Post Bubble Housing Price Stagnation (USA)

The US housing bubble peaked in 2006 nationally and burst by Q1 2009. Then the housing market stagnated nationally until Q1 2012.  Among 100 urban areas, 15 had positive price changes, the largest gain 11 percent, while 35 had losses of more than 10 percent, the largest losses around 25 percent. There was a marked change in the volatility and the geographic concentration of losses during the crash. The Sunbelt states experienced the largest losses in the crash but during the stagnation metro areas like Chicago and Seattle cracked the top 10 in losses.  Interestingly, several metro areas suffered more during the stagnation than the crash (see appendix). Prices change varied geographic by urban area as did underling causes including unemployment, psychology, foreclosures, previous price changes, rental prices, home construction, and local economic and population changes. From Q1 2012 to Q4 2013 prices have increased in all US cities in the Federal Housing Finance Administration (FAHA) data set (see next post).

Housing_Prices_2009_2011

Looking at the data we see a mixed picture of housing prices during the stagnation. Geographically, Texas faired well as did non-coastal states north of Texas. Energy prices and the lack of a large housing bubble resulting in less foreclosures helped Texas cities no doubt. The other non-coastal cities to the north of Texas also did not experience a housing bubble which left them with less foreclosures and uncertainty post crash. Two outliers in different directions are Washington DC, Metro and Las Vegas, Metro. DC experienced a sharp rise during the stagnation 11 percent much greater than the 7 percent gain of the next closest city (San Jose, CA), even after a fall in housing price during the housing bust of 27 percent. No doubt lots of government spending, relatively low unemployment and in migration positively influenced prices post crash. The city DC itself had fewer risky mortgages than the ‘burbs which ameliorated the bubble. Like other metros Las Vegas was hit very hard but the housing crash 49 percent but in contrast also continued large declines in this period, 25 percent, and 1 in 10 houses were in foreclosure in 2010. Boise decreased about the same amount but only lost 14 percent during the crash so it was a different story.

Performance during the crash did  appeared to have some influence on the price during the housing stagnation. The same general pattern of Texas and the interior Midwest and West out performing the rest of urban America held. However, several cities like DC and in sun belt states of California and Florida (Miami) did quite well during the stagnation but were hit hard during the bubble.

 

Housing_Prices_bottom

 

 

These declines in metro areas by 2010 fueled fears of wave after wave of foreclosures. There were repeated calls for federal intervention in the refinancing and equity of mortgages. While some efforts were made in reality little was done. Most of the largest foreclosure areas had already experienced a slowdown in rates by 2010[2].  On can see that cities 4 Sunbelt states California, Arizona, Florida, and Nevada lead the way in foreclosures in 2010.

forclosure

There was increase in regulations and down payments probably prudent moves given new found volatility of the housing market. This period up to today has seen little home construction or new home owners nationally and a sharp increase in job creation [1]. Also new renters outstripped new owner during the last 7 years as a result the number of new homes built also decreased sharply [1]. Problems with financing in addition to the slow economy may have contributed to slow home starts [1].

Population growth in metro areas varied and was not tied to the housing market and often even macro economic trends. The upper Midwest and NY State having large losses in population for 2010-2011 while places like Tucson and Phoenix and Las Vegas with large price losses gained population over all. Again there were many factors at play natural increase, international migration, region economy. (note not all metros like LA were Core Based Statistical Areas or Metro Division as so the geographic areas may be slightly different, and this map has many more small urban areas)

CBSA_Figure2b_2011

So we see a mixed picture among urban areas both temporally and spatially. We don’t see any one clear pattern emerging during the stagnation period geographically that can be reduced to a few patterns. This is in contrast to the boom period were almost all metro areas had increasing values often with huge increases of 90 or 100 percent usually in Sun Belt States with risky subprime mortgages and high numbers investment property purchases. This post just gives a picture or exploration of what was going and should not be interpreted as a causal explanation of the housing market during the time period.

1. http://www.realtor.org/presentations/housing-market-and-economic-outlook

2. http://www.creativeclass.com/_v3/creative_class/tag/housing-prices/

 

Appendix (st-crash is the is the difference in the losses during the crash vs stagnation)

NAME Stag Crash bubble St-Crash
Boise City, ID Metro Area −25.9% −14.8% 57.7% 11.1%
Las Vegas-Henderson-Paradise, NV Metro Area −25.7% −49.8% 82.8% −24.1%
Tucson, AZ Metro Area −24.5% −20.6% 66.3% 3.9%
Elgin, IL Metro Division −23.8% −13.0% 26.1% 10.8%
Orlando-Kissimmee-Sanford, FL Metro Area −22.3% −32.4% 78.2% −10.1%
Seattle-Bellevue-Everett, WA Metro Division −18.1% −5.2% 48.8% 12.9%
Tacoma-Lakewood, WA Metro Division −18.1% −16.4% 54.2% 1.7%
Charleston-North Charleston, SC Metro Area −18.1% −7.5% 47.1% 10.5%
Chicago-Naperville-Arlington Heights, IL Metro Division −17.7% −14.8% 30.1% 2.9%
Lake County-Kenosha County, IL-WI Metro Division −16.5% −14.2% 21.1% 2.3%
Jacksonville, FL Metro Area −16.2% −23.8% 59.8% −7.7%
Atlanta-Sandy Springs-Roswell, GA Metro Area −15.8% −14.1% 12.3% 1.7%
New Haven-Milford, CT Metro Area −15.4% −8.2% 35.4% 7.2%
Portland-Vancouver-Hillsboro, OR-WA Metro Area −15.2% −6.0% 52.7% 9.2%
Virginia Beach-Norfolk-Newport News, VA-NC Metro Area −15.0% −7.8% 68.5% 7.1%
Phoenix-Mesa-Scottsdale, AZ Metro Area −14.9% −39.6% 83.7% −24.7%
Fresno, CA Metro Area −14.7% −41.0% 79.6% −26.4%
Wilmington, DE-MD-NJ Metro Division −14.4% −8.9% 45.8% 5.5%
Tampa-St. Petersburg-Clearwater, FL Metro Area −14.4% −32.0% 71.9% −17.7%
West Palm Beach-Boca Raton-Delray Beach, FL Metro Division −14.2% −41.8% 81.3% −27.6%
Allentown-Bethlehem-Easton, PA-NJ Metro Area −13.6% −8.6% 45.2% 5.0%
Sacramento–Roseville–Arden-Arcade, CA Metro Area −13.0% −41.3% 54.5% −28.3%
Richmond, VA Metro Area −12.8% −5.5% 44.4% 7.2%
Providence-Warwick, RI-MA Metro Area −12.7% −14.6% 34.4% −1.9%
Camden, NJ Metro Division −12.6% −11.4% 49.1% 1.2%
Charlotte-Concord-Gastonia, NC-SC Metro Area −12.5% 5.7% 17.4% 6.8%
Newark, NJ-PA Metro Division −12.0% −11.2% 42.0% 0.9%
Milwaukee-Waukesha-West Allis, WI Metro Area −11.7% −5.4% 22.7% 6.3%
Worcester, MA-CT Metro Area −10.6% −16.4% 20.4% −5.8%
Salt Lake City, UT Metro Area −10.5% 0.6% 40.7% 10.0%
Bakersfield, CA Metro Area −10.4% −46.4% 97.9% −36.0%
Greensboro-High Point, NC Metro Area −10.0% 1.5% 11.4% 8.5%
New York-Jersey City-White Plains, NY-NJ Metro Division −9.9% −9.2% 44.5% 0.7%
Minneapolis-St. Paul-Bloomington, MN-WI Metro Area −9.6% −19.4% 19.7% −9.7%
Albuquerque, NM Metro Area −9.6% −4.5% 45.0% 5.1%
Nassau County-Suffolk County, NY Metro Division −9.4% −11.0% 39.5% −1.6%
Baltimore-Columbia-Towson, MD Metro Area −9.4% −11.6% 63.0% −2.3%
Wichita, KS Metro Area −9.1% 5.2% 11.2% 3.9%
Detroit-Dearborn-Livonia, MI Metro Division −8.9% −36.3% −1.1% −27.5%
Hartford-West Hartford-East Hartford, CT Metro Area −8.3% −5.3% 28.9% 3.0%
St. Louis, MO-IL Metro Area −8.3% −7.3% 21.6% 1.0%
Birmingham-Hoover, AL Metro Area −7.6% −3.5% 23.1% 4.1%
Knoxville, TN Metro Area −6.8% 3.9% 27.5% 2.9%
Columbia, SC Metro Area −6.7% 0.5% 21.2% 6.2%
Stockton-Lodi, CA Metro Area −6.7% −57.7% 64.2% −51.0%
Montgomery County-Bucks County-Chester County, PA Metro Division −6.6% −5.5% 37.3% 1.2%
Bridgeport-Stamford-Norwalk, CT Metro Area −6.1% −15.3% 33.8% −9.2%
Riverside-San Bernardino-Ontario, CA Metro Area −6.0% −49.4% 87.7% −43.4%
Winston-Salem, NC Metro Area −5.8% 2.9% 10.9% 3.0%
Dayton, OH Metro Area −5.8% −9.1% 10.5% −3.3%
Kansas City, MO-KS Metro Area −5.6% −5.3% 12.2% 0.3%
Colorado Springs, CO Metro Area −5.6% −7.9% 18.7% −2.3%
North Port-Sarasota-Bradenton, FL Metro Area −5.4% −45.9% 77.1% −40.5%
Los Angeles-Long Beach-Glendale, CA Metro Division −5.3% −36.4% 88.4% −31.1%
Cincinnati, OH-KY-IN Metro Area −5.3% −6.5% 11.3% −1.2%
El Paso, TX Metro Area −5.1% −0.8% 36.0% 4.4%
Cleveland-Elyria, OH Metro Area −5.0% −15.6% 6.6% −10.6%
Nashville-Davidson–Murfreesboro–Franklin, TN Metro Area −5.0% 0.1% 27.1% 4.9%
Fort Lauderdale-Pompano Beach-Deerfield Beach, FL Metro Division −4.6% −43.6% 82.5% −39.0%
Akron, OH Metro Area −4.2% −11.5% 6.1% −7.3%
Memphis, TN-MS-AR Metro Area −4.0% −10.6% 13.9% −6.7%
Gary, IN Metro Division −3.9% −7.7% 17.2% −3.7%
Cambridge-Newton-Framingham, MA Metro Division −3.7% −10.1% 16.1% −6.4%
Philadelphia, PA Metro Division −2.9% −0.3% 53.2% 2.6%
San Francisco-Redwood City-South San Francisco, CA Metro Division −2.8% −10.1% 24.2% −7.3%
Silver Spring-Frederick-Rockville, MD Metro Division −2.7% −24.4% 65.4% −21.7%
Omaha-Council Bluffs, NE-IA Metro Area −2.7% −5.3% 12.2% −2.6%
Raleigh, NC Metro Area −2.6% 4.4% 17.2% −1.8%
Miami-Miami Beach-Kendall, FL Metro Division −2.6% −41.2% 83.2% −38.7%
Columbus, OH Metro Area −2.5% −5.0% 10.6% −2.5%
Urban Honolulu, HI Metro Area −2.5% 0.7% 75.9% 1.8%
Anaheim-Santa Ana-Irvine, CA Metro Division −2.3% −28.8% 66.4% −26.4%
Greenville-Anderson-Mauldin, SC Metro Area −2.2% 4.4% 13.8% −2.2%
Baton Rouge, LA Metro Area −2.0% 6.4% 27.0% −4.4%
Grand Rapids-Wyoming, MI Metro Area −1.9% −16.5% 5.8% −14.6%
Oxnard-Thousand Oaks-Ventura, CA Metro Area −1.6% −35.9% 60.2% −34.3%
Albany-Schenectady-Troy, NY Metro Area −1.3% −1.5% 46.8% −0.3%
Rochester, NY Metro Area −1.1% 3.6% 13.1% −2.5%
Indianapolis-Carmel-Anderson, IN Metro Area −1.0% −5.6% 8.5% −4.6%
Warren-Troy-Farmington Hills, MI Metro Division −0.9% −34.6% 3.3% −33.6%
Boston, MA Metro Division −0.9% −13.3% 18.1% −12.4%
Syracuse, NY Metro Area −0.3% 1.4% 20.9% −1.1%
San Antonio-New Braunfels, TX Metro Area −0.2% 8.6% 22.9% −8.3%
Louisville/Jefferson County, KY-IN Metro Area 0.0% −1.9% 13.8% −1.9%
Dallas-Plano-Irving, TX Metro Division 0.2% 3.4% 10.4% −3.2%
Fort Worth-Arlington, TX Metro Division 0.4% 2.4% 10.1% −1.9%
Tulsa, OK Metro Area 0.5% 6.0% 11.3% −5.5%
Oakland-Hayward-Berkeley, CA Metro Division 0.5% −43.9% 42.6% −43.4%
Cape Coral-Fort Myers, FL Metro Area 0.7% −53.1% 83.5% −52.4%
New Orleans-Metairie, LA Metro Area 0.8% −9.7% 35.2% −8.9%
Oklahoma City, OK Metro Area 1.1% 4.4% 19.4% −3.3%
San Diego-Carlsbad, CA Metro Area 1.2% −34.1% 47.5% −33.0%
Little Rock-North Little Rock-Conway, AR Metro Area 1.2% −0.5% 19.7% 0.6%
Denver-Aurora-Lakewood, CO Metro Area 2.1% −4.0% 9.8% −1.8%
Austin-Round Rock, TX Metro Area 4.0% 12.2% 17.9% −8.2%
Buffalo-Cheektowaga-Niagara Falls, NY Metro Area 5.4% 6.5% 15.9% −1.1%
Houston-The Woodlands-Sugar Land, TX Metro Area 6.0% 7.1% 15.7% −1.1%
Pittsburgh, PA Metro Area 6.5% 3.3% 14.4% 3.2%
San Jose-Sunnyvale-Santa Clara, CA Metro Area 7.3% −31.2% 38.2% −23.9%
Washington-Arlington-Alexandria, DC-VA-MD-WV Metro Division 11.5% −27.1% 67.1% −15.6%

 

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About Matthew Mulbrandon

I really like maps, as I am a geographer, and with the help of my more artistic partner I make cool maps. My focus in work and education has been centred on urban problems particularly housing and transportation. I have built and am working on several agent-based housing models. I am also interested in developing innovative ways to combat urban congestion using buses and electric kick scooters. Also it has led me to more theoretical pursuits such as how we determine if a model or methodology is sound (epistemology). How individuals relate to their social and built environment and their resulting interactions (social theory). Cities and really all our institutions are made of people with all their issues, virtues, and dreams and cannot be discounted when examining policy or predicting behaviours.

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