Himmelberg, C.; Mayer, C.; and Sinae T.

Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions

September 2005

11643

Paper Website

Ian Gorovoy

2006-10-4

2006-10-9

Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions

With the current situation of an extremely active “seller’s housing market, some question whether this is a trend or a bubble. Often conventional metrics (growth rate of house prices, price-to-rent ratios, and price-to-income ratios) predict over aggressive market activity and its impending fall back to equilibrium, such as the internet bubble in 2000. However, these metrics don’t include important information for long-term trends. When the research factors in these statistics, the results are dismissive of a bubble. The same quantitative measurements were corroborated when they determined that housing prices were over-valued during the bubble of the 1980s. Housing prices may decline if the market fundamentals change due to factors like an increase in interest rates and specificity of each measurement to one locality. What is expensive for an area may be typical for another. Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions by Himmel et al. argues that although some conventional metrics may signal an impending housing bubble, these numbers may be misleading because they do not account for real long-term interest rates and predictable differences in the long-run growth of housing prices.

A housing bubble occurs when investors agree to a high price today because, despite contradiction from market determining factors, they believe they will receive a high price tomorrow. Of course, the prices cannot escape market factors in the long run, and once it is realized that these inflated priced are unjustified, prices plummet. The real price of housing has increased from an average raise of 0.5% annually between 1975 and 1995 to 3.6% in the last ten years. Therefore, question is how one predicts the difference between real growth due to the fundamental factors of supply and demand and an unsustainable bubble. Through their model, the authors can predict the housing bubble that occurred in the late 1980s, but do not foresee one in the near future. The growth in prices today is probably caused by an increase in demand due to easier means to borrow money in new mortgage plans, lower interest rates, and higher incomes.

Stemming from the criticism of the major fallacies used in measuring relative house prices, the best insights highlighted are as follows:

  1. Because prices are not the same as the annual costs of owning, rising house prices does not necessarily mean ownership is becoming more expensive.
  2. High growth in prices does not necessarily indicate an impending bubble.
  3. Housing must be studied in each individual locality because of differences in taxes and expected appreciation of land value in the area.
  4. Because prices are especially sensitive when long-term interest rates are low (i.e prices increase because a mortgage is so affordable) and expected growth is high, an acceleration of price growth does not inevitably lead to a bubble.

The authors demonstrate how conventional metrics (growth rate of house prices, price-to-rent ratios, and price-to-income ratios) can provide a false positive for an approaching bubble. For example, if housing prices rise quickly in an area, but one is moving from one home to another in the area, then the rise in pricing affects the price of the new home as well as the old home house, meaning that housing did not become more expensive for that person. Using data from the Office of Federal Housing Enterprise Oversight, the researchers determine that housing has become more expensive in recent years, but is less expensive than prior to the 1980s bubble, especially when factors like new mortgage types are factored in. Some cities like San Francisco, Boston, and New York City are very expensive, but they have always been this way and will probably continue the trend.

My view

Overall, this paper provided sound evidence to support its hypothesis that conventional metrics makes housing look more expensive than it is, which would falsely predict a housing bubble. It effectively notes the limitations of the proposed model too. Of course, these limitations mean that a definitive conclusion for a potential housing bubble cannot be reached. For example, the model cannot be used to determine if rents are over-priced and because much of the housing in New York City is condominiums, it is difficult to gain an accurate picture there. Also, they admit that their model cannot factor in the transaction costs of switching between ownership and rental, and these expenses are significant. One of the strengths of the argument is that limitations are thoroughly outlined. They predict that a housing bubble will not occur soon, but admit that changes in fundamental factors or limitations of the model can change this outcome.

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