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H-1 APPENDIX H COMMUNITY LIVABILITY BENEFITS The Value of Bicycle Trail Proximity on Home Purchases INTRODUCTION As the literature describes various methods to assign value to housing characteristics, there exist opportunities to increase the Many cities--through public dialogues, community initiatives, explanatory power of hedonic models. Recent contributions include and other land use-transportation policies--are developing strategies accessibility, perceived school quality, and measures of environ- to increase the "livability" of their communities. While "livability" is mental amenities. For example, Franklin and Waddell (230) used a a relatively ambiguous term, there is emerging consensus on the hedonic model to predict home prices in King County, Washington, following: the ease by which residents can travel by walking or as a function of accessibility to four types of activities (Commercial, bicycling represents a critical component of this goal. Communities University, K-12 Schools, and Industrial). In assessing the relation- well endowed with non-motorized infrastructure, either in the form ship between public school quality and housing prices, Brasington of sidewalks, bicycle paths, or compact and mixed land uses are (231) found that proficiency tests, per-pupil spending, and student/ hypothesized to be more livable than those without. This is an often teacher ratios most consistently capitalize into the housing market. relied-upon argument used by advocates of bicycle paths or sidewalks. Earnhart (232) combined discrete-choice hedonic analysis with If livability is a cherished commodity among residents, and one choice-based conjoint analysis to place a value on adjacent environ- important component of livability includes bicycle paths, then prox- mental amenities such as lakes and forests. imity to bicycle paths should be capitalized into the value of home Our application here focuses on the relative impact of bicycle lanes purchases. Documenting this relationship would go a long way for and trails. To the casual observer, bicycle lanes and trails may be con- advocates of bicycle facilities who often seek ways to monetize the sidered as a single facility where any type of bicycle trail would have value of these facilities. Such an endeavor would be especially bene- the same attraction. More careful thinking, however, suggests other- ficial since bicycle facilities are non-market goods, making it difficult wise, especially for different types of bicycle facilities. Consider, for to attach an economic value to them. example, the three different types of trails/lanes shown in Figure 20. Social or economic benefits can be measured either through stated Some trails are on existing streets (demarcated by paint striping, preferences, in which users are asked to attach a value to non-market hereafter "on-street lanes"); some trails are adjacent to existing road- goods, or through revealed preferences. The revealed preference ways (hereafter "roadside trails") but are separated by curbs or mild approach measures individuals' actual behavior. In this study we landscaping (these facilities are sometimes referred to as "black measure homebuyers' revealed preferences in the form of hedonic sidewalks" because they are nothing more than blacktop in the usual modeling to learn if and how much residents value proximity to location of sidewalks); other trails are clearly separated from traffic bicycle paths. The first part of this paper reviews previous literature and often within open spaces (hereafter "non roadside trails"). For on hedonic modeling focusing primarily on the dimension of open this last category, it is important to explain and control for the degree space and trails. The second part describes the setting for this work, to which open space versus the bike trail contained within the open our data, descriptive statistics, and methodological approach. Part space contribute to a home's value. In many metropolitan areas bike three describes the results of a hedonic regression model, and part trails and open space share a spatial location and at minimum exhibit four reports on the policy implications and relevant conclusions. similar recreational qualities. On-street lanes or roadside trails are often on or near roads. In some cases they will be on well-used col- lector streets or trunk highways; in others they may be on neighbor- REVIEW OF RELEVANT LITERATURE hood arterial streets. Home buyers tend to dislike proximity to busy AND CONCEPTS roadways. Much of the attraction of these facilities therefore depends on the design speed of the roadway facility and the average daily traf- Discerning the relative value of non-market goods using hedonic fic. Any research failing to account for any of these factors will mis- modeling techniques is a method with a long history. Taylor (229) estimate the independent value of bicycle trails. used what are now called hedonic techniques to explain the price of It is therefore important to consider relevant literature estimating cotton with its internal qualities, and later applications by Lancaster the value of open space. For example, Quang Do (136 ) found that (128) and Rosen (129) standardized the method for consumer prod- homes abutting golf courses sell for a 7.6 percent premium over ucts such as houses. An extensive review of this literature (130) doc- others. Other studies include measures of proximity and size of vari- uments nearly 200 applications that have examined home purchases ous open spaces (233, 234 ). Geoghegan (133 ) compared the price to estimate values of several home attributes including structural fea- effects of the amount of permanent and developable open space tures (e.g., lot size, a home's finished square footage, and number of within a one-mile radius. Smith et al. (235) examined the distinction bedrooms), internal and external features (e.g., fireplaces, air condi- between fixed and adjustable open spaces along a new Interstate high- tioning, garage spaces, and porches), natural environment features way corridor. Other approaches further disaggregate developable and (e.g., scenic views), attributes of the neighborhood and location (e.g., non-developable open space in terms of ownership type and land crime, golf courses, and trees), public services (e.g., school and infra- cover (236 ). Some studies seek to attach values to views of open structure quality), marketing, and financing. space. Benson et al. (132) created a series of dummy variables for

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H-2 Hypothesized Relationship with Home Value City Residents Suburban Residents ON- STREET BICYCLE LANE NON- ROAD SIDE BICYCLE TRAIL ROAD- SIDE BICYCLE TRAIL Figure 20. Representative photographs of an on-street bicycle lane, non-roadside trail, and roadside trail, hypothesized relationships. four different qualities of ocean views, as well as lake and mountain and West. Their analysis suggests that open spaces and by associa- views. Luttik (135) combined the vicinity and view approaches, tion, bicycle facilities, may be perceived and valued differently dividing the geography into three levels of proximity. depending on whether they are located in the city or suburbs. Anderson and West's work (131) is particularly helpful for this spe- Unlike other attributes which tend to be more universally valued cific application. They modeled both proximity and size of six specific (e.g., home size, number of bathrooms), we hypothesize that trails open space categories, comparing effects on home prices between the may be more appreciated by a subset of the population. Households city and suburb. They found that proximity to golf courses, large who choose to live in the city are more likely to walk or bike, partic- parks, and lakes has a positive effect on home prices in the city, with ularly to work, (53, 239) and therefore more likely to value bicycle no significant results in the suburbs. The effects of open space on facilities. Because we specify three different types of facilities with home prices also increased with the size of the open space. Proxim- two populations who may value such facilities differently, we present ity to small parks and cemeteries tended to reduce sale prices. To our Figure 20 displaying the nature and relative magnitude of our hy- knowledge, only one application focuses on proximity to bicycle pothesized relationships. trails. Lindsey (72) performed a hedonic analysis of 9,348 home sales, identifying properties falling inside or outside a half-mile buffer around fourteen greenways in Marion County, Indiana. This research SETTING AND DATA found that some greenways have a positive, significant effect on property values while others have no significant effect. A survey in Our investigation is based in the Twin Cities (Minnesota) Metro- Vancouver found that the majority of realtors perceive little effect of politan Area which proves to be an almost ideal laboratory for a vari- bicycle trails on home values, either positive or negative (237 ). How- ety of reasons. First, the Twin Cities boasts an almost unparalleled ever, two-thirds of respondents also indicated that they would use system of off-street bike paths for a major metropolitan area in the bicycle trail proximity as a selling point. United States, totaling more than 2,722 kilometers (1,692 miles). Given the novelty of the application presented herein, theory is While not nearly as extensive, striped on-street bike lanes are com- derived from a combination of sources, including existing published mon as well. The network of on- and off-street trails is accessible to work (described in part above), consumer theory, and anecdotal evi- most Twin Citians, with 90 percent of homes within 1,600 meters dence. Our first underpinning is derived from a local county com- (one mile) of an off-street trail. In fact, in many communities within missioner who claims that bike facilities--like libraries--are goods the metropolitan area, over 90 percent of the homes have some form everyone appreciates (238). Such a claim maps well with the asser- of facility within 400 meters (one-quarter mile). tions of bicycle trail advocates. Assuming an ability to account for the Second, several municipalities and county governments pursue possible disutility of living on a busy arterial, bicycle facilities--no active roles in constructing and maintaining these facilities. The matter their type--positively contribute to home value. However, this Grand Rounds Parkway in Minneapolis, considered by many to be hypothesis needs to be tempered based on the findings of Anderson the crown jewel of parks and recreational trails in Minnesota, con-

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H-3 TABLE 26 Descriptive statistics of sample Variable Standard Name Description Mean Deviation Median Contrnr CITY: distance to nearest on-street bicyble lane (meters) 1276.31 947.90 1023.55 Cnrtrnr CITY: distance to nearest non-roadside bicycle trail (meters) 799.42 517.82 711.29 Crstrnr CITY: distance to nearest roadside bicycle trail (meters) 1293.81 716.20 1219.16 Sontrnr SUBURBS: distance to nearest on-street bicycle lane (meters) ) 1580.51 2240.18 979.82 Environmental Snrtrnr SUBURBS: distance to nearest non-roadside bicycle trail (meters) ters) 1099.89 1732.29 602.92 Amenities (A) Srstrnr SUBURBS: distance to nearest roadside bicycle trail (meters) 1359.35 1728.01 911.83 Cactive CITY: distance to nearest active open space (meters) 340.15 203.41 315.35 Cpassive CITY: distance to nearest passibe open space (meters) 683.10 396.64 633.76 Cactive SUBURB: distance to nearest active open space (meters) 569.92 1176.45 290.07 Spassive SUBURB: distance to nearest passive open space (meters) 760.73 641.12 613.09 Bedrooms Number of bedrooms 3.12 0.91 3.00 Bathroom Number of bathrooms 2.14 0.88 2.00 Homestea Homestead status 0.86 0.34 1.00 Structural Age Age of house 35.88 28.97 27.00 Attributes (S) Lotsize Size of lot (square meters) 2097.98 8053.17 968.00 Finished Finished square feet of floor space 1871.01 908.66 1708.00 Firepls Number of fireplaces 0.70 0.76 1.00 Garagest Number of garage stalls 1.72 1.02 2.00 Hwynear Distance to nearest major highway (meters) 1672.32 1821.44 1149.58 Location (L) Cbdnear Distance to nearest central business district (meters) 17558.59 10409.61 16374.75 Busy Home is on a busy street 0.05 0.21 0.00 mca5_att Standardized test score in school district 4760.46 276.78 4836.10 Neighborhood Pctnonwt Percent nonwhite in census tract 12.51 14.02 7.82 Attributes (N) Avghhsiz Persons per household in census tract 2.67 0.40 2.66 sists of 69 km (43 miles) of off-street paved trails along the city's totaling 35,002xxii home sale purchases, including structural attributes chain of lakes, the Mississippi River, and Minnehaha Creek. Hen- of each home. The address of each home was mapped and paired with nepin County, which includes the city of Minneapolis and many of GIS features for spatial analysis using ArcGIS. its suburbs, works in cooperation with the Three Rivers Park District Table 26 lists each variable, its definition, and descriptive statistics. to build and maintain the largest network of off-street trails in the We measure location attributes through simple calculations of linear metro area (240). Many off-street trails in Hennepin and other coun- distance to the nearest central business district (either Minneapolis or ties are located on former railroad rights of way for the dual purposes St. Paul) (cbdnear) and the nearest major highway (hwynear). A third of recreation and preservation of the land for future transit corridors. location variable (busy) indicates the presence of an arterial street Other off-street trails in the Twin Cities follow arterial and collector fronting the home. streets. The cities of Chanhassen, Eden Prairie, and Plymouth have Neighborhood attributes include school district and demographic extensive networks of these roadside trails, with somewhat smaller variables. Standardized test scores capitalize into home sale prices networks in Maple Grove, Roseville, Eagan, and Apple Valley. and are an effective measure of perceived school quality (231). Roseville is the only inner-ring suburb with a substantial network of Mca5_att represents the sum of the average math and reading scores off-street trails. Third, Twin Citians appear to cherish such trails, achieved by fifth grade students taking the Minnesota Comprehensive particularly in the summer months. For example, Minneapolis ranks Assessment. Scores associated with suburban homes are measured at among the top in the United States in the percentage of workers com- the school district level, while Minneapolis and St. Paul scores are muting by bicycle (55). assigned to elementary school attendance areas. Demographic vari- Consistent with the prevailing literature, our hedonic model ables are derived from the 2000 United States Census. We include the assumes a competitive market in which homebuyers are seeking a set percentage of people in the census tract who do not classify them- of home attributes that can be tied to a location. Locations are defined selves as Caucasian (Pctnonwt) and the average number of people in by structural attributes (S ) (including internal and external attributes), each household in the census tract (Avghhsize). neighborhood characteristics (N ), location and accessibility (L), and environmental amenities (A). We build an equilibrium hedonic price function on these assumptions, where the market price of a home (Ph ) MEASURES OF INTEREST depends on the quantities of its various attributes: AND METHODOLOGY Ph = P ( S , N , L , A) Measures of Distance to Bicycle Facility The Regional Multiple Listing Services of Minnesota, Inc., The measures of interest for this application center on bicycle facil- (RMLS) maintains home sale data from major real estate brokers in ities and to a certain extent, open space. Examples of the facilities and Minnesota. This database includes all home sales in Anoka, Carver, trails in this setting are shown in Figure 21. Detailed GIS data allowed Dakota, Hennepin, Ramsey, Scott, and Washington counties in 2001, us to discern all bike trails in the region, separately identifying

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H-4 Figure 21. Examples of off-street bicycle trails, on-street bicycle lanes, and open space. on- and off-street facilities which are distributed across both major ers. They might value a well-connected system of trails, which are open space corridors (e.g., railway lines, rivers, and lakes) and other prevalent in many areas throughout the Twin Cities metropolitan roadways. We pair the MLS data for every home sale in the seven area. We therefore calculate the kilometers of trails within buffer dis- county study area from 2001 with the location of these trails. tance. See, for example, Figure 22 showing an example home in Min- Some on-street and off-street trails are located alongside busy traf- neapolis and how we measured open space and density of bicycle ficked streets, which is presumably a propelling characteristic for facilities by differing radii of 200, 400, 800, and 1,600 meters. home locations. We therefore divide the off-street layer into roadside and non-roadside trails based on proximity to busy streets. We then calculate distance to the nearest roadside trail, non-roadside trail, and Interaction Terms on-street bicycle lane for each home. As previously mentioned, we also measure distance to open space as a central variable, classifying Many of the structural attributes used in this application are uni- such areas by type: active or passive.xxiii versally valued (e.g., home size, number of bathrooms). Several of the spatial attributes employed, however, are hypothesized to vary by segments of the population (urbanites versus suburbanites). Again, Measures of Density of Bicycle Facilities this distinction was found by Anderson and West's application for the same region. We therefore generate interaction terms (e.g., city mul- Motivated by Anderson and West's (131) findings that proximity tiplied by independent variable) to measure the attributes that may and size of open space matters, we also theorize it to be important to vary spatially. Doing so allows us to pool the sample of urban and consider not only the distance to facilities but also the density of trails suburban homes, thereby parsimoniously estimating a single model around a particular home. The overall density (length) of different that preserves the integrity of the differing preferences. This single facilities within a buffer area may also be appreciated by homebuy- model provides coefficients that describe the effect of common attrib-

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H-5 Figure 22. Off-street trails and open space within 200, 400, 800, and 1,600 meters of a Minneapolis house. utes while producing different coefficients for the spatial attributes to a bicycle trail, assuming a household has already decided to locate that may vary across suburbanites and urbanites.xxiv in one of the 104 MLS areas in the region. While more accurate, this process makes it difficult to identify the impact of bicycle trail prox- imity because it in effect reduces the variation of the variables of Fixed Effects interest. Michaels and Smith (241) support this claim, showing that dividing a market into submarkets results in less robust estimates of Finally, as with any analysis of this type there are omitted attrib- the effects of hazardous waste site proximity. utes to consider. When estimating phenomena associated with the real estate market this dimension is particularly important. There are likely spatial attributes--not captured by any of our measures-- RESULTS AND DISCUSSION which invariably affect home value. These attributes may include but are not limited to general housing stock of neighboring homes, Our final model (shown in Table 27) is an OLS regression which the reputation effects of different neighborhoods or unobserved char- determines the effect of bicycle trail proximity on home sale prices. acteristics of the neighborhood. We employ a logged dependent variable and also log transforma- Without fixed effects, variation across all observations in all neigh- tions of several continuous independent variables, indicated by an ln borhoods is used to identify the effect of interest. But given the likely following the variable name. All structural and location variables spatial correlation between proximity to bicycle facility with other are statistically significant and have the expected signs. Home val- variables, this effect is susceptible to omitted variable bias. We con- ues increase with number of bedrooms, bathrooms, lot size, finished trol for bias introduced by potential omitted variables by using local square footage, fireplaces, garage stalls, proximity to a central busi- fixed effects, a dummy variable for each RMLS-defined market area ness district, and school quality. Home values decrease with age and (104 areas in our region). These boundaries mostly follow city limits percent non-white in the census tract. Similarly, proximity to a free- in suburban areas and divide the central cities into several neighbor- way has a negative effect on home value, which implies that the dis- hoods that closely follow similarly natured real-estate markets. By amenity effects of freeways (e.g., noise, pollution) likely outweigh controlling for fixed effects we are estimating the effect of proximity any accessibility benefits within particular neighborhoods. Looking

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H-6 TABLE 27 Regression results Effect of Variable Description Coefficient Standard t-statistic 400m Closer Name Error Contrln CITY: distance to nearest on-street bicycle lane (ln) 0.003950 0.002689 1.47 Cntrln CITY: distance to nearest non-roadside bicycle trail (ln) 0.007851 0.003732 2.1* $ 509.85 Crstrln CITY: distance to nearest roadside bicycle trail (ln) 0.022772 0.003777 6.03** $ (2,271.63) Sonrln SUBURBS: distance to nearest on-street bicycle lane (ln) 0.003334 0.001272 2.62** $ (364.02) Snrtrln SUBURBS: distance to nearest non-roadside bicycle trail (ln) 0.003858 0.001325 2.91** $ (239.65) Srstrln SUBURBS: distance to nearest roadside bicycle trail (ln) 0.010230 0.001419 7.21** $ (1,058.73) Cactive CITY: distance to nearest active open space (meters) 0.000024 0.000012 1.96* Cpassive CITY: distance to nearest passive open space (meters) 0.000065 0.0000007 9.08** $ 3,860.35 Cactive SUBURB: distance to nearest active opn space (meters) 0.000006 0.000001 3.88** $ (442.80) Spassive SUBURB: distance to nearest passive open space (meters) 0.000028 0.000002 12.86** $ 2,066.40 Bedrooms Nubmer of bedrooms 0.033037 0.001570 21.05** Bathroom Number of bathrooms 0.079976 0.002018 39.63** Homestea Homestead status 0.027259 0.003481 7.83** Ageln Age of house (ln) 0.092578 0.001759 52.65** Lotsize Size of lot (squaremeters) 0.000003 0.000000 21.68** Finished Finished square feet of floor space 0.000168 0.000002 82.14** Firepls Number of fireplaces 0.068749 0.001768 38.89** Garagest Number of garage stalls 0.075257 0.001268 59.37** Hwynear Distance to nearest major highway (meters) 0.000009 0.000001 10.35** $ (637.20) Cbdnrln Distance to nearest central business district (ln) 0.056065 0.006926 8.09** $ 9,861.10 Busy Home is on a busy street 0.03351 0.005096 6.54** mca5_att Standardized test score in school district 0.000160 0.000010 15.31** Pctnonwt Percent nonwhite in censu tract 0.004014 0.000183 21.99** Avghhsiz Persons per household in census tract 0.038961 0.004481 8.7** Constant 11.314800 0.079957 141.51** Number of observations: 35,002 ** Significant at p<0.01 Adjusted R-squared: 07920 * Significant at p<0.05 at some of the location and amenity variables reveals a different acting phenomena may be taking place. Some suburbanites may story. Open space coefficients are generally consistent with Anderson indeed value such trails. However, their preferences may be over- and West's (131) findings. Suburbanites value passive open space shadowed by a combination of the following factors. Some of the over active recreational areas. City residents also value lakes and suburban trails are along former railway beds. If these property val- golf courses, but active open space does not affect sale price. ues were formally depressed because of such an externality, such Focusing on the variables of interest in this application, our analy- legacy effect may likely still be in effect. Uncertainty surrounding sis of bicycle facilities reveals a relatively complex story. It fails to be future uses of such corridors, such as commuter rail, could com- crisp and clean because we measure three types of facilities for two pound any legacy affect. Snowmobiling introduces additional exter- different populations (urban and suburban). Our discussion sepa- nalities common to exurban trails. Most notable, many suburbanites rates the findings for city and suburban residents. First, city resi- simply appreciate the seclusion of their settings. Proximity to trails-- dents clearly value proximity to non-roadside trails (after control- no matter their character--may be an indication of unwanted peo- ling for open space). As Minneapolis is well endowed with many ple passing by or other symptoms that run counter to factors that off-road facilities and appears to exhibit a relatively high cycling pop- prompted their decision. One need only refer to several newspaper ulation, this comes as little surprise. The opposite is true for trails headlines (Figure 23) to learn of instances in which suburbanites alongside busy streets, however, even when controlling for adjacency oppose nearby trails. to the streets themselves. On-street bicycle lanes have no significant Similar analysis employing measures of the density of bicycle effect in the city. The possible reason for this is that in general, the facilities did not reveal statistically significant findings in any of the nature of on-street facilities differs considerably between Minneapo- models estimated. lis and St. Paul.xxv Because the policy variables of interest and the dependent vari- As in the city, suburban homes near roadside trails sell for less able are logged, the coefficients can be directly interpreted as elas- than those further away, even when controlling for busy streets. The ticities. However, we provide the results of an effect analysis to more same is true for on-street bicycle lanes, for which there was not sta- concretely estimate values. In Table 27 the last two columns present tistically significant effect in the city. Suburban off-street trails the effect of moving a median-priced home 400 meters closer to each appear to negatively influence home prices, unlike in the city. There facility than the median distance, all else constant.xxvi We find that in are possibly several reasons for this. First, it may be the case that the city, the effect of moving a median-priced home 400 meters closer because of decreased cycling use, suburbanites simply do not value to a roadside bicycle trail reduces the sale price $2,272. Assuming a access to trails. Such proximity may not even factor into their use home was 400 meters closer to a non-roadside trail would net $510. or option value of their home purchase locations. Second, counter- While all relationships between bicycle facility proximity and home

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H-7 The Seattle Times, Nov. 1, 2003. P. 1A. L ake Sammamish rails-to-trails vision: `One person 's dream, another's nightmare.'" The Boston Globe, Aug. 26, 2004. Globe West P. 9. "Friends, foes of Nobscot rail trail take up sides." The Washington Post, June 10, 2001. Loudoun Extra P. 5. "Trail's appeal is lost on some; Neighbors: not in our back yards." Figure 23. Newspaper headlines showing suburban trail opposition. sale prices are negative in the suburbs, the effect analysis shows use of interaction terms between city and suburb reveals this differ- significant variation in the magnitude of those relationships. The ence in preferences between city dwellers and suburbanites. We effects of moving a suburban home 400 meters closer to a road- measure bicycle facilities in different ways. Distance to nearest facil- side bicycle trail is -$1,059 compared with only -$240 for a non- ity is the measure discussed in detail above. Models that were esti- roadside trail. mated to examine the role of trail density did not produce statistically significant findings. The comprehensiveness of the Twin Cities' bicycle trails may contribute to a lack in variation among trail den- CONCLUSION AND FUTURE RESEARCH sities near homes. Further refinements would enhance our approach to estimating the There are several important implications for our results which value of bicycle facilities. Introducing a stated preference element confirm the hypothesis that the three types of trails influence home akin to Earnhart's (232) application could yield more robust esti- sale prices in different ways. They demonstrate the importance of mates. Additional stratification of variables would also augment our controlling for bias induced by omitted spatial variables. Such bias understanding. We have divided bicycle trails into on-street, road- is especially relevant for large complex and polycentric housing side and non-roadside facilities in the city and suburbs. Further data markets (such as in the Twin Cities, with two CBDs) and in areas collection efforts aimed at identifying other differentiating charac- where factors that influence home price differ tremendously by teristics among facilities, such as trail width and adjacent land cover, neighborhood. We use local neighborhood fixed effects to reduce would allow the implementation of a hedonic travel cost model to spatial autocorrelation and also lead to more robust coefficient esti- place a value on such characteristics (242). mates. Of course, using this methodology--while technically sound Assigning future benefits based on a hedonic model presents and robust--also makes it more difficult to detect the effects of such complications, as new environmental amenities can take years proximity because we are now comparing homes within MLS areas. to capitalize into housing prices. Ridell (243) shows that cross- Our results are also able to robustly test for the fact that urbanites sectional studies may underestimate the benefits of these goods, and and suburbanites perceive and value bicycle facilities differently. The provides an approach for capturing delayed benefits. In addition to

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H-8 delayed benefits, future benefits also present an opportunity for ing residential location decisions. Using fixed effects detects such refining model specification. Shonkweiler's (244) methodology considerations in terms of neighborhood quality and character. Over- accounts for the potential conversion of rural land to urban uses, all, our results suggest that off-street bicycle trails add value to home revealing that this qualitative consideration reduces estimation sale prices in the city, implying a contribution to social livability. No error. positive or significant relationship, however, is found for other types From a policy perspective, this research produces three important of facilities in either city or suburb. In fact, bicycle trails exhibit a insights. First, type of trail matters. On-street trails and roadside trails disutility in suburban settings. This suggests that urban planners and may not be as appreciated as many city planners or policy officials advocates need to be aware of the consequences of providing for think. Second, city residents have different preferences than suburban bicycle facilities, as the change in welfare is not necessarily positive residents. Third, larger and more pressing factors are likely influenc- for all homeowners.