1# RECORD 14eecd7db66f7a02fa146490275ce97a19765a1a2# TITLE: The impact of a new light rail system on single-family property values in Charlotte, North Carolina3# YEAR: 2012.0 VENUE: nan DOI: 10.5198/jtlu.v5i2.2614# AUTHORS: Si-Si Yan; Eric Delmelle; Michael Duncan5# PAGES: 8 CANDIDATE TABLE PAGES (1-based): [6]6# ABSTRACT: This paper examines the impact of a new light rail system on single-family housing values in Charlotte, North Carolina, from 1997 to 2008. We use a Hedonic Price Analysis (HPA) to estimate how proximity to light rail, housing characteristics, and spatial components (at a block group level) affect single-family housing values. The same method is applied to each of the four time periods (t1, t2, t3, t4) that coincide with the pre-planning, planning, construction, and operation phase of the light rail system. We observe a trend that suggests a greater desirability to live closer to a light rail station as the transit system becomes operational.789===== PAGE 1 (score 15, numeric-paren 0, table-words 0, var-words 15) =====1011 http://jtlu.org12. 5 . 2 [2012] pp. 60–67 doi: 10.5198/jtlu.v5i2.26113Sisi Yan14Environmental Systems Research 15Institute (ESRI)16Eric Delmelle17The University of North Carolina a18Michael Duncan19Florida State University b20Abstract: This paper examines the impact of a new light rail system on single-family housing values in Charlotte, North Carolina, from 211997 to 2008. We use a Hedonic Price Analysis (HPA) to estimate how proximity to light rail, housing characteristics, and spatial components 22(at a block group level) affect single-family housing values. The same method is applied to each of the four time periods (t1, t2, t3, t4) that 23coincide with the pre-planning, planning, construction, and operation phase of the light rail system. We observe a trend that suggests a greater 24desirability to live closer to a light rail station as the transit system becomes operational. 25Copyright 2012 Sisi Yan, Eric Delmelle and Michael Duncan.26Licensed under the Creative Commons Attribution – NonCommercial License 3.0.271 28Introduction 29From the latter part of the 20th century until today, city and 30regional planners have promoted reliable public transportation 31as a sustainable element of their transportation systems. This 32can be attributed to an increasing awareness of sustainability 33among planners and the general public. Although personal au34tomobiles are convenient and provide travel flexibility, they also 35cause congestion and accidents, increase energy consumption 36and emissions, generate sprawl, and exclude low-income and 37disabled populations (Richardson 1999). To reduce personal 38reliance on the automobile and address the social and environ39mental issues mentioned above, most major European cities 40have invested in rail systems as an alternate mode of transporta41tion. In the United States, cities such as Portland, Boston, and 42San Francisco have adopted light rail systems, which has influ43enced their morphology over time (Muller 1995). The benefits 44public transit offer are relatively inexpensive transportation and 45the flexibility for workers to reside in suburban areas instead 46of the center city. Finally, public transit raises transportation 47equity standards for people with limited access to private trans48portation (Dunbaugh 2008; Ong and Houstion 2002). 49Transit impact studies are generally based on hypotheses 50that assert that improved accessibility will increase land value. 51Alonso (1964) suggested that locating closer to the central busi52ness district (CBD) reduces commuting costs at the expense of 53higher land rents. The underlying principle of Alonso’s work 54broadly applies to any location with a high level of accessibil55ity, such as areas near highway interchanges and rail stations. 56It is generally acknowledged that transportation investments 57such as corridor development will improve the value of land 58adjacent to these developments, since commuting time may be 59reduced. In addition, increasing accessibility will attract more 60activities and spatial interaction to these places (Gatzlaff and 61Smith 1993). 62In this study, we are interested in evaluating whether in63dividuals are willing to invest more money when purchasing a 64residential property located in proximity to a light rail station in 65Charlotte, North Carolina. What makes this study unique are 66the characteristics of the light rail line: (1) it is a short line; (2) 67it is located in a fairly low-density urban area; and (3) it uses the 68track of a former freight line. In addition, the light rail was con69ceived and built during a period of rapid urban expansion. Re70cent studies suggest that a rapid transit station is likely to affect 71the price of housing within a certain proximity, usually with72in1600 meters (roughly one mile) of a station. (Gatzlaff 1993; 73Huang 1996; Ryan 1999). Many studies have found positive 74relationships between real estate values and proximity to fully 75developed light rail systems (Davis 1970; Lee 1973; Baldassare 76et al. 1979; Benjamin and Sirmins 1996; Lewis-Workman and 77Brod 1997; Weinstein and Clower 2002; Cervero and Duncan 782002). Conversely, no relationship was found between home 79prices and proximity to the semi-developed rail system of Mi80ami (Gatzlaff and Smith 1993). For a detailed analysis of this 81literature, we refer the reader to a handful of papers that have 82been dedicated to reviewing research about the impacts of rail 83on real estate values: Bartholomew and Ewing (in press), De84brezion, Pels and Rietveld (2007), Ryan (1999), and Knight 85and Trigg (1977).86Most of the previous work in this area applies a cross-sec87tional approach. In other words, there is no temporal compo88nent to the analysis, and the implicit price of station proximity 89The impact of a new light rail system on single-family property values in Charlotte, 90North Carolina91a eric.delmelle@uncc.edu92b mdduncan@fsu.edu939495===== PAGE 6 (score 61, numeric-paren 38, table-words 7, var-words 9) =====96976598The impact of a new light rail system on single-family property values in Charlotte, North Carolina99time periods, which confirms the hypothesis that proximity to 100a light rail station at time period t4 contributes positively to 101house prices relative to earlier time periods. As t2 and t3 are not 102significantly different from t1, it appears that the rail invest103ment did not affect Charlotte single-family home prices until 104the rail system actually began operation. This stands in contrast 105to other studies that have illustrated price increases during the 106planning stages of a rail system (Damm et al. 1980; McMillen 107and McDonald 2004). Possible reasons may be attributed to 108(1) the planning phase of the light rail with only part of the sys109tem being implemented, (2) significantly less-congested traffic 110in Charlotte compared to Boston or Chicago, which reduces 111the attraction of living closer to light rail, and hence, (3) the 112limited awareness of public transportation.113Table 4: Z test for coefficients comparison among models114 115t1-2116t1-3117t1-4118t2-3119t2-4120t3-4121Z-test 122(blockgroup)123-1.29124-0.641251.641260.601272.951282.25129Sensitivity Analysis 130Using the coefficients of each variable from our hedonic model, 131a sample of adjusted price is computed holding other variables 132constant while changing the value of the network distance. We 133consider two similar 139.35-square-meter (1500-square-foot) 134houses in excellent condition (building grade = 5), 20 years old 135with air conditioning, one fireplace each and regular fuel, but 136with one located 402 meters (¼ mile) from the nearest station 137and the other 1609 meters (1 mile). Figure 3 indicates that, for 138each time period, as the network distance to the light rail in139creases, so do housing prices. However, the difference in price 140for two houses becomes marginal in time t4, indicating that it 141is becoming more desirable to live closer to the light rail. This 142empirically shows that the light rail may be offsetting the nega143tive influence that previously existed in this corridor. 144Figure 3: Variation in adjusted house price ($) at all four time periods at a 145distance of 402 meters (0.25 mile) and 1609 meters (1 mile). 1465 147Conclusion148In this paper, we apply a hedonic regression model (hedonic 149Table 3: Regression coefficients for the four time periods, block group level.150 151T1152T2153T3154T4155Variable156Coefficient157Coefficient158Coefficient159Coefficient160(constant)1618.196*1627.406*1638.123*1648.249*165Property characteristics166age167-0.004*168-0.001169-0.003170-0.006*171squared age 1724.37E-05*1732.59E-051744.16E-05*1758.21E-05*176height1770.125*1780.062*1790.076*1800.053181no fuel182-0.796*1830.3021840.032185-0.794*186central air conditioning1870.045*1880.080*1890.090*1900.101*191building grade1920.034*1930.027*1940.032*1950.059*196# fireplaces1970.089*1980.064*1990.057*2000.004201ln (heated area)2020.337*2030.392*2040.338*2050.455*206Rail Impact207ln (network distance) 208(std. error)2090.123(.028)*2100.169(.022)*2110.148(.027)*2120.052(.033)213R22140.779*2150.786*2160.811*2170.837*218Moran’s I (residuals)2190.097*2200.110*2210.167*2220.021*223*Note: significant at p < 0.05 224225226===== PAGE 7 (score 14, numeric-paren 0, table-words 0, var-words 14) =====22722866229JOURNAL OF TRANSPORT AND LAND USE ?.?230price analysis) at a block group scale to evaluate the impact of 231a newly added light rail system on single-family housing values 232in Charlotte, North Carolina. Most research efforts have been 233cross-sectional, and this study takes a longitudinal approach by 234repeating the same hedonic regression analysis at four different 235time periods, coinciding with the pre-planning, planning, con236struction, and operation phase of the light rail system. 237Results from this study reveal that before the rail system 238began operation, proximity to the future rail corridor had a 239negative influence on home prices. This is likely due to the 240presence of industrial land use zones around existing stations. 241However, when compared across the four time periods, hous242ing prices have started to react positively to light rail investment 243during the operational phase. This may suggest that accessibil244ity to reliable transportation has improved the attractiveness of 245single-family houses in the vicinity of light rail stations, or that 246some of the unattractive industrial uses have disappeared, or 247that light rail investment has improved the image of the area. 248Locally, it is possible that after the light rail system went into 249operation, the accessibility provided by newly built light rail 250stations improved the attractiveness of these areas. Another 251explanation is that, due to the concept of transit-oriented de252velopment of the Charlotte land planning commission, it is 253likely that multiple-family houses or commercial properties 254have started to concentrate in the vicinity of light rail stations. 255In this paper, we have used a distance of 1609 meters (1 256mile) around light rail stations to extract those single-family 257houses for which sale prices would potentially be influenced 258by the proximity to transit. O’Sullivan and Morrall (1996) in259dicate that individuals living in suburban neighborhoods are 260willing to overcome a greater distance to get to a transit station 261than those individuals living in denser, urban areas. As future 262research, the distance buffer can be modified to be station-263specific, better reflecting the type and complexity of neighbor264hoods surrounding them. 265In this study, we were limited in our ability to fully mea266sure the effect on home values of the railway operating over a 267number of years, after it had matured. As a future study, we 268would like to extend our analysis over a wider period of time 269after the rail entered its operational phase. Spatially explicit re270gression methods exist to test whether the price premium asso271ciated with rail proximity will fluctuate from station to station, 272especially with varying distance from the CBD. 273References274Agostini, C. and G. Palmucci. 2008. 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