5 Consequences of Housing Bubbles

Equation (4.15) repeats the idea that the current account can be defned as the difference between the demand and the supply of assets (including the bubble). We discuss two periods: (i) pre-bubble (1990–1995) and (ii) bubble episodes (1996–2007).

In the pre-bubble period, there was no bubble in the United States. In this case, the globalization process affects the current account only through its effect on the interest rate. As we have seen, when the fnancially developed economy integrates with a fnancially underdeveloped economy, the interest rate falls (see Fig. 4.2). The supply of assets (DU) declines with the interest rate and the demand of assets (AU) increases with the interest rate. Thus, a decline in the interest rate implies that the difference between DU and AU increases. That is, the current account falls when the interest rate declines. In other words, the current account falls as fnancial globalization progresses because middle-aged agents in fnancially underdeveloped economies purchase assets in the United States. As we can see from Fig. 4.1, this empirical prediction fts the evolution of the current account in the United States during this period. Indeed, the current account (over GDP) decreased from −1.3% in 1990 to −1.5% in 1995.

Next, we turn to the bubbles period. Equation (4.15) implies that at the start of the Dot-Com Bubble (1996) there is a further decline in the current account. That is, when there is a bubble, the current account declines because the size of the bubble increases with the level of fnancial globalization. In 2000, the bubble burst and, thus, the current account should suddenly improve. In 2002, a second bubble emerges (the Housing Bubble) which should decrease the current account again. The peak of bubble is 2006. Thus, the current account should suddenly increase in 2007 once the bubble has burst. Note that for these predictions, it does not matter if the bubble is attached to houses or stocks. They only depend on the prediction that the size of the bubble increases with the level of fnancial globalization. The evolution of the current account in the United States during 1996 and 2007 fts very well this narrative. The trend on the current account (over GDP) was negative between 1996 and 2000 (it went from −1.5 to −3.9%). It reached the minimum in 2000 (the peak of the Dot-Com Bubble) and it continued to decrease from 2001 to 2006 (it declined from −3.6 to −5.8%). The current account stopped decreasing in 2006 (the peak of the Housing Bubble) and it suddenly increased from 2006 to 2007. After 2007, the economy enters into a global recession and the fnancial globalization

process stalls. This could explain the increase in the US current account in the aftermath of the fnancial crisis.

Therefore, the evolution of the current account in the United States could be explained through the lenses of our simple model. We now want to argue that this model can also explain the evolution of houses prices at the local level. To perform this exercise, we extend the model to include n municipalities in the fnancially developed economy. In particular, we assume that each municipality in the fnancially developed economy has its own housing market but the capital market is integrated. Basically, the only thing that changes is that house prices will depend on the housing supply elasticity of the municipality.

We are interested in the effect of fnancial globalization on house prices of each municipality during three periods: (i) no bubble, (ii) DotCom Bubble and (iii) Housing Bubble. From Eq. (4.14), it is straightforward to check that houses prices in a municipality in a fnancially developed country in each of these (steady-state) regimes is given by,

where Bi(τ ) is the size of the bubble in municipality i, σi is the fundamental demand of houses in municipality i and εi is the housing supply elasticity in municipality i. By defnition, i Bi(τ ) = B(τ), where B(τ) is defned in Eq. (4.13).

If there is no bubble (Eq. 4.16), house prices are only affected by fnancial globalization through the interest rate. If there is a bubble but it is not attached to houses (Eq. 4.17), house prices should not be affected by fnancial globalization because the interest rate is equal to one when there is a bubble.6 Finally, when there is a housing bubble (Eq. 4.18), house prices are affected by fnancial globalization because

p NB i (τ ) = σ i(R(τ ))

1 1+εi

, (4.16)

p Dot i −Com(τ ) = σ i(R = 1)

1 1+εi

, (4.17)

pHousingBubble i (τ ) = Bi(τ) + σ i(R = 1)

1 1+εi

, (4.18)

6 For the advanced reader, note that we have been assuming that each τ is associated to a different steady-state and we directly switch between steady-states.

the size of the bubble increases with globalization and the bubble is attached to houses, which raises housing demand.

From these equations we want to emphasize two general predictions. First, if there is a bubble, the effect of globalization on house prices is larger if there is a housing bubble. The intuition is that if the bubble is not attached to houses, the increase in capital infows is used to sustain the bubble. However, since the bubble is not attached to houses, it does not affect housing demand. Second, the effect of globalization on house prices should be larger in housing supply inelastic municipalities. The reason is that, given the same increase in housing demand, the effect on house prices should be higher in municipalities where it is not easy to build new houses. Therefore, we have two empirical predictions. The frst is that the effect of fnancial globalization on house prices should be positive and larger in housing supply inelastic municipalities. The second is that this effect should be exacerbated during the housing bubble.

To assess the empirical validity of this prediction, we need a measure of housing supply elasticity. That is, an indicator of how hard it is to build a house in a given municipality. Saiz (2010) built a housing supply elasticity index for metropolitan statistical areas in the United States. Loosely speaking, Saiz computes the share of land available in each municipality. The interested reader is referred to Saiz (2010) for more details. The idea behind his work is that if a municipality is located next to the sea or close to a mountain, it has not much space to build new houses or it will be very costly. Similarly, if the municipality is located in the middle of the desert, it has plenty of space for building new houses at a low cost. In the frst case, an increase in housing demand will increase house prices a lot because it will be diffcult to increase the stock of houses. In the second case, we have the symmetric result. The increase in housing demand can be matched with an increase in the stock of houses and houses prices will not change much.

We can take a look at the ranking of municipalities in Saiz (2010) to better understand his measure of housing supply elasticity. For example, Miami (FL), San Francisco (CA), San Diego (CA) and New York (NY) are in the top-10 of municipalities with low housing supply elasticity. This ranking should not surprise the reader. These municipalities are close to the ocean and they are geographically constrained to increase their stock of housing. On the other end of the ranking, we fnd cities like Columbia (MO), Fargo (ND-MN), Wichita (KS) or Longview (TX). These cities have a large housing supply elasticity, which implies that they are not geographically constrained to expand their housing stock.

This is a good measure of housing supply elasticity because it is exogenous to housing demand. In other words, the geographical location of the municipalities does not depend on the spread of fnancial globalization. This measure of housing supply elasticity has been used in different papers. The incomplete list includes Glaeser et al. (2008), Mian and Suf (2011), Chaney et al. (2012) and Basco (2014).

Once we have given empirical content to the concept of housing supply elasticity, we are ready to test the empirical predictions of the model. First, we would like to observe that the effect of fnancial globalization on house prices is larger in housing supply inelastic municipalities. Second, this differential effect across municipalities should be exacerbated during the housing bubble. Basco (2014) formally tests and confrms these empirical predictions using the current account defcit (over GDP) of the United States as a proxy of fnancial globalization. In this book, we informally test these predictions graphically. Figure 4.3 represents the annual real house price growth in the different municipalities of the United States during the Housing Bubble (2002–2006) and the Dot-Com Bubble (1996–2000). The blue dots represent the Housing Bubble and the red dots the Dot-Com Bubble. We emphasize two features of this fgure. First, house price growth was higher during the Housing Bubble (blue dots are generally above the red ones). Second, the difference in growth between housing supply inelastic and elastic municipalities was higher during the Housing Bubble (i.e., the correlation between housing supply elasticity and house price growth is more negative for the blue dots). Note that these facts are consistent with the empirical predictions of the model. The current account defcit is an aggregate shock to the United States, which has a heterogenous effect across municipalities. During the Dot-Com Bubble, the increase in the current account defcit in the United States did not increase the demand of housing and, thus, real house prices grew more or less the same in all municipalities. In contrast, during the Housing Bubble, the increase in the current account defcit raised the size of the bubble. Since the bubble was attached to housing, the current account had a direct effect on housing demand. This increase in housing demand translated into high house price growth in housing supply inelastic municipalities and a more muted effect in housing supply elastic municipalities. Glaeser et al. (2008) performed a similar exercise to the one described in Fig. 4.3. They use the same measure of housing supply elasticity and also argue that geographical conditions explain the difference in house price growth across

Fig. 4.3 House prices and housing supply elasticity. Notes Author’s calculations. Housing supply elasticity from Saiz (2010). House price index is from Offce of Federal Housing Enterprise Oversight (OFHEO). https://www.fhfa.gov/ DataTools/Downloads/Pages/House-Price-Index-Datasets.aspx. CPI index, used to compute real prices, is from Bureau of Labor Statistics

municipalities. One main difference is that they consider the 1996–2006 boom as a unique period. In our case, we separate this period to highlight the differences between the two asset price bubble episodes.

To conclude, in this section, we have seen that the evolution of both the current account of the United States and house prices at the municipality level can be explained through the lenses of our model. The increase in fnancial globalization was conducive to asset price bubbles in the United States and to an exacerbation of the decline in the current account. When the bubble was attached to houses, there was a large increase in house prices in the municipalities with low housing supply elasticity and a much lower effect in the municipalities with a high housing supply elasticity.

references

Basco, S. (2014). Globalization and Financial Development: A Model of the

Dot-Com and the Housing Bubbles. Journal of International Economics, 92(1), 78–94. Bernanke, B. (2005). The Global Saving Glut and the US Current Account

Defcit. Richmond, VA: Sandridge Lecture. Broner, F., Didier, T., Erce, A., & Schmukler, S. L. (2014). Crises. Journal of

Monetary Economics, 60(1), 113–133. Caballero, R., Farhi, E., & Gourinchas, P.-O. (2008). An Equilibrium Model of

“Global Imbalances and Low Interest Rates”. American Economic Review, 98(1), 358–393. Chaney, T., Sraer, D., & Thesmar, D. (2012). The Collateral Channel: How

Real Estate Shocks Affect Corporate Investment. American Economic Review, 102(6), 2381–2409. Glaeser, E., Gyourko, J., & Saiz, A. (2008). Housing Supply and Housing

Bubbles. Journal of Urban Economics, 64(2), 198–217. Laibson, D., & Mollerstrom, J. (2009). Capital Flows, Consumption and Asset

Bubbles: A Behavioral Alternative to the Savings Glut Hypothesis. Journal of

International Economics, 120(544), 354–374. Mian, A., & Suf, A. (2011). House Prices, Home Equity-Based Borrowing, and the US Household Leverage Crisis. American Economic Review, 101(5), 2132–2156. Pavlidis, E., Yusupova, A., Paya, I., Peel, D., Martínez-García, E., Mack, A., et al. (2016). Episodes of Exuberance in Housing Markets: In Search of the

Smoking Gun. Journal of Real Estate Finance and Economics, 53, 419–444. Saiz, A. (2010). The Geographic Determinants of Housing Supply. The

Quarterly Journal of Economics, 125(3), 1253–1296.

CHAPTER 5

Consequences of Housing Bubbles

Abstract In this chapter, we describe the economic consequences of housing bubbles. During the boom, the rise in house prices results in an increase in consumption, borrowing and investment. These effects are reversed during the bust. The collapse of housing bubbles is conducive to fnancial crises, which tend to be longer and deeper than normal recessions. Finally, we discuss how housing bubbles can distort the optimal allocation of resources and reduce the aggregate productivity of the economy.

Keywords Consumption · Investment · Housing bubble Financial crisis · Misallocation

In the previous chapters, we have provided different explanations on the origin of asset price bubbles. In this chapter, we do not take a stand on the origin of the bubble, but we want to explain how the economy behaves when there is a housing bubble. In particular, we will be interested in whether the emergence of a housing bubble may distort the choices of agents in the economy. To be precise with the language, we say that the bubble distorts the optimal allocation of inputs if the allocation of inputs with the bubble is different than the allocation of inputs without the bubble. We next discuss these effects for households and frms.

© The Author(s) 2018 S. Basco, Housing Bubbles, https://doi.org/10.1007/978-3-030-00587-0_5 65

Let us start with a possible distortion on the choice of households. How can households be affected by the housing bubble? There is one immediate distortion, which is at the heart of the notion of housing bubble. This distortion is that the housing demand with the bubble is larger than without the bubble. In other words, there is a demand for houses, which is not related to the utility derived from living in the house. Going back to the model discussed in Chapter 3, there was a fundamental housing demand (young agents purchased the house because they would live there when middle-aged) and a bubbly housing demand (middle-aged agents purchased the house only as a store of value to sell it next period). If there were no housing bubble or the bubble were attached to another asset, this bubbly demand for houses would not exist. Therefore, since the bubble is attached to houses, this extra demand for houses is a distortion. A measure of this distortion is the overvaluation of house prices. We can use the municipality data for the United States to have a sense of the size of the housing bubble. For each municipality, we have a measure of housing supply elasticity and house prices. Theoretically, bubbles cannot arise in municipalities with a very high housing supply elasticity. As we discussed, in these municipalities, the extra demand for houses will have no effect on prices but it will just increase the stock of houses. Analogously, in municipalities with a very low elasticity (the stock of houses is fxed), the increase in demand will be matched with an increase in the price. Thus, we can compare the evolution of house prices in these two sets of municipalities to gauge the size of the housing bubble. In particular, we defne as “bubbly municipalities” and “no-bubbly municipalities” those municipalities in the bottom and top quartile of the housing supply elasticity distribution, respectively. If we perform this exercise with the data described above, we fnd that between 2002 and 2006 real house prices increased 43.7% in “bubbly” municipalities and only 6.1% in “non-bubbly” municipalities. Therefore, we can say that the distortion in housing demand represented an overgrowth in real house prices of 37.6%.

The housing bubble may also distort the borrowing choice of households. There are two main channels that can explain how house prices affected borrowing. The frst one is through households who purchase new houses. Let us imagine that some households decide that they want to participate in the housing bubble and purchase a house. They will go to the bank to ask for funds to purchase the house. If the bank grants a loan to this household, the amount borrowed by the household will increase.