# RECORD 0f88c3ee1b9c25f3d6602c7c07d55400a39f254b # TITLE: End of the line: The impact of new suburban rail stations on housing prices # YEAR: 2023.0 VENUE: Journal of Transport and Land Use DOI: 10.5198/jtlu.2023.2199 # AUTHORS: Rhea Acuña # PAGES: 20 CANDIDATE TABLE PAGES (1-based): [11, 14] # ABSTRACT: This study leverages the staggered opening of new Metro stations in a suburb of Washington, DC to estimate the impact of proximity to public rail transit on housing prices. Both hedonic and repeat sales models indicate that housing prices increase as distance increases, suggesting that living near public transportation in Prince George’s County is primarily viewed as a disamenity. For properties at one mile from the nearest station, the preferred repeat sales model estimates a marginal price increase of 4.6 percent for a one-mile increase in distance. I argue that the suburban environment may be key in explaining the results. In the suburbs, a greater share of the population relies on automobiles, and rail stations are typically equipped with large parking lots. The suburban environment allows households the opportunity to both benefit from public transportation access and mitigate the negative externalities associated with living right next to the station. ===== PAGE 1 (score 17, numeric-paren 0, table-words 0, var-words 17) ===== 1 Introduction Public transportation is a ubiquitous part of urban environments. Benefits of public transportation are wide-ranging, such as reducing traffic congestion, improving air quality, and expanding access to jobs (Baum-Snow & Kahn, 2000; Bhatta & Drennan, 2003). Additionally, reducing barriers to public transportation can help reduce the gap between higher income households who can afford private modes of transportation and lower income households who rely on public transportation to access jobs (Baum-Snow & Kahn, 2000). However, investments in public transportation are not without controversy. Many cities continue to grapple with declines in public transit usage and persistent con- cerns regarding the impact of transit-oriented development on proximate neighborhoods and residents (Baum-Snow & Kahn, 2000; Manville & Cummins, 2015; Padeiro et al., 2019). As government continues to invest in public transportation, researchers have attempted to quan- tify the relationship between proximity to public transportation and housing prices. Whether proximity to public transportation is an amenity or disamenity is theoretically ambiguous. For example, housing prices may be higher near stations as households may be willing to pay more to be closer and take ad- vantage of accessibility benefits. On the other hand, housing prices may be lower near stations as house- holds may be willing to pay more to avoid nuisances associated with station areas. Hedonic regressions that control for housing and locational characteristics have been widely used to estimate the relationship Article history: Received: March 21, 2022 Received in revised form: October 19, 2022 Accepted: November 27, 2022 Available online: February 21, 2023 Copyright 2023 Rhea Acuña http://dx.doi.org/10.5198/jtlu.2023.2199 ISSN: 1938-7849 | Licensed under the Creative Commons Attribution – Noncommercial License 4.0 The Journal of Transport and Land Use is the official journal of the World Society for Transport and Land Use (WSTLUR) and is published and sponsored by the University of Minnesota Center for Transportation Studies. T J  T  L U jtlu.org V. 16 N. 1 [2023] pp. 67–86 Rhea Acuña Syracuse University racuna@syr.edu End of the line: The impact of new suburban rail stations on housing prices Abstract: This study leverages the staggered opening of new Metro stations in a suburb of Washington, DC to estimate the impact of proximity to public rail transit on housing prices. Both hedonic and repeat sales models indicate that housing prices increase as distance increases, suggesting that living near public transportation in Prince George’s County is primarily viewed as a disamenity. For properties at one mile from the nearest station, the preferred repeat sales model estimates a marginal price increase of 4.6 percent for a one-mile increase in distance. I argue that the suburban environment may be key in explaining the results. In the suburbs, a greater share of the population relies on automobiles, and rail stations are typically equipped with large parking lots. The suburban environment allows households the opportunity to both benefit from public transportation access and mitigate the negative externalities associated with living right next to the station. ===== PAGE 11 (score 30, numeric-paren 10, table-words 6, var-words 8) ===== 77 End of the line: The impact of new suburban rail stations on housing prices housing prices drove the location selection of the expansion lines. In addition to the selection of the new stations, I am also concerned about housing prices responding in anticipation of the new stations, which I address in the robustness checks. 6 Results Table 3 presents the results of the main findings.7 Both the hedonic and repeat sales models estimate positive coefficients on distance, suggesting that housing prices increase as distance to the nearest sta- tion increases, and a negative squared term, indicating that housing prices are increasing at a decreasing rate. The hedonic model estimates that for properties at one mile from the nearest station, a one-mile increase in distance to the nearest station leads to a marginal price increase of 2.4 percent.8 Among the housing characteristics, structural area, whether the house has a basement, is a split level, and in below average conditions were statistically significant. Not surprisingly, having a larger home, basement, and being a split-level increased housing prices, while being in below average condition decreased housing prices. The repeat sales model suggests that the hedonic model understates the effects. For properties at one mile from the nearest station, the repeat sales model estimates that a one-mile increase in distance to the nearest station leads to a marginal price increase of 4.6 percent. Table 3. Main regression results Variables (1) Hedonic (2) Repeat Sales Estimate SE Estimate SE Intercept 8.940*** 0.128 11.800*** 0.037 Distance Distance 0.028*** 0.007 0.050*** 0.011 Distance squared -0.002** 0.001 -0.002* 0.001 Housing Characteristics Age -0.001 0.001 Age squared -0.000 0.000 Structural area (ln) 0.411*** 0.019 Basement 0.161*** 0.010 Below average -0.109*** 0.017 Split level 0.060*** 0.012 Parking Parking 0.000 0.000 0.000 0.000 Fixed Effects Housing X ZIP code X Time X X ZIP code linear time trend X X Observations 57,912 16,511 Note: The dependent variable is the natural logarithm of sales price, adjusted to 2000 dollars. *Statistical significance level at *** p<0.001, ** p<0.01, * p<0.05, + p<0.10. 7 See Appendix Table A2 for an alternative specification where sales price (dependent variable) and structural area are not logged. 8 Since I use a quadratic model and include linear and quadratic terms for distance, I calculate the marginal price effect by taking the partial derivative of sales price with respect to distance at one mile. For the hedonic model, ∂/∂D(β0 + β1 Di,t) + β2 D2 i,t + β3 Parkings + αi + δt + γn T + ϵi,t) = β1 + 2β2 Di,t = .028 - 2(.002)(1) = .024. ===== PAGE 12 (score 3, numeric-paren 0, table-words 1, var-words 1) ===== 78 JOURNAL OF TRANSPORT AND LAND USE 16.1 Another way to illustrate the effect is by evaluating the effect size for the average property. In both samples, the average distance is about four miles. The predicted sales price at four miles is $254,127 in the full sample and $236,779 in the repeat sales sample. The hedonic model estimates a marginal effect of 1.4 percent at four miles, which suggests that the marginal household is willing to pay about $3,558 to live one mile farther. The repeat sales model estimates a marginal effect of 3.5 percent at four miles, which suggests that the marginal household is willing to pay about $8,287 to live one mile farther. This further highlights that the hedonic model underestimates how much residents are willing to pay to be incrementally farther from the station. Figure 4 plots the hedonic price (i.e., predicted sales price) and implicit price functions (i.e., mar- ginal effects) for both the hedonic (red) and repeat sales (blue) models.9 Each circle represents the point estimate at a specific distance and the whiskers represent the associated 95 percent confidence interval. Although, I do not restrict the models, I restrict the figures to 10 miles since nearly 100 percent of my sample is located within 10 miles of the nearest station. The hedonic price functions demonstrate the non-linear positive relationship between distance and sales price, graphically illustrating that sales prices are increasing at a decreasing rate as distance increases. For both models, the predicted sales prices rapidly increase at lower distances then eventually level off. Figure 4. Hedonic and implicit price functions Note: Sales price in 2000 dollars. Turning to the implicit price functions, the marginal effects are higher for the repeat sales model across distances, but the patterns are otherwise similar, leading to two key observations. First, the mar- ginal effects are greatest closest to the station, suggesting that nuisance elements may be strongest right next to the station. This is not surprising given that certain nuisance elements, such as unsightliness and 9 I generated the implicit price function by taking the partial derivative of price with respect to distance. ===== PAGE 14 (score 42, numeric-paren 23, table-words 5, var-words 9) ===== 80 JOURNAL OF TRANSPORT AND LAND USE 16.1 7 Robustness checks I employ several robustness checks to test the sensitivity of my results to alternative samples. Table 4 presents the results of these robustness checks.10 I apply the repeat sales specification to each of the ro- bustness check. Table 4. Summary of robustness checks Variable Regression Models 1 2 3 4 5 6 7a Distance 0.050*** 0.044*** 0.058+ 0.078*** 0.062*** 0.042*** 0.019 (0.011) (0.01) (0.031) (0.021) (0.012) (0.007) (0.012) Distance squared -0.002* (0.001) -0.001+ (0.001) -0.000 (0.005) -0.005* (0.003) -0.003** (0.001) -0.001* (0.001) -0.001 (0.001) Marginal effects .046 .042 .058 .068 .056 .042 .017 Joint F test 19.95 20.98 16.51 17.21 21.44 20.98 1.16 Observations 16,511 11,243 8,956 12,132 15,980 32,231 32,231 Note: The dependent variable is the natural logarithm of sales price, adjusted to 2000 dollars. Each column is a separate re- gression that applied equation 2 to alternative repeat sales samples: 1=preferred repeat sales sample; 2=limit to properties built before 1990; 3=limit to properties less than 5 miles; 4=limit to properties less than 7 miles; 5=limit to properties to less than 10 miles; 6=expand to include all properties sold between 1994-2011; 7=treatment date based on construction groundbreak- ing. Controls include house fixed effects, parking is represented by the number of all-day spaces in parking lots/garages within one mile of the station, time fixed effects represented by sales year and quarter, and ZIP code-specific linear time trends. Robust standard errors in parenthesis. a Includes all properties that sold more than once between 1994-2011. *** Statistical significance level at *** p<0.001, ** p<0.01, * p<0.05, + p<0.10. For Model 2, I remove properties that could have potentially responded to the announcement and construction of the new stations by limiting the sample to properties that were built before 1990. The results are consistent with the main estimate. For Models 3-5, I restrict the sample to only include prop- erties that were within the following distances – five, seven, and ten miles of the nearest station – to assess sensitivity to distance thresholds. I estimate the same general pattern when omitting farther properties, supporting the main finding that proximity to rail is considered a disamenity and effects should eventu- ally level off. Furthermore, the greater marginal effects relative to the main estimate provide evidence that properties from farther distances are not driving my results. For Model 6, I extend the pre- and post- periods by four years to assess if the window of time being studied explains the results.11 The marginal effects and general patterns are consistent with the main estimate. For Model 7, I test for anticipatory effects to ensure that the estimated effects are in response to the opening. I use the extended sample of re- peat sales transactions but change the treatment date from its opening to its construction date, artificially assigning post treatment distance to properties sold during construction years even though the stations are not yet in operation.12 A joint hypothesis test reveals that the study cannot reject the null hypothesis that both distance coefficients are zero at the 10 percent level, providing evidence that the increase in housing prices estimated by the main analysis is in response to the opening of the new stations. 10 For Table 4, figures of the hedonic price and implicit price functions are available upon request. 11 I select four years given that the northern end of the Green Line expanded in 1993. 12 I artificially change the treatment date from opening, 2001, to groundbreaking, 1995, for the Green Line and change the treatment date from opening, 2005, to groundbreaking, 2001, for the Blue Line. ===== PAGE 15 (score 6, numeric-paren 0, table-words 1, var-words 4) ===== 81 End of the line: The impact of new suburban rail stations on housing prices I also consider the robustness of my model to the quadratic distance specification. A key concern in using the hedonic framework is using the appropriate functional form (Yinger & Nguyen-Hoang, 2016; Yu et al., 2017). As Yinger and Nguyen-Hoang (2016) underscored, a nonlinear hedonic speci- fication is required for the bid function and associated envelope to be mathematically consistent and allow for sorting. I selected a quadratic specification due to the shape of my underlying data, but another commonly used nonlinear form in the literature is a categorical distance term.13 I decided not to use a categorical distance measure as it excludes useful identifying variations. In a repeat sales model with a categorical distance measure, my identification strategy would be based off houses that sold more than once and changed distance categories between sales as a result of the new stations; therefore, properties that changed distance but not categories would not be contributing to the model. However, as an ad- ditional robustness check, I assess if my findings are sensitive to the selection of functional form by esti- mating my hedonic and repeat sales models using a categorical distance specification. The results of the categorical distance models provide support for a quadratic distance specification and similarly conclude that distance to the station is considered a disamenity.14 Overall, the robustness checks provide further evidence that the introduction of the new stations is not endogenous to recent trends in price and that sample selection and functional forms are not driving my results. The preferred repeat sales specification makes a strong case for causal inference as it controls for all time-invariant housing and locational characteristics, time-specific shocks, and time-varying char- acteristics that follow a linear trend within a ZIP code; however, bias can stem from time-varying hous- ing and locational characteristics that are not accounted for by the various covariates and fixed effects in the model. Data limitations prevented the inclusion of specific time-varying housing and locational characteristics and contextual limitations did not allow for the inclusion of a difference-in-differences strategy, which would have further reduced the threat of omitted variable bias. 8 Discussion and conclusions This study sheds light on how the suburban context may complicate or even reverse findings on the well-studied relationship between proximity to rail and housing prices. I use hedonic and repeat sales models with an unrestricted distance specification to estimate the impact of improved access to public transportation produced by the opening of new Metro stations in Prince George’s County. This study contributes to the literature by focusing on the suburban environment and employing models that are better suited for the car-centric suburbs. Both the hedonic and repeat sales models suggest that being near the station is considered a disa- menity in Prince George’s County with the preferred repeat sales model estimating larger marginal price increases. It appears that households are evaluating the tradeoffs between the costs and benefits of liv- ing near a rail station and the negative elements may be offsetting accessibility and other benefits. The main findings are inconsistent with previous studies on central cities that find a premium for properties located near stations. I argue that the suburban context may be critical in explaining the overall trend of higher prices for houses located farther from the stations. When comparing the urban and suburban context, a critical element to take into account is the mode of access to the rail stations. A 2012 survey of riders conducted by the WMATA revealed that the majority of the riders from Prince George’s County rely on cars to access the Metro (Metropolitan Wash- 13 Appendix Figures A3 and A4 show that the underlying data supports a quadratic specification. 14 Appendix Table A3 presents the regression results for models using distance categories where houses were assigned to one- mile interval distance categories based on their distance to the nearest station. Appendix Figure A5 plots the coefficients and associated 95 percent confidence intervals from the hedonic and repeat sales models.