Augmenting Housing Sales Data to Improve Hedonic Estimates of Golf Course Frontage

Transcription

1 Augmenting Housing Sales Data to Improve Hedonic Estimates of Golf Course Frontage Abstract Hedonic price modeling quantified the impact of golf course frontage on single-family housing prices at 20 different golf courses across the Midwestern City of Omaha, Nebraska. Geographic information system analyses tripled a sample of golf frontage sales which allowed courses to be valued by detailed access and ownership types, and for 15 individual courses. Private-non-equity courses had the greatest impact on adjacent housing prices (28%) followed by public courses (15%), municipal courses (9%) and private-equity courses (5%). However, a wide range of price impacts across individual courses was noted which indicates the benefits of conducting course specific analyses. Key Words: Golf Course Frontage, Geographic Information Systems, Hedonic Pricing 1

2 Augmenting Housing Sales Data to Improve Hedonic Estimates of Golf Course Frontage 1. Introduction The ability to accurately estimate the impact that golf courses have on adjacent singlefamily housing values is of interest to appraisers, tax assessors, real estate and/or golf course developers, and of course, the buyers and sellers of golf course frontage homes. Previous research on this topic has successfully utilized hedonic price models to estimate the incremental effects (marginal prices) of golf course frontage, proximity, and/or views on single-family housing values. Most of these studies have relied on multiple-listing service (MLS) data and relatively small numbers of golf course frontage sales which limited their ability to estimate the price impacts of a full range of different golf courses types. In fact, most of these studies have not classified golf courses by type or have classified them as being simply private versus public courses. Finally, no previous studies have attempted to value all of the courses within a particular city (or market) and most of the published research to date has concentrated on golf courses in the southwestern or southeastern parts of the United States. This study integrates MLS sales data with a Geographic Information System (GIS) parcel-level database in the mid-western city Omaha, Nebraska in order to augment the sample size of sold golf course frontage homes. The overall goal is to estimate impact of a wide variety of different types of golf courses (classified by both access and ownership characteristics) on adjacent housing values. The course types evaluated include: municipal, public, private-equity, and private-non-equity courses. As well, hedonic price models are estimated for 20 individual golf courses across the city in order to determine the full range of the implicit values of golf course frontage and the variability of these values within specific course types. 2

3 2. Previous Literature The extent of how golf courses impact nearby residential property values is frequently discussed by both the national and local media. For example, a December 2006 Wall Street Journal article reported that decreasing numbers of golfers across the U.S. coupled with extensive new golf course construction activity are likely factors behind sharp reductions in price premiums for golf course homes (Fletcher, 2006). The article also states that in some communities, golf club membership fees and maintenance costs (often paid by adjacent homeowners) have been rising faster than golf course housing prices and that disputes between homeowners and the owners of golf courses over maintenance issues and proposals to close and/or re-develop courses are increasingly common. Previous attempts to measure the impact of golf courses on single-family property values fall into two categories, those that have focused specifically (and almost exclusively) on golf courses (Asabare and Huffman, 1996; Do and Grudnitski, 1995; Grudnitski and Do, 1997; and Grudnitski, 2003) versus more general housing and/or open space related studies in which golf course frontage/views/proximity are only one of many different amenity-based explanatory variables (Espey and Owus-Edusei, 2001; Shultz, and Fridgen, 2001; Lutzenhiser and Netusil, 2001; among others). There are also many other hedonic studies that have likely incorporated golf courses within wider (or more general) classifications of green space or recreation areas and most of these have focused the impact of frontage and/or direct views impacts (Chan et al. 1998, and Simons and Saginor, 2002). [Table 1 Here] Most hedonic studies of golf courses have utilized the MLS as a data source while a few others rely on county sale records. The advantage of using the MLS is that it includes a wide 3

4 variety of housing characteristic and transaction data including an indication of whether a home is located on a golf course. In some studies, particularly those with city or region-wide focus, the number of golf course frontage sales are often low in comparison to all sales. For example, an extensive MLS based hedonic study in the Philadelphia, Pennsylvania area with a sample of 28,828 sales had only 89 golf course sales representing only 0.03% of all sales (Sirmans and Macpherson, 2003). However other studies such as Do and Grudnitski (1995) and Grudnitski and Do (1995) which have used MLS data and focused on a single (or a few) courses have done a much better job of identifying golf frontage sales. The issue however is whether this can be accomplished using MLS data for a large number of golf courses across an entire city or market. Studies such Grudnitski (2003) which utilize county sale records often utilize geographic information system (GIS) technologies and/or or the use of plat maps to identify whether sold homes were located on golf courses or within golf course communities. However highly detailed housing characteristics and transaction data is not always collected and/or disseminated by all counties which leads many researchers to rely on MLS data (which again may have questionable classifications of golf courses and which is often not linked to GIS databases. Past hedonic studies have reported general golf courses (all courses without classifications by type of access) to have frontage impacts of between 8% and 10% and proximity impacts of around 13% (Table 1). Studies that have estimated impacts separately for public versus private courses have generally reported a wider range of price impacts: Frontage impacts at public courses range from 4.8% to 27% versus 4.8% and 8% at private courses. Proximity impacts have been reported to be between 6% and 13% at public courses and around 12.5% for private courses. Only one known study attempted to estimate the value for both 4

5 frontage/views and proximity within in a single hedonic model (Espey and Owus-Edusei, 2001) and the only study to date that has used a relatively large sample size of golf course sales (1,906 sales in close proximity to 15 individual golf courses), and which simultaneously valued three distinct golf course types was conducted by Grudnitski (2003) in the Las Vegas, Nevada area. This study reported price impacts of estimates of 6% for both public and semi-private courses, and 12.5% for private courses. This present study makes three unique contributions to the hedonic-golf course valuation literature. First, no known prior studies have utilized a high proportion of golf course sales to value the frontage/view aspects of golf courses on single-family housing values in a Midwestern city. Second, this is potentially the first study to value a wide range of course types classified by both access and ownership characteristics (municipal, public, private-equity, and private-nonequity courses) rather than only public/private comparisons. Third, and most importantly this is the first known study to estimate the impact of golf course frontage across most the golf courses in a community. 3. Procedures 3.1 Data Residential sales for the Omaha area from January 1, 2000 to May 30, 2006 were extracted from the Great Plains Realtors MLS and joined to a parcel level GIS database created and maintained by the two adjoining county governments (Douglas and Sarpy) based on parcel identification numbers and/or addresses. A GIS coverage of 27 golf courses in the Omaha area was created by digitizing golf course boundaries based on true color aerial imagery and a street coverage (Figure 1). Seven of these golf courses were subsequently excluded from the study due to their atypical characteristics. In all but one case these were older municipal courses in highly 5

6 urbanized areas with few nearby housing sales. The other course that was removed from the study was a private course that was completely fenced off from adjacent homes in a neighborhood of older historical homes. Distances from sold homes to golf courses were calculated using the ArcINFO Near tool (ESRI, 2006) and all sales more than one-half mile away from golf courses were dropped from the study population to be consistent with the previous golf course frontage studies of Do and Grudnitski (1995) and Grudnitski and Do (1997), and because such distances were needed to capture homogenous housing stocks and distinct neighborhoods surrounding golf courses in Omaha. The determination of whether sold homes possessed golf course frontage and/or a view were made using manual GIS parcel selections based on overlaying the boundaries of sold parcels and golf courses. Golf course frontage homes are defined as properties whose lots directly abut a golf course. While developers have clearly integrated golf courses with residential housing developments in Omaha, there do not exist any gated golf course communities that are exclusively associated with a particular golf course like those that occur in many Sun-belt states and which have been evaluated using hedonic models by Grudnitski (2003) 3.2 The Hedonic Model Specification Hedonic price models allow the estimation of the marginal implicit prices of differentiated characteristics of real property (Rosen, 1974; Palmquist, 1984). Such models specify the sale price of a home to be a function of a vector of a physical characteristic of a home S, a matrix of time trend variables representing the year sold T, and a vector representing if the sold home has golf course frontage G: n Pricei = β 0 + β jsij+ βttit+ β ggi+ u. (3.1) i= 1 6

7 Although other environmental amenities such as open space, parks, pollution or other environmental characteristics are known to influence housing values, they are not considered relevant in our hedonic model since the sample of non-golf course homes were limited to within a half mile of golf courses. In other words, we assume that the only variation in Q across the sold homes in our sample is the location of a home in relation to whether or not a home is located on a golf course. The marginal implicit price of golf course frontage can therefore be estimated by: U g Price = = βg. (3.2) U g δ Palmquist (1991) and others note that economic theory alone does not provide sufficient guidance for selecting the functional form of particular explanatory variables within hedonic equations. Alternatively, a Box-Cox Transformation is often used to evaluate alternative functional forms but this procedure is not appropriate for dummy (binary) variables as used in the present study (Box and Cox, 1964). Since this present study focuses on the magnitude of implicit prices of different types of golf courses, the use of a single functional form for all models is required. The semi-log (or log-linear) hedonic specification is therefore used due to its ease of interpretation and, in particular, because the coefficient of a dummy variable can then be interpreted as the percentage change in the dependant variable (house price) associated with the independent variable (golf course frontage). However, Halvorsen and Palmquist (1980) and Kennedy (1981) have demonstrated that a direct interpretation of such dummy variable coefficients is not valid. Therefore, our estimate of the marginal implicit price of golf course frontage is based on the following equation derived by Kennedy (1981): gˆ = e( cˆ ( ) V( cˆ)) 1 (3.3) 1 2 7

8 where ĝ is the percentage effect of the dummy variable as adjusted by the variance of the coefficient Vc. () ˆ The selection of the remaining explanatory variables in the model are based on the results of previous hedonic studies conducted in other Midwestern cities and Omaha itself (Shultz and Fridgen, 2001; Decker, Sindt and Nielsen, 2006). These include: ln Lot Size which is the natural log of lot size (square feet); House Size which is the natural log of house size (square feet); Age which is the age in years at date of sale of the home; Walkout accounting for whether a home has a walk out basement; Fireplace, whether a home has a fireplace; and Garage Stalls measuring the number of garage stalls. All of these particular explanatory variables are expected to have positive impact on sale prices. Time variables represented by dummy variables are intended to account for price variations over the duration of the study area (which was modest in the Omaha market during the 2000 to 2006 time period). It is well documented that hedonic price models focused on housing values suffer from both spatial autocorrelation and heteroskedasticity defined as non-constant variance amongst the error terms which may cause t-statistics (and levels of statistical significance). In most cases this is associated with a wide variation in housing sizes and corresponding variations in related housing characteristics. Unfortunately the most common solution to heteroskedasticity (the use of weighted least square regression) does not always solve the problem and such weighting makes it impossible to compare regression results across different models (all of which need to be weighted differently), which is a major focus of the present study in which 25 separate regression models are estimated. However, this problem is to some extent naturally controlled for in the present study design, since our study sample is limited only to areas within half a mile of golf courses, meaning that the variation in house sizes and non-constant variances in error 8

9 terms) is greatly limited as compared to city-wide hedonic studies. For this reason, ordinary least squares regression (without the use of weights) is considered acceptable Evaluating Different Types of Golf Courses Seven of the 27 golf courses in the Omaha study area individual golf courses excluded from all analyses. Most of these were municipal courses and all were either were adjoining multi-use parks and were located on adjoining high traffic roads (although six municipal courses with few nearby residential sales and/or high traffic flows were removed from the study sample). Using the remaining 20 courses, marginal implicit prices of golf courses are estimated at three different levels based on course access and ownership. The first three models use highly generalized course access classifications (all courses combined, versus public and private courses) in order to generate equivalent estimates of the implicit price of golf course types that have been reported by previous studies, and to provide baseline value estimates for comparisons with more detailed access/ownership classifications planned in this present study. It was hypothesized that the marginal prices of private courses are larger than the prices of public golf courses due to the exclusivity and the potentially higher quality of private courses. The second level of analysis involved estimating four separate hedonic models based on the following course access and ownership designations: municipal, public, private-equity, private-non-equity, public, and municipal courses. Municipal course are publicly owned and offer complete access to the public and have the lowest green fees of all courses. Public courses are privately owned and open to the public (i.e. the do not have any membership costs). Privateequity courses require memberships and are owned by either member-players and/or homeowners of properties within or abutting courses. In contrast, private-non-equity courses which are privately owned by investors rather than player-members and/or adjacent 9

10 homeowners. Finally It is hypothesized that municipal courses will have lower marginal prices than public courses since the privately owned public course are assumed to be better maintained and managed by owner operators. Since no prior hedonic based studies of golf courses have differentiated between the implicit prices of private courses based on ownership characteristics (equity versus non-equity), no hypotheses regarding these impacts in the Omaha market are made. One argument in support of private-non-equity courses having larger implicit prices than private-equity courses is due to the relatively higher membership costs and financial obligations associated with equity courses. However, this expected effect might be offset by the fact that none of the private-equity courses in Omaha formerly require abutting homeowners to become co-owners of courses as is the case with various golf courses in other parts of the country, particularly those located in gated golf course communities in the southwest and southeast. An additional reason why private-equity courses could have a larger impact on adjacent housing values than non-equity courses is that such a ownership structure provides homeowners with greater control and certainty over the maintenance, upkeep, and/or continued existence of golf courses which adjoin and/or are in close proximity to their properties. This may in fact be a relevant issue in the Omaha market where recently two non-equity golf courses (one private and one public) have been sold and/or proposed to be dissolved and developed into housing and/or commercial properties. The final level of analysis involved estimating implicit values separately for each of 20 individual courses across Omaha (if and when there were at least 30 frontage sales associated with a course over the study period. The goal was to evaluate whether the augmentation of golf course sales using GIS approaches would generate enough sales to successfully estimate implicit prices and to determine the range of implicit values across the City. A related objective of these 10

11 model specifications is to determine how closely the aggregated model estimates account for the variation across individual (course-specific) estimates of the impacts of golf courses on adjacent housing values. 4. Results 4.1 Numbers and Types of Golf Course Sales Based MLS data combined with a GIS analysis, it was determined that between January 1, 2000 and May 30, 2006 that 5,782 single-family home sales occurred within a half mile of the 20 golf courses selected for analysis. MLS records (based on listing agents checking a golf course frontage data field) indicated that 530 of these sales were golf course homes. However, our GIS-based analyses determined that 1,324 of golf course frontage sales actually occurred (an increase of 150%). The augmented number of golf course sales (n=1,324) represents approximately 3.2% of all sales in Omaha during the 2000 to 2006 time-period, and it was determined that 74% of these were on public access courses (57% on ten privately owned public courses, and 17% on three municipal courses). In contrast 5% of golf course sales were on five different private-equity courses versus 21% on two private-non-equity courses. Figure 1 shows the location of these courses by type while Figure 2 provides and illustrative example both nearby (comparable) and golf frontage sales associated with a single golf course. [Figures 1 & 2 Here] 4.2. Characteristics of Golf Course Sales Golf course frontage homes were slightly newer and larger than nearby (comparable) homes, and had consistently higher mean prices (Table 2). The most expensive golf course 11

12 frontage homes were associated with private non-equity courses (mean: $367,000) followed by public ($305,000), private-equity ($254,000), and municipal ($265,000) courses. Average green fees (18 holes of weekend golf) were lowest at municipal courses ($25) followed by public courses ($31) versus $75 at private equity courses and $75 at private nonequity courses. Some researchers (Fanning, 2003, and Gimmy, 2003) have independently indicated that there is a strong correlation between green fees and course quality. However exceptions to this relationship do occur with regards to certain types of courses in the Omaha area. For example, the green fees at municipal courses are often set administratively (and can be unrelated to course quality), and the membership fees of many private courses are often used to offset green fees. [Table 2 Here] 4.2 Hedonic Regression Results The hedonic regression model results estimated separately for all courses combined (n=20), versus all public access courses (n= 10) and all private access courses (n=10), are summarized in Table 3. The models have adjusted R 2 values that range from 0.86 to 0.90 and all of the explanatory variables in each model are statistically significant with the expected coefficient signs. Similarly, the F-statistics of each model are statistically significant from zero indicating that all of the explanatory variables in each model considered jointly have a statistically significant influence on the sale price of homes. All courses considered jointly have a marginal impact of 15% on frontage property values, versus 13% for public courses and 23% for private courses. The expectation that private courses would have a larger impact on the prices of adjacent properties is confirmed and it is expected that this is the direct result of these courses being of higher quality, better maintained, 12

13 and possibly due to exclusivity or snob effect. These marginal values are higher than those reported by previous studies although none of those studies were focused in a mid-sized Midwestern City such as Omaha, Nebraska. [Table 3 Here] The results of the four hedonic models based on more specific (detailed) access and ownership criteria are summarized in Table 4. The adjusted R 2 values for the models range from 0.79 to 0.81 and again, the majority of the coefficients of the explanatory variables are all statistically significant with expected signs across all the course-types. Of particular importance is the fact that the coefficient representing golf course frontage is statistically significant in each model. The marginal implicit prices of specific course-types listed in order of magnitude are: 28% (private non-equity), 15% (public), 9% (municipal), and 5% (private-equity). The wide range of these values along with the differences between course-specific estimates general course-type estimates (all, public, and private courses) demonstrates the importance of classifying courses by course-types when making hedonic estimates of the marginal impacts of courses on abutting housing values. The expectation regarding public courses having a greater impact on adjacent property values than municipal courses, turned out to be correct. And while there was a suspicion that private-non-equity course would be preferred by homebuyers over private-equity courses, the magnitude of this differential was surprising. The fact that golf course frontage premiums associated with private-equity courses (5%) are so relatively low (even lower than municipal courses) suggests that golf-course home buyers in the Omaha market prefer to not have to be part owners of those golf courses that they view and/or play. [Table 4 Here] 13

14 Finally, marginal implicit price estimates of golf course frontage were successfully estimated for 15 of the 20 golf courses in the study based on the dual criteria of at least 30 frontage sales and statistically significant coefficients for the golf course frontage variables. We do not report the full results for each of the these 15 course specific hedonic models, but rather the range of estimated marginal implicit prices of golf course frontage are summarized and compared to implicit prices for the more generalized access/ownership categories in Table 5. From this, it can be seen that across all course that there is a wide variation in the implicit values of golf course frontage which illustrates the potential inaccuracy of relying on generalized golf course valuation estimates for valuing particular courses. In most cases it appears that the marginal effects based on general access/ownership classifications tend to under-estimate the value of specific courses. The exception to this trend is with municipal courses where the general implicit estimate is at the higher range of implicit values reported for individual courses. [Table 5 Here] 5. Conclusions The ability of this study to measure the implicit prices of golf course frontage by detailed access/ownership classifications of courses, and for a majority of the individual courses in Omaha, was made possible due to the augmentation of MLS sales data and in particular the use of GIS technology to determine whether or not sold homes were adjacent to golf courses. The under-reporting of actual of golf courses sales by the MLS in Omaha is suspected to result from individual real estate agents not always recording golf course frontage data into the MLS databases. Therefore, caution is urged in future hedonic studies of golf courses that rely solely on MLS classifications of golf course frontage (or other amenities). 14

15 The ability to increase the sample size of golf course frontage sales threefold was critical for estimating the marginal price impact of different types of golf courses on residential sale prices. From this it was determined that golf courses impact adjacent housing values differently in Omaha, Nebraska than in other parts of the country and that different types of courses have varying impacts on property values within the Omaha market. This reinforces the need to classify golf courses by their type (access and/or ownership structure) in order to accurately estimate the impact of specific courses on abutting property values. Similarly, this study demonstrated the importance of valuing different types of golf courses individually when this is possible, as there are many cases where price impacts at individual courses differed substantially from price impacts based on more generalized (aggregated) classifications. Since this is the first known golf course valuation study to compare hedonic price estimates of generalized course classifications based on both access and ownership structure (among individual courses), it would be prudent to replicate the research in other parts of the country where golf course and housing value relationships may differ. Depending on the accuracy of MLS classifications of golf course sales in other markets, such analyses may require the augmentation of sales by conducting GIS based analyses of homes abutting golf courses which would also facilitate the classification of golf course types. Future studies should also attempt to evaluate how the specific characteristics of individual golf courses (course layout, landscaping, levels of privacy, etc.) may influence abutting residential housing values. In the meantime, based on these study results, it is recommended that caution be used by home buyers/sellers, appraisers, and tax assessors in valuing the impact of golf course frontage on property values when those estimates were based on city ore region-wide hedonic modeling estimates. 15

16 References: Asabare, P., and F. Huffman. Negative and positive impacts of golf course proximity on home prices. The Appraisal Journal (4), Box, G. E. P., and D. R. Cox. An Analysis of Transformations Journal of the Royal Statistical Society. Series B (Methodological) (2), Chan, S. S. Chu, G. Lentz, and K.Wang Intra-project Externality and Layout Variables in Residential Condominium Appraisals. Journal of Real Estate Research, 15:1/2: Decker, C., R. Sindt and D. Nielsen. Is Pollution a Homogeneous Determinant of Value? Evidence from the Omaha Residential Housing Market. The Appraisal Journal (2), Do., Q, and G. Grudnitski. Golf courses and residential house prices: An empirical evaluation. Journal of Real Estate and Finance Economics (3) Espey, M., and K. Owusu-Edusei. Neighborhood Parks and Residential Property Values in Greenville, South Carolina Working Paper (#012203), Department of Agricultural and Applied Economics, Clemson, University, South Carolina < Fanning, S.F Segmentation of Golf Course Markets. The Appraisal Journal. January Fletcher, J. Golf Course Homes Hit the Rough. Wall Street Journal, March, 16, 2006 Gimmy, A.E Analysis and Valuation of Golf Courses. The Appraisal Institute. Halvorsen, R., and R. Palmquist. The Interpretation of Dummy Variables in Semilogarithmic Equations The American Economic Review (3) Grudnitski, G. and Q. Do. Adjusting the value of houses located on a golf course. The Appraisal Journal (3), , Grudnitski, G. Golf Course Communities: The Effect of Course Type on Housing Prices. The Appraisal Journal (2), Kennedy., P. Estimation with correctly interpreted Dummy variables in semi-logarithmic equations. American Economic Review (4), 801 Leutzenhiser, M. and N. Netusil. The Effect of Open Spaces on a Home's Sale Price. Contemporary Economic Policy (3),

17 Palmquist, R. B. Estimating the Demand for Characteristics of Housing. Review of Economics and Statistics , Palmquist, R. Hedonic Methods. In Measuring the Demand for Environmental Quality, J.B. Braden and C.D. Kolstad. Elsevier Science Publishers Rosen, S. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy , Shultz, S. and P. Fridgen. Floodplains and housing values: Implications for flood mitigation projects. Journal of the American Water Resources Association (3), Simons, R. and J. Saginor A Meta-analysis of the Effect of Environmental Contamination and Positive Amenities on Residential Property Values. Journal of Real Estate Research, 28(1): Sirmans G. and D. Macpherson Value of Housing Characteristics. National Association of Realtors, Washington, DC. 17

18 Table 1. Previous Hedonic Price Studies of Golf Courses Author/Year Location & Scope Sample Sizes Data Source Marginal Impacts Golf All A) Golf Specific Hedonic Studies Grudnitski, 2003 Las Vegas (15 Golf courses/communities) (Frontage homes dropped) Grudnitski and Do, 1997 Suburban San Diego: Frontage homes on 3 private golf courses Do & Grudnitski, 1995 Suburban San Diego, Abutting homes on 3 private golf courses Asabare, 1996 Suburban New Jersey. Frontage homes on a single public course Espey, Kwame Owusu-Edusei, 2001 Frontages/views and Nearby, Greenville South Carolina (single course) 1,906 2,311 Metro-Scan (similar to MLS data) Private: 12.5% Semi-Private: 6% Public: 6% MLS Private 4.8% MLS Private: 7.6% MLS Public: 8% View: 16 Near: County sale records Frontage: 27% Near: 15% B) Some Other Hedonic Studies (that included a golf variable) Sirmans & Macpherson, 2003 Philadelphia, Frontage homes (views) 89 28,828 MLS All: 8.1% Lutzenhiser and Netusil, 2001 Portland, Nearby homes (1500 sft), ,636 Govt. Databases All: 13% Shultz and Fridgen, Fargo: Frontage homes 270 4,500 MLS All: 9.6% 18

19 Table 2: Summary Statistics for the Sample Size of Golf Course Frontage and Nearby (Comparable) Sales, Omaha Area (excluding the Urban Core Courses), Type Non Golf Frontage Sales (within half a mile of golf courses) Golf Frontage Sales Statistic Municipal n 218, ,000 median price 3 Courses 2 2 Avg. Green Fee $25 mean age 2,396 2,626 mean Sq. Ft. Public 2, n 154, ,489 median price 10 Courses Avg. Green Fee $ mean age 2,213 3,127 mean Sq. Ft. Private Non-Equity n 194, median price 2 Courses Avg. Green Fee $ mean age 2, mean Sq. Ft. Private Equity 1, n 140, median price 5 Courses Avg. Green Fee $ mean age 2, mean Sq. Ft. All Courses 4,458 1,324 n 169, ,997 median price 20 Courses Avg. Green Fee $ mean age 2,190 3,165 mean Sq. Ft. 19

20 Table 3. Hedonic Regression Results for All Courses Versus Private and Public Access Courses All Courses Public Private Coef. SE P>t Coef. SE P>t Coef. SE P>t D Frontage Ln Lot SF Ln SF Age Walk Fire Gar Con Obs F p>f R Adj. R Root MSE % Effect* 15.14% 12.92% 22.97% $ Amount** $46,694 $34,837 $85,238 * Calculated using equation 5 (Kennedy, 1981) for statistically significant frontage coefficients. ** From a linear specification of the model 20

21 Table 4. Hedonic Regression Results By Specific Golf Course Type Municipal Public Private Equity Private Non-Equity Coef. SE P>t Coef. SE P>t Coef. SE P>t Coef. SE P>t D Frontage Ln Lot SF Ln SF Age Walk Fire Gar Con Obs F p>f R Adj. R Root MSE % Effect* 8.54% 14.79% 4.57% 27.88% $ Amount** $23,282 $38,318 $20,112 $89,502 * Calculated using equation 5 (Kennedy, 1981) for statistically significant frontage coefficients. ** From a linear specification of the mode 21

22 Table 5. A Summary of Hedonic Regression Results (Frontage Impacts) By General Course Classifications and for Individual Courses Marginal Effect: General Classifications (%) Individual Course Models Successfully Estimated Marginal Effects for Individual Courses (%) Municipal Public Private-Non- Equity Private-Equity 11.3% (n=3) 13.1% (n=10) 27.7% (n=2) 10.6% (n=5) % & 11.3% 7.8% to 56.4% 13.1% & 52.3% 32.7% & 39.8% 22

23 Figure 1. Golf Course Locations by Type, Omaha, NE 23

24 Figure 2. Residential Housing Sales Near and Abutting a Sample Golf Course ( ) 24