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The association between residential housing prices, bicycle infrastructure and ridership volumes
Abstract
City officials and planners have shown increased interest in pedestrian- and bicycle-friendly designs aimed at addressing urban problems such as traffic congestion, pollution, sprawl and housing availability. An important planning consideration is the economic impact associated with existing or planned infrastructure, especially in relation to home property values. In this study, we use measures of infrastructure and ridership to evaluate the relationship between bicycling infrastructure and activity and single-family home values in Tempe, Arizona. We apply a hedonic modelling approach and find that bicycle infrastructure density is positively associated with home sale price, while ridership density around home locations has no significant relationship with sale price. Our results inform discourse related to the potential economic values of residential bicycle infrastructure, especially in areas where property tax is a source of local public finance revenue. We show that the characteristics of bicycle-friendly design may be the same characteristics valued by homebuyers and the resulting increased home sale values may lead to increased property tax revenue in Tempe, Arizona.
... In principle cycling infrastructure can have two impacts on home values: by increasing the utility of cycling (active transportation accessibility benefit), and by directly impacting the streetscape in front of a home (aesthetic/property impact access). Significant and positive effects are more reported than negative ones (Conrow et al., 2021), implying that the benefits from improved cycling accessibility seem to outweigh negative effects (Bartholomew & Ewing, 2011). However, the existing studies vary in the local context, accessibility measure, model design, and the type of cycling facility (Connolly et al., 2019;Welch et al., 2016), rendering the "positive impacts" less transferrable to policymakers in different cases. ...
... Their results found overall positive price capitalization associated with proximity to on-road facilities in Franklin County, Ohio, and the added value increased when bike facilities were connected with local public open space. A recent study (Conrow et al., 2021) also demonstrated a positive association between Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
Cycling is increasingly valued as a healthy active transport mode. Many cities across the world are investing in on-street bike lanes or multi-use trails to support more sustainable travel. When cycling infrastructure plans are proposed, however, local communities often express concern about loss of driveway access, on-street parking and street trees, also asserting that bike lanes will reduce their property values, termed “Bikelash” by planners. Our research examines correlations between cycling facilities and residential property values, applying space–time multi-level hedonic models in Kitchener, a growing mid-sized Canadian city. We consider three types of cycling facilities: (1) on-road bike lanes, (2) separated bike lanes, and (3) multi-use trails. The models use 2013–2018 housing transaction data and compare two types of proximity metrics: distance buffers and network distance. The results present no evidence that on-road bike lanes or multi-use trails in proximity to single-family or multi-family homes reduce property values. These findings can help planners inform the local community of the actual relationship between cycling infrastructure and property values and lower the resistance facing municipalities when making cycling infrastructure improvements.
... A large body of literature also demonstrates that bike facilities appreciate home values (Connolly et al., 2019;Conrow et al., 2020;Kashian et al., 2018;Liu & Shi, 2017). Liu and Shi (2017) examined distance to on-street bike facilities and density of the bike network. ...
... Even recreational bikeways had a significant and positive impact on housing values (Kashian et al., 2018). However, busy bike paths around residence may not positively affect sales prices (Conrow et al., 2020). Connolly et al. (2019) studied the impact of interconnection of bike facilities with other local amenities and found the heterogeneity of economic benefits depending on the type of local amenities. ...
Calgary offers many public transportation options, including light rail transit (LRT), bus rapid transit (BRT), and local buses as well as facilities for non-motorized travelers. Yet the individual and synergistic effects of these mobility options on residential property values in Calgary have not been clearly understood. By using a multilevel hedonic modeling approach, we examine the independent and interactive economic impacts of different transportation options on Calgary's residential properties in 2017. Our findings suggest that proximity to LRT and better neighborhood walkability can appreciate residential property values, whereas proximity to major roads and bus service tend to depreciate the values, although the degree and significance vary by property type. When proximity to LRT is coupled with high walkability, more appreciation of property values is expected, particularly in the case of attached houses and apartment properties. Our results imply that urban planners and city officials should strive to create safe and pedestrian-friendly environments near LRT stations that could lead to more dynamic civic activities.
... Bikemetro transfer increases significantly when the plot ratio exceeds 0.2, and housing prices show a nonlinear effect, with higher bike-metro transfer in both very high-and very lowpriced areas. This may occur because bicycles, as a low-cost transportation mode, are more favored in low-price housing areas, while high-price areas often provide more comprehensive non-motorized infrastructure and services [50]. The influence of cultural POI density on bike-metro transfer is particularly prominent across different times and transfer flows, possibly because areas with dense cultural POIs serve as centers of regional vitality with diverse services and attractions [51], especially under morning access and evening egress models. ...
Dockless bike-sharing (DBS) plays a crucial role in solving the "last-mile" problem for metro trips. However, bike-metro transfer usage varies by time and transfer flows. This study explores the nonlinear relationship between the built environment and bike-metro transfer in Shenzhen, considering different times and transfer flows while incorporating spatial dependence to improve model accuracy. We integrated smart card records and DBS data to identify transfer trips and categorized them into four types: morning access, morning egress, evening access, and evening egress. Using random forest and gradient boosting decision tree models, we found that (1) introducing spatial lag terms significantly improved model accuracy, indicating the importance of spatial dependence in bike-metro transfer; (2) the built environment's impact on bike-metro transfer exhibited distinct nonlinear patterns, particularly for bus stop density, house prices, commercial points of interest (POI), and cultural POI, varying by time and transfer flow; (3) SHAP value analysis further revealed the influence of urban spatial structure on bike-metro transfer, with residential and employment areas displaying different transfer patterns by time and transfer flow. Our findings underscore the importance of considering both built environment factors and spatial dependence in urban transportation planning to achieve sustainable and efficient transportation systems.
... As an important carrier of social and economic development [4,5], cities affect the efficient and sustainable development of society and the country. However, as the urbanization process continues to accelerate, the rapid expansion of urban development has shown several problems, such as overpopulation [6], soaring housing prices [7], uneven allocation of public resources [8], and industrial decline [9]. At present, as a powerful tool for resource allocation optimization and efficient information interaction, smart cities provide a new direction for the efficient governance of urban issues and have received great attention worldwide [10][11][12]. ...
As China is the largest developing country and the country with the largest volume of smart city construction, the Chinese government has promulgated a series of policies to develop smart cities vigorously. Thus, the understanding of smart city policies from the perspective of policy science theory is helpful in achieving a full understanding of the development stage and evolution path of smart cities and improving the implementation of smart city policies. In this study, text content mining and quantitative empirical analysis are used to investigate the structure and function of China’s smart city policy. The results demonstrate that China’s smart city policy has largely realized the evolutionary process of the policy keynote from “taking” to “giving” and the policy subject from “single” to “pluralistic”. The organizational structure has gradually turned from “multidisciplinary” to “cooperative governance”. The analysis results indicate that the policy guidance for smart cities should focus on the positive interaction between social needs and urban development regulations. It is also important to enhance citizen participation and redefine the role of the government in the development of smart cities.
... After the epidemic, the national tourism industry has surged to a new height. The demand for homestays and accommodation requirements are gradually increasing [16]. According to the 2023 Homestay Industry Insight Report released by Feizhu Travel, the keywords for homestays in the hot search are shown in Table I. ...
... In order to evaluate the association between bicycle infrastructure and activity and single-family house values in Tempe, Conrow, Mooney and Wentz employed measures of infrastructure and cycling traffic. They discovered that bicycle infrastructure density was positively correlated with home selling prices [26]. Based on a survey of apartment inhabitants in Perth, Melbourne, and Sydney, De Gruyter, Hooper, and Foster evaluate the sufficiency of off-street parking for flat households and give insights into the factors influencing both an under-and an oversupply of off-street parking. ...
The long-term rental apartment market in China is steadily growing to be a trillion-dollar sector, but excessive market growth can lead to several issues. Due to the high demand for long-term rentals, many investors will enter the market. Nevertheless, without direction and supervision, it is simple to overdevelop the supply of long-term rentals, which would have negative effects on the real estate market. Long-term rental apartments involve a variety of companies, and it is vital that we drive their beneficial growth. To provide other operators with a comparison to find flaws, enhance improvements, and prevent irrationally increasing the stock, this paper uses the Pythagorean fuzzy decision-making method to identify the most well-liked long-term rental apartment operators and the most significant rental needs of tenants in the market environment. The results of the study show that real estate developers’ flats are the most popular among the four major operators, and that C4: Providing emotional value, C7: Ability to resist risk and C8: Ability to prevent social incidents are aspects that tenants value more than others. The results of the study provide real estate operators with directions for optimization, provide other operators with criteria for improvement, prevent blind increases in rental stock and provide operators with a healthy competitive environment, which is of great significance to the healthy development of long-term rental apartments in China.
... Especially in the context of sustainable development, the role of infrastructure markets is undeniable given its important nexus of economic productivity, poverty reduction and growth (World Bank, 2019). Prior studies document important linkages between infrastructure and housing markets such as infrastructure and housing supply (Stover, 1987); urban infrastructure, housing finance, affordability and development (Bryant, 2017;Collier & Venables, 2016;Gallent et al., 2020;Kim, 1997); airport, local, bicycle infrastructure and housing prices (Conrow, Mooney, & Wentz, 2020;Lipscomb, 2003), infrastructure for new housing (Kellett & Nunnington, 2019). ...
This paper performs a formal investigation on the volatility connectedness, stability and potential breaks among global infrastructure and property markets. Global Infrastructure markets include utilities, industrials, energy, and Global Property markets contain property diversified, healthcare, industrials, office space, and residential. Unknown break tests, cumulative sum tests, granger causality test and the DCC-MGARCH model, the study provides the critical findings as follows: (1) the selected markets present a good stability through cumulative sum tests; however, there are still potential break dates detected via the potential unknown break tests in some of those markets and the Global Infrastructure Energy presents its earliest breaks compared to the other peer ones. (2) While the connectedness and systematic risk transmission among the sample markets are presented globally, the property markets show a stronger spill over effects compared to the infrastructure markets via the DCC-MGARCH pairwise correlation examinations. (3) the causality is well presented among both the infrastructure and property markets detected via the granger causality Wald tests. Our results imply the limitation in systematic risk diversification among the markets, especially within the property markets globally.
Using hedonic and spatial regressions, this paper estimates a significantly larger association between proximity to bicycle networks and property prices than previously reported. As cities face increasing challenges of congestion and pollution, many are implementing policies to integrate bicycle facilities and other active modes of transport. However, policymakers are slow to support these initiatives and remain skeptical due to the investment costs required and appropriation of limited land. Drawing on a large dataset of approximately 253,000 transactions in Greater Manchester, over a 9-year period, we find clear evidenced that a 1 km reduction in distance to the nearest bicycle network is associated with property values being around 2.8% higher, on average, and 7.7% higher in the central borough of Manchester. We also provide an applied example to rank new bicycle routes by comparing their benefit-to-cost ratios and a discussion on the property tax system.
Bicycle-metro integration, in which bicycling is used as a flexible feeder mode to connect with public transport nodes presents new opportunities for sustainable transportation. It is known that the built environment can influence travel attitudes and choice, yet the empirical evidence for the role of built environment features in shaping the bicycle-metro integration remains rare. Inspired by the idea of text mining, this article is an attempt to demonstrate a data-driven semantic framework to capture key topic-based features of land use and bicycle-metro integrated usage in the vicinity of metro stations as well as their interactions. Latent Dirichlet Allocation topic modeling is analogously implemented here to generate a range of probability-based land use patterns and mobility patterns, and the associations between them are investigated by multivariate linear regression. A case study from Shanghai shows that the mixed land use and diversification of urban functions in the catchment areas of the metro stations can be detected effectively by 11 identified land use patterns. Based on 7 derived mobility patterns, this paper gives a probabilistic explanation to the time-varying properties of the bicycle-metro usage. All of the above thematic topics exhibit notably heterogeneous patterns in spatial distribution. The topic compositions in terms of land use pattern and mobility pattern at the station level reveal the current performance of station areas. Plus, results from the regression analysis confirm that most of the land use patterns that are related to various mixed use have close relationships with mobility patterns of bicycle-metro integration. Yet it is noteworthy that the effects of land use patterns often differ and change over time, namely affecting different mobility patterns. This study gives rise to alternative insights into the synergy between bike sharing and metro, which may help policymakers to develop more targeted TOD strategies.
Existing problems such as waste of resources and lack of public access indicate that sustainable smart city construction should consider the balanced development of construction efficiency and performance. The study adopts the slacks-based measurement (SBM)-DEA model to measure the efficiency of smart city construction, a subsampling technique is adopted as a bootstrapping method, and applies the entropy weight technology to measure the performance of smart city construction. Finally, a model for categorizing the smart city construction is developed using the Boston matrix technique. Results show the following: (a) The smart city development patterns of the 33 prefecture-level cities show gradual improvement during the study period. Seven cities, including Yinchuan, Shenzhen, and Xiamen, are smart city pioneers. (b) Regional differences characterize the level of smart city construction in prefecture-level cities, with the eastern region exhibiting better smart city construction status than the central and western regions. (c) The four municipalities achieve outstanding results in terms of the smart city construction performance. However, apart from Beijing, which is a pioneer in smart city construction, the other three municipalities are not efficient in their construction. Finally, the study proposes policy recommendations for the promotion of the development of different types of smart cities.
This study investigates factors influencing housing prices in Tehran's metropolitan area and predicts their values. We analysed a sample of 32,162 housing units using Hedonic Model and Artificial Neural Networks for prediction and comparison. The Results indicate that distance to certain transportation facilities, such as airports, bus terminals, and road intersections, positively affects housing prices, potentially due to their capacity to attract traffic and environmental externalities. Conversely, distance to facilities like public bike stations and bus rapid transit stations negatively impacts prices, signalling their role in enhancing accessibility. Notably, ANN outperformed hedonic in predicting housing prices. These findings hold significant implications for policymakers, investors, and housing market stakeholders, shedding light on the intricate relationship between transportation infrastructure and housing prices. The study underscores the importance of employing diverse modelling methods to capture the non-linear dynamics of housing markets. These insights are crucial for evidence-based decision-making and can inform more effective policies and strategies in metropolitan Tehran and other similar cities. By aligning transportation policies with housing market needs, cities can enhance urban development and promote sustainable growth.
Smart cities produce high volumes of data that could be shared through APIs. This open data could be used to overcome the challenges of urbanization. This research aims at using the Location-based-services APIs to measure the attractiveness of a residence location for people who are searching for long-term rental apartments. The research uses the days on market as an indicator for residence attractiveness. This will occur through the developing a machine learning model to predict days on market whereas the urban features are part of the predictors. The research utilizes transaction data and profile information for long term rental apartments crawled from a real estate website. The research assumption has been tested using the principal component regression. Several machine learning models have been evaluated. The Gradient Boosting Regression is selected based on its performance. Shapley value is used to determine the comportment of the predictors in machine learning model. Although the urban feature component is a relatively weak predictor, but it is statically significant, whereas walkability, transit availability, and bikeability negatively influence the days on market while traffic congestion has a positive impact. These shows that renters prefer well serviced areas even when it is congested.
Rapid urbanization not only helps the urban economy achieve rapid growth but also imposes great pressure on resources and the environment. The green and blue infrastructure (GBI) in urban areas appears to be an important measure to enhance the economic benefits of the urban environment, but related research is still limited. In this article, we combine big data and machine learning methods to investigate the economic benefits of GBI in the city of Wuhan in Hubei Province. Specifically, the daily travel routes of citizens are analyzed at two scale levels (the housing and neighborhood scales); in our analysis, we start with the structural attributes of citizens’ own houses, move on to the city street views that citizens walk through, pass by the locational amenities where citizens spend their leisure time, and arrive at open spaces located throughout the city. The above trajectory dataset includes 30 variables, which were used as the input into ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale GWR (MGWR). The results show that MGWR (adj. R² = 0.524 at the housing scale, while adj. R² = 0.869 at the neighborhood scale) with all control variables and the GBI attribute variables have the highest goodness of fit: the closer GBI is to urban residents, the higher the economic benefits are regardless of the area; and the higher the street visible green rate is, the greater the economic benefits are. Therefore, urban areas can appropriately increase the number of well-designed small GBIs located near urban residents. Our research provides insights into how big data and machine learning can be employed in frameworks to characterize the economic benefits of GBI and can be applied in other countries and regions.
Cycling is a sustainable mode of transportation with numerous health, environmental and social benefits. Investments in cycling specific infrastructure are being made with the goal of increasing ridership and population health benefits. New infrastructure has the potential to impact the upgraded corridor as well as nearby street segments and cycling patterns across the city. Evaluation of the impact of new infrastructure is often limited to manual or automated counts of cyclists before and after construction, or to aggregate statistics for a large region. Due to methodological limitations and a lack of data, few spatially explicit approaches have been applied to evaluate how patterns of ridership change following investment in cycling infrastructure. Our goal is to demonstrate spatial analysis methods that can be applied to emerging sources of crowdsourced cycling data to monitor changes in the spatial-temporal distribution of cyclists across a city. Specifically, we use crowdsourced ridership data from Strava to examine changes in the spatial-temporal distribution of cyclists in Ottawa-Gatineau, Canada, using local indicators of spatial autocorrelation. Strava samples of bicyclists were correlated with automated counts at 11 locations and correlations ranged for 0.76 to 0.96. Using a local indicator of spatial autocorrelation, implemented on a network, we applied a threshold of change to separate noise from patterns of change that are unexpected given a null hypothesis that processes are random. Our results indicate that the installation or temporary closure of cycling infrastructure can be detected in patterns of Strava sample bicyclists and changes in one location impact flow and relative volume of cyclists at multiple locations in the city. City planners, public health professionals, and researchers can use spatial patterns of Strava sampled bicyclists to monitor city-wide changes in ridership patterns following investment in cycling infrastructure or other transportation network change.
Policymakers pay much attention to effectively increasing frequency of people’s cycling in the context of developing sustainable and green cities. Investigating associations of environmental characteristics and cycling behaviour could offer implications for changing urban infrastructure aiming at encouraging active travel. However, earlier examinations of associations between environmental characteristics and active travel behaviour are limited by low spatial granularity and coverage of traditional data. Crowdsourced geographic information offers an opportunity to determine the fine-grained travel patterns of people. Particularly, Strava Metro data offer a good opportunity for studies of recreational cycling behaviour as they can offer hourly, daily or annual cycling volumes with different purposes (commuting or recreational) in each street across a city. Therefore, in this study, we utilised Strava Metro data for investigating associations between environmental characteristics and recreational cycling behaviour at a large spatial scale (street level). In this study, we took account of population density, employment density, road length, road connectivity, proximity to public transit services, land use mix, proximity to green space, volume of motor vehicles and traffic accidents in an empirical investigation over Glasgow. Empirical results reveal that Strava cyclists are more likely to cycle for recreation on streets with short length, large connectivity or low volume of motor vehicles or on streets surrounded by residential land.
The aim of this chapter is to discuss hedonic pricing models that are often employed in housing research. Parametric models (loglinear OLS, Box-Cox OLS, and weighted least square models), nonparametric and kernel estimates (nearest neighbour and local polynomial estimates models) and semi-parametric models (Robinson-Stock, Yatchew's differencing and Clapp's local regression models) are discussed. The discussion shows that parametric models are easy to estimate and interpret but impose some strong assumptions, which can produce misleading results when the assumptions are violated. Even though the nonparametric models avoid the strong assumptions of parametric models, their computations are technical and complicated. The semi-parametric models combine elements of the parametric and nonparametric models, thereby, reducing the problems inherent in such models. Even though the simple parametric approach seems to be the most widely employed model, the chapter provides useful insights about other possible alternative models that can be applied in housing research. However, it is important to note that based on the advantages and disadvantages of each approach, no particular approach is necessarily superior to others. The overarching factors that ultimately determine the choice of an approach are research aim and objectives.
This article investigates the impact of neighborhood walkability, measured by Street Smart Walk Score and sidewalk density, on property values by analyzing the 2010–2012 single-family home sale transactions in Austin, Texas. The Cliff-Ord spatial hedonic model (also known as the General Spatial Model, or SAC) is used to control for spatial autocorrelation effects. Results show that improving walkability through increased access to amenities in car-dependent neighborhoods does not appear to increase property values; adding sidewalks in these neighborhoods leads to a minimal increase in property values. Investments in neighborhood amenities and sidewalks will yield a greater home price increase in a walkable neighborhood than in a car-dependent neighborhood.
This study investigated how the benefits of a walkable neighborhood were reflected in the American real estate market by examining the economic values of urban environmental factors supporting walking activities. Property values were used as a proxy measure for economic value and analyzed in relation to land use characteristics that have been known to correlate with walking at the neighborhood scale. Four aspects of the built environment supporting walking were included in the analyses: development density, land use mix, public open space and pedestrian infrastructure. Hedonic models were employed where the property value was regressed on the measures of the four sets of correlates of walking in a neighborhood. Models were estimated for four land use types – single-family residential, rental multi-family residential, commercial and office. The findings did not support previous arguments that increasing density weakens the quality of a neighborhood. To the contrary, the positive association of higher development density with the value of single-family residential properties detected in King County suggested that high development density might increase surrounding property values. The pedestrian infrastructure and land use mix significantly contributed to increases in rental multi-family residential property values. Higher development density with higher street and sidewalk coverage were also favored by retail service uses. In relation to land use mix, mixing retail service uses and rental multi-family residential uses helped make rental housings more attractive.
Planners and policy makers are increasingly aware that local amenities can play an important role in community and regional development and greenways represent one such amenity that can be locally developed. As planning initiatives compete for scarce resources it becomes vital that decision makers have ex ante information about the impact of policy options. This paper provides empirical estimates of changes in land value that should be expected if a planned greenway, the Catawba Regional Trail, is developed in Mecklenburg County, North Carolina. Passing through economically modest neighborhoods, the trail holds potential to increase equity in the distribution of open space though the aggregate impact on property values is limited by neighborhood characteristics. Using a hedonic land value model, we estimate the real estate premium associated with the trail and find that most of the impact to land values will be captured within 1000 ft of the planned greenway.
EXECUTIVE SUMMARY: Planners propose multi-use urban greenways to enhance urban form, promote conservation of habitat and biodiversity, provide opportunities for fitness, recreation, and transportation, and promote economic development. This paper presents a taxonomy of the values of greenways and illustrates how two particular types of values can be measured using complementary techniques. Impacts of greenways on property values in Indianapolis, Indiana are measured with geographic information systems (GIS) and hedonic price modeling using residential real estate sales data from 1999. Recreation values are measured for a greenway trail in Indianapolis with the travel cost method using data from a 2000 survey of trail users and counts of trail traffic taken in 1996. We show that some but not all greenways have a positive, significant effect on property values and that the recreation benefits of a trail exceed costs. Limitations of the approaches are reviewed, and the importance of values not amenable to quantification is discussed. The paper concludes with discussion of the implications of our findings. Planners can use the findings to illustrate the benefits of greenways, to address concerns about negative impacts of greenway systems, and to inform and design research studies.
The objective of this paper is to provide an empirical analysis of the impact traffic noise has on the values of single-family houses. Under the assumption that negative externalities are capitalized into house values, the hedonic price method is used. Issues of asymmetric information and disequilibrium are discussed and tested. Furthermore, the cost-benefit valuation has been corrected for the existence of property tax. Noise pollution was found to have a substantial negative effect on housing values. A single-family house of SEK975 000 would sell for SEK650 000 if located near a road where noise is loud, equivalent to a total discount of 30%.
This study examines the impacts of trails and greenbelts and other amenities on home value. Using the hedonic framework the
study provides analyses of a database consisting of roughly 10,000 sales of homes occurring from April 2001 to March 2002
in and around San Antonio, Bexar County, Texas. Among other things, our study shows that trails, greenbelts, and trails with
greenbelts (or greenways) are associated with roughly 2, 4, and 5%, price premiums, respectively. The following amenities:
proximity to golf course, neighborhood playground, tennis court, neighborhood pool, view, and cul-de-sac, all add significantly
to home value.
Providing safe, convenient places for walking and bicycling can reduce barriers to participating in regular physical activity. We examined bicycle- and pedestrian-related investments authorized by federal transportation legislation in 3,140 counties in the United States by region, population size and urbanization, social and economic characteristics, and indicators of travel-related walking and bicycling. From 1992 to 2004, states and counties implemented 10,012 bicycle- and pedestrian-related projects representing $3.17 billion in federal expenditures. We found disparities in implementation and system-building outcomes according to population size and location and social and economic indicators. Counties characterized by persistent poverty (odds ratio=0.69, 95% confidence interval 0.53-0.91) or low educational status (odds ratio=0.66, 95% confidence interval 0.52-0.84) were less likely to implement projects. Three key policy recommendations for improving public health outcomes are drawn from this research: Improved data tracking, more explicit linkages between transportation projects and public health, and improved planning assistance to underserved communities are all seen as essential steps.
We used Comparative Risk Assessment methods to estimate the health effects of alternative urban land transport scenarios for two settings-London, UK, and Delhi, India. For each setting, we compared a business-as-usual 2030 projection (without policies for reduction of greenhouse gases) with alternative scenarios-lower-carbon-emission motor vehicles, increased active travel, and a combination of the two. We developed separate models that linked transport scenarios with physical activity, air pollution, and risk of road traffic injury. In both cities, we noted that reduction in carbon dioxide emissions through an increase in active travel and less use of motor vehicles had larger health benefits per million population (7332 disability-adjusted life-years [DALYs] in London, and 12 516 in Delhi in 1 year) than from the increased use of lower-emission motor vehicles (160 DALYs in London, and 1696 in Delhi). However, combination of active travel and lower-emission motor vehicles would give the largest benefits (7439 DALYs in London, 12 995 in Delhi), notably from a reduction in the number of years of life lost from ischaemic heart disease (10-19% in London, 11-25% in Delhi). Although uncertainties remain, climate change mitigation in transport should benefit public health substantially. Policies to increase the acceptability, appeal, and safety of active urban travel, and discourage travel in private motor vehicles would provide larger health benefits than would policies that focus solely on lower-emission motor vehicles.
This study examines how errors in measuring marginal attribute pric es vary with the form of the hedonic price function. In simulations, consumers with known utility functions bid for houses with given attributes. Various forms of the hedonic function are estimated using equilibrium housing prices. Errors in estimating marginal attribute prices are calculated by comparing each consumer's equilibrium marginal bid vector with the gradient of the hedonic function. When all attributes are observed, linear and quadratic Box-Cox forms produce lowest mean percentage errors; however, when some attributes are unobserved or are replaced by proxies, linear and linear Box-Cox functions perform best. Copyright 1988 by MIT Press.
Some claim that cardependent cities contribute to obesity by discouraging walking and bicycling. In this article, we use household activity data from the San Francisco region to study the links between urban environments and nonmotorized travel.
We used factor analysis to represent the urban design and land-use diversity dimensions of built environments. Combining factor scores with control variables, like steep terrain, that gauge impediments to walking and bicycling, we estimated discrete-choice models. Builtenvironment factors exerted far weaker, although not inconsequential, influences on walking and bicycling than control variables.
Stronger evidence on the importance of urban landscapes in shaping foot and bicycle travel is needed if the urban planning and public health professions are to forge an effective alliance against cardependent sprawl.
Conventional bicycling data have critical limitations related to spatial and temporal scale when analyzing bicycling as a transport mode. Novel crowdsourced data from smartphone apps have the potential to overcome those limitations by providing more detailed data. Questions remain, however, about whether crowdsourced data are representative of general bicycling behavior rather than just those cyclists who use the apps. This paper aims to explore the gap in understanding of how conventional and crowdsourced data correspond in representing bicycle ridership. Specifically, we use local indicators of spatial association to generate locations of similarity and dissimilarity based on the difference in ridership proportions between a conventional manual count and crowdsourced data from the Strava app in the Greater Sydney Australia region. Results identify where the data correspond and where they differ significantly, which has implications for using crowdsourced data in planning and infrastructure decisions. Fourteen count locations had significant low-low spatial association; similarity was found more often in areas with lower population density, greater social disadvantage, and lower ridership overall. Five locations had high-high spatial association, or were locations of dissimilar rank values indicating that they did not have a strong spatial match. Higher coefficients of variation were associated with population density, the number of bicycle journey to work trips, and percentage of residential land use for the significant locations of dissimilarity. IRSD and bicycle infrastructure density were lower than the locations that were not significantly dissimilar. For the significant locations of similarity, all coefficient of variation measures were lower than the locations that were not significant. Areas where ridership show locations of similarity are those where it may be suitable to substitute conventional data for the more detailed crowdsourced data, given further investigation into potential bias related to rider demographics.
As many cities are investing in street improvements or transportation infrastructure upgrades to provide better bike access or more complete bike networks, many practitioners, planners, and policy makers are seeking more conclusive evidence about the economic value of bike infrastructure and bike facilities. With the use of residential property values as indicators of consumer preferences for bicycle infrastructure, many scholars have shown the importance of green space and off-street bike trails as amenities valuable to property owners. However, empirical evidence on the relationship of on-street bike facilities and property values remains relatively inconsistent. The unique focus of this study was advanced bike facilities that represented higher levels of bike priority or bike infrastructure investments shown to be more desirable to a larger portion of the population. Through the separate estimation of ordinary least squares hedonic pricing models and spatial autoregressive hedonic models of single and multifamily properties, it was found that proximity to advanced bike facilities (measured by distance) had significant and positive effects on all property values, which highlighted household preferences for high-quality bike infrastructure. Furthermore, the study showed that the extensiveness of the bike network (measured by density) was a positive and statistically significant contributor to the prices for all property types, even after proximity was controlled for with respect to bike facilities and other property, neighborhood, and transaction characteristics. Finally, estimated coefficients were applied to assess the property value impacts of the Green Loop (i.e., the proposed Portland, Oregon, signature bike infrastructure concept), which illustrated the importance of considering the accessibility and the extensiveness of bike facility networks.
Bicycle transportation increasingly has become a central focus of urban regions invested in the improvement of livability, sustainability, and public health outcomes. Recently, transportation agencies across North America have deployed travel surveys with a smartphone application to gain a better understanding of bicycling travel behavior to forecast travel, to invest in infrastructure, and for a variety of other purposes. A potential limitation of data sets crowdsourced with smartphones is sampling bias (i.e., the demographic characteristics of the smartphone application users may not match the characteristics of the cycling population). Such a bias can be caused by the passive nature of sample recruitment, by differences in access to smartphone ownership or in familiarity with the technology, or both. This study examined the characteristics of several user samples from bicycle smartphone application deployments in North America. Differences between these samples were highlighted, and the smartphone samp...
A house is made up of many characteristics, all of which may affect its value. Hedonic regression analysis is typically used to estimate the marginal contribution of these individual characteristics. This study provides a review of recent studies that have used hedonic modeling to estimate house prices. The findings indicate that slanted versus flat roof, sprinkler system, garden bath, separate shower stall, double oven and gated community positively affect selling price while not having attic space, living in an earthquake zone, proximity to a hog farm, proximity to a landfill, proximity to high voltage lines, corporate-owned properties, percentage of Blacks or Hispanics in an area and properties that require flood insurance negatively affect selling price.
Research on the role of bicycling for health through physical activity has been limited by the lack of information on where bicyclists ride. New big data sources available through smartphone-based applications provide a rich source to provide bicycle volume data more comparable to the scale of information available for automotive and public transit modes. In the case of smartphone apps for fitness tracking, results of this data can be used similar to the growing application of global positioning systems for automotive travel surveying. The authors evaluate data from Travis County, Texas for the purpose of determining where bicyclists ride, primarily for fitness purposes. Ride trip volumes are evaluated with residential and employment density, land use diversity, bicycle facilities and terrain to characterize places chosen for bicycling for health. Though limited to bicycle rides and routes voluntarily logged using the smartphone app, this method provides promise for applications in multi-modal transportation planning and health impact assessment studies.
To better understand bicyclists’ preferences for facility types, GPS units were used to observe the behavior of 164 cyclists in Portland, Oregon, USA for several days each. Trip purpose and several other trip-level variables recorded by the cyclists, and the resulting trips were coded to a highly detailed bicycle network. The authors used the 1449 non-exercise, utilitarian trips to estimate a bicycle route choice model. The model used a choice set generation algorithm based on multiple permutations of path attributes and was formulated to account for overlapping route alternatives. The findings suggest that cyclists are sensitive to the effects of distance, turn frequency, slope, intersection control (e.g. presence or absence of traffic signals), and traffic volumes. In addition, cyclists appear to place relatively high value on off-street bike paths, enhanced neighborhood bikeways with traffic calming features (aka “bicycle boulevards”), and bridge facilities. Bike lanes more or less exactly offset the negative effects of adjacent traffic, but were no more or less attractive than a basic low traffic volume street. Finally, route preferences differ between commute and other utilitarian trips; cyclists were more sensitive to distance and less sensitive to other infrastructure characteristics for commute trips.
How can people be encouraged to walk or cycle more? This article draws on the results of over 300 well-designed empirical studies to provide guidance on how specific strategies can influence walking or cycling for travel: community design, infrastructure availability, infrastructure quality, programming, pricing, and combined strategies. Urban environments with high levels of walking and cycling for travel typically represent a combination of many factors that help promote these modes. The most compelling argument, particularly for cycling, is that only via an integrated range of built environmental features (including infrastructure and facility improvements), pricing policies, or education programmes will substantive changes result. This is what has been occurring in The Netherlands, Denmark, and parts of Germany for decades. By linking research to practical advice, the article fills a gap between (a) the many excellent literature reviews pointing to where further research is needed and (b) useful practice-oriented guidelines based on experience.
Objective
We examine the effects of neighborhood walkability on house values. Recent research claims that walkability makes homes more valuable, ceteris paribus. We contend that some studies report a spurious effect of walkability because of differences between areas with high and low walkability. Methods
We replicate the positive effect of walkability on prices for single-family homes and condominiums in Miami, Florida, using a unique data set of house values and characteristics. We employ a fixed effects regression model instead of a traditional ordinary least squares regression model to account for the unobserved heterogeneity of neighborhoods. ResultsWe find that walkability's impact on housing value becomes statistically insignificant at the margin after controlling for heteroscedasticity and neighborhood fixed effects. Conclusions
The significant impact of the fixed effects suggests that something other than walkability is affecting prices and that better specified models are needed to discern the real price effects of walkability.
Recent consumer surveys and demographic analyses have indicated a growing market for pedestrian- and transit-designed development. Theoretically, this market shift should be reflected in the price people are willing to pay for those styles of development. This article traces the literature that uses hedonic price methods for testing this hypothesis, either by assessing pedestrian/transit-design development holistically or by evaluating its component parts. The literature confirms that the market shift is, indeed, being capitalized into real estate prices and demonstrates that the amenity-based elements of transit-designed development play an important positive role in urban land markets, independent of the accessibility benefits provided by transit.
This paper examines the impacts of a multi-purpose trail on residential property values in a hedonic model. Using a large housing data set in combination with street network distances, we show that proximity to trail entrances positively effects property values. Among other things, our study compares the hedonic model results from three different spatial specifications. We pay specific attention to the direct and indirect effects on residential property prices associated with potential changes in house characteristics. In addition, our study predicts property values around trail entrances using a ‘modified spatial predictive process’ approach that is well suited for capturing spatial dependence in large data sets.
The effect of greenways on surrounding residential property values remains somewhat of an unknown quantity. Though several studies have ascertained that nearby residents tend to view greenways as positive or neutral amenities that increase or have no discernible impact on property values and saleability, these results are mostly based on anecdote rather than actual market data. Using the hedonic pricing method, this study demonstrates that greenways may indeed have significant positive impacts on proximate properties' sales prices. Adjacency to a greenbelt produced significant property value premiums in two of three neighborhoods. Physical access to a greenbelt had a significant, positive impact in one case, but was insignificant in two others. No negative greenway impacts were recorded.
This study assesses the impact of proximity to light rail transit stations on residential property values in Buffalo, New York, where light rail has been in service for 20 years, but population is declining and ridership is decreasing. Hedonic models are constructed of assessed value for residential properties within half a mile of 14 light rail stations and independent variables are included that describe property characteristics, neighbourhood characteristics and locational amenities. The model suggests that, for homes located in the study area, every foot closer to a light rail station increases average property values by 0.99 (using network distance). Consequently, a home located within one-quarter of a mile radius of a light rail station can earn a premium of $1300-3000, or 2-5 per cent of the city's median home value. Model results further suggest that three independent variables-the number of bathrooms, size of the parcel and location on the East side or West side of Buffalo-are more influential than rail proximity in predicting property values. Individual regression models for each of the light rail system's 14 stations suggest that effects are not felt evenly throughout the system. Proximity effects are positive in high-income station areas and negative in low-income station areas. An analysis of the actual walking distance to stations (along the street network) versus the perceived proximity to stations (measured by straight-line distance) reveals that the results are statistically more significant in the network distance than the straight-line distance model, but the effects are greater in the straight-line distance model, which suggests that apparent proximity to rail stations is an added locational advantage compared with physical walking distance to the station.
A number of studies have focused on estimating the effects of accessibility on housing values by using the hedonic price model. In the majority of studies, estimation results have revealed that housing values increase as accessibility improves, although the magnitude of estimates has varied across studies. Adequately estimating the relationship between transportation accessibility and housing values is challenging for at least two reasons. First, the monocentric city assumption applied in location theory is no longer valid for many large or growing cities. Second, rather than being randomly distributed in space, housing values are clustered in space often exhibiting spatial dependence. Recognizing these challenges, a study was undertaken to develop a spatial lag hedonic price model in the Seoul, South Korea, metropolitan region, which includes a measure of local accessibility as well as system wide accessibility, in addition to other model covariates. Although the accessibility measures can be improved, the modeling results suggest that the spatial interactions of apartment sales prices occur across and within traffic analysis zones, and the sales prices for apartment communities are devalued as accessibility deteriorates. Consistent with findings in other cities, this study revealed that the distance to the central business district is still a significant determinant of sales price.
This article reports the results of two different approaches to valuing some of what are thought to be benefits of bicycle trails and lanes. First, an adaptive stated preference survey is used to measure how much travel time individuals are willing to spend to obtain particular features of on- and off-street bicycle facilities. These findings indicate that bicycle commuters in Minneapolis and St. Paul prefer bicycle lanes on existing streets over off-street bicycle trails, and also prefer them over streets that have no onstreet parking but lack designated bicycle lanes. Second, I used home sales data to learn the effect of bicycle trail proximity on home value. Though proximity to bicycle facilities is valued differently for different types, it actually significantly reduced home value in suburban locations. Suburban home values were most reduced by proximity to roadside trails, which also reduced home values significantly in the cities. Proximity to other types of bicycle facilities in the citie...
The literature shows single-use, low- density land development and discon- nected street networks to be positively associated with auto dependence and negatively associated with walking and transit use. These factors in turn appear to affect health by influencing physical activity, obesity, and emissions of air pollutants. We evaluated the association between a single index of walkability that incorporated land use mix, street connec- tivity, net residential density, and retail floor area ratios, with health-related outcomes in King County, Washington. We found a 5% increase in walkability to be associated with a per capita 32.1% increase in time spent in physically active travel, a 0.23-point reduction in body mass index, 6.5% fewer vehicle miles traveled, 5.6% fewer grams of oxides of nitrogen (NOx) emitted, and 5.5% fewer grams of volatile organic compounds (VOC) emitted. These results connect development patterns with factors that affect several prevalent chronic diseases. Lawrence D. Frank is an urban planner
Planning and policy efforts at all levels of transportation planning aim to increase levels of bicycling. An initial step in doing so is to ensure that a variety of facilities exist for bicycling, such as relatively wide curb lanes, on-street bike paths, or off-street bike paths. However, providing such facilities costs money. In some eyes, spending for such facilities is considered a luxury and the merits of such expenditures are increasingly being called into question. In austere economic times, bicycle advocates are grasping for ways to "economize" their beloved facilities. How can they demonstrate that their facilities matter and will make a difference towards inducing increased use? The purpose of this paper is to review and interpret the literature that evaluates the economic benefits of bicycle facilities and to propose methods for their estimation. We first identify and factors that confound the degree to which one can estimate bicycle benefits using a consistent framework. Following this, we briefly describe 25 studies that speak to economic dimensions of bicycle facilities. The final section interprets the existing literature by describing six core benefits of municipal and regional bicycle facilities and suggests strategies for how each could be estimated. These benefits include social transportation benefits (congestion, air quality, energy), user transportation benefits, social benefits, (livability and option value), user safety benefits, user health benefits, and agency benefits from right-of-way preservation. We conclude by proposing how this framework could be built upon and the increased challenges that lie ahead.
In a survey of 1,402 current and potential cyclists in Metro Vancouver, 73 motivators and deterrents of cycling were evaluated.
The top motivators, consistent among regular, frequent, occasional and potential cyclists, were: routes away from traffic
noise and pollution; routes with beautiful scenery; and paths separated from traffic.In factor analysis, the 73 survey items
were grouped into 15 factors. The following factors had the most influence on likelihood of cycling: safety; ease of cycling;
weather conditions; route conditions; and interactions with motor vehicles. These results indicate the importance of the location
and design of bicycle routes to promote cycling.
KeywordsBicycle-Survey-Infrastructure-Influence-Non-motorized transport
This paper presents spatially explicit analyses of the greenspace contribution to residential property values in a hedonic
model. The paper utilizes data from the housing market near downtown Los Angeles. We first used a standard hedonic model to
estimate greenspace effects. Because the residuals were spatially autocorrelated, we implemented a spatial lag model as indicated
by specification tests. Our results show that neighborhood greenspace at the immediate vicinity of houses has a significant
impact on house prices even after controlling for spatial autocorrelation. The different estimation results from non-spatial
and spatial models provide useful bounds for the greenspace effect. Greening of inner city areas may provide a valuable policy
instrument for elevating depressed housing markets in those areas.
KeywordsHousing value-Urban greenspace-Hedonic pricing model-Spatial dependence
To successfully stimulate cycling, it is necessary to understand the factors that facilitate or inhibit cycling. Little is known about how changes in the neighborhood environment are related to changes in cycling behavior.
This study aimed to identify environmental determinants of the uptake of cycling after relocation.
The RESIDential Environment Project (RESIDE) is a longitudinal natural experiment of people moving into new housing developments in Perth (Western Australia). Self-reported usual transport and recreational cycling behavior, as well as self-reported and objective built environmental factors were measured before and after residential relocation. Participants who did not usually cycle at baseline in 2003-2004 were included in the study. Logistic regression models were used to relate changes in built environmental determinants to the probability of taking up cycling after relocation (2005-2006). Analyses were carried out in 2010-2011.
At baseline, 90% (n=1289) of the participants did not cycle for transport and 86% (n=1232) did not cycle for recreation. After relocation, 5% of the noncyclists took up transport-related cycling, and 7% took up recreational cycling. After full adjustment, the uptake of transport-related cycling was determined by an increase in objective residential density (OR=1.54, 95% CI=1.04, 2.26) and self-reported better access to parks (OR=2.60, 95% CI=1.58, 4.27) and other recreation destinations (OR=1.57, 95% CI=1.12, 2.22). Commencing recreational cycling mostly was determined by an increase in objective street connectivity (OR=1.20, 95% CI=1.06, 1.35).
Changes in the built environment may support the uptake of cycling among formerly noncycling adults.
Urban open space provides a number of valuable services to urban populations, including recreational opportunities, aesthetic enjoyment, environmental functions, and may also be associated with existence values. In separate meta-analyses of the contingent valuation (CV) and hedonic pricing (HP) literature we examine which physical, socio-economic, and study characteristics determine the value of open space. The dependent variable in the CV meta-regression is defined as the value of open space per hectare per year in 2003 US$, and in the HP model as the percentage change in house price for a 10 m decrease in distance to open space. Using a multi-level modelling approach we find in both the CV and HP analyses that there is a positive and significant relationship between the value of urban open space and population density, indicating that scarcity and crowdedness matter, and that the value of open space does not vary significantly with income. Further, urban parks are more highly valued than other types of urban open space (forests, agricultural and undeveloped land) and methodological differences in study design have a large influence on estimated values from both CV and HP. We also find important regional differences in preferences for urban open space, which suggests that the potential for transferring estimated values between regions is likely to be limited.
Data file: Police general offense
- City Of Tempe
Maricopa County Tax Levy
- Maricopa County
Location Database (SLD) (2010) Smart Location Database 2.0. Accessed using EPA’s Clip and Ship Tool
- Us Epa
- Smart
American Community Survey 5-year Estimates
- Us Census
- Bureau
Transit-oriented development and joint development in the United States: A literature review
- R Cervero
- C Ferrell
- S Murphy
Modes less traveled – Bicycling and walking to work in the United States: 2008–2012
- B Mckenzie
Maricopa County 2016 Tax Rates
- Maricopa County
Modes less traveled - Bicycling and walking to work in the United States
- B Mckenzie