Data Analysis of Nassau County Home Sales for Realtors

Posted on May 3, 2020
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Project summary:

Currently, real estate markets are analyzed on a large scale (ex: country or state) in order to provide an overarching view of data trends and patterns. While invaluable to economists, their analysis is not always relevant to local real estate agents and the areas they operate in.

My project hopes to solve this issue by allowing agents to observe and analyze the markets they operate in. It accomplishes this by analyzing historical home sales based on specific districts and towns chosen by the user. The data analyzed so far only includes home sales within Nassau County, Long Island since 2017 and is acquired from the Multiple Listing Service: a database used for listing and selling homes on Long Island.

The app’s analysis focuses on 4 areas:

  • Seasonal patterns
  • Home prices within school districts
  • Friction between buyers and sellers
  • Migration of sales

Seasonal Data Patterns:

Identifying seasonal patterns allows agents to take advantage of the repetitive movements within the market. The two parameters I have chosen to analyze for seasonal patterns are “Average Price of Home Sold” and “Total Home Sales.”  At a glance, it appears that both parameters not only exhibit a seasonal trend but also move in the same direction together. Colder seasons observe fewer home sales and a lower average price sold while warmer seasons observe the opposite.

It is a real estate agent’s responsibility to position their client in the most advantageous manner. Upon realizing the patterns in home sales and total sales throughout the year, depending of if the client is a seller or buyer, the agent may advise them to time their entrance into the market to time a peak or trough that suits them.

Home Prices Within School Districts:

Many move out to the suburbs to start a family or to relocate an existing one and therefore, important factors considered are the school district and the size of the home. My app allows you to analyze home prices within a school district by trend and average price per number of bedrooms.

By visualizing the average price of homes in each town located within the users chosen school district, it allows a client to narrow down their search to towns within their price range based on the number of bedrooms required. Conversely, it also assists in finding an anchoring point for a seller’s listing price.


Friction Between Buyers and Sellers:

I chose to measure friction between buyers and sellers by the number of days a home has spent on the market: with a large number indicating greater friction and vice versa. Agents can view the towns with homes that spent the most and least days on the market filtered by year. In addition, agents can also set a price range for the home sold further segment the data for deeper analysis.

By understanding the friction between buyers and sellers in a town, agents can better prepare their clients for how long their home might stay on the market. This knowledge will also assist agents in acquiring listings through cold calls, as they can provide a more accurate timeline suggestion of when they should list their home in order to move out by their desired date.


Migration of Sales:

Total home sales help paint a picture of which towns have busy markets. Similar to the “Friction Between Buyers and Sellers” tab, agents can view towns with the most and least sales filtered by price range and year.

By analyzing which towns have more sales, agents can focus more of their cold calls on homes in those areas as demand and supply might be high, thereby capturing listings with greater potential to sell.






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