The Impact of Broadband Deployment on Recreational and Seasonal Property Values- A Hedonic Model

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1 1 The Impact of Broadband Deployment on Recreational and Seasonal Property Values- A Hedonic Model Authors Russell Kashian, PhD University of Wisconsin Whitewater Jose Zenteno University of Wisconsin Whitewater Abstract In recent times, Internet access has become vital not only for the daily activities of individuals and businesses, but also for sustainable growth in a region. However, there is a remarkable difference in accessibility to broadband Internet between rural and urban areas. In Door County, WI, access to high-speed Internet connection in recreational or seasonal properties is either non-existent or deficient. This paper outlines the results of a housing market hedonic analysis conducted in Door County regarding the effect that Internet connection has on residential property prices. The results of the study show that availability of Internet connection on residential and seasonal homes is statistically significant and has a positive effect on property prices. Note: This is a working paper and should not be cited without explicit permission from the authors.

2 2 I. Introduction Door County, WI attracts around 2 million people, spending approximately $289 million annually in visitor spending, supporting 2,948 jobs (Door County Visitor Bureau, 2012). While manufacturing and agriculture play important roles in the economy, tourism and part-time residents are the primary economic engine (Door County Visitor Bureau, 2012). Tourism also put $31.8 million in state and local tax coffers and generated nearly $20 million in federal tax revenue during 2012 (Door County Visitor Bureau, 2012). However, Internet connection in Door County is sporadically existent. This paper explores whether seasonal and recreational homes with availability of broadband Internet connection have a significantly higher value. Properties represent a great portion of total wealth that part-time residents have. For that reason, Sheppard (1999) argues that housing and residential construction play a crucial role in determining the level of welfare in society and the level of aggregate economic activity in a region. This analysis specifically attempts to measure the extent to which availability of Internet connection affects property prices in Door County, WI. Individuals consider multiple factors when making a real estate purchase. For instance, location of the home, quality of the school district, level of crime in the area or property tax rates. Another factor that consumers consider when making a real estate purchase might be availability of reliable and fast Internet connection in the property. This paper hypothesizes that seasonal and recreational homes with access to this crucial service are worth significantly more than houses without access to Internet service. This study can be applied to regions that have similar socioeconomic characteristics as Door County such as a large number of part-time residents, remoteness of the region, and a deficient or nonexistent Internet connection.

3 3 A hedonic analysis of Door County s housing market was conducted to quantify and measure the difference in property prices between seasonal homes in the area with availability of Internet connection from those without access to Internet connection. In hedonic pricing analysis, we assume that structural characteristics and access to specific services are going to determine a property s price. In order to get at causality, we hold constant all structural characteristics of the home to examine the impact of availability of Internet connection on property prices. According to Rosen (1974) hedonic prices are defined as the implicit prices of attributes and the specific amounts of characteristics associated with them. The assumption behind a hedonic examination is the idea that houses are homogenous assets that are comprised of many differentiated features that will influence the demand and value of a particular house. For instance, price of homes is impacted by square footage, location, number of bathrooms and bedrooms, proximity or access to a specific service, age of the property, etc. Many of these house characteristics do not have an explicit selling price in the real estate market; rather, a residence is sold as a complete bundle that includes multiple components. In hedonic pricing modeling, multivariate regression analysis is implemented to determine the specific impact of multiple attributes that together make up the overall monetary value of the residences examined. II. Previous Studies There is an extensive body of research that analyzes hedonic pricing models. Waugh, (1928) first analyzed hedonic pricing models prior to the Great Depression, followed by research by Griliches (1971), and Lancaster (1971). Furthermore, Rosen (1974) examined housing as a bundle of characteristics and concluded that the demand for house features requires estimating a hedonic housing model in order to

4 4 determine the implicit prices of these features. Econometrically speaking, implicit prices are estimated by multivariate regression analysis in the construction of hedonic price indexes. Sheppard (1999) defined a hedonic regression as an approach that provides a methodology for identifying the structure of prices of the attributes (estimation of the hedonic price function). Additionally, Goodman (1978) appears to clarify several aspects of housing analysis using hedonic prices, with respect to market segmentation, functional form and behavior of prices within submarkets. Harding et al. (2003) argue that different components of a home provide a unique level of value based on the characteristics of these differentiated components. In addition to house structural characteristics, other features were considered in other empirical studies. Haurin (1980) examines the impact of local climate while Roback (1982) analyze the effect of labor and wages on housing markets. Peek and Wilcox (1991) later study the effect of demographic factors such as the size and age distribution of the population. Furthermore, Potepan (1994) examines a simultaneous equation model in which house prices are determined by population growth through migration, while migration is determined by houses prices in the metropolitan areas. Berger and Blomquist (1992) also suggest that house prices are a function of quality of life using as proxies the amount of crime in the area, climate, and environmental quality. Literature on hedonic pricing analysis is extensive and continues to grow at a high rate. The primary question researchers have attempted to answer using hedonic models is the effect of different house attributes and characteristics on property prices. However, we decided to go on a slightly different route and analyze the specific effect that availability of Internet connection has on property values. The California Broadband Task Force (2008) argues that without Internet

5 5 connection, communication is limited, innovation is restrained, productivity decreases and quality of life is depressed. There is remarkable difference in broadband deployment and adoption between rural and urban areas. Prieger (2013) in an empirical study of the FCC and CPS broadband data, finds that for faster forms of fixed broadband, availability of broadband service and the number of providers in rural areas is lower than in urban areas. In addition, Prieger finds that for fixed broadband, there is still a sizeable gap in usage rates between urban and rural areas. Technology improvements and Internet availability have become a necessity in the lives of individuals and performance of businesses. Economists as early as 60 years ago have determined technological improvements as one of the key determinants of economic progress and labor productivity growth (Abramovitz, 1986; Kendrick, 1956; Solow, 1957). Improvements in broadband infrastructure have the capacity to boost economic growth at different stages. At first, we anticipate an increase in economic growth derived from the initial investment to deploy broadband, which includes: purchasing equipment, laying down fiber, and installing the network required for broadband service in a region. Nonetheless, this initial level of investment is insignificant compared to the ensuing benefits derived from broadband Internet. According to Firth and Mellor (2005), high-speed Internet has the potential to offer a specific area improved quality of education and health services, improved connectedness of government with society, provide jobs and economic prosperity. Kolko (2012) also suggests that policymakers understand that broadband leads to job creation and economic growth, and many, especially in rural areas, put broadband investment at the core of their economic development strategy. He finds a positive relationship between broadband expansion and employment growth and suggests that the relationship is causal.

6 6 FCC former chairman Copps, (2012) argues that areas without high-speed Internet are economically disadvantaged because they lack critical infrastructure to provide a service that is already becoming key to our nation s system of education, commerce and jobs. Nevertheless, Internet providers are aware that the costs associated with broadband deployment may be higher than the potential benefits. Chaudhuri et al. (2005) suggests that it is reasonable to expect that Internet providers would be willing to make the greater investment necessary for broadband only in richer areas where it would be easier to extract a service cost premium from the customers, if high-speed access is assumed to be a normal good. Lehr, Gillet, Osorio, and Sirbu (2005) using econometric techniques, find evidence that broadband connectivity increases economic growth. Specifically, they analyze communities in which broadband was widely deployed from They find evidence that these communities experienced more rapid growth in employment and the number of business in the region; however, the effect of broadband deployment on wages was insignificant. Furthermore, they find evidence that broadband availability has a positive and significant effect on market rates for rental housing. Crandall, Lehr, and Litan (2007) examine private employment growth in the 48 contiguous states from They find evidence that suggests that broadband penetration (measured as broadband subscriber lines per capita in a state) is positively associated with private employment growth. However, their regression analysis does not control for the fact that broadband deployment tends to be greater in areas with higher economic growth. Nonetheless, they find that the association between broadband and employment growth remains after

7 7 controlling for multiple factors such education of workface, wage level, tax climate and unionization rates. Kolko (2010) finds that broadband expansion is correlated with economic growth over the period He addresses the issue of reverse causality by using slope terrain in a ZCTA as an instrumental variable. Using this method, the relationship between broadband expansion and employment growth remains positive and statistically significant, suggesting a causal relationship. In another study, Kolko (2012) suggests that broadband could raise property values and tax bases; however, this theory was not quantified. Consequently, throughout this study, we seek to estimate the impact of high-speed Internet on property values. III. Methodology and Data This study focuses on determining the impact of a set of different attributes and characteristics on recreational and seasonal houses in Door County, WI. The source of data for this study is a survey questionnaire designed by the University of Wisconsin -Whitewater Fiscal and Economic Research Center (FERC) with input from the University of Wisconsin-Extension. The FERC surveyed full-time and part-time residents of Door County to obtain different socioeconomic characteristics. This survey provided with variables such as household income, spending habits, level of education, and availability of Internet connection in their property. In addition, a dataset provided by Door County Assessor s Office database contains assessed values of homes, and variables related to the structural characteristics (e.g. square footage, number of bedrooms, number of bathrooms) of homes. The variable names and descriptive statistics are presented in Table 1.

8 8 Table 1: Descriptive Statistics Variable N Mean Std. Dev. Total Assessed Value , ,239 Seasonal Home with Internet Seasonal Home without Internet Full-time Home with Internet Full-time Home without Internet Number of Bedrooms Number of Full Bathrooms Number of Half Bathrooms Square Footage 225 1, Great emphasis was given to the dataset to ensure accuracy. Observations were removed if the property had missing data for any of the variables used in the estimation. In this analysis, the dataset contains 225 observations without any missing values. As previously mentioned, to determine the impact that each characteristic of a home has on its assessed value, a hedonic pricing model is estimated. This analysis specifies the assessed value of the home to be a function of structural characteristics, and variables created from dummy interactions between variables SeasonalHome (where seasonal home =1 and full-time home=0) and InternetService (where availability of Internet in home =1 and absence of Internet in home =0). We estimate a multivariate regression model with total assessed value of the home (proxy for price) as our dependent variable on various tangible and intangible home characteristics as independent variables. The estimation is by ordinary least squares with heteroskedasticconsistent robust standard errors with results presented in Table 2. In particular, we hypothesize that seasonal and recreational homes with access to Internet connection are worth significantly more than homes without access to this vital service.

9 9 The hedonic pricing model is shown below: AValueHomes i = β! + β! PartTimeInternet i + β! PartTimeNotInternet i + β! FullTimeInternet i + β! SFLA i + β! Bedrooms i + β! FullBathrooms i + β! HalfBathrooms i + ε i (1) where AValueHomes i is the assessed value of the property in dollars, PartTimeInternet i is a dummy variable indicating that the house is a seasonal home and has access to Internet, PartTimeNotInternet i is a dummy variable indicating that the house is a seasonal home and does not has access to Internet connection, FullTimeInternet i is a dummy variable indicating that the home is full-time and does has access to Internet (reference group for dummy variables is full time homes without access to Internet), SFLA i is square footage of the home, Bedrooms i is the number of bedrooms the home has, FullBathrooms i is the number of full bathrooms the home has, HalfBathrooms i is the number of half bathrooms the home has, ε i is the stochastic error term β! is the constant term and β! - β! are the estimable coefficients. Note: Dummy variable (FullTimeNotInternet) could not be included in estimating model because the set of Internet interaction dummies would have caused perfect multicollinearity in the model). We expect all house structural characteristics to have a positive coefficient, reflecting the assumption that a house with more square footage, more bedrooms and more bathrooms is predetermined to have a higher assessed value. In addition, we expect dummy variables containing Internet connection to have a positive coefficient.

10 10 Table 2: Regression Results Dependent variable is the total assessed value of the home Coefficient (Robust Standard Error) t-stat Seasonal Home with Internet γ 142,355** (55,347) 2.57 Seasonal Home without Internet γ 130,540*** (49,238.18) 2.65 Full-time Home with Internet γ 32,270 (59,011.59) 0.55 Number of Bedrooms -59,751* (30,956.5) Number of Full Bathrooms 51,919 (54,067.94) 0.96 Number of Half Bathrooms -9,746 (48,896.76) Square Footage 209*** ( ) 4.66 Constant 18,999 (116,294.2) 0.16 *** signifies that the coefficient is significantly different from zero with a 0.01 or less probability of a type I error for OLS estimate ** signifies that the coefficient is significantly different from zero with between a 0.01 and a 0.05 probability of a type I error for OLS estimate * signifies that the coefficient is significantly different from zero with between a 0.05 and a 0.10 probability of a type I error for OLS estimate γγγ reference group is full-time home without Internet IV. Results Table 2 presents the results of our main model. Among the housing structural characteristics, only the explanatory variables SFLA and Bedrooms are significant. However, the coefficient on Bedrooms is negative which is not what we expected. FullBathrooms is not statistically significant although it has the expected positive sign. HalfBathrooms despite the fact that it has a negative coefficient is not statistically significantly. Additionally, among dummy variables interacted, results show a significant difference in price between seasonal or recreational homes with Internet (PartTimeInternet) and those without access to Internet (PartTimeNotInternet). This confirms our original theory and

11 11 suggests that owners of part-time residences highly value access to Internet connection. Specifically, we find that seasonal homes with access to Internet connectivity are worth $11,815 more than seasonal homes without availability of Internet, which is a significant difference in assessed value. Furthermore, the coefficient on the variable FullTimeInternet is positive and insignificant, possibly indicating that full-time owners in the region are not as concerned about their homes having Internet connection. This would suggest that Internet price elasticity of demand for full-time residents is elastic while Internet price elasticity of demand for part-time residents is inelastic. V. Conclusion The hedonic analysis conducted in this study demonstrates that there is a significant difference in assessed values (proxy for price) between seasonal and recreational homes with Internet and those homes without access to this service. Multiple studies have estimated the economic benefits of broadband deployment to include: job creation, local growth and higher wages. However, this study specifically finds that availability of broadband has a direct impact on property values. The hedonic analysis implemented in this study estimates that on average, residential properties with availability of Internet connection are worth $11,815 more than those properties without Internet availability. This corroborates our hypothesis stating that part-time residents highly value the availability of Internet connection in their seasonal or recreational property. Additionally, higher property values generate higher property taxes, increasing tax revenue for the county. These taxes will continue to support local education, county government, and local infrastructure in the area.

12 12 The impact of broadband Internet on residential property values is understudied. However, this analysis acts as a starting point and is relevant to regions where there is a significant number of part-time residence and sporadic Internet availability. This analysis has its limitations and richer dataset containing information about services a house has access to in addition to more structural house characteristics would be ideal to corroborate the results. We suggest any region without access to high-speed Internet to explore the net benefits of broadband deployment, as there is growing evidence of benefits provided such as job creation, local economic growth, higher tax revenues and increased property values.

13 13 References Abramovitz, M. (1986). Catching up, forging ahead, and falling behind. The Journal of Economic History, 46(2), Berger, M. C., & Blomquist, G. C. (1992). Mobility and destination in migration decisions: The roles of earnings, quality of life, and housing prices. Journal of Housing Economics, 2(1), Chaudhuri, A., Flamm, K., & Horrigan, J. (2005). An analysis of the determinants of Internet access. Telecommunications Policy, 29(9-10), Crandall, R., Lehr, W., & Litan, R. (2007). The effects of broadband deployment on output and employment: A cross-sectional analysis of U.S. data. Issues in Economic Policy, 6. Brookings Institution. Door County Visitor Bureau. (2012). Door County Fact Sheet. Available online: Federal Communications Commission. (FCC) (2012a). Eighth broadband progress report. FCC Released August 21, Firth, L., & Mellor, D. (2005). Broadband: benefits and problems. Telecommunications Policy, 29(2), Griliches, Z. (1971). Price Indexes and Quality Change: Studies in New Methods of Measurements. Harvard University Press, Cambridge, MA. Goodman, A. C. (1978). Hedonic prices, price indices and housing markets. Journal of Urban Economics, 5(4), Harding, J.P., S.S. Rosenthal, and C.F. Sirmans. Estimating bargaining power in the market for existing homes. Review of Economics and Statistics, 2003, 85:1, Haurin, D. R. (1980). The regional distribution of population, migration, and climate, Quarterly Journal of Economics, XCV, Kendrick, J. W. (1956). Productivity trends: Capital and labor. Review of Economics and Statistics, 38, Kolko, J. (2010). Does broadband boost local economic development? Public Policy Institute of California. Retrieved from / Kolko, J. (2012). Broadband and local growth. Journal of Urban Economics, 71(1), Lancaster, K. (1971). Consumer Demand: A New Approach. Columbia University Press, New York, NY.

14 14 Lehr, W., Gillett, S., Osorio, C., & Sirbu, M. (2005). Measuring broadband s economic impact. Broadband Properties, Peek, J., & Wilcox, J. A. (1991). The measurement and determinants of single family house prices. Real Estate Economics, 19(3), Potepan, M. J. (1994). Intermetropolitan migration and housing prices: simultaneously determined? Journal of Housing Economics, 3(2), Prieger, J. (2013). The broadband digital divide and the economic benefits of mobile broadband for rural areas. Telecommunications Policy, 37, Roback, J. (1982). Wages, rents, and the quality of life. Journal of Political Economy 90 (6), Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. The Journal of Political Economy, Sheppard, S. (1999). Hedonic analysis of housing markets. Handbook of Regional and Urban Economics, 3, Solow, R. M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics, 39(3), The California Broadband Task Force. (2008). The state of connectivity: Building innovation through broadband. Available online: Waugh, F. V. (1928). Quality factors influencing vegetable prices. Journal of Farm Economics, 10(2),

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