................................................... 11
Housing Statistics
October 2017
HPI® YOY Chg 7.0%
HPI YOY Chg XD 6.1%
NegEq Share (Q3 2017) 6.3%
Cash Sales Share
(as of January 2017)
36.5%
Distressed Sales
(as of January 2017)
7.0%
The MarketPulseVolume 6, Issue 12December 2017Data as of October 2017 (unless otherwise stated)
News Media Contact
Alyson [email protected]
949.214.1414 (office)
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 1
The MarketPulse g December 2017 g Volume 6, Issue 12 | Articles
Peering into 2018: The Outlook for U.S. Housing MarketsErosion of housing affordability likely to spread to more markets
By Frank E. Nothaft
A central theme for the 2018 housing market
will be the continuing erosion of housing
affordability, an issue that will permeate a
growing list of American neighborhoods.
Today housing affordability is already a
major concern in many high-cost markets,
and will spread to more moderate-cost
places across the nation. Let’s look at the
economic factors that we expect will further
weaken affordability in the coming year.
One is the projected rise in interest rates.
The Federal Reserve has signaled its
plan to increase its federal funds target,
pushing other short-term interest rates
up including initial rates on ARMs, and to
reduce its portfolio of long-term Treasury
and mortgage-backed securities. And
while fixed-rate mortgage rates remain at
historically low levels, they are already up
about three-fourths of a percentage point
above their record low. Fixed-rate loans are
forecast to rise in 2018 by at least one-half
a percentage point to as much as a full
percentage point. (Figure 1)
A second factor is the increasing price of
buying a home. CoreLogic’s national Home
Price Index has been rising at a 6 percent
or better clip over the past year with
less expensive homes rising even faster.
When combined with the rise in mortgage
rates, the price increase for lower-priced
homes translates into approximately a
15 percent rise over the last year in the
monthly principal and interest payment for
a first-time buyer.1 (Figure 2) We expect
this trend to continue in 2018, with the
CoreLogic Home Price Index for the U.S.
up another 5 percent.
Third, we expect the very low for-sale
inventory, especially for ‘starter’ homes,
to continue. As low inventory confronts
the rising desire for homeownership by a
growing number of millennials, home sale
conditions will favor the seller with low time-
on-market, multiple contracts per home,
and more homes that sell at or above list
price. These phenomena will be particularly
FIGURE 1. MORTGAGE RATES HEADING UP IN 2018Interest Rate on 30-Year Fixed-Rate Mortgages (percent)
3%
4%
5%
6%
7%
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
4.6%
GreatRecession
Forecast
Dec. 2018:
April 2011:
5.1%
Tax ImplementedAugust 2016
nothaft: fig 1Peak 18.4%
Source: Freddie Mac Primary Mortgage Market Survey®; forecast is an average of MBA, Fannie Mae, Freddie Mac, NAHB, NAR and IHS Markit projections.
FIGURE 2. 'STARTER' HOME PRICES HAVE GROWN FASTERCoreLogic home Price Index (Percent change, September 2016 to September 2017)
8.9%
6.4%
5.2%
0%
2%
4%
6%
8%
10%
Price < 75% of Median All Homes Price > 125% of Median
Tax ImplementedAugust 2016
nothaft: fig 2Peak 18.4%
Source: CoreLogic Home Price Index (December 5, 2017 release).
Dr. Frank Nothaft
Executive, Chief Economist,
Office of the Chief Economist
Frank Nothaft holds the title executive, chief economist for CoreLogic. He leads the Office of the Chief Economist and is responsible for analysis, commentary and forecasting trends in global real estate, insurance and mortgage markets.
Continued on page 4
1 Calculation used $160,000 as the median loan amount
application for a first-time home buyer in August 2016, the
August 2016 30-year FRM rate of 3.44% (Freddie Mac Primary
Mortgage Market Survey), and compared with a loan that was
8.9% larger (September 2016-to-September 2017 increase in
CoreLogic HPI for homes that sold for less than 75% of the local
area median price) in August 2017 with FRM rate of 3.88%.
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.2
Articles | The MarketPulse December 2017 Volume 6, Issue 12
Housing InventoryInventory Constraints Driving Up Home Prices
By Sam Khater
We recently took a look at how low available
inventory is contributing to rising home
prices. As you can see in Figure 1, For Sale
Inventory is at its lowest level since 2005 at
approximately 4-months’ supply compared
to a “normal” market of 6-months’ supply.
We found that Unsold Inventory is even
lower than traditional metrics might
suggest. Because the bulk of entry-level
supply, especially fi rst-time homebuyers,
is so constrained, it’s eff ectively keeping
potential buyers out of the market. Figure 2
illustrates the low price tier pressure. These
price tier are based on median price, which
means 100 is the median, 125 is 25% above
the median, etc. The highlighted area shows
that the “aff ordable” price tier’s inventory
is shrinking and now represents less than
3-months’ supply of homes for sale.
When the housing market faces lower
inventories, it has a mirror eff ect in speeding
up the velocity or lowering days on the
market. Figure 3 shows the increase in
the percentage of homes sold in less than
30 days—17% of homes sold in less than
30 days, an all-time high since we’ve been
tracking this metric. It also demonstrates
another dramatic shift on the other end of
the spectrum, Unsold Homes on the market
over 180 days. This has dropped to an
all-time low and 50% down from the level
during the mortgage and housing crisis.
Not surprisingly, the price pressure that
results from this market velocity has had a
direct impact on listing vs. sold prices. The
smaller the inventory, the more impact on
driving selling prices up. This lack of supply
is disproportionately driving up low-end
home prices. In Figure 4, we compared
low-end to high tier prices for the top 20
markets and found that the low tier’s lack of
Con nued on page 6FIGURE 2. MONTHS' SUPPLY BY PRICE TIERMonths' Supply
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0-50 50-75 75-100 100-125 125-150 150-175 175-200
Price Tier
Aug-17 Aug-16
Source: CoreLogic
FIGURE 1. MONTHS' SUPPLY OF HOMES FOR SALEMonths' Supply
3
5
7
9
11
13
15
Jun
-82
Feb
-84
Oct
-85
Jun
-87
Feb
-89
Oct
-90
Jun
-92
Feb
-94
Oct
-95
Jun
-97
Feb
-99
Oct
-00
Jun
-02
Feb
-04
Oct
-05
Jun
-07
Feb
-09
Oct
-10
Jun
-12
Feb
-14
Oct
-15
Jun
-17
Source: NAR
Sam Khater
Executive, Research & Insights,
Deputy Chief Economist,
Offi ce of the Chief Economist
Sam Khater holds the title executive, Research & Insights, and Deputy Chief Economist at CoreLogic, America’s largest provider of advanced property and ownership information, analytics and services. He is responsible for analysis and commentary on the real estate and mortgage markets and is regularly quoted by trade publications and national news outlets, such as The Wall Street Journal, New York Times, Bloomberg, etc.
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 3
The MarketPulse g December 2017 g Volume 6, Issue 12 | Articles
How Much Is Your Home’s Collateral Value?Traditional Appraisal and Automated Valuation Models Don’t Always See Eye to Eye
By Yanling Mayer
Recently the two government-sponsored
enterprises (GSEs) Fannie Mae and
Freddie Mac announced plans to waive
the requirement of a professional appraisal
on qualified purchase loans with a loan-
to-value ratio at or below 80 percent.1 For
Fannie Mae, the new waiver option extends
the Property Inspection Waiver program
which was initially only applicable to
refinancing loans. Similarly for Freddie Mac,
the move has expanded lenders’ option to
use automated evaluation tools, in lieu of a
traditional appraisal, on both purchase and
refinancing loans when working with its
Loan Advisor Suite.
The GSE announcements came amid
reports of a shortage of state-certified
and licensed appraisers, especially in rural
areas.2 Nonetheless, the announcement
was not without controversy. The Appraisal
Institute (AI), the country’s largest trade
association of real estate appraisers, has
raised safety and soundness concerns
of eliminating the appraisal requirement
and is seeking a legislative rollback as it
regards “the requirement for the completion
of full appraisals to determine the true
equity position of individual properties”
fundamental to prudent risk management
for the mortgage finance sector.3 Under the
federal banking regulations for real estate
transactions, automated appraisal methods
are generally reserved as a due diligence
tool rather than as the primary valuation.4
From a market economics perspective,
a clash between automated evaluations
and traditional appraisal seems rather
inevitable, as advanced analytics and
big data technology have steadfastly
pushed the boundaries of collateral
evaluation capabilities. Today’s automated
valuation alternatives are often powered
by large databases that can capture
information on a given property as well
as transaction records in and around the
property in consideration.
In mortgage underwriting and securitization,
collateral risk is typically quantified by loan-
to-value (LTV) ratios. For purchase loans, the
LTV ratios at origination are valued at the
lesser of purchase price and appraised value.
Since traditional appraisals infrequently
come in below purchase price—about 10
percent of the time among loan applications
or less than 4 percent among funded
loans5—a loan’s collateral risk measure is
typically unaffected by appraisal.
But that could change quickly using an
automated valuation model (AVM). Here
is a quick look at the difference between
traditional appraisal and AVMs, with
implications for origination LTV. This blog
analyzed a sample of recently appraised
single-family homes purchased with
mortgage financing for which a CoreLogic
AVM value was also available.6 The sample
consists of approximately 190,000 purchase-
loan properties appraised between
July 2016 and June 2017.
Figure 1 shows the distribution of the
properties’ traditional appraisal value
relative to their purchase price. A majority
FIGURE 1. 9-IN-10 APPRAISALS HAVE NO EFFECT ON UNDERWRITING LOAN-TO-VALUE RATIOPercent of Loans
0%
10%
20%
30%
40%
50%
60%
Lo
wer
-20
% o
r m
ore
At
leas
t 15
%
At
leas
t 10
%
At
leas
t 5%
At
leas
t 3%
At
leas
t 1%
low
er
Lo
wer
Iden
tial
Hig
her
At
leas
t 1%
hig
her
At
leas
t 3%
At
leas
t 5%
At
leas
t 10
%
At
leas
t 15
%
Hig
her-
20%
or
mo
re
Appraisal Value Relative to Pre-Closing Contract Price
mayer: fig 1
Mean: 1.6%Standard deviation: 5.7%Low appraisals: 9.8%
Source: CoreLogic 2017
Continued on page 4
Yanling Mayer
Principal, Economist
Yanling Mayer holds the title principal economist for CoreLogic in the Office of the Chief Economist, and conducts analysis of housing and mortgage markets. A financial economist by training, Yanling has more than 15 years of professional experience in economic and market research.
1 The property must be a single-family, primary residence or
second home with a value less than $1 million; additional
restrictions apply.2 See the Interagency Advisory on the Availability of Appraisers,
issued by the federal banking regulators on May 31, 2017. https://
www.occ.gov/news-issuances/news-releases/2017/nr-ia-2017-
60a.pdf.3 The Appraisal Institute press release, “Appraisal Institute Joins 35
Groups Seeking to Halt Appraisal Waivers,” September 7, 2017.4 See the Interagency Appraisal and Evaluation Guidelines 2010,
which was originally issued in 1994 by the FDIC, OCC, FBR, and
OTC, in accordance with Title XI of the 1989 FIRREA.
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.4
Articles | The MarketPulse December 2017 Volume 6, Issue 12
How Much is Your Home's Collateral Value? con nued from page 3
of the appraisals were either exactly at the
contract price (31.6 percent) or slightly
above it (58.6 percent), leaving about
10 percent of the properties appraised
below the purchase price. With very few
appraisals on the low end, the purchase
price eff ectively determined origination LTV
during loan underwriting.
Figure 2 shows the distribution of the
AVM values relative to the purchase price:
45.4 percent of the AVM values were at
or above the contract price, while 54.6
percent were below it. Compared with
traditional appraisals, the AVM values were
more symmetrically distributed about the
purchase price but with thicker tails on both
ends (that is, greater uncertainty in the
valuation). For the 5-in-9 properties with
an AVM value below the purchase price, the
LTV ratios for these loans would be higher
had the AVM valuations been used instead
of a traditional appraisal.7
Since the odds of an AVM coming in
below the purchase price were 55-45 in
this analysis, compared with 10-90 for
traditional appraisals, AVM usage will
increase the underwriting LTV on a much
larger number of loans. And the ‘fatter tail’
of the distribution below the contract price
means that the upward LTV adjustment
will more often be larger than for a
traditional appraisal.
While the industry may debate which
valuation method is likely more accurate
than the other, or more importantly, which
is more useful than the other in predicting
default risk and loan performance, there is
one thing we can all agree on: Lenders and
mortgage investors need reliable information
about a loan’s and portfolio’s collateral
risk to make informed underwriting and
investment decisions. ■
5 A recent study by researchers at Fannie Mae reported less than
4 percent of the purchase loans guaranteed by the agency
during 1992-2015 had an appraisal below the purchase price. The
study can be accessed at http://www.fanniemae.com/resources/
file/research/datanotes/pdf/working-paper-102816.pdf.6 The AVM valuation date (or, AVM “as of” date) did not fall
exactly on the appraisal date, but ranged from 15 days to about
3½ months after the appraisal date.7 Because the data set did not include the buyers’ loan amount,
analysis by LTV ratio could not be performed. It remains to be
seen whether the distribution of AVM valuations or appraisal is
affected by leverage. However, if the valuations are unbiased, we
should not expect leverage to affect the valuation outcome.
FIGURE 2. MORE THAN 1-IN-2 AVM VALUES COULD EFFECT UNDERWRITING LOAN-TO-VALUE RATIOPercent of Loans
0%
10%
20%
30%
40%
50%
60%
Lo
wer
-20
% o
r m
ore
At
leas
t 15
%
At
leas
t 10
%
At
leas
t 5%
At
leas
t 3%
At
leas
t 1%
low
er
Lo
wer
Iden
tica
l
Hig
her
At
leas
t 1%
hig
her
At
leas
t 3%
At
leas
t 5%
At
leas
t 10
%
At
leas
t 15
%
Hig
her-
20%
or
mo
re
AVM Value Relative to Pre-Closing Contract Price
Mean: -0.04%Standard deviation: 8.8%Low AVMs: 54.6%
Source: CoreLogic 2017
FIGURE 3. INVENTORY 'SHORTAGE' ACUTE FOR ENTRY-LEVEL BUYERSMonths' Supply by Price Tier, August 2017
2.0
2.5
3.0
3.5
4.0
4.5
5.0
< 50 50-75 75-100 100-125 125-150 150-175 175-200
Price Tier (Percent of Median Price)
Entry Level Supply
Source: CoreLogic (chart excludes data for homes priced > 200% of local-area median, which was 7.6 months in August 2017)
Peering into 2018 con nued from page 1
acute in the fi rst-time buyer segment,
where there is already a shortage of for-sale
inventory. (Figure 3)
Declining aff ordability can be alleviated
by new construction and rehabilitation of
older housing stock. We expect housing
starts to increase 5 percent in 2018, but
more building is necessary to alleviate the
aff ordability challenges in many higher-
cost American cities.
Best wishes for a healthy and successful
2018. ■
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 5
The MarketPulse December 2017 Volume 6, Issue 12 | Articles
Con nued on page 6
Credit Characteristics of RentersPatterns In Risk Factors
By Matt CannonCo-author John Wang
Mortgage lenders have known for a long
time that debt-to-income (DTI) and credit
history (based on credit bureau data),
among other factors, are critical for sound
underwriting and managing credit risk on
a mortgage portfolio. Similar analysis can
be used to evaluate a prospective tenant’s
likelihood of making the rent payments
agreed to in the lease or the share of a
building’s rent roll that may go delinquent.
This has become increasingly important for
rental management companies as the renter
share of households has risen to its highest
in 50 years (Figure 1).
Rental property owners and managers use
the CoreLogic® Rental Property Solutions
platform to evaluate the credit risk of rental
applicants. Information from this platform
can be used to examine trends in renter
credit quality over time.
Figure 2 shows the average rent-to-income
ratio for rental applicants. A higher rent-to-
income ratio is generally associated with
increased credit risk, as renters devote
a higher percentage of their income to
paying rent. The rent-to-income ratio has
trended upward between 2009 and 2017, as
the increase in rents has outpaced income
growth. At the national level, it has increased
from 25.4 percent in the second quarter
of 2009 to 28.1 percent in the second
quarter of 2017, a 10.6 percent increase over
an eight-year period.
Rent to income is one factor aff ecting
renter payment risk. Additional sources of
information, such as credit bureau data,
public records, and other information in the
renter application, can also provide insight
into renter performance risk. The CoreLogic
ScorePLUS® model is a statistical model
that brings together multiple sources of
information to predict renter applicant’s
risk of lease default. Information related to
credit bureau history, subprime loan history,
eviction and rental collection history, as well
as the renter’s application information all
factor in to the SafeRent® Score risk score.
Figure 3 shows the average SafeRent Score
over time for rent applicants contained in
the Rental Property Solutions platform.
Similar to a FICO score, a higher SafeRent
Score is associated with lower risk. Two
trends stand out in the average renter risk
scores. First, the scores exhibit a seasonal
trend. The seasonal trend is supported
by seasonal trends in credit bureau
characteristics of rent applicants. Second,
the average score has been improving
(renter applicant risk has been declining)
since 2010. This is consistent with the
general improvement of credit performance
as borrowers continue to recover from the
2008–2009 recession. The improvement in
applicants’ credit characteristics has more
than off set the upward trend in rent to
income shown in Figure 2, resulting in an
improving rental risk score over time.
Matt Cannon
Principal, Science & Analytics Team
Matt Cannon holds the title principal with the Science & Analytics Team at CoreLogic. He currently is responsible for the models contained in the LoanSafe Appraisal Manager™ and LoanSafe Collateral Manager™ products, as well as contributing analytic support to CoreLogic’s Tax team. During his 12 years at CoreLogic, Matt has contributed to a range of analytic models, including automated valuation model (AVM) cascades, house price indices and forecasts, loan level mortgage prepayment and default models, and distressed valuation models.
FIGURE 2. RENT-TO-INCOME RATIO
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.3
200
9Q
2
200
9Q
4
2010
Q2
2010
Q4
2011
Q2
2011
Q4
2012
Q2
2012
Q4
2013
Q2
2013
Q4
2014
Q2
2014
Q4
2015
Q2
2015
Q4
2016
Q2
2016
Q4
2017
Q2
Source: CoreLogic 2017
FIGURE 1. RENTAL RATE IN THE U.S.(Not Seasonally Adjusted)
30.0
31.0
32.0
33.0
34.0
35.0
36.0
37.0
38.0
200
0Q
1
200
0Q
4
200
1Q3
200
2Q2
200
3Q1
200
3Q4
200
4Q
3
200
5Q2
200
6Q
1
200
6Q
4
200
7Q3
200
8Q
2
200
9Q
1
200
9Q
4
2010
Q3
2011
Q2
2012
Q1
2012
Q4
2013
Q3
2014
Q2
2015
Q1
2015
Q4
2016
Q3
2017
Q2
Source: Census Bureau 2017
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.6
Articles | The MarketPulse g December 2017 g Volume 6, Issue 12
Housing Inventory continued from page 2
Credit Characteristics continued from page 5
inventor, and the borrowers competing for
those homes, is outpacing price growth for
high tier properties.
In our next blog, we’ll take a closer look
at how these market forces are impacting
rental prices. ■
FIGURE 4. TOP 20 MARKET YEAR-OVER-YEAR PERCENT CHANGEAugust 2017
0%
2%
4%
6%
8%
10%
12%
14%
16%
Sea
ttle
, WA
Den
ver,
CO
Dal
las,
TX
Mia
mi,
FL
Riv
ersi
de,
CA
Lo
s A
ngel
es, C
A
Atl
anta
, GA
Oak
land
, CA
Min
neap
olis
, MN
Pho
enix
, AZ
San
Die
go
, CA
Was
hin
gto
n, D
C
Bal
tim
ore
, MD
Nas
sau
Co
unty
,N
Y
Ana
hei
m, C
A
Ho
ust
on,
TX
St.
Lo
uis,
MO
Chi
cag
o, I
L
New
Yo
rk, N
Y
Low Tier
High Tier
khater: fig 4
Note: High tier is for home prices that are 25% or more above the median priced home in that market and low tier are for home prices 75% or less below the median priced home in that market.Source: CoreLogic
FIGURE 3. VELOCITY OF SALES IS HIGH AND LESS DESIRABLE INVENTORY IS RAPIDLY DECLININGPercent of Homes Sold in < 30 Days Percent of Unsold Inventory on Market > 180 Days
5%
7%
9%
11%
13%
15%
17%
19%
Jan
-01
Feb
-02
Mar
-03
Ap
r-0
4
May
-05
Jun
-06
Jul-
07
Aug
-08
Sep
-09
Oct
-10
No
v-11
Dec
-12
Jan
-14
Feb
-15
Mar
-16
Ap
r-17
khater: fig 3
15%
20%
25%
30%
35%
40%
45%
Jan
-01
Feb
-02
Mar
-03
Ap
r-0
4
May
-05
Jun
-06
Jul-
07
Aug
-08
Sep
-09
Oct
-10
No
v-11
Dec
-12
Jan
-14
Feb
-15
Mar
-16
Ap
r-17
Source: CoreLogic
This blog provides an introductory overview
of renter credit risk. A follow-up blog will
examine trends in renter payment risk
as well as other renter characteristics in
greater detail. ■
FIGURE 3. AVERAGE RENTER RISK SCORE
300
350
400
450
500
550
600
2010
Q1
2010
Q2
2010
Q3
2010
Q4
2011
Q1
2011
Q2
2011
Q3
2011
Q4
2012
Q1
2012
Q2
2012
Q3
2012
Q4
2013
Q1
2013
Q2
2013
Q3
2013
Q4
2014
Q1
2014
Q2
2014
Q3
2014
Q4
2015
Q1
2015
Q2
2015
Q3
2015
Q4
2016
Q1
2016
Q2
2016
Q3
2016
Q4
2017
Q1
2017
Q2
cannon: fig 3
Source: CoreLogic 2017
In the News
MarketWatch, December 8, 2017
California wildfires could mean over $27
billion in damages to homes, CoreLogic
says
More than 86,000 homes in Southern California are
at risk as wildfires rage through Southern California,
according to a CoreLogic analysis. Of that total, 16%
are at significant risk of damage and fall into “high” and
“extreme” categories. That represents a reconstruction
cost value of more than $5 billion.
24/7 Wall St., December 8, 2017
More Than 86000 Homes at Risk From
Southern California Wildfires
CoreLogic estimates that 4,645 homes face High risk
from the fire and 968 face Extreme risk. Total RCV for
the homes at High risk is about $2.24 billion, and the
RCV for homes at Extreme risk is about $396 million.
HousingWire, December 8, 2017
CoreLogic: Another quarter million
homes no longer underwater on the
mortgage
Mortgage data tracker and analytics firm CoreLogic
today released its Q3 2017 home equity analysis and
found 260,000 mortgaged properties regained equity
between the second and third quarters of 2017. That’s
among the 63% of all homeowners with a mortgage,
data from CoreLogic tracks.
Mortgageorb, December 8, 2017
CoreLogic: About 2.5 Million US Homes
Still in Negative Equity
The number of U.S. homeowners who were
“underwater” on their properties continued to decrease
in the third quarter, falling 9% compared with the
second quarter to 2.5 million homes, or 4.9% of all
mortgaged properties, according to CoreLogic.
National Mortgage News, December 8,
2017
Homeowner equity rises by $871B in the
third quarter
Homeowners with mortgages, or 63% of the total, have
collectively seen their equity increase 11.8% from the
third quarter of 2016 to 3Q17, representing a gain of
$871 billion year-over-year, according to CoreLogic.
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 7
The MarketPulse g December 2017 g Volume 6, Issue 12 | Analysis
“Single-family residential sales and prices continued to heat up in October. On a year-over-year basis, home prices grew in excess of 6 percent for four consecutive months ending in October, the longest such streak since June 2014. This escalation in home prices reflects both the acute lack of supply and the strengthening economy.”
Dr. Frank Nothaft,
chief economist for CoreLogic
Home Price Index State-Level Detail — Combined Single Family Including Distressed October 2017
StateMonth-Over-Month
Percent ChangeYear-Over-Year Percent Change
Forecasted Month-Over-Month
Percent Change
Forecasted Year-Over-Year Percent Change
Alabama 0.7% 5.4% 0.3% 4.0%Alaska 0.3% 2.8% 0.2% 5.4%
Arizona 0.5% 6.2% 0.2% 5.8%Arkansas 0.8% 4.6% 0.3% 4.5%California 0.4% 7.6% 0.4% 8.2%Colorado 0.9% 8.2% 0.3% 5.4%
Connecticut −0.2% 2.5% 0.2% 6.3%Delaware 1.2% 4.1% 0.2% 3.7%
District of Columbia −0.5% 2.4% 0.3% 3.8%Florida 0.4% 6.1% 0.3% 6.4%
Georgia 0.3% 6.2% 0.2% 3.7%Hawaii 0.5% 8.1% 0.4% 5.3%Idaho 0.5% 9.0% 0.3% 4.4%Illinois −0.2% 3.7% 0.2% 4.7%
Indiana 0.6% 4.7% 0.3% 4.9%Iowa −0.2% 4.0% 0.0% 3.4%
Kansas 0.1% 3.2% 0.1% 3.6%Kentucky 0.2% 5.8% 0.2% 3.8%Louisiana 0.4% 5.3% 0.1% 2.2%
Maine −1.2% 5.6% −0.2% 4.7%Maryland −0.2% 3.3% 0.1% 3.9%
Massachusetts −0.1% 6.8% 0.1% 4.3%Michigan 0.3% 8.2% 0.2% 5.2%
Minnesota 0.1% 6.0% 0.1% 3.0%Mississippi −0.2% 3.8% 0.2% 3.3%
Missouri 0.3% 5.9% 0.2% 4.2%Montana −0.5% 5.1% −0.1% 3.3%
Nebraska 0.1% 5.5% 0.1% 3.5%Nevada 0.8% 10.1% 0.6% 8.2%
New Hampshire −0.2% 5.9% 0.3% 6.3%New Jersey −0.4% 2.6% 0.2% 4.9%New Mexico −0.3% 2.1% −0.1% 3.6%
New York 1.5% 5.9% 0.3% 4.6%North Carolina 0.6% 5.3% 0.2% 4.0%North Dakota 2.2% 7.1% 0.4% 2.8%
Ohio 0.4% 5.6% 0.2% 4.1%Oklahoma 0.2% 2.3% 0.1% 3.1%
Oregon −0.1% 8.1% 0.1% 5.7%Pennsylvania −0.3% 3.4% 0.1% 4.3%Rhode Island 0.4% 7.5% 0.2% 3.7%
South Carolina 0.2% 4.9% 0.2% 3.8%South Dakota 0.1% 7.5% 0.0% 2.9%
Tennessee −0.5% 6.3% 0.2% 2.9%Texas 0.3% 5.5% 0.1% 2.2%Utah 0.4% 10.1% 0.3% 3.8%
Vermont 1.1% 4.9% 0.5% 5.2%Virginia −0.2% 2.9% 0.2% 4.0%
Washington 0.5% 12.5% 0.2% 4.9%West Virginia −0.6% 0.4% 0.5% 5.0%
Wisconsin 0.3% 6.5% 0.2% 3.9%Wyoming 0.9% 2.2% 0.5% 3.2%
Source: CoreLogic October 2017
10 Largest CBSA — Loan Performance Insights Report September 2017
CBSA
30 Days or More Delinquency Rate
September 2017 (%)
Serious Delinquency Rate September
2017 (%)Foreclosure Rate
September 2017 (%)
30 Days or More Delinquent Rate
September 2016 (%)
Serious Delinquency Rate September
2016 (%)Foreclosure Rate
September 2016 (%)
Boston-Cambridge-Newton MA-NH 3.8 1.4 0.5 4.1 1.8 0.7
Chicago-Naperville-Elgin IL-IN-WI 5.2 2.3 0.9 5.6 2.9 1.1
Denver-Aurora-Lakewood CO 2.0 0.6 0.1 2.3 0.8 0.2
Houston-The Woodlands-Sugar Land TX 10.5 2.1 0.2 5.8 2.0 0.4
Las Vegas-Henderson-Paradise NV 4.5 2.3 0.8 5.4 3.2 1.2
Los Angeles-Long Beach-Anaheim CA 2.8 0.9 0.2 3.2 1.2 0.3
Miami-Fort Lauderdale-West Palm Beach FL 9.6 3.1 0.9 7.4 4.1 1.6
New York-Newark-Jersey City NY-NJ-PA 6.9 3.9 2.0 7.8 5.0 2.7
San Francisco-Oakland-Hayward CA 1.8 0.6 0.1 2.0 0.8 0.2
Washington-Arlington-Alexandria DC-VA-MD-WV 4.1 1.7 0.5 4.5 2.0 0.7
Source: CoreLogic September 2017
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.8
Analysis | The MarketPulse g December 2017 g Volume 6, Issue 12
OVERVIEW OF LOAN PERFORMANCENational Delinquency Rates
Source: CoreLogic September 2017
5.0
2.4
0.70
0.30
1.3
1.6
0.6
5.2
2.1
0.70 0.30
1.5
2.0
0.8
0.0
1.0
2.0
3.0
4.0
5.0
6.0
30+ days 30 to 59 days 60 to 89 days 90 to 119 days 90+ days (not infcl)
120+ days In Foreclosure
Per
cent
age
Rat
e
5.3
0.0
1.0
2.0
3.0
4.0
5.0
6.0
30+ days 30 to 59 days 60 to 89 days 90 to 119 days 90+ days (not infcl)
120+ days In Foreclosure
Per
cent
age
Rat
e2.61x5.11 / 2.69x4.98
loan performance sep 2017: national overview
August 2016
August 201790-119 Days
Past Due120+ DaysPast Due
60-89 DaysPast Due
30-59 DaysPast Due
30 Days or MorePast Due
90+ Days(not in fcl)
HOME PRICE INDEXPercentage Change Year Over Year
Source: CoreLogic October 2017
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
Ap
r-0
4
Oct
-04
Ap
r-0
5
Oct
-05
Ap
r-0
6
Oct
-06
Ap
r-0
7
Oct
-07
Ap
r-0
8
Oct
-08
Ap
r-0
9
Oct
-09
Ap
r-10
Oct
-10
Ap
r-11
Oct
-11
Ap
r-12
Oct
-12
Ap
r-13
Oct
-13
Ap
r-14
Oct
-14
Ap
r-15
Oct
-15
Ap
r-16
Oct
-16
Ap
r-17
Oct
-17
2.62x5.02hpi as of oct 2017
Including DistressedIncluding Distressed
Charts & Graphs
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 9
The MarketPulse g December 2017 g Volume 6, Issue 12 | Analysis
CORELOGIC HPI® MARKET CONDITION OVERVIEWOctober 2017
Source: CoreLogic
CoreLogic HPI Single Family Combined Tier, data through October 2017.
CoreLogic HPI Forecasts Single Family Combined Tier, starting in November 2017.
Legend
Normal
Overvalued
Undervalued
CORELOGIC HPI® MARKET CONDITION OVERVIEWOctober 2022 Forecast
Source: CoreLogic
CoreLogic HPI Single Family Combined Tier, data through October 2017.
CoreLogic HPI Forecasts Single Family Combined Tier, starting in November 2017.
Legend
Normal
Overvalued
Undervalued
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.10
Analysis | The MarketPulse g December 2017 g Volume 6, Issue 12
NATIONAL HOME EQUITY DISTRIBUTIONBy LTV Segment
Source: CoreLogic Q3 2017
2.62x5.02q3 equity as of q3 2017
Including Distressed
Q2 2017
Q3 2017
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
50%
to
54
%
55%
to
59
%
60
% t
o 6
4%
65%
to
69
%
70%
to
74
%
75%
to
79
%
80
% t
o 8
4%
85%
to
89
%
90
% t
o 9
4%
95%
to
99
%
100
% t
o 1
04
%
105%
to
10
9%
110
% t
o 1
14%
115%
to
119
%
120
% t
o 1
24%
125%
+
Loan-to-Value Ratio
“While homeowner equity is rising nationally, there are wide disparities by geography. Hot markets like San Francisco, Seattle and Denver boast very high levels of increased home equity. However, some markets are lagging behind due to weaker economies or lingering effects from the great recession. These include large markets such as Miami, Las Vegas and Chicago, but also many small- and medium-sized markets such as Scranton, Pa. and Akron, Ohio.”
Frank Martell,
president and CEO of CoreLogic
MAP OF AVERAGE YEAR-OVER-YEAR EQUITY GAIN PER BORROWERAs of Q3 2017
Vermont, West Virginia, Maine, Mississippi, South Dakota have insufficient equity data to report.
Source: CoreLogic Q3 2017
1.6%
1.7%
3.2%
9.0%
1.5%
7.2%4.5%
1.7%
3.0%
2.3% 3.5%
4.5%
3.3%
4.9%
1.5%10.1%
5.5%
3.9%
5.1%
3.3%
5.7%
8.7%
6.2%
2.4%6.9%
4.1%
3.7%
4.3% 4.6%
9.0%
4.1%
3.7%
5.3%
4.0%
4.6% 4.4%
1.7%
2.5%
1.6%
1.7%
3.2%
9.0%
1.5%
7.2%4.5%
1.7%
3.0%
2.3% 3.5%
4.5%
3.3%
4.9%
1.5%10.1%
5.5%
3.9%
5.1%
3.3%
5.7%
8.7%
6.2%
2.4%6.9%
4.1%
3.7%
4.3% 4.6%
9.0%
4.1%
3.7%
5.3%
4.0%
4.6% 4.4%
1.7%
1.7%
3.5%7.7%
6.0%
7.6%
8.3%7.5%
5.2%2.5%
Legend
Very High
High
Moderate
Low
© 2017 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 11
The MarketPulse g December 2017 g Volume 6, Issue 12 | Analysis
Variable Descriptions
Variable Definition
Total Sales The total number of all home-sale transactions during the month.
Total Sales 12-Month sum The total number of all home-sale transactions for the last 12 months.
Total Sales YoY Change 12-Month sum
Percentage increase or decrease in current 12 months of total sales over the prior 12 months of total sales
New Home Sales The total number of newly constructed residentail housing units sold during the month.
New Home Sales Median Price
The median price for newly constructed residential housing units during the month.
Existing Home Sales The number of previously constucted homes that were sold to an unaffiliated third party. DOES NOT INCLUDE REO AND SHORT SALES.
REO Sales Number of bank owned properties that were sold to an unaffiliated third party.
REO Sales Share The number of REO Sales in a given month divided by total sales.
REO Price Discount The average price of a REO divided by the average price of an existing-home sale.
REO Pct The count of loans in REO as a percentage of the overall count of loans for the reporting period.
Short SalesThe number of short sales. A short sale is a sale of real estate in which the sale proceeds fall short of the balance owed on the property's loan.
Short Sales Share The number of Short Sales in a given month divided by total sales.
Short Sale Price Discount The average price of a Short Sale divided by the average price of an existing-home sale.
Short Sale Pct The count of loans in Short Sale as a percentage of the overall count of loans for the month.
Distressed Sales Share The percentage of the total sales that were a distressed sale (REO or short sale).
Distressed Sales Share (sales 12-Month sum)
The sum of the REO Sales 12-month sum and the Short Sales 12-month sum divided by the total sales 12-month sum.
HPI MoM Percent increase or decrease in HPI single family combined series over a month ago.
HPI YoY Percent increase or decrease in HPI single family combined series over a year ago.
HPI MoM Excluding Distressed
Percent increase or decrease in HPI single family combined excluding distressed series over a month ago.
HPI YoY Excluding Distressed
Percent increase or decrease in HPI single family combined excluding distressed series over a year ago.
HPI Percent Change from Peak
Percent increase or decrease in HPI single family combined series from the respective peak value in the index.
90 Days + DQ Pct The percentage of the overall loan count that are 90 or more days delinquent as of the reporting period. This percentage includes loans that are in foreclosure or REO.
Stock of 90+ Delinquencies YoY Chg
Percent change year-over-year of the number of 90+ day delinquencies in the current month.
Foreclosure Pct The percentage of the overall loan count that is currently in foreclosure as of the reporting period.
Percent Change Stock of Foreclosures from Peak
Percent increase or decrease in the number of foreclosures from the respective peak number of foreclosures.
Pre-foreclosure FilingsThe number of mortgages where the lender has initiated foreclosure proceedings and it has been made known through public notice (NOD).
Completed ForeclosuresA completed foreclosure occurs when a property is auctioned and results in either the purchase of the home at auction or the property is taken by the lender as part of their Real Estate Owned (REO) inventory.
Negative Equity ShareThe percentage of mortgages in negative equity. The denominator for the negative equity percent is based on the number of mortgages from the public record.
Negative Equity
The number of mortgages in negative equity. Negative equity is calculated as the difference between the current value of the property and the origination value of the mortgage. If the mortgage debt is greater than the current value, the property is considered to be in a negative equity position. We estimate current UPB value, not origination value.
Months' Supply of Distressed Homes (total sales 12-Month avg)
The months it would take to sell off all homes currently in distress of 90 days delinquency or greater based on the current sales pace.
Price/Income RatioCoreLogic HPI™ divided by Nominal Personal Income provided by the Bureau of Economic Analysis and indexed to January 1976.
Conforming Prime Serious Delinquency Rate
The rate serious delinquency mortgages which are within the legislated purchase limits of Fannie Mae and Freddie Mac. The conforming limits are legislated by the Federal Housing Finance Agency (FHFA).
Jumbo Prime Serious Delinquency Rate
The rate serious delinquency mortgages which are larger than the legislated purchase limits of Fannie Mae and Freddie Mac. The conforming limits are legislated by the Federal Housing Finance Agency (FHFA).
corelogic.com
End Notes | The MarketPulse g December 2017 g Volume 6, Issue 12
© 2017 CoreLogic, Inc. All rights reserved.
CORELOGIC, the CoreLogic logo, CORELOGIC HPI, SAFERENT and SCOREPLUS are trademarks of CoreLogic, Inc. and/or its subsidiaries. All other trademarks are the property of their respective holders.
17-MKTPLSE-1217-00
Source: CoreLogicThe data provided is for use only by the primary recipient or the primary recipient's
publication or broadcast. This data may not be re-sold, republished or licensed to any
other source, including publications and sources owned by the primary recipient's parent
company without prior written permission from CoreLogic. Any CoreLogic data used for
publication or broadcast, in whole or in part, must be sourced as coming from CoreLogic,
a data and analytics company. For use with broadcast or web content, the citation
must directly accompany first reference of the data. If the data is illustrated with maps,
charts, graphs or other visual elements, the CoreLogic logo must be included on screen
or website. For questions, analysis or interpretation of the data, contact CoreLogic at
[email protected]. Data provided may not be modified without the prior written
permission of CoreLogic. Do not use the data in any unlawful manner. This data is compiled
from public records, contributory databases and proprietary analytics, and its accuracy is
dependent upon these sources.
For more information please call 866-774-3282
The MarketPulse is a newsletter published by CoreLogic, Inc. ("CoreLogic"). This information is made
available for informational purposes only and is not intended to provide specific commercial, financial or
investment advice. CoreLogic disclaims all express or implied representations, warranties and guaranties,
including implied warranties of merchantability, fitness for a particular purpose, title, or non-infringement.
Neither CoreLogic nor its licensors make any representations, warranties or guaranties as to the quality,
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