The MarketPulse DECEMBER The MarketPulse g December 2017 g Volume 6, Issue 12

Transcription

1 The MarketPulse g December 2017 g Volume 6, Issue 12 The MarketPulse DECEMBER 2017 i 2017 CoreLogic Proprietary. This material may not be reproduced in any form without express written permission.

2 Table of Contents The MarketPulse g December 2017 g Volume 6, Issue 12 The MarketPulse Volume 6, Issue 12 December 2017 Data as of October 2017 (unless otherwise stated) 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% 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 Distressed Sales (as of January 2017) 7.0% Credit Characteristics of Renters... 5 Patterns In Risk Factors In the News Largest CBSA Loan Performance Insights Report September Home Price Index State-Level Detail Combined Single Family Including Distressed October Home Price Index... 8 Overview of Loan Performance... 8 CoreLogic HPI Market Condition Overview... 9 October 2017 October 2022 Forecast National Home Equity Distribution...10 Map of Average Year-Over-Year Equity Gain per Borrower...10 Variable Descriptions News Media Contact Alyson Austin alaustin@corelogic.com (office) ii 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 Peering into 2018: The Outlook for U.S. Housing Markets Erosion 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 timeon-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 2018 Interest Rate on 30-Year Fixed-Rate Mortgages (percent) 7% 6% 5% 4% Great Recession April 2011: 5.1% 3% Forecast 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 FASTER CoreLogic home Price Index (Percent change, September 2016 to September 2017) 10% 8.9% Continued on page 4 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. 1 Calculation used $160,000 as the median loan amount application for a first-time home buyer in August 2016, the August 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%. Dec. 2018: 4.6% 8% 6.4% 6% 5.2% 4% 2% 0% Price < 75% of Median All Homes Price > 125% of Median Source: CoreLogic Home Price Index (December 5, 2017 release) CoreLogic Proprietary. This material may not be reproduced in any form without express written permission. 1

4 Articles The MarketPulse December 2017 Volume 6, Issue 12 Housing Inventory Inventory Constraints Driving Up Home Prices By Sam Khater FIGURE 1. MONTHS' SUPPLY OF HOMES FOR SALE Months' Supply Sam Khater Executive, Research & Insights, Deputy Chief Economist, Office 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. 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 first-time homebuyers, is so constrained, it s effectively 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 affordable 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 effect 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 Jun-82 Feb-84 Source: NAR 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 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 FIGURE 2. MONTHS' SUPPLY BY PRICE TIER Months' Supply Con nued on page Aug-17 Aug Price Tier Source: CoreLogic CoreLogic Proprietary. This material may not be reproduced in any form without express written permission.

5 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 loanto-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 loanto-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 loans 5 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 purchaseloan properties appraised between July 2016 and June Figure 1 shows the distribution of the properties traditional appraisal value relative to their purchase price. A majority Continued on page 4 FIGURE 1. 9-IN-10 APPRAISALS HAVE NO EFFECT ON UNDERWRITING LOAN-TO-VALUE RATIO Percent of Loans 60% 50% 40% 30% 20% 10% 0% Lower-20% or more At least 15% Source: CoreLogic 2017 At least 10% At least 5% At least 3% At least 1% lower Lower Idential Higher At least 1% higher At least 3% Appraisal Value Relative to Pre-Closing Contract Price 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, The Appraisal Institute press release, Appraisal Institute Joins 35 Groups Seeking to Halt Appraisal Waivers, September 7, 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. Mean: 1.6% Standard deviation: 5.7% Low appraisals: 9.8% At least 5% At least 10% At least 15% Higher-20% or more 2017 CoreLogic Proprietary. This material may not be reproduced in any form without express written permission. 3

6 Articles The MarketPulse December 2017 Volume 6, Issue 12 How Much is Your Home's Collateral Value? con nued from page 3 5 A recent study by researchers at Fannie Mae reported less than 4 percent of the purchase loans guaranteed by the agency during had an appraisal below the purchase price. The study can be accessed at file/research/datanotes/pdf/working-paper 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. 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 effectively 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 FIGURE 2. MORE THAN 1-IN-2 AVM VALUES COULD EFFECT UNDERWRITING LOAN-TO- VALUE RATIO Percent of Loans 60% 50% 40% Mean: -0.04% Standard deviation: 8.8% Low AVMs: 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 in this analysis, compared with 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. 30% 20% 10% 0% Lower-20% or more At least 15% Source: CoreLogic 2017 At least 10% At least 5% At least 3% At least 1% lower Lower Identical Higher At least 1% higher At least 3% AVM Value Relative to Pre-Closing Contract Price At least 5% At least 10% At least 15% Higher-20% or more 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. Peering into 2018 con nued from page 1 FIGURE 3. INVENTORY 'SHORTAGE' ACUTE FOR ENTRY-LEVEL BUYERS Months' Supply by Price Tier, August acute in the first-time buyer segment, where there is already a shortage of for-sale inventory. (Figure 3) Entry Level Supply Declining affordability 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 affordability challenges in many highercost American cities < Price Tier (Percent of Median Price) Source: CoreLogic (chart excludes data for homes priced > 200% of local-area median, which was 7.6 months in August 2017) Best wishes for a healthy and successful CoreLogic Proprietary. This material may not be reproduced in any form without express written permission.

7 Credit Characteristics of Renters Patterns In Risk Factors By Matt Cannon Co-author John Wang The MarketPulse December 2017 Volume 6, Issue 12 Articles 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 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 This is consistent with the general improvement of credit performance as borrowers continue to recover from the recession. The improvement in applicants credit characteristics has more than offset the upward trend in rent to income shown in Figure 2, resulting in an improving rental risk score over time. Con nued on page 6 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 shows the average rent-to-income ratio for rental applicants. A higher rent-toincome 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. FIGURE 1. RENTAL RATE IN THE U.S. (Not Seasonally Adjusted) Q1 2000Q4 2001Q3 2002Q2 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1 2012Q4 2013Q3 2014Q2 2015Q1 2015Q4 2016Q3 2017Q2 Rent to income is one factor affecting Source: Census Bureau 2017 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 2. RENT-TO-INCOME RATIO Source: CoreLogic Q2 2009Q4 2010Q2 2010Q4 2011Q2 2011Q4 2012Q2 2012Q4 2013Q2 2013Q4 2014Q2 2014Q4 2015Q2 2015Q4 2016Q2 2016Q4 2017Q CoreLogic Proprietary. This material may not be reproduced in any form without express written permission. 5

8 Articles The MarketPulse g December 2017 g Volume 6, Issue 12 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 Housing Inventory continued from page 2 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 3. VELOCITY OF SALES IS HIGH AND LESS DESIRABLE INVENTORY IS RAPIDLY DECLINING Percent of Homes Sold in < 30 Days Percent of Unsold Inventory on Market > 180 Days 19% 45% at risk as wildfires rage through Southern California, 17% 40% 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. 15% 13% 11% 9% 35% 30% 25% 24/7 Wall St., December 8, % 20% More Than Homes at Risk From Southern California Wildfires 5% Jan-01 Feb-02 Mar-03 Apr-04 May-05 Jun-06 Jul-07 Aug-08 Sep-09 Oct-10 Nov-11 Dec-12 Jan-14 Feb-15 Mar-16 Apr-17 15% Jan-01 Feb-02 Mar-03 Apr-04 May-05 Jun-06 Jul-07 Aug-08 Sep-09 Oct-10 Nov-11 Dec-12 Jan-14 Feb-15 Mar-16 Apr-17 CoreLogic estimates that 4,645 homes face High risk Source: CoreLogic 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. FIGURE 4. TOP 20 MARKET YEAR-OVER-YEAR PERCENT CHANGE August % 14% Low Tier HousingWire, December 8, % High Tier CoreLogic: Another quarter million 10% homes no longer underwater on the mortgage Mortgage data tracker and analytics firm CoreLogic 8% 6% 4% 2% today released its Q home equity analysis and 0% found 260,000 mortgaged properties regained equity between the second and third quarters of That s among the 63% of all homeowners with a mortgage, Seattle, WA Denver, CO Dallas, TX Miami, FL Riverside, CA Los Angeles, CA Atlanta, GA Oakland, CA Minneapolis, MN Phoenix, AZ San Diego, CA Washington, DC Baltimore, MD Nassau County, NY Anaheim, CA Houston, TX St. Louis, MO Chicago, IL New York, NY data from CoreLogic tracks. 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 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. Credit Characteristics continued from page 5 This blog provides an introductory overview of renter credit risk. A follow-up blog will examine trends in renter payment risk FIGURE 3. AVERAGE RENTER RISK SCORE 600 as well as other renter characteristics in greater detail. National Mortgage News, December 8, 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 Q1 2010Q2 2010Q3 2010Q4 2011Q1 2011Q2 2011Q3 2011Q4 2012Q1 2012Q2 2012Q3 2012Q4 2013Q1 2013Q2 2013Q3 2013Q4 2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4 2017Q1 2017Q2 Source: CoreLogic 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 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 Chicago-Naperville-Elgin IL-IN-WI Denver-Aurora-Lakewood CO Houston-The Woodlands-Sugar Land TX Las Vegas-Henderson-Paradise NV Los Angeles-Long Beach-Anaheim CA Miami-Fort Lauderdale-West Palm Beach FL New York-Newark-Jersey City NY-NJ-PA San Francisco-Oakland-Hayward CA Washington-Arlington-Alexandria DC-VA-MD-WV Source: CoreLogic September 2017 Home Price Index State-Level Detail Combined Single Family Including Distressed October 2017 State Month-Over-Month Percent Change Year-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% 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 This escalation in home prices reflects both the acute lack of supply and the strengthening economy. Dr. Frank Nothaft, chief economist for CoreLogic Source: CoreLogic October CoreLogic Proprietary. This material may not be reproduced in any form without express written permission. 7

10 Analysis The MarketPulse g December 2017 g Volume 6, Issue 12 Charts & Graphs HOME PRICE INDEX Percentage Change Year Over Year 20% 15% 10% 5% 0% -5% -10% -15% -20% Including Distressed Apr-04 Oct-04 Apr-05 Oct-05 Apr-06 Oct-06 Apr-07 Oct-07 Apr-08 Oct-08 Apr-09 Oct-09 Apr-10 Oct-10 Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14 Apr-15 Oct-15 Apr-16 Oct-16 Apr-17 Oct-17 Source: CoreLogic October 2017 OVERVIEW OF LOAN PERFORMANCE National Delinquency Rates Percentage Rate August 2016 August Days 30+ days or More to Days days to Days days to 119 Days days days Days (not in Past Due Past Due Past Due Past Due (not fcl) in fcl) 120+ days Days In Foreclosure Past Due Source: CoreLogic September 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 CORELOGIC HPI MARKET CONDITION OVERVIEW October 2017 Legend Normal Overvalued Undervalued Source: CoreLogic CoreLogic HPI Single Family Combined Tier, data through October CoreLogic HPI Forecasts Single Family Combined Tier, starting in November CORELOGIC HPI MARKET CONDITION OVERVIEW October 2022 Forecast Legend Normal Overvalued Undervalued Source: CoreLogic CoreLogic HPI Single Family Combined Tier, data through October CoreLogic HPI Forecasts Single Family Combined Tier, starting in November CoreLogic Proprietary. This material may not be reproduced in any form without express written permission. 9

12 Analysis The MarketPulse g December 2017 g Volume 6, Issue 12 NATIONAL HOME EQUITY DISTRIBUTION By LTV Segment 10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% Q Q % to 54% 55% to 59% 60% to 64% 65% to 69% 70% to 74% 75% to 79% 80% to 84% 85% to 89% 90% to 94% 95% to 99% Loan-to-Value Ratio 100% to 104% 105% to 109% 110% to 114% 115% to 119% 120% to 124% 125% + Source: CoreLogic Q MAP OF AVERAGE YEAR-OVER-YEAR EQUITY GAIN PER BORROWER As of Q % 2.3% 3.5% 1.7% 9.0% 3.2% 2.5% 1.5% 3.0% 1.7% 4.5% 3.3% 3.3% 5.7% 5.1% 8.7% 3.9% 6.2% 2.4% 6.9% 4.1% 4.0% 5.3% 4.6% 4.4% 7.6% 6.0% 7.7% 3.5% 5.2% 7.5% 8.3% 7.2% 4.5% 4.9% 5.5% 3.7% 3.7% 4.1% Legend 4.3% 4.6% Very High High Moderate 1.7% 1.5% 10.1% 9.0% Low 1.7% Vermont, West Virginia, Maine, Mississippi, South Dakota have insufficient equity data to report. Source: CoreLogic Q 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 CoreLogic Proprietary. This material may not be reproduced in any form without express written permission.

13 The MarketPulse g December 2017 g Volume 6, Issue 12 Analysis Variable Descriptions Variable Total Sales Total Sales 12-Month sum Total Sales YoY Change 12-Month sum New Home Sales New Home Sales Median Price Existing Home Sales REO Sales REO Sales Share REO Price Discount REO Pct Short Sales Short Sales Share Short Sale Price Discount Short Sale Pct Distressed Sales Share Distressed Sales Share (sales 12-Month sum) HPI MoM HPI YoY HPI MoM Excluding Distressed HPI YoY Excluding Distressed HPI Percent Change from Peak 90 Days + DQ Pct Definition The total number of all home-sale transactions during the month. The total number of all home-sale transactions for the last 12 months. Percentage increase or decrease in current 12 months of total sales over the prior 12 months of total sales The total number of newly constructed residentail housing units sold during the month. The median price for newly constructed residential housing units during the month. The number of previously constucted homes that were sold to an unaffiliated third party. DOES NOT INCLUDE REO AND SHORT SALES. Number of bank owned properties that were sold to an unaffiliated third party. The number of REO Sales in a given month divided by total sales. The average price of a REO divided by the average price of an existing-home sale. The count of loans in REO as a percentage of the overall count of loans for the reporting period. The 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. The number of Short Sales in a given month divided by total sales. The average price of a Short Sale divided by the average price of an existing-home sale. The count of loans in Short Sale as a percentage of the overall count of loans for the month. The percentage of the total sales that were a distressed sale (REO or short sale). The sum of the REO Sales 12-month sum and the Short Sales 12-month sum divided by the total sales 12-month sum. Percent increase or decrease in HPI single family combined series over a month ago. Percent increase or decrease in HPI single family combined series over a year ago. Percent increase or decrease in HPI single family combined excluding distressed series over a month ago. Percent increase or decrease in HPI single family combined excluding distressed series over a year ago. Percent increase or decrease in HPI single family combined series from the respective peak value in the index. 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 Percent change year-over-year of the number of 90+ day delinquencies in the current month. YoY Chg Foreclosure Pct Percent Change Stock of Foreclosures from Peak Pre-foreclosure Filings Completed Foreclosures Negative Equity Share Negative Equity The percentage of the overall loan count that is currently in foreclosure as of the reporting period. Percent increase or decrease in the number of foreclosures from the respective peak number of foreclosures. The number of mortgages where the lender has initiated foreclosure proceedings and it has been made known through public notice (NOD). A 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. The percentage of mortgages in negative equity. The denominator for the negative equity percent is based on the number of mortgages from the public record. 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 Ratio CoreLogic HPI divided by Nominal Personal Income provided by the Bureau of Economic Analysis and indexed to January 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 Proprietary. This material may not be reproduced in any form without express written permission. 11

14 End Notes The MarketPulse g December 2017 g Volume 6, Issue 12 MORE INSIGHTS The CoreLogic Insights Blog (corelogic.com/blog) provides an expanded perspective on housing economies and property markets, including policy, trends, regulation and compliance. Please visit the blog for timely analysis, thoughtprovoking data visualizations and unique commentary from our team in the Office of the Chief Economist. Source: CoreLogic The 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 newsmedia@corelogic.com. 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 CoreLogic CoreLogic Econ CoreLogic Insights On The Go. Download our free App now: 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, reliability, suitability, truth, accuracy, timeliness or completeness of the information contained in this newsletter. CoreLogic shall not be held responsible for any errors, inaccuracies, omissions or losses resulting directly or indirectly from your reliance on the information contained in this newsletter. This newsletter contains links to third-party websites that are not controlled by CoreLogic. CoreLogic is not responsible for the content of third-party websites. The use of a third-party website and its content is governed by the terms and conditions set forth on the third-party s site and CoreLogic assumes no responsibility for your use of or activities on the site 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 corelogic.com