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Article Excerpt
This article carries out an asset-pricing analysis of the U.S. metropolitan housing market. We use ZIP code-level housing data to study the cross-sectional role of volatility, price level, stock market risk and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the dynamic role of volatility and housing returns within submarkets over time, our risk--return analysis is cross-sectional and covers the national U.S. metropolitan housing market. The study provides a number of important findings on the asset-pricing features of the U.S. housing market. Specifically, we find (i) a positive relation between housing returns and volatility, with returns rising by 2.48% annually for a 10% rise in volatility, (ii) a positive but diminishing price effect on returns and (iii) that stock market risk is priced directionally in the housing market. Our results on the return-volatility-price relation are robust to (i) metropolitan statistical area clustering effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density.
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It is well known that investment assets trading in financial markets typically exhibit a positive relation between risk and return. For example, as an asset class, the more volatile small-cap stocks exhibit higher returns over the long run than large-cap stocks. Does such a relation also exist in the U.S. housing market where housing has the dual role of consumption and investment and where transaction costs and liquidity risk are high? In other words, do riskier, more volatile housing markets also provide higher returns? Furthermore, what is the impact of the house-price level on this risk-return relation and how does exposure to the stock market affect housing returns?
When studying housing risk or talking about the possibility of bubbles, the national market is not very relevant to most home owners. "No one owns the median home in the USA or even in a MSA. They own property in a submarket." (1) In this article, we empirically examine the questions posed above by using disaggregate housing sale price data at the ZIP code level. Prices at this level will correspond more closely to an individual perspective. Here we investigate the role of housing return volatility, price level, stock market exposure and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the longitudinal role of volatility and housing returns within metropolitan statistical areas (MSAs), our risk-return analysis is cross-sectional and covers the national U.S. metropolitan housing market. (2) Our study uses disaggregate ZIP code-level housing data from the International Data Management Corporation (IDM) and consists of 155 MSAs and 7,234 ZIP codes. The use of ZIP codes as the spatial unit provides a more localized delineation of housing submarkets for examining the risk-return structure across submarkets.
We find that MSAs explain only 19.6% of the overall ZIP code-level variation in housing returns, implying that cross-sectional analysis at this level would eliminate 80% of the return variation in our data. This suggests that aggregation to the MSA level blurs the heterogeneity of hedonic factors that defines neighborhoods more locally and masks their influence on property values. For example, neighborhoods with higher priced homes where households tend to be employed in managerial occupations may be more sensitive to changes in the stock market through an income/wealth effect. Moreover, a low-risk MSA may still contain higher-risk submarkets and vice versa.
While there is some arbitrariness in the use of ZIP codes to define submarkets, empirical studies show that they provide a reasonable spatial delineation that is correlated with important factors impacting property values. For example, Goodman and Thibodeau (1998, referred to as GT) propose a hierarchical hedonic model for identifying housing submarket boundaries based on public school quality which is used to estimate property value by Goodman and Thibodeau (2003). (3) The study finds that the prediction mean square error for (logged) house prices is 0.04335 when ZIP codes are used to define neighborhoods while the same under the GT approach is 0.0420. The authors conclude: "Indeed, given the arcane formulation of ZIP codes, it is surprising how well they characterize submarkets. Moreover, they are the easiest submarket indicator to use--everyone knows his or her ZIP code" (p. 19).
Goetzmann and Spiegel (1997) also estimate ZIP code-level housing returns where all repeat sales in a metropolitan areas are weighted using distance functions based on geographical and socioeconomic characteristics. They find that submarket return indices often deviate dramatically from the citywide index in San Francisco indicating the need to further explore and understand these differences in submarket price movements. In this regard broad metropolitan area indices may be misleading to lenders and investors as a proxy for capital appreciation or risk. Given the well established use of ZIP codes as a spatial unit, we believe that the use of ZIP codes to delineate submarkets is a reasonable and practical start to investigating the cross-sectional role of risk and return across the U.S. housing market.
Our empirical results provide a number of important insights into the asset-pricing features of the U.S. metropolitan housing market. First, we find that the U.S. metropolitan real estate market is in conformance with the general risk-return hypothesis where higher volatility is rewarded by higher return. Housing returns increase by 2.48% annually for a 10% rise in volatility. Second, the return on housing investment is positively affected by the price level, although the price effect declines as the house-price level increases.
Third, we find that stock market risk is also priced by the housing market, and a more complex effect emerges based on the direction of the stock market. Submarket sensitivity to the stock market is measured through "housing betas" estimated by regressing housing returns to S & P 500 index returns. We find that submarkets with higher exposure to the stock market experience higher returns over the period where the market rises (1996-1999) while returns decline when the market falls (2000-2003). Regression estimates imply that a submarket with a housing beta of 0.5 yields an expected 8.21% higher return over 1996-1999 than a zero beta submarket, while it yields a 7.9% lower return than the zero beta submarket over the 2000-2003 stock market downturn.
One possible explanation follows from the degree to which household income and wealth in various submarkets is sensitive to the wider economy, whose leading indicator is the stock market. Houses in ZIP codes that are more sensitive to the stock market have the potential of greater price appreciation in states of the stock market that provide those households with higher income and wealth (when, e.g., higher corporate profits increase compensation, bonuses and stock options to managers). Because housing supply is relatively fixed in urban submarkets in the short run, housing demand can rise sharply with income, leading to higher housing returns in ZIP codes that are more sensitive to the stock market. This suggests a positive relation between return and beta in periods of rising stock market performance. (4)
The same mechanism leads to a fall in demand when the stock market declines because household income is affected more negatively in submarkets with greater market sensitivity. This implies a declining relation between return and beta in falling periods of the stock market. Due to the dependence of the return-beta relation on the direction of the stock market, aggregation of returns over the entire 1996-2003 period then lead to a U-shaped pattern of returns with respect to beta (see Figures 6 and 8).
Fourth, the return-volatility-price relation identified in the article is robust to (i) MSA fixed effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density. While differences among the 155 MS As explain 20% of the total return variation among ZIP codes, the inclusion of volatility and price level explains an additional 40% of the total return variation. Among the six socioeconomic variables, median household income, gross rent and population density exert a significant positive effect on returns while percentage managerial employment have a negative effect (the unemployment rate and percentage owner-occupied are not significant). Further, while price and income have a positive impact on housing returns, their interaction is negative, suggesting that housing returns fall in submarkets where income and price level simultaneously rise. An implication of this empirical finding is that, for any given price level, investment in a relatively lower income submarket leads to higher housing investment returns than in higher income submarkets.
Finally, we analyze the house-price effect as a Fama-French type factor. This allows us to confirm that house prices impact the return generating process across submarkets and in not merely a statistical artifact. Fama and French (1992, referred to as FF) define the "Small Minus Big" (SMB) factor as the return between low and high market capitalization stocks and estimate its impact on stock returns by including it in the capital asset pricing model (CAPM) regression. Using the analogy between house price and a company's market capitalization, we similarly construct the house price FF factor by sorting median-priced houses by ZIP code into three price ranked subportfolios each year and then taking the difference between the average return between the lowest and highest priced groups (SMB). The estimation reveals that the house price FF factor is statistically significant in explaining housing returns in the cross-section.
There have been a number of studies on housing-price dynamics, from Ozanne and Thibodeau (1983) to Bourassa et al. (2005). Some of the empirical literature examines the efficiency and predictability of the housing market or explains price change while more recent work examines the dynamic relation between volatility and house prices within localized metropolitan areas. In comparison, the focus of our article is on the cross-sectional asset-pricing relation between risk, price level and housing returns across the U.S. metropolitan housing market at the submarket level. A discussion of the related literature is given below.
In addition to Goodman and Thibodeau (2003) and Goetzmann and Spiegel (1997) as mentioned above, a number of other studies have also used ZIP codes as the spatial unit of analysis. (5) Dolde and Tirtiroglu (1997) observe time-varying volatility and positive relations between conditional variance and returns in Connecticut and San Francisco over the period from 1971 to 1994. Dolde and Tirtiroglu (2002) identified 36 volatility events in four regional housing markets from 1975 to 1993 and suggest that price volatility surges are associated with changes in economic conditions. Miller and Peng (2006) use generalized autoregressive conditional heteroskedasticity (GARCH) models and a panel vector autoregressive (VAR) model to analyze the time variation of home value appreciation and the interaction between volatility and economic growth. They find evidence of time-varying volatility in about 17% of the MS As and find that volatility is Granger-caused by the home appreciation rate and GMP growth rate.
A notable early study on housing market efficiency by Rayburn, Devaney and Evans (1987) used 15 years of housing-price data for ten submarkets of Memphis, Tennessee, and estimates an ARIMA time-series model of differenced log prices based on the means of sale price per square foot of single-unit residential properties. After adjusting for transaction costs, all submarkets were deemed weak-form efficient because of the inability to exploit the time-series pattern to create an arbitrage profit. Case and Shiller (1989, 1990) found evidence of positive autocorrelation in real house prices and performed weak and strong form efficiency tests on weighted repeated sales price data for Atlanta, Chicago, Dallas and San Francisco during the 1970-1986 period. They also analyzed the performance of a trading rule where individuals wishing to purchase a home buy if the forecasted price change was greater than...
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