New-York-Housing-Analysis

Below is a brief description regarding the project and the approach. The R Notebook for the analysis can be found here.

Problem Statement and Solution Approach

This project aims at looking at the cost and revenue data of houses in New York, aimed at recommending key areas of profitability. Data is sourced from two key locations for the same purpose. The idea would be to understand the key features of profitability of a house by identifying the various features, and digging into the

The problem aims at consulting for a real estate company that is looking to invest in two bedroom properties in New York City. The company has a niche of purchasing the properties and renting it out on a short-term basis. The particular ask to identify which zip codes are the best to invest in.

This solution aims at digging deep into the available data sources, finding the key variables/metrics that can be used to best define and describe the neighourhood and help calculate the key performance indices (like revenue, reviews and so on). The approach splits the main ask into a number of sub-questions in order to tackle the problem and the analyses on the individual tasks aim to provide a holistic solution.

The key ask - “What are the best zip codes in New York City to invest in a 2-bed room apartment for short-term rentals?” is broken down as such into certain key questions aimed at providing a specific perspective.

What is the neighbourhood that has the most listings in NYC?

The analyses uses the AirBnB listing data (Revenue) and Zillow data (cost). The Analysis is seperated into the following key sections