Effects Of Urban Rail Transit On Housing Prices

Urban rail transit, with its high capacity of service, high efficiency and low pollution, provides better access to central business districts, employment locations and other places. Since urban rail transit stations are the points of access to a transit system and the ability to access them conveniently and quickly influences property values. Assuming that access to destinations reachable by public transit is an important determinant in the value of property, higher property values are expected in places with superior access to transit stations. Previous researches investigating the impact of rail transit on property values reveal results of varying magnitude that are sometimes contradictory and most studies deal with basic influences of such impact and few in-depth empirical analysis was done. Furthermore, most studies are unique to a given locale and do not provide a firm basis to judge future impacts. As a matter of fact, cities experiencing different changes of population and economic conditions probably react to urban rail transit construction differently. Therefore, the case study of Hangzhou subway system and its impact on nearby property values, is both practical and of great significance.This study, based on theoretical and empirical analysis, estimates the impact of proximity to rail stations on residential property values from two perspectives. The first empirical section estimates the proximity effect of transit stations based on cross-sectional data, and the main focuses of the cross-sectional analysis include distance effects, decay rate, and spatial inhomogeneity. We explore how effects differ as rail travels through diverse and uneven urban locations and test two methods for measuring the distance between a residential property and a light rail station-straight-line distance and network distance. The second section investigates whether premiums were paid for the expected benefits offered by a new urban rail transit before its completion. We compare the anticipation effects between different times and different periods, and examine the variation of spatial price gradient of residential properties adjacent to transit stations. The results are as follows. Implications from cross-section analysis for proximity effect:(1) The proximity effects of rail station varies greatly. The price premium between houses in and outside the radius of 500 meters around station is highly significant; 1000 meters, fairly significant, and 1500 meters insignificant. The price premium for 500 meters is 9.4%, for 1000 meters 7.6%.(2) The straight-line distance models produce more positive effects on property values than the network distance models and that the model results have greater statistical significance. A greater premium for straight-line distance than for walking distance suggests that geographical distance to a transit station is perhaps more important than walking distance in the case under study.(3) The price premium effects around rail stations vary dramatically with the distance from central business district. In central locations, proximity effect is relatively lower, and in near suburb area larger, and distant suburb largest. This result indicates that the proximity effect is spatially inhomogeneous.(4) The proximity effects differ across submarkets of high price residential property and medium-low price residential property. Chow-test substantially support the existence of such submarket and the proximity effect between submarkets, submarket and the whole market are significantly different. The medium-low price properties have greater price premium, the high price much lower.Results from anticipation effects:(5) The anticipation effect changes over time during the period of urban rail transit planning and construction, and positive price expectation effects were found before the completion of the rail. The results also showed that there was no significant price premiums during rumor period, and for the construction period, no price decrease found, on the contrary, price premium increases significantly. The eco-metric results indicate the anticipation effect increases with time. The results support the hypothesis of rational expectation.(6) Empirical results showed that anticipation effects between the period before and after the announcement of subway construction planning differ significantly, so is the effects between the period before and after the construction. The results also suggest that the spatial range of proximity effect increase with time.(7) The later the rail starts to operate, the smaller the anticipation effect would be. For rail line project of long-medium term, the anticipation effect is insignificant.(8) The price gradient analysis showed that price gradient along the rail line did not change between two time points, and the price gradient around the rail station decreases significantly by 0.067% for each 1% increase of distance to station, and the price gradients between central business district and properties along rail line drop significantly as well. Among three measurements of price gradient, the time cost approach is the best.This study may have several practical implications. Firstly, the methodologies devised in this paper improve the traditional analysis on improvements in transport and the results found here are useful for a in-depth and thorough understanding of proximity effect and anticipation effect. Secondly, the findings have far-reaching implications for government policies on land sales and rail station and rail line planning. The expectation effects will enable the government to finance infrastructure projects by selling land parcels in the affected districts in advance. The empirical results can also be valuable to urban transportation planning programs.