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Confusing demand with inequality
Every so often I read a claim that the housing crisis is caused not by supply or demand, but by inequality. It occurred to me today that I should dig up information on inequality by metro area.
I discovered pretty quickly that the four most unequal large metros (measured by GIni coefficient) are Miami, New York, Los Angeles, and San Francisco: all high-cost metros. So at first glance, it thus appears that there is a strong correlation between inequality and housing prices.
But then I looked at the fifth through tenth most unequal metros, and a different picture emerged. These were, in order of Gini coefficient: Memphis, Houston, Boston, Tampa, Cleveland, and Chicago. All of these except Boston are low and medium cost metros; for example, the median home price in Memphis is under $300,000. And the differences between these metros and the high-cost metros are not enormous: San Francisco’s coefficient is .489 and Memphis’s is .486.
The least unequal metros with over a million people were mostly low and medium-cost metros,* but with a few exceptions: in Washington, Riverside and Denver the median home price was just over $600,000.
So what distinguishes the high-inequality/high-cost metros from the other high-inequality metros? As one might guess from conventional economic theory, income. The highest-cost high-inequality metros (Miami, New York, Los Angeles and San Francisco) have median household incomes over $90,000. So do Washington and Denver (although Riverside barely misses the cut at $87,000). By contrast, Memphis’s median income is just under $65,000, Cleveland’s is just under $68,000 and Houston’s is just under $80,000. In other words, high inequality plus high income = high prices. High inequality + low income = low prices.
*The bottom 10 included: Salt Lake City, Washington, Virginia Beach, Riverside, Grand Rapids, Kansas City, Minneapolis, Indianapolis, Denver, and Raleigh.
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Great point that inequality alone doesn’t explain the housing crisis—the overall income levels of the population are a critical piece of the puzzle.
If we look at the situation in Lithuania (especially Vilnius or coastal towns like Palanga), we see a very similar dynamic. High inequality or a concentration of higher incomes in major cities wouldn’t be a problem on its own if supply could keep up with demand. However, when you have high (or rapidly growing) average incomes combined with strict urban planning regulations and construction bottlenecks (inelastic supply), you get exactly what this article describes: soaring prices that lead people to blame inequality, even though the root cause is restricted housing supply in the face of growing demand.
When incomes rise while new residential developments are stalled by bureaucratic or planning obstacles, even middle-income households start feeling priced out of the market. In Lithuania, it is quite evident that in smaller regions where incomes are lower and housing development faces fewer restrictions, prices remain at a completely different level, regardless of the country’s overall inequality statistics.