Traffic congestion, accessibility to employment, and housing prices: A study of single-family housing market in Los Angeles County

Abstract

This study mainly addresses two main questions: (1) whether traffic congestion negatively affects single-family house price by constraining accessibility to jobs; (2) whether congestion effects and accessibility effects vary by income groups within a metropolitan area. This study uses a multilevel hedonic price model to estimate the marginal price of accessibility while controlling for other neighbourhood attributes and the correlation of proximal housing sales. The congestion effects are identified by comparing the implicit price of accessibility between congested-flow and free-flow. The results show that the accessibility measured with congested time yields higher marginal price, suggesting that households are willing to pay more to avoid locations with high congestion delays and accessibility loss. The results also suggest that accessibility effects are more valued by homebuyers in middle-income neighbourhoods, compared with those in the lowest or highest income neighbourhoods.

Interaction of Public Transport Accessibility and Residential Property Values Using Smart Card Data

“…Future research may consider developing alternative indicators which could capture the two effects independently from one another. Lastly, high access to employment opportunities positively influences property values, which is in accordance with many related studies [72,74]. This is also in line with the existing literature on housing [75][76][77], where job accessibility is frequently found to be an important factor when households determine their housing location choice.…”

“…A similar pattern was also observed in the number of evening peak trips except in and around the CBD, where a negative relationship with property values exists. This may be due to the possible nuisances of living in the city's core, such as noise pollution [71] and congestion [72]. The aforementioned annoyances are also true for properties located near transit hubs.…”

The Impact of Population Migration on Urban Housing Prices: Evidence from China’s Major Cities

“…With regard to the research into demographic factors, the existing literature has focused on the demographic age structure [41- With the booming development of the real-estate market and the rapid growth of housing prices, the factors influencing housing prices have attracted wide attention from scholars at home and abroad. Focused on the factors affecting housing prices, the existing research has achieved very fruitful results, including in macroeconomics (real-estate investment [9], economic growth [10,11], monetary policy [12,13] and inflation [14]), politics (urban hierarchy [15] and government policy [8]), society (population factors [16,17], social environment [18] and urbanization [19,20]), physical geography (geographical location [21], natural features [22] and environmental health risks [23,24]) and micro-aspects of real-estate characteristics (walled buildings [25], cost of construction and installation [26,27], house type [28,29], locational conditions [30], educational resources [31], infrastructure [32][33][34] and neighborhood factors [35,36]), and the household and individual characteristics of household buyers (income [37,38], the Dutch index of consumer confidence [39] and consumer expectations [40]). As the main subject of investing, consuming, using and disposing of real estate, the human factor has gradually become the research focus.…”

Light at the end of the tunnel:The impacts of expected major transport improvements on residential property prices

“…Models 1 and 2 have the same specification but estimated by using OLS and multilevel regression (MLR) technique (Raudenbush and Bryk, 2002), respectively. MLR is often used when data are nested, for example, subway stations within districts (see, for example, Hou, 2017;Zolnik, 2020). By taking into account the variations of property prices within each neighbourhood, MLR results have more reliable coefficient estimates and standard errors.…”