# RECORD c8acd4a142f8c89ba5c53a61a81bfd91957160a6
# TITLE: Do people value farmers markets: A spatial hedonic pricing model approach
# YEAR: 2024.0  VENUE: Agricultural and Resource Economics Review  DOI: 10.1017/age.2024.1
# AUTHORS: Yanan Zheng; Meng Yang; Henry An; Feng Qiu
# PAGES: 21  CANDIDATE TABLE PAGES (1-based): [9, 11, 12, 13, 14, 15, 17]
# ABSTRACT: 
 Farmers markets can generate positive externalities by improving food access and negative externalities through pollution. The presence of both may influence people’s willingness to pay (WTP) for living nearby. This study employs spatial hedonic pricing models to estimate the WTP for living near farmers markets in Edmonton, Canada. We find an inverted U-shaped relationship between proximity to a farmers market and property values. Our results suggest that local governments might consider the economic impact of building new or relocating existing farmers markets on residential housing values, alongside the benefits of improved access to high-quality food sources.


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RESEARCH ARTICLE
Do people value farmers markets: A spatial
hedonic pricing model approach
Yanan Zheng1, Meng Yang2
, Henry An3 and Feng Qiu3
1Department of Agricultural and Resource Economics, University of California, Davis, Davis, CA, USA,
2School of Public Administration, Zhongnan University of Economics and Law, Wuhan, Hubei, China and
3Department of Resource Economics and Environmental Sociology, University of Alberta, Edmonton, AB,
Canada
Corresponding author: Meng Yang; Email: yangmeng@zuel.edu.cn
(Received 24 May 2023; revised 25 October 2023; accepted 29 December 2023; first published online 14 February
2024)
Abstract
Farmers markets can generate positive externalities by improving food access and negative
externalities through pollution. The presence of both may influence people’s willingness
to pay (WTP) for living nearby. This study employs spatial hedonic pricing models to
estimate the WTP for living near farmers markets in Edmonton, Canada. We find an
inverted U-shaped relationship between proximity to a farmers market and property
values. Our results suggest that local governments might consider the economic impact of
building new or relocating existing farmers markets on residential housing values,
alongside the benefits of improved access to high-quality food sources.
Keywords: Externality; farmers market; spatial hedonic pricing model; willingness to pay
JEL Classifications: D12; R30
Introduction
The extensive growth of farmers markets in Canada and worldwide has been advocated by
various agents including governments, private entrepreneurs, and consumers over the past
decades with the goal of improving healthfulness, access to fresh produce, and
environmental sustainability (Hunt 2007; Feagan and Morris 2009; Farmer et al. 2019;
Garner 2019). Many regional governments expand local food systems through the
promotion of farmers markets as a way to facilitate public health and promote local
economic development (Gillespie et al. 2008; Wittman et al. 2012; Kahin et al. 2017;
Malagon-Zaldua et al. 2018). Private entrepreneurs also argue for the creation of new
farmers markets by emphasizing the reduction in food miles from supporting local
producers (Martinez et al. 2010).
Consumers also have various motivations to support farmers markets. Some consumers
value farmers markets for their perceived higher quality and freshness of food products
(Ruelas et al. 2012; Dodds et al. 2014; Yu et al. 2017). Others view farmers markets as places
© The Author(s), 2024. Published by Cambridge University Press on behalf of Northeastern Agricultural and Resource
Economics Association This is an Open Access article, distributed under the terms of the Creative Commons Attribution
licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction,
provided the original article is properly cited.
Agricultural and Resource Economics Review (2024), 53, 277–297
doi:10.1017/age.2024.1
https://doi.org/10.1017/age.2024.1 Published online by Cambridge University Press


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Table 1. Variable definition and summary statistics
Variables
Description
Mean
Std. dev.
Dependent variable
Price
Log of transacted price (in 2016 CAD$)
12.98
0.34
Key variable of interest
Farmers market
Distance to the nearest Farmers market
2.86
1.49
Structural variables
Living area
Square feet of living space
1,559.64
620.80
Lot size
Square feet of lot size
5,873.90
4,338.71
Bedroom
Number of bedrooms
2.92
0.65
Bathroom
Number of bathrooms
1.64
0.66
House condition
4 if the house condition is excellent, 3 if the house condition is good, 2 if the house
condition is average, and 1 if the house condition is poor
2.99
0.83
Basement condition
4 if the house condition is excellent, 3 if the house condition is
good, 2 if the house condition is average, and 1 if the house condition is poor
2.44
0.85
Garage
Number of garages
1.83
0.47
House age
Age of the house
29.20
23.21
Locational variables
Shopping center
Number of shopping centers within each house’s 1,000-m buffer
0.16
0.43
Grocery store
Number of grocery stores within each house’s 1,000-m buffer
0.30
0.72
Bar
Number of bars within each house’s 1,000-m buffer
0.53
1.25
Downtown
Distance to the downtown area
10.57
4.31
(Continued)
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Table 1. (Continued )
Variables
Description
Mean
Std. dev.
University
Distance to the University of Alberta
11.44
3.96
Hospital
Distance to a hospital
5.05
2.35
River buffer
1 if the house is located within a 2 km buffer area of the North
Saskatchewan River, 0 otherwise
0.31
0.46
Golf
Area (in m2) of a golf course within a 600-m buffer
7,155.26
40,950.36
Green space
Area (in m2) of park and natural preserve within a 600-m buffer
55,037.07
87,327.32
Neighborhood variables
Population density
Neighborhood-level population density
3,071.33
1,036.51
Low education
The share of residents whose highest level of education is high school or lower
0.37
0.12
Low income
The share of households who have a relative low income (less than C$30,000)
0.12
0.10
Unemployment
The share of residents who are unemployed
0.09
0.04
Senior
The share of the senior population aged over 65
0.14
0.08
Public school
The share of residents who are located in a public school zone
0.75
0.06
Married
The share of married residents
0.49
0.06
Walk
The share of residents who rely upon walking as the primary mode of transportation
0.03
0.04
Public
transportation
The share of residents who rely upon public transportation as the primary
mode of transportation
0.13
0.05
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The parameter estimates of the OLS and SAR models are shown in Table 3. The
coefficients on the spatially lagged dependent variable from the four SAR models (i.e., ρ)
are positive and significant, which confirm the presence of spatial dependence and the
necessity of employing spatial HPMs. Therefore, the rest of the section only discusses the
estimates from the spatial models and omits the discussion of the biased OLS estimates.
Our key variables of interest are Farmers Market and Farmers Market2. The positive
and significant coefficients on Farmers Market and the negative and significant coefficients
on Farmers Market2 from four out of the five SAR models suggest an inverted U-shaped
relationship between distance to farmers markets and property values. The only exception
is the specification using the nearest 10 weight, which is less suitable than the nearest 20
and nearest 30 specifications based on Z-scores from a spatial autocorrelation test.
Specifically, it indicates that property values were valued the highest when the distance to
the nearest farmers market was around 4.60 km to 4.90 km depending on the weighting
matrix.3 When the distance to the nearest farmers market was below this benchmark,
property values fell as the distance dropped. This might be attributable to the negative
externalities such as congestion and noise brought by the farmers markets. Even though
farmers markets also generate positive externalities for nearby residents, negative
externalities may offset the positive ones. Moreover, when the distance to the nearest
farmers market was above the benchmark, property values fell as the distance increased.
This might be due to the fact that residents were not able to enjoy the benefits brought by
farmers markets when living too far away. Though Edmonton citizens were car-
dependent, a 4.5-km drive typically took approximately 10 min. Overall, the results show
property values initially increased and then decreased with the distance to farmers
markets, which is consistent with our conceptual hypothesis. Our results regarding the
dominance of negative externalities seem to be in contrast with previous studies (e.g.,
Collins 2020) that found the opposite, that is, that positive externalities dominate, and
housing values decrease with proximity to a farmers market. Our finding is not wholly
unexpected because the effect of the farmers market on housing values is likely to be
context- and/or region-specific. Edmonton is a large urban city, and its two most
prominent farmers markets are very large and attract a large clientele. In fact, Collins
(2020) noted that “nuisances are more likely to be associated with larger, well-established
markets,” which is an apt description of Edmonton’s main farmers markets.
Table 2. Results of spatial dependence testing
Tests
Moran’s I
LM spatial lag
Robust LM spatial lag
800-m weights
0.155***
1,245.300***
207.430***
1000-m weights
0.133***
1,028.200***
170.950***
Nearest 10 weights
0.214***
1,398.800***
264.840***
Nearest 20 weights
0.188***
1,515.400***
288.030***
Nearest 30 weights
0.173***
1,415.300***
248.750***
Note: Asterisks denote levels of significance (***for 1%).
3Based on the coefficient estimates from the SAR model with the nearest 20 weight matrix, the turning
point is calculated to be about 4.60 km to the nearest farmers market. Similarly, with the 1,000-m weight
matrix, the turning point is calculated to be about 4.90 km.
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Table 3. Regression estimation results
Variables
OLS
Spatial lag models
Nearest 30 weights
800-m weights
1000-m weights
Nearest 10 weights
Nearest 20 weights
Farmers market
0.0111**
0.0106***
0.0114***
0.0092**
0.0096**
0.0105**
(0.0044)
(0.0041)
(0.0041)
(0.0040)
(0.0040)
(0.0041)
Farmers market2
−0.0009
−0.0011*
−0.0012*
−0.0010
−0.0010*
−0.0011*
(0.0007)
(0.0006)
(0.0006)
(0.0006)
(0.0006)
(0.0006)
Structural variables
Living area
0.0003***
0.0002***
0.0002***
0.0002***
0.0002***
0.0002***
(4.07E-06)
(3.95E-06)
(3.96E-06)
(3.99E-06)
(3.97E-06)
(3.96E-06)
Lot size
7.04E-06***
6.67E-06***
6.81E-06***
6.29E-06***
6.58E-06***
6.73E-06***
(3.80E-07)
(3.58E-07)
(3.61E-07)
(3.53E-07)
(3.54E-07)
(3.56E-07)
Bedroom
−0.0188***
−0.0120***
−0.0135***
−0.0101***
−0.0106***
−0.0117***
(0.0029)
(0.0027)
(0.0028)
(0.0027)
(0.0027)
(0.0027)
Bathroom
0.0319***
0.0275***
0.0287***
0.0270***
0.0263***
0.0268***
(0.0031)
(0.0029)
(0.0029)
(0.0029)
(0.0029)
(0.0029)
House condition
0.0180***
0.0164***
0.0168***
0.0161***
0.0158***
0.0159***
(0.0020)
(0.0019)
(0.0019)
(0.0018)
(0.0018)
(0.0019)
Basement condition
0.0449***
0.0445***
0.0446***
0.0431***
0.0440***
0.0443***
(0.0020)
(0.0019)
(0.0019)
(0.0019)
(0.0019)
(0.0019)
(Continued)
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Table 3. (Continued )
Variables
OLS
Spatial lag models
Nearest 30 weights
800-m weights
1000-m weights
Nearest 10 weights
Nearest 20 weights
Garage
0.1169***
0.1105***
0.1122***
0.1041***
0.1057***
0.1069***
(0.0039)
(0.0037)
(0.0037)
(0.0037)
(0.0037)
(0.0037)
House age
−0.0024***
−0.0022***
−0.0022***
−0.0023***
−0.0022***
−0.0022***
(0.0001)
(0.0001)
(0.0001)
(0.0001)
(0.0001)
(0.0001)
Locational variables
Shopping center
−0.0035
−0.0039
−0.0042
−0.0046
−0.0042
−0.0039
(0.0039)
(0.0037)
(0.0037)
(0.0036)
(0.0036)
(0.0036)
Grocery store
−0.001
−0.0015
−0.0016
−0.0008
−0.0008
−0.0009
(0.0024)
(0.0022)
(0.0022)
(0.0022)
(0.0022)
(0.0022)
Bar
−0.0023*
0.0013
0.0012
0.0002
0.001
0.0013
(0.0014)
(0.0013)
(0.0013)
(0.0013)
(0.0013)
(0.0013)
Downtown
−0.0054***
−0.0127***
−0.0143***
−0.0081***
−0.0096***
−0.0108***
(0.0014)
(0.0013)
(0.0013)
(0.0013)
(0.0013)
(0.0013)
University of Alberta
−0.0114***
−0.0028***
−0.0012***
−0.0070***
−0.0056***
−0.0043***
(0.0015)
(0.0015)
(0.0015)
(0.0014)
(0.0014)
(0.0014)
Hospital
−0.0037***
−0.0050***
−0.0054***
−0.0035***
−0.0043***
−0.0049***
(0.0011)
(0.0010)
(0.0010)
(0.0010)
(0.0010)
(0.0010)
River buffer
0.0302***
0.0106***
0.0088***
0.0189***
0.0164***
0.0158***
(0.0044)
(0.0042)
(0.0042)
(0.0041)
(0.0041)
(0.0041)
(Continued)
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Table 3. (Continued )
Variables
OLS
Spatial lag models
Nearest 30 weights
800-m weights
1000-m weights
Nearest 10 weights
Nearest 20 weights
Golf
1.29E-07***
8.55E-08***
1.11E-07***
9.61E-08***
1.00E-07***
1.21E-07***
(3.99E-08)
(3.76E-08)
(3.80E-08)
(3.71E-08)
(3.72E-08)
(3.74E-08)
Green space
4.12E-07***
2.22E-07***
2.38E-07***
2.11E-07***
2.05E-07***
2.11E-07***
(2.07E-08)
(2.05E-08)
(2.06E-08)
(2.02E-08)
(2.03E-08)
(2.04E-08)
Neighborhood variables
Population density
−1.21E-05***
−8.11E-06***
−9.33E-06***
−6.01E-06***
−6.22E-06***
−7.99E-06***
(1.94E-06)
(1.83E-06)
(1.85E-06)
(1.81E-06)
(1.81E-06)
(1.82E-06)
Low education
−0.6492***
−0.3169***
−0.3494***
−0.3163***
−0.2940***
−0.2971***
(0.0291)
(0.0293)
(0.0295)
(0.0285)
(0.0288)
(0.0292)
Low income
−0.1937***
−0.1819***
−0.1761***
−0.1678***
−0.1733***
−0.1710***
(0.0383)
(0.0361)
(0.0364)
(0.0356)
(0.0357)
(0.0359)
Unemployment
−0.3507***
−0.0319
−0.0162
−0.1392*
−0.1048
−0.0769
(0.0819)
(0.0776)
(0.0784)
(0.0763)
(0.0766)
(0.0771)
Senior
0.1006***
0.1018***
0.1040***
0.0975***
0.0959***
0.0847***
(0.0316)
(0.0298)
(0.0300)
(0.0293)
(0.0294)
(0.0296)
Public school
0.053
0.1528***
0.1471***
0.1364***
0.1327***
0.1346***
(0.0349)
(0.0330)
(0.0334)
(0.0324)
(0.0325)
(0.0328)
Married
−0.1510***
−0.3225***
−0.3283***
−0.2247***
−0.2480***
−0.2591***
(0.0545)
(0.0518)
(0.0525)
(0.0506)
(0.0508)
(0.0512)
(Continued)
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Table 3. (Continued )
Variables
OLS
Spatial lag models
Nearest 30 weights
800-m weights
1000-m weights
Nearest 10 weights
Nearest 20 weights
Walk
1.0992***
0.6467***
0.6997***
0.7167***
0.6742***
0.7033***
(0.0703)
(0.0675)
(0.0679)
(0.0662)
(0.0664)
(0.0667)
Public transportation
−0.4968***
−0.4335***
−0.4605***
−0.3662***
−0.3746***
−0.4024***
(0.0559)
(0.0526)
(0.0531)
(0.0520)
(0.0521)
(0.0523)
Constant
Control
Control
Control
Control
Control
Control
Monthly dummies
Control
Control
Control
Control
Control
Control
Adjusted R2
0.823
Rho
0.407
0.403
0.353
0.386
0.395
Log likelihood
4,419.84
4,869.08
4,801.74
4,952.95
4,950.07
4,911.76
AIC
−8,721.68
−9,618.20
−9,483.50
−9,785.90
−9,780.10
−9,703.50
Note: Asterisks denote levels of significance (*for 10%, **for 5%, ***for 1%).
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Nearly every coefficient on the structural variables has the expected sign and is
statistically significant. The results suggest that larger houses with better conditions tended
to be more highly priced than others. In particular, property values benefited from an
increase in basement and house conditions, the number of bathrooms and size of garages,
as well as the square feet of living area and lot size. The lot size had a very small effect on
housing values, which is consistent with other studies such as Feng and Humphreys
(2018). Property values were penalized for older age. One unexpected finding is the
negative and statistically significant coefficient on the number of bedrooms. Other studies
that found a similar result (e.g., Li et al. 2015) proposed energy cost as the possible
explanation.
Almost all locational attributes are found to have capitalization effects on property
values. For the five variables controlling for the effects of commercial activities (shopping
center, grocery stores, bars, the downtown area, and the University of Alberta), we find
property prices increased as the household got closer to the downtown area and the
University of Alberta, which is in accordance with existing studies (e.g., Morancho 2003;
Jim and Chen 2006). Similarly, living close to hospitals in general positively affected
property prices. Good accessibility to green space and golf courses, as well as better scenic
river views, were also positively related to housing values. Our results consistently support
the positive contribution of water bodies, golf courses, parks, and public reserves to the
increasing house values, as suggested by numerous empirical studies (Larson and Perrings
2013; Sander and Polasky 2009; Samad et al. 2020).
In terms of the neighborhood characteristics, the results show that property values
tended to be higher if they were located in neighborhoods with a lower population density
and a higher portion of senior residents. Property values also benefited from an increase in
the percentage of residents who were located in public school zones and relied on walking
as the primary mode of transportation. On the other hand, property values were lower if
the neighborhoods had a higher proportion of residents with low education, low income,
and who were married. Property values were also penalized for being in an area with a large
percentage of residents who relied upon public transportation as the primary mode of
transportation. One potential reason is that properties in such neighborhoods tended to
have better access to transportation infrastructure, and while accessibility to public
transportation could have a positive effect due to convenience, the negative effect caused
by congestion, noise, and pollution may dominate the positive effect. Moreover, about 83%
of the residents in the sampled neighborhoods had cars, and this may explain why people
do not value public transportation in Edmonton as highly as in other cities.
Valuing marginal effects and quantifying marginal WTP/WTA
We calculate the marginal effects and marginal WTP/WTA for distance to farmers
market using seven distance points: mean distance (2:68 km), lowest distance (0.63 km =
mean distance −1.5 S.D), lower distance (1.37 km = mean distance −1 S.D), low distance
(2.12 km = mean distance −05 S.D), high distance (3.60 km = mean distance  0.5 S.D),
higher distance (4.35 km = mean distance  1 S.D), and highest distance (5.10 km = mean
distance  1.5 S.D). Total marginal effects and marginal WTP/WTA are reported in
Table 4. Although the magnitude of marginal effects and marginal WTP/WTA varies by
weight matrix, the sign and statistical significance remain the same. Because the SAR
model with the nearest 20 weights has the lowest AIC value and the highest log-likelihood
value, the following discussion focuses on the estimation results from this model.
The results indicate that at the mean distance level, property values increased by 0.59%
of the average property price if their distance to the nearest farmers market increased by
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1 km. Its estimated range is from 1.35% for houses located at the lowest distance (0.63 km)
away from a farmers market to −0.17% for houses located at the highest distance (5.09 km)
away from a farmers market.
For houses located 2.68 km (the mean distance) away from a farmers market, our
estimates suggest that households were willing to pay C$2,724.72 to live 1 km further away.
For houses located at the lowest distance (0.63 km) away from a farmers market, the
households were willing to pay C$6,225.82. At the other end, for houses located at the
highest distance (5.1 km) away from a farmers market, the households were willing to
accept C$776.38 to live 1 km further away. The estimation results are consistent with our
conceptual framework assumptions: people’s marginal WTP for living further away from
farmers markets declines with the average distance to farmers markets. Using the
transaction values of individual houses and their distances to the nearest farmers market,
Table 4. Marginal Effects and WTP/WTA for Access to Farmers Markets
800-m
weights
1000-m
weights
Nearest 10
weights
Nearest 20
weights
Nearest 30
weights
Marginal
effects
Lowest
distance
0.0155***
0.0166***
0.0122
0.0135**
0.0149**
Lower
distance
0.0126***
0.0137***
0.0099
0.0110**
0.0121**
Low
distance
0.0097***
0.0108***
0.0076
0.0084**
0.0093**
Mean
distance
0.0069***
0.0079***
0.0053
0.0059**
0.0065**
High
distance
0.0040***
0.0050***
0.0030
0.0034**
0.0037**
Higher
distance
0.0011***
0.0022***
0.0006
0.0008**
0.0009**
Highest
distance
−0.0017***
−0.0007***
−0.0017
−0.0017**
−0.0019**
Marginal
WTP
Lowest
distance
7,128.77***
7,638.13***
5,629.11
6,225.82**
6,870.90**
Lower
distance
5,808.79***
6,309.01***
4,562.53
5,058.78**
5,583.56**
Low
distance
4,488.81***
4,979.89***
3,495.95
3,891.75**
4,296.22**
Mean
distance
3,168.83***
3,650.76***
2,429.37
2,724.72**
3,008.87**
High
distance
1,848.84***
2,321.64***
1,362.79
1,557.68**
1,721.53**
Higher
distance
528.86***
992.51***
296.21
390.65**
434.19**
Highest
distance
−791.12***
−336.61***
−770.37
−776.38**
−853.16**
Note: Asterisks denote levels of significance (**for 5%, ***for 1%).
Agricultural and Resource Economics Review
293
https://doi.org/10.1017/age.2024.1 Published online by Cambridge University Press


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we further derive the total economic effect of farmers markets on the prices of all houses
sold during the study period. At the aggregate level, we find that the negative externalities
generated by farmers markets dominate the positive ones. The reduction in transacted
housing values is estimated to be C$23,190,452. Using the 2019 property tax rate (0.9%),
the annual reduction in tax revenue from these transacted houses was about C$208,714
(City of Edmonton 2023a).
Conclusion
Farmers markets may generate both positive and negative externalities for nearby
residents, which may influence how they value the presence of these markets when
evaluating housing options. However, little attention is paid to quantify people’s WTP/
WTA for living in close proximity to farmers markets. By employing SAR models, this
study examines the impact of proximity to farmers markets on property values and
estimates people’s marginal WTP/WTA for living near farmers markets. The results
suggest that the relationship between property values and proximity to farmers markets
was nonlinear, with an ideal distance (i.e., highest value) of approximately 4.60– 4.90 km.
In terms of the corresponding WTP values, our estimates suggest that at the mean distance
(2.86 km) to a farmers market, households were willing to pay C$2,724.72 for every 1-km
increase in the distance to the farmers market. Moreover, households’ marginal WTP for
living further away from a farmers market decreased with their distance to the farmers
market. Overall, we find the negative externalities associated with farmers markets
dominated the positive ones, resulting in a C$23,190,452 reduction in transacted housing
values. Assuming a 0.9% property tax rate, the annual reduction in tax revenues from those
houses is estimated to be about C$208,714.
This study does have several limitations. First, like Warsaw and Phaneuf (2019) and
Collins (2020), there is a potential for endogeneity bias in the location of farmers markets.
That is, it is possible that housing values in a particular area are influencing the location of
farmers markets rather than vice versa. We have attempted to address this potential bias as
best we can with our commercial controls and spatial approach but it remains a potential
issue. Therefore, we recommend that the reader interprets the effect as correlational
instead of causal.
Our findings have several implications. First, introducing new farmers markets to
improve a neighborhood’s food environment may lead to some unintended consequences.
When the distance to the nearest farmers market is below a certain threshold, the value of
the property falls as its distance to a farmers market drops. Because property taxes are the
most substantial source of a municipality’s revenue, decreased property values might result
in substantial revenue loss, which could lead to reduced supplies of public goods and
services. Second, for neighborhoods with limited access to farmers markets, the local
government can introduce new farmers markets or relocate the existing ones because our
results show that when the distance to the nearest farmers market was above certain a
threshold, people required compensation to live further away from the farmers market.
When making the locating or relocating decisions, governments should take proximity to
farmers markets into consideration. Last, to reduce the potential negative externalities
associated with larger farmers markets, greater consideration should be given to improve
the operation of farmers market and limit the nuisance effects during market hours. This
may include enhanced traffic control, expanded street and sidewalk cleaning services, and
a greater police or security presence during market hours in the surrounding area.
294
Yanan Zheng et al.
https://doi.org/10.1017/age.2024.1 Published online by Cambridge University Press