Manhattan Rolling Sales
Data Visualisation using
Tableau
presented by:
Mankirat Singh (60001180029)
Prachita Punde (60001180042)
Yash Pawar (60001180063)
Project Guide: Dr. Manish Jain
Background
The properties anywhere tend to fluctuate based on various factors and thus, having a keen eye while buying or selling property is a key requirement for any investor who wishes to invest in property.
The project described here tries to provide a holistic view for all properties sold in New York City in the year 2020 and 2021. This project aims to visualise all the details of the various types of properties sold and collate them in a set of visualisations and a working dashboard. This will help in recognizing best areas to buy/lease/sell property in the NYC area and best time and types of properties which can be bought or sold.
Tools and Technologies used
Tableau-
Tableau is basically a data visualisation tool which provides pictorial and graphical representations of data. Tableau is a visual analytics engine that makes it easier to create interactive visual analytics in the form of dashboards. These dashboards make it easier for non-technical analysts and end users to convert data into understandable, interactive graphics. With features like advanced visualisation, maps, trend lines and predictive analysis, Tableau is the perfect tool suited for our analysis.
Jupyter notebook-
We have utilised jupyter notebook as the platform for running the python code for certain analysis and answering a set of questions.
Preprocessing of Data
Step 1: Collection of data
We have downloaded the NYC rolling sales data from the website mentioned below. On the webpage, we have downloaded specifically, the Manhattan sales data in the excel format.
Link: https://www1.nyc.gov/site/finance/taxes/property-rolling-sales-data.page
Step 2: Cleaning of data
Since the data we obtained from the above-mentioned website had some null values and errors, we cleaned the data in MS excel itself.
Step 3: Correlation of parameters
Perfmormed EDA on the large dataset and then deduced he correlation amongst various parameters to understand the data in a comprehensive manner
Forming insightful questions
Every property in the city is assigned to one of four tax classes (Classes 1, 2, 3, and 4), based on the use of the property.
By making use of this information, we formulated four major questions which could be answered using the dataset and also gave a holistic view of the sales in the NYC area.
· How many 1 to 3 family homes were sold in each neighbourhood?
· For any property sold for over $1,000,000, when did the building boom associated with it occur?
· What is the average square footage sold to each tax class?
· Geographically speaking, how close are businesses to large cooperatives and apartment buildings?
32,663.5 sq.ft
The average gross square feet area occupied by a building
$809,912,583
Highest sale price for a building
Biggest area occupied by a building
2,400,000 sq.ft.
How many 1 to 3 family homes were sold in each neighbourhood?
For any property sold for over $1,000,000, when did the building boom associated with it occur?
What is the average square footage sold to each tax class?
Geographically speaking, how close are businesses to large cooperatives and apartment buildings?
Sale Price by Neighbourhood
Impact of size on sale price
Conclusion
Further Research and Development
References
➔ Literature Survey- Visualising NYC 2017 House Sales Data
Thank You.