This study contributes to the hedonic pricing literature in housing. Despite the large number of such studies and the broad range of variables examined, prior studies have not systematically considered the impact on housing prices of the physical condition of their immediate neighborhood.;Most likely this failure is attributable to the fact that such data do not exist in published form. Most hedonic studies base their analysis on published data or data easily derived from satellite images. Few studies make use of data collected in the field. Yet both theory and casual observation suggest that the physical condition of neighboring houses should contribute to explaining housing prices. The size of this impact is important also as it plays a role in the theory of neighborhood change, which attributes the downward slide of neighborhoods to the initial deterioration of a few houses, which through their negative externalities start a broader trend.;The study first reviews the available literature suggesting the impact of physical disorder on neighborhood change. Much of this literature relates to the relationship between disorder and fear of crime, and the way it affects neighborhood change. It also reviews the theoretical background of the hedonic price model and some of the neighborhood variables already investigated in the literature.;Second, it develops a methodology to measure physical disorder through a field survey. Data were collected for 406 houses for sale in the City of Columbus, looking at two types of neighborhoods: a micro neighborhood that consists of the immediately adjacent houses, typically eight or less buildings; and an intermediate neighborhood of houses on the same block, usually up to a distance of 225 feet. The field survey focused on both private and public disorder, including the presence of graffiti, yard conditions, private trash, exterior building deterioration, abandonment, presence of utility poles, public area appearance, and adjacency to a major road.;Third, it assembles a wider data base that combines the survey information with published data from the Columbus Board of Realtors, Franklin County Auditor, U.S. Census Bureau, and others. These data act as control variables, and include the housing characteristics of each of the 406 for sale houses in the sample, accessibility to amenities, and the socio-economic characteristics of their larger neighborhood.;Fourth, it provides descriptive statistics on disorder. This confirms expectations based on the literature. Specifically, (1) disorder variables are highly correlated with each other, though this correlation is not sufficiently high to justify multicollinearity concerns; (2) there is a high correlation between public and private disorder, i.e., when individual houses suffer from disorder, so does the surrounding public space; (3) there is a high correlation between disorder at the micro and intermediate neighborhood level, confirming the contagion predicted by the literature; (4) disorder is correlated to accessibility, such as distance to downtown, airport, schools, and other public (dis)amenities; and (5) disorder is highly correlated to property sales prices, with the average sales price in the presence of disorders generally less than half that in their absence.;Fifth, it investigates alternative formulations of the hedonic equation and selects the semi-log model for detailed analysis. The analysis confirms the importance of disorder in explaining housing values and derives the impact on price of individual disorder characteristics. Even though disorder is present in only one third of all for sale properties, it explains a high share of the total variation in final sales prices. The R2 also is much higher than almost any found in the literature, rising from a value that rarely exceeds .49 to .82. The point then is that real estate agents and tax assessors neglect disorder at their own peril. To the extent that assessed property values disregard disorder, those who live in neighborhoods with disorder---usually the poor, overpay on property taxes. There is no substitute for a field visit to estimate housing prices.;Finally, this study investigates the extent to which real estate agents correctly estimate the final sales price of properties, and the impact of disorder on this price. Agents typically use the market comparison method to arrive at a listing price, based on comparison property provided by a search of MLS data. The study shows that the resulting listing prices are excellent predictors of final sales price, and that the final sales price discounts the listing price by just 4.6 percent. However, in the presence of micro neighborhood disorder, this discount is 10 percent, while in its absence, it is about 2 percent. In the presence of disorder, therefore, listing prices are upward biased. Real estate agents currently lack the data and methods to arrive at reliable price estimates. Whether the same bias extends to the assessed values used for taxation is a question to be addressed in future research. In summary, the study shows the cost that neighbors can inflict on each other, by not maintaining their houses and neighborhood. For the first time, it provides specific estimates of the cost associated with individual disorder attributes. The results also are indicative of the importance externalities play in explaining neighborhood change.
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