# RECORD 4326e33d5aa8be80e2f1d1ae91adb42331da513d # TITLE: E-Grocery and Housing Markets: Evidence from the Amenity Effect of Amazon Fresh as a Digital Amenity # YEAR: 2026.0 VENUE: Journal of real estate finance and economics DOI: 10.1007/s11146-026-10055-w # AUTHORS: Jamie Chung; Liuming Yang # PAGES: 41 CANDIDATE TABLE PAGES (1-based): [12, 14, 19, 22, 23, 24, 25, 27, 30, 38, 39] # ABSTRACT: This paper examines the impact of e-grocery entry on the housing market, focusing on Amazon Fresh, which provides grocery items and delivery services without a physical store. We find that areas receiving Amazon Fresh services experience a 2.5% increase in housing prices compared to areas within the same metropolitan region but do not receive such services. Local households show increased online consumption following Amazon Fresh’s entry, highlighting its amenity effect. Furthermore, the price effect is stronger in areas where households are more inclined to use online platforms and in socio-economically disadvantaged areas, including those with lower income, less education, higher rental occupancy, higher minority populations, lower vehicle ownership, and limited retail access. These patterns have policy implications for alleviating the " Food Desert " issue in urban areas through technological advancements in grocery markets by leveraging digital grocery services. ===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) ===== The Journal of Real Estate Finance and Economics https://doi.org/10.1007/s11146-026-10055-w Abstract This paper examines the impact of e-grocery entry on the housing market, focus­ ing on Amazon Fresh, which provides grocery items and delivery services without a physical store. We find that areas receiving Amazon Fresh services experience a 2.5% increase in housing prices compared to areas within the same metropolitan region but do not receive such services. Local households show increased online consumption following Amazon Fresh’s entry, highlighting its amenity effect. Fur­ thermore, the price effect is stronger in areas where households are more inclined to use online platforms and in socio-economically disadvantaged areas, including those with lower income, less education, higher rental occupancy, higher minor­ ity populations, lower vehicle ownership, and limited retail access. These patterns have policy implications for alleviating the "Food Desert" issue in urban areas through technological advancements in grocery markets by leveraging digital gro­ cery services. Keywords  E-grocery · Digital amenity · Housing market · Retail desert JEL Classification  D12 · D63 · J15 · L81 · R12 · R32 Received: 18 June 2025 / Accepted: 24 March 2026 © The Author(s) 2026 E-Grocery and Housing Markets: Evidence from the Amenity Effect of Amazon Fresh as a Digital Amenity Jamie Chung1  · Liuming Yang2 Jamie Chung and Liuming Yang contributed equally to this work. Jamie Chung jeonghyunchung@unomaha.edu Liuming Yang liumingyang@cuhk.edu.hk 1 University of Nebraska Omaha, Omaha, USA 2 The Chinese University of Hong Kong, Hong Kong, China 1 3 ===== PAGE 12 (score 17, numeric-paren 0, table-words 2, var-words 10) ===== J. Chung, L. Yang value of online consumption is $123,355, which reveals that online shopping con­ stitutes a minor proportion of overall consumption throughout the observed time­ frame. By computing the share, it's evident that online expenditure contributes to around 2.25% of the total expenditure. Despite falling below, the national average, which stands at 5.6% over 2004–2019 according to the Census Bureau's 2022 data (Lee & Zheng, 2025), NHCP dataset holds potential for identifying the changes in patterns of online and offline consumptions.14 As mentioned, the majority of total consumption comes from offline consumption in NHCP, which accounts for around 97.57% ($4,960,819/$5,084,173) on average. Panel C illustrates the socioeconom­ ics characteristics at the ZIP Code level, which are included as control variables in the model. On average, each ZIP Code comprises a population of approximately 33,299 individuals, with 37.32% of them holding bachelor’s degree or higher edu­ cational qualifications. Furthermore, 37.30% of households opt to rent rather than 14 Literature about using Nielsen Household Panel Data to explore the online consumption pattern could be found in Baker, et al. (2021); He (2023); Lee and Zheng (2025). Table 1  Summary statistics Panel A. Property Values Obs Mean Std. Dev Min Max Home Value ($) 118,517 364,891 241,803 28,063 1,074,548 Home Value ($)—1 bedroom 54,608 223,019 150,847 16,975 698,865 Home Value ($)—2 bedrooms 107,186 254,341 170,592 21,068 766,425 Home Value ($)—3 bedrooms 115,619 343,948 219,075 36,146 1,004,458 Home Value ($)—4 bedrooms 106,735 447,683 288,195 43,963 1,421,147 Home Value ($)—5 bedrooms 87,603 623,715 463,457 54,768 2,467,025 Panel B. Household Consumptions Obs Mean Std. Dev Min Max Total Consumption ($) 183,011 5,084,173 4,845,732 670 63,700,000 Online Consumption ($) 183,011 123,355 400,733 0 22,600,000 Offline Consumption ($) 183,011 4,960,819 4,727,535 0 60,400,000 Share of Online Consumption (%) 183,011 0.0225 0.0603 0.00 100.00 Panel C. Socioeconomics Obs Mean Std. Dev Min Max Population 118,517 33,299 19,688 615 111,893 High Education (%) 118,517 37.32 19.66 2.56 86.26 Renter Occupied (%) 118,517 37.30 18.61 1.14 88.96 Non-White Population (%) 118,517 38.60 23.86 1.52 99.25 Median Age 118,517 36.91 5.15 21.10 76.20 Median Household Income ($) 118,517 69,628 28,483 12,994 190,995 Non-Vehicle Ownership (%) 118,517 13.96 16.74 0.00 85.50 Household Size 118,517 2.77 0.48 1.42 5.18 Vacancy Rate 118,517 0.07 0.47 0 0.54 Competing Retail Est. per households 118,517 0.09 0.07 0 0.82 This table presents the summary statistics for the data used in the analysis. Panel A provides summary statistics for home values, both overall and by property type. Panel B offers summary statistics for household consumption, with the Share of Online Consumption calculated by dividing online consumption by total consumption. Lastly, Panel C outlines the socio-economic factors at the ZIP Code level. 1 3 ===== PAGE 13 (score 16, numeric-paren 0, table-words 2, var-words 12) ===== E-Grocery and Housing Markets: Evidence from the Amenity Effect of… own a home. About 38.6% of the population identifies as non-White. The median age and median household income are 37 years old and $69,628, respectively. The ownership of automobiles can potentially impact the utilization of online services, and it’s noteworthy that around 14% of households do not possess a car. The aver­ age household size is 2.77, and the housing vacancy rate is approximately 7%. Local retail market dynamics may influence the adoption of e-grocery services and consequently affect our results. Following An & Chung (2025), we identify competing retail stores relevant to e-grocers, including supermarkets, convenience stores, and retailers of daily necessity goods.15 On average, there are 0.09 compet­ ing retail stores per household within each ZIP Code. Methodology Our main research question is whether e-grocery service is treated as an amenity and increases housing values. To answer this question, we run the difference-in-difference regression described as: Yl,t = α + β1EGroceryl + β2Postt + β3EGroceryl × Postt + βiXl,t + γl + δt + ϵl,t where the dependent variable is the logarithm of home value in ZIP Code l at year- month t. EGrocery is an indicator variable which equals to 1 if Amazon Fresh service is available in ZIP Code l and 0 if Amazon Fresh service is not available although it is located within the same MSA. This approach is used in a recent paper by An & Chung (2025) that investigates the impact of Amazon Fresh on the local competing retail businesses. Post is a time indicator variable which equals to 0 for 36 months prior to the entry of Amazon Fresh and 1 for 36 months after the entry of Amazon Fresh. The interaction term of EGrocery and Post is the main variable of our interest, which captures the impact of Amazon Fresh service on the local home values. If β3 is positive and statistically significant, it indicates that Amazon Fresh service has a positive amenity effect on local home values. Conversely, if β3 is negative or statisti­ cally insignificant, it describes that the e-grocery service either results in a decrease in home values or has no discernible impact. As our dependent variable is the average home values at the ZIP Code level, we include local socio-economic factors that could affect the housing market. Specifi­ cally, Xl,t includes a vector of local factors such as the logged population, the logged median age, the logged number of individuals in the labor force, the percentage of the non-White population, the percentage of individuals with a bachelor’s degree or higher, the logged median household income, the vacant unit rate, unemployment rate, and the logged median household size at the ZIP Code level. We include a ZIP Code fixed effect to account for time-invariant location-related factors and a year- month fixed effect to mitigate the influence of seasonal effects exist in the housing market. Standard errors are clustered at the ZIP Code level. 15 See An and Chung (2025) for the complete list of NAICS codes for competing retail stores. 1 3 ===== PAGE 14 (score 32, numeric-paren 10, table-words 7, var-words 7) ===== J. Chung, L. Yang Empirical Findings Baseline: Impact of Amazon Fresh on Housing Prices Table 2 represents the results when we run the regression following Eq. 1. In column 1, where ZIP Code level control variables are excluded, we observe a 2.5% increase in home values following the entry of Amazon Fresh. When local control variables are introduced in column 2, the estimated coefficient remains positive and statisti­ cally significant at the 1% level. The magnitude of the effect is unchanged, indicating an average increase in home values of approximately 2.5%, which corresponds to about $9,122 over a three-year period within each ZIP Code.16 This positive price response suggests that Amazon Fresh is perceived as a valuable local amenity in the housing market. Notably, the magnitude of the effect is compara­ ble to the estimated amenity value of physical grocery stores documented in the prior literature (e.g., Pope & Pope, 2015).17 Increases in housing prices are typically ben­ eficial for homeowners, reflecting a positive amenity effect. While some may raise concerns that higher assessed values could lead to increased property tax burdens, particularly for homeowners who do not sell, higher house values nonetheless rep­ resent an improvement in housing wealth. They could also expand opportunities for extracting home equity through mechanisms such as refinancing, home-equity loans, or reverse mortgages, even in the absence of a transaction. 16 It indicates the variation of ZIP Code level controls are largely absorbed in ZIP Code fixed effects. 17 For example, Pope & Pope (2015) find that the opening of a Walmart leads to an increase in nearby housing prices by 2% to 3%. Table 2  The impact of e-grocery on housing prices (1) (2) (3) (4) (5) Dep. Variable: ln(ZHVI) Baseline Anticipation Effects Announcement  Within Market Across Market  Amazon Fresh x Post 0.025*** 0.025*** 0.029*** 0.020*** 0.028*** (0.001) (0.001) (0.003) (0.002) (0.002) Observations 118,517 118,517 80,304 93,154 97,413 R-squared 0.989 0.990 0. 929 0.990 0.989 Controls no yes yes yes yes ZIP Code Fixed Effect yes yes yes yes yes Year x Month Fixed Effect yes yes yes yes yes This table presents the influence of Amazon Fresh on home values. In column 1, we exclude the ZIP Code level control variables while in column 2, we include the control variables in the model. ZIP Code fixed effect and year by month fixed effect are included in columns 1 and 2. In column 3, we exclude two markets, San Francisco and New York, where Amazon Fresh announced entry prior to the commencement of actual operations. Column 4 addresses within-market anticipation effects by excluding ZIP Codes that are immediately adjacent to treated ZIP Codes. Column (5) addresses across- market anticipation effects by excluding three markets, San Francisco, San Diego, and Philadelphia, where nearby markets received the service prior to their own openings. Standard errors are clustered at the ZIP Code level. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 1 3 ===== PAGE 15 (score 7, numeric-paren 0, table-words 2, var-words 3) ===== E-Grocery and Housing Markets: Evidence from the Amenity Effect of… Anticipation Effects We next examine whether anticipation effects influence our estimates. Although entry dates generally align with the timing of actual operations and are verifiable for most markets, we identify two exceptions in which early media coverage preceded the official service launch. In New York, an August 2014 report documented Amazon Fresh’s anticipated entry ahead of its official October launch, while in San Francisco, an NPR article published in June 2013 described the service’s forthcoming arrival several months before its official December launch.18 Such early coverage may induce announcement effects in local housing markets. To address this concern, we exclude these two markets from the sample and re-estimate the model in column 3 of Table 2. The estimated effect remains robust, with home values increasing by 2.9%. We further consider two types of potential anticipation spillovers. The first is within-market anticipation effects, whereby residents in ZIP Codes adjacent to treated ZIP Codes may anticipate imminent service expansion. To mitigate this con­ cern, we exclude all geographically adjacent ZIP Codes from the control group and re-estimate the model using only non-adjacent ZIP Codes as the comparison group in column 4. This restriction substantially reduces the scope for within-market spill­ overs. The estimated effect declines modestly to 2.0%, suggesting some anticipation among immediate neighbors. Importantly, however, the estimate remains positive and statistically significant, and its magnitude is consistent with the baseline results, indicating that within-market anticipation does not materially affect our conclusions. The second concern is across-market anticipation effects, which may arise when nearby markets experience closely timed openings. We identify three such markets, San Francisco, San Diego, and Philadelphia, that launched Amazon Fresh within three years of larger neighboring markets (Los Angeles or New York) and are located within 150 miles or within the same state. To test for cross-market spillovers, we exclude these later-opening markets in column 5. After removing these markets, the estimated effect increases by 0.3% (2.8% compared to the baseline estimate of 2.5%). This pattern suggests that within-market anticipation is more pronounced than across-market anticipation, likely reflecting greater exposure to and familiarity with the service at close geographic proximity. Overall, the results across all specifications remain robust, indicating that anticipation effects are limited and do not drive our main findings. Parallel Trend To examine the timing of the price effects, we conduct dynamic analyses of Amazon Fresh’s entry. Figure 3 presents coefficient plots from these event-study specifica­ tions, illustrating the annual effects of Amazon Fresh’s entry on home values rela­ tive to the year prior to entry. In Panel A, which uses a balanced three-year pre- and post-entry window, the estimated coefficients in the pre-entry period are statistically 18 ​h​t​t​p​s​:​/​/​w​w​w​.​b​i​z​j​o​u​r​n​a​l​s​.​c​o​m​/​n​e​w​y​o​r​k​/​b​l​o​g​/​t​e​c​h​f​​l​a​s​h​/​2​0​1​4​/​0​8​/​s​p​o​t​t​e​d​-​i​n​-​n​y​c​-​a​m​a​z​o​n​f​r​e​s​h​-​t​r​u​c​k​s​.​h​t​m​ l; ​h​t​t​p​s​:​/​/​w​w​w​.​n​p​r​.​o​r​g​/​s​e​c​t​i​o​n​s​/​t​h​e​s​a​l​t​/​2​0​1​3​/​0​6​/​0​5​/​1​8​8​9​5​9​9​6​9​/​a​m​a​z​o​n​-​s​-​g​r​o​c​e​r​y​-​d​e​l​i​v​e​r​y​-​a​-​t​r​o​j​a​n​-​h​o​r​s​ e​-​t​o​-​g​e​t​-​i​n​t​o​-​y​o​u​r​-​d​o​o​r 1 3 ===== PAGE 19 (score 35, numeric-paren 14, table-words 7, var-words 5) ===== E-Grocery and Housing Markets: Evidence from the Amenity Effect of… Table 3  Robustness tests (1) (2) (3) (4) (5) (6) (7) Dep. Variable: ln(ZHVI) FHFA HPI Weighted Sample Treated vs Not-yet-Treated Focusing on Neighboring ZIP Codes Excluding Alternative E-grocery Service Atlanta Case Initial Service ZIP Codes Continued Treated vs. Never Treated Amazon Fresh x Post 0.026*** 0.028*** 0.015*** 0.019*** 0.045*** 0.073*** 0.089*** (0.002) (0.001) (0.001) (0.006) (0.001) (0.010) (0.027) Observations 10,669 118,517 191,827 50,736 99,029 19,465 4,088 R-squared 0.990 0.990 0.991 0.991 0.990 0.985 0.984 Controls yes yes yes yes yes yes yes ZIP Code Fixed Effect yes yes yes yes yes yes yes Year x Month Fixed Effect yes yes yes yes yes yes yes This table presents the results of robustness tests. Column 1 uses the Federal Housing Finance Agency (FHFA) home price index as the dependent variable. Column (2) re-estimate the baseline specification by weighting observations by the share of treated ZIP Codes within each market. Column 2 re-estimates the baseline model using weights equal to each market’s share of treated ZIP Codes. Column 3 compares the impacts between treated areas and not-yet-treated areas, where the not-yet-treated areas are the cities that received Amazon Fresh services in later years, beyond the sample period. Column 4 incorporates control ZIP Codes that are geographically adjacent to the treated ZIP Codes; treated ZIP Codes without adjacent control ZIP Codes are excluded from this analysis. Column 5 excludes ZIP Codes within the service areas of alternative e-grocery providers to ensure the results are not confounded by overlapping service regions. Standard errors are clustered at the ZIP Code level. The last two columns focus on the Atlanta metropolitan market: column 6 utilizes the initial Amazon Fresh service area data from 2016, while column 7 uses the continued service area from 2016 to 2022, comparing these two areas where Amazon Fresh was never introduced during the sample period. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 1 3 ===== PAGE 20 (score 13, numeric-paren 0, table-words 3, var-words 7) ===== J. Chung, L. Yang Subsample Analysis: Neighboring ZIP Codes Although the coefficient plots (Fig. 3) indicate that home values are almost iden­ tical within the metropolitan statistical areas prior to Amazon Fresh’s entry, we may still be concerned that ZIP Codes that receive e-grocery services are system­ atically different from those without such services. To address this concern, we use an alternative approach by exclusively comparing geographically adjacent ZIP Codes because geographically contiguous regions often share similar unobservable characteristics, such as local economic conditions and labor market dynamics. Fol­ lowing the methodology of Dube, et al. (2010), who leverage spatial proximity to estimate the effects of minimum wage policies on employment and earnings, we restrict our analysis to only adjacent ZIP Codes, rather than including all ZIP Codes in the market. Specifically, we focus on treated ZIP Codes located along the edge of the Ama­ zon Fresh service area and use their neighboring ZIP Codes (e.g., shaded in green in Fig. 2) as the control group.21 This approach allows us to compare nearby ZIP Codes that are likely similar in economic and demographic factors, differing pri­ marily in access to Amazon Fresh services. The results are presented in Table 3 column 4. We find that areas with Amazon Fresh services experience a robust 1.9% increase in home values compared to areas without Amazon Fresh services. These findings suggest that the positive pricing effects observed in our analysis are pri­ marily driven by the entry of Amazon Fresh, indicating that e-grocery services can function as a positive amenity for local households, similar to the effect of a physi­ cal big-box store. Alternative e-grocer: Instacart While Amazon Fresh emerged as a dominant player, accounting for approximately one-third of the U.S. online grocery market during the study period, other e-grocers may have simultaneously entered focal markets, potentially introducing estimation bias. To address this concern, we design a regression model that excludes overlap­ ping periods between Amazon Fresh and Instacart. We focus on Instacart as the pri­ mary alternative for two reasons. First, Instacart experienced rapid expansion during the sample period and became the second-largest e-grocer nationally.22 Second, like Amazon Fresh, Instacart publicly delineates its service areas at the ZIP Code level on its website, allowing for precise spatial identification of market entry. Our analy­ sis uses a pre-period spanning three years prior to the first opening of either service (Amazon Fresh or Instacart) and a post-period covering three years after Amazon Fresh’s entry.23 The results, presented in Table 3 column 5, indicate that the posi­ tive pricing impact remains significant when using this clean pre- and post- period 21 We exclude treated ZIP Codes that are not geographically adjacent to any control ZIP Codes. 22 According to eMarketer, Instacart held a 3.7% market share of the U.S. digital grocery sales market in 2017, which increased to 5.9% in 2018, 10.2% in 2019, and 21.5% in 2020. ​h​t​t​p​s​:​/​/​w​w​w​.​o​b​e​r​l​o​.​c​o​m​/​s​t​a​t​i​ s​t​i​c​s​/​i​n​s​t​a​c​a​r​t​-​m​a​r​k​e​t​-​s​h​a​r​e Accessed in June 2025. 23 This empirical setting is illustrated graphically in Figure A3. 1 3 ===== PAGE 22 (score 19, numeric-paren 4, table-words 5, var-words 5) ===== J. Chung, L. Yang appeal of online grocery services (CBRE, 2022). Lastly, the Atlanta metropolitan area features a highly diverse and youthful population, including a large proportion of professionals and university students, who are among the most active users of digital platforms. These combined factors likely amplify the positive housing market effects observed in the Atlanta case, relative to other urban markets. However, it is more crucial to emphasize that, even after accounting for potential dynamic changes in service areas, we can still conclude that the e-grocery service amenity is capital­ ized into housing prices. Heterogeneous Treatment Effects Although our analysis focuses on 11 markets that adopted Amazon Fresh over a rela­ tively short period (2013–2016), the two-way fixed effects (TWFE) estimator may still be limited in its ability to accommodate heterogeneous treatment effects or staggered adoption. To address this concern, we follow De Chaisemartin and d’Haultfoeuille (2023) and implement two alternative estimators: (i) the did_multiplegt estimator and (ii) the did_imputation estimator. Both approaches recover group–time average treatment effects and avoid the negative-weight problem associated with TWFE esti­ mators. Using these methods, we continue to find positive and statistically significant effects of Amazon Fresh access on housing prices, with estimated increases of 2.6% and 4.4%, respectively, as reported in columns 1 and 2 of Table 4. These robustness checks indicate that our main conclusions are not driven by the TWFE specification or by heterogeneous treatment effects arising from staggered adoption. Mechanism Household Consumption Patterns We find that the entry of Amazon Fresh services positively impacts local home values. We hypothesize that this effect is attributable to the perception of e-gro­ (1) (2) Dep. Variable: ln(ZHVI) did_multiplegt did_imputation Amazon Fresh x Post 0.026*** 0.044*** (0.008) (0.0001) Observations 118,517 118,517 Controls yes yes ZIP Code Fixed Effect yes Yes Year x Month Fixed Effect yes yes This table shows the results when we use did_multiplegt and did_imputation estimators suggested by De Chaisemartin and d’Haultfoeuille (2023). All regressions include ZIP Code fixed effects, year-by-month fixed effects, and the full set of baseline controls. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Table 4  Heterogeneous treat­ ment effects 1 3 ===== PAGE 23 (score 32, numeric-paren 6, table-words 11, var-words 3) ===== E-Grocery and Housing Markets: Evidence from the Amenity Effect of… cery services as an amenity for local households. To understand the underlying mechanism behind this positive pricing effect, we examine whether local house­ holds alter their consumption behavior subsequent to Amazon Fresh’s entry. Using the Nielsen Household Consumer Panel (NHCP) data, we run the regressions described in Eq. 1 on the household consumption patterns. The results are pre­ sented in Table 5. First, we conduct a regression on the logarithm of total consumption to gauge whether there are changes in overall household consumption subsequent to Ama­ zon Fresh’s introduction. In column 1, we find that total consumption experiences a 3.7% increase, amounting to $188,114 over the three-year period within each ZIP Code after Amazon Fresh’s arrival. While the economic impact of Amazon Fresh’s entry on the total consumption is notable, it is statistically insignificant. Secondly, we confirm that the surge in total consumption primarily stems from heightened online consumption following Amazon Fresh’ entry. When we use the logarithm of online consumption as the dependent variable in column 2, we ascertain that local households enhance their online consumption by 29.1% (approximately $35,896) in comparison to households residing in ZIP Codes where Amazon Fresh service is not available. However, local households exhibit no significant altera­ tions in their offline consumption, as demonstrated in column 3. Lastly, we adopt the online consumption share as the dependent variable in column 4 and consis­ tently find that households’ proportion of online consumption elevates by 0.3% following the entry of Amazon Fresh. Relative to the mean value, it is about 13.3% (0.3%/2.25%) increases of the proportion of online consumption over the total consumption. These results suggest that the operation of Amazon Fresh prompts an upswing in local households’ consumption via the online channel. This augmented online con­ sumption appears to be an additional expenditure driven by the convenience and variety brought by Amazon fresh. These findings support our main findings that the entry of Amazon Fresh services increases home values, which also proposes a poten­ Table 5  Mechanism: household consumption patterns (1) (2) (3) (4) Dep. Variables: ln(Total Con­ sumption + 1) ln(Online Consumption + 1) ln(Offline Consumption + 1) ln(Online Share + 1) Amazon Fresh x Post 0.037 0.291*** 0.033 0.003** (0.023) (0.099) (0.023) (0.001) Observations 183,011 183,011 183,011 183,011 R-squared 0.692 0.355 0.689 0.293 Controls yes yes yes yes ZIP Code Fixed Effect yes yes yes yes Year x Month Fixed Effect yes yes yes yes This table presents the influence of Amazon Fresh on consumption at the ZIP Code level. The dependent variables are the natural logarithm of the amount of consumption (plus 1), the natural logarithm of the amount of online and offline consumption (plus 1) as well as the online consumption share in column 1–4, respectively. Standard errors are clustered at the ZIP Code level. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 1 3 ===== PAGE 24 (score 31, numeric-paren 10, table-words 5, var-words 9) ===== J. Chung, L. Yang tial avenue whereby Amazon Fresh services function as a positive amenity within the housing market. Local Market Changes Another mechanism contributing to the increase in home values is the potential improvement in local markets following the entry of Amazon Fresh. The availability of this new service often drives significant investments in infrastructure and may bol­ ster the local labor market. For instance, Chung (2023) demonstrates that the open­ ing on Amazon fulfillment centers leads to increased retail property values, through improvements in the labor market within the transportation and warehousing sector. If Amazon Fresh exerts similar impacts on local markets, we would expect that its entry may lead to significant changes in local economic and demographic factors. To explore this possibility, we analyze how these factors evolve in response to the introduction of Amazon Fresh services. As shown in Table 6, Amazon Fresh services appear to marginally improve local market conditions. Following its entry, the total population in treated areas increases by 0.3%, and the labor force grows by 0.4%. Additionally, the median age rises by 0.3%. However, the economic impacts of these changes are relatively small, making it difficult to argue that Amazon Fresh significantly alters local market demographics. Other factors, such as median household income, household size, and unemployment rates, show no statistically significant changes. These findings provide evidence that online platforms like Amazon Fresh may not fundamentally drive local market trans­ formations but instead serve as an added amenity for local households. Moreover, the lack of significant changes in economic factors suggests that the observed increase in home values is unlikely to be driven by broader local market shifts, such as gentrifi­ cation or demographic changes. Table 6  Local market changes (1) (2) (3) (4) (5) (6) (7) Dep. Variables: Population Laborforce Income Median Age House­ hold Size Unemploy­ ment Rate Employ­ ment Size Amazon Fresh x Post 0.003** 0.004*** −0.002 0.003*** −0.002 0.002 −0.001 (0.001) (0.001) (0.001) (0.001) (0.001) (0.004) (0.0005) Observations 118,517 118,517 118,517 118,517 118,517 118,517 118,517 R-squared 0.998 0.998 0.990 0.972 0.984 0.904 0.917 ZIP Code Fixed Effect yes yes yes yes yes yes yes Year x Month Fixed Effect yes yes yes yes yes yes yes This table presents the results when using the economic and demographic factors as dependent variables. The dependent variables are logged. Standard errors are clustered at the ZIP Code level. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 1 3 ===== PAGE 25 (score 38, numeric-paren 10, table-words 6, var-words 15) ===== E-Grocery and Housing Markets: Evidence from the Amenity Effect of… Heterogeneity Property Characteristics E-commerce is frequently favored by younger generations and considering that younger customers often occupy smaller units with fewer bedrooms, while fami­ lies with children tend to reside in larger units with more bedrooms, we posit that the amenity effect of Amazon Fresh could differ based on the number of bedrooms. Using the ZHVI data by the number of bedrooms, we run the regressions on home values of each property type in column 1–5 of Table 7. We find that home values of smaller units are sold at higher prices when compared to similar size homes in areas without Amazon Fresh service following the entry of Amazon Fresh. Specifically, homes with 1 bedroom to 3 bedrooms are transacted at higher prices following the entry of Amazon Fresh. The magnitude is larger when a home has 1 bedroom at 2.3% and 2 bedrooms at 2.9%, and the effect decreases to 2.0% when a home has 3 bed­ rooms. Homes with 4 bedrooms and 5 bedrooms also have positive amenity effect from Amazon Fresh entry, but with smaller impacts of 1.5% and 1.3%, respectively. Our findings suggest the positive pricing effects of the Amazon Fresh’s entry are more pronounced on homes where young people are likely to reside, i.e. units with smaller the number of bedrooms, which is consistent with the previous literature that e-grocery services are advantageous for the younger demographic (e.g., Couture, et al., 2021). Socioeconomic Status Urban areas with low socioeconomic status are often referred to as “Food Deserts” due to their lack of accessible grocery stores and nutritious food options (e.g., All­ cott, et al., 2019). Furthermore, the distribution of retail outlets varies depending on the income of the neighborhood; for instance, areas with high poverty rates tend to exhibit lower business density, including fewer retail stores or chain supermarkets Table 7  Heterogeneity tests—property types (1) (2) (3) (4) (5) Dep. Variable: ln(ZHVI) 1-bedroom 2-bedroom 3-bedroom 4-bedroom 5-bedroom Amazon Fresh x Post 0.023*** 0.029*** 0.020*** 0.015*** 0.013*** (0.004) (0.002) (0.002) (0.002) (0.002) Observations 54,535 106,987 115,420 106,473 87,477 R-squared 0.987 0.990 0.992 0.992 0.993 Controls yes yes yes yes yes ZIP Code Fixed Effect yes yes yes yes yes Year x Month Fixed Effect yes yes yes yes yes This table illustrates the impacts of Amazon Fresh on home values based on property type. Column 1 employs ZHVI for 1-bedroom units, column 2 for 2-bedroom units, column 3 for 3-bedroom units, column 4 for 4-bedroom units, and column 5 for 5-bedroom units. Both ZIP Code and year-month fixed effects are included. Standard errors are clustered at the ZIP Code level. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 1 3 ===== PAGE 26 (score 14, numeric-paren 0, table-words 2, var-words 10) ===== J. Chung, L. Yang (e.g., Alwitt & Donley, 1997; Schuetz, et al., 2012). Our study aims to investigate the potential mitigating effect of e-grocery services on these retail disparities in eco­ nomically disadvantaged urban neighborhoods. Specifically, we explore whether the impacts of Amazon Fresh’s entry on property values differ across regions with vary­ ing socioeconomic statuses. To address this question, we utilize household demo­ graphic and economic data at the ZIP Code level from the American community Survey. We create dummy variables based on the median values of each demographic and economic factor and intersect these dummy variables with the interaction term of the treatment and post variables. Specifically, we explore whether the results vary according to local socioeconomic factors and if so, which attributes have significant effects. The findings from our heterogeneity analysis are presented in Table 8. In column 1, where we use median age as the criterion, residential properties located in younger-population areas see a greater rise in home values. Focusing on the median household income in column 2 of Table 8, we find that areas with higher income experience a lower home value increase following the entry of Amazon Fresh. While higher income households are more inclined to shop online (e.g., Dolfen, et al., 2023), we find the opposite impact that the entry of e-grocery services, which provide daily necessities, may be more beneficial to lower income households through ame­ nity channel. Analyzing the proportion of renter-occupied households in column 3, we find that areas with high renter occupancy witness a larger boost in home values following the entry of Amazon Fresh. When examining the ratio of the highly edu­ cated population (bachelor’s degree and above) in column 4, we find that homes in less educated areas encounter a greater increase in home values following the entry of Amazon Fresh. Considering household race in column 5, we discover that home val­ ues increase more in areas with minority households (non-White). These results are in line with the previous literature that e-commerce offers greater benefits to younger population and minorities in rural households (e.g., Couture, et al., 2021). Further­ more, since e-grocery services can be useful when local households are not able to utilize automobiles, we investigate whether local automobile ownership rates affect the results. Notably, we find that areas with lower automobile ownership exhibit a stronger and positive pricing effect following the entry of Amazon Fresh in column 6. These findings suggest that areas with underdeveloped socioeconomic factors benefit more from the availability of e-grocery services, experiencing a greater appreciation in home values. Additionally, we investigate whether local interest in Amazon Fresh affects the results in column 7. Unlike physical stores, online platforms are not directly visible to buyers, though they provide significant convenience to local customers. In our study, we assume that local households are aware of e-grocery services or use them signifi­ cantly enough to impact the local housing market. To connect local household inter­ est in Amazon Fresh to housing market outcomes, we use Google Trends’ keyword search outcomes for ‘Amazon Fresh’ as a proxy for local interest in e-grocery ser­ vices. We find that the pricing effect is more pronounced when local household inter­ est in e-grocery services is higher. This result supports our argument that e-grocery services serve as an additional amenity to the local housing market and are priced in home values. The presence of such services may act as a signal of neighborhood desirability and investment potential. 1 3 ===== PAGE 27 (score 62, numeric-paren 45, table-words 4, var-words 4) ===== E-Grocery and Housing Markets: Evidence from the Amenity Effect of… (1) (2) (3) (4) (5) (6) (7) (8) (9) Dep. Variables: ln(ZHVI) Age Income Renter Education Minority No Vehicle Google Trends Competing Retail Est All Amazon Fresh x Post 0.060*** 0.055*** −0.019*** 0.046*** −0.006* 0.004 0.0033 0.033*** 0.009 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.025) (0.001) (0.039) x Age −0.072*** −0.004*** (0.007) (0.001) x Income −0.059*** 0.00005** (0.007) (0.001) x Renter Occupied 0.086*** 0.122*** (0.006) (0.031) x Education −0.041*** −0.0003 (0.006) (0.001) x Minority 0.064*** 0.110*** (0.008) (0.018) x Non-Vehicle 0.043*** 0.001** (0.007) (0.001) x Interest 0.0363*** 0.021*** (0.005) (0.009) x Competing Retail Est −0.016*** 0.00006 (0.007) (0.001) Observations 118,517 118,517 118,517 118,517 118,517 118,517 118,517 118,517 118,517 R-squared 0.991 0.990 0.991 0.990 0.990 0.990 0.990 0.990 0.991 Controls yes yes yes yes yes yes yes yes yes Table 8  Heterogeneity tests – socio-economic factors 1 3 ===== PAGE 28 (score 15, numeric-paren 0, table-words 3, var-words 9) ===== J. Chung, L. Yang (1) (2) (3) (4) (5) (6) (7) (8) (9) Dep. Variables: ln(ZHVI) Age Income Renter Education Minority No Vehicle Google Trends Competing Retail Est All ZIP Code Fixed Effect yes yes yes yes yes yes yes yes yes Year x Month Fixed Effect yes yes yes yes yes yes yes yes yes This table provides the results of the heterogeneity test analyzing the impact of the entry of e-grocery services on housing prices. We use the median value of each indicator to create dummy variables, which are then intersected with the interaction term of Amazon Fresh (Treat) and Post. Demographic and economic factors at the ZIP Code level are from the 2010 American Community Survey. Income is median household income, age is median household age, and education is the ratio of educated individuals with a bachelor's degree and above. Minority is the ratio of non-White population. No Vehicle indicates the share of household without vehicle. Google Trends indicates the search engine outcomes for the keyword “Amazon Fresh” at the market level. Competing retail establishments (Competing Retail Est.) indicates the share of retail establishments that compete with e-grocer compared to the total retail establishment. Standard errors are clustered at the ZIP Code level. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Table 8  (continued) 1 3 ===== PAGE 30 (score 34, numeric-paren 12, table-words 6, var-words 9) ===== J. Chung, L. Yang tically significant, with estimated effects of 16.6%, 14.9%, and 19.3%, respectively. This pattern indicates that online consumption increases more in ZIP Codes experi­ encing larger housing-price effects and in areas with housing structures and tenure patterns, specifically smaller housing units and higher owner-occupancy, that are more likely to benefit from Amazon Fresh. Overall, these findings provide additional support for the proposed consumption channel. As the Nielsen data do not identify whether online purchases are specifically attributable to Amazon Fresh, the results should be interpreted as suggestive evidence consistent with the proposed mecha­ nism, rather than as a direct measure of the treatment mechanism. Conclusion The e-grocery industry has experienced rapid expansion in recent decades, yet its economic impact remains underexplored. This study contributes to the literature by exploring the impact of e-grocery services on the housing market. We use Amazon Fresh, which provides both products and delivery services to local households as a representative case of e-grocers as Amazon Fresh is a leading e-grocer during our Table 9  Heterogeneity tests—online consumptions (1) (2) (3) Dep. Variable: ln(Online Consumption) Amazon Fresh x Post 0.221* 0.213** 0.252** (0.119) (0.102) (0.125) Amazon Fresh x Post x D(Strong Price Effects) 0.166** (0.071) Amazon Fresh x Post x D(Higher Share Units with 1–3 Bedrooms) 0.149*** (0.049) Amazon Fresh x Post x D(Higher Share Owner-Occupied Units) 0.193** (0.080) Observations 183,011 183,011 183,011 R-squared 0.356 0.354 0.355 Controls yes yes yes ZIP Code Fixed Effect yes yes yes Year x Month Fixed Effect yes yes yes This table examines heterogeneity in the effect of Amazon Fresh entry on online consumption using household-level Nielsen data. The dependent variable is Online Consumption, an index of online grocery spending at the ZIP Code–month level (exact construction described in the main text). Amazon Fresh × Post equals one for ZIP Codes in metros where Amazon Fresh is available in month t and zero otherwise. In column (1), D(Strong Price Effects) equals one for ZIP Codes whose estimated house- price response to Amazon Fresh entry is above the sample median and zero otherwise. In column (2), D(Higher Share Units with 1–3 Bedrooms) equals one for ZIP Codes with an above-median share of 1–3 bedroom units. In column (3), D(Higher Share Owner-Occupied Units) equals one for ZIP Codes with an above-median owner-occupancy rate. Interaction terms capture how online consumption responds in ZIP Codes with stronger price effects and housing structures that are more likely to benefit from e-grocery. All specifications include ZIP Code fixed effects, year-by-month fixed effects, and the full set of baseline controls. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 1 3 ===== PAGE 31 (score 10, numeric-paren 0, table-words 1, var-words 8) ===== E-Grocery and Housing Markets: Evidence from the Amenity Effect of… study’s sample period. Furthermore, we accurately define the exact servicing areas of e-grocer by using detailed data on e-grocer servicing areas at the ZIP Code level. Our study reveals that e-grocery services are perceived as a valuable amenity by local residents. Specifically, we discover that the entry of Amazon Fresh positively influences the housing market, resulting in a 2.5% increase in local housing prices. Our findings also remain robust when we redefine treated and control groups to focus on neighboring ZIP Codes and consider alternative e-grocer impacts on local home values. Additionally, our mechanism analysis shows that the presence of Amazon Fresh results in increased local household consumption through online shopping channels. This supports our argument that e-grocery services provide amenity effects comparable to those of physical retail stores in the housing market. Our heterogeneity analysis indicates that the positive impact on home values is more pronounced in areas with lower socioeconomic status, including those with lower income, less education, higher renter occupancy, and lower automobile own­ ership. Moreover, residents in "Retail Desert" areas, regions with limited access to physical stores, benefit particularly from e-grocery services compared to those in areas with better retail access. Because e-grocery services do not require physical storefronts and can cover broader geographies efficiently (for example, an Amazon Fresh fulfillment center can serve a larger area than a single retail store), increased accessibility to daily necessities can help address disparities in the housing market. This study provides new insights into the complex interactions among technologi­ cal innovation, consumer behavior, and housing markets. The expansion of e-grocery services, exemplified by Amazon Fresh, has the potential to generate meaningful ben­ efits for local housing markets, particularly in areas facing socioeconomic challenges. However, this study does not directly examine whether the observed increases in home values translate into improved housing affordability for low-income or under­ served households. While greater access to online grocery services may enhance convenience and welfare in underserved areas, rising home values could also pose affordability challenges, especially for renters. Future research could address this issue by incorporating household-level measures of housing affordability. Moreover, an important concern relates to the potential predictability of Ama­ zon Fresh entry through facility site selection, development approvals, or permitting processes. Such activities could render entry partially anticipated and thus weaken a strict “surprise” interpretation of entry announcements. Because we are unable to obtain comprehensive permitting or zoning data, we rely instead on systematic media coverage to identify entry timing. This approach is reasonable insofar as most households are more likely to learn about Amazon Fresh’s entry through news reports than by directly observing development or permitting activity. Future research could extend our analysis by explicitly examining the developmental and pre-operational stages of e-grocery entry. As digital services continue to reshape urban life, understanding these nuanced dynamics remains essential for informing policy discussions and promoting equi­ table access to digital amenities. In particular, the rapid expansion of other e-grocery providers beyond our sample period (post-2019) both across markets and over time may moderate or reshape amenity capitalization effects. Future research can inves­ tigate the dynamic effects of e-grocers on local economies to fully assess how digi­ 1 3 ===== PAGE 38 (score 43, numeric-paren 16, table-words 7, var-words 7) ===== J. Chung, L. Yang (1) (2) (3) All Sample Matched Sample Considering Alternative E-grocery Dep. Variable: ln(Home Value) Instacart x Post 0.003*** -0.000 -0.004*** (0.00) (0.00) (0.00) Observations 784,958 458,038 284,752 R-squared 0.991 0.991 0.991 Controls yes yes yes ZIP Code Fixed Effect yes yes yes Year x Month Fixed Effect yes yes yes This table presents the influence of Instacart on home values. We use all the markets that received Instacart services between 2013 and 2016 in the first column while we use the matched markets to the Nielsen data in the next column. Column 3 excludes areas serviced by alternative e-grocery provider’s servicing areas from the sample. ZIP Code fixed effect and year by month fixed effect are included. Standard errors are clustered at the ZIP Code level. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Table A12  The impact of insta­ cart on housing prices Treated Group Control Group T-test Mean Std. dev Mean Std. dev Difference Home Value ($) 367,181 31,178 330,874 23,227 36,307 (0.92) Population 30,392 2,257 26,230 2,214 4,161 (1.31) Education Rate 0.664 0.042 0.568 0.040 0.097 (1.66)* Renter Oc­ cupied Rate 0.230 0.018 0.209 0.015 0.021 (0.86) Minority Rate 0.304 0.028 0.234 0.023 0.07 (1.89)* Median Age 39.685 0.564 40.878 0.591 -1.19 (1.46) Median HH Income ($) 87,945 4,211 84,137 3,845 3,807.98 (0.66) Non-Vehi­ cle (%) 6.872 1.408 4.867 0.567 2.004 (1.27) Household Size 2.876 0.057 2.843 0.048 0.033 (0.44) Vacancy Rate 0.057 0.004 0.072 0.008 -0.015 (-1.84)* Competing Retail Est. Rate 0.262 0.057 0.364 0.065 -0.10 (-1.17) Table A11  Pre-event summary statistics This table shows the summary statistics of pre-event period and t-test difference in the last column. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 1 3 ===== PAGE 39 (score 27, numeric-paren 6, table-words 8, var-words 4) ===== E-Grocery and Housing Markets: Evidence from the Amenity Effect of… Funding  No funding was received for conducting this study. Data Availability  The datasets contain confidential information and cannot be shared publicly. Declarations Ethics Approval  This manuscript is original, has not been published, and is not currently under consider­ ation for publication elsewhere. Consent to Participate  Not applicable. Consent to Publish  Not applicable. Competing Interests  The authors have no financial or non-financial interests to disclose. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit ​h​t​t​p​:​/​/​c​r​e​a​t​i​v​e​c​o​m​m​o​n​s​.​o​r​g​/​l​i​c​e​n​ s​e​s​/​b​y​/​4​.​0​/​.​ (1) (2) (3) (4) Dep. Variables: ln(Total Consump­ tion + 1) ln(Online Consump­ tion + 1) ln(Offline Consump­ tion + 1) ln(Online Share + 1) Amazon Fresh x Post -0.010 0.129** -0.013 0.002*** (0.012) (0.053) (0.012) (0.001) Observations 302,065 302,065 302,065 302,065 R-squared 0.693 0.343 0.690 0.258 Controls yes yes yes yes ZIP Code Fixed Effect yes yes yes yes Year x Month Fixed Effect yes yes yes yes This table presents the influence of Instacart on consumption at the ZIP Code level. The dependent variables are the natural logarithm of the amount of consumption (plus 1), the natural logarithm of the amount of online and offline consumption (plus 1) as well as the online consumption share in column 1–4, respectively. ZIP Code fixed effect and year by month fixed effect are included. Standard errors are clustered at the ZIP Code level. Robust standard errors, clustered at the ZIP Code level, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Table A13  Instacart: household consumption patterns 1 3 ===== PAGE 40 (score 18, numeric-paren 0, table-words 0, var-words 18) ===== J. Chung, L. Yang References Agarwal, S., Ding, Y., Kuang, W., & Zhu, X. (2023). Are environmental punishments good news or bad news? Evidence from China. Journal of Environmental Economics and Management, 120, Article 102847. Allcott, H., Diamond, R., Dubé, J. P., Handbury, J., Rahkovsky, I., & Schnell, M. (2019). Food deserts and the causes of nutritional inequality. Quarterly Journal of Economics, 134(4), 1793–1844. Alwitt, L. F., & Donley, T. D. (1997). Retail stores in poor urban neighborhoods. Journal of Consumer Affairs, 31(1), 139–164. An, B. Y., & Chung, J. (2025). Who bears the brunt of disruptive innovation? 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