1# RECORD 28fd8140cfc928298ce0dcc59bc4a1264ace9c6c2# TITLE: Exploring the quantitative impact of medical marijuana dispensaries on residential sale prices in Oklahoma3# YEAR: 2026.0 VENUE: Empirical Economics DOI: 10.1007/s00181-026-02894-64# AUTHORS: Joshua Clark; Michael S. Delgado5# PAGES: 25 CANDIDATE TABLE PAGES (1-based): [10, 11, 16, 17, 19, 20, 22]6# ABSTRACT: 7 Medical marijuana was legalized in Oklahoma as recently as 2018, and since then Oklahoma has rapidly grown to have the largest number of medical marijuana dispensaries of any states in the USA. What have been the impacts of this rapid proliferation of legalized medical marijuana? We use a hedonic pricing model to assess how legalized marijuana dispensary activity (both distance to the nearest dispensary and the number of nearby dispensaries) has impacted residential property values. The dataset spans six different counties from across the State of Oklahoma, and we explore a variety of different regression specifications to rigorously explore the effects of dispensary activity on local residents. While we find evidence of some heterogeneity in the dispensary effects, one broad finding is that residents prefer not to live in immediate proximity to a dispensary but prefer access to dispensaries moderately distanced from their home. Understanding these broad trends and the localized heterogeneity in effects are important for residents and policymakers alike in Oklahoma and in other states that may be considering similar legislation.8910===== PAGE 1 (score 3, numeric-paren 0, table-words 0, var-words 3) =====1112Empirical Economics (2026) 70:6113https://doi.org/10.1007/s00181-026-02894-614Exploring the quantitative impact of medical marijuana15dispensaries on residential sale prices in Oklahoma16Joshua Clark1 · Michael S. Delgado117Received: 20 December 2024 / Accepted: 28 January 2026 / Published online: 21 March 202618© The Author(s) 202619Abstract20Medical marijuana was legalized in Oklahoma as recently as 2018, and since then21Oklahoma has rapidly grown to have the largest number of medical marijuana dispen-22saries of any states in the USA. What have been the impacts of this rapid proliferation23of legalized medical marijuana? We use a hedonic pricing model to assess how legal-24ized marijuana dispensary activity (both distance to the nearest dispensary and the25number of nearby dispensaries) has impacted residential property values. The dataset26spans six different counties from across the State of Oklahoma, and we explore a27variety of different regression specifications to rigorously explore the effects of dis-28pensary activity on local residents. While we find evidence of some heterogeneity in29the dispensary effects, one broad finding is that residents prefer not to live in immedi-30ate proximity to a dispensary but prefer access to dispensaries moderately distanced31from their home. Understanding these broad trends and the localized heterogeneity in32effects are important for residents and policymakers alike in Oklahoma and in other33states that may be considering similar legislation.34Keywords Medical marijuana · Hedonic · Public policy351 Introduction36Oklahoma State Question 788 (Initiative Petition No. 412), or SQ 788, was filed with37the Oklahoma Secretary of State on April 11, 2016, and proposed the legalization of the38purchase and consumption of medical marijuana for adults with a state issued medical39marijuana license. SQ 788 established that a regulatory office—the Oklahoma Medical40Marijuana Authority (OMMA)—be opened under the Oklahoma State Department of41Health to regulate and receive applications for dispensaries, consumption, growing,42and packaging licenses. SQ 788 passed on June 26, 2018, with a 56.86 percent major-43B Michael S. Delgado44delgado2@purdue.edu45Joshua Clark46joshclarkstudent1@gmail.com47148Department of Agricultural Economics, Purdue University, West Lafayette, IN 47907-2056, USA49123505152===== PAGE 10 (score 60, numeric-paren 41, table-words 1, var-words 8) =====53546155Page 10 of 2556J. Clark, M. S. Delgado57Table 1 Descriptive statistics for the pooled sample of four counties and separately by county58Pooled59Comanche60Wagoner61Washington62Oklahoma63Tulsa64Payne65Sample66County67County68County69County70County71County72Statistic73(n = 28, 429)74(n = 5, 941)75(n = 396)76(n = 286)77(n = 21, 806)78(n = 27, 374)79(n = 676)80Property price81Sale price82253,060.3083209,286.1084232,053.0085182,763.2086266,290.0087302,299.5088325,171.1089(243,574.30)90(284,027.50)91(150,395.20)92(106,298.60)93(232,476.20)94(450,721.40)95(466,785.50)96Property characteristics97Beds983.196993.0791003.3541013.3951023.2231033.083104(0.666)105(0.599)106(0.629)107(0.755)108(0.679)109(0.968)110Baths1112.2151121.7751132.1821141.9441152.3391161.8201172.052118(0.898)119(0.578)120(0.702)121(0.742)122(0.936)123(0.764)124(0.874)125Age12640.29612743.30112837.34612960.69913039.26413150.042132(23.885)133(24.852)134(13.651)135(19.337)136(23.625)137(24.564)138SQFT1391,904.9871401,643.8161412,107.5451422,020.2271431,970.952144(895.220)145(599.074)146(854.591)147(968.378)148(947.487)149Stories1501.1541511.1491521.181153(0.260)154(0.287)155(0.321)156Garage area157388.317158521.715159403.213160(245.387)161(161.457)162(246.642)163Garage Dummy1640.7871650.9971660.825167(0.410)168(0.050)169(0.380)170Non-Ag acres1710.557172(0.700)173Depreciation loss174−51,702.83175(33,454.04)176Dist to CBD (km)17739.787178124.58617933.03918065.14918116.47318214.52418377.832184(44.451)185(8.678)186(15.225)187(3.227)188(6.255)189(6.886)190(6.199)191123192193194===== PAGE 11 (score 80, numeric-paren 62, table-words 1, var-words 3) =====195196Exploring the quantitative impact of medical marijuana…197Page 11 of 2519861199Table 1 continued200Pooled201Comanche202Wagoner203Washington204Oklahoma205Tulsa206Payne207Sample208County209County210County211County212County213County214Statistic215(n = 28, 429)216(n = 5, 941)217(n = 396)218(n = 286)219(n = 21, 806)220(n = 27, 374)221(n = 676)222Neighborhood223203,309.40224139,660.60225177,009.70226139,379.10227221,966.40228241,517.80229173,123.90230(155,612.00)231(58,822.90)232(101,887.20)233(60,546.30)234(169,922.10)235(250,292.60)236(135,425.30)237Density23824.80123925.1762403.4072413.70324225.36424313.49724436.544245(18.367)246(15.728)247(4.823)248(2.259)249(18.906)250(8.618)251(42.698)252Dispensary exposure253minDist2541,409.4142551,726.0252562,624.3292571,260.1352581,303.0492591,224.9622603,209.315261(1385.480)262(1866.693)263(1893.913)264(983.981)265(1187.434)266(1064.254)267(2953.432)268Count5002690.3402700.3092710.1312720.2452730.3532740.3192750.109276(0.894)277(0.923)278(0.567)279(0.612)280(0.893)281(0.776)282(0.525)283Count10002841.5272851.1972860.4442871.1712881.6422891.4512900.361291(2.387)292(2.282)293(1.857)294(2.101)295(2.414)296(2.103)297(0.941)298Count20002995.6573004.1523011.4723023.4133036.1723045.5643051.370306(6.614)307(6.106)308(3.714)309(3.208)310(6.725)311(8.618)312(3.155)313Year indicators314Year20203150.2483160.1313170.1693180.1543190.2833200.2233210.392322(0.432)323(0.338)324(0.375)325(0.361)326(0.450)327(0.417)328(0.489)329Year20213300.4683310.3233320.4123330.3503340.5093350.4443360.229337(0.499)338(0.468)339(0.493)340(0.478)341(0.500)342(0.497)343(0.421)344Year20223450.2363460.3253470.2903480.4093490.2083500.3333510.272352(0.424)353(0.468)354(0.455)355(0.493)356(0.406)357(0.471)358(0.445)359Year20233600.0493610.2203620.1293630.0873640.0003650.107366(0.215)367(0.414)368(0.335)369(0.283)370(0.000)371(0.309)372Numbers reported are sample means, with sample standard deviations in parentheses below373The variable “neighborhood” refers to our historical neighborhood value index calculated prior to the passage of SQ788374123375376377===== PAGE 12 (score 24, numeric-paren 2, table-words 9, var-words 4) =====37837961380Page 12 of 25381J. Clark, M. S. Delgado382Fig. 1 Scatterplot of historical neighborhood values and current property prices383Table 2 Univariate correlation between neighborhood values and ln(Price/100)384Dependent variable: ln(Price/100)385ln(Neighborhood)3860.708∗∗∗387(0.006)388Constant389−0.936∗∗∗390(0.072)391Observations39228,022393R23940.333395Adjusted R23960.333397∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01398correlation—thus, a descriptive statistic that motivates our use of neighborhood as a399control variable—and should not be taken as evidence that dispensaries are located in400areas characterized in any particular way (e.g., by race, income, or other status).401Turning to the dispensary-related variables, we see that, on average, the minimum402distance to the nearest dispensary is about 1.4 kilometers (Table 1). These properties403have, on average, 0.34 dispensaries within a 500m radius of the property; 1.5 dis-404pensaries within 1km of the property; and 5.7 dispensaries within 2 kilometers of the405property. It is worth noting that Washington, Oklahoma and Tulsa counties have prop-406erties that are in closest proximity, on average, to dispensaries and with the highest407average density of proximate dispensaries.408123409410411===== PAGE 16 (score 105, numeric-paren 82, table-words 3, var-words 5) =====41241361414Page 16 of 25415J. Clark, M. S. Delgado416Table 4 Benchmark model and sub-models to further assess minDist417Dependent variable: ln(Price/100)418No counts &419No counts &420Benchmark421No counts422minDist2423Interaction424minDist4252.197e-05***4261.467e-05***4271.135e-06428−1.613e-05**429(2.881e-06)430(2.598e-06)431(7.023e-06)432(7.761e-06)433minDist24343.041e-09**4352.437e-09*436(1.466e-09)437(1.470e-09)438minDist ×4398.908e-11***440Neighborhood441(1.707e-11)442Count5004430.003444(0.003)445Count1000446−0.004**447(0.002)448Count20004490.004***450(0.001)451Age452−0.012***453−0.012***454−0.012***455−0.012***456(0.0004)457(0.0004)458(0.0004)459(0.0004)460Age24618.527e-05***4628.834e-05***4638.786e-05***4648.726e-05***465(4.010e-06)466(3.975e-06)467(3.981e-06)468(3.981e-06)469SQFT4700.001***4710.001***4720.001***4730.001***474(0.00001)475(0.00001)476(0.00001)477(0.00001)478SQFT2479−3.323e-08***480−3.328e-08***481−3.318e-08***482−3.355e-08***483(1.372e-09)484(1.373e-09)485(1.374e-09)486(1.375e-09)487Beds488−0.043***489−0.043***490−0.043***491−0.043***492(0.005)493(0.005)494(0.005)495(0.005)496Baths4970.069***4980.069***4990.069***5000.068***501(0.005)502(0.005)503(0.005)504(0.005)505Density5060.0003**5070.0003*5080.0003**5090.0003**510(0.0001)511(0.0001)512(0.0001)513(0.0001)514Dist to CBD (km)515−0.0007*516−0.001***517−0.001***518−0.001***519(0.0004)520(0.0004)521(0.0004)522(0.0004)523Neighborhood5243.823e-07***5253.736e-07***5263.3739e-07***5272.815e-07***528(1.721e-08)529(1.713e-08)530(1.713e-08)531(2.463e-08)532Year2020533−0.169***534−0.169***535−0.169***536−0.170***537(0.013)538(0.013)539(0.013)540(0.013)541Year2021542−0.075***543−0.076***544−0.075***545−0.076***546(0.012)547(0.012)548(0.012)549(0.012)550Year20225510.087***5520.087***5530.087***5540.086***555(0.012)556(0.012)557(0.012)558(0.012)559Oklahoma Co5600.086**5610.0315620.0465630.022564(0.043)565(0.042)566(0.043)567(0.043)568123569570571===== PAGE 17 (score 53, numeric-paren 20, table-words 10, var-words 10) =====572573Exploring the quantitative impact of medical marijuana…574Page 17 of 2557561576Table 4 continued577Dependent variable: ln(Price/100)578No counts &579No counts &580Benchmark581No counts582minDist2583Interaction584Washington Co585−0.080**586−0.127***587−0.116***588−0.126***589(0.033)590(0.032)591(0.033)592(0.033)593Wagoner Co5940.012595−0.041596−0.029597−0.040598(0.043)599(0.043)600(0.042)601(0.042)602Constant6036.814***6046.903***6056.894***6066.941***607(0.053)608(0.051)609(0.052)610(0.052)611Observations61228,02261328,02261428,02261528,022616R26170.6406180.6396190.6396200.640621Adjusted R26220.6406230.6396240.6396250.640626∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01627the average home price, this is a decrease of about 1085.63 dollars. At the same time,628the coefficient on Count2000 indicates that adding one more dispensary in the 1000–6292000m range, holding minDist, the other Count variables, and controls constant,630results in a 0.427 percent increase in property values (or about 1080.57 dollars at the631average). Taken at face value, these estimates indicate that homeowners prefer to have632a marginal dispensary located farther away from the home.633It is worth discussing the effects and significance of the control variables on prop-634erty prices before digging deeper into the dispensary exposure variables and their635relationship to property prices. Still looking at the benchmark model in Column 1 of636Table 4, we see statistically significant quadratic relationships between the age and637square footage of the property and property values. In particular, property age has an638inverse-U-shaped relationship with property prices, with a minimum property value639occurring at a property age of 70 years. The average age of the home in the pooled640sample is 40 years (Table 1). Square footage has a U-shaped relationship with property641values, with the maximum occurring at 15,046 square feet. From Table 1, the average642square footage in the pooled benchmark sample is 1904.99, and the detailed descrip-643tive statistics tables in the appendix indicate that this turning point is greater than the644maximum square footage in our data; in effect, the square footage effect on property645prices is monotonically increasing but at a decreasing rate. We see that the number of646bedrooms is significantly negative; given that we control for square footage, the neg-647ative coefficient on bedrooms means that home buyers prefer fewer but larger rooms648for a home of a given size. The number of bathrooms and neighborhood home density649are significantly positive, while distance to the nearest CBD is significantly negative.650The historical neighborhood value is significantly positive, which is in line with the651correlations shown in Fig.1 and Table 2, and indicates that historical average neigh-652borhood values are positively and significantly correlated with current sales prices.653This estimate, therefore, reinforces our intuition to include this variable as an impor-654tant control for neighborhood quality that in part explains current property prices but655123656657658===== PAGE 18 (score 23, numeric-paren 0, table-words 10, var-words 3) =====65966061661Page 18 of 25662J. Clark, M. S. Delgado663might also correlate with dispensary activity (distance and exposure). Finally, we see664some significance of the year of sale indicators relative to the baseline year of 2023,665and significance of the county indicators relative to the Comanche County baseline.666We note that the R2 is 0.640, reflecting a reasonable fit of the regression.6674.2 Distance to the nearest dispensary668Moving beyond the benchmark regression, in Columns 2–4 of Table 4 we explore669more deeply the relationship between minDist and property values. In Column 2, we670remove the Count variables and explore the linear effect of minDist on the natural671log of property values: We find the results remain qualitatively unchanged from the672benchmark regression (Column 1), with a significantly positive effect of minDist on673property values. This estimate is fairly smaller in magnitude compared to the estimate674in Column 1, this time implying an effect of 3.71 dollars per meter, or about 371675dollars for a 100m distance.676In Column 3, we consider a quadratic specification and find that only the quadratic677term is statistically significant, and in Column 4 we consider both the quadratic678minDist specificationincludinganinteractionwiththehistorical neighborhoodindex.679In this latter model, we find that all coefficients are significant. Exploring these esti-680mates further, using both the average property value and average neighborhood value681index (203,309.40 from Table 1), we find that on average the minDist marginal effect682on property prices is about 224 dollars per 100m. In looking across the models in683Table 4, we see that sharpening our estimates (i.e., Column 4) results in a smaller684estimated average effect of minDist on property prices. Yet, from the perspective of685these models, dispensaries are viewed by homeowners as a negative amenity.6864.3 Intensity of dispensary exposure687Next, we look more deeply into the relationship between the Count variables and688property values; regression results are found in Table 5. The first model is a version689of the benchmark regression from Table 4 except that we omit minDist. Models 2–4690consider the three Count variables separately, one at a time. The dependent variable691remains ln(Price/100).692The results in the first column are very similar to the results shown in the bench-693mark regression (Column 1 of Table 4). That is, removing minDist does not change694the results from the benchmark model. This result mirrors Column 2 of Table 4 in695that removing the Count variables did not change the effect of minDist on property696price. That is, we continue to find an insignificant coefficient on Count500, a sig-697nificantly negative coefficient on Count1000, and a significantly positive coefficient698on Count2000 all with magnitudes very similar to those discussed in the previous699subsection.700It is interesting that when considering the Count variables separately, as shown in701Models 2–4, only Count2000 remains significant (and still has a positive coefficient).702It is important to remember that in these three regressions, the interpretations of the703Count variables coefficients are different from the joint model. Since the other bands704123705706707===== PAGE 19 (score 81, numeric-paren 63, table-words 3, var-words 5) =====708709Exploring the quantitative impact of medical marijuana…710Page 19 of 2571161712Table 5 Models to further assess significance of count variables713Dependent variable: ln(Price/100)714All715Count500716Count1000717Count2000718Count5007190.00017200.001721(0.003)722(0.003)723Count1000724−0.005***7250.0004726(0.002)727(0.001)728Count20007290.003***7300.002***731(0.001)732(0.0004)733Age734−0.013***735−0.012***736−0.012***737−0.013***738(0.0004)739(0.0004)740(0.0004)741(0.0004)742Age27430.00009***7440.00009***7450.00009***7460.00009***747(0.000004)748(0.000004)749(0.000004)750(0.000004)751SQFT7520.0006***7530.0006***7540.0006***7550.0006***756(0.00001)757(0.00001)758(0.00001)759(0.00001)760SQFT2761−0.0000000***762−0.0000000***763−0.0000000***764−0.0000000***765(0.00000)766(0.00000)767(0.00000)768(0.00000)769Beds770−0.043***771−0.043***772−0.043***773−0.043***774(0.005)775(0.005)776(0.005)777(0.005)778Baths7790.070***7800.070***7810.070***7820.070***783(0.005)784(0.005)785(0.005)786(0.005)787Density7880.000067890.000087900.000087910.00006792(0.0001)793(0.0001)794(0.0001)795(0.0001)796Dist to CBD (km)797−0.0002798−0.001*799−0.001*800−0.0002801123802803804===== PAGE 20 (score 85, numeric-paren 66, table-words 6, var-words 0) =====80580661807Page 20 of 25808J. Clark, M. S. Delgado809Table 5 continued810Dependent variable: ln(Price/100)811All812Count500813Count1000814Count2000815(0.0004)816(0.0004)817(0.0004)818(0.0004)819Neighborhood8200.0000004***8210.0000004***8220.0000004***8230.0000004***824(0.0000000)825(0.0000000)826(0.0000000)827(0.0000000)828Year2020829−0.171***830−0.171***831−0.171***832−0.171***833(0.013)834(0.013)835(0.013)836(0.013)837Year2021838−0.077***839−0.077***840−0.077***841−0.078***842(0.012)843(0.012)844(0.012)845(0.012)846Year20228470.086***8480.086***8490.086***8500.086***851(0.012)852(0.012)853(0.012)854(0.012)855Oklahoma Co8560.141***8570.094**8580.096**8590.143***860(0.043)861(0.041)862(0.042)863(0.043)864Washington Co865−0.059*866−0.094***867−0.093***868−0.059*869(0.033)870(0.032)871(0.032)872(0.033)873Wagoner Co8740.072*8750.0258760.0278770.074*878(0.042)879(0.041)880(0.041)881(0.042)882Constant8836.803***8846.860***8856.858***8866.797***887(0.053)888(0.051)889(0.052)890(0.053)891Observations89228,02289328,02289428,02289528,022896R28970.6398980.6398990.6399000.639901Adjusted R29020.6399030.6399040.6399050.639906∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01907123908909910===== PAGE 21 (score 14, numeric-paren 0, table-words 4, var-words 6) =====911912Exploring the quantitative impact of medical marijuana…913Page 21 of 2591461915are not held constant, the coefficient on Count1000, for instance, indicates the effect916on log price of an additional dispensary located anywhere within 1000m of the prop-917erty, whereas previously it pertained only to an additional dispensary being located918within the 500–1000m distance band from the property. When taken separately, these919intensity variables indicate that a change in intensity of dispensary exposure does not920significantly affect property values within 500m (see Column 1) or up to 1000m (see921Columns 2–3) of the property. A marginal increase in intensity up to 2000m from the922property results in an increase in the property value of about 0.19 percent, or about923480.81 dollars based on the average property sale price. Broadly speaking, these results924also indicate that residents prefer a marginal dispensary to be located farther from the925property, though they may be indifferent to a marginal dispensary being located nearer926to the property.9274.4 County-specific regressions928Thus far we have focused exclusively on both distance and intensity measures of929dispensary exposure for our four-county benchmark sample. To ascertain the extent930to which there may be heterogeneity in the dispensary effects across counties, and to931exploitdataontwoothercounties,wenowdividethesampleandturntocounty-specific932regressions. Note that from the four benchmark counties, the majority of observations933come from Oklahoma County, with a large group also coming from Comanche County.934Relatively, few observations come from Wagoner and Washington counties. Thus, it935is interesting to assess the extent to which the pooled sample results merely reflect936Oklahoma and perhaps Comanche counties, or are representative of all counties more937generally. For these county-specific models, we are no longer bound by a common set938of control variables across counties; for each county-specific regression, we include939as many control variables as are available for that county. Also, for each county we940use the Fitzpatrick and Parmeter (2021) cross-validation method to select the optimal941minDist threshold, and then use all property sales within 2 times that threshold for our942county-optimal sample. We report the county-specific regression results in Table 6. To943save space, we withhold estimates of all control variables, and provide the estimates944of the dispensary effects.9945Looking first the the coefficients on minDist across Table 6, we see that minDist946is significantly negative in Comanche and Payne counties, insignificant in Wagoner947and Washington counties, and significantly positive in Oklahoma and Tulsa counties.948Count500 is only significant in Tulsa County, wherein we find the estimate is positive.949Count1000 is significantly negative in Oklahoma County and significantly positive950in Tulsa County, while Count2000 is significantly positive in Oklahoma County and951significantly negative in Payne County. Taking stock of the first four columns, relative9529 In terms of additional variables per county, we note that Comanche County has a dummy variable953indicating whether the property has a garage, and a township location indicator; Wagoner County has954the number of stories per home, the garage dummy, amount of non-ag acreage of the property, and measure955of property depreciation loss; Washington County has the number of stories and garage dummy; Oklahoma956County has the number of stories and rooms in the home; Payne County lacks data the age and square957footage of the home; and Tulsa County lacks data on square footage but has available the number of stories958in the home.959123960961962===== PAGE 22 (score 65, numeric-paren 45, table-words 6, var-words 0) =====96396461965Page 22 of 25966J. Clark, M. S. Delgado967Table 6 County-specific benchmark regressions968Dependent variable: ln(Price/100)969Comanche970Wagoner971Washington972Oklahoma973Payne974Tulsa975minDist976−0.00013***9770.00001978−0.000019790.00002***980−0.00005**9810.00011***982(0.00003)983(0.00001)984(0.00002)985(0.000003)986(0.00002)987(0.00001)988Count500989−0.0059900.054991−0.0089920.0019930.0239940.019***995(0.011)996(0.055)997(0.035)998(0.003)999(0.110)1000(0.006)1001Count100010020.0041003−0.00810040.0011005−0.008***10060.01210070.011***1008(0.006)1009(0.027)1010(0.013)1011(0.002)1012(0.077)1013(0.003)1014Count20001015−0.0031016−0.0031017−0.00710180.006***1019−0.044***1020−0.0021021(0.003)1022(0.011)1023(0.008)1024(0.001)1025(0.015)1026(0.001)1027Constant10287.768***10296.501***10305.715***10316.815***10324.989***10337.043***1034(0.465)1035(0.345)1036(0.458)1037(0.021)1038(0.464)1039(0.038)1040Control variables?1041Yes1042Yes1043Yes1044Yes1045Yes1046Yes1047minDist threshold1048907.8710494884.9110504884.9110512655.6610523719.5110531192.781054Observations10554,06810563961057286105821,4631059622106024,2331061R210620.24610630.53610640.77210650.73210660.30710670.1971068Adjusted R210690.24310700.51110710.75610720.73210730.29310740.1961075∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.011076123107710781079===== PAGE 23 (score 12, numeric-paren 0, table-words 3, var-words 6) =====10801081Exploring the quantitative impact of medical marijuana…1082Page 23 of 251083611084to the benchmark pooled sample regressions, it seems apparent that the results found1085with the four pooled counties are largely driven by the Oklahoma County effects. Table10866 shows that residents in Comanche County experience a negative minDist effect,1087indicating that property prices fall when dispensaries are located farther away from1088the home, though the intensity of dispensary activity does not affect property values.1089Looking toward Payne and Tulsa counties, we see that Payne County bears similar1090effects to Comanche County, while Tulsa County accords with Oklahoma County. It1091is worth pointing out the following. First, there is quite a bit of heterogeneity across1092counties in terms of both the optimal minDist threshold and the R2 measures for1093each regression, and readers should bear in mind that both Payne and Tulsa counties1094are missing several key variables (which is why they were left out of the baseline1095four-county model). In terms of the R2, Oklahoma County seems to have the best fit,1096relative to the regressions for the other counties.10975 Discussion and conclusion1098We study the effects of medical marijuana dispensaries on residential property prices in1099Oklahoma as a means of measuring the extent to which dispensaries are seen as desir-1100able or undesirable neighborhood amenities and thereby affect property prices. We1101have motivated the uniqueness of Oklahoma for our study setting: not only has Okla-1102homaveryrecentlylegalizedmedicalmarijuana(effectiveAugust2018)butOklahoma1103has seen a rapid rise in the proliferation of medical marijuana dispensaries and has1104become a leader in the nation in terms of the number of operating dispensaries. More-1105over, Oklahoma is an interesting case because of the strongly conservative electorate,1106despite marijuana legalization being typically seen as a politically liberal interest.1107Thus, we believe that Oklahoma is an ideal setting for studying the effects of medical1108marijuana legalization on nearby property values with lessons for citizens of Okla-1109homa as well as the public or policymakers in other states. Using data from six counties1110in Oklahoma, we conduct a state-wide hedonic pricing analysis to investigate the net1111effects, as capitalized in property values, of local dispensary activities on residential1112property prices. We measure dispensary activity with both distance to nearest dispen-1113sary as well as intensity of dispensary activity in the proximate area (within 500m,11141000m, and 2000m from the property). We consider pooled sample regressions as well1115as county-specific regressions, linear and nonlinear specifications. In looking across1116these six counties, we find some heterogeneity in our results. However, our results1117point to a few broad trends across most of Oklahoma. First, a general pattern is that1118residents view local proximity to a medical marijuana dispensary as a net negative,1119with property prices being significantly lower when either the minimum distance to1120the nearest dispensary is closer to the home or when a marginal dispensary is opened1121within 0.5 kilometers from the home. We find the effects to be quite small, with even1122the largest effects being less than one percent of the property’s value. At the same time,1123residents seem to prefer relatively easy access to medical marijuana dispensaries, with1124property prices increasing for marginal dispensaries being located at a medium dis-1125tance (i.e., between 1 and 2 kilometers) from the home. Second, it is worth noting that1126Washington and Wagoner counties generally have insignificant effects of dispensary11271231128