Housing Value, Costs, and Measures of Physical Adequacy

Paul Emrath, Heather Taylor , National Association of Home Builders

As with the articles in this issue, this introduction reflects the views of the authors and does not necessarily reflect the views of the U.S. Department of Housing and Urban Development.

Part of the U.S. Department of Housing and Urban Development’s (HUD’s) mission is to create quality affordable homes for all. To accomplish this mission, HUD must define quality and must develop a method for detecting physically inadequate housing units. In the past, researchers have relied on summary indicators of inadequacy provided on the American Housing Survey (AHS) public use data file. These measures are designed by HUD and are used by HUD for HUD purposes. This article reexamines these standard indicators in a hedonic regression framework, using AHS data to develop models that estimate house values and rent. The hedonic models are then used to define a new indicator of physical inadequacy that has a statistically significant negative effect on house values and rents, in contrast to the traditional indicators that are not statistically significant and often have the wrong sign. The new indicator indentifies a substantially larger number of housing units in the United States as being physically inadequate, especially single-family units, suggesting that the need for housing assistance is more widespread than is generally recognized. Housing units identified as inadequate under this new criterion are concentrated in the older stock and are disproportionately occupied by households with children. The new criterion also identifies a substantial number of nonseasonal, vacant single-family housing units as being physically inadequate, implying that the inventory of existing homes on the market may be effectively overstated. The statistical models used to derive these results also illustrate the practical utility of a large number of variables in different sections of the AHS. Many neighborhood characteristics are shown to have a significant effect on home values, for example, which is information of potentially great value to homeowners and local governments.