Kenneth Joh

Retrofitting the Suburbs to Increase Walking: Evidence from a Land-use-Travel Study

This paper reports results from a detailed travel diary survey of 2125 residents in the South Bay area of Los Angeles County—a mature, auto-oriented suburban region. Study areas were divided into four centres, typical of compact development or smart growth, and four linear, auto-oriented corridors. Results show substantial variation in the amount of walking across study areas. Trips are shorter and more likely to be via walking in centres. A key to the centres’ increased walking travel is the concentration of local shopping and service destinations in a commercial core. Yet the amount of business concentration that is associated with highly pedestrian-oriented neighbourhoods is from three to four times as large as what can be supported by the local resident base, suggesting that pedestrian-oriented neighbourhoods necessarily import shopping trips, and hence driving trips, from larger surrounding catchment areas. The results suggest both land use and mobility strategies that can be appropriate for suburban regions.

Can Built and Social Environmental Factors Encourage Walking among Individuals with Negative Walking Attitudes

We investigate whether the design of the built environment encourages walking above and beyond individuals’ attitudes toward walking. With data from a regional travel survey, we use regression analyses to examine differences in neighborhood walking trips among residents with positive and negative attitudes toward walking. The results show that built and social environment factors have a differential impact on walking trips depending on a person’s walking attitudes. Therefore, strategies to promote positive walking attitudes should be pursued in tandem with land use policies to encourage neighborhood walking.

Assessing Benefits of Neighborhood Walkability to Single-Family Property Values A Spatial Hedonic Study in Austin, Texas

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.

From car-dependent neighborhoods to walkers' paradise: Estimating walkability premiums in the condominium housing market

Movement toward compact and walkable communities and low-carbon travel has gained momentum through various policy initiatives. However, residents’ willingness to pay for neighborhood walkability is still unknown. This study addresses that knowledge gap by quantifying walkability premiums based on 3,899 condominium sale transactions from 2010 to 2012 in Austin, Texas. Two measures of walkability were compared: Walk Score and Street Smart Walk Score. A spatial hedonic modeling approach improved estimation accuracy by controlling for spatial autocorrelation effects. The premium for walkability was found to be influenced by the pedestrian collision rate, street connectivity, length of sidewalks, speed limit, and various sociodemographic factors. A positive walkability premium was identified for 57% of sampled condominiums, most of which were located in neighborhoods that were at least somewhat walkable. The results could be used to identify investment priorities for creating walkable neighborhoods.

Unpacking the impacts of the Low-Income Housing Tax Credit program on nearby property values

Most existing research on the impacts of the Low-Income Housing Tax Credit (LIHTC) on neighbouring property values is limited in terms of providing causal attribution and uncovering nuances in the role of housing market and neighbourhood composition. This article addresses these shortcomings by investigating the impacts of the LIHTC program in Charlotte, North Carolina and Cleveland, Ohio. Levels and trends in housing prices before and after LIHTC developments in neighbourhoods are examined based on parcel-level housing sales data from 1996 to 2007. The Adjusted Interrupted Time Series-Difference in Differences (AITS-DID) model is used to clarify the causal direction of impacts of LIHTC developments. The results show that LIHTC developments have negative impacts in Charlotte, while having upgrading effects in Cleveland. Also, these impacts vary across neighbourhoods’ income heterogeneity. Thus, care should be taken when siting LIHTC developments to minimise negative impacts and enhance its use for community revitalisation across different housing market conditions.

Impacts of the Low-Income Housing Tax Credit Program on Neighborhood Housing Turnover

Although the Low-Income Housing Tax Credit (LIHTC) program has grown into the largest subsidized housing program in the United States, we have limited understanding of how these units affect the neighborhoods in which they are located. This article examines impacts of the LIHTC program on neighborhood stability in the cities of Charlotte and Cleveland. We examine housing turnover before and after the introduction of LIHTC developments into the neighborhood, based on housing sales data from 1996 to 2007. Location data on LIHTC units are merged with parcel-level sales data from Mecklenburg and Cuyahoga Counties. We estimate an extended Cox hazard model with the difference-in-difference specification to determine whether LIHTC units are causing increased turnover of neighborhood housing. We also explore impacts on neighborhood stability from LIHTC developments citywide and in neighborhoods stratified by income. We find significant impacts on the stability of LIHTC developments in both cities. Particularly, in higher-income submarkets, strong spillover effects were observed in Charlotte. Our results suggest that the stability of neighborhoods may be an important factor when considering the siting of LIHTC developments.

A Spatial and Longitudinal Analysis of Unmet Transportation Needs During Hurricanes Katrina and Rita

Abstract Hurricanes Katrina and Rita struck the US Gulf Coast in 2005, leading to the largest mass evacuation in US history and straining the region’s transportation infrastructure and services. This case study addresses the topic of disaster response to transportation unmet needs through an unprecedented spatial and longitudinal analysis of transportation-related disaster 2-1-1 call data collected in real-time, allowing for the investigation of unmet transportation needs by location and disaster phases. The authors analyze 25,205 transportation-related calls logged in Texas’ 25 regional 2-1-1 Area Information Centers from August 1 to December 31, 2005, including a baseline period before Hurricane Katrina, evacuation and landfall, and 3-months recovery post-Hurricane Rita. The spatial results show that transportation unmet needs were concentrated in Texas’ major metropolitan areas, especially in Houston-Galveston, and along highway evacuation routes. However, after controlling for population size, areas close to the landfall site and evacuation destinations had greater unmet transportation needs. Longitudinally, transportation unmet needs surged during evacuation and immediate disaster response then returned to baseline levels during recovery. Based on the results of the case study analyzing Texas 2-1-1 call data of unmet transportation needs, strategies and policies for improving mass evacuation and transportation support services are proposed and discussed.