Spatial unconditional quantile regression: application to Japanese parking price data
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Spatial unconditional quantile regression: application to Japanese parking price data Hajime Seya1 · Kay W. Axhausen2 · Makoto Chikaraishi3 Received: 14 May 2019 / Accepted: 24 February 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The present study develops a spatial unconditional quantile regression by extending Firpo et al.’s (Econometrica 77:953–973, 2009) unconditional quantile regression and empirically investigates the determinants of parking prices at different quantiles of prices in Japan. The empirical results suggest that spatial competition in terms of unit price and the unit time play important roles in determining parking prices. On the contrary, price is unaffected by demand, approximated by adopting several employment density variables and aggregated people flow data obtained from cell phones. Besides, significant differences exist among the factors that affect parking prices during the day and at night as well as among the unconditional quantiles. JEL Classification C21 · R41 · R12
1 Introduction Approaches to determining pricing for parking are changing quickly. A decade ago, Shoup (2005) proposed using smart meters and ground sensors to measure parking occupancy and adjust prices accordingly. The Shoup (2005) proposals adjust park‑ ing prices on each block for each hour of the day until there is always at least one * Hajime Seya [email protected]‑u.ac.jp Kay W. Axhausen [email protected] Makoto Chikaraishi [email protected] 1
Departments of Civil Engineering, Graduate School of Engineering Faculty of Engineering, Kobe University, 1‑1 Rokkodaicho, Nada, Kobe, Hyogo 657‑8501, Japan
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Institute for Transport Planning and Systems, ETH Zürich, Stefano‑Franscini‑Platz, 58093 Zurich, Switzerland
3
Graduate School for International Development and Cooperation, Hiroshima University, 1‑5‑1, Kagamiyama, Higashi‑hiroshima, Hiroshima 739‑8529, Japan
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free space available for an arriving driver. When prices are set in this manner, driv‑ ers need not search for parking spaces or even slow down on the way to their desti‑ nation. This approach, which essentially matches supply and demand, removes the externalities of the search, and provides an incentive for more off-street supply, as developers can directly observe drivers’ willingness to pay. Indeed, recent experi‑ ments in San Francisco based on Shoup’s (2005) proposals and reported in MillardBall et al. (2013, 2014) and Pierce and Shoup (2013) show the feasibility of marketbased pricing1 (see also Barter (2010) for a cautionary note). Axhausen et al. (2015) argued that Japanese cities are a good approximation of the Shoup (2005) approach because most parking lots in cities are market-priced (Morikawa et al. 2010; Barter 2011).2 The present study adds to the evidence in the literature on parking pricing by conducting an empirical analysis of parking price setting under nearly free market conditions—the Japanese coin-parking mar‑ ket—with a case study of Hi
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