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This paper contributes to the growing literature that aims at identifying and measuring the impact of social context on individual economic behavior. We develop a model of housing structure demand with neighborhood effects and neighborhood choice. Modeling neighborhood choice is of fundamental importance in estimating and understanding endogenous and contextual neighborhood effects. Controlling for non-random sorting into neighborhoods allows for unbiased estimates and provides a means for identifying endogenous neighborhood effects.Estimation of the model exploits a household-level data set that has been augmented with contextual information at two different levels ("scales") of aggregation. One is at the neighborhood level, consisting of about ten neighbors, with the data coming from the neighborhood clusters sub-sample of the American Housing Survey. A second level is the census tract to which these dwelling units belong. These data were geocoded by means of privileged access to confidential US Census data. Our results for the neighborhood choice model indicate that individuals prefer to live near others like themselves. Our estimates of the housing structure demand equation confirm that neighborhood effects are important. In particular, one's demand for housing depends on the mean of neighbors' demand for housing. JEL Classification Codes: R21, C31.

The American Housing Survey (AHS) includes the owner's valuation of the house as a measure of the house's value. If owner-stated values are accurate, the AHS (as well as other survey instruments) can be used by researchers studying a variety of topics. In this study we use the metropolitan version of the AHS for three cities over fourteen years to compare owners' valuations with sales prices of houses that sold in the twelve months prior to an interview. We find that, on average, recent buyers report house values that are 8.4% higher than the stated sales prices. Further analysis indicates that these recent buyers, when compared with owners with longer tenure, overvalue their houses by 3.3%, on average. Thus, we find that the average owner overvalues his house by 5.1%. Also, differences between sales prices and owners' valuations are not related to particular characteristics of the house, occupants (other than length of tenure), or neighborhood. Thus, the use of the owners' valuations will result in accurate estimates of house price indexes and will provide reliable estimates of the prices of house and neighborhood characteristics. Copyright American Real Estate and Urban Economics Association.

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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.