THE BIG DIG OF BOSTON
ECONOMIC ASSESSMENT OF URBAN TRANSFORMATION Prof. A. Caragliu
GROUP Carrara Marco Cifarelli Dario Dealexandris Andrea Gaglione Giacomo
Management of Built Environment Academic Year 2018 /2019
INDEX 1. ABSTRACT………………..…………………………………..…...…...…..…3 2. BIG DIG, THE PROJECT..……………………………………………………4 3. DATA..…………………………………………………………………………4 3.1
GIS DATA..……………………………………….…………………5
3.2
VARIABLES DESCRIPTION..……………..……………………….6
4. HEDONIC EMPIRICAL MODEL 1..…………………………………………7 4.1
ANALYSIS OF THE RESIDUALS..……………………..…………9
5. MODEL 2..………………………………………………………………...…10 6. COMPARED RESULTS..………………………………………………….....11 7. CONCLUSIONS 8. References……………………………………………………..……………12 9. Attachments……………………………………………………..………….12
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1. ABSTRACT
This analysis focuses on the benefits given by the Big Dig to the single residential unit. The Big Dig project of Boston is one of the largest, technically difficult and environmentally challenging infrastructure projects ever undertook in the United States. Starting from the data collection of single unit transactions, spread around the whole downtown area of Boston, it has been done an analysis due to understand the different impact, on the real estate value of the selected area, of an underground highway instead of a ground level one. The big cities rarely have large green spaces available within the downtown; therefore, additional parks are important and possibly relevant asset, and this is possible only if the highway will be buried. The analysis has been carried out through the hedonic price method, because in this scenario it has been tried to assess the variance made by the change in one environmental context characteristic of the city center
of
Boston.
Furthermore, starting from the assumption that city centers are areas with a high rate of users, a large park could possibly benefit many people: residents, commuters, weekend visitors and tourists. How could the underground highway, covered by parks, impact on the single residential unit price?
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2. BIG DIG, THE PROJECT The Big Dig is a mega-scale urban construction, which brought 30 acres of new open space in the very center of the city. The project mainly consists in hiding the highway and all its negative externalities under the ground level. The project is unique both for its cost and for the large green area it created in an existing urban center. Because of its scale and impacts, the Big Dig has been a major issue in urban politics for more than two decades. Therefore, it is of particular interest to estimate the benefits of the new open spaces and examine the distribution of the benefits among urban stakeholders.
3. DATA Hedonic pricing requires actual property data of single real estate transaction, and to focus on the housing market related to the Big Dig area and its surrounded, it has been used property data of all apartment units in Boston’s zip codes from 02108 to 02118 except for the 02117. For the property prices and other housing attributes, it has been used data from the city of Boston Assessing Department. Geospatial datasets referring to Open Space in Boston are taken from Boston Analyze (https://data.boston.gov/) and then “filtered” using qGIS. The data used for the first analysis are: 1. All the parks owned by the Municipality greater than 1 acre, in order to quantify the relative distance variable 2. The distance from the highway path from each single dwelling 3. The distance of the unit from the Big Dig park The variable distances to CBD are potentially important, but it is not been used because all sample properties are either part of, or within a few miles to, the CBD. Therefore, it is possible to observe a small differential. Due to lack of data it’s impossible assess the values relative to it parcel data before the intervenvention.
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In our analysis the distances both to parks and highways are the primary variables in the hedonic model, used to estimate marginal willingness-to-pay for proximity to these facilities. The first hypothesis is that the farther from a highway a unit is, the higher the its price will be; and the farther from a park is, the lower the cost of housing will be. If the price differentials on these location values are significant, they imply that in the city center a highway is an undesirable environmental public good, while a park is a desirable good. In the following chapters it will proceed with the explanation of the chosen data and the calculation made.
3.1 GIS DATA: • • •
Parcel_data_full 2016: filtered per zipcode and Residential Condo Unit https://data.boston.gov/dataset/parcels-2016-data-full Open space: filtered per Parks > 1 acre owned by Municipality of Boston / MA https://data.boston.gov/dataset/open-space Boston Street Segments: filtered per speed limit >45 mph due to highlight only the highways. https://bostonopendataboston.opendata.arcgis.com/datasets/cfd1740c2e4b49389f47a9ce2dd23 6cc_8
Outcomes from the abovementioned shapefile allow to measure the following distances: • • •
Distance per each unit to the closest park. Distance per each unit to big dig area. Distance per each unit to the closest highway.
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3.2 VARIABLES DESCRIPTION VARIABLE DESCRIPTION (single apartment of a dwelling) Single apartment of a dwelling: 21411 observations Variables AV_TOTAL
Description (unit) Price for a single apartment transaction (in US dollar $) Intercept AV_TOTAL AGE_BUILT Year of construction of the building LIVING_AREA_M2 Area of the apartment (in sqm) NUM_FLOORS At which floor the apartment is located U_BASE_FLO Total number of floors of the building DUMMY VARIABLES It's 0 if not present and 1 if present U_NUM_PARK Parking lot U_FPLACE Fire safety system U_TOT_RMS Number of rooms of the apartment U_BDRMS Number of bedrooms U_FULL_BTH Number of bathroom DIST_PARKS Distance from the nearest park DIST_BIGDIG Distance from the Big Dig park DIST_HIGHWAY Linear distance from the highway DIST_HIGHdif Distance from the point where the highway is visible (out of the ground)
Model 1 x x x x x x x x x x x x x x
Model 2 x x x x x x
Name y α X1 X2 X3 X4 X5 X9 X6 X7 X8 X10 X11 X12
x x x x
X13
Table 1 – Variable descriptions
Table 2- Summary statistics per variable
Here above in Table 2 are explicated the summary statistics per each variable containing min, max, median and mean values.
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4. HEDONIC EMPIRICAL MODEL 1 The following form of hedonic pricing equation is employed.
Dummy variables values are taken as factor for the regression performed in R studio (see attachment for full references).
Table 3 - First linear regression
After a first regression, it has been pointed out that variables relative to total rooms, age built, numbers of bathrooms and presence of fireplace are not so significant, and they could be ignored, resulting the following purged formula:
Table 4- Summary of Purged regression
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The Pr(>t) relates to the probability of observing any value equal or larger than t. A small p-value indicates that it is unlikely we will observe a relationship between the predictor and response variables due to chance. It has been performed a test to highlight the outliers whose residual is much higher (or lower) than the Table 5 – Excerpt of outlierTest average that are 10 out of over 20’000. Those observations have been deleted, in order to improve the model relevancy reaching an R2 of 0.85 as shown in the below table (see Attachment 4).
The key coefficients of the hedonic regression are those on the distances to parks and to highways, so it has been clarified its confidence interval and its sign. As it was expected being closer to a park affects price positively while distance from highways acts oppositely.
Table 6 – confidence interval of key variable
There is a probability of 95% that the above intervals do include β, highlighting the influences sign of the variables. Being closer to parks affects price positively distance to highway works the opposite.
Table 7 – VIF test for multicollinearity
Variance Inflation Factor tests for multicollinearity, a phenomenon that leads to statistical unstable regression coefficients’ points out that the explanatory variables selected are not correlated to each other
4.1 LOG - LEVEL TRANSFORMATION It has been applied Log transformation to the dependent variable of price in order to interpret the change in price in percentage.
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- every extra 100 meters of distance from the closest park mean a decrease of 2,2% in the price.
- every extra 100 meters of distance from the highway mean a increase of 0,65%i n the price. Distance from bid dig doesn’t affect the price so much.
Table 8 -Log-Level transformation
5. ANALYSIS OF RESIDUALS From Figx it’s possible to see that the residuals have a skewed distribution. Applying the LogLevel regression it is obtained a more symmetrical and normal distribution around zero of the residuals. In the case in question stand residual doesn’t fit perfectly the normal distribution. Even also after having deleted the outliers (Table 5) the dataset consists of various observations which residuals are much higher.
Fig 3A – Standardized Residual Plot and 3B – Distribution of residuals for Linear Model 1
Fig 3A shows that the standardized predicted values and residuals not perfectly homogeneously distributed above and below the line of their expected value also after the elimination of the outliers. The histogram in Fig 3B present strong symmetry around zero but result a little positively skewed in the approximation of the normal distribution.
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Fig 4A – Standardized Residual Plot and 4B – Distribution of residuals for Log-Linear Model 1
Log-linear Model analysis of residuals doesn’t show any difference from the linear acting as acts as a magnifying glass.
Fig 5 _ncvTest
Finally, it has been performed a Breusch-Pagan (Fig5) test to the Linear Model in order to assess the presence of heteroscedasticity and tests the null hypothesis of homogeneity of the residuals’ variance. In this case it is possible to exclude the presence of omoschedasticity as the p-value is below 1.
Fig 6 – Residuals vs fitted value of linear model
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6. MODEL 2 Model 2 consists of the same variables taken into account in Model 1 except for the variable which take into account the distance to the closest highway, in fact, in this model the segment of highway that has been buried underground isn’t considered, resulting in the following formula:
every extra 100 meters of distance from the closest park mean a decrease of 2,08% in the price. every extra 100 meters of distance from the highway mean an increase of 0,4% in the price. Distance from bid dig doesn’t affect the price so much.
Table 9 -Log-level model 2
Table 10 – confidence interval of key variables - Model 2
Model 2 confidence interval of Model 2 key variables shows a less significative strength in the sign resulting distance from the highway more favorable.
7. COMPARED RESULTS
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Model I entail location distance from highway variables considering the segment buried underground in the Big Dig area; and Model II entails the highway variable that represents the distance from the highways left above ground after the Big Dig. As expected, it is shown that distances from parks have negative coefficients, and the distance from the highway has a positive coefficient on the property price of condominium units in both models. Therefore, it is desirable to be located close to a park, and it is not desirable to be located close to a highway. Model 1 results are 1) an increase of 0,7% in the price every extra 100 meters to the closer highway. And 2) decrease of about 2,2 every extra 100 meters to the closer Municipal park.
8. COCLUSIONS Parks and open spaces in a central city have an important role in enhancing the local environment, raising the quality of urban life. In urban centers where the population is large and land scarce, open spaces are expected to play an even larger role. The Big Dig project in Boston, Massachusetts exemplifies the creation of new green spaces in central city through an urban mega-project. To show the environmental benefits of this project, the impact of proximity to parks on housing values in Boston has been estimated through the hedonic pricing method. Assessed values of condominium units of central Boston are used to estimate the implicit prices for their location attributes that were added to the property public data of Boston using the Geographic Information Systems (GIS). The empirical analysis performed through the hedonic pricing method highlights: A) Based on the current land use and observed property values of condominium units in the selected area, shorter distances to urban open space in central Boston have positive impacts on property prices. The benefits of parks enjoyed by city visitors, however, can’t be measured by the hedonic pricing method. And, 2) Shorter distances to a highway have negative impacts on property prices. Combined with the impact of proximity to parks, the highway demolition and creation of open spaces on the Central Artery Corridor have caused significant increases in prices of neighborhood properties.
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REFERENCES Altshuler, A. A., & Luberoff, D. (2003). Mega-projects: The changing politics of urban public investment. Washington, DC: Brookings Institution Press. Rosen, S., 1974. Hedonic prices and implicit markets: product differentiation in pure competition. J. Polit. Econ. 8, 34–55. W. Edwards. “Project History”. Presentation to the National. Research Council and the National Council of Engineering Committee for Review of the Project Management Practices Employed on the Central Arthery/Tunnel Project, 2001 https://data.boston.gov/ https://massachusetts.hometownlocator.com/zip-codes/zipcodes,city,boston.cfm
ATTACHMENTS Attachment 1 – databasecsv Attachment 2 – database_purged.csv Attachment 3 - file.qgis Attachment 4 – Console_r.txt
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