hw-1.pdf - MGTF-415 Homework 1 Real Estate This assignment builds off of Lectures#2 and#3 Students should email their assignments to the course teaching

hw-1.pdf - MGTF-415 Homework 1 Real Estate This assignment...

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MGTF-415 Homework 1: Real Estate This assignment builds off of Lectures #2 and #3. Students should email their assignments to the course teaching assistant by 9:00am on October 23, including “MGTF-415” in the subject line of the email. Save your assignment as a .pdf file, and include your code in the same file as your homework assignment. If you are using Python with a Jupyter Script, recall that you can install new modules by inserting the following commands at the beginning of your script: import sys !{sys.executable} -m pip install your_module_name 1 House Price Dynamics In this exercise, you will study how the riskiness of returns on residential real estate varies with dis- tance to the city center. As in lecture, you will perform this analysis using the FHFA All-Transactions Price Index, which covers single-family homes. If you are using Python, you will likely need to import the module pandas . (a) Write a command that will download and read a .csv file of price returns by census tract (i.e. submarket) and year from the FHFA’s website. Use the same link as in lecture. (b) Filter your dataframe such that the first year is 1990. Then, convert the data type of the variable annual change to a float. (c) Read the crosswalk file tract-metro-crosswalk.csv and create a new dataframe consisting of a list of census tracts in San Diego. Note that San Diego’s metro code is 41740. (d) Read the auxiliary dataset tract-centrality.csv and drop observations with missing data on dis- tance to the city center. (e) Merge the dataframe on centrality with the dataframe containing the list of San Diego census tracts, using an inner join. Should we use an inner join if we wanted to include non-San Diego census tracts in our analysis?
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