# RECORD c98a43acf9eb55645cbcf1d65650ddce2b8ab2c6
# TITLE: The social value of a ridesharing platform: a hedonic pricing approach
# YEAR: 2022.0  VENUE: Empirical Economics  DOI: 10.1007/s00181-022-02310-9
# AUTHORS: Yongwook Paik; C. Makridis
# PAGES: 26  CANDIDATE TABLE PAGES (1-based): [11, 16, 17, 18, 19]
# ABSTRACT: Governments around the world have been facing the challenges of regulating the ever-increasing prevalence of ridesharing platforms, leading to a proliferation of academic and policy debates about the social value of such platforms. However, the overall social value is still unclear because when analyzing the net benefit provided by ridesharing platforms, prior studies have focused on either supply side (i.e., drivers) or demand side (i.e., passengers) users without considering nonusers. Conversely, some studies that have considered the spillover effect of a ridesharing platform on nonusers have not simultaneously considered the net benefit for users. In this study, we propose that a hedonic pricing model can be used as a reasonably straightforward approach to collectively quantify the social value of a ridesharing platform for both users and nonusers. Using this approach, we exploit plausibly exogenous variation in the staggered entry of Uber into different metropolitan areas between 2010 and 2016 and find that the entry of Uber leads to, on average, a 2.8% increase in median housing prices per square foot within a metropolitan area. We also find that there is substantial spatial heterogeneity, with benefits concentrated in areas with greater dependence on public transportation and higher levels of traffic delay. By quantitatively computing the social value of a ridesharing platform with a hedonic pricing model, our study helps government regulators generate policies that are more likely to promote public welfare and become less susceptible to political whims.


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Empirical Economics (2023) 64:2125–2150
https://doi.org/10.1007/s00181-022-02310-9
The social value of a ridesharing platform: a hedonic pricing
approach
Yongwook Paik1
· Christos A. Makridis2
Received: 15 June 2021 / Accepted: 13 September 2022 / Published online: 25 September 2022
© The Author(s) 2022
Abstract
Governments around the world have been facing the challenges of regulating the ever-
increasing prevalence of ridesharing platforms, leading to a proliferation of academic
and policy debates about the social value of such platforms. However, the overall social
value is still unclear because when analyzing the net benefit provided by ridesharing
platforms, prior studies have focused on either supply side (i.e., drivers) or demand
side (i.e., passengers) users without considering nonusers. Conversely, some studies
that have considered the spillover effect of a ridesharing platform on nonusers have
not simultaneously considered the net benefit for users. In this study, we propose that a
hedonic pricing model can be used as a reasonably straightforward approach to collec-
tively quantify the social value of a ridesharing platform for both users and nonusers.
Using this approach, we exploit plausibly exogenous variation in the staggered entry
of Uber into different metropolitan areas between 2010 and 2016 and find that the entry
of Uber leads to, on average, a 2.8% increase in median housing prices per square foot
within a metropolitan area. We also find that there is substantial spatial heterogeneity,
with benefits concentrated in areas with greater dependence on public transportation
and higher levels of traffic delay. By quantitatively computing the social value of a
ridesharing platform with a hedonic pricing model, our study helps government reg-
ulators generate policies that are more likely to promote public welfare and become
less susceptible to political whims.
Keywords Platform · Hedonic pricing · Social value · Ridesharing · Uber
Yongwook Paik and Christos A. Makridis have equal contribution and are listed in random order.
B Yongwook Paik
ywpaik@kaist.ac.kr
Christos A. Makridis
cmakridi@stanford.edu
1
KAIST College of Business, Korea Advanced Institute of Science and Technology, 85 Hoegiro,
Dongdaemoon-gu, Seoul 02455, Korea
2
Stanford University, 210 Panama Street, Stanford, CA 94305, USA
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The social value of a ridesharing platform: a hedonic pricing approach
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Table 1 Characterizing the quasi-random nature of Uber entries into metropolitan areas. Sources: Census
Bureau, Zillow, Quarterly Census of Employment and Wages, 2010–2016. The table reports the coefficients
associated with logit regressions of Uber’s entry (at a metro × month frequency) on housing price per square
foot, employment growth, and wage growth conditional on year and month fixed effects and a range of
metro × year demographics, including logged population, the share of males, the age distribution (0–17,
18–34, 35–64), the race distribution (the share of African Americans), the share of married individuals,
and the education distribution (no high school, high school, some college, college). The table shows that
contemporaneous and one-year lagged housing prices, employment, and wage growth do not predict Uber’s
entry into a metro area. Standard errors are clustered at the metro level and are shown using brackets
Dependent variable =
Uber entry into area
(1)
(2)
Housing price growtht
−2.86
−6.11
[2.15]
[4.74]
Employment growtht
2.50
3.36
[4.08]
[3.72]
Wage growtht
0.21
0.33
[1.89]
[1.55]
Housing price growtht-1
1.95
[3.08]
Employment growtht-1
−0.57
[1.97]
Wage growtht-1
−0.14
[1.05]
R-squared
0.76
0.76
Sample size
9347
9232
Metro controls
Yes
Yes
Year + month FE
Yes
Yes
4 Results
4.1 Net effect of Uber on local property values
Table 2 presents our main results associated with Eq. (1) when our outcome variable is
logged median housing prices per square foot at the zip code level. Not surprisingly, the
cross-sectional estimator suggests that there is a strong positive correlation between the
areas Uber enters and housing prices when we do not control for confounding factors.
For example, Column (1) shows that the property values tend to be 11.0% higher in
places where Uber entered. However, once we add control variables for demographic
conditions to model (2), the conditional correlation declines to an implied 5.4% rise
in property values following the entry of Uber. However, these correlations may still
be potentially biased by time-invariant factors across locations or by seasonal factors
because fixed effects are not added.
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Table 2 Estimating the effects of uber entry on housing values. Sources: Census Bureau, Zillow, 2010-2016.
The table reports the coefficients associated with regressions of logged median housing prices per square
foot on an indicator for the month × year that Uber entered a metropolitan area, conditional on metropolitan
controls, which include logged population, logged(traffic delay indicator), logged(air quality indicator), the
share of males, the age distribution (0–17, 18–34, 35–64), the race distribution (white, black), the share of
married individuals, and the education distribution (no high school, high school, some college, college).
Standard errors are clustered at the metro level and are shown using brackets
Dependent variable =
ln(median housing price/sq ft)
(1)
(2)
(3)
1[Uber entry]
0.110∗∗∗
0.054∗
0.028∗∗
[0.020]
[0.028]
[0.010]
R-squared
0.02
0.30
0.98
Sample size
398,209
225,176
224,824
Metro controls
No
Yes
Yes
Zip code FE
No
No
Yes
Year + quarter FE
No
No
Yes
*** p value < 0.01, ** p value < 0.05, * p value < 0.10
Consistent with this view, the inclusion of zip code and time fixed effects reduces
the marginal effect of Uber’s entry to 0.028, as shown in model (3). Nonetheless, the
estimated effect of Uber’s entry on the local housing market remains positive and statis-
tically significant, implying that there is a 2.8% net gain in median property values, on
average, after Uber’s entry. To the extent that we interpret Column (3) as our preferred
causal estimate of Uber’s entry, approximately 25.5% (= 0.028/0.110) of the overall
association between entry and property values is driven by the causal impact that Uber
has on local amenities—that is, after we control for selection effects.15 The R-squared
in model (3) is quite large (e.g., 0.98) relative to models (1) and (2), reflecting the fact
that much of the variation in median zip code-level housing prices is explained by
time-invariant fixed factors within a zip code (e.g., neighborhood conditions, location
of house, size of house, number of bedrooms and bathrooms, parking space) and hous-
ing market seasonality trends. It also implies that our hedonic pricing model does not
suffer materially from potentially omitted variables, as the variation in housing prices
is already sufficiently accounted for in our preferred specification (R-squared = 0.98)
(Oster 2019). Given that the mean of the median housing price per square foot across
all zip codes in our sample is $98.62/sq ft, Column (3) implies that the marginal effect
of Uber’s entry would increase the median housing price per square foot to $101.38/sq
ft. This increase would imply, for example, that the value of a 2,000-square-foot home
would undergo a gradual increase of approximately $5,520 after Uber becomes avail-
able in the local market. Thus, our result is not only statistically significant but also
economically significant and realistic.
15 We find it useful to decompose selection and causal effects in our setting to inform policy debates, as is
common in many other settings (e.g., in labor economics; see Lazear (2000)). Understanding the magnitude
of the causal effect, above and beyond the selection effect, provides insight into other factors that might
explain Uber’s entry and the adoption of ridesharing services.
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Table 4 Heterogeneous treatment effects of Uber entry on housing values. Sources: Census Bureau, Zillow, 2010–2016. The table reports the coefficients associated with
regressions of the logged median housing price per square foot on an indicator for the month × year that Uber entered a metropolitan area and interaction terms testing
various heterogeneous effects of Uber’s entry. Metro controls include logged population, logged income, the share of males, the age distribution (0–17, 18–34, 35–64), the
race distribution (white, black), the share of married individuals, and the education distribution (no high school, high school, some college, college). Heterogeneity effects
are tested by comparing areas above versus below the median value of a key characteristic that represents various local conditions. The median values of these key variables,
all obtained from zip code-level census data, refer to the following: at least 26.4% of individuals with a college degree or more, at least 11.3% of individuals below the
poverty line, the share of workers over the age of 16 who use public transportation (including taxis) as a means of transportation to work equal to at least 0.8%, average time
to commute to work of at least 25 min, and vehicles per capita (16 + and older) equal to at least 0.52. Standard errors are clustered at the metro level and are shown using
brackets
Dep. var. =
ln(median housing price/sq ft)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1[Uber entry]
0.017∗
0.021∗∗∗
0.004
0.033∗∗∗
0.040∗∗∗
−0.001
0.023∗∗
−0.04
[0.009]
[0.007]
[0.007]
[0.008]
[0.009]
[0.009]
[0.009]
[0.17]
1[college > median]
0.312∗∗∗
0.274∗∗∗
[0.013]
[0.017]
× 1[Uber entry]
0.022∗∗
0.013
[0.008]
[0.010]
1[poverty > median]
−0.207∗∗∗
−0.111∗∗∗
[0.017]
[0.017]
× 1[Uber entry]
0.015
0.000
[0.006]
[0.006]
1[pct public transport > median]
−0.122∗∗∗
−0.046∗∗∗
[0.017]
[0.015]
× 1[Uber entry]
0.047∗∗∗
0.018∗∗
[0.009]
[0.008]
1[commute > median]
−0.021
−0.054∗∗∗
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Table 4 (continued)
Dep. var. =
ln(median housing price/sq ft)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
[0.022]
[0.021]
× 1[Uber entry]
−0.008
−0.013
[0.008]
[0.12]
1[vehicles per capita > median]
0.142∗∗∗
0.033∗
[0.019]
[0.019]
× 1[Uber entry]
−0.022∗∗
−0.007
[0.008]
[0.009]
1[traffic delay > median]
−0.042∗∗
−0.037∗∗
[0.019]
[0.018]
× 1[Uber entry]
0.056∗∗∗
0.052∗∗∗
[0.019]
[0.017]
1[air quality > median]
0.023∗∗∗
0.021∗∗
[0.007]
[0.008]
× 1[Uber entry]
0.017
0.000
[0.020]
[0.015]
R-squared
0.68
0.63
0.59
0.58
0.60
0.58
0.58
0.70
Sample Size
397,601
397,601
397,601
397,464
397,464
224,856
397,601
224,784
Metro Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Metro FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year + Quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
*** p value < 0.01, ** p value < 0.05, * p value < 0.10
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Consequently, Uber is more appreciated and highly valued in markets with more
educated people.
Column (2) shows that areas where Uber enters have higher property values (β =
0.021***) regardless of the level of poverty. Unsurprisingly, areas with higher levels
of poverty are also negatively correlated (β = −0.207***) with property values in
that area. In addition, areas with higher levels of poverty potentially benefit more from
Uber’s entry and may gain a point estimate of 1.5% (β = 0.015) more in property values
than areas with lower levels of poverty. While this result may potentially suggest the
benefits offered by the gig economy platform, caution is needed, as the coefficient is
not statistically significant.
Column (3) shows that zip codes where people rely more on using public trans-
portation (compared to driving their own car) to commute to work are less desirable
residential areas in general (β = −0.122***) but may benefit more from Uber’s entry
than places where people do not need public transportation as much because they use
their own cars. In fact, the net gain that Uber offers to local markets is actually concen-
trated in zip codes where dependence on public transportation is relatively high, as the
own effect (1[Uber entry]), while still positive, becomes statistically insignificant in
Column (3). The interaction effect in Column (3) implies that there is an approximately
4.7%increaseinpropertyvaluesafterUber’sentryinareaswhereusageofpublictrans-
portation is high but not elsewhere. The estimate is in line with theory because it is
precisely these areas (i.e., where residents are less dependent on driving their own car)
that stand to gain the most by having a more robust and active ridesharing economy,
as more individuals value having access to a low-cost alternative transportation mode.
It is worth noting that the construction of our variable, 1[pct public transport], relies
on 2010 census data that combine taxi users and users of other public transportation
modes. Thus, it may be useful to consider the heterogeneous effect of Uber’s entry
based on residents’ average commute time to tease out some of the nuances and gain
more insight because as commute time increases, it probably becomes less likely that
people rely solely on taxis on a daily basis.
Column (4) shows that zip codes where people experience longer commute times
may be less desirable residential areas in general (β = −0.021, although not statisti-
cally significant) and that, consistent with previous results, the net gains Uber offers to
a local market are positive and statistically significant (β = 0.033***). In addition, the
interaction term (β = −0.008) in Column (4), while negative, shows that there is no
statistically significant difference between locations where people experience longer
versus shorter commute times. This nonsignificance most likely occurs because peo-
ple living in locations with longer commute times rely on their own cars or low-cost
transit rather than taxis. Thus, Uber (which functions similarly to local taxis) may not
provide much benefit to consumers with longer commute times, especially if they use
Uber sporadically rather than for commuting purposes on a daily basis. Therefore,
the results in Columns (3) and (4), taken together, imply that Uber is similar to a taxi
service that may be used for shorter trips (e.g., the “last mile” after getting off a train
station) or for occasional rides, rather than longer commutes, but potentially offers
greater convenience and cost-effectiveness.
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In Column (5), the interaction term shows that the entry of Uber may actually be
undesirable in areas where the level of vehicle ownership is already high, as the coef-
ficient is negative and statistically significant (β = −0.022**). If vehicle ownership
is already high, then there may be less demand for ridesharing services or for other
public transportation options (e.g., the correlation between the share of the population
taking public transportation and the measure of vehicles per capita within a zip code
is −0.67). Nonetheless, these areas must still endure the negative externalities of
ridesharing services, such as increased congestion (e.g., frequent stops by rideshar-
ing drivers to load and unload passengers) or increased air pollution (Keating 2019).
Hence, areas with high vehicle ownership seem to appreciate the entry of Uber the
least because people rely on driving their own cars.
Column (6) shows that, unsurprisingly, zip codes that suffer more traffic delays are
less desirable residential areas in general (β = 0.042***) but may benefit more from
Uber’s entry than other places where traffic delays are not as bad because consumers
can benefit from the convenience of using a low-cost ridesharing service such as Uber
rather than stressfully driving to navigate local traffic congestion. The interaction
effect in Column (6) implies that there is an approximately 5.6% gain in property
value after Uber’s entry in areas where traffic delay is severe compared to areas where
traffic delay is not so severe. Similar to Column (3), the net gain Uber offers to local
markets is concentrated in zip codes where traffic delay is relatively high, as the own
effect (1[Uber entry]) becomes statistically insignificant in Column (6). The result in
column (6), as well as the result in column (3), shows that local traffic conditions are
an important factor that unleashes the potential benefits Uber offers.
Column (7) shows that areas with better air quality may be more desirable (β =
0.023***) to reside in general. However, the interaction term between air quality and
Uber’s entry is not significant (β = 0.017), which implies that the benefit of Uber’s
entry does not differ across areas depending on their air quality. Column (8) includes all
interaction terms simultaneously and finds that the inclusion of all interaction terms
does not materially alter our results and interpretations. Importantly, while we find
that the benefit of Uber’s entry had substantial spatial heterogeneity depending on
various local market conditions, we find that the benefits were concentrated in areas
with greater dependence on public transportation or with higher levels of traffic delay
according to column (8).
Overall, our quantitative results from the hedonic pricing model point to the follow-
ing conclusions: after Uber’s entry, there is a nonnegligible average economic gain in
property value that is equivalent to an increase of 2.8% in the median housing value
per square foot. While our hedonic pricing model suggests that Uber has a net positive
social value on average, there is substantial heterogeneity across regions based on local
conditions. Regions with greater dependence on public transportation (as opposed to
driving their own vehicle) and with greater traffic delay benefit the most after Uber’s
entry. These findings are corroborated in Column (8) when all interaction terms are
included simultaneously, as this column shows results that are qualitatively similar
to those in Columns (1)–(7). In summary, our main findings are consistent with the
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Y. Paik, C. A. Makridis
view that Uber, and ridesharing more generally, functions as a relatively low-cost,
time-efficient for-hire transportation mode that directly competes with local taxis.17
4.4 Aggregate social value of a ridesharing platform and policy implications
We now compute the aggregate welfare gains from having access to ridesharing. Under
the assumption that the MWTP for access to ridesharing is homogeneous, we can take
our preferred gradient of 0.028 and scale it by the number of individuals who had
access to ridesharing as of the end of 2016.18 We take the total population in these
metropolitan areas with exposure to ridesharing platforms such as Uber and scale it
by the number of individuals, which equals approximately 209,256,147 people. As
of 2016, among the set of metropolitan areas with an Uber presence, the population-
weighted average of the median housing price per square foot was $130.9. Given that
we find that Uber entry is associated with a 2.8% causal increase in housing prices,
the marginal increase amounts to $3.67 per square foot. Once we scale this figure
across all individuals exposed to ridesharing in these markets, we obtain a total of
$768 million of net value created to society. Thus, our results show that ridesharing
platforms represent a net gain for society.
The policy implications of these results deserve some attention. While our results
show the net gain to society represented by Uber’s entry and the heterogeneity of social
value accrued across regions, our method cannot speak to the heterogeneity of social
value accrued to each agent (e.g., taxi drivers, Uber drivers, renters, homeowners, or
nonusers of Uber) within a market.19 We only know that in aggregate, the benefit that
residents (renters and homeowners alike) enjoy from having access to Uber outweighs
the cost that they suffer from the disruption generated by Uber. The net benefit is
thus translated into a higher willingness to pay for being a resident. In this regard,
some policymakers may be responding to the harm done by Uber to specific groups
by banning Uber against a background of society-wide gains. For example, this was
the case in Austin, Texas, London, UK and other international markets (Lee 2017).
In addition, due to spatial heterogeneity, if regions with greater dependence on public
transportation or greater traffic delays increase property taxes (for example, up to 2.8%,
according to our estimates20), the additional tax revenue can be used, for example, to
17 Paik et al. (2019) reports that Uber offers a significant economic benefit in the form of a 45% discount,
on average, relative to a typical taxi fare for the same 5-mile distance due to ridesharing.
18 If the MWTP for access to ridesharing is not homogeneous, then the welfare gains may be lower than
our estimate. The reason is that if homebuyers who care about Uber are likely driving up the prices for
homes, then individuals other than homebuyers may have a lower MWTP. Our welfare estimates will be
the upper bound in this case.
19 The welfare implication of Uber for each agent is the subject of many prior microlevel studies (e.g.,
Barrios et al., 2020; Cachon et al., 2017; Chen et al., 2019; Greenwood & Wattal, 2017; Hagiu & Wright,
2019; (Park, Pang, Kim, and Lee 2021) https://pubsonline.informs.org/doi/abs/10.1287/isre.2020.0978).
However, the purpose of this study is to synthesize and compute the aggregate social value created by Uber
that accrues to both users and nonusers of Uber so that we can complement prior studies.
20 Given that platform businesses such as Uber intentionally incur accounting losses for many years while
offering promotions and discounts to users, the increase in property taxes should be much lower than the
2.8% we estimated because it is likely that Uber will eventually begin to increase prices once it starts to
enjoy a strong network effect (Katz & Shapiro, 1985; Parker & Van Alstyne, 2005; Zhu & Iansiti, 2012).
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