Incorporating GIS into Hedonic Pricing Models

Transcription

1 Blanchette 1 Alex Blanchette NRS /10/2016 Incorporating GIS into Hedonic Pricing Models One of the central themes in environmental economics is determining the value individuals derive from various environmental amenities. However, this can also be a difficult task, as amenities as typically non-market goods, meaning their price is not explicitly stated. In order to deal with this problem, economists can either imply stated or revealed preference methods. Stated preference methods are typically survey based and explicitly ask individuals their willingness to pay for various amenities. The revealed preference method is when economists determine an individuals wiliness to pay through their behavior, these results can be dramatically difference from the consumers stated preference. The revealed preference method, if implemented properly, can provide more reliable results, and one of the most popular revealed preference methods is the hedonic pricing model (Lang & Opaluch 2013). The typical hedonic pricing model uses property values to try and reveal individuals preferences by defining home h based on structural attributes S h, Neighborhood characteristics N h, and environmental amenities A h. Typical structural attributes can include bedrooms and bathrooms, neighborhood characteristics can include crime rate and school quality, and amenities can include open space or parks near a given property. Based on a collection of home data, we can characterize a home, a hedonic pricing model can define homes price P h as a function of these attributes P h =f(sh,n h,a h ) (Anderson & West 2006). Utilizing spatial data harvested from GID databases, economist can use this price function to compare the prices of home and measure the benefits those homes receive from various environmental amenities. Economists recognized the importance of spatial relationships long before modern GIS was available. Some of the earliest spatial models attempted to model variations in income and expenditure across different regions (Bateman et al. 2002). These early studies were fairly simple, as data was difficult to collect. However, the recent advancements in GIS have allowed us to conduct complex spatial analysis using large scale digital data that was not previously possible (Lake et al. 2000). The spatial analysis conducted through GIS provides economists with spatial variables that allow them to determine the value of non-market goods. Typically, economists will use GIS in a variety of ways for their studies. Some of the common purposes of GIS include estimating location of properties through geocoding, extracting local economic and demographic conditions, measuring the distance to various amenities, and measuring the size and shape of various land uses for study (Bohlen 2009, Anderson & West 2006, Kong et al. 2007, Mila et al., Lang et al. 2013). In addition to size and distance, GIS can also be used to convert elevation data into visibility variables, which economists have found to have an impact on the value of amenities (Paterson and Boyle 2002, Cavailhes et al. 2209). Many economists have incorporated GIS into their hedonic models. Anderson and West (2006) attempted to use GIS methods and Hedonic analysis to estimate the effects of proximity to open space on property values. In order to do this they collect data on single family home sales prices and structural characteristics. They then geocoded these homes in GIS and calculated to distance of each home to the

2 Blanchette 2 nearest central business district as well as the distance to and size of a variety of open space types. Utilizing the data pulled from their GIS database, they concluded that sales price of an average home increased with proximity to open space. However, they found this effect varied widely with neighborhood characteristics and location, and that dense neighborhoods near a CBD place a substantially higher value on proximity to open space. Similarly to Anderson & West, Kong et al. (2007) Attempts to value urban green space amenities in Jinan City, China using GIS and a hedonic model. Rather than using GIS to calculate explicit distance from open space amenities, they create accessibility variables based off travel time to various amenities. They do this using a distance/cost weighted measure tool to calculate the time it would take a public bus, or an individual walking, to reach the nearest amenity. They then generated spatial landscape characteristics based on richness, density, and aggregation of green space within both 300 and 500 meters of a home. Utilizing these variables, they were able to find that there is a positive relationship between proximity to amenities and housing prices. Although hedonic models are typically used to place a value on positive externalities, they can also be used to find the costs associated with the negative impacts of disamenities. Recently the construction of wind turbines has started to become a debated topic with many individuals claiming they produce loud noises and have other negative impacts that could lower they property value. Lang an Opaluch (2013) attempt to quantify the alleged negative impacts of turbines on housing prices in Rhode Island by utilizing GIS and a hedonic pricing model. After collecting housing characteristics and sales data, find each home s latitude and longitude by using the RI GIS E-911 geolocater. Once they had located each home geocoded, they calculated the Euclidian distance from each home to the nearest turbine. They then removed unnecessary observations by using a buffer to limit their dataset to only include homes within 5 miles of a turbine. Utilizing this data, they found that there was no statistical evidence to support the claims that wind turbines have negative impacts on housing price, and that there was virtually no change in home prices within ½ a mile of a turbine after construction was completed. Another example how GIS can be used to estimate negative externalities is a study conducted my Mila et al. (2005). Their study attempts to estimate the negative effect that proximity to Hog farms has on residential property values in North Carolina. In order to answer this question they collected data on 26 different hog farms that varied in size, as well as parcel data for home sales transactions between January 2000 and July Using ArcView, they generated parcel attributes by applying geometric distance functions. They then generated center points for each residential polygon and calculated distance to each swine operation. Utilizing the dataset generated through GIS technologies, they found that there was a statistically significant externality associated with animal feeding operations. They recommended that policies be implemented to create a 1.75 to 2.5 mile buffer for hog farms near residential properties in order to avoid the negative impacts on property values. As spatial issues become more prevalent, the role of GIS in economics will become increasingly important. Recent advancements in GIS technology have allowed economist to utilize spatial analysis techniques that used to be either impractical or impossible. Today, economists have the ability to value various types of land use, as well as asses property damages associated with negative externalities. The combining of geographical and econometric models has the potential to open up new perspectives and GIS has the potential to considerably enhance the incorporation of spatial issues into economics

3 Blanchette 3 (Cavilhes). As GIS continues to advance and additional data becomes available, the capabilities of Hedonic Analysis will continue to expand. Annotated Bibliography Anderson, Soren T. and Sarah E. West. "Open Space, Residential Property Values, And Spatial Context". Regional Science and Urban Economics 36.6 (2006): Web. 16 Nov This study attempts to use GIS methods and Hedonic analysis to estimate the effects of proximity to open space on property values. In order to do this they collect data on single family home sales prices and structural characteristics in Minneapolis Minnesota. They obtain this data from Multiple Listing Services and then geocoded these homes in GIS and calculated to distance of each home to the nearest central business district as well as the distance to and size of a variety of open space types. Utilizing the data pulled from their GIS database, they concluded that sales price of an average home increased with proximity to open space. However, they found this effect varied widely with neighborhood characteristics and location, and that dense neighborhoods near a CBD place a substantially higher value on proximity to open space. Bateman, Ian J., et al. "Applying geographical information systems (GIS) to environmental and resource economics." Environmental and Resource Economics (2002): This is a review article covering a variety of GIS applications that are applicable to the field of Environmental and Resource Economics. It examines the contributions GIS has made to complex spatial analysis and reviews much of the research that has already been conducted. In addition to reviewing the literature, they also discuss more detailed applications that are possible using GIS. Some of these additional methods include the incorporation of distance in the valuation of nonmarket goods, the hedonic pricing method, determining recreational values, implementing natural resource policy, and determining the effectiveness of these policies. Each section reviews the methods necessary to conduct each type of analysis and reviews literature that has done so. Bohlen, Curtis and Lynne Y. Lewis. "Examining The Economic Impacts Of Hydropower Dams On Property Values Using GIS". Journal of Environmental Management 90 (2009): S258-S269. Web. This paper seeks to estimate the effects that hydropower dams have on property values. The area of focus for their study was the Penobscot watershed located in Maine. There are more than 120 dams in the 8600 square mile watershed including 26 licensed hydropower facilities. Using Multiple Listing Service records, they collected residential transaction data for single family home along the Penobscot River from 1997 to Once this data was collected they utilized GIS in order to estimate the locations of these properties, extract local economic conditions from the US census, determine the distance between houses and the Penobscot river and dams, as well as calculate landscape metrics to

4 Blanchette 4 describe the land surrounding each home. Based on their analysis they concluded that open space has a positive but diminishing effect on housing value. They also found that waterfront landowners along the Penobscot river paid a 16% premium for the waterfront property, however properties that did not have a water view had lower values with increased proximity to the river. Additionally, they found that homeowners paid a small premium for living near a hydropower dam. Cavailhès, Jean et al. "GIS-Based Hedonic Pricing Of Landscape". Environmental and Resource Economics 44.4 (2009): Web. This article seeks to provide hedonic price estimates of landscapes along the urban fringe of Dijon, France. Their primary goal was to determine the impact that visibility plays into the valuation of local amenities. In order to conduct this analysis, they collected data on 2757 residential property sales from 1995 to This data was readily available through real-estate lawyers in France. Once they had this data collected the located each home in GIS as well as locating local amenities and roads. Rather than using the distance to the amenities, they calculate the amount of amenities visible from each property. They focus specifically on trees, bushes, and agriculture. Based on their analysis, they were able to determine that when in the line of sight of a property, trees and farmland both have a positive impact on housing prices. Conversly their results showed that visible roads had the opposite effect and negatively impacted housing prices. These effects were only observed when the landscape was in view, as the effect on prices when the amenities were out of sight was insignificant. Kong, Fanhua, Haiwei Yin, and Nobukazu Nakagoshi. "Using GIS And Landscape Metrics In The Hedonic Price Modeling Of The Amenity Value Of Urban Green Space: A Case Study In Jinan City, China". Landscape and Urban Planning (2007): Web. Urban green space are important amenities that provide both leisure opportunities as well as aesthetic enjoyment, however their value can be difficult to quantify. This paper seeks to estimate the value of urban green space in Jinan City China. They examine three types of urban green space in their study which include plazas, parks, and scenery forests. They then assign a weight to each open space according to its expected amenity effect. Rather than using GIS to calculate explicit distance from open space amenities, they create accessibility variables based off travel time to various amenities. They do this using a distance/cost weighted measure tool to calculate the time it would take a public bus, or an individual walking, to reach the nearest amenity. They then generated spatial landscape characteristics based on richness, density, and aggregation of green space within both 300 and 500 meters of a home. Utilizing these variables, they found that there is a positive relationship between proximity to amenities and housing prices. Lake, Iain R., et al. "Using GIS and large-scale digital data to implement hedonic pricing studies." International Journal of Geographical Information Science 14.6 (2000): This article introduces how GIS can be used to convert large-scale vector data into polygons. Generating polygons for building areas, residential properties boundaries, and land use is critical to hedonic analysis. Besides just describing the creation of these polygons, they also detail the critical

5 Blanchette 5 components of hedonic analysis, including the derivation of structural, neighborhood, and local housing characteristics. In addition to outline the standard hedonic model, they also describe how to generate visibility variables using DEM. Visibility variables are included in hedonic analysis to determine whether or not given amenities are visible from a given property. This analysis shows that the incorporation of these variables is crucial for a meaningful analysis, and their exclusion could lead to bias results. Lang, Corey, and James Opaluch. "Effects of Wind Turbines on Property Values in Rhode Island." University of Rhode Island (2013). Recently the construction of wind turbines has started to become a debated topic with many individuals claiming they produce loud noises and have other negative impacts that could lower they property value. This study attempts to quantify the alleged negative impacts of turbines on housing prices in Rhode Island by utilizing GIS and a hedonic pricing model. They conduct their study using nearly all Rhode Island residential property transactions between January 2000 and February After collecting housing characteristics and sales data, find each home s latitude and longitude by using the RI GIS E-911 geolocater. Once they had located each home geocoded, they calculated the Euclidian distance from each home to the nearest turbine. They then removed unnecessary observations by using a buffer to limit their dataset to only include homes within 5 miles of a turbine. Utilizing this data, they found that there was no statistical evidence to support the claims that wind turbines have negative impacts on housing price, and that there was virtually no change in home prices within ½ a mile of a turbine after construction was completed. Milla, Katherine, M. Thomas, and Winsbert Ansine. "Evaluating the effect of proximity to hog farms on residential property values: a GIS-based hedonic price model approach." URISA Journal 17.1 (2005). Their study attempts to estimate the negative effect that proximity to Hog farms has on residential property values in North Carolina. To answer this question, they collected data on 26 different hog farms that varied in size, as well as parcel data for home sales transactions between January 2000 and July Data regarding hog farms was obtained from the North Carolina Department of Environmental and Natural Resources, while residential property data was obtained from the Craven County tax assessor s property parcel GIS database. Using ArcView, they generated parcel attributes by applying geometric distance functions. They then generated center points for each residential polygon and calculated distance to each swine operation. Utilizing the dataset generated through GIS technologies, they found that there was a statistically significant externality associated with animal feeding operations. They recommended that policies be implemented to create a 1.75 to 2.5 mile buffer for hog farms near residential properties in order to avoid the negative impacts on property values. Paterson, Robert W., and Kevin J. Boyle. "Out of sight, out of mind? Using GIS to incorporate visibility in hedonic property value models." Land economics 78.3 (2002):

6 Blanchette 6 This paper utilizes GIS data to generate variables representing the physical characteristics and visibility of various land use features in a hedonic analysis of residential properties in Simsbury and Avon Connecticut. The area for this study was chosen through discussion with local realtors who claim that these two are commonly considered jointly by potential homebuyers. From Multiple Listing Service data, they gather sales price information and property characteristics for all single-family home transactions from 1997 to early Four types of land use were considered in this analysis, including agriculture, forests, developed areas, and surface water. The GIS data used to identify land use patterns and topography were obtained from the Connecticut Department of Environmental Protection The spatial variables developed through GIS include the percentage of land occupied by residential and commercial development, as well as the other types of land use within a on-kilometer radius of each property, as well as the percentage of each type of land area visible within one kilometer of each property. They then estimate three equations that only vary in how environmental variables are treated. The first model only includes the physical area of each land cover, the second equation adds a variable representing total land area that is visible, and the final equation adds a visibility variable for each type of land use. Overall, they determined that including visibility variables are important and that amenities or disamentites that are out of sight have far less of an impact on property values than those that are visible.