In collaboration with Ryan Haugland and Raquel Fernandez
Housing prices directly affect the decision individuals make when pursuing employment in certain areas. Rational actors make decisions that maximize individual benefit, which create incentives to relocate to areas where this condition is met. Understanding the role that housing prices play on the dynamics of labor migration can give us some insight on the measures policy makers can take to attract workers.
What do government spending, social security, education, and international trade all have in common? They are all controlled by the government and their regulations. In every election of a new president these are the topics that potential candidates are being asked about. These seem to be the focus of everyone’s attention and tend to get the most media coverage. All these items are great for America as a whole, but do they necessarily affect each of us individually? Not substantially at least, but one area that does affect almost everyone is house pricing. This is the area that most individuals end up sinking most of their income into. This is the factor that when house prices go up people feel richer and when they go down they feel poorer. House prices drive most decisions for individuals and families, and government regulation, which may vary across locations, affect house pricing in different ways.
What are the effects of housing prices on labor migration? Raven Saks (2008) explores this important question and finds that the elasticity of house prices, which is a result of barriers to construction in certain local metropolitan markets, has a large effect on the amount of labor migration to certain areas. He also claims that places with low barriers to construction, meaning that houses can be built with hardly any frictions, will have very little change in house prices, thus making them more attractive to labor migration, whereas zones with high barriers to construction will have large upward shifts in house prices, thus making them less attractive to migrating workers.
Saks (2008) starts out by introducing several constraints that can be found when facing construction planning; one of them is Government regulation, which affects house pricing in different ways. This is a complex factor, as regulations vary from city to city, due to the fact that land use policy is controlled by local governments. To study the impact of such various policies, the author uses six different sources to show the effect of government regulations on local house prices.
On the one hand, the first source is a survey conducted by the Wharton Urban Decentralization Project in the late 1980s, one of Saks (2008) main sources, and uses questions on zoning approval and permit processing time to draw conclusions about local government housing supply regulations.
Then, the second survey was undertaken by the Fiscal Austerity and Urban Innovation project and shows how the strict limitation of growth control is being used as a tool on housing development.
Moreover, a third difficulty for construction which is faced is the preservation of historic areas; such as historic districts, singular buildings, or archaeological sites. So as to find evidence on this factor the author collects information on the amount of land used for historical reasons from the National Register of Historic Places (NRHP).
The fourth issue that is addressed in Sack’s paper is how the state government also implements regulations on land for environmental reasons, or to control the development of new towns. This data is collected from a study implemented by the American Institute of Planners.
Finally, he also includes information from two surveys of local government officials concerning the overall degree of housing supply regulation in each place. The first survey is a survey of the Regional Council of Governments and the second survey is by the International City Managers Association.
The author constructs a model supposing that elasticity of housing supply is a function of regulation and other factors including some fixed effects which are: the year and the metropolitan area. Therefore, the omitted factors such as geographic amenities, which are constant over time, will not affect the estimation. Taking into account the data found on the paper, on average a 1-percent increase in labor demand is associated with a 0.25-percent increase in housing stock and a 0.8-percent increase in housing prices. The more regulation there is, the less houses that are constructed and the higher are the house prices.
Constraints on the supply of housing have an impact on local labor markets. In every metropolitan area, the marginal product of labor declines with the level of employment so that the demand for labor is downward sloping. Labor demand depends on how much output a certain city produces; for example, back in the early 90s Detroit was producing the most cars—an utopia for auto workers—but today it is in financial ruins and hardly able to keep any workers because wages are so low. On the other hand, labor supply is determined by the size of the population, which affects housing supply levels. Population varies due to several factors which are an increase in wages, decreases in house prices, and personal preferences.
An increase in labor demand causes wages to increase in a certain area. These wage increases attract workers from other regions, which causes population (labor supply) to increase; as new workers arrive in the city they need houses; therefore, they cause a rise in housing prices. In inelastic housing supply areas, the increase in house prices is greater than areas with a more elastic housing supply, because it is easier for elastic housing supply areas to construct houses, apartments, and other buildings to face the rise in housing demand for the workers who migrated. This increase in housing supply keeps house prices around the same level they were before the demand shock. All of the effects described above are short-run effects.
In the long run employment steadily rises slightly above a 1.5-percent increase and then falls to about a 1-percent increase from its initial level about 15 years after the shock. Wages decline gradually and after about 25 years return to their initial preshock level. House prices rise by roughly 2 percent at the start and stay that way for longer than all other variables, but in the end they fall below their initial level and end at a long-run equilibrium. Finally, employment effects vary between inelastic and elastic areas. In elastic areas, a 1-percent increase in labor demand leads to a 1-percent increase in employment, but in inelastic areas a 1-percent increase in labor demand only leads to a 0.9-percent increase in employment, so there is a small gap of unemployment increase.
Although the author feels his model does a more than sufficient job in showing the relationship between government regulations and housing supply, he does discuss a few problems he feels could occur while evaluating the model. First, Saks states that some of the numbers may be biased due to areas that have high constraints on other things as well and not just housing for example, employment constraints, environmental constraints, and so on. Second, weather could be a decisive factor because people would desire to live in areas that have a warmer climate, such as California. Third, the age of the metropolitan area can also affect the decision of workers because older areas tend to be less productive than newer areas. Fourth, he states that his index might be correlated with geographic factors that make the supply of housing more inelastic in some areas relative to others. Finally, he says local governments might impose stricter regulations in areas that have higher expected future demand of housing.
In conclusion, once the authors model is fully constructed it is clear to see that areas with inelastic housing supply will have higher wages, lower employment, and higher house prices and areas with elastic housing supply will have lower wages, lower house prices, and more employment. In the short run there are many effects, but in the long run house prices settle at a price below their initial value. Moreover, it is important to note that the authors model is not perfect, but it does paint a good picture of how housing supply affected house prices, employment, and wages during the financial crisis.1
Multiple Choice Questions
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1. According to Sacks (2008), which of the following statements is false?
a. Inelastic housing areas will have higher house prices, higher wages, and lower employment levels
b. Elastic housing areas will have higher house prices, higher wages, and lower employment levels
c. Elastic housing areas will have lower house prices, lower wages, and higher employment levels
d. It is easier to construct house in elastic areas, as opposed to inelastic areas
Explanation: The answer is (b). Elastic housing areas will have lower house prices, lower wages, and higher employment levels because they have an easier time constructing houses, thus making it easier for them to adapt to employment migration. (a) is incorrect because it is indeed inelastic housing that displays those characteristics. (c) is incorrect because that is the definition of elastic house prices. (d) is incorrect because it is easier to construct houses in elastic areas as opposed to inelastic areas.
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2. According to Sack (2008), which is not a short-/long-run effect of housing supply?
a. There is a strong relationship between an increase in labor demand and an increase in labor employment
b. In more constrained labor supply areas, an increase in labor supply leads to higher house prices, which leads to a higher wage level and an increase in employment
c. An increase in labor demand is also associated with higher wages, and a large significant change in house prices
d. In the long run house prices, after a slight rise in the short run, fall below their initial level and eventually settle at a long-run equilibrium point
Explanation: The answer is (c), because in the short run there is a small/insignificant change in housing supply and not a large change. (a) is incorrect because there is a strong correlation between labor demand and labor employment. (b) is incorrect because there is a small increase in unemployment of 0.01 percent (1 to 0.9 percent). (d) is incorrect because that is a long-run effect.
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3. Which of the following is a problem with the model constructed by Sack (2008)?
a. Local governments might impose stricter regulations in areas that have higher expected future demand of housing
b. Weather as a factor because people want to live in areas that are nicer in terms of weather
c. The age of the metropolitan area because older areas tend to be less productive than newer areas
d. All of the above are problems with the model
Explanation: The answer is (d) because all of the above are stated problems listed in Sack’s (2008) paper.
References
Luque, J. (2014), “Wages, Local Amenities, and the Rise of the Multi-skilled City,” Annals of Regional Science 52, 457–467.
Luque, J. (2013), “Heterogeneous Tiebout Communities with Private Production and Anonymous Crowding,” Regional Science and Urban Economics 43, 117–123.
Mian, A. and A. Sufi (2014), “House of Debt,” University of Chicago Press: Chicago, USA.
Sacks, E. R. (2008), “Job Creation and Housing Construction: Constraints on Metropolitan Area Employment Growth,” Journal of Urban Economics 64, 178–195.
1In contrast to Sacks’ (2008) arguments however, Mian and Sufi (2014) claim that sometimes unemployed workers have less incentives to move to other locations to look for a job, due to the possibility to skip mortgage payments, which delays foreclosure and government assistance. This could explain why sometimes housing prices would not adjust to changes in labor demand and supply, because workers might be less willing to move if they have other sources of income apart from labor wage. Moreover, housing prices are also affected by several factors which are not only related to construction.