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This article presents a methodology for producing a quarterly transactions-based index (TBI) of property-level investment performance for U.S. institutional real estate. Indices are presented for investment periodic total returns and capital appreciation (or price-changes) for the major property types included in the NCREIF Property Index. These indices are based on transaction prices to avoid appraisal-based sources of index “smoothing” and lagging bias. In addition to producing variable-liquidity indices, this approach employs the Fisher-Gatzlaff-Geltner-Haurin (Real Estate Econ., 31: 269–303, 2003) methodology to produce separate indices tracking movements on the demand and supply sides of the investment market, including a “constant-liquidity” (demand side) index. Extensions of Bayesian noise filtering techniques developed by Gatzlaff and Geltner (Real Estate Finance, 15: 7–22, 1998) and Geltner and Goetzmann (J. Real Estate Finance Econ., 21: 5–21, 2000) are employed to allow development of quarterly frequency, market segment specific indices. The hedonic price model used in the indices is based on an extension of the Clapp and Giacotto (J. Am. Stat. Assoc., 87: 300–306, 1992) “assessed value method,” using a NCREIF-reported recent appraised value of each transacting property as the composite “hedonic” variable, thus allowing time-dummy coefficients to represent the difference each period between the (lagged) appraisals and the transaction prices. The index could also be used to produce a mass appraisal of the NCREIF property database each quarter, a byproduct of which would be the ability to provide transactions price based “automated valuation model” estimates of property value for each NCREIF property each quarter. Detailed results are available at http://web.mit.edu/cre/research/credl/tbi.html . Copyright Springer Science+Business Media, LLC 2007Transaction-based index, Commercial real estate investment performance, NCREIF database, Commercial property type returns, Constant liquidity index, Ridge regression techniques,

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This paper compares housing price indices estimated using three models with several sets of property transaction data. The commonly used hedonic price model suffers from potential specification bias and inefficiency, while the weighted repeat-sales model presents potentially more serious bias and inefficiency problems. A hybrid model combining hedonic and repeat-sales equations avoids most of these sources of bias and inefficiency. This paper evaluates the performance of each type of model using a particularly rich local housing market database. The results, though ambiguous, appear to confirm the problems with the repeat sales model but suggest that systematic differences between repeat-transacting and single-transacting properties lead to bias in the hedonic and hybrid models as well. Copyright American Real Estate and Urban Economics Association.

The recent slump notwithstanding, substantial increases in house prices in many parts of the United States have served to highlight housing affordability for moderate-income households, especially in high-cost, supply-constrained coastal cities such as Boston. In this article, we develop a new measure of area affordability that characterizes the supply of housing that is affordable to different households in different locations of a metropolitan region. Key to our approach is the explicit recognition that the price/rent of a dwelling is affected by its location. Hence, we develop an affordability methodology that accounts for job accessibility, school quality and safety. This allows us to produce a menu of town-level indexes of adjusted housing affordability. The adjustments are based on obtaining implicit prices of these amenities from a hedonic price equation. We thus use data from a wide variety of sources to rank 141 towns in the greater Boston metropolitan area based on their adjusted affordability. Taking households earning 80% of area median income as an example, we find that consideration of town-level amenities leads to major changes relative to a typical assessment of affordability. Copyright (c) 2009 American Real Estate and Urban Economics Association.

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This paper explores rich longitudinal data to gain a better understanding of the importance of spatial mismatch in lower-paid workers' job search. The data infrastructure at our disposal allows us to investigate the impact on a variety of job search-related outcomes of localized and individual-specific job accessibility measures using identification strategies that mitigate the impact of residential self-selection. Our results suggest that better access to jobs causes a statistically significant, but modest decrease in the duration of joblessness among lowerpaid displaced workers, while an abundance of competing searchers for those jobs increases duration modestly. Search durations for older workers, Hispanic workers, and those displaced from manufacturing jobs are especially sensitive to job accessibility. * NOTE: An earlier version of this paper was prepared for the American Real Estate and Urban Economics Association meetings, January 2011. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the Office of the Comptroller of the Currency, the Department of Treasury or the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The authors want to thank Sheharyar Bokhari for his valuable research assistance, participants at various seminars and conferences for their suggestions, and Kevin McKinney and Ron Jarmin for their comments on an earlier draft.

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