NYC Real Estate Data Analytics - Manhattan 2017
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Overview
'When it comes to real estate business, choosing the 'real deal' is the hardest step!'
Investors search for properties that have higher tendency to be sold with better price. The more deals they have, the more money they earn. However, it is not that simple! Having the wrong deal in buying properties means losing millions of dollars. So prior to buying a property, there should be deep data analysis about the property itself, the previous sales, the previous prices, the location etc. A lot of factors help in having the real deal!
The following application helps investors in making their decision by analyzing previous sales transactions. It provides the user some computed analysis results with statistical insights and user interactive analysis features. For the time being, this project shows all the property sales that occurred in Manhattan for year 2017.
Application Link: https://project1-fatimahamdan.shinyapps.io/shiny-project_salesinmanhattan/
Note: The application still needs some enhancements and updates.
Data Set
The data is mainly collected for 36 different locations through 17,464 sales transactions.
It provides the following about each transaction: Neighborhood, Building Class Category, Tax Class at Present, Block, Lot, Easement, Building Class at Present, Address, Zip Code, Residential Units, Commercial Units, Total Units, Land Square Feet, Gross Square Feet, Year Built, Building Class at Time of Sale, Sales Price, Sale Date.
From an investor's point of view, this data set provides the investor all the information needed to analyze previous sales. He/She will get answers for
the following questions:
1. What is the best location in Manhattan to invest in? (whether because it has the highest sales rate or the highest sale price).
2.What is the best time of the year to invest? (depending on the sale prices changes during the year)
3. What location do people prefer for residential units as well as for commercial units?
4. Where are the old buildings located? Are people still buying apartments or houses in an old building?
A lot of questions can be answered from this data set.
The NYC Real Estate Data Analysis Application
Statistical Data Insights
The statistical insights tab answers the user two questions:
- When is the best time to invest in Manhattan?
- Where is the best location to invest?
The four graphs are based on the following:
- The average price of each month
This graph shows that the best time of the year for investment in Manhattan -based on price- is December! As for the minimum average of sale price, it is 261,156 $, and the maximum average of sale price is 5,411,472 $.
- The average number of sales of each month
In this graph, we can see that the best time of the year for investment in Manhattan-based on number of sales- is August!
Why there are two different months for the best investment?! and which month is better?
The answer is in the hand of the investor.. it depends on whether he/she wants to earn more money or sell more properties.
- The average price of each location
The variation in this graph shows that the best place to invest in is MidTown CBD! this is based on the sale price.
- The average number of sales of each location
As for this graph,it shows people's interests for some locations over the others since there are more sales in those locations. The highest number of sales is in Manhattan Valley and the second highest is Midtown CBD (which is the same place that has the highest average price as well).
The Map
The map shows the 36 locations in Manhattan with average sale price and number of sales for each location. The map is user interactive as well.
User Interactive Analysis
The interactive analysis feature provides the user the option to analyse the information himself/herself. The user can modify 5 inputs which are: the location,the type of building, show only old buildings feature, show only new buildings feature, and the price. After that, he can visualize the result in a table or graph.
For the following specific demo example: The user’s inputs are: ANY LOCATION, ANY TYPE OF BUILDING, old and new buildings, and price rage 200,000,000 to 600,000,000 $
- The inputs features
- Visualizing the Table: the data will appear in a table filtered by the user's inputs values
- Visualizing the Graphs: These interactive graphs show hows the sale price vary over time and locations.
Future Enhancements
Live People Searches
Since people's interest in buying or renting a property varies with time, it is important to keep track with what people are up to! nothing is better that analyzing google trends to visualize people's searches on buying in a certain location. This tab will show the investor on daily basis and even lively what locations people are interested in the most.
Contact information:
For more information about the application, contact me on fatima-hamdan@live.com
About Author
