The Long-Term Property Value Effects of Transit Investments: A Comprehensive Multi-Method Approach

Transcription

1 The Long-Term Property Value Effects of Transit Investments: A Comprehensive Multi-Method Approach Timothy F. Welch Assistant Professor School of City and Regional Planning Georgia Institute of Technology Atlanta, GA 30308, United States Phone: (404) welch@gatech.edu August 1, 2014 Submitted for Peer Review and Compendium of Papers: 94 th Annual Meeting of the Transportation Research Board Washington, DC January 2015 Word Count: Words (4,735) + 6 Table and 5 Figures (11x 250 = 2,750) = 7,585 1

2 Valuing Access: The long-term property value effects of transit investments Abstract This study investigates the relationship between rail transit infrastructure investment and real estate market capitalization of transit station access. All property sales data recorded in the Metropolitan Atlanta Rapid Transit Authority (MARTA) rail service area of Fulton and DeKalb Counties in the Atlanta, Georgia from 1975 to 2009 are analyzed, applying hedonic regression and sales methods to measure capitalization. By comparing multiple grouped cross sections (tranches) the very long-term period to period change in value-added is tracked. The study takes into account different types of residential property - single family and owner-occupied multifamily units - to measure changes in the relationship between transit proximity and property values. The results of the study show that proximity to a rail station in Atlanta had virtually no impact on property values for two decades. During the last decade of the study period, there was a substantial shift in results indicating that since 1997 single family and multifamily residential units in the MARTA rail service area benefited greatly from proximity to rail stations. The scope of this study s dataset, both in time and space is in unprecedented, shedding new light on the housing value and transit proximity relationship. Keywords: Transportation and land use; transit proximity; value of transit access 1. Introduction Residents of Atlanta tend to know their city and its transportation system inside and out. There are passionate legions of citizens that argue the merits of each highway expansion project and debate about the best routes around the city. Riders of heavy rail, the Metropolitan Atlanta Rapid Transit Authority (MARTA) system, know the condition of the system, the type of person they ride with and the schedule of trains. Those who are residents of the city and patrons of the roads and trains may well know what their city looks like and to some degree, how their neighborhood has been shaped and influenced by MARTA. Most Atlantans know the condition of the highway system, the general traffic patterns and levels of congestion that often occur along the roadway. What these transportation consumers may not readily know is the amount of money their home s proximity to MARTA service or highway access points has either added or detracted from their real estate value. Though many studies, some even of Atlanta, have attempted to provide information on this interaction, most have left readers with little satisfaction. This is because a vast majority of prior work examines a single snapshot (cross-section) of the city, its real estate and its transportation system. The narrow frame of one cross section misses long-term 2

3 trends, changing behavior, the reshaping of land uses and the movement of jobs and residences. The body of knowledge we have of transportation systems and property values captures very little of the long-term strategies engendered in most transportation investments. This study sheds new light on the ever-changing relationship between property values and transportation systems, particularly the rail network. New information is uncovered through the statistical analysis of decades of quantitative data. The end result is more than simply an understanding of a city and its transportation system; it is to know the real long-term effects of transportation in a major US city, proving insight into the impact of transportation on property values not of a single moment in time, but across three full decades. Study Area Fulton and DeKalb Counties, together, constitute the most populous area in Georgia and are situated in the center of a sprawling 28 county metropolitan area (Figure 1). The two counties are home to the city of Atlanta. The city of Atlanta itself is by any measure, a vastly sprawling metropolis. The city s political boundaries stretch a staggering square miles; with no natural boundaries to confine it, the city and especially its suburbs have grown and spread out enormously in the past century. 3

4 Figure 1 Map of Atlanta Region 2. Literature Review The relationship between property values and rail transit, though extensively examined in the literature, still is a matter of contention. Many derivations of the relationship exist and scholars have provided a good deal of data both supporting and rejecting the strength of the relationship over time. The basic supposition is that access to transit and its cost advantages (in terms of generalized travel cost, especially in high density urban environments) is capitalized in the value of real estate (1). At the same time, determining with any certainty the mode by which transit investments affect property values is an extensive subject unto itself, as it can be quite difficult to measure the value added or to some extent detracted from the overall land value, due to the complexity of the real estate market. 4

5 Property Value Effects Several studies indicate that rail transit investments increase the value of properties near stations, including residential land values (2, 3), commercial property values (4), industrial land values and office rents (5) and residential rental rates (6). A clear and recurring theme among these studies and others is proximity matters. That is, land closer to stations receives more value added than those farther away (7, 8). Studies that have analyzed initial property value responses to transportation investments have found that values increase simply with the announcement of the planned rail investment (9). When the study period is extended, the results indicate a strong initial capitalization for station proximity with the amount of premium leveling off over time (10). The positive influence of rail proximity on property values appears to hold true on most continents including the North America, Europe and Asia (11 13) This study focuses on the on long term effects of rail proximity in Atlanta, Georgia. One previous study of the MARTA rail system (5) measured the effect of close proximity to a rail station on residential property values. The results of the study revealed that properties within a quarter of a mile of rail stations and lines actually lost roughly 19% of their value, compared to all other residential property in and around the city of Atlanta. Additionally, properties located within one to three miles of rail stations were valued much higher than those more than three miles away, controlling for many other factors (5). Here, the authors used a hedonic model to uncover what factors led to the price differentiation. The authors found that areas within one quarter mile of rail stations suffered from higher crime rates (presumably due to better access to stations); thus, those lots had lower values. At the same time, proximity of one to three miles of rail stations was convenient enough to enhance property values but inaccessible enough for would-be criminals that crime rates remained low and property prices were higher. Whether this criminal accessibility paradox was really to blame or the areas where stations and rail exist were already blighted, was not discussed in the study. An earlier study of MARTA s east line indicated that there was added property value for station proximity which ranged from $.96 to $1.05 every foot closer the property was to a station (14). In another study of MARTA, conducted by Bollinger and Ihlandfeld (15), MARTA s effect on population and employment distribution was measured. The authors found little relation between rail proximity and either employment or population distribution. However, these 5

6 MARTA studies, like many others are somewhat limited due to each study s relatively short time horizon. Many of the studies covered less than a decade. Because of the long-term nature of the built environment, the average lifespan of a commercial building is 65 to 80 years (16), many proponents and opponents of light rail and transit oriented development (TOD) contend that land use changes and ultimately population and employment changes will not take place in the short term (17). Geographic scope A limitation of previous studies is the small geographic coverage of the study area. In almost every transportation and land use related study the geographic area of analysis has been constrained to one mile or less from a rail station, with a few exceptions extending the study area to three miles (18). This is particularly problematic when examining large fixed rail systems. Since such systems are designed to serve regional transit needs and tend to have multiple and variety economic effects (19, 20), the value they add and the total impact on land development on the region as a whole is not fully quantified when the study area is only a short distance from each station. A recent meta-analysis found that property values tend to be most positively impacted be rail proximity within a distance of about 500 to 800 meters (13). Additionally, the full effects of other infrastructure such as rail lines (21, 22), highway ramps (23, 24) and roadways (25, 26) are not captured when the study area is focused only around rail stations rather than around the full extent of the transportation infrastructure. It is important to control for the effects from transit and the many other parts of transportation infrastructure, particularly access to highways, in order to accurately measure the net impact of rail investments (8, 27). Method and concluding remarks By far, the most common method of analysis to capture the impact of transit station proximity on property values in the last several decades has been hedonic regression (28). The hedonic method controls for several variables thought to influence property value, to determine the level of influence change in a single variable (i.e. distance from a transit station) has on the total value (29). The least common method, but often cited as the most reliable, is repeat sales 6

7 analysis. This method compares aggregate rates of change in property values (the change in sale price from the first sale to the next) for locations near transit stations to the aggregate rate of change in a larger geographic area (30, 31). This study is unique as it is based on a one of a kind dataset of all property transactions in the entire Metropolitan Atlanta Rapid Transportation Authority (MARTA) rail service area (see Figure 2) for the entire life of the MARTA rail system from 1975 until the end of this study period (2009). Employing a multiple cross sectional hedonic approach and a repeat sales method, the changing market conditions attributable to transit station proximity over the long term can be studied for the first time. 3. Study Area and Data This study covers the entire MARTA rail service area that extends to the borders of Fulton and DeKalb Counties in Atlanta Georgia (Figure 2). Within this geography are 21 incorporated cities, including the cities of Atlanta and Decatur. The two counties have an estimated 2008 population of nearly 1,755,000. The study includes all recorded sales of single and multifamily houses. In the two-county area in 2008, there were over 750,000 residential units (U.S. Census Bureau 2009); this study incorporates a majority of those units. The study also includes sales of commercial tax parcels within each county during the study period spanning 1975 to Atlanta s central business district (CBD) is located in the middle of Fulton County. Though the CBD was traditionally the economic center for the state, over the past several decades, employment has decentralized outside the CBD. Today Atlanta s CBD employment is less than 12% of the total Atlanta metropolitan area employment (Brown and Thompson 2009). Much like population growth in the Atlanta region, employment growth has increasingly occurred outside of the MARTA rail service area. The CBD is however, the main hub of the MARTA rail system. The Five Points station in the CBD is the meeting place of all four MARTA rail lines. Newer and emerging affluent areas in the central part of the city are the Midtown and Arts Center neighborhoods north of the city. Still farther north, the neighborhood of Buckhead, one of the location control variables in this study, is traditionally an affluent area in mid-northern Fulton County. 7

8 Figure 2. Map of MARTA Rail and Atlanta Highway System The MARTA rail network was the first heavy rail system in the Southeast and is the seventh most utilized largest heavy rail system (by unlinked trips) in the US (32). The full system consists of four major lines and a single spur line with 38 rail stations covering 48.6 rail miles and a rolling stock of over 300 cars with continuous service since 1979 (ARC 2008). The system is laid-out in a radial spine which connects with the grid-like bus network (Brown and Thompson 2009). In 2007 MARTA rail had a daily average ridership of nearly 500,000 boardings and experienced a 3.36% increase in revenue from the previous year. The rail service alone experienced a nearly 8% increase in rail boardings for one year (ARC 2008). Of the half million daily boardings, MARTA riders are typically lower income, non-choice riders travelling from suburb to suburb (Brown and Thompson 2009). 8

9 The time period covered by this study ranges across 34 years, which are dived into several tranches 1. The first data set (data set zero) contains sales and property attributes beginning in 1975 and going until 1979, the year before the official start of operation of the MARTA east line, the first to come online. The rest of the data sets, segmented by station construction date are listed in Table Table 1 Data Set Construction by Station Opening Date Tranche Station Code Station Name Station Opening Date 1 E7 Avondale 30-Jun E4 Edgewood/ Candler Park 30-Jun E5 East Lake 30-Jun E3 Inman Park/ Reynoldstown 30-Jun E1 Georgia St. 30-Jun E2 King Memorial 30-Jun E6 Decatur 30-Jun Five Five Points 22-Dec W2 Vine City 22-Dec W1 Omni/Dome/ GWCC 22-Dec W4 West Lake 22-Dec W5 Hamilton E. Holmes 22-Dec W3 Ashby 22-Dec S1 Garnett 4-Dec N3 North Ave. 4-Dec N2 Civic Center 4-Dec N5 Arts Center 11-Sep N1 Peachtree Ctr. 11-Sep S2 West End 11-Sep N4 Midtown 11-Sep N6 Lindbergh Ctr. 15-Dec S4 Lakewood/ Fort McPherson 15-Dec NE7 Lenox 15-Dec S3 Oakland City 15-Dec NE8 Brookhaven/ Oglethorpe Univ. 15-Dec S5 East Point 16-Aug NE9 Chamblee 19-Dec S6 College Park 18-Jun S7 Airport 18-Jun P4 Bankhead (Proctor Creek line) 12-Dec NE10 Doraville 29-Dec E8 Kensington 26-Jun E9 Indian Creek 26-Jun N7 Buckhead 8-Jun N9 Dunwoody 8-Jun N8 Medical Ctr. 8-Jun N11 North Springs 16-Dec N10 Sandy Springs 16-Dec A term traditionally used in finance, it simply refers to something that for one part of a larger whole. 9

10 Method Hedonic Model To study the long-term effects of transportation proximity, and the access that transportation provides to an area, a multiple cross sectional approach was developed for the first method. The time period covered by this study was dived into eight tranches. The first dataset (dataset zero) contains sales and property attributes beginning in 1975 and continuing until 1979, one day before the official start of operation of the MARTA east line, the first to come online. The rest of the data sets, segmented by major line and station opening dates are listed in Table 2. Table 2 Data Set Construction, by Date Tranche Number Begin Date Period End Date Length (Days) 0 1-Jan Jun Jun Dec Dec Dec Dec Aug Aug Dec Dec Dec Dec Jun Jun Dec Dec Sep This paper uses hedonic price regression to estimate the impact of distance from transportation infrastructure on three vectors of dependent variables for property sales in Fulton and DeKalb Counties. The vectors for this model are the specific housing characteristics, control vectors which are location specific for each sale, and transportation infrastructure proximity. To estimate the level of impact, two models will be used, one for each type of property, single family residential and owner occupied multifamily residential. The general format of the three models is represented by equation 1. Where: SP i = Selling price of home i SP! = f S, C, T (1) 10

11 S C T = Structural Characteristic = Control vectors (fixed effect estimator) = Transportation Infrastructure Proximity A variety of forms have been used in the specification of hedonic models. A common form is one of the Box-Cox transformations (33) involving the quadratic transformation of the dependent variable regressed in a second stage with the variance from the first stage OLS regression. Fitting the model is an important part of specification. It has been found that since each housing market is unique, the functional form of the hedonic equation should be somewhat unique to that market. This study deals with a geographically constrained market, but with much variance in time between transaction periods. The likely result of this variation in time is that the form of the model in tranche one may best fit a Box-Cox transformation while the fit in tranche five is better suited for linear form. To best accommodate the possible fluctuation in best-fit model forms, and to make tranche to tranche comparisons easier, a simple linear form has been selected for all of the hedonic models. Thus, equation 1 can be rewritten in into equation 2.!!! SP! = α! +!!! β! S! +!!! β! C! +!!! β! T! + ε (2) Where S is the vector of housing attributes, C is the vector of fixed effect estimators, T is the vector of transportation infrastructure proximity, β is the coefficient of the regression and ϵ is a normal, independently distributed error term Repeat Sales Repeat sales analysis was first developed in the 1960s (30) as a method for creating a measure of average area-wide home prices and tracking year-to-year changes in value. The method did not gain substantial recognition until it was modified and generalized for the housing investment market in the 1980s (34). The method is now used by Standard and Poor s Case- Shiller Home Price Indices which indexes US home prices for 20 metropolitan areas (35). The purpose of the index is to measure the average annual change in home prices in a specific geographic area. 11

12 The Case-Shiller method of analysis was later adapted by Gatzlaff to measure the change in home prices for a local housing market due to a specific locational attribute (31). Gatzlaff measured the change by constructing two home price indices, one for a larger geographic area and another for an area within a specific range (distance) of the locational attribute. The difference in the index levels was compared to find the change in home prices related to the locational attribute. Appreciation with the repeat sales method in this study is estimated with ordinary least squares (OLS) regression on a set of dummy variables representing the of year sale. The time period of the study is broken up into individual years beginning in the year 1975 and ending with The change in price between two sales is the dependent variable represented by the natural log value transformation of the property selling price at the time of the second sale divided by the selling price of the first sale. Each dependent variable receives dummy variables for each year corresponding to a sale or no sale, except for those selling in the first year (1975). The dummy variables are coded with -1 for the first sale; 0 if there was no sale in the specified year and +1 for the second sale. The complete equation is represented by equation Where: SP 1i = Sale Price at time of first sale SP 2i = Sale Price at time of second sale LN!"!!!"!! = β! Y1975! + + β!" Y2009! (3) β = Coefficient Representing Appreciation Y = Year, from The coefficients from the regression equation are then indexed to provide a standardized measure of appreciation. The index for the repeat sales method was constructed by arbitrarily setting the index to 100. The yearly index figure is the regression coefficient for each year, from equation 4, starting with the second year 1976 in exponential form multiplied by 100. The result is an index for each year for each of the study areas. ı! = 100e!!, s = 2,, S (4) 303 s! = 100 (100 x e!!, with τ! set to zero) 12

13 Where, e t s = is an exponential (2.718) of the estimated appreciation, derived from the coefficient of the regression equation; for all tranches (years) beginning in tranche two. The database for the repeat sales regression was created by matching multiple parcel sales. Repeat sales that occurred within less than 365 days were removed. Houses less than 800 square feet and greater than 6,000 square feet, were removed from the dataset. Any property that had a year construction date later than the initial sale date was removed. Since only two sales are needed to construct a database, any subsequent sales were removed with selection of the earliest repeat sale. This allowed substantial bias (changes resulting from renovation, land use changes or neighborhood improvement) to be limited since the time between sales was greatly reduced. 5. Results The regression analysis results for the nine phases of MARTA construction and operation for single-family residential unit sales for all locations in Fulton and DeKalb counties are provided in Table 3. The proximity coefficients during each tranche are statistically significant at the.001 level for all but one instance (noted below), where the significance level is.01. Sales prior to the operation of MARTA show a negative price effect for proximity to a station that was planned or under construction. Home prices continued to lose value for proximity to MARTA rail stations until a small premium began to accrue during the tranche beginning December 19, In late 1981, there is a $.0002 per square foot premium for proximity to a station. The premium disappears from approximately December 12, 1992 to June 8, In the last two tranches of the study, there was a growing level of premium for single family home prices associated with proximity to MARTA rail stations. Beginning in June 1996 until December 2000 there was a $.0006 premium for rail station proximity. After December 2000 the premium increased to $.0021, a much higher level than any other premium for infrastructure proximity. To put these seemingly small numbers into context, in the first period of operation for MARTA rail (tranche one), for every mile farther away from a MARTA station a home was sited, there was a $1.58 per square foot premium paid the for the home. By the Fifth tranche ( ) the relationship had flipped so that for every mile farther away from a MARTA station a home was, it sold for about $1.20 per square foot less. This relationship significantly 13

14 strengthened over the next decade so that a home lost approximately $11.09 per square foot for every mile away from a MARTA station it was sited. Comparing two houses selling in the same period ( ) with similar characteristics and an average 2,000 square feet but one a mile farther away from a MARTA station, may sell for $22,176 less, simply from the lack of proximity to a MARTA station. This relationship exists for every home in the MARTA service areas of Fulton and DeKalb County. On the other hand, the sale price decreased by $0.002 per square foot for every foot ($10.56/mile) a home was situated away from a freeway ramp. Table 3 Hedonic Regression Results, Single Family Station Distance Rail Distance One Mile from Station or Ramp Ramp Distance Highway Distance Summary Statistics Tranche B Sig. B Sig. B Sig. B Sig. OBS F R , , ,465 1, , , ,336 1, ,765 1, ,200 3, ,197 9, Proximity effects can be somewhat diluted when the geographic scope of the analysis is as large as the full MARTA service area. When the spatial scale is reduced to just one network mile of a station, the effect can be measured for locations within a 20-minute walk (assuming an average three mile per hour pace) of a transit station. Table 4 shows the results of the geographically reduced regression analysis for single family property sales. There is a negative relationship between price and distance for the pre-operation period (tranche 0) and for the first year of operation. At the end of tranche one there was a small reversal of the trend, but the same negative correlation reappeared by tranche three and continues for another tranche. A positive correlation between property values and proximity to a rail station to within one mile occurred from tranche five to tranche six. Beginning in MARTA tranche seven (June 1996) and continuing until the end of the study (2009) there is moderate negative relationship between single family home prices and distance to a MARTA station. The station proximity coefficient for several of the observational periods (tranches one, two and five) is not statistically 14

15 significant. Since the significance waivers so greatly, it is difficult to infer a specific relationship for home prices within this more refined geography. Table 4 Regression Results, One Mile from Station or Ramp, Single Family Station Distance Rail Distance Summary Statistics Ramp Distance Highway Distance Summary Statistics Tranche B Sig. B Sig. F R 2 B Sig. B Sig. F R , , The relationship between distance from a rail station and owner-occupied multifamily property values is provided in Table 5. In general, the results for the multifamily hedonic regression analysis show the effect on values for this type of housing is much greater than that for single family residential units. For all property locations and all tranches, there is a negative relationship between property values and distance from a station. The farther a multifamily residential unit is from a station the lower the value of the property for both Fulton and DeKalb Counties. The penalty for distance from a rail station beginning in tranche one was $.0006 per square foot per foot away from a station. In other words, multifamily units sold for about $3.17 per square foot less for every mile away from a rail station in the first year of MARTA operation. This penalty decreased after the initial tranche and did not regain the original value until tranche six. The market for rail proximity appears to have cooled down between the years (tranches two - five) but for the final three tranches of the study ( ) the relationship strengthened significantly. By the final tranche ( ) the penalty for distance from a MARTA station was $.0047 per square foot per foot, or a reduced sales price of $24.82 per square foot for every mile away from a station. Similar to single family housing, there was a benefit of $ per square foot for every foot away from MARTA rail or a highway. Table 5 Hedonic Regression Results, Multifamily 15

16 Station Distance Tranche B Sig. B Sig. B Sig. B Sig. OBS F R 2 MRS MRS , MRS , MRS , MRS , MRS , MRS , MRS ,867 1, MRS ,101 3, One Mile from Station or Ramp Multifamily properties located within one mile of a station consistently benefited from proximity to a rail station. Table 6 shows that in tranche one ( ) multifamily property within one mile of a station lost $.0094 per square foot for each foot the property was separated from a station. This relationship showed some variation from tranches two through five (similar to the tend for single family houses). By tranche six, the level of premium stabilized at just over $0.01 per square foot per foot of distance from a station. In the final tranche ( ), a multifamily unit sold for approximately $67.06 less per square foot for every mile away from a MARTA station. Table 6 Regression Results, One Mile from Station, Multifamily Station Distance Rail Distance Rail Distance Ramp Distance Summary Statistics Highway Distance Ramp Distance Highway Distance Summary Statistics Summary Statistics Tranche B Sig. B Sig. F R 2 B Sig. B Sig. F R , , To summarize the results of the hedonic analysis, a graphical summary of the results is provided in Figure 3. The graphic shows the effect of proximity for any property in Fulton and DeKalb counties for both single family and multifamily housing units. The chart, spanning 34 16

17 years, clearly shows the long-term positive impacts of rail investment on both types of residential properties. Dollars Per Square Foot per Mile -$ $ $ $ $ $ $9.00 -$6.00 -$3.00 $0.00 $3.00 $ Study Tranche Multi-family Units Single Family Units Figure 3 Summary of Hedonic Regression Results Repeat Sales The second component of this statistical analysis constructs a repeat sales analysis to track levels of housing price appreciation. Two indices are compared, one measuring appreciation over the study period for property sales occurring within one mile of a MARTA rail station and one for property sales farther than one mile from a station. Two sets of repeat sales analysis is conducted, one for each of the two housing types. Figure 4 (and Table 7 in the appendix) provides the results from the repeat sales analysis for single family houses selling twice between 1975 and The results indicate that prior to the operation of MARTA rail in 1979, property located within one mile of a MARTA station depreciated and appreciated at approximately the same rate as property located in the rest of the two county MARTA service area. In 1981, property values within one mile of a station appreciated at a rate much higher than the rest of the Atlanta area. This is significant because in this year gas prices were at a record high, indicating to some degree, an increased value in access to public transportation is related to the price of gas. This is also evidenced by the sharp increase in MARTA ridership following the gas price spike. The repeat sales index indicates that property values near stations 17

18 increased as gas prices reached historical highs. Properties within one mile of a station tracked the rate of appreciation experienced by all other similar housing types for the next several years until 1997 (tranche seven) when single family property values within one mile of a MARTA station appreciated for the next nine years at a higher rate than the rest of the Atlanta area. The repeat sales index also shows the impact on all housing prices of the housing crisis that began in The index does however indicate that property values within one mile of a station declined at a faster rate than the rest of Atlanta until the end of the study period in 2009 when both indexes were roughly equal. 300 Repeat Sales Index Year Farther than one mile from a station Figure 4 Repeat Sales Results, Single family Within one mile of a station Figure 5 graphs the rate of appreciation for all multifamily properties in Fulton and DeKalb Counties. In the four years prior to the operation of MARTA, multifamily properties located within one mile of a station experienced marginally lower rates of appreciation when compared to the same type of property located more than a mile from a MARTA rail station. In 1981, similar to the single family unit effect, the rate of appreciate spiked for property located within one mile of a MARTA rail station. This is also likely linked to the price of gas. For the next several years from 1982 until 1991 the rates of appreciation for property within one mile of a station lagged behind the rate of appreciation for similar properties types located beyond one mile. In 1992 the rate of appreciation for properties within one mile of a MARTA station began 18

19 exceed the appreciation for properties all other multifamily properties. This trend of higher appreciation rates continued for property within a 20-minute walk of a MARTA station through Repeat Sales Index Figure 5 Repeat Sales Results, Multifamily Conclusion Year Farther than one mile from a station Within one mile of a station Transportation is a long-term investment; its more subtle impact beyond immediate mobility and accessibility enhancements may take several years or decades to materialize. This long-term effect makes measuring the impact of transportation infrastructure in a timeframe short of a few decades, difficult. The vast majority of studies in this critical area focus on market value capitalization in a single study period or cross-section. However the nature of fixed rail transportation investments does not lend itself well to the snapshot view of a single cross-section. It is often the case that significant transportation investments require major shifts in travel behavior, complimentary infrastructure and changes in land use, all of which take a significant amount of time to efficiently form around new rail access locations. Further, many studies have been limited to measuring property value effects only within one mile of a transit station. However, to capture the true regional impact of transit it is necessary to measure property sales 19

20 located within the entire service area. To measure the true effect of these investments on factors like property values, a study must examine the change in sale prices over a long period over time. This type of longitudinal study has been difficult in the past due to limited amounts high quality, year-after-year parcel-level sales data. This paper addresses many of the past transit and property value relationship research shortcomings. First, the dataset covers an extremely long period of rail operations, capturing all single family and multifamily property sales starting five years before the rail system began operation (1975) and continuing 34 years (2009). The scope of the data captured transactions within the entire two-county MARTA rail service area. Finally, to fully capture the relationship between property values and transit investments two types of analysis is conducted. Hedonic regression, which controls for a variety of variables including housing and neighborhood characteristics and proximity to other transportation infrastructure, reveals the dollar value per square foot of property based on the distance to a transit stop. Repeat sales indices are created to measure how proximity to a transit station influences property value appreciation. The results of the study, divided into tranches spanning periods of rail development, show that proximity to a rail station in Atlanta had virtually no impact on property values for nearly twenty years. During the last decade of the study period, there was a substantial shift in results indicating that since 1997 single family and multifamily residential units in Fulton and DeKalb Counties benefited from proximity to rail stations. In the final study tranche, for every mile away from a transit station a single family home was situated, it sold for approximately $11.09 per square foot less. For a 2,000 square foot single family home, one mile difference in distance to a MARTA station could change the selling price by as much as $22,176. Multifamily houses lost approximately $24.82 per square foot for every mile away from a transit station. Single family and multifamily property located within one mile of a rail station benefitted substantially from close proximity to a transit station. Typically the properties within this 20- minute walking distance appreciated in value much faster and to a greater extent than all other residential property in the two-county MARTA service area. The results of this study demonstrate the importance of recognizing rail transit as a longterm investment. Policy should recognize that property value impacts can and very often do take years if not decades to be fully realized as the urbanized area catches up with the new investment. As such, it is appropriate to measure the full benefits of transit over several decades. 20

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