Chapter 1: Discovering the Opportunity

Recently, I decided to browse Craigslist for quick gig options, even though I usually avoid searching on weekends. To my surprise, I stumbled upon a newly posted gig just an hour prior to my search. The task involved gathering contact details for real estate agents in the Kalamazoo, Michigan area.

The client’s response was as follows:

“Thanks for your inquiry regarding my Craigslist ad. I’m looking to compile a database of about 500-900 Realtors from the Kalamazoo area, as there are over 900 associated with the Great Kalamazoo Association of Realtors. This database will be used for email marketing aimed at realtors through services like Constant Contact or MailChimp.

Unlike many other sectors, real estate broker websites typically list their agents along with their contact information. However, personal email addresses are often omitted to prevent spam; they usually just have general addresses like ‘[email protected].’ Realtors, on the other hand, prefer direct contact from potential clients. I’m simply in need of the first name, last name, brokerage/company, and their email address, all compiled into a straightforward Excel spreadsheet (an example is attached).”

After I sent the initial list, the client responded with the following message. There was potential for me to earn even more, but I was away from home and couldn’t provide additional details right away.

“Wow, impressive! A couple of things… there are only 930 Realtors in the Greater Kalamazoo area, and some in your list need to be filtered out as they aren’t actual Realtors. This list appears to cover only Kalamazoo zip codes (which must have been your criteria), but the Greater Kalamazoo area includes Portage (49002, 49024), which has several significant companies and agents. I’m willing to pay for up to 900 contacts. If you can add the Portage Realtors, that would be fantastic. Thank you.”

Chapter 2: Leveraging My Database

Recognizing this as an ideal opportunity, I knew I could utilize my extensive database of approximately 64 million records. I had already organized it by state for quicker access, so I just needed to run a simple query to obtain the necessary information.

First, I imported the pandas library:

import pandas as pd

Next, I created a list of locations for my search:

places = ['Portage', 'Kalamazoo', 'Richland', 'Mattawan', 'Vicksburg', 'Schoolcraft', 'Plainwell', 'Otsego', 'Galesburg', 'Augusta', 'Paw Paw']

Using a join statement, I could search multiple terms at once:

search = '|'.join(places)

Then, I read my dataframe:

df = pd.read_pickle('d:\USPickle\USMI.pkl')

To focus on relevant businesses, I filtered my dataframe for the real estate sector:

df = df[df.Industry.str.contains('Real Estate')].copy()

To obtain the appropriate entries, I searched my list using the previously created search term:

df = df[df.City.str.contains(search, na=False)]

Finally, I saved the refined dataframe to my hard drive without the indices:

df.to_excel('d:/realtors_2ndbatch.xlsx', index=False)

I promptly emailed the completed list to my client, who quickly sent me $200 via PayPal. Ironically, it took me longer to write this blog post than it did to create the script and send the list.

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