(April 1, 2013) – Traditional "Large Lot Zoning" is "Greener" than "Smart Growth" within Urban Growth Boundaries . . . Copyright 2009 – 2013 . . . Tom Lane . . . Photographing California, Arizona, Nevada, New Mexico, Colorado, Utah, Oregon, and Seattle, Washington
Superstar Cities are populated by the very rich elite among us, as shown by Dr. Joseph Gyourko, certainly one of the nation’s number one real estate economists. The poor and middle class are forced out of superstar cities, as prices have escalated over decades. Home values increase, as smart growth and urban growth boundaries decrease the supply of land. This post is a summary of Dr. Gyourko’s paper (click here for a .pdf file from his web site). You can also view Dr. Gyourko’s power point presentation.
Superstar cities, and their surrounding suburbs, include San Francisco, Los Angeles, Portland, Seattle, Santa Cruz, Santa Rosa, Boulder (CO), Boston, etc. The photos above and below show a new subdivision in Sammamish, Washington (30 miles east of Seattle) where new homes (in 2010) start at almost $700,000!
Based on observing how expensive some cities are, Dr. Gyourko used complex math formulaes to calculate four types of cities. This web site distills economic data to a level for the general public. Therefore, for Dr. Gyourko’s mathematical models, I will refer interested readers to the Differential Calculus presented in his paper at the blue .pdf link above.
This entry remains under construction, as I continue to translate Dr. Gyourko’s model for a general audience.
*Superstar Cities, Region A of the Graph below, 22 total nationwide, where the rich live (Seattle, San Francisco, L.A., Boston, Boulder, etc.)
These are high demand, higher average income, slow growth areas with implied high land use controls and/or smart growth and/or geographical barriers to construction (i.e. oceans, mountains). High rate of housing appreciation over time.
By 1980, San Francisco and Los Angeles were the first two markets (out of several hundred surveyed) to qualify as superstars. Since then, 20 additional markets have achieved superstar status, for a total of 22 (as of 2006 when the paper was published).
*Non Superstar Cities, Region C on the graph, where the poor can afford homes ( Las Vegas, Phoenix, Austin, etc.). High demand, lower average income, rapid growth, and implied less land use regulations, or geographical barriers to construction. Low rate of housing appreciation over time.
*“In Between” High Demand Markets, Region B on the graph, more expensive than Non Superstar Cities (Raleigh-Durham, NC, Atlanta, etc.), and slower growth than non-superstars.
*Low Demand Markets, Region D on the graph, growing slowly with very little appreciation (El Paso, Bakersfield, etc.)
Disclaimer: One should be careful not to generalize Dr. Gyourko’s Superstar status to smart growth principles. While Dr. Gyourko’s paper does not mention “smart growth,” I present a summary of his work, interpreted from a smart growth perspective. Indeed, Dr. Gyourko states in his study that he did not measure the degree of land use controls (i.e. smart growth) in his math equations. Dr. Gyourko says:
“We neither observe (in this study) the true state of demand, nor the elasticity of supply (i.e. he means from land use regulations or geographical barriers to construction), so we define a superstar city using housing market outcomes (i.e. the variables are in his mathematical equations).”
In fact, land use regulation is the topic of his Wharton Land Use Regulatory Index, where he contacted over 3,000 City officials nationwide to find out the effects of land use restrictions on housing construction costs. I’ve summarized that data in another post also under construction (link).
Superstar cities generally have limited supplies of land, perhaps due to geographical barriers to development (i.e coastlines, mountains), smart growth principles, and urban growth boundaries.
As a result, housing prices increase, since demand in the 22 superstar cities is greater than supply.
But why is the demand high? It’s due to rich people in white collar industries (i.e. business, finance, and software). The very rich engineers and scientists compete among themselves for expensive properties, increasing prices even further.
Since land use controls, such as urban growth boundaries, decrease the amount of land available to developers, then land is sold at a premium for construction. Of course, the rich white collar workers already have the money to pay $200,000 for a lot for their new home, or, for a $2,000,000 home looking over San Francisco Bay.
Therefore, Dr. Gyourko defines the 22 Superstar Cities as meeting these criteria (derived by way of his mathematical formulae):
*Demand is greater than supply.
*The supply of land is limited, if not fixed (…where we can interpret this as perhaps due to smart growth, urban growth boundaries, or geographical barriers to construction…).
*Therefore, high demand results in significant price growth over time, due to the limited supply of land, leading to superstar status.
*The sum of price growth and quantity growth is above the sample median (in his math formulaes), and the supply of land is inelastic (meaning fixed, or artificially limited, perhaps due to geography or land use restrictions).
*Defining the supply of land as inelastic (versus elastic, unlimited, as in desert areas or Texas), is determined by a high rate of price growth over time, compared to growth in the quantity of housing over time. Again, it’s not determined by directly measuring urban growth boundaries, regulations on construction, geography, or smart growth principles.
*Rich families in Dr. Gyourko’s analysis are defined as making more than $156,000 (in 2000A.D. dollars).
*Poor families make less than $40,000. Of course, in some small rural areas, $40,000 is considered rich.
Overall, the rich have become richer, and concentrated into the 22 superstar cities. Over the last 50 years, the number of rich, highly educated white collar workers has become highly concentrated in superstar cities. Over the last 50 years, the gap between “liberal” big blue cities and “conservative” red small town America has caused political tension. Over time, the number of high income individuals has increased not only nationally, but especially in superstar cities.
First, since the 1960’s, the number of rich families has increased every decade (the yellow and green indicate familial incomes exceeding $72,000/year). As a result, the rich have been able to afford more expensive homes:
Clearly, one can see that in the last 50 years, the US has seen growth in the absolute number, population share (percentage of the total US population), and income share — of rich families, defined by Dr. Gyourko as making more than $140,000 per year (in my area, that’s over $72,000, the green zone above).
Therefore, superstar cities have gained rich folks over time, while losing poor folks to the surrounding suburbs. This is clearly demonsrated in San Francisco, where the number of rich, where the green and yellow has increased much faster than the national average (represented by the previous graph).
Superstar cities such as San Francisco (category A), of course, have higher average incomes than the other three categories of cities (B, C, D) developed by Dr. Gyourko.
Non-superstar cities, such as Las Vegas, Phoenix, and Austin, have unlimited room to grow, less land use restrictions, and do not have as many smart growth principles. Again, to not misrepresent the original thesis of Dr. Gyourko, he did not calculate smart growth principles or land use regulation.
However, Dr. Gyourko does say that in these markets, a house costs its construction costs alone. Poor individuals (defined by Dr. Gyourko as making less than $40,000 a year) can easily purchase a home in Vegas or Austin!
Dr. Gyourko shows that unlike San Francisco, Las Vegas has not become gradually richer over time. Below, you can see that the rich zones (green and gold) have increased at the same rates as the other income groups over the past 50 years:
Therefore, for young people who are not engineers, lawyers, and IT specialists, cities such as Austin, Phoenix, Reno, and Vegas and good choices for home ownership, rather than buying smart growth condos WITHOUT a yard in Superstar cities (i.e. photos all over this web site).
And, of course, the total number and population share (percentage) of both the poor and the rich gradually increases over time in non-superstar cities, while the poor exit places such as San Francisco (Comparison Figures).
With less land use restrictions and lots of open Mojave desert, Las Vegas grew from a town of less than 50,000 families in 1960, to the size of San Francisco by 2000. The real home price growth rate per year was well below the national average.
Do Superstar cities discriminate against the poor?
Dr. Gyourko asks:
“In addition, this dynamic has profound implications for the evolution of urban areas, because it implies that even large metro areas might evolve into communities that are affordable only by the rich, just as exclusive resort towns have done. Is such a MSA (metropolitan statistical area, a populated area for census purposes), or does it lose the vibrancy that makes it unique? Should public policy ensure that living in a particular city is available to all families, or, since superstar cities and towns are like luxury goods, is it reasonable that low income workers can no longer afford to buy homes in superstar cities?”
Well, those are questions my City Councilors should be asking! Sounds like Dr. Gyourko cares a lot about home ownership than most local smart growth City Councils! He’d have to take a pay cut if he ran for City Council, here, and his papers will have much more influence, anyway, especially in the Internet age. Nevertheless, if a million City Council candidates with the intelligence of Dr. Gyourko ran for City Council, the housing market would turn around and unemployment would decrease by two thirds.
Other observations from Dr. Gyourko’s work:
1. Under limited land use supply, a location may fill up with homes, and it many not decide to increase its density. Home prices increase, and it transitions to superstar status. For example, in San Francisco, appreciation rates have increased 3.5% per year from 1950-2000, 2 percentage points above the national average. By 2000 A.D., San Francisco average home price was $550,000, over three times the national average.
2. Home prices in superstar cities do not rise due to increasing values of local amenities, or, increasing worker productivity. The model predicts four types of cities without considering these factors, which are irrelevant. Coastal towns have always had the same coastal amenities; Boulder has always had the same mountain environment.
3. Dr. Gyourko asks his readers if they think home prices and salaries continue to rise. (This paper was published before the housing bubble.) I would say yes, for the superstar cities such as Boulder that haven’t been hit with that many foreclosures. Dr. Gyourko points out that over the last 50 years, 8 times as many American families make a minimum of $140,000 per year (in 2000A.D. dollars). And, the fraction of income from the top 1% richest families increased from 11.4% in 1950, to 16.9% in 2000. Dr. Gyourko also points out that Superstar cities attract rich immigrants as scientists and engineers. I would say that globalization, at least in this case, may help our economy, as immigrants can afford to buy million dollar homes in Information Technology areas such as Seattle, San Jose, and Boulder.
4. Rich cities become richer with time, as long as enough rich people still can afford it, or, in a close suburb. Dr. Gyourko also refers to Superstar Suburbs.
5. Rich resort, college, and/or retirement towns (i.e. Flagstaff, AZ, Aspen, CO, Mammoth Lakes, CA, and Ashland, OR), and coastal cities (i.e. Santa Cruz, CA, San Francisco, and L.A.), fill up due to geographical barriers (coastlines, national forests, mountains, or urban growth boundaries in the cases of Mammoth Lakes, Santa Cruz, and Ashland). San Francisco and Los Angeles reached capacity between 1960 and 1980; Boston and NYC 1970-1990.
The top ten MSA’s for real annualized price growth, 1950-2000, with populations greater than 500,000:
1) San Francisco 3.53%
2) Oakland 2.82%
3) Seattle 2.74%
4) San Diego 2.61%
5) Los Angeles 2.46%
6. Portland 2.36%
7. Boston 2.30%
8. Bergen-Passaic, NJ 2.19%
9. Charlotte 2.18%
10. New Haven 2.12%
The bottom ten at 1.18% or less (in descending order): San Antonio, Milwaukee, Pittsburg, Dayton, Albany, Cleveland, Rochester, Youngstown, Syracuse, and Buffalo (at 0.54%).
6. A “superstar suburb” simply has more rich folks than poor ones, such as Orinda, Moraga, Piedmont, and Berkeley, California (in the San Francisco area), or Kirkland or Issaquah, Washington near Seattle.
In conclusion, the rich are getting richer. Young families move to the suburbs where housing is cheaper. Of course, smart growth proponents cause price escalations with growth management.
Finally, a look at 1980, when just two superstar cities existed (Region A), and Santa Cruz and Santa Rosa (California coastal communities) in region B are about to cross into region A (i.e. achieve superstar status).
Cost of Living Maps
Superstar cities have expensive homes, rents, food, a very high cost of living index, and generally a high percentage of white collar workers and college graduates.
Often, these markets have high foreclosure rates and high unemployment, yet even non-superstar cities such as Las Vegas and Phoenix have high foreclosures and unemployment.
A recent study by the Bureau of Labor Statistics (BLS) on July 22, 2010, found that cities who might meet the Superstar criteria of Dr. Joseph Gyourko had the highest wages, in every occupation.
The New York Times offers an interactive chart, for many major metro areas.
As the chart shows, San Francisco, the first city that acheived Superstar Status along with Los Angeles, offers the highest pay for all categories, followed by a hierarchy that professional realtors will guess: New York, Salinas, Boston, Hartford, Seattle, Springfield (MA), Los Angeles, Minneapolis, Sacramento, San Diego, Washington DC, and Chicago.
The lowest paid cities are in Texas, the Southwest, and the Southeast.
However, this study has its limitations. The US ACCRA Cost of Living Index is much higher in Superstar and Smart Growth cities (and states), so one should not just head for San Francisco or Seattle and expect to get rich.
The publishers of the index jealously guard the copyright over their data, and offer very little free information such as this post at http://coli.org. However, some information is available at various free web sites, such as these maps.
Furthermore, for aspiring homeowners, Superstar and Smart Growth Cities may offer very high housing costs. This chart by Dr. Wendell Cox designates Smart Growth cities colloquially as “high cost losers,” as their Median Multiples are exceptionally high.
For those looking for a job, the coastal cities with high salaries in the BLS Study may be too expensive. Alternative include the Rocky Mountain States, Great Plains, and Texas, where unemployment remains low, as shown on the BLS unemployment web site, for hundreds of MSA’s nationwide.
Richard Florida writing in The Atlantic offers several maps of average salaries, by county. First, he lists the average wage for the working, service, and creative classes, combined:
The second map shows metro salaries for the working class, exclusively:
Third, metro salaries for the service class, exclusively:
Fourth, metro salaries for the creative class, exclusively:
Here are expensive homes in Sammamish, the most expensive suburb in the superstar city of Seattle, Washington: