The Baby Boom: Predictability in House Prices and Interest Rates

The Baby Boom: Predictability in House Prices and Interest Rates
Robert F. Martin

Abstract: This paper explores the baby boom's impact on U.S. house prices and interest rates in the post-war 20th century and beyond. Using a simple Lucas asset pricing model, I quantitatively account for the increase in real house prices, the path of real interest rates, and the timing of low-frequency fluctuations in real house prices. The model predicts that the primary force underlying the evolution of real house prices is the systematic and predictable changes in the working age population driven by the baby boom. The model is calibrated to U.S. data and tested on international data. One surprising success of the model is its ability to predict the boom and bust in Japanese real estate markets around 1974 and 1990.

This paper explores the extent to which the baby boom has impacted U.S. house prices and interest rates over the latter half of the 20th century. Without question, the baby boom has had a direct and large impact on the dynamics of the U.S. age profile. These dynamics have manifested themselves in changes to the proportion of the population that is of working age. The boomers entered the labor force en masse between 1967 and 1973 and, over the subsequent 35 years, they increased both the size and the growth rate of the potential labor force. The baby boomers are now about to leave the workforce. As they retire, the size of the working age population will fall as will output per person.

These changes in the working age population and the associated impact on output have had and will continue to have a first order effect on house prices and interest rates in the United States. This paper builds on the intuition of the well known paper by Mankiw and Weil (1988). That paper predicted that the baby boom would lead to a peak in house prices in 1989 (they were right) and then to a large permanent fall in the real price of housing from that point forward (obviously wrong). The intuition for this result is simple. If households exhibit hump-shaped demand for housing over the life-cycle and if housing is relatively difficult to build, then house prices should exhibit a peak at the time that the baby boomers reach their peak demand and should decline as the boomer’s demand for housing wanes. The obvious gulf between their predictions and the increases in house prices between 1995 and the present have caused many to dismiss both the Mankiw and Weil paper and the impact of the baby boom on house prices in toto.

This paper will resurrect their intuition. Mankiw and Weil missed the upturn only because they worked in a partial equilibrium environment and over the course of the 1990s general equilibrium effects began to dominate the contemporaneous demand effects. In other words, they neglected both the impact of discount rates on house prices and the impact of the baby boom on the discount rate. By working in a structural model, this paper is able to consider the general equilibrium effects without neglecting the demand effects which are so important in the work of Mankiw and Weil. Indeed, I will show that, if the interest rate effect is shut down in my model, the model will replicate the predictions of Mankiw and Weil.

Using a parsimonious and standard Lucas asset pricing framework, the model demonstrates that the demographic profile in the United States is a likely driver of both house prices and interest rates in the post-war era. In this paper, changes in the size of the working age population determine the labor input into a neoclassical production function. The model takes as given the endowment of capital, labor, and the stock of housing. As I work in an endowment economy, the model will not replicate the time series of capital accumulation — neither business nor residential. Since both of these stocks are assumed fixed, the model also misses the increase in consumption of both goods and housing services observed in the data. However, the model exactly replicates the increase in the ratio of consumption of goods and services to the consumption of housing services as reported in the National Income and Product Accounts. That is, the ratio of consumption to housing services increases by 18 percent in both the model and in the NIPA data over the period 1974 to 2004.


When the size of the working age population is temporarily high as it is now, output is temporarily high and households wish to transfer assets to the future. Because ability to do so is limited2 , there is upward pressure on real asset prices — house prices rise, interest rates fall.3 The increase in house prices occurs now despite the certain knowledge that once the baby boomers retire real house prices will fall.

Without changing the parameters of the model, I test the model’s ability to replicate the pattern of long-term real interest rates.6 Ignoring high frequency movement in the real rate, the model is consistent with interest rates over the period 1950 to 1972 during which interest rates rose and then fell and over the period 1990 to 2005 during which interest rates fell. In the intervening period, the model interest rate is too high relative to the data in the 1970s and too low relative to the data in the early 1980s. To restate this result, using the parameters which are designed to replicate house prices, the model exactly matches long-term interest rates for 37 of the past 55 years.


The question remains “How rational do agents have to be in order to incorporate the output increases observed in the 1990s into the price of bonds being traded in the 1970s?” Do the agents need to have a demographic model like the one presented in this paper in order to anticipate the change in output? The answer is no. Individual agents simply looks forward to their own path of lifetime labor income. The agents in the model know their life time working hours and realize that their highest output years are yet to come. They realize that in the future their own household output per person will rise (their children becoming adults). Therefore, each agent wishes to borrow against these future increases in wealth. Unfortunately for them, the shock that they face turns out to be aggregate — their cohort goes to the bond market with them and interest rates rise. This is simply the flip side of why interest rates are low now.

One of the most surprising results of this paper is its ability to replicate the decline in output volatility in 1984. The volatility in output implied by our model falls by half in the post-1984 world. McConnell et al. (1999) identify a decline in the volatility of U.S. real GDP in 1984. This decline has been attributed alternatively to improved monetary policy (Clarida et al (2000)), improved business practices (McConnell et al. (1999), improved mortgage markets associated with regulation Q (Dynan et al (2005), or simply to a reduction in the volatility of macroeconomic shocks (Stock and Watson (2003)). The demographic model has none of these channels, implying that the good luck noted by Ahmed et al (2002) can simply be named predictable demographic changes. As the baby boom moderated so did the economy.

While it is a success in itself to match both house prices and interest rates in a such a parsimonious model, we further test the model’s ability to match house prices in other industrialized countries. I test the model on data for the United Kingdom, Japan, and Ireland. The model succeeds in each case in matching both the trend in house prices and most of the major peaks in each country. Japan is the most remarkable case.

Between 1970 and 2005, Japanese real house prices exhibited peaks in 1974 and 1990. Since 1990, real house prices have fallen around 34 percent. The sharp increase in real estate prices in the 1980s followed by a fifteen-plus year fall in prices has led many to refer to 1980s Japan as the original bubble economy (Bayoumi and Collyns (2003)). This simple model, using Japanese demographic data, successfully predicts the 1974 and the 1990 house price peaks. Strikingly, the model also predicts a 30 percent decline in real house prices over the fifteen years following the 1990 peak.

The economy consists of a single representative agent. While more complicated demographics could easily have been implemented in the model, this framework ensures the absence of the life-cycle type savings behavior which is the focus of so much of the previous work. That is, the dynamics in this model are not driven by dissaving on the part of some sub-set of agents but rather by aggregate fluctuations in output.


Japan
Japan makes an excellent test case for the model. Japanese real house prices rose almost 40 percent between 1986 and 1990. After the 1990 peak, real house prices have fallen around 34 percent, nominal house prices have fallen even further.20 Japan is widely thought to have experienced an asset price bubble in the late 1980s, with a primary affected asset class being real estate (see for example Bayoumi and Collyns (2000), Ahearne et al. (2002), and Charkaborty (2005)). These papers give many reasons why the bubble occurred and subsequently collapsed mainly associated, in one form or another, with imperfections in the banking system. Hayashi and Prescott (2005) dispute the finding that a breakdown in the financial system caused the decade long stagnation. Instead they attribute the decline to a decade long slowdown in total productivity growth.

Demographics, if not the only driver, appears to have played a significant role in the run-up in house prices in Japan in the 1980s and appears linked to the decline in house prices over the 1990s. Figure 13 shows the time path of real house prices (dashed black line) and the data output by the model (the bold solid line). The model is capable of replicating both the 1974 peak in real house prices and the 1990 peak, albeit the model predicts the peak to occur in 1992 rather than 1990. The model, surprisingly, also predicts the fall in house prices over the last fifteen years. From the model peak year, the model predicts a 30 percent decline in house prices over fifteen years, remarkably close to the actual fall.

Ireland
The model is applied to Ireland. I wanted to end with Ireland because it is a country without a dire prediction for house prices going forward because Ireland has a very young population . One result of the very young population is that the model does not predict a decline in house prices until the year 2033. Figure 17 shows real house prices and simulated house prices from the model. Among the countries examined in this paper, Ireland has the smoothest predicted price of housing. The model comes close to predicting the stagnant housing market between 1970 and 1994 and is able to make the turn to high house price growth from 1994 forward. The model exactly matches house prices from 2002 to 2005. The model’s predictions for house prices between now and 2010 are remarkable. Over this time period, the model predicts a further doubling of house prices.


The model predicts such different house price growth in Ireland because the Irish baby boom was quite different from the other countries. Irish population data is available beginning in 1950. Inferring from the growth rate of the 20 to 24 year old population, the original “post-war” baby boom in Ireland occurred quite late. The birth rate may have begun to pick-up in 1940 but did not substantially peak until 1960. More importantly for our result is that Ireland had a baby boom quite recently in the very late 1970s. This cohort enters the workforce around 1994 implying that most of the current run-up in house prices in Ireland is a result of increased output as the labor supply increases. (Note, this is also the reason the model predicts a slight decline in house prices earlier.) That same cohort does not begin to retire until around 2033. But for Ireland, even in 2033, the case for house prices is not so dire. This results from the fact that Ireland is currently in the middle of a sizable baby boomlet (the boomlet causes the change in slope of prices around 2000). This second cohort does not enter the labor force until between 2025 and 2030, somewhat cushioning the falling prices later. This cohort retires around 2065 to 2070.