My whole world seems to be about land-use. Land, land, land. So many competing uses. Can’t easily or inexpensively make more of it artificially. Unlike, say, a goat, land cannot move around. This immobility means that land’s location is fixed. Because a land’s location is fixed, its value is affected by its absolute location (i.e. where it is) and its relative location (i.e. the use of land around it). Y’all know the real estate turn of phrase, “Location, location, location!” Absolutely and relatively, the location of land affects its value. In addition to that, the land’s use (e.g. residential, agriculture, wilderness) will also affect its value.
Bringing it back to economics: Markets will allocate land to its highest valued use. Consider big cities; you tend to find the higher rent downtown than farther out in the country. Economics uses a bid-rent functions to model this relationship between distance to the center of the city and the maximum net benefits from each type of land use. Allow me to stress that this is a model, and the real world does not work exactly like this. Models, one of the major tools of economics, allows you to simplify and illustrate complex processes. You want to simplify these processes because there are so many factors and constraints, like legal requirements and individual/firm decisions, affecting these processes. There are also random fluctuations. As much as we like to say that people are 100% rational, people tend to not always follow through. Modeling all of these factors and constraints would be nearly impossible, so we simplify the models.
And that’s exactly what I’m doing for my project. Modeling how housing sales pries respond to proximity to open green spaces. Of course, not every house will follow the model; there are real-world considerations, such as negotiating the price.
Well, I do try to include images in my posts, and, really, what is economics without graphs? So here’s my crudely drawn model of the bid-rent function:
Yes, this is what economics looks like! Allow me to explain. On the x-axis, we have a distance from the Central Business District (think of it as downtown). On the y-axis, we have the net benefits per acre (think of it as money). Then we have 3 functions, one for residential land-use, one for agricultural land-use, and one for wilderness land-use. Points A, B, and C illustrate distances at which the land-use type changes. At Point A, the net benefits from agriculture exceeds the net benefits from residential use, so the land-use type changes from residential to agricultural. Same goes for Point B: at this point the land-use changes from agriculture to wilderness.
I keep using the phrase “green open spaces.” From my conversations with people regarding my project, I realize that this very phrase can lead to confusion. People ask me if I include parking lots, undeveloped private, or community garden plots among other spaces. The purpose of this post is to clarify just what I mean when I say “green open spaces.” When I use “green open space,” I mean parcels of undeveloped land that feature vegetation and outdoor recreational opportunities. Think more parks, nature preserves, and nature trails than empty parking lots. My open green spaces are not developed for residential, commercial, or industrial purposes.
For example, I would include city parks like University Park or Elm Park since they provide a lil’ bit of nature and walking opportunities.
However, I wouldn’t include the following open space in my analysis. My urban economics class, taught by Professor Brown, did a local field trip of some of Worcester’s factories. It is brownfield land, a piece of land on which there used to be an industrial or commercial facility. This used to be a factory site in Worcester.
When I began dreaming up my project, I initially used the term “open spaces.” Talking with other people truly proved to be beneficial to me. Although I may have a clear concept of what I’m doing in my head, I realized that other people may not have the same picture as I do. Communication and feedback help. I joined “green” with “open spaces” to emphasize that I want to research outdoorsy open spaces. Like the following:
Institutions and groups also subscribe to different definitions of open space. For example, from my data gathering, I learned that the City of Worcester includes community gardens and cemeteries as open spaces. I’m debating whether or not to include community gardens. I immediately chose to not include cemeteries as open spaces. I’m looking for spaces with an outdoor recreation component. I do not believe that people regularly go hiking in a cemetery. Therefore, I will exclude cemeteries from my collection of green open spaces.
Welcome to my LEEP blog! For my project, I’ll estimate how people value open green spaces, like parks and nature preserves, by how much they are willing to pay for housing based on that house’s proximity to these open, green spaces in Worcester, MA. We’re very much in economics-land now. A housing consumer’s willingness to pay for housing, whether it be an apartment or house, reveals his or her preference of certain housing attributes, like the number of bathrooms or quality of of the school district. One assumption is that housing are heterogeneous, multi-attribute goods. If such a person considers an attribute, say a low crime rate, an amenity, he or she can be expected to be willing to pay more for it. Conversely, if a person considers an attribute, say lots of air pollution, a disamenity, he or she can be expected to be willing to pay less for it. My goal is to test my hypothesis that people are willing to pay more for housing that features closer proximity to open spaces. People may be willing to pay more or less or nothing for this feature. I’ve got to test it to find out!
Simply, the main thrust of my project consists of seeing how housing prices respond to proximity to open green spaces in Worcester, MA. To accomplish this, I’ll use data from the Warren Group, the Greater Worcester Land Trust (GWLT), and MassGIS. However, this is the real world, and I need to clean the data to make them fit for analysis. Working with Clark University’s economics graduate students and under the guidance of Professors John Brown and Jacqueline Geoghegan of Clark’s Economics Department, I’ll geocode and map housing data from the Warren Group. The Greater Worcester Land Trust (GWLT) is a non-profit organization that advocates and seeks to expand open spaces in the greater Worcester area. The GWLT uses GIS data and GIS-produced maps to keep track of open spaces until its management or control. MassGIS, the Massachusetts Geographic Information System provides Massachusetts GIS data, such as political town boundaries. The MassGIS data will provide the context under which the Warren Group housing data and the GWLT open space data will operate.
Once all these data are cleaned up, which is easier said than done, I’ll use the data in STATA, a statistics software package, to estimate a regression model of how proximity to open spaces, like the ones the GWLT manages, affects a house’s market sales price while controlling for other housing and neighborhood characteristics. Adding a spatial component to my analysis, I’ll utilize ArcGIS to determine statistically significant spatial relationships between houses and open spaces in Worcester, MA.