link Conclusion below
The results generated by the hedonic models for 1996 and 1999 are consistent with previous results in the literature. The structural variables are generally of the expected sign. For railroad-related variables, smaller houses of up to 1,250 square feet and located within 250 feet, 500 feet, or 750 feet of a railroad track experienced a statistically significant loss in sale price of $4,300 within 250 feet, $3,800 within 500 feet, and $5,800 within 750 feet from a freight track line; this is equivalent to losses of 5%-7% of sale price. For the medium and larger units, many had negative signs, but only the middle ring (251-500 feet away) was statistically significant at a 95% confidence level, with a discount of about 5%. The lack of a consistent declining pattern implies that markets perceive a zonal rather than gradient effect for this negative amenity when modeled with pure proximity.
Proximity to a gated railroad crossing at grade was associated with a reduction in sale price of about 5% under some circumstances, but results were not robust over all subcategories of sales.
Results improved substantially when freight train trip counts, separate from simple proximity to a track, were modeled. In 1996, prior to announced track reconfigurations, trip counts had little effect on prices, with only one cell having results indicating market awareness of trip counts. In 1999, after the announced changes, among smaller units each trip count was associated with a reduction in sale price of around $194 per additional average daily freight train trip within 250 feet. The reduction in sale price decreased to about $85 and $94 per trip within 500 feet and 750 feet away, respectively. Medium-sized units exhibited a gradient-type effect ranging from $262 to $72, at generally lower significance levels. Larger units also had a drop in sale price of $264 per trip at the closest distance. Thus, adding trip counts substantially improved pricing effects of train trips. It also represents more of a gradient, rather than zonal, pattern of impact.
To put this into perspective, for example, if a $100,000 house were located near a freight train track, and the daily train count were to go from 10 trains per day to 30 trains per day, this would imply a reduction in value of $5,000 (20 trips times $250/trip), or 5%. This is a new finding and represents a contribution to the literature.
In a recent financial settlement related to the train reorganization in the Cleveland area, the railroads negotiated with communities for mitigation of noise and safety concerns, but no funds were provided specifically to compensate residents for losses in property value. Of course, this research has not calculated the net effect (some lines gained trips, some lost), so there is no statement made here about the fairness of these payments, but loss in property values should be included in future negotiations of this type. The train-trip count impact was insignificant before the merger talks and accompanying newspaper publicity. After the publicity, significant modest price reductions were evident and these were consistent with theory. This is evidence that the markets were able to price the train volume data reasonably well, and that the talk of train line reorganization did have a substantial effect on the parameter estimates after the change in trip volumes.
The models appear to work better for smaller-sized units, regardless of distance from the tracks. One possible explanation could be that a higher percentage of the larger units are located in affluent suburbs outside the central city, where other locational amenities outside the model (e.g., school districts) may be affecting value. Smaller sales tended to be in the central city or in a few, inner-ring working-class suburbs.
The implication of this research for appraisers is that they should include proximity to rail lines, train trip counts, and potentially gated crossings in determining the value of residential property
