Tag Archives: Real estate pricing

NRR: Lumber, log prices up. Home recovery expected to rise for next 3 years

Lumber, log prices up. Home recovery expected to rise for next 3 years


December 23, 2013

Timber Industry Report
By Rick Sohn PhD.
Umpqua Coquille LLC

Mills are competing for logs and prices are rising. This month is good for homebuyers, including a slight drop in Median Home values and a big drop in interest rates. Seven-year trend of lumber, logs, housing, and mortgage statistics are shown below.



Lumber and logs are both in nice rising price trends. Logs are climbing into a winter price range as mills compete for a limited supply to fill inventory. Economists say we are in a recovering housing market that could last another 3 years anyway. Expect to see continued strength in lumber and logs through the winter and beyond, with seasonal dips.

Building Permits were reported up to a record high level for the year, and as the media likes to say, finally recovering to the 2008 levels. That is NOT saying much. You will hear comparisons to 2008 a lot, since it was such a volatile, falling year. The 2008 high was 1.2 million permits and the low was 554,000. We will have made some real progress when housing and other stats are compared to 2007, or better yet, 2006.

The weekly average for 30-year fixed rate mortgages is bouncing around. It was at 4.57 the week of Sept 13, but dipped as low as 4.10, in the first week of November, and now is at 4.22. It is bouncing around these low levels.

At the same time, the Zillow report shows clearly that median home prices have plateau’ed and have started to dip slightly. The high for this cycle was August’s $163,000 national median. This mirrors the data for Portland from the Regional Multiple Listing Service data which also shows a home price drop, and reduced inventory of homes for sale, which fits the season. This is all very favorable for homebuyers.

In speaking with one local producer who sells into the European Clears market, recovery in that market has yet to occur. This coincides with the generally lackluster European economy, which is has worldwide impacts.

For the second month in a row, Housing Starts are not reported, due to the Government Shutdown. Data “Does not meet Production Standards.” According to the US Dept of Commerce, the results for September, October and November will be reported on Dec 18 – lets hope for a pleasant surprise.

Data reports used with permission of: 1Random Lengths. Kiln Dried 2×4-8′ PET #2/#2&Btr lumber. 2RISI, Log Lines. Douglas-fir #2 Sawmill Log, Southern Oregon region. 3 US Dept of Commerce. 4Regional Multiple Listing Service RMLSTM courtesy of Janet Johnston, Prudential Real EstateProfessionals, Roseburg, OR. Portland, Oregon data. 5Freddie Mac. National monthly average. 6Mortgage-X, national average, most recent week. 7Zillow.com, National Median Issue #6-11. © Copyright Rick Sohn, Umpqua Coquille LLC please e-mail rsohn@umpquacoquille.com

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JCHS: How Helpful is the Price-to-Income Ratio in Flagging Bubbles?

How Helpful is the Price-to-Income Ratio in Flagging Bubbles?

 by Rocio Sanchez-Moyano
Research Assistant
With the continued growth of house prices across the country, talk of a housing bubble is beginning to reappear in the headlines. House price-to-income ratios are often used to indicate a bubble, as prices have historically had a relatively stable relationship with incomes (both mean and median).  In the US, nationally, the price-to-income ratio remained relatively stable throughout the 1990s.  It began to increase around 2000 and surpassed its long-run average of 3.65 by 2002 (Figure 1).  The national price-to-income ratio continued to increase in the mid-2000s, reaching a high of 4.63 in 2006 before rapidly declining in 2007 and 2008 and eventually hitting a low of 3.26 in 2011.  The classic bubble shape is clearly visible in this trend.  Recent price gains, viewed in this context, do not seem to indicate the return of a bubble; price-to-income ratios today match their early 1990s rates and still have some room for growth before reaching their long-run average.  (Click chart to enlarge.)
Notes: Prices are 1991 National Association of Realtors® Median Existing Single-Family Home Prices, indexed by the FHFA Expanded-Data House Price Index.  Incomes are median household incomes.
Sources: JCHS tabulations of FHFA Expanded-Data House Price Index;  US Census Bureau, Moody’s Analytics Estimates.
However, despite the seemingly straightforward relationship between house prices and incomes in Figure 1, this indicator can be difficult to interpret.  To start, many data sources are available for measuring prices.  One used frequently is the National Association of Realtors ® (NAR) Single-Family Median Home Price as it is widely available for many metros and provides an actual house price (rather than an index showing change in values) that can be compared to income levels.  The disadvantage to this measure is that NAR house prices also capture changes in the types of units that are being sold over time and so does not reflect how the value of the same home changes.  Repeat sales indices, like the Federal Housing Finance Authority’s (FHFA) Expanded-Data House Price Index, which was used to produce the figures in this post, are designed to take into account changes in the values of homes themselves by tracking sales of the same homes over time.  However, the FHFA index can be more difficult to interpret since, as an index, it does not provide information about current prices.  Price-to-income ratios using this data must peg the index to a starting or ending house price.
Furthermore, identifying bubbles or other price anomalies from price-to-income ratios can be difficult because it is not clear what is an appropriate baseline value of the measure for comparison.  Even in the aggregate US case, where the ratio did not fluctuate more than one percent in either direction for much of the 1990s, the linear trend is not flat and the long run average is above the 1990s levels.  This becomes even murkier when observing trends at the metro level.  Some metros, like Dallas, had stable price-to-income ratios over the last two decades (Figure 2).  Dallas did not experience a significant bubble in the mid-2000s and its long-run average mirrors the linear trend.  In other metros, like Phoenix, the boom-bust period led to significant fluctuations in the price-to-income ratio after having been relatively stable in the 1990s.  If the 1990s levels are to be considered normal for Phoenix, then current price-to-income ratios remain below average and recent growth in prices can be considered a return to normal after an overcorrection.
For other metros, the price-to-income trend is more difficult to interpret.  Ratios in Cleveland are well below their long-run average, but the historical trend has been drifting downwards, so ratios in recent years could be indicating a reset of the ratio in Cleveland to lower levels.  At another end, San Francisco has experienced a wide range of price-to-income ratios in recent history.  Price-to-income ratios boomed in the late 1980s, decreased throughout much of the 1990s, and then surged through the mid-2000s.  Compared to its long-run average, ratios in San Francisco are above historical norms, but, when the historical trend is considered, prices can continue to increase before they appear “too high.”  Finally, if a fundamental relationship exists between prices and incomes, it is unclear why the ratio can vary significantly from metro to metro.  The national average is around 3.6.  In the metros observed here, Cleveland and Dallas both have historic averages below 3.0 while San Francisco’s is double the national average. (Click chart to enlarge.)
Notes: Prices are 1991 National Association of Realtors® Median Existing Single-Family Home Prices, indexed by the FHFA Expanded-Data House Price Index.  Incomes are median household incomes.
Sources: JCHS tabulations of FHFA Expanded-Data House Price Index;  US Census Bureau, Moody’s Analytics Estimates
Given this variation, what can we make of the price-to-income ratio?  On a national level, this ratio does a relatively good job of identifying substantive shifts in the market.  In the aggregate, there appears to be a “normal” price-to-income ratio and prolonged deviation from this trend can signal an underlying shift.  However, on a metro-by-metro level, where it can be difficult to identify an appropriate baseline value, long-run historical context is necessary to interpret point-in-time estimates.  In markets like Dallas and Phoenix, historical trends are consistent enough that it can be useful to compare the current ratio to past ones.  In others, like Cleveland and San Francisco, the price-to-income ratio on its own is not especially helpful since there is no clear way to identify a “normal” price-to-income ratio.

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