The Impact of Ride-Hailing Services on Real Estate Prices in Urban Districts PDF Download

Author: Amit Serusi
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Languages : en
Pages : 0

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Book Description
Throughout history, real estate prices and transportation accessibility have had a strong relationship (Debrezion et al., 2011; Rodriguez & Targa, 2004). One of the most common sayings in the real estate industry is "location, location, location", especially when it comes to choosing and valuing a property (Atack & Margo, 1998). One critical element in determining whether a property's location is ideal, is its accessibility to a variety of desired locations, such as center city regions (Czamanski, 1966). Accessibility is determined by convenience, specifically the time and cost of the commute to and from desired destinations whether by foot, self-owned cars, or traditional public transportation methods such as rapid rail, trains, and buses (Pivo & Fisher, 2011; John, 1996; Raymond & Love 2000; Bowes & Ihlanfeldt, 2001). Furthermore, proximity to public transportation increases accessibility, and as a result, properties near public transportation stations enjoy higher real estate prices (Grass, 1992; Agostini & Palmucci, 2008; Sharma & Newman, 2018; Cervero, 2006; Czamanski, 1966).While the use of traditional transportation methods is still widespread, over the last several years, there has been a shift and more people are relying on new transportation solutions (Sadowsky & Nelson, 2017; Dong, 2020), with one of the most popular being ride-hailing services (Clewlow & Mishra, 2017), such as companies like Uber and Lyft. These ride hailing services impact the usage of different public transportation methods (Clewlow & Mishra, 2017; Badar, 2020; Rayle et al., 2014; Dong, 2020; Sadowsky & Nelson, 2017) by providing a new, convenient, and affordable method of transport (Nguyen-Phuoc et al., 2020) without the need for proximity to public transportation stations. In an effort to expand on the current body of literature and based on previous studies that have shown the impact of a property's proximity to a rapid rail station on the property's price (Agostini & Palmucci, 2008; Grass, 1992), this study will explore the impact of ride-hailing services on real estate prices for properties located in different distance categories from a rapid rail station. Rapid rail also known as subway, metro railway, elevated railway, and rapid transit (American Public Transit Association, 1994; New York State Department of Transportation, n.d) includes all underground and elevated stations (Metropolitan Transportation Authority, n.d). The following hypothesis (H1) was formulated based on the existing literature and the relationships discussed above: Ride-hailing services will have a significant effect on real estate prices per square foot. To further explore the effect of ride-hailing services on real estate prices based on property location, this study examines the effect of ride hailing usage on prices of properties located in an immediate distance to rapid rail stations versus those that are located further away. Past studies have demonstrated that a desired accessible distance from public transportation is about 0.3 miles (Al-Mosaind et al., 1993; Olszewski and Wibowo, 2005; Hess et al., 2007; Daniels and Mulley, 2013). As mentioned above, ride-hailing services, as a new convenient, and affordable method of transport (Nguyen-Phuoc et al., 2020) provide an alternative to public transportation. Thus, the impact of ride-hailing services on public transportation usage (Clewlow & Mishra, 2017; Badar, 2020; Rayle et al., 2014; Dong, 2020; Sadowsky & Nelson, 2017), may in turn decrease the attractiveness of properties located in the immediate location of rapid rail stations and increase the attractiveness of properties located further away. One goal of this study was to explore this further by examining the relationship between ride-hailing services and real estate prices for properties located less than 0.3 miles versus those located greater than or equal to 0.3 miles from rapid rail stations and the following hypotheses were formulated: H1.a) Ride-hailing services will have a significant negative effect on real estate prices per square foot for properties located in a distance less than 0.3 miles from a rapid rail station; H1.b) Ride-hailing services will have a significant positive effect on real estate prices per square foot for properties located in a distance greater than or equal to 0.3 miles from a rapid rail station. Past studies have found a positive relationship between a property's proximity to a rapid rail station and a property's price (Agostini & Palmucci, 2008; Grass, 1992; Czamanski, 1966). While the first hypotheses in this study explore the relationship between ride-hailing services and real estate prices, the second hypothesis will examine the impact of a property's proximity to a rapid rail station on the relationship between ride-hailing services and real estate prices. Therefore, the second hypothesis (H2) suggests that a property's proximity to a rapid rail station will moderate the relationship between ride-hailing services and real estate prices per square foot. To explore the hypotheses above, this study uses an extensive data set of historic sales price records in New York City for each of the five boroughs (Manhattan, The Bronx, Brooklyn, Queens, Staten Island). The data set includes various variables per sales transaction including address, sales price, total square foot (SF), total units, borough, neighborhood, zip code, building classification, and sales transaction date. In order to determine the proximity of each of the properties to the nearest rapid rail station, the distance from a property's address to all 472 rapid rail stations in New York City was analyzed and the shortest distance to a rapid rail station was then used as an additional variable. In order to measure the level of usage of ride-hailing services in New York City during the sales transaction years, this study uses public data from New York City Open Data (2020), that shows the number of dispatched trips per month per ride hailing company between 2015 and 2019. To analyze the relationship between ride-hailing usage and property's sale price per SF, general linear model (GLM) analysis was conducted with total units as the control variable, and year of sale, building classification, borough, neighborhood, rapid rail station, and zip code as fixed effects for hypotheses H1, H1.a, H1.b, and H2. The main data set in this study which pertained to the five boroughs of New York City was used for the analyses of all hypotheses. To further support the findings in this study, two additional analyses were conducted. First, additional analyses of all hypotheses (H1, H1.a, H1.b, and H2) were examined using data from Queens alone, the largest borough in New York City. Second, in addition to the distance category of 0.3 miles, the distance category of 0.35 miles, was used to examine hypotheses H1.a, H1.b, and H2. As expected, a significant positive relationship between ride-hailing usage and property's sale price per SF was observed. The results in this study found that the usage of ride-hailing services positively contributed to an increase in real estate prices. Furthermore, in general, the study shows that an increase in property's distance from the nearest rapid rail station positively moderates the relationship between ride-hailing usage and real estate prices. These findings imply that ride-hailing services, as a new method of transportation accessibility, increase the attractiveness and ultimately the price of properties located further from rapid rail stations. In other words, ride-hailing services enable accessibility and negate the need to be located in the immediate area of a rapid rail station. The current transportation revolution may impact the real estate industry even more than the change observed in the 1950s, after the mass adoption of automobiles (CBRE, 2019). When considering all the recent transportation changes, it is likely that real estate location preference and urban real-estate design will be impacted. In other words, the relatively new "player" in the transportation industry, ride-hailing, will impact the traditional relationship between transportation accessibility and real estate prices, as suggested by the study. The results from this pioneering study provide valuable information to city and state authorities, financial institutions, real estate investors and developers, and home buyers.