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.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?

Abstract:In this study, we provide new evidence on the performance measurement and reporting of commercial real estate returns. We do so by examining the accuracy of commercial-real-estate appraisals that occurred prior to the sale of properties from the NCREIF National Property Index ("NPI") during 1984 -2010, a period which spans two up-and-down cycles of the market. We find that, on average, appraisals are more than 12% above, or below, subsequent sales prices that take place two quarters following the appraisal. Even in a portfolio context, allowing for offsetting positive and negative differences, appraisals are off by an average of 4% -5 % of value, even after adjusting for capital appreciation during those two quarters. We also provide new evidence regarding how, and by how much, appraised values lag behind sales prices. We find that appraisals appear to lag the true sales prices, falling significantly below in hot markets and remaining significantly above in cold markets. This new evidence provides guidance to investors, regulators and others about how to interpret real-estate indices like the NPI that are based upon appraised values, in both a rising and falling market. Finally, we find that this "appraisal error" is largely systematic; we can explain more than half of the variation in the signed percentage difference in sales price and appraised value. Hence, appraisal errors are not due solely to property-specific heterogeneity.

In this study, we present panel-data evidence on REIT liquidity and its determinants over the 1988 -2007 period. We focus upon liquidity measures that do not require microstructure data (1) to facilitate use of our results as benchmarks for comparisons with results from international markets for which micro-structure data may be unavailable, (2) to provide benchmarks that do not require access to costly (and voluminous) micro-structure data. We find that REIT liquidity improved during the early and mid-1990s, deteriorated during the late 1990s, and then improved dramatically during 2000 -2006, with the notable exception of 2007. Liquidity improved the most for REITs traded on the NYSE, and was an order of magnitude better than liquidity of REITs traded on the AMEX or NASDAQ. We link the deterioration in liquidity observed in 2007 to the investment portfolio of a REIT. We find that the percentage bid-ask spread is highly correlated with the measure of price impact proposed by Amihud (2002). We provide panel-data evidence on the key determinants of the percentage bid-ask spread that largely confirms the results reported by Bhasin, Cole and Kiely (1997) for 1990and 1994: the percentage spread is a positive function of the volatility of stock returns, and a negative function of dollar volume turnover, share price and market capitalization. Finally, we provide evidence that these results obtained using daily closing bid-and ask-prices are not qualitatively different from those obtained using market micro-structure data. This suggests that we can use liquidity measures based upon readily available daily return data rather than being forced to rely upon market micro-structure data.

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Abstract:In this study, we provide new evidence on the performance measurement and reporting of commercial real estate returns. We do so by examining the accuracy of commercial-real-estate appraisals that occurred prior to the sale of properties from the NCREIF National Property Index ("NPI") during 1984 -2010, a period which spans two up-and-down cycles of the market. We find that, on average, appraisals are more than 12% above, or below, subsequent sales prices that take place two quarters following the appraisal. Even in a portfolio context, allowing for offsetting positive and negative differences, appraisals are off by an average of 4% -5 % of value, even after adjusting for capital appreciation during those two quarters. We also provide new evidence regarding how, and by how much, appraised values lag behind sales prices. We find that appraisals appear to lag the true sales prices, falling significantly below in hot markets and remaining significantly above in cold markets. This new evidence provides guidance to investors, regulators and others about how to interpret real-estate indices like the NPI that are based upon appraised values, in both a rising and falling market. Finally, we find that this "appraisal error" is largely systematic; we can explain more than half of the variation in the signed percentage difference in sales price and appraised value. Hence, appraisal errors are not due solely to property-specific heterogeneity.