Competitive Analytics | Is A Housing Recovery Underway? - Competitive Analytics

Is A Housing Recovery Underway?

August 11, 2009

Is there a housing recovery underway?  Not yet.  We illustrate the fundamental weakness underpinning the current housing sector…and what DID NOT happen during the recent housing boom.

The media and myriad sources are reporting signs of a housing recovery.  Is this true?  No.  If we want to look at a glass 1/50th full, our numbers do show an iota of a glimmer of hope for 2010…but nothing material yet to hang anyone’s hat on.  To illustrate reality, let’s introduce a metric I created over twelve years ago called “The Cumulative Housing Gap.”

In simple terms, The Cumulative Housing Gap (“CHG”) is a proxy for housing demand LESS housing supply by applying cumulative mathematics.  A positive number reflectspent-up demand and a negative number reflects an oversupply.  The backstory about the CHG is below.

For week 10, let’s keep things simple.  We’ll examine employment growth and new home sales over the prior 20 years (covering approximately three economic cycles) and calculate a short-term and long term CHG measurement.

Here are your take-aways:

“Employment-Based New Housing Demand” decelerated during 2000 and NEVER rebounded to levels realized during the mid-1990s. What about the greatest housing boom in history? Here’s the quick explanation: Housing demand during the mid-1990s was based on the fundamental of strong employment growth in contrast to the 2000s.  From July 1990 to July 1999, employment increased by 19.4 million and new home sales equaled 6.6 million…which results in a “Job Growth-to-New Home Sales Ratio” of 2.92.  This provided a strong correlative foundation for housing demand during the 1990s.  However, the period between July 2000 and July 2009 was a different story – Employment growth was an anemic 2.3 million yet new home sales climbed to 9.2 million…which results in a “Job Growth-to-New Home Sales Ratio” of 0.25.  In other words, THE 1990s CREATED NEARLY THREE JOBS FOR EVERY NEW HOME SALE…YET DURING THE 2000s FOUR NEW HOMES SOLD FOR EVERY SINGLE JOB CREATED.  In essence, housing demand during the 2000s completely ignored the fundamentalrelationship between employment growth and new home sales (typically a highly correlative relationship).  What drove housing demand during the 2000s was not employment, income, and employed wages…it was (please excuse my repetitiveness from week 9) low interest rates, myopic mortgage banking, avaricious investment bankers, and a public rife withimmediate valuation gratification that drove new home sales far above equilibrium and drove home prices far beyond sanity.

The current “12 Month Moving Cumulative Housing Gap” is NEGATIVE 3.9 million. This is approximately five times lower than July 2008′s CHG of NEGATIVE 793,244.  If you thought July 2008 was bad, then how would you describe July 2009?  I think I’m beginning to tire of searching for negative adjectives.

The current level of New Home Sales is NOT supported by current employment trends. Monthly New Home Sales during 2009 are averaging 32,214.  To “justify” this level of new home sales, we need to add 40,084 jobs PER MONTH.  To “justify” a longer-term level of 66,000 new home sales per month, we need to add 104,775 jobs PER MONTH.  Instead, we are LOSING an average of 477,667 jobs PER MONTH over the prior 12 months.

CAVEAT: With all these large red numbersillustrated in the table below, it’s challenging to not get depressed.  HOWEVER (and note this is a big however), specific geo-submarkets are revealing strong forecasted housing demand within specific product segments.  So although the macro perspective is obviously still dismal… exceptions abound.

The Cumulative Housing Gap – United States of America

July
Cumulative
New Home Sales
Cumulative
Job Growth
3 MM
Cumulative 
Housing Gap
12 MM
Cumulative 
Housing Gap
Cumulative 
Job Growth to
New Home Sales
1990
351,000
-98,000
– 17,496
-0.28
1991
843,000
-1,645,000
– 164,087
– 1,466,488
-1.95
1992
1,411,000
-1,090,000
+ 18,118
– 218,394
-0.77
1993
2,042,000
1,115,000
+ 257,606
+ 757,976
0.55
1994
2,731,000
4,599,000
+ 371,622
+ 1,505,646
1.68
1995
3,389,000
7,381,000
– 61,236
+ 1,094,441
2.18
1996
4,113,000
10,016,000
+ 274,331
+ 935,843
2.44
1997
4,901,000
13,083,000
+ 249,472
+ 1,143,969
2.67
1998
5,756,000
16,205,000
+ 183,307
+ 1,111,614
2.82
1999
6,652,000
19,412,000
+ 222,362
+ 1,124,157
2.92
2000
7,515,000
22,149,000
– 66,520
+ 861,094
2.95
2001
8,427,000
22,060,000
– 491,378
– 968,063
2.62
2002
9,346,000
20,333,000
– 441,087
– 2,006,874
2.18
2003
10,392,000
19,923,000
– 465,110
– 1,304,268
1.92
2004
11,565,000
21,707,000
– 193,165
– 49,220
1.88
2005
12,825,000
24,038,000
– 88,693
+ 208,346
1.87
2006
13,980,000
26,281,000
– 202,370
+ 257,913
1.88
2007
14,881,000
27,752,000
– 192,283
+ 25,614
1.86
2008
15,467,000
27,423,000
– 447,551
– 793,244
1.77
2009
15,850,500
21,691,000
– 750,760
– 3,994,209
1.37
90-99
6,652,000
19,412,000
+ 222,362
+ 1,124,157
2.92
00-09
9,198,500
2,279,000
– 973,122
– 5,118,366
0.25

Note: The Equilibrium Jobs to New Home Sales Ratio of 1.5875 was applied in the above analysis.

Backstory regarding The Cumulative Housing Gap: The Cumulative Housing Gap was created back in 1998 during my tenure as Director of Market Research for The Irvine Company.  Back in the late 1990s this metric supported the tremendous pent-up demand for both apartments and for-sale housing on the Irvine Ranch as well as many other submarkets within California…and also signaled the pending housing weakness that was to follow.  The Cum Housing Gap is still an excellent leading indicator of future housing demand and has multiple analogue applications for many other industries and sectors.  Note of caution: a comprehensive and precision analysis of a geo-submarket’s past, current, and forecasted housing demand MUST include multiple scenarios of  The Cumulative Housing Gap (i.e. running models with numerous groupings of demand and supply drivers, numerous starting points, and various equilibrium weights in order to triangulate an accurate housing gap estimate).  For a specific project Competitive Analytics applies hundreds of demand and supply drivers and calculates a CHG based on an exhaustive amount of calculations with different starting points and moving averages/totals.

To be master of any branch of knowledge, you must master those which lie next to it. – Oliver Wendell Holmes, Jr.