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US Economic Observations: January 2018

It is well known that there are issues in the home purchase market, but there is less information on the single-family rental market, which makes up one-half of residential rentals. The CoreLogic Single Family Rental Index reflects rents paid on single-family houses and condos, and using this index we can dissect rent growth by both price tier and metro area.

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Figure 1 shows the 12-month change in our national rental index from 2005 to today. Rents for single-family fell during the Great Recession but then bounced back strongly from their low point in mid-2009 and have been trending up, mirroring home price growth. In October 2017, the index measured rent growth of 2.7 percent from a year ago. We can also show rent changes for the high-end (those rents 25 percent or more above the median rent in that market) and the low end (those rents 25 percent or less below the median in that market). The low-end single-family rental tier lagged the high-end tier from mid-2009 to early 2014, but then the low-end began steadily outpacing the high-end and the difference is growing. This mirrors the same high demand, low- supply forces that have caused low-end home prices to outpace high-end prices, as evidenced by shorter days-on-market and tighter inventory for low-end homes. Investors who entered the market to buy up distressed properties during the housing crisis might be exacerbating this trend in the rental market. High-end rents increased 2 percent in October from a year ago, while low-end rents increased by more than twice as much – 4.2 percent.

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We can also look at the difference between low-end and high-end rent growth by metro area. Figure 2 shows that low-end rents have been increasing in the largest 20 markets, with Seattle leading the large metros with the biggest increase in rents at 7.9 percent in October. Austin had the smallest increase in low-end rents of the large metros. In most of the 20 markets shown in the chart, low-end rents are increasing faster than high-end rents, and the trend is happening all over the country, not just in one region. The one exception is Warren, Mich., where low-end and high-end rents are increasing at about the same rate. The biggest spread in low-end and high-end rent increases was in Charlotte, N.C., where the low-end increased 5.6 percent and the high-end showed no increase.

The single-family rental market is an important and often overlooked segment of the and is affected by rising demand and constrained supply just like the rest of the housing market. The demand and supply pressures are especially apparent for lower-cost homes, for which rents are increasing at a much faster rate than for higher-cost homes

February 06, 2018, Irvine, Calif. –

  • Largest Price Gains During 2017 Were in California, Idaho, Nevada, Utah and Washington
  • Affordability Continues to Erode, Especially in Low-Price Range
  • Home Prices Projected to Increase by 4.3 Percent by December 2018

CoreLogic® (NYSE: CLGX), a leading global property information, analytics and data-enabled solutions provider, today released its CoreLogic Home Price Index (HPI) and HPI Forecast for December 2017, which shows home prices are up both year over year and month over month. Home prices nationally increased year over year by 6.6 percent from December 2016 to December 2017, and on a month-over-month basis home prices increased by 0.5 percent in December 2017 compared with November 2017,* according to the CoreLogic HPI.

Looking ahead, the CoreLogic HPI Forecast indicates that home prices will increase by 4.3 percent on a year-over-year basis from December 2017 to December 2018, and on a month-over-month basis home prices are expected to decrease by 0.4 percent from December 2017 to January 2018. The CoreLogic HPI Forecast is a projection of home prices using the CoreLogic HPI and other economic variables. Values are derived from state-level forecasts by weighting indices according to the number of owner-occupied households for each state.

“The number of homes has remained very low,” said Dr. Frank Nothaft, chief economist for CoreLogic. “Job growth lowered the unemployment rate to 4.1 percent by year’s end, the lowest level in 17 years. Rising income and consumer confidence has increased the number of prospective homebuyers. The net result of rising demand and limited for-sale inventory is a continued appreciation in home prices.”

According to CoreLogic Market Condition Indicators (MCI) data, an analysis of housing values in the country’s 100 largest metropolitan areas based on housing stock, 35 percent of metropolitan areas have an overvalued housing market as of December 2017. The MCI analysis categorizes home prices in individual markets as undervalued, at value or overvalued by comparing home prices to their long-run, sustainable levels, which are supported by local market fundamentals such as disposable income. Also, as of December, 28 percent of the top 100 metropolitan areas were undervalued and 37 percent were at value. When looking at only the top 50 markets based on housing stock, 48 percent were overvalued, 14 percent were undervalued and 38 percent were at value. The MCI analysis defines an overvalued housing market as one in which home prices are at least 10 percent higher than the long-term, sustainable level, while an undervalued housing market is one in which home prices are at least 10 percent below the sustainable level.

“Home prices continue to rise as a result of aggressive monetary policy, the economic and jobs recovery and a lack of housing stock. The largest price gains during 2017 were in five Western states: California, Idaho, Nevada, Utah and Washington,” said Frank Martell, president and CEO of CoreLogic. “As home prices and the cost of originating loans rise, affordability continues to erode, making it more challenging for both first time buyers and moderate-income families to buy. At this point, we estimate that more than one-third of the 100 largest metropolitan areas are overvalued.”

*November 2017 data was revised. Revisions with public records data are standard, and to ensure accuracy, CoreLogic incorporates the newly released public data to provide updated results.

Methodology

The CoreLogic HPI is built on industry-leading public record, servicing and securities real-estate databases and incorporates more than 40 years of repeat-sales transactions for analyzing home price trends. Generally released on the first Tuesday of each month with an average five-week lag, the CoreLogic HPI is designed to provide an early indication of home price trends by market segment and for the “Single-Family Combined” tier representing the most comprehensive set of properties, including all sales for single-family attached and single-family detached properties. The indexes are fully revised with each release and employ techniques to signal turning points sooner. The CoreLogic HPI provides measures for multiple market segments, referred to as tiers, based on property type, price, time between sales, loan type (conforming vs. non-conforming) and distressed sales. Broad national coverage is available from the national level down to ZIP Code, including non-disclosure states.

CoreLogic HPI Forecasts are based on a two-stage, error-correction econometric model that combines the equilibrium home price—as a function of real disposable income per capita—with short-run fluctuations caused by market momentum, mean-reversion, and exogenous economic shocks like changes in the unemployment rate. With a 30-year forecast horizon, CoreLogic HPI Forecasts project CoreLogic HPI levels for two tiers—“Single-Family Combined” (both attached and detached) and “Single-Family Combined Excluding Distressed Sales.” As a companion to the CoreLogic HPI Forecasts, Stress-Testing Scenarios align with Comprehensive Capital Analysis and Review (CCAR) national scenarios to project five years of home prices under baseline, adverse and severely adverse scenarios at state, Core Based Statistical Area (CBSA) and ZIP Code levels. The forecast accuracy represents a 95-percent statistical confidence interval with a +/- 2.0 percent margin of error for the index.

More Than A High Appraisal

Homes appraised above contract price had above-market appreciation rates

Housing Trends

For homebuyers, the outcome of appraisal is one of these three scenarios: (1) appraised value closely matches sales price, (2) appraisal falls short of sales price or (3) appraisal is higher than sales price. If a home sells for less than its appraised value, does that mean that the buyers got ‘a bargain,’ and should anticipate above-average appreciation during their ownership period?  Conversely, if a home sells for more than its appraised value, does that mean the buyers may have ‘overpaid,’ and could expect a below-market rate of price growth during the length of time they own the home?

Evidence seems to support the hypothesis that there is “money left on the table” in high-appraisal transactions. When property price appreciation was calculated for twice turned-over in the California market – first sale observed with a full appraisal and sales closing price in 2010 or later, and then a second time with a sale by the owner – homes previously appraised with a sizable premium above the contract sales price were found to have above-market appreciation rates.

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As shown in Figure 1, excess rates of price appreciation averaged about 3.3 percent per year.  By comparison, closely appraised homes appreciated at about the market average, while homes with appraised value below their contract sales price appreciated 0.3 percent per year slower than the market.  Excess appreciation rates were annualized price gains at re-sale—annualized percentage difference between prior purchase price and subsequent re-sale price, in excess of average market appreciation during the same ownership period.  The CoreLogic county-level Home Price Index (HPI) was used as the benchmark of market-wide appreciation.

Yanling Mayer Blog Post

Figure 2 shows that high-appraisal homes – whether a distressed sale or not – had above-market price appreciation, averaging 3.15 percent among non-distressed sales or 3.9 percent among distressed sales. Real estate owned (REO) and short sales exhibited above-market appreciation rates across all three appraisal valuation outcomes, likely driven by their below-market pricing to motivate sales.  Investors’ value-enhancing repair and refurbishing work could also be a factor for their higher re-sale values – despite that only homes that were held for at least 18 months since initial purchase/appraisal were included in the analysis.  For both non-distressed and distressed sales, median prices of high- and low-appraisal homes were lower than closely appraised homes. Since both high- and low-appraisal homes may have drawn disproportionately from lower-priced homes, faster price appreciation experienced by low-valued homes alone could not explain away the large disparities in price appreciation between the two.[1]

In Figure 3, sample homes were further sub-grouped by the year in which they were initially purchased and appraised. Given significant market dynamics during 2010-2015, property appreciation rates were likely to vary depending on the timing of initial purchase.  They ranged between 2 and 5 percent, reaching the highest during the 2012 market bottom when market-wide underpricing was likely the severest.

Yanling Mayer Blog Post

A city-level breakdown is shown in Figure 4. Stockton (5.87 percent) and Riverside (5.22 percent) had the highest excess price gains, followed by San Francisco (4.62 percent), Los Angeles (4.35 percent), Bakersfield (4.24 percent), and San Jose (4.04 percent).  Due to the use of county-wide HPIs for benchmarking, some cities – such as Oakland, Riverside and others – that may have experienced faster-rising prices than its county as a whole could well see across-the-board positive excess price appreciation.

Regardless of the reason(s) why a home may have sold for less than its appraised value, the buyers appear to have benefitted by having a faster-than-market appreciation during their ownership tenure.

How Much Is Your Home’s Collateral Value?

Traditional Appraisal and Automated Valuation Models Don’t Always See Eye to Eye.

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.  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.  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.  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.

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.

 What Title of figure 2 is

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 loans – 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 purchased with mortgage financing for which a CoreLogic AVM value was also available.  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 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 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.

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.

[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.

[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 3 ½ 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.